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10.1371/journal.pntd.0001779 | The MASP Family of Trypanosoma cruzi: Changes in Gene Expression and Antigenic Profile during the Acute Phase of Experimental Infection | Trypanosoma cruzi is the etiological agent of Chagas disease, a debilitating illness that affects millions of people in the Americas. A major finding of the T. cruzi genome project was the discovery of a novel multigene family composed of approximately 1,300 genes that encode mucin-associated surface proteins (MASPs). The high level of polymorphism of the MASP family associated with its localization at the surface of infective forms of the parasite suggests that MASP participates in host–parasite interactions. We speculate that the large repertoire of MASP sequences may contribute to the ability of T. cruzi to infect several host cell types and/or participate in host immune evasion mechanisms.
By sequencing seven cDNA libraries, we analyzed the MASP expression profile in trypomastigotes derived from distinct host cells and after sequential passages in acutely infected mice. Additionally, to investigate the MASP antigenic profile, we performed B-cell epitope prediction on MASP proteins and designed a MASP-specific peptide array with 110 putative epitopes, which was screened with sera from acutely infected mice.
We observed differential expression of a few MASP genes between trypomastigotes derived from epithelial and myoblast cell lines. The more pronounced MASP expression changes were observed between bloodstream and tissue-culture trypomastigotes and between bloodstream forms from sequential passages in acutely infected mice. Moreover, we demonstrated that different MASP members were expressed during the acute T. cruzi infection and constitute parasite antigens that are recognized by IgG and IgM antibodies. We also found that distinct MASP peptides could trigger different antibody responses and that the antibody level against a given peptide may vary after sequential passages in mice. We speculate that changes in the large repertoire of MASP antigenic peptides during an infection may contribute to the evasion of host immune responses during the acute phase of Chagas disease.
| The parasite Trypanosoma cruzi is the etiologic agent of Chagas disease, a neglected tropical disease. A major finding of the T. cruzi genome project was the discovery of a multigene family that encodes mucin-associated surface proteins (MASP), a highly polymorphic family expressed at the surface of infective forms of the parasite. We speculate that MASP may contribute to the ability of T. cruzi to infect several host cells and/or participate in host immune evasion mechanisms. To begin investigating this hypothesis, we analyzed the MASP expression profile in trypomastigotes derived from different host cells and in bloodstream parasites after sequential passages in mice. We also investigated the MASP antigenic profile in acutely infected mice. We observed more pronounced MASP expression changes by comparing bloodstream and tissueculture trypomastigotes and between bloodstream forms from sequential passages in infected mice. We also found
that MASP peptides could trigger different IgG and IgM antibody responses and that the antibody level against a given peptide may vary after sequential passages in mice. We speculate that changes in the large repertoire of MASP antigenic peptides during the course of an infection may contribute to the evasion of host immune responses during the acute phase of Chagas disease.
| Trypanosoma cruzi is the etiological agent of Chagas disease, a major public health problem in Central and South America. Currently there are approximately 10 million people infected and 40 million people at risk of acquiring the disease [1], [2]. Trypomastigotes are the bloodstream circulating form that infect a wide variety of nucleated host cells and subsequently differentiate into the intracellular replicative amastigote forms. After several rounds of binary division, amastigotes differentiate into trypomastigotes, which are released into the extracellular medium and bloodstream. The repetitive cycle of cell infection triggers the acute phase of Chagas disease, characterized by high blood parasitaemia, broad tissue parasitism, and a strong host immune response. The chronic phase is achieved after the host immune system controls the parasitaemia but fails to completely eliminate the parasite [3].
The annotation of the T. cruzi genome revealed a new multigene family composed of approximately 1,300 genes, which became known as mucin-associated surface protein (MASP) as they were not randomly distributed throughout the genome but instead clustered with genes encoding mucins and other surface protein families [4]. A previous study on the molecular characterization of a few members found that MASP proteins are expressed on the surface of the circulating infective forms of the parasite and can be shed into the extracellular medium [5]. MASP expression in the trypomastigote stage was also demonstrated by recent proteomic studies [6], [7]. Moreover, the MASP family is characterized by a strikingly variable and repetitive central region composed of peptides shared among its members, thus contributing to the extended repertoire of parasite polypeptides that could be exposed to the host cells and immune system [5]. The MASP repertoire of peptides could also contribute to cell-type-specific interactions, because the polymorphism of T. cruzi surface proteins has been suggested to be an important factor in the parasite's ability to infect multiple cell types [8], [9]. Taken together, this evidence has prompted us to examine a possible role of the MASP family in host–parasite interactions, such as the host-cell-dependent expression profile or/and immune evasion mechanisms. We observed differential expression of a few MASP genes between trypomastigotes derived from epithelial and myoblast cell lines. The more pronounced MASP expression changes were observed when comparing bloodstream and tissue-culture trypomastigotes and between bloodstream forms from sequential passages in acutely infected mice. Furthermore, in this work we describe the antibody recognition of several MASP peptides. Taken together, our findings prompt us to speculate that variations in the large repertoire of antigenic peptides derived from the MASP family may favor the parasite's escaping the host immune response during the acute phase of infection.
The following steps for CL Brener trypomastigote culture and collection are detailed in Figure 1.
cDNA samples were generated by reverse transcriptase Super Script II (Invitrogen) reactions using random primers and 3–4 µg total RNA. cDNA quality was tested by PCR using specific primers for aromatic L-alpha-hydroxy acid dehydrogenase (AHADH2) (AHADH2_F 5′ CCAAATGTTTCGCCACTCG 3′ and AHADH2_R 5′ CACGCTGCGGAGGGATCTC 3′) and for the DNA repair gene RAD51 (RAD51_F 5′ GGCTGTCAAGGGTATCA 3′ and RAD51_R 5′ AACCACTGCGGATGTAA3′), a gene with low expression in trypomastigotes [11]. Approximately 200 ng of each cDNA or 250 ng of total RNA (negative control) were used in PCR reactions, with 200 µM dNTPs, 2 µM primers, and 1.25 U Taq DNA polymerase in 50 mM KCl, 10 mM Tris-HCl pH 8.4, 1% X-100 Triton, and 1.5 mM MgCl2. To ensure that the samples were not contaminated with genomic DNA, total RNA samples were tested by PCR for the T. cruzi gene AHADH2 using the primers described above. DNA contaminated samples were treated with DNase I (Fermentas), following manufacturer's recommendations, and submitted to RNA clean-up using the RNeasy kit (Qiagen). Treated samples were also PCR-tested. PCR amplification was observed in agarose ethidium bromide or polyacrilamid silver stained gels.
To construct expression libraries, several primer combinations were tested in silico by electronic PCR (e-PCR) (http://www.ncbi.nlm.nih.gov/projects/e-pcr/) and experimentally to amplify the majority of the MASP genes. A semi-nested PCR was set up using the following primer combinations: SL 5′ AACGCTATTATTGATACAGTTTCTGTACTATATTG 3′ for the 35 pb spliced leader region and 3′UTR1 (reverse) 5′ GTGTGCTTCGTGGGGTGAGGTG 3′ for the 3′UTR in the first reaction and SL and 3′UTR2 5′ CTCACTCTCACGCGGCCACCACCACCG 3′ also for 3′UTR (more internal localization) in the second reaction. The semi-nested RT-PCR reactions with these primers were performed with 2 µL of amplified product of the first reaction or 400 ng of the cDNA with 200 µM dNTPs, 2 µM of primers, and 3.75 U High Fidelity Taq DNA polymerase (Invitrogen). The amplicons between 500 and 2,000 bp of the second reaction were cloned in pGEM-T (Promega), and at least 96 ampicillin-resistant clones were selected and cultured. After plasmid extraction, a PCR of each clone was performed using primers for the insert flanking regions M13F (forward) 5′ CGCCAGGGTTTTCCCAGTCACGAC 3′ and M13R (reverse) 5′ TCACACAGGAAACAGCTATGAC 3′. Amplified products were precipitated with 20% polyethylene glycol 8000 and 2.5 M NaCl and submitted to sequencing at one end in the ABI Prism 3730xl DNA Analyser (Applied Biosystems) by Macrogen Inc (Korea). The libraries constructed for each trypomastigote sample are presented in Table 1. The EST sequences have been deposited in the GenBank database under Accession Number JK743993 - JK744782.
Sequences from the expression libraries were processed by an in-house pipeline. The EST sequences were processed by the Phred algorithm and then filtered after a cross-match with the vector sequence (pGEM-T- Promega). The MASP genes were identified using BLASTN algorithm against two T. cruzi sequence databases: one with all T. cruzi predicted features of the genome (coding sequences, retroelements and structural RNAs), and the other with T. cruzi contigs. The e-value (expected value) cutoff of the BLASTN searches used was 10−10, and the minimum identity between the ESTs and the database entries was 70%. The uncertainty of the hierarchical cluster analysis of all expression libraries was calculated using the Pvclust package [12] using the R software platform [13]. Pvclust calculates probability values (p-values) for each cluster using bootstrap re-sampling techniques. AU (approximately unbiased) p-value was used, which is calculated by multi-scale bootstrap re-sampling and has superiority in bias over BP (bootstrap probability) according to the authors [12].
MASP sequence variability and expression profile were visualized in a multidimentional scaling plot (MDS). To this end, we calculated the pairwise distance of all 1,377 annotated MASP genes and generated the distance matrixes using the package PHYLIP [14]–[15]. To provide a visual representation of the distance matrix, we used an MDS plot with two dimensions (2D). The k-means method [16] was used to define six clusters or subgroups. Each dot in the MDS represents a MASP member and its graphic localization, the sequence similarity among the genes. The expressed MASP genes were plotted in the MDS graphics, where the size of the dots represents the frequency of the MASP genes in the library and the colors represent their group classification. The databases generated by the e-PCR predictions were also plotted in the MDS graphics. The MDS, hierarchical clustering, statistical analyses, and graphing were performed using the R software platform [13].
The genes targeted for Real Time PCR (qRT-PCR) were selected after comparative analysis of the expression libraries. Primers were designed using Allele ID 7 (Premier Biosoft, demo version), and NCBI T. cruzi database BLAST searches were performed to exclude primers with cross homology with other MASP members and other T. cruzi genes. The design also included the template's secondary structure test at 60°C. The MASP complete genes analyzed by qRT-PCR were: MASP2 (Tc00.1047053510359.460), MASP4 (Tc00.1047053508541.110), MASP14 (Tc00.1047053504039.230), MASP16 (Tc00.1047053510693.190), MASP23 (Tc00.1047053511089.19), and MASP27 (Tc00.1047053506615.100). The primer sequences used in the analysis for each MASP gene are listed in Table S1. Reactions in triplicate were prepared with 1 µM forward and reverse primers, SYBR Green Supermix (Applied Biosystems), and each of the diluted template cDNAs (1∶4 in DNAse free water) and were performed using cycling conditions as recommended by the manufacturer (Applied Biosystem). Standard curves were used for the calculation of relative quantity (Rq) values of each sample for each target. qRT-PCRs for MSH2 and RAD51 genes (Table S1) were performed, and the average value between them was used to normalize the MASP gene results. The results were analyzed by a one-way ANOVA test, and graphics were constructed in GraphPad Prism 5.0 (GraphPad Inc.).
MASP predicted protein sequences derived from expressed genes were submitted to B-cell linear epitope prediction using the BepiPred algorithm [17], and the output was parsed by an in-house PERL script to select 15-mer amino acid peptides whose prediction score according to their quality as an epitope was >1.3. One or two peptides from specific MASP genes identified in the expression libraries were selected to be synthesized in pre-activated cellulose membranes according to the SPOT synthesis technique [18]. The SPOT synthesis was employed using a method for the preparation of approximately 5 nmol of immobilized peptides. The assembly of the peptides was performed utilizing the previously described Fmoc-chemistry [18]. Briefly, 0.5 mM of each activated Fmoc (9-fluorenylmethoxycarbonyl) amino acid was automatically spotted on pre-activated membranes using the MultiPep SPOT synthesizer (Intavis AG). Each cycle of amino acid coupling was followed by a 10% acetic anhydride blocking and deprotection of Fmoc amino acids by adding 25% 4-methyl piperidine. The coupling and deprotection of Fmoc amino acids were confirmed after each cycle by staining the membrane with 2% bromophenol blue. After the synthesis, the side chain deprotection was performed by adding a 25∶25∶1.5∶1 solution of trifluoroacetic acid, dichloromethane, triisopropylsilane and water. The side-chain deprotection was also confirmed by staining with 2% bromophenol blue. The synthesized peptides are listed in Table S2. Membranes were blocked with 5% BSA and 4% sucrose in PBS overnight and incubated for 1.5 hours with pools of diluted mice sera (1∶500 for IgG or 1∶5,000 for IgM) in blocking solution. After washing three times for 10 minutes in PBS-T (PBS; 0.1% Tween 20), membranes were incubated for 1.5 hours with secondary HRP-conjugated anti-mouse IgM or IgG antibody (Sigma-Aldrich), diluted 1∶2,000 in blocking solution. After a second washing, membranes were revealed by ECL Plus Western blotting (GE Healthcare), in the Gel Logic 1500 Imaging system (KODAK). Synthetic peptides corresponding to epitopes of a trans-sialidase [19] and L7A ribosomal protein [20] were included in the experiments as positive controls. As negative controls, membranes were submitted to the same experimental conditions using sera of uninfected Swiss mice. Densitometry measurements and analysis of each peptide were performed using Image Master Platinum (GE), and the relative intensity ratio (RI) cutoff for positivity was determined as 2.0. Reactive spots in the positive blottings (using the infected mouse serum pool) were only selected for analysis when not reactive in the negative blotting. Graphics were constructed in GraphPad Prism 5.0 (GraphPad Inc.).
Seven peptides with the highest RI values and a peptide derived from trans-sialidase SAPA (shed acute phase antigen) [21] (Table S3) were submitted to soluble synthesis (Peptide 2.0) and ELISA experiments. Flexible ELISA polyvinylchloride plates (BD Falcon) were sensitized with 2 µg of soluble peptides or trypomastigote extract in water at 37°C overnight. After blocking with 2.5% BSA in PBS for 2 hours at 37°C, the plates were incubated with sera from uninfected and infected mice (dilution 1∶100) for 1.5 hours at 37°C. After washing in 0.05% Tween 20-PBS, the plates were incubated with secondary antibodies anti-mouse IgM or IgG (dilution 1∶2000; Sigma). After several washes in PBS-0.05% Tween 20, the plates were revealed with OPD (o-phenylenediamine; Sigma), in citric acid buffer (50 mM Na2HPO4, 27 mM, citric acid, pH 5.0) and hydrogen peroxide and read at 492 nm. The reactivity of the trypomastigote extract was used in the normalization of ELISA results using the peptides. The results were analyzed by one-way ANOVA test and graphics were constructed with GraphPad Prism 5.0 (GraphPad Inc.). Trypomastigote total extracts were obtained by ultrasound lysis of purified and PBS-washed parasite. Protein quantification was determined by the BCA™ Protein Assay Kit (Pierce).
Low-affinity antibodies were eluted by adding an incubation step with 6 M urea for 5 minutes at room temperature after the mouse serum incubation. Avidity index was expressed as (mean OD of urea-treated sera/mean OD urea-untreated sera)×100%. Affinity indexes <40% or >40% were considered low and intermediate affinity levels, respectively [22]. The three mouse serum pools (after 2, 10, and 12 passages) were tested in triplicate.
Trypomastigotes derived from L6 cells after 17 passages were purified by centrifugation at 400× g followed by incubation at 37°C for 4 h to allow motile trypomastigotes to swim up. The trypomastigotes were then used to infect LLC-MK2 and L6 cells as follows. LLC-MK2 or L6 cells ressuspended in RPMI supplemented with 10% FBS were plated (4×104 cells/well) in 24-well plates containing coverslips and incubated at 37°C and 5% CO2 for 36 hours prior to infection. Infection was performed by exposing cells to purified trypomastigotes for 30 min at 37°C at a multiplicity of infection (MOI) of 50. Cells were then washed five times with PBS to remove extracellular parasites and fixed with 4% (wt/vol) paraformaldehyde/PBS overnight at 4°C. After fixation, coverslips with attached cells were washed three times in PBS, incubated for 20 min with PBS containing 2% BSA, and processed for an inside/outside immunofluorescence invasion assay using an anti-T. cruzi rabbit polyclonal antibody and a secondary anti-rabbit IgG antibody conjugated with Alexa Fluor 546 (Life technologies) according to a previous protocol [23]. In this step, all extracellular, non-washed parasites were stained. Coverslips were then washed twice with PBS, incubated with 10 g/ml DAPI (4′,6′-diamidino-2-phenylindole; Sigma) in PBS for 2 min, washed three times in PBS and mounted on slides using a fluorescence mounting solution containing 1 mg/ml of PPD (p-phenylenediamine) in Glycerol/Tris-HCl. At least 250 cells (10 fields) were analyzed per coverslip in triplicate, and invasion rates were calculated as the number of intracellular parasites/100 host cells. Graphs were plotted using GraphPad Prism 5.0 (GraphPad Inc.) and statistically significant differences were determined using Student's t test.
All animal procedures were approved by the animal-care ethics committee of the Federal University of Minas Gerais (Protocol # 232/2009) and were performed under the guidelines from COBEA (Brazilian College of Animal Experimentation) and strictly followed the Brazilian law for “Procedures for the Scientific Use of Animals" (11.794/2008).
Prior to investigating the MASP expression profile, we performed sequence clustering analysis of the family to identify subgroups, whose expression profiles were then analyzed. To this end, we performed pairwise alignments of the coding sequences of all MASP genes, resulting in a distance matrix that was used to generate a multidimensional scaling (MDS) plot (Figure S1). The k-means method was used to define six clusters or subgroups (Figure S1A) (Table S4). Due to the extensive sequence variability of the MASP family, we performed a series of electronic PCR analyses to select the primers suitable to amplify most MASP transcripts. These analyses suggest that most of the MASP complete genes (77.8% or the equivalent to 630 genes) and eventually those derived from pseudogenes (55.9%, 317 members) could be amplified in a semi-nested RT-PCR using primers for the spliced leader sequence (SL) and MASP 3′UTR (3′UTR1 and 3′UTR2) (Figure S1B). The SL primer was used in both reactions of the semi-nested RT-PCR to guarantee that the amplified transcripts were mature, whereas the 3′UTR1 and 3′UTR2 primers were derived from the MASP 3′UTR, which is the most conserved region of MASP transcripts [5]. In addition, because of the mosaic structure of MASP genes having shared fragments among the members [4], this combination of primers has the advantage of generating amplification products containing the entire MASP coding region and therefore would allow an unequivocal identification of the expressed genes. Using these primers, five of the six different MASP subgroups can be amplified (Figure S1).
We hypothesized that the large repertoire of MASP peptides may contribute to ability of T. cruzi to infect and/or survive within several host-cell types and/or participate in host immune evasion mechanisms [5]. To begin investigating this hypothesis, we constructed seven expression libraries from tissue culture trypomastigotes derived from two cell types (epithelial cells and myoblasts), and from bloodstream trypomastigotes recovered after sequential passages in mice (Table 1). For all libraries, the amplification profile by semi-nested RT-PCR presented a smear in both reactions (Figure S2), indicating the co-expression of several MASP transcripts with different lengths. Fragments ranging from 500 to 2,000 bp were cloned into pGEM-T vector (Promega) and a total of 960 clones were sequenced. Based on the percentages of valid sequences, ranging from 76.4% to 90.6%, the libraries were considered to be of good quality (Table 2). The T. cruzi genes corresponding to the best hit of each valid EST sequence are shown in the Table S5. We retrieved a total of 94 MASP complete genes by analyzing the content of the libraries altogether, even though the number of sampled transcripts per library was restricted to 20 to 32 genes. Although the proportion of sequences with non-MASP best hit ranged from 50% to 83.9%, the majority of these genes are short hypothetical proteins that appear to be unreal because they were predicted within the MASP 3′UTR, which contains the annealing sites of the reverse primers used to construct the libraries. Other non-MASP hits include chimeric sequences containing the MASP C-terminal coding sequence and the downstream MASP 3′UTR and TcMUC mucin genes and retroelements followed by fragments of the MASP 3′UTR. We also detected the transcription of pseudogenes, ranging from 4.8% to 37.8% of the MASP hits in all the libraries. Because the library of bloodstream trypomastigotes after 10 passages (Blood.Tryp.10p) presented a higher proportion of pseudogenes (37.8% versus an average of 12.3% for all other libraries), a larger number of sequences from this library was analyzed (Table 2). We analyzed 96 more sequences of the Blood.Tryp.10p expression library than of the other libraries, and the number of MASP genes sampled in this library was similar to those of the other libraries. We mapped the MASP cDNAs in the MDS distribution to represent a visual analysis of the expressed members. A transparency was applied to the dots so that differences in their size would represent differences in the level of MASP expression (Figure 2).
In a previous study, we had identified several MASP transcripts in a cDNA library constructed from tissue culture trypomastigotes derived from Vero cells [5]. Here, we investigated whether MASP is also expressed in bloodstream trypomastigotes during the acute phase of experimental infection. In addition, we compared two expression libraries constructed from bloodstream trypomastigote forms after sequential passages in mice. As shown in Figure 2A, bloodstream trypomastigotes derived from a given passage co-express MASP genes belonging to all five different groups, indicating a broad expression of different MASP genes. MASP expressed genes for which we could design specific primers were analyzed by qRT-PCR. Significant differential expression was observed by qRT-PCR for MASP2, MASP16, and MASP27 between bloodstream trypomastigotes from sequential passages (Figure 3A). MASP2 and MASP27 were significantly more expressed in bloodstream trypomastigotes after 10 passages in mice compared to trypomastigotes after two passages. In contrast, MASP16 was significantly more expressed in bloodstream forms after two passages in mice. In addition to the temporal changes in the expression of a given gene after sequential passages in mice, we also found that the level of expression of distinct MASP transcripts varies significantly in the trypomastigote population. For instance, MASP27 is 100 times more expressed than MASP 23 in all libraries (Figure 3). These results indicate that the expression profile of distinct MASP genes is heterogeneous and may vary after sequential passages in mice.
To investigate whether tissue culture trypomastigotes had a distinct MASP expression profile compared with the bloodstream forms, part of the trypomastigote population collected after two passages in mice was used to infect myoblast (L6) and epithelial cells (LLC-MK2), and after 4 passages in culture, the RNA was extracted for library construction. Both libraries, Tryp.4p.llcmk2 and Tryp.4p.l6, showed a very similar pattern of expression (Figure 2A). In fact, no significant difference in gene expression between these two libraries was observed for those genes analyzed by qRT-PCR (Figure 3A). In contrast, we detected differences in the expression profile of bloodstream forms after two passages (BloodTryp.2p) compared with both Tryp.4p.llcmk2 and Tryp.4p.l6 libraries (Figure 2A). This was confirmed by qRT-PCR: MASP2 and MASP27 were more expressed in Tryp.4p.l6 compared to the BloodTryp2 library, whereas MASP16 was more expressed in bloodstream forms after two passages compared with L6- and LLC-MK2-derived trypomastigotes after four passages (Figure 3A).
Because we did not detect significant changes in MASP expression when comparing trypomastigotes after four passages in the two types of host cells (myoblast and LLC-MK2 cells), we decided to analyze the MASP expression profile after a larger number of passages in these two types of tissue culture cells (14 passages). Similar to what was observed in the other libraries, in both Tryp.14p.llcmk2 and Tryp.14p.l6 libraries, we also detected co-expression of MASP genes in five different groups. Furthermore, MASP27 is one of the most represented genes in the sequenced clones, while MASP23 was not sampled in these libraries (Figure 2B). This data were validated by qRT-PCR since we detected remarkably high levels of expression of MASP27 compared to the other analyzed genes, while MASP23 was approximately 100 times less expressed in all libraries (Figure 3B). More importantly, more notable differences in MASP expression in trypomastigotes derived from both host cells were observed after 14 passages compared to the expression profile after 4 passages. We observed by qRT-PCR that MASP2, MASP14, and MASP16, belonging to the Red subgroup, were more expressed in L6-derived trypomastigotes compared to LLC-MK2-derived trypomastigotes after 14 passages (Figure 3B). Whether these specfiic MASP members are implicated in trypomastigote invasion, replication, and/or survival within L6 cells remain to be investigated. Nevertheless, by performing invasion assay, we confirmed an association between the MASP profile and the infectivity of L6-derived trypomastigotes (Figure S3). Specifically, we evaluated the rate of invasion of L6 and LLC-MK2 cells by the same population of trypomastigotes that were maintained for 17 consecutive passages in L6 cells. We found a higher rate of invasion of L6 cells compared with LLC-MK2, reinforcing our findings and suggesting that successive passages of trypomastigotes in a given host cell may configure a specific expression profile that optimizes the rate of invasion.
The differences observed by qRT-PCR are in agreement with the hierarchical clustering analysis of all expression libraries using the pvclust package (Figure 4). The dendrogram derived from the pvclust analysis shows that, after four passages, tissue culture trypomastigotes derived from either L6 or LLC-MK2 cells are very similar. In contrast, tissue culture trypomastigotes libraries are distantly clustered from bloodstream trypomastigote libraries. Taken together, these results indicate that tissue culture and in vivo infection may selectively configure a distinct MASP expression profile in trypomastigotes.
To investigate the MASP antigenic profile in the acute phase of the experimental infection, we performed B-cell linear epitope prediction on the MASP proteins derived from expressed members using the Bepipred algorithm [17]. Only predicted epitopes exclusive for MASP proteins were selected. A total of 110 peptides for 64 MASP expressed genes, from a total of 94 genes identified in the expression libraries, are member specific and were analyzed by immunoblotting. We found that 74 to 88% of the analyzed MASP members were recognized by sera of acutely infected mice, having at least one reactive peptide (RI>2.0) against one serum pool (Figure 5). Additionally, 21 to 33% of the MASP members were recognized by all three serum samples. The remaining peptides were not reactive or were excluded from the analysis after the normalization step with sera of uninfected mice.
Following the peptide screenings on SPOT arrays, seven peptides with high RI values were submitted for soluble synthesis to be used in ELISA experiments (Table S3). As expected, IgM and IgG antibodies from infected mice were reactive against all MASP peptides and also against SAPA, a known T. cruzi epitope derived from a repetitive region of the trans-sialidase enzyme [21] (Figure 6). MASP genes with low expression levels had reactive peptides, such as peptide D10, derived from MASP23. However, it is important to emphasize that mRNA and protein levels in T. cruzi are not always linearly associated, and therefore we cannot assume that the level of expression of the MASP23 protein is also low. A variable level of reactivity against MASP peptides was also observed between the sequential passages in mice. Furthermore, variable levels of recognition by the two immunoglobulin types (IgG and IgM) of each peptide were observed. High levels of IgM were observed against peptide B5, derived from MASP27, after ten passages in mice. It is worth noting that, as mentioned before, MASP27 had the highest expression levels in all cDNA libraries. High levels of IgM were also observed against the peptides H5 and J10 after two and 12 passages in mice. Both peptides are derived from MASP genes with low expression levels. The other peptides tested by ELISA that were derived from genes expressed at low levels, displayed a distinct level of antibody recognition. SAPA reactivity by IgM had high values, and the results also indicated that there was a differential recognition by IgM antibodies between the sequential passages in mice. The affinity levels of the IgG and IgM antibodies against MASP peptides were also measured (Figure S4). The IgG antibodies against all tested MASP peptides presented intermediate affinity, ranging from 52 and 58.3%, which were higher levels than that of the affinity of antibodies against SAPA (40%). In contrast, IgM antibodies against MASP peptides presented variable levels of affinity. IgM antibodies against C5 peptides presented intermediate affinity (54.9%), whereas antibodies against MASP peptides C3, B5, D10, H1, and SAPA presented low affinity, ranging from 17.8 to 33.2%. The distinct antibody affinity levels are most likely related to differences in peptide composition rather than to the total antibody levels against the peptides. For instance, antibodies against the peptides C5 had the highest affinity index, despite the fact that the total antibody against this peptide had the lowest level (Figure S4).
Overall, these results showed that different members of the MASP family are expressed during acute T. cruzi infection and constitute parasite antigens recognized by IgG and IgM antibodies. The results also indicated that distinct MASP peptides could trigger different antibody responses and that the antibody level against a given peptide may vary after sequential passages in mice.
A key T. cruzi strategy to survive in a mammalian host is its ability to actively invade a wide variety of non-phagocytic host cells. The acute phase of Chagas disease is characterized by intense parasitaemia and tissue parasitism involving the infection of heart, skeletal and smooth muscle cells, as well as liver, fat, and brain cells [24]–[27]. Furthermore, during the chronic phase, there are several reports on differences of T. cruzi tropism to host tissue, which is associated with the pathogenesis of Chagas disease [28]–[31]. Therefore, an important aspect to understanding T. cruzi infection is the identification of molecular components of both parasite and host cells that play a role in the infection of a broad range of cell types. In this regard, several studies have investigated changes in gene expression during T. cruzi infection. However, these studies have focused mainly on the modulation of gene expression of the host cells [32]–[40]. Although several trypomastigote surface proteins have been implicated in host-cell recognition/invasion [41]–[46], so far there is no clear association between a T. cruzi expression profile and its ability to invade/proliferate in a given host cell.
In our previous work on the molecular characterization of the MASP family [5], we speculated that these proteins may be involved in host–parasite interactions because of their surface localization on the infective circulating trypomastigote forms. Moreover, the MASP family is highly polymorphic and can be secreted by the parasite, thus contributing to a large T. cruzi polypeptide repertoire that could be exposed to the host cells and the host's immune system [5]. In fact, it has been shown recently that a MASP protein is able to induce endocytosis in Vero cells [47], a process whereby the trypomastigote forms of the parasite actively invade host cells [48]. As an attempt to investigate whether MASP members could be implicated in interactions with specific cell types, we investigate the MASP expression profile in trypomastigotes derived from epithelial (LLC-MK2) and myoblast (L6) cell lines. We selected these cell types because LLC-MK2 is widely used for maintaining T. cruzi in in vitro culture, whereas myoblasts give raise to muscle cells, which are target by T. cruzi during acute and chronic phase of Chagas disease [26], [49]. We did not detected significant changes in the MASP expression profile between these two host cells after 4 passages in tissue culture. However, differential expression of MASP genes were detected by sequencing and by qRT-PCR analyses between trypomastigote forms derived from these two host cells after a larger number of tissue culture passages: MASP2, MASP14 and MASP16 were significantly more expressed in myoblasts compared to epithelial cells after 14 passages (Figure 3B). This is an indirect evidence that different MASP genes may be implicated in the interaction with distinct cell types. As indicated before, it was recently demonstrated that one MASP member (named MASP52, Tc00.1047053504239.220) is secreted by trypomastigote forms upon contact with non-phagocytic Vero cells and is able to induce endocytosis [47]. This MASP gene was not sampled in our sequenced clones. Nevertheless, an association between the selection of a MASP profile and the infectivity of L6-derived parasites is suggested by the invasion assay experiment (Figure S3). Whether peptides derived from MASP2, MASP14 and MASP16 are involved in myoblast recognition/invasion and/or parasite proliferation/survival within myoblast cells remains to be investigated.
We have also investigated whether the transition from in vivo to in vitro infection would affect the MASP expression profile. To this end, the same trypomastigote population that was recovered from passage 2 in mice was used to infect myoblast and epithelial cells, and after four passages in each cell type, the MASP expression profile was analyzed. We found noticeable differences in the MASP family expression profile when tissue-culture and bloodstream forms were compared (Figures 2A, 3A and 4). Significant differential expression of the genes MASP2, MASP16 and MASP27 was observed by qRT-PCR in tissue-culture derived trypomastigotes after four passages (Tryp.4p.llcmk2 and Tryp.4p.l6) compared to bloodstream trypomastigotes after two passages (BloodTryp.2p) (Figure 3A). These observations suggest that selective pressure driven by tissue culture or in vivo infection may induce distinct MASP expression profiles. Differences in infections using bloodstream- and tissue culture-derived trypomastigotes have already been reported. Specifically, it has been shown that the rate of in vitro infection of distinct host cell types does not correlate with the level of parasitaemia of experimentally infected mice [50]. We also found a heterogeneous MASP expression profile when bloodstream trypomastigotes recovered from mice after 2 and 10 passages were compared: MASP2 and MASP16 were significantly more expressed in bloodstream forms after 10 passages compared with bloodstream forms from passage 2, and the inverse was observed for MASP16 (Figure 3A). In addition, a large number of MASP pseudogenes were expressed in bloodstream forms after 10 passages, which was not observed for any other library (Table 2). Sequencing a larger number of clones of this library did not significantly change this profile. Pseudogenes may contribute to the diversity of the sequence repertoire through recombination. Indeed, the involvement of pseudogenes in the generation of variant surface glycoprotein (VSG) diversity has already been described in T. brucei [51], and it was hypothesized that this mechanism may explain the large number of VSG pseudogenes in the T. brucei genome [52]. Whether there is a selective pressure imposed by the vertebrate host immune system to increase the sequence diversity of the MASP family in the circulating trypomastigote forms at the expenses of generating pseudogenes remains to be investigated.
It is well established that several T. cruzi surface proteins are co-expressed at a given time in the parasite population, leading to the phenomenon of antigenic variability [53]. Here, we found that, in fact, several MASP genes are co-expressed, although the level of expression of each transcript is very variable in a given library (Figure 3), indicating a heterogeneous expression of the family. This expression profile was also observed in the proteomic study of the trypomastigote form of the Y strain [7]. In this study, 37 unique MASP peptides found in 167 MASP proteins were identified at different expression levels in a parasite population derived from LLC-MK2 host cells. Although the conditions and strains used in both studies were different, it is interesting that two MASP members sampled in our expression libraries from trypomastigotes derived from LLC-MK2 host cell were also represented in this proteomic study (Tc00.1047053508253.10 and Tc00.1047053510163.30). We have previously analyzed the MASP expression in individual trypomastigotes by performing immunofluorescence using non-permeabilized cells and an affinity-purified antibody specific to a MASP subgroup. Only a few trypomastigotes were labeled with the antibody, suggesting that, at least for some MASP proteins, their expression on the surface of trypomastigotes is not uniform in the parasite population [5]. The present study added another layer of complexity to the expression of the MASP family since we detected temporal changes in gene expression of the same gene after sequential passages in mice and also in trypomastigotes derived from epithelial and myoblast cells after a large number of in vitro passages (Figures 2 and 3).
How the parasite modulates the expression of MASP genes during the infection is an open question. We did not detect a correlation between chromosomal location of the MASP genes sampled in our cDNA libraries and their expression levels (data not shown), suggesting that there is no apparent bias regarding the chromosomal location of expressed MASP genes. It is well established that the control of gene expression in Trypanosomatids operates almost exclusively at a post-transcription level, primarily mediated by regulatory elements with the 3′UTR of the transcripts that modulate the mRNA stability by means of interactions with regulatory proteins [reviewed in 54]. We have previously shown that the 3′UTR of MASP transcripts is highly conserved among the family members [5] and therefore may not be involved in the differential expression of the distinct MASP genes. Nevertheless, subtle nucleotide differences in these regions and/or alternative polyadenylation sites among the different transcripts may favor or abolish specific interactions with regulatory proteins. How the parasite changes the expression of the same MASP gene under different in vitro and in vivo conditions is also intriguing. In this case, it is possible that the host cell and/or the host immune system may configure a specific MASP expression profile.
Another possible MASP function that may explain the high level of polymorphism of the family would be its involvement in immune evasion mechanisms. In addition to its extreme polymorphism, localization at the trypomastigote surface, and shedding properties, another MASP feature that reinforces this hypothesis is the large repertoire of distinct repetitive motifs of the MASP proteins [5]. It has been shown that several parasitic repetitive proteins are targets for strong B-cell responses [53], [55]–[56]. In fact, in silico predictions performed by our group on the entire MASP proteome suggest the occurrence of a large repertoire of B-cell epitopes in the family (data not shown). In the present study, we validated these predictions by showing that several peptides derived from MASP-expressed members reacted with sera from acutely infected mice (Figures 5 and 6). The antibody recognition of several MASP peptides supports the interaction of the MASP family with the host immune system during acute T. cruzi infection.
We have also investigated whether the MASP antigenic profile changes during acute infection. Indeed, variable antigenic profiles between the trypomastigotes isolated from sequential passages in experimentally infected mice were observed by immunoblotting and ELISA (Figures 5 and 6). The MASP family, along with other T. cruzi surface proteins, may contribute to the polyclonal lymphocyte activation that leads to hypergammaglobulinemia and the delayed specific humoral immune response, that are characteristic of the acute phase of Chagas disease. These phenomena are suggested to be an immune evasion mechanism [57]–[59]. Polyclonal lymphocyte B activation could scatter the immune response, preventing the development of a specific and neutralizing response against the parasite and its complete elimination. T-cell independent responses may contribute to hypergammaglobulinemia [57]. Indeed, the SAPA repetitive C-terminal region of the trans-sialidase protein was reported to be a T-cell independent B mitogen and inducer of non-specific Ig secretion [56]. It is possible that MASP peptides could mediate both specific T-dependent or unspecific T-independent immune responses, a hypothesis that is partially supported by the differential recognition of MASPs by the two immunoglobulin types (IgM and IgG) and the difference in the antibody affinity levels against each of the synthetic peptides. We speculate that variations in the large repertoire of antigenic peptides derived from the MASP family may contribute to the mechanism of immune evasion during the acute phase of the infection.
This is the first report on the antigenic properties of the MASP family, supported by the description of the antibody recognition of expressed MASP peptides in the acute phase of the experimental infection. MASP expression in bloodstream trypomastigotes is also first described in this study, as well as the differential expression of its members in trypomastigotes derived from distinct host cells and during acute experimental infection. The MASP expression profile is likely to be even more complex than reported here due to the limitations of our approach. The construction of the expression libraries in our investigation was limited by the similarity of the MASP 3′UTRs, and by the limited number of clones sequenced. Nevertheless, this study revealed a much more complex pattern of MASP expression than was previously described [5]. The use of an RNA-seq approach to study the transcription of bloodstream, tissue-culture derived parasites and infected host cells will reveal a comprehensive picture of the expression of genes involved in T. cruzi–host cell interactions.
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10.1371/journal.pcbi.0030031 | Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity | Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses.
| The paper describes a new biologically plausible mechanism for generating intermediate-level visual representations using an unsupervised learning scheme. These representations can then be used very effectively to perform categorization tasks using natural images. While the basic hierarchical architecture of the system is fairly similar to a number of other recent proposals, the key differences lie in the level of description that is used—individual neurons and spikes—and in the sort of coding scheme involved. Essentially, we have found that a combination of a temporal coding scheme where the most strongly activated neurons fire first with spike timing dependent plasticity leads to a situation where neurons in higher order visual areas will gradually become selective to frequently occurring feature combinations. At the same time, their responses become more and more rapid. We firmly believe that such mechanisms are a key to understanding the remarkable efficiency of the primate visual system.
| Temporal constraints pose a major challenge to models of object recognition in cortex. When two images are simultaneously flashed to the left and right of fixation, human subjects can make reliable saccades to the side where there is a target animal in as little as 120–130 ms [1]. If we allow 20–30 ms for motor delays in the oculomotor system, this implies that the underlying visual processing can be done in 100 ms or less. In monkeys, recent recordings from inferotemporal cortex (IT) showed that spike counts over time bins as small as 12.5 ms (which produce essentially a binary vector with either ones or zeros) and only about 100 ms after stimulus onset contain remarkably accurate information about the nature of a visual stimulus [2]. This sort of rapid processing presumably depends on the ability of the visual system to learn to recognize familiar visual forms in an unsupervised manner. Exactly how this learning occurs constitutes a major challenge for theoretical neuroscience. Here we explored the capacity of simple feedforward network architectures that have two key features. First, when stimulated with a flashed visual stimulus, the neurons in the various layers of the system fire asynchronously, with the most strongly activated neurons firing first—a mechanism that has been shown to efficiently encode image information [3]. Second, neurons at later stages of the system implement spike timing dependent plasticity (STDP), which is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early [4,5]. We demonstrate that when such a hierarchical system is repeatedly presented with natural images, these intermediate-level neurons will naturally become selective to patterns that are reliably present in the input, while their latencies decrease, leading to both fast and informative responses. This process occurs in an entirely unsupervised way, but we then show that these intermediate features are able to support categorization.
Our network belongs to the family of feedforward hierarchical convolutional networks, as in [6–10]. To be precise, its architecture is inspired from Serre, Wolf, and Poggio's model of object recognition [6], a model that itself extends HMAX [7] and performs remarkably well with natural images. Like them, in an attempt to model the increasing complexity and invariance observed along the ventral pathway [11,12], we use a four-layer hierarchy (S1–C1–S2–C2) in which simple cells (S) gain their selectivity from a linear sum operation, while complex cells (C) gain invariance from a nonlinear max pooling operation (see Figure 1 and Methods for a complete description of our model).
Nevertheless, our network does not only rely on static nonlinearities: it uses spiking neurons and operates in the temporal domain. At each stage, the time to first spike with respect to stimulus onset (or, to be precise, the rank of the first spike in the spike train, as we will see later) is supposed to be the “key variable,” that is, the variable that contains information and that is indeed read out and processed by downstream neurons. When presented with an image, the first layer's S1 cells, emulating V1 simple cells, detect edges with four preferred orientations, and the more strongly a cell is activated, the earlier it fires. This intensity–latency conversion is in accordance with recordings in V1 showing that response latency decreases with the stimulus contrast [13,14] and with the proximity between the stimulus orientation and the cell's preferred orientation [15]. It has already been shown how such orientation selectivity can emerge in V1 by applying STDP on spike trains coming from retinal ON- and OFF-center cells [16], so we started our model from V1 orientation-selective cells. We also limit the number of spikes at this stage by introducing competition between S1 cells through a one-winner-take-all mechanism: at a given location—corresponding to one cortical column—only the spike corresponding to the best matching orientation is propagated (sparsity is thus 25% at this stage). Note that k-winner-take-all mechanisms are easy to implement in the temporal domain using inhibitory GABA interneurons [17].
These S1 spikes are then propagated asynchronously through the feedforward network of integrate-and-fire neurons. Note that within this time-to-first-spike framework, the maximum operation of complex cells simply consists of propagating the first spike emitted by a given group of afferents [18]. This can be done efficiently with an integrate-and-fire neuron with low threshold that has synaptic connections from all neurons in the group.
Images are processed one by one, and we limit activity to at most one spike per neuron, that is, only the initial spike wave is propagated. Before presenting a new image, every neuron's potential is reset to zero. We process various scaled versions of the input image (with the same filter size). There is one S1–C1–S2 pathway for each processing scale (not represented on Figure 1). This results in S2 cells with various receptive field sizes (see Methods). Then C2 cells take the maximum response (i.e., first spike) of S2 cells over all positions and scales, leading to position and scale invariant responses.
This paper explains how STDP can set the C1–S2 synaptic connections, leading to intermediate-complexity visual features, whose equivalent in the brain may be in V4 or IT. STDP is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between pre- and postsynaptic spikes: an excitatory synapse receiving a spike before a postsynaptic one is emitted is potentiated (long-term potentiation) whereas its strength is weakened the other way around (long-term depression) [19]. The amount of modification depends on the delay between these two events: maximal when pre- and postsynaptic spikes are close together, and the effects gradually decrease and disappear with intervals in excess of a few tens of milliseconds [20–22]. Note that STDP is in agreement with Hebb's postulate because presynaptic neurons that fired slightly before the postsynaptic neuron are those that “took part in firing it.” Here we used a simplified STDP rule where the weight modification does not depend on the delay between pre- and postsynaptic spikes, and the time window is supposed to cover the whole spike wave (see Methods). We also use 0 and 1 as “soft bounds” (see Methods), ensuring the synapses remain excitatory. Several authors have studied the effect of STDP with Poisson spike trains [4,23]. Here, we demonstrate STDP's remarkable ability to detect statistical regularities in terms of earliest firing afferent patterns within visual spike trains, despite their very high dimensionality inherent to natural images.
Visual stimuli are presented sequentially, and the resulting spike waves are propagated through to the S2 layer, where STDP is used. We use restricted receptive fields (i.e., S2 cells only integrate spikes from an s × s square neighborhood in the C1 maps corresponding to one given processing scale) and weight-sharing (i.e., each prototype S2 cell is duplicated in retinotopic maps and at all scales). Starting with a random weight matrix (size = 4 × s × s), we present the first visual stimuli. Duplicated cells are all integrating the spike train and compete with each other. If no cell reaches its threshold, nothing happens and we process the next image. Otherwise for each prototype the first duplicate to reach its threshold is the winner. A one-winner-take-all mechanism prevents the other duplicated cells from firing. The winner thus fires and the STDP rule is triggered. Its weight matrix is updated, and the change in weights is duplicated at all positions and scales. This allows the system to learn patterns despite changes in position and size in the training examples. We also use local inhibition between different prototype cells: when a cell fires at a given position and scale, it prevents all other cells from firing later at the same scale and within an s/2 × s/2 square neighborhood of the firing position. This competition, only used in the learning phase, prevents all the cells from learning the same pattern. Instead, the cell population self-organizes, each cell trying to learn a distinct pattern so as to cover the whole variability of the inputs.
If the stimuli have visual features in common (which should be the case if, for example, they contain similar objects), the STDP process will extract them. That is, for some cells we will observe convergence of the synaptic weights (by saturation), which end up being either close to 0 or to 1. During the convergence process, synapses compete for control of the timing of postsynaptic spikes [4]. The winning synapses are those through which the earliest spikes arrive (on average) [4,5], and this is true even in the presence of jitter and spontaneous activity [5] (although the model presented in this paper is fully deterministic). This “preference” for the earliest spikes is a key point since the earliest spikes, which correspond in our framework to the most salient regions of an image, have been shown to be the most informative [3]. During the learning, the postsynaptic spike latency decreases [4,5,24]. After convergence, the responses become selective (in terms of latency) [5] to visual features of intermediate complexity similar to the features used in earlier work [8]. Features can now be defined as clusters of afferents that are consistently among the earliest to fire. STDP detects these kinds of statistical regularities among the spike trains and creates one unit for each distinct pattern.
We evaluated our STDP-based learning algorithm on two California Institute of Technology datasets, one containing faces and the other motorbikes, and a distractor set containing backgrounds, all available at http://www.vision.caltech.edu (see Figure 2 for sample pictures). Note that most of the images are not segmented. Each dataset was split into a training set, used in the learning phase, and a testing set, not seen during the learning phase but used afterward to evaluate the performance on novel images. This standard cross-validation procedure allows the measurement of the system's ability to generalize, as opposed to learning the specific training examples. The splits used were the same as Fergus, Perona, and Zisserman [25]. All images were rescaled to be 300 pixels in height (preserving the aspect ratio) and converted to grayscale values.
We first applied our unsupervised STDP-based algorithm on the face and motorbike training examples (separately), presented in random order, to build two sets of ten class-specific C2 features. Each C2 cell has one preferred input, defined as a combination of edges (represented by C1 cells). Note that many gray-level images may lead to this combination of edges because of the local max operation of C1 cells and because we lose the “polarity” information (i.e., which side of the edge is darker). However, we can reconstruct a representation of the set of preferred images by convolving the weight matrix with a set of kernels representing oriented bars. Since we start with random weight matrices, at the beginning of the learning process the reconstructed preferred stimuli do not make much sense. But as the cells learn, structured representations emerge, and we are usually able to identify the nature of the cells' preferred stimuli. Figures 3 and 4 show the reconstructions at various stages of learning for the face and motorbike datasets, respectively. We stopped the learning after 10,000 presentations.
Then we turned off the STDP rule and tested these STDP-obtained features' ability to support face/nonface and motorbike/nonmotorbike classification. This paper focuses more on feature extraction than on sophisticated classification methods, so we first used a very simple decision rule based on the number of C2 cells that fired with each test image, on which a threshold is applied. Such a mechanism could be easily implemented in the brain. The threshold was set at the equilibrium point (i.e., when the false positive rate equals the missed rate). In Table 1 we report good classification results with this “simple-count” scheme in terms of area under the receiver operator characteristic (ROC) and the performance rate at equilibrium point.
We also evaluated a more complicated classification scheme. C2 cells' thresholds were supposed to be infinite, and we measured the final potentials they reached after having integrated the whole spike train generated by the image. This final potential can be seen as the number of early spikes in common between a current input and a stored prototype (this contrasts with HMAX and extensions [6,7,26], where a Euclidian distance or a normalized dot product is used to measure the difference between a stored prototype and a current input). Note that this potential is contrast invariant: a change in contrast will shift all the latencies but will preserve the spike order. The final potentials reached with the training examples were used to train a radial basis function (RBF) classifier (see Methods). We chose this classifier because linear combination of Gaussian-tuned units is hypothesized to be a key mechanism for generalization in the visual system [27]. We then evaluated the RBF on the testing sets. As can be seen in Table 1, performance with this “potential + RBF” scheme was better.
Using only ten STDP-learnt features, we reached on those two classes a performance that is comparable to that of Serre, Wolf, and Poggio's model, which itself is close to the best state-of-the-art computer vision systems [6]. However, their system is more generic. Classes with more intraclass variability (for example, animals) appear to pose a problem with our approach because a lot of training examples (say a few tens) of a given feature type are needed for the STDP process to learn it properly.
Our approach leads to the extraction of a small set (here ten) of highly informative class-specific features. This is in contrast with Serre et al.'s approach where many more (usually about a thousand) features are used. Their sets are more generic and are suitable for many different classes [6]. They rely on the final classifier to “select” diagnostic features and appropriately weight them for a given classification task. Here, STDP will naturally focus on what is common to the positive training set, that is, target object features. The background is generally not learned (at least not in priority), since backgrounds are almost always too different from one image to another for the STDP process to converge. Thus, we directly extract diagnostic features, and we can obtain reasonably good classification results using only a threshold on the number of detected features. Furthermore, as STDP performs vector quantization from multiple examples as opposed to “one-shot learning,” it will not learn the noise, nor anything too specific to a given example, with the result that it will tend to learn archetypical features.
Another key point is the natural trend of the algorithm to learn salient regions, simply because they correspond to the earliest spikes, with the result that neurons whose receptive fields cover salient regions are likely to reach their threshold (and trigger the STDP rule) before neurons “looking” at other regions. This contrasts with more classical competitive learning approaches, where input normalization helps different input patterns to be equally effective in the learning process [28]. Note that “salient” means within our network “with well-defined contrasted edges,” but saliency is a more generic concept of local differences, for example, in intensity, color, or orientations as in the model of Itti, Koch, and Niebur [29]. We could use other types of S1 cells to detect other types of saliency, and, provided we apply the same intensity–latency conversion, STDP would still focus on the most salient regions. Saliency is known to drive attention (see [30] for a review). Our model predicts that it also drives the learning. Future experimental work will test this prediction.
Of course, in real life we are unlikely to see many examples of a given category in a row. That is why we performed a second simulation, where 20 C2 cells were presented with the face, motorbike, and background training pictures in random order, and the STDP rule was applied. Figure 5 shows all the reconstructions for this mixed simulation after 20,000 presentations. We see that the 20 cells self-organized, some of them having developed selectivity to face features, and others to motorbike features. Interestingly, during the learning process the cells rapidly showed a preference for one category. After a certain degree of selectivity had been reached, the face-feature learning was not influenced by the presentation of motorbikes (and vice versa), simply because face cells will not fire (and trigger the STDP rule) on motorbikes. Again we tested the quality of these features with a (multiclass) classification task, using an RBF network and a “one-versus-all” approach (see Methods). As before, we tested two implementations: one based on “binary detections + RBF” and one based on “potential + RBF”. Note that a simple detection count cannot work here, as we need at least some supervised learning to know which feature (or feature combination) is diagnostic (or antidiagnostic) of which class. Table 2 shows the confusion matrices obtained on the testing sets for both implementations, leading, respectively, to 95.0% and 97.7% of correct classifications on average. It is worth mentioning that the “potential + RBF” system perfectly discriminated between faces and motorbikes—although both were presented in the unsupervised STDP-based learning phase.
A third type of simulation was run to illustrate the STDP learning process. For these simulations, only three C2 cells and four processing scales (71%, 50%, 35%, and 25%) were used. We let at most one cell fire at each processing scale. The rest of the parameters were strictly identical to the other simulations (see Methods). Videos S1–S3 illustrate the STDP learning process with, respectively, faces, motorbikes, and a mix of faces, motorbikes, and background pictures. It can be seen that after convergence the STDP feature showed a good tradeoff between selectivity (very few false alarms) and invariance (most of the targets were recognized).
An interesting control is to compare the STDP learning rule with a more standard hebbian rule in this precise framework. For this purpose, we converted the spike trains coming from C1 cells into a vector of (real-valued) C1 activities XC1, supposed to correspond to firing rates (see Methods). Each S2 cell was no longer modeled at the integrate-and-fire level but was supposed to respond with a (static) firing rate YS2 given by the normalized dot product:
where WS2 is the synaptic weight vector of the S2 cell (see Methods).
The S2 cells still competed with each other, but the k-winner-take-all mechanisms now selected the cells with the highest firing rates (instead of the first one to fire). Only the cells whose firing rates reached a certain threshold were considered in the competition (see Methods). The winners now triggered the following modified hebbian rule (instead of STDP):
where a decay term has been added to keep the weight vector bounded (however, the rule is still local, unlike an explicit weight normalization). Note that this precaution was not needed in the STDP case because competition between synapse naturally bounds the weight vector [4]. The rest of the network is strictly identical to the STDP case.
Figure 6 shows the reconstruction of the preferred stimuli for the ten C2 cells after 10,000 presentations for the face stimuli (Figure 6, top) and the motorbikes stimuli (Figure 6, top). Again we can usually recognize the face and motorbike parts to which the cells became selective (even though the reconstructions look fuzzier than in the STDP case because the final weights are more graded). We also tested the ability of these hebbian-obtained features to support face/nonface and motorbike/nonmotorbike classification once fed into an RBF, and the results are shown in Table 1 (last column). We also evaluated the hebbian features with the multiclass setup. Twenty cells were presented with the same mix of face, motorbike, and background pictures as before. Figure 7 shows the final reconstructions after 20,000 presentations, and Table 2 shows the confusion matrix (last columns).
The main conclusion is that the modified hebbian rule is also able to extract pertinent features for classification (although performance on these tests appears to be slightly worse). This is not very surprising as STDP can be seen as a hebbian rule transposed in the temporal domain, but it was worth checking. Where STDP would detect (and create selectivity to) sets of units that are consistently among the first one to fire, the hebbian rule detects (and creates selectivity to) sets of units that consistently have the highest firing rates. However, we believe the temporal framework is a better description of what really happens at the neuronal level, at least in ultrarapid categorization tasks. Furthermore, STDP also explains how the system becomes faster and faster with training, since the neurons learn to decode the first information available at their afferents' level (see also Discussion).
While the ability of hierarchical feedforward networks to support classification is now reasonably well established (e.g., [6–8,10]), how intermediate-complexity features can be learned remains an open problem, especially with cluttered images. In the original HMAX model, S2 features were not learned but were manually hardwired [7]. Later versions used huge sets of random crops (say 1,000) taken from natural images and used these crops to “imprint” S2 cells [6]. This approach works well but is costly since redundancy is very high between features, and many features are irrelevant for most (if not all) of the tasks. To select only pertinent features for a given task, Ullman proposed an interesting criterion based on mutual information [8], leaving the question of possible neural implementation open. LeCun showed how visual features in a convolutional network could be learned in a supervised manner using back-propagation [10], without claiming this algorithm was biologically plausible. Although we may occasionally use supervised learning to create a set of features suitable for a particular recognition task, it seems unrealistic that we need to do that each time we learn a new class. Here we took another approach: one layer with unsupervised competitive learning is used as input for a second layer with supervised learning. Note that this kind of hybrid scheme has been found to learn much faster than a two-layer backpropagation network [28].
Our approach is a bottom-up one: instead of intuiting good image-processing schemes and discussing their eventual neural correlates, we took known biological phenomena that occur at the neuronal level, namely integrate-and-fire and STDP, and observed where they could lead at a more integrated level. The role of the simulations with natural images is thus to provide a “plausibility proof” that such mechanisms could be implemented in the brain.
However, we have made four main simplifications. The first one was to propagate input stimuli one by one. This may correspond to what happens when an image is flashed in an ultrarapid categorization paradigm [1], but normal visual perception is an ongoing process. However, every 200 ms or 300 ms we typically perform a saccade. The processing of each of these discrete “chunks” seems to be optimized for rapid execution [31], and we suggest that much can be done with the feedforward propagation of a single spike wave. Furthermore, even when fixating, our eyes are continuously making microsaccades that could again result in repetitive waves of activation. This idea is in accordance with electrophysiological recordings showing that V1 neuron activity is correlated with microsaccades [32]. Here we assumed the successive waves did not interfere, which does not seem too unreasonable given that the neuronal time constants (integration, leak, STDP window) are in the range of a few tens of milliseconds whereas the interval between saccades and microsaccades is substantially longer. It is also possible that extraretinal signals suppress interference by shutting down any remaining activity before propagating the next wave. Note that this simplification allows us to use nonleaky integrate-and-fire neurons and an infinite STDP time window. More generally, as proposed by Hopfield [33], waves could be generated by population oscillations that would fire one cell at a time in advance of the maximum of the oscillation, which increases with the inputs the cell received. This idea is in accordance with recordings in area 17 of cat visual cortex showing that suboptimal cells reveal a systematic phase lag relative to optimally stimulated cells [34].
The second simplification we have made is to use only five layers (including the classification layer), whereas processing in the ventral stream involves many more layers (probably about ten), and complexity increases more slowly than suggested here. However, STDP as a way to combine simple features into more complex representations, based on statistical regularities among earliest spike patterns, seems to be a very efficient learning rule and could be involved at all stages.
The third main simplification we have made consists of using restricted receptive fields and weight sharing, as do most of the bio-inspired hierarchical networks [6–10] (networks using these techniques are called convolutional networks). We built shift and scale invariance by structure (and not by training) by duplicating S1, C1, and S2 cells at all positions and scales. This is a way to reduce the number of free parameters (and therefore the VC dimension [35]) of the network by incorporating prior information into the network design: responses should be scale- and shift-invariant. This greatly reduces the number of training examples needed. Note that this technique of weight sharing could be applied to other transformations than shifting and scaling, for instance, rotation and symmetry. However, it is difficult to believe that the brain could really use weight sharing since, as noted by Földiák [36], updating the weights of all the simple units connected to the same complex unit is a nonlocal operation. Instead, he suggested that at least the low-level features could be learned locally and independently. Subsequently, cells with similar preferred stimulus may connect adaptively to the same complex cell, possibly by detecting correlation across time thanks to a trace rule [36]. Wallis, Rolls, and Milward successfully implemented this sort of mechanism in a multilayered hierarchical network called Vis-Net [37,38]; however, performance after learning objects from unsegmented natural images was poor [39]. Future work will evaluate the use of local learning and adaptative complex pooling in our network, instead of exact weight sharing. Learning will be much slower but should lead to similar STDP features. Note that it seems that monkeys can recognize high-level objects at scales and positions that have not been experienced previously [2,40]. It could be that in the brain local learning and adaptative complex pooling are used up to a certain level of complexity, but not for high-level objects. These high-level objects could be represented with a combination of simpler features that would already be shift- and scale-invariant. As a result, there would be less need for spatially specific representations for high-level objects.
The last main simplification we have made is to ignore both feedback loops and top-down influences. While normal, everyday vision extensively uses feedback loops, the temporal constraints almost certainly rule them out in an ultrarapid categorization task [41]. The same cannot be said about the top-down signals, which do not depend directly on inputs. For example, there is experimental evidence that the selectivity to the “relevant” features for a given recognition task can be enhanced in IT [42] and in V4 [43], possibly thanks to a top-down signal coming from the prefrontal cortex, thought to be involved in the categorization process. These effects, for example, modeled by Szabo et al. [44], are not taken into account here.
Despite these four simplifications, we think our model captures two key mechanisms used by the visual system for rapid object recognition. The first one is the importance of the first spikes for rapidly encoding the most important information about a visual stimulus. Given the number of stages involved in high-level recognition and the short latencies of selective responses recorded in monkeys' IT [2], the time window available for each neuron to perform its computation is probably about 10–20 ms [45] and will rarely contain more than one or two spikes. The only thing that matters for a neuron is whether an afferent fires early enough so that the presynaptic spike falls in the critical time window, while later spikes cannot be used for ultrarapid categorization. At this point (but only at this point), we have to consider two hypotheses: either presynaptic spike times are completely stochastic (for example, drawn from a Poisson distribution), or they are somewhat reliable. The first hypothesis causes problems since the first presynaptic spikes (again the only ones taken into account) will correspond to a subset of the afferents that is essentially random, and will not contain much information about their real activities [46]. A solution to this problem is to use populations of redundant neurons (with similar selectivity) to ensure the first presynaptic spikes do correspond on average to the most active populations of afferents. In this work we took the second hypothesis, assuming the time to first spike of the afferents (or, to be precise, their firing order) was reliable and did reflect a level of activity. This second hypothesis receives experimental support. For example, recent recordings in monkeys show that IT neurons' responses in terms of spike count close to stimulus onset (100–150 ms time bin) seem to be too reliable to be fit by a typical Poisson firing rate model [47]. Another recent electrophysiological study in monkeys showed that IT cell's latencies do contain information about the nature of a visual stimulus [48]. There is also experimental evidence for precise spike time responses in V1 and in many other neuronal systems (see [49] for a review).
Very interestingly, STDP provides an efficient way to develop selectivity to first spike patterns, as shown in this work. After convergence, the potential reached by an STDP neuron is linked to the number of early spikes in common between the current input and a stored prototype. This “early spike” versus “later spike” neural code (while the spike order within each bin does not matter) has not only been proven robust enough to perform object recognition in natural images but is fast to read out: an accurate response can be produced when only the earliest afferents have fired. The use of such a mechanism at each stage of the ventral stream could account for the phenomenal processing speed achieved by the visual system.
Here is a detailed description of the network, the STDP model, and the classification methods.
S1 cells detect edges by performing a convolution on the input images. We are using 5 × 5 convolution kernels, which roughly correspond to Gabor filters with wavelength of 5 (i.e., the kernel contains one period), effective width 2, and four preferred orientations: π/8, π/4 + π/8, π/2 + π/8, and 3π/4 + π/8 (π/8 is there to avoid focusing on horizontal and vertical edges, which are seldom diagnostic). We apply those filters to five scaled versions of the original image: 100%, 71%, 50%, 35%, and 25%. There are thus 4 × 5 = 20 S1 maps. S1 cells emit spikes with a latency that is inversely proportional to the absolute value of the convolution (the response is thus invariant to an image negative operation). We also limit activity at this stage: at a given processing scale and location, only the spike corresponding to the best matching orientation is propagated.
C1 cells propagate the first spike emitted by S1 cells in a 7 × 7 square of a given S1 map (which corresponds to one preferred orientation and one processing scale). Two adjacent C1 cells in a C1 map correspond to two 7 × 7 squares of S1 cells shifted by six S1 cells (and thus overlap of one S1 row). C1 maps thus subsample S1 maps. To be precise, neglecting the side effects, there are 6 × 6 = 36 times fewer C1 cells than S1 cells. As proposed by Riesenhuber and Poggio [7], this maximum operation is a biologically plausible way to gain local shift invariance. From an image processing point of view, it is a way to perform subsampling within retinotopic maps without flattening high spatial frequency peaks (as would be the case with local averaging).
We also use a local lateral inhibition mechanism at this stage: when a C1 cell emits a spike, it increases the latency of its neighbors within an 11 × 11 square in the map with the same preferred orientation and the same scale. The percentage of latency increase decreases linearly with the distance from the spike, from 15% to 5%. As a result, if a region is clearly dominated by one orientation, cells will inhibit each other and the spike train will be globally late and thus unlikely to be “selected” by STDP.
S2 cells correspond to intermediate-complexity visual features. Here we used ten prototype S2 cell types, and 20 in the mixed simulation. Each prototype cell is duplicated in five maps (weight sharing), each map corresponding to one processing scale. Within those maps, the S2 cells can integrate spikes only from the four C1 maps of the corresponding processing scale. The receptive field size is 16 × 16 C1 cells (neglecting the side effects; this leads to 96 × 96 S1 cells, and the corresponding receptive field size in the original image is [96 / processing scale]2). C1–S2 synaptic connections are set by STDP.
Note that we did not use a leakage term. In the brain, by progressively resetting membrane potentials toward their resting levels, leakiness will decrease the interference between two successive spike waves. In our model we process spike waves one by one and reset all the potentials before each propagation, and so leaks are not needed.
Finally, activity is limited at this stage: a k-winner-take-all strategy ensures at most two cells that can fire for each processing scale. This mechanism, only used in the learning phase, helps the cells to learn patterns with different real sizes. Without it, there is a natural bias toward “small” patterns (i.e., large scales), simply because corresponding maps are larger, and so likeliness of firing with random weights at the beginning of the STDP process is higher.
Those cells take for each prototype the maximum response (i.e., first spike) of corresponding S2 cells over all positions and processing scales, leading to ten shift- and scale-invariant cells (20 in the mixed case).
We used a simplified STDP rule:
where i and j refer, respectively, to the post- and presynaptic neurons, ti and tj are the corresponding spike times, Δwij is the synaptic weight modification, and a+ and a− are two parameters specifying the amount of change. Note that the weight change does not depend on the exact ti − tj value, but only on its sign. We also used an infinite time window. These simplifications are equivalent to assuming that the intensity–latency conversion of S1 cells compresses the whole spike wave in a relatively short time interval (say, 20–30 ms), so that all presynaptic spikes necessarily fall close to the postsynaptic spike time, and the change decrease becomes negligible. In the brain, this change decrease and the limited time window are crucial: they prevent different spike waves coming from different stimuli from interfering in the learning process. In our model, we propagate stimuli one by one, so these mechanisms are not needed. Note that with this simplified STDP rule only the order of the spikes matters, not their precise timings. As a result, the intensity–latency conversion function of S1 cells has no impact, and any monotonously decreasing function gives the same results.
The multiplicative term wij · (1 − wij) ensures the weight remains in the range [0,1] (excitatory synapses) and implements a soft bound effect: when the weight approaches a bound, weight changes tend toward zero.
We also applied long-term depression to synapses through which no presynaptic spike arrived, exactly as if a presynaptic spike had arrived after the postsynaptic one. This is useful to eliminate the noise due to original random weights on synapses through which presynaptic spikes never arrive.
As the STDP learning progresses, we increase a+ and
To be precise, we start with a+ = 2−6 and multiply the value by 2 every 400 postsynaptic spikes, until a maximum value of 2−2. a− is adjusted so as to keep a fixed a+/a− ratio (−4/3). This allows us to accelerate convergence when the preferred stimulus is somewhat “locked,” whereas directly using high learning rates with the random initial weights leads to erratic results.
We used a threshold of 64 (= 1/4 × 16 × 16). Initial weights are randomly generated, with mean 0.8 and standard deviation 0.05.
We used an RBF network. In the brain, this classification step may be done in the PFC using the outputs of IT. Let X be the vector of C2 responses (containing either binary detections with the first implementation or final potentials with the second one). This kind of classifier computes an expression of the form:
and then classifies based on whether or not f(X) reaches a threshold. Supervised learning at this stage involves adjusting the synaptic weights c so as to minimize a (regularized) error on the training set [27]. The Xi correspond to C2 responses for some training examples (1/4 of the training set randomly selected). The full training set was used to learn the ci. We used σ = 2 and λ = 10−12 (regularization parameter).
The multiclass case was handled with a “one-versus-all approach.” If n is the number of classes (here, three), n RBF classifiers of the kind “class I” versus “all other classes” are trained. At the time of testing, each one of the n classifiers emits a (real-valued) prediction that is linked to the probability of the image belonging to its category. The assigned category is the one that corresponds to the highest prediction value.
The spike trains coming from C1 cells were converted into real-valued activities (supposed to correspond to firing rates) by taking the inverse of the first spikes' latencies (note that these activities do not correspond exactly to the convolution values because of the local lateral inhibition mechanism of layer C1). The activities (or firing rates) of S2 units were computed as:
where WS2 is the synaptic weight vector of the S2 cell. Note that the normalization causes an S2 cell to respond maximally when the input vector XC1 is collinear to its weight vector WS2 (neural circuits for such normalization have been proposed in [27]). Hence WS2 (or any vector collinear to it) is the preferred stimulus of the S2 cell. With another stimulus XC1 the response is proportional to the cosine between WS2 and XC1. This kind of tuning has been used in extensions of HMAX [26]. It is similar to the Gaussian tuning of the original HMAX [7], but it is invariant to the norm of the input (i.e., multiplying the input activities by 2 has no effect on the response), which allows us to remain contrast-invariant (see also [26] for a comparison between the two kinds of tuning).
Only the cells whose activities were above a threshold were considered in the competition process. It was found useful to use individual adaptative thresholds: each time a cell was among the winners, its threshold was set to 0.91 times its activity (this value was tuned to get approximately the same number of weight updates as with STDP). The competition mechanism was exactly the same as before, except that it selected the most active units and not the first one to fire. The winners' weight vectors were updated with the following modified hebbian rule:
a is the learning rate. It was found useful to start with a small learning rate (0.002) and to geometrically increase it every ten iterations. The geometric ratio was set to reach a learning rate of 0.02 after 2,000 iterations, after which the learning rate stayed constant.
Here we summarize the differences between our model and their model [6] in terms of architecture (leaving the questions of learning and temporal code aside).
We process various scaled versions of the input image (with the same filter size), instead of using various filter sizes on the original image: S1 level, only the best matching orientation is propagated; C1 level, we use lateral inhibition (see above); S2 level, the similarity between a current input and the stored prototype is linked to the number of early spikes in common between the corresponding spike trains, while Serre et al. use the Euclidian distance between the corresponding patches of C1 activities.
We used an RBF network and not a Support Vector Machine. |
10.1371/journal.ppat.0040030 | A Role for NKG2D in NK Cell–Mediated Resistance to Poxvirus Disease | Ectromelia virus (ECTV) is an orthopoxvirus (OPV) that causes mousepox, the murine equivalent of human smallpox. C57BL/6 (B6) mice are naturally resistant to mousepox due to the concerted action of innate and adaptive immune responses. Previous studies have shown that natural killer (NK) cells are a component of innate immunity that is essential for the B6 mice resistance to mousepox. However, the mechanism of NK cell–mediated resistance to OPV disease remains undefined. Here we show that B6 mice resistance to mousepox requires the direct cytolytic function of NK cells, as well as their ability to boost the T cell response. Furthermore, we show that the activating receptor NKG2D is required for optimal NK cell–mediated resistance to disease and lethality. Together, our results have important implication towards the understanding of natural resistance to pathogenic viral infections.
| Ectromelia virus (ECTV) causes mousepox, a murine disease that is the equivalent of human smallpox. ECTV normally penetrates through the periphery but rapidly spreads through the lymphatic system to vital organs. In mousepox-sensitive strains of mice, ECTV infection culminates with either rapid death or overt symptoms of mousepox due to very high loads that the virus reaches in vital organs, particularly the liver. However, some strains of mice such as C57BL/6 (B6) and 129 also become infected with ECTV but naturally resist mousepox by controlling the virus loads in vital organs and clearing the virus without clinical symptoms of disease. Natural killer (NK) cells are cells of the innate immune system previously shown to play an important role in natural resistance to mousepox. However, how NK cells protect from this disease is still unknown. In this paper we show that NK cells directly contribute to antiviral defenses by curbing virus dissemination to vital organs and also indirectly by augmenting the antiviral T cell response. We also demonstrate that optimal protection requires the activating NK cell receptor NKG2D which facilitates killing of ECTV-infected cells. Our work has important implications for the understanding of natural resistance to viral disease.
| Ectromelia virus (ECTV), the causative agent of mousepox, is an orthopoxvirus (OPV) with host specificity for the mouse. It is genetically very similar to vaccinia virus (VACV), the human pathogen variola virus (the agent of smallpox), and monkeypox virus [1], which sporadically infects people in Africa and produced a recent outbreak in the midwestern United States [2,3]. Like smallpox, mousepox is a severe disease with high mortality and infectivity. Thus, mousepox constitutes an excellent model to study smallpox and exanthematous diseases in general [4].
Although all mouse strains can be infected with ECTV, the outcome of the infection following footpad inoculation varies. Some sensitive strains, such as DBA/2, A/J, and BALB/c, develop mousepox and suffer high mortality during the first 2 wk post-infection, whereas other strains, such as C57BL/6 (B6), clear the infection without visible symptoms of systemic disease [5]. The resistance of B6 mice to mousepox is not due to an inherent decreased ability of the virus to replicate in this strain, but is a result of the combined action of the innate and adaptive immune systems [6–8].
Natural killer (NK) cells are innate effector cells serving as a first line of defense against certain viral infections and tumors [9,10]. For example, NK cell–deficient individuals become sick or succumb to normally non-life-threatening infections with the herpes virus human cytomegalovirus (HCMV) or varicella zoster [11–13]. In addition, C57BL/6 mice, which are normally resistant to mouse cytomegalovirus (MCMV), become susceptible when NK cells are depleted [14]. Similarly, NK cells have been shown to be crucial for the resistance of B6 mice to mousepox [15–17].
The activation of NK cells is regulated by a balance of signals transduced by activating and inhibitory receptors [18,19]. It is thought that continuous signaling through inhibitory receptors maintain NK cells in a resting state, and the loss of inhibitory signals (i.e., due to downregulation of MHC class I on target cells) or the expression of ligands for activating receptors on target cells results in NK cell activation [19]. Previous studies have shown that Rmp1, a dominant gene important for the ability of B6 mice to resist mousepox, maps to the “NK complex,” a region of chromosome 6 that encodes a large number of NK cell–activating and inhibitory receptors [16,18]. Well-defined activating receptors encoded by polymorphic genes within the NK complex in B6 mice include NKR-P1C (NK1.1, the prototypic marker defining NK cells in B6 mice), the Ly49 family members Ly49H and Ly49D, NKG2D, CD94-NKG2C, and CD94-NKG2E. Cmv1, a gene responsible for the resistance of B6 mice to MCMV infection and also mapped to the NK complex, has been shown to be the activating receptor Ly49H that binds to the MCMV-encoded MHC-like protein m157 expressed on the surface of infected cells [20–23]. On the other hand, the positive identification of any activating NK cell receptor involved in resistance to mousepox is still lacking, even though it has been speculated that Rmp1 might be the prototypic activating receptor NK1.1 [24].
In this study, we show that NK cells directly contribute to antiviral defenses by curbing virus dissemination to central organs and also indirectly by augmenting the antiviral T cell response. We also demonstrate that the activating receptor NKG2D is involved in the NK cell–mediated resistance to mousepox, whereas NK1.1 is not.
B6 mice are normally resistant to mousepox ([5] and Table 1). To determine whether and when NK cells are required for resistance to mousepox, we depleted NK cells in B6 mice at different times following ECTV infection with either anti-asialo GM1 antiserum or anti-NK1.1 mAb PK136. Anti-asialo GM1 antiserum depletes NK cells and some activated T cells but not NKT cells, whereas PK136 depletes NK cells and NKT cells but not T cells. Thus, the use of both Abs allowed us to better distinguish the role of NK cells from that of other lymphocyte populations. As shown in Table 1, depletion of NK cells with either Ab on or before day 4 post-infection (PI) with 3,000 pfu ECTV resulted in very high mortality, while mock-depleted mice (normal rabbit sera for anti-asialo GM1 and mouse IgG2a for PK136) were still resistant (data not shown). On the other hand, resistance to mousepox was maintained when mice were depleted of NK cells on day 6 PI, indicating an important role for the physical presence of NK cells at the early, but not later, stages of infection. In additional experiments, we found that five out of five and two out of five PK136-depleted B6 mice succumbed when infected with 100 and 1 pfu ECTV, respectively. Identical results were obtained for BALB/c mice infected with the same virus stock. This shows that the susceptibility of B6 mice depleted of NK cells on the day of infection is similar to that of genetically susceptible BALB/c mice. In agreement with the increased mortality, NK cell depletion resulted in severe necrosis and splenic lymphopenia as compared to non-depleted (intact) B6 mice (Figure 1A). Furthermore, 7 d PI, NK cell–depleted mice had more than 103-fold higher viral titers in both the spleen and liver than intact mice (Figure 1B), indicating a more severe systemic infection in the absence of NK cells. Thus, NK cells are required in the early phase of infection to control viral loads in central organs and to resist lethal mousepox.
We next evaluated the NK cell response at different times PI by flow cytometry. NK cell production of IFN-γ and granzyme B (GzB) in the draining lymph nodes (D-LN) was already induced 24 h PI, reaching the peak at 48 h PI when ∼20% of the NK cells produced IFN-γ and 43% produced GzB (Figure 1C and 1D), a time when T cell responses were not yet detected [25]. This was accompanied by a 2- to 4-fold increase in the proportion of NK cells in the D-LN (Figure 1D). NK cell responses in non-draining LN and spleen, however, did not peak until day 5 PI (Figure 1C). In addition, histopathological analysis of the infected footpads on day 2 PI did not show leukocyte infiltration or other signs of inflammation in either intact or NK cell–depleted B6 mice (data not shown). Thus, at very early stages of infection the NK cell response is restricted to the D-LN.
To distinguish whether the increase in NK cell number in D-LN resulted from recruitment and/or proliferation, we inoculated mice at different stages of infection with BrdU IP and sacrificed the mice 3 h later to determine BrdU incorporation by flow cytometric analysis. Very few NK cells incorporated BrdU in the D-LN during the first 3 d PI, suggesting increased migration to the D-LN at early times PI. In fact, on day 3 and 5 PI, the BrdU incorporation in NK cells in spleen and liver was higher than in the D-LN (Figure 1E). Thus, the increase of NK cells in the D-LN on day 2 PI is mostly due to recruitment rather than proliferation.
A recent report by Hayakawa and Smyth revealed that peripheral NK1.1+ NK cells can be divided into three subsets according to their expression of CD11b and CD27, which they designated R1 (CD27+, CD11b−), R2 (CD27+, CD11b+), and R3 (CD27−, CD11b+), that represent NK cells at distinct developmental stages [26,27]. R1 cells are immature, whereas R2 and R3 fractionate the mature cells into two populations. While adoptively transferred R2 cells can differentiate into R3 cells, R3 cells appear to be terminally differentiated. We, therefore, investigated whether ECTV infection altered the maturation phenotype of NK cells in the D-LN and found a substantial increase in NK cells with a mature (R3) phenotype on day 2 PI (Figure 1F, upper panels). Of interest, the majority of effectors (IFN-γ+ and/or GzB+) were within this mature NK cell population (Figure 1F, lower panels). Together, these data demonstrate that the increased NK cell number in the D-LN at early time points PI is mainly a result of recruitment. Moreover, the data show that the NK cells responding to ECTV infection are mature or mature very rapidly after infection.
We hypothesized that the observed early recruitment to and activation of NK cells in the D-LN may contribute to the prevention of virus dissemination to central organs. Thus, we depleted NK cells in B6 mice with anti-NK1.1 mAb (PK136) and determined viral titers in spleen and liver on day 3 PI. To rule out a role for T cells at this stage we also depleted both CD4+ and CD8+ T cells by using a combination of anti-CD4 (GK1.5) and anti-CD8 (2.43) mAbs. Depletion of NK cells resulted in >103-fold increase in viral loads in the spleen, while a much lower increase in virus titers was observed in mice depleted of T cells. The slight increase in virus titers in the T cell–depleted mice was not statistically significant (p = 0.09) and was not reproducible. We also observed a significant increase in virus titers on day 3 PI in the livers of NK cell–depleted mice, but not T cell–depleted mice (p = 0.07) (Figure 1G). Therefore, NK cells, and not T cells, are responsible for limiting the early dissemination of ECTV from the site of infection to central organs.
NK cells can control viral infections by producing antiviral cytokines, such as IFN-γ, or by perforin (Pf)-mediated killing of infected cells [28]. Others have shown that B6 mice deficient in either Pf or IFN-γ are susceptible to mousepox [29,30]. However, whether the early control of virus dissemination (day 3 PI) by NK cells requires IFN-γ and/or Pf-mediated killing is unknown. We, therefore, compared virus titers on day 3 PI in the spleens of wild-type (WT), IFN-γ-deficient, and Pf-deficient B6 mice. We found that both IFN-γ-(Figure 1H) and Pf-deficient mice (Figure 1I) had significantly elevated virus titers as compared to B6 mice. Thus, the early control of virus dissemination by NK cells requires both the antiviral effects of IFN-γ and Pf-mediated cytotoxicity.
Our previous studies and those of others showed that an optimal CD8+ T cell response is required for resistance to mousepox [8,25,31]. The data above indicate that NK cells have a direct effect in preventing mousepox by curbing virus dissemination during early infection. However, NK depletion at early stages resulted in an increase in spleen and liver viral titers, splenic lymphopenia, liver pathology, and death on day 7 PI (Figure 1 and Table 1), a time when the presence of NK cells was no longer necessary (Table 1) and when the T cell response should have already peaked [25]. This suggested that the absence of NK cells may have an impact on the establishment of the adaptive anti-ECTV T cell response. Thus, we compared in vivo T cell proliferation in intact mice and mice depleted of NK cells on day 0 PI. We found that on day 5 PI (when the T cell response normally peaks in D-LN), only a few CD8+ and CD4+ T cells incorporated BrdU in the D-LN of NK cell–depleted mice, while ∼40% of CD8+ T cells and 20% of CD4+ T cells incorporated BrdU in intact mice (Figure 2A). Mice depleted of NK cells on day 0 PI also had substantially reduced T cell proliferation in spleen and liver on day 7 PI (the time when the T cell response normally peaks in the spleen, data not shown). We also determined the CD8+ T cell response by intracellular staining for the effector molecules, GzB and IFN-γ. On 7 d PI, there was a strong T cell response in spleen with ∼70% of CD8+ T cells producing GzB and more than 10% of CD8+ T cells producing IFN-γ in intact B6 mice (Figure 2B). However, in mice depleted of NK cells on day 0 PI, the CD8+ T cell response was greatly reduced, with only ∼30% of CD8+ T cells producing GzB and ∼5% CD8+ T cells producing IFN-γ. Furthermore, because infected NK cell–depleted mice had severely lymphopenic spleens (Figure 1A), they also had a 16-fold reduction in the absolute number of effector CD8+ T cells as compared with infected intact mice (data not shown). These results indicate that the adaptive T cell response was substantially reduced in the absence of NK cells.
To gain insight into the mechanism whereby NK cells become activated during ECTV infection, we explored whether various known NK cell–activating receptors are involved in resistance to mousepox. We first focused on NK1.1 and on the activating receptors of the Ly49 family. We focused on these receptors because NK1.1 is not encoded in mousepox-susceptible DBA/2 and BALB/c mice. Furthermore, it has been speculated that NK1.1 might be Rmp1 [24]. In addition, there is good precedent for the involvement of Ly49H in NK cell–mediated antiviral responses, more specifically to MCMV [20–22]. Ly49D and Ly49H are the only activating receptors of the Ly49 family in B6 mice. Because B6 mice selectively deficient in NK1.1, Ly49H, or Ly49D are not available, we first used 129/Sve mice, which do not express any of these receptors [22]. Similar to B6 mice, 129/Sve mice were naturally resistant to mousepox but became sensitive when depleted of NK cells with anti-asialo GM1 Ab (PK136 could not be used because 129/Sve mice do not express NK1.1) (Table 2). This demonstrates that NK cells are also required for 129/Sve mice resistance to mousepox but that NK1.1, Ly49D, or Ly49H are not essential for resistance because 129/Sve do not possess these receptors.
The results above suggested, but did not formally prove that Ly49H, Ly49D, and/or NK1.1 are not required for the resistance of B6 mice to mousepox, because it remained possible that Ly49H, Ly49D, and/or NK1.1 play a role in B6 resistance but that other activating receptors substitute their function in 129/Sve mice. Although it is not possible to definitively rule out the participation Ly49H or Ly49D in B6 resistance with currently available reagents, we took advantage of FcɛRIγ-deficient B6 mice [32] to rule out a role for NK1.1. It is known that NK1.1 signaling requires association with the ITAM-containing FcɛRIγ adapter [33]. FcɛRIγ-deficient B6 mice, however, were completely resistant to mousepox (Table 2), demonstrating that NK1.1 is not essential for the resistance of B6 mice to mousepox. In addition, because Abs are required for long-term resistance to mousepox [8,31] and FcɛRIγ is required for Ab-dependent cellular cytotoxicity (ADCC) [32], these data also demonstrate that ADCC is not involved in Ab-dependent resistance to mousepox.
We addressed whether the activating receptor NKG2D might be involved in mousepox resistance. NKG2D is expressed by NK cells and some T cells, including γδ-TcR+ T cells and activated CD8+ T cells. Although not polymorphic, this activating receptor is expressed at somewhat lower levels on activated NK cells of mousepox-susceptible NOD and BALB/c mice than in B6 mice ([34] and unpublished data). Because NKG2D-deficient mice are not available, we took advantage of the anti-NKG2D mAb CX5 that, in vivo, blocks binding of NKG2D to its ligands and causes its internalization without depleting NKG2D-bearing cells [35–37]. We inoculated B6 mice with CX5 mAb or control rat IgG 1 d before and 2 d PI. As shown in Table 2, CX5 treatment resulted in 50% mortality suggesting that NKG2D is involved in the NK cell–mediated resistance to mousepox.
In mice, NKG2D signals through either DAP10 or DAP12 adapter proteins, whereas Ly49H and D signal through DAP12 [38–40]; therefore, we determined mousepox susceptibility in either DAP12- or DAP10-deficient mice on a B6 mousepox-resistant background. We found that more than 50% of DAP12-deficient mice developed the typical skin rash, but only 10% died following ECTV infection, whereas all DAP10-deficient mice survived without showing ostensible symptoms of mousepox (Table 2).
To get further insights into the role of NKG2D and its adapters in resistance to mousepox, we determined cell numbers and virus titers 7 d PI in NKG2D-blocked as well as in DAP12- and DAP10-deficient mice. Results showed that NKG2D blockade and DAP12- or DAP10-deficiency resulted in significantly decreased splenic lymphocytosis as compared with wild type, untreated B6 mice (Figure 3A). More important, the viral loads in NKG2D-blocked mice were almost 103 times higher than in unblocked mice, whereas those of DAP12- and DAP10-deficient mice were also significantly elevated, but to a lesser degree (102 and 10 times higher, respectively) (Figure 3B). Thus, the degree of splenic lymphocytosis and viral loads on day 7 PI was consistent with the lethality and symptoms of mousepox that we observed under the different conditions. Together, these results show that NKG2D is involved in resistance to mousepox. The data also suggest that for this process NKG2D preferentially, but not exclusively, uses DAP12 as the signaling adapter. Most likely, the absence of one adapter is partially compensated by the presence of the other. The data further suggest that Ly49H and Ly49D are not essential for B6 resistance to mousepox because DAP12-deficient mice were only mildly susceptible to mousepox infection. This contrasts with prior studies demonstrating that DAP12-deficient mice on a B6 MCMV-resistant background are exquisitely sensitive to MCMV infection [41].
NKG2D is expressed by NK cells [42] and its expression is further increased during MCMV infection (J. Sun and L. L. Lanier, unpublished data). We, therefore, determined whether ECTV infection also increased NKG2D expression on NK cells. As shown in Figure 4A, NK cells increased NKG2D expression at the cell surface, and the proliferating NK cells after ECTV infection were NKG2Dhigh.
The data above showed that NK cells are the main population-controlling early virus dissemination to visceral organs, and that absence of NKG2D signaling results in increased susceptibility to mousepox. However, NKG2D is not only expressed by NK cells but also by some T cells. We hypothesized that if NKG2D has a role in NK cell–mediated resistance to mousepox, NKG2D blockade should result in increased early virus dissemination to organs independent of T cells. To test this hypothesis, we measured early viral titers in the spleens (day 3 PI) of mice treated with the anti-NKG2D mAb CX5. To rule out a role of NKG2D on T cells at this time, we also included groups of mice that were T cell–depleted or NKG2D-blocked and T cell–depleted. As shown in Figure 4B, NKG2D-blocked and T cell–depleted + NKG2D-blocked mice had virus titers that were comparable to those of NK cell–depleted mice and that were significantly higher than those of mice that were only T cell–depleted. Together, these data indicate that NKG2D is involved in the NK cell–mediated resistant to mousepox.
We next tested whether NKG2D blockade and DAP10- or DAP12-deficiency affected the recruitment of NK cells to D-LN (Figure 4C, upper panels) or their ability to produce IFN-γ and GzB (Figure 4C, lower panels). However, we found that none of these parameters were decreased. In fact, in both DAP10- and DAP12-deficient mice more NK cells were recruited to the D-LN and a larger proportion produced IFN-γ and GzB as compared with wild-type B6 mice. This indicates that NKG2D is required for effective NK cell–mediated control of ECTV, but is not required for their activation.
Because NK cell recruitment and activation in the D-LN was not diminished by NKG2D blockade or DAP12 and DAP10 deficiency (Figure 4C), we hypothesized that NKG2D may be required for optimal NK cell–mediated cytotoxicity. Thus, we examined whether NK cells from NKG2D-blocked mice were defective in their ability to kill various targets. For this purpose, mice were infected with ECTV and treated or not with anti-NKG2D Ab. Five days later, NK cells were purified from their spleens and immediately analyzed by flow cytometry to confirm NKG2D receptor blockade or downregulation in the CX5-treated mice (Figure 4D) and were used as effectors in 4-h 51Cr release assays. Because we were unable to show specific killing of ECTV-infected cells in vitro, we tested the purified NK cells against a variety of uninfected targets that constitutively express NKG2D ligands at their cell surface (MC57G, MEF, and YAC-1) or, as a control, CHO-K1 whose killing is dependent on Ly49D [43,44], but not NKG2D. We found that the NK cells from mice infected with ECTV (whether treated with anti-NKG2D Ab or not) killed all targets much more effectively than those from uninfected mice. This enhanced killing did not require the targets to be infected. However, the NK cells from mice treated with CX5 were less effective killers of NKG2D ligand-bearing target cells as compared to those from untreated mice, except against CHO-K1 cells for which the NK cell killing is dependent on Ly49D and not on NKG2D (Figure 4E). Furthermore, purified NK cells from ECTV-infected intact mice also demonstrated reduced, but not absent cytotoxicity when anti-NKG2D mAb was added to the assay (Figure 4F). Thus, ECTV infection enhances spontaneous NK cell–mediated cytotoxicity, which is partially reduced by NKG2D blockade. Together, these data suggest that NKG2D signaling is not required for the recruitment and activation of NK cells during ECTV infection, but contributes to their optimal ability to kill targets expressing NKG2D ligands. Because anti-NKG2D did not reduce cytotoxicity to the same levels as those in uninfected mice, the data also indicate that activating receptors other than NKG2D are involved in the cytotoxicity induced by ECTV infection.
NKG2D-mediated killing requires the recognition of ligands on the surface of target cells. The ligands of NKG2D are host cell–encoded MHC class I–like proteins that are expressed by tumors and stressed cells and also following infection of cells with some viruses. Identified cellular ligands for NKG2D in mice include H60, MULT1, and Rae-1 [45–49]. To determine if ECTV infection induces the upregulation of NKG2D ligands, we infected mouse embryo fibroblasts (MEFs) with 0.5 pfu/cell ECTV expressing enhanced green fluorescence protein (EGFP), and the expression of NKG2D ligands on infected and uninfected cells was determined by flow cytometry. Consistent with the ability of ECTV-activated NK cells to spontaneously kill them, MEFs constitutively express NKG2D ligands as revealed by staining with mouse NKG2D-Fc fusion protein [50] (Figure 5A). However, infection with ECTV increased NKG2D-Fc staining and resulted in clear upregulation of MULT1 (Figure 5A) but not Rae-1 (data not shown). We also observed increased staining with NKG2D-Fc and anti-pan Rae-1 and anti-MULT1 mAbs in the fibrosarcoma cell line MC57G, and an increase in staining with NKG2D-Fc and anti-pan Rae-1 mAb in peritoneal lymphocytes (data not shown). Moreover, using quantitative RT-PCR, we detected a 2.8-fold increase of Rae-1 transcripts in the D-LN of ECTV-infected mice at 12 h PI, as compared with uninfected controls (Figure 5C). Thus, these results show that ECTV infection can induce the expression of NKG2D ligands, suggesting that binding of NKG2D with these ligands might result in improved NK cell killing of infected cells in vivo and better control of ECTV dissemination.
In this study, we confirmed previous reports demonstrating that NK cells are required for natural resistance to mousepox [15–17]. More importantly, we define several of the mechanisms whereby NK cells afford this resistance.
To determine whether and when NK cells are required for resistance to mousepox we depleted mice of NK cells with either anti-asialo GM1 or anti-NK1.1 Abs at different times PI. Although separately these two approaches have caveats, together they provide conclusive evidence that the presence of NK cells during the first 4 d PI, but not beyond day 5 PI, is essential for resistance to mousepox. First, even though anti-NK1.1 Ab depletes NKT cells, the loss of mousepox resistance upon Ab treatment cannot be due to the elimination of NKT cells because anti-asialo GM1 Ab does not eliminate this population [51,52]. Furthermore, Parker et al. recently demonstrated that NKT cells are dispensable for resistance to mousepox because ECTV infection of mice deficient in NKT cells (i.e., CD1d- and Vα14Jα281 TCR-deficient mice) did not result in symptoms of mousepox or an increase in virus titers when compared to wild-type B6 mice [17]. Second, although anti-asialo GM1 can eliminate some activated T cells [53], the loss of resistance cannot be attributed to T cell depletion because asialo GM1 is not expressed on virus-specific T cells within the first few days after viral infection. Moreover, we show that anti-NK1.1 and -asialo GM1 Abs do not affect resistance when given on day 6 PI (the peak of the T cell response). Furthermore, treatment with anti-NK1.1, but not depletion of T cells one day before infection, resulted in significantly increased virus titers on day 3 PI. In addition, treatment with anti-asialo GM1 Ab accelerated ECTV lethality in RAG-1-deficient mice, which lack adaptive immunity (data not shown). γδ T cell–deficient mice are also resistant to mousepox [17], indicating that the possible depletion of some γδ T cells by anti-asialo GM1 Ab cannot account for the dramatic increase in ECTV lethality after treatment with this Ab. Although work in other laboratories has already shown that depletion of NK cells with either anti-NK1.1 or asialo-GM1 results in susceptibility to mousepox [6,15,17], depletion at different times PI has not been described previously. Sequential depletion of NK cells allowed us to establish that the presence of NK cells is required only during the very early stages of infection. This is consistent with the concept that NK cells serve as the first line of defense after infection, thereby providing sufficient time to mount a full-fledged T and B cell response. We also established, however, that the contribution of NK cells to mousepox resistance extends beyond the need for their physical presence to kill virus-infected cells early after infection because NK cell depletion before day 4 PI resulted in higher virus titers and death after this time. This is most likely due to the role of NK cells in allowing a potent antiviral T cell response to develop, as discussed below.
We also followed the kinetics of NK proliferation and activation in spleen, liver, and D-LN of ECTV-infected mice. Interestingly, although we detected some proliferation, we did not find activated NK cells in liver and spleen on day 3 PI. This was despite the fact that NK cell–mediated control of virus loads in spleen and liver already occurred at this time PI. In fact, the peak of NK cell activation in spleen and liver took place on day 5 PI. On the other hand, the proportion of total NK cells, as well as the proportion of activated NK cells in the D-LN, peaked as early as day 2 PI, and these parameters were still substantially elevated on day 3 PI, notwithstanding that the proliferation of NK cells at these times was not yet detectable in D-LN. In addition, we found that the increase in NK cell numbers in the D-LN on day 2 PI was mostly due to an increase in mature NK cells and that these mature NK cells were preferentially activated. Together, these data suggest that the prompt NK cell response in the D-LN is responsible for the early control of virus loads in central organs and that this response is mostly due to the recruitment and activation of mature NK cells rather than their expansion. To spread to central organs from the footpad, ECTV must pass through the D-LN [5,54–56]. Recently, we have shown that in mousepox-susceptible BALB/c mice, memory CD8+ T cells protect from mousepox, at least in part, by curbing the spread of ECTV from the D-LN to central organs and allowing for the establishment of a full-fledged adaptive response [54]. Our results here suggest that NK cells may use the same strategy, furthering a model where LNs are not only sites where lymphocytes are primed and proliferate but also the place where a major fight against virus spread takes place [54].
In addition to NK cells, strong adaptive T cell responses are essential for resistance to mousepox. Our experiments here show that NK cells have a direct role in controlling ECTV because they reduced virus loads on day 3 PI when the T cell response is still undetectable (data not shown and [25]). However, if the only role of NK cells were direct, one would expect mice depleted of NK cells to become sick, but recover once the adaptive immune system takes control. Yet, while the presence of NK cells on day 6 PI was not required for resistance to mousepox, their presence before day 6 PI was vital for controlling virus titers and preventing pathology and death after this time. This could be explained by the finding that depletion of NK cells on the day of infection resulted in a substantially reduced T cell response. There are three non-excluding possibilities that may explain this effect. First, NK cell production of cytokines such as IFN-γ may directly modulate the adaptive T cell response [28]. A caveat to this hypothesis is that NK cells produce as much or more IFN-γ in ECTV-infected mousepox-susceptible BALB/c and DBA2/J mice as in resistant B6 mice (unpublished data), but these susceptible mice fail to mount an effective T cell response to ECTV. Second, NK cells may indirectly affect the activation of T cells through their interaction with antigen-presenting cells such as macrophages and dendritic cells (DC). This hypothesis has the same caveats as the one above. Third, it is possible that in the absence of NK cells, the uncontrolled virus replication of a highly pathogenic virus overwhelms the T cell response. In support of this possibility, infection of mousepox-susceptible BALB/c mice with a highly attenuated strain of ECTV results in the induction of a strong T cell response (unpublished data), and depletion of NK cells does not affect the T cell response to the poorly pathogenic vaccinia virus (unpublished data) with which ECTV shares most of the dominant CD8+ T cell epitopes [57].
Previous work by Delano and Brownstein showed that Rmp1, a gene important for resistance to mousepox, maps to the NK complex in distal chromosome 6 [16]. Moreover, they proposed that Rmp1 encoded NK1.1 [24]. However, we found that NK1.1 is not required for resistance to mousepox. In addition, our work suggests that the activating receptors Ly49D and Ly49H are likely not involved. On the other hand, using in vivo signaling blockade we found that NKG2D is involved in resistance to mousepox and that ECTV infection results in the upregulation of NKG2D ligands in vivo and in vitro. That NKG2D is involved in antiviral responses has precedents. For example, previous studies have shown that cytomegalovirus (CMV) induces the upregulation of NKG2D ligands and that mouse and human CMV encode immune evasion molecules that downregulate NKG2D ligands and are important for their pathogenesis [28]. However, a direct role for NKG2D in the response to poxviruses has not been described previously. Interestingly, Campbell et al. have recently shown that cowpox virus encodes a soluble competitive antagonist of NKG2D [58]. Although this gene has been lost in other OPVs, such as ECTV, VACV, VARV, and MPXV, these data support our finding that NKG2D is involved in resistance to some OPV infections. On the other hand, NKp30, NKp44, and NKp46, but not NKG2D, were found to be responsible for the recognition of VACV-infected cells by human NK cells [59]. Whether this reflects differences between NK cell recognition of different OPVs, differences between human versus mouse NK cells, or both, will require further investigation.
Because mouse NKG2D signals through either the adapter protein DAP12 or DAP10, we further tested the susceptibility of mice lacking one or the other adapter. Of interest, when considering virus titers and spleen cellularity, the susceptibility of these two strains of mice was intermediate between intact and NKG2D mAb–blocked B6 mice. This indicates that for resistance to mousepox, the two adapters likely have overlapping, but not completely redundant, effects. Still, DAP12 seems to be the preferred adapter because DAP12-deficient mice were more susceptible to mousepox than DAP10-deficient mice, although it is possible that other DAP12-associated receptors might contribute to resistance to mousepox. Our results predict that DAP10 + DAP12 double-deficient mice would be highly susceptible to mousepox. Unfortunately, these mice are not yet available.
NKG2D is expressed by most NK cells but also by activated CD8+ T cells. In fact, we have observed that in ECTV-infected B6 mice a large proportion of virus-specific CD8+ T cells express NKG2D beginning 7 d PI (unpublished data). Thus, the experiments reported here do not rule out the additional contribution of NKG2D in the T cell–mediated resistance to mousepox, a very interesting possibility that we are currently investigating. Nevertheless, our experiments clearly implicate NKG2D in the NK cell–mediated response to ECTV because in vivo NKG2D blockade resulted in enhanced virus loads in central organs before the onset of the T cell response and because in vivo NKG2D blockade reduced the cytotoxicity of ECTV-activated NK cells. Still, several lines of evidence indicate that NKG2D is not the only activating receptor involved in the anti-ECTV NK cell–mediated response and that its most likely role is as a co-stimulator that facilitates cytotoxicity rather than being required for the initial NK cell activation: (1) NK cell depletion was much more effective than NKG2D blockade at rendering B6 mice susceptible to mousepox; (2) NKG2D-blockade did not affect NK cell proliferation (data not shown), IFN-γ and GzB production by NK cells, or recruitment of NK cells into the D-LN of ECTV-infected mice; (3) In vivo and in vitro NKG2D blockade significantly decreased, but did not abrogate, the cytotoxicity of ECTV-activated NK cells. Ongoing studies in our laboratory are aimed at identifying other activating receptor(s) and signaling pathway(s) required for NK cell–mediated resistance to mousepox.
In summary, our work demonstrates that NK cells contribute to the natural resistance of B6 mice to mousepox by using direct effector functions (most likely the killing of infected cells) to curb virus dissemination and by supporting a strong adaptive T cell response. Moreover, our data suggest that the activating receptor NKG2D, but not NK1.1 or Ly49 family members, has a role in this NK cell–mediated resistance to mousepox by promoting optimal NK cell–mediated killing. Thus, our data provide substantial insights into the mechanisms of natural resistance to ECTV and possibly other OPV infections. Together, our work furthers our understanding of host-pathogen interactions and the mechanisms whereby NK cells protect from viral disease.
YAC-1 and CHO-K1 cell lines were obtained from Dr. Kerry Campbell (Fox Chase Cancer Center, Philadelphia, Pennsylvania), and A9, MC57G, and BSC-1 cells were obtained from the ATCC. MEF cells were made from day 11 to 13 embryos from B6 mice. As standard tissue culture media, we used RPMI-10 that consisted of RPMI-1640 medium (Invitrogen) supplemented with 10% fetal calf serum (Sigma), 100 IU/ml penicillin and 100 μg/ml streptomycin (Invitrogen), 10 mM Hepes buffer (Invitrogen), and 0.05 mM 2-mercaptoethanol (Sigma). MEF were grown in DMEM medium containing 15% fetal calf serum. When indicated, RPMI 2.5 (as above but with 2.5% FCS) was used. When required, 10 U/ml interleukin 2 (IL2) was added to RPMI 10 (RPMI 10-IL2). All cells were grown at 37 °C and 5% CO2.
The production of ECTV stocks for infection of mice and the determination of titers in stocks and organs were done as described previously [8]. To generate ECTV 189898-p7.5-EGFP, we adapted the method described by Johnston and McFadden [60]. Briefly, a construct containing the ECTV Moscow fragment 189543–189897, the VACV early/late promoter p7.5, the sequence of EGFP, and the ECTV Moscow fragment 189950–190297, in that order, was made by recombinant PCR and cloned into plasmid Bluescript II SK+ to generate the targeting vector pBS-EVM189898-p7.5-EGFP. This targeting vector was used to transfect mouse A9 cells using Lipofectamine 2000 as per manufacturer's instructions (Invitrogen). The transfected cells were infected with wild-type ECTV (Moscow strain, 0.3 pfu/cell) in 6-well plates. 2 d later, transfected/infected A9 cells were harvested using a rubber policeman, frozen and thawed, and different dilutions of cell lysates were used to infect BSC-1 cells in 6-well plates. 2 h after infection, the cells were overlaid with media containing 0.5% agarose. 4 d later, green-fluorescent plaques were picked with a pipette tip and used to infect a new set of cells. The purification procedure was iterated five times until all plaques were fluorescent. The resulting virus, ECTV 189898-p7.5-EGFP, carries EGFP in a non-coding region and is as pathogenic as wild-type ECTV Moscow (not shown). For preparation of ECTV stock for infection of different cell lines, A9 cells were infected with 0.2 pfu ECTV/cell, and incubated at 37 °C, 5% CO2. After 5 d, the cells were collected, frozen and thawed three times, and then sonicated in a water-bath sonicator. The solid material was pelleted by centrifugation, and the supernatant was stored in aliquots at −80 °C. The DAP10-deficient mice [61,62] (generously provided by Dr. Joe Phillips) and DAP12-deficient mice [63] on the C57BL/6 background were bred at UCSF. All the other mice were bred at the Fox Chase Cancer Center Laboratory Animal Facility in specific pathogen-free rooms or were purchased from Jackson Laboratories. IFN-γ-deficient C57BL/6 mice were generously provided by Dr. Glenn Rall. For infections, sex-matched animals 8–12 wk old were transferred to a biosafety level 3 room. For ECTV infection, anesthetized mice were infected in the left footpad with 25 μl PBS containing 3 × 103 pfu ECTV. Following infections, mice were observed daily for signs of disease (lethargy, ruffled hair, weight loss, skin rash, and eye secretions) and imminent death (unresponsiveness to touch and lack of voluntary movements). Moribund mice were euthanized by halothane inhalation. All of the experimental protocols involving animals were approved by the Fox Chase Cancer Center Institutional Animal Care and Use Committee.
For ECTV infection of cells, 3–5 × 105 cells were plated in 6-well plates and cultured overnight to allow cells to adhere. The cells were then infected with 0.5 pfu ECTV/cell for 18 h, collected, washed, stained, and analyzed for surface expression of various markers. For ECTV infection of peritoneal cells, the mice were euthanized by halothane inhalation and injected i.p. with PBS, the abdomen massaged gently, and the peritoneal cells were collected by aspiration and washed.
Depletion of NK cells was performed by i.p. inoculation of 200 μg anti-NK1.1 mAb PK136 or 20 μl anti-asialo GM1 antisera (Wako), as indicated. Antibody treatment was done 1 d before or on the indicated days after virus infection. For depletion of T cells, mice were injected i.p. with 200 μg anti-CD4 mAb GK1.5 and 200 μg anti-CD8 mAb 2.43 1 d before infection. For NKG2D blockade mice were inoculated with purified 200 μg CX5 Ab 1 d before and 2 d PI.
Mice were exsanguinated from the orbital cavity to decrease the amount of blood in the liver. The liver was removed and passed through a cell strainer (BD Falcon) to obtain a single cell suspension. The cells were resuspended in 35% Percoll solution (in PBS) containing 100 U/ml heparin and centrifuged at 830 × g for 15 min at room temperature. The upper liquid phase was removed from the tube, the lymphocyte pellet resuspended in 0.84% NH4Cl solution to lyse the red blood cells, and then washed twice with medium.
At the indicated time post-infection (PI), mice were injected with 2 mg BrdU i.p. 3 h later, spleens and lymph nodes (LNs) were removed and made into single cell suspensions. The liver lymphocytes were obtained as described above. The cells were then stained for cell surface molecules, fixed, and permeabilized using the Cytofix/Cytoperm kit (BD Pharmingen) according to the manufacturer's instructions, incubated with DNase at 37 °C for 1 h, and subsequently stained with FITC-conjugated anti-BrdU mAb (eBiosciences).
Determination of cytokine production by intracellular staining was done as described previously [8]. To determinate NK cell responses in LNs, intact organs were incubated at 37 °C for 1 h in media containing 10 μg/ml brefeldin A, made into single cell suspensions, stained, and analyzed as described above. To evaluate NK cell responses in the spleen, the organs were made into single-cell suspensions, RBC were lysed with 0.84% NH4Cl, and the lymphocytes were washed and incubated at 37 °C for 1 h with brefeldin A, followed by staining and analysis as described. To determine expression of NKG2D ligands, MEFs were infected with 0.5 pfu ECTV 189898-p7.5-EGFP for 18 h. Infected cells (∼15%) were identified by EGFP expression. Gated infected and uninfected cells were analyzed for expression of NKG2D ligands by staining with: a PE-labeled rat anti-mouse Rae-1 (IgG2a isotype, R&D Systems) or a PE-labeled rat isotype-matched control IgG2a; with rat anti-mouse MULT1 (IgG2a isotype, R&D Systems) and secondary PE-labeled donkey anti-rat IgG2a or secondary Ab alone as a control; or with mouse NKG2D-human Fc fusion protein (R&D Systems) and an APC-labeled anti-human IgG (BD) secondary Ab or secondary Ab alone as control.
NK cells were purified from spleens using anti-CD49b-conjugated microbeads and a LS column (Miltenyi Biotec) according to the manufacturer's instructions and were stained for flow cytometric analysis with PE-conjugated anti-NKG2D mAb CX5. NK cells were resuspended in RPMI 10, and serially diluted in round-bottom 96-well plates in triplicate in 100 μl/well. The indicated target cells were prepared by incubation with 200 μl 51Cr (0.1 mCi) in 100 μl of FCS for 2 h. Cells were thoroughly washed, resuspended in RPMI 10, and 50 μl (5×103 targets) were added to the wells containing effector cells. The plates were incubated at 37 °C for 4 h. Where indicated, 20 μg/ml of a neutralizing anti-NKG2D mAb (clone 191004, R&D Systems) was added at the initiation of cytotoxicity assays. 50 μl supernatants were transferred to white 96-well plates containing 75-μl Microscint-40 scintillation fluid (PerkinElmer). Controls included wells with target cells alone for spontaneous release and wells with target cells and 1% Triton-X for maximal release. Radioactivity was measured by using a Packard Topcount instrument (PerkinElmer). Specific lysis was determined by using the formula [(experimental release − spontaneous release)/(full release − spontaneous release)]×100. Three animals were used per experimental group.
Primers and probes for Rae-1 were purchased from Applied Biosystems. The amplicon was 95 bp and the sequence of the probe was GGAAAAGCCAAGATCAACCTCAAGG. The primers and probe for β-actin were synthesized at the DNA Synthesis Facility in the Fox Chase Cancer Center. The primers and probe used for β-actin were: sense, 5′-CACCGAGGCCCCCCT-3′; anti-sense, 5′-CAGCCTGGATGGCTACGTACA-3′, and the probe was 5′-6-FAM-AACCCTAAGGCCAACCGTGAAAAGATGA-BHQ1–3′. Total RNA extracted from infected cell lines and LNs of infected mice was treated with Dnase I, and the first-strand cDNA was synthesized by using random primers. qRT-PCR was carried out by using the ABI 7500 (Applied Biosystems). The cycling conditions for real-time PCR were: 50 °C for 10 min, followed by 45 cycles of 95 °C for 30 s, and 60 °C for 2 min. Data were analyzed by using the Sequence Detection v1.2 Analysis Software (Applied Biosystems).
We used a two-tailed t test for two samples for means with a confidence level (alpha) of 0.05 using Excel Analysis Tool Pack (Microsoft). Differences in survival and disease were determined at the FCCC Biostatistics and Bioinformatics Facility using Log Rank Test with the STATE SE/9.2 software. In all cases, differences were considered significant when p-values were ≤ 0.05. |
10.1371/journal.ppat.1006686 | TRIM5α SPRY/coiled-coil interactions optimize avid retroviral capsid recognition | Restriction factors are important components of intrinsic cellular defense mechanisms against viral pathogens. TRIM5α is a restriction factor that intercepts the incoming capsid cores of retroviruses such as HIV and provides an effective species-specific barrier to retroviral infection. The TRIM5α SPRY domain directly binds the capsid with only very weak, millimolar-level affinity, and productive capsid recognition therefore requires both TRIM5α dimerization and assembly of the dimers into a multivalent hexagonal lattice to promote avid binding. Here, we explore the important unresolved question of whether the SPRY domains are flexibly linked to the TRIM lattice or more precisely positioned to maximize avidity. Biochemical and biophysical experiments indicate that the linker segment connecting the SPRY domain to the coiled-coil domain adopts an α-helical fold, and that this helical portion mediates interactions between the two domains. Targeted mutations were generated to disrupt the putative packing interface without affecting dimerization or higher-order assembly, and we identified mutant proteins that were nevertheless deficient in capsid binding in vitro and restriction activity in cells. Our studies therefore support a model wherein substantial avidity gains during assembly-mediated capsid recognition by TRIM5α come in part from tailored spacing of tethered recognition domains.
| TRIM5α is a cytosolic protein that provides effective protection for mammalian cells against retroviral infection. This anti-viral defense mechanism is an unprecedented example of how the cell can recognize entire capsids, which are large, megadalton-sized particles. TRIM5α achieves this by assembling into a hexagonal scaffold that coats the capsid. An important unresolved question is how the capsid-binding SPRY domain of TRIM5α is positioned to optimize its contact points on the capsid surface. Here, we use a variety of techniques to determine that the SPRY domains in the TRIM lattice are organized in pairs and likely to be stably tethered against the hexagonal scaffold. Such an arrangement maximizes the avidity of capsid binding, and allows TRIM5α to act as a “molecular ruler” that matches the spacings and orientations of the capsid subunits.
| TRIM5α is a restriction factor that recognizes and binds the incoming cores of retroviruses such as HIV [1–3], and represents a first-line intracellular antiviral defense mechanism. Upon core binding, TRIM5α induces accelerated capsid dissociation or uncoating, inhibits reverse transcription, and activates innate immune signaling pathways [1, 3, 4]. Like other members of the TRIM family [5], TRIM5α contains a tripartite or RBCC motif at its N-terminus (RING, B-box 2, and coiled-coil domains)–the RING domain mediates E3 ubiquitin ligase effector functions required to inhibit reverse transcription and signal interferon [4, 6, 7], whereas the coiled-coil and B-box 2 domains respectively mediate TRIM5α dimerization and higher-order assembly [8–16]. The TRIM5α RBCC motif is connected by a long linker (L2 or linker 2) to a C-terminal SPRY domain that directly contacts retroviral capsids [1–3, 17].
Retroviral capsids are higher-order macromolecular assemblages composed of about 1,500 viral CA protein subunits, which assemble on a hexagonal lattice of several hundred hexamers and 12 pentamers [18, 19]. Accordingly, TRIM5α also undergoes higher-order assembly in order to bind retroviral capsids [10, 20]. Although an individual SPRY domain does not have appreciable affinity for the capsid (estimated to be in the mM range [21]), dimerization of the coiled-coil domain [8, 9, 12] and trimerization of the B-box 2 domain [15, 16, 20, 22] creates a hexagonal TRIM lattice that displays an array of SPRY domains for multivalent, avid capsid binding [10, 23]. This “pattern recognition” model and the architecture of the TRIM lattice are supported by structural and biochemical studies of in vitro TRIM5α/capsid complexes and crystal structures of individual domains and fragments of TRIM5α and other TRIM proteins [3, 8–16, 20, 22, 23]. However, the molecular details of SPRY domain positioning–whether it is flexibly displayed or tethered–remain experimentally undefined. This unresolved issue is a core concept of the avidity-driven recognition mechanism.
The L2 linker that connects the SPRY domain to the coiled-coil is likely to facilitate positioning of the SPRY domain. Mutagenesis studies have shown that an intact L2 sequence is required for efficient retroviral restriction [24, 25], and L2 polymorphisms have been reported to correlate with susceptibility to HIV-1 infection [26]. Furthermore, evolutionary sequence analysis has shown that some L2 residues are under positive selection, even though the linker itself does not contact the capsid [17]. In crystal structures of TRIM protein dimerization domains, the L2 linkers display substantial degrees of disorder but have been seen to fold into a short C-terminal helix that packs against the center of the coiled-coil dimer [11–14]. This packing interaction can be therefore quite flexible, and this flexibility has been proposed to underlie degenerate binding of the SPRY domain to capsid surface epitopes as well as a mechanism to destabilize the capsid lattice [11–13, 27, 28]. Alternatively, it has been proposed that the C-terminal L2 helix is integrated with the downstream SPRY domain fold, and that this L2/SPRY helix packs more stably against the coiled-coil helices and thereby positions two SPRY domains at a defined spacing and orientation relative to each other [11, 12]. This issue has not yet been resolved, in part because structures of TRIM5α constructs containing the coiled-coil, L2, and SPRY domains have been notoriously difficult to obtain. Here, we describe biochemical, biophysical, and cell biological experiments to test the models for SPRY domain positioning. Our results are consistent with a tethered mechanism: the residues at the L2/SPRY boundary are indeed helical, and packing of this helix to the main coiled-coil helix not only facilitates capsid recognition, but also modulates stability of the TRIM5α dimer, efficiency of higher-order assembly, and overall antiviral activity.
In the published crystal structure of the TRIM5 B-box 2/coiled-coil/L2 fragment, both of the subunits in the antiparallel dimer had substantial disorder in their L2 regions, but in one subunit the L2 C-terminus was folded into a short α-helix [12]. Crystal structures of other TRIM proteins displayed similar variations in L2 configurations, and some of the variations appeared to have been caused by crystal packing interactions [11, 13]. We therefore first sought confirmation that L2 packed against the coiled-coil helix in solution, using site-directed spin labeling and paramagnetic double electron-electron resonance (DEER) spectroscopy (Fig 1 and S1 Fig). In this experiment, the distance of separation between a pair of labels can be determined provided that phase modulation can be reliably measured (<8 nm) [29]. Measurements were performed on a purified recombinant CC-L2 fragment of rhesus TRIM5α (residues 133–300) that includes the full sequence of the putative L2/SPRY helix (281PDLKGMLDMFRELTDARRYW300) [11].
In a control experiment, we first confirmed that labels appended to the main coiled-coil helix (W196R1) had a single distance distribution peaking at the expected distance–about 3 nm–between the two labels (Fig 1A). In contrast, labels appended to the terminal L2 helix (D288R1, E292R1, W300R1) returned progressively broader distance distributions with multiple peaks as the labels approached the C-terminus (Fig 1B–1D). These are indicative of either a dynamic helical configuration or dynamic packing of the L2/SPRY helix against the coiled-coil. These results are consistent with the crystal structures [11–13], as well as a recent biochemical analysis of the CC-L2 fragment of rhesus TRIM5α [28].
Given the likelihood that L2 disorder in CC-L2 was caused by the absence of the SPRY domain, it was important to perform a comparative DEER analysis with a protein construct containing an intact SPRY. Unfortunately, we were unable to perform these experiments because the recombinant CC-L2-SPRY protein did not tolerate removal of its 7 native cysteine residues to allow for site-directed thiol-based labeling. We therefore determined the effect of the SPRY domain on L2 flexibility by comparing CC-L2 and CC-L2-SPRY stabilities using a thermal melting experiment called differential scanning fluorimetry. In this assay, protein unfolding is monitored with a dye that fluoresces upon binding hydrophobic residues that become exposed with increasing temperature [11]. As shown in Fig 2A, the CC-L2 construct (blue curve) displayed the expected melting profile for a coiled-coil protein, with a single transition reflecting the coupled folding and dimerization; the apparent melting point (Tm) was about 41°C. The SPRY domain alone also displayed a single transition, consistent with its monomeric configuration in isolation, with a Tm of about 50°C (green curve). The CC-L2-SPRY protein (maroon curve) had an intermediate Tm about halfway between CC-L2 and SPRY. Most notably, the CC-L2 profile had significantly elevated signals at the start of the experiment, which is distinct from the flat profiles of CC-L2-SPRY and SPRY (boxed area in Fig 2A). The elevated signals indicated that CC-L2 had exposed hydrophobic residues even at low temperature. The simplest interpretation of this observation is that the L2 linker was undergoing dynamic packing (association and dissociation) with the coiled-coil in this construct. Conversely, the flat signals for CC-L2-SPRY suggested that in the presence of the SPRY domain, L2 is more stably packed against the coiled-coil.
We next performed a complementary analysis of the putative L2/SPRY helix in context of the isolated SPRY domain. Published structures of the TRIM5α SPRY domain have revealed that residues 292–300 (the C-terminal half of the L2/SPRY boundary) are indeed helical and appear to pack stably against the main body of the domain [30, 31]. However, in one of the structures, residues that form part of the predicted helix (287–291) adopt a non-helical, random coil configuration [31]. To test whether these and additional N-terminal residues would actually adopt a helical configuration in solution, we used NMR spectroscopy to analyze a SPRY construct starting at residue 281 and compared this to a truncated construct starting at residue 292. Control spectra indicated that the two SPRY proteins had the same fold (S2 Fig). Importantly, we found three complementary indications that the additional residues in the longer construct were likely to be helical. First, significant chemical shift perturbations were observed for residues located in a loop (encircled in Fig 3A) that would physically encounter the extended helix. Second, analysis of chemical shift deviations from random coil values for backbone carbon and proton atoms indicated the presence of contiguous α-helical secondary structure in the segment spanning residues 283–300 (Fig 3B). Third, we observed three 4-residue segments (285GMLD288, 290FREL293, and 293LTDA296) with fortuitously well-resolved sequential proton-amide cross-peaks in an 15N-filtered NOESY spectrum (Fig 3C). The absolute peak intensities obey the expected sequential pattern for α-helical segments, with strong/medium i→i+1 cross-peaks, very weak i→i+2, and weak i→i+3. Collectively, these data indicated that the helical termini observed separately in the crystal structures of the CC-L2 and SPRY fragments of TRIM5α probably constitute a single, contiguous helix in the full-length protein.
Given the above results and in the absence of an experimentally determined structure as yet, we computed a molecular model of the CC-L2-SPRY dimer (Fig 4A) and designed a mutagenesis study to test it. The model was built by first symmetrizing the B-box 2/coiled-coil/L2/lysozyme structure [12] to obtain ordered L2 regions for both subunits in the dimer, and then modeling a contiguous helix spanning residues 283–300 in the L2/SPRY boundary by superimposing matching residues in the isolated SPRY domain structure [30]. In this model, the orientations of the SPRY domains were dictated primarily by interactions between the two L2/SPRY helices and the middle of the two coiled-coil helices, as previously suggested [11, 12]. Given significant model uncertainties in the positions of the L2/SPRY residues in the interface, we focused on the reliably defined coiled-coil residues for mutagenesis. Fourteen coiled-coil residues (7 per subunit) were buried within the putative packing interface, and we selected these for alanine substitution (Fig 4B and 4C). For biochemical experiments, mutations were made in the CC-L2 fragment described above and in TRIM5-21R, a chimeric construct described in previous studies as a useful recombinant surrogate for TRIM5α [8–10, 15, 23, 32]. Mutations were also made in context of full-length rhesus TRIM5α in a mammalian expression vector for analysis of assembly and restriction phenotypes in cells. The collective functional data are summarized in Table 1.
Studies of the related protein, TRIM25, have shown that single alanine substitutions in the center of the elongated coiled-coil helix can severely destabilize the dimer, even though the dimerization interface is quite extensive [11]. We therefore used the thermal melting assay above to determine which of the model-based TRIM5α mutants were deficient in dimerization. In context of the CC-L2 construct, we found that the F187A, L190A, and L194A mutants were very prone to aggregation and could not be purified easily, indicating that the mutations severely destabilized the dimer. These results are consistent with their positions within the interface, as these three hydrophobic residues bridge contacts between the coiled-coil helices as well as between the coiled-coil and L2/SPRY helices (Fig 4B and 4C). Similar results were observed for equivalent mutations in the CC-L2 dimer of TRIM25 [11]. In contrast, the remaining mutations produced purifiable CC-L2 proteins. Of these, I193A measurably reduced the stability of the dimer (Tm = 37°C), whereas the others (D186A, E197A, E201A) had no effect or even slightly increased the apparent Tm compared to the wildtype control (Fig 2B).
To more rigorously determine oligomerization states, we also analyzed the CC-L2 mutants by using SEC-MALS (size exclusion chromatography coupled with multi-angle light scattering). Consistent with the thermal shift data, SEC-MALS showed that D186A (Fig 5B), I193A (Fig 5C), and E201A (Fig 5E) were dimeric just like wildtype control (Fig 5A). The E197A mutant was likewise dimeric, but the major peak also had a significant trailing shoulder indicating dissociation into monomer (Fig 5D). Thus, this CC-L2 mutant was also deficient in dimerization, although not to the same extent as the F187A, L190A, and L194A mutants.
We next tested the mutations in context of TRIM5-21R, a restriction-competent chimeric construct wherein the RING domain of rhesus TRIM5α has been replaced by that of human TRIM21 [32, 33]. It was previously shown that this protein expresses both as a monomer and dimer–the two oligomers can be cleanly separated by sequential anion exchange and size exclusion chromatography steps (S3A and S3B Fig) [8–10]. In this case, all the mutants were purifiable, but the monomer fractions of F187A, L190A, L194A, and E197A during initial purification steps comprised 50% or more of the total protein, consistent with significant defects in dimerization (S3C Fig). In contrast, the D186A, I193A, and E201A mutants were more similar to wildtype, with the dimers being the major fraction (S3D Fig). For thermal melting analysis, we purified the dimer fraction for each mutant. Compared to the CC-L2-SPRY construct, the melting curve of wildtype TRIM5-21R had a sharper transition and higher Tm of 50°C (Fig 2C, black curve). The increased stability is likely due to “capping” of the coiled-coil ends by the B-box 2 domains, as observed in crystal structures [12, 15, 16]. Consistent with severe destabilization of the F187A, L190A, and L194A CC-L2 constructs, the equivalent TRIM5-21R proteins were still clearly unstable, with high signals at early time points similar to the wildtype CC-L2 construct (Fig 2C). In particular, F187A, which eliminated a significant proportion of the hydrophobic core, produced a non-canonical melting profile (Fig 2C, green curve). The remaining mutants did not significantly perturb the Tm of the TRIM5-21R dimer, but also had elevated signals at low temperature, which we interpret to mean that the mutations also weakened packing of L2/SPRY against the coiled-coil, as predicted by the computational model.
On the basis of the thermal melting and chromatography data, we classified mutations within the putative CC/L2/SPRY interface into two groups: class I mutants (F187A, L190A, L194A, and E197A; orange in Fig 4B and 4C) had significant or measurable effects on dimerization, whereas class II mutants (D186A, I193A, and E201A; pink in Fig 4B and 4C) had little or no effect on dimerization but still likely important for CC/L2/SPRY packing. We therefore considered this second group to be more informative in the experiments below.
If the positions of the SPRY domains in the TRIM5α dimer were tailored to match the spacing of epitopes on retroviral capsids, then destabilization of coiled-coil/L2/SPRY packing would also disrupt capsid recognition. We therefore tested the mutant TRIM5-21R proteins for their ability to bind disulfide-stabilized HIV-1 CA tubes by using an established centrifugation assay [3, 10, 15, 34]. Using our specific protocol, about 50% of wildtype TRIM5-21R was consistently found to co-pellet with the CA tubes [15]. Given the propensity of TRIM5-21R to spontaneously assemble and the reduced stabilities of the mutants, experiments were performed right after purification. Each mutant was analyzed at least twice with independent protein preparations, and always in parallel with a wildtype control. Representative results are shown in Fig 6.
Consistent with expectation that the dimer is the minimal capsid-binding unit of TRIM5α, the F187A class I mutant pelleted only at background levels. The L190A and L194A mutations had less severe defects (with 34% and 29% pellet, respectively), consistent with the less severe biophysical defects observed in the thermal stability assays. E197A pelleted efficiently with the tubes (61%), although we consider this mutant to be also an intermediate binder because in four independent experiments it reproducibly showed high levels of background pelleting (20–30%), likely due to aggregation. For reference, these levels of residual binding are similar to those observed for the R121E and W117E mutations, which disrupt higher-order interactions mediated by the B-box 2 domain and essentially abolish restriction activity [10, 15]. Among the class II mutants, D186A had significant residual binding (36%), whereas I193A and E201A only pelleted at background levels. Thus, there is good correlation between the expected structural effects of the mutations, the biophysical properties of the mutant proteins, and their capsid binding activities.
Mutations in the L2 linker have been previously shown to disrupt TRIM5α self-association and higher-order assembly [24, 25, 27, 35]. To test the alternative possibility that the capsid binding defects we observed simply reflected this property, we overexpressed YFP-tagged rhesus TRIM5α in HeLa cells and tested the mutants for their ability to form fluorescent puncta called cytoplasmic bodies. Although not yet definitively proven, these cytoplasmic bodies are reasonably believed to reflect the intrinsic ability of purified TRIM5α proteins to assemble in vitro [1, 10, 15]. In these experiments, the wildtype control produced around 80–100 individual puncta per cell (Fig 7A and 7B). As expected, the class I mutants that were appreciably deficient in dimerization were also significantly impaired in cytoplasmic body formation (S4 Fig). F187A and L194A, which were the most severe mutations in our in vitro assays, produced virtually no cytoplasmic bodies (S4A and S4C Fig), again confirming that the dimer is the fundamental building block of higher-order assemblies of TRIM5α.
Results also showed that the three class II mutants retained the ability to assemble into cytoplasmic bodies (Fig 7). Importantly, I193A and E201A, which showed only background levels of capsid binding in vitro, assembled puncta about as efficiently as wildtype (Fig 7D and 7E). To correlate these results with in vitro assembly phenotypes, we assembled purified TRIM5-21R harboring these two mutations, and confirmed that both mutant proteins efficiently assembled into a large hexagonal lattice with the expected unit cell dimensions (Fig 8) [10, 15, 23]. These results also provide further evidence that the mutants were stably dimeric, because monomeric TRIM5-21R is severely impaired in assembly in vitro [10]. We therefore conclude that the significant binding defects caused by the I193A and E201A mutations were not due to disruption of dimerization or higher-order assembly, but more likely due to impaired positioning of the SPRY domain relative to the coiled-coil domain.
We then determined the ability of our mutants to inhibit HIV-1 replication in cultured HeLa cells. Consistent with expectation from the above analysis, the class I mutants were significantly impaired in restriction, and the extent of impairment correlated with the degree to which each mutant was deficient in dimerization in vitro (Fig 9A). Of the class II mutations, D186A only had a minor defect in restriction (Fig 9B, maroon), which correlated with its in vitro properties and intermediate defect in the capsid-binding assay (Fig 6). This residue is located at the outer edges of the modeled SPRY/coiled-coil interface, and therefore probably does not significantly contribute to the packing interaction (Fig 4C). In contrast, the I193A and E201A mutants were more significantly impaired in restriction (Fig 9B, blue and pink), which again correlated with more severe loss of binding activity in vitro and more significant interactions with the L2/SPRY helix in the modeled TRIM5α dimer (Fig 4B and 4C). Note, however, that both the I193A and E201A mutants still retained measurable levels of antiviral activity. We speculate that the relatively high protein expression levels in our stably transfected cell lines, combined with the intact ability of the mutant proteins to form higher-order assemblies, may have buffered the effects of the mutations (see also Discussion below).
Finally, we tested the mutations in context of owl monkey TRIMCyp, which we predicted would be relatively insensitive to positioning effects due to its higher intrinsic affinity for the HIV-1 capsid protein [36–40]. Indeed, both the TRIMCyp I192A and E200A mutants (equivalent to TRIM5α I193A and E201A) were just as restriction-competent as the wildtype control (Fig 10). Importantly, these results also help exclude pleiotropic effects of the mutations.
Multivalency is commonly found in nature to achieve tight binding, even though each component univalent interaction is by itself very weak, by bonding or linking together multiple copies of the interacting subunits. In these systems, simple clustering of binding domains can already result in significant binding, but truly substantial avidity gains are observed when the spacing of tethered recognition domains is matched to the spacing of their corresponding epitopes [41]. Here, we provide experimental evidence that in the TRIM5α dimer, the two SPRY domains are tethered by an α-helical segment that integrates into the SPRY domain fold and packs against the center of the coiled-coil scaffold. The simplest interpretation of our structural and biophysical data (and those of others) is that this molecular tether is a single contiguous helix that spans residues 283–300 [11, 12], although precise molecular details will have to await direct structure determination. We propose that CC/L2/SPRY packing limits the flexibility and range of orientations that the two SPRY domains can adopt relative to each other, and that this tethering mechanism significantly contributes to the avidity gains observed upon multivalent binding of TRIM5α to retroviral capsids by allowing more precise (or tailored) matching with the spacing of binding epitopes on the capsid surface. In support of this model, we identified mutations (I193A and E201A) within the putative CC/L2/SPRY packing interface that did not significantly affect dimerization or higher-order assembly but still abrogated capsid binding in vitro and disrupted restriction activity in cells. The simplest explanation of our data is that the impaired restriction activities of these two mutants arise from impaired CC/L2/SPRY packing. Our studies therefore support the proposed “minimum design feature” of capsid-dependent restriction factors [12, 42, 43], in which the minimal capsid-binding unit is a dimer. Higher-order assembly of the TRIM hexagonal lattice further amplifies affinity, both by spreading the interactions across the entire capsid surface and by matching the rotations of the subunits in the capsid lattice. The basal positioning mechanism occurs in context of the dimer, however, which explains the observation that TRIM5α proteins impaired in higher-order assembly (e.g., B-box mutants or deletions) can still retain the ability to bind CA tubes or other capsid mimics in vitro, provided that the reagents are supplied at high enough concentrations in the binding reactions [3, 10, 15, 44]. We also note that this model is compatible with the ability of a single TRIM5α protein to restrict multiple different retroviruses, because even though CA proteins have widely divergent sequences, retroviral capsids have the same underlying hexagonal arrangement with a conserved lattice spacing. Interestingly, the relative spacings of interaction modules have been found to play an important role in defining binding specificity in some avidity-driven systems [45–47]. Capsid binding specificity is dictated by the SPRY domain, and we speculate that coiled-coil/SPRY packing may contribute to specificity because productive recognition will still not occur if otherwise compatible binding epitopes on the surface of a capsid are not within the reach of allowable SPRY spacings and orientations. Testing this model will require a more precise understanding of the local SPRY/CA contacts than currently known.
It is notable that the I193A and E201A mutations did not abolish restriction activity of TRIM5α, despite apparently causing complete loss of binding in our centrifugation assay. Because the I193A and E201A mutant proteins were not impaired in higher-order assembly, our interpretation of these results is that the mutants still retained some capsid-binding activity in cells that was amplified by multivalent clustering and hexagonal lattice formation (and perhaps also further mitigated by high expression levels). Thus, SPRY/coiled-coil interactions do not appear to be fundamentally essential to recognition, but rather help to optimize avidity and maximize binding efficiency. This is consistent with studies showing that artificial restriction factors can be created by appending exogenous capsid-binding domains to the TRIM5α tripartite motif [48–50]. In these artificial systems, it is unlikely that the exogenous domains are tethered in the same manner as the native SPRY domain, yet the avidity afforded by multivalency is sufficient to generate measurable anti-viral activity.
An interesting counter-example is TRIMCyp, which contains the TRIM5α tripartite motif but harbors a cyclophilin domain for capsid binding instead of SPRY [2]. The L2 linker in TRIMCyp retains the C-terminal helical segment, but the helix is followed by an additional 11 random coil residues such that the two cyclophilin domains are likely to be flexibly connected to the coiled-coil [12]. We suggest that this high degree of flexibility is compensated for by both higher-order assembly and the significantly higher intrinsic affinity of the cyclophilin fold for the HIV-1 CA protein (with a dissociation constant of about 10 μM [36–40]). Consistent with this idea, we found that the I192A and E200A mutations had no effect on TRIMCyp’s restriction activity. The degree of affinity amplification required by TRIMCyp to allow for functional capsid recognition therefore appears to be more relaxed compared to TRIM5α.
Recombinant TRIM5-21R and TRIM5α133–300 (CC-L2) proteins were expressed and purified as described [9–11]. Briefly, TRIM5α133–300 proteins were expressed as a His-SUMO-tagged construct in E. coli BL21(DE3) cells using the autoinduction method [51]. The tagged construct was initially purified on Ni-NTA resin (Qiagen), the His-SUMO leader sequence was cleaved with Ulp1 protease, and the released protein was purified to homogeneity by using anion exchange and size exclusion chromatography. TRIM5-21R proteins were expressed in Sf9 cells with a Strep-FLAG leader sequence. The tagged construct was initially purified on StrepTactin resin (GE Healthcare), the leader sequence was removed by incubation with Prescission protease (GE Healthcare), and the protein purified to homogeneity using anion exchange and size exclusion. The latter two steps were particularly important in separating contaminating monomers from the dimer fraction that we used for these studies (S3A and S3B Fig). TRIM5α133–497 (CC-L2-SPRY) was made from the TRIM5-21R construct by deleting the RING and B-box 2 domains, expressed in Sf9 cells, and purified in the same manner as the full-length protein. All single point mutations were introduced into the appropriate constructs using the Quikchange method (Agilent) and mutants were expressed and purified in the same way as their respective wildtype constructs. Given the established propensity of TRIM5 proteins to spontaneously aggregate or assemble in vitro, biochemical assays were performed as soon as possible after purification. SPRY constructs lacking the V1 loop were expressed as His-GB1 fusion proteins (derived from a plasmid kindly provided by D. Ivanov) and purified as described [30].
Purified TRIM5α133–300 harboring the W196C, D288C, E292C, and W300C mutations were briefly incubated with excess dithiothreitol to reduce the exogenous cysteines, then exchanged into labeling buffer (20 mM Tris, pH 8, 100 mM NaCl) using a desalting column. The proteins were then incubated with excess MTSL (S-(1-oxyl-2,2,5,5-tetramethyl-2,5-dihydro-1H-pyrrol-3-yl)methylmethanesulfonothioate) overnight at 4°C. After removing unreacted label with a desalting column, the labeled proteins were concentrated to 20 μM. Deuterated glycerol (Cambridge Isotope Laboratories) at approximately 10% final concentration was added to the samples before freezing. DEER EPR measurements were performed and analyzed as described previously [52]. In brief, samples were placed inside quartz capillaries and frozen using a dry ice/isopropanol bath. Standard four-pulse DEER measurements [53] were performed with a Q-band Bruker Elexsys E580 spectrometer and EN5107D2 dielectric resonator (Bruker Biospin). Dipolar evolution data were processed and distance distributions determined using Tikhonov regularization, as implemented in the DeerAnalysis2015 software package [54]. The validation route in DeerAnalysis was used to estimate uncertainty in the distance distributions due to subtraction of the background form factor. Distance estimates from the molecular model of CC-L2 were made using the PyMol plug-in MTSSLWizard [55].
Thermofluor melting assays were performed as previously described [11], with final protein concentrations of 1 mg/mL for TRIM5-21R constructs and 2 mg/mL for CC-L2, CC-L2-SPRY, and SPRY constructs. Each sample was set-up in 3 or 4 replicates, and melting curves for each protein were determined at least twice, with independent protein preparations.
SPRYΔV1 constructs for NMR experiments were uniformly labeled with 15N and/or 13C by growing transformed bacteria in minimal media supplemented with 15NH4Cl and/or 13C-glucose. Assignments for SPRY292-497 were kindly provided by D. Ivanov [30]. Resonance assignments (including all N-terminal residues) were independently determined for SPRY281-497 by using the following experiments: 15N/1H HSQC [56], CBCA(CO)NH [57], HNCA [58], HNCACB [59], HNCO [58], HNCOCA [60]. NOE cross-peaks were obtained from an 15N-edited NOESY-HSQC [56, 61]. Spectra were recorded on a Varian Inova 600 MHz spectrometer, processed with NMRPipe [62], and analyzed using the tools in SPARKY [63]. Chemical shift indices were calculated using the program PREDITOR [64]. Normalized chemical shift changes were calculated as described [65].
The TRIM5α coiled-coil/L2/SPRY model was built as described in the main text, using PyMol software (Schrödinger Scientific).
These experiments were performed as described [15]. Purified CC-L2 mutants (50 μL) were injected into the column at 0.5–1 mg/mL concentrations.
In vitro pull-down assays with disulfide-stabilized HIV-1 CA tubes and purified TRIM5-21R proteins were performed as described [15].
Assembly and negative stain electron microscopy imaging of the I193A and E201A TRIM5-21R mutants were performed as described [10].
The hemagglutinin (HA)-tagged and/or yellow fluorescent protein (YFP)-labeled rhesus TRIM5α and owl monkey TRIMCyp constructs were generated as previously described [27]. Overlapping PCR was used to generate single point mutations in the coiled-coil regions. HeLa and human embryonic kidney 293T (HEK293T) cells (from an already existing collection in-house) were cultured in complete Dulbecco’s modified Eagle’s medium (DMEM) containing 10% fetal bovine serum, penicillin (100 U/mL), and streptomycin (100 μg/mL). Vectors expressing YFP- or HA-tagged rhesus TRIM5α were made by transfecting 293T cells with the respective wildtype and mutant TRIM5 plasmids along with VSV-G and pCig-B. HeLa cells stably expressing the YFP-tagged proteins were generated by G418 (400 μg/mL) selection at 48 h post-transduction. The stable cell lines were then analyzed by immunofluorescence and western blotting.
HeLa cells (from an already existing collection in-house) stably expressing YFP-tagged TRIM5α proteins were plated onto fibronectin-treated coverslips, allowed to adhere, and fixed with 3.7% formaldehyde and stained with DAPI. Images were collected with a DeltaVision microscope (Applied Precision) equipped with a digital camera (CoolSNAP HQ; Photometrics), using a 1.4 numerical aperture objective lens, and were deconvolved with SoftWoRx deconvolution software (Applied Precision). Z-stack images of each cell line were acquired by using identical acquisition parameters. Deconvolved images were analyzed for fluorescent cytoplasmic bodies by using the Surface Finder function of the Imaris software package (Bitplane).
Vesicular stomatitis virus G protein (VSV-G)-pseudotyped R7ΔEnv HIV-GFP was produced by transfecting HEK293T cells as previously described [66]. Virus infectivity was assessed by infecting equivalent numbers of cells in a 24-well plate, and green fluorescent protein (GFP) expression was determined at 48h post-infection by using a FACSCanto II flow cytometer (Becton, Dickinson).
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10.1371/journal.pntd.0004072 | An Estimation of Private Household Costs to Receive Free Oral Cholera Vaccine in Odisha, India | Service provider costs for vaccine delivery have been well documented; however, vaccine recipients’ costs have drawn less attention. This research explores the private household out-of-pocket and opportunity costs incurred to receive free oral cholera vaccine during a mass vaccination campaign in rural Odisha, India.
Following a government-driven oral cholera mass vaccination campaign targeting population over one year of age, a questionnaire-based cross-sectional survey was conducted to estimate private household costs among vaccine recipients. The questionnaire captured travel costs as well as time and wage loss for self and accompanying persons. The productivity loss was estimated using three methods: self-reported, government defined minimum daily wages and gross domestic product per capita in Odisha.
On average, families were located 282.7 (SD = 254.5) meters from the nearest vaccination booths. Most family members either walked or bicycled to the vaccination sites and spent on average 26.5 minutes on travel and 15.7 minutes on waiting. Depending upon the methodology, the estimated productivity loss due to potential foregone income ranged from $0.15 to $0.29 per dose of cholera vaccine received. The private household cost of receiving oral cholera vaccine constituted 24.6% to 38.0% of overall vaccine delivery costs.
The private household costs resulting from productivity loss for receiving a free oral cholera vaccine is a substantial proportion of overall vaccine delivery cost and may influence vaccine uptake. Policy makers and program managers need to recognize the importance of private costs and consider how to balance programmatic delivery costs with private household costs to receive vaccines.
| The price of vaccine and the costs of its delivery are two important economic measures considered by governments and various international organizations in their decisions on the use of a new vaccine. However, the costs to the vaccine recipients resulting from their travel, time and wage loss are hardly considered and rarely documented. Even if the vaccine is provided for free, the costs borne by vaccine recipients could be sufficient enough to be a hurdle for taking vaccine. We elucidate this less explored angle of “vaccine recipient cost” in the context of oral cholera vaccine mass campaign in Odisha, India. Our research shows that the potential loss of income for individuals for receiving oral cholera vaccine ranged from 25% to 38% of overall vaccine delivery costs. We believe our findings have global implications on future decisions and policy making on vaccine introduction in balancing programmatic delivery costs with private household costs to receive vaccines.
| Several large cholera outbreaks in the sub Saharan Africa, Asia and Caribbean regions [1–3] have renewed interest in the use of oral cholera vaccines (OCV) in recent years. Considering the public health importance of cholera, the World Health Organization (WHO) recommends targeting OCVs to vulnerable populations living in high-risk areas in conjunction with other control measures [4]. The WHO prequalified OCV Shanchol is reported to confer 65% protective efficacy over five years against clinically-significant cholera [5].This vaccine has been used in several OCV mass campaigns worldwide in recent years [6–10] and available eligible countries through WHO stockpile [11].
When deploying OCVs during a vaccination campaign, budget-constrained public health staff will seek to minimize costs, particularly staff time, equipment, vaccine transport, and vaccine procurement costs. However, health staff may give less attention to the costs in transportation and lost wages incurred by individuals who seek vaccination. The travel and time costs borne by households are known to be crucial determinants in population-level access and uptake of vaccines. It has previously been reported that high out-of-pocket expenditure resulted in lower uptake in routine vaccination settings [12–16]. Similarly, higher indirect costs measured as travel distance and time has an adverse impact on vaccination coverage [16–19]. In Beira, Mozambique, the likelihood of participation and household cholera vaccine uptake was inversely related to travel costs [20]. Other than this Beira study, the household costs for OCV delivered through mass campaign settings has not been included in published estimates of vaccine delivery costs [8,21–24].
In this paper, we explore private household costs to receive free vaccine during an OCV campaign conducted in Odisha, India in 2011. These findings are potentially applicable to many other settings and vaccination programs.
Based on a cross sectional survey, we estimated the private household costs for receiving OCV during a mass vaccination campaign conducted in Orissa, India in 2011. These costs included direct costs or out-of pocket expenses and indirect costs such as income loss due to the time spent for vaccination by the recipients and their caretakers.
There are two types of direct out-of pocket costs: medical and non-medical costs. The direct medical cost may include co-payments for vaccines or treatment for vaccine-related adverse events. There were no direct medical costs for this study because the vaccine was given free of cost and there were no adverse events following vaccination that required medical care [8]. Direct non-medical costs included transport costs from home to the vaccination site and were estimated based on a cross sectional survey described below.
Indirect costs or costs related to income loss were estimated using a human capital approach based on the lost productivity of those vaccinated during the campaign [25]. In the human capital approach, the vaccine recipient perspective was taken and any hour not worked while receiving a vaccine was counted as hourly income loss. The income loss for adults was calculated by multiplying the self-reported participation time by daily wage rates. Wage loss was accounted for using two distinct methods: based on self-report by vaccine recipients (Method 1) and based on Odisha government minimum wage of INR 145 (USD 3.3) paid to semi-skilled individuals per day (Method 2) [26]. A third approach to measure productivity loss based on gross domestic products (GDP) per capita income per day (Method 3) was used in sensitivity analysis.
A minimum wage is the supportive lowest daily remuneration that employers may legally pay to workers for the skill category. Minimum wage is a conservative estimate. We weighed the minimum wage by age and job category. Many children do not earn wages but are often significant contributors to the economy and therefore their time should be monetised [27–29]. We applied age-specific wages separately for adults (15+ years); school aged children (5–14 years) and young children (1–4 years). Average hourly minimum wage was applied 100% for adults, 50% for school aged children and 25% for young children respectively [28,29]. To estimate the productivity loss from foregone non-market activities such as routine household chores, childcare, leisure time and school time, which is valued by the individuals, household and farm work was given 70% of daily/hourly wage, while leisure time was given 50% of daily/hourly wage [28,29].
For Method 3, we applied the GDP per capita in Odisha to value time. The time cost of each individual was valued equally irrespective of age or occupation. The state GDP per capita of Indian Rupee (INR) 53,578 (USD 1,205.5) was obtained from Indian Ministry of Statistics and Programme Implementation data for 2011–2012 [30]. Assuming 365 work days and 8 hours of productivity a day [31], estimated GDP-hour is translated to INR 146.79 (USD 3.30) per day or INR18.35 (USD 0.41) per hour. The assumption of 8 hours of productivity in GDP per capita estimation was used because non-market household production is usually not accounted in GDP estimation [32,33]. We did a sensitivity analysis where we assumed 24 hours of productivity a day in GDP estimation.
For conversion to USD from INR we used an exchange rate for April 1st 2011 (1USD = 44.45 INR) based on Reserve Bank of India data [34] which is beginning of fiscal year [35].
The 2-dose OCV campaign was described in detail elsewhere [8]. In short, a baseline census was conducted from household surveys used to map the target population in the proposed vaccination areas of 10 health sub centers of Alagum community health center, Satyabadi block, Puri district, India. Subsequently, the OCV campaign was conducted at 62 vaccination booths located in the community, mostly at schools from May 5 to June 4, 2011. Schools were not in session at the time of vaccination campaign. Of the total 51,865 residents listed in the census, 31,552 eligible persons received the first dose of vaccine and 23,751 of these completed their second dose. This corresponds to coverage rates of 61% for the first dose and 46% for the second dose. The vaccine cost at market price was $1.85 and the public health vaccine delivery cost was $0.49 based on the review of project expenditure records and interview of key personnel [8]. GIS mapping of households and vaccination kiosks were used to quantify the physical distance between households and their nearest vaccination booths.
The direct out-of-pocket costs for travel to the vaccination site and the time in minutes waiting to receive vaccination was self-reported by recipients during a cross sectional survey. For the survey, nine villages were selected via stratified, simple random sampling. Villages were stratified by economic status (low-middle-high), location (more/less remote), size (number of households), and level of vaccine uptake (low-middle-high) during the campaign to give sufficient representation to these characteristics. All houses in the 9 selected villages were further stratified by 1) all or some household members taking two doses, 2) at least one household member receiving one dose, and 3) no household members receiving the vaccine. In total, 600 households, 200 from each of three categories (two doses, one dose and no dose) were randomly selected for conducting the socio-behavioral survey presented elsewhere [36]. The private costing questionnaire described here was administered to the subset of households that received one or two doses of vaccines only. For each household, one eligible individual (i.e., a permanent household member aged 18 years or older) was asked a series of questions regarding out-of-pocket and time costs for the last dose of vaccine they had received. The questions included the number of household members that received vaccines, travel cost, travel time, waiting time, loss of wages, what other activities they would have engaged in, the number of accompanying members, and income loss for accompanying members.
Double data entry was done in Microsoft FoxPro 7.0 (Microsoft, Seattle, WA, USA) and the data was analyzed using open source statistical software R [37]. All the costs presented here are incurred in year 2011 and presented in INR 2011 and USD 2011 without discounting. The GIS map and the distance between households and vaccination booth was estimated based on ArcGIS Desktop 9.3.1 (ESRI Redlands, CA, USA).
Written informed consent was obtained from the respondents. For participants unable to sign, a witness observed the consenting process and signed the consent form. The study protocol including consent process where witness observed the consenting process for participants unable to sign was approved by relevant ethical committees. This included Institutional Review Board of the International Vaccine Institute (IVI), Seoul, Korea (Ref. No: IVI IRB# 2010–003) and Human Ethical Committee of the Regional Medical Research Center (RMRC), Bhubaneswar, Odisha (letter dated 19th January 2010).
Among the 600 randomly selected households, five households could not be reached. Of the remaining households, the 337 households that reported one or more members received vaccine during the mass vaccination campaign were interviewed.
The most common occupation of the interviewed heads of households was farming (n = 122; 36.2%), followed by work with daily wage compensation (n = 63; 18.7%), trading (n = 40; 11.9%), unemployed (n = 27; 8%) and retired (n = 17; 5%). The majority of household heads were literate without formal education (n = 69; 20.5%) or attended primary school (n = 98; 29.1%). Some of them had secondary school (n = 62; 18.4%) or high school and higher education (n = 63; 18.7%) while, 13.4% (n = 45) household heads were illiterate. A majority of the interviewed households (n = 240; 71.2%) reported practicing open field defecation.
Seventy three percent (245/337) of the respondents were females, many men were at work. The mean age of female and male respondents were 37.0 (SD = 13.0) and 44.2 years (SD = 15.7) respectively. The mean number of members in interviewed households was 5.7 (SD = 2.8) of which on average 4.0 (SD = 2.3) household members were vaccinated with at least one dose. Nearly 70% of the vaccine recipients in the interviewed household were adults. The household demographic characteristics are presented in Table 1.
Most households were located adjacent to the vaccination booths (Fig 1) with an average distance of 282.7 meters from nearest vaccination booth (SD = 254.5, median 202.6, minimum 3.2, maximum 1,303). The average distance to the nearest vaccination booth in interviewed households (N = 337) was lower than the average distance to nearest vaccination booth in all vaccinated households (N = 9,166; mean = 311.8, SD = 240.5, p value = 0.03) in the study site. This distance between households and vaccination booths was calculated from GIS maps, and does not account for physical barriers such as absence of road, ponds, rivers, and lakes between houses and booths.
Most household members walked from their homes to vaccination booths (92%; n = 309/337). The remainder used bicycles (10%; n = 33/337) or motorbikes (2%; n = 7/337), and one household used a car. The total percentage is over 100 as some households reported using more than one mode of transport. Interviewed people reported an average of 26.5 minutes of travel time to reach vaccination booths and a waiting time of 15.7 minutes to receive vaccination (Table 2). Some individuals spent up to 3 hours travelling and up to 2 hours waiting for vaccination. Around one half of the people in households missed a part of their work time to receive the vaccination (Table 2).
The out-of-pocket travel cost per family for receiving vaccination was negligible (INR 0.68; USD 0.02). The productivity loss depended on the calculation method (Table 3). The productivity loss estimated based on minimum wages (Method 2) was least, followed by estimation based on reported potential loss of income (Method 1) while, productivity loss based on GDP (Method 3) was almost twice that of the former two methods. If 24 hours of productivity was assumed in estimating GDP per capita, the productivity loss in Method 3 was least at $0.09 ($0.0 to $0.62).
The private cost of receiving oral cholera vaccine was 24.6%–38.0% of the total vaccine delivery costs depending on the method used to value vaccine recipients’ productivity loss (Table 4). If 24 hours of productivity was assumed in GDP calculation, the private cost of receiving oral cholera vaccine was 17.0% of vaccine delivery costs.
Our analysis shows that private cost, i.e., direct travel costs and indirect productivity losses during the Odisha mass campaign ranged from 0.16 to 0.30 USD per dose (0.62 to 1.18 USD per family). This is the marginal costs to vaccine recipients, despite the vaccination booths were organized close proximity to the households. Although the vaccine was provided for free during the campaign, vaccine recipients and those who accompanied them had to forego time and money. This indicates a need for operational approaches and robust planning to reduce private household costs in future OCV campaigns and potentially in other vaccination programs.
In recent years, there have been several recommendations for approaches to reduce out of pocket expenditure and improve vaccination uptake in routine settings [15,38,39]. As such, an OCV campaign has distinct challenges related to private costs- unlike most childhood vaccines. The OCV is targeted to populations greater than one year of age (except for pregnant women), of whom a large proportion may be working men and women. High coverage rates among working men and women are difficult to achieve because of their greater opportunity costs relative to children. Although approaches such as house-to-house vaccine delivery have helped to achieve nearly universal coverage of the oral polio vaccine among children [40], such approaches may not produce similar coverage rates in OCV campaigns since adults may be unreachable during work days. Moreover, house-to-house visits are more costly for service providers and logistically challenging. In Odisha, there were no out-of-pocket payments required to receive OCVs, and extra efforts were made to set-up vaccination booths close to households [8] so that private travel costs could be minimized. Despite these measures, the productivity losses were nearly one third of total vaccine delivery costs due to potential or forgone income loss. The vaccination booths operated from 7 am to 5 pm which fell during working hours for many participants. People then had to choose between receiving the vaccine and performing their routine activities based on their perceived benefits. Modified approaches such as flexible location and time may suit the work-leisure patterns of local populations and may be beneficial to reduce private vaccination costs in future programs. Similarly, it is valuable to have context and vaccine specific approaches that can reduce private household costs and improve vaccination coverage.
Our finding on the need to reduce private costs for receiving OCV is applicable to other routine vaccination programs. As described in the introduction high out of pocket expenditures and indirect costs measured by travel distance and time adversely affect routine vaccine uptake [12–19]. Program managers should consider developing site specific approaches to reduce private costs as vaccine recipients costs are under recognized in program planning and implementation.
There are certain limitations of this research owing to study design and study timing. The indirect cost is a conservative estimate as the average distance to vaccination booth from sampled households was lower than average distance to vaccination booth from all vaccinated households (282.7 meters vs. 311.8 meters). Assuming the same travel time per meter among sampled population and all vaccinated population, the travel distance of 311.8 meters would have taken 29.18 minutes per dose instead of 26.46 estimated in the sampled population. Thus the time cost is underestimated. Reported out-of-pocket costs and waiting time were collected nearly four months after vaccination and subject to recollection and reporting biases commonly encountered in population based surveys. Moreover, the participant time cannot be directly valued in the marketplace, especially for women and children not attending school. This leads to imprecision regardless of the method used to value their time.
Oral cholera mass vaccination involves costs related to productivity loss to vaccine recipients which may adversely influence vaccination decision. Locally appropriate programmatic approaches are necessary to reduce time and costs involved in receiving OCV. Future program planning and vaccination costing studies should account for costs to service provider as well as service recipients. Global, regional and country level decision makers as well as local program managers should account for the potential implications of private household costs on coverage levels while deploying new vaccines and consider the costs to recipients as important as the cost to providers.
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10.1371/journal.pcbi.1004005 | Systems Level Analysis of Systemic Sclerosis Shows a Network of Immune and Profibrotic Pathways Connected with Genetic Polymorphisms | Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.
| Systemic sclerosis (SSc) is a rare autoimmune disease characterized by skin thickening (fibrosis) and progressive organ failure. Previous studies of SSc skin biopsies have identified molecular subsets of SSc based upon gene expression termed the inflammatory, fibroproliferative, normal-like, and limited intrinsic subsets. These gene expression signatures are large and although the biological processes are conserved, the exact list of genes can vary across datasets due to random variation, as well as minor differences in the composition of the study cohorts (e.g. early vs. late disease). We developed a computational tool to identify the consensus genes underlying the subsets across heterogeneous data and characterized the biological role of the consensus genes in SSc in order to obtain a systems level perspective of the SSc subsets. Our analysis reveals a complex network of genes connecting two of the major SSc intrinsic subsets, inflammatory and fibroproliferative. Many genetic loci associated with SSc risk show connections with the consensus genes of the intrinsic subsets, indicating that differential expression of genes defining the subsets may be related to genetic risk for SSc, thus for the first time placing the genetic risk factors in the context of, and showing putative relationships with, the intrinsic gene expression subsets.
| Genome-scale gene expression profiling of systemic sclerosis (SSc) skin has identified distinct intrinsic molecular subsets (inflammatory, fibroproliferative, and normal-like) within the subset of patients diagnosed with diffuse cutaneous SSc (dSSc) based upon the extent of skin involvement. These subsets are identified by an intrinsic gene analysis [1] that shifts the focus to differences between patients rather than patient biopsies. The inflammatory subset is characterized by increased expression of genes associated with inflammation and extracellular matrix (ECM) deposition, while the fibroproliferative subset is characterized by increased expression of genes associated with cell proliferation [1], [2]. Biopsies from patients in the normal-like subset show gene expression most similar to healthy control skin biopsies. The presence of three distinct molecular SSc subsets within patients diagnosed with dSSc underscores the molecular heterogeneity of SSc. However, it is unclear whether the subsets represent distinct diseases with different etiologies or whether they represent disease progression. To address this question, we identified the conserved molecular pathways characteristic of each subset that are reproducible between different datasets from multiple patient cohorts, and examined the connectivity of these genes and SSc-associated polymorphic genes in a predicted functional network.
SSc is a rare disease without validated disease progression markers and no known cure. SSc affects between 49,000–276,000 Americans; one in three patients dies within 10 years of diagnosis [3]. The rarity of SSc makes this disease an excellent test case for a genomic meta-analysis to understand disease mechanism. Using this approach, we have begun to understand the molecular and clinical complexity of SSc. Our findings may assist in generating patient-specific therapies [4] and delivering real-time quantitative feedback regarding therapeutic response during clinical trials [4], [5].
High-throughput gene expression data has demonstrated that genes that function together are almost always co-expressed and thus highly correlated with each other [6], [7]. Indeed, the expressed genes in a biopsy form a co-expression network, where genes serve as nodes and correlations as links between genes. (The language of networks is technical and beyond the scope of this paper. The supporting information (S1 Text) contains a glossary of keywords that are used in a technical sense in the main body of the paper.) This observation forms part of the basis for “network medicine” [8], [9]. The co-expression network contains groups of highly correlated genes that represent the biological processes at work in the tissue [6], [10]. These groups of genes can be found using a variety of procedures for co-expression clustering (e.g. [6], [10]). All of these procedures group highly correlated genes together, i.e. they partition the genome into non-overlapping groups of genes with similar expression patterns sometimes called modules. The output of co-expression clustering is a data-driven partition of the expressed genes in the genome. As a result of technical differences in data acquisition protocols as well as true biological variation (patient heterogeneity, treatment), the exact modules identified in one SSc dataset differ somewhat from those identified in another dataset, although the same pathways, biological processes, and many core genes are found in each dataset. We developed a tool to compare gene co-expression modules derived from multiple disparate datasets to identify the modules that are reproducibly expressed in each dataset. We then derived gene sets from the overlaps between conserved modules across datasets. These “consensus clusters” are the gene clusters that are conserved across all datasets.
A naive approach to solving this problem is to simply intersect the “intrinsic gene lists” derived for each of the cohorts [1], [4], [11]. The methodological issue with this approach is that these lists are derived under a large multiple hypothesis testing burden, and although the same biological processes and some genes are found reproducibly, the gene sets do not exactly recapitulate across data sets [11]. This simple intersection approach would be much too conservative and consequently exclude many biologically important genes. Our alternative approach is to consider “modules first”. In brief, our goal is to identify the modules that are conserved across datasets first and then extract the consensus genes as those that are consistently assigned to those modules. This transfers the multiple testing burden onto the much smaller list of modules and allows genes to be included in the consensus even if they do not achieve extremely high statistical significance in all datasets simultaneously.
We developed this idea into a novel data mining procedure called Mutual Information Consensus Clustering (MICC) to identify conserved gene expression modules across multiple gene expression datasets. Consensus clustering is a set of techniques from computer science and bioinformatics that refers to strategies for extracting robust clusters from an ensemble of partitions. Typically this is done using a large ensemble of partitions. Early work focused on weak clustering algorithms and consensus clustering was used to “boost” the weak partitions into an aggregate, consensus partition [12]. In bioinformatics, consensus clustering algorithms have been developed to aggregate ensembles of partitions that are derived from data resampling [13]. These techniques have in common that they do not “trust” a particular partition from one of their clustering algorithms. Here, we use a strong clustering algorithm called weighted gene co-expression network analysis (WGCNA). Our ensemble of partitions is the collection that we obtain from having multiple, clustered datasets from independent cohorts. While WGCNA extracts meaningful signals in each data set, the potentially interesting modules in one dataset are not precisely replicated in all others.
Mutual information [14] provides a rigorous criterion by which modules from different datasets can be said to have significant overlap (i.e. are conserved) and allows one to identify when the available information between two partitions is exhausted. Mutual information is a sum of positive and negative contributions from each pair of modules across datasets, and MICC automatically disregards all overlaps that do not contribute positively to the total mutual information, thus giving an objective measure of conserved gene expression that is both comprehensive and parsimonious. As such, MICC is a metaclustering procedure that “clusters the clusters” [12], but does not produce a complete partition. Instead, it generates only a partition of the subset of the genome that has strongly conserved gene co-expression.
Using previously published gene expression data from skin biopsies from patients with SSc recruited at three independent academic centers [1], [4], [11] and new samples analyzed as part of this study (Table 1), we identified the consensus clusters that were present in all datasets. Due to the unbiased nature of high-throughput screening, these datasets contain information about SSc-specific biology as well as the general biology of skin. We showed that MICC yields consensus clusters that are biologically specific. At the level of the whole transcriptome, we demonstrated that the consensus clusters are enriched for hubs defined by co-expression network analysis. We then filtered the consensus clusters down to those that were intrinsic subset-specific. The existence of the intrinsic subsets is a robust observation in each of these studies and the consensus clusters associated with them provide a rigorous picture of the core gene set underlying the subsets. The comprehensive and concise annotation of the conserved differential gene expression that we developed suggests that the intrinsic subsets represent pathophysiological states of one disease. Our major findings include the following: 1. We show that the subset-specific consensus clusters are part of a gene-gene network and for the first time to our knowledge, demonstrate putative connections between the intrinsic gene expression subsets of SSc and SSc-associated genetic polymorphisms identified by candidate and genome-wide association studies (GWAS). 2. We provide additional unbiased data to support the hypothesis that immune system activation is an early event and plays a central role in SSc pathogenesis as SSc risk alleles are linked to the immune system nodes of our network. 3. The consensus gene-gene network provides insights into genes that may be central to the major disease processes and identifies genes and pathways that may connect these major groups of genes. 4. We show a link between the inflammatory and fibroproliferative patient groups through a shared TGFβ/ECM subnetwork, suggesting a theoretical path by which these gene expression subsets may be linked. Collectively, these findings demonstrate that MICC is a powerful tool that identifies the reproducible signals in gene expression data across multiple datasets and shows how they may relate to the genetic polymorphisms associated with SSc.
We analyzed a compendium of three whole transcriptome datasets from SSc skin biopsies (Milano et al. [1], Pendergrass et al. [11], and an expanded version of Hinchcliff et al. [4]; see Materials and Methods). These datasets consist of 70 patients with dSSc, 10 patients with limited SSc (lSSc), 4 morphea samples, and 26 healthy controls (Table 1). Our aim was a comprehensive picture of the gene expression abnormalities in SSc skin and we integrated several publicly available tools with a novel consensus clustering procedure. As demonstrated in Fig. 1, our analysis began with gene coexpression clustering (Fig. 1A), followed by a novel post-processing step called Mutual Information Consensus Clustering (MICC) that identified conserved gene expression modules across the three cohorts (Fig. 1B). The outputs from MICC were consensus clusters, i.e. modules that were conserved across datasets, which were the objects of further study, including ontology annotation and functional interaction analysis (Fig. 1C).
To understand the molecular processes at work in SSc skin biopsies, we constructed data-driven partitions of the expressed genes across multiple SSc skin gene expression datasets using weighted gene co-expression network analysis (WGCNA) [10] (Figs. 1A, 2). Each co-expression cluster, or module, in the partition corresponds to a collection of correlated molecular processes present in the SSc tissue at the time of biopsy. To compare these modules across SSc datasets, we used mutual information to detect when a module from one dataset is present in another dataset. The partitions of the genome-wide expression data vary from one dataset to the next due to clinical heterogeneity and treatment effects, as well as technical variation in RNA processing protocols. All samples were analyzed on Agilent DNA microarrays with the same DNA probes in the same laboratory, providing consistency of the gene expression data and genes analyzed.
To identify genes with conserved expression across multiple datasets, we developed a procedure called Mutual Information Consensus Clustering (MICC) that detects significant conservation of a piece of a module and groups these conserved modules into collections called communities, which are sets of modules with considerable mutual overlap between datasets. Each community is associated with a gene set; namely all genes that are annotated to a module in that community for each dataset. We call these gene sets consensus clusters. The basis of MICC is the concept of mutual information from information theory [14]. Specifically, we use mutual information of partitions (MIP), which is an information measure specific for partitions. MIP quantifies the amount of information one partition has about another; i.e. it measures the correlation of cluster labels across datasets. MICC identifies consensus clusters using MIP to build a module similarity network of significant module overlaps, which we call the information graph (Figs. 1B, 3A). Then MICC algorithmically identifies communities in that network (Fig. 1B; Fig. 3A, colored nodes). These communities are collections of modules that have substantial overlap among each other, and they represent nearly all of the mutual information between the genomic partitions. In this way, MICC extracts almost all of the available information present in the separate clusterings of individual datasets and reports the clusters that are conserved across the three cohorts (see Materials and Methods for a detailed description).
Weighted gene coexpression network analysis (WGCNA) [10] is a gene co-expression clustering procedure that automatically detects the number of modules in a dataset and removes outlier genes. WGCNA performed on a single SSc skin dataset (Milano et al. [1], S1 Data file), demonstrates the complexity of comparative studies across multiple datasets (Fig. 2). The molecular subsets termed ‘SSc intrinsic subsets’ were first identified by Milano et al. [1]. The Milano et al. dataset is the best characterized dataset showing the SSc intrinsic subsets that has been analyzed to date. These data were clustered into modules by WGCNA, and the resulting modules were summarized by their first principal component or module eigengene (Fig. 2). Module eigengenes are a one-dimensional summary of the gene expression within a module that captures the bulk of the variance within that module. To identify those modules that were intrinsic subset-specific, we performed Kruskal-Wallis tests on the module eigengenes with groups defined according to intrinsic subsets. Of the 54 total modules, 23 had a significant p-value for association with the intrinsic subsets (all p<0.05 after Bonferroni correction; Fig. 2 shows six representative examples). These 23 modules comprise approximately 40% of the expressed genes in the genome (Table 2), demonstrating that the intrinsic subsets found in SSc skin are defined by deregulation of a very large fraction of the expressed genes in any given cell. Gene expression for six significant modules along with their corresponding module eigengenes demonstrates clear association with the intrinsic subsets (Fig. 2). These modules are enriched for broad functional categories previously associated with SSc, including chemokine signaling, NFκB signaling, RAS-RAC signaling in the inflammatory subset, and cell cycle processes in the fibroproliferative subset [1], [4], [11].
To identify the core set of genes reproducibly found in each SSc intrinsic subset, we performed WGCNA on two additional SSc skin gene expression datasets: Pendergrass et al. [11] and an expanded version of Hinchcliff et al. [4] (Data files S2–S3). The Hinchcliff data are from an ongoing clinical trial of mycophenolate mofetil (MMF). Preliminary data have been published [4], but we also analyzed data for an additional 82 unpublished samples from that trial (the full data available from NCBI GEO at GSE59787). A summary of the three cohorts, including the expanded Hinchcliff cohort, is available in Table 1. Each of the three datasets had approximately 60 modules. The module eigengenes were tested for association with the intrinsic subsets, and it was found that, within a dataset, between 18% and 67% of the modules were subset-specific (Table 2). This shows that for each dataset a substantial fraction of modules was associated with the intrinsic subsets and, that 4,004–10,373 genes out of the approximately 19,500 in the human genome were in differentially regulated modules associated with the intrinsic subsets (Tables 2 and 3).
The gene co-expression modules represent biological processes that are active in skin and some are reflective of disease pathogenesis. To determine which processes were conserved across all three datasets, we constructed the information graph for the three separate WGCNA partitions of the genome (Fig. 3A). The information graph is a network where a node in the network is a module from one dataset, and a link between modules indicates that the overlap between those modules is significantly larger than would be expected at random. In other words, an edge represents conservation of a significant part of a module across two datasets (see Materials and Methods for a detailed discussion of module overlap scores). Triangles in the information graph correspond to a significant three-way overlap of modules or, equivalently, a module conserved across all three datasets. We enumerated all triangles in the information graph to identify all such conserved modules. There were 157 triangles and approximately 2000 genes in their corresponding triple overlaps. Most (129 out of 178) of the modules across all SSc datasets are present in at least one triangle, i.e. most co-expression modules had a significant portion co-expressed in the other datasets (Table 2, bottom row). This indicates that the WGCNA-derived modules are reproducible features of SSc gene expression. Nine of the triangles had all three nodes (modules) significantly associated with the subsets (five inflammatory, four fibroproliferative; see below and Fig. 3).
The consensus genes are hubs in the gene-gene co-expression networks. To see this, we noted that module eigengenes represent hubs in the gene-gene correlation network [15]. A module eigengene does not correspond to an actual gene, but rather represents a theoretical gene that is most central in the module. Therefore, genes that are highly correlated to their module eigengene are more central within their module. We calculated the correlation of each gene to its corresponding module eigengene (S1 Fig.). The density of these gene-eigengene correlations is shown for all genes in the genome (blue curve) and for only the consensus genes (red curve). The consensus genes are significantly more correlated with their module eigengene than randomly selected genes are with their module eigengene, indicating that the consensus genes are significantly enriched for hub genes in their (dataset-specific) co-expression network. This is a useful positive control for the MICC method because it shows that the consensus genes are enriched for “hubness” in the SSc co-expression network and thus MICC finds genes that have salient network features.
While most modules are partially conserved between the three datasets and many of them are intrinsic subset-specific, not all intrinsic subset-specific modules are conserved across all datasets (S4 Data file). To find the conserved, intrinsic subset-specific modules, we noted that the information graph has groups of triangles with considerable mutual edge-sharing (Fig. 3A). Many of the triangles in the information graph overlap and form communities of triangles (Fig. 3A, S2 Fig.). This was intriguing because it opened up a broader interpretation of “consensus cluster”. If the information graph had been a disconnected collection of single triangles, this would have implied that there was a one-to-one mapping between the modules from different datasets. Instead, a single module from one dataset gets broken into pieces in the other datasets. The community structure of the information graph indicates what we have known from many prior microarray studies, namely that specific groups of genes are commonly expressed together and that the aggregate set of genes underlying these multiple co-expression clusters constitutes the truly conserved processes in SSc [1], [6], [16].
We detected communities in the information graph using a variant of clique percolation [17], a network community detection procedure that, in this case, explicitly identifies communities of triangles (S1 Text). Clique percolation identified 26 communities, 13 of which were single, isolated triangles, while the rest were groups of more than one triangle (Fig. 3A, S2 Fig.).
To derive a gene set associated with a community in the information graph, we took all modules within the community, computed their union within datasets, and computed their intersection across datasets (S3 Fig.). In this way, we captured all genes whose co-expression was conserved across the three datasets. (A mathematical description of this procedure is presented in the Materials and Methods.) We termed these community-derived gene sets consensus clusters (CCs). Using g:Profiler [18], we found that the consensus clusters are enriched for many biological processes (summary in Table 3; raw data in S5 Data file) present in both healthy and SSc biopsies. For example, CCs 1, 4, 5, 7, 8, and 11 are enriched for basic metabolic and cellular processes, while CC 12 showed enrichment for keratinocyte-specific processes (Table 3; Fig. 3A, cyan). These consensus clusters show that MICC extracts biologically coherent sets of genes that are known to be active in skin as consensus clusters. This provides an additional positive control for the MICC method.
More importantly, CC3 and CC9 showed enrichment for processes implicated in SSc (Table 3; S5 Data file). CC3 was enriched for response to interferons, B cell receptor signaling, monocyte chemotaxis, and TGFβ and PDGF signaling, as well as ECM remodeling processes. CC9 showed enrichment for cell cycle and cell proliferation processes, as well as integrin interactions with fibrin. Note that CC3 and CC9 both show enrichment for distinct ECM-related molecular processes. These data are consistent with the analysis of experimentally derived pathway signatures [19].
The consensus clusters CC3 and CC9 map to the major intrinsic subsets previously described [1]. We tested every module for association with the intrinsic subsets (see Materials and Methods) and we constructed a “heatmap” of the triangles in the information graph by dataset (Fig. 3B). The rows were ordered by community membership and the columns were ordered by dataset. We concatenated each of these plots so all subsets, datasets, and consensus clusters can be viewed simultaneously. Only consensus clusters 3 and 9 were enriched for SSc intrinsic subset specificity. Consensus cluster 3 contained modules that are almost all significantly expressed at high levels in the inflammatory group of patients (Fig. 3A, purple nodes; Fig. 3B). Consensus cluster 9 contains modules that are almost all significantly expressed at high levels in the fibroproliferative group (Fig. 3A, red nodes; Fig. 3B). We also included tests for all SSc biopsies versus healthy controls to determine if there were any consensus clusters that were generally conserved across all SSc biopsies. There were no consensus clusters that were enriched for all SSc versus healthy controls, which illustrates quantitatively SSc heterogeneity. Furthermore, there were no consensus clusters that were consistently expressed at low levels in any of the subsets.
Some consensus clusters are enriched for a subset in some of the datasets, but are not replicated across all datasets (Fig. 3B). For example, CC2 is expressed at high levels in the inflammatory subset and low levels in the proliferative subset in Milano, but neither of the other datasets. Inflammatory-specific CC3 is expressed at low levels in the proliferation subset in Milano and in the normal-like subset in Pendergrass and Hinchcliff, and is expressed at high levels in all SSc versus healthy controls in Pendergrass and Hinchcliff only. Similarly, CC9, which is proliferative-specific, is expressed at low levels in the inflammatory subset in Milano only. These observations demonstrate that genes with increased expression should be the focus in SSc.
The biology of CC3 and CC9 show the processes common to the intrinsic subsets that have been observed across multiple gene expression datasets: inflammation, cell interactions with ECM, and cell proliferation (Table 3). To determine if there was a more interconnected relationship between these conserved processes (such as genes related to specific cell types) than could be gained from an ontological annotation analysis like g:Profiler, we used CC3 and CC9 as a query gene set for the IMP gene-gene interaction Bayesian network (IMP) (Fig. 4) [20]. IMP is a gene-gene interaction network developed using a large compendium of high-throughput biological data including all publicly available microarray data that predicts the probability that pairs of genes have a co-expression interaction. A list of genes is imported into IMP, and a list of high-probability interactions between the genes on the imported list and (up to 50 additional genes in) the rest of the genome is generated. IMP is completely agnostic to SSc-specific biology and reports predicted interactions that are based on the preponderance of evidence across all publicly available gene expression data. As our query, we pooled the two consensus clusters CC3 and CC9 to discover possible molecular links between the inflammatory and fibroproliferative intrinsic subsets. We added polymorphic genes from genome-wide association studies (GWAS), as well as genes from candidate gene studies that have been replicated in at least one follow up study (see Materials and Methods; S6 Data file). In addition, we added four genes that are putative predictors of Modified Rodnan Skin Score (MRSS), a widely used clinical measure of skin fibrosis [5].
The output network from IMP was dominated by one large interconnected network that had five distinct subnetworks (Fig. 4; S7–S9 Data files). The five molecular subnetworks were each enriched for a distinct biological process: interferon response, M2 macrophage activation, adaptive immunity, ECM deposition and remodeling and TGFβ signaling, and cell proliferation.
One subnetwork was dominated by interferons and interferon-inducible genes (Fig. 4, top middle; S9 Data file). The interferon subnetwork contained genes solely from the inflammatory consensus cluster (Fig. 4, purple nodes). This subnetwork contained the interferon inducible genes IFI16 and IFI44, the latter of which is a putative biomarker of fibrosis [5]. This subnetwork also contains the polymorphic interferon regulatory factor genes IRF5, IRF7, and IRF8.
A second subnetwork contained genes characteristic of M2 macrophage activation (Fig. 4, bottom left; S9 Data file). The genes in this network, which include major histocompatibility complex (MHC) class II genes with SSc-associated polymorphisms, are derived primarily from the inflammatory consensus cluster, implicating macrophages as mediators of inflammation. Polarized macrophages can broadly be categorized as “classically activated” (M1) or “alternatively activated” (M2), although it is important to recognize that macrophage polarization encompasses a broad spectrum of activation states. M1 macrophages may be elicited through stimulation with IFN-γ and LPS, are microbicidal, and promote Th1-mediated immune responses. In contrast, M2 cells, which mediate immune suppression, may be activated by various stimuli, including IL-4 and/or IL-13, which are elevated in SSc sera [21], [22]. Genes associated with M2 activation, including CX3CR1 [23], IL10R [24], and HLA-DMB [25], were consistently expressed in this subnetwork, in accordance with previous studies that found increased M2-polarized macrophages in SSc skin compared to healthy skin [26]. As M2-polarized cells regulate vascularization and are a potent source of TGFβ, PDGF, and inflammatory cytokines [27]–[29], activated M2 macrophages may play a role in mediating fibrosis and inflammation in SSc.
A third molecular subnetwork contained genes related to adaptive immunity (Fig. 4, top left; S9 Data file). There are relationships to both B and T cells in the genes in this subnetwork. Two chains of the T cell receptor complex are represented: CD3G (gamma chain of the T cell receptor (CD3)) and CD247 (the zeta chain of the T cell receptor), which contains SSc-associated polymorphisms. The IL-12 pathway, which mediates Th1 cell differentiation and activation [30], [31], is represented through IL12RB2. Binding of IL-12 to IL12RB2 on activated T cells initiates a signal transduction cascade that results in activation of STAT transcription factors, including STAT4 [32] (also represented in this subnetwork), which regulate T cell signaling and immune activation [33]. Aberrant expression of IL12RB2 has been reported in autoimmune and infectious diseases [34], [35], implicating this gene as an important regulator of inflammation and immune defense.
B cell receptor activation and signaling are also represented in this subnetwork. DOCK10 expression is up-regulated in B cells by pro-inflammatory IL-4 [36], and BANK1 and BLK are B cell proteins that have polymorphisms associated with SSc. Both LYN and CSK appear in this subnetwork and are directly connected to each other. The tyrosine kinase LYN, which plays a critical role in down-regulating B cell activation and mediating self-tolerance [37], [38], is phosphorylated by CSK [39]. Polymorphisms in CSK have been linked to both SSc and systemic lupus erythematosus (SLE) and are associated with aberrant B cell signaling [40]. CSK also associates with Lyp [41], which is the product of the tyrosine phosphatase PTPN22. The PTPN22 gene also contains an SSc-associated polymorphism. Mutations in PTPN22 that interfere with its ability to bind to CSK also interfere with both B and T cell receptor activation [42], [43]. Moreover, mutations in PTPN22 have been reported in a variety of other autoimmune diseases, including SLE, rheumatoid arthritis, and type 1 diabetes [44].
Negative regulators of B and T cell activation such as SOCS2 and SOCS3, are included in this network. SOCS3 has been shown to directly inhibit IL-12-induced STAT4 activation [45]. The co-occurrence of pro- and anti-inflammatory signals in this subnetwork is notable and is likely because our data are derived from whole skin biopsies (see Discussion).
The fourth molecular subnetwork contained TGFβ pathway genes (which have long been implicated in the activation of fibrosis in SSc [46], [47]) and ECM structural proteins (Fig. 4, bottom middle). This TGFβ/ECM subnetwork contained genes from both the inflammatory and fibroproliferative consensus clusters (Fig. 4, red and purple nodes; S9 Data file). We also found expression of genes associated with Notch signaling such as NOTCH4, which contains SSc-associated polymorphisms, and with the epithelial-mesenchymal transition (EMT) such as LATS2. Alternatively activated macrophages are known to produce large quantities of TGFβ in SSc pulmonary fibrosis [29], suggesting that the M2 macrophage subnetwork could drive activation of the TGFβ/ECM subnetwork.
The final molecular subnetwork contained cell cycle/cell proliferation genes, which were primarily from the fibroproliferative consensus cluster (Fig. 4, right). The expression of proliferation genes is commonly observed in cancer [2], [7], [48] and their presence in the gene expression data of SSc was a surprising and unexpected finding [1]. The large and densely interconnected subnetwork of genes in Fig. 4 (right, red nodes) was composed almost exclusively of cell cycle-regulated genes including AURKA/B, CCNA2, CCNB1, CHK1, and DHFR [7]. This subnetwork was conserved and showed increased expression in the fibroproliferative subset of patients across all three cohorts, and constituted the core gene expression signature in that subset of patients (Fig. 4, red nodes). Therefore, the cell proliferation signature of the fibroproliferative subset of patients first observed in Milano et al. [1] is a conserved feature of SSc across three independent cohorts from three separate clinical centers. This molecular subnetwork has connections to each of the other four subnetworks (interferon, M2 macrophages, adaptive immunity, and TGFβ/ECM) suggesting that cell proliferation in SSc skin is modulated by the inflammatory and ECM remodeling processes in skin.
IMP predicts that the genes linked to SSc-associated polymorphisms (30/41 total) and the putative MRSS biomarker genes of Lafyatis and co-workers (4/4) have interactions within this large component of the molecular network (Fig. 4). Polymorphisms in IRF5, IRF7, and IRF8 were linked to the interferon subnetwork. IRF7 is also differentially expressed in the inflammatory subset. The polymorphisms associated with human leukocyte antigen (HLA) alleles predominantly have interactions with the M2 macrophage subnetwork of genes. Polymorphisms in and differential expression of NOTCH4 were linked to the TGFβ/ECM subnetwork. The same was true for the MRSS biomarker genes; IFI44 was linked to the interferon subnetwork; SIGLEC1 was linked to the M2 macrophage subnetwork; and both COMP and THBS1 were linked to the TGFβ/ECM subnetwork. These results suggest that prediction of worsening skin disease requires sampling genes from each molecular subnetwork.
The molecular network contains genes that are hubs (i.e. highly connected nodes) of the subnetworks.
Interferon-induced protein 44 (IFI44) is a hub of the interferon subnetwork. It has conserved high expression across all three of our cohorts in the inflammatory subset and is one of the most highly connected genes in the interferon subnetwork (Fig. 5, top right). IFI44 is predicted to have co-expression interactions with several other interferon-inducible and interferon-regulating genes, including IFI16, IRF7, IFITM2, ISG20, GBP1, and TRIM22.
Allograft Inflammatory Factor 1 (AIF-1) is a hub of the M2 macrophage. AIF1 is consistently highly expressed in the inflammatory subset across all three SSc skin cohorts and is one of the most highly connected genes in the M2 macrophage subnetwork (Fig. 5, bottom left). In the molecular network (Fig. 5, bottom left), AIF1 has many links including: ITGB2, a binding partner of the monocyte marker ITGAL, and MHC class II genes HLA-DMB, HLA-DPA1, and HLA-DQB1. In addition, AIF1 has connections to chemokine receptors CCR1 and CX3CR1, which are connected to chemokines CX3CL1 (fractalkine) and CCL2 (MCP-1).
The tyrosine kinase gene LYN is a hub of the adaptive immunity subnetwork (Fig. 5, top left). LYN has predicted edges with four polymorphic genes in this subnetwork: BLK, BANK1, CSK, and GRB10. LYN also has connections to the polymorphic, bridge genes PLAUR and LCP2 (see below), and suppressors of cytokine signaling genes SOCS2 and SOCS3. The conserved finding of high expression of LYN in the inflammatory subset and its centrality within the adaptive immune subnetwork suggests that LYN plays a key role in the adaptive immune component of SSc in skin.
Fibrillin-1 (FBN1) is a hub of the TGFβ/ECM subnetwork (Fig. 5, bottom right). High expression of FBN1 is conserved across the inflammatory subset of all three cohorts of SSc skin, and FBN1 is highly connected within the TGFβ/ECM subnetwork of the molecular network (Fig. 5, bottom right). The TGFβ/ECM subnetwork includes genes that primarily show high expression in the inflammatory subset but also includes genes that are highly expressed in the fibroproliferative group, thus providing a putative molecular link between the two groups. FBN1 has predicted connections to many genes whose increased expression is conserved, including: pro-fibrotic genes including COL1A2, COL5A2, and elastin (ELN), CTGF, SPARC, THBS1, THBS4, COMP, TNC and ECM remodeling and wound response genes LOX, NNMT, and FBLN5. In addition, FBN1 has connections with growth factor genes and receptors such as HTRA1 and NOTCH4; cell adhesion genes CDH11 and LAMA4; as well as the complement system gene C1S.
In addition to containing discrete subnetworks, the molecular network also shows genes that bridge the subnetworks (Fig. 6). These genes are of particular interest because they have predicted connections between multiple, distinct subnetworks. The primary reason for using CC3 and CC9 simultaneously as queries to IMP was to identify possible molecular connections between the core molecular processes of the inflammatory and fibroproliferative intrinsic subsets. The bridge genes live at the interfaces between the subnetworks that constitute these core molecular processes.
The genes CXCR4 and LCP2 are the major connections between the adaptive immunity subnetwork and the M2 macrophage subnetwork (Fig. 6). LCP2 (SLP-76), which modulates T cell activation [49], has predicted interactions with AIF1 and IL10RA in the M2 macrophage subnetwork and to SOCS2, SOCS3, STAT4, LYN, and CSK in the adaptive immunity subnetwork (see edges extending from LCP2 in Fig. 6). The chemokine CXCR4 has predicted interactions with the cytokines/chemokines IL10RA, CX3CR1, CCR1, and the polymorphic CCR6 in the M2 macrophage subnetwork (Fig. 6). CXCR4 has predicted interactions with SOCS3 and JAK3 in the adaptive immunity subnetwork.
GRB10 contains an SSc-associated polymorphism and is also expressed at high levels in the inflammatory subset (see blue GRB10 node, Fig. 6). GRB10 is part of a complex path from the adaptive immune subnetwork to the M2 macrophage subnetwork that includes genes containing pleckstrin homology domains including PLEKHO1, PLEKHO2, CYTH4 and ADAP2.
The major connection between the M2 macrophage subnetwork hub AIF1 and the interferon subnetwork hub IFI44 is through RAC2. RAC2 encodes a member of the Rac family of signaling molecules and has multiple predicted interactions with both the interferon subnetwork and the M2 macrophage subnetwork (Fig. 6). In the interferon subnetwork (Fig. 6, upper middle), RAC2 connects to CTSC (cathepsin C), IFITM1 and IFI16, as well as the Rho GTPase related genes ARHGDIB and RAB31. In the M2 macrophage subnetwork (Fig. 6, lower left), RAC2 connects to ITGB2, the actin cytoskeleton related proteins LCP1 and COTL1, and GMFG. COTL1 is also related to leukotriene biosynthesis through a known interaction with ALOX5. These diverse interactions suggest that RAC2 is involved simultaneously in macrophage motility, leukotriene biosynthesis, and interferon signaling.
The major bridges between the M2 macrophage subnetwork and the ECM subnetwork are THY1 (CD90) and CD14 (Fig. 6, lower left). THY1 connects to SIGLEC1, MXRA5 and COL1A2. THY1 mediates adhesion of leukocytes and monocytes to endothelial cells and fibroblasts [50], may also have a role in lung fibrosis (a major complication of SSc); THY1 knockout mice have increased lung fibrosis [51], [52]. CD14 is a cell surface protein mainly expressed by macrophages, is inducible by and connected to AIF1 [53]. It also has connections to the polymorphic genes TLR2 and HLA-DRA (Fig. 6, lower left).
PLAUR (UPAR) contains a putative SSc-associated polymorphism, is a member of the interferon subnetwork, and has numerous links with the ECM, M2 macrophage, and the adaptive immunity subnetworks (Fig. 6). PLAUR encodes the plasminogen activator, urokinase receptor protein and is a pleiotropic gene at the interface of ECM remodeling, as a component of the fibrinolysis system, and in both adaptive and innate immune processes, including monocyte migration [54]. PLAUR is inducible by proinflammatory cytokines IL1β and TNFα. PLAUR connects to the tyrosine kinase LYN, the hub gene of the adaptive immunity subnetwork, and to the integrin gene ITGB2 in the M2 macrophage subnetwork. It is also connected to the polymorphic genes TNFSF10B and TNFAIP in the interferon network and to TPM4, INHBA, THBS1, and CCL2 in the ECM subnetwork. The centrality of PLAUR within the consensus gene network suggests that PLAUR may be a key mediator of inflammatory and ECM remodeling signals in SSc skin.
The proliferation subnetwork has predicted interactions with the inflammatory and ECM subnetworks. The most pronounced connection is between the ECM subnetwork and the cell proliferation subnetwork through TGFβ pathway genes (Fig. 6). The TGFβ pathway is known to modulate cell proliferation. There are multiple paths from the TGFβ pathway genes TGFB3 and TGFBR2 to the cell proliferation subnetwork through the polymorphic genes IRAK1 and PXK, which have predicted interactions with the serine/threonine kinases LATS2, WNK4, and PRKAA1. Serine/threonine kinases are well known to be important regulators of cell proliferation and they are bridges between the ECM subnetwork and cell proliferation network.
The intrinsic subsets of SSc have been found in multiple skin gene expression datasets. Until now, the majority of experimental data has indicated that the subsets are mutually exclusive—i.e. patients are categorized as being in one of the subsets, and that the core molecular processes and subsets of genes, are reproducible across cohorts. Despite this consistency, the exact set of intrinsic genes varies across datasets. We address both of these issues here. Our consensus clustering approach allowed us to detect a conserved set of genes from a module perspective across the three independent SSc patient cohorts by considering molecular processes first and constituent genes second. The predicted co-expression interactions between these consensus genes indicate that the key processes represented by the consensus genes (inflammation, ECM remodeling, and cell proliferation) may interact at a molecular level, with specific links between the subnetworks. Thus, we have demonstrated theoretical connections between the genes of the SSc intrinsic subsets that are difficult to capture experimentally.
It is clear that in addition to its clinical heterogeneity, SSc is a genetically complex disease. Many risk alleles for SSc have been identified, but each has only a modest odds ratio and the complete picture of SSc will likely develop from the interactions between various risk factors. The network of consensus genes demonstrates that a significant fraction of the genes with risk alleles for SSc have probable interactions with the consensus genes that underlie the intrinsic gene expression subsets. This implicates these polymorphic genes as interacting with genes differentially expressed in the subsets. This simultaneously provides a picture of the key gene expression abnormalities in the intrinsic subsets and the validated genetic associations at a systems level.
These data and the resulting network were developed from a detailed meta-analysis of SSc skin gene expression datasets using MICC, a consensus clustering framework we developed. Our method reports only consensus clusters that are conserved across all input datasets and dispenses with non-conserved gene expression. The concept of mutual information gives MICC a theoretical foundation, but like any data mining algorithm, its value is gauged by performance on real data.
The rationale for gene coexpression clustering algorithms like WGCNA is that co-expression networks are inherently modular and that co-expression hub genes are likely related to the regulation of the modules. This has been borne out by several studies in humans [55], mice [56], and even across species [57]. The genes identified by MICC are disproportionately more hub-like than a random population of the same size (S1 Fig.). Therefore, MICC does not identify spurious overlaps but rather detects network-relevant overlaps that are enriched for key hub genes. At the same time, the information graph used by MICC is not simply a disconnected set of triangles, which would indicate a one-to-one mapping of modules between datasets. Instead, the modules in one dataset are broken into a small set of pieces that are re-assorted to build the modules in another dataset. This is likely due to variations in study design and protocols between the datasets, but also the inherent heterogeneity of SSc, therapy effects, and environmental exposures. The MICC method is explicitly designed to handle this unavoidable variance by broadening the definition of consensus cluster to allow for imperfect conservation of gene coexpression. We also note that MICC is completely general with respect to the data that are clustered and which clustering algorithms are used. In principle, gene expression from different tissues (e.g. blood and skin) or different species (e.g. mouse and human) or data from multiple experimental modalities (e.g. transcriptomics and proteomics) can be compared using MICC. These types of data exist for multiple tissues in SSc and multiple animal models of SSc. Follow-up studies will integrate these to further elaborate the molecular underpinnings of SSc.
The consensus clusters from MICC show both skin-specific processes that represent basic biological processes in this tissue as well as disease-specific processes. Nearly all (24 out of 26) consensus clusters are enriched only for general cellular or otherwise skin-specific biology: metabolism, cell turnover, keratinocyte-specific gene expression, etc. We view these consensus clusters as a useful positive control for the MICC method. Such housekeeping processes are clearly biologically relevant and MICC would be missing important structure in the data if these were not found. By taking a “module first” approach, MICC is able to identify consensus genes that are specifically clustered into pathologically active modules (the subset-specific modules).
Two of the consensus clusters were SSc subset-specific (Fig. 3B). These clusters contain the key gene expression abnormalities in SSc that are conserved across all three cohorts. The consensus clusters are enriched for inflammatory process Gene Ontology terms, as well as TGFβ signaling, PDGF signaling, and cell proliferation (Table 3). Most (30 out of 41) of the genes with replicated SSc-associated polymorphisms are predicted to interact with genes in the consensus clusters; 28 out of 30 of these interact in the immune (interferon, M2 macrophage, and adaptive immunity) and TGFβ/ECM subnetworks (Fig. 4). The inflammatory-specific consensus cluster also contains the genes FBN1 and AIF1. Previous work implicates FBN1 in SSc pathogenesis, as a duplication of FBN1 causes fibrosis in the Tsk1 mouse [58] and a point mutation in FBN1 causes the fibrotic phenotype in the Stiff Skin Syndrome mouse [59]. Fibrillin-1 forms a matrix of elastic microfibrils that provide a scaffold for elastins and collagens, and a means for sequestering matricellular growth factors. Mouse embryonic fibroblasts expressing the Tsk1 mutant FBN1 have altered microfibril morphology that results in increased collagen deposition [58]. While polymorphisms in FBN1 might cause dosage effects that result in fibrosis in some models (e.g. Tsk1), it is possible that chronic inflammation causes chronic high expression of FBN1 to similar effect in humans. Rare polymorphisms in FBN1 have been associated with SSc in some subpopulations [60]–[62].
Similarly, AIF1 is implicated in SSc disease progression. A SNP in AIF1 has been implicated in anticentromere antibody (ACA) positive SSc [63]. Moreover, AIF-1 is interferon-inducible, constitutively expressed in macrophages [64], and plays a role in vasculogenesis and endothelial cell proliferation and migration [65]. In the Sclerodermatous Graft-Versus-Host Disease (sclGVHD) mouse model of SSc, AIF1 was found to be highly expressed in skin [66] and to induce fibroblast and monocyte chemotaxis [53]. AIF1 has many predicted interactions with chemokine receptors CCR1 and CX3CR1, which are connected to chemokines CX3CL1 (fractalkine) and CCL2 (MCP-1). The genes CX3CL1 and CCL2 are M1 and M2 macrophage-related genes respectively [67] and are chemotactic for monocytes, macrophages, and T cells [68], suggesting enhanced recruitment of inflammatory cells to this subnetwork. A recent study of a mouse model of SSc demonstrated that both CCR2 and CX3CR1 regulate skin fibrosis, further implicating these mediators in the pathogenesis of SSc [69]. In addition, CCL2 has been shown to induce M2 macrophage polarization [70], which may result in persistent M2 activation. The repeated and conserved finding of high AIF-1 levels in the inflammatory subset and its tight connection to innate immune mediators of inflammation suggest it may be involved in enhanced macrophage chemotaxis and activation in SSc skin.
LYN, a hub of the adaptive immunity subnetwork, modulates B cell activation and plays a role in self-tolerance. B cell signaling has been implicated in SSc development and progression, as B cells have been shown to play a role in both the development of autoantibodies and cutaneous fibrosis in the Tight Skin 1 (Tsk1) mouse model of SSc. Notably, LYN is overactive in response to overexpression of CD19 in this model [71]. Thus, LYN may play a role in the autoimmune component of SSc in human patients.
The consensus gene network (Figs. 4 and 6) also implicates genes as bridges between the subnetworks. These notably include the polymorphic genes PLAUR, IRAK1, PXK, and GRB10. In addition, we find differentially expressed genes straddling the subnetworks including RAC2 and LCP2. The interconnections between the subnetworks present possible molecular paths through which these processes interact.
The finding that most SSc-associated polymorphisms are associated with immune system mediators suggests that the initial events in SSc are likely to be immune-regulated and to involve interferon activation (Fig. 7). The immune response in SSc likely differs from a normal response because of predisposing genetic variants in these and associated genes. This may lead to the secondary recruitment of macrophages via RAS-RAC signaling (Fig. 7).
We predict that the interferon network suppresses cell proliferation, given the clear distinction between the inflammatory and fibroproliferative subgroups. This inference is based on known interferon biology and not on the network itself. In contrast, it is possible that the ECM network stimulates cell proliferation through the TGFβ pathway and serine/threonine kinases IRAK1, LATS2, WNK4, and PRKAA1. In this model, inflammatory gene expression creates a balancing feedback loop that modulates fibroproliferative gene expression (Fig. 7).
A major strength of the IMP network and its data integration capabilities derives from its ability to provide a more detailed picture of SSc development and progression compared with more conventional approaches. For example, while all of the purple nodes in Fig. 4 are highly expressed in the inflammatory group across all data sets, the IMP network provides information regarding gene-gene interactions in addition to expression data. In this example, the IMP network indicates which subnetworks correspond to discrete processes (interferon, M2 macrophages, ECM, and adaptive immunity) and which interactions are mediated through the network. Thus, we gain insight by recognizing that the interferon component is distinct from the M2 macrophage component, despite their co-expression and known interdependence. The value of the IMP network is as much in the connections that are not present as those that are.
Since the original publication of the intrinsic subsets, two important questions have been central to their interpretation and their clinical relevance: First, can a patient's subset change over the course of their disease? And second, can the subsets predict therapeutic response?
Pendergrass et al. [11] demonstrated that a patient's subset is stable over time scales of 6 to 12 months. This means either that patients never change subsets and the intrinsic subsets are effectively distinct diseases, or that the subsets are long-lived states of the same disease. Our analysis shows that the inflammatory and fibroproliferative subsets share a molecular network containing TGFβ pathway genes and ECM component genes, suggesting that inflammatory patients may transition to the fibroproliferative subset, perhaps in response to successful immunosuppressive therapy. Indeed, immunosuppressive therapy has not been widely successful for treatment of SSc [72]. On the other hand, fibroproliferative biopsies still have some activation of the TGFβ/ECM network despite the absence of the inflammatory signature (Fig. 4). The connection of the subsets through the TGFβ/ECM subnetwork indicates that the fibroproliferative subset shares a common pathway with the inflammatory subset and that the fibroproliferative subset is tied to chronic TGFβ activation and ECM deposition. Thus, based on the molecular network, it is possible that immunosuppressive therapy can move patients to the fibroproliferative subset rather than restoring their gene expression to that of healthy skin. Our data from an ongoing MMF clinical trial and analysis of mouse models of SSc suggests that gene expression changes precede clinical changes [4], [66]; therefore gene expression could act as a readout for the effectiveness of a drug. This idea should be rigorously tested in clinical trials that carefully monitor gene expression in patient skin biopsies.
The pathogenesis of SSc has been enigmatic, but a number of genetic risk factors have been identified by genome-wide association studies and candidate gene studies. Three of these polymorphic genes, NOTCH4, IRF7, and GRB10, are in the inflammatory consensus cluster, and hence are consistently differentially expressed in the inflammatory subset (Fig. 4). This suggests that these may be cis-acting alleles and demonstrates the need for candidate gene studies to determine if differential expression is genetically driven in a subset of patients. The IMP functional network predicts that twenty-five of the remaining forty-one polymorphic genes interact with genes from the inflammatory consensus cluster (Fig. 4). Rather than being scattered evenly across all of the subsets or unrelated to any of the consensus genes, the risk alleles are overwhelmingly related to the inflammatory subset. The genetic studies, however, did not stratify their patients by intrinsic gene expression subset. The studies were carried out as case versus control or case versus case, when stratified by autoantibody status or other clinical outcomes. Risk alleles associated with a particular gene expression subset have not been reported. We reemphasize the fact that we found no consensus clusters that were differentially regulated in all SSc vs. healthy control biopsies.
These data support the hypothesis that the subsets are related to disease progression and that SSc starts with immune activation, perhaps in response to an environmental trigger [73], [74]. The SNPs associated with SSc would then likely be risk factors for an aberrant immune response to this trigger. Should such a model be correct, we are still left with the question of why we have different subsets that generally show little or no correlation with disease duration. The simplest explanation for this result is that patients progress through the gene expression subsets at dramatically different rates and that our measures of disease duration are currently inadequate.
Another possibility is that any given patient transitions between these intrinsic gene expression groups in a dynamic manner that we do not observe using serial skin biopsies across 6–12 month time interval. This would mean that cross-sectional studies of patients would still capture all subsets while maintaining a weak correlation to disease duration. We think this is unlikely because serial biopsies are generally found in the same subset.
The final possibility is that the subset a patient stays in, and the duration in which they remain, is dependent on many outside and as yet poorly characterized factors. These could include environmental stimuli that trigger an inflammatory response, or genetic factors that determine the rate at which one progresses through the mechanistic stages of SSc. It is possible that patients in each intrinsic subset have a different set of predisposing genetic polymorphisms or similar environmental triggers. This can only be addressed if we can look for genetic risk factors in a cohort of patients stratified by gene expression subset for genetic risk alleles. There may be genetic risk factors that cause a patient to “stall” at particular point along the progression from inflammatory to proliferative to normal-like. Genetic modifiers of the molecular links in the consensus gene network (Fig. 4) might hold the key to showing why many patients go into spontaneous remission while others experience rapid clinical progression, and indeed, our network analysis suggests candidates for explaining this (Figs. 6, 7). For example, IRAK1 and PXK are polymorphic genes that exist on paths in the network between the TGFβ/ECM network and the cell proliferation network. This strongly argues for future studies that test their possible roles in TGFβ-modulated cell proliferation, with particular attention to their roles in influencing other serine-threonine kinases that modulate the cell cycle.
The presence of antinuclear autoantibodies in patient serum is a widely used biomarker of SSc. To date the intrinsic subsets have shown no clear association with autoantibody status [1], [4], [11], which is consistent with a model by which the subsets represent disease progression. Several genetic polymorphisms are associated with autoantibody status (S9 Data file), including BLK and BANK1, which are related to ACA- and ATA-positive SSc respectively. These B cell proteins are already attractive candidates for autoantibody production, as they are directly associated with the cells that produce the antibodies, but our network analysis also shows that they are functionally related to adaptive immune genes that are highly expressed in the inflammatory subset.
A primary role of bioinformatics in complex diseases is to pare down the possibilities to a coherent set of candidates for future study. The many risk alleles for SSc each have modest odds ratio and the final picture of SSc will likely lie in the interactions between various risk factors, but the number of possible interactions between these combinatorial factors is prohibitively large. It is here that the network approach may be most useful in delineating candidates for interaction studies. We might speculate, for example, that SSc results from the presence of multiple, functionally distinct alleles, but that it does not matter what gene is mutated as long as the mutation has a particular functional outcome. The predicted interactions in the network suggest which alleles might be functionally related and which might be distinct from each other, as the alleles either cluster within a subnetwork or straddle the subnetworks.
This report is limited by our utilization of whole skin biopsies, which are complex mixtures of cells, and in that the studies were observational. The use of whole skin means that we cannot directly ascribe gene expression to specific cell types. For example, we infer that the M2 macrophage subnetwork is related to that cell type based on the coherent expression of monocyte markers and cytokines related to M2 polarization of macrophages. Our study is therefore hypothesis generating. Mechanistic studies will be needed to evaluate the existence of the molecular links suggested by the network analysis.
Nevertheless, our analyses place the intrinsic subsets as a possible readout of SSc pathology. The consensus gene expression of the subsets implicates a number of molecular mechanisms that have been associated with SSc and suggests functional roles for a large fraction of the replicated SSc-associated polymorphisms. We demonstrate that the core molecular processes of the inflammatory and fibroproliferative subsets are molecularly connected to each other. This suggests the possibility that SSc subsets may be dynamic and interconnected.
The analysis of prospectively collected human samples in this study was approved by the Committee for the Protection of Human Subjects at Dartmouth College (CPHS#16631) and by the IRB review panel at Northwestern Feinberg School of Medicine (STU00004428). All subjects in the study provided written consent, which was approved by the IRB review panels at Dartmouth College and Northwestern Feinberg School of Medicine.
This study used data from three previously published cohorts (Table 1). Each of the studies is available from NCBI GEO at the following accession numbers: Milano et al. (GSE9285), Pendergrass et al. (GSE32413) and Hinchcliff et al. (GSE45485). We used an expanded version of the Hinchcliff dataset that contained an additional 12 SSc patients, 1 healthy control and 1 morphea patient beyond what was included in Hinchcliff et al.[4] (GSE59785).
Each of the three study cohorts contained patients with SSc defined using the 1980 ACR criteria. Specifically, all patients met the American College of Rheumatology classification criteria for SSc [75] and were further characterized as the diffuse (dSSc), or the limited (lSSc) subsets. Limited SSc patients had 3 of the 5 features of CREST syndrome, or had Raynaud's phenomenon with abnormal nail fold capillaries and scleroderma-specific autoantibodies.
All three studies used Agilent Technologies 44,000 element DNA microarrays representing the full human genome. All samples were processed and all microarrays hybridized in the Whitfield lab providing consistency between the datasets. The DNA probes between these datasets are identical and thus were indexed using the same probe identifiers allowing direct mapping from one data set to another without significant loss of data. Microarray data from each cohort were Log2 Lowess-normalized and only spots with mean fluorescent signal at least 1.5 greater than median local background in Cy3- or Cy5- channels were included in the analysis. Genes with less than 80% good data were excluded. Since a common reference experimental design was used for all cohorts, each probe was centered on its median value across all arrays. Data were multiplied by -1 to convert them to Log2(Cy3/Cy5) ratios.
The three cohorts were clustered into coexpression modules using the WGCNA procedure. We used the WGCNA R package available on the Comprehensive R Archive Network (http://cran.r-project.org) and described in [10]. We used the default parameters for running the software except that we used the “signed” network option and a soft thresholding parameter d = 12. These parameters are described in depth in [10], [15]. Genes that were classified as outliers were discarded from further analysis.
To each pair of modules from different datasets we associate an overlap score W. Specifically, if Ci is a module in, say, Milano et al. and Cj a module in Pendergrass et al., then we definewhere N is the total number of genes in the genome. The W-scores can be interpreted as edge weights in a module-module network (the information graph). This network encodes the mutual information between the WGCNA-derived genomic partitions. We computed the W-scores between each pair of modules across all three datasets by the above formula and set the small and negative W-scores below a threshold to zero (S4 Fig.). A mathematical derivation of the relationship between the W-scores and mutual information and a detailed description of the thresholding procedure are available in the supporting information (S1 Text). The resulting 3-partite information graph was mined for consensus clusters.
Since triangles in the information graph represent a module conserved across all three datasets, we clustered the information graph using a variant of triangle percolation [17], which is a community detection procedure designed to find sets of modules that are members of many triangles together. Specifically, from the information graph we constructed an auxiliary graph, called the triangle graph, and detected communities in the triangle graph by greedy modularity maximization [76]. A description of the construction of the triangle graph is available in the supporting information (S1 Text).
We define a final consensus cluster as all of the genes that are contained in a module from the community for each of the three data sets community. Note that triangle percolation allows for overlapping communities in the underlying information graph. For example, the inflammatory consensus cluster and the keratinocyte consensus cluster overlap by one module (Fig. 3A). This is one of MICC's strengths because it does not require a whole module from one dataset to be associated with only one consensus cluster. To derive a gene set associated to the consensus cluster, we took all modules within that community, computed their unions within their dataset, and then computed their intersection across datasets. In symbols, let Comm denote a set of modules that form a community in the information graph (e.g. the dotted circles Fig. 1B and the colored nodes of Fig. 3A). Let MComm, PComm, and HComm denote respectively the sets of Milano, Pendergrass and Hinchcliff modules within Comm. Let m, p, and h denote modules in the Milano, Pendergrass, and Hinchcliff data sets respectively; note that these are sets of genes. We associate a gene set CCComm with the community Comm through the following formula:We call CCComm the consensus cluster associated with the community Comm and it consists of all genes that are present in a module from each data set within the community. The elements of CCComm are the consensus genes. It is clear by definition that the consensus clusters are nonoverlapping even though communities can share modules. This is because a gene needs to be present in a module in the community from each of the three data sets. Since modules do not overlap within data sets, consensus clusters cannot either.
To determine if a WGCNA-derived module was significantly differentially regulated in a subset, we performed one-tailed Wilcoxon rank sum tests. Specifically, we computed the module eigengene of each module by first normalizing the gene expression so that each gene expression vector had Euclidean length 1. The module eigengene is the first principal component of the normalized gene expression vectors within the WGCNA module. The module eigengene is a one-dimensional summary score for the module's gene expression across all biopsies. To determine if the module was significantly up- or down-regulated in a particular subset, we determined if the median of the module eigengene for that subset was above or below that of the whole population, and then performed a one-tailed Wilcoxon rank sum test to determine the significance of the median being above or below that of the population as a whole. We used the subset assignments reported in the previous papers describing these datasets [1], [4], [11]. We used Bonferroni corrections for multiple comparisons. There were 178 modules in total across the datasets. In Table 2, we corrected for 178×3 tests for each of the subset-specificity tests. In Fig. 3B, we corrected for 178×4 tests because we included tests for all non-normal-like SSc versus normal-like SSc and healthy controls (see also S4 Data file).
The IMP Bayesian network is available through an online interface at (http://imp.princeton.edu). To build our network, we queried IMP with four gene sets: inflammatory and fibroproliferative consensus genes derived from the consensus clusters, SSc-associated polymorphisms (as described below), and the four gene MRSS biomarker reported in [5]. IMP provides export of the subnetwork corresponding to the query genes as a weighted edge list (a three-column table indicating which genes are connected and with what probability). IMP automatically thresholds the probabilities at 0.5 and exports the network with up to an additional 50 genes that provide extra context for the query genes. In our case, the 50 genes were predominantly cell cycle genes. This is probably because the cell cycle is heavily studied in the microarray compendium from which IMP was built. In that case, IMP would be highly confident about predicting interactions between the fibroproliferative genes and other cell cycle genes.
We developed in-house Matlab and R scripts to transform the edge list data into the Graph Exchange Format (gexf), which allows for manipulation in Gephi, an open source network visualization program [77]. Data files S7-S8 contain post-processed networks and Data files S10-S11 provide R data and code snippets for manipulating the network programmatically.
We collected genes with SSc-associated polymorphisms from the literature and curated them according to the following criteria. We included polymorphic genes that were reported in genome-wide association studies of SSc [78]–[82], from a recent study using the Immunochip platform [83] and from case-control candidate gene studies that were replicated in at least one other study [54], [84]–[105]. This resulted in a list of 41 polymorphic genes (S6 Data file).
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10.1371/journal.pbio.0050328 | Basic Math in Monkeys and College Students | Adult humans possess a sophisticated repertoire of mathematical faculties. Many of these capacities are rooted in symbolic language and are therefore unlikely to be shared with nonhuman animals. However, a subset of these skills is shared with other animals, and this set is considered a cognitive vestige of our common evolutionary history. Current evidence indicates that humans and nonhuman animals share a core set of abilities for representing and comparing approximate numerosities nonverbally; however, it remains unclear whether nonhuman animals can perform approximate mental arithmetic. Here we show that monkeys can mentally add the numerical values of two sets of objects and choose a visual array that roughly corresponds to the arithmetic sum of these two sets. Furthermore, monkeys' performance during these calculations adheres to the same pattern as humans tested on the same nonverbal addition task. Our data demonstrate that nonverbal arithmetic is not unique to humans but is instead part of an evolutionarily primitive system for mathematical thinking shared by monkeys.
| Adult humans possess mathematical abilities that are unmatched by any other member of the animal kingdom. Yet, there is increasing evidence that the ability to enumerate sets of objects nonverbally is a capacity that humans share with other animal species. That is, like humans, nonhuman animals possess the ability to estimate and compare numerical values nonverbally. We asked whether humans and nonhuman animals also share a capacity for nonverbal arithmetic. We tested monkeys and college students on a nonverbal arithmetic task in which they had to add the numerical values of two sets of dots together and choose a stimulus from two options that reflected the arithmetic sum of the two sets. Our results indicate that monkeys perform approximate mental addition in a manner that is remarkably similar to the performance of the college students. These findings support the argument that humans and nonhuman primates share a cognitive system for nonverbal arithmetic, which likely reflects an evolutionary link in their cognitive abilities.
| The fact that humans and nonhuman animals represent numerical values nonverbally using a common cognitive process is well established [1–7]. Both human and nonhuman animals can nonverbally estimate the numerical values of arrays of dots or sequences of tones [8–12] and determine which of two sets is numerically larger or smaller [13–19]. When adult humans and nonhuman animals make approximate numerical comparisons, their performance is similarly constrained by the ratio between numerical values (i.e., Weber's law; [7]). Thus, discrete symbols such as number words and Arabic numerals are not the only route to numerical concepts; both human and nonhuman animals can represent number approximately, in a nonverbal code.
The parallel psychophysics for number discrimination in adult humans and various nonhuman animal species implicates an evolutionarily ancient system for representing number. Within this system, numerical representations take on an analog-magnitude format: mental representations of numerical values are proportional to the numerosities they represent (e.g., [8,16]). A key advantage for representing number in an analog format is that these representations can enter into arithmetic operations such as ordering and addition [7]. However, although there is a great deal of evidence that animals represent the ordinal relationships among numerosities (e.g., [14–17]), few studies have addressed whether animals can perform other arithmetic operations, and even fewer studies have directly compared performance between adult humans and nonhuman animals on the same arithmetic task.
Arithmetic operations—such as addition, subtraction, division, and multiplication—require mental transformations over numerical values. Addition is an arithmetic operation that involves combining two or more quantitative representations (addends) to form a new representation (the sum). The ability to mentally combine representations is inherent to many aspects of human cognition including language and symbolic mathematical expression [20]. One possibility, then, is that the ability to combine representations, whether linguistic or arithmetic, is unique to humans.
There is, however, already some evidence that nonhuman animals can perform approximate, nonverbal addition on numerical values [21–28]. For instance, Flombaum, Junge, and Hauser [21] found that when untrained rhesus monkeys watched as two groups of four lemons were placed behind a screen, they looked longer when the screen was lowered to reveal only four lemons (incorrect outcome) than when the correct outcome of eight lemons was revealed (see also [22,23]). Thus, as measured by their looking time, monkeys spontaneously form numerical expectations when they view addition events. Moreover, Beran and colleagues [24–26] have demonstrated that nonhuman primates reliably choose the larger of two food quantities, even when this requires tracking one-by-one additions to multiple caches over time. Such data provide important evidence that animals can form numerical representations when this requires one-by-one accumulation, but they leave open the question of whether animals can perform nonverbal arithmetic by combining set-level representations.
Other studies have trained animals to associate arbitrary symbols with numerosities and then tested the animals' ability to add symbols [27,28]. For example, pigeons reliably chose the combination of two symbols that indicated the larger amount of food [27]. However, when the number of food items associated with the symbols was varied but total reward value (mass) was held constant, the pigeons failed to determine the numerical sum of the food items, suggesting that they performed the addition task by representing the total reward value represented by the two symbols, rather than by performing numerical arithmetic. Thus, food items may not be an optimal stimulus for testing pure numerical arithmetic in nonhuman animals.
To date, the most persuasive test of arithmetic in a nonhuman animal was conducted on a single chimpanzee [28]. In this study, a symbol-trained chimpanzee chose the Arabic numeral that corresponded to the sum of hidden sets of oranges, for sets that summed to less than four items, over 14 test trials. In contrast, studies of adult human nonverbal addition have tested many trials with a large range of numerical values and arithmetic problems (e.g., [4,13,29,30]). Thus, although there is suggestive prior evidence that nonhuman animals may perform mental arithmetic, the data are not definitive. An important limitation of all prior studies of nonhuman arithmetic is that they used drastically different methods from those used to test adult human nonverbal arithmetic. The degree to which nonhuman arithmetic parallels the nonverbal arithmetic of adult humans is therefore undetermined.
Several studies provide compelling evidence that without verbally counting, adult humans can choose the approximate sum of two or more sets. These studies required subjects to add two arrays of arbitrary elements and then select the correct sum, over hundreds of trials, testing a wide range of numerical values (e.g., [4,13,29,30]). For example, in one study [13], adults were presented with two arrays of dots (of 1–62 elements) and were required to mentally add the numerical values of the sets to determine whether a third test array was approximately equal to their sum. Performance was modulated by the subjective difference between the correct sum and the test array (i.e., Weber's law); accuracy declined as the ratio between the choices (smaller value/larger value) approached one. Thus, adult humans have the capacity for precise symbol-based arithmetic, and they are also able to perform approximate addition on nonsymbolic quantities.
Our goal was to compare directly the nonverbal arithmetic abilities of monkeys and adult humans using the same task and stimuli. Monkeys (n = 2) and college students (n = 14) were presented with two sets of dots on a touch screen monitor separated by a delay (Figure 1). Following the presentation of these two sets, subjects were required to choose between two arrays: one with a number of dots equal to the sum of the two sets and a second, distractor array, which contained a different number of dots. Our results indicate that monkeys perform approximate mental addition in a manner comparable to college students tested on the same addition task.
During the initial phase of training for the addition task, we presented monkeys with a limited set of addition problems (1 + 1 = 2, 4, or 8; 2 + 2 = 2, 4, or 8; 4 + 4 = 2, 4, or 8). Monkeys performed at a level significantly greater than chance on each of these three problems within 500 trials (Figure 2). Performance on the 2 + 2 addition problem was significantly worse than performance on the 1 + 1 and 4 + 4 problems for both monkeys (p < 0.05). This finding suggests that monkeys' performance resulted from approximate arithmetic even during this early stage of training, because the discrimination ratio of the sum to the choice stimuli was more difficult for the 2 + 2 problems than either the 1 + 1 or 4 + 4 problems. More specifically, the distractor values we tested resulted in more difficult numerical discriminations for the 2 + 2 problems (mean discrimination ratio = 0.5) than the 1 + 1 and 4 + 4 problems (mean discrimination ratios = 0.38). However, during this initial training, monkeys may have learned the specific relationships between the addends and sums for this limited set of problems rather than performing true addition. That is, monkeys may have formed associations between a particular pair of addends and its resulting sum.
Next, we expanded the range of addition problems to include the numerical values 2, 4, 8, 12, and 16. All possible permutations of addends summing to these values were tested (e.g., sum of 8 = 1 + 7, 2 + 6, 3 + 5, 4 + 4, 5 + 3, etc.) and all values were equally likely to occur as correct and incorrect choices. Monkeys' performance was modulated by the ratio between the numerical values of the choice stimuli; they performed significantly better when the numerical difference between the choice stimuli was easier to discriminate (Figure 3).
To confirm that monkeys' performance was modulated by the ratio between the numerical values of the sum and choice stimuli, we tested monkeys' performance against a mathematical model developed by Stanislas Dehaene ([4,13] and see [31] for full description of model] for human nonsymbolic arithmetic performance.
This model represents each of the two addends (n1, n2) and the distractor value (n3) as a Gaussian distribution with a mean equal to their numerical value and a standard deviation that increases proportional to the mean. In addition, the model includes a parameter for the internal Weber fraction (w), which reflects the amount of variability, or noise, in the distributions. This version of the model includes a parameter (λ), which modulates additional variability associated with the sum of the addends after they have been added together and stored, temporarily, in memory [31]. Here, the best fitting value for λ ranged from 1.3–1.5, although simpler implementations of this model have set λ = 0 (e.g., [4,13]). As a consequence of these parameters, the predicted probability of selecting the correct sum from the distractor choice depends on the ratio between the numerical values of the sum and distractor choice, the degree to which numerical values at this ratio are internally distinct (w), and the added variability associated with forming the initial representation of the sum (λ). In short, this model predicts the probability of success on a given addition problem under Weber's law.
We implemented this model to obtain the predicted performance for the addition problems tested and used a goodness of fit test (r2) to determine the w that best accounted for the monkeys' performance (Figure 3). We found that this model predicted a significant amount of the variance in monkeys' performance (Boxer: R2 = 0.83, p < 0.0001; Feinstein: R2 = 0.85, p < 0.0001), demonstrating that monkeys' addition performance was modulated by the numerical ratio of the sum and distractor. It is noteworthy that even during this training period, monkeys' approach to these addition problems was comparable to the process used by adult humans on parallel tasks (e.g., [4,13]).
To determine whether monkeys relied on an abstract mental addition process that could be applied to both familiar and unfamiliar numerical values, we tested them with novel addition problems. We tested all possible addends of the novel sums 3, 7, 11, and 17. To prevent learning on these novel test trials, monkeys were rewarded regardless of which of the two choice stimuli they selected as the sum. Performance on these nondifferentially reinforced test trials was significantly greater than that predicted by chance (one-sample t-test of accuracy on novel addition problems vs. chance; Boxer: 70% versus 50%; t(11) = 4.20, p < 0.01; Feinstein: 75% versus 50%; t(11) = 4.45, p < 0.001). Furthermore, addition performance with novel numerical values was modulated by numerical ratio, just as with the familiar values. That is, performance on both the familiar and novel numerical values decreased as the ratio between the choice stimuli approached one; the model of ratio-dependent addition performance presented in Equation 1 well-accounted for monkeys' performance on both trial types (Figure 4). This finding demonstrates that monkeys performed addition on the novel numerical values using the same cognitive process that they used for the familiar numerical values. Therefore, monkeys added the values of the two sets of elements together regardless of the absolute value of the sets and independent of their familiarity with particular values or addition problems.
Additional analyses confirmed that monkeys' performance was based purely on the sum of the two addends. First, monkeys were not simply choosing the numerically larger of the two choice stimuli. Monkeys performed significantly above chance on addition problems regardless of whether the distractor stimulus was larger or smaller than the sum (distractor larger: Boxer, 75%, t(15) = 6.43, p < 0.001, Feinstein, 82%, t(15) = 6.32, p < 0.001; distractor smaller: Boxer, 70%, t(15) = 4.69, p < 0.001, Feinstein, 75%, t(15) = 5.85, p < 0.001). In addition, both monkeys performed significantly better than chance, even when the first addend was equal to the numerical value of the distractor choice (binomial tests; Boxer, n = 178, 0.75 versus 0.5, p < 0.001; Feinstein, n = 149, 0.81 versus 0.5, p < 0.001). Similarly, accuracy was better than chance when the distractor value was equal to the second addend (Boxer, n = 148, 0.76 versus 0.5, p < 0.001; Feinstein, n = 143, 0.73 versus 0.5, p < 0.001) and the largest addend (Boxer, n = 114, 0.62 versus 0.5, p < 0.01; Feinstein, n = 98, 0.62 versus 0.5, p < 0.01). Thus, performance was unimpaired when a strategy based on matching a single addend predicted the incorrect choice. Rather than using a simple heuristic, monkeys mentally added the two sets and based their choices on the sum of the two addends.
To confirm that monkeys were not performing addition across the spatial extent of the dots as opposed to their number, we examined their performance as a function of the cumulative surface area of the addends and choice stimuli. As described in Materials and Methods, the cumulative surface area of the elements in the stimuli was varied to create trials in which a strategy based on the cumulative surface area of the elements would result in error. On approximately 25% of all trials, the cumulative surface area of the dots in the distractor stimulus was closer to the cumulative surface area of the dots in the two addends. If monkeys were using the cumulative surface area of the addition sets to perform this task, their performance should be below chance on these trials, because the incorrect numerical choice was the correct choice for cumulative surface area. This was not the case. Instead, monkeys performed significantly above chance on this subset of trials, indicating that they based their choices on the numerical sum of the objects, not their surface area (binomial test of accuracy on area control trials versus chance; Boxer: n = 1571, 0.83 versus 0.5, p < 0.00001; Feinstein: n = 1460, 0.88 versus 0.5, p < 0.00001).
Finally, performance was equivalent on trials that required addition and “single-set” trials that did not require addition (dependent sample t-test on addition trials versus single-set trials; Boxer: t(31) = 0.56, p = 0.58; Feinstein: t(31) = −0.70, p = 0.49). On single-set trials, all of the dots were presented simultaneously, in a single set, and monkeys simply had to select the correct numerical match between this single set and one of the two choice stimuli (see [13] for a similar result with adult human subjects). The numerical values tested on these trials were identical to those tested on the addition trials. The equivalent, ratio-dependent performance on addition and single-set trials confirms that monkeys used a mental computation during addition that is linked to their broader set of numerical skills in the sense that they invoke a common form of numerical representation during addition and numerical estimation
Overall accuracy across the 40 different addition problems was higher for adult humans (mean = 94%) than for monkeys (mean = 76%) on the addition trials (t(38) = 3.90, p < 0.001). However, the mean response time of monkeys (mean = 1,099 ms) and humans (mean = 940 ms) was not significantly different (t(38) = 1.43, p = 0.16). Thus, humans responded at the same rate as monkeys but were more accurate overall.
Despite these quantitative differences in performance, however, monkeys and humans produced qualitatively similar patterns of accuracy and response time in the addition task (Figure 5). Monkeys and humans alike exhibited a correlation between the numerical ratio of the choice stimuli and their speed in choosing the correct arithmetic outcome (humans: R2 = 0.88, p < 0.005; monkeys: R2 = 0.53, p = 0.06). Moreover, predicted performance from the model of ratio-dependent addition captured the accuracy data from both monkeys and humans (humans: R2 = 0.95, p < 0.0001; monkeys: R2 = 0.90, p < 0.0001). The precision variable in the model (w) that produced the best-fitting predicted performance for humans (w = 0.22) indicated that humans were able to make finer numerical discriminations than monkeys were (w = 0.45). Overall, however, the robust relationships among numerical ratio, accuracy, and response time indicate that the primary constraint for humans and monkeys in solving addition problems was the numerical ratio between the correct sum and the distractor choice. The data from the individual numerical values of the sum–distractor pairs that contributed to this analysis are presented in Figure S1.
In addition to the effect of numerical ratio, there was also an effect of the numerical magnitude of the sum on monkeys' and humans' performance; accuracy decreased as the sum increased for the addition trials (Figure 6). This sum size effect is also predicted by Equation 1, because it includes a parameter (λ) that modulates additional variance contributed by the representation of the sum, after the addends have been added together and stored in memory [31]. The effect of sum size in our data confirms that the numerical magnitude of the sum of the two sets contributes additional noise to the representational process of adding two sets together and choosing the correct sum from two choice stimuli.
Finally, like monkeys, adult humans performed similarly on addition trials and single-set trials. Humans exhibited no significant difference in accuracy between addition trials and single-set trials in which all of the elements were presented all at once (t(13) = 1.19, p = 0.26). The lack of a difference in performance between comparison and addition has also been found when adult humans perform approximate arithmetic [13]. Thus, humans' capacity to perform rapid, approximate arithmetic appears to be linked to their broader skill set for estimating numerical values [16]. However, one peculiar finding is that humans' performance on these single-set trials was also affected by the magnitude of the sample stimulus above and beyond the effect of numerical ratio. Specifically, adult humans were more accurate at a given ratio when the sample value was relatively small on single-set trials (r = −0.48, p < 0.05) although this was not the case for monkeys (r = −0.17, p = 0.5).
Overall, the data from the addition performance of adult humans reinforce the claim that the basic arithmetic ability we have observed in the current study belongs to a primitive mathematical toolkit that deals in approximate, analog representations of numerical values with a limiting performance factor of numerical ratio. Monkeys invoke this mathematical system to solve quantitative problems, whereas humans invoke this primitive system when precise, symbolic mathematics is not a viable option, as was the case in the current study.
Our results provide definitive evidence that monkeys can perform mental addition. Furthermore, monkeys' accuracy in combining numerical values was ratio-dependent, suggesting that they performed addition by combining analog-numerical representations. The qualitative similarity between the performance of monkeys and humans in this study is striking; when monkeys and humans nonverbally add two sets of objects together to represent their sum, their performance is similarly modulated by the ratio between the numerical values of choice stimuli (see also [4]). Humans and nonhuman primates thus appear to share a cognitive system for basic nonverbal arithmetic, which likely reflects an evolutionary link in their cognitive abilities.
Although it is impossible to know precisely the function for which numerical arithmetic may have been selected in our evolutionary past, a few studies have shown that extant nonhuman animals use numerical information to determine the number of animals in an unfamiliar group during territorial disputes [32,33] and to choose a relatively large amount of food during foraging [34]. Numerical information thus appears to be influential in both social and foraging decisions. Numerical arithmetic may be important during social and foraging decisions under circumstances in which groups of animals or food items are widely separated in space and/or time. For example, if conspecifics or food items are widely distributed in space or time, an animal may have no choice but to perform addition in order to update an initial quantitative representation.
Our results demonstrate that when monkeys mentally add numerical values together, their performance is modulated by numerical ratio, just as when they compare or equate stimuli based on numerical values [16]. Thus for monkeys, addition is a computation that belongs to a mathematical toolkit with an overarching set of psychological principles. In support of this claim, recent studies have demonstrated that nonhuman primates exhibit ratio-dependent performance when they abstract numerical values across stimuli with high perceptual variability [17] and even across sensory modalities (Jordan KE, MacLean EL, Brannon EM, unpublished data). Additionally, the process that monkeys and humans use to compare numerical values seems to obey the same algorithm [15,16]. It is becoming increasingly apparent that the set of nonverbal mathematical skills shared by humans and nonhuman animals is remarkably abstract and computationally powerful.
The precise computational and neural mechanism by which humans or monkeys perform nonverbal addition is unknown. Gelman and Gallistel [7] proposed that nonverbal addition functions in a manner parallel to histogram arithmetic. Discrete quantities are represented as analog magnitudes that are isomorphic to the quantities they represent, much like the process by which analog machines represent discrete quantities in currents or voltages. In this sense, mental representations of numerical values are analogous to the bars on a histogram in which height is an index of numerical magnitude. To perform addition, these analog representations of number might be combined in a manner equivalent to spatially combining the bars of a histogram. In this case, the bars of the histogram represent the numerical sum of sets of discrete objects. The result of histogram addition is a new mental magnitude (the sum) that is directly proportional to the combined numerical magnitude of the two original quantities. This kind of mechanism may underlie nonverbal numerical arithmetic in humans and nonhuman animals.
In the current study, when monkeys selected the sum of the two sets of dots, they based their decisions on a representation (the sum) that they generated by mentally combining two existing numerical representations (the addends). The ability to combine mental representations is a capacity that humans invoke regularly to solve cognitive problems and especially to produce symbolic mathematical expressions. Our results demonstrate that, like humans, monkeys are capable of combining mental representations of numerical values together to solve mathematical problems. Indeed, the qualitative similarity between the performance of monkeys and humans on our addition task is evidence that they likely compute simple nonverbal arithmetic outcomes in much the same way. This conclusion is bolstered by our finding that a single equation accounts equally well for monkeys' and humans' success in performing addition nonverbally (see also [31]).
Studies of animal cognition from a variety of domains describe cases in which animals appear to perform computations that require combining mental representations. For example, to locate objects via echolocation, bats must combine information from phase shifts in both the original and reflected sound emissions across many different frequencies [6,35]. In addition, rats can integrate information about the metric relations among surfaces in their environment with information about their position within that space during navigation (e.g., [36]). Our data add to the evidence that nonhuman animals combine representations by providing evidence of combinatorial computations that operate over numerical representations of discrete objects to represent approximate arithmetic outcomes.
In short, our data advance the hypothesis that numerical addition is a component of the primitive, language-independent set of numerical capacities that has a common evolutionary origin among primates, including humans. More broadly, our data demonstrate that the ability to combine mental representations, which is a characteristic of sophisticated aspects of human cognition, is a capacity that nonhuman animals use within the numerical domain. These findings underscore the existence of extraordinary continuity in the processes governing numerical thought for human and nonhuman primates.
Nonhuman primate subjects were two adult female rhesus macaques, named Feinstein and Boxer, who were socially housed along with two other rhesus macaque females. All animal care procedures are in accordance with an IACUC protocol. Human participants were 14 adults (mean age = 23 y, standard deviation = 3.45, 5 male) that currently attend Duke University.
Monkeys were tested in sound-attenuated touch screen booths while seated in Plexiglas primate chairs. Adult humans were tested at a touch screen computer station. For both species, stimuli were presented on a touch screen in randomly selected locations. To begin a trial, subjects were required to press a start stimulus, a small red square presented in the bottom left corner of the screen. Following this response, two sets of dots were presented, separated by a delay of 500 ms. Then, subjects were presented with two choice stimuli and were required to select the stimulus that contained the numerical sum of these two sample sets. A trial terminated when a subject touched one of the two choice stimuli.
Both species received positive visual (light-up border) and auditory (chime) feedback for correct choices and negative visual (black screen) and auditory (warning tone) feedback for incorrect responses. Incorrect responses were also followed by a 2–5 s timeout period. Monkeys were also rewarded with small amounts of Kool-Aid for selecting the correct sum. When monkeys failed to select the correct choice, they received no juice reward. Humans were given $10 to participate in the study.
For all subjects, all numerical values tested were equally likely to occur as the correct and incorrect choices; thus the incorrect choice could be smaller or larger than the sum on any trial. Stimuli were trial-unique, in the sense that a computer program randomly selected the sizes and locations of the elements in each array from a parameter distribution. Thus the sizes and locations of the elements could not be used to solve the task. A video of a monkey and an adult human performing this task can be found at: http://rd.plos.org/pbio.0050328.
Prior to training on the addition task, monkeys were trained on a numerical matching task in which a sample array of 1–9 dots was presented and they were rewarded for selecting the array that numerically matched the sample set from two choices (see [9]). Monkeys reached a 70% criterion on this numerical matching task before training on the addition task.
For the initial training on the addition task, monkeys were presented with a limited range of addition problems: 1 + 1 = 2, 4, or 8; 2 + 2 = 2, 4, or 8; 4 + 4 = 2, 4, or 8. Monkeys completed ∼9,000 trials on this phase of training; however, as reported in the results section, their performance was above chance within the first 500 trials. Next, we expanded the range of addition problems by testing all possible addends of the sums 2, 4, 8, 12, and 16. For example, when 8 was the sum, the addends could be 1 + 7, 2 + 6, 3 + 5, 4 + 4, 5 + 3, 6 + 2, or 7 + 1. Each sum was equally likely to occur as the correct and incorrect choices. Monkeys completed approximately 5,000 trials on this phase of training before we tested them with novel addition problems. Throughout training and testing, we included trials in which the monkeys were not required to add. On these trials, a single set of dots was presented on monkeys were required to select the choice stimulus that corresponded to its numerical value. These single-set trials were analyzed separately from the addition trials as a measure of monkey's numerical performance in the absence of arithmetic computation.
Monkeys were tested on addition problems that they have never been trained to compute. All possible addends of the novel values 3, 7, 11, and 17 were tested. These novel values were equally likely to occur as correct and incorrect choices during test trials. Thus, there were 12 different novel sum-distractor pairs. Approximately 50 trials were completed by each monkey on each novel pair. Novel addition problems were presented randomly within a session and comprised 20% of the total trials. To prevent monkeys from learning the solutions to the novel problems, they were rewarded no matter which of the two choice stimuli they selected as the sum. During the second half of testing sessions, a green rectangle appeared during the delay between the two addend sets rather than a blank black screen.
Adult humans were instructed to press the start stimulus to initiate each trial and then to attend to the number of dots in each set, add them together without verbally counting, and rapidly select the box that contained their sum from two choices. The task was demonstrated by the experimenter for 3–5 trials, the subject practiced the task for 3–5 trials, and then testing began.
The task used to test adult humans was identical to that used for the final phase of monkey training. Adult humans were tested on all possible addends of the sums 2, 4, 8, 12, and 16. Each sum was equally likely to occur as the correct and incorrect choices. Half of the trials were single-set trials, wherein the total number of items was presented simultaneously in a single set rather than across two sets. Each adult completed 500 trials on this task over a 50-min period.
For both species, the training and testing stimuli consisted of red dots on a black background. Stimuli were trial-unique in the sense that the surface area, location, and spacing of the elements were varied. For the sample sets, the location of each element in a set was randomly drawn (barring overlap among elements) from all x- and y- coordinates within approximately 10 cm in any direction of the center of the screen. For each choice stimulus, elements were randomly placed (again, barring overlap among elements) in a 9 cm × 7.5 cm stimulus. To control for cumulative surface area, the physical sizes of the elements were varied such that the cumulative surface area of the sample sets varied between 250 and 11,000 pixels, and the cumulative surface area of the choice stimuli was either 1,000 or 10,000 pixels. For each of the numerical values tested, the cumulative surface area values of the choice stimuli were equally likely to occur as the correct and incorrect choice. Consequently, on a percentage of trials, the incorrect choice had the closer cumulative surface area value to the cumulative surface area of the sample sets. If monkeys were using cumulative surface area to perform this task, as opposed to number, they would have failed to choose the correct sum on these trials. In the Results section, we separately analyzed trials in which a strategy based on the cumulative surface area of the elements would lead to failure.
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10.1371/journal.pgen.1005035 | A Meta-analysis of Gene Expression Signatures of Blood Pressure and Hypertension | Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p<0.05). Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%–9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2). Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension.
| The focus of blood pressure (BP) GWAS has been the identification of common DNA sequence variants associated with the phenotype; this approach provides only one dimension of molecular information about BP. While it is a critical dimension, analyzing DNA variation alone is not sufficient for achieving an understanding of the multidimensional complexity of BP physiology. The top loci identified by GWAS explain only about 1 percent of inter-individual BP variability. In this study, we performed a meta-analysis of gene expression profiles in relation to BP and hypertension in 7017 individuals from six studies. We identified 34 differentially expressed genes for BP, and discovered that the top BP signature genes explain 5%–9% of BP variability. We further linked BP gene expression signature genes with BP GWAS results by integrating expression associated SNPs (eSNPs) and discovered that one of the top BP loci from GWAS, rs3184504 in SH2B3, is a trans regulator of expression of 6 of the top 34 BP signature genes. Our study, in conjunction with prior GWAS, provides a deeper understanding of the molecular and genetic basis of BP regulation, and identifies several potential targets and pathways for the treatment and prevention of hypertension and its sequelae.
| Systolic and diastolic blood pressure (SBP and DBP) are complex physiological traits that are affected by the interplay of multiple genetic and environmental factors. Hypertension (HTN) is a critical risk factor for stroke, renal failure, heart failure, and coronary heart disease [1]. Genome-wide association studies (GWAS) have identified numerous loci associated with BP traits [2,3]. These loci, however, only explain a small proportion of inter-individual BP variability. In aggregate the 29 loci reported by the International Consortium of Blood Pressure (ICBP) consortium GWAS account for about one percent of BP variation in the general population [3]. Most genes near BP GWAS loci are not known to be mechanistically associated with BP regulation [3]. Therefore, further studies are needed to determine whether the genes implicated in GWAS demonstrate functional relations to BP physiology and to uncover the molecular actions and interactions of genetic and environmental factors involved in BP regulation.
Alterations in gene expression may mediate the effects of genetic variants on phenotype variability. We hypothesized that characterizing gene expression signatures of BP would reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. We additionally hypothesized that by integrating gene expression profiling with genetic variants associated with altered gene expression (eSNPs or eQTLs) and with BP GWAS results, we would be able to characterize the genetic architecture of gene expression effects on BP regulation.
Several previous studies have examined the association of global gene expression with BP [4,5] or HTN [6,7]. Most of these studies, however, were based on small sample sizes and lacked replication [4,5,6,7]. To address this challenge, we conducted an association study of global gene expression levels in whole blood with BP traits (SBP, DBP, and HTN) in six independent studies. In order to avoid the possibility that the differentially expressed genes we identified reflect drug treatment effects, we excluded individuals receiving anti-hypertensive treatment. The eligible study sample included 7017 individuals: 3679 from the Framingham Heart Study (FHS), 972 from the Estonian Biobank (EGCUT), 604 from the Rotterdam Study (RS) [8], 597 from the InCHIANTI Study, 565 from the Cooperative Health Research in the Region of Augsburg [KORA F4] Study [9], and 600 from the Study of Health in Pomerania [SHIP-TREND] [10]. We first identified differentially expressed BP genes in the FHS (n = 3679) followed by external replication in the other five studies (n = 3338). Subsequently, we performed a meta-analysis of all 7017 individuals from the six studies, and identified 34 differentially expressed genes associated with BP traits using a stringent statistical threshold based on Bonferroni correction for multiple testing of 7717 unique genes. The differentially expressed genes for BP (BP signature genes) were further integrated with eQTLs and with BP GWAS results in an effort to differentiate downstream transcriptomic changes due to BP from putatively causal pathways involved in BP regulation.
After excluding individuals receiving anti-hypertensive treatment, the eligible sample size was 7017 (FHS, n = 3679; EGCUT, n = 972; RS, n = 604; InCHIANTI, n = 597; KORA F4, n = 565 and SHIP-TREND, n = 600). Clinical characteristics of participants from the four studies are presented in Table 1. The mean age varied across the cohorts (FHS = 51, EGCUT = 36, RS = 58, InCHIANTI = 71, KORA F4 = 72 and SHIP-TREND = 46 years) as did the proportion of individuals with hypertension (11% in FHS, 19% in EGCUT, 35% in RS, 45% in InCHIANTI, 26% in KORA, and 12% in SHIP).
At a Bonferroni corrected p<0.05, we identified 73, 31, and 8 genes that were differentially expressed in relation to SBP, DBP, and HTN, respectively in the FHS, which used an Affymetrix array for expression profiling, and 6, 1, and 1 genes in the meta-analysis of the 5 cohorts that used an Illumina array (Illumina cohorts): EGCUT, RS, InCHIANTI, KORA F4 and SHIP-TREND (S1 Table). For each differentially expressed BP gene in the FHS or in the Illumina cohorts, we attempted replication in the other group. At a replication p<0.05 (Bonferroni corrected), 13 unique genes that were identified in the FHS were replicated in the Illumina cohorts, including 10 for SBP (CD97, TAGAP, DUSP1, FOS, MCL1, MYADM, PPP1R15A, SLC31A2, TAGLN2, and TIPARP), 5 for DBP (CD97, BHLHE40, PRF1, CLC, and MYADM), and 2 for HTN (GZMB and MYADM) (Table 2). Each of the unique BP signature genes in the Illumina cohorts, 6 for SBP (TAGLN2, BHLHE40, MYADM, SLC31A2, DUSP1, and MCL1), 1 for DBP (BHLHE40) and 1 for HTN (SLC31A2), replicated in the FHS. All 6 Illumina cohorts BP signature genes that replicated in the FHS were among the 13 FHS BP signature genes that replicated in the Illumina cohorts. The BP signature genes identified in the FHS showed enrichment in the Illumina cohorts at pi1 = 0.88, 0.75, and 0.99 for SBP, DBP, and HTN respectively (pi1 value indicates the proportion of significant signals among the tested associations [11]; see details in the Methods section). Fig. 1 shows that the mean gene expression levels of the top BP signature genes were consistent with the BP phenotypic changes observed in the FHS and the Illumina cohorts.
The 73 SBP signature genes in the FHS (55 of these 73 genes were measured in the Illumina cohorts) at a Bonferroni corrected p<0.05 in aggregate explained 9.4% of SBP phenotypic variance in the Illumina cohorts, and the 31 DBP signature genes from the FHS (22 of these 31 genes were measured in the Illumina cohorts) in aggregate explained 5.3% of DBP phenotypic variance in the Illumina cohorts. These results suggest that in contrast to common genetic variants identified by BP GWAS, which explain in aggregate only about 1% of inter-individual BP variation [3], changes in gene expression levels explains a considerably larger proportion of phenotypic variance in BP.
A meta-analysis of differential expression across all six cohorts revealed 34 differentially expressed BP genes at p<0.05 (Bonferroni corrected for 7717 genes that were measured and passed quality control in the FHS and Illumina cohorts), including 21 for SBP, 20 for DBP, and 5 for HTN (Table 2 and S2 Fig.). All of the 34 differentially expressed BP signature genes showed directional consistency in the FHS and the Illumina cohorts (Table 2). The 34 BP signature genes included all 13 genes that were cross-validated between the FHS and the Illumina cohorts. Of the 34 BP signature genes, 27 were positively correlated with BP and only 7 genes were negatively correlated. MYADM and SLC31A2 were top signature genes for SBP, DBP, and HTN. At FDR<0.2, 224 unique genes were differentially expressed in relation BP phenotypes including 142 genes for SBP, 137 for DBP, and 45 for HTN (details are reported in the S1–S2 Text, and S3–S5 Table).
We used gene set enrichment analysis (GSEA) to identify the biological process and pathways associated with gene expression changes in relation to SBP, DBP, and HTN in order to better understand the biological themes within the data. As shown in Table 3, the GSEA of genes whose expression was positively associated with BP showed enrichment for antigen processing and presentation (p<0.0001), apoptotic program (p<0.0001), inflammatory response (p<0.0001), and oxidative phosphorylation (p = 0.0018). The negatively associated genes showed enrichment for nucleotide metabolic process (p<0.0001), positive regulation of cellular metabolic process (p<0.0001), and positive regulation of DNA dependent transcription (p = 0.0021).
Among the 34 BP signatures genes from the meta-analysis of all 6 studies, 33 were found to have cis-eQTLs and 26 had trans-eQTLs (Fig. 2A and S2 Table) based on whole blood profiling [12,13]. Of these, six master trans-eQTLs mapped to either five or six BP signature genes (no master cis-eQTL was identified). Five master trans-eQTLs (rs653178, rs3184504, rs10774625, rs11065987, and rs17696736) were located on chromosome 12q24 within the same linkage disequilibrium (LD) block (r2 >0.8, Fig. 2B). We retrieved a peak cis- and trans-eQTL for each BP signature gene. The peak cis-eQTL explained 0.2–20% of the variance in the corresponding transcript levels, in contrast, the peak trans-eQTL accounted for very little (0.02–2%) of the corresponding transcript variance. Westra et al. also reported a similar small proportion of variance in transcript levels explained by trans-eQTLs [12].
We then linked the cis- and trans-eQTLs of the 34 BP signature genes with BP GWAS results from the ICBP Consortium [3] and the NHGRI GWAS Catalog [14] (Fig. 2 and S2 Table). We did not find any cis-eQTLs for the top BP signature genes that also were associated with BP in the ICBP GWAS [3]. However, the 6 master trans-eQTLs were all associated with BP at p<5e-8 in the ICBP GWAS [3] and were associated with multiple complex diseases or traits (Table 4). For example, rs3184504, a nonsynonymous SNP in SH2B3 that was associated in GWAS with BP, coronary heart disease, hypothyroidism, rheumatoid arthritis, and type 1 diabetes [12], is a trans-eQTL for 6 of our 34 BP signature genes from the meta-analysis (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2; Fig. 2A-B and Table 4). These 6 genes are all highly expressed in neutrophils, and their expression levels are correlated significantly (average r2 = 0.04, p<1e-16). rs653178, intronic to ATXN2 and in perfect LD with rs3184504 (r2 = 1), also is associated with BP and multiple other diseases in the NHGRI GWAS Catalog [14]. It also is a trans-eQTL for the same 6 BP signature genes (Table 4). These two SNPs are cis-eQTLs for expression SH2B3 in whole blood (FDR<0.05), but not for ATXN2 (FDR = 0.4). We found that the expression of SH2B3 is associated with expression of MYADM, PP1R15A, and TAGAP (at Bonferroni corrected p<0.05), but not with FOS, S100A10, or FGBP2. The expression of ATXN2 was associated with expression of 5 of the 6 genes (PP1R15A was not associated). S3 Fig. shows the coexpression levels of the eight genes that were cis- or trans- associated with rs3184504 and rs653178 genotypes. These results suggest that there may be a pathway or gene co-regulatory mechanism underling BP regulation involving these genes that is driven by this common genetic variant (rs3184504; minor allele frequency 0.47) or its proxy SNPs.
We further checked whether the cis- or trans-eQTLs for the top 34 BP signature genes are associated with other diseases or traits in the NHGRI GWAS catalog [14]. We identified 12 cis-eQTLs (for 8 genes) and 6 trans-eQTLs (for 6 genes) that are associated with other diseases or traits in the NHGRI GWAS catalog [14] (Table 4).
Our meta-analysis of gene expression data from 7017 individuals from six studies identified and characterized whole blood gene expression signatures associated with BP traits. Thirty-four BP signature genes were identified at Bonferroni corrected p<0.05 (224 genes were identified at FDR<0.2, reported in the S1 Text). Thirteen BP signature genes replicated between the FHS and Illumina cohorts. The top BP signature genes identified in the FHS (55 genes for SBP and 22 genes for DBP) explained 5–9% of interindividual variation in BP in the Illumina cohorts on average.
Among the 34 BP signature genes (at Bonferroni corrected p<0.05), only FOS [15] and PTGS2 [16] have been previously implicated in hypertension. We did not find literature support for a direct role of the remaining signature genes in BP regulation. However, we found several genes involved in biological functions or processes that are highly related to BP, such as cardiovascular disease (GZMB, ANXA1, TMEM43, FOS, KCNJ2, PTGS2, and MCL1), angiogenesis (VIM and TIPARP), and ion channels (CD97, ANXA1, S100A10, PRF1, ANTXR2, SLC31A2, TIPARP, and KCNJ2). We speculate that these genes may be important for BP regulation, but further experimental validation is needed.
Seven of the 34 signature genes, including KCNJ2, showed negative correlation of expression with BP. KCNJ2 is a member of the potassium inwardly-rectifying channel subfamily; it encodes the inward rectifier K+ channel Kir2.1, and is found in cardiac, skeletal muscle, and nervous tissue [17]. Most outward potassium channels are positively correlated with BP. Loss-of-function mutations in ROMK (KCNJ1, the outward potassium channel) are associated with Bartter's syndrome, and ROMK inhibitors are used in the treatment of hypertension [18,19]. Previous studies reported that greater potassium intake is associated with lower blood pressure [20,21,22,23]. These data suggest that KCNJ2 up-regulation may be a means of lowering BP.
By linking the BP signature genes with eQTLs and with BP GWAS results, we found several SNPs that are associated with BP in GWAS and that also are trans associated with several of our top BP signature genes. For example, rs3184504, a non-synonymous SNP located in exon 3 of SH2B3, is associated in GWAS with BP, coronary heart disease, hypothyroidism, rheumatoid arthritis, and type I diabetes [12]. rs3184504 is a common genetic variant with a minor allele frequency of approximately 0.47; the rs3184504-T allele is associated with an increment of 0.58 mm Hg in SBP and of 0.48 mm Hg in DBP [2]. rs3184504 is a cis-eQTL for SH2B3, expression of this gene was not associated with BP or hypertension in our data. However, rs3184504 also is a trans-eQTL for 6 of our 34 BP signature genes: FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2. These 6 genes are highly expressed in neutrophils [12], and are coexpressed. Prior studies have suggested an important role of neutrophils in BP regulation [24]. We speculate that these 6 BP signature genes, all driven by the same BP-associated eQTL, point to a critical and previously unrecognized mechanism involved in BP regulation. Further experimental validation is needed.
One limitation of our study is the use of whole blood derived RNA for transcriptomic profiling. GSEA showed that the top enriched biological processes for the differentially expressed BP genes include inflammatory response. Numerous studies have shown links between inflammation and hypertension [25,26,27]. The top ranked genes in inflammatory response categories provide a guide for further experimental work to recognize the contributions of inflammation to alterations in BP regulation. We speculate that using similar approaches in other tissues might identify additional differentially expressed BP signature genes.
In conclusion, we conducted a meta-analysis of global gene expression profiles in relation to BP and identified a number of credible gene signatures of BP and hypertension. Our integrative analysis of GWAS and gene expression in relation to BP can help to uncover the genetic and genomic architecture of BP regulation; the BP signature genes we identified may represent an early step toward improvements in the detection of susceptibility, and in the prevention and treatment of hypertension.
This investigation included six studies (the Framingham Heart Study (FHS), the Estonian Biobank (EGCUT), the Rotterdam Study (RS) [8], the InCHIANTI Study, the Cooperative Health Research in the Region of Augsburg (KORA F4) Study [9], and the Study of Health in Pomerania (SHIP-TREND) [10], each of which conducted genome-wide genotyping, mRNA expression profiling, and had extensive BP phenotype data. Each of the six studies followed the recommendations of the Declaration of Helsinki. The FHS: Systems Approach to Biomarker Research (SABRe) in cardiovascular disease is approved under the Boston University Medical Center’s protocol H-27984. Ethical approval of EGCUT was granted by the Research Ethics Committee of the University of Tartu (UT REC). Ethical approval of the InCHIANTI study was granted by the Instituto Nazionale Riposo e Cura Anziani institutional review board in Italy. Ethical approval of RS was granted by the medical ethics committee of the Erasmus Medical Center. The study protocol of SHIP-TREND was approved by the medical ethics committee of the University of Greifswald. KORA F4 is a population-based survey in the region of Augsburg in Southern Germany which was performed between 2006 and 2008. KORA F4 was approved by the local ethical committees. Informed consent was obtained from each study participant.
Hypertension (HTN) was defined as SBP ≥140 mm Hg or DBP ≥90 mm Hg. We excluded individuals receiving anti-hypertensive treatment because of the possibility that some of the differentially expressed genes we identified would reflect treatment effects. The eligible study sample included 7017 individuals: 3679 from FHS, 972 from EGCUT, 604 from RS, 597 from InCHIANTI, 565 from KORA F4, and 600 from SHIP-TREND.
RNA was isolated from whole blood samples that were collected in PaxGene tubes (PreAnalytiX, Hombrechtikon, Switzerland) in FHS, RS, InCHIANTI, KORA F4 and SHIP-TREND, and in Blood RNA Tubes (Life Technologies, NY, USA) in EGCUT. Gene expression in the FHS samples used the Affymetrix Exon Array ST 1.0. EGCUT, RS, InCHANTI, KORA F4, and SHIP-TREND used the Illumina HT12v3 (EGCUT, InCHANTI, KORA F4, and SHIP-TREND) or HT12v4 (RS) array. Raw data from gene expression profiling are available online (FHS [http://www.ncbi.nlm.nih.gov/gap; accession number phs000007], EGCUT [GSE48348], RS [GSE33828], InCHIANTI [GSE48152], KORA F4 [E-MTAB-1708] and SHIP-TREND [GSE36382]). The details of sample collection, microarrays, and data processing and normalization in each cohort are provided in the S2 Text.
The association of gene expression with BP was analyzed separately in each of the six studies (Equation 1). A linear mixed model was used in the FHS in order to account for family structure. Linear regression models were used in the other five studies. In each study, gene expression level, denoted by geneExp, was included as the dependent variable, and explanatory variables included blood pressure phenotypes (SBP, DBP, and HTN), and covariates included age, sex, body mass index (BMI), cell counts, and technical covariates. A separate regression model was fitted for each gene. The general formula is shown below, and the details of analyses for each study are provided in the S2 Text and S6 Table.
geneExp=BP+∑j=1mcovariates
The overall analysis framework is provided in S1 Fig.. We first identified differentially expressed genes associated with BP (BP signature genes) in the FHS samples (Set 1) and attempted replication in the meta-analysis results from the Illumina cohorts (Set 2, see Methods, Meta-analysis). We next identified BP signature genes in the Illumina cohorts (Set 2), and then attempted replication in the FHS samples (Set 1). The significance threshold for pre-selecting BP signature genes in discovery was at Bonferroni corrected p = 0.05 (in FHS, corrected for 17,318 measured genes [17,873 transcripts], and in illumina cohorts, corrected for 12,010 measured genes [14,222 transcripts] that passed quality control). Replication was established at Bonferroni corrected p = 0.05, correcting for the number of pre-selected BP signatures genes in the discovery set. We computed the pi1 value to estimate the enrichment of significant p values in the replication set (the Illumina cohorts) for BP signatures identified in the discovery set (the FHS) by utilizing the R package Qvalue [11]. Pi1 is defined as 1-pi0. Pi0 value provided by the Qvalue package, represents overall probability that the null hypothesis is true. Therefore, pi1 value represents the proportion of significant results. For genes passing Bonferroni corrected p<0.05 in the discovery set for SBP, DBP and HTN, we calculated pi1 values for each gene set in the replication set.
We performed meta-analysis of the five Illumina cohorts (for discovery and replication purposes), and then performed meta-analysis of all six cohorts. An inverse variance weighted meta-analysis was conducted using fixed-effects or random-effects models by the metagen() function in the R package Meta (http://cran.r-project.org/web/packages/meta/index.html). At first, we tested heterogeneity for each gene using Cochran’s Q statistic. If the heterogeneity p value is significant (p<0.05), we will use a random-effects model for the meta-analysis, otherwise use a fixed-effects model. The Benjamini-Hochberg (BH) method [28] was used to calculate FDR for differentially expressed genes in relation to BP following the meta-analysis of all six cohorts. We also used a more stringent threshold to define BP signature genes by utilizing p<6.5e-6 (Bonferroni correction for 7717 unique genes [7810 transcript] based on the overlap of FHS and illumina cohort interrogated gene sets).
To estimate the proportion of variances in SBP or DBP explained by a group of differentially expressed BP signature genes (gene 1, gene 2, …, gene n), we used the following two models:
Full model:
BP=∑i=1ngenei+∑j=1mcovariates
Null model:
BP=∑j=1mcovariates
The proportion of variance in BP attributable to the group of differentially expressed BP signature genes (hBP_sig2) was calculated as:
hBP_sig2=max(0,σG.null2+σerr.null2−σG.full2−σerr.full2σBP2)
where σBP2 is the total phenotypic variance of SBP or DBP, σG.full2 and σerr.full2 are the variance and error variance when modeling with the tested group of gene expression traits (gene 1, gene 2, …, gene n), and σG.null2 and σerr.null2 are the variance and error variance when modeling without the tested group of gene expression traits.
The proportion of the variance in BP phenotypes attributable to the FHS BP signature genes was estimated in the five Illumina cohorts, respectively, and then the average proportion values were reported. In turn, the proportion of the variance in BP phenotypes attributable to the Illumina BP signature genes was estimated in the FHS.
SNPs associated with altered gene expression (i.e. eQTLs) were identified using genome-wide genotype and gene expression data in all available FHS samples (n = 5257) at FDR<0.1 (Joehanes R, submitted, 2014, and a brief summary of methods and results are provided in the S2 Text). A cis-eQTL was defined as an eQTL within 1 megabase (MB) flanking the gene. Other eQTLs were defined as trans-eQTLs. We combined the eQTL list generated in the FHS with the eQTLs generated by meta-analysis of seven other studies (n = 5300) that were also based on whole blood expression[12].
For every BP signature gene, we estimated the proportion of variance in the transcript attributable to the corresponding cis- or trans-eQTLs (heQTL2) using the formula:
heQTL2=max(0,σeQTL.null2+σerr.null2−σeQTL.full2−σerr.full2σgene2)
where σgene2 was the total phenotypic variance of a gene expression trait; σeQTL.full2 and σerr.full2 were the variance and the residual error, respectively, when modeling with the tested eQTL; σeQTL.null2 and σerr.null2 were the variance and the residual error when modeling without the tested eQTL.
In order to understand the biological themes within the global gene expression changes in relation to BP, we performed gene set enrichment analysis[29] to test for enrichment of any gene ontology (GO) biology process[30] or KEGG pathways[31]. “Metric for ranking gene” parameters were configured to the beta value of the meta-analysis, in order to look at the top enriched functions for BP associated up-regulated and down-regulated gene expression changes respectively. One thousand random permutations were conducted and the significance level was set at FDR≤ 0.25 to allow for exploratory discovery [29].
Steering Committee (alphabetical)
Gonçalo Abecasis, Murielle Bochud, Mark Caulfield (co-chair), Aravinda Chakravarti, Dan Chasman, Georg Ehret (co-chair), Paul Elliott, Andrew Johnson, Louise Wain, Martin Larson, Daniel Levy (co-chair), Patricia Munroe (co-chair), Christopher Newton-Cheh (co-chair), Paul O'Reilly, Walter Palmas, Bruce Psaty, Kenneth Rice, Albert Smith, Harold Snider, Martin Tobin, Cornelia Van Duijn, Germaine Verwoert.
Members
Georg B. Ehret1,2,3, Patricia B. Munroe4, Kenneth M. Rice5, Murielle Bochud2, Andrew D. Johnson6,7, Daniel I. Chasman8,9, Albert V. Smith10,11, Martin D. Tobin12, Germaine C. Verwoert13,14,15, Shih-Jen Hwang6,16,7, Vasyl Pihur1, Peter Vollenweider17, Paul F. O'Reilly18, Najaf Amin13, Jennifer L Bragg-Gresham19, Alexander Teumer20, Nicole L. Glazer21, Lenore Launer22, Jing Hua Zhao23, Yurii Aulchenko13, Simon Heath24, Siim Sõber25, Afshin Parsa26, Jian'an Luan23, Pankaj Arora27, Abbas Dehghan13,14,15, Feng Zhang28, Gavin Lucas29, Andrew A. Hicks30, Anne U. Jackson31, John F Peden32, Toshiko Tanaka33, Sarah H. Wild34, Igor Rudan35,36, Wilmar Igl37, Yuri Milaneschi33, Alex N. Parker38, Cristiano Fava39,40, John C. Chambers18,41, Ervin R. Fox42, Meena Kumari43, Min Jin Go44, Pim van der Harst45, Wen Hong Linda Kao46, Marketa Sjögren39, D. G. Vinay47, Myriam Alexander48, Yasuharu Tabara49, Sue Shaw-Hawkins4, Peter H. Whincup50, Yongmei Liu51, Gang Shi52, Johanna Kuusisto53, Bamidele Tayo54, Mark Seielstad55,56, Xueling Sim57, Khanh-Dung Hoang Nguyen1, Terho Lehtimäki58, Giuseppe Matullo59,60, Ying Wu61, Tom R. Gaunt62, N. Charlotte Onland-Moret63,64, Matthew N. Cooper65, Carl G.P. Platou66, Elin Org25, Rebecca Hardy67, Santosh Dahgam68, Jutta Palmen69, Veronique Vitart70, Peter S. Braund71,72, Tatiana Kuznetsova73, Cuno S.P.M. Uiterwaal63, Adebowale Adeyemo74, Walter Palmas75, Harry Campbell35, Barbara Ludwig76, Maciej Tomaszewski71,72, Ioanna Tzoulaki77,78, Nicholette D. Palmer79, CARDIoGRAM consortium80, CKDGen Consortium80, KidneyGen Consortium80, EchoGen consortium80, CHARGE-HF consortium80, Thor Aspelund10,11, Melissa Garcia22, Yen-Pei C. Chang26, Jeffrey R. O'Connell26, Nanette I. Steinle26, Diederick E. Grobbee63, Dan E. Arking1, Sharon L. Kardia81, Alanna C. Morrison82, Dena Hernandez83, Samer Najjar84,85, Wendy L. McArdle86, David Hadley50,87, Morris J. Brown88, John M. Connell89, Aroon D. Hingorani90, Ian N.M. Day62, Debbie A. Lawlor62, John P. Beilby91,92, Robert W. Lawrence65, Robert Clarke93, Rory Collins93, Jemma C Hopewell93, Halit Ongen32, Albert W. Dreisbach42, Yali Li94, J H. Young95, Joshua C. Bis21, Mika Kähönen96, Jorma Viikari97, Linda S. Adair98, Nanette R. Lee99, Ming-Huei Chen100, Matthias Olden101,102, Cristian Pattaro30, Judith A. Hoffman Bolton103, Anna Köttgen104,103, Sven Bergmann105,106, Vincent Mooser107, Nish Chaturvedi108, Timothy M. Frayling109, Muhammad Islam110, Tazeen H. Jafar110, Jeanette Erdmann111, Smita R. Kulkarni112, Stefan R. Bornstein76, Jürgen Grässler76, Leif Groop113,114, Benjamin F. Voight115, Johannes Kettunen116,126, Philip Howard117, Andrew Taylor43, Simonetta Guarrera60, Fulvio Ricceri59,60, Valur Emilsson118, Andrew Plump118, Inês Barroso119,120, Kay-Tee Khaw48, Alan B. Weder121, Steven C. Hunt122, Yan V. Sun81, Richard N. Bergman123, Francis S. Collins124, Lori L. Bonnycastle124, Laura J. Scott31, Heather M. Stringham31, Leena Peltonen119,125,126,127, Markus Perola125, Erkki Vartiainen125, Stefan-Martin Brand128,129, Jan A. Staessen73, Thomas J. Wang6,130, Paul R. Burton12,72, Maria Soler Artigas12, Yanbin Dong131, Harold Snieder132,131, Xiaoling Wang131, Haidong Zhu131, Kurt K. Lohman133, Megan E. Rudock51, Susan R Heckbert134,135, Nicholas L Smith134,136,135, Kerri L Wiggins137, Ayo Doumatey74, Daniel Shriner74, Gudrun Veldre25,138, Margus Viigimaa139,140, Sanjay Kinra141, Dorairajan Prabhakaran142, Vikal Tripathy142, Carl D. Langefeld79, Annika Rosengren143, Dag S. Thelle144, Anna Maria Corsi145, Andrew Singleton83, Terrence Forrester146, Gina Hilton1, Colin A. McKenzie146, Tunde Salako147, Naoharu Iwai148, Yoshikuni Kita149, Toshio Ogihara150, Takayoshi Ohkubo149,151, Tomonori Okamura148, Hirotsugu Ueshima152, Satoshi Umemura153, Susana Eyheramendy154, Thomas Meitinger155,156, H.-Erich Wichmann157,158,159, Yoon Shin Cho44, Hyung-Lae Kim44, Jong-Young Lee44, James Scott160, Joban S. Sehmi160,41, Weihua Zhang18, Bo Hedblad39, Peter Nilsson39, George Davey Smith62, Andrew Wong67, Narisu Narisu124, Alena Stančáková53, Leslie J. Raffel161, Jie Yao161, Sekar Kathiresan162,27, Chris O'Donnell163,27,9, Stephen M. Schwartz134, M. Arfan Ikram13,15, W. T. Longstreth Jr.164, Thomas H. Mosley165, Sudha Seshadri166, Nick R.G. Shrine12, Louise V. Wain12, Mario A. Morken124, Amy J. Swift124, Jaana Laitinen167, Inga Prokopenko51,168, Paavo Zitting169, Jackie A. Cooper69, Steve E. Humphries69, John Danesh48, Asif Rasheed170, Anuj Goel32, Anders Hamsten171, Hugh Watkins32, Stephan J.L. Bakker172, Wiek H. van Gilst45, Charles S. Janipalli47, K. Radha Mani47, Chittaranjan S. Yajnik112, Albert Hofman13, Francesco U.S. Mattace-Raso13,14, Ben A. Oostra173, Ayse Demirkan13, Aaron Isaacs13, Fernando Rivadeneira13,14, Edward G Lakatta174, Marco Orru175,176, Angelo Scuteri174, Mika Ala-Korpela177,178,179, Antti J Kangas177, Leo-Pekka Lyytikäinen58, Pasi Soininen177,178, Taru Tukiainen180,181,177, Peter Würtz177,18,180, Rick Twee-Hee Ong56,57,182, Marcus Dörr183, Heyo K. Kroemer184, Uwe Völker20, Henry Völzke185, Pilar Galan186, Serge Hercberg186, Mark Lathrop24, Diana Zelenika24, Panos Deloukas119, Massimo Mangino28, Tim D. Spector28, Guangju Zhai28, James F. Meschia187, Michael A. Nalls83, Pankaj Sharma188, Janos Terzic189, M. J. Kranthi Kumar47, Matthew Denniff71, Ewa Zukowska-Szczechowska190, Lynne E. Wagenknecht79, F. Gerald R. Fowkes191, Fadi J. Charchar192, Peter E.H. Schwarz193, Caroline Hayward70, Xiuqing Guo161, Charles Rotimi74, Michiel L. Bots63, Eva Brand194, Nilesh J. Samani71,72, Ozren Polasek195, Philippa J. Talmud69, Fredrik Nyberg68,196, Diana Kuh67, Maris Laan25, Kristian Hveem66, Lyle J. Palmer197,198, Yvonne T. van der Schouw63, Juan P. Casas199, Karen L. Mohlke61, Paolo Vineis200,60, Olli Raitakari201, Santhi K. Ganesh202, Tien Y. Wong203,204, E Shyong Tai205,57,206, Richard S. Cooper54, Markku Laakso53, Dabeeru C. Rao207, Tamara B. Harris22, Richard W. Morris208, Anna F. Dominiczak209, Mika Kivimaki210, Michael G. Marmot210, Tetsuro Miki49, Danish Saleheen170,48, Giriraj R. Chandak47, Josef Coresh211, Gerjan Navis212, Veikko Salomaa125, Bok-Ghee Han44, Xiaofeng Zhu94, Jaspal S. Kooner160,41, Olle Melander39, Paul M Ridker8,213,9, Stefania Bandinelli214, Ulf B. Gyllensten37, Alan F. Wright70, James F. Wilson34, Luigi Ferrucci33, Martin Farrall32, Jaakko Tuomilehto215,216,217,218, Peter P. Pramstaller30,219, Roberto Elosua29,220, Nicole Soranzo119,28, Eric J.G. Sijbrands13,14, David Altshuler221,115, Ruth J.F. Loos23, Alan R. Shuldiner26,222, Christian Gieger157, Pierre Meneton223, Andre G. Uitterlinden13,14,15, Nicholas J. Wareham23, Vilmundur Gudnason10,11, Jerome I. Rotter161, Rainer Rettig224, Manuela Uda175, David P. Strachan50, Jacqueline C.M. Witteman13,15, Anna-Liisa Hartikainen225, Jacques S. Beckmann105,226, Eric Boerwinkle227, Ramachandran S. Vasan6,228, Michael Boehnke31, Martin G. Larson6,229, Marjo-Riitta Järvelin18,230,231,232,233, Bruce M. Psaty21,135*, Gonçalo R Abecasis19*, Aravinda Chakravarti1, Paul Elliott18,233*, Cornelia M. van Duijn13,234*, Christopher Newton-Cheh27,115, Daniel Levy6,16,7, Mark J. Caulfield4, Toby Johnson4
Affiliations
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10.1371/journal.pgen.1000361 | Deletion of the Mitochondrial Superoxide Dismutase sod-2 Extends Lifespan in Caenorhabditis elegans | The oxidative stress theory of aging postulates that aging results from the accumulation of molecular damage caused by reactive oxygen species (ROS) generated during normal metabolism. Superoxide dismutases (SODs) counteract this process by detoxifying superoxide. It has previously been shown that elimination of either cytoplasmic or mitochondrial SOD in yeast, flies, and mice results in decreased lifespan. In this experiment, we examine the effect of eliminating each of the five individual sod genes present in Caenorhabditis elegans. In contrast to what is observed in other model organisms, none of the sod deletion mutants shows decreased lifespan compared to wild-type worms, despite a clear increase in sensitivity to paraquat- and juglone-induced oxidative stress. In fact, even mutants lacking combinations of two or three sod genes survive at least as long as wild-type worms. Examination of gene expression in these mutants reveals mild compensatory up-regulation of other sod genes. Interestingly, we find that sod-2 mutants are long-lived despite a significant increase in oxidatively damaged proteins. Testing the effect of sod-2 deletion on known pathways of lifespan extension reveals a clear interaction with genes that affect mitochondrial function: sod-2 deletion markedly increases lifespan in clk-1 worms while clearly decreasing the lifespan of isp-1 worms. Combined with the mitochondrial localization of SOD-2 and the fact that sod-2 mutant worms exhibit phenotypes that are characteristic of long-lived mitochondrial mutants—including slow development, low brood size, and slow defecation—this suggests that deletion of sod-2 extends lifespan through a similar mechanism. This conclusion is supported by our demonstration of decreased oxygen consumption in sod-2 mutant worms. Overall, we show that increased oxidative stress caused by deletion of sod genes does not result in decreased lifespan in C. elegans and that deletion of sod-2 extends worm lifespan by altering mitochondrial function.
| In this paper, we examine the oxidative stress theory of aging using C. elegans as a model system. This theory proposes that aging results from the accumulation of molecular damage caused by reactive oxygen species (ROS). To test this theory, we examined the effect of deleting each of the five individual superoxide dismutase (SOD) genes on lifespan and sensitivity to oxidative stress. Since SOD acts to detoxify ROS, the oxidative stress theory predicts that deletion of sod genes should increase oxidative stress and decrease lifespan. However, in contrast to yeast, flies, and mice, where loss of either cytoplasmic or mitochondrial SOD results in decreased lifespan, we find that none of the sod deletion mutants in C. elegans exhibits a shortened lifespan despite increased sensitivity to oxidative stress. Surprisingly, we find that sod-2 mutant worms have extended lifespan and even worms with the primary cytoplasmic, mitochondrial, and extracellular sod genes deleted can live longer than wild-type worms. By examining genetic interactions with other genes known to extend lifespan and by comparing the phenotype of worms lacking sod-2 to that of known long-lived mitochondrial mutants such as clk-1 or isp-1, we provide evidence that the loss of sod-2 extends lifespan through alteration of mitochondrial function.
| The oxidative stress theory of aging proposes that reactive oxygen species (ROS) generated by normal metabolism cause damage to macromolecules within the cell and that the accumulation of this damage over time leads to cellular dysfunction and eventually organismal death [1]–[3]. The majority of ROS present in the cell is thought to be generated in the mitochondria. In order to counteract this process, cells have a number of defense mechanisms which serve to detoxify ROS. Superoxide dismutase (SOD) is a detoxification enzyme that converts superoxide to hydrogen peroxide, which can subsequently be converted to water [4].
The oxidative stress theory of aging predicts that loss of SOD activity should result in increased sensitivity to oxidative stress, since the organism would be less able to detoxify ROS. This should, in turn, result in a shortened lifespan. This is essentially what is observed in yeast, flies and mice for both cytoplasmic SOD (SOD1, CuZnSOD) and mitochondrial SOD (SOD2, MnSOD). In yeast, knocking out sod1 has been shown to decrease clonal and replicative lifespan [5],[6] and accelerate chronological aging [7],[8]. In flies, knocking out Sod1 decreases lifespan [9]. In mice, targeted inactivation of Sod1 results in high oxidative stress and a 30% decrease in lifespan [10].
For SOD2, yeast knockouts show decreased chronological and replicative lifespan [6]–[8]. Reduction of Sod2 in flies by either RNA interference (RNAi) or genetic deletion results in marked reductions in lifespan [11],[12]. In mice, Sod2 knockouts exhibit high degrees of oxidative stress and neonatal or perinatal lethality [13],[14]. In contrast, loss of extracellular SOD (SOD3, EC-SOD) does not appear to impact lifespan despite an increased sensitivity to hyperoxia [15]. Thus, in support of the oxidative stress theory, the effect of deleting Sod1 or Sod2 in all three model species is increased oxidative stress and decreased lifespan or early lethality in the case of Sod2 mice.
In contrast, lifespan in C. elegans may be relatively unaffected by decreased sod expression. Using an RNAi approach to knockdown either sod-1 or sod-2, Yang et al. showed a mild decrease in lifespan with sod-1 RNAi but no effect of sod-2 RNAi, despite the fact that both knockdowns resulted in increased sensitivity to paraquat and an increase in oxidatively damaged proteins [16]. However, it is possible that if the RNAi did not completely abolish sod expression then the remaining low level of SOD activity is sufficient for normal lifespan.
Here, we examine the effect of eliminating SOD on lifespan and sensitivity to oxidative stress in C. elegans and thereby test the oxidative stress theory of aging. Whereas most organisms have only three SODs (one cytoplasmic, one mitochondrial and one extracellular), C. elegans has five sod genes [17]. sod-1, sod-2 and sod-4 encode the primary cytoplasmic, mitochondrial and extracellular SODs respectively [18]–[22] (equivalent to Sod1, Sod2 and Sod3 in mice). In addition, sod-3 is expressed in the mitochondrial matrix and sod-5 is expressed in the cytoplasm, thereby providing C. elegans with two cytoplasmic and two mitochondrial SODs [18],[23]. By examining C. elegans mutants with deletions in each of the five sod genes, we find that elimination of individual sod genes can increase sensitivity to oxidative stress but does not decrease lifespan. Furthermore, we find that sod-2 mutant worms are long-lived and propose that their lifespan extension is due to an alteration of mitochondrial function.
The oxidative stress theory of aging predicts that increasing oxidative stress should result in decreased lifespan. To test this hypothesis, we assessed the lifespan of worms lacking each of the five individual sod genes in C. elegans (the location and size of the mutation for each allele tested are shown in Figure S1). For sod-1, sod-2 and sod-5 we assessed two independent alleles. We found that lifespan was not affected by the disruption of sod-1, sod-3, sod-4 or sod-5 (Figure 1A,C–E; all lifespan data is included in Table S1). This is particularly surprising in the case of sod-1 since SOD-1 accounts for the majority of SOD activity in the cell [23]. In addition we found that deletion of sod-2 resulted in a significant increase in lifespan (Figure 1B). This is also a surprising result given that SOD-2 is the primary SOD present in the mitochondrial matrix and the mitochondria is a major site of superoxide production in the cell.
To ensure that the lifespan extension in sod-2 mutant worms resulted from the deletion of the sod-2 gene we generated heteroallelic mutants. This was accomplished by crossing sod-2(gk257) males with sod-2(ok1030) hermaphrodites and following lifespan in the male offspring (since these must be cross progeny) and by crossing sod-2(gk257) males with either dpy-17 (control) or sod-2(ok1030);dpy-17 hermaphrodites and following the lifespan of the resulting non-dumpy hermaphrodite offspring. In both cases, we found that heteroallelic sod-2(gk257)/sod-2(ok1030) mutant worms lived significantly longer than their corresponding controls (Figure S2).
Since the loss of individual SODs failed to decrease lifespan, we next sought to determine whether the deletion of individual sod genes had an impact on oxidative stress. As it is currently not possible to accurately quantify the levels of ROS in worms, we used paraquat and juglone to assess sensitivity to oxidative stress as has been described in previous experiments [24]–[26]. Both of these compounds are reduced upon entry into the cell and are thought to induce oxidative stress by generating superoxide from oxygen during their subsequent reoxidation [27],[28].
To assess paraquat sensitivity, we examined the survival of 7 day old adult worms on plates containing 4 mM paraquat. We found that sod-1 mutant worms were very sensitive to paraquat with all of the worms dying within one or two days (Figure 1F). We also found that sod-2 and sod-3 mutant worms were more sensitive to paraquat than wild-type worms although not as sensitive as sod-1 mutant worms (Figure 1G,H). In contrast, sod-4 and sod-5 mutant worms showed similar survival to wild-type worms (Figure 1I,K)
In order to confirm our observation of increased sensitivity to oxidative stress, we assessed sensitivity to juglone. One day old worms were transferred to plates containing 240 µM juglone and survival was monitored for the following 10 hours. As with the paraquat assay, both sod-1 deletion strains showed markedly increased sensitivity to oxidative stress as no worms survived to the 4 hour time point (Figure 1L). The sod-2 deletion strains also showed increased sensitivity to juglone which was not as severe as the sod-1 mutants (Figure 1M). In contrast, deletion of sod-3, sod-4 or sod-5 did not make worms significantly more sensitive to juglone-induced oxidative stress (Figure 1N–P).
Next, we assessed sensitivity to paraquat during development by exposing eggs to plates containing 0.2 mM paraquat and determining the latest developmental stage attained for each strain. While exposure to paraquat slowed development in all strains, including wild-type N2 worms, we found that all of the sod deletion mutants except for sod-2 were able to develop to adulthood (Figure S3). The sod-2 mutants were found to arrest at the L1 stage. Thus, sod-2 mutant worms are the most sensitive of all of the sod deletion mutants to oxidative stress during development while sod-1 mutant worms are the most sensitive in adulthood.
In order to confirm the absence of individual sod expression in the deletion strains, we assessed the levels of each of the five sod mRNAs by qRT-PCR (quantitative real time reverse transcription polymerase chain reaction). Importantly, we also sought to determine whether the deletion of individual sod genes is compensated for by the upregulation of other sod genes. In all strains, the deletion mutation resulted in decreased levels of the corresponding mRNA (in most cases no mRNA was detected) (Figure 2A). In both sod-2 mutant strains, sod-1, sod-3 and sod-4 mRNAs were significantly elevated and there was a trend towards increased expression of sod-5 (Figure 2A). Similarly, sod-3 mutant worms showed increased expression of sod-1 and sod-4 mRNA and a trend towards increased expression of sod-2 and sod-5 (Figure 2A). One of two sod-5 mutants showed significantly increased expression of sod-1 mRNA, while sod-1 and sod-4 mutant worms showed no significant changes in mRNA levels of the other four sod mRNAs (Figure 2A). Overall, we observed some compensatory upregulation of other sod mRNAs in the sod deletion strains but the degree of upregulation was small, generally 2-fold or less. The fact that sod-3 mutant worms showed a similar upregulation of sod mRNA as sod-2 mutant worms suggests that the lifespan extension in the sod-2 mutants does not result from the observed upregulation of sod mRNA.
Since a compensatory increase in SOD expression could also occur at the translational level, we examined the level of SOD-1 and SOD-2 protein in the sod deletion mutants (antibodies to the other SOD proteins are currently not available). We observed no SOD-1 protein in sod-1 deletion mutants or SOD-2 protein in sod-2 deletion mutants (Figure 2B). As with mRNA expression we did not observe a dramatic upregulation of SOD-1 or SOD-2 in any of the sod deletion mutants (Figure 2C).
Although the magnitude of compensatory upregulation of other sod genes, when present, was small, it is possible that this could have accounted for the normal or extended lifespan we observed in the sod single deletion mutants. To investigate this possibility, we sought to determine whether elimination of a second sod gene would shorten the lifespan of the sod single deletion mutants. Accordingly, we generated a panel a sod-sod double mutants consisting of all of the double mutants for sod-1 and sod-2, since these are the major contributors to SOD activity in the cytosol and mitochondria respectively, and sod-3; sod-5 (this mutant lacks both of the “extra” sod genes found in C. elegans).
Examining the lifespan of sod-1 double deletion mutants revealed that deletion of sod-3, sod-4 or sod-5 did not shorten the lifespan of sod-1 mutant worms (Figure 3A). In contrast, sod-1;sod-2 mutant worms lived significantly longer than wild-type N2 worms (Figure 3A). Among the sod-2 double deletion mutants, all of the worms maintained the extended lifespan seen in sod-2 single deletion mutants indicating that in no case is the upregulation of another sod gene entirely responsible for the long life observed in sod-2 mutant worms (Figure 3B). Finally, we found that sod-3;sod-5 mutant worms had a similar lifespan to wild-type worms (not shown).
Next, we examined the sensitivity to oxidative stress among the sod-sod double mutants using both paraquat and juglone. Examining the survival of one day old adult worms on 4 mM paraquat plates, we found that all of the sod-1 double mutants, including the long-lived sod-1;sod-2 mutant worms, had decreased survival compared to N2 worms (Figure 3C). Among the sod-2 double mutants, sod-2;sod-3 mutant worms were hypersensitive to paraquat, while sod-2;sod-4 and sod-2;sod-5 mutant worms appeared to be only mildly more sensitive than N2 worms (Figure 3C).
A similar pattern of sensitivity to oxidative stress was observed on juglone plates. All of the sod-1 double mutants as well as sod-2;sod-3 mutant worms were more sensitive to juglone than N2 worms (Figure 3D). There was also a trend towards decreased survival in the remaining double mutant strains (Figure 3D). Overall, the sod-sod double mutants showed increased sensitivity to oxidative stress but normal or extended longevity. Thus, we did not observe any correlation between sensitivity to oxidative stress and lifespan.
We also examined sod mRNA expression levels in the sod-sod double mutant worms (Figure S4). As with the sod single deletion mutants, we observed some compensatory upregulation of other sod genes but the magnitude of this increase was small and failed to rescue the observed increase in sensitivity to oxidative stress.
Based on our finding that even the elimination of two sod genes together does not shorten the lifespan of C. elegans, we assayed lifespan in a selection of sod triple mutants. To eliminate the possibility that the reason why sod-1 and sod-2 mutants of C. elegans do not show decreased lifespan is because C. elegans has duplicate SODs in the cytoplasm and mitochondria, we generated sod-1;sod-3;sod-5 and sod-2;sod-3;sod-5 triple mutants to model sod-1 and sod-2 knockouts in species with only three sod genes. We also generated sod-1;sod-2;sod-4 worms which lack the primary cytoplasmic, mitochondrial and extracellular sod genes. Examination of worm lifespan revealed that sod-1;sod-3;sod-5 mutant worms live as long as wild-type worms while both sod-2;sod-3;sod-5 and sod-1;sod-2;sod-4 mutant worms live significantly longer than wild-type (Figure 4A–C). This clearly indicates that the normal lifespan observed in sod-1 worms does not result from the overlapping expression of sod-3 in the mitochondria or sod-5 in the cytoplasm. Since sod-2;sod-3;sod-5 triple mutant worms do not survive as long as sod-2 single deletion mutants, it is possible that the mild upregulation of sod-3 and sod-5 may contribute to the increased lifespan of sod-2 mutant worms. However, the fact that similar upregulation of sod mRNA in sod-3 mutant worms does not result in extension of lifespan and that upregulation of sod-3 and sod-5 in sod-2 mutant worms is insufficient to prevent increased levels of oxidative damage (see below) suggests that other mechanisms are involved in the long life of sod-2 mutant worms.
In order to investigate possible mechanisms of lifespan extension in sod-2 mutant worms, we generated double mutants with genes known to extend lifespan which are representative of different lifespan extending mechanisms including daf-2 (insulin/IGF signaling)[29], clk-1 (decreased mitochondrial function)[30], isp-1 (decreased mitochondrial function)[25], eat-2 (dietary restriction)[31] and glp-1 (germ-line ablation)[32]. Deletion of sod-2 did not extend the lifespan of daf-2 worms (Figure 5A). clk-1 worms showed a marked extension of lifespan when sod-2 was deleted (Figure 5B). In contrast, sod-2 deletion greatly shortened the lifespan of isp-1 worms such that isp-1;sod-2 worms had a shorter lifespan than wild-type N2 worms (Figure 5C). eat-2;sod-2 mutant worms showed a small increase in lifespan compared to eat-2 worms (Figure 5D). Finally, deletion of sod-2 in glp-1 worms resulted in a modest increase of mean but not maximum lifespan (Figure 5E). Overall, we found that sod-2 deletion had the greatest impact on the lifespan of mutants which exhibit extended longevity as a result of alterations in mitochondrial function.
Based on our finding that sod-2 deletion interacts with long-lived mutants with altered mitochondrial function and the fact that SOD-2 is localized to the mitochondria, we hypothesized that deletion of sod-2 extends lifespan by decreasing mitochondrial function. In C. elegans a number of genes have been identified that affect mitochondrial function and at the same time increase lifespan [25],[30],[33],[34]. Although these genes do not necessarily interact and the exact mechanism of lifespan extension is unclear, these mutants are generally grouped together since it is believed that the alteration of mitochondrial function is the key to their long life. In addition to impaired mitochondrial function and extended longevity, these mutants, sometimes referred to as Mit mutants, are characterized by slow development, slow defecation rate and decreased brood size. Accordingly, we quantified the development, brood size and defecation rate of sod-2 mutant worms and compared this with two prototypes of this class of mutants - clk-1 and isp-1 worms [25],[30],[35]. We also examined clk-1;sod-2 and isp-1;sod-2 double mutants to determine if the loss of sod-2 enhanced the phenotypes observed in clk-1 and isp-1 worms.
Examination of post-embryonic development (PED) revealed that sod-2, clk-1 and isp-1 worms all developed slower than wild-type worms (Figure 6A). On a clk-1 and isp-1 background, sod-2 deletion resulted in further increase in PED time (Figure 6A). Examination of defecation cycle length revealed a slow rate of defecation in sod-2, clk-1 and isp-1 worms compared to wild-type N2 worms (Figure 6B). Deletion of sod-2 had opposite effects on defecation cycle length in clk-1 and isp-1 worms. sod-2 deletion further lengthened the defecation cycle of clk-1 worms but shortened the defecation cycle length of isp-1 worms (Figure 6B). Self-brood size was decreased in sod-2, clk-1 and isp-1 worms and deletion of sod-2 further decreased the brood size in clk-1 and isp-1 worms (Figure 6C). Finally, a comparison of lifespan between these strains revealed that isp-1;sod-2 worms were short-lived, sod-2 and clk-1 worms were long-lived and clk-1;sod-2 and isp-1 worms were very long-lived (Figure 6D). Clearly, sod-2 deletion mutants exhibit the key characteristics of extended longevity mitochondrial mutants and modulate these phenotypes in clk-1 and isp-1 worms.
The phenotypic similarity of sod-2 mutant worms to extended longevity mitochondrial mutants as well as the ability of sod-2 deletion to alter these characteristic phenotypes in clk-1 and isp-1 worms suggests that sod-2 extends lifespan through a similar mechanism. Based on this hypothesis, we would predict that mitochondrial function would be altered in sod-2 mutant worms. To assess this, we measured whole worm oxygen consumption, which has previously been shown to be decreased in both clk-1 and isp-1 worms [16],[25]. We found that oxygen consumption in one day old adult worms was significantly decreased in sod-2 mutant worms compared to wild-type worms (Figure 7A).
We have previously reported that both clk-1 and isp-1 worms exhibit decreased levels of oxidatively damaged proteins [16]. Here, we find that sod-2 worms are hypersensitive to oxidative stress, suggesting that there may be an increase in oxidatively damaged proteins in these worms. To determine the level of oxidative damage in sod-2 mutant worms, we quantified carbonylated proteins in sod-2 mutant worms and wild-type worms. We found that sod-2 mutant worms had significantly more oxidative damage than wild-type worms (Figure 7B).
In order to determine whether the sensitivity of sod-2 mutant worms to paraquat and juglone was the result of a specific sensitivity to oxidative stress or a sign of general weakness, we examined the ability of sod-2 worms to withstand heat stress [36] or osmotic stress [37]. After exposure to 35 degree Celsius heat stress for a period of 6 hours or 9 hours, we found that sod-2 mutant worms survived as well as wild-type worms (Figure 7C). Similarly, exposing sod-2 mutant worms to osmotic stress on 500 mM NaCl NGM plates for 20 hours revealed that sod-2 mutant worms survive osmotic stress as well as wild-type N2 worms (Figure 7D). Combined these results suggest that the sensitivity of sod-2 mutant worms to oxidative stress is a specific sensitivity resulting from their decreased ability to detoxifying ROS.
In this paper we test the oxidative stress theory of aging in C. elegans. Examination of lifespan and sensitivity to oxidative stress in mutants with deletions in one, two or three sod genes reveals normal or extended lifespan in worms with markedly increased sensitivity to oxidative stress. In contrast to observations in other species, deletion of sod-2 results in long life despite increased levels of oxidative damage. The lifespan extending mechanism of sod-2 interacts most strongly with that of clk-1 and isp-1 mutations, which extend lifespan by decreasing mitochondrial function. The phenotypic similarity of sod-2 mutant worms with these mitochondrial mutants, the mitochondrial localization of SOD-2 and the decreased oxygen consumption in sod-2 mutant worms suggest that sod-2 deletion extends lifespan through alterations in mitochondrial function.
Since its origins in 1956, the oxidative stress theory of aging has been extensively tested in multiple organisms both by observing variations in natural populations and through genetic intervention [1]–[3]. Thus far there have been many experiments that support this theory, but also experiments which challenge the notion that molecular damage from ROS leads to aging (reviewed in [38]).
In this paper, we find that the effect of sod deletion on lifespan in C. elegans is unique from other organisms. In concordance with the oxidative stress theory of aging, yeast, flies and mice lacking either cytoplasmic or mitochondrial SOD show either decreased lifespan or lethality (in the case of Sod2 knockout mice) [5], [7], [9], [10], [12]–[14] while mice lacking extracellular SOD live as long as wild-type mice [15]. Here, we demonstrate that none of the sod deletion mutants in C. elegans show decreased lifespan. One possible explanation for this discrepancy is the fact that C. elegans has five sod genes, rather than three, including two cytoplasmic SODs and two mitochondrial SODs. To eliminate this explanation, we show that the lifespan of sod-1;sod-3;sod-5 and sod-2;sod-3;sod-5 triple mutants, which model sod-1 and sod-2 deficient organisms in species with only three sod genes, is not decreased.
Another possible explanation for why C. elegans sod mutants exhibit a normal lifespan would be compensatory upregulation of other sod genes. In support of this hypothesis, we observed sod mRNA upregulation in sod-2 and sod-3 mutant worms as well as one of two sod-5 mutants. However, the magnitude of this upregulation was small (2-fold or less) and we observed no significant sod upregulation in sod-1 or sod-4 mutant worms which also exhibit a normal lifespan. These results are in general agreement with studies of Sod knockouts in flies and mice where either no change in other SOD activity is reported [12], [15], [39]–[41] or changes with magnitudes of less than 50% [10],[14],[42].
Although the compensatory upregulation of other sod genes was small in magnitude and not present in all sod deletion mutants, it is possible that this small increase contributed to the normal or extended lifespans observed in these strains. It is also possible that changes in SOD protein levels or activity contributed to the preservation of lifespan in these strains. To investigate these possibilities, we used the genetic approach of generating sod double and triple mutants. The loss of an additional sod gene did not decrease the lifespan in sod-1 mutant worms, nor did it revert the lifespan of sod-2 mutant worms to wild-type. This indicates that the increased expression or activity of any single other sod gene is not responsible for the normal lifespan observed in sod-1 mutant worms or extended lifespan observed in sod-2 mutant worms. Similarly, all of the sod triple mutants were able to live at least as long as wild-type worms. The lack of lifespan shortening in worms with multiple sod genes deleted is in concordance with studies in mice where the loss of extracellular SOD [43] or the loss of glutathione peroxidase (another ROS detoxifying enzyme) and one copy of Sod2 [38] does not further decrease lifespan in Sod1 knockout mice. The lack of additive effects between different compartments can be explained by the inability of superoxide to cross biological membranes [44],[45]. A compartment specific effect of genes involved in ROS detoxification on lifespan has also been observed in C. elegans with genes encoding catalase, where deletion of peroxisomal catalase (ctl-2) results in decreased lifespan while deletion of cytoplasmic catalase (ctl-1) has no effect on lifespan [46].
A comparison of our results for lifespan and sensitivity to oxidative stress reveals no correlation. None of the sod single or double mutants exhibited a shortened lifespan despite many strains showing markedly increased sensitivity to oxidative stress. Most strikingly, sod-1;sod-2 mutants show the highest sensitivity to oxidative stress in combination with the longest lifespan. Similarly, our laboratory has recently shown that decreasing levels of sod-1 or sod-2 by RNAi increases paraquat sensitivity and oxidative damage to proteins in N2 as well as in multiple long-lived strains (daf-2, clk-1 and isp-1) yet does not decrease lifespan in these strains [16]. Initial experiments examining sensitivity to oxidative stress in long-lived worms indicated that increased resistance to oxidative stress occurs with increased lifespan [24],[47],[48]. It was also found that when longevity was selected for in flies, increased longevity was accompanied by resistance to oxidative stress [49]. More recently, in the reverse experiment examining the lifespan of worms that were resistant to paraquat-induced oxidative stress, it was found that only 84 of 608 RNAi treatments that increased stress resistance also increased lifespan [50]. Similarly, examination of the relationship between paraquat resistance and lifespan in 138 lines of flies revealed only a weak positive correlation [51]. In mice, Sod1 knockouts show increased sensitivity to oxidative stress and decreased lifespan [10],[39], mice heterozygous for the targeted inactivation of Sod2 showed a normal lifespan despite increased oxidative damage [52], while Sod3 knockout mice show increased sensitivity to oxidative stress and a normal lifespan [15]. Combined with our results, it appears that the correlation between sensitivity to oxidative stress and lifespan is weak at best.
SOD2 is the primary, and normally sole, SOD present in the mitochondrial matrix. Since the mitochondria is a major source of superoxide within the cell and superoxide is not able to pass through membranes[44],[45], SOD2 may be the most critical SOD within the cell for decreasing superoxide-induced damage. This conclusion is supported by findings that decreasing or eliminating SOD2 expression affects lifespan more than elimination of SOD1 or the extracellular SOD. In flies, eliminating SOD1 reduces lifespan from about 60 days to 11.8 days [9] while eliminating SOD2 decreases lifespan to less than 1 day [12]. Similarly, Sod1 knockout mice show a 30% decrease in lifespan living an average of 20.8 months [10], while Sod2 knockout mice exhibit either peritnatal or neonatal lethality [13],[14].
In contrast to what is observed in other species, we find that sod-2 deletion in C. elegans results in extended lifespan. While these worms show small but significant increases in sod-1, sod-3 and sod-4 mRNA expression, deleting sod-1, sod-3 or sod-4 in sod-2 mutant worms does not revert their lifespan to wild-type suggesting that this upregulation of sod expression is not responsible for the lifespan increase in sod-2 mutant worms. Our observation of decreased lifespan in sod-2;sod-3;sod-5 mutant worms compared to sod-2 mutant worms suggests the possibility that upregulation of sod-3 and sod-5 partially contributes to the extended lifespan observed in sod-2 mutant worms. However, the fact that we observe similar upregulation of other sod genes in sod-3 mutant worms without the lifespan extension supports the conclusion that the mild compensatory upregulation of other sod genes is not responsible for the long life of sod-2 mutants. Furthermore, the fact that sod-2 mutant worms show increased oxidative damage indicates that the upregulation of sod-3 and sod-5 is not sufficient to reduce mitochondrial oxidative stress in sod-2 mutant worms.
To gain insight into the mechanism of lifespan extension in sod-2 mutant worms, we examined the effect of sod-2 deletion on other mutants with extended longevity. sod-2 deletion did not extend lifespan in daf-2 worms, which extend lifespan through the insulin-IGF1 pathway [29] but did result in a modest extension of lifespan in eat-2 worms, which extend lifespan through caloric restriction [31] and glp-1 worms, which extend lifespan through the germ-line ablation [32]. In clk-1 worms, which extend lifespan by decreasing mitochondrial function [30],[53], deletion of sod-2 resulted in a 15 day increase in mean lifespan. In contrast, sod-2 deletion decreased the lifespan of isp-1 worms by 25 days despite the fact that isp-1 also extends lifespan through via a decrease in mitochondrial function [25].
The clear interaction of sod-2 deletion with mutants that extend lifespan through alterations in mitochondrial function suggested the possibility that sod-2 also increases longevity through a similar mechanism. In C. elegans a number of mutants have been identified by genetic deletion or RNAi that affect mitochondrial function and extend lifespan [25],[30],[33],[34],[54]. In addition to decreased mitochondrial function and extended lifespan, the group of mitochondrial mutants also share a number of characteristic phenotypes such as slow rate of development, slow rate of defecation and decreased brood size [25],[35],[55]. Phenotypic characterization of sod-2 mutant worms demonstrates that sod-2 mutants exhibit all of the phenotypes of the extended longevity mitochondrial mutants including slow development, slow defecation rate, decreased brood size, decreased mitochondrial function and increased lifespan.
While we have previously shown that clk-1 and isp-1 worms have decreased levels of oxidatively damaged proteins [16], here we find that sod-2 mutant worms exhibit an increase in oxidative damage. The fact that all three strains have a long lifespan suggests that both high and low levels of oxidative damage are compatible with long life. Moreover, the fact that oxidative damage in clk-1 and isp-1 worms can be increased to a level that is significantly greater than wild-type worms without diminishing the long life of these two strains suggests that the low levels of oxidative damage in clk-1 and isp-1 worms does not contribute to their extended longevity [16].
In addition to those genes which impair mitochondrial function and increase lifespan, there are at least two mutations, mev-1 [56] and gas-1 [57], which decrease mitochondrial function and decrease lifespan. While it is currently uncertain why these mutations have a different effect on lifespan compared to the extended longevity mitochondrial mutants, it appears that there are at least two ways in which decreasing mitochondrial function can lead to decreased lifespan. First, the severity of the mutation can be incompatible with long life. This has recently been demonstrated using an RNAi dilution series against genes involved in mitochondrial function [55]. These authors find that RNAi against the same gene can increase lifespan at low concentration (i.e. mildly inhibited mitochondrial function) and decrease lifespan at high concentration (i.e. severely inhibited mitochondrial function). In our work, we hypothesize that isp-1;sod-2 worms are another example whereby the overall mitochondrial function in the double mutant worm is severely affected leading to a shortened lifespan. Second, the decreased lifespan can be the result of the way in which mitochondrial function is altered. For example, RNAi targeted against any of the four subunits of electron transport chain complex II results in decreased lifespan [58] while RNAi targeted against proteins in any other complex of the electron transport chain can result in increased lifespan [33]. Furthermore, recent work examining the effect of an RNAi dilution series against mev-1 indicates that it is not the severity of this mutation that prevents it from extending lifespan, since mev-1 RNAi failed to increase the lifespan of wild-type worms at any concentration [55].
Examination of clk-1;sod-2 double mutants shows that sod-2 deletion enhances all of the mitochondrial mutant phenotypes of clk-1 worms. However, a different pattern is observed with isp-1 worms, where sod-2 deletion further slows development and decreases brood size but quickens defecation towards wild-type and decreases lifespan below wild-type N2 worms. We propose that the reason for the different effects of sod-2 deletion on clk-1 and isp-1 worms results from differences in the initial degree of mitochondrial function. Based on previous measurements of oxygen consumption, respiration is only mildly impaired in clk-1 worms [16],[53] while it is more than 50% reduced in isp-1 worms [25]. By comparing the other phenotypes of N2, clk-1 and isp-1 worms it can be seen that as mitochondrial function decreases, development time gets longer, defecation gets slower, self brood size decreases and lifespan increases. However, according to mitochondrial threshold theory, once a certain threshold of mitochondrial dysfunction is reached, the cell is no longer able to compensate and lifespan decreases [59]. This theory was recently explored in C. elegans through the use of an RNAi dilution series to show that progressively decreasing mitochondrial function resulted in increased lifespan only until a certain threshold after which lifespan began to decrease [55]. Based on these findings, we propose a model in which the shortened lifespan that we observe in isp-1;sod-2 worms results from the sod-2 deletion pushing mitochondrial function past the threshold at which the organism is able to compensate for the lost mitochondrial function and accordingly lifespan is decreased (Figure 8). Similarly, we propose that the increased lifespan in clk-1;sod-2 worms results from the sod-2 deletion reducing the mitochondrial function to a level similar to isp-1 worms. In line with our demonstration of decreased oxygen consumption in sod-2 mutant worms, deletion of sod-2 has also been shown to decrease mitochondrial function in mouse models [60]–[62].
Although the first of the extended longevity mitochondrial mutants was identified more than a decade ago [30],[35],[54], the precise mechanism by which these mutants extend lifespan is still unresolved. Nonetheless, a number of potential mechanisms have been suggested [63]. Future studies will need to more precisely define how mitochondrial mutants, such as sod-2, extend lifespan and to determine how C. elegans is able to cope with reduced SOD activity. It will be particularly interesting to examine the interaction between SODs and other proteins involved in ROS detoxification (catalases, peroxidases, thioredoxins, peroxiredoxins) in order to obtain a more complete understanding of the relationship between oxidative stress and lifespan.
The following strains were used in these experiments: N2 (wild-type), sod-1(tm776), sod-1(tm783), sod-2(gk257), sod-2(ok1030), sod-3(tm760), sod-4(gk101), sod-5(tm1146), sod-5(tm1246), clk-1(qm30), eat-2(ad1116), daf-2(e1370), isp-1(qm150), glp-1(e2141). Strains obtained from external sources were outcrossed with our N2 worms for 5-10 generations. For these experiments the following double and triple mutant strains were generated: sod-1(tm783);sod-2(ok1030), sod-1(tm783);sod-3(tm760), sod-1(tm783);sod-4(gk101), sod-1(tm783);sod-5(tm1246), sod-2(ok1030);sod-3(tm760), sod-2(ok1030);sod-4(gk101), sod-2(ok1030);sod-5(tm1246), sod-3(tm760);sod-5(tm1246), clk-1(qm30);sod-2(ok1030), eat-2(ad1116);sod-2(ok1030), daf-2(e1370);sod-2(ok1030), isp-1(qm150);sod-2(ok1030), glp-1(e2141);sod-2(ok1030), sod-1(tm783);sod-2(ok1030);sod-4(gk101), sod-1(tm783);sod-3(tm760);sod-5(tm1246), and sod-2(ok1030);sod-3(tm760);sod-5(tm1246). All of the sod deletions were confirmed by PCR. All strains were maintained at 20°C.
Lifespan studies were completed at 20°C with a minimum of 3 independent trials and an initial number of 80 worms per strain per trial. Initial lifespan assays for sod single deletion mutants, sod-sod double deletion mutants and sod-2 double mutants with genes in known pathways of lifespan extension were completed on normal NGM plates. As some sod double mutant strains bagged extensively subsequent lifespan studies were completed on plates containing 100 µM FUDR (Sigma). Results obtained on NGM plates were all repeated and confirmed on FUDR plates. Survival plots shown represent pooled data from multiple trials on FUDR plates. For glp-1 and glp-1;sod-2 lifespan analyses worms were grown at 25°C and then transferred to 20°C at adulthood.
Paraquat and juglone sensitivity assays were completed in triplicate with 30–40 worms per strain per trial at 20°C. To assay paraquat sensitivity, 7 day old adult worms were transferred to plates containing 4 mM paraquat (Sigma) and survival was monitored daily. Initially, paraquat assays were performed on 1 day old adult worms. However, by day 3 of adulthood, paraquat causes most of the worms to have internal hatching of progeny (bagging) such that more worms die of this than of paraquat toxicity.
Juglone sensitivity was assessed in 1 day old adult worms on plates containing 240 µM juglone (Sigma). For this assay, plates were made fresh on the day of the assay as the toxicity of juglone decreases rapidly over time. Survival was monitored for 6 to 10 hours. To assess the ability of worms to develop under oxidative stress, a minimum of 40 eggs were placed on plates containing 0.2 mM paraquat and seeded with OP50 bacteria.
RNA was isolated from young adult worms using TRIZOL reagent (Invitrogen). Subsequently, 1 µg of RNA was converted to cDNA using the Quantitect Reverse Transcription kit (Qiagen). 1 µl of the resulting cDNA preparation was used for quantitative real-time PCR using the Quantitect SYBR Green PCR kit and a Biorad iCycler RT-PCR machine. Primer sequences for sod mRNAs were previously validated [64]. A combination of three control primer sets (cdc-42, pmp-3 and Y45F10D.4) were used as has been previously described [65]. Results represent the average of three independent biological samples, each of which was amplified in triplicate.
Eggs were collected and allowed to hatch over a period of 3 hours. After 3 hours, L1 worms were transferred to a new plate and monitored for development to an adult worm. Results are the average of at least three independent trials with 20 worms per trial.
Defecation cycle length in young adult worms was measured as the average time between consecutive pBoc contractions. Results represent a minimum of 3 trials with 10 worms per trial.
To determine the average number of progeny produced by each strain, L4 worms were placed on individual NGM plates. Worms were transferred daily until egg laying ceased and the total number of live progeny produced was counted.
Gravid adult worms were collected from five to ten 100 mm NGM plates and bleached to recover eggs. Eggs were allowed to hatch in M9 buffer over a period of 5 days before L1 worms were transferred to NGM plates. At adulthood worms were collected in M9 buffer, washed free of bacteria and oxygen consumption was measured using a Clark electrode for a 10 minute period. Subsequently worms were pelleted and frozen for protein quantification. Proteins were quantified using a bicinchonic acid protein assay kit (Thermo Scientific) according to the manufacturer's protocol.
Western blotting for SOD proteins was completed as described previously [16]. Antibody dilutions were as follows: SOD-1 (1∶1000), SOD-2 (1∶1000), tubulin (1∶10,000). Levels of protein were compared in three independent samples of one day old adult worms. Oxidative damage was assessed using an Oxyblot assay kit (Millipore) to detect carbonylated proteins as previously described [16]. In this assay carbonyl groups are derivatized to 2,4-dinitrophenylhydrazone (DNP-hydrazone) which can then be detected by western blotting with a DNP specific antibody. The Oxyblot assay was completed according to the manufacturer's protocol using 10 samples of N2 worms and 8 samples of sod-2 mutant worms (Millipore). 9 µg of protein lysate was loaded in each lane. Quantification of carbonylated proteins was achieved by taking the ratio of DNP staining to tubulin staining.
Heat stress experiments were based on previously developed protocols [36]. Briefly, young adult worms on NGM plates were incubated at 35 degrees Celsius for a period of 6 or 9 hours. Worms were then transferred to a 20 degree Celsius incubator. Two days later the percentage of worms surviving was determined. Osmotic stress experiments were also done according to previously developed protocols [37]. Young adult worms were transferred to NGM plates containing 500 mM NaCl. After 20 hours, worms were washed off salt plates in M9 buffer containing 300 mM NaCl and transferred to normal NGM plates. After one day of recovery, the percentage of worms surviving was determined. Results for both stress assays are the average of three independent trials.
Survival plots were compared using the log-rank test. The maximum lifespan of a given strain was measured as the average of the lifespan of the ten longest living worms. A student's t-test was used to compare maximum lifespan between strains. Significance between strains for paraquat and juglone sensitivity assays were assessed by ANOVA. Oxygen consumption results were compared by student's t-test. Error bars show standard deviation.
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10.1371/journal.pntd.0006453 | The role of case proximity in transmission of visceral leishmaniasis in a highly endemic village in Bangladesh | Visceral leishmaniasis (VL) is characterised by a high degree of spatial clustering at all scales, and this feature remains even with successful control measures. VL is targeted for elimination as a public health problem in the Indian subcontinent by 2020, and incidence has been falling rapidly since 2011. Current control is based on early diagnosis and treatment of clinical cases, and blanket indoor residual spraying of insecticide (IRS) in endemic villages to kill the sandfly vectors. Spatially targeting active case detection and/or IRS to higher risk areas would greatly reduce costs of control, but its effectiveness as a control strategy is unknown. The effectiveness depends on two key unknowns: how quickly transmission risk decreases with distance from a VL case and how much asymptomatically infected individuals contribute to transmission.
To estimate these key parameters, a spatiotemporal transmission model for VL was developed and fitted to geo-located epidemiological data on 2494 individuals from a highly endemic village in Mymensingh, Bangladesh. A Bayesian inference framework that could account for the unknown infection times of the VL cases, and missing symptom onset and recovery times, was developed to perform the parameter estimation. The parameter estimates obtained suggest that, in a highly endemic setting, VL risk decreases relatively quickly with distance from a case—halving within 90m—and that VL cases contribute significantly more to transmission than asymptomatic individuals.
These results suggest that spatially-targeted interventions may be effective for limiting transmission. However, the extent to which spatial transmission patterns and the asymptomatic contribution vary with VL endemicity and over time is uncertain. In any event, interventions would need to be performed promptly and in a large radius (≥300m) around a new case to reduce transmission risk.
| Visceral leishmaniasis (VL), a fatal parasitic disease transmitted by sandflies, has been targeted for elimination as a public health problem in the Indian subcontinent by 2020. The goal has been reached in the majority of endemic regions in Bangladesh, India and Nepal, but the disease persists in several hotspots. Better understanding of spatial clustering of VL cases and the role of asymptomatically infected individuals in transmission is required to improve control interventions and sustain the elimination target. To address this issue, we have fitted an individual-level spatiotemporal model of VL transmission to geo-located incidence data from Bangladesh to estimate the rate at which VL risk decreases with distance from a case and the potential contribution of asymptomatic individuals to transmission. Our results suggest that VL risk decreases quickly with distance and that symptomatic individuals are the main drivers of transmission, highlighting the potential for spatially-targeted control interventions to reduce transmission.
| Visceral leishmaniasis (VL), the world’s second most lethal vector-borne parasitic disease, has been targeted for elimination as a public health problem in the Indian subcontinent (ISC) by 2020 [1]. The target is an incidence of less than 1 VL case/10,000 people/year at subdistrict level in Bangladesh and India and district level in Nepal. The reported annual number of cases in the ISC has decreased by approximately 80% since 2011 (from ∼37,000 to ∼6,750) [2], and as of 2017 the target had been reached in all subdistricts in Bangladesh, in approximately 85% of endemic subdistricts in India, and in all districts in Nepal for the previous 4 years [3–7]. Bangladesh has seen a more than 90% reduction in VL incidence since 2011 (from 2874 cases in 2011 to just 255 in 2016) [2], following a large epidemic wave lasting over a decade [8, 9]. The district that has consistently had the highest incidence is Mymensingh, within which Fulbaria (the source of the data analysed in this study) and Trishal have been the most endemic subdistricts and the last to reach the elimination target [9]. Despite the overall decline in incidence in the ISC, several subdistricts that have not had cases for a number of years have reported new cases in 2016 and 2017 [7, 10, 11], highlighting the need to maintain surveillance and control efforts as the target is approached and address gaps in understanding of VL transmission. Two key knowledge gaps are understanding of spatial variation in transmission and the role of asymptomatically infected individuals (individuals infected with Leishmania donovani who do not develop clinical symptoms) in transmission [4, 12–14].
Asymptomatic individuals significantly outnumber clinical VL patients in the ISC, with estimates of the asymptomatic-to-symptomatic incidence rate ratio varying from 4:1 [15] to 17:1 [4, 16]. Parasites have been demonstrated in the blood smears of asymptomatic individuals [17] and modelling has suggested that asymptomatic individuals could be the main drivers of transmission if they are infectious to sandflies [18–20]. Evidence of ongoing transmission, in the form of sporadic outbreaks, in regions in Nepal and Bhutan where there has historically been little symptomatic disease but asymptomatic individuals are present [21, 22] suggests that asymptomatic individuals can infect sandflies and contribute to transmission.
In recent xenodiagnosis experiments, asymptomatic individuals (defined by high anti-leishmanial antibody titres by the direct agglutination test (DAT) or rK39 enzyme-linked immunosorbent assay (ELISA) and no symptoms or prior history of VL) failed to infect sandflies fed upon them, whereas clinical VL cases and post kala-azar dermal leishmaniasis (PKDL) cases infected sandflies [23, 24]. However, only 48 asymptomatic individuals were tested and the experiments are ongoing.
Several studies have shown that proximity to past or current VL cases is a risk factor for infection and disease [15, 25–32] (see S1 Table). In addition to higher incidence of VL in households with past/current VL cases than households without VL, higher prevalence of positivity and incidence of conversion on diagnostic tests such as the rK39 ELISA, DAT, and leishmanin skin test (LST) have been observed in households with past/current VL cases, suggesting that transmission intensity is greater near VL cases. Picado et al [27] observed that incident VL was also associated with the presence of seropositive individuals in the same household at the baseline survey in their study (odds ratio (OR) for VL vs remaining DAT-negative = 2.43, 95% CI 1.55–3.79, p < 0.001) and the presence of other seroconvertors in the same household (OR = 2.22, 95% CI 1.38–3.58, p = 0.001) and within 50m of the household (OR = 8.73, 95% CI 2.59–29.41, p < 0.001) over the 2.5 years of the study. Incident asymptomatic DAT seroconversion was associated with presence of VL cases (OR = 1.66, 95% CI 1.16–2.36, p = 0.005), DAT-positive individuals (OR = 1.37, 95% CI 1.12–1.67, p = 0.002) and DAT seroconvertors (OR = 2.22, 95% CI 1.79–2.75, p < 0.001) in the same household. Incidence of asymptomatic infection and disease were more strongly associated with recent VL (i.e. VL that occurred in the relatively short time frame of the study) than VL that occurred in the 18 months before the study. These findings suggest that infection and transmission are highly spatially and temporally clustered.
Bern et al [15, 25] analysed spatial variation in risk of VL and asymptomatic infection in more detail using the dataset from Mymensingh, Bangladesh, analysed in this study. Associations between VL risk and asymptomatic infection risk (as measured by rK39 ELISA positivity or LST positivity) and distance from a VL case were assessed in terms of the odds ratios of having VL or being rK39 ELISA/LST-positive if living in the same household as, and a different household but <50m from, the closest previous VL case compared to living >50m from them. The risk of VL was substantially higher for individuals living in the same household as a previous case (OR = 6.37, 95% CI 3.30–12.28, p < 0.0001) and decreased relatively quickly with distance (OR = 1.85, 95% CI 0.95–3.60, p = 0.07, for individuals in a household without VL less than 50m from a case), while rK39 ELISA positivity and LST positivity were also strongly associated with proximity to previous VL cases. A multivariable logistic regression model comparing VL to recent asymptomatic infection (defined by rK39 ELISA positivity and LST negativity at baseline) showed that living in the same household as a previous case was associated with significantly increased risk of symptomatic infection (OR = 2.85, 95% CI 1.45–5.61, p = 0.003). Other studies have suggested that the risk of progression from infection to VL is higher for individuals living in households with past/current VL cases [28, 33, 34], and a study comparing transmission in households with recently treated VL cases against households with rK39-rapid-test-positive individuals or PKDL cases (or neither) found higher transmission (in terms of rK39-positivity) in VL households [35]. These studies lend support to the view that VL cases are the primary drivers of transmission, at least in highly endemic villages.
As VL incidence declines in the ISC, understanding spatial heterogeneity in transmission is particularly important for optimising control interventions. The main interventions currently employed are early case detection and treatment, and indoor residual spraying of insecticide (IRS) to try to reduce the density of the Phlebotomus argentipes sandfly vectors [6]. In endemic situations, the WHO recommendation is for IRS to be applied in affected villages and timed to precede maximum sandfly density, while during epidemics large-scale IRS covering all buildings, including houses and animal shelters is recommended [36]. An important question currently facing the control programme is whether more effort should be invested in improving case detection or IRS to reach and sustain the elimination target [7, 12, 20, 37]. This depends on the effectiveness of IRS, but also on the range of transmission around infected individuals and how much asymptomatic individuals contribute to transmission.
Spatially-targeted active case detection and IRS strategies are being considered as part of the national elimination programmes in India and Bangladesh due to resource constraints (the economic and practical infeasibility of performing active case detection and blanket spraying insecticide in all villages) and falling case numbers [3, 6, 9, 38, 39]. These strategies involve checking for infection and VL, and spraying insecticide, in all houses within a certain radius of a new VL case to try to limit transmission. Spatially targeted active case detection strategies have been tested in Bangladesh, India and Nepal with varying levels of success, potentially due to protocol differences (e.g. differences in the definition of ‘index’ cases, from any previous cases to only active cases, and the radii around index cases in which case detection was performed, from 50m to 200m), different levels of incidence, and implementation issues [40–42]. Currently, spatial targeting of IRS is being trialled only at village level in the Vaishali and Saran districts of Bihar state, India, with adjusted IRS village micro-plans in which all villages neighbouring (less than 500m from) a village with cases in the previous year are sprayed (based on an estimated maximum sandfly flight range of 500m [43–45]), along with all villages that have had any cases in the previous 3 years (as in the previous micro-plans) [38]. More precise estimates of the spatial and temporal range of transmission via sandfly dispersal are required to target IRS at a household level.
Although the epidemiological studies described above provide evidence of the importance of case proximity in transmission, they all use relatively simple statistical analyses with crude measures of spatial and temporal proximity to assess transmission patterns. In order to accurately account for variation in the infectious pressure on individuals in both space and time, a more fine-grained spatiotemporal model is required. Existing transmission models of VL [12, 18–20, 46, 47] fail to account for spatial heterogeneity in transmission, and as a result may overestimate the contribution of asymptomatic individuals to transmission [48]. Two key questions that need to be addressed are:
One approach that has been taken to estimate spatial variation in disease risk is using spatial kernel transmission models [49], in which the transmission rate between individuals is scaled by a function of the distance between them (the spatial kernel). Methods for inferring spatial kernels from geo-located incidence data were first developed for foot-and-mouth disease in livestock [50, 51], and have since been extended to handle a number of complexities, including missing data and unobserved infections [52–55], and been applied to other livestock diseases such as avian influenza and swine flu [56–58]. However, they have rarely been applied to human diseases [59] or vector-borne diseases [60] due to a number of challenges, including limited information on human movement [49]. Inference of spatial transmission of VL is particularly challenging due to long infectious periods, the long and highly variable incubation period (reported to last anywhere between 10 days and 2 years, though generally thought to be 2-6 months [25, 61], with an estimated average duration of 5 months (95% CI 4.3–5.5 months) based on diagnostic data [62]), the potential role of asymptomatic individuals in transmission, and the sparsity of data on the flight range of P. argentipes sandflies.
To start to address the questions above we have developed an individual-level spatial kernel transmission model for VL and fitted it to a detailed epidemiological dataset from a highly endemic setting in Bangladesh [15, 25] using a Bayesian inference framework. Our aim in developing and parameterising the model is to eventually use it to predict the impact of spatially-targeted control interventions in the ISC, once it has been validated against data from different settings.
The data used in this study were collected in a longitudinal epidemiological study in a highly endemic village in Fulbaria upazila, Mymensingh district, Bangladesh between January 2002 and June 2004. Full details of the study design and data collection have been provided previously [15, 25, 62], but aspects particularly relevant to the present study are briefly described here. The data consist of detailed information on 2507 individuals living in 509 households in the 3 hamlets (or ‘paras’), out of the 9 in the village, that had the highest reported VL incidence in the years before the study. The 3 study paras are situated in an area approximately 2.7km×2km in size. Longitudinal VL incidence data—including dates of onset of symptoms, treatment, relapse, relapse treatment and death due to VL—was collected for 183 VL cases with symptom onset between January 1999 and June 2004 (retrospectively for those with onset before 2002), and onset years were recorded for 41 individuals who had VL prior to 1999 (see S1 and S3 Data). Annual censuses to record demographic information (births and deaths) and cross-sectional diagnostic surveys using rK39 ELISA and LST were conducted on the entire study population from January-April in 2002-2004 (S1 Data). All households present in 2002 were mapped with a Global Positioning System (GPS) device accurate to ±10m, and the GPS positions of households built in the study area after 2002 were imputed as being halfway between those of the two closest households (when these were known). Thus GPS coordinates were available for 506 of the 509 households (Fig 1). All analyses were restricted to the 2494 inhabitants of these households. Individuals were assigned GPS coordinates according to the household they belonged to and pairwise distances between all individuals calculated using the haversine formula [63].
We describe VL transmission using an individual-level spatiotemporal susceptible-exposed-infectious-recovered (SEIR) model. In the model, individuals can be in one of four states at any particular time:
and can only progress from being susceptible to pre-symptomatically infected to symptomatic to recovered, and back to symptomatic if they suffer a relapse (Fig 2). Asymptomatically infected individuals are not explicitly modelled (see below for how transmission due to asymptomatic infection is implicitly incorporated). Note also that recovered individuals who do not relapse are assumed to possess lifelong immunity to VL. This is different to several previous transmission models of VL [18–20], which assume that individuals who have recovered from symptomatic or asymptomatic infection return to being susceptible after a number of years, and thus can be reinfected. Whilst this may be true for asymptomatic infection, multiple VL episodes are relatively rare [25] and the limited available evidence suggests that the majority of these are due to relapse rather than reinfection [65, 66]. PKDL cases are also not included, since there were only 4 confirmed cases of PKDL during the study period and no information was recorded on time of PKDL onset.
Time is measured in units of months (running from t = 1 (January 1998) to t = T = 78 (June 2004)) since this is a natural time scale for VL progression (the durations of infection and disease typically lasting for months) and is the finest time resolution to which some dates (such as birth and death) were recorded. We label individuals that developed VL during the study by i = 1, 2, …, nI, and the remainder of the study population by i = nI + 1, nI + 2, …, n, where n = 2494 is the total study population. The vectors of birth, infection, onset, treatment, relapse, relapse treatment, and death times of all individuals are denoted by B = (Bi)i=1, …, n, E = (Ei)i=1, …, n, I = (Ii)i=1, …, n, R = (Ri)i=1, …, n, I R = ( I i R ) i = 1 , … , n, R R = ( R i R ) i = 1 , … , n, and D = (Di)i=1, …, n. We define the sets of susceptible, pre-symptomatic, symptomatic and recovered individuals at time t by
S ( t )≔ { i : B i ≤ t < min ( E i , D i ) } , E ( t )≔ { i : E i ≤ t < I i } , I ( t )≔ { i : I i ≤ t < min ( R i , D i ) orI i R ≤ t < R i R } , and R ( t )≔ { i : R i ≤ t < min ( D i , I i R ) orR i R ≤ t < D i } , i = 1 , … , n ,
respectively, where we adopt the notation E i = I i = R i = I i R = R i R = ∞ if individual i did not have VL during the study.
We compared the goodness of fit of the background-transmission-only model and the different spatial kernel models defined by Eqs (2)–(4), with and without additional within-household transmission, using the Deviance Information Criterion (DIC) [101]. DIC measures the trade-off between model fit and model complexity, and is the Bayesian equivalent of the Akaike Information Criterion (AIC) [102]. Since some data is unobserved, we required a modified version of the standard DIC that takes into account the missing data. We used a version of DIC for missing data models proposed by Celeux et al [103] that is based on the complete data likelihood L(θ; A) and calculated using the modes of the posterior distributions for the parameters (see S1 Text for further details). As for AIC, lower DIC values indicate better model performance, though differences of less than 5 units between models do not suggest a substantial difference in goodness-of-fit [104]. Given that there are potential issues with using DIC for model comparison [105], we also compared the posterior distributions of the deviances of the models to assess differences in quality of fit, where the deviance is defined up to an additive constant as
D ( θ ) = - 2log( L ( θ ; A ) ) . (11)
Demographic and VL incidence data for the three paras are presented in Table 2 and Fig 3. Average household size was relatively consistent across the three paras, but there were considerable differences in VL incidence and the average number of VL cases per household with VL. All three paras had high incidence from January 1999 to June 2004, but para 1 had the highest average incidence (248/10,000/yr) and average number of cases per VL household (1.65), and para 2, the largest para, the lowest (70/10,000/yr and 1.10 respectively). Most of the difference in incidence was due to much higher incidence in para 1 between 1999 and 2002, prior to the start of the study (Fig 3A). The para-level incidences are similar to those that were observed during 1-3 year ‘micro-epidemics’ in highly endemic clusters of hamlets in the data from Muzaffarpur, Bihar State, India, analysed by Bulstra et al [106]. Fourty-one households, 29 of which were in paras 1 and 3, had multiple cases between 1999 and 2004 (Fig 3B). Paras 1 and 3 also had greater maximum numbers of cases per household: all 3 households that had 4 cases were in para 1 in close proximity to each other and several households that had 3 and 2 cases, and there were 3 households that had 3 cases in para 3, but no household in para 2 had more than 2 cases. The evolution of the spatial pattern of VL cases in each para (Fig 4 and S1 and S2 Figs) from 1999-2004 suggests highly focal transmission around VL cases, with new cases in each year generally appearing very close to VL cases with onset in the previous year. Fig 5 shows the distributions of the distances between all pairs of individuals and all pairs of VL cases for each of the paras. The differences between the distributions for each para, e.g. the higher density in the case distribution at short distances (<100m) and the clumping of the case distribution, reflect the high degree of spatial clustering of cases.
Fig 6 shows the output of the MCMC algorithm for the exponential kernel model, including the log-likelihood trace plot and the posterior distributions for the transmission parameters, β, α, and ϵ, and incubation period distribution parameter, p. It is clear from the plots that the MCMC chain converged rapidly and mixed well, and that the parameters are well defined by the data. The chain was initialised from various regions of parameter space to assess convergence and in all cases nearly identical distributions for the parameters and log-likelihood were obtained.
The estimates (posterior modes and 95% highest posterior density intervals (HPDIs)) for the transmission parameters and DIC for each of the models tested are presented in Table 3. Comparing the DIC values for the different models, it is clear that those in which transmission risk decreases with distance from a VL case fit the data much better than the models with no variation with distance (i.e. the ‘background-only’ model and constant kernel model, for which the differences in DIC from the best-fitting model are ΔDIC = 247.1 and ΔDIC = 64.1 respectively). The best-fitting model is the exponential kernel model. However, the DIC differences between this model and the Cauchy kernel model and the models with additional within-household transmission are all small (ΔDIC < 7), and their deviance distributions are virtually completely overlapping (S3 Fig), suggesting that the data is similarly well described by a Cauchy or exponential kernel and models with additional within-household transmission.
The parameter estimates are highly consistent across the Cauchy and exponential kernel models. The estimates for the spatial transmission rate constant β, background transmission rate ϵ and additional within-household transmission rate can be interpreted as follows. In the absence of any background transmission, the estimated monthly probability of developing VL from living in the same household as a single symptomatic case was 14–16 in 10,000 (95% HPDI 6–31 in 10,000) according to the models without extra within-household transmission, and 26–28 in 10,000 (95% HPDI 7–70 in 10,000) according to models with extra within-household transmission. The monthly probability of developing VL from background transmission if there were no VL cases nearby was approximately 3 in 10,000 (95% HPDI 1.8–4.6 in 10,000). Put another way, the models suggest that at least 134–138 of the 183 VL cases in the 3 paras from 1999-2004 were infected due to being near to VL cases, while the remaining cases originated from background transmission (or at least cannot be explained by spatial proximity to active VL cases or infectious pre-symptomatic individuals).
All but one of the posterior modes for the distance decay rate parameter, α, are ≤ 100m, suggesting VL risk decreased relatively quickly with distance from a case. Although the α estimates for the exponential kernel models are greater than those for the Cauchy kernel models, they actually correspond to a very similar rate of decrease in risk, due to the different shapes of the kernels. Accounting for background transmission, the estimates for the distance at which the risk of VL halves compared to living in the same household as a case (the ‘half-risk distance’) are 79m (95% HPDI 38–197m) and 87m (95% HPDI 50–166m) for the Cauchy and exponential kernel models without extra within-household transmission, and 33m (95% HPDI 4–105m) and 0m (i.e. double the risk in the same household as a case) (95% HPDI 0–69m) for the models with additional within-household transmission. Fig 7 shows the estimated spatial kernel (in terms of the transmission rate from a single VL case βK(d) a distance d away) and its 95% HPDI, for the exponential kernel model. The relatively rapid decay in risk with distance is clear, but there is considerable uncertainty in the estimated transmission rate up to 100m. The estimated background transmission rate and its 95% HPDI are also shown, indicating that the transmission rate from VL cases is higher than the background rate up to around 150m from a case (Box 1).
Including additional within-household transmission in the model led to an increase in the estimates for α since a flatter kernel shape was required to compensate for the extra within-household transmission. The estimate for the additional within-household transmission rate δ for the exponential kernel model suggests an approximately 2-fold increase in risk (95% HPDI 0.2–5.4) from occupying the same household as an active case compared to living right next to a case, but this result should be interpreted with caution given the wide 95% credible interval and the fact that this model did not fit significantly better than the models without additional within-household transmission (Box 1).
Including the LST data and treating individuals as immune upon becoming LST-positive did not significantly affect the estimates for α and ϵ, which both increased by only a small amount (Table 3). However, it resulted in an increase in the estimate for β, such that the estimated monthly probability of developing VL living in the same household as a case increased (from 14 in 10,000 to 19 in 10,000 for the exponential kernel model). This is as expected, given the reduction in the susceptible population from treating LST-positive individuals as immune and the proximity of a large proportion of the LST-positive individuals to VL cases [15].
The posterior distribution for the incubation period distribution parameter, p, was very similar for all models apart from the background-only model (posterior modes 0.50–0.51, 95% HPDIs (0.40–0.62)–(0.41–0.63)), suggesting that the incubation period distribution was relatively well constrained by the data given its assumed shape. The estimated posterior distribution for p corresponds to a mean incubation period duration of approximately 4 months (95% HPDI 2.7–5.1 months).
This study is the first individual-level fully spatiotemporal analysis of VL transmission in the Indian subcontinent and the first attempt to estimate a spatial transmission kernel for VL. It builds upon the work of several field studies that have identified proximity to a recent or current VL case as a strong risk factor for infection and disease, and given estimates of the increase in risk from living with or nearby a VL case, by providing a framework for a more refined spatiotemporal analysis of VL transmission. In particular the framework we have developed accounts for the large variation in the incubation period of the disease, the unobserved infection times of cases, the number of individuals infectious at the infection time of each new case and their distances from the new case, and potential transmission from asymptomatic individuals.
The results of our analysis suggest that the risk of being infected and developing VL decreases relatively quickly with distance from a case (halving within 90m) in a high endemicity setting. This estimate broadly agrees with the results of previous studies that used different methods of analysis. A multivariable logistic regression model of VL risk for the same dataset [15], found that the OR for VL, relative to living more than 50m from a previous case, decreased from 6.37 in the same household as a case to 1.85 in a household within 50m of a case. Another study on data from India and Nepal also found elevated risk of VL when living within 50m of a case with VL in the previous 18 months, compared to being more than 50m away (OR = 2.14, 95% CI 1.27–3.62, p = 0.004) [27]. Given that the spatial kernel in our transmission model represents the spread of infection from person to person due to the sandfly movement, the estimated kernel is also in agreement with the results of recent mark-release-recapture sandfly dispersal studies performed in Bihar, India, in which most sandflies only flew short distances (∼20m), and very few more than 150m [78]. Nevertheless, the estimated distance from a case over which risk decays is relatively small, and it is possible that other aspects of the local environment, such as housing density and configuration, are important in transmission at this scale. In future work we will investigate the potential role of these fine-scale environmental factors in the spatial patterns of VL cases.
The estimated mean incubation period of 4 months is consistent with our previous estimate of ∼5 months (95% CI 4.3–5.5 months) from a multi-state Markov model of the individual-level diagnostic data (including the serological data, which was not used in the present analysis) and case onset and treatment data from the same study [62], and falls within the 2-6 months range reported as typical in the literature [25, 61]. It also appears to agree relatively well with data from longitudinal studies on times from being measured sero-/PCR-positive to developing VL [26, 28, 33, 34, 107–109], which suggest most disease conversion occurs within 6 months.
The estimated relative contributions of transmission from nearby VL cases and background transmission to the observed number of cases suggest that VL cases contribute significantly more to transmission than asymptomatic individuals (at least three times as much) when incidence is high. Given that the background transmission rate also covers potential transmission from animals, the fact that it is much smaller than the case-to-case transmission rate suggests that the contribution of animals to transmission (if it exists) is small. Together with the relatively limited range of transmission around VL cases implied by the estimated spatial kernel, this suggests that reactive spatially-targeted interventions could be effective for reducing VL risk (Box 2). However, the relationship between reaction time to new cases and the radius around them in which interventions would need to be performed is uncertain and needs quantification [110]. It is likely that such a strategy would rely on early detection of new cases and interventions being performed soon after their detection and in a large radius around them (≥ 300m according to the estimated kernel). Even short delays could render the strategy ineffective, since by the time spraying and active case detection were performed the index cases would probably have already infected the next generation of cases within their transmission range.
With regard to implications for spatially-targeted control, it is important to note that the estimated kernel is a single static estimate of how VL risk varies with distance from a case based on incidence over 5.5 years in a highly endemic setting. This is also true of the estimated asymptomatic contribution. How spatial transmission patterns and the asymptomatic contribution vary across settings with different endemicities and longer periods of time is uncertain. The approach used here should be applied to more recent data and data from other geographical areas to assess this variation. This is particularly important given the large differences in incidence between Bangladesh, India and Nepal and the significant declines in incidence in all three countries since the study was conducted. Although existing data suggests that proximity to VL cases is a strong risk factor for infection in all three countries and across different endemicity levels [15, 26, 27, 111], it is possible that spatial patterns of transmission and the asymptomatic contribution have changed as incidence has declined, and further work is needed to assess the generalisability of the results to low-incidence and outbreak settings. Bern et al [25] observed that even over the course of the study, the pattern of VL cases spread from being highly-clustered to saturating major parts of each para (see Fig 4, S1 and S2 Figs), as a large proportion of the population became exposed and those that did not develop disease gained some level of immunity to the parasite. They suggested that this saturation could occur within 2-3 years in high transmission intensity settings, which agrees with observations of 1-3 year micro-epidemics in hamlets in Muzaffarpur, Bihar, India [106]. Thus, spatially-targeted interventions would need to be implemented as early as possible in a micro-epidemic to maximise their impact.
Another important consideration is the practical and economic feasibility of performing spatially-targeted IRS and case detection at a sub-village level, as it would require the ability to mount a rapid response to new cases and delineate the households that would receive the interventions, and focal active case detection could be time-consuming and expensive to implement. Given that VL incidence is also strongly clustered at hamlet level [106], it might make more sense to implement targeted control at this level depending on resource constraints. Based on our estimates, the policy of spraying all villages within 500m of villages with cases in the previous year that is currently being trialled in India seems sensible, as it should account for the fact that the disease may have been transmitted through more than one generation of cases by the time the spraying is performed.
Given the uncertainties in the potential impact of reactive focal IRS and active case detection and in the time window after detection of a new case in which they would be effective, and the difficulties of trialling them, in future work we will predict the impact of different spatially-targeted control strategies by simulating them with the parameterised spatiotemporal transmission model.
As with any spatiotemporal analysis of transmission of a disease such as VL, with a complicated natural history and transmission cycle, our analysis has a number of limitations. In particular, we have not included the serological data from the annual cross-sectional surveys in the model and have not explicitly modelled asymptomatic infection, but incorporated potential transmission from asymptomatic individuals via a background transmission rate that is constant in space and time. Although we have considered the implications of LST-convertors being immune, this was via a very simple age-dependent model for the probability of being LST-positive, which does not take account of spatial and temporal heterogeneity in the force of infection. Asymptomatic infection is known to be associated with spatial and temporal proximity to VL cases, so if asymptomatic individuals transmit it is likely that their contribution varies in space and time. Incorporating the diagnostic data into the model would enable explicit modelling of asymptomatic infection and could help to constrain the possible infection times of cases (e.g. by assuming that individuals cannot be seropositive before they are infected). However, constructing the model and performing the inference on the full data is technically very challenging since the diagnostic surveys only cover the prospective part of the study (2002-2004), the tests (in particular the LST) have imperfect sensitivity and specificity, many individuals are missing tests, and there are a large number of observed combinations of test results. An inference approach that can account for unobserved infections, such as reversible jump MCMC [53, 54], would thus be required, along with a hidden Markov model for the true infection status underlying the observed test results [97]. Both the infection and recovery times of asymptomatically infected individuals are unobserved, so it would also be necessary to impute these from the diagnostic data.
Potential edge effects in space—contributions from infected individuals in unsurveyed paras neighbouring the study paras—are not accounted for in the model. Based on evidence that P. argentipes flies have a limited flight range and the study paras being more than 500m apart, we assumed that there was no transmission between cases in different paras in the absence of migration. However, some of the unsurveyed paras are within the maximum flight range of the sandfly and had cases during the study period, so it is possible that there was inter-para transmission from infectious individuals in households near the edges of unsurveyed paras. A related issue is that the model does not explicitly account for migration (which is covered via the background transmission rate), and assumes individuals were infected in or very nearby their households. In reality, there is a significant amount of seasonal migration for work in rural Bangladeshi communities, which may affect transmission, and there is uncertainty about how much transmission occurs indoors vs. outdoors [14]. These issues will be addressed in future work analysing spatial transmission over a longer period in the same paras and the unsurveyed paras using geo-located incidence and migration data [112].
Another limitation is that the model treats VL risk as purely a function of the local number of infectious individuals and their spatial proximity, and does not include other potential risk factors such as age, sex, bed net use and socioeconomic status. Such risk factors can be included in the model by treating the transmission rate between individuals (λij(t) in Eq (1)) as a function of these variables, but were omitted here to keep the analysis as simple as possible and because case proximity was identified as the strongest risk factor in the original analyses [15, 25]. Sandfly density, which varies seasonally [113], is also likely to affect transmission risk, but sandflies are not explicitly represented in the model due to a lack of comprehensive data on sandfly abundance in the study paras, and uncertainties in the relationship between sandfly and host densities and infection prevalences [12, 14] (Box 3).
The unknown infectiousness profile of VL cases over time (before and during symptoms) and how it varies between individuals is also a potential source of uncertainty in the estimated spatial kernel and transmission rates. In households with multiple cases during the study period, there were long intervals between the onsets of the cases (longer than would be expected if the later cases were infected by the earlier cases, based on the estimated incubation period), possibly suggesting that some cases can remain infectious even after apparently successful treatment, although this is complicated by potential transmission from asymptomatic individuals and other nearby cases.
Finally, we note that the results of this analysis need to be validated. Unfortunately, there are relatively few geo-located VL incidence datasets available with which to do this. However, as part of a more formal validation of the model, in future work we will implement the equivalent stochastic spatial simulation model and test that it reproduces similar spatial patterns of incidence to those observed.
Our analysis shows that in a high-endemicity setting VL transmission is focused around cases and cases are the main drivers of transmission. This suggests that reactive spatially-targeted control interventions could be effective at reducing transmission in high endemicity areas if implemented promptly and in a sufficiently large area around a case. Nevertheless, it is necessary to validate the model against data from different settings and to use it to predict the impact of different control strategies to assess whether spatially-targeted interventions would be effective, and under what conditions. If targeted interventions were predicted to be effective, the results could then be used to inform control policy to help achieve and maintain the 2020 elimination target for VL in the ISC.
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10.1371/journal.pcbi.1003456 | In Silico Single-Molecule Manipulation of DNA with Rigid Body Dynamics | We develop a new powerful method to reproduce in silico single-molecule manipulation experiments. We demonstrate that flexible polymers such as DNA can be simulated using rigid body dynamics thanks to an original implementation of Langevin dynamics in an open source library called Open Dynamics Engine. We moreover implement a global thermostat which accelerates the simulation sampling by two orders of magnitude. We reproduce force-extension as well as rotation-extension curves of reference experimental studies. Finally, we extend the model to simulations where the control parameter is no longer the torsional strain but instead the torque, and predict the expected behavior for this case which is particularly challenging theoretically and experimentally.
| Video game techniques are designed to simulate rigid body dynamics of macroscopic bodies, e.g. characters or vehicles, in a realistic manner. However they are not able to deal with temperature effects, hence they are not able to deal with molecules. In order to extend these powerful techniques to molecular modeling, we implement here Langevin Dynamics in an open source library called Open Dynamics Engine. Moreover we add a “global thermostat” to this Langevin Dynamics, which accelerates the simulation sampling by two orders of magnitude. With these radically new simulation techniques, we prove that we can accurately reproduce single-molecule manipulation experiments in silico, in particular force-extension as well as rotation-extension curves of reference experimental studies. The method developed here represents an unparalleled tool for the study of more complex single molecule manipulation experiments, notably when DNA interacts with proteins. Furthermore the simulation technique that we propose here has all the functionalities required to tackle the nuclear organization of chromosomes at every length scale, from DNA to whole nuclei.
| The mechanical and topological properties of DNA and protein-DNA assemblies are of primary importance in many biological processes, including transcription, replication, chromatin organization and remodeling. Since techniques have become available enabling the manipulation of single-molecules [1], [2], a large amount of experimental data have been accumulated on the mechanical response of DNA and protein-DNA assemblies under stretching forces and twisting torsions, in particular from optical and magnetic tweezers experiments [2]–[5]. In magnetic tweezers experiments, a DNA molecule is grafted at one end to a coverslip and at the other end to a magnetic bead. The bead is trapped in the magnetic field of a pair of magnets that may be translated, thus exerting a varying force on the bead. Moreover the pair of magnets may be rotated at a certain number of turns, thus constraining the linking number of the DNA molecule. After the stretching force and the number of turns are applied to the bead, the only physical variable that can be directly measured is the DNA extension, i.e. the distance between its two ends. Therefore the interpretation of the experimental results requires an important modeling effort, particularly in the more complex cases where DNA is associated with proteins, as for instance in chromatin assemblies [6], [7]. Although theoretical approaches may be successful in some cases [8]–[10], simulations are often crucial tests of the proposed model validity, when they are not the unique possible way of dealing with the system complexity.
In this spirit, we aim to develop an efficient tool to manipulate single-molecules in silico reproducing optical and magnetic tweezers experiments. This task is challenging since the DNA model should have precise specifications to reproduce the behavior of DNA accurately. We need to: (i) model a polymer, i.e. an articulated chain; (ii) reproduce the effective diameter of DNA (depending on electrostatic conditions) and, when proteins are present, have the possibility to model their shape and steric hindrance; (iii) deal with collisions, especially in order to reproduce DNA supercoiled structures (plectonemes) and steric effects in DNA-protein assemblies; (iv) reproduce DNA twisting and bending elasticities; (v) include statistical mechanics features to account for temperature and thermal motion. Beside these essential points, we also wish to simulate the system dynamics, which may be important in some cases, e.g. when hysteresis is observed under magnetic tweezers [7] or for in vivo chromosome dynamics experiments in the cell nucleus [11], [12].
This ambitious list of specifications is beyond the reach of traditional simulation approaches where particles interact through 2-body potentials (as in Molecular Dynamics or Monte Carlo simulations [13], [14] with a given force field). The need to deal with frozen degrees of freedom in coarse grained modeling may be addressed through holonomic constraints, as in the SHAKE algorithm [15], [16], where an iterative approach is adopted. However, collision detection and steric hindrance may only be accounted for in this scheme by introducing additional steps. More recently, non iterative algorithms have been developed [17], that subsequently led to the development of new powerful tools, called “physics engines”. These have been designed by the engineering and robotics communities to reproduce the dynamic behaviour of articulated systems of rigid bodies. Physics engines are acquiring an increasing importance, notably in the fields of computer graphics and video games, where they are now widely used to simulate rigid body motion under realistic conditions and in real-time. Open Dynamics Engine (ODE) is one of the most popular rigid-body dynamics open source library for robotics simulation applications [18]. As other physics engines, ODE simulates the kinematics of articulated systems by using permanent joints that impose holonomic constraints, instead of bond potentials. The same method is used to manage collisions: when overlapping between bodies is detected, a temporary joint is locally created that reproduces the action of the contact forces, without the need for explicit permanent 2-body interaction potentials (see section “Materials and Methods” for details on how ODE manages joints and collisions).
These extremely efficient simulators haven't, up to now, been used in statistical mechanics. Although well adapted to mechanical simulations, physics engines lack coupling to a thermal bath. The main novelty of our approach is the implementation of Langevin-Euler equation in the ODE software. Moreover we improve the simulation efficiency of this Langevin dynamics by extending the “global thermostat” algorithm designed by Bussi and Parinello in 2008 [19] to physics engines. This algorithm allows much faster yet unbiased sampling of the phase-space. As a first step toward simulating DNA-protein assemblies, we focus here on bare DNA and show how to perform in silico single molecule manipulation of DNA.
In rigid body dynamics simulations run with ODE, the state of a system consisting of rigid bodies is described by the positions of their centres of mass, a quaternion representation of their orientations , and their linear and angular velocities and respectively. These velocities are collected in the column vector . We use the superscript T to denote the transpose of a vector or a matrix everywhere in this article. The vector then collects all linear and angular momenta, where is a block diagonal matrix whose elements are the mass matrices and inertia matrices of the N bodies (with the identity matrix). The Newtonian dynamics equation then reads where the generalized force is a vector collecting forces and torques applied to the system. These forces and torques may be external, due for example to gravity or magnetic fields, or internal, as a consequence of the mechanical constraints between the rigid bodies that make up the system.
Most notably, in articulated systems, as is the case of polymers, rigid bodies are connected by mechanical joints. A joint is a relationship that is enforced between two bodies so that they can have only certain positions and orientations relative to each other, and ODE provides different types of joints according to the kind of articulation that has to be implemented, e.g. ball-and-socket, hinge, slider or universal.
Mathematically a joint imposes some holonomic constraint between both connected bodies. Such a constraint is an equation that reads where is the distance between both joint bearings, e.g. the center of the ball of one body and the the center of the socket of the other one. The constrained distance is purely geometrical, depending only on the relative position and orientation of both jointed bodies. The position and orientation of each of the bodies the articulated system is composed of depend on time . Therefore the constraints of the articulated system can be derived with respect to time to get the kinematic constraints in the form where we introduce the jacobian matrix of constraints (see subsection “Exact solution for when there are no collisions” for a detailed example). This velocity-based description is used in ODE as in most game/physics engines.
So, mechanical joints exert reaction forces and torques on the joint bearings. These internal mechanical constraints can be collected into a generalized constraint force which, by virtue of the principle of virtual work , reads where is a vector of Lagrange multipliers that precisely accounts for the reaction forces and torques coming from the joint bearings [16]. The Newtonian dynamics equation therefore reads where and stand for the external and internal contributions to the generalized force respectively. As the constraint force reads , the Newtonian dynamics equation becomes an equation for in the form: .
Solving this equation for should moreover satisfy the holonomic constraints at every timestep . However the discretization used in the numerical calculation results in errors on so that is generally not equal to 0. Then, in order to have at the next timestep, the kinematic constraint should be adapted accordingly. Indeed according to the Euler semi-implicit integration scheme which is used in velocity-based algorithms. Hence . But then this implies that the kinematic constraint is not equal to zero at time , i.e. , so that the joint bearings will continue to move apart afterwards. In order to keep both and close to zero at every timestep, ODE introduces an error reduction parameter in the kinematic equation [18]. This parameter has to be adjusted to some optimal value between 0 (no correction at all) and 1 (complete correction of in one timestep). However setting is not recommended since, as said above, this would imply that the joint bearings will continue to move apart afterwards with maximal velocities. ODE recommends values between and .
In addition to , ODE introduces a second ingredient to soften the rigid constraints by allowing the violation of the constraint equation by an amount proportional to the restoring force . More explicitly, a “constraint force mixing” diagonal matrix is defined, such that (implicit integration) [18]. This is equivalent to introducing a spring-damper system (spring constant of and damping constant of ) with implicit integrator between the joint bearings; this can be understood as analogous to a bead-spring model. Nevertheless there is a major difference between this effective spring and a regular spring: the term constrains the velocity whereas a regular spring constrains the acceleration. As a result, no energy is stored in this effective spring, at odds with a regular spring which stores an averaged energy (see below subsection “Preliminary tests of validity and performance of the global thermostat” along with the histograms of energy in Figure 1).
In particular, ODE uses a powerful software called libccd [20] to detect collisions between two convex shapes. Whenever overlapping is detected between two rigid bodies, ODE attaches a temporary joint between them called a “contact joint”. Defining vector (resp. ) that connects the center of mass of body 1 (resp. 2) to the contact point and denoting the common normal to both bodies at the contact point (directed from 2 to 1), the kinematic constraint imposed by the contact joint would read in the perfect case when the holonomic constraint imposed by the contact joint reads exactly . However, in practice is not equal to 0 because of discretization errors, hence the kinematic constraint imposed by the contact joint actually reads:(1)with(2)(3)
The right hand side of Eq.(1) deals with the already existing overlapping of the two bodies in contact at time when collision is first detected, or with their residual overlapping while the contact joint exists.
By inserting the constraint force into the equation of motion and taking the first-order discretisation of this equation, one can easily get the following expression to be solved for :(4)
This equation is of the form . Importantly, the addition of the term to each diagonal term of the matrix provides a symmetric positive definite matrix , thus greatly increasing the solution accuracy of Eq.(4). From this equation, the vector of Lagrange multipliers, hence the constraint force , can be determined. Then the motion solver (semi-implicit Euler integrator) gives the new positions and orientations of the articulated bodies at time . It is advantageous to choose the Exponential Map parametrization [21] for the quaternion integration.
In general Eq.(4) has to be solved numerically and ODE has two algorithms to do so, one based on the Successive-Over-Relaxation (SOR) method [22] and the other based on the Linear Complementary Problem (LCP) [23]. LCP time complexity is of order and space complexity (memory) of order where is the number of constraint rows [18]; whereas SOR time complexity is of order where is the number of successive-over-relaxation and space complexity of order [18]. Both algorithms have equivalent performances when . But in general LCP is more accurate, although much more time consuming, than SOR. We compared these two algorithms for a chain of length without noticing significant differences in the errors on (error on the colocalization of joint bearings). In order to save computational time, we preferentially run the SOR method with a value of for the relaxation factor and . These values are different from the default values in ODE and work well for a linear chain of rigid bodies connected with ball-and-socket joints. But in some cases, the SOR method does not converge and we then switch to the LCP method, which always converges. However in the case when there are no collisions between the rigid bodies the articulated system is composed of, we were able to derive an exact solution for (see next subsection). Therefore we solve Eq.(4) according to the following scheme:
For the sake of clarity, let us first consider the example of four rigid cylinders of length each connected with ball-and-socket joints at the extremities with the first one anchored to some fixed point, taken as the origin of the coordinates. The vector is the tangent to the cylinder . The jacobian matrix associated with this system is tridiagonal when there are no collisions, in which case it reads:(5)
For each cylinder, the antisymmetric matrix is associated with the cross product and has the property :(6)
The transpose Jacobian matrix and mass matrix are given by:(7)
Then we get:(8)and we deduce the final result for the symmetric matrix :(9)where we define the matrix . We then write in the associated principal axis body frame as :(10)
The vector collects the Lagrange multipliers associated to each joint respectively . The equation gives us a system of coupled equations on . Note that ODE solves in one time all the constraints of this articulated system. This is not the case with the SHAKE algorithm where an unconstrained step is first performed, before correcting the positions and orientations iteratively to get the constraints satisfied eventually. The term from Eq.(4) is given by:(11)where we write and . We can then write the constraint forces and torques (12)
We can now generalise the previous example to the case of a linear chain of rigid cylinders connected with ball-and-socket joints with the first one anchored to the ground. We denote the matrix with the following properties:(13)(14)(15)(16)
Using the following decomposition for the matrix where is a block lower matrix with block identity matrix on the diagonal and where is the block diagonal matrix it is easy to show that for . From these we get the following equations:(17)(18)(19)
In order to solve the linear system of equations we define and solve the problem in an iterative way:(20)(21)and we get the final solution for by solving the problem :(22)(23)
The method explained here is the exact solution of the problem where no collisions are present in the system. With this exact resolution the simulation is faster than the SOR algorithm ( and ) with a gain of .
To model a DNA molecule, we build a linear chain of rigid cylinders of length each, corresponding to 10 base pairs (), which amounts to the double helix pitch. We connect the cylinders to each other by ball-and-socket joints. The radius of the cylinders is set according to the salt buffer concentration of the experimental data we compare with. Indeed, since the DNA molecule is highly negatively charged, DNA-DNA electrostatic repulsion affects the double helix response in single molecule experiments [9], [24]–[27]. This effect can be easily and implicitly included in simulations and theoretical models by introducing an effective DNA radius where is the crystallographic radius of the DNA double helix and accounts for the DNA-DNA electrostatic repulsion [28]–[31]. It turns out that may be set equal to the Debye length of the salt buffer solution. As with c the salt concentration given in and in , we set the effective radius to in mmol monovalent salt buffer for comparison with the reference experimental data of Mosconi et al [32]. Alternatively we use an effective radius to fit the experimental data obtained in mmol monovalent salt buffer by Smith et al [1].
We performed all our in silico single molecule experiments with a DNA molecule of contour length . The corresponding number of DNA cylinders in the chain is therefore . The DNA molecule is anchored, at one end, to a planar surface (mimicking the microscope coverslip), and at the other end, to a rotatable bead (mimicking the magnetic bead). We set the bead radius to in order to prevent the DNA from looping around it. At both ends of the DNA chain, the rigid cylinders are tangent to their attachment surface.
The final problem that remains to be addressed is how to obtain a correct definition of the bending and twisting behavior of DNA. We have solved this problem by a special choice of the connecting joints and by introducing appropriate restoring torques reacting to the bending and twisting deformations. This has been done based on the bending and twisting energies that are defined according to the usual expressions and respectively. The rigidity constants and are related to the bending and twisting persistence lengths and respectively, through the following equations:(24)(25)(26)where is the Langevin function (see supplementary Text S1). The bending angle and twisting angle are related to the standard Euler transformation ZXZ and are given by(27)(28)(29)where is the tangent vector of cylinder i, a vector normal to and . These three vectors are the principal axis of cylinder i.
We finally get the following expression for the global restoring torque between two connected DNA segments (see supplementary Text S1 for the complete derivation of this equation):(30)
We recall that, for DNA, estimates of the bending persistence length give for salt buffer (see Refs. [1], [33]–[35]); whereas estimates of the twisting persistence length give for salt buffer [9], [32]. According to the size of the unit cylinder we find and .
Although well adapted to mechanical simulations, ODE lacks coupling to a thermal bath. As physics engines impose to deal with dynamics equations including inertial terms, in particular for computing constraint forces (collected in ), we need to turn to some implementation of stochastic isothermal molecular dynamics in order to thermalize the system: isothermal to simulate the system at constant temperature, stochastic to ensure ergodicity. The corresponding algorithms are all related to Langevin dynamics and can be cast into local and global thermostats. In local thermostats, such as standard Langevin dynamics, a correction force including both a frictional term and a stochastic term is exerted on each particle to drive the system to the canonical distribution at a prescribed temperature. Global schemes of Langevin dynamics are designed to minimize the perturbation introduced by the thermostat on the Hamiltonian trajectory (so called “disturbance” as defined originally in [36]), hence on the dynamical properties, such as autocorrelation functions, and related quantities, such as diffusion coefficients. In these globally applied thermostats the stochastic term of the correction force acting on each particle is proportional to the momentum of that particle. Two main global algorithms have been designed so far: (a) Stochastic Velocity Rescaling methods, most notably the “global thermostat” introduced by Bussi and Parrinello [19], (b) the Nosé-Hoover Langevin thermostat [37]. Here we first show how to implement Langevin-Euler equation in the ODE software. Moreover we show that the global thermostat introduced by Bussi and Parrinello is so remarkably adapted to this implementation that it improves quite significantly the sampling efficiency with respect to local Langevin dynamics (by two orders of magnitude in typical situations), while preserving the time-dependent properties such as autocorrelation functions. The sampling efficiency is defined as usual as the number of independent configurations generated during the time necessary to reach thermal equilibrium.
To begin with, we add to the “mechanical” forces an additional, thermal contribution containing a frictional term and a random force vector . is the matrix of the coupling frequencies to the thermostat, the matrix of white noise amplitudes and a generalized vector of normalized and independent Wiener processes. and are related through the fluctuation-dissipation theorem, which reads here(31)where with the temperature of the thermal bath and where the superscript denotes that the matrices and are chosen to be diagonal in the principal axis body frame (where the matrix is diagonal by definition). For simplicity, we choose to fix all the to a common frequency . Note that is the relaxation time of the thermostat, i.e. the autocorrelation time of the kinetic energy (see supplementary Figure S1).
We then improve the sampling efficiency of this Langevin dynamics by extending the “global thermostat” algorithm designed by Bussi and Parinello in 2008 [10] to physics engines. This algorithm allows faster yet correct sampling of the phase space in the canonical ensemble. However, it is designed for the translational degrees of freedom only. In order to apply it to an articulated rigid body system, we therefore have to extend it to the rotational degrees of freedom and adapt it to the ODE software. To this aim, we replace the traditional Langevin-Euler correction force (local thermostat) by a corresponding global version , which reads(32)Eq.(32) shows that is proportional to , so that the stochastic force and torque globally associated with the thermostat is in the same direction as . Hence, a free particle, i.e. a particle not connected to any other particle, will move on a straight line between two collisions. Note that, nevertheless, the particle will undergo Brownian motion along this straight trajectory. The global version of the Langevin dynamics minimizes the disturbance induced by the thermostat on the Hamiltonian trajectory (equal to according to its definition in Ref. [19], but extended here to the rotational degrees of freedom), nevertheless retaining the same thermalization speed as usual Langevin dynamics (see supplementary Figure S1).
When used in the framework of a velocity-based algorithm such as ODE, the global thermostat presents a remarkable advantage. This is because, in this case the global Langevin contribution is decoupled from the constraint forces, in the sense that it cancels out in the equations for . More precisely, with our definition of (see Eq.(32)), the contribution to the term in Eq.(4) is always zero. In other words, not only minimizes the disturbance of the Hamiltonian trajectory , but also does not disturb the generalized constraint force . Both effects cooperate to achieve a dramatic acceleration of the simulation sampling, that is, in the case of our model, approximately times faster than with the local thermostat (see below “Preliminary tests of validity and performance of the global thermostat” and Figure 2). Importantly this acceleration is compatible with the correct computation of dynamical properties, such as autocorrelation functions.
In all DNA simulations presented in this article, we choose the length of the cylinders as the unit length , the mass of the cylinders as the unit mass and the unit of thermal agitation as the unit of energy , from which we deduce the unit of time . The complete set of parameters of our simulations is given in Table 1. We also choose to deal collisions with a restitution coefficient equal to without surface friction. Hence, when two rigid bodies collide, the constraint force imposed by the contact joint that temporarily connects them is directed along their common normal at the contact point. We finally choose an error reduction parameter and a constraint force mixing parameter .
We start to validate our methodology by simulating a DNA molecule without any constraint applied on the bead (neither stretching nor twisting). To this aim, we first check the equipartition theorem. When the system is at thermal equilibrium, its temperature is related to the kinetic energy through the equation(33)where is the number of non-redundant holonomic constraints. This relation is standard since is just the number of degrees of freedom (dof) of the system. Moreover the distribution of the kinetic energy of the system at thermal equilibrium follows a Boltzmann law and therefore reads:(34)with . We checked this relation for a DNA molecule of length µm coupled to the two different Langevin thermostats, local and global respectively. The resulting histograms are shown in Figure 1, confirming that: (i) the kinetic energy is correctly sampled at thermal equilibrium with both thermostats, (ii) there is indeed no energy stored in the joints, although these have been softened by effective springs (see above the error reduction parameter in subsection “Introduction to physics engines”).
We then quantified the simulation sampling efficiency by means of the autocorrelation function of the end-to-end distance of a DNA molecule of length µm coupled to the two different Langevin thermostats, local and global respectively. Here the average is performed over the time and denotes the lag-time of the autocorrelation function. A demonstration of the performance of the global thermostat in terms of relaxation rapidity is given in Figure 2. Fitting the exponential decrease of both relaxation curves shows that with use of the global thermostat we reach the saturation value at whereas this value is reached at in the case of the local thermostat, thus resulting in an acceleration factor of about for this system composed of articulated rigid bodies. Note that the dimensionless lag-time is equal to (see Table 1), with the corresponding number of time steps. Then a typical run using the global thermostat is of the order of tens of millions of time steps, whereas it is of the order of billions of time steps with usual Langevin dynamics. A striking illustration of the sampling acceleration provided by the global thermostat is also given in supplementary Video S1.
We also compute the tangent-tangent correlation function along the polymer with both local and global thermostats. No significant deviations were found between both thermostats. Results obtained with the global thermostat are plotted in supplementary Figure S2 along with the corresponding theoretical curves.
A simple calculation shows that the tangent-tangent correlation function decreases as , from which one can calculate the average bending . With the DNA persistence length , this quantity amounts to , to be compared to the result from a fit of the simulation curves, giving . The same comparison can be done for the twist angle (with ), for which the simulation average matches the theoretical value . These comparisons show that our simulation results are in very good agreement with the analytical formulae, thus validating (i) our implementation of the bending and twisting rigidities and (ii) the correct sampling of the DNA conformation space by means of the global Langevin thermostat.
We then simulate reference force-extension curves, both theoretical and experimental. We thus perform simulations at given stretching force along the z-axis (normal to the DNA anchor surface), and without torsional constraints on the magnetic bead. In order to fit the experimental data obtained by Smith et al [1] in monovalent salt buffer, we set here the DNA radius (i.e. the radius of the unit cylinders) to . The resulting force-extension curve is given in Figure 3 where we plot (red circles) the dimensionless stretching force as a function of the dimensionless mean relative extension . Here denotes the mean DNA extension, i.e. the mean distance between the bead and the anchor surface, at zero torsional constraint. For comparison, we also plot (black solid line) the analytical Worm-Like-Chain (WLC) interpolation fitting curve proposed by Bouchiat et al [35] as well as the numerical solution of the WLC model (black triangles) obtained by Marko and Siggia with the same persistence length [34]. The simulation reproduces pretty well the WLC behavior, thus validating our implementation of the DNA bending rigidity. Note that, at low forces, the extension saturates at a value greater than zero because of the impenetrable ground and magnetic bead that both confine the DNA molecule. This effect is more pronounced than in the experimental curve [1] because the ratio of the bead radius to the DNA length is higher in our simulations.
In Figure 3, we also show the results obtained in the limit case , for which (see Eqs.(24–25)), and when there are no collisions. In this case we expect to observe a Freely-Jointed-Chain response (with segments of each). The analytical force-extension relation for FJC is given by the well-known expression as a Langevin function and it is also reproduced in Figure 3. Again, the simulation results are in very good agreement with the theoretical formula.
More interestingly, magnetic tweezers also allow the application of a torsional strain on a single DNA molecule at constant stretching force. This torsional strain is equal to the number of turns of the magnetic bead around the z-axis due to the rotation of the magnets. The number of turns of the bead is also equal to , the variation of the linking number of the DNA double helix with respect to the intrinsic twist of the DNA double helix with the pitch of the DNA. And we define as usual the DNA relative overtwist as .
The method developed here can conveniently describe all physiological situations involving DNA positive supercoiling, which are of main importance for DNA transcription or replication. One limitation of the modeling developed so far is that extensive modifications of the double helix structure are not accounted for, e.g. base pair opening that occurs when negative supercoiling is applied (DNA denaturation), or the S-DNA transition observed at extremely high force, or the P-DNA transition under very positive torque. Nevertheless this methodology gives an unparalleled opportunity to study more complex biological systems, such as protein-DNA complexes: in particular, we are currently addressing the modeling of magnetic tweezers response of chromatin fibres [7], by associating our in silico DNA model with a rigid body representation of the histone octamer. More recently, in vivo experiments also enable the measurement of the dynamics of chromosome loci in the cell nucleus [11], [12], [46].
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10.1371/journal.pntd.0003138 | The Spatial Dynamics of Dengue Virus in Kamphaeng Phet, Thailand | Dengue is endemic to the rural province of Kamphaeng Phet, Northern Thailand. A decade of prospective cohort studies has provided important insights into the dengue viruses and their generated disease. However, as elsewhere, spatial dynamics of the pathogen remain poorly understood. In particular, the spatial scale of transmission and the scale of clustering are poorly characterized. This information is critical for effective deployment of spatially targeted interventions and for understanding the mechanisms that drive the dispersal of the virus.
We geocoded the home locations of 4,768 confirmed dengue cases admitted to the main hospital in Kamphaeng Phet province between 1994 and 2008. We used the phi clustering statistic to characterize short-term spatial dependence between cases. Further, to see if clustering of cases led to similar temporal patterns of disease across villages, we calculated the correlation in the long-term epidemic curves between communities. We found that cases were 2.9 times (95% confidence interval 2.7–3.2) more likely to live in the same village and be infected within the same month than expected given the underlying spatial and temporal distribution of cases. This fell to 1.4 times (1.2–1.7) for individuals living in villages 1 km apart. Significant clustering was observed up to 5 km. We found a steadily decreasing trend in the correlation in epidemics curves by distance: communities separated by up to 5 km had a mean correlation of 0.28 falling to 0.16 for communities separated between 20 km and 25 km. A potential explanation for these patterns is a role for human movement in spreading the pathogen between communities. Gravity style models, which attempt to capture population movement, outperformed competing models in describing the observed correlations.
There exists significant short-term clustering of cases within individual villages. Effective spatially and temporally targeted interventions deployed within villages may target ongoing transmission and reduce infection risk.
| Transmission of dengue virus has long been studied in Kamphaeng Phet, Northern Thailand, but how cases are related in time and space is still unclear, as is the role of human movement in generating these patterns. Because of these knowledge gaps, public health officials cannot make educated decisions on how to target vector control interventions and mechanisms of virus dispersal are not known. We mapped the homes of dengue cases admitted to the main hospital in the province capital from 1994–2008 and quantified the spatial correlation between them. We found an almost three times greater chance that cases from the same month came from the same village than expected, given the overall distribution of cases. Some clustering was also observed between cases in neighboring villages with the overall epidemics experienced by neighboring communities also more correlated than epidemics in villages farther apart. The short-term clustering observed within individual villages implies that effective spatially targeted interventions deployed within villages may reduce infection risk. As the distance between neighboring communities exceeds the typical flight range of the dengue vector, these findings also suggest a potential role for human movement in driving the wider spread of the virus.
| Dengue remains a major public health concern throughout global tropical and subtropical regions. An estimated 390 million people are infected by the mosquito-borne virus each year, of which 96 million develop symptomatic disease [1]. Thailand, like most countries in Southeast Asia, has experienced endemic dengue circulation of all four serotypes for decades [2], [3]. An effective dengue vaccine remains elusive and intervention measures will continue to rely on mosquito control for the foreseeable future. These efforts include the detection and removal of potential oviposition sites, the spraying of insecticides, and potentially the future releases of Wolbachia-infected mosquitoes that have been shown to reduce the mosquitoes' ability to transmit dengue [4]. Effective use of these measures requires a good understanding of the spatial distribution of cases. Of particular use is an understanding of where other cases are likely to be found on detection of an index case. Characterizing the spatial dependence between dengue cases can also provide insight into potential mechanisms of disease spread.
The home locations of individuals hospitalized with dengue in Bangkok have been shown to exhibit significant spatial dependence at distances of around a kilometer [5]. Such spatial structure suggests focal transmission events are driving viral dispersal in this large, super-urban population. The situation in rural areas, which make up the majority of the country, may be markedly different. Phylogenetic studies have shown widespread genetic and serotype diversity across the rural Thai province of Kamphaeng Phet with some clustering of lineages within villages [6], [7]. In addition, cluster studies in the same region detected infected individuals within 15 days of an index case at distances of 100 m within villages [8], [9]. However, the extent at which spatial dependence is observed in these areas is not known. Unlike continuously inhabited urban centers such as Bangkok, rural communities in Thailand tend to be separated by wide expanses of uninhabited farmland or forests. The distance between neighboring rural communities is typically far beyond the short flight range of the main dengue vector, Aedes aegypti [10]. For sustained transmission to occur between rural communities, movement of infected individuals is likely necessary. If human movement between neighboring communities were key to DENV dispersal in this region, we would expect short-term spatial dependence between cases occurring at between-community scales. Further, we would expect that patterns of population flows would correlate with the spatio-temporal location of infections. It has previously been shown that individuals tend to move to larger and closer communities [11]–[13]. Such population flows can be captured using gravity models that incorporate the size of populations and the distance between them. Similar approaches have previously been used in phylogenetic analyses to describe dengue viral flow in Vietnam [14]–[16].
Appropriate data necessary to describe the spatio-temporal patterns of dengue virus require, 1) a long time series, 2) availability of address data for patients, and proper diagnostics to confirm DENV infection. We used a unique dataset that meets all of these criteria: the geocoded home addresses of 4,768 individuals who were admitted to the provincial hospital in Kamphaeng Phet, Thailand over a fourteen-year period (1994–2008). The objective of our study was to characterize the short-term spatial dependence between dengue cases, to quantify the correlation in the long-term epidemics experienced by different communities and to explore the ability of human movement models to describe the observed correlations.
Data were collected from existing records without personal data. The research components of this project received approval from the Ethical Research Committee of Faculty of Public Health, Mahidol University and U.S. Army Medical Research Materiel Command (USAMC-AFRIMS Scientific Review Committee) review and approval.
Kamphaeng Phet is a largely rural province in northern Thailand with an area of 8,600 km2 (Figure 1) [17]. It had a population of 797,000 people in the 2010 census, mainly residing in villages. The largest town in the province is the capital (Mueang Kamphaeng Phet) with 30,000 inhabitants. The landscape is dominated by rolling hills with large portions of the province covered by forests. Since 1994, the Armed Forces Research Institute of Medical Sciences (AFRIMS) has conducted dengue surveillance at Kamphaeng Phet Provincial Hospital (KPPPH). KPPH is the largest hospital in Kamphaeng Phet, located in the capital, and such receives referral cases as well as walk-in patients of all ages from throughout the province. For each suspected dengue case, DENV infection is confirmed using semi-nested RT-PCR and IgM/IgG ELISA. In addition, home address information is collected on each patient. We geocoded the home address down to the village level for each individual using detailed base maps of the region. Individuals from the same village were given the same coordinates (Table 1).
To characterize the short-term spatial dependence between rural dengue cases, we used the statistic on all cases occurring outside the provincial capital [5]. This statistic estimates the probability of two cases occurring both within distances d1 and d2 and within a month of each other relative to the independent probabilities of observing two cases within d1 and d2 over the entire time series and of observing two cases within a month of each other over the whole study area. This approach therefore measures the interaction in time and space of cases and has previously been used to characterize the spatial dependence of dengue cases in Bangkok [5].Where is the set of cases that occur both within a 30 day period and within d1 and d2 of case i; is the set of cases within d1 and d2 of case i over the entire time series and is the set of cases that occur within a 30 day period from case i over the study area. Importantly, as underlying spatial biases such as population density and hospital utilization rate differences impact both the numerator and the denominator in the same way, they do not bias our estimates of spatial dependence. We estimate as follows (see [5] for details):We generated bootstrapped confidence intervals for by resampling the cases with replacement 500 times. Ninety-five percent confidence intervals were calculated from the 2.5% and 97.5% quantiles from the resulting distribution. Patterns of spatial dependence may have changed over the time period of the study. We therefore recalculated using cases from annual incremental five-year windows from between 1994 and 2008.
We explored whether any short-term spatial dependence between individual cases resulted in correlation in the epidemics experienced by different communities. In this analysis, to avoid excessively small numbers of cases per location over the entire time period, villages were grouped into clusters by placing a grid over the province. The distance between each grid point was 3 km and villages were assigned to the closest grid point. Only village clusters with at least 40 cases over the time series were used in the analysis. The population of each village cluster was extracted from LandScan data [18]. LandScan uses a combination of satellite imaging and census data to construct population estimates throughout the world.
To make the epidemic curves between locations as comparable as possible, we down-sampled each epidemic curve (to create “down-sampled curves”) by randomly selecting 40 cases (the minimum number of cases at within a village cluster) with replacement from all the cases that occurred at that location. We calculated the Pearson correlation coefficient between all pairs of down-sampled curves. We calculated the loess curve of the relationship between the Euclidean distance and correlation between village cluster pairs. We repeated the down-sampling process 500 times and reported the mean of the resulting distribution. In addition, 95% confidence intervals for the loess curves were estimated from the 2.5% and 97.5% quantiles.
We compared our estimate of the expected correlation by distance separating communities to a theoretical complete-synchrony scenario where there was no distance effect. The complete-synchrony distribution was generated by randomly reassigning the location of all cases, keeping the month in which they occurred fixed. The total number of cases within any location over the whole time series was unchanged. The resulting distribution is that expected under a scenario of complete synchrony of cases over the province. The mean and confidence intervals for the complete-synchrony distribution were calculated by repeating the process above in generating down-sampled curves, repeating each resampling event 500 times.
There exist alternative measures of correlation. We explored the consistency of our findings to a different measure: the Spearman rank correlation coefficient. In this sensitivity analysis, we recalculated the correlation coefficients for both the observed data and the theoretical complete-synchrony scenario.
Gravity models can be used to describe population flows [11]–[13]. Here we used them to explore their ability to explain the correlation in the epidemic curves between pairs of village clusters:where pop1 and pop2 are the populations of the two settlements and dist is the Euclidean distance between the two settlements. By log-transforming the equation, we can estimate the exponents α and β through linear regression:We used Akaike's Information Criterion (AIC) to compare the performance of the gravity model to an intercept only model and a univariate model incorporating Euclidean distance only (Table 2) [19]. All of the models were performed using the correlation coefficients from each set of down-sampled curves (500 in all). We reported the mean coefficient across all sets of down-sampled curves for each model. In addition we calculated 95% confidence intervals using the 2.5% and 97.5% quantiles from the distribution of coefficient estimates.
All analyses were conducted in R 2.15.2 [20].
Between 1994 and 2008, 4,768 dengue inpatients at KPPPH were successfully geocoded (93% of all cases) (Table 1) coming from 568 different villages (Figure 1). The provincial capital, where KPPPH was located, had 732 cases (15% of all cases). The mean age of cases was 11.0 years and 59% of cases suffered from the more severe hemorrhagic form of the disease (Table 1). On average, villages were separated by 1.4 km from their closest neighboring village.
We characterized the short-term spatial dependence between the home locations of the cases presenting at KPPH using the φ(d1, d2) statistic. We found that cases were 2.9 times more likely (95% confidence interval of 2.7–3.2) to occur both within the same community and to be infected within the same month of each other than the independent probabilities of occurring within the same community over the entire study period and occurring within the same month across the entire province (Figure 2). This fell to 1.4 times (1.2–1.7) for communities separated by between 0.5 km and 1.5 km and to 1.2 times (1.1–1.3) for communities separated by 2.5 km −3.5 km. We observed significant spatial dependence, albeit at low levels, at distances up to 5 km. However, when we divided the entire time series into smaller subsets covering five year time periods only, there was a clear trend in the spatial extent of spatial dependence (Figure S1). Cases from the 1990s exhibit spatial dependence at larger distances than more recent cases.
To explore whether short-term spatial dependence between individual cases resulted in similar patterns of disease observed between communities, we compared the correlation of the epidemic curves between communities by the distance separating them. We divided the villages into 24 village clusters with each village cluster having at least 40 cases over the 14 years. The locations of the village clusters are illustrated by the red dots in Figure 1. The mean correlation in the monthly epidemic curves between all village cluster pairs was 0.19, however, there existed substantial structure in the correlation: village clusters that were under 5 km apart had a mean correlation of 0.28 (95% confidence interval of 0.25–0.31), whereas village clusters separated by between 20 km and 25 km had a mean correlation of 0.16 (95% confidence interval: 0.14–0.17) (Figure 3).
We estimated that a (theoretical) scenario of complete synchrony across the entire province would result in a mean correlation of 0.32, irrespective of distance between village clusters (Figure 3). This correlation was much less than 1.0 as there are fewer cases than locations for many time points resulting in occasional small peaks in the epidemic curves that were not matched across all locations. The correlation under full synchrony and the observed correlations looked very similar when the alternative Spearman rank correlation coefficient was used instead (Figure S2).
We explored whether different statistical models could explain the observed correlation between community-pairs (Table 2). We found that univariate model incorporating only the Euclidean distance separating communities explained only 7% of the variance in the correlations (Table 3). Incorporating population sizes (model 3) substantially improved the fit of the model although the majority of the variance remained unexplained (R2 of 0.13). Model 3 was also strongly favored by AIC [21].
We have used a large dataset from a long time series with geocoded addresses to explore the spatial patterns of dengue cases in a rural region with endemic circulation. We have shown substantial short-term clustering of dengue cases within communities, consistent with transmission chains circulating at small spatial scales. We observed a large drop in the clustering of cases from within-community to between community scales. Our findings suggest that upon discovering an infected individual, there is a significant risk that other individuals from his or her village will also be infected. The removal of mosquitoes in that community could potentially reduce the risk of onward transmission.
While lower than within-community estimates, significant short-term spatial dependence was nevertheless observed at inter-settlement scales. This observation is consistent with viral movements between neighboring communities, distances greater than the flight range of the dengue vector [10]. These findings point to a potential role for human movement in driving the spread of the virus. This was further supported by a clear reduction in the correlation in the epidemic curves between communities with increasing spatial separation between them. Gravity models are regularly used to describe human population flows [11]–[13]. Here a related formulation of gravity models that describes the correlation in the epidemic curves between communities was found to outperform competing models. This finding supports previous findings from gravity models fit to phylogeographic data from southern Vietnam [15]. Human movement has also been suggested to play a major role in the dengue epidemic in Iquitos, Peru [22]. Spatial correlation in ecological conditions (e.g., vector density) or in behavioral factors (e.g. the use of screens on windows) between communities may also explain these observations. We cannot definitively differentiate between these potential explanations here. Further research using information on the infecting pathogen, such as serotype or genetic information could help disentangle these competing hypotheses.
Our findings of focal patterns of disease support the results of previous cluster studies in the region [8], [9]. In addition, a previous study in Bangkok observed short-term spatial dependence in the homes of hospitalized cases between 1995 and 1999 at distances up to around 1 km [5]. Overall, we observed spatial dependence at larger distances than in the Bangkok study although when we looked at 5-year subsets of the data, the spatial extent of clustering was shorter among more recent cases. Higher levels of movement across the province as a whole suppresses spatial dependence by promoting the global mixing of the population. Our observations are therefore consistent with increased movement across the province in more recent years.
Mosquito control efforts are widely used throughout Southeast Asia and center on the use of insecticides. Insecticide fogging has been shown to temporarily reduce the number of mosquitoes in any location [23]. However, the ability of insecticides to reduce the risk of dengue infection remains unclear. Insecticide effectiveness may be limited by an inability to reduce mosquito density sufficiently or for a long enough period to prevent transmissions from viremic individuals. This is supported by a lack of a clear relationship between vector density and dengue transmission risk [24]. In addition, spraying may be too spatially restricted, allowing mosquitoes outside spray zones to rapidly repopulate fogged spaces. Finally spraying is sometimes only deployed in outdoor areas whereas Aedes aegypti mosquitoes tend to be found inside households. Estimating the impact of insecticides on dengue infection is difficult. The majority of dengue infections are not detected and the appropriate characteristics of control populations for any study are unclear. Nevertheless, further studies are needed to provide a sound evidence base for the widespread use of these measures.
The study has some limitations. The mean correlation between the epidemics experienced by pairs of communities appeared low (mean of 0.19). However, this was only slightly less than expected if all cases at any time point were randomly distributed throughout the communities (mean of 0.32), resulting in synchronous epidemics. This low level of correlation occurs because of the small numbers of cases (all the epidemic curves were down-sampled to only 40 cases). Even in the scenario of complete synchrony, tiny fluctuations were regularly present in the epidemic curve in one location and not in the curves of others, deflating correlation. These observations illustrate the problems in using the absolute correlation as a marker of similarity when many time points have no cases. Nevertheless, trends in correlation over distance and comparisons to a distribution expected under complete synchrony remain useful. Our data consists of cases that presented at hospital only. The majority of infections, however, result in asymptomatic or only mildly symptomatic. The spatial dependence between these infections may be different. We could only geocode individuals to the village level. We could not therefore explore spatial differences within any village. Future work using exact home locations may allow elucidation of finer scale spatial dependence between case homes. Finally, the relationship between gravity models fit to population flows directly and those fit to the correlation in epidemic curves may be complex and setting specific. Further work using simulated data may help provide insight into their relationship.
In conclusion, cases of dengue appear highly spatially correlated within villages in rural Thailand; however, neighboring communities nevertheless appear to observe correlated epidemics. Human movement patterns may be a key driver of dengue dispersal in this region. Future studies that incorporate movement diaries or GPS trackers would help describe population flows and allow the development of mechanistic models for the dispersal of dengue.
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10.1371/journal.pgen.1005512 | Multimer Formation Explains Allelic Suppression of PRDM9 Recombination Hotspots | Genetic recombination during meiosis functions to increase genetic diversity, promotes elimination of deleterious alleles, and helps assure proper segregation of chromatids. Mammalian recombination events are concentrated at specialized sites, termed hotspots, whose locations are determined by PRDM9, a zinc finger DNA-binding histone methyltransferase. Prdm9 is highly polymorphic with most alleles activating their own set of hotspots. In populations exhibiting high frequencies of heterozygosity, questions remain about the influences different alleles have in heterozygous individuals where the two variant forms of PRDM9 typically do not activate equivalent populations of hotspots. We now find that, in addition to activating its own hotspots, the presence of one Prdm9 allele can modify the activity of hotspots activated by the other allele. PRDM9 function is also dosage sensitive; Prdm9+/- heterozygous null mice have reduced numbers and less active hotspots and increased numbers of aberrant germ cells. In mice carrying two Prdm9 alleles, there is allelic competition; the stronger Prdm9 allele can partially or entirely suppress chromatin modification and recombination at hotspots of the weaker allele. In cell cultures, PRDM9 protein variants form functional heteromeric complexes which can bind hotspots sequences. When a heteromeric complex binds at a hotspot of one PRDM9 variant, the other PRDM9 variant, which would otherwise not bind, can still methylate hotspot nucleosomes. We propose that in heterozygous individuals the underlying molecular mechanism of allelic suppression results from formation of PRDM9 heteromers, where the DNA binding activity of one protein variant dominantly directs recombination initiation towards its own hotspots, effectively titrating down recombination by the other protein variant. In natural populations with many heterozygous individuals, allelic competition will influence the recombination landscape.
| During formation of sperm and eggs chromosomes exchange DNA in a process known as recombination, creating new combinations responsible for much of the enormous diversity in populations. In some mammals, including humans, the locations of recombination are chosen by a DNA-binding protein named PRDM9. Importantly, there are tens to hundreds of different variations of the Prdm9 gene (termed alleles), many of which are predicted to bind a unique DNA sequence. This high frequency of variation results in many individuals having two different copies of Prdm9, and several lines of evidence indicate that alleles compete to initiate recombination. In seeking to understand the mechanism of this competition we found that Prdm9 activity is sensitive to the number of gene copies present, suggesting that availability of this protein is a limiting factor during recombination. Moreover, we found that variant forms of PRDM9 protein can physically interact suggesting that when this happens one variant can influence which hotspots will become activated. Genetic crosses in mice support these observations; the presence of a dominant Prdm9 allele can completely suppress recombination at some locations. We conclude that allele-dominance of PRDM9 is a consequence of protein-protein interaction and competition for DNA binding in a limited pool of molecules, thus shaping the recombination landscape in natural populations.
| Genetic recombination in mammals is restricted to hotspots: short, 1–2 kb-long sites scattered throughout the genome [1,2]. With the exception of canids [3,4], their locations in mammals are determined by the sequence-specific DNA binding protein, PRDM9 (MGI:2384854) [5,6,7]. PRDM9 initiates recombination by binding DNA at hotspots where it locally trimethylates histone H3 at lysine 4 (H3K4me3) using a conserved PR/SET domain [8,9,10,11]. This signals the correct locations of programmed meiotic double-strand breaks (DSB) that are required for the physical exchange of material between homologous chromatids during meiosis and the eventual formation of genetic crossovers and noncrossovers [9,10,12].
Prdm9 function is essential for meiosis; null alleles lead to sterility in both sexes of mice [13], and point mutations in PRDM9 are found in azoospermic human patients [14,15]. In addition, Prdm9 is a key player in evolution by creating hybrid sterility. Male intersubspecific F1 hybrid mice that are heterozygous for particular Prdm9 alleles and carry the M. m. musculus-derived chromosome (Chr) X are infertile, thus creating postmating reproductive barriers that contribute to incipient speciation [16].
Prdm9/PRDM9 is highly polymorphic, both within and between mammalian species. This includes humans [5,6,7,17,18,19], mice [5,7,9,20], chimps [21,22,23], cattle [24], and equids [25], which all harbor diverse alleles of Prdm9. Most of the naturally occurring sequence polymorphisms in Prdm9 change the identity of the amino acids contacting DNA and/or the number and arrangement of individual fingers in the DNA-binding zinc-finger domains. This allows PRDM9 variants to target a large number of DNA sequences, thereby expanding the distribution of recombination sites.
Three laboratories simultaneously came to the identification of PRDM9 as the key protein determining the location of mammalian hotspots [5,6,7]. In our case, we identified hotspots in genetic crosses between C57BL/6J (B6) and CAST/EiJ (CAST) mice whose activation depended on a trans-acting factor [26]. Genetic mapping identified the key factor as the CAST allele of Prdm9 [7]. Importantly, the same experiments also identified hotspots whose activities were quantitatively reduced rather than activated by the presence of CAST alleles, and others whose activities were completely suppressed. Similar variation in recombination rates has been observed at human hotspots depending on the identities and combinations of PRDM9 alleles present [17,18,27].
These observations coincide with previous evidence that Prdm9 alleles in heterozygous individuals do not show simple additive behavior. In both humans [28,29] and mice [10,30] there is allelic dominance in which a predominance of hotspots in heterozygotes are activated by one of the alleles present. This phenomenon is of considerable biological importance given the extensive polymorphism of Prdm9 and that heterozygotes represent a considerable majority of some natural populations.
Together, the available evidence indicates a complex regulation of hotspot activity in heterozygous individuals. However, little is known of the specific mechanisms and molecular players involved in hotspot suppression and the observed competition between Prdm9 alleles. Here we report that both of these observations are the functional consequence of a direct interaction between PRDM9 protein variants in a limited pool of PRDM9 molecules in meiotic cells. Using genetic strategies, we now show that, while Prdm9 is required for activation of hotspots, it is also the trans-acting factor responsible for the quantitative modulation of recombination rate and allelic dominance in heterozygous mice. In cell cultures, we show that PRDM9 protein variants form both homo- and heteromeric complexes, and that heteromeric complexes bind and trimethylate nucleosomes at hotspot DNA sequences. We find that Prdm9 function is dosage-sensitive; in heterozygous male Prdm9+/- null mice, where Prdm9 is present in a single copy, the numbers and activity of PRDM9-defined H3K4me3 hotspots are reduced and animals have increased abnormalities in meiotic prophase I. In addition, replacing the null allele with one from a different mouse subspecies is sufficient to fully suppress recombination at some hotspots, suggesting direct interaction between protein variants.
Taken together, the data point to a model in which quantitative activity at recombination hotspots is partially controlled through PRDM9 occupancy at hotspots, which in turn is dependent on both the number of PRDM9 molecules available in meiotic cells and the DNA-binding affinity of each allele. Our data suggests that in heterozygous individuals, PRDM9 forms heteromers that preferentially bind to and activate recombination at the stronger allele’s hotspots, thereby suppressing recombination at hotspots otherwise activated by the weaker allele.
To determine factors regulating recombination rate, we first focused on the hotspot Pbx1, as evidence indicates that genetic background has a strong effect on the recombination rate at this hotspot in mice [26]. Pbx1 has a sex-averaged recombination rate of 2.38 cM in the B6 background, but shows a significant 5.8-fold down-regulation when CAST alleles are introduced in (B6xCAST)F1 hybrids (0.41 cM, Fisher’s exact test p < 10−4) [31]. To genetically map factors controlling the quantitative activity of Pbx1 we used N2 and F2 mapping crosses (S1A Fig) that allowed us to detect the influence of dominant, recessive or additive alleles [26]. We collected 49 N2 and 75 F2 males heterozygous for B6/CAST on distal 100 Mb on Chr 1, the region containing the Pbx1 hotspot, genotyped them at 165 markers spaced across the genome, and isolated their sperm DNA to measure the recombination rate at Pbx1. Crossing over at Pbx1 was determined using a DNA sequencing assay that takes advantage of SNPs located on either side of the hotspot and counts the number of recombinant and parental molecules in sperm samples (S1B Fig). Comparing the frequencies of parental and recombinant molecules from many thousands of individual sperm (each representing a potential offspring) provided a measure of recombination rates at Pbx1 in individual male mice.
Using the recombination rate at Pbx1 as our phenotype, genome scans performed on individual crosses, and pooled data from both crosses, resulted in a single significant QTL peak on proximal Chr 17 (Figs 1A, S2A and S2B). The 1.5-LOD support interval for this QTL is from ~4–30 Mb along Chr 17, with the approximate QTL located around 14 Mb. Mice homozygous for B6 at Chr 17 had the highest rate of recombination; heterozygous mice had an intermediate level of recombination, and mice homozygous for CAST had the lowest (Fig 1B). This pattern suggests an additive effect of the QTL dependent on the B6 haplotype on Chr 17.
The position of the QTL on Chr 17 implicates Prdm9 in regulating recombination at the Pbx1 hotspot. Prdm9 is located on Chr 17 at 15.5 Mb, is currently the only known recombination regulator locus in mice, and B6 and CAST mice carry two different Prdm9 alleles (Prdm9Dom2 and Prdm9Cst respectively) [7,26]. Furthermore, the PRDM9Dom2 protein variant, found in B6 mice where Pbx1 is active, shows binding to the Pbx1 DNA sequence in vitro and regulates H3K4me3 level in the surrounding region in vivo [31]. The QTL analysis above suggests that Prdm9 is also a modifier of recombination rate at Pbx1; two copies of Prdm9Dom2 result in a higher recombination rate at Pbx1 compared to one copy. The apparent low recombination rate in homozygous CAST at the QTL locus mice is largely a measure of the frequency of false-recombinants (see methods). To test for the presence of additional modifiers of hotspot activity, we conditioned on the identity of the Prdm9 allele present by selecting the set of N2 and F2 mice that were homozygous Prdm9Dom2/Dom2 and performed an additional genome scan on these mice alone; however, we did not detect any other significant QTLs (S2C Fig).
The genetic evidence above suggests that PRDM9 activation of hotspots is sensitive to Prdm9 dosage, indicating that PRDM9 is limiting in meiotic cells, or sensitive to competition between alleles. A plausible molecular mechanism by which two alleles can directly influence each other is through their physical interaction [28,29]. To test if this is the case and PRDM9 interacts with itself, we cloned both the human PRDM9A allele, the primary allele found in humans from European ancestry, and the PRDM9C allele, more prevalent in populations with African ancestry [7,18], for expression in cultured human HEK293 cells similar to previous reports [32,33].
Expressing PRDM9 in cultured HEK293 cells resulted in a significant increase in total H3K4me3 levels, which depended on the conserved PR/SET methyltransferase domain present in PRDM9 (Fig 2A), similar to results previously described [13,33]. In order to test if PRDM9 retains DNA-binding specificity in HEK293 cells we expressed PRDM9A, PRDM9C, or empty vector and performed ChIP for H3K4me3. Several human hotspots have previously been characterized as being responsive to either PRDM9A (for example hotspots S and F) or PRDM9C (hotspots 5A and 22A) by measuring recombination in pooled sperm samples [17,18]. We found that expression of either PRDM9A or PRDM9C in HEK293 cells resulted in increased H3K4me3 levels at the center of these hotspots in an allele-specific manner as measured by qPCR (Fig 2B and 2C).
To identify genome-wide PRDM9-defined H3K4me3 sites we used ChIP DNA for deep sequencing, for each allele, and compared these H3K4me3 maps to the recently published genome-wide position of meiotic DSBs identified by chromatin immunoprecipitation of the meiotic recombinase DMC1 from men (S3 Fig and S1 Table, DMC1 SSDS data available at GEO: GSE59836) [28]. DSB hotspots were classified as PRDM9C-defined if they were uniquely identified in the DMC1 SSDS data from the heterozygous A/C individual but not found in the homozygous A/A1 individual (S3B Fig). For both alleles, approximately one-third of unique allele-specific H3K4me3 sites identified here in HEK293 cells overlap with DSB hotspots identified in testis (S3C and S3D Fig). To visualize allele-specificity, heat maps were generated for each H3K4me3 ChIP and DMC1 ChIP data set at shared hotspots by aligning the position of identified PRDM9 motifs (Fig 2D). H3K4me3 signal at PRDM9C-defined DSB hotspots was increased only after expression of PRDM9C but not after expression of PRDM9A or in empty vector controls. Similarly, expression of PRDM9A in HEK293 cells resulted in increased H3K4me3 only at PRDM9A-defined hotspots. H3K4me3 signal is readily detected at promoter regions, including empty vector control, highlighting the PRDM9-defined H3K4me3 at hotspots (S4 Fig). For both PRDM9 alleles, H3K4me3 modified nucleosomes are organized in a symmetrical pattern around a central PRDM9 sequence motif as previously seen in mouse germ cells [9], near the maximum signal of DSB intensity found from testis (Fig 2E and 2F). These data show that ectopically expressed PRDM9 can bind and modify chromatin at hotspot sequences in somatic cells in an allele-specific manner.
To examine if PRDM9 can interact with itself, we assessed interaction between these two human alleles in HEK293 cells. Both alleles were cloned to contain either an N-terminal FLAG or N-terminal V5 epitope tag to facilitate detection and allow discrimination of protein variants. Both the FLAG- and V5-tagged versions of each PRDM9 allele were expressed, either separately or together, in HEK293 cells (Fig 3A). Immunoprecipitation using the FLAG monoclonal antibody directed against FLAG-PRDM9A or FLAG-PRDM9C showed an enrichment for the V5-tagged PRDM9C only when the two proteins were co-expressed (Fig 3A, lanes 11 and 12). Likewise, reciprocal immunoprecipitation with V5-PRDM9C displayed the same result, as it enriched for both FLAG-PRDM9 protein variants (Fig 3A, lanes 17 and 18). These data show that PRDM9 can form both homo- and heteromeric protein complexes when co-expressed.
A macromolecular complex containing two PRDM9 protein variants would have two distinct zinc-finger arrays, with the potential capability to bind two motifs. This predicts that in cells expressing two PRDM9 alleles, the non-activating protein variant might be found at hotspots at which it does not typically bind. For example, human PRDM9A would be found at a C-defined hotspot only when in a heteromeric complex with PRDM9C. To test for the presence of heteromeric complexes at hotspots, we expressed V5-PRDM9C, FLAG-PRDM9C, and FLAG-PRDM9A alone, or co-expressed V5-PRDM9C and FLAG-PRDM9A together, and tested for their presence at several C- and A-defined hotspots using ChIP (Figs 3B–3E and S5). As expected, expression of FLAG-tagged PRDM9C (FLAG-C) resulted in enrichment for DNA at C-hotspots following immunoprecipitation using anti-FLAG antibody (Fig 3B, 3C and 3D); while expression of FLAG-PRDM9A (FLAG-A) did not. In addition, using anti-FLAG antibody there was no enrichment for C-hotspots after expression of V5-PRDM9C (V5-C), showing antibody specificity. The lack of PRDM9A signal at C-defined hotspots is not due to inactivity of the protein variant, as PRDM9A can readily bind to an A-hotspot (Fig 3E). Importantly, there were increases in enrichment at C-defined hotspots when co-expressing FLAG-PRDM9A along with V5-PRDM9C (V5-C + FLAG-A) compared to either protein expressed alone (Fig 3B, 3C and 3D). Thus PRDM9A, which does not bind to or modify C-defined hotspots alone, is nevertheless found at C-hotspots when co-expressed with PRDM9C, a situation potentially similar to heterozygous individuals.
Given that in heteromeric complexes at least two PRDM9 molecules can be found at hotspot sequences, we wanted to test if the protein variant that does not bind DNA can still catalytically function to modify hotspot nucleosomes. To do so we expressed the catalytically-dead FLAG-PRDM9C-G278A alone or co-expressed FLAG-PRDM9C-G278A with V5-PRDM9A and performed ChIP for H3K4me3 (Fig 3F). As expected, expression of FLAG-PRDM9C-G278A alone did not lead to H3K4me3 at C-hotspots. However, when co-expressed with a functional V5-PRDM9A protein variant, C-defined hotspots had a clear, albeit weak, H3K4me3 signal and organized the nucleosome pattern at these hotspots. These data show that bringing together a functional PR/SET domain of one allele, with the zinc-finger DNA-binding domain of a different allele, is sufficient to mark hotspots.
Together, these data provide strong evidence for the formation of functional heteromeric complexes that can bind and modify hotspots.
The above cell culture data show that two PRDM9 variants can be found in the same protein complex bound at hotspot DNA sequences. If PRDM9 activity is limiting in meiotic cells, the two protein variants might directly compete for hotspot activation in heterozygous individuals by influencing which allele’s hotspots the heteromeric complexes bind. To investigate if PRDM9 activity is limiting in meiotic cells we next characterized mice made heterozygous null at Prdm9.
To test the effect of lowered Prdm9 dosage on hotspots in vivo, we measured genome-wide H3K4me3 levels in male germ cells from mice heterozygous for the targeted null allele Prdm9tm1Ymat (B6-Prdm9Dom2/-) and compared them to those from homozygous B6 (Prdm9Dom2/Dom2). B6-Prdm9Dom2/- males have reduced level of PRDM9 protein compared to homozygous littermates [30,34], suggesting reduced availability of the catalytic domain. Using H3K4me3 ChIP-seq we identified approximately half the number of detectable H3K4me3 hotspots seen in mice with two copies of Prdm9Dom2. Among 97,117 total H3K4me3 peaks identified in B6-Prdm9Dom2/- germ cells, 9,707 were associated with PRDM9Dom2-defined H3K4me3 hotspots, the remainder being associated with promoters and other functional elements. This is in contrast to nearly twice as many H3K4me3 hotspots previously measured in Prdm9Dom2/Dom2 mice (18,849) [9]. The PRDM9-defined H3K4me3 hotspots identified in the heterozygous null mice correspond to those with the highest level of H3K4me3 in homozygous B6 (Fig 4A).
Next, we compared the relative activity (normalized read counts) of H3K4me3 hotspots present in both B6 and B6-Prdm9Dom2/- heterozygous null males. As a class, PRDM9Dom2-defined H3K4me3 hotspots have one-third of the level of H3K4me3 in heterozygous null mice compared to B6 (Fig 4B). This reduction in H3K4me3 is also sensitive to the intrinsic strength of the hotspot (as measure by average H3K4me3 level); the weaker the hotspot is (lower average H3K4me3), the greater the fold difference in H3K4me3 levels between B6-Prdm9Dom2/- and B6 mice. Importantly, other PRDM9-independent H3K4me3 sites, such as gene promoters, are not affected by Prdm9 copy number (Fig 4B, blue points). Together, the H3K4me3 ChIP-seq data show that B6-Prdm9Dom2/- male mice have about half the number H3K4me3 hotspots compared to B6 mice, and those that are present have reduced levels of H3K4me3, with the greatest reductions seen at the weakest hotspots.
Male mice that are homozygous null for Prdm9 have a complete meiotic arrest [13,35,36,37]. However, there is conflicting evidence on the effect of removing one allele of Prdm9 on fertility and meiotic progress. Heterozygous male mice for the targeted null mutation Prdm9tm1Ymat have testes weights and sperm counts similar to wild-type males when on a mixed (129*B6) [13] or B6 [36] background. Males heterozygous for another allele, Prdm9M1045Lja, that expresses a truncated protein [34] displayed lower testes weight, reduced number of spermatids, and azoospermia [37]. To determine the effect of heterozygosity of the Prdm9 tm1Ymat allele in the B6 background (B6-Prdm9Dom2/-) on meiotic progress and fertility, we used indirect immunofluorescence labeling of spread adult testicular cells to detect meiotic arrest. Compared to homozygous Prdm9Dom2/Dom2 B6 littermate controls, B6-Prdm9Dom2/- males displayed a mild, but significantly increased fraction of abnormal pachytene stage cells, either completely lacking or having an abnormal sex body (Fig 4C and 4D). This increased number of abnormal pachytene spermatocytes was also seen in heterozygotes when using a different M.m. domesticus allele, Prdm9Dom3, on a C3H/HeN genetic background (Fig 4D). To assess the effect of lowered Prdm9 dosage on overall fertility, we crossed heterozygous null (B6-Prdm9Dom2/-) males to homozygous B6 (Prdm9Dom2/Dom2) females. The B6-Prdm9Dom2/- males produced fewer offspring compared to B6 controls (4.3±1.4 versus 6.2±1.0 per female per month, p = 0.01, Welsch’s t-test) and needed on average 7.4 more days to sire their first litter (p = 0.01, Welsch’s t-test). Thus Prdm9 is partially haploinsufficient for meiotic progress and fertility.
In total, these data comparing Prdm9+/- heterozygous mice to homozygous mice demonstrate that PRDM9 function is dosage-sensitive.
Because the number of H3K4me3 hotspots decreased with lowered Prdm9 dosage, and B6-Prdm9Dom2/- males have increased abnormal pachytene stage cells, we wanted to test if meiotic DSBs are reduced in B6-Prdm9Dom2/- males. To accomplish this, meiotic DSBs were counted in early zygonema using indirect immunofluorescence microscopy with a mix of antibodies directed against DSB-repair proteins RAD51 and DMC1 on staged surface-spread testicular nuclei (S6 Fig). The B6-Prdm9Dom2/- males displayed 189±18 (mean ± standard deviation) DSBs per cell and their B6-Prdm9Dom2/Dom2 littermates 202±29 DSBs per cell; this difference was not significant (p = 0.12, Welsch´s t-test), confirming a previous report of no reduction using different combinations of Prdm9 alleles [34].
The combined evidence suggests that PRDM9 activity is limiting in meiotic cells and that PRDM9 variants can self-interact. Together these data suggest that, if heteromeric complexes exist in meiotic cells, the two variants might compete for DNA binding and activation of hotspots in heterozygous individuals. Recombination at some hotspots in Prdm9Dom2/Cst heterozygous mice are completely suppressed when CAST alleles are introduced in trans [26]. One such example is the PRDM9Dom2-defined hotspot Ush2a (genomic position: Chr 1 190,124,179–190,127,477 Mb). By genotyping progeny from crosses [26], we found that the sex-averaged recombination rate at Ush2a is 0.61 cM in the B6 background and completely suppressed when CAST alleles are present (Fisher’s exact test p < 10−4). Nested allele-specific PCR, using primers to amplify either parental or recombinant molecules from pooled sperm, confirms the genetic cross data (Fig 5A). Crossovers at Ush2a are detected in sperm DNA from (B6 x B6.CAST-1T)F1 hybrids that are heterozygous B6/CAST at the hotspot on distal Chr1 and otherwise homozygous B6/B6 (Fig 5A, lanes 3 and 4), but fully suppressed in sperm DNA from (B6 x CAST)F1 hybrids that are heterozygous B6/CAST across all of the genome (Fig 5A, lanes 1 and 2).
To test if suppression of recombination is due to reduced Prdm9Dom2 dosage, competition between PRDM9Dom2 and PRDM9Cst, or the action of a novel regulatory factor, we compared recombination at Ush2a in sperm DNA from co-isogenic mice that are either heterozygous Prdm9Dom2/Cst or heterozygous Prdm9Dom2/-, heterozygous B6/CAST on distal Chr 1 (to allow detection of crossing over at Ush2a), and uniformly B6/B6 over the rest of the genome. We did so by using appropriate progeny from two crosses: B6-Prdm9CAST-KI (KI), a co-isogenic strain in which the Prdm9Dom2 allele has been replaced by the Prdm9Cst allele from CAST mice [9], crossed to B6.CAST-1T (KI x CAST-1T), and B6-Prdm9Dom2/- crossed to B6.CAST-1T (KO het x CAST-1T). Importantly, similar to (B6 x CAST)F1 hybrid males, recombination at Ush2a is suppressed in (KI x CAST-1T)F1 hybrid males, where the only difference is the presence of Prdm9Cst in one copy (Fig 5A, lanes 5 and 6). However, recombination persists in (KO het x CAST-1T)F1 hybrid males, which only have one allele of Prdm9Dom2 (Fig 5A, lanes 7 and 8). Moreover, the rate of recombination at Ush2a is similar in B6 mice with two doses of Prdm9Dom2 and heterozygous B6-Prdm9Dom2/- mice with one dose (S2 Table). These data show that the Prdm9Cst allele alone is sufficient to directly suppress the activity of the Prdm9Dom2 allele at the Ush2a hotspot.
We next tested the extent to which Prdm9Cst can influence Prdm9Dom2 activity on a genome-wide basis. Previous reports found that, when tested for either H3K4me3 initiation sites or DMC1 DSB sites, the number of PRDM9Dom2-defined hotspots represent much less than the predicted 50% of all hotspots in F1 hybrids carrying two different Prdm9 alleles, indicating some form of competition between alleles [9,10]. In progeny from both B6xCAST and CASTxB6 crosses, the majority (65%) of all hotspots were PRDM9Cst-activated [30]. However, interpretations of genome-wide hotspot behavior in traditional F1 hybrids between inbred strains are complicated by the presence of novel hotspots not found in either parent that result from the action of one parents Prdm9 allele on the genome of the other parent [30], and by the fact that the entire genome is heterozygous, potentially introducing additional trans control mechanisms.
To remove these complications and test for competition in a genetically uniform background, we crossed B6 mice to co-isogenic B6-Prdm9CAST-KI mice and measured H3K4me3 levels in germ cells of the resulting heterozygous Prdm9Dom2/Cst F1 male progeny. The total number of putative PRDM9-defined H3K4me3 hotspots in Prdm9Dom2/Cst progeny (n = 21,894, Fig 5B) is less than the sum of the parental strains (n = 18,849 and n = 28,475, for B6 and CAST-KI respectively) [9], similar to previous result for DSBs in crosses involving Prdm9Dom2 [10], likely reflecting the sensitivity of hotspot numbers to the total amount of PRDM9 protein. In addition, only ~ 26% of H3K4me3 hotspots in these F1 mice are PRDM9Dom2-activated, while ~ 74% are PRDM9Cst-activated (Fig 5B). The PRDM9Dom2-activated H3K4me3 hotspots that are found in the (B6xKI)F1 mice are a subset of the PRDM9Dom2 hotspots found in Prdm9Dom2/- heterozygous mice (Fig 5C), and are therefore those with the highest activity in B6 mice. Together these data confirm competition between alleles in mice heterozygous for Prdm9 and may suggest that PRDM9Cst has a greater affinity for its binding sequence.
In both mouse and humans, recombination rates can be influenced by heterozygosity at Prdm9 [17,18,26]. Here, using mouse genetics, we identified a single QTL influencing the recombination rate at the Pbx1 hotspot that overlaps with Prdm9 (Fig 1). The QTL mapping data suggested that Prdm9 function is more complex than simple activation of hotspots; in particular, that it is dosage-sensitive and subject to competition between alleles in heterozygous individuals. We found that PRDM9 can form homo- and heteromeric complexes, and that these complexes are bound to DNA at hotspots (Fig 3), providing a molecular explanation for competition between alleles in both mouse and humans. Moreover, measuring H3K4me3 levels at hotspots in heterozygous null and homozygous mice confirmed that Prdm9 is dose-sensitive (Fig 4). Finally, we found that hotspot suppression extends beyond simple dosage of Prdm9 in heterozygous mice, showing that the PRDM9Dom2-activated hotspot Ush2a is directly suppressed by the presence of only the Prdm9Cst allele (Fig 5).
Our data indicate that Prdm9 is partially haploinsufficient for mouse fertility on the B6 background. Further phenotypic evidence for Prdm9 dosage sensitivity comes from genetic studies of hybrid sterility [16,36,38]. Crosses between certain M. m. musculus-derived mice and M. m. domesticus strains carrying Prdm9Dom2 result in sterile males with the severity of the pachytene-stage arrest in spermatogenesis being dependent on the parental origin of Chr X [39]. Thus, there are complex genetic interactions between PRDM9 protein variants and another locus on Chr X [38,40]. The F1 hybrid male sterility can be rescued by either making the Prdm9Dom2 allele homozygous; replacing the Prdm9Dom2 allele with another M. m. domesticus allele; or adding extra copies of Prdm9 (independent of which M. m. domesticus allele is added); or it can be partially rescued by removing Prdm9Dom2, creating a heterozygous null M. m. musculus state, these results together further implicate allelic interactions in the hybrid sterility phenomenon [36]. These data, together with our findings on the capacity of PRDM9 to form homo- and heteromeric complexes, indicate that the sterility phenotype is Prdm9 dosage-sensitive and may be partially explained by incompatibilities of different homo- versus heteromeric PRDM9 complexes.
In the absence of Prdm9, meiotic DSBs persist [13], although they are relocated away from recombination hotspots to other Prdm9-independent H3K4me3 sites such as those found at promoters and enhancers [10], resulting in complete meiotic arrest. We found that B6-Prdm9Dom2/- heterozygous mice have a partial failure in meiotic progression (Fig 4). One possible explanation is that the reduced number of PRDM9-dependent H3K4me3 hotspots in a single cell may lead DSBs to be redirected to other, PRDM9-independent, H3K4me3 sites, which are subsequently not properly repaired, as occurs in the homozygous null mouse [10].
Evidence for competition between PRDM9 alleles is also seen in humans [17,18,29]. The recombination rate at several hotspots was measured in men carrying various combinations of A-type and C-type PRDM9 alleles. While men homo- or heterozygous for PRDM9C have similar recombination rates at C hotspots, recombination rates at A hotspots are reduced in heterozygous PRDM9A/C men when compared to homozygous PRDM9A/A men [29]. In addition, in one heterozygous PRDM9A/C man, 56% of the DSBs were due to PRDM9C protein variant, and PRDM9C hotspots were on average stronger than PRDM9A hotspots [28]. Data from these observations, combined with our finding that PRDM9A and PRDM9C can form heteromers and that PRDM9A can be found at C hotspots, support the idea that competition between human PRDM9 alleles results from PRDM9C being partially-dominant to PRDM9A, a relationship similar to that of PRDM9Cst and PRDM9Dom2 in mice.
In addition to Prdm9, there are 16 orthologous PRDM genes in primates and 15 orthologs in rodents, many of which function in multi-protein complexes [41,42]. PRDM proteins are characterized as containing a PR/SET domain, which can catalyze a variety of chromatin modifications, and most also have C-terminal DNA-binding zinc finger domains. Two other PRDM-family proteins, PRDM6 and PRDM2 (also known as Riz1), also form homomeric complexes, in part through interactions involving their PR/SET domains [43,44]. PRDM9 also contains a KRAB domain known to facilitate protein-protein interactions [45]. The mouse and human genomes both contain hundreds of other KRAB-Zinc finger proteins [46], and several are known to form both homo- and heterodimers [47]. Together, these observations suggest that multimer formation may be a common feature of PRDM and KRAB domain containing proteins.
The phenomenon of dominance among Prdm9 alleles is most simply explained by assuming that different alleles have different intrinsic DNA binding affinities determined by the allele-specific zinc finger domains. For example, in a heterozygous mouse, such as the F1 offspring of a cross between B6 and CAST mice, putative PRDM9 dimers would consist of PRDM9Dom2 homodimers, PRDM9Dom2-PRDM9Cst heterodimers, or PRDM9Cst homodimers, in approximate ratios of 1:2:1 (Fig 6). If PRDM9Cst is dominant over PRDM9Dom2, as the suppression of Ush2a suggests, and overall PRDM9 activity is limiting, as the results from the B6-Prdm9Dom2/- studies indicate, PRDM9Dom2-PRDM9Cst heterodimers would activate PRDM9Cst-defined hotspots more often than PRDM9Dom2 hotspots, predicting the 3:1 over-representation of PRDM9Cst-defined hotspots that are active in (B6 x KI)F1 hybrids (Figs 5B and 6). The dominance relationship seen between these two alleles is enhanced by the fact that PRDM9Dom2 hotspots have undergone greater evolutionary hotspot erosion in B6 mice compared to PRDM9Cst-defined hotspots, resulting in PRDM9Cst hotspots having greater binding affinity in the B6 background [9,30]. However, not all allelic pairs show such large bias in hotspot selection. For example, in (WSB x PWD)F1 hybrids, containing two different PRDM9 alleles, 32% of hotspots are defined by the WSB allele and 40% of hotspots are defined by the PWD allele, and the remaining hotspots are unique to the F1 [30]. In any particular combination of alleles, relative dominance will be determined by the intrinsic binding strength of each allele for the hotspots found in that genetic background. This model is supported by the following evidence: Prdm9 activity is dosage dependent (Fig 4), suggesting a limited molecular activity within meiotic cells, PRDM9 protein variants directly compete for hotspot binding [30], for H3K4me3 activity (Fig 5B), DSBs [10,28], and genetic recombination (Fig 5A) [17,18,29], and finally, PRDM9 can form heteromeric complexes that allow protein variants to directly influence each other (Fig 3).
In general, if the average affinity of a PRDM9 allele for its hotspots is appreciably stronger than that of a different PRDM9 allele for its hotspots, and the two protein variants are found in complex together, this difference in affinity in heterozygotes would create a molecular tug-of-war with the stronger allele winning more often, further diminishing the effective dose of the weaker allele. As a result, in heterozygotes, a complex containing both PRDM9 protein variants would more often be bound at hotspots corresponding to the stronger allele. Given the very high population frequencies of Prdm9 heterozygotes [17,18,23,28,48,49], these effects can seriously influence patterns of inheritance in some natural populations.
The animal care rules used by The Jackson Laboratory and Institute of Molecular Genetics are compatible with the regulations and standards of the U.S. Department of Agriculture, National Institutes of Health, and European Union Council Directive 86/609/EEC and Appendix A of the Council of Europe Convention ETS123. The protocols were approved by the Animal Care and Use Committee of The Jackson Laboratory (Summary #04008) and Committee on the Ethics of Animal Experiments of the Institute of Molecular Genetics (Permit Nos. 137/2009, 61/2013).
C57BL/6J (stock number 000664) and CAST/EiJ (stock number 000928) mice were used. The generation and characterization of the B6.CAST-1T, B6-Prdm9CAST-KI/Kpgn and B6-Prdm9tm1Ymat strains were described previously [9,13,26,38,50]. The C3H-Prdm9tm1Ymat mice were derived from Prdm9tm1Ymat mice by repeated backcrossing to C3H/HeN resulting in a 98% C3H/C3H background; the differential segment of Chr 17 carrying Prdm9 was approximately 36 Mbp.
Surface-spread testicular nuclei were prepared using the hypotonic treatment protocol described previously [51] with a few modifications [36,38]. The following antibodies were used: rabbit polyclonal anti-RAD51 (Santa Cruz, sc-8349), anti-DMC1 (Santa Cruz, sc-22768), and anti-SYCP1 (Abcam, ab15087); mouse monoclonal anti-γH2AFX (Upstate, #05–636) and anti-SYCP3 (Santa Cruz, sc-74569); goat anti-Rabbit IgG Alexa Fluor 488 (Molecular Probes, A-11034); and goat anti-Mouse IgG Alexa Fluor 568 (Molecular Probes, A-11031) and IgG Alexa Fluor 647 (Molecular Probes, A-21236). The images were acquired using a Nikon E400 microscope with a DS-QiMc mono-chrome CCD camera (Nikon) and processed in the NIS-Elements program (Nikon). The spread nuclei were staged based on axis development and synaptonemal complex formation.
An important goal for counting recombination at Pbx1 was to bring distant (1–2 kb) SNPs that define recombination hotspots into close proximity for DNA sequencing in a single molecule, while being able to multiplex DNA from hundreds of animals in one sequencing lane. This was achieved using a series of enzymatic steps designed to reduce false-recombinant molecules and incorporate DNA barcoded primers (S1B Fig). Using this system each molecule sequenced represents a single sperm DNA and therefore a potential recombinant DNA. Epididymal sperm was collected from adult mice and DNA purified using the automated sample handling system Maxwell 16 (Promega) with the Tissue LEV Total RNA Purification Kit (Promega). All mice were genotyped as described previously [26].
Step 1: First-round of PCR. DNA primers were design to amplify Pbx1 and contained NotI restriction sites (all primers are listed in S3 Table). PCR reactions for each sample were seeded with ~20,000–25,000 haploid genomes (75 ng total sperm DNA) using 0.25 μl Phusion II enzyme with the HF Buffer (New England Biolabs), 0.8 μM of each primer, 5% DMSO, 0.2 mM dNTPs (NEB) in a total reaction volume of 25 μl. First-round PCR conditions include an initial 98°C 30 second denaturing step followed by 11 cycles of 98°C for 10 seconds, and a 70°C annealing step for 30 seconds followed by 72°C extension step for 45 seconds. The final cycle was followed by 10 minutes at 72°C. Step 2: The entire PCR reaction was brought to 50 μl supplemented with 1 μ NotI (NEB) and appropriate restriction buffer and incubated for 60 minutes at 37°C, followed by heat-inactivation at 80°C for 15 minutes. Step 3: To facilitate intra-molecular ligation and create circularized DNA, the restriction digests were diluted to 200 μl using 5% polyvinylpyrrolpidone (SIGMA), 20 μl T4 ligase buffer, and 1 μl T4 Ligase (NEB). Ligations were performed at 15°C for 15 minutes. The ligation reactions were treated with Exonuclease I and III and incubated at 37°C for 15 minutes to digest any remaining linear DNA molecules. Exonuclease was heat-inactivated by incubating at 95°C for 2 minutes. DNA was concentrated using standard ethanol precipitation and diluted in 10 mM Tris pH 8.0. Step 4: The second round of PCR was performed to generate small DNA molecules amenable to paired-end sequencing. PCR reaction conditions were similar to the first round of PCR in a total reaction volume of 25 μl. Second-round PCR primers were designed to include an 8-bp DNA barcode on the 5’ end in order to allow multiplexing different mouse samples. PCR cycling conditions were also similar to the first round of PCR using 24 cycles. Step 5: After the second round of PCR all individual 25 μl reactions were pooled together and concentrated using ethanol precipitation and resuspended in 10 mM Tris pH 8.0. DNA was run on 2% agarose gel for size selection and purification using QIAquick Gel Extraction Kit (Qiagen). The resulting samples were then subject to high throughput DNA sequencing (see below).
Even with the protocol described above, the DNA sequences generated by high-throughput sequencing consistently reported a low rate of recombination in control samples. To measure false-recombination using deep sequencing we mixed equal amounts of spleen DNA prepared from B6 and CAST mice separately prior to the first-round of PCR; this analysis resulted in a false-recombination rate of 0.22 ± 0.05 cM (mean ± standard deviation). Because recombination cannot occur in these control samples, we conclude that the observed chimeric molecules are created from incomplete extension in one PCR cycle synthesizing a DNA molecule that is subsequently used to prime DNA in following rounds of PCR, so called template-switching or ‘jump-PCR’.
QTL analysis was performed using R (http://www.R-project.org/) and the r/qtl package [52]. Single-QTL scans were performed using the scanone function using imputation method. Genome-wide LOD significance thresholds were defined by performing 5,000 permutations.
The PRDM9B allele was purchased from OriGene (Rockville, MD). Oligonucleotide primers were designed to include a 5’ V5 epitope tag and used to amplify the full-length PRDM9 and cloned into pCEP4 expression vector (Invitrogen) to create pCB09. A 6X-HIS-3X-FLAG tag was inserted in frame replacing the V5-tag using yeast-based homologous recombination [53]. The zinc-finger arrays for both PRDM9A and PRDM9C were amplified from human genomic DNA [7] and cloned into pBAD-HisC (Invitrogen). These zinc-finger arrays were subcloned into the pCEP4 vectors using restriction enzymes AflII and HindIII (NEB) to create full-length tagged versions of FLAG-PRDM9C (pCB51), V5-PRDM9C (pCB47), and FLAG-PRDM9A (pCB53), and V5-PRDM9A (pCB48) for expression in mammalian cell culture. The V5-PRDM9C-G278A allele was created using QuikChange II site-directed mutagenesis (Agilent Technologies) to change glycine 278 to alanine to create pCB56. All cloning oligonucleotides are listed in S3 Table.
HEK293 cells were cultured in DMEM (Gibco, Life Technologies) supplemented with 10% FBS (Gibco) at 37°C and 5% CO2. 24 hours prior to transfection, cells were seeded at with 10 ml 2.5·105 cell/ml in 10-cm culture-treated plates. Cells were transfected using X-tremeGene HP transfection reagent (Roche) following manufacturer’s protocol using a ratio of 3:1 reagent to DNA with 10 μg total plasmid DNA.
H3K4me3 ChIP-seq from mouse spermatocytes was performed as previously described [9]. ChIP from HEK293 cell cultures were performed with modifications. After transfection cells were allowed to grow for 48 hours. For H3K4me3 ChIP cells were crosslinked by adding formaldehyde (SIGMA) to a final concentration of 1%, and incubated for 10 minutes. For FLAG-tagged PRDM9 ChIP, cells were crosslinked using freshly prepared paraformaldehyde added to a final concentration of 1% and incubated for 5 minutes. Excess formaldehyde was quenched by adding glycine to a final concentration of 125 mM. The medium was removed and the cells were washed once with phosphate-buffered saline (PBS, SIGMA). The PBS was removed and 2 ml of fresh PBS was added supplemented with protease inhibitor cocktail (SIGMA). The cells were collected by scrapping into a 2-ml Eppendorf tube and pelleted by centrifugation at 5000 x g at 4°C for 5 minutes. The PBS was removed and the cell pellet frozen in liquid nitrogen and stored at -80°C. For H3K4me3 ChIP chromatin isolation, MNase digestion, and immunoprecipitation steps were carried out as previously described for spermatocytes. For FLAG ChIP, chromatin was sheared using sonication and immunoprecipitation performed as described for mouse PRDM9 [30].
Pooled DNA samples from the Pbx1 recombination assay were prepared for sequencing using the TruSeq DNA PCR-Free Sample Preparation Kit (Illumina) in order to avoid PCR amplification, which could lead to template switching during amplification, in turn leading to false recombinant molecules. After library preparation Pbx1 DNA was size-selected using the Pippin Prep (Sage Science). DNA from ChIP experiments was prepared for sequencing using NEXTflex ChIP-Seq Kit (Bioo Scientific) for H3K4me3 ChIP from mouse spermatocytes, or Kapa Hyper Prep Kits (Kapa Biosystems) for H3K4me3 ChIP from HEK293 cells without size-selection and 14-cycle PCR amplification. Sequencing for mouse samples was performed at The Jackson Laboratory using the Illumina HiSeq 2000 platform. Sequencing for HEK293 samples was performed at the New York Genome Center using Illumina HiSeq 2500 platform. Base calls were made using CASAVA and mapped to either the mouse genome (mm9) or the human genome (hg19) using BWA [54] with default settings. Custom software was developed to count parental and recombinant molecules, and to de-multiplex individual mice from the Pbx1 recombination assay. For ChIP-seq, alignment files were filtered to keep only uniquely mapped reads. DMC1 SSDS (DSB) ChIP data was previously described [28] (GEO accession no. GSE59836). Peak calling was performed using MACS (v.1.4.2) [55] using ChIP samples for treatment and, for H3K4me3 ChIP, sequenced input DNA as controls with the following settings:-p 1e-5 –keep-dup = ‘all’. Coverage profiles presented in figures were generated with the UCSC genome browser (settings: mean, smoothing window 5) using bedgraphs generated from MACS after tag-shifting. Motif identification and searching for PRDM9C and PRDM9A allele-specific motifs was performed using the MEME Suite (v. 4.9.0) [56]. To locate hotspot centers for heat maps, for each hotspot with more than one motif instance only the top scoring motif was retained (threshold—p-value < 0.0001). Analysis of H3K4me3 peak differences between B6 and heterozygous B6-Prdm9Dom2/- null mice was performed using the R package DiffBind [57].
Heat maps for H3K4me3 ChIP from HEK293 cells and DMC1 ChIP were created using seqMiner [58] for peaks with identified PRDM9 motifs that overlapped both H3K4me3 and DMC1 datasets. For heat maps, tag extension was set at 150 bp for H3K4me3 and 450 bp for DMC1 ChIP, determined by the MACS tag-shifting model, and a wiggle step of 1 bp. Summaries of H3K4me3 ChIP-seq and DMC1 SSDS datasets are presented in S1 Table and S3 Fig. Analysis of peak locations between datasets was performed using bedtools [59].
Quantitative PCR (qPCR) was performed using Quantifast SYBR Green PCR Kit (Qiagen) on the real-time PCR system MasterCycler ep realplex (Eppendorf). Primers were designed using OligoPerfect primer design software (Life Technologies) with 40–60% GC with a product size of 80–120 bps (all primer sequences are listed in S3 Table). All PCR reactions were set up in technical triplicates with 2 μl of ChIP DNA and 0.5 μM forward and reverse primers. Reactions were run for 40 cycles followed by melting curve analysis, and cycle threshold numbers were determined by automated threshold. All ChIP samples were normalized to purified input DNA controls.
Whole-cell protein was extracted from HEK293 cells using RIPA buffer (SIGMA) supplemented with 1 mM PMSF, 1X protease inhibitor cocktail (SIGMA), 1 mM EDTA, 1 mM DTT, and 1 μl Benzonase (SIGMA). Cells were lysed at 4°C for 30 minutes mixed every 5 minutes. For Histone extraction, cells were first incubated for 30 minutes with rotation in hypotonic lysis buffer (10 mM Tris-HCL, pH 8.0; 1 mM KCl, 1.5 mM MgCl2) supplemented with 1 mM PMSF and 1X protease inhibitor cocktail. Nuclei were pelleted by centrifugation at 10,000 x g for 10 minutes at 4°C. Histones were recovered by diluting nuclei in 0.2 N HCl and incubating at 4°C with rotation for 2 hours. Cell lysate was cleared by centrifugation at 10,000 x g for 10 minutes at 4°C. Protein samples were normalized for equal loading using Bradford Reagent (BioRad) and diluted in SDS gel-loading buffer and heat-denatured for 5 minutes at 98°C.
For immunoprecipitation, cleared whole-cell lysate was diluted to 500 μl in RIPA buffer. Magnetic protein-G Dynabeads (Invitrogen) were pre-washed with RIPA and treated with anti-FLAG or anti-V5 antibodies for 20 minutes with rotation at room temperature, and washed again with RIPA. Dynabeads were added to the whole-cell lysates and incubated with rotation at 4°C for 3 hours. Immunocomplexes bound to beads were washed 3 times with 500 μl RIPA buffer and eluted using 2X SDS loading buffer.
For western blotting, protein was loaded into 4–15% Tris-Glycine gels (mini-protean, BioRad) and electrophoresis was carried out at 150 V for 60 minutes. Protein was transferred to nitrocellulose membranes using the iBlot system (Invitrogen) with a 7-minute transfer. Westerns were developed using the SNAP i.d. 2.0 Protein Detection System (Millipore/EMD) with the following antibodies diluted 1:1000: anti-FLAG M2 (SIGMA, F1804), anti-V5 (Invitrogen, R960-25), anti-H3K4me3 (Millipore/EMD, 07–473), anti-H3 (Millipore/EMD, 06–755), and VeriBlot secondary antibody HRP (Abcam, ab131366). Blots were visualized using enhanced chemiluminescent substrate SuperSignal West Femto (Life Technologies) and images digitally captured using a G:BOX gel document system (SYNGENE).
Sperm DNA was amplified by two rounds of nested PCR using allele-specific primers in each PCR reaction similar to previously described [7]. The two pairs of primers were orientated in 5’-3’ CAST-B6 combination. The 5’ forward primers were both designed to the CAST haplotype. The 3’ reverse primers were designed as either CAST or B6. Primers were PTO-modified at the last two nucleotides in the 3’ end (primer sequences found in S3 Table). The first-round PCR was performed using 50 ng sperm DNA, 0.25 mM of each dNTP, 0.25 μM of each primer, 1x Titanium Taq PCR buffer, and 0.5 U Titanium DNA Taq Polymerase (Clontech Laboratories Inc). PCR cycling conditions included an initial denaturizing step at 94°C for 5 minutes, then 12 cycles of 94°C for 1 minute, 64°C for 40 seconds, and extension time of 68°C for 3 minutes, followed by a final extension time at 68°C for 10 minutes. The amplified DNA product was diluted 10 times and 2 μl used for the second-round allele-specific PCR. Second-round PCR cycling conditions used an initial denaturing step at 94°C for 5 minutes, then 40 cycles of 94°C for 1 minute, 55°C for 40 seconds, and extension time of 68°C for 3 minutes, followed by a final extension time at 68°C for 10 minutes.
Quantitation of recombination rates was done by determining the number of crossover and parental molecules in the same sample of sperm DNA. PCR amplification was carried out in serial dilutions where the starting amount of DNA was diluted two times in each consecutive reaction. The last positive and the first negative dilution reactions were used to perform 20 PCR reactions each in parallel. The number of negative reactions in each pool determines the number of amplifiable molecules through the Poisson distribution.
High-throughput sequencing files and processed data for ChIP-seq experiments associated with this manuscript can be found at Gene Expression Omnibus under accession numbers GSE52628 and GSE67673.
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10.1371/journal.pntd.0002025 | Coverage and Effectiveness of Kyasanur Forest Disease (KFD) Vaccine in Karnataka, South India, 2005–10 | Kyasanur forest disease (KFD), a tick-borne viral disease with hemorrhagic manifestations, is localised in five districts of Karnataka state, India. Annual rounds of vaccination using formalin inactivated tissue-culture vaccine have been conducted in the region since 1990. Two doses of vaccine are administered to individuals aged 7–65 years at an interval of one month followed by periodic boosters after 6–9 months. In spite of high effectiveness of the vaccine reported in earlier studies, KFD cases among vaccinated individuals have been recently reported. We analysed KFD vaccination and case surveillance data from 2005 to 2010.
We calculated KFD incidence among vaccinated and unvaccinated populations and computed the relative risk and vaccine effectiveness. During 2005–2010, a total of 343,256 individuals were eligible for KFD vaccination (details of vaccination for 2008 were not available). Of these, 52% did not receive any vaccine while 36% had received two doses and a booster. Of the 168 laboratory-confirmed KFD cases reported during this 5-year period, 134 (80%) were unvaccinated, nine each had received one and two doses respectively while 16 had received a booster during the pre-transmission season. The relative risks of disease following one, two and booster doses of vaccine were 1.06 (95% CI = 0.54–2.1), 0.38 (95% CI = 0.19–0.74) and 0.17 (95% CI = 0.10–0.29) respectively. The effectiveness of the vaccine was 62.4% (95% CI = 26.1–80.8) among those who received two doses and 82.9% (95% CI = 71.3–89.8) for those who received two doses followed by a booster dose as compared to the unvaccinated individuals.
Coverage of KFD vaccine in the study area was low. Observed effectiveness of the KFD vaccine was lower as compared to the earlier reports, especially after a single dose administration. Systematic efforts are needed to increase the vaccine coverage and identify the reasons for lower effectiveness of the vaccine in the region.
| Kyasanur forest disease (KFD), a tick-borne viral disease with hemorrhagic manifestations, occurs as seasonal outbreaks in five districts of Karnataka state, India. Vaccination with formalin inactivated tissue-culture vaccine is the key strategy for the prevention of the disease in the region. In spite of high effectiveness of the vaccine reported in earlier studies, KFD cases among vaccinated individuals have been recently reported. We analysed KFD vaccination and case surveillance data from 2005 to 2010 to estimate the coverage and efficacy of the vaccine under programme conditions. Vaccination coverage was low with less than 50% of the population vaccinated. The effectiveness of the vaccine was 62.4% (95% CI = 26.1–80.8) among those who received two doses and 82.9% (95% CI = 71.3–89.8) for those who received an additional booster dose as compared to the unvaccinated individuals. Systematic efforts are needed to increase the vaccine coverage and identify the reasons for lower efficacy of the vaccine in the region.
| Kyasanur forest disease (KFD) is a tick-borne viral disease characterised by sudden onset of fever and/or headache followed by hemorrhagic manifestations such as conjunctival congestion, bleeding gums, epistaxis, haemoptysis, haematemesis and malena [1]–[3]. The disease which was reported for the first time from Shimoga district of Karnataka state, India in 1957, is localised in five districts (Shimoga, Chikamagalur, Uttar Kannada, Dakshina Kannada and Udupi) of the state and occurs as seasonal outbreaks during December to May when the nymphal activity of ticks in the forest is maximum.
Prior to the currently used formalin inactivated KFD virus (KFDV) vaccine produced in chick embryo fibroblasts, several vaccines were tested for the control of the disease. These included 5–10% suspension of formalin-inactivated Russian Spring Summer Encephalitis (RSSE) virus [4]–[6], formalin inactivated vaccine from mice brain [7] as well as tissue culture source [8]–[9] and a live attenuated vaccine through serial tissue culture passages [10]. Field studies conducted in 1970–71 with the formolized KFDV demonstrated a serological response in 59% of the vaccinees after two doses [11]–[12].
Based on these findings, a vaccine production unit was established at Shimoga, Karnataka and the indigenously manufactured vaccine was licensed for use in the affected districts. Subsequently, the vaccine production was shifted to the Institute of Animal Husbandry and Veterinary Biologicals, Hebbal, Bangalore. Immunization with this vaccine has remained the key strategy for prevention of KFD in Karnataka since 1990. The focal immunization strategy involves annual rounds of vaccination using formalin inactivated tissue-culture vaccine. These campaigns are conducted during the months of August-November in the areas that reported KFD activity (defined as laboratory evidence of confirmed case/s in monkeys/humans or infected ticks) in the previous transmission seasons and surrounding villages within a radius of 5 Km [13]. Two doses of the vaccine are administered to individuals aged 7–65 years at an interval of one month. As the immunity conferred by the vaccination is short-lived, booster doses are recommended within 6–9 months after primary vaccination and repeated for five consecutive years after the last confirmed case in the area [13]. If cases of KFD are reported in the area in spite of vaccination during the pre-transmission season, additional vaccination campaigns are conducted. As part of the KFD vaccination programme, information about the number of individuals eligible for vaccination as well as coverage of vaccine is routinely compiled.
In the previous field evaluation of the vaccine during 1990–92 in Shimoga, Uttar Kannada and Chikamagalur districts, an effectiveness of 79.3% (95% CI: 64.7–87.9) with one dose and 93.5% (95% CI: 87.9–96.6) with two doses was reported [14]. However, despite routine vaccination, an increasing number of KFD cases have been reported during 1999–2005 suggesting sub-optimal efficacy of the current vaccine or vaccination protocol [15]. During the recent KFD outbreak in Shimoga in December 2011–March 2012, two doses of the vaccine given during April–May 2011 were found to confer no protection amongst the cases reported during Dec 2011–Mar 2012 [16]. With this background, we analysed the KFD vaccination and surveillance data from the five KFD endemic districts of Karnataka for the period 2005–2010 to estimate the coverage and the effectiveness of the KFD vaccine in the region.
As part of KFD surveillance in the region, health workers conduct door to door search for identifying suspected case-patients (defined as sudden onset of fever, headache and myalgia) in a radius of 5 km. surrounding the villages reporting recent monkey deaths or laboratory-confirmed KFD cases. Blood samples are collected from all the suspected case-patients and are tested for the nested polymerase chain reaction (RT-PCR) [17] and/or intra-cerebral inoculation of the sera into suckling mice. Information about number of doses of the KFD vaccine received is also collected from the case-patients. We reviewed the KFD vaccination and the surveillance data for the year 2005–2010 from the five KFD endemic districts.
We calculated the annual and overall incidence of KFD during 2005–10 among the vaccinated population and compared the same with the incidence of the disease among unvaccinated individuals to calculate the relative risk (RR) associated with vaccination. Using Epi-6 software (CDC, Atlanta), we calculated the effectiveness of the vaccine (VE) and its 95% confidence intervals (CI).
The study primarily involved analysis of the archived data of the KFD vaccination programme and surveillance conducted by Karnataka state health department. Permission from the district health authorities was obtained to access the data.
During 2005–10, a total of 343,256 individuals from the five districts were eligible for KFD vaccination as these individuals resided in villages within 5 km radius of villages that reported monkey deaths or a laboratory confirmed human case of KFD or tick positivity. The vaccination details for 2008 were not available and hence were not included in the calculation of vaccine effectiveness.
About 52% of the eligible population did not receive any vaccine while 36% of the population received two doses and a booster dose (Table 1). The remaining 12% of the population received one or two doses of the vaccine. A total of 168 laboratory confirmed KFD cases were reported during this five year period of which 134 (80%) were unvaccinated. Of the remaining 34 cases, nine each had received one and two doses respectively while 16 had received a booster in the pre-transmission season. The incidence of KFD among the individuals who had received one dose of the vaccine was similar to the incidence among those who were unvaccinated (8 per 10,000 in both groups; RR: 1.06, 95% CI: 0.54–2.1). Compared to unvaccinated individuals, the incidence was significantly lower among the individuals who received two doses (3 per 10,000, RR: 0.38, 95% CI: 0.19–0.74) or two doses and booster (1 per 10,000, RR: 0.17, 95% CI: 0.10–0.29) dose of the vaccine. The effectiveness of the vaccine was 62.4% (95% CI: 26.1–80.8) among those who received two doses and 82.9% (95% CI: 71.3–89.8) for those who received two doses and a booster dose as compared to unvaccinated individuals.
The findings of our analysis of surveillance and vaccination data indicated lower effectiveness of the vaccine as compared to the earlier report of effectiveness of 79% and 94% with one and two doses of the vaccine respectively [14]. In particular, the administration of one dose was found to be non-efficacious as the incidence of the disease among such individuals was comparable with unvaccinated individuals. Our findings of lower effectiveness of the vaccine corroborate well with earlier reports of lower efficacy by other investigators [15]. It is pertinent to note that the breakthrough cases of KFD occurred only during 2005 and 2006 and there were no cases among the vaccinated individuals in subsequent years. The breakthrough cases were reported from three of the five districts.
Several reasons have been postulated for the lower efficacy of the vaccine including the possibility of drifts and diversity in the recently currently circulating strains of the KFD virus in contrast to the strain used for vaccine preparation (isolated in 1950s) [15]. However, the genotyping studies conducted on 48 KFD viruses isolated over the past five decades showed a low level of diversity with a maximum of 1.2% nt and 0.5% aa differences seen among these viruses [17]. It is necessary to evaluate other reasons for lower efficacy of the vaccine including the issues related with cold chain maintenance.
The coverage of the vaccine in the region was low with nearly half of the target population being unvaccinated. About 45% of the population received two doses or two doses and a booster, which were found to be protective. Achieving high coverage with two doses and/or booster doses of vaccine appears to be the key for the control of KFD in the endemic districts.
Our study had certain limitations. First, we used the available surveillance and programmatic data to estimate the coverage and the effectiveness of the vaccine. The number of KFD cases reported in the surveillance system is likely to be affected by its notification efficiency. However as the awareness about the disease in the area is high and most of the cases seek treatment from public health facilities in the area, we believe that the surveillance data reflects the true situation of KFD in the area and the number of missed cases is likely to be negligible. Second, the vaccination coverage estimated using administrative method in our analysis is likely to be higher than the actual coverage estimated through surveys.
In conclusion, the coverage of KFD vaccine in the five KFD endemic districts of Karnataka was low. The effectiveness of the vaccine was also found to be lower as compared to the earlier studies especially among those who received single dose of the vaccine. Systematic efforts are needed to increase the coverage of the vaccine among the areas targeted for vaccination to more effectively control this well-localized spread of KFD virus in Karnataka. It is also necessary to understand the reasons of lower uptake of the vaccine. The vaccine associated side effects such as pain as well as the number of doses to be taken over a period of five years are some potential deterring factors. Epidemiological studies are also needed to assess the long-term protection offered by boosters. Future research needs to focus on further refinement of the vaccine candidate to eliminate these deficiencies to make the vaccine safer and more effective in order to avoid the need for periodic boosters.
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10.1371/journal.pcbi.1007184 | Network analyses to quantify effects of host movement in multilevel disease transmission models using foot and mouth disease in Cameroon as a case study | The dynamics of infectious diseases are greatly influenced by the movement of both susceptible and infected hosts. To accurately represent disease dynamics among a mobile host population, detailed movement models have been coupled with disease transmission models. However, a number of different host movement models have been proposed, each with their own set of assumptions and results that differ from the other models. Here, we compare two movement models coupled to the same disease transmission model using network analyses. This application of network analysis allows us to evaluate the fit and accuracy of the movement model in a multilevel modeling framework with more detail than established statistical modeling fitting methods. We used data that detailed mobile pastoralists’ movements as input for 100 stochastic simulations of a Spatio-Temporal Movement (STM) model and 100 stochastic simulations of an Individual Movement Model (IMM). Both models represent dynamic movement and subsequent contacts. We generated networks in which nodes represent camps and edges represent the distance between camps. We simulated pathogen transmission over these networks and tested five network metrics–strength, betweenness centrality, three-step reach, density, and transitivity–to determine which could predict disease simulation outcomes and thereby be used to correlate model simulation results with disease transmission simulations. We found that strength, network density, and three-step reach of movement model results correlated with the final epidemic size of outbreak simulations. Betweenness centrality only weakly correlated for the IMM model. Transitivity only weakly correlated for the STM model and time-varying IMM model metrics. We conclude that movement models coupled with disease transmission models can affect disease transmission results and should be carefully considered and vetted when modeling pathogen spread in mobile host populations. Strength, network density, and three-step reach can be used to evaluate movement models before disease simulations to predict final outbreak sizes. These findings can contribute to the analysis of multilevel models across systems.
| Epidemics of infectious disease vary geographically and vary through time. A large part of this variation is caused by movement of individuals who are susceptible to the disease or infected with the disease. To study how movement affects epidemics, researchers often combine movement models with transmission models. However, multiple movement models have been proposed, and their effect on infectious disease model output is not well understood. Here, we combine two different movement models that we developed to represent mobile pastoralists in the Far North Region, Cameroon, with the same disease transmission model. We use network metrics to test how different movement models can affect the output of the disease transmission model. We found that three metrics could be applied to movement model output in order to predict epidemic model output. We conclude that movement models coupled with disease transmission models can affect disease transmission results and should be carefully considered and vetted when modeling epidemics.
| Host movement influences disease dynamics [1–3]. In situations where the population size is too small to sustain long-term chains of transmission, movement of infected hosts can spark local outbreaks and increase incidence counts. Alternatively, migration of susceptible hosts away from outbreak locations can reduce abundance or local density of the susceptible population, reduce contact rates, and decrease incidence counts or outbreak duration [1]. In order to quantify and predict pathogen transmission in a mobile host population, researchers couple mathematical and statistical models of disease dynamics with host movement models to more accurately represent local abundance of diseased hosts in a multilevel modeling framework [4–9]. The problem still remains, however, of how to best represent host movement since host behavior is complex and full movement trajectories are rarely observed.
To account for heterogeneity in host movement, multiple models have been proposed. These models range from stochastic models of movement, such as the set of random walk models [10], to more mechanistic models of movement [11], including distance-based and gravity-based movement models [12, 13]. Each movement model has its own set of assumptions and simulated movement trajectories can differ. When different movement models are coupled with disease transmission models [4–9] the movement model selected can impact results and conclusions relating to disease dynamics. It is important that the movement model used in a multilevel modeling framework with a disease transmission model accurately represent contacts that result in disease transmission. Statistical methods have been developed to resolve this specific problem and help find the most parsimonious movement model (reviewed in [14] and [15]).
One approach is to develop multilevel models that incorporate alternative hypothesized modes of movement and use statistical techniques to find which model fits the data best. For example, one multilevel model would include a movement model at level one and a disease transmission model at level two; another multilevel model would include a different movement model at level one and the same disease transmission model at level two. Using classical methods, one could find which multilevel model best fits the data using a goodness of fit method–e.g., maximum likelihood–and infer that whichever movement model best represents movement patterns in the host population [15]. This approach works well when host location and disease incidence data are detailed enough to accurately select the most parsimonious model and when the outcome desired is the selection of the best-fit model at the population level. However, when model fit varies through time or by host, additional methods are needed for model evaluation and selection.
Here, we extend traditional methods of model selection by developing a suite of network analysis tools for multilevel disease transmission models that incorporate host movement. Networks consist of nodes, which represent hosts, that are connected by edges, or contacts among nodes through which pathogens propagate. This network approach has three advantages. First, networks permit flexibility in representing heterogeneous behavior that can affect disease transmission and overcome homogeneous behavior assumed in some traditional models [16]. Second, networks permit flexibility in representing host-pathogen systems–as the edge can represent any relevant mode of transmission–and surmount the need to develop specific methodology for each pathogen transmission scenario [17–19]. Third, networks can be implemented in a way that displays and measures effects of the movement level in a multilevel model even if these effects vary through time or vary by host. Together, these advantages highlight the usefulness of network analysis for model evaluation and selection.
To demonstrate our network analysis tools, we use livestock data from the Far North Region, Cameroon. This Region hosts many important livestock diseases–including five different serotypes of foot-and-mouth disease virus [20, 21]–making disease quantification and prediction important here. Host movement shapes disease transmission, because 7% of the estimated 650,000 cattle in this area participate in seasonal transhumance as a livestock management practice [22]. During transhumance, herders move their camps, which consist of mixed-species livestock herds and households, over great distances (>100 km). While pastoralists follow the same general patterns of movement [23], detailed spatiotemporal characteristics vary between herds. Each pastoralist makes a decision about the location of the campsite and the dates of arrival and departure and considers multiple factors for each move [24], suggesting that movement is a complex process.
To account for the uncertainty regarding in pastoralist movements in the Far North Region, Cameroon, we developed two movement models that represent camp movement and resulting contacts as dynamic processes. The first model is a spatial-temporal mobility model (STM) that estimates the probability of pastoralist movements based on detailed movement survey data [25]. This model categorizes pastoralists into three groups based on the shape of their movement trajectories and assigns corresponding probability distributions to herd locations [26]. The second model is called Individual Movement Model (IMM) that uses a set of rules in an agent-based simulation of pastoralist movements. This model assigns a herd to a sequence of seasonal grazing areas with random movements within these seasonal grazing areas [27]. We couple these movement models with disease transmission models to develop a multilevel modeling framework to quantify incidence among livestock in the Far North Region.
In this work, our objective is to determine which to determinine how effective each network analysis tool quantifies the effect of movement model selection and its impact on disease simulations, considering both temporal variation and the effect of host variation. First, we generate networks from stochastic simulations of both cattle movement models and analyze these networks. Second, we simulate and quantify disease transmission across networks stochastically generated from both cattle movement models. Finally, we determine which network connectivity metrics correlate with simulated disease incidence. By evaluating the accuracy of competing movement models and quantifying the impact of movement models on predicted outbreaks in this system with consideration of temporal variability and the effect of herd characteristics, we contribute information that could help control of infectious diseases among these mobile pastoralists. More broadly, we contribute a set of model evaluation tools that can be applied across systems quantified and analyzed with multilevel models.
Location data were obtained from 72 mobile camps, which comprise a near complete set of all Cameroonian mobile pastoralists who visited the Logone Floodplain on any date between August 16, 2007 and August 15, 2008 [25]. Each camp contained, on average, approximately 8 households, 30 people, and 600 cattle [24]. Data were collected in two phases. First, locations of pastoral households were observed two times–in February 2008 and August 2008 –and the global positioning system (GPS) coordinates of campsite locations were recorded. Second, locations of pastoral households were obtained by structured interviews in August and September of 2008. These interview data represent campsite location at a finer temporal scale and inserted information about campsite location at dates that fell between first phase data collection events, thereby documenting the entire annual transhumance. Herders provided names of all locations in which they erected a campsite during the previous year and number of days spent at each location. Members of the research team visited each of the places that pastoralists listed in the transhumance interviews to obtain geographic coordinates. The full location data set consists of the GPS coordinates of the centroid of each named location for every date between August 16, 2007 and August 15, 2008. The data set is publically available at MoveBank [28].
The data collection protocol was reviewed and approved by the Ohio State University Institutional Review Board / Human Research Protection Program (Federal-wide Assurance #00006378 from the Office for Human Research Protections in the Department of Health and Human Services: protocol 2010B0004). We obtained informed consent after explaining the protocol and potential risks.
Location data are input for stochastic simulations of the STM and IMM models, both of which model camp movement and resulting contacts dynamically. The STM model simulates 71 mobile camps and the IMM model simulates 67 mobile camps (Fig 1). We ran 100 simulations of the STM model and 100 simulations of the IMM model [26, 27]. These produced 200 sets of simulated locations from which we generated networks.
To generate the networks, we first calculated symmetrical daily adjacency matrices in which we defined adjacency as all camps with simulated locations within a given distance k. If two camps, i and j, were separated by k km or a shorter distance, the {i,j} entry of the daily adjacency matrix would contain a one; if two camps, i and j were separated by more than k km, the {i,j} entry of the daily adjacency matrix would contain a zero.
We defined adjacency by setting k at three different distances: 0 km, 5 km, and 10km. At 0 km, camps are have simulated locations at the same coordinates, and share the same simulated location by definition. In practice, a single coordinate was chosen to represent many camps located at the same general campsite. At 5 km, we consider camps that have overlapping cattle grazing zones to be connected, because the grazing radius of a camp in the Far North Region is approximately 5 km [29]. At 10 km, we consider camps that have adjacent cattle grazing zones to be connected, again based on the 5 km grazing radius. Daily adjacency matrices were calculated for each of the 366 days for which location data and model outputs were available, because 2008 was a leap year. In total, we calculated 3 daily adjacency matrices for each of the 200 sets of simulation locations generated from two movement models, which resulted in a total of 219,600 daily adjacency matrices for the 2007–2008 year.
To simulate disease across networks, it is important that the adjacency matrices summarize a duration equal to the infectious period of the pathogen [30]. Since the infectious period of foot-and-mouth disease is approximately one week in cattle [31], we summarized the daily adjacency matrices as weighted weekly adjacency matrices. To calculate the weighted weekly adjacency matrices, we summed daily matrices over seven days, resulting in 31,200 matrices. From these weekly matrices, we generated 31,200 weighted weekly networks.
We simulated disease transmission across weekly networks using a susceptible, infected, recovered (SIR) framework in which each herd was categorized in a single disease state and transmission occurred between herds. Susceptible herds had not previously experienced disease, were immunologically naïve, and capable of catching the pathogen. Infected herds harbored the pathogen and were capable of transmitting the pathogen to susceptible herds. Recovered herds mounted convalescent immunity after experiencing infection and were removed from chains of transmission [32].
Transmission propagated through the networks when an infected herd was connected to a susceptible herd by an edge. We simulated various values of R0, or the number of secondary cases produced by a primary infectious case in a completely susceptible population. We performed transmission simulations under three values for R0 (1, 5, 10), looping through the following protocol until no additional nodes could be infected.
This transmission propagation procedure keeps the number of secondary infections caused by each infectious node relatively constant and not greater than the value at which R0 is set. The nodes receiving the secondary infection are randomly chosen from the set of all possible nodes connected to the infectious node. We assume that cattle herds are infectious for 7 days, which is longer than the mean duration of infectiousness of an individual animal (4.5 days [33]), but implies that most of the herd gets infected within days. This duration was chosen considering the size of herds in the Far North Region, Cameroon, the proximity of animals in these mobile herds, and our assumption that the entire herd is susceptible.
We performed multiple disease transmission simulations for each value of R0 for a maximum of 366 days. Disease was initiated at each node and on each day, with simulations lasting from the day of initiation to the end of the 366-day period. For each of the 31,200 weighted weekly adjacency matrices, we performed simulations at 3 different R0 values, with disease initiated on each of the 52 weeks, for a total of 4,867,200 disease transmission simulations.
We used five different connectivity metrics to compare the structure of weekly networks: three node-level metrics averaged across the entire network and two network-level metrics [34]. The node-level metrics were strength, betweenness centrality, and 3-step reach. Strength is calculated for each node by summing the weights of all connected edges. Strength is a single metric that represents both the number of connections between the given node and all other nodes plus the duration of those connections. Betweenness centrality is calculated for each node by counting the number of shortest paths between any two other nodes on which the node lies; to do this, we used the betweenness command in R. This metric represents how many other nodes a given node plays a role in connecting; in terms of disease transmission, a camp with high degree centrality is crucial for propagating disease transmission. Three-step reach is calculated by counting the number of nodes within three edge lengths or three “steps” from any given node. This metric indicates how connected the entire network is; if three-step reach is high, an epidemic will propagate though a large part of the network in three time steps. The network-level metrics were network density and network transitivity. Network density is calculated as the sum of weighted adjacency matrices divided by the total number of edges possible in the network. This metric is a different way to calculate how connected the entire network is and also has implications for the speed and extent of infectious disease transmission. Network transitivity is calculated as a ratio where the numerator is the proportion of closed triads and the denominator is the proportion of open triads [34]. This metric measures the cliquishness of a network; infections initiated in a network with high transitivity will show pockets where disease transmitted and other pockets that the disease would not reach. To measure transitivity, we used the transitivity command in R.
To compare the two movement models, we calculated the value for each of these 5 network metrics for each of the 31,200 weekly adjacency matrices. We quantified how the 5 metrics correlated with the final epidemic sizes of disease simulations using Pearson’s correlation coefficient and p-value implemented with the cor.test function in R. The metrics that correlated well with outbreak size can be used as tools for analysis of the movement level of a multilevel model.
Because network characteristics might not immediately correlate with infectious disease spread, we quantified temporal variation in the movement model network metrics and disease simulation results. We tested lag periods of 0 weeks, 1 week, 2 weeks, 3 weeks, 4 weeks, and 5 weeks. For each lag period, we used Pearson’s correlation coefficient described to quantify the correlation between connectivity metrics and outbreak size. For each connectivity metric, we identify maximum correlation coefficient and report it, along with the time period that produced the maximum correlation coefficient. This method detects correlations that might have been obscured because they did not happen simulatelously with an increase in final epidemic size.
Here, we report results from analyses conducted when adjacency was defined at 5 km. Results from analyses conducted when adjacency was defined at 0 km and 10 km are reported in the supporting information.
In order to quantify network connectivity with node-level metrics, we found the strength, betweenness centrality, and 3-step reach for each node in the weekly networks for 100 simulations of the IMM and 100 simulations of the STM and averaged these values across each network (Fig 2). Average strength in the IMM and STM were similar, especially at the start and end of the year; however, average strength showed three peaks in the IMM simulations that were not observed in the STM simulations (Fig 2A). Average betweenness centrality showed small peaks in the IMM and STM simulations that differed in timing (Fig 2B). Average 3-step reach was greater in the STM simulations than in the IMM simulations and showed greater peaks in the STM simulations (Fig 2C). In this way, the connectivity differed between the two movement models.
In order to quantify connectivity with network-level metrics, we found the density and transitivity in the weekly networks for 100 simulations of the IMM and 100 simulations of the STM (Fig 3). Density in the IMM and STM were similar, especially at the start and end of the year (Fig 3A) and mirrored the average strength results (Fig 2A); again, density values showed peaks in the IMM simulations that were not observed in the STM simulations (Fig 3A). Transitivity values were high in networks drawn from simulations of both movement models but IMM and STM values differed through time; however, the IMM values showed more peaks (Fig 3B). Similar to the individual connectivity metrics, the network-wide connectivity differed between the two movement models.
In order to quantify disease transmission across all networks, we ran SIR simulations (Eqs 1 and 2) across all networks. With R0 = 1, most simulations showed small final epidemic sizes, similar to those described as stuttering chains of transmission in homogeneously mixing models, and many outbreaks failed to take off (Fig 4A and 4B). With R0 = 5, simulations showed slightly larger final epidemic sizes; still, though, many outbreaks failing to take off (Fig 4B and 4E). Results with R0 = 10 were similar to results with R0 = 5, indiciating transmission saturation of the network (Fig 4C and 4F). Transmission simulated over the networks generated from IMM output tended to produce smaller outbreaks (Fig 4A–4C) than transmission simulated over the networks generated from STM output (Fig 4D–4F) and the STM output showed a bimodal distribution at higher values of R0. Therefore, the differences in the two movement models resulted in differences in disease simulation results.
In order to correlate connectivity metrics with disease transmission, we computed the correlation coefficient for each of the five network analysis metrics and the final epidemic size produced by transmission simulations. For the IMM, we found that 3-step reach consistently and positively correlated with final epidemic size across a range of R0 values (Table 1). Betweenness centrality also showed consistent positive correlations, although the magnitude of the association was smaller (Table 1).
For the STM, we found a different pattern in associations. In this case, strength and density consistently and positively correlated with final epidemic size across a range of R0 values (Table 2). Transitivity also showed consistent positive correlations, although the magnitude of the association was smaller (Table 2). In this way, multiple metrics appear to correlate with disease simulation when averaged across nodes and across simulations. However, this averaging diminishes the advantages of network analyses, which excel in ability to capture individual behavior.
To capture temporal variation in the movement model network metrics and disease simulation results, we correlated weekly connectivity metrics with weekly outbreak results between 0 and 5 weeks lagged. We found, in general, equal or higher correlation when considering time-variation. We found that lagged strength, lagged betweenness centrality, and lagged 3-step reach correlated with final outbreak size for the IMM; lagged strength and lagged 3-step reach correlated with final outbreak size for the STM (Table 3).
Transmission models are often matched with models of host movement seeking greater accuracy in rep resenting heterogeneity and other processes that drive disease spread [4–9]. Which host movement model is chosen to couple with a disease transmission model can affect incidence results. We found that network analysis metrics–strength, 3-step reach, network density–correlated with simulated transmission when network and disease dynamics were averaged across time. When network metrics show a large degree of temporal variation, lagged metrics provide even stronger correlations. However, when considering time-variation in both the movement model and disease transmission, betweenness centrality did not provide a correlation with final epidemic size. For these reasons, we can recommend mean strength, mean 3-step reach, and network density as analysis tools that can evaluate the results of a movement model and correlate with disease transmission in both time invariant and time-varying scenarios.
The practical significance of betweenness centrality measures for networks has been noted for medicine, veterinary health, and public health. Because this metric quantifies the number of shortest paths on which a node lies [35], it can be used to identify individuals to target for vaccination that would most effectively cease outbreak transmission [36], especially in assortative networks and at the start of an outbreak [37]. These works suggest that betweenness centrality is a metric that captures the characteristic of network structure that affects disease spread. It is surprising, then, that in our study, betweenness centrality very weakly correlated only when averaged across the IMM network or with time-lagged values from the IMM simulations; there were no positive correlations with any of the STM simulations. This suggests that nodes that have high betweenness centrality might change over the course of the simulations in the STM simulations. High betweenness centrality might not be an intrinsic quality of a node in this system, but it might be a product of movements, seasonality, and network structure.
Quantifying network density leads to a different set of practical applications. Interestingly, we found that this metric was time-varying. Based on the networks drawn from empirical data, we found that network density was low in August and September at the end of the rainy season. When herds begin to move to their dry season pastures, in October 2007, herds cluster together and display the highest network density of the entire year, because network density steadily declines until the following August. Because herds are geographically clustered, this mustering before dispersal presents a time period in which information or vaccinations could be efficiently disseminated among herders enrolled in this study. We quantify this seasonality in contacts and simulate its effects on transmission of an infectious disease in another study [38].
Uncertainty is inherent in location data. To address uncertainty in location data, we considered herds to be adjacent at three different distances. Adjacency defined at 5 km or 10 km can be thought of as analogous to a 5 km or 10 km error in location data. Interestingly, we found similar quantities of infected herds from disease transmission models over networks with adjacency defined at 5 km and 10 km. This suggests that precise locations of each camp or each animal are not needed to make accurate conclusions about network characteristics and disease transmission, similar to what has previous findings that full location data are not needed to determine optimal control [39]. Disease dynamics can be inferred with reasonable accuracy with datasets at less resolution than perfect observation of all animals.
Our transmission model has been formulated to represent generic pathogens that spread either by direct contact or by aerosol through a population using a simulation procedure that keeps R0 near the value set in simulations. Simulations parametrized as R0 = 1 represent the threshold value between epidemics occurring and not occurring; with the stochasticity inherent in our transmission algorithm, this results in some stuttering chains of transmission and other instances where the epidemic fails to take off. Because of the way transmission was propogated, the variation in simulation outcomes was relatively low and the number of stochastic fadeouts might have been lower than for other more random simulation procedures. For the other values of R0 simulated, these models would be especially applicable to livestock pathogens such as foot-and-mouth disease virus. Nevertheless, livestock diseases are spread by many other modes, including environmental or vector borne transmission. Conceptually, our model with R0 = 5 that assumes direct transmission will correspond to a model with a lower R0 that assumes environmental transmission, because our networks are defined by sharing geographic locations. It remains a limitation of our model that not all modes of transmission are represented and that these mobile camps might interact with livestock reared in sedentary farms or traded at markets that are not included in the system.
Nevertheless, we have found that the choice of a movement model that is coupled with a disease transmission model can influence connectivity of the host population and disease transmission results. We have found that strength, network density, and 3-step reach can be used to analyze the results of a movement model simulation in order to correlate with disease transmission simulations. We advise quantitative analysis of the movement model, evaluating sensitivity to movement and location parameters, as a requisite to multilevel modeling of disease dynamics in a mobile host population.
Overall, these results have two major implications for pathogen spread among mobile camps. First, they indicate that identity of centrally connected camps can change over time. This suggest that connectivity might not be an innate property of a camp but a property that emerges though some combination of movement, seasonality, and other factors. Second, they indicate that multiple movement models should be considered and vetted before using to make a single movement model in combination with an infectious disease transmission model for decisions about the management infectious disease transmission but exact camp locations are not needed. For this reason, models of infectious disease transmission could greatly inform preventative policies and decisions about infectious disease control.
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10.1371/journal.pcbi.1005293 | Normal Modes Expose Active Sites in Enzymes | Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes.
| In this paper, we present an improved technique to predict active sites in enzymes. Our technique is based on changes of solvent accessibility that accompany normal mode dynamics. We assert the technique strength using several enzyme datasets with known catalytic residues. We show the technique successfully locates the active site in most cases, and consistently surpasses the accuracy of other techniques. We show how the technique is advantageous and paves the way for high precision prediction of active sites.
| Prediction of functional sites in proteins is essential for a range of bioinformatics applications such as molecular docking, and structure based drug design. Traditional methods for predicting functional sites include three approaches: 1). The first approach uses sequence homology to find evolutionary conserved residues with functional activity. 2). The second approach utilizes structural homology with other proteins of known function to locate functional regions. 3). The third and last approach uses geometry and physico-chemical attributes of the protein structure and sequence to identify areas with functional activity.
Over the years, several techniques based on the third approach have been developed. These techniques include LIGSITE [1], POCKET [2], POCKET-FINDER [3], SURFNET [4], CAST [5], PASS [6], Cavity Search [7], VOIDOO [8], APROPOS [9], LigandFit [10], 3DLigandSite [11], MSPocket [12], Fpocket [13], McVol [14], Ghecom [15], PocketDepth [16], PocketPicker [17], VICE [18], as well as consensus techniques which use a combination thereof such as MetaPocket [19]. Other methods analyze the protein surface for pockets [20, 21], cavities [22–24], and channels [25] using pure geometric characteristics, and do not require any prior knowledge of the ligand or of sequence homology. Other computational techniques use geometric characteristics in combination with physico-chemical traits. Such methods include FOD [26], and Elcock [27] that analyze the hydrophobicity distribution under the assertion that functionally important residues are often in electrostatically unfavorable positions. Similarly, THEMATICS [28] uses geometric characteristics in combination with theoretical microscopic titration analysis, while the methods of Goodford [29] and Rupert et al. [30], and SiteHound [31] identify ligand binding sites based on analyses of the binding energies of probes placed on a grid around the protein. Another purely geometric method, EnSite, uses the proximity of catalytic residues to the molecular centroid to accurately detect the active sites of enzymes with high accuracy [32].
When used in combination with sequence and structure homology, geometric techniques are enhanced and prediction is improved. Some techniques use a vast combination of parameters ranging from conservation, residue type, accessibility, 2D structure propensity, cleft depth, B-factors, etc. to predict active site residues. Using such parameters, Gutteridge et al. predicted the location of active sites in enzymes using a neural network and spatial clustering [33]. Similarly Petrova et al. used Support Vector Machine with selected protein sequence and structural properties to predict catalytic residues [34]. In both cases, about 90% of the actual catalytic residues were correctly predicted. From these data it is clear, that one should rely on sequence and structure homology when possible, and over the past decade, multiple methods to detect binding sites and functional pockets based on geometric, structural, and genetic data were developed [35–39]. Several webservers of ligand binding sites have also been constructed and may be used to infer unknown ligand binding sites based on homology and other attributes such as Pocketome [40], FunFold [41], scPDB [42], IBIS [43], Multibind [44], fPop [45], and FINDSITE [46]. To date however, no comprehensive study comparing geometry based techniques has been performed.
Normal-mode analysis is one of the standard techniques for studying long time dynamics and, in particular, low-frequency motions. In contrast to molecular dynamics, normal-mode analysis provides a very detailed description of the dynamics around a local energy minimum. Even with its limitations, such as the neglect of the solvent effect, the use of harmonic approximation of the potential energy function, and the lack of information about energy barriers and crossing events, normal modes have provided much useful insight into protein dynamics. Over the past years, several techniques have been described to calculate large-scale motions using simplified normal-mode analysis [47–51]. Based on these techniques, several executable programs to calculate normal modes have been released, such as ElNemo [52], GROMACS [53], and STAND [49].
Recently, several studies have drawn attention to the allosteric effect of ligand binding on normal modes dynamics [54]. From these studies, a clear correlation between binding in the native site and perturbation of normal modes was identified. The same allosteric effect of ligand binding on molecular dynamics was also pointed out by Bhinge [55] and Ming [56] which proceeded to use molecular dynamics simulations in predicting ligand binding sites. It is based on these recent advances, that we became aware of the capacity of normal modes in predicting active sites.
In this paper we present a novel structure based technique using normal modes to predict the location of active sites in enzymes. The technique exploits the normal mode opening and closing motion of enzymes and the accompanied change of solvent accessibility and highlights residues of the active site. The idea behind the presented technique is that active sites pockets become exposed in normal mode dynamics (Fig 1).
The hypothesis that active sites are surrounded by a shell of flexibility is not new and has been proposed in the dynamic lock-and-key model for biomolecular interactions. The shell of flexibility allows the enzyme to adapt to its ligand through an induced fit. The hypothesis was demonstrated in several studies notably by Weng et al. in a recent study on the flexibility of enzyme active sites [57], and less recently by Babor et al [58].
The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique may also be used in association with other methods to rank geometrically calculated pockets according to their solvent exposure. First, the prediction strength of EXPOSITE is trained extensively in a dataset containing 133 enzymes with known active sites from the Catalytic Site Atlas (CSA) database [59]. Then, EXPOSITE is tested in a dataset containing 845 enzymes and found to be more robust than other structure-based techniques. EXPOSITE’s high success rate is valuable for structure-based identification of active sites and clearly shows the added value of using normal modes for finding active sites. The technique does not attempt to withdraw from the importance of using genetic data, and clearly, a combination of both structural and genetic data would be more useful for predicting active sites than any of them on their own.
To assemble a training dataset containing 133 enzymes with known active sites, enzymes were selected from the CSA database [59], version 2.2.1. The dataset enzymes were selected according to the following two criteria: 1). The enzyme active site is known from the literature (LIT), and not derived by homology. 2). The biologically active enzyme is composed of a single polypeptide chain and a single oligomerization state.
To assemble a test dataset containing 845 enzymes, enzymes were selected from the CSA database [59], version 2.2.1. The test dataset was compiled by extracting chain A of all LIT entries that were not included in the 133 training dataset. These two datasets were used for training and testing EXPOSITEs prediction consistency respectively.
To calculate normal modes of the dataset enzymes, two programs were utilized namely STAND [49] and ElNemo [52] and were run locally. For STAND, both real normal modes (REA) and Tirion modes (TIR) were calculated. For speed, the STAND option of coarse graining, 1 point (1 pt), which accelerates the calculations yet does not flaw the results was used, and defaults values of deformation amplitude were used. For ElNemo, default values of DQMIN -100 and DQMAX 100 were utilized. The DQMIN and DQMAX parameters correspond to the deformation amplitude in the direction of a single normal mode. For both STAND and ElNemo, deformation amplitudes were not scaled, and the same amplitude produces smaller deformation for larger molecules. For both STAND and ElNemo, only the 10 non-trivial lowest frequency modes were calculated. For each of these 10 modes, 40 PDB files were generated by STAND and 10 PDB files were generated by ElNemo all distorted along the particular mode. The two methods are very different in that STAND (REA) minimizes the structure and then calculates modes in φ and ψ torsion angle space whereas STAND (TIR) and ElNemo avoid minimization by using Tirion modes [50] and then calculate modes in Cartesian coordinate space. For STAND, the opposite extremes of the harmonic motion were empirically chosen as the 1st and 14th structure out of 40 respectively. At these extremes, the structures look fully “distorted” from each other. For ElNemo, the opposite extremes of the harmonic motion are the 1st and 10th structure out of 10.
To calculate the solvent accessible surface (SAS) area of amino acids in the generated PDB files, the DSSP program was used [60]. For each mode, SAS for each residue in the two structures at opposite extremes of the harmonic motion were calculated, and the absolute change of SAS between the extreme mode distortions, |ΔSAS| was used.
To calculate pockets, LIGSITE [61] was run locally using default parameters. In each case, the 10 largest pockets were calculated and the pocket center as well as the pocket size were collected.
The predicted active site was defined as the geometrical center (centroid) of the Cα coordinates of all residues with a solvent exposure |ΔSAS|, in the range 20-40Å2
The observed active site was defined as the geometrical center (centroid) of the Cα coordinates of the active site residues specified in the CSA database [59].
The predicted and observed active sites were represented each by a single coordinate in Cartesian space. The distance between these two coordinates was defined as the distance between the predicted and observed sites.
The success of a prediction was based on the distance between the predicted and observed sites in the training and test datasets. If the distance between the predicted and observed sites was less than 12Å, then a prediction was considered successful. Conversely, if the distance was larger than 12Å, then a prediction was deemed incorrect.
In the special case of the PLD dataset and for easy comparison with other techniques, a prediction was considered successful if any atom coordinate of the ligand was within 4Å of the predicted site. If no atom coordinate of the ligand was within 4Å of the predicted site, then the prediction was considered wrong.
To compare EXPOSITE with other techniques, several software were run on all datasets namely, the training dataset of 133 enzymes, and the testing dataset of 845 enzymes, as well as a dataset containing 48 proteins derived from the PLD dataset [62] and engineered by Huang et al [61]. First, each of the following software was downloaded: LIGSITE, CAST, PASS, and SURFNET. For EnSite, no software was available, and the script was reconstructed based on the algorithm described in the original paper [32]. Then, each of the software was run locally on a PC running under Windows or Linux. In the case of the training and test datasets (which lacks ligands), a prediction was considered successful if the predicted and observed active site were less than 12Å apart. In the case of the PLD dataset (which contains ligands), a prediction was considered successful if the predicted active site was less than 4Å apart from any ligand atom.
To reliably assess the success rate of our technique in an sizeable ensemble, two datasets were assembled from the CSA database [59]. The CSA database contains 23,265 enzymes with known active sites. Of these, only 845 had an active site known from the literature (LIT), and comprised the test dataset. Of these, only 133 were composed of a single chain that is biologically active as a monomer in a single oligomerization state, and comprised the training dataset. The PDB IDs of the 133 selected enzymes of the training dataset are listed in S1 Table. The PDB IDs of the 845 enzymes of the test dataset are listed in S2 Table. To test for homology within the datasets, the enzyme commission (EC) numbers were retrieved. Although, some homologues were found within a single dataset, no homologues were found between the training and test dataset.
A number of programs were tested to calculate geometric pockets of biomolecular structures, i.e. POCKET [2], LIGSITE [1], POCKET-FINDER [3], SURFNET [4], CAST [5], PASS [6]. The program LIGSITECSC [61] provides a list of pocket centers and size in a PDB format and was subsequently utilized in all our calculations.
Surprisingly, there are significant differences between SAS of residues calculated by DSSP and other techniques such as ENCAD, CNS, and Accelrys. These differences arise from the different approaches used in calculating SAS. Nonetheless, when calculating the change of surface areas, ΔSAS, these differences cancel out and all programs produce comparable ΔSAS values.
Biologically relevant modes are not always represented in the lowest frequency modes. Sampling more data, i.e. by calculating more modes could provide better results. Similarly, changing the |ΔSAS| thresholds could also lead to a higher success rate by allowing more exposure data to be included. To test this assertion and optimize the success rate of EXPOSITE the following parameters were varied: the threshold value of |ΔSAS| and the number of normal modes sampled. The number of modes sampled was varied from 0 to 10 and the |ΔSAS| minimum and maximum thresholds were changed from 0 to 60 Å2.
As seen in S4 Table, the optimal |ΔSAS| thresholds for ElNemo were around 20 and 40 Å2 respectively. Below the threshold of 10 Å2, normal exposure fluctuations contribute little to EXPOSITE’s accuracy. Above the threshold of 40 Å2, exposure changes arise from the normal mode tip effect (bond breaking and exaggerated exposure) and contribute little to the EXPOSITE accuracy. For STAND, the optimal |ΔSAS| threshold values were 20 and 40 Å2 respectively. This difference of |ΔSAS| thresholds between STAND and ElNemo is due to the fact that STAND uses coarse graining, inherently reducing the surface area, whereas ElNemo does not. STAND uses coarse graining and represents each amino acid with a single bead, while ElNemo uses a heavy atoms representation. In both cases, the maximum deformation amplitude were not chosen and default values were used. Also, the maximum deformation amplitude was not scaled in this study.
The optimal number of mode sampling peaks to a plateau around modes 8, 9, and 10 for both STAND and ElNemo (S5 Table). Below this sampling number important information is lost. Intriguingly, when using no threshold for |ΔSAS|, the accuracy of EXPOSITE is consistently 86%, no matter how many modes are sampled.
EXPOSITE uses solvent accessibility changes in normal-modes to predict the location of active sites in enzymes. As seen in Fig 2, residues experiencing large accessibility changes (colored cyan and green) are likely to be found in proximity to active site residue (shown in text). In contrast, residues experiencing little exposure change (colored blue) are less likely to be found in vicinity of the active site. The proximity between residues experiencing large |ΔSAS| and the experimentally observed active site residues is an indicator of the precision of EXPOSITEs prediction.
On average, the predicted and observed active sites in the training dataset are separated by 7.9 Å, and a standard deviation of 4.4 Å (S1 Fig).
The maximum success rate of EXPOSITE in the training dataset consisting of 133 enzymes was 92%. Curiously, in the training dataset, the binding pocket coincides mostly with the largest pocket (82%) but not always (18%). This finding accounts for the pitfall of other techniques which rely on pocket size only for ranking.
Also interesting is the fact that no active site was found in pockets with a size less than 7 Å3. Such pockets are too small to accommodate ligands and validate our convention of discarding them as insignificant.
Shown in Fig 3 is a histogram of distances between the predicted and observed active sites in the 845 enzyme test dataset. In this dataset, the predicted and observed catalytic sites are separated by an average of 9.2 Å, 11.5 Å, and 14.1 Å for EXPOSITE, ENSITE, and LIGSITE respectively (Fig 3). Significantly, if a successful prediction is arbitrarily defined by a distance cutoff of 4 Å, then the number of hits of EXPOSITE (16.1%) is almost double that of ENSITE (8.7%). Similarly, if a successful prediction is arbitrarily defined by a distance cutoff of 3 Å, then the number of hits of EXPOSITE (10.4%) is 2.4 times that of ENSITE (4.3%).
To test the robustness of EXPOSITE, we tested its success rate in a dataset containing 845 enzymes (S2 Table). Not surprisingly, the success rate is much lower than in the 133 enzyme dataset. Reliably however, EXPOSITE is better that EnSite in predicting the active site by >2%. The sharp decrease of prediction success rate in the 845 enzymes dataset is not surprising, as the dataset does not discriminate between real homomonomeric enzymes with high success rates, and homomultimeric enzyme assemblies with low success rates (close to 0). Even if statistically robust, the large 845 enzyme dataset does not reflect the real success-rate of prediction techniques, and the smaller 133 enzyme dataset should be regarded as a more representative alternative. The large 845 enzymes dataset is too diverse, and demonstrates the difficulty in assembling representative datasets.
EXPOSITE highlights the binding site of proteins of the Protein Ligand Dataset (PLD) published elsewhere [62]. Shown in Fig 4 (and in S2 Fig) are a few examples of ligand binding site prediction. Residues experiencing large accessibility changes (colored green) are likely to be found in proximity to the ligand (colored red), whereas residues experiencing little exposure change (colored blue) are further away. The proximity of residues with large accessibility changes and residues of the observed active site is a success indicator of EXPOSITEs predictions.
On average, the predicted and observed centers in the protein PLD dataset are separated by 7 Å with a standard deviation of 3.3 Å. Intriguingly, the separation in the PLD dataset is smaller than that of the CSA dataset by almost 1 Å, and it is probably a flaw due to the handpicked nature of the PLD dataset.
To accurately and robustly compare EXPOSITE with other techniques, all other software were run on all datasets namely the training dataset of 133 enzymes, the testing dataset of 845 enzymes. A prediction was considered accurate if the distance between the predicted and observed sites was less than 12Å. If the distance was larger than 12Å, then a prediction was considered inaccurate. The calculated prediction accuracies are listed in Table 1.
When compared to other geometric techniques EXPOSITE is advantageous due to its high success rate. As seen in Table 1, EXPOSITE is only slightly better than EnSite at predicting active sites and EnSite is still superior to EXPOSITE in speed as it is ingenious in simplicity. Also note that prediction of binding sites in unbound proteins is less successful than that of ligand-bound proteins simply because the ligands occupy and expose the binding site through induced fit thereby easing its identification.
To accurately and robustly compare EXPOSITE with other techniques, all other software were run on the bound and unbound PLD dataset [61]. A prediction was considered accurate if any ligand atom was within 4Å of the predicted site. If no ligand atom was within 4Å of the predicted site, then the prediction was considered inaccurate. The calculated prediction accuracies are listed in Table 2. The data for EXPOSITE and Ensite is reported by us, the data for VICE was reported by Tripathi et al [18], the data for Fpocket was reported by Le Guilloux et al. [13], the data for PocketPicker was reported by Weisel et al. [17], the data for LIGSITEcs, CAST, PASS and SURFNET were first reported by Huang et al. [63]. Please note that EXPOSITE is not always successful, such as in the case of PDB 1igj, 3gch, 3mth, and 2tmn as may be seen in Fig 5.
Intriguingly, the classically accepted metric for binding site prediction is 4Å, and we used this metric in the classical PLD dataset when comparing the classical performance of EXPOSITE, Ensite, VICE, Fpocket, PocketPicker, LIGSITEcs, CAST, PASS and SURFNET (Table 2). However, in the unclassical training and test datasets which were never tested before, we relied on an unclassical distance of 12Å. The training and test datasets contain 20 times more proteins than the hand-picked PLD dataset, and if the classical distance of 4Å was used, then the performance of all techniques sank drastically. To maintain good performances for all techniques in the training and test datasets, the classically accepted metric for binding site prediction was raised to an unclassical 12Å.
Generally speaking, the success rate in the handpicked PLD dataset is higher than in the non-handpicked 845 test dataset. This discrepancy suggests that the PLD dataset was not randomly picked, and could artificially increase prediction success rates.
EXPOSITE’s feature, of highlighting active sites is very useful for ranking pockets. Indeed, the technique is capable of ranking enzyme pockets according to their degree of exposure in normal mode dynamics. This ranking enables EXPOSITE to choose the correct binding pockets from a list of pockets calculated by LIGSITE. The assumption that the active site pockets is usually in the largest pocket [1, 4, 64] is being used by several pocket detection programs and the top site is generally the largest one. However, this assumption is not always true and in several instances, the active site corresponds to the second, third, or fourth largest pocket.
The rationale behind the success rate of EXPOSITE is fairly simple. For proper enzyme activity, protection from the surrounding water is often necessary as shown by normal modes closure of the active site. Proteins in general and enzymes in particular often act as environment protectors. They envelop substrates to catalyze chemical reaction that would otherwise not take place in aqueous solution. They conceal prosthetic groups to coordinate binding thus increasing affinity which is negligible in water. They act as small shielding cases displaying alternating motions of opening and closing to allow ligand entrance and protection respectively. Throughout this motion, protein residues located at various distances from the active site are exposed to the solvent to a different degree. Residues in proximity to the active site are exposed more than those faraway. This idea lays down the foundation for EXPOSITE suggesting the pocket closest to the maximum exposure center is the active site.
The change in solvent accessibility between the X-ray structure and the largest deformation of either of the normal mode extremes could also have been used. However, the maximum effect of motion is observed between the two extremes which vibrate around the X-ray structure corresponding to a local minimum.
EXPOSITE takes into account several parameters such as accessibility change in normal modes, centroid distance from pockets, as well as pocket size. Normal modes by their own virtue take into account more parameters such as contact network and distances. Together, these parameters resemble those used in neural network techniques [33, 34] where they are analogous to accessibility, cleft depth, B-factors, etc… As much as these techniques seem different, the analogy between the parameters is astounding.
The success rate is not affected by the different types of normal modes techniques, STAND and ElNemo. The success rate remains unchanged even when STAND and ElNemo are used in different combinations with accessibility calculators (i.e. ElNemo with ENCAD accessibility calculator [65]. The success rate does not originate from the difference in the atomic representation used by ElNemo and STAND. In fact, when running STAND in full-atom representation the success rate remains unchanged. These data indicate that coarse graining which ignores the amino acid type and accessible surface does not influence the success rate of EXPOSITE. In fact, adding heavy atoms to the PDB files generated by STAND also does not decrease the success rate of EXPOSITE. We conclude that coarse-graining and accessibility calculation methods do not affect the success rate of EXPOSITE.
Care should be taken when using our technique on structures composed of several domains. Practical interpretation of normal modes of multi-domain structures tend to be problematic in the sense that bending and twisting of one domain relative to another tend to overshadow modes with biological meaning. One way to circumvent this problem is to run normal modes of single domains to predict its active site. We excluded multi chain enzymes which are biologically active in oligomeric states from our CSA dataset. Similarly, care should be taken when using EXPSOITE on structures with elongated termini or exceedingly flexible loops. Such structures often present odd normal modes around these areas which tend to overshadow modes with biological meaning. Some strongly recommended ways to circumvent the problem of exaggerated motion is simply to clip out (or edit out) the stretches and rerun normal mode computation or to set an upper value for the cutoff of |ΔSAS| of 75 Å2 when calculating modes with ElNemo (40 Å2 for STAND). The cutoff should minimize the effect of loose and flexible termini with exaggerated exposure change. A complete list of success and failures is provided in S6 and S7 Tables.
A distinction should be made between the concepts “binding site” and “active site”. Usually, an active site is found in a single copy in an enzyme, while binding sites may be present in multiple copies in proteins. Thus, prediction of active sites and ligand binding sites are very different, and whereas only one prediction is correct for enzymes, several predictions are correct for proteins. To complicate things further, some enzymes are composed of multiple chains, each equipped with a distinct active site, and so much care should be taken so as not to over interpret a prediction. As a rule of thumb structure based predictions (EXPOSITE, EnSite, etc) are more accurate in single chain polypeptide enzymes.
In an attempt to correlate between pocket size and active site, the following parameters of active site were calculated in the PLD dataset: 1). The number of Cα atoms of the active site was derived from the CSA database. 2). The number of heavy atoms in the substrate was calculated from the PLD database. 3). The number of residues of with high accessibility change was calculated from EXPOSITE. 4). The size of the predicted pocket in Å3 was from LIGSITE. These parameters all reflect on the size of the active site yet there is no obvious correlation among them. There was no correlation (R = 0.12) between pocket size and the number of active site residues. This is partially due to fractionation of active sites into adjacent pocket (POK) which decrease “real” active site size. This fractioning of active sites is a problem often encountered in pocket calculating programs. Adjoining sizes of vicinal pockets did not improve the correlation significantly.
Over the past years normal modes have enjoyed a revival. In this article, the biological relevance of normal modes is illustrated in a new technique. The presented technique exposes active sites of enzymes with high success rates. As pocket detection methodologies normal mode techniques improve so will our technique. In the future, EXPSOITE is expected to become publicly available as a basic tool (website and/or program) for predicting active sites of enzymes. The Perl code used in this study is freely available in the supplementary data. Note that DSSP, LIGSITE, ElNemo, and/or STAND must be obtained from third parties, and that the time bottleneck of the method is normal mode calculation.
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10.1371/journal.pntd.0004202 | Parasitological, Hematological and Biochemical Characteristics of a Model of Hyper-microfilariaemic Loiasis (Loa loa) in the Baboon (Papio anubis) | Loiasis, a filarial infection caused by Loa loa usually thought to cause relatively minor morbidity, can cause serious and often fatal reactions in patients carrying very high levels of circulating Loa loa microfilariae (mf) following administration of microfilaricidal drugs. An experimental model of this condition would greatly aid the definition of the optimal management of this important clinical presentation.
Fifteen baboons (Papio anubis) were infected with 600 infective larvae (L3) isolated from Chrysops vector flies. Animals were observed for any clinical changes; blood samples were collected every 1–2 months for 22 months, and analysed for parasitological, hematological and biochemical profiles using standard techniques. All animals became patent but remained clinically normal throughout the study. The parasitological pre-patent period was between 4–8 months, with a majority (60%) of animals becoming patent by 5 months post infection (MPI); all animals were patent by 8 MPI. Microfilarial loads increased steadily in all animals and reached a peak at 18 MPI. By 10 MPI >70% of animals had mf >8,000 mf/mL, and at 18 MPI >70% of animals had mf >30,000mf/mL with 50% of these animals with mf >50,000mf/mL. Absolute eosinophil, creatinine, Ca2+ and K+ levels were generally above normal values (NV). Positive associations were seen between microfilariaemia and eosinophilia, Hb, Ca2+, and gamma-GT values, whilst significant negative associations were seen between microfilariaemia and potassium, glucose and mononuclear leukocyte levels.
Infection of splenectomised baboons with L. loa can induce levels of circulating microfilariae, and corresponding haematological profiles, which parallel those seen in those humans in danger of the severe post-microfilariacide clinical responses. Utilization of this experimental model could contribute to the improved management of the loiasis related adverse responses in humans.
| Loiasis is a filarial infection of humans that, in addition to causing severe direct clinical effects, is of concern to the global community’s efforts to eliminate the important filarial diseases, onchocerciasis and lymphatic filariasis, through causing interruption to mass drug distribution activities. Hyper-microfilariaemia has been seen to be the characteristic parameter in patients suffering from post ivermectin encephalopathy, a condition which sometimes leads to death. Understanding and developing appropriate approaches to the treatment and prevention of these severe adverse reactions has been difficult due to the lack of suitable models. As primates can be infected with human L. loa, and can develop hyper-microfilariaemia, it is likely that they therefore can serve as suitable models for the investigation of this syndrome in humans. This current study shows that following splenectomy the circulating microfilarial loads are similar to those seen in humans, and that the clinical pathology profile following infection also appears to be similar. The consistent ability to induce microfilariae levels of above 30,000 mf/ml in more than 70% of the tested animals suggests that this is indeed a practical model for investigating the adverse events occurring in hyper-loiasis.
| Loa loa is a parasitic filarial nematode of humans, and a member the super family Filariodea which includes infections that are targeted for elimination, such as lymphatic filariasis (Wuchereria bancrofti, Brugia sp), and onchocerciasis (Onchocerca volvulus). L. loa is found in the tropics [1] restricted to the rainforest and forest fringes of West and Central Africa [2] and causing a relatively well tolerated medical condition known as loiasis. The geographic distribution of this infection is limited by the presence of the two biting tabaniid vectors, Chrysops silicea and C. dimidiata, which generally prefer rainforest-like environment; however, recently the disease has been described in the Guinean savannah [3]. It is estimated that some 14.4 million people live in high risk areas where the prevalence of loiasis (i.e. a history of “eye worm”) is greater than 40%, with 15.2 million in intermediate risk areas where the estimated eye worm prevalence is between 20 and 40% [4].
This disease has been recognized as one of public health importance, not so much because of its own clinical manifestations, but because of its negative impact on the control of onchocerciasis and lymphatic filariasis in areas of co-endemicity. There have been increasing reports in the past decade of serious adverse events (SAE) following the administration of ivermectin for these two major filariae, onchocerciasis and lymphatic filariasis, in L.loa endemic areas. These SAE are characterized by a severely disabling and potentially fatal encephalopathy. Evidence exists that these SAE appear to correlate with high loads of L.loa microfilariaemia (>30,000 mf/mL) [5–8].
Despite advances in defining the epidemiological aspects of these L.loa associated SAE, their pathogenesis and treatment still remains obscure. It is not known if the genesis of the encephalopathy is associated with the increased presence of L.loa in the brain tissue, although a vasculopathy associated with the presence of microfilariae has been proposed as a possible aetiology [9,10]. To be able to properly manage these cases, it is necessary to better understand the mechanism of pathogenesis of the post-ivermectin events in these heavily infected individuals. Very limited progress has been made in research on the pathogenesis of encephalopathy, due in most part to the lack of material from human cases, as well as a lack of any useful animal models to investigate the etiology and test new potential clinical management procedures.
Although the natural hosts of L. loa are humans, the entire life cycle of L. loa can be maintained experimentally. L. loa readily infects mandrills (Mandrillus leucophaeus) [11], baboons (Papio anubis) [12], and patas monkeys (Erythrocebus patas) [12]. Non-human primates (NHPs) are therefore considered the best models for the much needed investigation in human loiasis, especially as suitable in vitro models for loiasis have not yet been developed, and indeed such artificial models are not easily extrapolated to humans [13–15]. Although the mandrill is an excellent experimental host, there are ethical concerns with using this now protected animal for research, and it is no longer used in biomedicine. The use of Patas monkeys also is limited as the parasite does not behave in the same way like it does in the more human-like drill [12].
The baboon (P. anubis) however, has potential as an experimental model to study the mechanisms behind the SAEs that develop in L. loa infected people as in this animal the parasite here behaves essentially in the same way as it does in the drill [12], and therefore is comparable with the situation in humans. Secondly, the use of baboons in biomedical research is accepted by the International Union for Conservation of Nature-IUCN [16]. In the simian host, the spleen usually becomes enlarged and granulomatous in filarial infections as it is the site of destruction of a large proportion of circulating Loa microfilariae [17,18]. If the spleen is removed very high levels of circulating microfilariae develop in the blood (>50,000 mf/mL), levels that are similar to those found in patients developing the post-treatment Loa-associated encephalopathy. Thus the baboon, with its remarkable similarities to humans in many anatomical and physiological parameters [19, 20, 21], appears to likely be a suitable model for studying this important clinical phenomenon.
Ethical and administrative clearances for the use of baboons in this study were obtained from the Ministry of Scientific Research and Innovation of Cameroon (Research permit #028/MINRESI/B00/C00/C10/C12). The animal procedures were conducted in accordance with the guidelines with animal care and use committee at the National Institutes of Health (USA) and University of Georgia, Athens, USA. Ethical clearance for the involvement of human subjects in the production of infective larvae was obtained from the Institutional Review Board of the Medical Research Station of Kumba, Cameroon. All volunteers were handled according to the Helsinki declaration on the use of humans in biomedical research. The use of non-human primates for research was approved by the Committee on the Ethical Use of Animals in Research (CEUAR) within the Research Foundation for Tropical Diseases and Environment (REFOTDE), Cameroon. All relevant aspects of the International Primatological Society (IPS) 2007 guidelines on the acquisition, care and breeding of non-human primates for research were followed.
Baboons of both sexes were trapped in different parts of Cameroon according to IPS standard accepted procedures These animals were transported to the animal facilities in the Tropical Medicine Research Station, Kumba, South West Region and quarantined for a period of two months during which they were pre-screened for a panel of natural infections (loiasis, other blood-borne parasites, and intestinal worms). Each animal was observed daily by the veterinary staff to ensure that they were healthy, and any animal found to be ill was immediately given appropriate treatment, both in the quarantine period and during the main study period.
The animals were housed individually in large custom built cages that allowed the animals to move about freely and be allowed to display their normal repertoire of locomotor behavior (walking, climbing, running, jumping and swinging) by providing them with vertical climbing surfaces and perches. Horizontal surfaces were also provided to allow them to rest comfortably and perform their social interactions such as sprawling during grooming. The housing facility was well aerated and equipped with a system that provided water ad libitum for each animal. Each baboon’s behavior was regularly monitored to identify any indications of poor welfare. Baboons received a diet of food that mimicked their natural diet (leaves, grass, roots, bark, flowers, fruit, lichens, tubers, seeds, mushrooms, corms, and rhizomes). They were also fed a supplement of a nutritionally complete commercial-available diet.
The health and well-being of the baboons were regularly assessed during the study by an animal welfare officer who advised on matters such as disease prophylaxis, zoonoses, anesthesia, and methods of humane euthanasia and provision of health certificates. All measures were taken to minimize suffering during capture, captivity and experimentation. Health screening of workers in contact with the baboons was performed regularly to prevent animal losses from diseases transmitted from humans to baboons as well as zoonotic transmission of disease from baboons to workers. A total of 15 animals (6 males, 9 females) were used in this study with each animal being given a project animal number (BAB-1 to BAB -15).
Splenectomy was carried out by a licensed veterinarian following previously published procedures [17,18]. The animals were anaesthetized using an intra-peritoneal injection of 4 mg of betamethasone (Septon, Europe) and 5 mg ketamine (Imalgene, Merial, France); 2mg/kg morphine sulphate (Hamelin Pharmaceuticals Ltd, UK) as also added to the administration as an analgesic. Spleens were removed in approximately 25 minutes under aseptic conditions, the skin wound sutured and disinfected with an antibiotic spray (2.0g Chlortetracycline, 0.5g Gentian violet, 100 mL excipient), and the incision site bandaged. Dressing were changed daily and the animals given a daily 1 mL injection containing 1.2 million units of penicillin and 5 mg of streptomycin with 1 mL of anticoagulation factor (Vitamin K) for a week post-surgery. The surgical wound was dressed daily using an antimicrobial and insect repellent (Veto Spray—Vétoquinol) to protect against flies. The sutures were removed 7 to 8 days after splenectomy. The general welfare of the animals was monitored is on a daily basis for a period of approximately 2 months before infecting the animals with L. loa.
The normal ranges for various blood parameters in baboons used for comparison were those provided by the Association of Primate Veterinarians Primate Formulary (1999) as listed for baboons housed individually in large custom cages. The data were entered into Epi Info version 3.5.3 (C.D.C. Atlanta, GA, USA) and analysed using the Software Package SPSS version 20. Descriptive statistical analyses were performed to compute the mean, median and standard deviations of Loa microfilarial counts, different haematological and biochemical parameters in the general study group, and in both males and females. Graph PadPrism software was used to draw the scatter plots comparing microfilariaemia and the different haematological and biochemical parameters to test for any association. The Kruskal and Wallis test was used to test for significant differences in levels of microfilariaemia, haematological and biochemical parameters before inoculation and at different time points of observation in the general study population. The Mann-Whitney test was used to test for significant differences in the different haematological and biochemical parameters between males and females. The Jonchkeere-Terpstra (J-T) test was used to test for any trend of linearity in the different parameters at different months. All tests were performed to a 5% significance level.
All fifteen animals appeared well fed and remained healthy throughout the study. The surgical incision sites post-splenectomy all healed without any evidence of infection, and no animal showed fever nor any intestinal disturbances during the study. The pre-study screening did not detect the presence of malaria or any intestinal parasites, in the test animals.
The pre-patent period of this infection in baboons ranged from 4–8 months, with a median of 5 months. 1 of 15 (6.7%), 9 of 15 (60%) baboons, 4 of 15 (26.7%), 1 of 15 (6.7%) had pre-patent periods respectively of 4, 5, 6, and 8 MPI (Figs 1 and 2). The pre-patent period for males ranged from 4–6 months (median 5 months) with 3 out of 6 males (50%) becoming patent at 5 months post infection (Fig 1). The pre-patent period of females ranged from 5–8 months (median 5 months) with 6 out of 9 females (66.67%) becoming patent at month 5 post infection (Fig 1). There was no significant difference between the median pre-patent period of males and that of females (p = 0.504). The month at which each baboon started having microfilariae in blood (pre-patency period) and the month at which each baboon developed its highest microfilariaemia is shown in Fig 1.
Animals were followed up for 22 months by which time all animals had become patent (Fig 2). By month 4 post inoculation (MPI) about 7% of infected baboons had microfilariae present in their circulation, and by month 5 MPI >70% of infected animals had developed microfilariaemia. By month 8 MPI all animals were parasitologically positive (Fig 2B). Generally, the mf increased steadily in all animals from the onset of patency to reach a median of 48,790 mf/mL by month 18. The mf loads at different time points during the course of infection was highly variable (Fig 2A). Male baboons generally developed higher microfilariaemia than females (Fig 1), although this difference was not statistically significant (p = 0.06). By 6 MPI about 7% of animals had developed mf loads >8,000 mf/mL; at 8 MPI 50% of them had developed mf loads >8,000 mf/mL, and at 10 MPI >70% of animals had developed mf loads >8,000 mf/mL (Figs 1 and 2A).
With regards especially high blood microfilarial loads, about 20% of animals had developed mf loads >30,000 mf/mL by 10 MPI. At 14 MPI, 50% of infected animals had developed mf loads that were >30,000 mf/mL and at 18 MPI >70% of infected animals had developed mf loads >30,000 mf/mL (Fig 2B). By 10 MPI about 7% of infected animals had developed extremely high blood microfilarial loads of >50,000 mf/mL and by 18 MPI almost 50% of them had developed these very high microfilarial loads (Fig 2B).
RBC counts ranged from 2.65–4.3 x106 cells/mm3 (median 3.48 x106 cells/mm3; NV = 3.76–5.61 x106 cells/mm3). Again males differed significantly from females in RBC values (p<0.001). Most RBC values recorded in all animals were below the NV at the different time points (Fig 3A). Hemoglobin (Hb) values ranged from 10–16.2g/dl (median: 13.90g/dl) essentially close to normal values (NV) of 9.5–14.5g/dl (Fig 3B), with a slight upward trend during the infection (see S1 File). There was significant difference between males and females: Hb values in males ranged from 10–16.2g/dl (median: 14.4g/dl) whilst for females the values were 11–15.2g/dl (median 13.6g/dl); these were significantly different (p<0.01).
The total white cell counts were generally all within the NV (Fig 4A). Absolute neutrophil counts also stayed within NV ranging from 1,000–19,500 cells/mm3 (Median: 2,250 cells/mm3) (Fig 4B). Absolute mononuclear (lymphocyte + monocyte) cell counts ranged from 2,400–34,760 cells/mm3 and were within the NV (see S1 File) although was a marked variation between different time points (Fig 4C). with a median of 3,796 cells/mm3 (NV: 810–19,728 cells/mm3). The values for males were 1,800–39,000 cells/mm3 (median: 3,788 cells/mm3) while in females these counts ranged from 1,848–6,030 cells/mm3 (median: 3,810 cells/mm3). These mononuclear cell counts did not vary significantly between males and females (p = 0.904). Absolute counts at different time points however did vary significantly (p<0.001) and showed a significant linear trend (p<0.001). Mononuclear counts recorded were within the NV (Fig 4C).
Absolute eosinophil counts ranged from 0–6,500 cells/mm3 (Median: 978 cells/mm3; NV: 0–822 cells/mm3). Absolute eosinophil values in males ranged from 0–6,500 cells/mm3 (Median: 1,013 cells/mm3) while in females absolute eosinophil levels values ranged from 0–2,640 cells/mm3 with a median of 972 cells/mm3; these were not significantly different (p = 0.473). Eosinophil counts at different time points, however did vary significantly (p<0.001) and showed a general increase over the 18 months studied. All animals recorded absolute eosinophil values out of the normal range at different time points (Fig 5A) The absolute eosinophil results for each animal are given in Fig 5B. Basophils were not identified in these study samples.
There was no significant difference in SGPT values between males and females (p = 0.342), nor did the SGPT values at different time points vary significantly (p = 0.086) or show a significant linear trend (p = 0.110) with the majority of SGPT values being within the NV (Fig 6A). The SGOT values at different time points varied significantly (p<0.05) although there was no significant linear trend (p = 0.356). The majority of SGOT values were within the NV, even though all baboons except BAB 04 showed SGOT values both below or above the NV at different time points (Fig 6B). The γ-GT values at different time points varied significantly (p<0.001), and there was a significant linear relationship between microfilariaemia and the duration of infection (p<0.05). However, most of the γ-GT values were within the NV (Fig 6C).
The creatinine values at different time points varied significantly (p<0.001), although the values did not show any significant linear relationship with the duration of infection (p = 0.068). Most of the creatinine values were above the NV at the different time points (Fig 7A). Glucose values varied significantly (p<0.001) at different time points, and there was significant linear relationship between blood glucose level and the duration of infection (p<0.001). Majority of the glucose values were within the normal range, although all baboons showed glucose values out of the normal range at different time points (Fig 7B).
Calcium values varied significantly at different time points (p<0.001) and showed a significant linear relationship with the duration of infection (p<0.001). Majority of the calcium values in all animals at different time points were below the NV (Fig 8A). The potassium values at different time points varied significantly (p<0.001), and showed a significant negative linear relationship with the duration of infection (p<0.001). The majority of the potassium values in all animals were out of the NV (Fig 8B).
Hb values showed a slow increase from 12.4 g/dL before inoculation to 14.1 g/dL at 3 MPI after which the values dropped to 13.1 g/dL at 6 MPI and increased steadily over time to 14.1 g/dL at 16 MPI where a slight dropped to 13.3 g/dL was noticed as mf culminated at 18 MPI. Overall, there was a positive significant association (r = 0.180, p>0.05) between mf and Hb values (Fig 9B). The eosinophil count increased sharply from 0 cells/mm3 before inoculation to 1,500 cells/mm3 at 18 MPI when the mf reached its highest level. Overall, there was a strong positive significant association (r = 0.730, p<0.001) between eosinophil and mf (Fig 10C). The mononuclear count decreased steadily from 4,800 cells/mm3 at 1 MPI to 3,800 cells/mm3 at 16 MPI from where its values dropped slightly to 3,200 cells/mm3 at 18 MPI when the mf peaked. Overall, there was a negative significant association (r = -0.368, p<0.001) between mf and mononuclear counts (Fig 10D). There was no significant association between mf and RBC, WBC and neutrophil (Figs 9A, 10A and 10B).
The enzymes SGPT and SGOT did not show any significant association with mf (Fig 11A and 11B). Overall, there was a positive significant association (r = 0.281, p<0.001) between γ-GT and mf (Fig 11C). γ-GT values increased sharply from 0 IU/L before inoculation to 50 IU/L at 8 MPI, then decreased slightly over time to 29 at 14 MPI after which time its values decreased gradually over time to 42 IU/L at 18 MPI when mf peaked and then dropped again.
There was a negative, non-significant, association between mf and creatinine (Fig 12A). Glucose values decreased gradually from 1.5 g/L at 1 MPI to 0.6 g/L at 6 MPI from where the values increased slightly to 1.1 g/L at 10 MPI after which its values decreased to 0.6 g/L at 18 MPI. Overall, there was a negative significant association (r = -0.171, p<0.05) between glucose and mf, (Fig 12B).
Calcium values increased steadily over time from 9 mg/L before inoculation to 25 mg/L at 10 MPI at which point it increased sharply to 74 mg/L at 12 MPI after which its values decreased steeply over time to 15 mg/L at 20 MPI. Overall, there was a positive significant association (r = 0.410, p<0.001) between calcium and mf (Fig 13A). Potassium values plummeted from 170 mmol/L before inoculation to 5 mmol/L at 18 MPI when the mf peaked. Overall, there was a negative significant association (r = -0.423, p<0.001), between mf and potassium (Fig 13B).
This study extends the findings made a number of years ago showing that primates can be infected with human derived L. loa. In this present study the time course and kinetics of Loa microfilariaemia in the baboon (Papio anubis) were characterized in detail showing that all infected spelectomized animals become patent, and with the majority doing so by 5 months after inoculation. This finding is comparable to the earlier observations of Duke and Wijers [11], Duke [17], Orihel and Moore [12, 24] and Dennis et al [25], with a major difference being that in this present study the animals were splenectomised before inoculation. However, the pre-patency periods for the infection in the animals splenectomised before inoculation with Loa and those splenectomised after infection are the same suggesting that the spleen plays only a minor role in determining the pre-patency of these animals. The extended pre-patent period (8 months) observed in one baboon cannot be readily explained or attributed to anything other than a peculiarity in the individual host-parasite relationship. All the animals in this present study were able to attain very high microfilarial loads.
Although the spleen appears not to influence to any great degree the development of patency this organ may however have a more significant role in maintaining patency and possibly the microfilarial load eventually reached, i.e. be involved in the rate of removal of microfilariae from the circulation. It is also possible that after splenectomy there may be regeneration of functional splenic tissues; this possibility, and the previously reported data from mandrills, adds to the contention that the spleen is likely to be an important player in controlling the load of circulating microfilariae.
These findings indicate that the baboon is a very good experimental model for studying conditions related to high circulating microfilarial loads such as the Loa encephalopathy seen post ivermectin treatment. As the time course of Loa microfilariaemia steadily increases to reach a peak at month 18 (Fig 2) in a relatively regular manner it is possible to develop reproducible experimental protocols to test various questions related to specific microfilarial loads. It is thus possible, therefore, to test different possible treatment regimes in scenarios that parallel various human microfilarial loads, and can do this beginning at month 5, when baboons start showing microfilariaemia, up until around month 18, when baboons reached their highest microfilariaemia. It was also noted that although microfilariaemia rose steadily in both sexes of these baboons, male baboons often developed a slightly higher microfilarial loads than females. Generally, differences between gender regarding disease occurrence have been related to physiological causes particularly hormonal and genetic ones [26]. Nevertheless, as there were no major differences in the duration of pre-patency period, nor in the maximum attained microfilariaemia between male and female baboons, it is likely that both sexes can be used for this type of experiment.
In this present study we have used a relatively high number of infective larvae (600 L3s) to induce infection compared to that used previously in mandrills. We have found this provides the most consistent infection rate in baboons. The reason for the need for a higher number of L3s compared to those needed to consistently infect mandrills is not clear at this time and it possible that this parasite is more adapted to surviving in mandrills, where lower numbers of larvae have been used, compared to baboons; other technical reasons might be at play here as well.
In humans it has believed that a microfilariaemia of >8,000mf/mL is a marker characteristic of those who develop serious non-neurological conditions for treatment, although >30,000mf/mL has been proposed as the main danger point in humans [5–8] in terms of a risk, with those >50,000 mf/mL regarded as very high risk individuals. This baboon model, with its ability to produce microfilarial loads consistently, is an important tool for understanding in more detail relationships between microfilarial load and pathology. Most animals (13 out of 15–86.67%) of the infected animals developed microfilariaemia of >30,000mf/mL. These findings differ from Dennis et al. [25] who observed only low microfilariaemia (250–1,000mf/mL) in splenectomised rhesus monkeys; this difference may be due to the fact that baboons are natural hosts for L. loa and rhesus are not.
All infected animals develop eosinophilia well above the normal range, a finding expected in a filarial infection as with many helminths [27]. Hyper-eosinophilia and loiasis have been noted in previous studies in humans [28] but not previously described in experimental models of loiasis [29]. The fact that all infected baboons developed a significant increase in eosinophilia prior to the onset of patency and that the levels continued to increase after patency suggests that adult worm antigens and associated cytokines are most likely to be involved in this induction of the eosinophilia [30]. The strong significant positive association between eosinophil levels and microfilarial loads is to be expected in helminth infections [31] as eosinophils are major effector cells in the immune responses to the presence of this parasite. A major question that now remains is whether this significant blood eosinophilia is intimately involved in the pathogenesis of the SAE as has been suggested [9].
It is not clear as to the biological significance of the variations seen here in total red blood cell counts seen at different time points, nor with the positive significant association between microfilariaemia and haemoglobin levels. Although variations occurred in haemoglobin levels at different time points the majority of measured values were within the normal range. As no animals were found to be clinically anaemic, and as the animals were fed on a rich protein-diets, it is likely that the variations in these parameters were not a major characteristic of this infection; this interpretation is supported by the findings of Johnson et al. [32] with vervet monkeys (Chlorocebus aethiops) in captivity fed on a high protein diet who also did not develop anaemia. The observation that males had a significantly higher concentration of haemoglobin and red blood cells than females maybe due to the greater muscle mass of males [33], or perhaps the menstrual blood loss in females [34]. The increased neutrophil levels are hard to explain without further tissue level investigation of these animals; there were no obvious bacterial or other infections in these animals that could have explained such a neutrophilia. It is noted that neutrophils, eosinophils, as well as monocytes, have all been shown experimentally to be capable of damaging and destroying microfilarial of various species through complement-dependent, IgG mediated, mechanisms [35]; such phenomenon may be involved in the altered neutrophil levels.
The elevations in blood creatinine in some animals before inoculation and their increase during the course of infection could be related to kidney dysfunction [36]. The retention of creatinine in the body likely indicates that the kidneys were failing to efficiently excrete these catabolic products. Studies on filariasis in dogs have demonstrated an increase in serum creatinine levels in dogs infected with Dipetalonema reconditum [37]. However, other physiological factors or substances unrelated to the infection may have contributed to this creatinine response since no significant association was seen between the appearance of microfilariae and the rise in creatinine levels. SGPT, SGOT, γ-GT and glucose levels were relatively stable indicating a proper functioning of the liver in these animals. The values were recorded here are in general agreement with Núńez et al. [38] who reported minimal changes in liver function for healthy (uninfected) young and adult Cebus appella monkeys of both sexes. It should be noted, however, that although the baboons remained healthy throughout the study, the animals in our study often had histories of varied and different diets which could explain variations in liver enzyme activities [39, 40]. It is not clear as to what could have caused the negative significant association between glucose and microfilariaemia; further studies, including the effect of diet, are need to explain this finding. The positive significant association seen between γ-GT and microfilariaemia corroborates the findings of Molina et al. [41], where visual hepatic damage and serum levels of γ-GT were significantly positively related in cattle infected with a helminthic parasite. The positive significant association noticed between microfilariaemia and calcium and the negative significant association between potassium and microfilariaemia could probably be due to kidney dysfunction in these animals [42, 43].
Many of the biochemical parameters in this present study were within the normal range; however, it is noted that significant changes in hemoglobin related parameters have been observed with other infections in the baboon, some that can be directly related to the specific infection. For instance, Mustafa et al. [44] demonstrated that haemoglobin levels were found to be generally low in baboons infected with Plasmodium knowlesi parasites. Al-Tayib [45] demonstrated that infection of baboons with Balantidium coli showed severe anaemia and in increased values of SGOT and SGPT well above their normal ranges. The effect of diet with animals kept in captivity must also be considered; many biochemical parameters are known to vary greatly with the diet and a major shortcoming of this present study is that the various biochemical samples taken from these animals were not standardized on a fasting state. However, the three most significant alterations seen here, i.e. the increased eosinophil, creatinine and gamma-GT level, are three parameters that can be argued as most likely be related to the presence of the filarial infection. An intriguing question that emerges now is whether these specific alterations bear any role in the development of the post-ivermectin encephalopathic SAE.
This study is the first showing parasitological, haematological and biochemical characterization of hyper-microfilaraemic loiasis in the splenectomized baboon (P. anubis). Parasite pre-patency was between 4–8 months, with the majority (60%) of animals becoming patent 5 months post inoculation; all animals developed patency; Microfilariaemia rose steadily in all animals and culminated at a peak level by month 18 post infection with males showed higher microfilariaemia than females. By month 10 post inoculation >70% of infected animals developed microfilariaemia >8,000mf/mL; by month 18 post inoculation >70% of infected animals had developed microfilariaemia >30,000mf/mL, and 50% of them developed >50,000mf/mL a level where in humans that predisposed for severe adverse reactions post treatment. Significant positive associations were seen between microfilariaemia and eosinophil, haemoglobin, calcium and gamma-GT, whilst there was a negative significant correlation between microfilariaemia and mononuclear leucocytes, glucose and potassium. This model has the potential of helping to understand the mechanism(s) involved in the development of Loa-encephalopathy post-ivermectin treatment in heavily Loa microfilariaemic humans, and could help in designing improved management of such cases.
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10.1371/journal.pgen.1006118 | OVATE Family Protein 8 Positively Mediates Brassinosteroid Signaling through Interacting with the GSK3-like Kinase in Rice | OVATE gene was first identified as a key regulator of fruit shape in tomato. OVATE family proteins (OFPs) are characterized as plant-specific transcription factors and conserved in Arabidopsis, tomato, and rice. Roles of OFPs involved in plant development and growth are largely unknown. Brassinosteroids (BRs) are a class of steroid hormones involved in diverse biological functions. OsGKS2 plays a critical role in BR signaling by phosphorylating downstream components such as OsBZR1 and DLT. Here we report in rice that OsOFP8 plays a positive role in BR signaling pathway. BL treatment induced the expression of OsOFP8 and led to enhanced accumulation of OsOFP8 protein. The gain-of-function mutant Osofp8 and OsOFP8 overexpression lines showed enhanced lamina joint inclination, whereas OsOFP8 RNAi transgenic lines showed more upright leaf phenotype, which suggest that OsOFP8 is involved in BR responses. Further analyses indicated that OsGSK2 interacts with and phosphorylates OsOFP8. BRZ treatment resulted in the cytoplasmic distribution of OsOFP8, and bikinin treatment reduced the cytoplasmic accumulation of OsOFP8. Phosphorylation of OsOFP8 by OsGSK2 is needed for its nuclear export. The phospphorylated OsOFP8 shuttles to the cytoplasm and is targeted for proteasomal degradation. These results indicate that OsOFP8 is a substrate of OsGSK2 and the function of OsOFP8 in plant growth and development is at least partly through the BR signaling pathway.
| OVATE family proteins (OFPs) are characterized as plant-specific transcription factors and mainly function in affecting fruit shape, but the molecular mechanisms by which they function are largely unknown. Rice genome contains 31 OFPs, the roles of these OsOFPs involved in plant development and growth are not understood. Brassinosteroids (BRs) are a class of steroid hormones involved in diverse biological functions. Here we report in rice that OsOFP8 plays a positive role in BR signaling pathway by interacting with OsGKS2, a negative regulator in BR signaling pathway. Our results shed light on studying the functions of OFPs and provide a chance to explore the new components of BR signaling pathway.
| OVATE gene was first cloned in tomato and demonstrated to encode a hydrophilic protein with putative bipartite nuclear localization signal, and a C-terminal domain of approximate 70 amino acids which is designated as the OVATE domain and conserved in tomato, Arabidopsis, and rice [1–3]. As a plant-specific transcription factor family, OVATE family proteins (OFPs) control multiple aspects of plant growth and development [2, 4–6]. Sequence analysis showed that there are 18 OVATE genes in the Arabidopsis genome [2, 5–7]. AtOFP1 was shown to function as an active transcriptional repressor to suppress cell elongation [2]. Arabidopsis plants overexpressing AtOFP1 exhibited abnormal morphological phenotypes because AtOFP1 suppresses the expression of AtGA20ox1, the key gibberellin (GA) biosynthesis enzyme gene [2]. AtOFP4 was reported to interact with KNAT7 (Knotted1-Like Homeodomain Protein 7) in planta, this interaction enhances KNAT7’s transcriptional repression activity and regulates the secondary cell wall formation [5]. AtOFP5 is required for normal embryo sac development in Arabidopsis by suppressing the activity of BELL-KNOX TALE complexes [7].
In rice, there are 31 putative OFPs identified in the genome [3]. Although increasing evidence in Arabidopsis demonstrates that AtOFPs participate in multiple aspects of plant growth and development by regulating the transcriptional levels of target genes, little is known about the function and action mode of OsOFPs in rice.
Brassinosteroids (BRs) are a class of plant-specific steroidal hormones that are structurally related to animal and inset steroids. As a group of growth-promoting steroid hormones, BRs play pivotal roles in promoting cell expansion and division, regulating senescence, male fertility, fruit ripening, and modulating plant responses to various environmental signals [8]. Extensive studies in Arabidopsis have identified a nearly complete BR signaling pathway starting with BRI1 (Brassinosteroid insensitive 1) as the cell membrane receptor which perceives and binds to BR [9], then initiating a phosphorylation-mediated cascade involving BSK1 (BR-signaling kinase 1), BSU1 (BRI1 suppressor 1), BIN2 (BR-insensitive 2), and PP2A (Protein phosphatase 2A), and finally transducing the extracellular signal to the transcription factor BZR1 (Brassinazole resistant 1) [10–13]. In this signaling pathway, BIN2 acts as a negative regulator that interacts with and phosphorylates BZR1 to inhibit its function, thereby blocking BR signaling [14, 15]. BIN2 can also phosphorylate Auxin Response Factor 2 (ARF2), resulting in the inhibition of the DNA binding activity of ARF2, thus promoting downstream auxin responses [16]. In addition, BR regulates stomatal development through BIN2-mediated phosphorylation of YDA, a mitogen-activated protein kinase kinasekinase (MAPKKK) [17, 18]. These studies indicated that BIN2 acts as a multi-tasker in diverse cellular signal transduction pathways [19].
In rice, OsGSK2 is the counterpart of Arabidopsis BIN2, and acts as a negative regulator to mediate BR signaling [20]. The phosphorylated form of OsBZR1 was increased in OsGSK2 overexpression plants, and decreased in OsGSK2 RNAi plants, suggesting that OsGSK2 mediates BR signaling through OsBZR1 [20]. In addition to OsBZR1, two other proteins in rice have been found as substrates for OsGSK2. DLT (Dwarf and Low-Tillering), encoding a GRAS-family protein, is a direct target of OsGSK2 and functions similarly to OsBZR1 [20–22]. In contrast to DLT, OsLIC (LEAF and TILLER ANGLE INCREASED CONTROLLER), another substrate of OsGSK2, acts as an antagonistic transcription factor of OsBZR1 and plays a negative role in BR signaling [23]. These studies suggested the vital role of OsGKS2 in BR signaling.
We report here the characteristics of OsOFP8, a member of OVATE family protein genes in rice. The gain-of-function mutant Osofp8 and OsOFP8 overexpression transgenic lines showed enhanced lamina joint bending, whereas OsOFP8 RNAi lines showed upright leaves and tight architecture. Further analysis revealed that OsGSK2 interacts with and phosphorylates OsOFP8, and phosphorylated OsOFP8 shuttles to the cytoplasm and is targeted for the proteasomal degradation.
In the experiment of generation of T-DNA mutants, we identified a T-DNA insertion mutant showing lamina joint bending phenotype at the maturation stage, especially for the flag leaves (Fig 1A). To identify the gene in which T-DNA was inserted, we performed TAIL-PCR analysis [24], DNA sequence comparison showed that the T-DNA was inserted into the 3’ region of LOC_Os01g64430 at the site of 27 bp downstream of the stop codon (Fig 1B), and there is no other annotated genes in the 5.5 kb region downstream of LOC_Os01g64430 (http://rice.plantbiology.msu.edu). LOC_Os01g64430 encodes an OVATE family protein (hereafter designated as OsOFP8). To investigate the effect of T-DNA insertion on the expression of OsOFP8, we carried out quantitative real-time RT-PCR (qRT-PCR), which showed that T-DNA insertion causes an increase in the expression of OsOFP8 (Fig 1C), thus T-DNA insertion generates a gain-of-function mutant Osofp8.
To further investigate the function of OsOFP8 gene, we generated both OsOFP8 overexpression and RNA-interference (RNAi) transgenic lines. OsOFP8 overexpression lines OE7 and OE10 phenocopied the Osofp8 mutant, showing increased lamina joint bending phenotype, by contrast, RNAi transgenic lines RNAi2 and RNAi4 showed upright leaves and tight architecture (Fig 1D). Furthermore, we examined the leaf inclination degrees of the three uppermost leaves. Compared to the wild-type (WT) plants, OsOFP8 overexpression plants showed largely increased leaf inclination for all three uppermost leaves, and the flag leaf showed the largest inclination angle, by contrast, RNAi transgenic plants showed reduced leaf angles (Fig 1E). Expression analysis by qRT-PCR showed that OsOFP8 expression was increased in overexpression lines and reduced in RNAi lines (Fig 1F). Tissue-specific expression of OsOFP8 was examined by qRT-PCR analysis, showing that OsOFP8 was expressed in various tissues (S1A Fig). Native promoter of OsOFP8 was fused to GUS gene to gain expression profile of OsOFP8, GUS activity was detected in different organs including roots, stem, leaf, lamina joint, inflorescence, and seeds (S1B–S1J Fig).
Gain-of-function Osofp8 mutant and OsOFP8 overexpression lines showed obvious leaf lamina joint bending phenotype, which is a classic phenotype of BR response. We hypothesized that OsOFP8 may be involved in BR signaling pathway. To this end, we tested the sensitivity of wild-type plants, OsOFP8 overexpression and RNAi lines to 24-epibrassinolide (BL) in lamina joint bending experiments. BL treatment caused a dose-dependent lamina joint inclination in both WT and OE10 with the latter plants were more sensitive to BL treatment, whereas RNAi4 plants were insensitive to BL treatment (Fig 2A and 2B). To further confirm this phenomenon, lamina joint assay was performed in the dark-grown seedlings by the excised leaf segment method [22]. We observed more severe inclination of leaf angle in OE10 than in WT plants, whereas RNAi4 plants did not show too much change in leaf angle when treated with BL (Fig 2C).
To investigate the effect of OsOFP8 on the expression of BR-related gene expression, we analyzed the expression levels of genes involved in BR biosynthesis and signaling. Rice D2/CYP90D2 (OsD2) gene is involved in the last step of brassinosteroid biosynthesis [25], and OsDWARF4/CYP90B2 functions in the rate-limiting step of brassinosteroid biosynthesis [26]. Overexpression of OsOFP8 suppressed the expression level of OsD2 but had little effect on the expression of OsDWARF4, whereas knockdown of OsOFP8 expression by RNAi led to significantly increased expression of OsD2 and OsDWARF4 (Fig 2D). OsOFP8 had little effect on the expression of OsGSK2, a negative regulator gene in BR signaling, by contrast, the expression levels of OsBZR1, a positive controller in BR signaling, were increased in OsOFP8 overexpression lines and significantly decreased in OsOFP8 RNAi lines (Fig 2D). We also measured the expression of OsOFP8 in BR signaling. BL treatment induced the mRNA transcript of OsOFP8 (Fig 2E). To further investigate the effect of BR signaling on OsOFP8 expression, we first treated the OsOFP8-YFP transfected protoplast cells with BRZ for 12 hr, and then BL was applied after the removal of BRZ. The protein level of OsOFP8 was induced by BL treatment (Fig 2F).
The different responses of OsOFP8 overexpression and RNAi plants to BL treatment prompted us to further explore the possible functions of OsOFP8 in BR signaling pathway. We performed yeast two-hybrid (Y2H) analysis to test the interactions between OsOFP8 and the components of BR signaling. OsGSK2, OsBZR1, and DLT are three important components in BR signaling pathway, we investigated the possible interactions between OsOFP8 and these three components. Y2H analysis showed that OsOFP8 interacted with OsGSK2, but not with OsBZR1 and DLT (Fig 3A). The interaction between OsOFP8 and OsGSK2 required the full length of OsOFP8 because neither the N-terminal nor the OVATE domain-containing C-terminal of OsOFP8 interacts with OsGSK2 (S2A and S2B Fig). The interaction between OsOFP8 and OsGSK2 was also confirmed by the coimmunoprecipitation (Co-IP) assay. The HA-tagged OsGSK2 protein (3HA-OsGSK2) and YFP-tagged OsOFP8 protein (OsOFP8-YFP) were co-transfected into Arabidopsis protoplast cells, the fusion protein 3HA-OsGSK2 can be immunoprecipitated by OsOFP8-YFP fusion but not by YFP protein (Fig 3B). Furthermore, BiFC assay was used to confirm the interaction between OsOFP8 and OsGSK2 (Fig 3C).
OsGSK2 protein phosphorylates proteins such as OsBZR1 and DLT with which it interacts [20]. In this scenario, we were interested to know whether OsGSK2 phosphorylates OsOFP8. To this end, we applied the biotin-pendant Zn2+-phos-tag and horseradish peroxidase-conjugated streptavidin method [27] to investigate the phosphorylation status of OsOFP8 when OsOFP8-YFP was expressed alone or co-expressed with 3HA-OsGSK2 in Arabidopsis protoplast cells. When OsOFP8-YFP was expressed alone in the protoplast cells, we only detected a faint band showing phosphorylated OsOFP8 (Fig 3D, asterisk), which is probably caused by the endogenous BIN2 of the protoplast cells. When OsOFP8-YFP and 3HA-OsGSK2 were co-expressed in protoplast cells, a stronger band was detected, indicating the increased phosphorylation status of OsOFP8 (Fig 3D, black arrow). The lower band showed the phosphorylated OsGSK2 (Fig 3D), which can be used as an internal reference for the system. This assay showed that OsGSK2 is able to phosphorylate OsOFP8. GSK3 kinases recognize a conserved sequence for phosphorylation (S/TXXXS/T, where S/T is serine or threonine and X is any amino acid), for example, BZR1 protein has 25 serine/threonine residues potentially phosphorylated by BIN2 [28]. Examination of OsOFP8 protein sequence revealed that there are 25 GSK3 kinase phosphorylation sites at the N-terminal region of OsOFP8 (S2C Fig), further supporting the notion that OsGSK2 phosphorylates OsOFP8.
To investigate the subcellular localization of OsOFP8, we made various OsOFP8 fusions in which YFP protein was fused to either the N-terminus or the C-terminus of OsOFP8, the fused OsOFP8 protein was transiently expressed in Arabidopsis protoplast cells to monitor the localization of OsOFP8. This assay showed that OsOFP8 localizes to the nucleus (Figs 4A, S3A and S3B). NLS-mCherry gene is expressed in the nucleus, the merged image of NLS-mCherry and OsOFP8-YFP showed that majority of OsOFP8 exists in the nucleolus, which was indicated by the strong fluorescence intensity in the round structure of the nucleus and seen in the differential interference contrast (DIC) images (Fig 4B). In addition, the fluorescent signals of OsOFP8-YFP and NLS-mCherry were well overlapped, further supporting the nuclear localization of OsOFP8 (Fig 4B).
To test whether the interaction with OsGSK2 alters subcellular localization of OsOFP8, we investigated the localization of OsOFP8 in the presence of OsGSK2. Co-expression with OsGSK2 clearly caused the cytoplasmic distribution of OsOFP8 (Fig 4C), and western blotting was carried out to show the presence of OsGSK2 (S3C Fig). A closer view of individual cells showed both the nuclear and cytoplasmic localization of OsOFP8 (Fig 4D, upper panel), and analysis of the fluorescent signal peaks showed that only one peak of OsOFP8-YFP was overlapped with that of NLS-mCherry, and three other peaks of OsOFP8-YFP were detected in the cytosol (Fig 4D, lower panel). These results indicate that interaction with OsGSK2 leads to the nuclear export of OsOFP8 to the cytoplasm.
To analyze the effect of BR signaling on the subcellular distribution of OsOFP8, we treated the OsOFP8-YFP transfected protoplast cells with BL. After BL treatment for two hours, the protein level of OsOFP8 was increased in the nucleus, and OsOFP8 was not detected in the cytoplasm (Fig 4E). When treated with BRZ, a BR biosynthetic inhibitor brassinazole, the protein level of OsOFP8 was detected both in the nucleus and in the cytoplasm, indicating that BRZ treatment altered the subcellular localization of OsOFP8 (Fig 4E). OsGSK2 interacts with and phosphorylates OsOFP8, the phosphorylation status of OsOFP8 may be required for its nuclear export. To this end, we treated the protoplast cells co-transfected by OsOFP8-YFP and 3HA-OsGSK2 with bikinin which inhibits the activity of BIN2 by acting as an ATP competitor. Western blotting showed that the protein level of OsOFP8 in the cytoplasm was largely reduced when the cells were treated with bikinin for two hours, indicating that phosphorylation of OsOFP8 by OsGSK2 is needed for its cytoplasmic localization (Fig 4F). Because presence of OsGSK2 induced the nuclear export of OsOFP8 (Fig 4D and 4F), we further analyzed the state of OsOFP8 and the phosphorylated OsOFP8 in the nucleus and cytoplasm, respectively. Nuclear and cytoplasmic fractions were prepared both from the protoplasts co-transfected with OsOFP8 and NLS-mCherry and with OsOFP8 and OsGSK2. In the absence of OsGSK2, OsOFP8 was only detected in the nucleus, which is consistent with the previous findings (Fig 4A and 4E). In the presence of OsGSK2, OsOFP8 was detected both in the nucleus and in the cytoplasm, but the band in the cytoplasm was much weaker than that in the nucleus (Fig 4G). When the co-transfected protoplasts were treated with MG132, a proteasome inhibitor, the intensity of the band in the cytoplasm was increased (Fig 4G), which suggests that the phosphorylated OsOFP8 is cytoplasm-localized and targeted for proteasomal degradation.
The OVATE gene was first identified as a major QTL controlling pear-shaped fruit in tomato [1, 29], and later on, studies in Arabidopsis show that the OVATE family proteins control multiple aspects of plant growth and development [2, 4–6]. The rice genome contains more number of OFPs than the Arabidopsis genome, but very few studies on OFP function in rice have been reported. We studied the function of OsOFP8 in rice, and demonstrated that OsOFP8 is involved in BR signaling. Elevated expression of OsOFP8 in rice leads to increased lamina joint bending phenotype and BR hypersensitivity (Fig 2).
In Arabidopsis, Atofp1-1D is a dominant, gain-of-function mutant, which has a T-DNA inserted at the 4332 bp downstream of the stop codon of the AtOFP1 gene and shows increased expression of AtOFP1 [2]. Osofp8 is also a gain-of-function mutant with T-DNA inserted in the 3’ region of OsOFP8 gene and shows increased expression of OsOFP8 gene (Fig 1). This coincidence of gain-of-function mutants generated by T-DNA insertion may imply a common mechanism regarding the expression regulation of OFP genes. In Arabidopsis, Atofp1-1D mutant shows reduced lengths in all aerial organs including hypocotyls, rosette leaf, inflorescence stem and floral organs. By contrast, the rice Osofp8 mutant displays increased lamina joint inclination but does not show reduced length of aerial organs (Fig 1). Furthermore, AtOFP1 is involved in GA signaling by repressing the expression of GA20ox1, a gene encoding a key enzyme in GA biosynthesis, but OsOFP8 is involved in BR signaling pathway and shows normal response to GA treatment (S4 Fig), indicating the functional diversity of these two genes in Arabidopsis and rice. AtOFP1 is expressed in roots, shoots, vasculatures, trichomes, and in mature flowers. Similarly, OsOFP8 is expressed in roots, shoots, and inflorescences (S1 Fig). Sequence analysis of AtOFP1 and OsOFP8 proteins also showed different functional domains. OVATE domain is the common feature for all OFP proteins, besides this, AtOFP1 contains an LXLXL motif (where L is leucine and X for any amino acid) in its OVATE region, which is not present in OsOFP8. The LXLXL motif has been shown to play an important role in repression of gene expression [2], although it only contributes marginal repression function to the AtOFP1.
OsGSK2 is an ortholog of Arabidopsis BIN2 gene and plays negative roles in BR signaling [20]. The expression levels of BR-biosynthesis related genes such as OsD2 and OsDWARF4 were increased in OsGSK2 overexpression line and decreased in OsGSK2 RNAi line [20]. By contrast, OsBZR1 and DLT plays positive roles in BR signaling, and OsD2 and OsDWARF4 expression levels were induced in OsBZR1 RNAi plants and dlt mutant [20, 22]. On the contrary, the expression levels of OsD2 and OsDWARF4 were increased in OsOFP8 RNAi lines, and the expression of OsD2 was reduced in OsOFP8 overexpression lines although in these lines OsDWARF4 did not show much change in expression when compared to its expression in wild-type plants. BL treatment (1 μM) increases OsOFP8 mRNA transcript and the protein amount at the translational level (Fig 2E and 2F), at this concentration the mRNA level of OsBZR1 and DLT was decreased [22, 23], suggesting OsOFP8 behaves differently to OsBZR1 and DLT.
OsGSK2 interacts with and phosphorylates the nuclear protein DLT [20], but we do not know whether the interaction with OsGSK2 causes subcellular distribution of DLT. OsGSK2 interacts with and phosphorylates OsOFP8, phosphorylation of OsOFP8 by OsGSK2 is required for its cytoplasmic localization. The phosphorylated OsOFP8 is exported from the nucleus and targeted for the proteasomal degradation in the cytoplasm. This phenomenon resembles the interaction between BIN2 and BZR1 [23, 30]. However, without BL treatment, BZR1 is located mainly in the cytoplasm [30], and BIN2 is distributed both in the nucleus and cytosol, as well as at the plasma membrane [31], but OsOFP8 is a nuclear protein. BR signaling converts phosphorylated BZR1 proteins to the dephosphorylated state [30], and BIN2 protein is rapidly depleted after 30 min treatment with 1 μM BL [32], whereas OsOFP8 protein level is increased under this treatment (Fig 2F). In rice, binding of 14-3-3 proteins to the phosthorylated OsBZR1 inhibits OsBZR1 function at least in part by reducing its nuclear localization [33]. Phosphorylated OsOFP8 may also adopt this mechanism to regulate its function. Indeed, there is a putative 14-3-3 motif in OsOFP8 protein (S3C Fig), providing a possibility that OsOFP8 may interact with 14-3-3 proteins to retain itself in the cytoplasm for degradation. Further studies are required to test the interaction between 14-3-3 and OsOFP8 and the possibility of cytoplasmic retention of phosphorylated OsOFP8 by 14-3-3 binding. Suppressing OsBZR1 expression by RNAi leads to dwarf phenotype and reduced lamina joint bending [33], however, reducing expression level of OsOFP8 by RNAi shows reduced lamina joint bending phenotype without dwarfism (Fig 1), suggesting OsOFP8 may have other biological functions in addition to participating in BR signaling.
The wild-type rice (Oryza sativa L.) plants Zhonghua 11 (japonica cv. ZH11) and OsOFP8 transgenic plants were grown in the experimental field at South China Botanical Garden in Guangzhou during the rice growing season. The angles between the leaf blades and the culms were measured with a protractor.
The Osofp8 mutant was identified from T-DNA transformation. Genomic DNA of the Osofp8 mutant was used as the template to amplify the flanking regions of the T-DNA insertion by high-efficiency thermal asymmetric interlaced PCR [24]. The primers are listed in S1 Table.
For promoter analysis, a 1983 bp promoter sequence upstream of the translation start codon of OsOFP8 was amplified by PCR. The PCR product was digested with EcoRI and NcoI, and inserted into the pCAMBIA1391z vector to generate the PromoterOsOFP8:GUS construct. Ten independent transgenic lines were obtained and showed β-glucuronidase (GUS) activity.
To overexpress OsOFP8, the full-length cDNA of OsOFP8 was PCR-amplified and inserted into the binary vector pCAMBIA1301-35S. The OsOFP8 RNAi lines were generated by RNA interference, using a 280 bp fragment of the OsOFP8 coding region. The fragment was inserted into an intermediate vector as positive and inverted directions, and then the whole cassette was cut out and inserted into the binary vector pCAMBIA1301-35S. The resulting constructs of overexpression and RNAi were introduced into Agrobacterium tumefaciens strain EHA105, respectively, and then transformed rice ZH11. The empty vectors were also transformed into ZH11 as controls.
Full-length OsOFP8 and OsGSK2 cDNAs were inserted into pBI221-YFP and pBI221-3HA vectors, respectively, to generate OsOFP8-YFP and 3HA-OsGSK2 constructs.
For BiFC assay, OsGSK2 and OsOFP8 coding sequence fragments were cloned into pSPYNE-35S and pSPYCE-35S vectors, respectively. The resulting constructs were co-transfected into protoplast cells, the transfected cells were incubated in dark for 12 h, and the fluorescence of YFP was observed.
Total RNAs were isolated with Trizol reagent (Invitrogen) according to the manufacturer’s instructions. Total RNAs were pre-treated with DNase I, and first-strand cDNA was synthesized from 2 μg of total RNAs using oligo (dT)18 as primers (Promega, http://cn.promega.com/). The first-strand cDNA product was used as template in a 20 μL PCR reaction. For quantitative RT-PCR, SYBR Green I was added to the reaction system and run on a Roche real-time PCR detection system according to the manufacturer’s instructions. The melting curve was acquired at the end. The transcript data were calculated by Roche’s Software, and were normalized using OsActin 1 as an internal control; the relative expression level was calculated by 2-ΔΔCt. Each experiment was performed with three replicates. The primers are listed in S1 Table.
GUS staining was performed according to the method as described [34]. Different tissues of the PromoterOsOFP8:GUS transgenic plants were incubated in a solution containing 50 mM NaPO4 buffer pH7.0, 5 mM K3Fe(CN)6, 5 mM K4Fe(CN)6, 0.1% Triton X-100 and 1 mM X-Gluc at 37°C overnight. Images were taken under the stereomicroscope (Leica M165c).
The lamina joint assay by the micro-drop method was performed as described previously [25]. A drop of ethanol (1 μL) containing 0, 10, 100 or 1000 ng of 24-epiBL, respectively, was spotted onto the top of lamina of the seedlings which were germinated for 2 days and grown for 3 days at 30°C. Images were taken after 3 days of incubation with 24-epiBL, and the angles of lamina joint bending were measured. The lamina joint assay using excised leaf segments was performed as described previously [35]. Synchronous seeds after 2 days germination were selected and grown in the dark for 8 days at 30°C. The entire segments comprising 1 cm of the second leaf blade, the lamina joint and 1 cm of the leaf sheath were floated on distilled water for 24 h and then incubated in 2.5 mM maleic acid potassium solution containing 1 μM 24-epiBL for 48 h in the dark. Lamina joint angles were measured, this experiment was repeated three times with similar results.
For transient expression assays, typically, 4×104 mesophyll protoplasts were isolated from 4-week-old Arabidopsis seedlings. Isolation of protoplasts and PEG-mediated transfection were as described [36]. For transient expression analysis of OsOFP8-YFP, 10 μg of the plasmid DNA were used to transfect the protoplast cells. The transfected cells were treated with 10 μM BRZ for 12 h, and then treated with 1 μM BL for 0, 0.5, 1 h after the removal of BRZ by washing. To test the OsGSK2-mediated cytosolic translocation of OsOFP8, plasmid DNAs containing OsOFP8-YFP, NLS-mCherry, and 3HA-GSK2 were co-transfected into protoplasts. After 8 h incubation, the protoplasts were incubated with or without 10 μM MG132 for 1h. All transient transfection experiments were repeated at least three times with similar results. YFP and RFP fluorescence was observed with a confocal laser scanning microscope (ZEISS-510 Meta). The signal intensities of YFP and RFP were quantitatively determined using LSM Image Browser Rel. 4.0 software.
For yeast two-hybrid analysis, OsOFP8, OsBZR1, OsGAK2 and DLT were cloned into either pGBKT7 vector or pGADT7 vector, their combinations were tested for interaction. The reported gene assay was performed following the manufacturer’s instructions (Clontech). In addition, the truncated fragments of OsOFP8 were also ligated into pGBKT7 vector for the analysis of protein-protein interacting sites.
For Co-immunoprecipitation analysis, OsOFP8-YFP, YFP, and 3HA-OsGSK2 were co-transfected into protoplasts in different combinations as indicated. After 8 h of incubation, total cell lysates from protoplasts were prepared in IP buffer (10 mM Tris-HCl pH 7.4, 150 mM NaCl, 0.5 mM EDTA, 0.2% Nonidet P-40, 5% glycerol, 1 mM dithiobis and 1 x Complete Protease Inhibitor Cocktail) and were then incubated with GFP-Trap agarose beads (ChromoTek) for 4 h at 4°C in a top to end rotator. After incubation, the beads were washed four times with ice cold washing buffer (10 mM Tris-HCl, pH7.4, 150 mM NaCl, and 0.5 mM EDTA) and then eluted by boiling in reducing SDS sample buffer. Samples were separated by SDS-PAGE and analyzed by immunoblot using appropriate antibodies.
For phosphorylation analysis, Plasmids of OsOFP8-YFP and 3HA-OsGSK2 were co-transfected into protoplasts. After 8 h incubation, total cell lysates from protoplasts were prepared in IP buffer (10m MTris-HCl pH 7.4, 150 mM NaCl, 0.5 mM EDTA, 0.2% Nonidet P-40, 5% glycerol, 1 mM dithiobis, 1 × Phosphatase Inhibitor Cocktail, and 1 x Complete Protease Inhibitor Cocktail) and were then incubated with GFP-Trap agarose beads (ChromoTek) for 4 h at 4°C in a top to end rotator. After incubation, the beads were washed four times with ice cold washing buffer (10 mM Tris-HCl, pH 7.4, 150 mM NaCl, and 0.5 mM EDTA) and then eluted by boiling in reducing SDS sample buffer. Samples were separated by SDS-PAGE and followed by immunoblotting with biotin-pendant Zn2+-Phos-tag (BTL-111) according to the manufacturer’s instructions (Western Blot Analysis of Phosphorylated Proteins-Chemiluminescent Detection using BiotinylatedPhos-tag).
Nuclear and cytoplasmic fractions in protoplasts were separated as described [37]. Protoplasts were lysed with a buffer (20 mM Tris-HCl, pH 7.0, 250 mM sucrose, 25% glycerol, 20 mM KCl, 2 mM EDTA, 2.5 mM MgCl2, 30 mM β-mercaptoethanol, 1 × protease inhibitor cocktail, and 0.7% Triton X-100) and fractionated by centrifugation at 3000 g for 15 min at 4°C. The supernatant was taken as the cytosolic fraction. The pellet was further washed with a resuspension buffer (20 mM Tris-HCl, pH 7.0, 25% glycerol, 2.5 mM MgCl2, and 30 mM β-mercaptoethanol) and reconstituted as the nuclear fraction. Each fraction was separated by SDS-PAGE and analyzed by Western blotting.
For total protein extraction from protoplasts, transformed protoplasts were harvested by centrifugation at 200 g for 3 min, followed by resuspension in lysis buffer containing 25 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM EDTA, and 1 × protease inhibitor cocktail. The protoplasts were further lysed by vortexing for 2 min. The total cell extracts were then centrifuged at 15,000 g for 30 min at 4°C; the supermatant were total protein and analyzed by immunoblotting with appropriate antibodies [38].
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10.1371/journal.pgen.1003316 | Genome-Wide Diversity in the Levant Reveals Recent Structuring by Culture | The Levant is a region in the Near East with an impressive record of continuous human existence and major cultural developments since the Paleolithic period. Genetic and archeological studies present solid evidence placing the Middle East and the Arabian Peninsula as the first stepping-stone outside Africa. There is, however, little understanding of demographic changes in the Middle East, particularly the Levant, after the first Out-of-Africa expansion and how the Levantine peoples relate genetically to each other and to their neighbors. In this study we analyze more than 500,000 genome-wide SNPs in 1,341 new samples from the Levant and compare them to samples from 48 populations worldwide. Our results show recent genetic stratifications in the Levant are driven by the religious affiliations of the populations within the region. Cultural changes within the last two millennia appear to have facilitated/maintained admixture between culturally similar populations from the Levant, Arabian Peninsula, and Africa. The same cultural changes seem to have resulted in genetic isolation of other groups by limiting admixture with culturally different neighboring populations. Consequently, Levant populations today fall into two main groups: one sharing more genetic characteristics with modern-day Europeans and Central Asians, and the other with closer genetic affinities to other Middle Easterners and Africans. Finally, we identify a putative Levantine ancestral component that diverged from other Middle Easterners ∼23,700–15,500 years ago during the last glacial period, and diverged from Europeans ∼15,900–9,100 years ago between the last glacial warming and the start of the Neolithic.
| Population stratification caused by nonrandom mating between groups of the same species is often due to geographical distances leading to physical separation followed by genetic drift of allele frequencies in each group. In humans, population structures are also often driven by geographical barriers or distances; however, humans might also be structured by abstract factors such as culture, a consequence of their reasoning and self-awareness. Religion in particular, is one of the unusual conceptual factors that can drive human population structures. This study explores the Levant, a region flanked by the Middle East and Europe, where individual and population relationships are still strongly influenced by religion. We show that religious affiliation had a strong impact on the genomes of the Levantines. In particular, conversion of the region's populations to Islam appears to have introduced major rearrangements in populations' relations through admixture with culturally similar but geographically remote populations, leading to genetic similarities between remarkably distant populations like Jordanians, Moroccans, and Yemenis. Conversely, other populations, like Christians and Druze, became genetically isolated in the new cultural environment. We reconstructed the genetic structure of the Levantines and found that a pre-Islamic expansion Levant was more genetically similar to Europeans than to Middle Easterners.
| The Levant is a geographical area in the eastern Mediterranean region bounded by Anatolia, Egypt, and the Arabian Desert. It includes Lebanon, Syria, Jordan, Israel, Palestine, and often Cyprus and historical Armenia. The region has been central to human cultural development, embracing the earliest civilizations, agricultural communities, and the rise of the first urban cities. The genetic diversity based on uniparental markers (i.e. Y-chromosome and mtDNA) of the Levantine populations shows a strong correlation with geography [1] and religion [2]–[4]. It has been suggested that the Islamic expansion from the Arabian Peninsula beginning in the 7th century CE introduced lineages typical of this Peninsula into those who subsequently became Lebanese Muslims, whereas the Crusader activity in the 11th–13th centuries CE introduced western European lineages into Lebanese Christians [5]. This recent differential penetration of exogenous Y-chromosome lineages into the Lebanese has probably been maintained by limited admixture between the religious groups, resulting in population stratifications in the present-day populations. However, it is not yet known if those structures are genome-wide and if they extend beyond Lebanese borders. Genome-wide surveys in the Levant are limited and most of our knowledge comes from studies assessing the relationship of Diaspora Jewish groups to a Levantine/Middle Eastern origin [6], [7]. These studies show that the Jews form a distinctive cluster in the Middle East, and it is not known whether the factors driving this structure would also involve other groups in the Levant. For example, would the Druze from Mount Lebanon have the same genome-wide diversity as the Druze from Mount Carmel, and would the predominantly Muslim populations in the Levant from Syria, Palestine, and Jordan have more genetic similarities to the populations of the Arabian Peninsula (Saudis, Yemenis) than would other non Muslims Levantines have? A recent study by Moorjani et al. [8], estimated that Jewish admixture with African genes ended much earlier (∼75 generations ago) than other Levantines (Muslims) (∼32 generations ago). However, it is not known if this different admixture history is the result of out-migration from the region and the discontinued gene flow from neighboring populations or if it is a result of cultural isolation in a predominantly Christian (∼100–650 CE) and later Muslim (∼650 CE-present) environment. Would today's Christians from the Levant also show older dates for cessation of African admixture than other Levantines, reflecting cultural/genetic isolation from their surrounding neighbors? By exploring the genetic isolation of populations like the Christians and Druze, it would then be possible to assess the pre-Islamic genetic structure of the Levantines and accurately construct the genetic relationships with neighboring populations.
In this study we analyze newly-generated genome-wide data from Lebanon in addition to individuals from 48 published global populations [7], [9]. We aim to assess the genome-wide genetic relationships of the Levantines and to resolve previous uncertainties about population structure in the Levant region. We pay particular attention to cultural influences on genetic structure, and explore the consequences of more than 2,000 years of cultural differentiation on the genetic composition of modern Levantines.
A multidimensional scaling (MDS) plot based on the identity-by-state (IBS) matrix shows strong stratification in Lebanon by religion, with separate clusters for Christians, Muslims, and Druze, irrespective of their geographic origin (Figure 1). The results suggest endogamous practices among the religious groups of Lebanon within a small geographical area not exceeding 10,452 km2 (half the size of the state of New Jersey or one third the size of Belgium). Christianity in Lebanon dates back to the first century CE, whereas Islam was brought to the Levant through the Islamic expansions in 635 CE. In 986 CE, the Druze faith developed as a movement within Islam, and from 1030 AD, a person could only be Druze if born Druze. This correlation of genetic structure within Lebanon with cultural traits was previously described by Haber et al. [3] based on the religious structuring of Y-chromosomal variation within Lebanon, but here we see it is genome-wide. In order to assess the proportion of putative ancestral components in the Lebanese, an unsupervised clustering method (ADMIXTURE) [10] was applied to the Lebanese dataset (Figure S1A). At K = 2, which showed the lowest cross-validation error (Figure S1B), Christians present one major component (∼82% on average per individual), which is also found in Druze and in lower frequencies in Muslims; in contrast, the second component is almost exclusive to Muslims with a lower representation in Druze. At K = 3 and K = 4, new components most abundant in Lebanese Muslims are shown, probably reflecting recent admixture after the split from the other Lebanese groups.
In order to assess the population structure of Levantine populations more generally, an MDS (Figure 2) and a normalized principle component analysis (PCA) (Figure S2) plots with 48 additional Old World populations (Table S1) were built. Only 25 randomly selected samples from each Lebanese group were used in order to avoid population size biases (Figure S3). The plots reveal a Levantine structure not reported previously: Lebanese Christians and all Druze cluster together, and Lebanese Muslims are extended towards Syrians, Palestinians, and Jordanians, which are close to Saudis and Bedouins. Ashkenazi Jews are drawn towards the Caucasus and Eastern Europe, reflecting historical admixture events with Europeans, while Sephardi Jews cluster tightly with the Levantine groups. These results are consistent with previous studies reporting higher European genome-wide admixture in Ashkenazi Jews compared with other Jews [11] and higher Y-chromosomal gene flow to Lebanese Muslims from the Arabian Peninsula compared with other Lebanese [5].
The previous analyses are based on linkage disequilibrium (LD) pruned data (r2<0.4) since LD can bias cluster analysis. However, identification of haplotypes shared between groups is a valuable tool to infer population history events [12]–[15]. Thus, we phased our data and generated a coancestry matrix using ChromoPainter [16] which reconstruct the haplotype of every individual using the haplotypes of each of the other individuals as possible donors. ChromoPainter computes a similarity measure which is the number of haplotype “chunks” used to reconstruct the recipient individual from each donor individual. We then used fineSTRUCTURE [16] which employ model-based Bayesian clustering to construct a tree that infer population relationships and similarities using ChromoPainter's coancestry matrix. The population tree (Figure 3A) splits Levantine populations in two branches: one leading to Europeans and Central Asians that includes Lebanese, Armenians, Cypriots, Druze and Jews, as well as Turks, Iranians and Caucasian populations; and a second branch composed of Palestinians, Jordanians, Syrians, as well as North Africans, Ethiopians, Saudis, and Bedouins. The tree shows a correlation between religion and the population structures in the Levant: all Jews (Sephardi and Ashkenazi) cluster in one branch; Druze from Mount Lebanon and Druze from Mount Carmel are depicted on a private branch; and Lebanese Christians form a private branch with the Christian populations of Armenia and Cyprus placing the Lebanese Muslims as an outer group. The predominantly Muslim populations of Syrians, Palestinians and Jordanians cluster on branches with other Muslim populations as distant as Morocco and Yemen. It should be noted here that the results depend significantly on populations included in the analysis as well as recent admixture events, and so should be treated as an approximate guide to similarity, rather than a full population history.
ChromoPainter's coancestry matrix (Figure 3B, Figure S4) shows the haplotype chunks donated from the world populations to the Levantines and shows that Jordanians, Palestinians, and Syrians receive more chunks from sub-Saharan Africans and from Middle Easterners compared with other Levantines. We explored the sub-Saharan/Middle Eastern gene flow to the Levantines further by employing a previously developed method (ROLLOFF) [8] that estimates the time since admixture with sub-Saharan African genes using the rate of exponential decline of admixture LD. Previous simulations [8] showed that bias from ROLLOFF estimates is removed with increased sample size, so we used the entire Lebanese religious subgroups after carrying out a rigorous outlier removal based on PCA [17] and keeping the main core clusters (336 Christians, 85 Druze, 747 Muslims) (Text S1). We found that Christians have the oldest admixture dates (2,375-2,025 years ago, y.a) with bounds coinciding with the decline of Phoenicia and the control of the region by the Hellenistic rulers. The time since the observed Druze admixture (1,275-1,025 y.a) closely precedes the development of the Druze faith and their divergence from other Muslims. The Muslims appear to have maintained contact with populations carrying sub-Saharan genes until 675-625 y.a, which overlaps with the rise of the Ottoman Empire and formation of a semi-autonomous state in Lebanon. Historical events coinciding with our observed admixture dates are some of the examples of population processes and demographic events that were occurring during this period in the Levant. These historical events, in addition to cultural adoptions and transitions, may have contributed to the differences among the religious groups through facilitating or restricting contact with other Middle Easterners carrying the sub-Saharan genes. It should also be noted here that ROLLOFF estimates dates assuming instantaneous mixture, without distinguishing between the patterns expected for instantaneous admixture and continuous gene flow. Previous simulations [8] show that for continuous gene flow, the dates from ROLLOFF reflect the average of mixture dates over a range of times, hence the date should be interpreted only as an average number.
The principal component plot performed with the coancestry matrix (Figure 3C, Figure S5) is similar to the pattern seen in West Asia with the MDS and PCA analysis based on LD-pruned SNPs.
In order to identify and quantify the ancestral components in the Levantines, an ADMIXTURE analysis [10] was performed with Old World samples (Figure S6A).
ADMIXTURE requires the assignment of a specific population number (K). We chose to assign a K = 10 (Figure S6, Table S3) since it captures many of the population structures identified by fineSTRUCTURE, particularly the formation of separate ancestral components for Levantines and Middle Easterners. ADMIXTURE's cross-validation (Figure S6B) shows that K = 8 has the lowest cross-validation (CV) error, however the CV effectiveness in predicting the “truth” K can be challenged when considering closely related populations [18]–[20]. Therefore, in this analysis we use the ChromoPainter/fineSTRUCTURE pipeline to identify fine populations subdivisions without the drawback of specifying a K value [16], [20], and use ADMIXTURE to estimate the genetic distances between the ancestral components independent of subsequent admixture events.
ADMIXTURE identifies at K = 10 an ancestral component (light green) with a geographically restricted distribution representing ∼50% of the individual component in Ethiopians, Yemenis, Saudis, and Bedouins, decreasing towards the Levant, with higher frequency (∼25%) in Syrians, Jordanians, and Palestinians, compared with other Levantines (4%–20%). The geographical distribution pattern of this component (Figure 4A, 4B) correlates with the pattern of the Islamic expansion, but its presence in Lebanese Christians, Sephardi and Ashkenazi Jews, Cypriots and Armenians might suggest that its spread to the Levant could also represent an earlier event. Besides this component, the most frequent ancestral component (shown in dark blue) in the Levantines (42–68%) is also present, at lower frequencies, in Europe and Central Asia (Figure 4A, 4C). We found that this Levantine component is closer to the European component (dark green) (FST = 0.035) than to the Arabian Peninsula/East Africa component (light green) (FST = 0.046). Our estimates show that the Levantine and the Arabian Peninsula/East African components diverged ∼23,700-15,500 y.a., while the Levantine and European components diverged ∼15,900-9,100 y.a. We note here that our divergence time estimates are based on the assumption that “effective population sizes” have not significantly changed overtime. We make this assumption, and obtain divergence times from genetic data which appear to coincide well with archeology.
The estimated time of divergence between the Levantine component and other Middle Easterners overlaps with evidence from archeological findings of a major cultural development in the Levant during the early Epipaleolithic period (23,000-14,500 y.a) [21]. The period of climatic warming after the Last Glacial Maximum (∼26,000-19,900 y.a) in the Levant was characterized by the spread of the microlithic technologies and the appearance of highly mobile populations between the Sinai Peninsula and southern Turkey. This Early Epipaleolithic phase formed a cultural continuity with the last Epipaleolithic phase, immediately preceding the appearance of the Natufian culture and the development of sedentism [22]. Our time estimate of divergence between the Levantine and European components (∼15,900-9,100 y.a) overlaps with the transition to agriculture in the Levant ∼11,000 y.a but is also slightly earlier than the proposed expansion to Europe starting at ∼9,000 y.a. [23]–[25]. In agreement with this, a recent study of complete mtDNA sequences also proposed earlier expansion dates (19,000-12,000 y.a) of certain female lineages from the Near East to Europe [26]. These results suggest that population migration to Europe from the Near East could have started after the LGM warming and continued until the Neolithic. In addition, these results show that the modern European genetic component is more recent than would be expected from a component that developed from the initial peopling of Europe in the Upper Paleolithic ∼40,000 y.a.
From the first ventures out of Africa, to admixture with archaic humans, to the earliest Neolithic transition, the developments in the Levant have marked the history of modern humans. However, the Levant had been underrepresented in genome-wide studies and little is known about its population structure. In this study, we show a multilayered history of the Levantines with multiple components that might be traced to different historical population events. We propose that the Levant and Middle Eastern modal components diverged after the LGM during the early Epipaleolithic period, which was characterized by behavioral variability and innovations accompanied by major life-style and technological changes in the Levant [21], [27], [28]. We also show that the Levantines and Europeans diverged between the last glacial warming and the start of the Neolithic age.
Finally, we show that although population movements and expansions during the Epipaleolithic marked the emergence of a Levantine component and made the Levantines genetically similar, recent cultural developments, such as the founding and spread of major world religions, have had a strong impact on population stratifications in the Levant.
Populations like the Levantines, where geography is not the only major correlate of genetic variation, are unusual. In addition to their importance in understanding human evolution and history, these unusual stratifications can be hard to control in association studies for mapping complex disease susceptibilities and therefore require particular attention.
Samples were collected from 1,341 Lebanese subjects with informed consent approved by the IRB of the Lebanese American University. Genotyping was performed on Illumina 610K or 660K bead arrays. PLINK [29] was used for data management and quality control. Genotyping success rate was set to 99%, sex-linked and mitochondrial SNPs removed, keeping 505,859 SNPs. After LD pruning (excluding r2>0.4) 244,919 SNPs remained. 75 Lebanese samples (Figure S3, Table S1) were selected through a stratified random sampling taking into consideration the distribution of the religious groups in Lebanon and merged with 994 samples from literature representing 48 populations (Table S2). The selected Lebanese data set is available at: bhusers.upf.edu/~mhaber/PLOS/
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10.1371/journal.pbio.1002399 | The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle | Bacterial Microcompartments (BMCs) are proteinaceous organelles that encapsulate critical segments of autotrophic and heterotrophic metabolic pathways; they are functionally diverse and are found across 23 different phyla. The majority of catabolic BMCs (metabolosomes) compartmentalize a common core of enzymes to metabolize compounds via a toxic and/or volatile aldehyde intermediate. The core enzyme phosphotransacylase (PTAC) recycles Coenzyme A and generates an acyl phosphate that can serve as an energy source. The PTAC predominantly associated with metabolosomes (PduL) has no sequence homology to the PTAC ubiquitous among fermentative bacteria (Pta). Here, we report two high-resolution PduL crystal structures with bound substrates. The PduL fold is unrelated to that of Pta; it contains a dimetal active site involved in a catalytic mechanism distinct from that of the housekeeping PTAC. Accordingly, PduL and Pta exemplify functional, but not structural, convergent evolution. The PduL structure, in the context of the catalytic core, completes our understanding of the structural basis of cofactor recycling in the metabolosome lumen.
| In metabolism, molecules with “high-energy” bonds (e.g., ATP and Acetyl~CoA) are critical for both catabolic and anabolic processes. Accordingly, the retention of these bonds during biochemical transformations is incredibly important. The phosphotransacylase (Pta) enzyme catalyzes the conversion between acyl-CoA and acyl-phosphate. This reaction directly links an acyl-CoA with ATP generation via substrate-level phosphorylation, producing short-chain fatty acids (e.g., acetate), and also provides a path for short-chain fatty acids to enter central metabolism. Due to this key function, Pta is conserved across the bacterial kingdom. Recently, a new type of phosphotransacylase was described that shares no evolutionary relation to Pta. This enzyme, PduL, is exclusively associated with organelles called bacterial microcompartments, which are used to catabolize various compounds. Not only does PduL facilitate substrate level phosphorylation, but it also is critical for cofactor recycling within, and product efflux from, the organelle. We solved the structure of this convergent phosphotransacylase and show that it is completely structurally different from Pta, including its active site architecture. We also discuss features of the protein important to its packaging in the organelle.
| Bacterial Microcompartments (BMCs) are organelles that encapsulate enzymes for sequential biochemical reactions within a protein shell [1–4]. The shell is typically composed of three types of protein subunits, which form either hexagonal (BMC-H and BMC-T) or pentagonal (BMC-P) tiles that assemble into a polyhedral shell. The facets of the shell are composed primarily of hexamers that are typically perforated by pores lined with highly conserved, polar residues [1] that presumably function as the conduits for metabolites into and out of the shell [5,6].
The vitamin B12-dependent propanediol-utilizing (PDU) BMC was one of the first functionally characterized catabolic BMCs [7]; subsequently, other types have been implicated in the degradation of ethanolamine, choline, fucose, rhamnose, and ethanol, all of which produce different aldehyde intermediates (Table 1). More recently, bioinformatic studies have demonstrated the widespread distribution of BMCs among diverse bacterial phyla [2,8,9] and grouped them into 23 different functional types [2]. The reactions carried out in the majority of catabolic BMCs (also known as metabolosomes) fit a generalized biochemical paradigm for the oxidation of aldehydes (Fig 1) [2]. This involves a BMC-encapsulated signature enzyme that generates a toxic and/or volatile aldehyde that the BMC shell sequesters from the cytosol [1]. The aldehyde is subsequently converted into an acyl-CoA by aldehyde dehydrogenase, which uses NAD+ and CoA as cofactors [10,11]. These two cofactors are relatively large, and their diffusion across the protein shell is thought to be restricted, necessitating their regeneration within the BMC lumen [3,12,13]. NAD+ is recycled via alcohol dehydrogenase [13], and CoA is recycled via phosphotransacetylase (PTAC) [3,12] (Fig 1). The final product of the BMC, an acyl-phosphate, can then be used to generate ATP via acyl kinase, or revert back to acyl-CoA by Pta [14] for biosynthesis. Collectively, the aldehyde and alcohol dehydrogenases, as well as the PTAC, constitute the common metabolosome core.
The activities of core enzymes are not confined to BMC-associated functions: aldehyde and alcohol dehydrogenases are utilized in diverse metabolic reactions, and PTAC catalyzes a key biochemical reaction in the process of obtaining energy during fermentation [14]. The concerted functioning of a PTAC and an acetate kinase (Ack) is crucial for ATP generation in the fermentation of pyruvate to acetate (see Reactions 1 and 2). Both enzymes are, however, not restricted to fermentative organisms. They can also work in the reverse direction to activate acetate to the CoA-thioester. This occurs, for example, during acetoclastic methanogenesis in the archaeal Methanosarcina species [15,16].
Reaction 1: acetyl-S-CoA + Pi ←→ acetyl phosphate + CoA-SH (PTAC)
Reaction 2: acetyl phosphate + ADP ←→ acetate + ATP (Ack)
The canonical PTAC, Pta, is an ancient enzyme found in some eukaryotes [17] and archaea [16], and widespread among the bacteria; 90% of the bacterial genomes in the Integrated Microbial Genomes database [18] contain a gene encoding the PTA_PTB phosphotransacylase (Pfam domain PF01515 [19,20]). Pta has been extensively characterized due to its key role in fermentation [14,21]. More recently, a second type of PTAC without any sequence homology to Pta was identified [4]. This protein, PduL (Pfam domain PF06130), was shown to catalyze the conversion of propionyl-CoA to propionyl-phosphate and is associated with a BMC involved in propanediol utilization, the PDU BMC [4].
Both pduL and pta genes can be found in genetic loci of functionally distinct BMCs, although the PduL type is much more prevalent, being found in all but one type of metabolosome locus: EUT1 (Table 1) [2]. Furthermore, in the Integrated Microbial Genomes Database [18], 91% of genomes that encode PF06130 also encode genes for shell proteins. As a member of the core biochemical machinery of functionally diverse aldehyde-oxidizing metabolosomes, PduL must have a certain level of substrate plasticity (see Table 1) that is not required of Pta, which has generally been observed to prefer acetyl-CoA [22,23]. PduL from the PDU BMC of Salmonella enterica favors propionyl-CoA over acetyl-CoA [4], and it is likely that PduL orthologs in functionally diverse BMCs would have substrate preferences for other CoA derivatives. Another distinctive feature of BMC-associated PduL homologs is an N-terminal encapsulation peptide (EP) that is thought to “target” proteins for encapsulation by the BMC shell [3,24]. EPs are frequently found on BMC-associated proteins and have been shown to interact with shell proteins [25,26]. EPs have also been observed to cause proteins to aggregate [27,28], and this has recently been suggested to be functionally relevant as an initial step in metabolosome assembly, in which a multifunctional protein core is formed, around which the shell assembles [24].
Of the three common metabolosome core enzymes, crystal structures are available for both the alcohol and aldehyde dehydrogenases. In contrast, the structure of PduL, the PTAC found in the vast majority of catabolic BMCs, has not been determined. This is a major gap in our understanding of metabolosome-encapsulated biochemistry and cofactor recycling. Structural information will be essential to working out how the core enzymes and their cofactors assemble and organize within the organelle lumen to enhance catalysis. Moreover, it will be useful for guiding efforts to engineer novel BMC cores for biotechnological applications [1,29,30].
The primary structure of PduL homologs is subdivided into two PF06130 domains, each roughly 80 residues in length. No available protein structures contain the PF06130 domain, and homology searches using the primary structure of PduL do not return any significant results that would allow prediction of the structure. Moreover, the evident novelty of PduL makes its structure interesting in the context of convergent evolution of PTAC function; to-date, only the Pta active site and catalytic mechanism is known [31]. Here we report high-resolution crystal structures of a PduL-type PTAC in both CoA- and phosphate-bound forms, completing our understanding of the structural basis of catalysis by the metabolosome common core enzymes. We propose a catalytic mechanism analogous but yet distinct from the ubiquitous Pta enzyme, highlighting the functional convergence of two enzymes with completely different structures and metal requirements. We also investigate the quaternary structures of three different PduL homologs and situate our findings in the context of organelle biogenesis in functionally diverse BMCs.
We cloned, expressed, and purified three different PduL homologs from functionally distinct BMCs (Table 1): from the well-studied pdu locus in S. enterica Typhimurium LT2 (sPduL) [3,4], from the recently characterized pvm locus in Planctomyces limnophilus (pPduL) [32], and from the grm3 locus in Rhodopseudomonas palustris BisB18 (rPduL) [2]. While purifying full-length sPduL, we observed a tendency to aggregation as described previously [4], with a large fraction of the expressed protein found in the insoluble fraction in a white, cake-like pellet. Remarkably, after removing the N-terminal putative EP (27 amino acids), most of the sPduLΔEP protein was in the soluble fraction upon cell lysis. Similar differences in solubility were observed for pPduL and rPduL when comparing EP-truncated forms to the full-length protein, but none were quite as dramatic as for sPduL. We confirmed that all homologs were active (S1a and S1b Fig). Among these, we were only able to obtain diffraction-quality crystals of rPduL after removing the N-terminal putative EP (33 amino acids, also see Fig 2a) (rPduLΔEP). Truncated rPduLΔEP had comparable enzymatic activity to the full-length enzyme (S1a Fig).
We collected a native dataset from rPduLΔEP crystals diffracting to a resolution of 1.54 Å (Table 2). Using a mercury-derivative crystal form diffracting to 1.99 Å (Table 2), we obtained high quality electron density for model building and used the initial model to refine against the native data to Rwork/Rfree values of 18.9/22.1%. There are two PduL molecules in the asymmetric unit of the P212121 unit cell. We were able to fit all of the primary structure of PduLΔEP into the electron density with the exception of three amino acids at the N-terminus and two amino acids at the C-terminus (Fig 2a); the model is of excellent quality (Table 2). A CoA cofactor as well as two metal ions are clearly resolved in the density (for omit maps of CoA see S2 Fig).
Structurally, PduL consists of two domains (Fig 2, blue/red), each a beta-barrel that is capped on both ends by short α-helices. β-Barrel 1 consists of the N-terminal β strand and β strands from the C-terminal half of the polypeptide chain (β1, β10-β14; residues 37–46 and 155–224). β-Barrel 2 consists mainly of the central segment of primary structure (β2, β5–β9; residues 47–60 and 82–154) (Fig 2, red), but is interrupted by a short two-strand beta sheet (β3-β4, residues 61–81). This β-sheet is involved in contacts between the two domains and forms a lid over the active site. Residues in this region (Gln42, Pro43, Gly44), covering the active site, are strongly conserved (Fig 3). This structural arrangement is completely different from the functionally related Pta, which is composed of two domains, each consisting of a central flat beta sheet with alpha-helices on the top and bottom [31].
There are two PduL molecules in the asymmetric unit forming a butterfly-shaped dimer (Fig 4c). Consistent with this, results from size exclusion chromatography of rPduLΔEP suggest that it is a dimer in solution (Fig 5e). The interface between the two chains buries 882 Å2 per monomer and is mainly formed by α-helices 2 and 4 and parts of β-sheets 12 and 14, as well as a π–π stacking of the adenine moiety of CoA with Phe116 of the adjacent chain (Fig 4c). The folds of the two chains in the asymmetric unit are very similar, superimposing with a rmsd of 0.16 Å over 2,306 aligned atom pairs. The peripheral helices and the short antiparallel β3–4 sheet mediate most of the crystal contacts.
CoA and the metal ions bind between the two domains, presumably in the active site (Figs 2b and 4a). To identify the bound metals, we performed an X-ray fluorescence scan on the crystals at various wavelengths (corresponding to the K-edges of Mn, Fe, Co, Ni, Cu, and Zn). There was a large signal at the zinc edge, and we tested for the presence of zinc by collecting full data sets before and after the Zn K-edge (1.2861 and 1.2822 Å, respectively). The large differences between the anomalous signals confirm the presence of zinc at both metal sites (S3 Fig).
The first zinc ion (Zn1) is in a tetrahedral coordination state with His48, His50, Glu109, and the CoA sulfur (Fig 4a). The second (Zn2) is in octahedral coordination by three conserved histidine residues (His157, His159 and His204) as well as three water molecules (Fig 4a). The nitrogen atom coordinating the zinc is the Nε in each histidine residue, as is typical for this interaction [33]. When the crystals were soaked in a sodium phosphate solution for 2 d prior to data collection, the CoA dissociates, and density for a phosphate molecule is visible at the active site (Table 2, Fig 4b). The phosphate-bound structure aligns well with the CoA-bound structure (0.43 Å rmsd over 2,361 atoms for the monomer, 0.83 Å over 5,259 aligned atoms for the dimer). The phosphate contacts both zinc atoms (Fig 4b) and replaces the coordination by CoA at Zn1; the coordination for Zn2 changes from octahedral with three bound waters to tetrahedral with a phosphate ion as one of the ligands (Fig 4b). Conserved Arg103 seems to be involved in maintaining the phosphate in that position. The two zinc atoms are slightly closer together in the phosphate-bound form (5.8 Å vs 6.3 Å), possibly due to the bridging effect of the phosphate. An additional phosphate molecule is bound at a crystal contact interface, perhaps accounting for the 14 Å shorter c-axis in the phosphate-bound crystal form (Table 2).
Interestingly, some of the residues important for dimerization of rPduL, particularly Phe116, are poorly conserved across PduL homologs associated with functionally diverse BMCs (Figs 4c and 3), suggesting that they may have alternative oligomeric states. We tested this hypothesis by performing size exclusion chromatography on both full-length and truncated variants (lacking the EP, ΔEP) of sPduL, rPduL, and pPduL. These three homologs are found in functionally distinct BMCs (Table 1). Therefore, they are packaged with different signature enzymes and different ancillary proteins [2]. It has been proposed that the catabolic BMCs may assemble in a core-first manner, with the luminal enzymes (signature enzyme, aldehyde, and alcohol dehydrogenases and the BMC PTAC) forming an initial bolus, or prometabolosome, around which a shell assembles [1]. Given the diversity of signature enzymes (Table 1), it is plausible that PduL orthologs may adopt different oligomeric states that reflect the differences in the proteins being packaged with them in the organelle lumen.
We found that not only did the different orthologs appear to assemble into different oligomeric states, but that quaternary structure was dependent on whether or not the EP was present. Full-length sPduL was unstable in solution—precipitating over time—and eluted throughout the entire volume of a size exclusion column, indicating it was nonspecifically aggregating. However, when the putative EP (residues 1–27) was removed (sPduL ΔEP), the truncated protein was stable and eluted as a single peak (Fig 5a) consistent with the size of a monomer (Fig 5d, blue curve). In contrast, both full-length rPduL and pPduL appeared to exist in two distinct oligomeric states (Fig 5b and 5c respectively, orange curves), one form of the approximate size of a dimer and the second, a higher molecular weight oligomer (~150 kDa). Upon deletion of the putative EP (residues 1–47 for rPduL, and 1–20 for pPduL), there was a distinct change in the elution profiles (Fig 5b and 5c respectively, blue curves). pPduLΔEP eluted as two smaller forms, possibly corresponding to a trimer and a monomer. In contrast, rPduLΔEP eluted as one smaller oligomer, possibly a dimer. We also analyzed purified rPduL and rPduLΔEP by size exclusion chromatography coupled with multiangle light scattering (SEC-MALS) for a complementary approach to assessing oligomeric state. SEC-MALS analysis of rPdulΔEP is consistent with a dimer (as observed in the crystal structure) with a weighted average (Mw) and number average (Mn) of the molar mass of 58.4 kDa +/− 11.2% and 58.8 kDa +/− 10.9%, respectively (S4a Fig). rPduL full length runs as Mw = 140.3 kDa +/− 1.2% and Mn = 140.5 kDa +/− 1.2%. This corresponds to an oligomeric state of six subunits (calculated molecular weight of 144 kDa). Collectively, these data strongly suggest that the N-terminal EP of PduL plays a role in defining the quaternary structure of the protein.
The hallmark attribute of an organelle is that it serves as a discrete subcellular compartment functioning as an isolated microenvironment distinct from the cytosol. In order to create and preserve this microenvironment, the defining barrier (i.e., lipid bilayer membrane or microcompartment shell) must be selectively permeable. The BMC shell not only sequesters specific enzymes but also their cofactors, thereby establishing a private cofactor pool dedicated to the encapsulated reactions. In catabolic BMCs, CoA and NAD+ must be continually recycled within the organelle (Fig 1). Homologs of the predominant cofactor utilizer (aldehyde dehydrogenase) and NAD+ regenerator (alcohol dehydrogenase) have been structurally characterized, but until now structural information was lacking for PduL, which recycles CoA in the organelle lumen [12,34]. Curiously, while the housekeeping Pta could provide this function, and indeed does so in the case of one type of ethanolamine-utilizing (EUT) BMC [2], the evolutionarily unrelated PduL fulfills this function for the majority of metabolosomes [2,4] using a novel structure and active site for convergent evolution of function.
The structure of PduL consists of two β-barrel domains capped by short alpha helical segments (Fig 2b). The two domains are structurally very similar (superimposing with a rmsd of 1.34 Å (over 123 out of 320/348 aligned backbone atoms, S5a Fig). However, the amino acid sequences of the two domains are only 16% identical (mainly the RHxH motif, β2 and β10), and 34% similar. Our structure reveals that the two assigned PF06130 domains (Fig 3) do not form structurally discrete units; this reduces the apparent sequence conservation at the level of primary structure. One strand of the domain 1 beta barrel (shown in blue in Fig 2) is contributed by the N-terminus, while the rest of the domain is formed by the residues from the C-terminal half of the protein. When aligned by structure, the β1 strand of the first domain (Fig 2a and 2b, blue) corresponds to the final strand of the second domain (β9), effectively making the domains continuous if the first strand was transplanted to the C-terminus. Refined domain assignment based on our structure should be able to predict domains of PF06130 homologs much more accurately. The closest structural homolog of the PduL barrel domain is a subdomain of a multienzyme complex, the alpha subunit of ethylbenzene dehydrogenase [35] (S5b Fig, rmsd of 2.26 Å over 226 aligned atoms consisting of one beta barrel and one capping helix). In contrast to PduL, there is only one barrel present in ethylbenzene dehydrogenase, and there is no comparable active site arrangement. The PduL signature primary structure, two PF06130 domains, occurs in some multidomain proteins, most of them annotated as Acks, suggesting that PduL may also replace Pta in variants of the phosphotransacetylase-Ack pathway. These PduL homologs lack EPs, and their fusion to Ack may have evolved as a way to facilitate substrate channeling between the two enzymes.
For BMC-encapsulated proteins to properly function together, they must be targeted to the lumen and assemble into an organization that facilitates substrate/product channeling among the different catalytic sites of the signature and core enzymes. The N-terminal extension on PduL homologs may serve both of these functions. The extension shares many features with previously characterized EPs [24,26,36]: it is present only in homologs associated with BMC loci, and it is predicted to form an amphipathic α-helix. Moreover, its removal affects the oligomeric state of the protein. EP-mediated oligomerization has been observed for the signature and core BMC enzymes; for example, full-length propanediol dehydratase and ethanolamine ammonia-lyase (signature enzymes for PDU and EUT BMCs) subunits are also insoluble, but become soluble upon removal of the predicted EP [27,28,11]. sPduL has also previously been reported to localize to inclusion bodies when overexpressed [4]; we show here that this is dependent on the presence of the EP. This propensity of the EP to cause proteins to form complexes (Fig 5) might not be a coincidence, but could be a necessary step in the assembly of BMCs. Structured aggregation of the core enzymes has been proposed to be the initial step in metabolosome assembly [1,37] and is known to be the first step of β-carboxysome biogenesis, where the core enzyme Ribulose Bisphosphate Carboxylase/Oxygenase (RuBisCO) is aggregated by the CcmM protein [37]. Likewise, CsoS2, a protein in the α-carboxysome core, also aggregates when purified and is proposed to facilitate the nucleation and encapsulation of RuBisCO molecules in the lumen of the organelle [36]. Coupled with protein–protein interactions with other luminal components, the aggregation of these enzymes could lead to a densely packed organelle core. This role for EPs in BMC assembly is in addition to their interaction with shell proteins [24–26,36,38].
Moreover, the PduL crystal structures offer a clue as to how required cofactors enter the BMC lumen during assembly. Free CoA and NAD+/H could potentially be bound to the enzymes as the core assembles and is encapsulated. However, this raises an issue of stoichiometry: if the ratio of cofactors to core enzymes is too low, then the sequestered metabolism would proceed at suboptimal rates. Our PduL crystals contained CoA that was captured from the Escherichia coli cytosol, indicating that the “ground state” of PduL is in the CoA-bound form; this could provide an elegantly simple means of guaranteeing a 1:1 ratio of CoA:PduL within the metabolosome lumen.
The active site of PduL is formed at the interface of the two structural domains (Fig 2b). As expected, the amino acid sequence conservation is highest in the region around the proposed active site (Fig 4d); highly conserved residues are also involved in CoA binding (Figs 2a and 3, residues Ser45, Lys70, Arg97, Leu99, His204, Asn211). All of the metal-coordinating residues (Fig 2a) are absolutely conserved, implicating them in catalysis or the correct spatial orientation of the substrates. Arg103, which contacts the phosphate (Fig 4b), is present in all PduL homologs. The close resemblance between the structures binding CoA and phosphate likely indicates that no large changes in protein conformation are involved in catalysis, and that our crystal structures are representative of the active form. The native substrate for the forward reaction of rPduL and pPduL, propionyl-CoA, most likely binds to the enzyme in the same way at the observed nucleotide and pantothenic acid moiety, but the propionyl group in the CoA-thioester might point in a different direction. There is a pocket nearby the active site between the well-conserved residues Ser45 and Ala154, which could accommodate the propionyl group (S6 Fig). A homology model of sPduL indicates that the residues making up this pocket and the surrounding active site region are identical to that of rPduL, which is not surprising, because these two homologs presumably have the same propionyl-CoA substrate. The homology model of pPduL also has identical residues making up the pocket, but with a key difference in the vicinity of the active site: Gln77 of rPduL is replaced by a tyrosine (Tyr77) in pPduL. The physiological substrate of pPduL (Table 1) is thought to be lactyl-CoA, which contains an additional hydroxyl group relative to propionyl-CoA. The presence of an aromatic residue at this position may underlie the substrate preference of the PduL enzyme from the pvm locus. Indeed, in the majority of PduLs encoded in pvm loci, Gln77 is substituted by either a Tyr or Phe, whereas it is typically a Gln or Glu in PduLs in all other BMC types that degrade acetyl- or propionyl-CoA. A comparison of the PduL active site to that of the functionally identical Pta suggests that the two enzymes have distinctly different mechanisms. The catalytic mechanism of Pta involves the abstraction of a thiol hydrogen by an aspartate residue, resulting in the nucleophilic attack of thiolate upon the carbonyl carbon of acetyl-phosphate, oriented by an arginine and stabilized by a serine [31]—there are no metals involved. In contrast, in the rPduL structure, there are no conserved aspartate residues in or around the active site, and the only well-conserved glutamate residue in the active site is involved in coordinating one of the metal ions. These observations strongly suggest that an acidic residue is not directly involved in catalysis by PduL. Instead, the dimetal active site of PduL may create a nucleophile from one of the hydroxyl groups on free phosphate to attack the carbonyl carbon of the thioester bond of an acyl-CoA. In the reverse direction, the metal ion(s) could stabilize the thiolate anion that would attack the carbonyl carbon of an acyl-phosphate; a similar mechanism has been described for phosphatases where hydroxyl groups or hydroxide ions can act as a base when coordinated by a dimetal active site [39].
Our structures provide the foundation for studies to elucidate the details of the catalytic mechanism of PduL. Conserved residues in the active site that may contribute to substrate binding and/or transition state stabilization include Ser127, Arg103, Arg194, Gln107, Gln74, and Gln/Glu77. In the phosphate-bound crystal structure, Ser127 and Arg103 appear to position the phosphate (Fig 4b). Alternatively, Arg103 might act as a base to render the phosphate more nucleophilic. The functional groups of Gln74, Gln/Glu77, and Arg194 are directed away from the active site in both CoA and phosphate-bound crystal structures and do not appear to be involved in hydrogen bonding with these substrates, although they could be important for positioning an acyl-phosphate.
The free CoA-bound form is presumably poised for attack upon an acyl-phosphate, indicating that the enzyme initially binds CoA as opposed to acyl-phosphate. This hypothesis is strengthened by the fact that the CoA-bound crystals were obtained without added CoA, indicating that the protein bound CoA from the E. coli expression strain and retained it throughout purification and crystallization. The phosphate-bound structure indicates that in the opposite reaction direction phosphate is bound first, and then an acyl-CoA enters. The two high-resolution crystal structures presented here will serve as the foundation for mechanistic studies on this noncanonical PTAC enzyme to determine how the dimetal active site functions to catalyze both forward and reverse reactions.
PduL and Pta are mechanistically and structurally distinct enzymes that catalyze the same reaction [4], a prime example of evolutionary convergence upon a function. There are several examples of such functional convergence of enzymes, although typically the enzymes have independently evolved similar, or even identical active sites; for example, the carbonic anhydrase family [40,41]. However, apparently less frequent is functional convergence that is supported by distinctly different active sites and accordingly catalytic mechanism, as revealed by comparison of the structures of Pta and PduL. One well-studied example of this is the β-lactamase family of enzymes, in which the active site of Class A and Class C enzymes involve serine-based catalysis, but Class B enzymes are metalloproteins [42,43]. This is not surprising, as β-lactamases are not so widespread among bacteria and therefore would be expected to have evolved independently several times as a defense mechanism against β-lactam antibiotics. However, nearly all bacteria encode Pta, and it is not immediately clear why the Pta/PduL functional convergence should have evolved: it would seem to be evolutionarily more resourceful for the Pta-encoding gene to be duplicated and repurposed for BMCs, as is apparently the case in one type of BMC—EUT1 (Table 1). There could be some intrinsic biochemical difference between the two enzymes that renders PduL a more attractive candidate for encapsulation in a BMC—for example, PduL might be more amenable to tight packaging, or is better suited for the chemical microenvironment formed within the lumen of the BMC, which can be quite different from the cytosol [44,45]. Further biochemical comparison between the two PTACs will likely yield exciting results that could answer this evolutionary question.
BMCs are now known to be widespread among the bacteria and are involved in critical segments of both autotrophic and heterotrophic biochemical pathways that confer to the host organism a competitive (metabolic) advantage in select niches. As one of the three common metabolosome core enzymes, the structure of PduL provides a key missing piece to our structural picture of the shared core biochemistry (Fig 1) of functionally diverse catabolic BMCs. We have observed the oligomeric state differences of PduL to correlate with the presence of an EP, providing new insight into the function of this sequence extension in BMC assembly. Moreover, our results suggest a means for Coenzyme A incorporation during metabolosome biogenesis. A detailed understanding of the underlying principles governing the assembly and internal structural organization of BMCs is a requisite for synthetic biologists to design custom nanoreactors that use BMC architectures as a template. Furthermore, given the growing number of metabolosomes implicated in pathogenesis [46–50], the PduL structure will be useful in the development of therapeutics. It is gradually being realized that the metabolic capabilities of a pathogen are also important for virulence, along with the more traditionally cited factors like secretion systems and effector proteins [51]. The fact that PduL is confined almost exclusively to metabolosomes can be used to develop an inhibitor that blocks only PduL and not Pta as a way to selectively disrupt BMC-based metabolism, while not affecting most commensal organisms that require PTAC activity.
Genes for PduL homologs with and without the EP were amplified via PCR using the primers listed in S1 Table. sPduL was amplified using S. enterica Typhimurium LT2 genomic DNA, and pPduL and rPduL sequences were codon optimized and synthesized by GenScript with the 6xHis tag. All 5’ primers included EcoRI and BglII restriction sites, and all 3’ primers included a BamHI restriction site to facilitate cloning using the BglBricks strategy. 5’ primers also included the sequence TTTAAGAAGGAGATATACCATG downstream of the restriction sites, serving as a strong ribosome binding site. The 6x polyhistidine tag sequence was added to the 3’ end of the gene using the BglBricks strategy and was subcloned into the pETBb3 vector, a pET21b-based vector modified to be BglBricks compatible.
E. coli BL21(DE3) expression strains containing the relevant PduL construct in the pETBb3 vector were grown overnight at 37°C in standard LB medium and then used to inoculate 1L of standard LB medium in 2.8 L Fernbach flasks at a 1:100 dilution, which were then incubated at 37°C shaking at 150 rpm, until the culture reached an OD600 of 0.8–1.0, at which point cultures were induced with 200 μM IPTG (isopropylthio-β-D-galactoside) and incubated at 20°C for 18 h, shaking at 150 rpm. Cells were centrifuged at 5,000 xg for 15 min, and cell pellets were frozen at –20°C.
For protein purifications, cell pellets from 1–3 L cultures were resuspended in 20–30 ml buffer A (50 mM Tris-HCl pH 7.4, 300 mM NaCl) and lysed using a French pressure cell at 20,000 lb/in2. The resulting cell lysate was centrifuged at 15,000 xg. 30 mM imidazole was added to the supernatant that was then applied to a 5 mL HisTrap column (GE Healthcare Bio-Sciences, Pittsburgh, PA). Protein was eluted off the column using a gradient of buffer A from 0 mM to 500 mM imidazole over 20 column volumes. Fractions corresponding to PduL were pooled and concentrated using Amicon Ultra Centrifugal filters (EMD Millipore, Billerica, MA) to a volume of no more than 2.5 mL. The protein sample was then applied to a HiLoad 26/60 Superdex 200 preparative size exclusion column (GE Healthcare Bio-Sciences, Pittsburgh, PA) and eluted with buffer B (20 mM Tris pH 7.4, 50 mM NaCl). Where applicable, fractions corresponding to different oligomeric states were pooled separately, leaving one or two fractions in between to prevent cross contamination. Pooled fractions were concentrated to 1–20 mg/mL protein as determined by the Bradford method [52] prior to applying on a Superdex 200 10/300 GL analytical size exclusion column (GE Healthcare Bio-Sciences, Pittsburgh, PA). Size standards used were Thyroglobulin 670 kDa, γ-globulin 158 kDa, Ovalbumin 44 kDa, and Myoglobin 17 kDa. For light scattering, the proteins were measured in a Protein Solutions Dynapro dynamic light scattering instrument with an acquisition time of 5 s, averaging 10 acquisitions at a constant temperature of 25°C. The radii were calculated assuming a globular particle shape.
Size exclusion chromatography coupled with SEC-MALS was performed on full-length rPduL and rPduL-ΔEP similar to Luzi et al. 2015 [53]. A Wyatt DAWN Heleos-II 18-angle light scattering instrument was used in tandem with a GE AKTA pure FPLC with built in UV detector, and a Wyatt Optilab T-Rex refractive index detector. Detector 16 of the DAWN Heleos-II was replaced with a Wyatt Dynapro Nanostar QELS detector for dynamic light scattering. A GE Superdex S200 10/300 GL column was used, with 125–100 μl of protein sample at 1 mg/ml concentration injected, and the column run at 0.5 ml/min in 20 mM Tris, 50 mM NaCl, pH 7.4.
Each detector of the DAWN-Heleos-II was plotted with the Zimm model in the Wyatt ASTRA software to calculate the molar mass. The molar mass was measured at each collected data point across the peaks at ~1 point per 8 μl eluent. Both the Mw and Mn of the molar mass calculations, as well as percent deviations, were also determined using Wyatt software program ASTRA.
For preparing protein for crystallography, expression cells were grown as above, except were induced with 50 μM IPTG. Harvested cells were resuspended in buffer B and lysed using a French Press. Cleared lysate was applied on a 5 ml HisTrap HP column (GE Healthcare) and washed with buffer A containing 20 mM imidazole. Pdul-His was eluted with 2 CV buffer B containing 300 mM imidazole, concentrated and then applied on a HiLoad 26/60 Superdex 200 (GE Healthcare) column equilibrated in buffer B for final cleanup. Protein was then concentrated to 20–30 mg/ml for crystallization. Crystals were obtained from sitting drop experiments at 22°C, mixing 3 μl of protein solution with 3 μl of reservoir solution containing 39%–35% MPD. Crystals were flash frozen in liquid nitrogen after being adding 5 μl of a reservoir solution. For heavy atom derivatives, 0.2 μl of 100 mM Thiomerosal (Hampton Research) was added to the crystallization drop 36 h prior to freezing. For phosphate soaks, 5 μl reservoir and 1.5 μl 200 mM sodium phosphate solution (pH 7.0) were added 2 d prior to flash freezing.
Enzyme reactions were performed in a 2 mL cuvette containing 50 mM Tris-HCl pH 7.5, 0.2 mM 5,5'-dithiobis-2-nitrobenzoic acid (DTNB; Ellman’s reagent), 0.1 mM acyl-CoA, and 0.5 μg purified PTAC, unless otherwise noted. To initiate the reaction, 5 mM NaH2PO4 was added, the cuvette was inverted to mix, and the absorbance at 412 nm was measured every 2 s over the course of four minutes in a Nanodrop 2000c, in the cuvette holder. 14,150 M-1cm-1 was used as the extinction coefficient of DTNB to determine the specific activity.
A multiple sequence alignment of 228 PduL sequences associated with BMCs [2] and 20 PduL sequences not associated with BMCs was constructed using MUSCLE [54]. PduL sequences associated with BMCs were determined from Dataset S1 of Reference [2], and those not associated with BMCs were determined by searching for genomes that encoded PF06130 but not PF03319 nor PF00936 in the IMG database [18]. The multiple sequence alignment was visualized in Jalview [55], and the nonconserved N- and C-terminal amino acids were deleted. This trimmed alignment was used to build the sequence logo using WebLogo [56].
Diffraction data were collected at the Advanced Light Source at Lawrence Berkeley National Laboratory beamline 5.0.2 (100 K, 1.0000 Å wavelength for native data, 1.0093 Å for mercury derivative, 1.2861 Å for Zn pre-edge and 1.2822 Å for Zn peak). Diffraction data were integrated with XDS [57] and scaled with SCALA (CCP4 [58]). The structure of PduL was solved using phenix.autosol [59], which found 11 heavy atom sites and produced density suitable for automatic model building. The model was refined with phenix.refine [59], with refinement alternating with model building using 2Fo-Fc and Fo-Fc maps visualized in COOT [60]. Statistics for diffraction data collection, structure determination and refinement are summarized in Table 2. Figures were prepared using pymol (www.pymol.org) and Raster3D [61].
Models of S. enterica Typhimurium LT2 and P. limnophilus PduL were generated with Modeller using the align2d and model-default scripts [62].
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10.1371/journal.ppat.1001143 | Wolbachia Stimulates Immune Gene Expression and Inhibits Plasmodium Development in Anopheles gambiae | The over-replicating wMelPop strain of the endosymbiont Wolbachia pipientis has recently been shown to be capable of inducing immune upregulation and inhibition of pathogen transmission in Aedes aegypti mosquitoes. In order to examine whether comparable effects would be seen in the malaria vector Anopheles gambiae, transient somatic infections of wMelPop were created by intrathoracic inoculation. Upregulation of six selected immune genes was observed compared to controls, at least two of which (LRIM1 and TEP1) influence the development of malaria parasites. A stably infected An. gambiae cell line also showed increased expression of malaria-related immune genes. Highly significant reductions in Plasmodium infection intensity were observed in the wMelPop-infected cohort, and using gene knockdown, evidence for the role of TEP1 in this phenotype was obtained. Comparing the levels of upregulation in somatic and stably inherited wMelPop infections in Ae. aegypti revealed that levels of upregulation were lower in the somatic infections than in the stably transinfected line; inhibition of development of Brugia filarial nematodes was nevertheless observed in the somatic wMelPop infected females. Thus we consider that the effects observed in An. gambiae are also likely to be more pronounced if stably inherited wMelPop transinfections can be created, and that somatic infections of Wolbachia provide a useful model for examining effects on pathogen development or dissemination. The data are discussed with respect to the comparative effects on malaria vectorial capacity of life shortening and direct inhibition of Plasmodium development that can be produced by Wolbachia.
| Malaria is one of the world's most devastating diseases, particularly in Africa, and new control strategies are desperately needed. Here we show that the presence of Wolbachia bacteria inhibits the development of a malaria parasite in the most important Anopheles mosquito species of Africa. In addition we show that the presence of Wolbachia results in the switching on of immune genes that are known to affect development of many species of malaria parasite. When added to the lifespan-shortening effects of this particular strain of Wolbachia, and the general ability of Wolbachia to spread through insect populations, our study provides a stimulus for the development of Wolbachia-based malaria control methods. It also provides new insights into the wide range of effects of Wolbachia in insects.
| Wolbachia pipientis is an intracellular maternally inherited bacterial symbiont of invertebrates that is very common in insects, including a number of mosquito species [1], [2]. It can manipulate host reproduction in several ways, including cytoplasmic incompatibility (CI), whereby certain crosses are rendered effectively sterile. Females that are uninfected produce infertile eggs when they mate with males that carry Wolbachia, while there is a ‘rescue’ effect in Wolbachia-infected embryos such that infected females can reproduce successfully with any males. Therefore uninfected females suffer a frequency-dependent reproductive disadvantage. Wolbachia is able to rapidly invade populations using this powerful mechanism [3]–[5].
A strain of Wolbachia called wMelPop has been identified that over-replicates in somatic tissues and roughly halves the lifespan of laboratory Drosophila melanogaster [6]. A transinfection of wMelPop from Drosophila into the mosquito Aedes aegypti also leads to a similarly shortened lifespan in the lab, as well as inducing strong CI, which has made it a very promising candidate for the development of new strategies for controlling mosquito-borne diseases [7]. All mosquito-borne pathogens require an extrinsic incubation period before they can be transmitted that is relatively long (∼9 days for malaria) compared to mean mosquito lifespan in the field; therefore, a reduction in the number of old individuals in the population will reduce disease transmission [8]–[11].
We recently found that the presence of wMelPop also produces a major upregulation of a large number of immune genes in Ae. aegypti and inhibits the development of filarial nematode worm parasites [12]. We hypothesized that the two effects are functionally related – higher levels of immune effectors in wMelPop-infected mosquitoes render them better able to kill parasites [12]. Homologs of some of the Ae. aegypti genes that are upregulated in the presence of wMelPop have been previously shown to have the ability to regulate development of Plasmodium parasites in Anopheles, for example a transgene encoding cecropin-A/a synthetic cecropin-B of Hyalophora cecropia; the NF-κB-like transcription factor Rel2 controlling the Imd pathway; and TEP (Thioester containing) opsonization proteins [13]–[20]. It has recently been shown that the wMelPop-infected Ae. aegypti line has impaired ability to transmit an avian malaria, Plasmodium gallinaceum [21]. It is possible that these effects of wMelPop could be particular to the Ae. aegypti transinfection; however, if comparable upregulation of orthologous immune genes, and inhibition of Plasmodium development are also seen in the important Anopheles vectors of human malaria, it may provide a stimulus to the development of new Wolbachia-based malaria control strategies.
To address this question we used Anopheles gambiae, the most important vector of malaria in Africa, which like Ae. aegypti is not naturally infected with Wolbachia. The creation of stably inherited lines of An. gambiae is likely to require a long period of microinjection and selection, as had to be performed for Ae. aegypti [7]. However, in advance of the successful creation of an An. gambiae stable transinfection, the effects of the presence of wMelPop on immunity and malaria transmission can be tested using an established wMelPop-infected An. gambiae cell line [22] and the ability to create somatic lifetime infections of Wolbachia in adult female mosquitoes by intrathoracic inoculation [23], [24]. The wMelPop strain forms disseminated somatic infections in its natural Drosophila host [6], in common with some but not all Wolbachia strains [25]. Given that a) Plasmodium parasites will travel solely through somatic tissues on their journey to the salivary glands, and b) that many of the known antimalarial immune effectors are humoral/systemic, we consider that the creation of somatic infections of Wolbachia via adult inoculation represents a useful model for stably inherited germline-associated infections. To examine this hypothesis further, we also created somatic wMelPop infections in Ae. aegypti, in order to compare the magnitude of the effects on mosquito immunity and filarial nematode parasite development with those observed in the stably wMelPop-transinfected line.
Given that a stable wMelPop infection of An. gambiae does not yet exist, it was necessary to create transient somatic infections by intrathoracic innoculation with purified Wolbachia. RNA from these transinfected females was then tested for expression levels of six immune genes, and upregulation of all these genes was observed compared to buffer injected and E. coli - injected controls (Figure 1). Of these genes, LRIM1 and TEP1 (whose products have been shown to interact in the opsonisation response) have previously been shown to have an important inhibitory or antagonistic effect on Plasmodium development [18]–[20]. Importantly, injected mosquitoes were left for eight days before Plasmodium challenge or qRT-PCR, and therefore the pulse of immune gene upregulation caused by the injury itself or by the E. coli challenge would be expected to have already passed [15].
The wMelPop infected cell line MOS55 [22] showed upregulation of all six selected immune genes compared to an uninfected cell line created by tetracycline curing of infected MOS55 (Figure 2). These data add confidence to the hypothesis that it is the presence of wMelPop itself that is inducing immune gene upregulation, and by extension Plasmodium inhibition, and that these effects are not artefacts of the intrathoracic injection process. The degree of upregulation was different for some genes in the cell line than observed for the somatic in vivo transinfection. However these differences would be expected given that many immune genes are primarily expressed in particular cell types/organs in adult mosquitoes, such as the fat body cells or in the case of TEP1, the haemocytes [18], and the cellular composition of this larval-derived cell line is unknown.
Three Plasmodium berghei challenge experiments were conducted on transiently Wolbachia-infected A. gambiae females compared to buffer injected, uninjected, and in one case E. coli-injected controls (Figure 3a–c). In all three experiments highly significant reductions in intensity of oocyst infection in the wMelPop transinfected females were observed compared to the other treatments, while there were no significant differences between any of the control treatments within each experiment. Mean P. berghei intensities were reduced in the wMelPop-infected mosquitoes by between 75% and 84% compared to the corresponding buffer injected control groups. A further experiment confirmed the lack of any significant differences in intensity between the E. coli-injected, buffer injected and uninjected controls (data not shown).
In order to obtain evidence for a causal link between the immune upregulation and the Plasmodium inhibition phenotypes, TEP1 knockdown was undertaken by injection of dsRNA at the same time as Wolbachia injection. Significantly higher oocyst numbers were observed compared to the control where dsLacZ was injected at the same time as Wolbachia (Figure 3d). This experiment provides evidence for a significant contribution of Wolbachia-induced TEP1 upregulation to the Plasmodium inhibition phenotype.
We assessed the utility of the transient wMelPop somatic infection model by comparing the effects on host immunity and pathogen development with those observed in stable inherited infections of wMelPop. To do this we utilized a filarial nematode-susceptible line of another mosquito species, Ae. aegypti, in which we have previously carried out Brugia pahangi challenges on a stable wMelPop-transinfected line [7], [12]. We created somatic wMelPop infections using exactly the same methodology as carried out for An. gambiae, and after eight days challenged them with B. pahangi or carried out qRT-PCR.
The somatic Wolbachia infection also induced upregulation of selected immune genes (PGRPS1, CECD, CLIPB37, CTL) (Figure 4a). The scale of upregulation was considerably lower than observed in the comparable Ae. aegypti stable transinfection as previously reported [12]. Likewise, challenge of the somatically wMelPop infected females with B. pahangi did produce a significant reduction in the numbers developing to the L3 (infectious) stage compared to the controls (Figure 4b), as was previously observed in the stable inherited wMelPop infected line, which showed >50% reduction in mean numbers of L3 compared to the Wolbachia-uninfected control at the same microfilarial challenge density [12]. Using quantitative PCR comparing three groups of two mosquitoes with the single copy genes ftsZ (Wolbachia) and Actin5C (Ae. aegypti) for normalization, we estimated that there were approximately 176±70 times more wMelPop cells in the stably infected line compared to the somatic infections. This may explain this reduced effect on gene upregulation. Therefore we conclude that intrathoracic inoculation can be a valuable way to test the effects of Wolbachia on host immunity and pathogen transmission. Although extrapolations to different mosquito species and parasites must be made with care, it does seem likely that the effects observed for somatic Wolbachia infections using the methodology reported here are likely to be smaller than for a stable inherited infection, and thus that the estimations made may be conservative.
An experiment to test whether the immune upregulation observed in wMelPop-infected mosquitoes affects the density of the Wolbachia itself was conducted using the stable inherited infection of wMelPop in an Ae. aegypti Refm background [7], [12]. Wolbachia ftsZ gene expression (used as a proxy for Wolbachia density) was found to be higher in dsRel2-injected than in dsLacZ-injected mosquitoes at both day six and day ten post-injection (Figure 4c). These data suggest that the immune effectors controlled by the Imd (Rel2-controlled) pathway can influence Wolbachia densities. The very high rate of maternal transmission observed in wMelPop-infected Ae. aegypti [7], despite chronic immune upregulation, means that the biological significance of this density difference is unknown, although potentially it could act to limit wMelPop pathogenicity to some degree. More comprehensive experiments addressing this question will make use of transgenic immune knockdown lines infected with wMelPop, which are currently being produced, and are expected to enable the effects of stronger and more long lasting immune pathway knockdown to be investigated.
The data reported strongly support the hypothesis that wMelPop can inhibit the development of Plasmodium in Anopheles malaria vector mosquitoes. The An. gambiae/P. berghei combination, although not one that occurs in nature, does represent a tractable and well studied model for which considerable information is already available about Plasmodium killing mechanisms; however we recognize the challenge experiments will ultimately need to be repeated with the far less tractable human parasite P. falciparum once a stably inherited Wolbachia transinfected line of An. gambiae has been created. The densities of P. berghei used in laboratory challenges such as these can be high compared to those of P. falciparum that would occur in nature, although the mean intensities recorded in these studies lie within the range recorded for P. falciparum in the field. The significant reductions in intensity we recorded in laboratory experiments are considered likely to translate to significant reductions in oocyst prevalence/transmission in a real-life setting.
The knockdown experiment provided evidence for a major role of TEP1, and by extension LRIM1 whose products interact as part of the same opsonization pathway [20], in the inhibition of P. berghei development. This is the first time a direct link between the Wolbachia pathogen inhibition and immune upregulation phenotypes has been made. A more detailed and exhaustive investigation of the relative contributions of different components of the Anopheles immune system to Plasmodium killing can be made once stable inherited Wolbachia infections have been established.
Taken together with the recent report of reduction in P. gallinaceum development in wMelPop-infected Ae. aegypti [21], the data increase the desirability of creating stably inherited wMelPop transinfections in important malaria vectors. The potential combination of lifespan shortening and direct inhibition of Plasmodium development in the mosquito would represent a very attractive control strategy, since both of these phenotypes are critical components of malaria vectorial capacity. A simple model exploring relative contributions of these two parameters to vectorial capacity is shown in Figure 5. Though lifespan reduction and Plasmodium inhibition can each substantially reduce the vectorial capacity of a mosquito population, together they act synergistically to reduce transmission. Depending on the scale of lifespan reduction that would be observed under field conditions, which is as yet unknown, the Plasmodium inhibition effect could dramatically increase the efficacy of the wMelPop infection in reducing malaria transmission.
Other Wolbachia strains might also show malaria inhibition effects, particularly if they reach high somatic densities and/or induce large-scale immune stimulation. Here we show that the use of transient somatic infections of Wolbachia by adult female inoculation followed by pathogen challenge is a valuable means to test likely effects on immunity and transmission. This is significant as it allows comparison and selection of strains for the most desirable properties prior to the lengthy, and technically very challenging, process of creating stably inherited Anopheles transinfections. If other Wolbachia strains can be identified which also inhibit Plasmodium transmission, they would represent an attractive alternative to wMelPop if they do not shorten lifespan to the same extent, since they are therefore likely to have much lower fitness costs. Only the wMelPop strain has to date been found to produce a strong life-shortening phenotype.
Laboratory estimates suggest that transinfection of wMelPop in Aedes aegypti can reduce fitness by around 50% [7]. This would appear to make it difficult for this strain of Wolbachia to spread by means of CI through natural populations [26], particularly where populations are fragmented. However, fitness estimates made in relatively benign laboratory conditions, where a comparatively large fraction of the population become old, can overestimate the relative costs of infection. In the field most mosquitoes die early and few live long enough to experience higher Wolbachia-induced mortality (although those that do are significant to disease control, if they would otherwise have lived long enough to transmit the infection). As shown in Figure 5 reductions in longevity and Plasmodium inhibition together determine vectorial capacity and it will also be important to understand the joint effects of the two phenotypes on mosquito fitness in the field. Detailed knowledge of the demographics of the target species is also important [27]. Selective pressures acting on the host would likely modulate the life-shortening phenotype over time, but this may not occur rapidly enough to prevent a sustained period of disease control.
Wolbachia is now known to inhibit the dissemination or development of a variety of insect pathogens and insect-borne pathogens – various Drosophila pathogenic viruses, dengue and chikungunya viruses of humans, and filarial nematode parasites in addition to Plasmodium [12], [21], [28]–[31]. Some of these pathogen-inhibition phenotypes have been reported in Drosophila species that naturally harbour Wolbachia, in other words they are not restricted to species such as Ae. aegypti or An. gambiae in which Wolbachia forms a novel transinfection. On a broader level these Wolbachia cases can be added to various other examples where bacterial symbionts have been shown to provide protective effects against one or more pathogens [32], [33], although the mechanisms involved are likely to be diverse. Parallels can also be drawn with the effects of entomopathogenic fungi, which can both reduce Anopheles lifespan and directly inhibit Plasmodium development [34]–[36]. Pathogen inhibition represents a new and increasingly significant component of our understanding of the effects of Wolbachia in insects, and provides excellent prospects for the development of novel malaria control strategies.
All procedures involving animals were approved by the ethical review committee of Imperial College and by the United Kingdom Government (Home Office), and were performed in accordance with United Kingdom Government (Home Office) and EC regulations.
Wolbachia wMelPop was purified from the infected An. gambiae cell line MOS55 [22], [37] as previously described [23], [24]. This protocol has previously been shown to allow Wolbachia replication in the recipient An. gambiae [24]. Cells obtained from one 75 CM2 flask were re-suspended in 100 µL of Schneider medium without antibiotics (optical density, OD = 0.09). 69 nL of this Wolbachia suspension (or 69 nL Schneider for the controls) were microinjected into the thorax of young An. gambiae females of the G3 strain or Ae. aegypti females of the Refm strain [38] using an Nanoject microinjector (Drummond). The mosquitoes were supplied with 10% sucrose ad libitum and left to recover for at least eight days prior to qRT-PCR or challenge experiments. A similar OD of 0.1 for E. coli was used to inject another set of controls.
Gene expression levels were monitored using qRT-PCR. Total RNA was extracted with Trizol reagent from groups of ten An. gambiae or Ae. aegypti females maintained at 26°C and 70% relative humidity, and cDNAs were synthesised from 1 µg of total RNA using SuperScript II enzyme (Invitrogen). qRT-PCR was performed on a 1 to 20 dilution of the cDNAs using dsDNA dye SYBR Green I. Reactions were run on a DNA Engine thermocycler (MJ Research) with Chromo4 real-time PCR detection system (Bio-Rad) using the following cycling conditions: 95°C for 15 minutes, then 45 cycles of 95°C for 10s, 59°C for 10s, 72°C for 20s, with fluorescence acquisition at the end of each cycle, then a melting curve analysis after the final one. The cycle threshold (Ct) values were determined and background fluorescence was subtracted. Gene expression levels of target genes were calculated, relative to the internal reference gene Actin5C or RS17 for Ae. aegypti and RS7R for An. gambiae. Primers were designed using Vectorbase (www.vectorbase.org) mosquito gene sequences/orthology criteria, and the wMel genome sequence [39], since wMel and wMelPop are closely related [40]. Primer pairs used to detect target gene transcripts are listed in Table 1.
The density of Wolbachia in somatic and stable infections of Ae. aegypti was estimated using both qPCR and qRT-PCR. DNA was extracted using the Livak method and qRT-PCR or qPCR equipment and protocols were the same as those described above. The single copy genes ftsZ (Wolbachia) and Actin5C and S7 (Ae. aegypti) were used to estimate relative numbers of Wolbachia normalized against the mosquito genome.
General parasite maintenance was carried out as previously described [41]. P. berghei ANKA 2.34 parasites were maintained in 4–10-week-old female Theiler's Original (TO) mice by serial mechanical passage (up to a maximum of eight passages). Hyper-reticulocytosis was induced 2–3 days before infection by treating mice with 200µL i.p. phenylhydrazinium chloride (6mg/ml in PBS; ProLabo UK). Mice were infected by intraperitoneal (i.p.) injection and infections were monitored on Giemsa-stained tail blood smears.
In four independent experiments, individual 4–10 week old Theiler's Original (TO) mice were treated with 200µL i.p. phenylhydraziuium chloride (PH; 6mg/ml in PBS; ProLabo UK) to induce hyper-reticulocytosis. Three days later mice were injected by intraperitoneal (i.p.) injection with 106 parasites of P. berghei ANKA 2.34 as described previously [41]. Three days post mouse infection, batches of 100 starved Anopheles gambiae strain G3 females, eight days post injection with Wolbachia, buffer, E. coli or uninjected controls, were allowed to feed on the infected mice. 24h after feeding, mosquitoes were briefly anesthetized with CO2, and unfeds removed. Mosquitoes were then maintained on fructose [8% (w/v) fructose, 0.05% (w/v) p-aminobenzoic acid] at 19–22°C and 50–80% relative humidity. At day 10 post-feeding, mosquito midguts were dissected, and oocyst numbers (intensity) and prevalence recorded. The Kruskal-Wallis test was used to compare oocyst counts (intensity of infection) and Fisher's exact test for prevalence (percentage of mosquitoes containing at least one oocyst).
T7-tailed primers (see Table 1) were used to amplify fragments of the TEP1 and REL2 gene from female cDNA template or the LacZ gene from E. coli total DNA. dsRNA was synthesized using the T7 Megascript kit (Ambion) and adjusted to a concentration of 3 or 4 µg/µl in RNAse free water for dsREL2 and dsTEP1 respectively. For REL2 KD 69nl of dsRNA were injected per female mosquito, For TEP1-wolbachia KD 69 nl of a mix of 2 parts dsRNA to 1 part of purified wMelPop in Schneider's medium (OD 0.3) were injected into the thorax of CO2 anesthetized female An. gambiae mosquitoes (total ∼200 per group). Five days after injection (in order to still fall within the gene knockdown period), mosquitoes were fed on a Plasmodium infected mouse.
Ae. aegypti mosquitoes of the filaria-susceptible Refm strain were fed on sheep blood containing 23 B. pahangi microfilaria per µL eight days post Wolbachia innoculation, plus buffer-injected controls of the same age; any females that did not feed properly were removed. Dissections were carried out 10 days after the infective blood meal under a dissecting stereomicroscope. Kruskal-Wallis tests were used to compare counts of B. pahangi L3 (infective stage larvae).
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10.1371/journal.pbio.1000235 | An aPKC-Exocyst Complex Controls Paxillin Phosphorylation and Migration through Localised JNK1 Activation | Atypical protein kinase C (aPKC) isoforms have been implicated in cell polarisation and migration through association with Cdc42 and Par6. In distinct migratory models, the Exocyst complex has been shown to be involved in secretory events and migration. By RNA interference (RNAi) we show that the polarised delivery of the Exocyst to the leading edge of migrating NRK cells is dependent upon aPKCs. Reciprocally we demonstrate that aPKC localisation at the leading edge is dependent upon the Exocyst. The basis of this inter-dependence derives from two-hybrid, mass spectrometry, and co-immunoprecipitation studies, which demonstrate the existence of an aPKC–Exocyst interaction mediated by Kibra. Using RNAi and small molecule inhibitors, the aPKCs, Kibra, and the Exocyst are shown to be required for NRK cell migration and it is further demonstrated that they are necessary for the localized activation of JNK at the leading edge. The migration associated control of JNK by aPKCs determines JNK phosphorylation of the plasma membrane substrate Paxillin, but not the phosphorylation of the nuclear JNK substrate, c-jun. This plasma membrane localized JNK cascade serves to control the stability of focal adhesion complexes, regulating migration. The study integrates the polarising behaviour of aPKCs with the pro-migratory properties of the Exocyst complex, defining a higher order complex associated with the localised activation of JNK at the leading edge of migrating cells that determines migration rate.
| Cell migration is an essential process in multicellular organisms during such events as embryonic development, the immune response, and wound healing. Cell migration is also instrumental in the development of pathologies such as cancer cell invasion of healthy tissues. To make cells move, key molecules must be engaged in a coordinated manner; understanding which molecules, and how and when they work (for example, under physiological versus pathological conditions) will impact on new therapies designed to suppress abnormal migration. Migrating cells must coordinate two key processes: extension of the front or ‘leading’ edge of the cell and retraction of the back edge. Both processes require the turnover of protein assemblies known as focal adhesion complexes. In this paper we show that two different groups of regulators of migration – aPKC, a protein kinase, and exocyst, a complex of proteins also known to be required for exocytosis – interact physically via the scaffold protein kibra. All these components are required for efficient cell migration and all are enriched at the leading edge of moving cells, in a mutually dependent manner. At the leading edge, these components control the local activation of two additional protein kinases, ERK and JNK. The activation of ERK and JNK at the front of migrating cells in turn controls the phosphorylation of paxillin, a component of focal adhesions. Phosphorylation of paxillin is associated with the presence of more dynamic focal adhesions. Our data thus indicate that an aPKC-kibra-exocyst complex plays a crucial role in delivering local stimulatory signals to the leading edge of migrating cells.
| Migration of cells is critical to the development and the normal physiology of organisms; it also plays a more sinister role in the dissemination of cancer towards metastatic disease, a process typically associated with poor prognosis. The process of migration involves a combination of cellular functions including those of altered attachment to surrounding contacts (cells or matrix), protrusion of a leading edge, polarisation in the creation or recognition of that leading edge, and mechanical movement [1]. Understanding the details of these processes represents an important objective in defining the collection of candidate targets that may offer new opportunities in restricting disease spread.
The atypical PKC isoforms (aPKCζ and aPKCι) comprise a branch of the serine/threonine protein kinase PKC superfamily with regulatory properties that distinguish them from the more typical diacylglycerol-regulated isoforms [2]. These kinases can be activated by acidic phospholipids such as the polyphosphoinositides [3], however specificity appears to be driven by activation through Par6/cdc42 [4]. Indeed, interactions with the Par6/Par3 complex have implicated aPKC isoforms in a number of polarity [5] and more recently migratory models [6]. In migrating astrocytes, the activation of aPKC leads to phosphorylation and inactivation of GSK-3β, which causes the adenomatous polyposis coli (APC) tumor suppressor protein to associate with microtubule plus ends at the leading edge [7]. The Par6-PKCζ complex also regulates the spatially localized association of Dlg1 and APC to control cell polarization [8]. PKCζ is required for epidermal growth factor-induced chemotaxis of human breast cancer cells [9], while PKCι has been shown to promote nicotine-induced migration and invasion of cancer cells via phosphorylation of m- and μ-calpains [10].
The Exocyst was first identified as a complex required for exocytosis in Saccharomyces cerevisiae [11]. In mammals, the Exocyst comprises a complex of eight proteins, which facilitates regulated exocytosis to regions of membrane activity [12],[13],[14],[15]. Recently it has been shown that a Ral-Exocyst pathway is involved in cell migration, with RalB activation leading to Exocyst assembly and recruitment to the leading edge [16].
It is anticipated that the various complexes and pathways involved in polarized migration may be co-regulated/coordinated and that elucidation of these relationships will lead to a more integrated understanding of migratory behaviour. The present study was stimulated by the finding that the scaffold protein Kibra, previously shown to interact with aPKCζ [17], was a binding partner of the Exocyst (see below). This has led to the specific hypothesis that there is a molecular and functional interaction between the Par/CDC42/PKCζ/ι pathway and the Ral/Exocyst pro-migratory pathways. We demonstrate that indeed there is a mutual dependence of aPKC and Exocyst in their behaviour in migratory cells and this is associated with their mutually dependent regulation of the delivery of signals to the leading edge of migrating cells. We identify key regulatory processes under the control of these local aPKC/Exocyst-dependent signals and go on to demonstrate that the regulation of focal adhesion stability represents a critical migratory output of the aPKC/Exocyst pathway.
A cooperative functional relationship between the Exocyst complex and aPKC in cell migration should be reflected in a shared requirement in a model system. The Exocyst has been shown previously to play an essential role in NRK cell migration. So to determine the requirements for PKCι and PKCζ in NRK cell migration, two independent siRNAs for each protein were employed to knock-down expression (Figure 1A). Depletion of either PKCι or PKCζ resulted in a cell migration defect as assessed in a monolayer wound healing model (Figure 1B) as well as in a Transwell migration assay (unpublished data). The speed of NRK cell migration in the wound assay is 15.3 µm/h. Depletion of either aPKC reduces this by ∼40%, whilst depletion of both reduces migration further to ∼6 µm/h (Figure 1C).
To distinguish between a non-catalytic, scaffold-only requirement for aPKC and a catalytic activity requirement, PKC inhibitors were employed. Selected combinations of these inhibitors can be exploited to provide circumstantial evidence on the requirements of PKC isoforms based upon their relative potency such that an aPKC activity involvement would be sensitive to the pan-PKC inhibitor Gö6983 (cPKC, nPKC, aPKC) while showing little sensitivity to BIMI (cPKC, nPKC) or Gö6976 (cPKC). Gö6983 had a profound effect on cell migration, while the cPKC inhibitor Gö6976 had no effect (Figure 1C and 1D). BIMI had a weaker effect than the Gö6983 on migration (Figure 1C and 1D). Although these inhibitors are not entirely PKC-specific, in combination with the siRNA data it can be concluded that aPKCs are required for efficient NRK cell migration, providing a model in which to probe an aPKC-Exocyst relationship in migration.
To assess a connection between aPKC isoforms and the Exocyst, we sought to determine their distribution in migrating cells. Examination of the location of aPKCs (combined PKCι and PKCζ) demonstrated that aPKC was localised at the leading edge of migrating NRK cells (Figure 2A). By contrast, in confluent cells, aPKCs are mainly cytosolic and sometimes partially at cell-cell contacts (unpublished data). A monoclonal antibody specific for PKCι confirmed its presence at the leading edge as well as within a perinuclear compartment; PKCζ specific reagents were found to cross-react with recombinant PKCι precluding PKCζ-specific immunostaining (Dr. M. Linch and PJP unpublished results). The Exocyst (here visualized by the subunits Sec6 and Exo70) is also in part localized at the leading edge of migrating cells and the pattern of aPKC distribution matches that observed for the Exocyst complex. The localisation of aPKC and the Exocyst at the leading edge of migrating cells is not simply a function of membrane ruffling. In subconfluent monolayers extensive membrane ruffling is observed without enrichment of aPKC or the Exocyst (Figure S1), while the ruffles at the leading edge of migrating cells are enriched with aPKC and the Exocyst.
To determine whether the Exocyst was responsible for aPKC accumulation at the leading edge, siRNA to Sec5 was employed [16]. Knock-down of Sec5 (Figure 2A) prevented aPKC localisation at the leading edge without disturbing the total amount of aPKC in the cells (Figure 2B). Knock-down of another component of the Exocyst complex, Exo84, also prevented aPKC localisation at the leading edge (see below, Figure 3C). When one of the components of Exocyst complex (either Sec5 or Exo84) is depleted, more than 50% of the cells have a total absence of PKCι at the leading edge. Consistent with this, migration was inhibited on knock-down of Sec5 and Exo84 by 60%. Reciprocally it was found that siRNA to aPKCs (Figure 2C and Figure S2A, S2C) or treatment with the inhibitor Gö6983 (unpublished data) suppressed Exo70 (for more than 60% of the cells) and Sec6 accumulation at the leading edge. This disruption of localisation is not due to a modification of the protein levels of Exo70 and Sec6 (Figure 1A). The recruitment of aPKC and Exo70 at the leading edge were quantified (Figure S2A, S2B, S2C). We also evaluated the specificity of the staining as well as the effect on the knock-down of the Exocyst (Sec5 or Exo84) on the presence of aPKC at the leading edge and reciprocally the effect on the knock-down of aPKC on the presence of Exo70 at the leading edge (Figure S2A, S2B). Notably, depletion of either PKCι or PKCζ partially inhibited the “tubularisation” of the Sec6 compartment seen in motile cells on methanol fixation (Figure S3C), suggesting that aPKC is required for the localization of Sec6 on microtubules in response to cell migratory cues. Depletion of PKCι or PKCζ or Sec5 did not dramatically affect the stability of the microtubules (unpublished data). To control for the possibility of global disruptive effects of aPKC on vesicle-staining patterns in migrating cells, β-COP proteins were monitored. The depletion of aPKC modified the localization of Sec6 but not β-COP, indicating the specificity of the aPKC effect (Figure S3C).
The Exocyst associates with microtubules [18],[19] providing a potential basis for Exocyst and aPKC movement. As predicted, depolymerisation of the microtubule network with nocodazole was found to block the accumulation of Exo70 at the leading edge (Figure 2D). Using methanol fixation, Sec6 is detected on tubular structures and these “tubules” of Sec6 are also dependent on the stability of the microtubules (Figure 2E). Because aPKCs were described to control MTOC orientation via the Dynein-Dynactin complex [20] and the recruitment of the Exocyst complex is dependent on the microtubule network, it is possible the effect of the depletion of aPKC on the recruitment of the Exocyst at the front of the cells is due to an indirect effect of aPKC on motor proteins. However, the presence of dominant negative CDC42, which is involved in aPKC effects on polarity, did not disturb the localization of Exo70 at the leading edge (Figure S3D). As predicted from the observations above, nocodazole treatment also blocks aPKC delivery to the leading edge (Figure 2F). The specificity of nocodazole treatment is reflected in the finding that treatment increases actin stress fibres showing that cells retained microfilament structures (unpublished data).
To investigate the basis of the mutual aPKC-Exocyst localisation relationship, assessment was made of the possible association of aPKC and the Exocyst in NRK cells. Antibodies to the Exocyst subunit Sec8 efficiently immunoprecipitated the native complex; probing this immunopurified complex for the presence of aPKC showed that aPKC was also associated. By contrast the related PKCε and PKCδ, which are also expressed in NRK cells, were not recovered in association with the immunopurified Exocyst complex (Figure 3A). Sec8 interacts with both PKCι and PKCζ, based on immunoprecipitation from NRK cells expressing PKCι or a myc tagged PKCζ construct (Figure S4A and S4B). Immunoprecipitation with anti-mycPKCζ or PKCι antibodies did not recover detectable Sec8 in the immunoprecipitate; it would seem that only a subfraction of PKCι or PKCζ is complexed to Sec8. Direct demonstration of the interaction in cells employing FRET approaches has not proved possible because the GFP-fusions of Exocyst subunits have been found not to enter into mature Exocyst complexes (unpublished data) in contrast to the findings in yeast [21].
Given the predicted requirement for activity in the migratory behaviour described above, it was important to determine whether the Exocyst associated aPKC was catalytically active. MBP was selectively phosphorylated in Sec8 immunoprecipitates and this phosphorylation was inhibited by a peptide inhibitor of aPKC (Figure 3B). The Sec8-associated aPKC thus appears to be active.
To assess if the interaction between aPKCs and the Exocyst is regulated, we examined activity and migratory requirements. Sec8 was immunoprecipitated following treatment with the inhibitor Gö6983. As shown in Figure S4C, the physical interaction between Sec8 and aPKC is not modulated in the absence of aPKC activity. To examine if cell migratory cues impact the stability of the interaction between the Exocyst and aPKC, a monolayer of confluent NRK cells was extensively scratched to maximize the number of “free edges” where cell-cell contacts are released. As shown in Figure 3C, cell migration promoted the interaction between aPKC and the Exocyst at 3 h and 6 h. The interaction with the Exocyst increased 1.5- to 2-fold (PKCι or both aPKCs) 6 h after monolayer wounding (Figure 3D). This increase of interaction during cell migration does not appear to be a non-specific stress response triggered by the multi-scratch assay, since no such influence is exerted by osmotic shock (unpublished data). These results sustain the idea that the function of these proteins requires their interaction during cell migration.
The Sec3 subunit of the Exocyst was used as a bait in a two-hybrid screen of a highly complex human placenta cDNA library (10 million independent clones). A total of 120 million interactions were screened (12 times library coverage) and four clones encoding human Kibra (NP_056053) were identified; Kibra is a known aPKC binding partner [17],[22]. Kibra's domain of interaction with Sec3 was defined by the smallest identified prey fragment and encompasses amino acids 129–526. This region has been found only in six screens amongst 935 screens performed against the same cDNA library, indicating that the Sec3-Kibra interaction is highly specific (Figure 3E). In the same screen with Sec3 as a bait, expected partners such as Sec5 (12 fragments, 4 different fusions; interacting domain is amino acids 96–252) and Sec8 (7 fragments, 2 different fusions; interacting domain is amino acids 28–167) were identified also. Confirmation of this Exocyst complex has come from independent studies as Kibra was identified as a partner of the Exocyst from an unbiased proteomic analysis of Exocyst-interacting proteins [23]. To determine the retention of this Kibra interaction in the context of the Exocyst complex, we submitted protein extracts from NRK cells expressing Myc-Kibra or Flag-Kibra constructs to immunoprecipitation with anti-Myc or Flag antibody and the immunoprecipitates were analyzed for the presence of Sec8 (a component of the complex but not Sec3 itself). Figure S5A and S5B show that ectopically expressed Myc-Kibra or Flag-Kibra co-immunoprecipitate with Sec8, whereas the beads alone or a myc antibody used as a negative control precipitated neither. For consistency, we tested if the interaction between Kibra and Exocyst (Sec8) and also between Kibra and aPKC are dependent on cell migration. As shown in Figure 3F, cell migration promoted the interaction between aPKC and Myc-Kibra and also that between Myc-Kibra and Sec8.
To ensure these observations were not a function of ectopic expression, we investigated the behaviour of the endogenous proteins. Endogenous Kibra was found to interact with Sec8 and aPKC, and this complex also increased during cell migration as observed above for the overexpressed Kibra (Figure 3F and 3G). This result sustains the idea that the function of these proteins requires their interaction during cell migration and is entirely consistent with the increased interaction between Sec8-aPKC during cell migration (Figure 3C).
Given that the region of interaction between Kibra and Sec3 defined by two-hybrid resides within the amino acid sequence 129–526 and the region of Kibra that binds PKCζ encompasses amino acids 953–996, there is no apparent conflict for Kibra in binding both PKCζ and Sec3. To assess whether the endogenous aPKCs are associated with the endogenous Exocyst via Kibra, we submitted protein extracts from scratch wounded NRK cells, depleted or not of endogenous Kibra, to immunoprecipitation with anti-Sec8. The immunoprecipitates were analyzed for the presence of PKCζ/ι. The interaction between Sec8 and PKCζ/ι decreased on depletion of Kibra, demonstrating that Kibra contributes to complex formation (Figure 3G and Figure S5D). Based upon this requirement for Kibra, it was predicted that Kibra knock-down by siRNA would inhibit migration and this was found to be the case (Figure 4A, 4B, and 4E). The fact that there was consistently only a 25% decrease in cell migration in the absence of Kibra using different knock-down strategies suggests that an alternate protein(s) might also participate in the complex between the Exocyst and aPKC.
We sought to assess the relevance of the aPKC-Kibra interaction in the context of the localisation of aPKC in migrating cells. As previously shown in podocytes [24], endogenous Kibra was found to be recruited to the leading edge as well in NRK cells. Endogenous Kibra colocalised with aPKC in an Exocyst dependent manner (Figure 4D). The depletion of aPKC decreased the recruitment of Kibra at the leading edge (Figure 4C, 4D, and 4E) consistent with the lack of recruitment of the Exocyst under these conditions (see above). Interestingly, overexpression of Myc-Kibra or Flag-Kibra inhibits NRK cell migration (Figure 4B), consistent with interference in the bivalent interaction observed here. To address this point, examination of the location of aPKCs (combined PKCι and PKCζ) demonstrated that aPKC was colocalised at the leading edge of migrating NRK cells when Myc-Kibra is weakly overexpressed but the recruitment of aPKC at the leading edge is disturbed when Myc-Kibra is strongly overexpressed (Figure S5C). Finally, to determine whether Kibra is responsible for aPKC delivery to the leading edge, siRNA to Kibra was employed. Knock-down of Kibra inhibited aPKC localisation at the leading edge (Figure 4C, 4E).
The function of the leading edge aPKC-Kibra-Exocyst complex was assessed in relation to the activation of the JNK pathway, since there is evidence both for aPKC involvement in JNK control in other contexts (e.g. in response to TNF or IL1 [25],[26]), as well as for JNK involvement in migration [27],[28],[29],[30]. In response to wound healing (multiple scratch wounds), JNK1 and JNK2 were activated in a biphasic fashion (Figure 5A). The effect of acute inhibition of aPKC was assessed using Gö6983 and for comparison the non-aPKC directed inhibitors, Gö6976 (Figure 5A) and BIMI (unpublished data). The inhibition of atypical, novel, and classical PKCs suppressed the activation of JNK1 without affecting JNK2. By contrast the PKC inhibitors not directed at the aPKCs, Gö6976, and BIMI modestly increased JNK1 phosphorylation (Gö6976 decreases JNK2 activation while BIMI had no such effect, indicative of a non-PKC dependent effect). On depletion of JNK1 by siRNA, the P-JNK1 immunoreactivity was also decreased indicating that the doublet identified by western is JNK1 and a splice variant/modified form of JNK1 (Figure S7D). Inhibiting aPKC (Gö6983), but not non-aPKC family members (Gö6976 nor BIM1), also affected the activation of ERK1/2 during cell migration (Figure S4D, S4E). None of these PKC inhibitors influenced the p38 pathway response in migrating NRK cells, indicative of specific pathways wherein aPKC activity is required for JNK1 and ERK1/2 activation during migration.
The effects of aPKC on JNK1 were reflected in the altered localised activation of JNK1/2 in migrating cells; under control conditions, JNK was found to accumulate in a phosphorylated state at the leading edge and this localised activation was lost on depletion of PKCζ, PKCι, Sec5, Exo84, or Kibra (Figure 5B–5D; see also Figure 6C, Figure S6A). Notably the depletion of these proteins did not influence the accumulation of active JNK in the nucleus, consistent with a lack of effect of Gö6983 on global JNK2 activation determined by Western. This Exocyst/Kibra/aPKC-dependence was also observed for the phosphorylation of ERK1/2 at the leading edge of migrating cells (Figure 5B–5D; see also Figure S6A). Quantitation of these responses is detailed in Figure S8. This confirms that there is a strong decrease of P-JNK and P-ERK at the leading edge without a significant disruption of the recruitment of the total ERK and JNK proteins at the leading edge (see below). It is noted that this demonstrates that this polarised leading edge in the migrating cells is still present even when aPKC and the Exocyst are non-functional. We confirmed the specificity of the phospho-specific antibody against P-ERK1/2 by using the MEK inhibitor U0126 and found that it suppressed the P-ERK staining (Figure S6B). We confirmed the specificity of the phospho-specific antibody against P-JNK1/2 by using a phospho-JNK (Thr183/Tyr185) blocking peptide and found that it suppressed the P-JNK staining (Figure S7F). Also, following depletion of JNK1 by siRNA, the staining of P-JNK at the leading edge decreased (Figure S7G). The controls monitoring the reduced protein expression associated with siRNA treatment is illustrated in Figure S7A and S7B (it is notable that Exo84 is reduced on Sec5 depletion suggesting that uncomplexed Exo84 may be subject to degradation).
This aPKC/Exocyst-dependent localised phosphorylation of MAPKinases (phosphorylated ERK1/2, as well as phosphorylated JNK) may be a consequence of their movement to the leading edge or of their activation at the leading edge. To distinguish between these two possibilities, the localisation of the kinases (phosphorylated and non-phosphorylated) was assessed in control cells or following aPKC, Kibra, Sec5, or Exo84 knock-down (protein depletion on knock-down is illustrated in Figure S7A and S7B). No effects on MAPkinase protein distribution were observed (Figure S6C, S6D, and S6E). Figure S6E shows that when the Exocyst/Kibra/aPKC is disrupted, P-ERK1/2 decreased at the leading edge whereas the non-phosphorylated form remained at the leading edge (note that the P-ERK1/2 staining appears a little different from that in Figure 5B–5D due to methanol fixation instead of paraformaldehyde fixation; the methanol fixation is better for the total ERK1/2 whereas the paraformaldehyde gave better staining for the P-ERK1/2). This result showed that cells retain a leading edge allowing the recruitment/retention of JNK and ERK proteins independent of the Exocyst and aPKC. Hence it is the localised activation that is critically under aPKC-Kibra-Exocyst control.
Analysis of the relevant upstream kinase(s) in the JNK pathway identified MKK4 and not MKK7 as showing increased phosphorylation after wounding (Figure 5A). Linking to aPKC function, wound-associated activation of MKK4 during cell migration is sensitive to the a/n/cPKC inhibitor Gö6983 (Figure 5A); Gö6976 (n/cPKC inhibitor) has no such effect (in fact it has the opposite effect to Gö6983, increasing the phosphorylation of MKK4). The constitutive phosphorylation of MKK7, the other JNK-kinase, was insensitive to Gö6983 (unpublished data).
The above evidence is indicative of a localised MKK4-dependent activation of JNK1 during cell migration, requiring the localised action of the aPKC-Kibra-Exocyst complex. To assess the specificity of these plasma membrane effects in relation to other compartments, we compared the aPKC-dependency of the phosphorylation of nuclear (c-Jun) and plasma membrane (Paxillin) both substrates for JNK [31]. The aPKC inhibitor Gö6983 blocked the phosphorylation of Paxillin on serine 178 but not of c-jun serine 63 (Figure 6A). Furthermore, siRNA directed against aPKCs or Sec5 does not decrease phospho-cJun in the nucleus (Figure S6E). The lack of effect on Jun phosphorylation is consistent with the fact that phospho-JNK in the nucleus is not modified after depletion of aPKCs (see Figure 5B). Demonstrating that these events are integral to the observed migratory response, it was shown that the depletion of ERK2 but not ERK1, depletion of JNK1 but not JNK2, and depletion of MKK4 (and also MKK7) significantly decreased cell migration (Figure 5E and 5F) as did the JNK inhibitor, SP600125 (Figure S7H and S7I; the controls for depletion are included in Figure S7C and S7F).
To address the effect of the absence of aPKC and Exocyst on Paxillin in migrating NRK cells, cells were stained for Paxillin and Actin. More Paxillin patches at focal adhesion complexes and also more actin stress fibres appeared in the absence of aPKC and the Exocyst (Figure 6B; quantified in Figure S9). Figure 6C shows that P-JNK colocalised with Paxillin and if the aPKC/Exocyst pathway is disrupted, P-JNK at the leading edge is abolished whereas there is an increase of Paxillin patches. This increase of Paxillin patches was mirrored with siRNAs against ERK2 and JNK1 consistent with the requirement for Exocyst/aPKC in the localised activation of ERK1/2 and JNK1 and the consequent distribution of Paxillin in focal adhesion complexes as opposed to the more static focal adhesion complexes. ERK1, ERK2, and JNK1 were depleted and the effect of their depletion on Paxillin Patches were quantified. Depletion of ERK2 and JNK1 and not ERK1 elicited an increase of Paxillin patches.
It is established here that aPKC isoforms via the Exocyst complex can confer efficient migration through their ability to control the leading edge activation of a distinct subpopulation of MAPKinases, conferring increased speed on the serum-dependent migratory response of cells. This localised process is enabled through the traffic of an aPKC-Exocyst complex to the leading edge of migrating cells. Assembly of this complex is dependent upon Kibra, which appears to act as a scaffold linking aPKC [17],[22] with Sec3 through non-overlapping binding domains. Whilst assembly is not dependent upon aPKC activity, it is promoted by migratory conditions and immuno-isolation of the Exocyst-aPKC complex demonstrates that the associated aPKC is catalytically active. The Exocyst complex is required for aPKC accumulation at the leading edge. Reciprocally, the association of the Exocyst with active aPKC correlates with a requirement for aPKC expression and activity for the traffic of the Exocyst to the leading edge of migrating cells. The Exocyst-Kibra-aPKC complex traffics in a microtubule-dependent fashion to the leading edge of migrating NRK cells and this is a necessary event to promote efficient/directed migration. The control of aPKCs on the Exocyst at the leading edge could be explained by control of microtubule dynamics by aPKC as suggested previously [32]. Consistent with this pattern of behaviour, knock-down of Exocyst subunits or aPKC isoforms, or inhibition of aPKC isoforms (Gö6983, a pan-PKC inhibitor) inhibits migration.
The migratory model itself requires the presence of serum and matrix interactions for migration. For NRK cells, migration occurs on fibronectin, laminin, and collagen with all three displaying a requirement for aPKC for optimum migration. By contrast in RPE1 cells, migration on fibronectin is aPKC-dependent but on laminin or collagen migration is relatively insensitive to knock-down of aPKC. As evident from the NRK cell model here, which is 60% dependent on aPKC, there are multiple modes of migration that display differential dependence on control mechanisms and these vary between cells.
Mechanistically, the aPKC-Exocyst assembly in the NRK cell model confers JNK and ERK activation at the leading edge and furthermore JNK1 (and not JNK2) as well as ERK2 (and not ERK1) inhibition blocks cell migration. JNK's effects appear to be mediated in part through the phosphorylation of Paxillin on serine 178, determining the dynamics of focal adhesions [31],[33]. aPKCs also control the phosphorylation of Paxillin by ERK1/2 on serine 126 (unpublished data). These phosphorylations were described to be important for the turnover of Paxillin at the focal adhesions [27],[31],[33],[34]. Indeed, the knock-down of aPKCζ/ι or Sec5 (or Exo84) causes Paxillin accumulation in large, static focal adhesions. Thus we have mapped a pathway from the assembly of the aPKC-Exocyst complex, through their mutual delivery to the leading edge of migrating cells, the activation there of ERK and JNK, and the consequent phosphorylation of Paxillin, influencing the dynamics of focal adhesion turnover and migration. Video microscopy experiments (Videos S1 and S2) are compatible with the regulation of Paxillin dynamics being under aPKC control. Co-immunoprecipitation experiments showed also an interaction between Sec8 and Paxillin in NRK cells. Paxillin was shown recently as a partner of Sec5 [35]. This interaction between Sec8 and Paxillin increased during NRK cell migration (Figure 6). These data suggest that there is an acute regulation of Paxillin by aPKC.
The action of aPKCs in conferring this promigratory behaviour reflects their role in directing the subcellular localisation of signals. Such spatially resolved behaviour of cellular regulators is increasingly recognised as a critical factor in determining the nature of their output. Here the evidence is for the localised action of the JNK pathway (in particular JNK1) in migration. The depletion of JNK1 decreases the P-JNK staining at the leading edge. Only JNK1 and not JNK2 knock-down inhibits cell migration and Gö6983 inhibits only the phosphorylation of JNK1 and not JNK2 during cell migration. These three observations provide compelling evidence that JNK1 is a key player in the JNK pathway responsible for aPKC-dependent NRK cell migration. Activation of JNK1 at the leading edge, effected through the aPKC-Kibra-Exocyst complex, is necessary for the phosphorylation of Paxillin in this compartment, while the migration-associated, JNK-dependent phosphorylation of nuclear c-jun is immune to aPKC-Kibra−-Exocyst function. Conversely it is evident that the activated JNK engaged in c-jun phosphorylation (probably JNK2) is not able to trigger Paxillin phosphorylation at the plasma membrane (Figure 7). The aPKC-dependent plasma membrane activation of both JNK and ERK is driven by delivery of upstream controls and not through the localisation of the JNK or ERK proteins themselves (Figure 7). This lack of effect on ERK and JNK recruitment at the leading edge when the Exocyst-aPKCs is disrupted by various siRNAs shows that a leading edge is preserved; aPKC/Exocyst disruption does not disorganise globally the leading edge, and cells still retain oriented protrusions. Moreover, the distribution of the actin cytoskeleton of migrating cells at the wound edge is retained as described by Guo et al. using siRNA against Exo70 [15]. These MAPKinases along with characteristic cortical actin structures retain their leading edge location independent of aPKC-Kibra-Exocyst action and their activation by phosphorylation. It is implicit that the polarised delivery and/or retention of these MAPKinases at the leading edge is dependent on distinct non-aPKC signal(s).
The upstream signals required for the activation of JNK appears to involve MKK4 and not MKK7 since the former shows sensitivity to inhibition of aPKC (pan-PKC) in its migration-induced activation, while the latter is insensitive. Although the depletion of MKK4 inhibits cell migration consistent with the proposed role for MKK4, so does the depletion of MKK7, preventing distinction to be made between these two JNK kinases. However, it is notable that the morphology of cells depleted of MKK4 (but not of MKK7) is a phenocopy of the cells depleted of aPKC (bigger, more spread cells), consistent with the selective effects of aPKC inhibition on MKK4; cells depleted of MKK7 displayed a stressed appearance after wounding that was quite distinct from the morphological phenotype of MKK4 and aPKC knock-down cells. It is concluded that MKK7, though not activated during migration, is probably required for migration in an aPKC independent pathway.
PKCι can control JNK via Par6 and Rac [36], however the control exerted during cell migration via MEK4 remains under investigation. HGK, a MAPKKKKinase specific for the JNK pathway, was described to interact with the Exocyst complex [37]. So one possibility is that aPKCs could control the interaction of HGK with Exocyst complex. Although the Exocyst has been associated with secretory events, the upstream trigger for activation is not thought to be dependent upon any factor(s) secreted following movement of the Exocyst to the leading edge, since conditioned medium from wounded cells does not rescue wounded cells where the Exocyst subunit Sec5 has been knocked down (Figure S3E, S3F). It would appear that the trigger for activation derives from changes in cell-cell/matrix interactions triggered by removal of cells from the monolayer (wounding).
In conclusion, aPKCs via the Exocyst complex and Kibra are shown to exert a pro-migratory role in NRK cells and do so through the regulated delivery of a signal to the leading edge of migrating cells through kinases upstream of JNK1 and ERK1/2. This regulation of the ERK and JNK pathways via aPKCs allows the phosphorylation of a common substrate, Paxillin (Figure 7), and consequently probably the turnover of Paxillin at the focal adhesion sites. The combined actions of this complex thus integrate the polarised, leading edge delivery of signals required for efficient migration.
Normal rat kidney (here denoted NRK, but specifically NRK-49F cells, confirmed by ß-catenin staining [38]; Figure S3A) cells were cultivated in Dulbecco's modified Eagle medium and 10% fetal calf serum under 5% CO2 on Falcon plastic dishes. Wounds were inflicted by scratching the cell surface with a plastic pipette tip. Images were recorded using a Zeiss microscope and an Orca ER CCD camera (Hamamatsu). All inhibitor treatments were performed without pre-incubation. Quantification of the speed of individual cells was performed using Metamorph, Tracker, and Mathematica software.
Cells were fixed in 4% paraformaldehyde (unless stated otherwise), permeabilized in 1% Triton X-100, and mounted using Prolong (Molecular Probes). Primary antibodies were obtained from Stressgen (Sec6), BD Bioscience (Sec8), Cell Signaling (phospho-extracellular signal-regulated kinase 1/2; P-ERK1/2, P-JNK1/2, P-cJun), or Sigma (fluorescein isothiocyanate (FITC)-coupled anti-tubulin). The antibody anti-Exo70 was generously provided by Dr. S. C. Hsu (Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, New Jersey, USA). Secondary antibodies were from Jackson Laboratories and Molecular Probes and were coupled to Cy3 and FITC, respectively. Images were acquired using a confocal laser scanning microscope (LSM510, Carl Zeiss Inc.) equipped with a 63×/1.4 Plan-Apochromat oil immersion objective. Alexa 488 was excited with the 488-nm line of an argon laser, Cy3 was excited with a 543-nm HeNe laser, and Cy5 was excited with a 633-nm HeNe laser. Each image represents a projection or a single section as indicated in the Figure legend.
For experiments involving protein recruitment at the leading edge, at least 70 cells at the wound were counted per experiment according the classification shown in Figure S2. Each figure shows the quantified recruitment of the specified protein at the leading edge from at least three independent experiments.
Where Paxillin spots and Actin stress fibers were quantified, an Array Scan II and the Cellomics analysis program were employed. Each cell was identified by nuclear staining (Dapi) and actin staining (phalloidin). Each area of interest for the analysis (spot or fiber) was delimited and represented as an object. The number of Paxillin spots as well as the total area of stress fibers per cell were measured (Figure S10B).
For whole-cell extracts, cells were lysed directly on plates in hot Laemmli sample buffer. For immunoprecipitation, cells were lysed in 20 mM Tris-HCl (pH 7.4), 100 mM NaCl, 1 mM MgCl2, 0.1 mM dithiothreitol, 1% Triton X-100, and 10% glycerol; antibodies were used at the concentrations recommended by suppliers for immunoprecipitation or for immunodetection on membranes. Proteins were visualized on membranes with a chemiluminescent detection system (ECL; Amersham). Quantitation of the immunoprecipitates were performed using Image J. Primary antibodies were obtained from Santa Cruz (PKCζ/ι, JNK1/2, Paxillin), BD Biosciences (PKCι and rSec8), UBI (ERK1/2), cell signaling (P-MKK4, MKK4, P-MKK7, MKK7, P-JNK1/2, P-ERK1/2), or Calbiochem (Phospho-Paxillin [ser178]). The Phospho-SAPK/JNK (Thr183/Tyr185) Blocking Peptide was obtained from Cell Signaling. The different inhibitors (Gö6976, Gö6983, BIM-I, SP600125, and UO126) were obtained from Calbiochem. For “scratch tests,” NRK cells were brought to confluence and scratched orthogonally at least 20 times with a p20-200 yellow tip. Cells were either further incubated for 3 h or harvested immediately. Preparations of cell extracts and co-immunoprecipitation were performed according to published procedures [16]. For detection of the activation of ERK1/2, JNK1/2, c-Jun, and MKK4, cells were scratched, allowed to migrate for different times (as indicated), and then harvested in Laemmli sample buffer, boiled, and processed for Western blotting. For direct comparison all cells were maintained in contact with the inhibitors for a 3 or 6 h period as indicated in the text or figure legends.
Full-length human Sec3 was cloned into pB27, derived from the original pBTM116 plasmid [39], and used as bait to screen a random-primed human placenta cDNA library constructed in pP6 [40]. A total of 120 million clones (12-fold library coverage) were screened using a mating approach with L40ΔGal4 (mata) and Y187 (matα) yeast strains [41]. His+ colonies were selected on medium lacking tryptophan, leucine, and histidine, supplemented with 2mM 3-aminotriazole to reduce bait autoactivation. Prey fragments of the positive clones were amplified by PCR and sequenced at their 5′ and 3′ junctions on a PE3700 Sequencer. The resulting sequences were used to identify the corresponding interactors in the GenBank database (NCBI) using a fully automated procedure.
siRNAs were transfected at 10 nM with Hyperfect (Qiagen) according to the recommendations of the manufacturer. siRNA were ordered from Dharmacon (rPKCζ-1) or Proligo (the others). Target sequences were:
GCAAGCUGCUUGUCCAUAAdTdT(rPKCζ-1),
GCAAACUGCUGGUUCAUAAdTdT (rPKCι-1),
GAAGAAAGAGCUCGUCAAUdTdT (rPKCι-2),
GCAGUGAGGUUCGAGAUAUdTdT (rPKCι-3),
GAACGAUGGUGUAGACC U UdTdT (rPKCζ-2).
Sequences for Sec5 were described previously [16]. The sequence for Exo84 is UGGGCAUGUUCGUGGAUGCdTdT.
A smart-pool untargeted plus from Dharmacon was used to deplete rat Kibra and independent siRNAs against rKibra AGGAGAUCUACCAGGUGAAdTdT (Kibra-330), AGCACGACUACAGUUCAAdTdT (rKibra-1), and CCACUCACCUUUGCUGACUdTdT (rKibra-2) were also employed. Myc-PKCζ and PKCι constructs were described previously [42]. Myc-Kibra and Flag-Kibra constructs were described previously [17],[22].
The siRNA for the JNK and ERK pathways were obtained from Qiagen; siJNK1 (SI03083185 and SI03105802) and a smart pool from Dharmacon was also used to deplete rat JNK1, siJNK2 (SI01906310 and SI02723588), siERK1 (SI01300593 and SI01906163), siERK2 (SI02672117 and SI02692326), siMKK4 (SI01533791 and SI01533798), and siMKK7 (SI04404680 and SI04404687).
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10.1371/journal.pcbi.1006406 | A fully autonomous terrestrial bat-like acoustic robot | Echolocating bats rely on active sound emission (echolocation) for mapping novel environments and navigating through them. Many theoretical frameworks have been suggested to explain how they do so, but few attempts have been made to build an actual robot that mimics their abilities. Here, we present the ‘Robat’—a fully autonomous bat-like terrestrial robot that relies on echolocation to move through a novel environment while mapping it solely based on sound. Using the echoes reflected from the environment, the Robat delineates the borders of objects it encounters, and classifies them using an artificial neural-network, thus creating a rich map of its environment. Unlike most previous attempts to apply sonar in robotics, we focus on a biological bat-like approach, which relies on a single emitter and two ears, and we apply a biological plausible signal processing approach to extract information about objects’ position and identity.
| Many animals are able of mapping a new environment even while moving through it for the first time. Bats can do this by emitting sound and extracting information from the echoes reflected from objects in their surroundings. In this study, we mimicked this ability by developing a robot that emits sound like a bat and analyzes the returning echoes to generate a map of space. Our Robat had an ultrasonic speaker mimicking the bat’s mouth and two ultrasonic microphones mimicking its ears. It moved autonomously through novel out-doors environments and mapped them using sound only. It was able to negotiate obstacles and move around them, to avoid dead-ends and even to recognize if the object in front of it is a plant or not. We show the great potential of using sound for future robotic applications.
| The growing use of autonomous robots emphasizes the need for new sensory approaches to facilitate tasks such as obstacle avoidance, object recognition and path planning. One of the most challenging tasks, faced by many robots, is the problem of generating a map of an unknown environment, while simultaneously navigating through this environment for the first time [1]. This problem, is routinely solved by echolocating bats that perceive their surroundings acoustically (other animals also solve this task on a daily basis using a range of sensory modalities) [2]. By emitting sound signals and analyzing the returning echoes, bats can orient through a new environment and probably also map it [3] [4]. Inspired by this ability, we present the ‘Robat’—a fully autonomous terrestrial robot that solely relies on bat-like SONAR to orient through a novel environment and map it. Using a biologically plausible system with two receivers (ears) and a single emitter(mouth) which produced frequency modulated (FM) chirps at a typical bat rate, the Robat managed to move through a large out-doors novel environment and map it in real-time.
There have been many attempts to use airborne sonar for mapping the environment and moving through it using non-biological approaches; for example by using an array of multiple narrow-band speakers [5, 6] [7] and or multiple microphones [8]. These studies proved, that by using multiple emitters, or by carefully scanning the environment with a sonar beam, as if it were a laser, one can map the environment acoustically, but these approaches are very far from the biological solution [9]. A bat emits relatively few sonar emissions towards an object, and it must rely on two receivers only (its ears) in order to extract spatial information from its very wide bio-sonar beam which can reach 60 degrees (6 dB double side drop in amplitude [10] [11] [12]). Unlike the narrow-band signals typically used in robotic applications, the bat’s wide-band signals provide ample spatial information allowing it to localize multiple reflectors within a single beam. This is the approach we aimed to test and mimic in this study.
Numerous studies have shown that echoes generated by emitting bat-like sonar signals contain spatial information that can be exploited for localization and identification of objects [13] [14] [15] [16] [17] [18]. Several previous attempts have been made to model and mimic bats’ spatial abilities of localization and mapping [19] [20]. One of the most comprehensive attempts to use a biological approach to map the environment was ‘BatSLAM’ [21], which relied on mammalian brain-like computation for simultaneous localization and mapping of a novel environment using biomimetic sonar. Using a biological representation of the data (the cochleogram) the BatSLAM algorithm generated topological maps in which the nodes represent unique places in the environment and the edges represent the robot’s displacements between them. The approach of recognizing a location based on its unique acoustic signature was further broadened by Vanderelst et al. [6] who classified a wide range of natural scenes based on their acoustic statistics, once again, without extraction of their spatial characteristics. Vanderelst et al. limited the information extracted from the echoes to the acoustic resolution available to a bat, and they were still successful in achieving useful scene recognition.
Our work differs from these former studies in two important respects: (1) Our Robat moved through the environment autonomously while the previous robots were driven by the user. (2) We mapped the 2D structure of the environment, while they mapped the position of the robot in the environment. Namely, in our approach the outline of the objects that were encountered by the Robat were delineated so that paths (free of obstacles) were revealed for future use. In these previous studies, objects in the environment were mapped as locations with a unique acoustic representation so that when encountered again, the agent could localize itself on the acoustic-map, but no spatial information about objects’ size or orientation was extracted. When moving autonomously, such information is essential for movement planning.
In addition to mapping, our Robat had to autonomously move through the environment while avoiding obstacles. Some previous attempts were made to model orientation and obstacle avoidance using a biological echolocation-based approach. For example, Vanderelst et al. [9], suggested a simple sensorimotor approach for obstacle avoidance based on turning away from the louder of the two echoes received by the ears. They showed that a simulated agent can move through a novel environment without any mapping of the positions or borders of the objects within it. This approach might be beneficial when an animal wants to move fast through the environment without an intention of returning to specific locations within it, but if the animal needs to find its way back to some point in this environment (e.g., to its roost), or to plan its movement to a specific location, some mapping must be performed. For example, the robust low-level sensorimotor heuristic presented in [9] could be combined with higher level mapping algorithms (e.g., [22]).
To our best knowledge, our Robat is the first fully autonomous bat-like biologically plausible robot that moves through a novel environment while mapping it solely based on echo information—delineating the borders of objects and the free paths between them and recognizing their type.
The Robat’s goal was to move through an environment that it has never experienced before, finding its path between vegetation and other obstacles while mapping their locations, delineating their borders and identifying them (when possible) similar to a bat flying through a grove or a shrubbery which it encounters for the first time (Fig 1A).
The Robat moved through the environment emitting echolocation signals every 0.5m thus mimicking a bat flying at 5m/s while emitting a signal every 100ms which is within the range of flight-speeds and echolocation-rates used by many foraging bats [23] [24, 25] [25] [26]. Every 0.5m, the Robat emitted three bat-like wide-band frequency-modulated sound signals while pointing its sensors (emitter and receivers) in three different headings: -60, 0, 60 degrees relative to the direction of movement (Fig 1A). This procedure aimed to overcome the narrow acoustic beam of the Robat and to better mimic a bat beam which is typically much wider than that of our speaker (see Methods) [27] [28] [10] [29] [11] [12].
Following echo acquisition, acoustic peaks of interest (representing objects) were identified in the echoes (Fig 1B). Equivalent peaks—i.e., peaks returning from the same object—received by the two ears were matched and the reflecting objects were localized. The time-delay between the emission and the arrival of the echoes was used to determine the distance of an object and the difference between the time of arrival of the echo to the two ears was used to determine its azimuth (i.e., Mapping was performed in 2D, Fig 1C, Turquoise points depict objects’ location, see Methods for full details). Importantly, the Robat was able to localized multiple objects whose echoes were received within a single beam (S1 Fig). This ability has not been reported in previous studies and bats are likely able to do so. After every 5 steps (i.e., 2.5m) the Robat applied an inflation and interpolation algorithm that incorporated the newly mapped objects into the map that has been created so far (based on the previous echoes, Fig 1C, yellow shaded area, see Methods). At each time step, following echo acquisition and object localization, the Robat planned its next movement according to the iterative map that has been created so far and according to the objects detected in the most recent acquisition. Movement planning was based on the bug algorithm [30] which can be simply described as turning 90 degrees to the right, whenever an obstacle is encountered ahead, and then turning left to maneuver around the obstacle.
The movement and mapping algorithms were tested in two outdoor environments: (1) The pteridophyte greenhouse (5m x 12m) and (2) The palm greenhouse (40m x 5m) both situated in the Tel Aviv University Botanical Garden.
The Robat successfully moved through both new environments without hitting objects and while mapping their locations and contour line (see Robat’s trajectory depicted in black in Fig 2A). When an obstacle was placed in the Robat’s way, it moved around it (Fig 2B). To quantify the mapping performance, we compared the contour of the objects as it was estimated by the Robat to the real contour (which we estimated from drone images in the Palm greenhouse and measured manually in the Pteridophyte greenhouse, see Methods). In the palm greenhouse, the mean distance between the two contours was 0.42 ± 0.74 (mean + STD) [m] meaning that along the 35m trail that the Robat passed and mapped in the Palm greenhouse, the estimated borders of the objects on both sides of the trail, were off by 42cm on average, relative to their real position. This might seem inaccurate when considering bats’ ability to estimate range with an accuracy of less than 1cm in a highly controlled experiment, [31] [32] but it should be emphasized that the Robat only detected and localized parts of the objects while their borders were delineated based on our inflation an interpolation algorithm (Methods). Moreover, note that many of the objects in our environment were plants with multiple branches so that the exact borders of the objects were inherently difficult to define (even in the drone images). Similar performance (0.44 ± 0.25 (mean + STD) [m]) was observed in the second environment (the Pteridophyte greenhouse, S2 Fig).
When moving through the environment, a real bat can probably use echoes in order to classify objects into categories (e.g., rocks, trees, bushes) and even to identify specific objects (e.g., a specific beech tree in its favorite foraging site). Such recognition would greatly assist the bat to navigate, for example, by recognizing specific landmarks at important turning points along its flight route and it could also assist its foraging, for example, by recognizing specific vegetation that is rich in fruit or insects [33] [17]. So far we demonstrated that the Robat can translate a novel natural environment into a binary map of open spaces and obstacles. In order to improve the mapping, we added a classification step to the algorithm, which was performed using a neural-network that was trained to distinguish between two object categories—plants and non-plants. To this end, a set of acoustic features were extracted from the echoes and used as input for the network (Methods). The Robat was able to classify objects as plants or non plants significantly above chance level (Fig 2C and Table 1) with a balanced accuracy of 68% (chance was 50%, P = 0.01, based on a permutation test with 100 permutations, the balanced accuracy is the number of correct classifications in each class, divided by the number of examples in each class, averaged over all classes. This measurement mitigates biases which could rise from unbalanced class sizes, see Methods). The classification performance is also shown in Fig 2a where colored points depict plants (green) and non-plants (gray).
Finally, we tested the functionality of this classification ability by purposefully driving the Robat into a dead end where it faced obstacles in all directions ahead (i.e., right, left and straight ahead, S7 Fig). The Robat had to determine which of the three obstacles was a plant, through which it could drive, and it did so successfully at ~70% of the cases (in accordance with its ~70% accurate classification rate, see movie: https://www.youtube.com/watch?v=LzGGuzvYSH8-second 49 and onward).
In this study, we managed to build an autonomous robot that moves through a novel environment and maps it acoustically using bat-like Bio-sonar. We achieved high mapping accuracy, despite our simple approach, proving the great potential of using active wide-band sound emissions to map the environment. We created a (2D) topographic map which would allow us to plan future movements through the environment (and not a topological map). The statistical approach presented in [9] is therefore complementary to ours, allowing classifying specific locations based on their echoes. For example, when navigating back to a specific location using the map created by the Robat, their approach could be used to validate the arrival at the desired location and also to help adjust the map to improve its accuracy.
The Robat was much slower than a real bat, stopping for ca. 30 seconds every 0.5m to acquire echoes. This slowness was however, merely a result of the mechanical limitations of our system and mainly the gimbal that was slow. Using a speaker with a wider beam (that eliminates the need to turn at each location) would allow the Robat to acquire echoes on the move, while moving as fast as a bat. Importantly, despite our stopping for echo recording, the acoustic information we acquired did not differ from that received by a bat, except for the fact that a bat’s echoes would also be slightly Doppler-shifted (but this would probably not affect any of our results).
In some respects, our processing was not fully bat-like. We used a sampling rate of 250kHz, which is higher than the theoretical time precision of the auditory system [34]. Bats and other small mammals have been shown to estimate azimuth with an accuracy of <10degrees (the exact accuracy depends on the azimuth, (e.g. [35, 36])). This accuracy accounts for an inter-aural time difference of <10μs which is in accordance with our sampling rate (sampling at 250kHz is equivalent to an error of ~5μs when estimating time differences between two ears). Therefore, even if our computation was different from that of a bat (which does not cross-correlate two highly sampled time signals) the overall accuracy allowed by our approach was not better than that of a bat. Moreover, due to the inflation and interpolation method that we used in order to delineate the borders of the objects, the effective accuracy of our mapping was much lower than that allowed by this high sampling rate, and probably much lower than that available to bats [31, 32]. Therefore, we hypothesize that using an auditory preprocessing model like that used in Batslam for example [21] would probably not change our results dramatically. Another advantage that we had over real bats was the relatively large distance between the two ears which were spaced 7cm apart—ca. two times more than in a large bat. This probably allowed more accurate azimuth estimations, but once again, we hypothesize that because of the use of inflation, this did not improve our performance dramatically. Importantly, we managed to extract information about multiple objects within a single sonar beam. On average, in each echo that contained reflections (some echoes did not) we detected 4.1 objects positioned in a range of azimuths between -50—50 degrees. Another important difference between the Robat and an actual bat is the lack of an external ear in the Robat. The angle-dependent frequency response of the external ear allows bats (and other animals) to gain information about the location of a sound source in three dimensions. Because we relied on temporal information for object localization, we used a first approximation of an ear. Adding a structure mimicking the external ear could have further improved our localization performance and it would be essential in order to expand our mapping to 3D.
In order to better mimic the bat’s beam, we used three beams (directed 60 degrees apart), but this made our task easier than a bat’s because we could analyze the echoes returning from each direction separately. We therefore also tested an approach in which we sum the three echoes collected (with different headings) at each acquisition point, thus mimicking a wider beam. Even with this degraded data, we were able to map the environment with a decent accuracy of 1.14 ± 0.70 [m] (mean + STD, S6b Fig), an accuracy that would allow future planning of trajectories while avoiding obstacles on the way.
In some respects our approach was probably much more simplistic than a bat. For example, the obstacle avoidance algorithm was very simple and a better approach would probably use control-theory to steer the Robat around obstacles [37]. In terms of mission priority, we used serial processing where the Robat first processes new incoming sensory information; it then performs the urgent low-level task of obstacle avoidance and path planning, and only every several acquisitions, it performs the high-level process of map integration. There is much evidence that the mammalian brain also performs sensory tasks sequentially (e.g., [38]) but it would be interesting to test some procedures for parallel processing in the future.
In addition to mapping the positions of objects in the environment, a complete map should also include information about the objects such as their type or identity. To show that such information is available in the echoes, we developed a classifier that can categorize objects based on their echo. We hypothesize that the medium classification performance that we achieved (68%) was a result of our choice of categories. We trained the classifier to distinguish between plant and non-plant objects but these are not always two well distinct groups. For example, the echo of an artificial object such as a fence will have vegetation-like acoustic features and indeed most of the classifier’s mistakes were recognition of non-plants as plants. Bats might thus divide their world of objects differently, perhaps to diffusive vs. glint-reflecting objects.
Altogether, we show how a rather simple signal processing approach allows to autonomously move and map a new environment based on acoustic information. Our work thus proves the great potential of using acoustic echoes to map and navigate, a potential that is translated into action by echolocating bats on a daily basis.
The Robat was based on the ‘Komodo’ robotic platform (Robotican, Israel). The Bio-sonar sensor was mounted on a DJI Ronin gimbal which allowed turning the sensing unit relatively to the base of the robot in a stable manner. The sensing unit included an ultrasonic speaker acting as the bat’s mouth (VIFA XT25SC90-04) and 2 ultrasonic microphones acting as the bat’s ears spaced 7cm apart (Avisoft-Bioacoustics CM16/CMPA40-5V Condenser). The speaker and the microphones were connected to A/D and D/A converters which were based on the USB-1608GX-2AO NI DAQ board, sampling at 250KS/s at each ear. The emitted signal was a 10ms FM chirp sweeping between 100-20kHz. It was amplified using a Sony Amplifier (XM-GS4). An uEye RGB camera, was used for image collection for validation purposes only. Three 2.4GHz/5.8GHz antennae were mounted at the rear of the Robat for wireless communication between the Robat and a stationary station. This allowed viewing the map created by the Robat in real time, but importantly, all calculations and decisions were performed on the Robat itself.
While moving, the Robat stopped every 0.5m (based on its odometry measurements) and the sonar system (emitter and receivers) was rotated to three different headings [0,60,-60 degrees] relative to the direction of movement, a sound signal (see above) was emitted, and echoes were recorded. Each recording was 0.035 sec long, equivalent to a range of ca. 6 meters (farther objects were thus ignored at each emission). The signal-to-echo delay time and the time of arrival differences of the echoes to the two ears (i.e., the Interaural Time Difference) were used together in order to map the environment. To this end, the received signals were cross-correlated with the theoretical emitted signal. The cross-correlated signal was normalized relative to the maximum value of the recording, and a peak detection function was used to find peaks of interest (python peakutil with a minimal peak distance of 0.002 sec, and a min amplitude of 0.3.).
To match peaks arriving at the right and left ears, for each peak detected in one ear, an equivalent peak was searched for in the other ear within a window of +/- 0.001 sec. If a peak was found, the Pearson correlation was used to determine if the two echoes were reflections of the same object. For this purpose, a segment of 0.01 seconds around each peak was cut and the correlation between the two time signals (one from each ear) was computed. Only correlations higher than 0.9 were accepted. This threshold was conservative thus potentially resulting in missing of objects, but it reduced the localization of artifact non-existent objects. Because the Robat emitted very 0.5 m—there was much overlap between echoes of consecutive emissions. We were therefore likely to detect an object several times, so a conservative approach was chosen. In addition to its position, each object on the map was defined by three parameters: “C |T |P”, where C is the Pearson correlation coefficient between the left and right ears for the specific point, T is the object’s type based on its acoustic classification—either artificial or a plant, and P is the classification probability (see more below about the classification process).
Results in the in-doors controlled environment showed that using two ears, the mean error in distance estimation was 1.3 ± 2.1 [cm] (mean + STD, S3 Fig) and the mean azimuth estimation error was 1.2 ± 0.7 [degrees] (mean+STD, S3 Fig). Importantly, these are the results for a single reflector, so accuracy in the real environment where many reflections are received at each point will be lower.
Every 5 Robat-steps, newly localized objects were integrated into the map that was created so far. This was done using an Iterative-Object-Inflation algorithm, which inflated points into squares and connected them. To this end, the entire area around the Robat was divided into a grid with 2000x2000 pixels (5x5cm2 each). Each detected object was placed in the corresponding pixel on the map and was inflated to an area of 20x20 pixels around its center (i.e., 1x1 m2, S4 Fig). This procedure creates a binary map with 1’s depicting objects and 0’s depicting a free path. Pixels along the trajectory that the Robat previously moved through always received the value 0 depicting an open path (even if they were within the 20x20 window of a detected object).
We chose a very simple obstacle avoidance approach also known as the ‘bug algorithm’ [39]. During the exploration process, the Robat moved forward in steps of 0.5m between consecutive acquisition points. When detecting an obstacle less than 1.2m in front of it, the Robat turned 90 degrees towards the right, and performed a 1m step towards the right (after checking that there is no obstacle ahead). After performing a 1m step to the right, the Robat turned 90 degrees to the left and acquired an echo. If no obstacle was detected (meaning that the obstacle has been passed) the Robat continued straight (i.e., in its previous direction before turning right). If the way was still blocked (i.e., the obstacle was not passed), the Robat turned again to the right and kept moving towards the right (90 degrees relative to its original direction).
In order to better mimic the bat, that has a beam much wider than Robat’s beam, we examine an approach of summing the echoes returning from the three different headings (mentioned above) into one superposition echo, and then running the same (detection, localization and mapping) algorithms as described above.
In order to examine the acoustic map generated by the Robat, inspired by [40], we collected aerial images using a drone (DJI Phantom 4, DJI), to construct a complete ground truth map of the area. This procedure was only performed for the large palm greenhouse (40x5 m2). The contour of the objects on both sides of the trail in the greenhouse was extracted and compared to the contour of the inflated map that was acoustically reconstructed by the Robat (both contours were marked manually). Each of the two contours was fit by a 55-coefficient order polynomial function which was then sampled at 500 points to get a high resolution description of the contour. The two contours (real and Robat-estimated) were compared by calculating the root-mean-square distance between them (the average over these 500 points, S5 Fig).
Acoustic based object classification was performed using a neural-network that was trained on a binary task—classifying whether and object was a plant or not. Only objects that were located closer than 3[m] from the sensing unit were classified. 0.035 s long echoes were used from both the right and left ear. These recordings were passed through three band pass filters, without the transmitted echo, (20-40kHz, 40-60kHz and 60-100kHz). Each echo was represented by 6 signals—3 filters x two ears. Next, a set of 21 acoustic features (Table 2) were extracted from each band-passed recording following T. Giannakopoulos [41]. Each echo was divided into seven windows equally spaced with an overlap of 40ms and the 21 features were extracted for each window generating a total of 147 dimensions per signal (21 features x 23 windows). The classifier was thus fed with 6 signals (483 dimensions each) and the decision of the majority of the six classifiers was used.
The data was fed into a neural network with the following architecture:
We used Python’s TensorFlow to construct and train a three-layer neural-network (using the Keras directory).
The training sets included 788 plant examples and 628 non-plant examples collected on several sites on campus. We used the camera that was on the Robat to label the echoes.
Finally, to assess the statistical significance of our classification, we ran 100 permutations in which we assigned the training data randomly into the two classes (plants and non-plants), trained a classifier for each permutation and tested it on the same test-data.
We also tested several additional classification methods before choosing the neural-network. We tested a KNN (K nearest neighbors) classifier with five different distance measurements: Mahalanobis, Euclidean, Correlation, Minkowski and Canberra. We also tested two additional approaches for dimensionality reduction (before using the KNN) including PCA and LDA. In addition, we also tested a linear SVM classifier. For all classifiers, we used the same input features (see above).
The results were similar for most classifiers, but the neural network performed slightly better than the other (S8 Fig).
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10.1371/journal.pgen.0030161 | Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis | It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce “surrogate variable analysis” (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.
| In scientific and medical studies, great care must be taken when collecting data to understand the relationship between two variables, such as a drug and its effect on a disease. In any given study there will be many other variables at play, such as the effects of age and sex on the disease. We show that in studies where the expression levels of thousands of genes are measured at once, these issues become surprisingly critical. Due to the complexity of our genomes, environment, and demographic features, there are many sources of variation when analyzing gene expression levels. In any given study, it is impossible to measure every single variable that may be influencing how our genes are expressed. Despite this, we show that by considering all expression levels simultaneously, one can actually recover the effects of these important missed variables and essentially produce an analysis as if all relevant variables were included. As opposed to traditional studies, the massive amount of data available in this setting is what makes the method, called surrogate variable analysis, possible. We hypothesize that surrogate variable analysis will be useful in many large-scale gene expression studies.
| Large-scale gene expression studies allow one to characterize transcriptional variation with respect to measured variables of interest, such as differing environments, treatments, time points, phenotypes, or clinical outcomes. However, a number of unmeasured or unmodeled factors may also influence the expression of any particular gene. Besides inducing widespread dependence in measurements across genes [1,2], these influential factors create additional sources of differential expression, which, unlike gene-specific fluctuations, represent common sources of variation in gene expression that can be observed among multiple genes.
We call “primary measured variables” (or primary variables) those variables that are explicitly modeled in the analysis of an expression study. These variables may or may not be associated with any given gene's expression variation. We classify all the remaining sources of expression variation into three basic types. “Unmodeled factors” are sources of variation explained by measured variables, but are not explicitly included in the statistical model (e.g., because their relationship to expression is intractable or the relevant measured variables were excluded because of sample size restrictions). “Unmeasured factors” are sources of expression variation that are not measured in the course of the study, so we also call these unmodeled factors. Finally, “gene-specific noise” refers to random fluctuations in gene expression independently realized from gene to gene.
As a simple example meant only for illustrative purposes, consider a human expression study where disease state on a particular tissue type is the primary variable. Suppose that in addition to changes in expression being associated with disease state, the age of the individuals also has a substantial influence on expression. Thus, some genes exhibit differential expression with respect to disease state, some with respect to age, and some with respect to both. If age is not included in the model when identifying differential expression with respect to disease state, we show that this may (a) induce extra variability in the expression levels due to the effect of age, decreasing our power to detect associations with disease state, (b) introduce spurious signal due to the fact that the effect of age on expression may be confounded with disease state, or (c) induce long-range dependence in the apparent “noise” of the expression data, complicating any assessment of statistical significance for differential expression. In practice, even if age were known, it may be one of dozens of available measured factors, making it statistically intractable to determine which to include in the model. Furthermore, even measured factors such as age may act on distinct sets of genes in different ways, or may interact with an unobserved factor, making the effect of age on expression difficult to model. “Expression heterogeneity” (EH) is used here to describe patterns of variation due to any unmodeled factor.
Major sources of expression variation are due to technical [3,4], environmental [5,6], demographic [7,8], or genetic [9–11] factors. It is well known that sources of variation due to experimental design or large-scale systematic sources of signal may be present in expression data [3,4,12,13], sometimes even after normalization has been applied [14]. Genetic factors can also have a large-scale impact on gene expression levels. Specific genetic loci have been shown to influence the expression of hundreds or thousands of genes in several organisms [10,11,15]. Expression heterogeneity is particularly pronounced in human expression data, especially in the study of complex systems, such as cancer or responses to stress [16–18]. Recently, Lamb et al. proposed the “Connectivity Map” for identifying functional connections between cancer subtypes, genetic background, and drug action [19]. Lamb et al. noted EH (e.g., due to cell type and batch effects) presented a major hurdle for extracting relevant biological signal from the Connectivity Map.
In each of these studies, expression variation with respect to one or at most a handful of variables is explored. However, it is likely that in each study multiple sources of EH will act on distinct, but possibly overlapping, sets of genes. Normalization techniques are commonly used to detect and adjust for systematic expression variation due to well-characterized laboratory and technical sources [12,13,20]. However, to date there has been no approach for identifying and accounting for all sources of systematic expression variation, including variation due to unmeasured or unmodeled factors of both biological and technical sources. We show here that biological sources of variation not modeled in the analysis can be just as problematic as technical sources of variation.
Here, we introduce “surrogate variable analysis” (SVA) to identify, estimate, and utilize the components of EH. Figure 1 shows the effects of failing to account for unmodeled factors in a differential expression analysis, and the potential benefits of the SVA approach. EH causes drastic increases in the variability of the ranking of genes for differential expression (Figure 1A), distorts the null distribution potentially causing highly conservative or anticonservative significance estimates (Figure 1B), and reduces the power to distinguish true associations between a measured variable of interest and gene expression (Figure 1C). However, employing SVA in these studies produces operating characteristics nearly equivalent to what one would obtain with no EH at all.
We apply SVA to three distinct expression studies [7,21,22], where each study contains clear patterns of EH (Figure S1). These studies represent major classes of gene expression studies performed in practice: genetic dissection of expression variation, differential expression analysis between disease classes, and differential expression over time. We show that SVA is able to accurately identify and estimate the impact of unmodeled factors in each type of study, using only the expression data itself. We further show that SVA improves accuracy and consistency in detecting differential expression. SVA orders the significant gene lists to more accurately and reproducibly reflect the ordering of the genes with respect to their true differential expression signal. SVA is particularly useful in producing reproducible results in microarray studies, because adjusting for surrogate variables reduces differential expression due to sources other than the primary variables. These results indicate that EH is prevalent across a range of studies and that SVA can be used to capture and account for these patterns to improve the characterization of biological signal in expression analyses.
We have developed an approach called surrogate variable analysis that appropriately borrows information across genes to estimate the large-scale effects of all unmodeled factors directly from the expression data. Figure 2A shows a simulated example of EH. The primary variable distinguishes the first ten arrays from the last ten (Figure 2B); however, the unmodeled factor may have a variety of effects on expression (Figure 2C). The SVA approach flexibly captures signatures of EH, including highly irregular patterns not following any simple model, by estimating the signatures of EH in the expression data themselves rather than attempting to estimate specific unmodeled factors such as age or gender. After the surrogate variables are constructed, they are then incorporated into any subsequent analysis as covariates in the usual way. The SVA algorithm, described in mathematical detail in Materials and Methods, can conceptually be broken down into four basic steps: (Step 1) Remove the signal due to the primary variable(s) of interest to obtain a residual expression matrix. Apply a decomposition to the residual expression matrix to identify signatures of EH in terms of an orthogonal basis of singular vectors that completely reproduces these signatures. Use a statistical test to determine the singular vectors that represent significantly more variation than would be expected by chance. (Step 2) Identify the subset of genes driving each orthogonal signature of EH through a significance analysis of associations between the genes and the EH signatures on the residual expression matrix. (Step 3) For each subset of genes, build a surrogate variable based on the full EH signature of that subset in the original expression data. (Step 4) Include all significant surrogate variables as covariates in subsequent regression analyses, allowing for gene-specific coefficients for each surrogate variable.
The four-step procedure is necessary both to ensure that the surrogate variables indeed estimate EH and not the signal from the primary variable (Step 1), to ensure an accurate estimate of each surrogate variable by identifying the specific subset of genes driving each EH signature (Step 2), to allow for correlation between the primary variable and the surrogate variables by building the surrogate variables on the original expression data (Step 3), and to take into account the fact that a surrogate variable may have a different effect on each gene (Step 4). The third and fourth steps are particularly important for maintaining unbiased significance with SVA, as demonstrated below.
The overall goal of SVA is to provide a more accurate and reproducible parsing of signal and noise in the analysis of an expression study when EH is present. One way in which signal is commonly quantified is through a significance analysis [23]. The most basic definition of a significance analysis being performed “correctly” is if the null distribution is calculated properly [24]. A straightforward means for determining whether this is true is to assess whether the p-values corresponding to true null hypotheses are Uniformly distributed between zero and one. Indeed, p-values are specifically defined so that those corresponding to true null hypotheses have a Uniform(0,1) distribution if and only if the null distribution has been correctly calculated [25]. Throughout this paper, we examine the distribution of p-values from null genes to determine whether various procedures are able to recover the correct null distribution in the presence of EH. To assess statistically any deviations from the Uniform distribution for the null p-values, we apply a nested Kolmogorov-Smirnov test that is robust to chance fluctuations that may be present in a single simulated dataset (see Text S1).
We performed a simulation study to investigate the properties of SVA with respect to large-scale significance testing. Specifically, we show that the SVA algorithm (a) accurately estimates signatures of expression heterogeneity, (b) corrects the null distribution of p-values from multiple hypothesis tests, (c) improves estimation of the false discovery rate (FDR) [23,26], and (d) is robust to confounding between the primary variable and surrogate variables. The primary variable for our simulation was a binary variable indicating two disease classes. We simulated 1,000 expression studies, drawn from the same hypothetical population. For each study, we simulated expression for 1,000 genes on 20 arrays divided between the two disease states. The first 300 genes were simulated to be differentially expressed between disease states and genes 200–500 were affected by an independent unobserved factor to simulate a randomized study (Materials and Methods).
Several recent studies have carried out the genetic dissection of expression variation at the genome-wide level [10,11,15]. Brem et al. [10, 21] measured expression genome wide in 112 segregants of a cross between two isogenic strains of yeast. They also obtained genotypes for each segregant at markers covering 99% of the genome (Materials and Methods). It was shown that many gene expression traits are cis-linking, i.e., the quantitative trait locus (QTL) linkage peak coincided with the physical location of the open reading frame for the expression trait [36]. At the same time, it was also shown that a number of gene expression traits are trans-linking, with linkage peaks at loci distant from the physical location of their open reading frames. In particular, several “pivotal” loci each appear to influence the expression of hundreds or even thousands of gene expression traits. Similar highly influential loci have been observed in other organisms [11,15]. These pivotal loci act as a major source of EH, regardless of whether genotypes have been measured in an expression study.
As proof of concept, the Brem et al. [10,21] dataset was used to show that well-defined EH exists in actual studies and that SVA can properly capture and incorporate this EH structure into the statistical analysis of measured variables of interest. First, we analyzed the full dataset to identify the expression traits under the influence of these pivotal trans-acting loci, as well as the patterns of EH induced by these loci. Then we applied SVA to only the expression data, ignoring the genotype data to identify relevant surrogate variables capturing EH. Linkage analysis was performed again including the surrogate variables as covariates, showing that the effects from the pivotal loci are now negligible. In other words, SVA was able to capture and remove the effects of these few pivotal loci without the need for genotypes.
A number of expression traits have significant trans-linking eQTL mapping to pivotal loci on Chromosomes II, III, VIII, XII, XIV, and XV (Figure 3A). In the SVA-adjusted analysis, the majority of the trans-linkages to the pivotal loci have been eliminated (Figure 3B). The pervasive trans-linkage signal mapping to the pivotal loci can be viewed as global expression heterogeneity. The reduction in trans-linkage to these loci in the SVA-adjusted significance analysis indicates that SVA effectively captures genetic EH.
Pivotal trans-linkage signals indicate large-scale effects of a few loci. However, subtle and potentially more interesting cis-linkage may be lost in the presence of substantial genetic heterogeneity. To assess the impact of SVA on power to detect cis-linkage, we calculated linkage p-values only for markers located within three centimorgans of the open reading frame of each trait. On chromosomes without a pivotal locus (Chromosomes I, IV, V, VI, VII, IX, X, XI, and XIII) the SVA-adjusted analysis finds substantially more cis-linkage signal. At an FDR cutoff of 0.05, the adjusted analysis finds 1,894 significant cis-linkages, compared with 1,604 for the unadjusted analysis. This increase is consistent across a range of FDR cutoffs (Table 1) and illustrates the potential increase in power obtained from applying SVA.
We applied the SVA approach to two human studies [7,22], representing the two common human study designs: disease state and timecourse.
Expression heterogeneity due to technical, genetic, environmental, or demographic variables is common in gene expression studies. Here we have introduced a new method, SVA, for identifying, estimating, and incorporating sources of EH in an expression analysis. SVA uses the expression data itself to identify groups of genes affected by each unobserved factor and estimates the factor based on the expression of those genes. Simulations show that SVA accurately detects expression heterogeneity and improves significance analyses. We performed SVA on experiments involving recombinant inbred lines, individuals of varying disease state, and expression measured over time to illustrate the broad range of studies on which SVA can be applied. One advantage of the SVA approach is the ability to disentangle correlated and overlapping differential expression signals. This approach may be particularly useful in clinical studies, where a large number of clinical variables may have a complicated joint impact on expression. We have implemented SVA in an open source package available for downloading at http://www.genomine.org/sva/.
Three publicly available datasets were employed to represent a broad range of gene expression studies performed in practice. The first dataset consists of gene expression measurements for 6,216 genes in 112 segregants of a cross between two isogenic strains of yeast, as well as genotypes across 3,312 markers [10,21]. The second dataset consists of gene expression for 3,226 genes in seven BRCA1 and eight BRCA2 mutation–positive tumor samples [22]; several genes with apparent outliers were removed as described [23] for a total of 3,170 genes. The third dataset consists of gene expression measurements in kidney samples from normal kidney tissue obtained at nephrectomy from 133 patients [7]; the 34,061 genes analyzed in [8] were also analyzed here. Seventy-four of the tissue samples were obtained from the cortex and 59 from the medulla. Details of the protocol for each study appear in the corresponding references. All expression data were analyzed on the log scale.
The SVA algorithm identified 14 significant surrogate variables from the expression data. We performed both an unadjusted and an SVA-adjusted linkage analysis for each expression trait. In the unadjusted analysis, linkage p-values were calculated from an F-test comparing an additive genetic model to the null model of no genetic association [42]. SVA-adjusted p-values were calculated from an F-test comparing the full model of genetic association and the null model of no association, both models including all significant surrogate variables as additive terms.
For each study, we simulated expression for 1,000 genes on 20 arrays divided between the two disease states. For simplicity, the expression measurements for each gene were drawn from a normal distribution with mean zero and variance one. We simulated expression heterogeneity with a dichotomous unmodeled factor independent of the disease state. The mean differences between disease states and states of the unmodeled factor were drawn from two independent normal distributions. For the real data example, we calculated the residuals from the regression of BRCA tumor type on expression for the Hedenfalk data [22]. Then, for each simulated study, we independently permuted each row of the expression data to create a matrix of residuals. To this matrix, we added signal, as in the case of the purely simulated data. The simulation studies were based on data generated using the R programming language [43]. All differential expression analyses were performed by a t-test based on standard linear regression. The genes were ranked for relative significance by the absolute values of their t-statistics.
Differential expression was calculated using a t-test based on standard linear regression for the disease class data. The method of Storey et al. [8] was applied for the time-course data. q-Values were estimated using previously described methodology [23].
Let Xmxn = (x1,..,xm)T be the normalized m × n expression matrix with n arrays for m genes, where xi = (xi1,..,xin)T is the vector of normalized expression for gene i. Let y = (y1,..,yn)T be a vector of length n representing the primary variable of interest.
Without loss of generality model xij = μi + fi(yj) + eij, where μi is the baseline level of expression, fi(yj) = E(xij | yj) − μi gives the relationship between measured variable of interest and gene i, and eij is random noise with mean zero. As a simple example, for a dichotomous outcomes yj ∈ {−1,1} we would employ the linear model xij = μi + βi yj +eij and estimate μi and βi by least squares. We could then perform a standard test of whether βi = 0 or not for each gene. This hypothesis test is exactly equivalent to performing a test of differential expression between the two classes.
Suppose in a microarray study there are L biologically meaningful unmodeled factors, such as age, environmental exposure, genotype, etc. Let gℓ = (gℓ1,...,gℓn) be an arbitrarily complicated function of the ℓth factor across all n arrays, for ℓ=1,2,...,L. Therefore, we can now model the expression for gene i on array j as xij = μi + fi (yj)+
, where γℓi is a gene-specific coefficient for the ℓth unmodeled factor. If unmodeled factor ℓ does not influence the expression of gene i, then γℓi = 0. The fact that we employ an additive model is actually quite general: it has been shown that even complicated nonlinear functions of factors can be represented in an additive fashion for a reasonable choice of a nonlinear basis [44]; we simply define the gℓ to be as nonlinear as necessary and make L as large as necessary to best fit the additive effect. Since there are n arrays, each gene's expression can be modeled by at most n linearly independent factors, and hence any dependence structure between genes can be represented using L ≤ n vectors in this additive fashion.
Due to this formulation, the inter-gene dependent eij have now been replaced with
, where
is the true gene-specific noise, now sufficiently independent across genes. In other words, we have broken the error eij into two terms, one that represents dependent variation across genes due to unmodeled factors,
, and one that represents gene-specific independent fluctuations in expression
.
It is not possible in general to directly estimate the unmodeled gℓ, and SVA does not attempt to do so. The key observation is to note that for L vectors in n space, it is possible to identify an orthogonal set of vectors hk , k = 1,...,K (K≤L) that spans the same linear space as the gℓ In other words, for any set of vectors gℓ and coefficients γℓi, it is possible to identify mutually orthogonal vectors hk and coefficients λki such that
and
Therefore, we do not need to estimate the specific variables gℓ. We only need to estimate the linear combination
, so we can choose a set of vectors that spans the same space but is statistically tractable. Here we choose the set of K orthogonal vectors (denoted by the hk) to be those that are the right non-zero singular vectors provided by the singular value decomposition of the m × n matrix with (i, j) entry
. This justifies the use of the singular value decomposition to identify orthogonal signatures of expression heterogeneity for surrogate variable estimates. We call these h1,h2,...,hK the “surrogate variables.”
An intuitive question that arises from an inspection of this formulation is about the model assumptions of the gℓj. Whereas the term fi (yj) is a model of the measured variable, yj, it is not generally possible to analogously formulate gℓj as a function of a well-defined, measured variable. Since we estimate the outcomes
directly from the expression data (as
), it is not necessary to determine a model of the gℓj in terms of a biologically meaningful variable. Thus, we can bypass the need to know what the most relevant model of a measured variable is for gℓj for the purposes of estimating the EH.
The goal of the SVA algorithm is therefore to identify and estimate the surrogate variables, hk , = (hk1,...,hkn)T, based on certain consistent patterns of expression variation. Methods for empirically identifying [37] and estimating [40] expression trends or clusters have previously been developed. However, care must be taken when estimating expression trends for use in subsequent analyses of measured variables of interest. Specifically, surrogate variables must represent signal due to sources other than the primary variable and allow for potential overlap with the primary variable. The SVA algorithm is designed to estimate surrogate variables that meet both requirements. We assume that n < m and, for simplicity, that there is only a single primary variable; the extension to multiple primary variables simply requires one to include all of them in the model fit occurring in each Step 1 below.
The algorithm is decomposed into two parts: detection of unmodeled factors and construction of surrogate variables. The basic form of the first algorithm has been proposed previously [27]. The second algorithm has been proposed and justified in this manuscript
Algorithm to detect unmodeled factors.
1. Form estimates
and
by fitting the model xij = μi + fi (yj) + eij, and calculate the residuals rij = xij −
(yj) to remove the effect of the primary variable on expression. Form the m × n residual matrix R, where the (i, j) element of R is rij.
2. Calculate the singular value decomposition of the residual expression matrix R = UDVT.
3. Let dℓ be the ℓth eigenvalue, which is the ℓth diagonal element of D, for ℓ=1,...,n. If df is the degrees of freedom of the model fit μ̂i +
f̂i (yj), then by construction the last df eigenvalues are exactly zero and we remove them from consideration. For eigengene k=1,..., n-df set the observed statistic to be
which is the variance explained by the kth eigengene.
4. Form a matrix R* by permuting each row of R independently to remove any structure in the matrix. Denote the (i, j) entry of R* by
.
5. Fit the model
and calculate the residuals
to form the m × n model-subtracted null matrix R0.
6. Calculate the singular value decomposition of the centered and permuted expression matrix R0 = U0D0
.
7. For eigengene k form a null statistic
as above, where d0ℓ is the ℓth diagonal element of D0.
8. Repeat steps 4−7 a total of B times to obtain null statistics
for b = 1,...,B and k = 1,...,n-df.
9. Compute the p-value for eigengene k as:
Since eigengene k should be significant whenever eigengene k′ is (where k′>k), we conservatively force monotonicity among the p-values. Thus, set pk = max (pk−1, pk) for k = 2,...,n-df.
10. For a user-chosen significance level 0≤α≤1, call eigengene k a significant signature of residual EH if pk ≤ α.
Algorithm to construct surrogate variables.
1. Form estimates
and
by fitting the model xij.= μi + fi(yj) + eij, and calculate the residuals rij = xij −
(yj) to remove the effect of the primary variable on expression. Form the m × n residual matrix R, where the (i, j) element of R is rij.
2. Calculate the singular value decomposition of the residual expression matrix R = UDVT. Let ek = (ek1,...,ekn)T be the kth column of V (for k=1,...,n). These ek are the residual eigengenes and represent orthogonal residual EH signals independent of the signal due to the primary variable.
3. Set
to the number of significant eigengenes found by the above algorithm. Note that “significant” means that the eigengene represents a greater proportion of variation than expected by chance.
For each significant eigengene ek k=1,...,
.
4. Regress ek on the xi (i = 1,...,m) and calculate a p-value testing for an association between the residual eigengene and each gene's expression. This p-value measures the strength of association between the residual eigengene ek and the expression for gene i.
5. Let π0 be the proportion of genes with expression not truly associated with ek; form an estimate
, as described previously [23] and estimate the number of genes associated with the residual eigengene by
. Let
be the indices of the genes with
smallest p-values from this test.
6. Form the
× n reduced expression matrix
. Since
is an estimate of the number of genes associated with residual eigengene k, the reduced expression matrix represents the expression of those genes estimated to contain the EH signature represented by some hk as described above. As was done for R, calculate the eigengenes of Xr, and denote these by
for j=1,...,n.
7. Let j* = argmax1≤j≤n cor
and set
. In other words, set the estimate of the surrogate variable to be the eigengene of the reduced matrix most correlated with the corresponding residual eigengene. Since the reduced matrix is enriched for genes associated with this residual eigengene, this is a principled choice for the estimated surrogate variable that allows for correlation with the primary variable.
8. In any subsequent analysis, employ the model xij = μi + fi(yj) +
, which serves as an estimate of the ideal model xij = μi + fi(yj) +
.
The singular value decomposition is employed in these SVA algorithms. It may be possible to utilize other decomposition methods, but since the singular value decomposition provides uncorrelated variables that decompose the data in an additive linear fashion with the goal of minimizing the sum of squares, we found this to be the most appropriate decomposition. If the primary variables are modeled for data that are not continuous, then it may make sense to decompose the variation with respect to whatever model-fitting criteria will be employed
SVA has been made freely available as an R package at http://www.genomine.org/sva/. |
10.1371/journal.pcbi.1005545 | Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation | The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.
| Characterizing the dynamics of biophysically modeled, large neuronal networks usually involves extensive numerical simulations. As an alternative to this expensive procedure we propose efficient models that describe the network activity in terms of a few ordinary differential equations. These systems are simple to solve and allow for convenient investigations of asynchronous, oscillatory or chaotic network states because linear stability analyses and powerful related methods are readily applicable. We build upon two research lines on which substantial efforts have been exerted in the last two decades: (i) the development of single neuron models of reduced complexity that can accurately reproduce a large repertoire of observed neuronal behavior, and (ii) different approaches to approximate the Fokker-Planck equation that represents the collective dynamics of large neuronal networks. We combine these advances and extend recent approximation methods of the latter kind to obtain spike rate models that surprisingly well reproduce the macroscopic dynamics of the underlying neuronal network. At the same time the microscopic properties are retained through the single neuron model parameters. To enable a fast adoption we have released an efficient Python implementation as open source software under a free license.
| There is prominent evidence that information in the brain, about a particular stimulus for example, is contained in the collective neuronal spiking activity averaged over populations of neurons with similar properties (population spike rate code) [1, 2]. Although these populations can comprise a large number of neurons [3], they often exhibit low-dimensional collective spiking dynamics [4] that can be measured using neural mass signals such as the local field potential or electroencephalography.
The behavior of cortical networks at that level is often studied computationally by employing simulations of multiple (realistically large or subsampled) populations of synaptically coupled individual spiking model neurons. A popular choice of single cell description for this purpose are two-variable integrate-and-fire models [5, 6] which describe the evolution of the fast (somatic) membrane voltage and an adaptation variable that represents a slowly-decaying potassium current. These models are computationally efficient and can be successfully calibrated using electrophysiological recordings of real cortical neurons and standard stimulation protocols [5, 7–10] to accurately reproduce their subthreshold and spiking activity. The choice of such (simple) neuron models, however, does not imply reasonable (short enough) simulation durations for a recurrent network, especially when large numbers of neurons and synaptic connections between them are considered.
A fast and mathematically tractable alternative to simulations of large networks are population activity models in terms of low-dimensional ordinary differential equations (i.e., which consist of only a few variables) that typically describe the evolution of the spike rate. These reduced models can be rapidly solved and allow for convenient analyses of the dynamical network states using well-known methods that are simple to implement. A popular example are the Wilson-Cowan equations [11], which were also extended to account for (slow) neuronal adaptation [12] and short-term synaptic depression [13]. Models of this type have been successfully applied to qualitatively characterize the possible dynamical states of coupled neuronal populations using phase space analyses [11–13], yet a direct link to more biophysically described networks of (calibrated) spiking neurons in terms of model parameters is missing.
Recently, derived population activity models have been proposed that bridge the gap between single neuron properties and mesoscopic network dynamics. These models are described by integral equations [14, 15] or partial differential equations [16, 17]
Here we derive simple models in terms of low-dimensional ordinary differential equations (ODEs) for the spike rate dynamics of sparsely coupled adaptive nonlinear integrate-and-fire neurons that are exposed to noisy synaptic input. The derivations are based on a Fokker-Planck equation that describes the neuronal population activity in the mean-field limit of large networks. We develop reduced models using recent methodological advances on two different approaches: the first is based on a spectral decomposition of the Fokker-Planck operator under two different slowness assumptions [18–20]. In the second approach we consider a cascade of linear temporal filters and a nonlinear function which are determined from the Fokker-Planck equation and semi-analytically approximated, building upon [21]. Both approaches are extended for an adaptation current, a nonlinear spike generating current and recurrent coupling with distributed synaptic delays.
We evaluate the developed low-dimensional spike rate models quantitatively in terms of reproduction accuracy in a systematic manner over a wide range of biologically plausible parameter values. In addition, we provide numerical implementations for the different reduction methods as well as the Fokker-Planck equation under a free license as open source project.
For the derived models in this contribution we use the adaptive exponential integrate-and-fire (aEIF) model [5] to describe individual neurons, which is similar to the model proposed by Izhikevich [6] but includes biophysically meaningful parameters and a refined description of spike initiation. However, the presented derivations are equally applicable when using the Izhikevich model instead (requiring only a small number of simple substitutions in the code).
Through their parameters the derived models retain a direct, quantitative link to the underlying spiking model neurons, and they are described in a well-established, convenient form (ODEs) that can be rapidly solved and analyzed. Therefore, these models are well suited (i) for mathematical analyses of dynamical states at the population level, e.g., linear stability analyses of attractors, and (ii) for application in multi-population brain network models. Apart from a specific network setting, the derived models are also appropriate as a spike rate description of individual neurons under noisy input conditions.
The structure of this article contains mildly redundant model specifications allowing the readers who are not interested in the methodological foundation to directly read the self-contained Sect. Results.
The quantity of our interest is the population-averaged number of spikes emitted by a large homogeneous network of N sparsely coupled aEIF model neurons per small time interval, i.e., the spike rate rN(t). The state of neuron i at time t is described by the membrane voltage Vi(t) and adaptation current wi(t), which evolve piecewise continuously in response to overall synaptic current Isyn,i = Iext,i(t) + Irec,i(t). This input current consists of fluctuating network-external drive Iext,i = C[μext(t) + σext(t)ξext,i(t)] with membrane capacitance C, time-varying moments μext, σ ext 2 and unit Gaussian white noise process ξext,i as well as recurrent input Irec,i. The latter causes delayed postsynaptic potentials (i.e., deflections of Vi) of small amplitude J triggered by the spikes of K presynaptic neurons (see Sect. Methods for details).
Here we present two approaches of how the spike rate dynamics of the large, stochastic delay-differential equation system for the 2N states (Vi, wi) can be described by simple models in terms of low-dimensional ODEs. Both approaches (i) take into account adaptation current dynamics that are sufficiently slow, allowing to replace the individual adaptation current wi by its population-average 〈w〉, governed by
d ⟨ w ⟩ d t = a ( ⟨ V ⟩ ∞ - E w ) - ⟨ w ⟩ τ w + b r ( t ) , (1)
where a, Ew, b, τw are the adaptation current model parameters (subthreshold conductance, reversal potential, spike-triggered increment, time constant, respectively), 〈V〉∞ is the steady-state membrane voltage averaged across the population (which can vary over time, see below), and r is the spike rate of the respective low-dimensional model. Furthermore, both approaches (ii) are based on the observation that the collective dynamics of a large, sparsely coupled (and noise driven) network of integrate-and-fire type neurons can be well described by a Fokker-Planck equation. In this intermediate Fokker-Planck (FP) model the overall synaptic input is approximated by a mean part with additive white Gaussian fluctuations, Isyn,i/C ≈ μsyn(t, rd) + σsyn(t, rd)ξi(t), that are uncorrelated between neurons. The moments of the overall synaptic input,
μ syn = μ ext ( t ) + J K r d ( t ) , σ syn 2 = σ ext 2 ( t ) + J 2 K r d ( t ) , (2)
depend on time via the moments of the external input and, due to recurrent coupling, on the delayed spike rate rd. The latter is governed by
d r d d t = r - r d τ d , (3)
which corresponds to individual propagation delays drawn from an exponentially distributed random variable with mean τd. The FP model involves solving a partial differential equation (PDE) to obtain the time-varying membrane voltage distribution p(V, t) and the spike rate r(t).
The first reduction approach is based on the spectral decomposition of the Fokker-Planck operator L and leads to the following two low-dimensional models: the “basic” model variant (spec1) is given by a complex-valued differential equation describing the spike rate evolution in its real part,
d r ˜ d t = λ 1 ( r ˜ - r ∞ ) , r ( t ) = Re { r ˜ } , (4)
where λ1(μtot, σtot) is the dominant eigenvalue of L and r∞(μtot, σtot) is the steady-state spike rate. Its parameters λ1, r∞, and 〈V〉∞ (cf. Eq (1)) depend on the total input moments given by μtot(t) = μsyn − 〈w〉/C and σ tot 2 ( t ) = σ syn 2 which closes the model (Eqs (1)–(4)). The other, “advanced” spectral model variant (spec2) is given by a real-valued second order differential equation for the spike rate,
β 2 r ¨ + β 1 r ˙ + β 0 r = r ∞ - r - β c , (5)
where the dots denote time derivatives. Its parameters β2, β1, β0, βc, r∞ and 〈V〉∞ depend on the total input moments (μtot, σ tot 2) as follows: the latter two parameters explicitly as in the basic model above, the former four indirectly via the first two dominant eigenvalues λ1, λ2 and via additional quantities obtained from the (stationary and the first two nonstationary) eigenfunctions of L and its adjoint L *. Furthermore, the parameter βc depends explicitly on the population-averaged adaptation current 〈w〉, the delayed spike rate rd, and on the first and second order time derivatives of the external moments μext and σ ext 2.
The second approach is based on a Linear-Nonlinear (LN) cascade, in which the population spike rate is generated by applying to the time-varying mean and standard deviation of the overall synaptic input, μsyn and σsyn, separately a linear temporal filter, followed by a common nonlinear function. These three components–two linear filters and a nonlinearity–are extracted from the Fokker-Planck equation. Approximating the linear filters using exponentials and damped oscillating functions yields two model variants: In the basic “exponential” (LNexp) model the filtered mean μf and standard deviation σf of the overall synaptic input are given by
d μ f d t = μ syn - μ f τ μ , d σ f d t = σ syn - σ f τ σ , (6)
where the time constants τμ(μeff, σeff), τσ(μeff, σeff) depend on the effective (filtered) input mean μeff(t) = μf − 〈w〉/C and standard deviation σeff(t) = σf. The “damped oscillator” (LNdos) model variant, on the other hand, describes the filtered input moments by
μ ¨ f + 2 τ μ ˙ f + ( 2 τ 2 + ω 2 ) μ f = 1 + τ 2 ω 2 τ ( μ syn τ + μ ˙ syn ) , (7) d σ f d t = σ syn - σ f τ σ , (8)
where the time constants τ(μtot, σtot), τσ(μtot, σtot) and the angular frequency ω(μtot, σtot) depend on the total input moments defined above. In both LN model variants the spike rate is obtained by the nonlinear transformation of the effective input moments through the steady-state spike rate,
r ( t ) = r ∞ ( μ eff , σ eff ) , (9)
and the steady-state mean membrane voltage 〈V〉∞ (cf. Eq (1)) is also evaluated at (μeff, σeff).
These four models (spec1, spec2, LNexp, LNdos) from both reduction approaches involve a number of parameters that depend on the strengths of synaptic input and adaptation current only via the total or effective input moments. We refer to these parameters as quantities below to distinguish them from fixed (independent) parameters. The computational complexity when numerically solving the models forward in time (for different parametrizations) can be greatly reduced by precomputing those quantities for a range of values for the total/effective input moments and using look-up tables during time integration. Changing any parameter value of the external input, the recurrent coupling or the adaptation current does not require renewed precomputations, enabling rapid explorations of parameter space and efficient (linear) stability analyses of network states.
The full specification of the “ground truth” system (network of aEIF neurons), the derivations of the intermediate description (FP model) and the low-dimensional spike rate models complemented by concrete numerical implementations are provided in Sect. Methods (that is complemented by the supporting material S1 Text). In Fig 1 we visualize the outputs of the different models using an example excitatory aEIF network exposed to external input with varying mean μext(t) and standard deviation σext(t).
Here, and in the subsequent two sections, we assess the accuracy of the four low-dimensional models to reproduce the spike rate dynamics of the underlying aEIF population. The intermediate FP model is included for reference. The derived models generate population activity in response to overall synaptic input moments μsyn and σ syn 2. These depend on time via the external moments μext(t) and σ ext 2 ( t ), and the delayed spike rate rd(t). Therefore, it is instrumental to first consider an uncoupled population and suitable variations of external input moments that effectively mimic a range of biologically plausible presynaptic spike rate dynamics. This allows us to systematically compare the reproduction performance of the different models over a manageable parameter space (without K, J, τd), yet it provides useful information on the accuracy for recurrent networks.
For many network settings the dominant effect of synaptic coupling is on the mean input (cf. Eq (2)). Therefore, we consider first in detail time-varying mean but constant variance of the input. Specifically, to account for a wide range of oscillation frequencies for presynaptic spike rates, μext is described by an Ornstein-Uhlenbeck (OU) process
μ ˙ ext = μ ¯ - μ ext τ ou μ + 2 τ ou μ ϑ μ ξ ( t ) , (10)
where τ ou μ denotes the correlation time, μ ¯ and ϑμ are the mean and standard deviation of the stationary normal distribution, i.e., lim t → ∞ μ ext ( t ) ∼ N ( μ ¯ , ϑ μ 2 ), and ξ is a unit Gaussian white noise process. Sample time series generated from the OU process are filtered using a Gaussian kernel with a small standard deviation σt to obtain sufficiently differentiable time series μ ˜ ext (due to the requirements of the spec2 model and the LNdos model). The filtered realization μ ˜ ext ( t ) is then used for all models to allow for a quantitative comparison of the different spike rate responses to the same input. The value of σt we use in this study effectively removes very large oscillation frequencies which are rarely observed, while lower frequencies [22] are passed.
The parameter space we explore covers large and small correlation times τ ou μ, strong and weak input mean μ ¯ and standard deviation σext, and for each of these combinations we consider an interval from small to large variation magnitudes ϑμ. The values of τ ou μ and ϑμ determine how rapid and intense μext(t) fluctuates.
We apply two performance measures, as in [21]. One is given by Pearson’s correlation coefficient,
ρ ( r N , r ) ≔ ∑ k = 1 M ( r N ( t k ) - r ¯ N ) ( r ( t k ) - r ¯ ) ∑ k = 1 M ( r N ( t k ) - r ¯ N ) 2 ∑ k = 1 M ( r ( t k ) - r ¯ ) 2 , (11)
between the (discretely given) spike rates of the aEIF population and each derived model with time averages r ¯ N = 1 / M ∑ k = 1 M r N ( t k ) and r ¯ = 1 / M ∑ k = 1 M r ( t k ) over a time interval of length tM − t1. For comparison, we also include the correlation coefficient between the aEIF population spike rate and the time-varying mean input, ρ(rN, μext). In addition, to assess absolute differences we calculate the root mean square (RMS) distance,
dRMS(rN,r)≔1M∑k=1M(rN(tk)−r(tk))2, (12)
where M denotes the number of elements of the respective time series (rN, r).
We find that three of the four low-dimensional spike rate models (spec2, LNexp, LNdos) very well reproduce the spike rate rN of the aEIF neurons: for the LNexp model ρ > 0.95 and for the spec2 and LNdos models ρ ≳ 0.8 (each) over the explored parameter space, see Fig 2. Only the basic spectral model (spec1) is substantially less accurate. Among the best models, the simplest (LNexp) overall outperforms spec2 and LNdos, in particular for fast and strong mean input variations. However, in the strongly mean-driven regime the best performing model is spec2.
We observe that the performance of any of the spike rate models decreases (with model-specific slope) with (i) increasing variation strength ϑμ larger than a certain (small) value, and with (ii) smaller τ ou μ, i.e., faster changes of μext. For small values of ϑμ fluctuations of rN, which are caused by the finite aEIF population size N and do not depend on the fluctuations of μext, deteriorate the performance measured by ρ (see also [21], p.13 right). This explains why ρ does not increase as ϑμ decreases (towards zero) for any of the models. Naturally, the FP model is the by far most accurate spike rate description in terms of both measures, correlation coefficient ρ and RMS distance. This is not surprising because the four low-dimensional models are derived from that (infinite-dimensional) representation. Thus, the performance of the FP system defines an upper bound on the correlation coefficient ρ and a lower bound on the RMS distance for the low-dimensional models.
In detail: for moderately fast changing mean input (large τ ou μ) the three models spec2, LNexp and LNdos exhibit excellent reproduction performance with ρ > 0.95, and spec1 shows correlation coefficients of at least ρ = 0.9 (Fig 2A), which is substantially better than ρ(rN, μext). The small differences between the three top models can be better assessed from the RMS distance measure. For large input variance σ ext 2 the two LN models perform best (cf. Fig 2A, top, and for an example, 2C). For weak input variance and large mean (small σext, large μ ¯) the spec2 model outperforms the LN models, unless the variation magnitude ϑμ is very large. For small mean μ ¯, where transient activity is interleaved with periods of quiescence, the LNexp model performs best, except for weak variations ϑμ, where LNdos is slightly better (see Fig 2A, bottom).
Stronger differences in performance emerge when considering faster changes of the mean input μext(t) (i.e., for small τ ou μ), see Fig 2B, and for examples, Fig 2C. The spec1 model again performs worst with ρ values even below the input/output correlation baseline ρ(rN, μext) for large mean input μ ¯ (cf. Fig 2B, left). The spec1 spike rate typically decays too slowly (cf. Fig 2C). The three better performing models differ as follows: for large input variance and mean (large σext and μ ¯), where the spike rate response to the input is rather fast (cf. increased ρ(rN, μext)), the performance of all three models in terms of ρ is very high, but the RMS distance measure indicates that LNexp is the most accurate model (cf. Fig 2B, top). For weak mean input LNexp is once again the top model while LNdos and, especially noticeable, spec2 show a performance decline (see example in Fig 2C). For weak input variance (Fig 2A, bottom), where significant (oscillatory) excursions of the spike rates in response to changes in the mean input can be observed (see also Fig 1), we obtain the following benchmark contrast: for large mean drive μ ¯ the spec2 model performs best, except for large variation amplitudes ϑμ, at which LNexp is more accurate. Smaller mean input on the other hand corresponds to the most sensitive regime where periods of quiescence alternate with rapidly increasing and decaying spike rates. The LNexp model shows the most robust and accurate spike rate reproduction in this setting, while LNdos and spec2 each exhibit decreased correlation and larger RMS distances–spec2 even for moderate input variation intensities ϑμ. The slowness approximation underlying the spec2 model likely induces an error due to the fast external input changes in comparison with the rather slow intrinsic time scale by the dominant eigenvalue, τ ou μ = 5 ms vs. 1/|Re{λ1}| ≈ 15 ms (cf. visualization of the spectrum in Sect. Spectral models). Note that for these weak inputs the distribution of the spike rate is rather asymmetric (cf. Fig 2B). Interestingly the LNdos model performs worse than LNexp for large mean input variations (i.e., large ϑμ) in general, and only slightly better for small input variance and mean input variations that are not too large and fast.
We would like to note that decreasing the Gaussian filter width σt to smaller values, e.g., fractions of a millisecond, can lead to a strong performance decline for the spec2 model because of its explicit dependence on first and second order time derivatives of the mean input.
Furthermore, we show how the adaptation parameters affect the reproduction performance of the different models in Fig 3. The adaptation time constant τw and spike-triggered adaptation increment b are varied simultaneously (keeping their product constant) such that the average spike rate and adaptation current, and thus the spiking regime, remain comparable for all parametrizations. As expected, the accuracy of the derived models decreases for faster adaptation current dynamics, due to the adiabatic approximation that relies on sufficiently slow adaptation (cf. Sect. Methods). Interestingly however, the performance of all reduced models (except spec1) declines only slightly as the adaptation time constant decreases to the value of the membrane time constant (which means the assumption of separated time scales underlying the adiabatic approximation is clearly violated). This kind of robustness is particularly pronounced for input with large baseline mean μ ¯ and small noise amplitude σext, cf. Fig 3B.
For perfectly balanced excitatory and inhibitory synaptic coupling the contribution of presynaptic activity to the mean input μsyn is zero by definition, but the input variance σ syn 2 is always positively (linearly) affected by a presynaptic spike rate–even for a negative synaptic efficacy J (cf. Eq (2)). To assess the performance of the derived models in this scenario, but within the reference setting of an uncoupled population, we consider constant external mean drive μext and let the variance σ ext 2 ( t ) evolve according to a filtered OU process (such as that used for the mean input μext in the previous section) with parameters σ 2 ¯ and ϑσ2 of the stationary normal distribution N ( σ 2 ¯ , ϑ σ 2 2 ), correlation time τ ou σ 2 and Gaussian filter standard deviation σt as before.
The results of two input parametrizations are shown in Fig 4. For large input mean μext and rapidly varying variance σ ext 2 ( t ) the spike rate response of the aEIF population is very well reproduced by the FP model and, to a large extent, by the spec2 model (cf. Fig 4A). This may be attributed to the fact that the latter model depends on the first two time derivatives of the input variance σ ext 2. The LN models cannot well reproduce the rapid spike rate excursions in this setting, and the spec1 model performs worst, exhibiting time-lagged spike rate dynamics compared to rN(t) which leads to a very small value of correlation coefficient ρ (below the input/output correlation baseline ρ ( r N , σ ext 2 )). For smaller mean input μext and moderately fast varying variance σ ext 2 ( t ) (larger correlation time τ ou σ 2) the fluctuating aEIF population spike rate is again nicely reproduced by the FP model while the rate response of the spec2 model exhibits over-sensitive behavior to changes in the input variance, as indicated by the large RMS distance (see Fig 4B). This effect is even stronger for faster variations, i.e., smaller τ ou σ 2 (cf. supplementary visualization S1 Fig). The LN models perform better in this setting, and the spec1 model (again) performs worst in terms of correlation coefficient ρ due to its time-lagged spike rate response.
It should be noted that the lowest possible value of the input standard deviation, i.e., σext (plus a nonnegative number in case of recurrent input) cannot be chosen completely freely but must be large enough (≳ 0 . 5 mV / ms) for our parametrization. This is due to theoretical reasons (Fokker-Planck formalism) and practical reasons (numerics for Fokker-Planck solution and for calculation of the derived quantities, such as r∞).
To demonstrate the applicability of the low-dimensional models for network analyses we consider a recurrently coupled population of aEIF neurons that produces self-sustained network oscillations by the interplay of strong excitatory feedback and spike-triggered adaptation or, alternatively, by delayed recurrent synaptic inhibition [16, 23]. The former oscillation type is quite sensitive to changes in input, adaptation and especially coupling parameters for the current-based type of synaptic coupling considered here and due to lack of (synaptic) inhibition and refractoriness. For example, a small increase in coupling strength can lead to a dramatic (unphysiologic) increase in oscillation amplitude because of strong recurrent excitation. Hence we consider a difficult setting here to evaluate the reduced spike rate models–in particular, when the network operates close to a bifurcation.
In Fig 5A we present two example parametrizations from a region (in parameter space) that is characterized by stable oscillations. This means the network exhibits oscillatory spike rate dynamics for constant external input moments μext and σ ext 2. The derived models reproduce the limit cycle behavior of the aEIF network surprisingly well, except for small frequency and amplitude deviations (FP, spec2, LNdos, LNexp) and larger frequency mismatch (spec1), see Fig 5A, top. For weaker input moments and increased spike-triggered adaptation strength the network is closer to a Hopf bifurcation [16, 23]. It is, therefore, not surprising that the differences in oscillation period and amplitude are more prominent (cf. Fig 5A, bottom). The bifurcation point of the LNexp model is slightly shifted, shown by the slowly damped oscillatory convergence to a fixed point. This suggests that the bifurcation parameter value of each of the derived models is not far from the true critical parameter value of the aEIF network but can quantitatively differ (slightly) in a model-dependent way.
The second type of oscillation is generated by delayed synaptic inhibition [22] and does not depend on the (neuronal) inhibition that is provided by an adaptation current. To demonstrate this independence the adaptation current was disabled (by setting the parameters a = b = 0) for the two respective examples that are shown in Fig 5B. Similarly as for the previous oscillation type, the low-dimensional models (except spec1) reproduce the spike rate limit cycle of the aEIF network surprisingly well, in particular for weak external input (see Fig 5B, top). For larger external input and stronger inhibition with shorter delay the network operates close to a Hopf bifurcation, leading to larger differences in oscillation amplitude and frequency in a model-dependent way (Fig 5B, bottom). Note that the intermediate (Fokker-Planck) model very well reproduces the inhibition-based type of oscillation which demonstrates the applicability of the underlying mean-field approximation. We would also like to note that enabling the adaptation current dynamics (only) leads to decreased average spike rates but does not affect the reproduction accuracy.
We would like to emphasize that the previous comprehensive evaluations for an uncoupled population provide a deeper insight on the reproduction performance–also for a recurrent network–than the four examples shown here, as explained in the Sect. Performance for variations of the mean input. For example, the (improved) reproduction performance for increased input variance in the uncoupled setting (cf. Fig 2) informs about the reproduction performance for networks of excitatory and inhibitory neurons that are roughly balanced, i.e., where the overall input mean is rather small compared to the input standard deviation.
We have developed efficient implementations of the derived models using the Python programming language and by employing the library Numba for low-level machine acceleration [24]. These include: (i) the numerical integration of the Fokker-Planck model using an accurate finite volume scheme with implicit time discretization (cf. Sect. Methods), (ii) the parallelized precalculation of the quantities required by the low-dimensional spike rate models and (iii) the time integration of the latter models, as well as example scripts demonstrating (i)–(iii). The code is available as open source software under a free license at GitHub: https://github.com/neuromethods/fokker-planck-based-spike-rate-models
With regards to computational cost, summarizing the results of several aEIF network parametrizations, the duration to generate population activity time series for the low-dimensional spike rate models is usually several orders of magnitude smaller compared to numerical simulation of the original aEIF network and a few orders of magnitude smaller in comparison to the numerical solution of the FP model. For example, considering a population of 50,000 coupled neurons with 2% connection probability, a single simulation run of 5 s and the same integration time step across the models, the computation times amounted to 1.1–3.6 s for the low-dimensional models (with order–fast to slow–LNexp, spec1, LNdos, spec2), about 100 s for the FP model and roughly 1500 s for the aEIF network simulation on a dual-core laptop computer. The time difference to the network simulation substantially increases with the numbers of neurons and connections, and with spiking activity within the network due to the extensive propagation of synaptic events. Note that the speedup becomes even more pronounced with increasing number of populations, where the runtimes of the FP model and the aEIF network simulation scale linearly and the low-dimensional models show a sublinear runtime increase due to vectorization of the state variables representing the different populations.
The derived low-dimensional (ODE) spike rate models are very efficient to integrate given that the required input-dependent parameters are available as precalulated look-up quantities. For the grids used in this contribution, the precomputation time was 40 min. for the cascade (LNexp, LNdos) models and 120 min. for the spectral (spec1, spec2) models, both on a hexa-core desktop computer. The longer calculation time for the spectral models was due to the finer internal grid for the mean input (see S1 Text).
Note that while the time integration of the spec2 model is on the same order as for the other low-dimensional models its implementation complexity is larger because of the many quantities it depends on, cf. Eqs (63)–(66).
In this contribution we have developed four low-dimensional models that approximate the spike rate dynamics of coupled aEIF neurons and retain all parameters of the underlying model neurons. These simple spike rate models were derived in two different ways from a Fokker-Planck PDE that describes the evolving membrane voltage distribution in the mean-field limit of large networks, and is complemented by an ODE for the population-averaged slow adaptation current. Two of the reduced spike rate models (spec1 and spec2) were obtained by a truncated spectral decomposition of the Fokker-Planck operator assuming vanishingly slow (for spec1) or moderately slow (for spec2) changes of the input moments. The other two reduced models (LNexp and LNdos) are described by a cascade of linear filters (one for the input mean and another for its standard deviation) and a nonlinearity which were derived from the Fokker-Planck equation, and subsequently the filters were semi-analytically approximated. Our approaches build upon [18–20] as well as [21], and extend those methods for adaptive nonlinear integrate-and-fire neurons that are sparsely coupled with distributed delays (cf. Sect. Methods).
We have compared the different spike rate representations for a range of biologically plausible input statistics and found that three of the reduced models (spec2, LNexp and LNdos) accurately reproduce the spiking activity of the underlying aEIF population while one model (spec1) shows the least accuracy. Among the best models, the simplest (LNexp) was the most robust and (somewhat surprisingly) overall outperformed spec2 and LNdos–especially in the sensitive regime of rapidly changing sub- and suprathreshold mean drive and in general for rapid and strong input variations. The LNexp model did not exhibit exaggerated deflections in that regime as compared to the other two models. This result is likely due to the importance of the quantitatively correct decay time of the filter for the mean input in the LNexp model, while the violations of the slowness assumptions for the spec2 and LNdos models seem more harmful in this regime. In the strongly mean-driven regime, however, the best performing model was spec2 for variations both in the mean drive (as long as those variations are not too strong and fast) and for variations of the input variance.
We have also demonstrated that the low-dimensional models well reproduce the dynamics of recurrently coupled aEIF populations in terms of asynchronous states (see Fig 1) and spike rate oscillations (cf. Fig 5), where mild deviations at critical (bifurcation) parameter values are expected due to the approximative nature of the model reduction.
The computational demands of the low-dimensional models are very modest in comparison to the aEIF network and also to the integration of the Fokker-Planck PDE, for which we have developed a novel finite volume discretization scheme. We would like to emphasize that any change of a parameter value for input, coupling or adaptation current does not require renewed precomputations. To facilitate the application of the presented models we have made available implementations that precompute all required quantities and numerically integrate the derived low-dimensional spike rate models as well as the Fokker-Planck equation, together with example (Python) scripts, as open source software.
Since the derived models are formulated in terms of simple ODEs, they allow to conveniently perform linear stability analyses, e.g., based on the eigenvalues of the Jacobian matrix of the respective vector field. In this way network states can be rapidly characterized by quantifying the bifurcation structure of the population dynamics–including regions of the parameter space where multiple fixed points and/or limit cycle attractors co-exist. For a characterization of stable network states by numerical continuation and an assessment of their controllability through neuromodulators using the LNexp model see [23] ch. 4.2 and [25]. Furthermore, the low-dimensional models are well suited to be employed in large neuronal networks of multiple populations for efficient simulations of population-averaged activity time series. Overall, the LNexp model seems a good candidate for that purpose considering its accuracy and robustness, as well as its computational and implementational simplicity.
In addition to the work we build upon [18–21] (cf. Sect. Methods) there are a few other approaches to derive spike rate models from populations of spiking neurons. Some methods also result in an ODE system, taking into account (slow) neuronal adaptation [17, 26, 36–38] or disregarding it [39]. The settings differ from the work presented here in that (i) the intrinsic neuronal dynamics are adiabatically neglected [17, 26, 36, 37], (ii) only uncoupled populations [38] or all-to-all connected networks [17, 36, 39] are assumed in contrast to sparse connectivity, and (iii) (fixed) heterogeneous instead of fluctuating input is considered [39]. Notably, these previous methods yield rather qualitative agreements with the underlying spiking neuron population activity except for [39] where an excellent quantitative reproduction for (non-adaptive) quadratic integrate-and-fire oscillators with quenched input randomness is reported.
Other approaches yield mesoscopic representations of population activity in terms of model classes that are substantially less efficient to simulate and more complicated to analyze than low-dimensional ODEs [14–17, 40–42]. The spike rate dynamics in these models has been described (i) by a rather complex ODE system that depends on a stochastic jump process derived for integrate-and-fire neurons without adaptation [40], (ii) by PDEs for recurrently connected aEIF [16] or Izhikevich [17] neurons, (iii) by an integro-PDE with displacement for non-adaptive neurons [42] or (iv) by integral equations that represent the (mean) activity of coupled phenomenological spiking neurons without [41] and with adaptation [14, 15].
Furthermore, the stationary condition of a noise-driven population of adaptive EIF neurons [32, 43, 44] and the first order spike rate response to weak input modulations [43, 44] have been analyzed using the Fokker-Planck equation. Ref. [32] also considered a refined approximation of the (purely spike-triggered) adaptation current including higher order moments.
It may be interesting for future studies to explore ways to extend the presented methods and relax some of the underlying assumptions, in particular, considering (i) the diffusion approximation (via shot noise input, e.g., [45, 46]), (ii) the Poisson assumption (e.g., using the concept from [47] in combination with results from [48]) and (iii) (noise) correlations (see, e.g., [49]).
Here we present all models in detail—the aEIF network (ground truth), the mean-field FP system (intermediate model) and the low-dimensional models: spec1, spec2, LNexp, LNdos—including step-by-step derivations and essential information on the respective numerical solution methods. An implementation of these models using Python is made available at GitHub: https://github.com/neuromethods/fokker-planck-based-spike-rate-models
We consider a large (homogeneous) population of N synaptically coupled aEIF model neurons [5]. Specifically, for each neuron (i = 1, …, N), the dynamics of the membrane voltage Vi is described by
C d V i d t = I L ( V i ) + I exp ( V i ) - w i + I syn , i ( t ) , (14)
where the capacitive current through the membrane with capacitance C equals the sum of three ionic currents and the synaptic current Isyn,i. The ionic currents consist of a linear leak current IL(Vi) = −gL(Vi − EL) with conductance gL and reversal potential EL, a nonlinear term Iexp(Vi) = gL ΔT exp((Vi − VT)/ΔT) that approximates the rapidly increasing Na+ current at spike initiation with threshold slope factor ΔT and effective threshold voltage VT, and the adaptation current wi which reflects a slowly deactivating K+ current. The adaptation current evolves according to
τ w d w i d t = a ( V i - E w ) - w i , (15)
with adaptation time constant τw. Its strength depends on the subthreshold membrane voltage via conductance a. Ew denotes its reversal potential. When Vi increases beyond VT, it diverges to infinity in finite time due to the exponentially increasing current Iexp(Vi), which defines a spike. In practice, however, the spike is said to occur when Vi reaches a given value Vs—the spike voltage. The downswing of the spike is not explicitly modeled; instead, when Vi ≥ Vs, the membrane voltage Vi is instantaneously reset to a lower value Vr. At the same time, the adaptation current wi is incremented by a value of parameter b, which implements suprathreshold (spike-dependent) activation of the adaptation current.
Immediately after the reset, Vi and wi are clamped (i.e., remain constant) for a short refractory period Tref, and subsequently governed again by Eqs (14) and (15). At the end of the Methods section we describe how (optionally) a spike shape can be included in the aEIF model, together with the associated small changes for the models derived from it.
To complete the network model the synaptic current in Eq (14) needs to be specified: for each cell it is given by the sum of recurrent and external input, Isyn,i = Irec,i(t) + Iext,i(t). Recurrent synaptic input is received from K other neurons of the network, that are connected in a sparse (K ≪ N) and uniformly random way, and is modeled by
I rec , i = C ∑ j J i j ∑ t j δ ( t − t j − d i j ) , (16)
where δ denotes the Dirac delta function. Every spike by one of the K presynaptic neurons with indices j and spike times tj causes a postsynaptic membrane voltage jump of size Jij. The coupling strength is positive (negative) for excitation (inhibition) and of small magnitude. Here it is chosen to be constant, i.e., Jij = J. Each of these membrane voltage deflections occur after a time delay dij that takes into account (axonal and dendritic) spike propagation times and is sampled (independently) from a probability distribution pd. In this work we use exponentially distributed delays, i.e., pd(τ) = exp(−τ/τd)/τd (for τ ≥ 0) with mean delay τd.
The second type of synaptic input is a fluctuating current generated from network-external neurons,
I ext , i = C [ μ ext ( t ) + σ ext ( t ) ξ ext , i ( t ) ] , (17)
with time-varying moments μext and σ ext 2, and unit Gaussian white noise process ξext,i. The latter is uncorrelated with that of other neurons j ≠ i, i.e., 〈ξext,i(t)ξext,j(t + τ)〉 = δ(τ)δij, where 〈·〉 denotes expectation (w.r.t. the joint ensemble of noise realizations at times t and t + τ) and δij is the Kronecker delta. This external current, for example, accurately approximates the input generated from a large number of independent Poisson neurons that produce instantaneous postsynaptic potentials of small magnitude, cf. [48].
The spike rate rN of the network is defined as the population-averaged number of emitted spikes per time interval [t, t + ΔT],
r N ( t ) = 1 N ∑ i = 1 N 1 Δ T ∫ t t + Δ T ∑ t i δ ( s − t i ) d s , (18)
where the interval size ΔT is practically chosen small enough to capture the dynamical structure and large enough to yield a comparably smooth time evolution for a finite network, i.e., N < ∞.
We chose values for the neuron model parameters to describe cortical pyramidal cells, which exhibit “regular spiking” behavior and spike frequency adaptation [7, 50, 51]. For the complete parameter specification see Table 1.
All network simulations were performed using the Python software BRIAN2 [52, 53] with C++ code generation enabled for efficiency. The aEIF model Eqs (14) and (15) were discretized using the Euler-Maruyama method with equidistant time step Δt and initialized with wi(0) = 0 and Vi(0) that is (independently) sampled from a Gaussian initial distribution p0(V) with mean Vr − δV and standard deviation δV/2 where δV = VT − Vr. Note that the models derived in the following Sects. do not depend on this particular initial density shape but allow for an arbitrary (density) function p0.
In the following sections we present two approaches of how simple spike rate models can be derived from the Fokker-Planck mean-field model described in the previous section, cf. Eqs (20), (21) and (23)–(32).
The derived models are described by low-dimensional ordinary differential equations (ODEs) which depend on a number of quantities defined in the plane of (generic) input mean and standard deviation (μ, σ). To explain this concept more clearly we consider, as an example, the steady-state spike rate, which is a quantity required by all reduced models. The steady-state spike rate as a function of μ and σ,
r ∞ ( μ , σ ) ≔ lim t → ∞ r ( t ; μ tot = μ , σ tot = σ ) , (37)
denotes the stationary value of Eq (29) under replacement of the (time-varying) total moments μtot and σ tot 2 in the probability flux qp, Eq (25), by (constants) μ and σ2, respectively. Thus the steady-state spike rate r∞ effectively corresponds to that of an uncoupled EIF population whose membrane voltage is governed by dVi/dt = [IL(Vi) + Iexp(Vi)]/C + μ + σξi(t) plus reset condition, i.e., adaptation and synaptic current dynamics are detached. For a visualization of r∞(μ, σ) see Fig 6.
When simulating the reduced models these quantities need to be evaluated for each discrete time point t at a certain value of (μ, σ) which depends on the overall synaptic moments μsyn(t), σ syn 2 ( t ) and on the mean adaptation current 〈w〉(t) in a model-specific way (as described in the following Sects.). An example trajectory of r∞ in the (μ, σ) space for a network showing stable spike rate oscillations is shown in Fig 5.
Importantly, these quantities depend on the parameters of synaptic input (J, K, τd, μext, σext) and adaptation current (a, b, τw, Ew) only through their arguments (μ, σ). Therefore, for given parameter values of the EIF model (C, gL, EL, ΔT, VT, Vr, Tref) we precalculate those quantities on a (reasonably large and sufficiently dense) grid of μ and σ values, and access them during time integration by interpolating the quantity values stored in a table. This greatly reduces the computational complexity and enables rapid numerical simulations.
The derived low-dimensional models describe the spike rate dynamics and generally do not express the evolution of the entire membrane voltage distribution. Therefore, the mean adaptation dynamics, which depends on the density p(V, t) (via 〈V〉, cf. Eq (23)) is adjusted through approximating the mean membrane voltage 〈V〉 by the expectation over the steady-state distribution,
⟨ V ⟩ ∞ = ∫ - ∞ V s v p ∞ ( v ) d v ∫ - ∞ V s p ∞ ( v ) d v , (38)
which is valid for sufficiently slow adaptation current dynamics [48, 58]. The steady-state distribution is defined as p∞(V) = limt → ∞ p(V, t; μtot = μ, σtot = σ), representing the stationary membrane voltages of an uncoupled EIF population for generic input mean μ and standard deviation σ. The mean adaptation current in all reduced models is thus governed by
d ⟨ w ⟩ d t = a ( ⟨ V ⟩ ∞ - E w ) - ⟨ w ⟩ τ w + b r ( t ) , (39)
where the evaluation of quantity 〈V〉∞ in terms of particular values for μ and σ at a given time t is model-specific (cf. following Sects.). Note again that the calculation of 〈V〉∞ slightly changes when considering an (optional) spike shape extension for the aEIF model, as described at the end of the Methods section.
The Fokker-Planck model does not restrict the form of the delay distribution pd, except that the convolution with the spike rate r, Eq (20), has to be well defined. Here, however, we aim at specifying the complete network dynamics in terms of a low-dimensional ODE system. Exploiting the exponential form of the delay distribution pd we obtain a simple ordinary differential equation for the delayed spike rate,
d r d d t = r - r d τ d , (40)
which is equivalent to the convolution rd = r * pd.
Note that more generally any delay distribution from the exponential family allows to represent the delayed spike rate rd by an equivalent ODE instead of a convolution integral [68]. Identical delays, rd(t) = r(t − d), are also possible but lead to delay differential equations. Naturally, in case of no delays, we simply have rd(t) = r(t).
To simulate the reduced models standard explicit time discretization schemes can be applied–directly to the first order equations of the LNexp model, and for the other models (LNdos, spec1, spec2)–to the respective equivalent (real) first order systems. We would like to note that when using the explicit Euler method to integrate any of the latter three low-dimensional models a sufficiently small integration time step Δt is required to prevent oscillatory artifacts. Although the explicit Euler method works well for the parameter values used in this contribution, we have additionally implemented the method of Heun, i.e., the explicit trapezoidal rule, which is second order accurate.
Linear-Nonlinear (LN) cascade models of neuronal activity are often applied in neuroscience, because they are simple and efficient, and the model components can be estimated using established experimental procedures [21, 74, 75]. Here we use the LN cascade as an ansatz to develop a low-dimensional model and we determine its components from the underlying Fokker-Planck model. This section builds upon [21] and extends that approach for recurrently coupled aEIF neurons; specifically, by taking into account an adaptation current and variations of the input variance. Furthermore, we consider an improved approximation of the derived linear filters and include an (optional) explicit description of the spike shape, cf. [23] (ch 4.2).
The cascade models considered here produce spike rate output by applying to the time-varying mean μsyn and standard deviation σsyn of the (overall) synaptic input, cf. Eq (21), separately a linear temporal filter, Dμ and Dσ, followed by a common nonlinear function F. That is,
r ( t ) = F ( μ f , σ f , ⟨ w ⟩ ) , (70) μ f ( t ) = D μ * μ syn ( t ) , (71) σ f ( t ) = D σ * σ syn ( t ) , (72)
where μf and σf denote the filtered mean and filtered standard deviation of the input, respectively. D μ * μ syn ( t ) = ∫ 0 ∞ D μ ( τ ) μ syn ( t - τ ) d τ is the convolution between Dμ and μsyn. The filters Dμ, Dσ are adaptive in the sense that they depend on the mean adaptation current 〈w〉 and on the (arbitrary) baseline input in terms of baseline mean μ syn 0 and standard deviation σ syn 0. For improved readability these dependencies are not explicitly indicated in Eqs (71) and (72). Note, that the nonlinearity F also depends on 〈w〉, which is governed by Eq (23). Since the mean adaptation current depends on the mean membrane voltage 〈V〉 we also consider a nonlinear mapping H for that population output quantity,
⟨ V ⟩ ( t ) = H ( μ f , σ f , ⟨ w ⟩ ) . (73)
For the derivation below it is instructive to first consider an uncoupled population, i.e., the input moments do not depend on rd for now. In particular, the input statistics are described by μ syn ( t ) = μ syn 0 + μ syn 1 ( t ) and σ syn ( t ) = σ syn 0 + σ syn 1 ( t ). In the following, we derive the components F, Dμ and Dσ from the Fokker-Planck model for small amplitude variations μ syn 1, σ syn 1 and for a slowly varying adaptation current (as already assumed). We then approximate the derived linear filter components using suitable functions such that the convolutions can be expressed in terms of simple ODEs. Finally, we account for time-varying baseline input (μ syn 0 ( t ), σ syn 0 ( t )) and for recurrent coupling in the resulting low-dimensional spike rate models.
In this contribution the membrane voltage spike shape has been neglected (typical for IF type neuron models) by clamping Vi and wi during the refractory period, justified by the observation that it is rather stereotyped and its duration is very brief. Furthermore, the spike shape is believed to contain little information compared to the time at which the spike occurs. Nevertheless, it can be incorporated in the aEIF model in a straightforward way using the following reset condition, as suggested in [43]: When Vi reaches the spike voltage Vs from below we let Vi decrease linearly from Vs to Vr during the refractory period and increment the adaptation current wi ← wi + b at the onset of that period. That is, Vi and wi are not clamped during the refractory period, instead, Vi has a fixed time course and wi is incremented by b and then governed again by Eq (15). This modification implies that the average membrane voltage in Eq (23) needs to be calculated over all neurons (and not only the nonrefractory ones), that is, 〈V〉 is calculated with respect to p + pref, where p ref ( V , t ) = ∫ 0 T ref r ( t - s ) δ ( V - V sp ( s ) ) d s with spike trajectory Vsp(t) = Vs + (Vr − Vs)t/Tref, cf. [43]. The same applies to the steady-state mean membrane potential in Eqs (1), (39) and (76), i.e., 〈V〉∞ is then given by
⟨ V ⟩ ∞ = ∫ - ∞ V s v p ∞ ( v ) d v + ( 1 - ∫ - ∞ V s p ∞ ( v ) d v ) V r + V s 2 , (93)
instead of Eq (38). Notably, the accuracy of the adiabatic approximation (Eq (15)) does not depend on the refractory period Tref in this case. That type of spike shape can therefore be considered in the FP model and the low-dimensional models in a straightforward way without significant additional computational demand. Note, however, that for the spec2 model a nonzero refractory period is not supported (see above). For an evaluation of the spike shape extension in terms of reproduction accuracy of the LN models see [23] (Fig. 4.15 in [23]).
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10.1371/journal.pbio.2001750 | Rictor positively regulates B cell receptor signaling by modulating actin reorganization via ezrin | As the central hub of the metabolism machinery, the mammalian target of rapamycin complex 2 (mTORC2) has been well studied in lymphocytes. As an obligatory component of mTORC2, the role of Rictor in T cells is well established. However, the role of Rictor in B cells still remains elusive. Rictor is involved in B cell development, especially the peripheral development. However, the role of Rictor on B cell receptor (BCR) signaling as well as the underlying cellular and molecular mechanism is still unknown. This study used B cell–specfic Rictor knockout (KO) mice to investigate how Rictor regulates BCR signaling. We found that the key positive and negative BCR signaling molecules, phosphorylated Brutons tyrosine kinase (pBtk) and phosphorylated SH2-containing inositol phosphatase (pSHIP), are reduced and enhanced, respectively, in Rictor KO B cells. This suggests that Rictor positively regulates the early events of BCR signaling. We found that the cellular filamentous actin (F-actin) is drastically increased in Rictor KO B cells after BCR stimulation through dysregulating the dephosphorylation of ezrin. The high actin-ezrin intensity area restricts the lateral movement of BCRs upon stimulation, consequently reducing BCR clustering and BCR signaling. The reduction in the initiation of BCR signaling caused by actin alteration is associated with a decreased humoral immune response in Rictor KO mice. The inhibition of actin polymerization with latrunculin in Rictor KO B cells rescues the defects of BCR signaling and B cell differentiation. Overall, our study provides a new pathway linking cell metablism to BCR activation, in which Rictor regulates BCR signaling via actin reorganization.
| As the central hub of cell metabolism, the mammalian target of rapamycin complex (mTORC) integrates immune signals and metabolic cues for the maintenance and activation of these systems. Rictor is the core component of the mammalian target of rapamycin complex 2 (mTORC2), and loss of this protein leads to an immunodeficiency that involves (among other things) impaired antibody production. B cell receptor (BCR) signaling is critical for antibody generation and although it has been shown that loss of Rictor in B cells negatively impacts this function, the underlying molecular mechanisms are unknown. Here, we show that both early and distal BCR signaling is reduced in Rictor knockout (KO) B cells. We find that the reduction in BCR signaling stems from defective clustering of BCRs during early B cell activation. This seems to be caused by the uncontrolled activation of the actin-connecting protein ezrin, which leads to a rigid actin fence that restricts the lateral movement of BCRs in the membrane. Interestingly, treatment of Rictor KO mice with an actin inhibitor rescues the BCR signaling. Our findings suggest that Rictor helps to allow effective BCR signaling in B cells by triggering reorganization of the actin network, thereby enabling an appropriate antibody response during infection.
| B cell receptor (BCR) signaling is vital for B cell development and function. When BCRs are cross-linked by antigens, it induces the conformational changes of signaling subunits immunoglobulin α chain (Igα) and immunoglobulin β chain (Igβ). The conformational changes of Igα and Igβ lead to the phosphorylation of immunoreceptor tyrosine-based activation motif (ITAM) domains of Igα and Igβ. The phosphorylated ITAM domain recruits LYN proto-oncogene, Src family tyrosine kinase (Lyn) for phosphorylation, and the phosphorylation of Lyn activates spleen tyrosine kinase (Syk). This initiates the activation of downstream signaling cascades, such as the activation of Brutons tyrosine kinase (Btk) and phospholipase C gamma 2 (PLCγ2) [1–3]. At the end of activation of BCR signaling, the negative regulators of BCR signaling are also activated, such as phosphorylated SH2-containing inositol phosphatase (pSHIP), which is regulated by Lyn [4–7]. Recently, with the development of the high-resolution technique of total internal reflection fluorescent microscopy (TIRFm), the molecular details of the initiation events in BCR activation have been revealed [8–10]. The conformational changes of the BCR expose the Cμ4 domain of membrane immunoglobulin M (IgM) for BCR self-aggregation [11] and ITAMs for signaling molecules to bind [12].
The role of actin on BCR signaling has been characterized recently with TIRFm as well. Actin is known to be involved in BCR capping [13,14], and the disruption of actin delays the attenuation of BCR signaling in B cells induced by soluble antigen (sAg) [15] or even induces BCR signaling alone, without antigen stimulation [16]. TIRFm coupled with single-molecule tracking techniques has dissected the underlying mechanism that links the actin network and BCR movement. In resting B cells, actin and ezrin together form a network that both defines compartments containing mobile BCRs and establishes boundaries restricting BCR diffusion. The BCR diffusion coefficient is inversely related to the actin intensity on the plasma membrane. Breaking down of the actin fence by latruculin treatment increases the diffusion coefficient of BCRs and induces BCR signaling comparable to that triggered by BCR cross-linking [17,18]. Therefore, actin depolymerization is essential for the initiation of BCR signaling.
As a core component of mTORC2, Rictor has been studied recently in all kinds of cells. Although Rictor deletion early in B cells using vav guanine nucleotide exchange factor (Vav)-Cre has a modest effect on the development of pro- and pre-B cells in the bone marrow by up-regulating forkhead box O1 (FoxO1) and recombination activating 1 (Rag-1) [19,20], it causes a severe impact on the peripheral development [19]. The reduction of marginal zone (MZ) B cells and B1a cells is more severe than folicular (FO) B cells. Furthermore, antibody production is greatly impaired when mature B cells lose Rictor expression after completing their development by using Cre-ERT2Rictorfl/fl mice[19]. Mechanistically, Rictor is vital for the induction of prosurvival genes, suppression of proapoptotic genes, nuclear factor κB (NF-κB) induction after BCR activation, and nuclear factor κB2/p52 generation [19]. Therefore, Rictor is critical for B cell survival signals initiated via Phosphotidylinositol 3 kinase (PI3K) [19]. However, it is unknown how Rictor affects BCR signaling or early B cell activation.
The activation of both BCR and T cell receptor (TCR) induces the dephosphorylation of ezrin-radixin-moesin (ERM) proteins that are the linkers between the plasma membrane and the actin cytoskeleton and induces the detachment of ERM from the actin cytoskeleton [21–23]. Similar to the role of the actin cytoskeleton in the steady state, ezrin also forms a network that, together with actin, restricts the movement of BCRs and slows the diffusion rate [17]. The transient inactivation of ERM, such as dephosphorylation of ezrin, can increase the diffusion rate of unengaged BCRs. The dephosphorylation of ezrin can alter the interaction between the actin cystoskeleton and plasma membrane, which can in turn alter the B cell’s morphology by modulating the filopodia. Consequently, this impairs BCR clustering and B cell spreading during B cell activation [24]. The BCR-mediated phosphorylation of ezrin negatively regulates activation events such as the phosphorylation of tyrosine kinases [25]. In systemic lupus erythematosus (SLE) T cells, the binding of autoantibodies to the cluster of differentiation 3 (CD3)-TCR complex induces the phosphorylation of ezrin and actin polymerization [26]. The inhibition of ezrin with pharmacological inhibitors or small interfering RNA (siRNA) reduces the formation of actin stress fibers [27]. The phosphorylation of ezrin is regulated by serine/threonine kinases including rho-associated coiled-coil-containing protein kinase (ROCK) and protein kinase C (PKC) [28,29]. Considering that Rictor is involved in the reorganization of actin, it is not clear whether Rictor links ezrin to regulate BCR signaling as well as the underlying mechanism.
In this study, we used cluster of differentiation 19 (cd19)-Cre to delete Rictor specifically in B cells and excluded the deletion outside of the B lineage by using Vav-Cre and mixed chimerism in non–B lineages, irradiation-induced load of apoptotic bodies when generating chimera mice. We found that Rictor positively regulates BCR signaling via up-regulating Btk and down-regulating SH2-containing inositol phosphatase (SHIP). Mechanistically, the reduction of BCR signaling is caused by the less mobile BCRs in the activation state, and Rictor deficiency disrupts the early actin depolymerization phase during BCR activation and enhances the actin polymerization and phosphorylation of ezrin. All of these account for the high intensity of ezrin-actin areas that restrict the diffusion of BCRs, which are essential for the triggering of BCR signaling. Furthermore, the reduction of FO B cells was more severe in immunized Rictor KO mice, but we did not observe changes for MZ B cells. Interestingly, the introduction of Latrunculin B, an actin inhibitor in vitro and in vivo, can rescue the defect of differentiation of FO B cells and BCR signaling.
To determine whether Rictor is involved in BCR activation or not, we examined the spatiotemporal relationship between BCR and Rictor using phosphorylated antibody specific for activated Rictor by confocal microscopy (CFm). At 0 min, phosphorylated Rictor (pRictor) was distributed on the plasma membrane evenly (Fig 1A). At 5 min and 10 min, pRictor was redistributed and cocapped with the BCR cluster (Fig 1A). At 30 min, the degree of cocapping of BCR with pRictor was decreased as BCRs started to be internalized (Fig 1A). We used a correlation coefficient to determine the colocalization of BCR and pRictor quantitatively. The colocalization between BCR and pRictor was increased over 10 min and decreased by 30 min. It increased significantly at 5 min and 10 min compared to 0 min (Fig 1B). Additionally, the levels of pRictor measured with mean fluroscence intensity (MFI) by NIS-Elements AR 3.2 software peaked at 10 min upon antigen stimulation (Fig 1C). These results suggest that Rictor is involved in BCR activation. In order to further determine whether Rictor signaling is also involved in BCR activation, we examined the location and expression levels of the downstream signaling molecule of Rictor, phosphorylated Akt (pAkt), in wild-type (WT) and Rictor KO B cells. First, in order to determine the deletion efficiency of cd19-Cre and line leakage, we examined the mRNA levels of rictor in B cells, CD4+, and CD8+ T cells using real time PCR (RT-PCR) and protein levels of Rictor in B cells using western blot. The mRNA levels of rictor and protein levels of Rictor were significantly lower in Rictor KO B cells but had no changes in CD4+ and CD8+ Rictor KO T cells (S1A and S1B Fig). This result suggests the cd19-Cre deletion efficiency is high in B cells without leakage in other types of immune cells. Similar to that of pRictor, the location of pAkt was distributed on the plasma membrane evenly at 0 min and cocapped with BCR at 5 min and 10 min and then began to have endocytosis at 30 min, together with BCR in WT B cells (Fig 1D). In contrast with that of WT B cells, the distribution of pAkt in Rictor KO B cells did not have obvious changes and BCR internalization was severely disrupted (Fig 1D and 1E). Additionally, the MFI of pAkt quantified with NIS-Elements AR 3.2 software in WT B cells was increased over 10 min and decreased by 30 min, and it was significantly decreased in KO B cells (Fig 1F). We also used flow cytomery to quantifiy the pAkt levels in WT and KO B cells after sAg stimulation and observed similar results as with CFm (Fig 1G). The colocalization of BCR with pAkt was increased by 10 min and decreased at 30 min but did not show obvious changes in WT B cells and was significantly decreased in KO B cells (Fig 1H). Taken together, these results suggest that Rictor as well as Rictor signaling is involved in BCR activation.
In order to determine the effect of Rictor deficiency on BCR signaling, we examined the levels of phosphorylated Brutons tyrosine kinase (pBtk) and pSHIP, the key postive and negative molecules of upstream BCR signaling, as well as total phosphotyrosine (pY) to indicate the total level of BCR signaling. The levels of pBtk and pY were increased over 10 min in WT B cells quantified by NIS-Elements AR 3.2 software and decreased by 30 min (Fig 2A, 2C and 2D). pBtk and pY were colocalized with BCR at 5 min and 10 min after stimulation and the degree of colocalization was decreased at 30 min in WT B cells (Fig 2A and 2E). In contrast to that of WT B cells, the levels of pBtk and pY were significantly decreased in KO B cells and the signalosomes of pBtk or pY were always distributed on the plasma membrane (Fig 2A–2D). The colocalization of pY and pBtk with BCR was increased over 10 min and decreased at 30 min in WT B cells, but it was dramatically decreased in KO B cells (Fig 2A, 2B and 2E). In order to further confirm the reduction of pY and pBtk in KO B cells, we examined the levels of pBtk and pY in WT and KO B cells stimulated by sAgs with flow cytometry. Similarly, we found the levels of pY and pBtk were significantly decreased in KO B cells (Fig 2F and 2G). Since the mammalian target of rapamycin (mTOR)/Akt and phospholipase C gamma 2 (PLCγ)/Ca2+ mobilization are seen as separate pathways downstream of the BCR, we examined the Ca2+ mobilization with flow cytometry. We found the Ca2+ mobilization was reduced in Rictor KO B cells after stimulation with sAg (Fig 2H). Additionally, we tested the distal BCR signaling levels such as phosphorylated extracellular regulated protein kinases (pErk) and we found that it was decreased in Rictor KO B cells (Fig 2I). To further confirm the down-regulation of BCR signaling by Rictor deficiency, we tested the levels of pBtk, pAkt, pErk, and pY with western blot after sAg stimulation and found a reduction in early and distal BCR signaling (Fig 2J). Furthermore, we examined the effect of Rictor deficiency on the recruitment of the negative signaling molecule, pSHIP. The MFI of pSHIP was increased over time until 30 min in WT B cells quantified by NIS-Elements AR 3.2 software, but it was significantly increased in KO B cells (Fig 3A–3C). Similar to the staining pattern of pY and pBkt (Fig 2A), pSHIP cocapped with BCR and went through internalization at 30 min in WT B cells (Fig 3A). In KO B cells, pSHIP was always colocalized with BCR (Fig 3B). To confirm the increase of pSHIP in KO B cells, we determined the levels of pSHIP in KO B cells by flow cytometry and found similar results (Fig 3D). We examined the colocalization of BCR and pSHIP using correlation coefficient in WT and KO B cells and found the colocalization of BCR and pSHIP was increased over time by 30 min in WT B cells and was significantly increased in Rictor KO B cells (Fig 3E). To exclude the effect of Rictor deficiency on BCR signaling that is due to B cell development, we examined the effect of Rictor deficiency on bone marrow and peripheral development in Rictor KO mice using flow cytometry. We found the frequency and number of pro-B cells were moderately increased and those of late pre-B cells and recirculating B cells were slightly decreased in Rictor KO mice (S2A–S2D Fig). We also examined the expression levels of interleukin 7 (IL-7) receptors and did not observe any changes (S2E Fig). Then, we examined the alteration of FO, MZ, and germinal center (GC) B cells in the spleen of Rictor KO mice without immunization. We found the frequency and number of FO and GC B cells was decreased and did not observe changes for MZ B cells (S3A–S3G Fig). Furthermore, we examined the expression levels of IgM and immunoglobulin D (IgD) and did not observe any differences of MFI of IgM and IgD between WT and KO B cells (S3H and S3I Fig). Overall, the deficiency of Rictor causes slight impact on the bone marrow and peripheral development. These results imply that Rictor regulates BCR signaling positively and the absence of Rictor leads to unbalanced positive and negative BCR signaling.
Rictor has been reported in several types of cells to regulate the actin cytoskeleton, although its coordination with actin in lymphocytes still remains elusive [30,31]. Rictor-mTOR complex modulates the phosphorylation of protein kinase C α (PKCα) and the actin cytoskeleton [32]. Our previous studies have shown that actin can offer feedback to BCR signaling [16,33–35]. In order to investigate that the effect of Rictor on BCR signaling is coincident with actin alteration, we examined the Rictor deficiency and actin reorganization in B cells after stimulation with sAgs and membrane-associated antigens (mAgs). Filamentous actin (F-actin) was stained with phallodin, the spatiotemporal position was examined by CFm, and the levels of actin were quantified by NIS-Elements AR 3.2 software and flow cytometry. Compared with the levels of F-actin in WT B cells, the levels of F-actin on the plasma membrane and in the cytoplasm of KO B cells were significantly enhanced at 10 min (Fig 4A–4C). However, the basal levels of F-actin examined by flow cytometry were not altered in KO B cells in the nonstimulated condition (Fig 4D). Interestingly, we found the total levels of F-actin were decreased by 5 min and then increased afterwards until 30 min by flow cytometry (Fig 4D), which indicates the depolymerization of actin in the early phase and polymerization of actin afterwards in WT B cells upon sAg stimulation (Fig 4D). However, we found a dramatic increase of the levels of F-actin by 5 min and a moderate decrease afterwards until 30 min in KO B cells (Fig 4D), and the levels of F-actin in KO B cells were always higher than that of WT B cells (Fig 4D). Because the levels of F-actin were highly condensed on the plasma membrane, we used TIRFm to determine the levels of F-actin in the contact zone between B cells and the antigen-tethered lipid bilayer. In WT B cells, the levels of F-actin on the contact zone were increased over 5 min and decreased at 7 min (Fig 4E and 4G), which is consistent with our previous study [16]. However, in KO B cells, the levels of F-actin were increased over time until 7 min and significantly higher than that of WT B cells (Fig 4E, 4F and 4G). We also determined the recruitment of BCR microclusters in the contact zone by measuring the MFI of the BCR cluster. In WT B cells, the MFI of the BCR cluster was increased over time and it was also increased over time in KO B cells, but it was significantly decreased in KO B cells compared to that of WT B cells (Fig 4E, 4F and 4H). The formation of the BCR microclusters triggers BCR signaling, and we examined the recruitment of activated Btk in the contact zone. The recruitment of pBtk was increased over 5 min and decreased by 7 min in WT B cells, and the recruitment of pBtk in KO B cells had a similar trend but was significantly decreased compared to that of WT B cells (Fig 4J). Our previous research has shown that upon stimulation with mAg, actin polymerizes first to facilitate spreading of B cells and depolymerizes later at the center to promote the formation of the central BCR cluster. During these events, F-actin colocalizes well with BCRs at first and then redistributes to the outer edge of the central BCR cluster [16,35]. As expected, in WT B cells, F-actin colocalized well with BCRs at early time points and redistributed to the outer edge of central BCR cluster (Fig 4E and 4K). The colocalization between BCRs and F-actin increased over 3 min and decreased thereafter (Fig 4E and 4K). In KO B cells, F-actin always colocalized well with BCRs for all the time points analyzed and the colocalization between BCRs and F-actin were increased until 7 min (Fig 4F and 4K). All these results suggest that actin reorganization has been altered in Rictor KO B cells and the absence of Rictor leads to enhanced actin polymerization both in the cytoplasm and on the plasma membrane.
Batista et al. have shown that the actin network restricts the movement of BCRs in the steady state. In the region with higher intensity of actin, the diffusion coefficient was decreased for BCRs [17]. In order to analyze the behavior of a single BCR, we used single-particle tracking and analysis as previously reported [11]. Analyses of the single BCR trajectory footprints suggested that single BCR molecules were more mobile in WT B cells after stimulation than in KO B cells after stimulation with mAg from 5 min to 15 min but no differences in the steady state (Fig 5A–5D). Tracking thousands of single BCR molecules from WT and KO B cells showed that their short-range mean-square displacements (MSDs) did not have differences in the resting state but were significantly decreased in KO B cells after activation (Fig 5E and 5F). The mean diffusion coefficient of the BCRs in KO B cells also decreased significantly during the activation status (Fig 5G and 5H). Moreover, the short-range diffusion coefficients of each individual BCR molecule were calculated and their distribution was analyzed and displayed as a cumulative distribution probability (CDP) plot. The CDP of KO B cells was decreased compared to that of WT B cells upon antigenic mAg stimulation but without changes in the steady state (Fig 5I and 5J). The normal mobility of BCRs in KO B cells for the steady state was consistant with the unchanged basal levels of actin without stimulation (Figs 4D, 5A, 5C, 5E, 5G and 5I). These results imply that the BCRs from the WT and KO B cells almost had the same mobility in the steady state, but the BCRs from KO B cells became less mobile than those of WT B cells after activation. Furthermore, these results suggest that the disrupted actin depolymerization in the early phase and ehanced levels of actin in KO B cells after stimulation with sAg and mAg restrict the movement of BCRs after activation.
In order to confirm the effect of actin on BCR signaling in Rictor KO B cells, we used Latrunculin B to reduce the levels of F-actin slightly [15] to see if the defect in BCR signaling and internalization can be rescued. Rictor KO B cells were pretreated with Latrunculin B for 30 min and stimulated with sAg in the presence of Latrunculin B. At 5 min, the levels of F-actin quantified by flow cytometry in KO B cells treated with Latrunculin B were decreased and then gradually increased, which had a similar trend to that of WT B cells, although the levels of F-actin were a little higher (Fig 6A–6D). The levels of F-actin in WT B cells treated with Latrunculin B were decreased compared to that of WT B cells without treatment (Fig 6D). To determine the coordination between actin and ezrin during BCR activation, we stained for activated ezrin by using phosphorylated antibodies. In WT B cells, the levels of phosphorylated Ezrin (pEzrin) decreased for the first 5 min and increased gradually to 30 min (Fig 6A and 6E), which is in line with the previous study [20]. However, in KO B cells, the basal level of pEzrin was significantly higher than that of WT B cells, decreased slowly to 30 min, but was still profoundly higher than that of WT B cells (Fig 6A–6C and 6E). The levels of pEzrin in WT B cells treated with Latrunculin B were decreased compared to those of WT B cells without treatment (Fig 6E). Latrunculin B treatment reduced the activation magnitude of ezrin significantly in KO B cells and induced the same trend as that of WT B cells (Fig 6A–6C and 6E). To further confirm the interplay between actin and ezrin, we used NSC668394 (an ezrin-specific inhibitor) and the inhibitors upstream of the ezrin signaling pathway, such as Y27632 (a ROCK-specific inhibitor) and bisindolylmaleimide I (Bis) (a PKC inhibitor). Not surprsingly, for all 3 inhibitors we found that the actin-polymerization phase starting at 5 min was completely disrupted and replaced with continuous actin depolymerization (S4 Fig). These results collectively suggest that actin and ezrin positively regulate with each other. For BCR internalization, KO B cells treated with Latrunculin B had some BCR caps at 10 min and further flow cytometry analysis found the BCRs remaining on the cell surface decreased significantly compared to that of untreated KO or treated WT B cells but were still higher than that of WT B cells (Fig 6A–6C and 6F). For BCR signaling, the levels of pY and pBtk in KO B cells treated with Latrunculin B increased profoundly compared to those of KO B cells after stimulation (Fig 6G–6K). The levels of pY and pBtk in WT B cells treated with Latrunculin B dropped down more slowly than those of WT B cells without treatment (Fig 6J and 6K). For pY, levels were comparable to those in WT B cells, although the levels of pBtk were still lower than those of WT B cells (Fig 6G–6K). We then looked at the colocalization between BCR, pY, and pBtk. The colocalization between BCR, pY, and pBtk was increased significantly in KO B cells treated with Latrunculin B compared to that of untreated KO B cells but still lower than that of WT B cells (Fig 6G–6I and 6L), and it was decreased in WT B cells treated with Latrunculin B compared to that of untreated WT B cells (Fig 6L). To further confirm that Latrunculin B can rescue the defect of differentiation of FO B cells and BCR signaling of Rictor KO mice in vivo, we fed the mice with 0.5 μM Latrunculin B every week for 2 months and then euthanized the mice to analyze the subpopulations and BCR signaling in splenocytes. Latrunculin B treatment largely restored the frequency and number of FO B cells in Rictor KO mice compared to Rictor KO mice treated with vector only but had no effect on the formation of MZ B cells (S5A–S5C Fig). Moreover, the levels of pY or pBtk were also recovered in a large degree in Rictor KO mice treated with Latrunculin B (S5D and S5E Fig). Taken together, these results suggest that enhanced actin polymerization in KO B cells causes the reduction of BCR signaling and differentiation defect of FO B cells.
In order to determine whether the distorted actin reorganization can affect the humoral immune response, we immunized the mice with T-cell dependent antigen–4-hydroxy-3-nitrophenylacetyl–keyhole limpet hemocyanin (NP-KLH). After 14 days, the mice were euthanized and analyzed for several key populations of B cells required for the humoral immune response. We found the percentage and number of FO B cells were profoundly reduced in KO mice after immunization but did not observe any changes for MZ B cells (Fig 7A–7C). Of note, the degree of the reduction of FO B cells in KO mice was greater in immunized mice than that of nonimmunized mice (S3 Fig, Fig 7A and 7C). Furthermore, we analyzed the frequency and number of GC B cells and they were decreased dramatically in KO mice compared to that of WT mice (Fig 7D and 7E). Additionally, we examined the effect of Rictor deficiency on the generation of antigen-specific memory B cells (MBCs); not surprisingly, we found a decrease of the percentage and number of MBCs in immunized KO mice (Fig 7F and 7G). Finally, we examined the plasma cells and plasmablasts in immunized WT and KO mice. We found a significant decrease of plasmablast (PBC) and plasma cell (PC) in immunized Rictor KO mice compared to that of WT mice (Fig 7H and 7J). To further confirm the effect of Rictor deficiency on humoral immune response, we examined the serum levels of NP-specific subclasses from the immunized mice and found the levels of both NP-specific IgM and IgG were decreased in Rictor KO mice (Fig 7K and 7L). Overall, all these results suggest that the distorted actin reorganization contributes to the noncompetent humoral immune response in Rictor KO mice.
This study examined the effect of Rictor deficiency on BCR signaling. We found that the absence of Rictor leads to down-regulation of BCR signaling via decreasing pBtk and increasing pSHIP. Furthermore, the levels of actin are enhanced in both cytoplasm and plasma membrane in Rictor KO B cells stimulated with sAg. Interestingly, the early actin depolymerization phase in WT B cells after stimulation by sAg is replaced with drastically enhanced actin polymerization in Rictor KO B cells. By using the mAg system, we also found an increased level of actin in the contact zone of B cells with the lipid bilayer as well as decreased BCR clustering, B cell spreading, and recruitment of signalosomes in Rictor KO B cells. The increased levels of actin in Rictor KO B cells led to the reduced diffusion coefficient of BCRs in the activation state. Interestingly, we found the phosphorylation of ezrin is increased and the attenuation of phosphorylation is delayed in Rictor KO B cells and that Latrunculin B treatment can rescue the defect of BCR signaling and internalization as well as the FO differentiation. Finally, Rictor deficiency leads to the reduction of FO B cells more severely in immunized mice. Altogether, to our knowledge, this is the first report of how Rictor regulates BCR signaling by altering the actin reorganization via ezrin.
To compare with what has been previously reported, Rictor deficiency causes an impact on the development of bone marrow B cells, although with varying degrees. These differences could be due to the different Cre systems used. Boothby’s group used VavCre and Yuan’s group used interferon-induced GTP-binding protein Mx1 (Mx1)Cre and in both, deletion is in the early stage of B cell development [19,20], and neither of them is B cell specific. In our cd19-Cre system, we found a slight impact on the progression of late pre-B cells and recirculating B cells and an increased accumulation of pro-B cells in Rictor KO mice. Boothby’s group also found a slight impact on the pro- and pre-B cells in Rictor KO mice and a profound reduction in MZ B cells that cannot be seen in the cd19-Cre KO mice [19]. Yuan’s group found pro-, pre-, and immature B cells are dramatically increased in Rictor KO mice [20]. To resolve the discrepancy between the different Cre systems that have different deletion stages, we are going to use cluster of differentiation 19 (cd19)-CreER mice to cross with Rictor flox/flox mice to observe any divergence caused by the deletion in different stages besides the deletion in different cells.
Another remaining issue is the detailed link between Rictor and BCR signaling molecules or ezrin. First, it would be interesting to explore the direct interaction between Rictor and BCR signaling molecules such as Btk and SHIP. mTORC2 and the key component, Rictor, specifically, has been shown to phosphorylate Akt and protein kinase B (PKB) on Serine 473 (Ser473). This phosphorylation activates Akt/PKB, whereas dysregulation of Akt/PKB has been correlated with cancer and diabetes [36]. Tyrosine phosphorylation of ezrin regulates the activation of c-Jun N-terminal kinase (JNK) after BCR stimulation [37]. Therefore, Rictor possibly can regulate the phosphorylation of Btk and SHIP. The phosphorylation of ezrin can be regulated by rho-associated coiled-coil-containing protein kinase (ROCK) activation, and additionally mTORC2 has been shown to regulate the actin cytoskeleton through its stimulation of F-actin stress fibers via activation of paxillin, ras homolog family member A (RhoA), ras-related C3 botulinum toxin substrate 1 (Rac1), cell division control protein 42 homolog (Cdc42), and PKCα [32]. Therefore, it would be of interest to explore the possible links between Rictor and the upstream molecules of ezrin, such as rho-associated coiled-coil-containing protein kinase (ROCK), RhoA, or even Wiskott-Aldrich syndrome protein (WASP). Another possibility is the regulation of BCR signaling through transcriptional levels. As a kinase, mTORC2 cannot regulate the genes via transcriptional levels unless it goes through the furthest downstream transcriptional factors such as FoxO1. Therefore, we can examine the mRNA levels of Btk and SHIP as well as other signaling molecules or by microarray to search for other candidate genes, and then to determine whether FoxO1 can regulate these candidate genes using chromatin immunoprecipitation-sequencing (Chip-seq).
In summary, this study has revealed not only a new pathway in BCR signaling but also the detailed molecular mechanism of how Rictor regulates BCR activation. Rictor deficiency leads to dysregulation of dephosphorylation of ezrin, which accounts for the enhanced actin polymerization. The high intensity ezrin-actin areas restrict the movement of BCRs after stimulation, which diminishes the triggering of BCR clustering and consequent BCR signaling. Overall, our study provides a new regulation pathway of Rictor to modulate BCR signaling by the actin-ezrin complex.
All animal work was reviewed and approved by the Institutional Animal Care and Usage Committee of Children’s Hospital of Chongqing Medical University following institutional and NIH guidelines and regulations.
Rictor conditional KO mice on a C57/BL6 background were obtained by crossing cd19-Cre mice with rictor flox/flox mice from Jackson lab. Splenic B cells were isolated as described previously [38].
Monobiotinylated Fab′ fragment of anti-mouse IgM+G Ab (mB-Fab′–anti-Ig) was made from the F(ab′)2 (Jackson ImmunoResearch Laboratories) as described before [39]. The disulfide bond that connects the 2 Fab′ was reduced using 20 mM 2-mercaptoethylamine and then biotinylated by maleimide-activated biotin (Thermo Scientific). Fab′ was purified by using Amicon Ultracentrifugal filters (Millipore) and examined by a biotin quantification kit (Thermo Scientific) and then conjugated with AF546 (Invitrogen). To stimulate B cells with sAg, B cells were incubated with AF546–mB-Fab′–anti-Ig (2 μg/ml) together with mB-Fab′–anti-Ig (8 μg/ml) for 30 min and streptavidin (1 μg/ml) for 10 min at 4°C. Streptavidin was omitted as a negative control. The cells were washed and warmed up to 37°C for different time points. To stimulate B cells with mAg, cells were incubated with AF546–mB-Fab′–anti-Ig and mB-Fab′–anti-Ig tethered to lipid bilayers with streptavidin at 37°C for different time points. As a control, B cells were incubated with AF546–Fab–anti-mouse IgM+G (2 μg/ml) at 4°C and then incubated with transferrin (Tf)-tethered lipid bilayers, on which the density of Tf was equal to that of AF546–mB-Fab′–anti-Ig.
The planar lipid bilayer was generated with previous protocol [40,41]. Liposomes were generated by sonicating 1,2-dioleoyl-sn-glycero-3-phosphocholine and 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-cap-biotin (Avanti Polar Lipids) in a 100:1 molar ratio in PBS to get 5 mM lipid. Aggregates in liposomes were discarded by ultra centrifugation and filtration. Coverslip chambers (Nalge Nunc International) were incubated with 0.05 mM liposomes for 10 min and then incubated with 1 μg/ml streptavidin (Jackson ImmunoResearch Laboratories) after extensive washes, followed by 2 μg/ml AF546-mB-Fab′–anti-Ig mixed with 8 μg/ml mB-Fab′–anti-Ig Ab.
Images were obtained using a Nikon A1R confocal and TIRF system on an inverted microscope (Nikon Eclipse Ti-PFS), installed with a 100×, NA 1.49 Apochromat TIRF objective (Nikon Instruments), an iXon EM-CCD camera (Andor), and 3 solid-state lasers with wavelengths 405, 488, and 546 nm.
To image intracellular-signaling molecules, B cells were incubated with an Ag-tethered lipid bilayer at 37°C for different time points. Cells were fixed with 4% paraformaldehyde and permeabilized with 0.05% saponin, followed by phallodin and Btk (pBtk, Y551; BD Biosciences) staining. The B cell contact area and MFI of each staining in the B cell contact zone were determined using IRM images and NIS-Elements AR 3.2 software. Background fluorescence generated by Ag tethered to lipid bilayers in the absence of B cells or secondary Ab controls was subtracted. For each set of data, >20 individual cells from 2 or 3 independent experiments were analyzed. In order to reduce the variability, we consistently dropped cells right above the PBS medium surface using the same volume (10 μl) and cell number (2 x 105) and started timing the early BCR signaling events. We took images from 8 random fields at each time point. We carefully evaluated the morphology and contact area of the B cells landing on the lipid bilayer at different time points. After finishing the analysis of all the individual cells, we pooled all the values together and removed the values that are usually in a very low percentage (<5%) and are far away from the normal range of the majority of the B cells based on the observed morphology and contact area together.
For confocal analyses, B cells were stimulated with AF546–mB-Fab′–anti-Ig without (−) or with streptavidin (sAg) at 4°C, washed, and warmed to 37°C for different time points. After fixation and permeabilization, the cells were stained for pRictor (T1135, Cell Signaling Technology), pY, pBtk, pSHIP, and pEzrin (T558, Cell Signaling Technology) and analyzed using CFm. For flow cytometric analyses, cell suspensions from BM and spleen were incubated with Fcγ receptor (FcγR) blocking Abs (anti-mouse CD16/CD32; BD Bioscience) on ice and stained at optimal dilutions of conjugated Abs in PBS supplemented with 1% FBS. Anti-mouse Abs and reagents used to stain BM cells included PB-anti-IgM (BioLegend), APC-anti-Ly-51 (BioLegend), PE-anti-CD43 (BioLegend), PerCP-Cy5.5-anti-B220 (BD Bioscience), and PE-Cy7-CD24 (BioLegend) [42,43]. Gating strategy was as follows: A-pre-pro-B cells (BP1-CD24-), B-pro-B cells (BP1-CD24+), C-early pre-B cells (BP1+CD24+), D-late pre-B cells (B220+IgM-), E-immature B cells (B220IntIgM+), and F-recirculating B cells (B220highIgM+) included BV510-anti-IgD (Southern Biotech), FITC-anti-B220 (BioLegend), and PB-anti-IgM (BD Biosciences) [44]. Gating strategy was as follows: FO B cells (B220high IgMlow IgDhigh), MZ B cells (B220high CD21highCD23low). Anti-mouse Abs and reagents used to stain splenic MZ B cells included APC-anti-CD21 (BioLegend), FITC-anti-B220, and PE-anti-CD23 (BD Biosciences) [44]. Anti-mouse Abs and reagents to stain splenic GC B cells included FITC-anti-CD95 (BD Biosciences), APC-anti-GL7, and PerCP-Cy5.5-anti-B220 (BD Biosciences) [45]. Anti-mouse Abs and reagents to stain splenic MBC, PBC, and PC B cells included FITC-anti-CD95 (BD Biosciences), Pac-Blue-anti-GL7 (Biolegend), BV510-anti-B220 (Biolegend), NP-PE (Biosearch Technologies), APC-anti-CD138 (Biolegend), PE-Cy7-anti-IgD (Biolegend), and PE-Cy7-anti-IgM (Biolegend). Anti-mouse Abs and reagents used to treat B cells for BCR signaling include: FITC-anti-B220. B cells were stimulated with F(ab′) -anti-Ig plus streptavidin (Jackson ImmunoResearch) at 37°C. The cells were fixed, permeabilized, and stained with pY (Millipore), phosphorylated Btk (pBtk, Y551; BD Biosciences), phosphorylated SHIP (pSHIP, Y1020; Cell Signaling Technology), phallodin, phosphorylated ezrin (pEzrin, T558; Cell Signaling Technology), phosphorylated Erk (pErk, T202/Y204; BD Biosciences), phosphorylated Akt (pAkt, S473;BD Biosciences). Stained cells were analyzed by a BD FACS Canto and analyzed using FlowJo software (Tree Star).
Splenic B cells were incubated with mB-Fab′–anti-Ig without (−) or with streptavidin (sAg) at 4°C, washed, and warmed to 37°C for indicated times and lysed. Cell lysates were analyzed with SDS-PAGE and western blot and probed for pAkt (Ser473; Cell Signaling Technology), pERK1/2 (T202/Y204; Cell Signaling Technology), pBtk (pBtk, Y551; BD Biosciences), and pY (Millipore). Anti-mouse β-actin Ab (Sigma-Aldrich) was used for loading controls.
For comparison of rictor gene expression in WT and Rictor KO B cells, RNA was isolated with RNAPURE kit (RP1202; BioTeke) and reverse transcribed with a PrimeScript RT reagent Kit (RR037A; Takara). The transcribed cDNA was used to analyze the expression of different genes with SsoAdvanced SYBR Green supermix (Bio-Rad) on a CFX96 Touch Real-Time System (Bio-Rad). rictor 5’primer:tgcgatattggccatagtga and 3’primer: acctcgttgctctgctgaat.
WT and Rictor KO mice were bred and maintained in a specific-pathogen–free animal facility. All mice were male and aged 6–8 weeks. For NP-KLH immunization, 400 μg NP-KLH (Biosearch Technologies) in 400 μl Ribi Adjuvant (MPL+TDM Adjuvant System; Sigma) was injected in the flank subcutaneously at day (d) 1. At d 14 after immunization, the spleen was harvested and immune cells were isolated by sucrose density centrifugation using Lymphocyte Separation Media (LSM; MPbio). For detection of serum levels of NP-specific subclasses, mice were immunized and boosted with the same 2 week later. Serum collected 5 d after the boost (19 d after primary immunization) was analyzed by ELISA using NP-bovine serum albumin–coated plates and Ig isotype specific secondary Ab (Southern Biotech).
B cells were pretreated with 0.05 μM Latrunculin B, 1 μM Bis, 10 μM NSC668394, or 10 μM Y27632 (Calbiochem, Gibbstown, NJ) for 30 min at 37°C before stimulation with Ag in the presence of inhibitors. Mice were fed with 0.5 μM Latrunculin B by IP injection every week for 2 months [46].
Splenic B cells were stimulated with biotinylated F(ab′)2-goat anti-mouse IgG+M (10 μg/ml; Jackson ImmunoResearch) at 4°C and pulsed at 37°C. Biotin-F(ab′)2–anti-IgG+M remaining on the cell surface after the stimulation was stained with PE-streptavidin and examined by flow cytometry. The data were shown as percentages of the cell-surface–associated biotin-F(ab′)2–anti-IgG+M at time 0.
Single BCR–molecule imaging was performed according to previous protocol [47]. In detail, prelabeled WT and Rictor KO B cells were imaged by TIRFm with a 640-nm laser in the epifluorescence mode at an output power of 10 mW at the objective lens. A region of 100 ×100 pixels of the area of the electron-multiplying CCD chip was used to obtain an exposure time of 30 ms/frame, the time resolution of which was enough to track the single-molecule BCRs as published [11,47]. Single-molecule tracking of BCR molecules was analyzed as described before [11,47]. Short-range diffusion coefficients and MSD for each BCR molecule trajectory were determined and plotted as CDPs from positional coordinates.
The level of calcium flux was examined by flow cytometry using the calcium-sensitive dyes Fluo4 AM and Fura Red (Life) according to the established protocols. The relative levels of intracellular calcium flux were measured by a ratio of Fluo4 to Fura Red emission using FlowJo software (Tree Star, Inc., Ashland, OR) [34].
Statistical significance was assessed using t-test or the Mann–Whitney U test. When multiple groups were compared, 1-way ANOVA with the Tukey test was performed (GraphPad Software, San Diego, CA). The p values were determined in comparison with WT or control B cells. * p < 0.01, ** p < 0.001.
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10.1371/journal.pgen.1005685 | Sae2 Function at DNA Double-Strand Breaks Is Bypassed by Dampening Tel1 or Rad53 Activity | The MRX complex together with Sae2 initiates resection of DNA double-strand breaks (DSBs) to generate single-stranded DNA (ssDNA) that triggers homologous recombination. The absence of Sae2 not only impairs DSB resection, but also causes prolonged MRX binding at the DSBs that leads to persistent Tel1- and Rad53-dependent DNA damage checkpoint activation and cell cycle arrest. Whether this enhanced checkpoint signaling contributes to the DNA damage sensitivity and/or the resection defect of sae2Δ cells is not known. By performing a genetic screen, we identify rad53 and tel1 mutant alleles that suppress both the DNA damage hypersensitivity and the resection defect of sae2Δ cells through an Sgs1-Dna2-dependent mechanism. These suppression events do not involve escaping the checkpoint-mediated cell cycle arrest. Rather, defective Rad53 or Tel1 signaling bypasses Sae2 function at DSBs by decreasing the amount of Rad9 bound at DSBs. As a consequence, reduced Rad9 association to DNA ends relieves inhibition of Sgs1-Dna2 activity, which can then compensate for the lack of Sae2 in DSB resection and DNA damage resistance. We propose that persistent Tel1 and Rad53 checkpoint signaling in cells lacking Sae2 increases the association of Rad9 at DSBs, which in turn inhibits DSB resection by limiting the activity of the Sgs1-Dna2 resection machinery.
| Genome instability is one of the most pervasive characteristics of cancer cells and can be due to DNA repair defects and failure to arrest the cell cycle. Among the many types of DNA damage, the DNA double strand break (DSB) is one of the most severe, because it can cause mutations and chromosomal rearrangements. Generation of DSBs triggers a highly conserved mechanism, known as DNA damage checkpoint, which arrests the cell cycle until DSBs are repaired. DSBs can be repaired by homologous recombination, which requires the DSB ends to be nucleolytically processed (resected) to generate single-stranded DNA. In Saccharomyces cerevisiae, DSB resection is initiated by the MRX complex together with Sae2, whereas more extensive resection is catalyzed by both Exo1 and Dna2-Sgs1. The absence of Sae2 not only impairs DSB resection, but also leads to the hyperactivation of the checkpoint proteins Tel1/ATM and Rad53, leading to persistent cell cycle arrest. In this manuscript we show that persistent Tel1 and Rad53 signaling activities in sae2Δ cells cause DNA damage hypersensitivity and defective DSB resection by increasing the amount of Rad9 bound at the DSBs, which in turn inhibits the Sgs1-Dna2 resection machinery. As ATM inhibition has been proposed as a strategy for cancer treatment, the finding that defective Tel1 signaling activity restores DNA damage resistance in sae2Δ cells might have implications in cancer therapies that use ATM inhibitors for synthetic lethal approaches that are devised to kill tumor cells with defective DSB repair.
| Programmed DNA double-strand breaks (DSBs) are formed during meiotic recombination and rearrangement of the immunoglobulin genes in lymphocytes. Furthermore, potentially harmful DSBs can arise by exposure to environmental factors, such as ionizing radiations and radiomimetic chemicals, or by failures in DNA replication. DSB generation elicits a checkpoint response that depends on the mammalian protein kinases ATM and ATR, whose functional orthologs in Saccharomyces cerevisiae are Tel1 and Mec1, respectively [1]. Tel1/ATM is recruited to DSBs by the MRX (Mre11-Rad50-Xrs2)/MRN (Mre11-Rad50-Nbs1) complex, whereas Mec1/ATR recognizes single-stranded DNA (ssDNA) covered by Replication Protein A (RPA) [2]. Once activated, Tel1/ATM and Mec1/ATR propagate their checkpoint signals by phosphorylating the downstream checkpoint kinases Rad53 (Chk2 in mammals) and Chk1, to couple cell cycle progression with DNA repair [2].
Repair of DSBs can occur by either non-homologous end joining (NHEJ) or homologous recombination (HR). Whereas NHEJ directly joins the DNA ends, HR uses the sister chromatid or the homologous chromosome to repair DSBs. HR requires that the 5’ ends of a DSB are nucleolytically processed (resected) to generate 3’-ended ssDNA that can invade an undamaged homologous DNA template [3,4]. In Saccharomyces cerevisiae, recent characterization of core resection proteins has revealed that DSB resection is initiated by the MRX complex, which catalyzes an endonucleolytic cleavage near a DSB [4], with the Sae2 protein (CtIP in mammals) promoting MRX endonucleolytic activity [5]. This MRX-Sae2-mediated DNA clipping generates 5’ DNA ends that are optimal substrates for the nucleases Exo1 and Dna2, the latter working in concert with the helicase Sgs1 [6–9]. In addition, the MRX complex recruits Exo1, Sgs1 and Dna2 to DSBs independently of the Mre11 nuclease activity [10]. DSB resection is also negatively regulated by Ku and Rad9, which inhibit the access to DSBs of Exo1 and Sgs1-Dna2, respectively [11–14].
The MRX-Sae2-mediated endonucleolytic cleavage is particularly important to initiate resection at DNA ends that are not easily accessible to Exo1 and Dna2-Sgs1. For instance, both sae2Δ and mre11 nuclease defective mutants are completely unable to resect meiotic DSBs, where the Spo11 topoisomerase-like protein remains covalently attached to the 5’-terminated strands [15,16]. Furthermore, the same mutants exhibit a marked sensitivity to camptothecin (CPT), which extends the half-life of DNA-topoisomerase I cleavable complexes [17,18], and to methyl methanesulfonate (MMS), which can generate chemically complex DNA termini. The lack of Rad9 or Ku suppresses both the hypersensitivity to DSB-inducing agents and the resection defect of sae2Δ cells [10–14]. These suppression events require Dna2-Sgs1 and Exo1, respectively, indicating that Rad9 increases the requirement for MRX-Sae2 activity in DSB resection by inhibiting Sgs1-Dna2 [13,14], while Ku mainly limits the action of Exo1 [10–12]. By contrast, elimination of either Rad9 or Ku does not bypass Sae2/MRX function in resecting meiotic DSBs [11,13], likely because Sgs1-Dna2 and Exo1 cannot substitute for the Sae2/MRX-mediated endonucleolytic cleavage when this event is absolutely required to generate accessible 5’-terminated DNA strands.
Sae2 plays an important role also in modulating the checkpoint response. Checkpoint activation in response to DSBs depends primarily on Mec1, with Tel1 playing a minor role [19]. On the other hand, impaired Mre11 endonuclease activity caused by the lack of Sae2 leads to increased MRX persistence at the DSB ends. The enhanced MRX signaling in turn causes unscheduled Tel1-dependent checkpoint activation that is associated to prolonged Rad53 phosphorylation [20–22]. Mutant mre11 alleles that reduce MRX binding to DSBs restore DNA damage resistance in sae2Δ cells and reduce their persistent checkpoint activation without restoring efficient DSB resection [23,24], suggesting that enhanced MRX association to DSBs contributes to the DNA damage hypersensitivity caused by the lack of Sae2. Persistently bound MRX might increase the sensitivity to DNA damaging agents of sae2Δ cells by hyperactivating the DNA damage checkpoint. If this were the case, then the DNA damage hypersensitivity of sae2Δ cells should be restored by the lack of Tel1 or of its downstream effector Rad53, as they are responsible for the sae2Δ enhanced checkpoint signaling [20,22]. However, while Rad53 inactivation has never been tested, TEL1 deletion not only fails to restore DNA damage resistance in sae2Δ cells, but it exacerbates their sensitivity to DNA damaging agents [23,24]. Therefore, other studies are required to understand whether the Tel1- and Rad53-mediated checkpoint signaling has any role in determining the DNA damage sensitivity of sae2Δ cells.
By performing a genetic screen, we identified rad53 and tel1 mutant alleles that suppress both the hypersensitivity to DNA damaging agents and the resection defect of sae2Δ cells by reducing the amount of Rad9 at DSBs. Decreased Rad9 binding at DNA ends bypasses Sae2 function in DNA damage resistance and resection by relieving the inhibition of the Sgs1-Dna2 resection machinery. Altogether our data suggest that the primary cause of the resection defect of sae2Δ cells is Rad9 association to DSBs, which is promoted by persistent Tel1 and Rad53 signaling activities in these cells.
We have previously described our search for extragenic mutations that suppress the CPT hypersensitivity of sae2Δ cells [13]. This genetic screen identified 15 single-gene suppressor mutants belonging to 11 distinct allelism groups. Analysis of genomic DNA by next-generation Illumina sequencing of 5 non allelic suppressor mutants revealed that the DNA damage resistance was due to single base pair substitutions in the genes encoding Sgs1, Top1, or the multi-drug resistance proteins Pdr3, Pdr10 and Sap185 [13]. Subsequent genome sequencing and genetic analysis of 2 more non allelic suppressor mutants allowed to link suppression to either the rad53-H88Y mutant allele, causing the replacement of Rad53 amino acid residue His88 by Tyr, or the tel1-N2021D allele, resulting in the replacement of Tel1 amino acid residue Asn2021 by Asp. Both rad53-H88Y and tel1-N2021D alleles restored resistance of sae2Δ cells not only to CPT, but also to phleomycin (phleo) and MMS (Fig 1A). While both rad53-H88Y and tel1-N2021D fully rescued the hypersensitivity of sae2Δ cells to phleomycin and MMS, the CPT hypersensitivity of sae2Δ cells was only partially suppressed by the same alleles (Fig 1A), suggesting that they did not bypass all Sae2 functions.
Both rad53-H88Y and tel1-N2021D suppressor alleles were recessive, as the sensitivity to genotoxic agents of sae2Δ/sae2Δ RAD53/rad53-H88Y and sae2Δ/sae2Δ TEL1/tel1-N2021D diploid cells was similar to that of sae2Δ/sae2Δ RAD53/RAD53 TEL1/TEL1 diploid cells (S1 Fig), suggesting that rad53-H88Y and tel1-N2021D alleles encode hypomorphic variants. Furthermore, both variants suppressed the hypersensitivity to DNA damaging agents of sae2Δ cells by altering the same mechanism, as sae2Δ rad53-H88Y tel1-N2021D triple mutant cells survived in the presence of DNA damaging agents to the same extent as sae2Δ rad53-H88Y and sae2Δ tel1-N2021D double mutant cells (Fig 1B).
The MRX complex not only provides the nuclease activity for initiation of DSB resection, but also it promotes the binding of Exo1, Sgs1 and Dna2 at the DSB ends [10]. These MRX multiple roles explain the severe DNA damage hypersensitivity and resection defect of cells lacking any of the MRX subunits compared to cells lacking either Sae2 or the Mre11 nuclease activity. As Sae2 has been proposed to activate Mre11 nuclease activity [5], we asked whether the suppression of sae2Δ DNA damage hypersensitivity by Rad53-H88Y and Tel1-N2021D requires Mre11 nuclease activity. Both rad53-H88Y and tel1-N2021D alleles suppressed the hypersensitivity to DNA damaging agents of sae2Δ cells carrying the nuclease defective mre11-H125N allele (Fig 1C). By contrast, sae2Δ mre11Δ rad53-H88Y and sae2Δ mre11Δ tel1-N2021D triple mutant cells were as sensitive to genotoxic agents as sae2Δ mre11Δ double mutant cells (Fig 1D), indicating that neither the rad53-H88Y nor the tel1-N2021D allele can suppress the hypersensitivity to DNA damaging agents of sae2Δ mre11Δ cells. Altogether, these findings indicate that both Rad53-H88Y and Tel1-N2021D require the physical presence of the MRX complex, but not its nuclease activity, to bypass Sae2 function in cell survival to genotoxic agents.
A single unrepairable DSB induces a DNA damage checkpoint that depends primarily on Mec1, with Tel1 playing a minor role [19]. This checkpoint response can be eventually turned off, allowing cells to resume cell cycle progression through a process that is called adaptation [25–27]. In the absence of Sae2, cells display heightened checkpoint activation that prevents cells from adapting to an unrepaired DSB [20,22]. This persistent checkpoint activation is due to increased MRX amount/persistence at the DSB that in turn causes enhanced and prolonged Tel1 activation that is associated with persistent Rad53 phosphorylation [20–22,28].
If the rad53-H88Y mutation impaired Rad53 activity, then it is expected to suppress the adaptation defect of sae2Δ cells by lowering checkpoint activation. We addressed this point by using JKM139 derivative strains, where a single DSB at the MAT locus can be generated by expression of the HO endonuclease gene under the control of a galactose-dependent promoter. This DSB cannot be repaired by HR because of the deletion of the homologous donor loci HML and HMR [27]. We measured checkpoint activation by monitoring the ability of cells to arrest the cell cycle and to phosphorylate Rad53 after HO induction. Both rad53-H88Y and sae2Δ rad53-H88Y cells formed microcolonies of more than 2 cells with higher efficiency than either wild type or sae2Δ cells (Fig 2A). Furthermore, the Rad53-H88Y variant was poorly phosphorylated after HO induction both in the presence and in the absence of Sae2 (Fig 2B). Thus, the rad53-H88Y mutation suppresses the adaptation defect of sae2Δ cells by impairing Rad53 activation.
DNA damage-dependent activation of Rad53 requires its phospho-dependent interaction with Rad9, which acts as a scaffold to allow Rad53 intermolecular authophosphorylation and activation [29–31]. Interestingly, the His88 residue, which is replaced by Tyr in the Rad53-H88Y variant, is localized in the forkhead-associated domain 1 of the protein and has been implicated in mediating Rad9-Rad53 interaction [32]. Thus, we asked whether the Rad53-H88Y variant was defective in the interaction with Rad9. When HA-tagged Rad9 was immunoprecipitated with anti-HA antibodies from wild type and rad53-H88Y cells grown for 4 hours in the presence of galactose to induce HO, wild type Rad53 could be detected in Rad9-HA immunoprecipitates, whereas Rad53-H88Y did not (Fig 2C). This defective interaction of Rad53-H88Y with Rad9 could explain the impaired checkpoint activation in sae2Δ rad53-H88Y double mutant cells.
Tel1 signaling activity is responsible for the prolonged Rad53 activation that prevents sae2Δ cells to adapt to the checkpoint triggered by an unrepairable DSB [20,22]. Although telomere length in tel1-N2021D mutant cells was unaffected both in the presence and in the absence of Sae2 (S2 Fig), the recessivity of tel1-N2021D suppressor effect on sae2Δ DNA damage hypersensitivity suggests that the N2021D substitution impairs Tel1 function. If this were the case, Tel1-N2021D might suppress the adaptation defect of sae2Δ cells by reducing the DSB-induced persistent Rad53 phosphorylation. When G1-arrested cell cultures were spotted on galactose-containing plates to induce HO, wild type, sae2Δ, tel1-N2021D and sae2Δ tel1-N2021D cells accumulated large budded cells within 4 hours (Fig 2A). This cell cycle arrest is due to checkpoint activation. In fact, when the same cells exponentially growing in raffinose were transferred to galactose, Rad53 phosphorylation was detectable about 2–3 hours after galactose addition (Fig 2B). However, while sae2Δ cells remained arrested as large budded cells for at least 30 hours (Fig 2A) and showed persistent Rad53 phosphorylation (Fig 2B), wild type, tel1-N2021D and sae2Δ tel1-N2021D cells formed microcolonies with more than 2 cells (Fig 2A) and decreased the amounts of phosphorylated Rad53 (Fig 2B) with similar kinetics 10–12 hours after HO induction. Therefore, the Tel1-N2021D variant impairs Tel1 signaling activity, as it rescues the sae2Δ adaptation defect by reducing the persistent Rad53 phosphorylation.
The N2021D substitution resides in the Tel1 FAT domain, a helical solenoid that encircles the kinase domain of all the phosphoinositide 3-kinase (PI3K)-related kinases (PIKKs) [33,34], suggesting that this amino acid change might reduce Tel1 kinase activity. Western blot analysis revealed that the amount of Tel1-N2021D was slightly lower than that of wild type Tel1 (Fig 2D). We then immunoprecipitated equivalent amounts of Tel1-HA and Tel1-N2021D-HA variants from both untreated and CPT-treated cells (Fig 2E, top), and we measured their kinase activity in vitro using the known artificial substrate of the PIKKs family PHAS-I (Phosphorylated Heat and Acid Stable protein) [35]. Both Tel1-HA and Tel1-N2021D-HA were capable to phosphorylate PHAS-I, with the amount of phosphorylated substrate being slighly higher in Tel1-N2021D-HA than in Tel1-HA immunoprecipitates (Fig 2E, bottom). This PHAS-I phosphorylation was dependent on Tel1 kinase activity, as it was not detectable when the immunoprecipitates were prepared from strains expressing either kinase dead Tel1-kd-HA or untagged Tel1 (Fig 2E, bottom). Thus, the tel1-N2021D mutation does not affect Tel1 kinase activity.
Interestingly, the FAT domain is in close proximity to the FATC domain, which was shown to be important for Tel1 recruitment to DNA ends [36], suggesting that the Tel1-N2021D variant might be defective in recruitment/association to DSBs. Strikingly, when we analyzed Tel1 and Tel1-N2021D binding at the HO-induced DSB by chromatin immunoprecipitation (ChIP) and quantitative real time PCR (qPCR), the amount of Tel1-N2021D bound at the DSB turned out to be lower than that of wild type Tel1 (Fig 2F). This decreased Tel1-N2021D association was not due to lower Tel1-N2021D levels, as the ChIP signals were normalized for each time point to the amount of immunoprecipitated protein. Thus, the inability of sae2Δ tel1-N2021D cells to sustain persistent Rad53 phosphorylation after DSB generation can be explained by a decreased association of Tel1-N2021D to DSBs.
As both Rad53-H88Y and Tel1-N2021D reduce checkpoint signaling in sae2Δ cells, we asked whether the increased DNA damage resistance of sae2Δ rad53-H88Y and sae2Δ tel1-N2021D cells was due to the elimination of the checkpoint-mediated cell cycle arrest. This hypothesis could not be tested by deleting the MEC1, DDC1, RAD24, MEC3 or RAD9 checkpoint genes, because they also regulate DSB resection [37–39]. On the other hand, an HO-induced DSB activates also the Chk1 checkpoint kinase [40], which contributes to arrest the cell cycle in response to DSBs by controlling a pathway that is independent of Rad53 [41]. Importantly, chk1Δ cells do not display DNA damage hypersensitivity and are not defective in resection of uncapped telomeres [38,41]. We therefore asked whether CHK1 deletion restores DNA damage resistance in sae2Δ cells. Consistent with the finding that Chk1 contributes to arrest the cell cycle after DNA damage independently of Rad53 [41], Rad53 was phosphorylated with wild type kinetics after HO induction in both chk1Δ and sae2Δ chk1Δ cells (Fig 3A). Furthermore, CHK1 deletion suppresses the adaptation defect of sae2Δ cells. In fact, both chk1Δ and sae2Δ chk1Δ cells spotted on galactose-containing plates formed microcolonies of more than 2 cells with higher efficiency than wild type and sae2Δ cells (Fig 3B), although they did it less efficiently than mec1Δ cells, where both Rad53 and Chk1 signaling were abrogated [41]. Strikingly, the lack of Chk1 did not suppress the hypersensitivity to DNA damaging agents of sae2Δ cells (Fig 3C), although it overrides the checkpoint-mediated cell cycle arrest.
To rule out the possibility that CHK1 deletion failed to restore DNA damage resistance in sae2Δ cells because it impairs DSB resection, we used JKM139 derivative strains to monitor directly generation of ssDNA at the DSB ends in the absence of Chk1. As ssDNA is resistant to cleavage by restriction enzymes, we followed loss of SspI restriction sites as a measure of resection by Southern blot analysis under alkaline conditions, using a single-stranded probe that anneals to the 3’ end at one side of the break. Consistent with previous indications that Chk1 is not involved in DNA-end resection [38], chk1Δ single mutant cells resected the DSB with wild type kinetics (Fig 3D). Furthermore, CHK1 deletion did not exacerbate the resection defect of sae2Δ cells (Fig 3E). Altogether, these data indicate that the prolonged checkpoint-mediated cell cycle arrest of sae2Δ cells is not responsible for their hypersensitivity to DNA damaging agents.
As the checkpoint-mediated cell cycle arrest was not responsible for the DNA damage hypersensitivity of sae2Δ cells, we asked whether Rad53-H88Y and/or Tel1-N2021D suppressed the sae2Δ resection defect. We first measured the efficiency of single-strand annealing (SSA), a mechanism that repairs a DSB flanked by direct DNA repeats when sufficient resection exposes the complementary DNA sequences, which can then anneal to each other [3]. The rad53-H88Y and tel1-N2021D alleles were introduced in the YMV45 strain, which carries two tandem leu2 gene repeats located 4.6 kb apart on chromosome III, with a HO recognition site adjacent to one of the repeats [42]. This strain also harbors a GAL-HO construct for galactose-inducible HO expression. Both Rad53-H88Y and Tel1-N2021D bypass Sae2 function in SSA-mediated DSB repair. In fact, accumulation of the SSA repair product after HO induction occurred more efficiently in both sae2Δ rad53-H88Y (Fig 4A and 4B) and sae2Δ tel1-N2021D (Fig 4C and 4D) than in sae2Δ cells, where it was delayed compared to wild type.
To confirm that Rad53-H88Y and Tel1-N2021D suppress the SSA defect of sae2Δ cells by restoring DSB resection, we used JKM139 derivative strains to monitor directly generation of ssDNA at the DSB ends. Indeed, sae2Δ rad53-H88Y (Fig 5A) and sae2Δ tel1-N2021D (Fig 5B) cells resected the HO-induced DSB more efficiently than sae2Δ cells, indicating that both Rad53-H88Y and Tel1-N2021D suppress the resection defect of sae2Δ cells.
The DSB resection defect of sae2Δ cells is thought to be responsible for the increased persistence of MRX at the DSB [43]. Because Rad53-H88Y and Tel1-N2021D restore DSB resection in sae2Δ cells, we expected that the same variants also reduce the amount of MRX bound at the DSB. The amount of Mre11 bound at the HO-induced DSB end turned out to be lower in both sae2Δ rad53-H88Y and sae2Δ tel1-N2021D than in sae2Δ cells (Fig 5C). Therefore, the Rad53-H88Y and Tel1-N2021D variants restore DSB resection in sae2Δ cells and reduce MRX association/persistence at the DSB.
Consistent with the finding that Rad53-H88Y and Tel1-N2021D do not fully restore CPT resistance in sae2Δ cells (Fig 1A), and therefore do not bypass completely all Sae2 functions, the rad53-H88Y and tel1-N2021D mutations were unable to suppress the sporulation defects of sae2Δ/sae2Δ diploid cells (Fig 5D), suggesting that they cannot bypass the requirement for Sae2/MRX endonucleolytic cleavage to remove Spo11 from meiotic DSBs.
The MRX complex not only provides the nuclease activity for initiation of DSB resection, but also allows extensive resection by promoting the binding at the DSB ends of the resection proteins Exo1 and Sgs1-Dna2 [6,7,10]. Suppression of the DNA damage hypersensitivity of sae2Δ cells by Rad53-H88Y and Tel1-N2021D requires the physical presence of the MRX complex but not its nuclease activity (Fig 1C and 1D). As the loading of Exo1, Sgs1-Dna2 at DSBs depends on the MRX complex independently of its nuclease activity [10], we asked whether the investigated suppression events require Exo1, Sgs1 and/or Dna2. This question was particularly interesting, as Rad53 was shown to inhibit resection at uncapped telomeres through phosphorylation and inhibition of Exo1 [38,44]. As shown in Fig 6A, sae2Δ suppression by Rad53-H88Y and Tel1-N2021D was Exo1-independent. In fact, although the lack of Exo1 exacerbated the sensitivity to DNA damaging agents of sae2Δ cells, both sae2Δ exo1Δ rad53-H88Y and sae2Δ exo1Δ tel1-N2021D triple mutants were more resistant to genotoxic agents than sae2Δ exo1Δ double mutant cells (Fig 6A).
By contrast, neither Rad53-H88Y nor Tel1-N2021D were able to suppress the sensitivity to DNA damaging agents of sae2Δ cells carrying the temperature sensitive dna2-1 allele (Fig 6B), suggesting that Dna2 activity is required for their suppressor effect. Dna2, in concert with the helicase Sgs1, functions as a nuclease in DSB resection [7]. The dna2-E675A allele abolishes Dna2 nuclease activity, which is essential for cell viability and whose requirement is bypassed by the pif1-M2 mutation that impairs the nuclear activity of the Pif1 helicase [45]. The lack of Sgs1 or expression of the Dna2-E675A variant in the presence of the pif1-M2 allele impaired viability of sae2Δ cells even in the absence of genotoxic agents. The synthetic lethality of sae2Δ sgs1Δ cells, and possibly of sae2Δ dna2-E675A pif1-M2, is likely due to defects in DSB resection, as it is known to be suppressed by either EXO1 overexpression or KU deletion [11]. Thus, we asked whether Rad53-H88Y and/or Tel1-N2021D could restore viability of sae2Δ sgs1Δ and/or sae2Δ dna2-E675A pif1-M2 cells. Tetrad dissection of diploid cells did not allow to find viable spores with the sae2Δ dna2-E675A pif1-M2 rad53-H88Y (Fig 6C) or sae2Δ dna2-E675A pif1-M2 tel1-N2021D genotypes (Fig 6D), indicating that neither Rad53-H88Y nor Tel1-N2021D can restore the viability of sae2Δ dna2-E675A pif1-M2 cells. Similarly, no viable sae2Δ sgs1Δ spores could be recovered, while sae2Δ sgs1Δ rad53-H88Y and sae2Δ sgs1Δ tel1-N2021D triple mutant spores formed very small colonies that could not be further propagated (Fig 6E and 6F). Finally, neither Rad53-H88Y nor Tel1-N2021D, which allowed DNA damage resistance in sae2Δ exo1Δ cells (Fig 6A), were able to suppress the growth defect of sgs1Δ exo1Δ double mutant cells even in the absence of genotoxic agents (Fig 6G). Altogether, these findings indicate that suppression by Rad53-H88Y and Tel1-N2021D of the DNA damage hypersensitivity caused by the absence of Sae2 is dependent on Sgs1-Dna2.
The Rad53-H88Y protein is defective in interaction with Rad9 (Fig 2C) and therefore fails to undergo autophosphorylation and activation, prompting us to test whether other mutations affecting Rad53 activity can bypass Sae2 functions. To this end, we could not use rad53Δ cells because they show growth defects even when the lethal effect of RAD53 deletion is suppressed by the lack of Sml1 [46]. We then substituted the chromosomal wild type RAD53 allele with the kinase-defective rad53-K227A allele (rad53-kd), which does not impair cell viability in the absence of genotoxic agents but affects checkpoint activation [47]. The rad53-kd allele rescued the sensitivity of sae2Δ cells to CPT and MMS to an extent similar to Rad53-H88Y (Fig 7A). Furthermore, accumulation of the SSA repair products occurred more efficiently in sae2Δ rad53-kd cells than in sae2Δ (Fig 7B and 7C), indicating that the lack of Rad53 kinase activity bypasses Sae2 function in SSA-mediated DSB repair.
Suppression of sae2Δ may be peculiar to Tel1-N2021D, which is poorly recruited to DSBs (Fig 2F), or it might be performed also by TEL1 deletion (tel1Δ) or by expression of a Tel1 kinase defective variant (Tel1-kd). Indeed, the Tel1-kd variant, carrying the G2611D, D2612A, N2616K, and D2631E amino acid substitutions that abolish Tel1 kinase activity in vitro (Fig 2E) [35], rescued the hypersensitivity of sae2Δ cells to genotoxic agents to an extent similar to Tel1-N2021D (Fig 8A). The lack of Tel1 kinase activity bypassed also Sae2 function in DSB resection, because sae2Δ tel1-kd cells repaired a DSB by SSA more efficiently than sae2Δ cells (Fig 8B and 8C). By contrast, and consistent with previous studies [23,24], TEL1 deletion was not capable to suppress the hypersensitivity to DNA damaging agents of sae2Δ cells (Fig 8A). Rather, tel1Δ sae2Δ double mutant cells displayed higher sensitivity to CPT than sae2Δ cells (Fig 8A). Altogether, these data indicate that the lack of Tel1 kinase activity can bypass Sae2 function both in DNA damage resistance and DSB resection, but these suppression events require the physical presence of the Tel1 protein.
As impairment of Tel1 function rescued the sae2Δ defects, we asked whether Tel1 hyperactivation exacerbates the DNA damage hypersensitivity of sae2Δ cells. We previously isolated the TEL1-hy909 allele, which encodes a Tel1 mutant variant with enhanced kinase activity that causes an impressive telomere overelongation [48]. As shown in Fig 8D, sae2Δ TEL1-hy909 double mutant cells were more sensitive to DNA damaging agents than sae2Δ single mutant cells. This enhanced DNA damage sensitivity was likely due to Tel1 kinase activity, as sae2Δ cells expressing a kinase defective Tel1-hy909-kd variant were as sensitive to DNA damaging agents as sae2Δ cells (Fig 8D). Thus, impairment of Tel1 activity bypasses Sae2 function at DSBs, whereas Tel1 hyperactivation increases the requirement for Sae2 in survival to genotoxic stress.
The absence of Tel1 failed not only to restore DNA damage resistance in sae2Δ cells (Fig 8A), but also to suppress their SSA defect (Fig 9A and 9B). The difference in the effects of tel1Δ and tel1-kd was not due to checkpoint signaling, as Rad53 phosphorylation decreased with similar kinetics in both sae2Δ tel1-kd and sae2Δ tel1Δ double mutant cells 10–12 hours after HO induction (Fig 9C). Interestingly, SSA-mediated DSB repair occurred with wild type kinetics in tel1-kd mutant cells (Fig 8B and 8C), while tel1Δ cells repaired a DSB by SSA less efficiently than wild type cells (Fig 9A and 9B), suggesting that Tel1 might have a function at DSBs that does not require its kinase activity. Indeed, TEL1 deletion was shown to slight impair DSB resection [19]. Furthermore, it did not exacerbate the resection defect [19] and the hypersensitivity to DNA damaging agents of mre11Δ cells (Fig 9D), suggesting that the absence of Tel1 can impair MRX function. Tel1 was also shown to promote MRX association at DNA ends flanked by telomeric DNA repeats independently of its kinase activity [49], and we are showing that suppression of sae2Δ by Tel1-N2021D requires the physical presence of the MRX complex (Fig 1D). Thus, it is possible that the lack of Tel1 fails to bypass Sae2 function at DSBs because it reduces MRX association at DSBs to a level that is not sufficient to restore DNA damage resistance and DSB resection in sae2Δ cells. Indeed, the amount of Mre11 bound at the HO-induced DSB was decreased in tel1Δ, but not in tel1-kd cells, compared to wild type (Fig 9E). In agreement with a partial loss of Tel1 function, the Tel1-N2021D variant, whose association to DSBs is diminished compared to wild type Tel1 but not abolished (Fig 2F), only slightly decreased Mre11 association to the DSB (Fig 9E). As the rescue of sae2Δ by Tel1-N2021D requires the physical presence of the MRX complex, this Tel1 function in promoting MRX association to DSBs can explain the inability of tel1Δ to bypass Sae2 function in DNA damage resistance and resection.
The suppression of the DNA damage hypersensitivity of sae2Δ cells by Rad53-H88Y and Tel1-N2021D requires Dna2-Sgs1 (Fig 6B–6G). Because Sgs1-Dna2 activity is counteracted by Rad9, whose lack restores DSB resection in sae2Δ cells [13,14], we asked whether suppression of the DSB resection defect of sae2Δ cells by Rad53 or Tel1 dysfunction might be due to decreased Rad9 association to the DSB ends. We have previously shown that wild type and sae2Δ cells have similar amounts of Rad9 bound at 1.8 kb from the DSB (Fig 10A) [43]. However, a robust increase in the amount of Rad9 bound at 0.2 kb and 0.6 kb from the DSB was detected in sae2Δ cells compared to wild type (Fig 10A) [14]. Strikingly, this enhanced Rad9 accumulation in sae2Δ cells was reduced in the presence of the Rad53-kd or Tel1-kd variant, which both decreased the amount of Rad9 bound at the DSB also in otherwise wild type cells (Fig 10A). Thus, Rad9 association close to the DSB depends on Rad53 and Tel1 kinase activity.
Rad9 inhibits DSB resection by counteracting Sgs1 recruitment to DSBs [13] and, as expected, Sgs1 binding to DSBs was lower in sae2Δ cells than in wild type (Fig 10B). By contrast, the presence of Rad53-kd or Tel1-kd variants increased the amount of Sgs1 at the DSB in both wild type and sae2Δ cells (Fig 10B). Together with the observation that the suppression of sae2Δ hypersensitivity to genotoxic agents by Rad53 and Tel1 dysfunctions requires Sgs1-Dna2, these findings indicate that the lack of Rad53 or Tel1 kinase activity restores DSB resection in sae2Δ cells by decreasing Rad9 association close to the DSB and therefore by relieving Sgs1-Dna2 inhibition. Although both rad53-kd and tel1-kd cells showed some lowering of Rad9 binding at DSBs compared to wild type cells (Fig 10A), they did not appear to accelerate SSA, suggesting that this extent of Rad9 binding is anyhow sufficient to limit resection in a wild type context.
Rad9 is known to be enriched at the sites of damage by interaction with histone H2A that has been phosphorylated on serine 129 (γH2A) by Mec1 and Tel1 [50–53]. As the lack of γH2A suppresses the SSA defect of sae2Δ cells [14], Tel1 activity might increase the amount of Rad9 bound at the DSB in sae2Δ cells by promoting generation of γH2A. Indeed, the hta1-S129A allele, which encodes a H2A variant where Ser129 is replaced by a non-phosphorylatable alanine residue, thus causing the lack of γH2A, suppressed the resection defect of sae2Δ cells (S3 Fig). Furthermore, γH2A formation turned out to be responsible for the enhanced Rad9 binding close to the break site, as sae2Δ hta1-S129A cells showed wild type levels of Rad9 bound at the DSB (Fig 10C). Finally, γH2A formation close to the DSB depends on Tel1 kinase activity, as γH2A at the DSB was not detectable in sae2Δ tel1-kd cells (Fig 10D). Altogether, these data indicate that Tel1 promotes Rad9 association to DSB in sae2Δ cells through γH2A generation.
Cells lacking Sae2 not only are defective in DSB resection, but also show persistent DSB-induced checkpoint activation that causes a prolonged cell cycle arrest. This enhanced checkpoint signaling is due to persistent MRX binding at the DSBs, which activates a Tel1-dependent checkpoint that is accompanied by Rad53 phosphorylation [20,22]. While failure to remove MRX from the DSBs has been shown to sensitize sae2Δ cells to genotoxic agents [23,24], the possible contribution of the DNA damage checkpoint in determining the DNA damage hypersensitivity and the resection defect of sae2Δ cells has never been studied in detail.
We show that impairment of Rad53 activity either by affecting its interaction with Rad9 (Rad53-H88Y) or by abolishing its kinase activity (Rad53-kd) suppresses the sensitivity to DNA damaging agents of sae2Δ cells. A similar effect can be detected also when Tel1 function is compromised either by reducing its recruitment to DSBs (Tel1-N2021D) or by abrogating its kinase activity (Tel1-kd). These suppression effects are not due to the escape of the checkpoint-mediated cell cycle arrest, as CHK1 deletion, which overrides the persistent cell cycle arrest of sae2Δ cells, does not suppress the hypersensitivity of the same cells to DNA damaging agents. Rather, we found that impairment of Rad53 or Tel1 signaling suppresses the resection defect of sae2Δ by decreasing the amount of Rad9 bound very close to the break site. As it is known that Rad9 inhibits Sgs1-Dna2 [13,14], this reduced Rad9 association at DSBs relieves inhibition of Sgs1-Dna2 activity that can then compensate for the lack of Sae2 function in DSB resection. In this view, active Rad53 and Tel1 increase the requirement for Sae2 in DSB resection by promoting Rad9 binding to DSBs and therefore by inhibiting Sgs1-Dna2. Consistent with a role of Sgs1 in removing MRX from the DSBs [54], the relieve of Sgs1-Dna2 inhibition by Rad53 or Tel1 dysfunction leads to a reduction of MRX association to DSBs in sae2Δ cells.
Our finding that Tel1 or Rad53 inactivation can restore both DNA damage resistance and DSB resection in sae2Δ cells is apparently at odds with previous findings that attenuation of the Rad53-dependent checkpoint signaling by decreasing MRX association to DSBs suppresses the DNA damage hypersensitivity of sae2Δ cells but not their resection defect [23,24]. Noteworthy, the bypass of Sae2 function by Rad53 or Tel1 dysfunction requires the physical presence of MRX bound at DSBs, which is known to promote stable association of Exo1, Sgs1 and Dna2 to DSBs [10]. Thus, we speculate that a reduced MRX association at DSBs allows sae2Δ cells to initiate DSB resection by relieving Rad9-mediated inhibition of Sgs1-Dna2 activity. As DSB repair by HR has been shown to require limited amount of ssDNA at DSB ends [55,56], the ssDNA generated by this initial DSB processing might be sufficient to restore DNA damage resistance in sae2Δ cells even when wild type levels of resection are not restored because DSB-bound MRX is not enough to ensure stable Sgs1 and Dna2 association.
Surprisingly, TEL1 deletion, which relieves the persistent Tel1-dependent checkpoint activation caused by the lack of Sae2, did not restore DNA damage resistance and DSB resection in sae2Δ cells. We found that the lack of Tel1 protein affects the association of MRX to the DSB ends independently of its kinase activity. As the rescue of sae2Δ by Tel1-N2021D requires the physical presence of the MRX complex, this reduced MRX-DNA association can explain the inability of TEL1 deletion to restore DNA damage resistance and resection in sae2Δ cells. Therefore, while an enhanced Tel1 signaling activity in the absence of Sae2 leads to DNA damage hypersensitivity and resection defects, a sufficient amount of Tel1 needs to be present at DSBs to support MRX function at DSBs.
How do Rad53 and Tel1 control Rad9 association to DSB? Rad53-mediated phosphorylation of Rad9 does not appear to promote Rad9 binding to the DSB [57,58]. Because Rad53 and RPA compete for binding to Sgs1 [59], it is tempting to propose that impaired Rad53 signaling activity might shift Sgs1 binding preference from Rad53 to RPA, leading to increased Sgs1 association to RPA-coated DNA that can counteract Rad9 binding and inhibition of resection. In turn, Tel1 and Mec1 can phosphorylate Rad9 [60,61], and abrogation of these phosphorylation events rescues the sensitivity to DNA damaging agents of sae2Δ cells [14], suggesting that Tel1 might control Rad9 association to DSBs directly through phosphorylation. On the other hand, Tel1 promotes generation of γH2A [50–53], which counteracts DSB resection by favoring Rad9 association at the DSB [43]. We show that expression of a non-phosphorylatable H2A variant in sae2Δ cells suppresses their resection defect and prevents the accumulation of Rad9 at the DSB. Furthermore, γH2A generation close to the break site depends on Tel1 kinase activity. Thus, although we cannot exclude a direct control of Tel1 on Rad9 association to DNA ends, our findings indicate that Tel1 acts in this process mostly through γH2A generation.
Altogether, our results support a model whereby Tel1 and Rad53, once activated, limit DSB resection by promoting Rad9 binding to DSBs and therefore by inhibiting Sgs1-Dna2. Sae2 activates Mre11 endonucleolytic activity that clips the 5’-terminated DNA strand, thus generating 5’ and 3’ tailed substrates that can be processed by Exo1/Sgs1-Dna2 and Mre11 activity, respectively (Fig 10E, left). When Sae2 function fails, defective Mre11 nuclease activity causes increased MRX persistence at the DSB that leads to enhanced and prolonged Tel1-dependent Rad53 activation. As a consequence, Tel1- and Rad53-mediated phosphorylation events increase the amount of Rad9 bound at the DSB, which inhibits DSB resection by counteracting Sgs1-Dna2 activity (Fig 10E, middle). Dysfunction of Rad53 or Tel1 reduces Rad9 recruitment at the DSB ends and therefore relieves inhibition of Sgs1-Dna2, which can compensate for the lack of Sae2 in DNA damage resistance and resection (Fig 10E, right). Altogether, these findings indicate that the primary cause of the resection defect of sae2Δ cells is an enhanced Rad9 binding to DSBs that is promoted by the persistent MRX-dependent Tel1 and Rad53 signaling activities.
ATM inhibition has been proposed as a strategy for cancer treatment [62]. Therefore, the observation that dampening Tel1/ATM signaling activity restores DNA damage resistance in sae2Δ cells might have implications in cancer therapies that use ATM inhibitors for synthetic lethal approaches to threat tumors with deficiencies in the DNA damage response.
The yeast strains used in this study are derivatives of W303, JKM139 and YMV45 strains and are listed in S1 Table. Cells were grown in YEP medium (1% yeast extract, 2% peptone) supplemented with 2% glucose (YEPD), 2% raffinose (YEPR) or 2% raffinose and 3% galactose (YEPRG).
To search for suppressor mutations of the CPT-sensitivity of sae2Δ mutant, 5x106 sae2Δ cells were plated on YEPD in the presence of 30μM CPT. Survivors were crossed to wild type cells to identify by tetrad analysis the suppression events that were due to single-gene mutations. Genomic DNA from two single-gene suppressors was analyzed by next-generation Illumina sequencing (IGA technology services) to identify mutations altering open reading frames within the reference S. cerevisiae genome. To confirm that rad53-H88Y and tel1-N2021D mutations were responsible for the suppression, either URA3 or HIS3 gene was integrated downstream of the rad53-H88Y and tel1-N2021D stop codon, respectively, and the resulting strain was crossed to wild type cells to verify by tetrad dissection that the suppression of the sae2Δ CPT sensitivity co-segregated with the URA3 or HIS3 allele.
DSB end resection at the MAT locus in JKM139 derivative strains was analyzed on alkaline agarose gels as previously described [63]. DSB formation and repair in YMV45 strain were detected by Southern blot analysis using an Asp718-SalI fragment containing part of the LEU2 gene as a probe as previously described [63]. Quantitative analysis of the repair product was performed by calculating the ratio of band intensities for SSA product with respect to a loading control.
Protein extracts for western blot analysis were prepared by TCA precipitation. ChIP assays were performed as previously described [64]. Data are expressed as fold enrichment at the HO-induced DSB over that at the non-cleaved ARO1 locus, after normalization of each ChIP signals to the corresponding amount of immunoprecipitated protein and input for each time point. Fold enrichment was then normalized to the efficiency of DSB induction. The kinase assay and coimmunoprecipitation were performed as previously described [48]. Rad53 was detected by using anti-Rad53 polyclonal antibodies (ab104232) from Abcam. γH2A was immunoprecipitated by using anti-γH2A antibodies (ab15083) from Abcam.
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10.1371/journal.pcbi.1004799 | Conflicting Selection Pressures Will Constrain Viral Escape from Interfering Particles: Principles for Designing Resistance-Proof Antivirals | The rapid evolution of RNA-encoded viruses such as HIV presents a major barrier to infectious disease control using conventional pharmaceuticals and vaccines. Previously, it was proposed that defective interfering particles could be developed to indefinitely control the HIV/AIDS pandemic; in individual patients, these engineered molecular parasites were further predicted to be refractory to HIV’s mutational escape (i.e., be ‘resistance-proof’). However, an outstanding question has been whether these engineered interfering particles—termed Therapeutic Interfering Particles (TIPs)—would remain resistance-proof at the population-scale, where TIP-resistant HIV mutants may transmit more efficiently by reaching higher viral loads in the TIP-treated subpopulation. Here, we develop a multi-scale model to test whether TIPs will maintain indefinite control of HIV at the population-scale, as HIV (‘unilaterally’) evolves toward TIP resistance by limiting the production of viral proteins available for TIPs to parasitize. Model results capture the existence of two intrinsic evolutionary tradeoffs that collectively prevent the spread of TIP-resistant HIV mutants in a population. First, despite their increased transmission rates in TIP-treated sub-populations, unilateral TIP-resistant mutants are shown to have reduced transmission rates in TIP-untreated sub-populations. Second, these TIP-resistant mutants are shown to have reduced growth rates (i.e., replicative fitness) in both TIP-treated and TIP-untreated individuals. As a result of these tradeoffs, the model finds that TIP-susceptible HIV strains continually outcompete TIP-resistant HIV mutants at both patient and population scales when TIPs are engineered to express >3-fold more genomic RNA than HIV expresses. Thus, the results provide design constraints for engineering population-scale therapies that may be refractory to the acquisition of antiviral resistance.
| A major obstacle to effective antimicrobial therapy campaigns is the rapid evolution of drug resistance. Given the static nature of current pharmaceuticals and vaccines, natural selection inevitably drives pathogens to mutate into drug-resistant variants that can resume productive replication. Further, these drug-resistant mutants transmit across populations, resulting in untreatable epidemics. Recently, a therapeutic strategy was proposed in which viral deletion mutants—termed therapeutic interfering particles (TIPs)—are engineered to only replicate by stealing their missing proteins from full-length viruses in co-infected cells. By stealing essential viral proteins, these engineered molecular parasites have been predicted to reduce viral levels in patients and viral transmission events across populations. Yet, a critical question is whether rapidly mutating viruses like HIV can evolve around TIP control by reducing production of the proteins that TIPs must steal in order to replicate (i.e., by ‘starving’ the TIPs). Here we develop a multi-scale model that tests whether TIP-starving HIV mutants can spread across populations to undermine TIP therapy campaigns at the population-scale. Strikingly, model results show that inherent evolutionary tradeoffs prevent these TIP-resistant HIV mutants from increasing in frequency (i.e., these TIP-resistant HIV mutants are continually outcompeted by TIP-sensitive mutants in both patients and populations). Maintained by natural selection, TIPs may offer a novel therapeutic approach to indefinitely control rapidly evolving viral pandemics.
| Defective interfering particles (DIPs) are ‘cheaters’ in a viral population. Rather than carrying a full set of genes essential for their own replication, these deletion mutants require co-infection by replication-competent ‘helper’ viruses to provide their missing components for replication, packaging, and spread [1, 2]. By stealing essential viral components from wild-type viruses, DIPs act as molecular parasites of viruses. Further, natural DIPs have been observed to arise spontaneously across a range of viruses, and have been predicted to reduce disease virulence by interfering with viral replication processes [3–9]. Consequently, DIPs have been proposed as novel antiviral therapeutics [5, 10–12].
While natural DIPs have never been documented in HIV infections, HIV-derived DIPs have been engineered artificially [10, 13–15] and shown to reduce HIV replication [10, 16, 17]. Here we quantitatively probe a subset of DIPs that are engineered to have basic reproductive ratios greater than 1 during co-infection with HIV (i.e., maintain stable TIP loads) while suppressing HIV viral loads. These stable and suppressive DIPs are termed therapeutic interfering particles (TIPs). Previous mathematical models predicted that TIPs would substantially outperform current state-of-the-art antiretroviral therapy campaigns [12, 18–21]. However, the models did not test whether TIP efficacy would be undermined at the population-scale by the evolution and spread of TIP-resistant HIV mutants.
Since TIP intervention is designed to reduce wild-type HIV viral loads within individual patients, it may pressure HIV to evolve. Specifically, reductions in HIV load correlate with reduced transmission of HIV [22, 23], so any TIP-mediated reductions would create a selection pressure for ‘resistant’ HIV mutants that are not suppressed. Arguably, the most direct way for HIV to evolve TIP-resistance is by reducing the amount of intracellular resource (e.g., capsid proteins) available for TIP parasitism. In that way, HIV may be able to ‘starve’ the parasitic TIP particles of the resources they parasitize. TIP-resistant HIV mutants would then have the potential to outcompete the wild-type HIV strains within a patient and spread through a host population, progressively nullifying the TIP intervention. This is a form of ‘unilateral escape’, evolutionary escape by mutations affecting a feature encoded by the viral genome but not the (reduced) TIP genome.
Recently, we analyzed the unilateral escape dynamics of HIV resistance at the level of an individual patient and found that HIV mutants that starve TIPs would be selected against within individual patients [19]. However, given their reduced suppression, these TIP-resistant HIV mutants would likely transmit from infected individuals more efficiently than the wild-type HIV [22]. Thus, even if disfavored in individual patients, TIP-resistant HIV mutants could supplant the wild-type HIV strain at the population-scale, undermining the effectiveness of a TIP therapy campaign.
We sought to test whether TIP-resistant HIV strains would spread through a population. Notably, the transmission of HIV strains through a population is primarily driven by small ‘core groups’ of infected individuals (~1–2% of the population) who engage in high-risk behaviors [24–26]. TIPs similarly concentrate within these high-risk groups, because TIPs spread via the same transmission routes and risk factors as HIV [18]. Further, the increased prevalence of TIPs within high-risk groups increases the selective pressure in favor of TIP resistance in these sub-populations. And even if TIP-resistance initially emerges in a disparate sub-population, transit through high-risk sub-populations is critical for the population-scale spread of TIP-resistant HIV strains. Consequently, we developed a mathematical model to quantify whether HIV mutants with increased TIP resistance could stably invade high-risk populations. Strikingly, model results show that as long as TIP genomes are initially engineered to express at least ~3-fold more genomic RNA transcripts than HIV expresses in co-infected cells, TIPs can generally maintain population-scale stability. Further, due to two intrinsic evolutionary tradeoffs, TIPs are shown to be evolutionary stable at the population-scale whenever they are evolutionarily stable at the patient-scale.
The model of HIV and TIP replication and transmission includes three levels of biological organization: a population-scale model, an individual patient-scale model, and a cellular-scale model (Fig 1A). Each scale is represented by a well-studied system of deterministic ordinary differential equations. The population-scale model is an epidemiological Susceptible-Infected (SI) model [27] extended to include the spread of TIPs [18] (Section C in S1 Text). The patient-scale model is a variant of the basic model of HIV dynamics [28, 29], again extended to account for the presence of TIPs (Section B in S1 Text). Finally, the cellular scale model is a ‘public-goods game’—a well-studied system in game theory and evolutionary biology [30, 31]—in which HIV and TIP sequences compete for HIV capsid elements within dually-infected cells (Section A in S1 Text). Each scale’s equations are shown below, with all model parameters derived in the Supporting Information and summarized in Tables 1 and 2.
At the single-cell scale, TIPs can only replicate and package by parasitizing essential trans-acting elements from full-length HIV in co-infected cells (Fig 1A, bottom left). In the absence of HIV, TIPs can only enter CD4+ T cells and integrate their genetic material into the cellular genomes—little to no TIP production occurs, since TIPs are engineered to lack essential trans-acting elements required for lentiviral replication and packaging. As a result, TIP genomic RNAs (gRNAs) only express after a cell is co-infected by HIV, at which point TIP gRNAs compete with HIV gRNAs for encapsidation by HIV capsid proteins. HIV capsids are thus intracellular ‘public goods’ that both HIV and TIP gRNAs utilize, as shown in the following equations:
dGHIVdt=θ︸production−kpckGHIVC︸packaging−αGHIV︸decay
dGTIPdt=mPθ︸production−kpckGTIPC︸packaging−αGTIP︸decay
dCdt=ηθ︸production−kpck(GHIV+GTIP)C︸packaging−βC︸decay
The three state variables represent the within-cell concentrations of HIV gRNA (GHIV), TIP gRNA (GTIP), and capsid proteins (C), respectively. As shown in the Supporting Information (Section A in S1 Text), the rate parameters kpck, α, and β can be grouped into a single composite waste parameter, κ, by non-dimensionalizing the model. The parameter m quantifies the number of TIP integrations, and results naturally from multiple TIP infections of a cell in the host scale model (below). The parameter θ is HIV’s gRNA production rate—θ serves to scale the size of all outputs, and is therefore absorbed into the host-scale parameters (Section B in S1 Text). Most importantly, η reflects the ratio of HIV-capsid to HIV-genome production and P corresponds to the ratio of TIP gRNA expression to HIV gRNA expression (i.e., the expression asymmetry). From previous studies, η and P are known to be two major parameters that determine TIP evolutionary stability within patients [18, 19]. Thus, we tracked TIP stability at the population scale as a function of η and P. For each value of (η, P), the single-cell scale equations are solved to determine HIV and TIP ‘burst sizes’ (i.e., the net numbers of HIV and TIP virions produced in the lifespan of an infected cell) (Eqs. 9–11 in S1 Text). The burst sizes are passed to the patient-scale equations, where they are used to calculate patient-wide viral set points, through the parameters n, ψm, and ρm (below).
At the patient scale, the model incorporates four fundamental asymmetries to determine TIP and HIV viral loads (Fig 1A, bottom right). (i) First, while TIPs require the presence of HIV to replicate, HIV can replicate in the absence of TIPs. (ii) Second, HIV gene expression (of the vpr gene) prevents the division of infected cells [32, 33]. Conversely, TIPs lack vpr and TIP infection is silent (absent HIV), so TIPs do not block cell division. (iii) Third, TIP-infected cells live as long as uninfected cells in the absence of HIV (due to this replicative silence); HIV infection results in rapid cell death [34, 35]. (iv) Fourth, HIV gene expression suppresses subsequent superinfection of a cell via the nef gene [33, 36]; TIPs lack nef and do not suppress superinfection. Thus, multiple copies of the TIP provirus can integrate into a cellular genome prior to HIV infecting that cell. These TIP infected cells can further divide to seed a reservoir of cells with a range of TIP proviruses. The resulting numbers of TIP and HIV particles in a patient are tracked in the following host-scale model:
dT0dt=b︸production+dhT0︸division−dT0︸death−kVHT0︸HIV infection−kVTT0︸TIP infection
dTmdt=dhTm︸division−dTm︸death−kVHTm︸HIV infection+kVTTm−1−kVTTm︸TIP infection,m≥1
dImdt=kVHTm︸HIV infection−δIm︸death,m≥0
dVHdt=nδI0+nδ∑m=1∞ψmIm︸HIV production−cVH︸clearance
dVTdt=nδ∑m=1∞ρmψmIm︸TIP production−cVT︸clearance
The patient-scale state variables quantify: the numbers of HIV-uninfected cells with m TIP integrations (Tm, m ≥ 0), the numbers of HIV-infected cells with m TIP integrations (Im, m ≥ 0), the number of HIV virions (VH), and the number of TIP virions (VT). All host-scale parameters are described in the Supporting Information (Section B in S1 Text), where non-dimensionalization is shown to reduce the number of parameters to four: R0, d/δ, c/δ, and h (Table 2). Notably, HIV viral loads are lower in TIP-treated individuals than in HIV-only infected individuals, because cells co-infected with both HIV and TIP produce fewer HIV virions than HIV-only infected cells. The steady-state TIP (VT) and HIV viral loads (VH) resulting from this model are passed to a population-scale model to calculate virulence and spread across a population, as in [22].
The population-scale model (Eqs. 1 and Eqs. 59–61 in Section C in S1 Text) is a standard SI model with a single, well-mixed population, corresponding to high-risk disease spreaders [26]. As is common, a constant influx of susceptible individuals is assumed. These susceptible individuals are converted into HIV-infected individuals upon contact with an HIV-infected patient at a rate dependent on HIV’s viral load in the infected ‘donor’ patient [22]. While HIV can directly infect susceptible individuals, TIPs are (conservatively) assumed to only infect patients already infected with HIV. Further, co-transmission of HIV and TIP is neglected, due to the evidence showing that only a single founder virus generally establishes in patients after transmission through mucosal bottlenecks [37, 38]. Based on epidemiological and patient data [22], HIV infection progresses to AIDS as a function of HIV’s viral load in the patient, which reduces the effective lifetime of an infected individual and is modeled as removal from the population. Superinfection with TIP slows progression to AIDS (and reduces transmission of HIV from that individual) by reducing the HIV viral load (Fig 1A, top panel). The equations describing these epidemiological processes are:
dSdt=λ︸input−cNβHISI−cNβHIDSID︸HIV infection−δSS︸death
dIdt=cNβHISI+cNβHIDSID︸HIV infection−cNβTIDIID︸TIP infection−δII︸death
dIDdt=cNβTIDIID︸TIP infection−δDID︸death
The state variables represent the prevalences of: individuals susceptible to HIV (S), HIV-infected individuals (I), and dually infected (HIV+TIP+) individuals (ID). As shown in the Supporting Information (Section C in S1 Text), non-dimensionalization reduces the population-scale system to:
dSdt=δI(1B(1−S)−R0pop(SI+μSID))dIdt=δI(R0pop(SI+μSID−ϕIID)−I)dIDdt=δI(R0popϕIID−τID)
(1)
The model parameters are defined as follows: δI is the rate of removal of HIV-infected individuals from the population (i.e. AIDS progression rate); B is the ratio of the removal rates of infected and uninfected individuals; R0pop is the basic reproductive ratio of HIV in a population; μ and ϕ are the respective HIV and TIP transmission rates from TIP-treated individuals relative to the HIV transmission rate from individuals infected with HIV alone; τ is the decrease in the AIDS-progression rate due to superinfection with TIP. As in [22], these parameters are directly calculated from the HIV and TIP viral loads in the individual-patient model (Eqs. 62–69 in S1 Text and Table 1).
Following a well-established approach [19, 39, 40], the three biological scales (the single cell, the individual patient, and the host population) are integrated into a single multi-scale model, by using the steady-state outputs from the lower scale models as inputs into the higher scale models. This separation of timescales approach is possible, because the timescales of the processes that occur on each scale are so disparate that the processes on the lower scales are approximately at steady state relative to those on the higher scales. More specifically, the cell-scale processes reach steady-state in hours, the patient-scale processes require days to months, and the population-scale processes play out over decades.
Using this separation of timescales approach, the multi-scale model is simulated by setting values for η and P within single-cells. Inputting these values into the cell-scale model outputs HIV and TIP viral burst sizes. Using these burst sizes as inputs, the patient-scale model outputs set-point viral loads for HIV and TIP (Section B in S1 Text). Finally, the viral set-points are used as inputs into the population-scale model to calculate the parameters in Eq (1), specifically: the progression time of infected individuals to AIDS (τ) and the relative transmission rates of HIV (μ) and TIP (ϕ) (see [22, 23] and Section C in S1 Text). Thus, the final output of the multi-scale model is the prevalence of HIV and TIP across a population as a function of the intracellular design parameters η and P.
Once we can calculate HIV and TIP viral loads and prevalence levels as functions of η and P, we can then map the regions of the (η, P) parameter plane in which HIV mutants are able to maintain higher steady-state levels in the presence of TIP than is the wild-type HIV strain. We term these HIV mutants to be ‘resistant’ mutants. Given that the model covers multiple scales of behavior, there are multiple scales at which resistance can arise. At the host scale, HIV resistance corresponds to increased viral loads. Thus, HIV mutants that are able to maintain higher viral loads than wild-type HIV in the presence of TIP are termed ‘viral-load increasing’ resistant mutants. At the population scale, HIV resistance corresponds to an increased prevalence of unsuppressed (HIV+TIP-) individuals in the population. Thus, HIV mutants that are able to maintain a higher prevalence of HIV+TIP- hosts in the presence of TIP are termed ‘prevalence-increasing’ resistant mutants. Notably, at both host and population-scales, whether or not an HIV mutant is TIP-resistant is defined relative to the wild-type HIV strain—resistance is therefore dependent upon the parameter (η) values of both the wild-type and mutant HIV strains.
After finding the parameter values that generate TIP-resistant viral phenotypes, the next step is to determine whether or not these resistant mutants can invade established populations of HIV and TIP to overcome a TIP intervention campaign. This invasion analysis is performed by taking the dominant eigenvalue of the Jacobian matrix of the system (Section D in S1 Text Eqs. 108–109), as is standard [41–43]. Behaviors at the host scale are independent of the population scale, so invasion within hosts can be solved agnostically of the population scale. On the other hand, whether or not a mutant can invade the population is dependent upon its behavior at the individual-host scale. To resolve this, we extend the model to allow for ‘host stealing’: take-over of hosts by the strain better able to propagate at the host scale. Because the time-scales differ greatly between the host and population scales, this host stealing is assumed to occur instantaneously from the perspective of the population. This ‘super-infection’ assumption is standard in multi-scale modeling studies in epidemiology [44]. Standard invasion analysis is then performed on the multi-scale model: a (TIP) treatment is termed ‘evolutionarily stable’ if no TIP-resistant HIV mutants are able to invade populations infected by wild-type HIV.
Using the multi-scale model described above, we first sought to determine the intracellular parameter regimes that result in TIP invasion and dynamic stability: i.e., the requirements for a small amount of TIP to invade a patient or population (R0TIP > 1) and reach a stable nonzero steady-state. At the patient-scale, model simulations show that HIV can only achieve a nonzero steady-state when η > ~0.1, matching earlier predictions [19]. When η < ~0.1, HIV replicates so poorly within an individual that neither HIV nor TIP can propagate (Fig 1B, left and S1 Fig)—resulting in dual HIV and TIP extinction. When η ≈ 0.1, HIV achieves stability, and there is a small slice of the (η, P) parameter plane at which HIV is stable but TIP is not (Fig 1B, left). Everywhere else in the (η, P) parameter plane, TIP co-stability with HIV can be achieved with a sufficient level of TIP gRNA overexpression (P). In particular, when P > Pcritical ≈ 3, TIPs are stable in the host across all η > ~0.1 and even remain stable as the TIP instability regime expands at η ≈ 1. Thus, engineering TIPs to express greater than ~3X more gRNA than HIV expresses is an essential design constraint at the patient-scale.
At the population-scale, P >~ 3 again generates TIP stability across a broad range of the (η, P) plane (Fig 1B, right). However, TIP stability also depends on the initial HIV prevalence in the high-risk population (in which HIV is highly prevalent) and on whether TIPs can ‘pre-immunize’ HIV-negative individuals (Fig 1C). For the maximally conservative assumption of no TIP pre-immunization, TIPs act as obligate secondary parasites and replicate only within HIV-infected individuals, so both the subpopulation available for TIP infection and the frequency of contacts within this subpopulation depend on the prevalence of HIV. Since the initial HIV prevalence depends on HIV’s basic reproduction ratio, R0pop [45] (see Eqs. S78), the effective reproduction ratio of TIPs, Reff, is also dependent on the reproduction ratio of HIV within the population. The requirement for TIP spread and stability in a population under the conservative assumption of no TIP pre-immunization is (see Table 1 and Section C in S1 Text):
Reff(η,P)=R0pop−Bτϕ(η,P)>1
(2)
Under this assumption, as the initial prevalence of HIV increases, the region of the (η, P) plane in which TIPs remain stable in the population expands and approaches the within-host stability region (Fig 1B). In particular, given a high-risk population with ~60% prevalence of HIV, if TIPs are stable in an individual, they stably coexist with HIV in the population. On the other hand, if pre-immunization is allowed and TIPs can (silently) pre-infect susceptible individuals and remain latent until subsequent infection with HIV (as was assumed in previous analyses [18]), the TIP stability region expands and is less sensitive to HIV’s initial prevalence (Fig 1C). Importantly, there is some evidence to suggest that pre-immunization is possible: lentiviruses are able to establish latency in rhesus monkeys even when suppressive antiretroviral therapy (which prevents replication) is started as little as three days post-infection [46]. Further, this latent state persists for more than six months [46]. In both cases, whether or not pre-immunization occurs, in regions of parameter space where TIPs coexist with HIV, TIPs will substantially suppress HIV/AIDS prevalence and incidence across a population (S2 Fig).
Having determined the critical engineering constraint for the dynamic stability of TIP treatments at both host and population scales—i.e., P > ~3—we next mapped the regions of parameter space at which HIV can achieve resistance to these stable TIPs. Since the model assumes no specific genotype-to-phenotype map, the difference between HIV strains is modeled phenotypically, as a difference in η. Importantly, the parameter η is likely to be under selection in vivo, as HIV has evolved suboptimal splicing (splicing increases η) and a molecular switch to control this splicing efficiency [47]. In contrast, the parameter P is assumed to be constant and independent of the HIV parameters (i.e., P depends on the TIP alone; see Discussion).
As defined in the Methods (above), η mutants can generate two types of resistance relative to the wild-type η strain. HIV mutants that generate increased viral loads in an individual are considered TIP-resistant within the host, and are termed ‘viral-load increasing’ mutants. HIV mutants that generate increased prevalence in the population are considered TIP-resistant within the population, and are termed ‘prevalence increasing’ mutants. In TIP+ individuals, HIV viral loads reach a maximum at a critical value of η near η = 1 (Fig 2A, left). Since this maximal HIV viral load depends on the value of P (i.e., the particular TIP variant), we denote this critical value as ηc(P). Any mutation in η toward ηc (i.e. any mutant for which |ηmut − ηc| < |ηwt − ηc|) is sufficient to increase the HIV load in TIP+ hosts (Fig 2A, left). Additionally, in a thin region of low η ≈ 0.1, TIP is destabilized in HIV+ hosts, so HIV loads again peak. So, any mutant with η ≈ 0.1 or with a value of η closer to ηc than the wild-type is a virus-load increasing mutant. In contrast, at the population-scale, mutants that reduce η reduce the population-level coverage of TIPs in the HIV-infected population, whatever the wild-type value of η (Fig 2A, right). Thus, any mutant with ηmut < ηwt is a prevalence-increasing mutant. Intuitively, both modes of TIP-resistance result from decreasing η to starve TIPs of the public goods (e.g., capsid proteins) they require. However, when η is decreased below ηc ≈ 1, decreasing capsid production begins to harm HIV’s own ability to propagate within hosts (so ηc ≈ 1 is effectively a ‘sweet-spot’).
Both viral-load increasing mutants and prevalence-increasing mutants can lead to either full or partial loss of HIV suppression, and, consequently, full or partial elimination of TIPs from the pertinent scale (Fig 2A and S2 Fig). Full resistance at each scale (individual or population) essentially drives the system to regions of P and η where TIPs are unstable (Fig 1B and 1C and S2 Fig). However, if P > 3, full resistance to TIP treatment only occurs at low η values of ~0.1, near the HIV extinction threshold (Fig 2A).
After mapping the regions in which HIV escape mutants arise, the next step is to whether or not these resistant mutants can spread across a population to undermine a TIP campaign. To do so, we examine the introduction of mutant HIV strains into the host population and analyze the competition between the wild-type HIV strain and each new mutant HIV strain. HIV mutants are introduced in small quantities into an individual with steady-state TIP and wild-type HIV viral loads. As in the stability analysis above, we rely on a time-scales separation, since a large number of viral replication events (i.e., viral generations) occur within each individual between inter-individual transmission events. Examining the dominant eigenvalues of the Jacobian, we determined the fitness landscape for HIV mutants at the individual-patient level from the rate of expansion or contraction of a mutant strain with slight differences in η relative to the wild-type (Section D in S1 Text). The Jacobian analysis enables us to calculate the net selective advantage (or disadvantage) of any TIP-resistant mutants.
There are two scales at which evolutionary fitness must be analyzed: the host scale and the population scale. At the host scale, the relative fitness of an HIV variant reflects the relative growth rate of that clone within a host. For any HIV strain, growth rate increases with its effective reproductive ratio [19], which depends on the viral burst sizes from individual cells and the distribution of TIP multiplicities among the cells. Critically, within a given cell, a larger η always corresponds to a larger viral burst size, regardless of the TIP multiplicity (S1 Text Eqs. S9-S13). Consequently, HIV mutants with larger values of η always have higher relative fitness within an individual, regardless of the presence or level of TIP (Fig 2B, left; and S3 Fig). This result can be understood intuitively as a ‘tragedy of the commons’ [48, 49]: enhanced capsid production favors the HIV strain that can achieve it, despite enabling increased TIP parasitism of all HIV mutants in the host.
At the population scale, the effective reproductive ratio of HIV is determined by its ability to transmit between members of the host population [22]. The HIV transmission rates are calculated (Eq 1, Table 1) from the viral load outputs from the individual-patient model as in [18] (see Section C in S1 Text). Since these patient-level viral loads themselves depend on the single-cell parameters, the transmission rates of HIV mutants are ultimately functions of η and P. Further, these transmission rates differ greatly between individuals only infected with HIV alone individuals infected with both HIV and TIP. In the absence of TIP, more resource production (i.e., a higher η) is always better for HIV, so HIV transmission always increases with η in TIP− individuals (Fig 2B, top right). However, in TIP+ individuals, the transmission rate and the viral load both peak at η = ηc (Fig 2B, bottom right). Intuitively, in TIP+ hosts, there is a balance between producing enough resources to propagate, and producing too many resources, which allows TIPs to establish a larger population. Taken together, the model results capture conflicting selection pressures driving HIV transmission from TIP+ individuals, HIV transmission from TIP− individuals, and HIV viral loads within patients. These conflicting pressures push HIV evolution in different directions along the η-axis, resulting in evolutionary conflicts on the value of η.
Overall, two evolutionary tradeoffs emerge from the model: an inter-scale conflict in TIP co-infected individuals between host-level HIV fitness and population-level HIV transmission, and an intra-scale (population-level) conflict between HIV transmission from individuals co-infected with TIP and individuals not co-infected with TIP (Fig 2B). The inter-scale conflict arises from the fact that when TIP is evolutionarily stable on the host level, evolution within TIP-treated hosts leads to higher η values. Yet, at the population-level (i.e., in TIP+ individuals), HIV variants with lower η values are evolutionarily beneficial, since they reduce TIP levels and attendant parasitism. The intra-scale conflict also arises from the benefit to HIV of reducing η in TIP+ populations—the tradeoff is that in TIP− populations, reducing η actually reduces HIV loads and transmission. Thus, the intra-scale conflict exists within the population scale alone and is dependent on the frequency of TIP+ hosts (i.e., it is a frequency-dependent effect).
Given these two evolutionary conflicts on the value of η, we probed which HIV mutants could spread through a population by extending the population-level model to include multiple HIV strains (Fig 3A and S1 Text Eqs. 100–104). For each HIV strain, relative transmission rates were calculated based on viral loads, as in the one-strain model (Eq 1, Table 1). When wild-type and mutant HIV strains co-infect the same host, host fitness comes into play at the population scale, since the within-host infection dynamics occur extremely rapidly relative to the population-scale dynamics [50]. As a result, the more fit HIV strain takes over within a host prior to population-scale transmission events. The rapid host take-over is due to ‘competitive exclusion,’ which precludes two strains from coexisting at steady state, regardless of the presence of TIP (dashed vertical arrows in Fig 3A). Therefore, we neglect individuals co-infected with multiple HIV strains—the most fit HIV strain rapidly excludes the others (see the Discussion for an analysis of cases where competitive exclusion does not occur, due to weakened within-host selection).
The dual-strain model was first used to track whether a representative HIV mutant with ηmut = 1 can invade a TIP-treated population with a wild-type HIV strain in which ηwt = 2 (Fig 3B). Given its decreased η value, this HIV mutant would increase the HIV viral load in HIV+TIP+ (co-infected) individuals and increase the prevalence of HIV+TIP- individuals in a population (Fig 2B). However, the mutant would be disfavored in TIP− populations and disfavored within individual hosts. The dual-strain model weighs the conflicting selective benefits at both scales to calculate whether the HIV mutant can spread.
In the particular example of ηwt = 2 and ηmut = 1, the dual-strain model demonstrates that the overall population-level trajectory of the resistant HIV mutant is toward extinction—given both large P (i.e., P > 3) and the presence of co-infection (Fig 3B and S4 Fig). Effectively, P > 3 drowns out the selective advantage of decreasing η: the modest increases in transmission from TIP+ individuals are matched by decreases in transmission from TIP- individuals. In contrast, when P = 2.5, decreasing η results in a major increase in transmission from TIP+ individuals (Fig 1B), which dwarfs the decrease in transmission from TIP- individuals. Thus, increasing P to a value greater than 3 is required for a robust intra-scale conflict. The trajectory of the mutant strain also depends on whether co-infection can occur, since co-infection results in the out-competition of reduced η mutants within hosts despite their population-level fitness advantage. In other words, co-infection is required for an inter-scale conflict. Competing mutant and wild-type HIV strains in the presence and absence of P > 3 and co-infection demonstrates that both intra-scale and inter-scale conflicts are necessary to prevent the establishment of TIP resistance (Fig 3B).
To determine the general conditions across all of (η, P > 3) parameter space under which a mutant virus (with parameter ηmut) can spread into a wild-type-infected host population (with parameter ηwt), we performed an invasion analysis [41–43] to examine the initial expansion rates of HIV mutants after introduction (Fig 3C). When ηmut < ηwt, the model shows that HIV mutants never expand (Fig 3C). This result is the key to determining parameter regions of P where TIPs would be safe from HIV escape mutants (i.e. the design criteria for engineering ‘resistance-proof’ TIPs). Indeed, if the small HIV-mutant population shrinks initially, it will never be able to outcompete wild-type HIV. Thus, when P is safely in the TIP stability region of P > ~3, HIV evolution is constrained to move toward larger η values and away from TIP-resistance.
In terms of the two types of resistant mutants discussed above, this invasion analysis (Fig 3C) shows the extinction of prevalence-increasing mutants anywhere, and the extinction of virus-load increasing mutants when ηwt > ηc. Virus-load increasing mutants do spread in a host and in a population when ηwt < ηc, in which case all the selection pressures align (S5 Fig), because an increase in η results in an HIV load increase. However, in this range (ηwt < ηc), HIV is pressured toward higher η values regardless of the presence of TIP (Fig 2B). This selection pressure arises not from the presence of TIP, but from the enhanced replication of HIV at the host scale at higher capsid production rates (regardless of TIP). At the population level, the selection pressure towards higher η even decreases when TIP is present (see the transmission rates in Fig 2B, right). In other words, the population-level instability comes from a pre-existing host-level instability, prior to the introduction of TIP.
Taken together, the results of the invasion analysis show that—given dynamic and evolutionary stability at the host scale (i.e., P > 3 and ηwt > ηc)—TIP interventions would be both dynamically and evolutionarily stable from unilateral HIV escape mutants at the population scale.
While the models used to test TIP evolutionary stability at both host and population-scales are well-established [45], as in any modeling study, our analysis necessarily utilizes simplifying assumptions. To determine whether these simplifying assumptions impacted model outcomes, we performed a number of sensitivity analyses in which model assumptions were relaxed. For example, a concern in the host-scale model is the function used to model target-cell division (since cell division enables the vertical transmission of provirally integrated TIPs across a host). To keep the uninfected T-cell population bounded, we assumed that the cell division rate ‘shuts off’ at high T-cell concentrations. Yet, the form of the function used to model this homeostatic shutdown could, in theory, affect the model’s outcomes. We thus tested disparate shutdown functions, finding that large changes in the shutdown function only result in small changes in the dependence of the equilibrium target-cell division rate (heq) on the maximal target-cell division rate (h0) (S7 Fig). This is because the equilibrium target-cell division rate is mostly driven by the asymptotic T-cell level, which is independent of the form of the shutdown function (SI Section B).
A second assumption is that all TIP-immunized hosts have significantly reduced HIV transmission rates, due to a TIP-mediated reduction in HIV viral loads. However, a large fraction of HIV transmission can occur prior to TIP suppression, especially during the acute phase of infection [51–53]. To account for the possibility that a large fraction of HIV transmission by a patient occurs during the acute phase of infection prior to TIP suppression of viral loads, we re-analyzed the model under the strong assumption that TIP therapy does not reduce HIV transmission at all in dually-infected (i.e., TIP+, HIV+) individuals. This is equivalent to the (worst-case) assumption that all of HIV’s spread occurs during acute infection prior to TIP inoculation. Importantly, the results of the model are virtually unchanged—both TIP dynamic stability (S8A Fig) and TIP evolutionary stability (S8B Fig) are preserved. Intuitively, the reason for the sustained TIP efficacy despite high HIV transmission is that increased HIV transmission enables increased TIP colonization of the population (middle columns of S8 Fig). Thus, the increased HIV levels only strengthen the evolutionary stability of TIPs to HIV mutants (last columns of S8 Fig).
Finally, we examined whether increased death rates reduce the transmission potential of higher η (i.e., TIP-susceptible) mutants and thereby select for lower η (i.e., TIP-resistant) strains. In fact, the increase in death rates due to increasing η only has a minimal effect on the transmission potential (S9 Fig). This robustness occurs because the increase in viral loads saturates as η is increased. Further, this saturation point is at a viral set-point of ~105, whereas the measured sharp decrease in transmission potential occurs at a set point >105 [22].
Overall, the results of these sensitivity analyses support the earlier model results, showing that TIPs can be engineered to be both dynamically and evolutionarily stable at the population-scale.
Here we developed a three-scale model of HIV dynamics to test whether interfering particles—which parasitize critical HIV proteins within individual cells—can be designed to stably control the rapidly evolving HIV virus throughout a high-risk population. In the absence of HIV mutation, the analyses show that TIP interventions can spread and stably persist at the population-scale, whenever there is sufficient prevalence of HIV within the high-risk population (Fig 1B). Importantly, even if HIV’s prevalence is initially low in the high-risk population, TIPs remain dynamically stable if they can pre-immunize HIV- individuals (Fig 1C). The analyses further tested the evolutionary stability of TIP interventions at the population-scale, probing whether ‘resistant’ HIV mutants that lead to increased HIV viral loads within patients or increased HIV prevalence across populations can undermine TIP efficacy. Critically, model results show that the spread of these TIP-resistant HIV mutants is limited by two fundamental evolutionary tradeoffs: their reduced transmission rates in untreated individuals and their reduced growth rates in both untreated and treated individuals (Fig 2B). In fact, when TIPs are evolutionarily stable within hosts, these evolutionary conflicts drive HIV to evolve toward increased (rather than decreased) TIP susceptibility at the population-scale (Fig 3C). Taken together, the analyses show that whenever TIPs can be designed to be dynamically and evolutionarily stable in individual patients, they remain dynamically and evolutionarily stable in populations.
The competition of mutant pathogen strains across multiple biological scales has previously been considered in a number of studies, as reviewed in [43]. Notably, these studies often predicted that co-infection would lead to increased pathogen virulence, because more virulent strains are likely to replicate more rapidly (i.e., have increased fitness) [49, 54, 55]. This increased virulence result is, in many ways, analogous to our finding that HIV always evolves toward higher η—except that increased η in the context of TIP therapy results in decreased HIV virulence. Importantly, decreased virulence is a predicted outcome in ‘public goods’ models in which selfish, but less virulent pathogens outcompete cooperative, virulent strains [31]. In the public goods framework at the host-scale, TIPs are the public goods shared among the HIV strains co-infecting a host. Critically, an individual HIV strain benefits when there is more TIP production, because increased capsid production increases both TIP production and that strain’s relative fitness. Yet, increased TIP production is deleterious to the overall HIV population, reducing all HIV strains uniformly. Thus, evolution toward increased TIP production can be viewed as evolution toward a cheating HIV strain, explaining the overall virulence reduction as a viral ‘tragedy of the commons’ [48, 49].
Still, a key question of this study was to determine whether the cheating HIV strain would outcompete cooperative HIV strains that produce fewer TIPs, since TIP production decreases HIV transmissibility at the population-scale. As analyzed in detail in [39, 56], whether or not a cheater outcompetes a cooperator in a multi-level evolutionary conflict depends on the relative strength of the within-host evolutionary pressures (which favor the cheater) and the between-host evolutionary pressures (which favor the cooperator). Our results capture the dominance of the within-host pressures in the context of HIV-TIP dynamics, because the between-host pressures in favor of decreased TIP production are absent (and in fact inverted) in the sub-population that remains TIP-.
In the multi-mutant model, new HIV mutants are assumed to arise infrequently relative to the strength of within-host selection—i.e., a weak-mutation, strong-selection regime is assumed. In fact, the multi-mutant model assumes an extreme weak-mutation regime, with HIV mutants only introduced into patients via co-infection. This neglects the de novo generation of new HIV mutants within hosts, which, in truth, occurs rapidly for an RNA-encoded virus. Fortunately, this minimization of HIV mutation represents a worst-case scenario for demonstrating TIP evolutionary stability at the population-scale. If within-host mutations were to arise more frequently, the effects of host-level selection would only become more pronounced at the population scale. This is because there would be greater numbers of cheater HIV mutants that increase TIP production (i.e., HIV mutants with higher η values than the wild-type). Further, all TIP-resistant mutants with lower η values than the wild-type would be lost due to their selective disadvantage within hosts. The increased mutation of HIV strains would thus enable the emergence of HIV mutants with higher η values, enhancing the evolutionary stability of TIP treatments. Consequently, once evolutionary stability has been established in a multi-mutant model in which host-scale effects are only exerted through co-infection, stability in a model with de novo generation of mutants follows.
In addition to changing the strength of mutation, one could also study how the results hinge on the strength of selection. The assumption that TIP-susceptible (i.e., higher η) HIV strains competitively exclude TIP-resistant (i.e., lower η) HIV strains within hosts depends on the strength of within-host selection. If host-scale selection is too weak, a distribution of mutants with different η values (i.e., TIP resistance levels) could persist within hosts. However, the presence of a distribution of mutants (rather than a single mutant) does not obviate either TIP dynamic or evolutionary stability. We begin by considering TIP dynamic stability in the context of decreased within-host selection. Given that HIV’s burst size increases monotonically in η, any within-host distribution of η mutants can be modeled as a single ‘characteristic' η mutant: the ‘characteristic’ HIV mutant whose η value gives rise to the average HIV burst size in the host. As long as the η value of this mutant is within the stability regime derived in Fig 1B for the single-mutant case (i.e., η > ~ 0.2), TIPs will remain dynamically stable at the host and population scales. For evolutionary stability, the key point is that TIP introduction leaves any distribution of η mutants essentially unchanged (if anything, it shifts the distribution slightly toward higher η: Fig 2B). This is because TIPs suppress all HIV mutants within a host essentially uniformly (they all feel the same TIP load). Thus, both the relative fitness values (Fig 2B) and relative transmission rates of the HIV mutants are unchanged by TIP introduction. As a result, there is no change in the patient-scale or population-scale distribution of η mutants and no selection for TIP-resistant strains. These arguments aside, if the strength of mutation were significantly increased, it might be possible for a beneficial mutant to arise that happens to have both a lower η and a net fitness advantage due to secondary beneficial mutations. We neglected these higher-order effects due to factors such as genetic linkage and clonal interference—as well as non-deterministic effects due to factors such as Muller’s ratchet and genetic drift—in our simplified model. Following a number of recent studies in theoretical population genetics [50, 57–59], these ideas could be the subject of future models.
A more basic assumption of the model is that HIV escapes TIP parasitism by reducing η—i.e., by unilaterally reducing the production of capsid elements available for TIP parasitism. Since TIPs do not encode trans elements such as capsids, the TIPs would have no ability to restore η to high values (i.e., η is an asymmetric parameter). Consequently, HIV’s unilateral evolution of η offers the most direct route for HIV to evade TIP-mediated suppression. Yet, as an alternative to this unilateral evolution in η, one could consider mutations in θ, which would alter HIV’s gRNA production rate.
Importantly, mutations in θ would be more symmetric, changing the production rates of both HIV and TIP. For example, by increasing HIV’s genomic RNA (gRNA) production rate, increasing θ would also increase the production of the HIV protein (Tat) that transactivates the LTR promoters of both HIV and TIP and increase the production of the HIV protein (Rev) that exports both HIV and TIP gRNAs into the cytoplasm for encapsidation. Thus, TIP gRNA production would be increased in symmetry with HIV gRNA production. A key point is that if the TIP:HIV gRNA overexpression ratio (i.e., P) were still sufficiently high—i.e., if P were still >3—the TIPs’ evolutionary stability would be expected to remain.
A further reason that HIV mutations in θ are unlikely to generate stable TIP-resistance is that increasing θ may not be an evolutionarily stable strategy for HIV—an intrinsic fitness cost may prevent HIV from increasing θ. In particular, a recent study [61] showed that increasing the rate of transcription (e.g., by adding transcription factor binding sites to the LTR) reduces the level of HIV replication, likely by disrupting an evolutionarily-tuned viral replication program. If θ has been optimized over the millennia of lentiviral evolution in primates, then a similar cross-scale evolutionary conflict to the one shown here for η could limit the emergence of HIV mutants with increased θ values, despite their increased TIP-resistance potential.
Conversely, if increasing θ were evolutionarily beneficial to lentiviruses, then TIPs could directly co-evolve to match HIV evolution for a ‘symmetric’ parameter such as θ. This is because, unlike trans (e.g., capsid) elements, TIPs encode all cis-acting elements. And given their shared error-prone reverse transcriptase enzyme, TIPs have the same evolutionary capacity and pressure to modify their LTR promoter towards increased gRNA production, should increased gRNA production prove beneficial within a cell. Thus, ‘red queen’ type selection races may arise [60], with both TIP and HIV particles simultaneously adapting to attempt to gain the upper hand in gRNA production (i.e., with both TIP and HIV evolving to modulate the level of gRNA overexpression, P). In fact, one numerical simulation study appears to have demonstrated this [20], although the particular methodologies and assumptions have been questioned [62, 63]. Taken together, given the potential of TIPs to co-evolve in θ and the simpler possibility that θ is already evolutionarily optimized, this study assumes that θ remains fixed, as in previous studies [12, 18, 19]. With θ fixed, P is similarly fixed once the TIP has been engineered.
A final non-unilateral escape mechanism involves mutations in the HIV trans elements that the TIP parasitizes. It has been argued that HIV is unlikely to win the resulting cis-trans arms races with TIP, due to an intrinsic mutational asymmetry between HIV and TIP [18, 19]. To escape TIP parasitism, HIV needs to almost simultaneously adapt both its cis and trans elements in a correlated way, so that the mutant trans still interacts with the mutant cis but not the original cis element (which remains in the TIP). Within this same adaptation timeframe, the TIP only needs to mutate its corresponding cis element to keep pace. Since both HIV and TIP maintain the same mutation rate—TIPs share the same error-prone reverse transcriptase protein that drives HIV mutations—and TIPs need to mutate fewer elements, TIPs would have a built-in evolutionary advantage. Notwithstanding this advantage, the outcomes of these arms races may depend on other factors, such as the ability of an individual TIP to parasitize distinct HIV mutants. Thus, detailed simulations of arms races between HIV and TIP will be carried out in a subsequent study.
As a result of the conflicting selection pressures induced by TIP, there are important caveats for modeling the evolutionary behavior of HIV in the presence of TIP. In general, the selective forces at the population level cannot be described by a fitness landscape. In order to express selection acting on a mutant as a fitness landscape, it must be possible to express the relative slope of expansion, s, as the difference in log fitness (f) between two strains, s = f(η1, η2) = f(η2) − f(η1), where η1 and η2 are initial and final values, respectively. However, this condition is violated in the present model for three reasons: (i) in the case of co-infection with different HIV strains (leading to competitive exclusion), the less host-fit mutant experiences a negative-selection pressure at the population level with the strength of this pressure depending on the frequency of contacts with individuals infected with fitter strains; (ii) the initial expansion rate of mutant strains has a term that does not depend on the magnitude of the parameter difference from the wild type (η1 − η2) but only on its sign, because of the speed of within-host competitive-exclusion relative to the population time-scale; (iii) even in the absence of within-host co-infection, the spread of a strain depends on the balance between TIP+ and TIP− individuals in the population and this ratio adjusts with the prevalence of the strains, causing long-term oscillations (S6 Fig).
Given the importance of the intra-scale (i.e., population-scale) conflict and the resultant frequency-dependent fitness effects at the population scale, it could be reasonably expected that similar features would appear at the host-scale. However, these frequency-dependent features can be safely neglected in this model, where we calculated a fitness landscape corresponding to incremental small changes in η (Fig 2B). Although modest frequency-dependent corrections would appear for large jumps in parameter η, these effects only adjust the strength of selection, not the direction. Because we assumed a strong separation of timescales between the host and population levels, only changes in the direction (not the magnitude) of host-level selection affect the final results. Within hosts, HIV fitness always increases as η increases regardless of how many TIP copies are in a cell (i.e., regardless of TIP and HIV loads in a host), since a larger η always results in a larger burst size (S1 Text Eqs. S9-S13). Hence, regardless of TIP frequency, the HIV strain with largest η always spreads fastest in a host, driving the other strains to extinction.
The predicted lack of unilateral evolution of HIV towards resistance to TIP is in striking contrast to HIV resistance to antivirals, which commonly arises in treated individuals due to poor adherence or suboptimal therapy regimens [64, 65]. HIV strains that are resistant to antivirals can then transmit from host to host, spreading through the population [65]. The critical difference between antivirals and TIPs is that TIPs parasitize HIV trans elements (i.e., steal HIV proteins), and this parasitism inexorably uses the same biochemical processes as HIV replication. So, to prevent TIPs from interfering, HIV must interfere with its own ability to replicate (i.e., ‘shoot itself in the foot’). In other words, the cost of mutation is always directly related to the benefit of the mutation. In contrast, HIV-escape from antiviral pharmaceuticals may produce some disadvantages for the mutant strain relative to the wild-type strain, but the benefit of the mutation and the cost of the mutation are not necessarily related.
With each TIP-evading mutation necessarily arising at a cost, our analysis quantifies the net-benefit (i.e., evolutionary viability) of these mutations across a population, where resistance may be beneficial to transmission. By capturing the parameter regimes under which resistance mutations are driven extinct, the analysis offers general guidelines for engineering therapies that obviate the spread of antiviral resistance within populations; in fact, these therapies are likely to direct pathogens toward increased susceptibility. As a result, these design constraints may aid in the engineering of resistance-proof interventions against a range of viral and bacterial pathogens beyond HIV.
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10.1371/journal.pcbi.1006471 | Quantum chemistry reveals thermodynamic principles of redox biochemistry | Thermodynamics dictates the structure and function of metabolism. Redox reactions drive cellular energy and material flow. Hence, accurately quantifying the thermodynamics of redox reactions should reveal design principles that shape cellular metabolism. However, only few redox potentials have been measured, and mostly with inconsistent experimental setups. Here, we develop a quantum chemistry approach to calculate redox potentials of biochemical reactions and demonstrate our method predicts experimentally measured potentials with unparalleled accuracy. We then calculate the potentials of all redox pairs that can be generated from biochemically relevant compounds and highlight fundamental trends in redox biochemistry. We further address the question of why NAD/NADP are used as primary electron carriers, demonstrating how their physiological potential range fits the reactions of central metabolism and minimizes the concentration of reactive carbonyls. The use of quantum chemistry can revolutionize our understanding of biochemical phenomena by enabling fast and accurate calculation of thermodynamic values.
| Redox reactions define the energetic constraints within which life can exist. However, measurements of reduction potentials are scarce and unstandardized, and current prediction methods fall short of desired accuracy and coverage. Here, we harness quantum chemistry tools to enable the high-throughput prediction of reduction potentials with unparalleled accuracy. We calculate the reduction potentials of all redox pairs that can be generated using known biochemical compounds. This high-resolution dataset enables us to uncover global trends in metabolism, including the differences between and within oxidoreductase groups. We further demonstrate that the redox potential of NAD(P) optimally satisfies two constraints: reversibly reducing and oxidizing the vast majority of redox reactions in central metabolism while keeping the concentration of reactive carbonyl intermediates in check.
| In order to understand life we need to understand the forces that support and constrain it. Thermodynamics provides the fundamental constraints that shape metabolism [1–5]. Redox reactions constitute the primary metabolic pillars that support life. Life itself can be viewed as an electron transport process that conserves and dissipates energy in order to generate and maintain a heritable local order [6]. Indeed, almost 40% of all known metabolic reactions are redox reactions [7,8]. Redox biochemistry has shaped the study of diverse fields in biology, including origin-of-life [9], circadian clocks [10], carbon-fixation [11], cellular aging [12], and host-pathogen interactions [13]. Previous work has demonstrated that a quantitative understanding of the thermodynamic parameters governing redox reactions reveals design principles of metabolic pathways. For example, the unfavorable nature of carboxyl reduction and carboxylation explains to a large degree the ATP investment required to support carbon fixation [1].
Developing a deep understanding of redox biochemistry requires a comprehensive and accurate set of reduction potential values covering a broad range of reaction types. However, only ~100 reduction potentials can be inferred from experimental data, and these suffer from inconsistencies in experimental setup and conditions. Alternatively, group contribution methods (GCM) can be used to predict a large set of Gibbs energies of formation and reduction potentials [14]. However, the accuracy of this approach is limited, as GCM do not account for interactions between functional groups within a single molecule and GCM predictions are limited to metabolites with functional groups spanned by the model and experimental data.
Quantum chemistry is an alternative modeling approach that has been used to predict redox potentials in the context of numerous applications, such as redox flow batteries, optoelectronics, and design of redox agents [15–27]. Unlike GCM, whose smallest distinct unit is a functional group, quantum chemistry directly relates to the atomic and electronic configuration of a molecule, enabling ab initio prediction of molecular energetics. Here, we adopt a quantum chemistry modeling approach from the field of redox flow battery design [25,26,28] to predict the reduction potentials of biochemical redox pairs. Our approach combines ab initio quantum chemistry estimates with (minimal) calibration against available experimental data. We show that the quantum chemical method can predict experimentally derived reduction potentials with considerably higher accuracy than GCM when calibrated with only two parameters. We use this method to estimate the reduction potentials of all possible redox pairs that can be generated from the KEGG database of biochemical compounds [7,8]. This enables us to decipher general trends between and within groups of oxidoreductase reactions, which highlight design principles encoded in cellular metabolism. We specifically focus on explaining the central role of NAD(P) as electron carrier from the perspective of the redox reactions it supports and the role it plays in lowering the concentration of reactive carbonyls.
To facilitate our analysis we divided redox reactions into several generalized oxidoreductase groups which together cover the vast majority of redox transformations within cellular metabolism (Fig 1A): (G1) reduction of an unmodified carboxylic acid (-COO) or an activated carboxylic acid–i.e., phosphoanhydride (-COOPO3) or thioester (-COS-CoA)–to a carbonyl (-C = O); (G2) reduction of a carbonyl to a hydroxycarbon (-COH, i.e., alcohol); (G3) reduction of a carbonyl to an amine (-CNH3); and (G4) reduction of a hydroxycarbon to a hydrocarbon (-C-C-), which usually occurs via an ethylene intermediate (-C = C-). We note that this categorization corresponds to the treatment of carbon oxidation levels in standard organic chemistry textbooks [29].
We developed a quantum chemistry method for predicting the standard transformed redox potential of biochemical redox reactions. We explored a range of different model chemistries, including combinations of DFT (density functional theory) functionals or wave-function electronic structure methods, basis sets, choice of implicit solvent, and choice of dispersion correction. We found that a DFT approach that uses the double-hybrid functional B2PLYP [32,33] gave the highest prediction accuracy (see Methods for detailed model chemistry description; other model chemistries also gave high accuracy as discussed in the Supplementary Information and S1 Fig). As each biochemical compound represents an ensemble of different chemical species–each at a different protonation state [31] –we applied the following pipeline to predict E’m (Fig 1, see also Methods): (i) a quantum chemical simulation was used to obtain the electronic energies of the most abundant chemical species at pH 0; (ii) we then calculated the difference in electronic energies ΔEElectronic between the product and substrate of a redox pair at pH 0, thus obtaining estimates of the standard redox potential, Eo; (iii) next, we employed empirical pKa estimates to calculate the energetics of the deprotonated chemical species and used the extended Debye-Huckel equation and the Alberty-Legendre transform [31] to convert Eo to the standard transformed redox potential E’m at pH = 7 and ionic strength I = 0.25 M (as recommended [34]), where reactant concentrations are standardized to 1 mM to better approximate the physiological concentrations of metabolites [1,35]. Finally, (iv) to correct for systematic errors, the predicted E’m values, of each oxidoreductase group, were calibrated by linear regression (two-parameter calibration) against a set of 105 experimentally measured potentials obtained from the NIST Thermodynamics of Enzyme-Catalyzed Reactions database (TECRDB) [30] and the Gibbs formation energy dataset of Robert Alberty [31] (Supplementary Information). We note that we observe empirically that the difference in electronic energies ΔEElectronic is strongly correlated with the Gibbs reaction energy ΔGr for these redox systems (S5 Fig) and so we estimate redox potentials using the former in order to reduce computational cost (see SI for details). We also note that the two-parameter calibration is needed mainly since we ignore vibrational enthalpies and entropies of the compounds (Supplementary Information).
As exemplified in Fig 2A and 2B and S2 Fig, the calibration by linear regression significantly improves the accuracy of our quantum chemistry predictions. As shown in Table 1, the predictions of quantum chemistry have a lower mean absolute error (MAE) than those of GCM for all reaction categories. (GCM has a higher Pearson correlation coefficient for category G1, but this is an artifact introduced by a single outlier value, S3 Fig). The improved accuracy is especially noteworthy as our quantum chemical approach derives reduction potentials from first principles and requires only two calibration parameters per oxidoreductase group (α and β in Fig 1E), as compared to GCM which uses 5–13 parameters while achieving lower prediction accuracy (Table 1). Therefore, our quantum chemistry approach can be extended to predict reduction potentials for a wide domain of redox reactions since it does not depend as heavily on empirical measurements. While the quantum chemistry method is computationally more expensive than GCM–with a cost that scales with the number of electrons per molecule (Supplementary Information)–it can still predict the potentials for several hundreds of reactions when run on a typical high-performance computing cluster.
Inconsistencies between our predictions and experimental measurements can be used to identify potentially erroneous experimental values. However, as such discrepancies might stem from false predictions, we used an independent method to estimate redox potentials. We reasoned that consistent deviation from two very different prediction approaches should be regarded as indicative of potential experimental error. The second prediction approach we used is based on reaction fingerprints [38], where the structure of the reactants involved is encoded as a binary vector (166 parameters without regularization, Supplementary Information). These binary vectors are then used as variables in a regularized regression to correlate structure against a physicochemical property of interest, such as redox potential [38,39]. This approach is similar to the group contribution method (GCM) in that it is based on a structural decomposition of compounds; however, unlike GCM, fingerprints encode a more detailed structural representation of the compounds.
To detect potentially erroneous experimental measurements, we focused on redox potentials of category G2 (carbonyl to hydroxycarbon reduction) as we have abundant experimental information for this oxidoreductase group (see S4 Fig for results with the other categories). As shown in Fig 2C, we normalized the prediction errors by computing their associated z-scores (indicating how many standard deviations a prediction error is from the mean error across all reactions). Two redox reactions stand out as having significantly different experimental and predicted values for both methods (Z>2): indolepyruvate reduction to indolelactate (indolelactate dehydrogenase, EC 1.1.1.110) and succinate semialdehyde reduction to 4-hydroxybutanoate (succinate semialdehyde reductase, 1.1.1.61).
We suggest an explanation for the observed deviation of the first reaction: in the experimental study, the K’eq of indolelactate dehydrogenase was measured using absorbance at 340 nm as an indicator of the concentration of NADH [40]. However, since indolic compounds also have strong absorption at 340 nm [41], this method probably resulted in an overestimation of the concentration of NADH, and thus an underestimation of K’eq. Indeed, the experimentally derived E’m is considerably lower (-400 mV) than the predicted one (-190 mV, via quantum chemistry). With regards to the second reaction, succinate semialdehyde reductase, we note that re-measuring its redox potential is of considerable significance as it plays a central role both in carbon fixation–e.g., the 3-hydroxypropionate-4-hydroxybutyrate cycle and the dicarboxylate-4-hydroxybutyrate cycle [11] –as well as in production of key commodities–e.g., biosynthesis of 1,4-butanediol [42].
We used the calibrated quantum chemistry model to predict redox potentials for a database of natural and non-natural redox reactions. We generated this dataset by identifying pairs of metabolites from KEGG [7,8] that fit the chemical transformations associated with each of the four different oxidoreductase groups (Methods). We considered only compounds with fewer than 7 carbon atoms, thus generating a dataset consisting of 652 reactions: 83 reductions of category G1; 205 reductions of category G2; 104 reductions of category G3; and 260 reductions of category G4 (Supplementary Dataset 1). Some of these redox pairs are known to participate in enzyme-catalyzed reactions while others are hypothetical transformations that could potentially be performed by engineered enzymes. We note that our approach to generate reactions is similar to that of the comprehensive Atlas of Biochemistry [43], but we focus solely on the four redox transformations of interest.
Fig 3A shows the distribution of all predicted redox potentials at pH = 7, I = 0.25 M and reactant concentrations of 1 mM, i.e., E’m [14,36]. Fig 3 demonstrates that the value of E’m is directly related to the oxidation state of the functional group being reduced. The general trend is that “the rich get richer” [1,44,45]: more reduced functional groups have a greater tendency to accept electrons, i.e., have higher reduction potentials. Specifically, the reduction potential of hydroxycarbons (G4, <E′m> = −15 mV) is higher than that of carbonyls (<E′m> = −225 mV for both G2 and G3) and the reduction potential of carbonyls is higher than that of un-activated carboxylic acids (G1, <E′m> = −550 mV). Categories G2 and G3 (reduction of carbonyls to hydroxycarbons or amines, respectively) have very similar potentials because the oxidation state of the functional groups involved is identical (note that this holds for the physiological E′m but not for E′o because reactions in the G3 category are balanced with an ammonia molecule as a substrate, thus introducing a factor of RTln(10−3) when converting to the mM standard state). For category G1, activation of carboxylic acids significantly increases their reduction potential (orange line in Fig 3) as the energy released by the hydrolysis of the phosphoanhydride or thioester (~50kJ/mol) activates the reduction: ΔE=50nF≅250mV (n being the number of electrons, F the Faraday constant).
The quantum chemical predictions further enable us to explore detailed structure-energy relationships within each of the general oxidoreductase groups. To exemplify this we focus on the G2 category, as shown in Fig 4. While we find no significant difference between the average E’m of aldehydes and ketones, we can clearly see that the identity of functional groups adjacent to the carbonyl has a significant effect on E’m, as expected. Alpha ketoacids and dicarbonyls have a significantly higher E’m than alpha hydroxy-carbonyls (Δ <E′m> ≅ 20 mV,p < 0.005) and carbonyls adjacent to hydrocarbons (Δ <E′m> ≅ 35 mV,p < 0.0005). Carbonyls next to double bonds or aromatic rings have a significantly lower E’m values than alpha hydroxy-carbonyls and carbonyls that are next to hydrocarbons (Δ <E′m> ≅ −50 mV, and Δ <E′m> ≅ −40 mV respectively, p < 0.0001). Lactones (cyclic esters), have redox potentials that are significantly lower than any other subgroup within the G2 category. As another validation of the predicted potentials, we found that the reduction potentials of open-chain sugars are significantly higher than those of closed-ring sugars that undergo ring opening upon reduction, where Δ <E′m> ≅ 60 mV (p < 10−5). This is consistent with the known thermodynamics of closed-ring sugar conformations, e.g., the Keq of arabinose ring opening is ~350[46], which translates to ΔE=RTln(350)nF≅75mV, close to the observed average potential difference between the subgroups (R is the gas constant, and T the temperature).
While myriad natural electron carriers are known to support cellular redox reactions, NAD(P) has the prime role in almost all organisms, participating in most (>50%) known redox reactions [7,8]. The standard redox potential of NAD(P) is ~ -330 mV (pH = 7, I = 0.25), but as [NADPH]/[NADP] can be higher than 50 and [NADH]/[NAD] can be lower than 1/500, the physiological range of the NAD(P) reduction potential is between -380 mV and -250 mV [35,47–51]. Most cellular redox reactions are therefore constrained to a limited reduction potential range determined by the physicochemical properties and physiological concentrations of NAD(P). By examining the fundamental trends of redox potentials of the different oxidoreductase groups we will show that NAD(P) is well-matched to the redox transformations most commonly found in cellular metabolism.
Fig 3 demonstrates that the reduction potentials of activated acids (activated G1) and carbonyls (G2 and G3) are very similar, such that NAD(P) can support both the oxidation and reduction of nearly all redox couples in these classes. Although the distributions associated with these redox reactions are not entirely contained in the NAD(P) reduction potential range (marked in grey), the reduction potential of a redox pair can be altered by modulating the concentrations of the oxidized and reduced species. As the concentrations of metabolites usually lie between 1 μM and 10 mM [1,4,35,52], the reduction potential of a redox pair can be offset from its standard value by up to ±RTln(104)nF≅±120mV (assuming two electrons are transferred). Therefore, NAD(P) can support reversible redox reactions of compound pairs with E’m as low as −380 − 120 = −500 mV and as high as −250 + 120 = −130 mV (indicated by the light grey regions in Fig 3), a range that encompasses almost all activated acids (activated G1) and carbonyls (G2 and G3 reactions). Outside this range, however, NAD(P)(H) can only be used in one direction of the redox transformation–either oxidation or reduction, but not both. Fig 3 shows that NAD(P)H can support irreversible reductions of hydroxycarbons to hydrocarbons and NAD(P) supports irreversible oxidation of carbonyls to carboxylic acids.
Next, we focus on a small set of redox reactions found in the extended central metabolic network that is shared by almost all organisms: (i) The TCA cycle, operating in the oxidative or reductive direction [53], as a cycle or as a fork [54], being complete or incomplete [54], or with some local bypasses (e.g., [55]); (ii) glycolysis and gluconeogenesis, whether via the EMP or ED pathway [56], having fully, semi or non-phosphorylated intermediates [57]; (iii) the pentose phosphate cycle, working in the oxidative, reductive or neutral direction; and (iv) biosynthesis of amino-acids, nucleobases and fatty acids. As schematically shown in Fig 5, and listed in Supplementary Dataset S2, the ≈ 60 redox reactions that participate in the extended central metabolism almost exclusively belong to one of the following groups: (i) reduction of an activated carboxylic acid to a carbonyl or the reverse reaction oxidizing the carbonyl (9 reactions, G1); (ii) reduction of a carbonyl to a hydroxycarbon or its reverse oxidation (20 reactions, G2); (iii) reduction of a carbonyl to an amine or its reverse oxidation (18 reactions, G3); (iv) irreversible oxidation of carbonyls to un-activated carboxylic acids (5 reactions, G1 in the direction of oxidation); and (v) irreversible reduction of hydroxycarbon to hydrocarbons (4 reactions, G4). Only two central metabolic reactions (marked in magenta background in Fig 5) oxidize hydrocarbons to hydroxycarbons (G4, in the direction of oxidation) and require a reduction potential higher than that of NAD(P): oxidation of succinate to fumarate and oxidation of dihydroorotate to orotate (While formally being oxidation of hydrocarbon to hydroxycarbon, the oxidations of prephenate to 4-hydroxyphenylpyruvate and of arogenate to tyrosine present a special case since they create a highly stable aromatic ring and hence have enough energy to donate their electrons directly to NAD(P)). Similarly, the extended central metabolic network does not demand the low reduction potential required for the reduction of un-activated carboxylic acids (G1).
The reduction potential range associated with NAD(P) therefore perfectly matches the vast majority of reversible redox reactions in extended central metabolism–i.e., reduction of activated carboxylic acids and reduction of carbonyls (orange, purple and blue distributions in Fig 3)–and can also support the common irreversible redox transformations of extended central metabolism–i.e., reduction of hydroxycarbons and oxidation of carbonyls to un-activated carboxylic acids (green and red distributions in Fig 3). Cells typically rely on secondary redox carriers like quinones and ferredoxins (Fig 3, S1 Table), to support less common reactions, i.e., oxidation of hydrocarbons and reduction of un-activated carboxylic acids.
Why is the reduction potential of NAD(P) lower than the E’m of most carbonyls (Fig 3)? As biosynthesis of an NAD(P) derivative with higher reduction potential presents no major challenge [58], why does this lower potential persist? We suggest that this redox offset plays an important role in reducing the concentrations of cellular carbonyls by making their reduction to hydroxycarbons favorable. It is well known that carbonyls are reactive towards macromolecules, as they spontaneously cross-link proteins, inactivate enzymes and mutagenize DNA [59,60]. As the reduction potential of NAD(P) is lower than most carbonyls, the redox reactions in category G2 (or G3) prefer the direction of reduction, thus ensuring that carbonyls are kept at lower concentrations than their corresponding hydroxycarbons (or amines). Assuming a value of E′ = −330 mV for NAD(P) and taking the average E’m of the G2 reactions (<E′m> ≅ −225 mV) results in an estimated equilibrium concentration ratio [hydroxycarbon][carbonyl]=exp(−(E’[NAD(P)]−<E’m>)nFRT)≅3500, thus ensuring very low levels of the carbonyl species. While we do not have many measurements to confirm this prediction, we note one central example: in E. coli, the concentration of oxaloacetate is 1–4 μM [61], while the concentration of its conjugated hydroxyacid, malate, is 2–3 mM [52].
For ketoacids and open-ring sugars (which are especially reactive due to the free carbonyl) this effect is even more pronounced as both have especially high reduction potentials (Fig 4). Indeed, the reduction potential of ketoacids is so high that the reverse, oxidative reaction is usually supported by electron donors with a higher potential than NAD(P), for example, quinones, flavins, and even O2 (e.g., lactate oxidase, glycolate oxidase). Interestingly, the reactions of category G2 that are supported by known enzymes in the KEGG database (75% of reactions in this category) have significantly lower E’m than the remaining reactions, which are not known to be catalyzed by natural enzymes (Δ <E′m> ≅ 20 mV,p < 0.005). As such, we suggest that the G2 transformations that are known to be enzyme-catalyzed are mainly those that are amenable to redox coupling with NAD(P) (Fig 4D). Within the subset of G2 transformations found in KEGG, those that use redox cofactors other than NAD(P) (such as cytochromes, FAD, O2, or quinones) have higher E’m values (Δ <E′m> ≅ 20 mV, not significant p = 0.03) than those that use NAD(P) (Fig 4).
Finally, we note that the reduction potential of NADP and activated carboxylic acids (activated G1) overlap almost completely, such that we would not expect NAD(P) to have a strong effect on the ratio between the concentrations of carbonyls and activated acids. This is to be expected as both carbonyls and activated carboxylic acids are reactive–e.g., acetylphosphate and glycerate bisphosphate acetylates proteins spontaneously [62] and acyl-CoA’s S-acetylates cellular peptides non-enzymatically [63]. As such, there is no sense in driving the accumulation of carbonyls at the expense of activated carboxylic acids or vice-versa–neither approach would ameliorate non-specific toxicity.
In this work, we present a novel approach for predicting the thermodynamics of biochemical redox reactions. Our approach differs radically from group contribution methods, which rely on a large set of arbitrarily-defined functional groups, assume no energetic interactions between groups, and are restricted to metabolites that are decomposable into the groups spanned by the model. In contrast, quantum chemistry directly takes into account the electronic structure of metabolites in solution.
Focusing on specific examples highlights the strengths of our quantum chemical approach as well as various weaknesses of GCM. For example, we find several reactions where the GCM predictions are obviously inaccurate as they are too high to be reasonable: 2-Hydroxy-5-methylquinone ⇔ 2,4,5-Trihydroxytoluene (GCM: E′m = 543 mV, QC: E′o = −158 mV); 2-Pyrone-4,6-dicarboxylate ⇔ 2-Hydroxy-2-hydropyrone-4,6-dicarboxylate (GCM: E′m = 1406 mV, QC: E′o = −375 mV); and Mevaldate ⇔ (R)-Mevalonate (GCM: E′m = 132 mV, QC: E′o = −190 mV). Close inspection of the group matrix underlying these estimates reveals errors in the decomposition of the compounds. Failures in the GCM decomposition are likely due to the complexity of molecular representations in the standard INCHI format [64] and usually occur with aromatic and delocalized electrons. This reflects challenges inherent in group decomposition, which are avoided when using the quantum chemistry approach.
A more illuminating example is that of 3-dehydroshikimate ⇔ shikimate (shikimate dehydrogenase), the sole redox reaction in the shikimate pathway, converting erythrose 4-phosphate and PEP into chorismate. (Chorismate is required for the biosynthesis of aromatic amino-acids, folates, quinones, and important secondary metabolites [65]). GCM predicts a value of E′m = −85 mV, which, if correct, indicates that the reduction of 3-dehydroshikimate with NAD(P)H is irreversible. On the other hand, quantum chemistry methods predict E′m = −268 mV, which corresponds to a 6 order-of-magnitude equilibrium concentration difference with respect to the GCM value. The quantum chemistry prediction thus implies reversibility of the oxidoreductase reaction with NAD(P)H. As oxidation of shikimate by NAD(P) has been shown to occur in-vivo in gram positive bacteria [66], it is clear that the GCM prediction is wrong and that the quantum chemistry approach provides a more accurate assessment of the thermodynamic potential of this important biochemical reaction.
Unlike previous efforts [67,68], our quantum chemistry approach relies on a two-parameter calibration for each oxidoreductase reaction category, which reduces computational cost by avoiding the need to calculate vibrational enthalpies and entropies (Supplementary Information). In future studies, improvements in accuracy could be achieved by exploring a larger space of quantum model chemistries, or—if more experimental data becomes available—calibrating using more sophisticated regression techniques, such as Gaussian Process regression [69]. Yet, as we have shown, the current procedure is sufficient to yield high coverage and accuracy at a reasonable computational cost.
In contrast with GCM methods, our calibration parameters can be at least partially interpreted. One important contribution to the systematic bias in the raw quantum calculations (i.e. the y-intercept in the linear regression) comes from neglecting the vibrational component of the molecular enthalpy. Interpreting the slope parameter is more complex, yet examples in the literature show that it can be traced back to the choice of solvation [70] or—in the context of modeling quinone derivatives–to the basis set incompleteness and the shortcomings of the DFT exchange correlation functionals [71]. We note, however, that faster computational resources will eventually enable full ab initio prediction of hundreds of standard transformed redox potentials, rendering the two-parameter calibration and the use of empirical pKa values obsolete [15,72].
Importantly, the quantum chemical strategy is not subject to the inconsistencies that plague experimental databases. Experimental values are measured in a wide range of different conditions, including temperature, pH, ionic strength, buffers, and electrolytes. In many cases, the exact measurement conditions are not reported, making it practically impossible to account for these factors. Thus, even if we were to gain access to more experimental data, the lack of systematically applied conditions makes such resources problematic. In contrast, quantum chemical simulations can be performed in consistent, well-defined conditions.
Why does the primary biological reduction potential range lie between -370 mV and -250 mV? One possibility is a frozen evolutionary accident. In this view, NAD(P) was available early in evolution and was found useful in supporting multiple redox reactions; as such, it was fixed as the central redox carrier before the Last Universal Common Ancestor (LUCA). While we cannot rule out this explanation, we suggest an alternative: that the primary reduction potential range represents a near optimal adaptation given biochemical constraints and selection pressures imposed throughout evolution. This idea is supported by the fact that most extant electron carriers already existed in LUCA [6], and yet none have as extensive a role in metabolism as NAD(P). Furthermore, derivatives of NAD(P) are simple to synthesize biochemically–e.g. deamino-NAD is a precursor of NAD–and can have considerably shifted reduction potentials [58]. Despite this, no organism has been found to rely on such derivatives. Finally, the deaza-flavin coenzyme F420 is a prominent electron carrier in the central metabolism of methanogens and other prokaryotes [73,74], and has a reduction potential around -340 mV [75], almost identical to that of NAD(P). Hence, even organisms that partially replace NAD(P) use a carrier with a similar reduction potential.
The enhanced resolution provided by quantum chemistry uncovers important patterns not accessible using traditional analyses. Exemplifying this, we found that the main cellular electron carrier, NAD(P), is ‘tuned’ to reduce the concentration of reactive carbonyls, thereby keeping the cellular environment more chemically stable. Yet, this protection comes at a price: the oxidation of hydroxycarbons is thermodynamically challenging and often requires the use of electron carriers with higher reduction potential. A recent study demonstrates the physiological relevance of this thermodynamic barrier: the NAD-dependent 3-phosphoglycerate dehydrogenase–the first enzyme in the serine biosynthesis route–can sustain high flux in spite of its unfavorable thermodynamics only through coupling with the favorable reduction of 2-ketoglutarate [76].
Our analysis further supports the previous assertion that the TCA cycle has evolved in the reductive direction [53,77]. While all the other electron transfer reactions in the extended central metabolism belongs to oxidoreductase groups that can be supported by NAD(P)(H), oxidation of succinate–a key TCA cycle reaction–cannot be carried by this electron carrier. As the reverse reaction, i.e., fumarate reduction, can be support by NADH [78,79], it is reasonable to speculate that the reaction first evolved in the reductive direction, and only later was adapted to work in the oxidative direction using an alternative cofactor.
So long as sufficient experimental data is available to allow for calibration, our approach can be extended to other types of biochemical reactions. For example, understanding the thermodynamics of carboxylating and decarboxylating enzymes–the “biochemical gateways” connecting the inorganic and the organic world– could pave the way for the identification of highly efficient, thermodynamically favorable carbon fixation pathways based on non-standard but promising reaction chemistries [80,81]. In this way, high-resolution thermodynamic analyses may provide much needed insight for the engineering of microbes to address global challenges.
We performed quantum chemical simulations—geometry optimizations followed by electronic single point energy (SPE) calculations—on the major species (MS) of each metabolite of interest at pH = 0, which corresponds to the most positively charged species (see below and SI for details on the model chemistries used). Running calculations on these reference protonation states yields estimates for the standard redox potential, Eo(major species at pH = 0). Using pKa values from the ChemAxon calculator plugin (Marvin 17.7.0, 2017, ChemAxon)—a cheminformatics software widely used in the field of biochemical thermodynamics [4,14,36,67,82–85] - the extended Debye-Huckel equation and the Alberty-Legendre transform, we converted Eo(MS at pH = 0) to the standard (standardized to 1 mM) transformed redox potential of interest, E′m(pH = 7,I = 0.25) [31,34]. To correct for systematic errors in both the quantum chemical predictions and the pKa estimates, we calibrate the resulting E′m(pH = 7,I = 0.25) values against experimental data using linear regression, performing a separate calibration for each of the four different redox reaction categories. We further detail each of these steps below (see also Supplementary Information).
For each metabolite, we generated ten initial geometric conformations using ChemAxon’s calculator plugin (Marvin 17.7.0, 2017, ChemAxon). Quantum chemistry calculations were performed using the Orca software package (version 3.0.3) [86]. Geometry optimizations were carried out using DFT, with the B3LYP functional and Orca’s predefined DefBas-2 basis set (see S3 Table for detailed basis set description). The COSMO implicit solvent model [87] was used, with the default parameter values of epsilon = 80.4 and refrac = 1.33. DFT-D3 dispersion correction [88] using Becke-Johnson damping [89] was also included.
Single point energy (SPE) calculations yield the value of the electronic energy EElectronic for each conformer at their optimized geometry. We used the optimized geometries obtained using DFT as inputs for SPE calculations (see below and SI for details on the SPE model chemistry selected). Substrate and product conformers were sampled according to a Boltzmann distribution. By taking the difference of products’ and substrates’ EElectronic values, we obtain ΔEElectronic, which we treat as directly proportional to the standard reduction potential of the major species at pH 0:
Eo(MSatpH=0)∼−ΔEElectronicnF
The use of ΔEElectronic to approximate the reduction potential as opposed to ΔGro (which includes rotational and vibrational enthalpies and entropies) reduces computational cost and is motivated by the empirical observation that there is a strong correlation between ΔEElectronic and ΔGro for these redox systems (S5 Fig, see SI for details). We note that we subtracted the energy of molecular hydrogen (obtained with the same SPE model chemistry) from ΔEElectronic in order to get redox the potentials relative to the standard hydrogen electrode. A similar approach has been used to model redox reactions in the context of organic redox flow batteries [28].
We use cheminformatic pKa estimates (Marvin 17.7.0, 2017, ChemAxon), the extended Debye-Huckel equation and the Alberty-Legendre transform (16, 17) to convert both the experimental standard redox potentials and the quantum chemical predictions of Eo(MS at pH = 0) to the transformed redox potentials standardized to 1 mM, E′m(pH = 7,I = 0.25). Then, independently for each redox category, we performed linear regressions between the E′m(pH = 7,I = 0.25) values and the available experimental redox potentials. The calibration via linear regression was implemented using the SciKit learn Python library.
In order to optimize prediction accuracy, we ran geometry optimization and SPE calculations using a large diversity of model chemistries, generated by selecting one of ten possible DFT functionals, two wave function electronic structure methods, three possible basis sets, the option of adding implicit solvation, as well as a correction to account for dispersion interactions (S1 Fig, and see SI for details). Optimizing for Pearson correlation coefficients r, we selected the following model chemistry to predict reactions without experimentally measured potentials: a DFT approach with the double-hybrid functional B2PLYP [32,33], the DefBas-5 Orca basis set (see S3 Table for detailed basis set description), COSMO implicit solvent [87], and D3 dispersion correction [88]. To avoid overfitting, we trained the model chemistry optimization procedure on the experimental data for the G3 reaction category (carbonyl to amine reduction), and validate its accuracy on the rest of the oxidoreductase reaction categories (Table 1 and S3 Fig). Hybrid and double-hybrid DFT functionals have been shown to accurately capture the thermochemistry and noncovalent interactions of molecules when compared with coupled cluster results [90,91]. Therefore, we select this double-hybrid DFT approach covers the relevant physics of our problem while minimizing computational cost and maximizing predictive power. Although we explored a large set of DFT functionals, wave function methods, and basis sets, further improvements could be achieved by exploring a larger space of model chemistries, including the geometry optimization procedure, conformer generation method, as well as explicit solvation models [15]. For example, adapting a recent highly accurate method (tested on four molecules) based on the Linear Response Approximation (LRA) to the large scale prediction of E’m values would be an interesting direction [72].
We used the RDKit software tool (http://www.rdkit.org), to obtain binary molecular fingerprints of each compound of interest. Because of the relatively small size of our training sets and in order to minimize overfitting, we used MACCS Key 166 fingerprints instead of other popular Morgan circular fingerprints [92]. We concatenated each redox half-reaction substrate/product fingerprint pair into a single reaction fingerprint [38] and used these as input training data for regularized linear regression. We then performed an independent regularized regression for each of the four different redox reaction categories.
To obtain group contribution estimates of redox potentials, we use the group matrix and the group energies of Noor et al. [36] used in eQuilibrator [37], an online thermodynamics calculator. We note that eQuilibrator uses the component contribution method (CCM) which combines group contribution energies with experimental reaction or formation Gibbs energies (“reactant contributions”) whenever these are available. That is, for reactions with available experimental data, eQuilibrator will return the experimental energies. Thus, for fair comparison against quantum chemistry we used the GCM code underneath eQuilibrator to obtain the group contribution estimates for all reactions in our test set. Just like the quantum chemical predictions, the GCM estimates were standardized to the E'm(pH = 7, I = 0.25) state.
We design a strategy to detect reactions with potentially erroneous experimental values as listed in the NIST Thermodynamics of Enzyme-Catalyzed Reactions Database (TECRDB) [30]. We identify reactions whose predicted potential deviates from experiment by a similar amount for both the calibrated quantum chemistry and fingerprint-based modeling approaches. In order to make the errors associated to the two different modeling methods comparable, we normalize the prediction errors by computing their associated z-scores: ZErr=(Err−μ)σ. We set a threshold value for the z-score of Z = 2, such that reactions with ZErr(QC) > 2 and ZErr(fingerprints) > 2 are assigned a high likelihood of having an erroneously tabulated experimental value in NIST-TECRDB.
To generate a database of all possible redox reactions involving natural compounds, we use a decomposition of all metabolites into functional groups as per the group contribution method [36]. We find pairs of metabolites in the KEGG database with functional group vectors whose difference matches the reaction signature of any of the redox reaction categories of interest. For example, pairs of metabolites in the G1 category will have a group difference vector with a +1/-1 in the element corresponding to a carbonyl/carboxylic acid functional group respectively (see SI for details). We note that every reaction generated by this strategy can be uniquely assigned to one of the four redox categories considered.
Using this method we succeeded in generating a rough database of redox reactions. However, additional manual and semi-automated data cleansing was required to get the final version of the database (see SI for further details). For example, use of the group difference vectors failed to account for the chirality of the metabolites, and in some instances stereochemistry was not maintained throughout the reaction. In order to solve this, we applied an additional filter, which used the conventions for assigning chirality (R/S, L/D) present in molecule names to match chirality between the substrate and product. Sugars proved to be especially problematic as those reactions did not maintain stereochemistry throughout; for these reactions, the above filtering method did not suffice, often keeping incorrect reactions such as L-Xylonate → L-Arabinose. For this, we used molecular naming conventions to eliminate the wrong reactions (see SI for further details).
We performed Welch’s unequal variance t-test to obtain the p-value for the null hypothesis that pairs of different reaction subcategories within group G2 have identical average E’m values (Fig 4). Welch’s t-test is an adaptation of Student’s t-test which does not assume equal variances.
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10.1371/journal.pcbi.1005574 | Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection | Fundamental to the function of nervous systems is the ability to reorganize to cope with changing sensory input. Although well-studied in single neurons, how such adaptive versatility manifests in the collective population dynamics and function of cerebral cortex remains unknown. Here we measured population neural activity with microelectrode arrays in turtle visual cortex while visually stimulating the retina. First, we found that, following the onset of stimulation, adaptation tunes the collective population dynamics towards a special regime with scale-free spatiotemporal activity, after an initial large-scale transient response. Concurrently, we observed an adaptive tradeoff between two important aspects of population coding–sensory detection and discrimination. As adaptation tuned the cortex toward scale-free dynamics, stimulus discrimination was enhanced, while stimulus detection was reduced. Finally, we used a network-level computational model to show that short-term synaptic depression was sufficient to mechanistically explain our experimental results. In the model, scale-free dynamics emerge only when the model operates near a special regime called criticality. Together our model and experimental results suggest unanticipated functional benefits and costs of adaptation near criticality in visual cortex.
| The cerebral cortex is versatile; depending on changes in behavioral context, the same neural circuit can exhibit dramatically different neural activity and perform different functions. A long-standing hypothesis at the interface of physics and neuroscience posits that such shifts in cortical operation are governed by the same basic principles as those governing phase transitions in certain physical systems. Importantly, this theory predicts changes in information processing as the system changes phases. Here we present experiments on the visual system of turtles, which are consistent with these theoretical predictions. As the system adapts to changes in visual input, we found that cortical dynamics shift towards a scale-free regime, as predicted at the critical point of a phase transition. At the same time, this shift in dynamical regime incurs a tradeoff between sensory detection and discrimination.
| Depending on behavioral and environmental context, the same neural circuits can perform different functions. Two essential functions performed by sensory cortices are stimulus detection and discrimination [1]. The ability to detect the presence or absence of certain stimuli is crucial when seeking or avoiding aspects of our environment (e.g. food, mates, predators), whereas the ability to discriminate among the finer details of sensory input is necessary in many other contexts. Can detection and discrimination be performed simultaneously by the same cortical network or do the two functions require different properties from the underlying circuit?
An important scenario in which this question arises is in visual cortex during adaptation to the onset of a strong visual stimulus [1–3]. In this context, we reframe our question (Fig 1); how do adaptive changes in the cortical network alter the ability of these circuits to detect and discriminate stimuli? Are detection and discrimination better during the transient response or after adaptation has reached a steady-state? Most traditional coding studies do not answer these questions because they have been based on brief (non-adapted) stimuli or, if they considered sustained stimuli, they focused only the steady-state, avoiding the transient response. Studies of the rat somatosensory system suggest that there is a tradeoff [4–7]; as discrimination improves during adaptation, detection worsens, but this remains debated in visual cortex [1,8,9]. In computational models, discrimination can improve or worsen depending on the details of the adaptation mechanisms [10]. Here we test the tradeoff hypothesis in visual cortex of turtles.
To further introduce the tradeoff hypothesis, we compare to some alternative scenarios. One possibility is that stimulus detection and discrimination could vary jointly, both getting worse as adaptation progresses (covarying scenario). In contrast, the tradeoff scenario posits that good discrimination comes at the cost of degraded detection; the two properties change oppositely during adaptation. In both scenarios, detection is most effective during the intense transient response at stimulus onset and decreases as adaptation progresses and response attenuates. How discrimination changes during adaptation is less obvious. In the covarying scenario, the onset response is most informative (for example [9,11,12])–best for discrimination–perhaps because synaptic depression during adaptation lowers spike rates and lowers response information capacity, thereby degrading discrimination. In contrast, the tradeoff scenario supposes that a sufficiently intense onset response can limit information capacity due to saturation and excessive correlations, while adaptation tunes the network into a regime better suited to discrimination [13–15]. Alternative scenarios, intermediate between the two extremes described here, could also exist; for example, discrimination might remain unchanged throughout adaptation (for example [12]).
A separate line of research also predicts the tradeoff hypothesis. Recent experiments [16] and theory [17] suggest that adaptation can tune cortex to a special regime of network dynamics called ‘criticality’. At criticality, network dynamics manifest as diverse spatiotemporal patterns of population activity, governed by specific statistical scaling laws [16,18,19]. For example, the sizes of population activation events are distributed according to a power-law, without a dominant spatiotemporal scale. Throughout this manuscript, we will refer to such power-law distributed population activity as ‘scale-free’. Scale-free dynamics and other features predicted at criticality were previously shown to emerge during adaptation, but were not present during the intense transient response following stimulus onset [16]. Importantly, cortical slice experiments [20] and theory [21–23] suggest that when a network operates near criticality, it is optimal for stimulus discrimination, although these studies did not address adaptation. Similarly, Fisher information is predicted to peak at criticality [24]. Thus, consistent with the tradeoff hypothesis, these studies predict the emergence of criticality and enhancement of discrimination during adaptation, but this prediction has yet to be tested.
Here our study was designed with two goals (Fig 1). First, we aimed to determine whether there is an adaptive trade-off between detection and discrimination or whether the two functions co-vary. Second, we sought to link adaptive changes in these two functions to changes in cortical network dynamics. We found that changes in network dynamics were consistent with adaptation tuning the cortex to a steady state near criticality after a transient response that was far from criticality. Discrimination was higher when the network was near criticality, while detection was higher during the non-critical transient. Thus, our results confirm the tradeoff hypothesis in visual cortex and associate a functional tradeoff with changes in cortical state near criticality.
To investigate adaptive changes in dynamics and function of cortical networks, we employed a visual stimulation paradigm that entails strong adaptation in the visual system. We suddenly switched from no visual input (darkness) to a movie projected onto the retina of the ex vivo turtle eye-attached whole-brain preparation [16,25] (Fig 2A). Using a microelectrode array we recorded local field potential (LFP) and multi-unit activity (MUA) from visual cortex (Fig 2B). We observed intense neural response during a brief transient following movie onset and attenuated response as the system adapted towards a steady-state during continued movie stimulation (Fig 2C). In the following sections, we first show how collective population dynamics change during adaptation, based on analysis of LFP. Then, we delineate adaptive changes in visual detection and discrimination, based on both LFP and MUA. Next, we show that these adaptive changes in function are not the same in all animals and that variability in population dynamics is correlated with variability in function. Finally, using a network-level computational model, we demonstrate that short-term synaptic depression provides a simple explanation of our experimental findings.
To characterize adaptive changes in cortical network dynamics, we examined spatiotemporal bouts of large amplitude, correlated LFP fluctuations, called neuronal avalanches [16,20,21,26,27]. Briefly, a neuronal avalanche was defined as a temporally correlated cluster of LFP peaks, often occurring on many electrodes (Fig 2B, Methods). Avalanche size was defined as the number of constituent LFP peaks (Fig 2B). After repeating the visual stimulation paradigm many times (n = 80), we combined all avalanches that occurred during transient periods (0–1 s after stimulus onset) into one group and, in a separate group, we combined all avalanches that occurred in a later period (2–5 s after stimulus onset), labeled the ‘baseline’ period in Fig 2D. Comparing probability distributions of avalanche sizes from the transient versus the baseline provided concise and quantitative characterizations of how cortical network dynamics change during adaptation.
During the transient period, we found that avalanche size distributions were bimodal in shape, indicating the tendency for large avalanches often spanning the entire recording area (Fig 2E). In contrast, as the visual cortex adapted towards a steady-state regime, avalanche sizes became more diverse with a scale-free size distribution, i.e. the size distribution became power-law in shape (Fig 2E). Using statistically rigorous methods [16], we found that avalanche sizes during the baseline period were power-law distributed (significance parameter q = 0.2 ± 0.26, see Methods), with exponents -1.8 ± 0.3, consistent with previous findings [16]. Importantly, such scale-free avalanche distributions are expected when a network operates at criticality, while bimodal distributions are inconsistent with criticality. Thus, in line with previous work [16], our results are consistent with the conclusion that the onset of stimulation drives the visual cortex into a state far from criticality and adaptation tunes the system towards criticality.
One interesting implication of this finding is that the spatiotemporal changes in visual input during the movie could also cause moment-to-moment deviations from scale-free dynamics. Indeed, the onset of the movie is simply a particularly intense change in visual input. Deviations from scale-free dynamics during the movie could be ‘averaged out’ when considering the entire baseline period (2–5 s after stimulus onset). Therefore, to test this possibility, we examined avalanche size distributions during a shorter time period well after the transient (4–5 s after stimulus onset), labeled ‘steady-state’ in Fig 2D. For both the steady-state periods and the transient periods, we computed the deviation δ from the baseline avalanche size distribution, based on summed differences between cumulative distributions, similar to a Kolmogorov-Smirnov statistic (Fig 2F; Methods). As expected, the deviation δ was large during the transient (0.32 ± 0.12, mean ± SD) and near zero in the steady-state (-0.02 ± 0.04; Fig 2F). However, there was substantial brain-to-brain variability in δ for the steady-state period.
To interpret the meaning of this variability in δ for the steady-state period, we note that the steady-state time period was deliberately chosen to overlap with the baseline time period. Thus, the steady-state distribution is based on a subset of all the avalanches that occur during the full baseline period. A non-zero deviation δ in the steady-state indicates a brief excursion from the time-averaged statistics of the baseline period. Experiments with positive values of steady-state δ suggest a brief excursion towards large-scale dynamics like those observed during the transient. Experiments with negative values of δ may indicate an excursion towards small-scale dynamics, consistent with a somewhat subcritical regime, in which large avalanches are relatively rare (further discussion of this possibility is in the model results below). In the following section, we will show that these variations in network dynamics revealed by δ are correlated with variations in how visual cortex processes input.
Next we sought to relate the adaptive changes in network dynamics described above to changes in how the cortex encodes the sensory input. We focused on two important aspects of cortical coding, stimulus detection and stimulus discrimination (Fig 1).
First, we measured adaptive changes in detection. We took the perspective of an ideal observer and asked to what extent the presence of the movie was detected based on the LFP cortical population response. We answered this question first based on activity recorded during the transient and then based on activity during the steady state (Fig 3A). To quantify stimulus detection, we calculated the mutual information I(R;S) of visual stimulus and neural response, adjusted for low sample count bias (see Methods). The stimulus set S was binary, consisting of many repetitions (n = 80) of two possible stimuli–movie off or movie on. The response set R was defined as the LFP peak count during a 1 s period while the movie was off or on. The ‘movie on’ response was taken from either the transient period or the steady state period, thus allowing us to compare the efficacy of detection during these two different stages of adaptation. We found that stimulus detection was high during the transient period (0.9 ± 0.1 bits) and typically reduced during the adapted and critical steady state (0.7 ± 0.2 bits; Fig 3B).
Next, we measured adaptive changes in discrimination. Here, our goal was to go beyond the rather coarse and simple detection considered above and assess how more detailed information about the visual input is represented in the cortex. To meet this goal, we modified the visual stimulation paradigm described above. We presented the same movie stimulus many times (n = 80), but now with four different ‘foreground’ stimuli, each presented 20 times in pseudorandom order, superimposed on the ‘background’ movie. The foreground stimulus was a red dot with four different levels of ‘redness’ ranging from gray to bright red (Fig 3A). This foreground red dot was presented either during the transient immediately following movie onset or later during the steady state. As with detection, we quantified discrimination using the mutual information I(R;S) of stimulus and response. However, the stimulus set S was different, now representing the four possible foreground red dot stimuli. Response was defined as the LFP peak count during a 1 s period immediately following the red dot presentation (Fig 3A). Stimulus discrimination was low during the transient period (-0.01 ± 0.1 bits) and higher in the adapted and critical steady state (0.2 ± 0.3 bits; Fig 3C).
Next we performed the same detection and discrimination analysis, but using MUA spike counts instead of LFP peak counts to define response. First we note that, in general, MUA spike rate is strongly correlated with LFP peak rate (Pearson’s ρ = 0.8 ± 0.2, p<10−4, p value based on permutation distributions, Fig 4A). Next, we computed detection and, like the LFP-based results, we observed that detection is strong in the transient response (0.8 ± 0.2 bits) and relatively weak in the steady state response (0.2 ± 0.2 bits) (Fig 4B). Likewise, discrimination improved from 0.001 ± 0.1 bits during the transient to 0.1 ± 0.2 bits during steady state (Fig 4C).
Thus, we conclude that, in visual cortex, there is an adaptive tradeoff between stimulus detection and discrimination during adaptation; detection drops from an initially high level while discrimination improves. To test whether these findings generalized beyond stimuli with naturalistic spatiotemporal structure, we also considered adaptation following the onset of a static gray screen, instead of a movie. The conclusions were largely the same as for the movie stimuli (Figs 3D, 3E, 4B and 4C).
In the preceding results, we have shown that both network dynamics and function evolve systematically over the time course of adaptation. The correlation between network dynamics and function becomes more apparent when we plot detection versus δ (Fig 3D) and discrimination versus δ (Fig 3E). Discrimination was anticorrelated with δ (for LFP ρ = -0.6, p<10−5; for MUA ρ = -0.38, p<0.004) and detection was correlated with δ (for LFP ρ = 0.6, p<10−5; for MUA ρ = 0.8, p<10−13). More interesting, we found that, even within the steady state period alone, variability in network dynamics, i.e. variability in δ, could explain a significant amount of the variability in discrimination (for LFP ρ = -0.6, p<0.02, Fig 3E; for MUA ρ = -0.56, p<0.03, Fig 4C). Such brain-to-brain variability in discrimination at the same time point following movie onset (4–5 s after movie onset) highlights the fact that the same movie background stimulus caused different neural response in different brains. Thus, presumably, differing degrees of adaptation were present in different brains. Moreover, the fact that stimulus discrimination improved with decreasing δ values, including into the δ < 0 regime, suggests that discrimination of foreground stimuli is optimal when network dynamics deviate from scale-free, towards the small-scale. In the context of the criticality hypothesis, this would indicate that slightly subcritical dynamics are better for discrimination than criticality.
In the above analysis, detection and discrimination were calculated assuming a rate based code (LFP peak rate in a fixed time window). Previous studies highlight the possibility that temporal or spatial patterns can encode information in addition to that encoded in rates [28–31]. Therefore, we next sought to determine if other forms of coding also exhibit an adaptive tradeoff between detection and discrimination. We tested this possibility for a temporal code and a spatial pattern code. First, we computed discrimination based on a 6-bit spatial pattern of response (normalized to remove dependence on activity rate.) Each bit represented a different, spatially contiguous group of electrodes in the matrix array. We found that spatial pattern of response did not carry significant information for discrimination of the foreground red dot stimuli. However, we next tested the temporal pattern of response and found adaptive improvement of discrimination based on such temporal coding. We defined the temporal response to a single stimulus to be a 5-bit binary ‘word’ corresponding to a sequence of 5 consecutive time bins, each 0.2 s in duration, following the onset of the foreground red dot (Fig 5A and 5B). Each bit was set to 1 if its LFP peak count (summed over all electrodes) was higher than the mean across the 5 time bins (counts were also normalized by variance across time). By this definition, the temporal pattern of response is normalized to emphasize temporal aspects of the response rather than the rate coding considered above (but may not be entirely independent of rate as discussed elsewhere [31]). We computed mutual information between this 5-bit temporal response and the stimulus (foreground red dot levels), corrected for low sample bias as done with the rate coding discrimination presented above. We found that the temporal pattern of response often did carry information about the stimulus, but only during the steady-state (0.15±0.17). During the transient response, temporal information was minimal (-0.01±0.06, Fig 5C). Moreover, temporal discrimination was significantly anticorrelated with changes in network state as measured by δ (ρ = -0.45, p<0.005). We note that when unstructured, static background stimulation (gray screen) was used instead of the movie background, adaptive improvement of temporal discrimination was less prominent and only observed in a minority of turtles. Comparing among the 9 turtles with significant discrimination during the steady-state, 6 had higher rate-based discrimination compared with timing-based discrimination. To determine if rate and timing were redundant or carried different information, we next computed discrimination using both rate and timing responses (joint mutual information). We found that this joint-discrimination was close to the individual values of rate-discrimination and timing-discrimination (difference of 0.04 ± 0.14 bits), which suggests that the rate and timing information was rather redundant. In contrast, if the rate and timing information was not redundant, i.e. coded different aspects of the stimulus, we would expect the joint-discrimination to be closer to the sum of the rate-discrimination and timing-discrimination. This difference was 0.36±0.24 bits. In summary, adaptive improvements of discrimination were most obvious when considering a rate code, but generalize beyond a simple rate code and apply to temporal population coding as well.
We also considered whether temporal response pattern is better for detection during the transient response than in the steady-state, as we found for rate-based response. First, compared to rate based detection, we found that detection based on temporal response was poor (but far from zero). This is perhaps not surprising since, during darkness, there is no expectation for temporally structured activity to encode the lack of stimulus. Nonetheless, there was a small, but significant decrease in detection from the transient to the steady state. Detection using temporal response was 0.42 ± 0.14 bits during the transient and 0.26 ± 0.17 bits during the steady state (Fig 5E and 5F).
What biophysical mechanisms could explain our observations of a trade-off between detection and discrimination as a network adapts from large-scale transient response towards scale-free dynamics (Figs 3 and 4)? Previous studies suggest that short-term synaptic depression is sufficient to explain adaptation towards criticality, where scale-free dynamics are expected [16,17]. Can such a simple mechanism also account for the observed trade-off between detection and discrimination? We investigated this possibility in a model network of excitatory and inhibitory probabilistic integrate-and-fire neurons with all-to-all connectivity and short-term synaptic depression [16] (Methods). To mimic the experimental visual stimuli (movie onset), the model was subjected to an abrupt increase in input rate followed by a slowly varying input rate (Fig 6A). This process was repeated 80 times. As in the experiments, we examined three time periods during the time course of adaptation following the onset in background stimulation–the transient, baseline, and steady state (Fig 6B). As found experimentally (Fig 2E), extremely large avalanches were common during the transient period (Fig 6B). Avalanches decreased in size as synapses depressed (Fig 6C). Specifically, the avalanche size distribution during the transient period displayed a distinct bimodal character, while a power-law distribution emerged as adaptation progressed into the baseline period (Fig 6D), consistent with the hypothesis that synaptic depression can tune the cortex towards criticality.
Deviations from the baseline power-law were large during the transient (δ = 0.18 ± 0.1). The steady state test period exhibited much smaller deviations from the baseline power-law (δ = 0.004 ± 0.06). Thus, the effects of synaptic depression on the model network dynamics matched well the adaptive changes in network dynamics we observed in our experiments (Fig 2F). We next investigated the impact of synaptic depression on detection and discrimination in the model. We focused on a rate code, since the majority of discrimination information in the experimental data was carried by a rate code.
Mirroring the experimental approach, we asked to what extent the presence of the elevated external input (Fig 6E) was detectable based on the simulated network spiking (Fig 6F) and how this detection was affected by synaptic depression. Detection was computed the same way as in the experimental data analysis presented in (Figs 3 and 4), with response defined in terms of spike count. For all networks and trials tested, stimulus detection was high (0.57 ± 0.19 bits) during the transient period at stimulus onset and significantly reduced (0.17 ± 0.10 bits) during the adapted and critical steady state. In this respect, the model reproduced the adaptive changes in stimulus detection in turtle visual cortex.
The model also successfully reproduced the dynamics of stimulus discrimination we observed experimentally. We asked to what extent four levels of a brief increase in external input rate (foreground stimulus) on top of the high external input rate (background stimulus) were discriminated based on the simulated network spiking (Fig 6E and 6F). For all networks and trials tested, stimulus discrimination was low (0.25 ± 0.10 bits) during the transient period at stimulus onset and higher (0.46 ± 0.11 bits) during the adapted and critical steady state. Thus, we conclude that short-term synaptic depression is sufficient to reproduce our experimentally observed tradeoff between detection and discrimination.
Our experiments exhibited substantial brain-to-brain variation in discrimination and detection (Figs 3B, 3C, 4B and 4C). This functional variability was partially explained by accounting for variability in the network state (the δ measure) (Figs 3D, 4E, 4B and 4C). As discussed above, one possible explanation of these variations in network state and function is that the same visual stimulus could result in a different time course of population response in different animals, and therefore, a different time course of adaptation. To test this idea in our model, we systematically varied the time course of the slowly varying component of the background stimulus (Fig 6A). We temporally shifted the peaks and valleys of the background stimulus with respect to the foreground stimulation onset and computed δ during the same transient and steady state time periods. This resulted in a range of δ from approximately 0 to 0.4 for the transient period and from approximately -0.1 to +0.1 in the steady state period. As found experimentally, discrimination in the steady state time period was anticorrelated with δ. Discrimination was highest when the system exhibited nearly scale-free dynamics, but slightly shifted towards small-scale (δ < 0) (Fig 6H), consistent with a slightly subcritical state.
These observations raise a question: if we push the cortex further towards small-scale dynamics (further subcritical) does discrimination continue to improve, or does discrimination drop in more extreme regimes? We used our model to answer this question (Fig 7). We made depression stronger (100 times stronger than the results in Fig 6) and the model steady state displayed extremely subcritical network dynamics, strongly deviating from scale-free avalanche sizes (Fig 7D). For such extreme synaptic depression, stimulus discrimination declined (Fig 7E). Together with our experiments (Figs 3E and 4C) and other recent model studies [22,32], this is consistent with the hypothesis that there is an optimal network state for stimulus discrimination near criticality, but slightly subcritical.
Cortical neural circuits are computationally versatile, capable of functionally reorganizing themselves to accommodate changes in sensory input. How two specific functions–stimulus detection and discrimination–emerge from the collective dynamics of the same neural circuit has remained an important and unanswered question [4–7]. Here, we found, using experiments and computational modeling, that adaptation to a change in sensory input incurred a shift in cortical dynamics and a trade-off between visual detection and discrimination. Visually-driven cortical dynamics shifted from an initially intense large-scale response to a more moderate, scale-free network dynamics, consistent with recent findings that adaptation tunes visual cortex towards criticality [16]. Concurrently, we found that adaptation towards scale-free dynamics coincided with enhancement of stimulus discrimination at the expense of decreased stimulus detection. Our simulations of a model network demonstrated that short-term synaptic depression can provide a mechanistic explanation for the computational trade-off during network adaptation towards criticality.
Our study offers an answer to an important, but typically overlooked, question. How do adaptive changes at the synaptic or single-neuron level impact network-level dynamics? It is well known that changing single neuron properties [33] or altering synaptic interactions [34] can result in dramatic changes in the collective dynamics of the cortical network. Moreover, many aspects of cortical function are sensitive to such changes in cortical state [33–35]. Given that adaptation can alter single neuron properties and synapses, it is important to determine how adaptation impacts cortical network dynamics. Traditional theoretical studies have focused on how adaptation impacts detection and discrimination at the single neuron, or single synapse level. Some of these studies even predicted a tradeoff between detection and discrimination, e.g. [36]. However, these studies left open the possibility that the observed neuronal or synaptic changes entail changes in large-scale network dynamics which, in turn, feed back and disrupt the conclusions found at the small scales. For example, most theoretical single neuron studies assume a certain level of background noise to account for the network input to the neuron. The assumption that such background noise is independent of single-neuron adaptive processes could lead to erroneous conclusions if adaptation changes the nature of the background network dynamics. Our results suggest that by tuning visual cortex towards a scale-free dynamical regime, adaptation facilitates improvements in discrimination.
Previous experiments [4–7] on mammalian somatosensory cortex observed a trade-off between detection and discrimination, similar to that we found in turtle visual cortex. This commonality in function is remarkable given the differences in structure between mammalian neocortex and the ancestral cortex of reptiles. For instance, turtle visual cortex is comprised of three layers similar to mammalian piriform cortex and hippocampus [37–39]. Together with studies of mammalian brains, our results suggest that the detection-discrimination trade-off either evolved independently for turtles and mammals or has been evolutionarily preserved for hundreds of millions of years with origins as early as the emergence of amniotes.
A simple interpretation for why the detection-discrimination trade-off might occur arises from considering some basic ethological aspects of how we interact with the environment. When a new object or feature of the environment is first encountered, the first job of our visual system is to detect its presence. A likely second step is to examine and discriminate the finer details of the newly-detected thing. The utility of this temporal sequence—detection followed by discrimination–suggests an explanation for our findings in the turtle visual cortex as well as similar findings in mammalian somatosensory cortex [4–7]. Moreover, our results suggest that this detection-discrimination sequence is facilitated by the flexibility in cortical state that comes of operating near criticality.
Although our measurements were done in cortex, it is also likely that retinal and thalamic adaptation contribute to our results. Our model suggests that short-term depression among thalamocortical and corticocortical synapses may be sufficient to explain our results, but future experiments are required to determine the relative importance of adaptation in different parts of the visual system. Indeed, detection and discrimination may relate differently at different stages of the visual system.
Our modeling focused on synaptic depression as the relevant adaptive mechanism to parsimoniously explain our results. However, other adaptive mechanisms could also play roles. Indeed, synaptic facilitation could result in a strong transient response similar to that we observed [40]. Differing time courses of excitatory and inhibitory response to thalamocortical input could also be relevant [41,42]. Further study is required to pinpoint which possible mechanisms contribute to our observations.
Our study focused on the adaptation that follows the onset of a sensory stimulus. However, it is also well established (at least in mammals) that ongoing activity without sensory input can manifest as bouts of intense network activity (e.g. up-states [43] and neuronal avalanches [26]). Our results suggest that adaptive changes during such internal activity may also incur a trade-off between discrimination and detection. This trade-off may help to explain how response to sensory input is modulated by ongoing activity [43–45].
We designed our visual stimulus to cause a very clear and reliable time course of adaptation—an abrupt transition from no stimulus to a rather intense, dynamic movie. However, in reality, such an intense change is rarely part of naturalistic visual input. More realistically, the visual system is constantly receiving input; the “movie” does not turn off until we close our eyes. Thus, for realistic visual input it may be unusual for detection and discrimination to reach the extremes we observed during the transient onset response. Nonetheless, our results do have practical implications for the countless experimental studies in which a visual stimulus is presented following a black screen. For sustained natural visual input, our results suggest that the visual cortex spends most of its time near a scale-free dynamical regime, like the visually-driven steady state period we discuss above.
Our work focused on two specific aspects of sensory function—detection and discrimination. However, the computational repertoire of sensory cortex is certainly broader than just detection and discrimination. Different functions may require the underlying cortical network to be tuned differently. For instance, sensory dynamic range has been shown to be maximized at criticality [34,46,47]. Other functions such as oscillatory binding and information transmission across cortical regions [48,49] may benefit from synchronous (perhaps supercritical) dynamics, while object representation has been suggested to benefit from asynchronous (perhaps subcritical) dynamics [22]. Our results demonstrate a clear case of switching between competing computational properties depending on context. We expect future studies to build upon our findings to obtain a more comprehensive understanding of the computational versatility of the cortex.
All procedures were approved by Washington University’s and University of Arkansas’ Institutional Animal Care and Use Committees and conform to the guidelines of the National Institutes of Health on the Care and Use of Laboratory Animals. Animal use protocol numbers were (20150248 for Wash U, and 16041 for U Ark) Adult red-eared turtles (n = 14, Trachemys scripta elegans, 150–200 g weight, 12–15 cm carapace length) were studied. Following anesthesia (Propofol 10 mg/kg) and decapitation, we surgically removed the brain, optic nerves, and eyes, from the cranium [16,50]. One eye was hemisected and drained, thus exposing the retina for visual stimulation; the other eye was removed. Two cuts allowed the cortex to be unfolded, exposing the ventricular surface, thus facilitating the subsequent insertion of the microelectrode array. The eye and the brain were continuously perfused with artificial cerebrospinal fluid (in mM; 85 NaCl, 2 KCl, 2 MgCl2, 45 Na HCO3, 20 D glucose, and 3 CaCl2 bubbled with 95% O2 and 5% CO2), adjusted to pH 7.4 at room temperature. Recordings began 2–3 hrs after induction of anesthesia.
Using a micromanipulator (Sutter, MP-285), we inserted a microelectrode array into the visual cortex. The array was comprised of a three dimensional grid of electrodes (4x4x8 grid, 16 shanks, 8 electrodes per shank, 300 μm inter shank spacing, 100 μm interelectrode spacing on each shank, Neuronexus). We analyzed data from every other electrode along each shank to avoid sampling redundant LFP. This included 48 electrodes in total. The array was inserted into visual cortex to a depth such that electrodes spanned the cortex from the ventricular to the dorsal surface. We recorded wideband (0.7 Hz– 15 kHz) extracellular voltages relative to a silver chloride pellet electrode in the bath at 30 kHz sample rate (Blackrock Microsystems, Cerebus). With post-processing filtering we extracted local field potential (LFP, band-pass 5–100 Hz) and multi-unit activity (MUA, band-pass 100–1000 Hz). MUA spike times were defined as the times of negative peaks that surpassed a -3 SD threshold.
Visual stimuli were created by a computer and delivered with a miniature video projector (Aaxa Technologies, P4X Pico Projector). The projector image was focused onto the retina with additional lenses (Fig 2a). The mean light intensity (irradiance) at the retina was 1 W/m2.
For our stimulus detection measurements, the stimulation consisted of a transition from darkness to one of two types of grayscale movie (5 s in duration). One was the first 5 s of a ‘motion-enhanced’ movie [51], the other was a spatiotemporally phase-shuffled version of this movie. We also tested a transition from darkness to a static, uniform gray visual field.
For stimulus discrimination, the same movies were considered as “background” stimulation, upon which we added 1 of 4 different foreground stimuli. The foreground stimuli were a circular patch (dot) of uniform color ranging from gray to red in linear steps in RGB space. The dot spanned ¼ of the visual field centered in the middle of the visual field. The dot was presented for 30 ms either during the transient period (300 ms after background onset) or during the steady state period (4 s after background onset).
The first step of avalanche detection was to compute the standard deviation of every LFP trace. Next we defined an ‘LFP peak’ as a period of time during which an LFP trace fluctuates beyond 3 standard deviations, due to either a positive or negative deflection (Fig 2b). For each LFP peak, we determined the time of its extreme value and the identity of the channel on which it was recorded. An avalanche was defined as a spatiotemporal cluster of consecutive LFP peaks with inter-peak intervals not exceeding a temporal threshold ΔT (channel information does not play a role in avalanche definition). For each turtle, ΔT was chosen to be the average inter-peak interval (<IPI>, inverse of population LFP peak rate), which was 19.3 ± 7.3 ms. The size of an avalanche was defined as the number of LFP peaks comprising the avalanche. Avalanches were grouped and analyzed separately depending on whether they occurred during the transient period or visually-driven baseline or steady state periods.
For avalanche size distributions in the baseline period, we used previously developed maximum likelihood fitting methods [16] to fit a truncated power law (truncated at both the head and tail). The fitting function was f(S)=S−τ(∑x=x0xMx−τ)−1, where the maximum size xM was assumed to be the largest observed avalanches size. The minimum size x0 and the exponent τ were fitting parameters. Minimum values for x0 were tried increasing from 0, but only up to the point when the fitted power law matches the data well enough to have a Kolmogorov-Smirnov statistic KS<1/Nsamp, where Nsamp is the number of avalanches comprising the dataset as established in previous work [16].
After finding the best-fit power law, the next step was to assess goodness-of-fit q [27,52]. We compared the experimental data to 1000 surrogate data sets drawn from the best-fit power law distribution with the same number of samples as the experimental data set. The deviation between the surrogate data sets and a perfect power law was quantified with the KS statistic. The quality q of the power law fit was defined as the fraction of these surrogate KS statistics which were greater than the KS statistic for the experimental data. A very conservative criterion for judging the data to be power law distributed is q>0.1. This is demonstrated visually in Fig 2e by plotting the experimental distribution over a green outlined region which delineates the 5–95 percentiles of the surrogate data sets.
We characterized changes in network dynamics with the measure δ. For the transient period δ was defined as the deviation between the baseline avalanche size distribution and the distribution of avalanches that occurred during the first second following stimulus onset. Similarly, for the “steady-state” period, δ is the deviation between the baseline distribution and the distribution of avalanches that occurred between 4–5 s following stimulus onset. To compute δ, we followed previously developed methods [16,47], first recasting the two compared distributions as cumulative distribution functions (CDF). Next, we took the mean of 10 differences between the two compared CDFs. The 10 differences were spaced logarithmically between minimum and maximum observed avalanche sizes. Thus, the range of possible δ is -1 to 1, with negative δ indicating a tendency for small-scale dynamics, positive δ indicating a tendency for large-scale dynamics, and δ = 0 indicating scale-free dynamics. In contrast, a Kolmogorov-Smirnov statistic is defined as the absolute value of the single largest difference between two cumulative distributions.
The mutual information calculations used to quantify detection and discrimination were adjusted to correct for potential finite sampling bias. We followed well established, non-parametric methods [53,54], sometimes referred to as ‘bootstrap correction’ [55]. More specifically, the naïve uncorrected mutual information was reduced by subtracting a surrogate ‘noise’ mutual information I(R;Sshuff) obtained by recomputing mutual information with the order of stimuli randomized. I(R;Sshuff) was computed for 100 independent randomizations of the stimuli order; error bars in Figs 3 and 4 reflect variability across these 100 randomizations. When the true mutual information is high, this correction scheme provides a conservative estimate, with a slight bias towards underestimating the true mutual information (S1 Text). However, the correction is less biased and particularly important when the true mutual information is low (S1 Text). Our primary conclusions–different discrimination and detection during the transient response compared with the steady-state response–would likely be even stronger if we were able to do more repetitions of the stimuli. To further mitigate low sampling bias, we also reduced the dimensionality of LFP and MUA response by categorizing each response into one of four bins defined by the following bin edges: 0, R20 + ΔR/4, R20 + ΔR/2, R20 + 3ΔR/4, Rmax. Here R20 is defined as the 20th quantile of all responses for a given time period (e.g. for all transient responses or all steady state responses), ΔR is the differences between the 80th and 20th quantiles, and Rmax is the largest response.
N = 1000 all-to-all connected binary neurons received input from outside the network. The ‘strength’ of the synapse from neuron j onto neuron i at time t is determined by the corresponding element of the synaptic weight matrix Wij(t). 20% of neurons are inhibitory, i.e. with negative entries in the weight matrix. Ωi(t) is the strength of the input synapse onto neuron i (all excitatory). The binary state si(t+1) of neuron i (s = 0 inactive, s = 1 spiking) is determined probabilistically based on the sum p(t+1) of its inputs p(t+1)=Ωi(t)σi(t)+∑j=1NWij(t)sj(t). If 0 < p < 1, then the neuron fires with probability p. If p ≥1, then the neuron fires with probability 1. If p ≤ 0, then the neuron does not fire. Time is discrete and state updates are synchronous. The input σi(t) from the ith input synapse is binary (1 with probability r(t)). The dynanmics of r(t), define the stimulus in the model as discussed further below. The update rules for synaptic dynamics are
Wij(t+1)=Wij(t)+τr−1(Wijo−Wij(t))−τd−1Wij(t)sj(t)
Ωi(t+1)=Ωi(t)+τr−1(Ωio−Ωi(t))−τd−1Ωi(t)σi(t).
The default weight matrix Wijo was constructed such that its largest eigenvalue Λ0 has absolute value equal to 1.03 (this results in synaptic strengths near 1.03/N on average) [16,56]. Default input synaptic strengths Ωio were 0.02. Synapses depress with a time constant of τd = 40 time steps following a presynaptic spike and recover exponentially with a time constant of τr = 400 time steps. These parameter choices were made based on previous work with this model [16] and with the goal of reproducing our experimental results.
To model the background stimulus used for studying detection, the onset of stimulation is modelled as a step increase from a constant low level (r = 5x10-4) to a higher level that slowly fluctuates as r(t) = 0.1 +0.075 sin(0.0075t + 2πφ), where φ was varied across trials (in linear steps from 0 on trial 1, to 1 on trial 80). This background stimulus is delivered to half of the neurons. To model the brief foreground stimulus used to study discrimination, we set r(t) = 0.05, 0.1, 0.2 or 0.4 (to mimic the 4 levels of foreground stimulus (red dots) in the experiments) for 30 model time steps for all neurons before returning to the background stimulus.
In the model, an avalanche is initiated by external input. Upon reaching a time step with no active neurons, the avalanche is considered to be ended. We simulated 80 trials of step increase in input for detection and 80 trials for discrimination (20 for each foreground stimulus). Each trial consisted of 2500 time steps (1 time step can be interpreted to be approximately 1 ms). The background stimulus onset was at time 500. The foreground stimulus was presented between times 530 and 560 (transient) or between times 2000 and 2030 (steady state).
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10.1371/journal.pgen.1008039 | Loss of atrx cooperates with p53-deficiency to promote the development of sarcomas and other malignancies | The SWI/SNF-family chromatin remodeling protein ATRX is a tumor suppressor in sarcomas, gliomas and other malignancies. Its loss of function facilitates the alternative lengthening of telomeres (ALT) pathway in tumor cells, while it also affects Polycomb repressive complex 2 (PRC2) silencing of its target genes. To further define the role of inactivating ATRX mutations in carcinogenesis, we knocked out atrx in our previously reported p53/nf1-deficient zebrafish line that develops malignant peripheral nerve sheath tumors and gliomas. Complete inactivation of atrx using CRISPR/Cas9 was lethal in developing fish and resulted in an alpha-thalassemia-like phenotype including reduced alpha-globin expression. In p53/nf1-deficient zebrafish neither peripheral nerve sheath tumors nor gliomas showed accelerated onset in atrx+/- fish, but these fish developed various tumors that were not observed in their atrx+/+ siblings, including epithelioid sarcoma, angiosarcoma, undifferentiated pleomorphic sarcoma and rare types of carcinoma. These cancer types are included in the AACR Genie database of human tumors associated with mutant ATRX, indicating that our zebrafish model reliably mimics a role for ATRX-loss in the early pathogenesis of these human cancer types. RNA-seq of p53/nf1- and p53/nf1/atrx-deficient tumors revealed that down-regulation of telomerase accompanied ALT-mediated lengthening of the telomeres in atrx-mutant samples. Moreover, inactivating mutations in atrx disturbed PRC2-target gene silencing, indicating a connection between ATRX loss and PRC2 dysfunction in cancer development.
| Somatic mutations in genes coding for epigenetic regulators such as ATRX are found across a diverse group of cancer types, suggesting their broad relevance in tumor induction and progression. However, tumors that have been linked to these chromatin remodelers can arise in many different molecular and cellular contexts, requiring studies with new experimental models to understand the extent and mechanisms of tumor development mediated by these regulatory proteins. Thus, we analyzed the tumor suppressive role of atrx in zebrafish that already harbored inactivating mutations of p53 and nf1. Homozygous deletion of atrx was lethal in developing fish, whereas the partial loss of this gene (atrx+/-) within the p53/nf1-deficient background led to a diverse spectrum of tumors not observed in animals that were wildtype for atrx, including epithelioid sarcoma, angiosarcoma, and rare carcinomas. Most of the cancer types we identified correspond to human tumors in the ATRX-mutant tumor sample cohort within the AACR Genie database, attesting to the relevance of our findings to human cancer. Further analysis revealed downregulation of telomerase during the lengthening of the telomeres through the ALT pathway, and disturbed function of the polycomb repressive complex 2 as key mechanistic components underlying atrx-linked tumorigenesis. These results demonstrate how a p53/nf1 compromised genetic background combined with ATRX haploinsufficiency leads to a broad spectrum of sarcomas and carcinomas associated with loss of this chromatin modulator.
| The alpha thalassemia/mental retardation syndrome X-linked (ATRX) protein is involved in the epigenetic regulation of gene expression. It is classified as a SWI/SNF-family chromatin remodeling factor due to its ATP-dependent helicase domain. In humans, germline loss of ATRX function causes mental retardation and alpha thalassemia that is associated with reduced alpha globin expression levels, lower blood-oxygen levels and hypochromia, anisocytosis, and poikilocytosis of red blood cells [1–5]. Because the ATRX gene is located on the X-chromosome in humans, females can carry a mutant allele heterozygously, without developing symptoms. Loss of ATRX leads to reduced levels of histone 3.3 (H3.3) incorporation, telomere destabilization and increased homologous recombination facilitating the development of ALT. ATRX binds to the death domain-associated protein 6 (DAXX) and recognizes H3K9me3 marks with its cysteine-rich domain termed ADD (ATRX-DNMT3-DNMT3L) [6]. The ATRX/DAXX-mediated deposition of H3.3 maintains the condensed heterochromatic state [7,8].
ATRX also guides the Polycomb repressive complex 2 (PRC2) to its targets for gene silencing by tri-methylation of histone 3 lysine 27 (H3K27me3) [9], a repressive epigenetic mark established by PRC2 [10]. This process is crucial for X-chromosome inactivation, mediated by the noncoding RNA XIST [11], which is expressed only from the to-be-silenced X-chromosome (Xi) and spreads along the Xi in cis [12]. ATRX binding to XIST is essential for PRC2 recruitment following XIST in cis along the Xi and also functions as an adaptor that affects PRC2 function beyond Xi inactivation. Upon ATRX knockdown in a human fibroblast cell line, PRC2 is unable to silence its target genes by deposition of the H3K27me3 mark at specific sites within the gene body. Instead, H3K27me3 is established at ectopic sites in the intergenic space and at non-canonical sites in the target genes, demonstrating the importance of ATRX for normal gene silencing by PRC2 [9].
Over the past 5 years, it has become apparent that mutations in epigenetic regulator genes are involved in the onset and progression of a large number of malignancies. The loss of ATRX in gliomas [13], neuroendocrine tumors [14] and various sarcoma types [15–19] facilitates alternative lengthening of the telomeres (ALT) and thereby stabilizes the genome of cancer cells during cancer development, a crucial step in the immortalization of cancer cells in general and a requirement for the formation of malignant tumors in humans [20]. There are two mutually exclusive mechanisms to elongate telomeres in tumor cells, i) telomerase (TERT) re-expression and ii) ALT activation. Individual tumor types differ in the frequency with which these mechanisms are activated [21]. For example, gliomas more frequently re-express TERT, but about 80% of ALT-positive pediatric high-grade gliomas are ATRX-deficient [13]. Comparable results demonstrating a strong association between ATRX deficiency and ALT were obtained in pancreatic neuroendocrine tumors (PanNETs) [22] and sarcomas [15–18]. ALT-positive cancer cell lines were recently found to preferentially have ATRX inactivation [23,24].
The mechanisms underlying ALT activation upon ATRX loss are not fully understood. It has been proposed that the heterochromatic state of the telomeres is disrupted when H3.3 can no longer be loaded by ATRX/DAXX, so that chromatin opens up and telomeres become more accessible [25,26]. Others have shown that ALT depends on telomere elongation by homologous recombination-mediated DNA replication (HR) [27]. It was has also been shown in a genetic mouse model of glioma that ATRX deficiency results in reduced activity of the non-homologous end joining pathway, which competes with HR in DNA repair [28]. This suggests that there may be increased HR-activity in ATRX-depleted cells, which further facilitates ALT.
Loss-of-function (lof) mutations in ATRX clearly promote the activity of ALT, which is an important step in establishing cellular immortality. However, the changes in tumor biology (other than activation of ALT) caused by ATRX loss are poorly understood. As ATRX loss is found in a variety of cancer types, we aimed to assess ATRX loss in combination with dysregulation of two of the most prevalent types of oncogenic pathways—the Ras pathway and the p53 pathway—to include a large spectrum of human cancers. Thus, we knocked out ATRX in the germline of the previously published p53- and nf1-deficient zebrafish model, in which mutants develop malignant peripheral nerve sheath tumors (MPNSTs) and high-grade gliomas [29]. Here we demonstrate that homozygous in activation of atrx leads to lethal alpha-thalassemia in zebrafish, while heterozygous loss of atrx in the context of combined p53/nf1-deficiency induces the onset of multiple histologic tumor types that are otherwise not observed in nf1/p53–deficient fish.
To create loss-of-function (lof) mutations in atrx in zebrafish germline, we induced frameshift-mutations in exon 4 of atrx using CRISPR/Cas9 (S1 Fig and S1 Table). This resulted in a truncated Atrx protein lacking both ADD and ATPase/helicase domains (Fig 1A) which represents a total loss of Atrx function. Mutant alleles were generated in both wildtype (strain AB) zebrafish and in the previously published nf1/p53-deficient zebrafish line that expresses the green fluorescent protein (GFP) marker under the control of the zebrafish sox10 promoter (sox10:GFP) [29].
After injection of gRNAs and Cas9 mRNA into one-cell-embryos, fish were raised to fertility and out-crossed with non-injected siblings. All 10 analyzed injected F0 fish of the p53/nf1-deficient line transmitted mutations into the F1 generation. When 25 F1-embryos derived from these F0 fish were examined for their genomic alterations at the target locus, we observed a frameshift-mutation rate of 40%, with 30% harboring deletions and 70% harboring insertions (S1 Table). Moreover, 48% of zebrafish exhibited the in-frame deletion of a specific triplet, two had other in-frame mutations (8%) and the single remaining fish (4%) had a 3 bp substitution. The most recurrent in-frame mutation deleted codon 98 residing 4–6 bases upstream of the PAM-sequence. It is known that certain gRNAs recurrently induce specific genomic alterations in knockout-approaches using CRISPR/Cas9. Thus, genome editing was highly efficient and the resulting genomic alterations are consistent with previous studies [30,31]. We chose two zebrafish lines with frameshift mutations in the p53/nf1-deficient background and one line in the wildtype background to conduct all experiments described in this manuscript (S1 Fig and S1 Table). All our here presented knockout lines carry frameshift mutations in atrx in germline and thus model a total loss of ATRX, as it is frequently observed in human tumors [13–19,32]. As ATRX is a tumor suppressor, our knockout recapitulates most closely the situation in broad carcinogenesis. To our knowledge, specific point mutations disturbing certain functions of ATRX or gain-of-function mutations are not described so far.
F1 fish of all three lines reached fertility at around three months of age, but never produced viable adult offspring with the atrx-/- genotype. Genotyping of developing embryos revealed that atrx-/- embryos of all lines died between 10 and 18 days post fertilization (dpf) (Fig 1B). Each of these embryos displayed a body curvature phenotype and a lack of the swim bladder. The embryonic lethality observed with the atrx-/- genotype is also observed in mice [33–35]. In zebrafish, this is also true for nf1 which is duplicated in fish, so that at least 1 out of 4 alleles of the two nf1-genes (either nf1a or nf1b) has to remain wildtype to enable normal development of the embryo [29].
Previous reports from human patients have shown that ATRX mutations are associated with reduced α-globin expression and lead to α-thalassemia myelodysplasia syndrome (ATMDS) [1–5]. To investigate the globin expression in atrx mutant zebrafish, we performed whole-mount in situ hybridization (WISH) using α-e1 and β-e1 globin probes. The results showed that the expression levels of α-e1 globin, but not β-e1 globin, were significantly reduced in atrx-/- homozygous mutants compared to atrx+/- heterozygous and wildtype siblings. This was observed during definitive erythropoiesis at 5 dpf, but not during primitive erythropoiesis at 22 hours post fertilization (hpf) (Fig 2A–2C), analogous to findings in humans [1]. Normal globin gene expression during primitive erythropoiesis likely reflects presence of maternal RNA or protein [36]. The expression levels of c-myb at both 36 hpf and 5 dpf did not show significant differences between atrx-/- homozygous mutants and wildtype embryos (S2A–S2C Fig), indicating that the development of hematopoietic stem/progenitor cells was not affected in atrx-/- homozygous mutants.
To study the effects of reduced globin expression on erythropoiesis, we bred the zebrafish atrx+/- mutant with the Tg(gata1:GFP) transgenic line [37]. In this line, GFP expression is driven by the zebrafish gata1 promoter, providing a useful marker to identify erythroid cells in the Tg(gata1:GFP) transgenic line. At both 7 dpf and 12 dpf, atrx-/- homozygous mutants showed a remarkable increase in GFP-expressing cells in the heart, kidney marrow and caudal hematopoietic tissue (CHT) compared to wildtype fish (Figs 2D and S2D), indicating the accumulation of erythroid progenitors in these regions. Furthermore, May-Grunwald-Giemsa (MGG) staining of peripheral blood smears from atrx-/- homozygous mutants showed that erythrocytes had an aberrant rounded shape compared with the characteristic flattened elliptical morphology observed in the wildtype fish at 7 dpf. Zebrafish atrx mRNA injection rescued the mutant phenotype (Fig 2E and 2F). The rounded erythrocytes indicate the presence of circulating erythroid progenitors in the atrx-/- homozygous mutants, reflecting a block in erythroid cell differentiation resulting from the lack of α-globin expression. Taken together, these data indicate that the zebrafish atrx knockout model closely resembles the phenotype of human thalassemia patients with ATRX mutation, showing reduced globin expression and the accumulation of erythroid progenitors [1].
Because ATRX is a tumor suppressor in many types of human cancers [13–19], we examined the oncogenic effects of haploinsufficiency for artx alone and in cooperation with the p53/nf1-deficient genetic background. Both, p53 and NF1 are known tumor suppressors in humans and are inactivated in various cancer types. Loss of NF1 removes a major source of GTPase-activation affecting RAS and thus prolongs and strengthens RAS-MAPK signaling, thus enhancing the proliferation and survival of tumor cells [38,39]. The loss of nf1 has previously been shown to synergize with p53 mutation in a zebrafish model of MPNSTs and high-grade gliomas [29]. In zebrafish, the nf1 gene is duplicated (nf1a and nf1b) resulting in four functional nf1 alleles. Since a complete loss of nf1 is lethal, we bred p53-/-;nf1b-/-;nf1a+/- fish, resulting in offspring with the p53-/-;nf1b-/-;nf1a+/- or p53-/-;nf1b-/-;nf1a+/+ genotypes. Zebrafish with both genotypes are prone to develop MPNSTs, but with much faster onset and increased penetrance in the nf1a+/- genotype fish [29]. Moreover, we have previously observed high grade glioma tumorigenesis in p53-/-;nf1b-/-;nf1a+/- fish arising with low penetrance. Using CRISPR/Cas9, we created the atrx+/- genotype in the p53-/-;nf1b-/-;nf1a+/- background. Since a total loss of both nf1 and atrx is lethal in development, in-cross of this line resulted in viable offspring with 4 genotypes, 1) p53-/-;nf1b-/-;nf1a+/-;atrx+/-, 2) p53-/-;nf1b-/-;nf1a+/+;atrx+/-, 3) p53-/-;nf1b-/-;nf1a+/-;atrx+/+, and 4) p53-/-;nf1b-/-;nf1a+/+;atrx+/+.
Fish of all these genotypes were carefully monitored for tumor onset. Both, atrx+/+ and artx+/- fish of this line developed visually identical tumors located in the eye, gill, head, tail and predominantly in the abdomen (Fig 3A). Surprisingly, tumor watch experiments revealed no differences in time of tumor onset and penetrance associated with the atrx+/- genotype (Fig 3B). However, histopathology analysis confirmed that 100% of the tumor bearing p53-/-;nf1b-/-;nf1a+/-;atrx+/+ and p53-/-;nf1b-/-;nf1a+/+;atrx+/+ control fish had MPNSTs (n = 21 and n = 14 respectively; Tables 1 and S2). In the atrx+/- siblings of both nf1a+/- and nf1a+/+ populations, MPNSTs were identified in between 83.3% and 97.1% of all tumor-bearing fish and were indistinguishable in histology from their atrx+/+ control counterparts (n = 34 and n = 12, respectively; S3A Fig and Tables 1 and S2). Because ATRX is known to influence PRC2-mediated gene silencing, we examined lysine 27 tri-methylation status of histone 3 (H3K27me3), which is an epigenetic modification associated with genes silenced by PRC2, and predicts a worse prognosis for patients when lost in MPNST tissue [40]. Immunofluorescence staining using an H3K27me3-specific antibody revealed that this epigenetic mark was clearly present in atrx+/- and atrx+/+ tumors in the p53/nf1-deficient background (S3B Fig). Thus, partial atrx loss did not appear to have an inhibitory effect on total H3K27me3 deposition.
Histopathologic analysis further revealed that 5 out of 34 (14.7%) analyzed tumor-bearing fish of the p53-/-;nf1b-/-;nf1a+/-;atrx+/- population had various tumor types other than or in addition to MPNSTs, including epithelioid sarcoma, biliary cancer, angiosarcoma, and an undifferentiated tumor of the eye with small round blue cell morphology (Table 1 and Fig 4). In the p53-/-;nf1b-/-;nf1a+/+;atrx+/- population this proportion was higher, with 3 out of 12 (25%) of zebrafish developing neoplasms consistent in histology with epithelioid sarcoma, undifferentiated pleomorphic sarcoma, or serous carcinoma of the ovary. These tumor types were never observed in atrx-wildtype siblings. It is noteworthy that many individual fish harbored more than one tumor site or type at the time of sacrifice. Epithelioid sarcomas were identified by the histologic appearance and positive cytokeratin (CK) staining of sections from paraffin embedded tumor tissue using the pan-CK antibody AE1/AE3 which is known to positively stain the vast majority of epithelioid sarcomas [41]. CK expression was also found in papillary serous carcinoma, undifferentiated pleomorphic sarcoma and biliary cancer (Fig 5). Faint positive staining for CK was detected in angiosarcoma tissue. All non-MPNST tumors in atrx+/- fish also stained strongly positive for the H3K27me3 mark (Fig 5). All detected sarcoma types other than MPNSTs are summarized below as soft tissue sarcomas.
In previous studies, ATRX was identified as a tumor suppressor gene involved in the pathogenesis of gliomas, sarcomas and neuroendocrine tumors [13–19]. Moreover, in the AACR Genie database, ATRX mutations are annotated in 49 cancer type categories (S3 Table), representing at least 45 distinct malignancies [32]. Within these, uterine sarcoma and glioma have the highest proportion of ATRX-mutant samples (19.60% and 16.74% respectively), whereas soft tissue sarcomas are ranked 15th with 6.42%. Moreover, the ATRX-mutation frequency in ovarian cancer is 2.65%, and 1.12% in hepatobiliary carcinoma. Thus, the tumor types observed in our model of combined p53/nf1/atrx-deficiency are faithfully reflected in the human ATRX-mutant tumor spectrum.
Soft tissue sarcomas and the other tumor types depicted in Fig 4 were not observed in the atrx-wildtype, p53/nf1-deficient controls or in the atrx+/-, p53/nf1-wildtype line. Thus, the atrx+/- genotype cooperated with the p53/nf1-deficient background in the development of these malignancies. Interestingly, p53/nf1/atrx-mutant fish developed various other tumor types regardless of whether two or three nf1 alleles were mutated (Tables 1 and S2), indicating atrx-deficiency synergizes with the combined loss of p53 rather than nf1. This cooperation between lof in p53 and atrx in our model is supported by previous data on the combined lof mutations in ATRX and p53 in high-grade gliomas [13] and leiomyosarcomas [19]. In the AACR Genie database, the overall p53 mutation rate considering all tumor samples analyzed is 38.64%, whereas among the ATRX-mutant samples 55.04% are co-mutants for p53 (Fig 6A) [32]. Gliomas, the tumors with the second highest prevalence of ATRX-mutation in the database (S3 Table) carry about twice as often a mutation in p53 if they have a mutation in ATRX (Fig 6A). In ATRX-mutant soft tissue sarcomas p53 mutations are observed almost 2.5 times as often as in the overall cohort. Together, these relationships indicate a selection advantage during the development of these particular tumor types for malignant clones with combined deficiencies in ATRX and p53. This does not extend as clearly to the loss of NF1 in soft tissue sarcomas, gliomas, and serous ovarian cancer (Fig 6B). However, when again all tumor samples were considered, similar results as for p53 were observed with 6.4% NF1-mutation rate in all tumor samples and 16.28% in the ATRX-mutated cohort. This indicates that both, loss of p53 and NF1, might cooperate with ATRX-deficiency in the right cancer type and genetic background. In hepatobiliary cancer in particular, there is a threefold higher incidence of nf1 mutations in atrx-mutant samples (Fig 6B). Thus loss of nf1b, and therefore RAS-MAPK pathway activation, appears to synergize with atrx deficiency in this cancer type. In tissues with predominate expression of nf1b compared to nf1a, the difference between loss of one allele or retention of both alleles could be very significant in terms of nf1 activity.
Despite the shift to more heterogeneous tumor types, we did not see an accelerated overall tumor onset in p53/nf1/atrx-deficient fish compared to p53/nf1-knockout control fish, and the proportion of MPNST-bearing fish decreased in the atrx+/- cohort. From this result, we conclude that atrx loss does not promote the pathogenesis of MPNSTs in our model. In the AACR Genie database ATRX mutations are annotated for 1 of 31 analyzed MPNSTs (3.23%), while 2 of 29 samples that were profiled for copy number alterations (CNAs) show a deletion of atrx (6.9%). However, CNAs can occur due to general genomic instability and do not have to be specific for a gene contained in a larger deletion. Thus, we cannot exclude the possibility that ATRX loss can contribute to MPNST malignancy in the right context (e.g. by supporting ALT), although it clearly it does not accelerate MPNST onset in the genetic background of our model. The observation of an undiminished H3K27me3 signal in MPNSTs upon heterozygous atrx knockout in our model fits well to the fact that patients with MPNSTs retaining the H3K27me3 mark have a better prognosis [42].
The soft tissue sarcomas and other tumor types detected in p53/nf1/atrx-deficient fish were never seen in atrx wildtype siblings and have not been described in previous genome-editing studies in the p53/nf1-deficient line [29,43]. Thus, we conclude that the onset of these tumors was caused by decreased ATRX activity associated with the atrx+/- genotype. To examine the effect of heterozygous loss of atrx on global gene expression we used mRNA isolated from zebrafish tumor tissue to compare the gene expression profiles of p53-/-;nf1b-/-;nf1a+/-;atrx+/- and p53-/-;nf1b-/-;nf1a+/-;atrx+/+ MPNSTs by RNA-seq with three biological replicates for each group. Strikingly, the telomerase encoding gene tert was significantly downregulated in atrx+/- tumor tissue (Figs 7A and S4 and S4 Table). Since ATRX loss in humans is associated with ALT, we used fluorescence in situ hybridization with a telomere-specific probe (TelC-FISH) to visualize the telomeres of atrx+/- and atrx+/+ tumors in our line. In this experiment, we detected an increased telomere signal consistent with previously described phenotypes of ALT in human tumors (Fig 7B) [13]. This indicates that longer telomeres associated with atrx depletion-mediated ALT triggers a downregulation of tert. Indeed, a negative feedback mechanism that regulates TERT expression in a telomere length-dependent manner has been previously described in human cancer cells [44], and is supported by our observations to occur in our model system. Quantification of the telomere FISH-signal (n = 6) relative to the DNA-signal revealed a significantly larger total telomere area in TelC-FISH images derived from atrx+/- tumors compared to images derived from atrx+/+ controls (Fig 7C and S5 Table; p = 0.0151). Moreover, the total number of detectable telomere spots also increased significantly (Fig 7D and S6 Table; p = 0.042). This effect was even stronger, when only larger telomere signals (>1.5μm2 area) were considered (Fig 7E and S6 Table; p = 0.0013). This shows that heterozygous loss of atrx resulted in a measurable increase in telomere size which indicated atrx-deficiency-related ALT.
In adult zebrafish tissues, the Tert protein regulates aging in a process similar to its role in human cells [45–47]. In a previous study, tert-/- zebrafish aged faster and developed earlier spontaneous age-related tumors [46]. Thus, in zebrafish, shortening of the telomeres promotes tumor onset, which can be explained by the need in early tumorigenesis to increase the mutational burden by genomic instability in order to develop a malignant cancer. In patients, similar findings were reported for the transition of adenomas to metastatic colon cancer, which is among the most well described cancer progression systems. In particular, cells within adenomas that give rise to metastatic cancer have critically short telomeres [48]. In this context, induction of ALT due to the loss of atrx would not be expected to accelerate the onset of tumors, but rather facilitate later phases of their progression. This may explain why the tumor onset was not uniformly accelerated in our model. Thus, ALT cannot alone be responsible for the atrx lof-mediated onset of various tumor types including soft tissue sarcomas detected in our model.
To identify factors that may have contributed to this carcinogenesis we analyzed mRNA-seq data for gene set enrichment that might contribute to tumor formation. In this survey, we found the enrichment of notch1-targets (one gene set) and jak-stat-signaling (one gene set), epithelial differentiation (three gene sets) and PRC2-function (11 gene sets) in p53/nf1/atrx-deficient tumors compared to the atrx wildtype controls (Figs 7F and S5, S7 and S8 Tables). Interestingly, 10 of 11 PRC2-related gene sets showed a significant up-regulation of PRC2-targets, SUZ12-targets or H3K27me3 marked genes from various stem and progenitor cell types. However, there was one EZH2-target gene set that was significantly downregulated in p53/nf1/atrx-depleted tumors. This indicates that the loss of atrx predominantly led to a re-expression of PRC2 target genes silenced by H3K27me3 deposition, even though this might not affect all PRC2-targets. Interestingly, this was accompanied by an undiminished nuclear H3K27me3 signal in p53/nf1/atrx-deficient tumors detected by immunofluorescence. This observation is consistent with a previous study in human cells where loss of ATRX was found to displace PCR2-deposited H3K27me3 silencing marks away from the target gene promoters to the intergenic space leading to their re-expression [9]. Thus, it is likely that ATRX loss does not abolish H3K27me3 marks in the genome, but rather deregulates their positioning throughout the genome.
In this study, we show the consequences of inactivation of atrx in the zebrafish germline, which resulted in the first zebrafish model atrx deficiency causing alpha-thalassemia and the contribution of loss of atrx to carcinogenesis in the background of p53 loss and RAS-MAPK pathway activation. Faithful models of both of these consequences of atrx loss will be valuable for future preclinical studies. We provide evidence that atrx and p53 cooperate in the carcinogenesis of soft tissue sarcomas and other malignancies. Our data are consistent with a negative feedback loop downregulating telomerase upon loss of atrx, causing alternative lengthening of the telomeres, and indicate that the role of atrx-deficiency in tumor initiation may also be linked to disturbed PRC2-mediated gene silencing.
All zebrafish studies and maintenance were done in accord with Dana-Farber Cancer Institute IACUC-approved protocol (#02–107).
Zebrafish were raised and maintained according to standard procedures. They were derived from the AB background strain, the Tg(gata1:GFP) line [37] or the p53/nf1-deficient background [29,49]. All p53-/- fish were homozygous for the p53-M214K mutation described previously [49]. The p53/nf1-deficient fish carried a frameshift mutation in exon 26 of nf1a and in exon 17 of nf1b which truncate the Nf1 protein before its functional GRD domain, as published in a previous study [29].
The zebrafish atrx mutant lines were generated by the CRISPR/Cas genome editing system using the previous described protocol [30]. The plasmid constructs pDR274 (#42250) and pMLM3613 (#42251) were purchased from Addgene. Oligonucleotides 5’-TAG GTC CTG AGT TCC GTA ACA A-3’ and 5’-AAA CTT GTT ACG GAA CTC AGG A-3’ were annealed and cloned into the pDR274 vector to generate single guide RNAs (gRNAs) targeting atrx exon 4. Each embryo was injected with 1 nl of solution containing 25 ng/μl gRNA and 600 ng/μl Cas9 mRNA at the 1 cell stage. Mosaic F0 fish with germline mutation were identified, and stable mutant lines were established by outcrossing. To genotype the atrx mutant line, we first amplified a DNA fragment containing the mutated site, using the primer pair atrx_E3I3Fw: 5’-GCA AGC TGC CAC AAG GTT AGT CC-3’ and atrx_I4Rv: 5’-GTC ACA AAC ACG TCA CCA CTT A-3’ with genomic DNA extracted from fin clips serving as template. The DNA product was either sent for sequencing with the primer atrx_seqI3Fw: 5’-TGT TCC GAT CAG TCT TCC TTA GC-3’ or digested by BslI. The wildtype DNA fragment was then digested into 2 fragments of 171 (bp) and 282 bp while the mutant one was resistant to digestion, revealing a band of 455 to 475 bp (please note that the mutant PCR product can be larger due to insertions). Alternatively, if PCR products were not sequenced, primers atrx_shortFw: 5’-GCAGGCACAGTAGTGGTGAAGCCA-3’ and atrx_shortRv: 5’- CACCAGGACGTTTCCGCGCACCCT-3’ were used. The wildtype DNA fragment was then digested into 79 and 45bp fragments. The mutant amplicon remained undigested at a size of 126 to 146 bp (please note that the mutant PCR product can be larger due to insertions).
All gRNA was transcribed in vitro from DraI (NEB) linearized pDR274-gRNA plasmid DNA using the MAXIscript T7 Kit (Ambion Inc.). Cas9 mRNA was transcribed in vitro from NotI (NEB, Ipswich, MA, United States) linearized pCS2-nCas9n plasmid DNA using the mMessage mMachine SP6 Kit (Ambion Inc., Foster City, CA, United States). Oligonucleotides were mixed between 1:5 and 1:1 with a 0.5% phenol red solution (Sigma-Aldrich, Burlington, MA, United States) to a final concentration of 25ng/μl gRNA and 600ng/μl Cas9 mRNA. To induce mutations in the genome of zebrafish, we injected one-cell-stage embryos with the oligonucleotide/phenol red mix described above within 30min after fertilization using a glass capillary mounted into an air pressure injector (Harvard Apparatus, Cambridge, MA, United States). The injection volume was 1nl oligonucleotide/phenol red mix per embryo. Dead embryos were removed 3 to 6 hours after injection using a Leica M420 microscope (Wetzlar, Germany). For zebrafish atrx rescue experiment, atrx was cloned into pCS2+ vector by PCR from cDNA of zebrafish embryos, using the primer pair atrx_Fw: 5’-CGG CTC GAG ATG GCA ACC AAT GAC GTA AAT ATT-3’ and atrx_Rv: 5’-CGG TAC GTA TTA CAG ACC CTT AGA TGG GCC TGG-3’. Zebrafish atrx mRNA was synthesized in vitro from NotI (NEB, Ipswich, MA, United States) linearized atrx-pCS2+ plasmid DNA using the mMessage mMachine SP6 Kit (Ambion Inc., Foster City, CA, United States). 1 nl atrx mRNA (500 ng/μl) containing phenol red dye was injected into zebrafish embryos at the one-cell stage.
The sox10:GFP zebrafish were genotyped at 2–3 months of age and separated into distinct tanks according to genotype. At least every 2 weeks, all fish were examined for tumor onset using a Nikon C-DSD115 fluorescence microscope (Tokyo, Japan). The time point of tumor onset was defined at the first observation of a GFP+ growth that did not regress within 2 weeks. Fish that died from tumor-unrelated causes were removed from the analysis. A survival analysis with Graph pad Prism 7 software was performed to visualize the tumor onset rate and tumor penetrance.
Tumor-bearing fish were sacrificed and fixed in 4% paraformaldehyde (PFA) in PBS at 4°C for 1–3 days. Subsequently, the fish were washed in PBS or 70% ethanol and embedded in paraffin. Paraffin sectioning (3-micron) and hematoxylin/eosin staining were performed at the DF/HCC Research Pathology Core according to standard protocols.
The indirect immunofluorescence protocol was adapted from previous studies [50,51]. Zebrafish tumor sections were incubated twice for 10 min each in xylol to remove paraffin. For rehydration the slides were washed for 5min each in 100% ethanol, 96% ethanol, 70% ethanol twice for 5min each in H2Odd. Epitopes were unmasked by heating for 2min and 30 sec in 10 mmol citric acid/NaCitrate-buffer (pH 6; 18% citric acid, 82% NaCitrate) followed by 5min incubation after each cooking step. Next, slides were blocked for 15min in PBS + 0.1% BSA at room temperature. The DNA was stained for 10 min with Hoechst 33342 (1mg/ml) and diluted 1:250 diluted in PBS + 0.1% BSA. Primary antibodies were diluted in PBS + 0.1% BSA and 100μl antibody-mix was incubated for at least 1h on fat surrounded area around the tissue (wet chamber in the dark). Secondary antibodies were diluted in PBS + 0.1% BSA and 100μl antibody-mix was incubated for 1h on fat surrounded area around the tissue (wet chamber in the dark). Slides were washed for 5-10min in PBS + 0.1% BSA after each staining step. After secondary antibody incubation the slides were washed 1x in PBS + 0.1% BSA, 1 x in PBS and 1x in H2Odd, dried with an absorbent paper and mounted in 30μl PromoFluor Antifade Reagent mounting medium (Promokine, Heidelberg, Germany) using a #1 cover glass. As primary antibodies were used: pan-cytokeratin AE1/AE3 (Novus Biologicals, Littleton, CO, USA), H3K27me3 C36B11 (Cell Signaling Technology, Danvers, MA, USA). Secondary antibodies were conjugated with Alexa 488, 568 (Thermo Fisher Scientific, Waltham, MA, USA). Slides were imaged by a Leica SP5X scanning confocal microscope (Wetzlar, Germany) at the Confocal and Light Microscopy core facility at Dana-Farber Cancer Institute.
Telomere PNA-FISH was performed as previously described [47]. Briefly, zebrafish paraffin sections were deparaffinized, followed by hybridization for 2 h at room temperature in the dark. Slides were washed, mounted and imaged by a Leica SP5X scanning confocal microscope at the Confocal and Light Microscopy core facility at Dana-Farber Cancer Institute. Cy3 labeled PNA TelC probes (CCCTAA repeats) were purchased from PNA Bio INC (Thousand Oaks, CA, USA). DNA was counterstained with DAPI. Individual images of the Cy3 and DAPI channels were analyzed in ImageJ to quantify the total area covered by Cy3 stained telomeres or DNA and the number of telomere signals (particles in Cy3 channel). Moreover, the average number of large telomere spots was quantified to determine changes in the abundance of large telomeres reminiscent of alternative lengthening of the telomeres. Large was defined as >0,01 inch2 size in digital image exported from LasX software (Leica) as single channel image which corresponds to ~1,556μm2 real size. For each image these values were normalized to the DNA content of the respective image to quantify the relative telomere signal within the nuclear compartment. For this the area percentage value of the DAPI signal in each image was determined using ImageJ. From this a “DNA content factor” was calculated by the formula 100/% DNA content. Raw values were subsequently multiplied with this DNA content factor. For each analysis the threshold was set to include the specific signal and exclude the background. The threshold was kept constant for all images compared. Images with background signals that could not be eliminated were excluded from the analysis. A two-tailed unpaired t-test type 2 was performed using GraphPad Prism software.
Riboprobe labeling and WISH were performed as described previously [52,53]. The digoxigenin (DIG)-labeled RNA probes to detect α-e1 and β-e1 globins [54], and c-myb [55] were as previously described.
Peripheral blood smears were prepared on glass slides as described previously [55]. The slides were fixed and stained with May-Grunwald–Giemsa (MGG) solution (Sigma-Aldrich, St. Louis, MO) according to the manufacturer’s instructions and visualized with a Zeiss AXIO microscope (Zeiss, Oberkochen, Germany).
RNA was isolated from one half of p53/nf1/atrx-deficient MPNSTs and p53/nf1-deficient control MPNSTs using the AllPrep DNA/RNA Mini Kit from Qiagen (Hilden, Germany) and the other half was analyzed by histopathology as described above. Library preparation, quality control, and next-generation sequencing were conducted by the Molecular Biology Core Facility of the Dana-Farber Cancer Institute following standard protocols.
Gene expression values were derived from paired end RNA-Seq data that compared samples from p53/nf1/atrx-deficient tumors to samples from atrx-wildtype controls (3 vs. 3 samples). FastQC was used to evaluate read quality on raw RNA-Seq reads and trimmed reads. Trimming of low quality reads and clipping of sequencing adapters was done using the program Trimmomatic [56] and all reads shorter than 36bp after trimming were dropped. Reads were aligned to the GRCz10 version of the zebrafish reference genome with TopHat [57] version 2.1.0. Bam sorting and indexing was done with SamTools [58] and duplicate reads were removed using Picard-tools (http://picard.sourceforge.net). Gene level counts were obtained with htseq-count version 0.9.1 [59]. Differential gene expression was evaluated with the R Bioconductor package DESeq2 [60] and normalized expression values for individual samples were obtained from DESeq2 using the variance stabilizing transform on the raw counts. The variance stabilizing transformed data were used for GSEA.
Gene Set Enrichment Analysis (GSEA) [61,62] was used to evaluate the association of genes with compared p53/nf1/atrx-deficient tumors to the atrx-wildtype controls. GSEA was run with 2500 permutations of the phenotype using signal-to-noise to rank genes. GSEA was performed with signatures from version 6.0 of the molecular signature database (MolSigDB) (http://www.broadinstitute.org/gsea/msigdb/index.jsp): the c2 curated gene sets from various sources such as online pathway databases, the biomedical literature, and knowledge of domain experts, the c3 motif gene sets, c5 gene ontology (GO) MF, CC, and BP ontologies, and the c7 immunologic signatures.
Data of the AACR Genie database (version 3.0 public release) were extracted online using the cBioPortal application (http://www.cbioportal.org/genie) and further processed in Microsoft Excel.
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10.1371/journal.pcbi.1003357 | Determinants of Cell-to-Cell Variability in Protein Kinase Signaling | Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds (‘pathway sensitivity’) and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.
| Cells sense their surroundings and respond to soluble factors in the extracellular space. Extracellular factors frequently induce heterogeneous responses, thereby restricting the biological outcome to a fraction of the cell population. However, the question arises how such cell-to-cell variability can be controlled, because some cellular systems show a very homogenous response at a defined level of an extracellular stimulus. We derived an analytical framework to systematically characterize the cell-to-cell variability of intracellular signaling pathways which transduce external signals. We analyzed how heterogeneity arises from fluctuations in the total concentrations of signaling proteins because this is the main source of variability in eukaryotic systems. We find that signaling pathways can be highly variable or inherently invariant, depending on the kinetic parameters and the structural features of the cascade. Our results indicate that the cell-to-cell variability can be reduced by negative feedback in the cascade or by signaling crosstalk between parallel pathways. We precisely define the role of negative feedback loops in variability suppression, and show that different aspects of the dose-response curve can be controlled, depending on the feedback kinetics and site of action in the cascade. This work constitutes a first step towards a systematic understanding of cell-to-cell variability in signal transduction.
| External stimuli typically induce cellular responses by binding to cell surface receptors. Intracellular signaling networks transduce the signal, ultimately triggering gene expression responses in the nucleus. The basic building blocks of eukaryotic signaling networks are protein kinase cascades (Figure 1A): The signaling proteins in the cascade act as enzymes (“kinases”) that catalyze the activation of downstream kinases by phosphorylation. Information is thus transmitted along the cascade by consecutive phosphorylation reactions (Figure 1A). The proto-typical example for such a signaling cascade is the conserved mitogen-activated protein kinase (MAPK) pathway which consists of three kinases (Raf, Mek, Erk) [1].
Signaling cascades can transduce information in different ways [2], [3]. The activity of the terminal kinase may quantitatively reflect the concentration of the extracellular stimulus, and the cascade is termed to behave gradually (or analog) in this case. Alternatively, the cascade may act as an ultrasensitive switch that responds in a digital (“all-or-none”) manner: low background signals are strongly dampened and rejected, while amplification and cellular commitment occur once a threshold stimulus is reached. Ultrasensitive signaling cascades therefore act as cellular decision making devices. Theoretical studies revealed that minimal models of multi-step protein kinase cascades show gradual dose-response behavior at steady state [4]. Ultrasensitive decision making requires additional regulation mechanisms which increase the steepness of the dose-response curve, e.g., strong enzyme saturation in the (de)phosphorylation reactions (“zero-order ultrasensitivity”), multisite phosphorylation, competitive inhibition, or positive feedback [3], [5].
The dose-response curve of a signaling pathway relates the signaling activity to the amount of extracellular stimulus applied. The dose-response curve of signaling pathways is typically sigmoidal in shape and can be quantitatively described by the so-called Hill equation (, with as the response to the stimulus ). The half-maximal stimulus () characterizes the stimulus concentration where the signal reaches 50% of its maximal activation level, and is thus a measure of the pathway sensitivity towards extracellular stimulation. The maximal activation level () describes how strongly the terminal kinase can be activated upon very strong stimulation, thereby reflecting amplification or dampening potential of the cascade. The Hill coefficient determines how steeply the pathway responds to external stimulation: the signaling cascade shows gradual behavior for , while ultrasensitive decision making is observed for . In the limit of very high the dose-response approaches a step-function and the pathway acts as a digital switch with the threshold stimulus .
Signaling networks show non-genetic variability, implying that the signaling activity can differ strongly between cells of a clonal population [6], [7]. Biological mechanisms underlying signaling variability include cell density effects [8] and cell-to-cell variability in signaling protein expression [6], [7], [9]. In the latter case, the stochasticity of protein biosynthesis indirectly hampers the precision of intracellular information transmission. An alternative source of variability may be the stochastic dynamics of signaling pathways operating at low molecule numbers [10]. Stochastic signaling fluctuations are typically fast compared to subsequent gene expression responses, and therefore should not impinge significantly on cellular decision-making. The variability of most signaling systems can therefore be understood by considering them as deterministic system with fluctuating initial signaling protein concentrations [6], [7], [9]. Single-cell measurements reveal that the level of each signaling protein differs by a factor of three among the cells of a clonal population [11], [12]. Thus, multi-component signaling systems may show strong variability, suggesting that regulation mechanisms exist which allow for variability suppression.
Cell-to-cell variability in the intracellular signaling pathway activity may be beneficial or deleterious depending on the biological system. Certain cellular responses such as apoptosis or differentiation should only be triggered in a subset of the cell population to maintain tissue homeostasis and to establish different cell lineages, respectively. The apoptosis and differentiation thresholds should thus be very different between individual cells and the system should exhibit strong variability [6], [13]. In cancer therapy, such strong heterogeneity may adversely affect the population responsiveness to drugs, thereby leading to incomplete killing of tumor cells [14]–[16]. Invariance of signaling thresholds is expected to be important in embryonic development: according to the so-called “French-flag model”, patterning is established by a single morphogen gradient that specifies multiple cell fates, each cell type requiring a different threshold morphogen concentration [17]. For sharp spatial boundaries to be established, signaling pathways that read of morphogen gradients should exhibit robust and invariant thresholds at which they respond. Similarly, a cell-to-cell invariant signaling threshold has been reported for yeast cells that sense positioning in an extracellular pheromone gradient [18], [19]. Low variability is also required for gradual signaling pathways which transduce information quantitatively. Taken together, the question arises how cellular systems are able to tune the variability of protein kinase signaling to ensure an appropriate response of the cell population.
In this work, we systematically characterize the cell-to-cell variability of protein kinase cascades. We focus on the dose-response behavior of signaling to investigate how synchronously a cell population responds to a change in a hormonal stimulus. We discuss how the variability can be actively modulated by parameter tuning, gene expression noise regulation or additional signaling mechanisms such as feedforward and feedback loops.
This work focuses on the cell-to-cell variability of protein kinase cascades. We study the general features of eukaryotic signaling pathways, but also try to specifically answer the question why the yeast pheromone pathway shows switch-like decision making with little cell-to-cell variability [18], [19]. The pheromone pathway initiates the mating of two haploid yeast cells by triggering various cellular responses, one of which is the so-called shmoo, a cellular projection in the direction of the mating partner that primes for cell fusion [18], [20]. Dose-response experiments with exogenously added pheromones revealed that shmooing occurs at a similar pheromone concentration for all cells in the population, implying that the signaling pathway shows little cell-to-cell variability [18], [19], [21]: The transition from no shmooing to complete shmooing of the whole cell population occurred within a 2-fold range of pheromone concentrations in one study [18], while others reported that the required pheromone increase is 4-fold [19] or 5-fold [21]. In this paper, we analyze the dose-response curves of signaling pathways to understand how a coordinated response of the whole cell population at a particular stimulus concentration can be realized. We study simplified models of signaling cascades with five levels to reflect the main steps of pheromone signaling, i.e., pheromone binding to a transmembrane receptor, receptor-mediated G protein activation and signal transduction through a three-tiered MAPK cascade [22].
We characterize the dose-response behavior at steady state. Steady state simulations imply that we focus on sustained signaling upon long-term stimulation and neglect the temporal features of the signal such as duration or area-under-curve. Steady state simulations likely provide physiologically relevant insights, because many cell fate decisions require ongoing signaling pathway activity over several hours [23]. Fast signaling events such as phosphorylation and dephosphorylation typically occur on a time-scale of minutes, and are thus expected to reach a (quasi-)steady state shortly after external stimulation.
Signaling dose-response curves may increase gradually and reflect the concentration of the extracellular stimulus, or the signaling pathway may act as an ultrasensitive switch that responds in a digital (“all-or-none”) manner (see Introduction). The shmooing of yeast cells is an all-or-none response [18]. Contradictory evidence exist in whether or not digital decision making already occurs at the level of MAPK signaling [18], [21], [24], but the pathway likely exhibits a certain degree of ultrasensitivity [20]. In this paper, we employ a bottom-up approach and initially study minimal signaling models with gradual dose-response curves, before turning to more complex systems capable of ultrasensitive signal transduction.
Cell-to-cell variability is introduced into the models by assuming fluctuations in initial signaling protein expression levels. In contrast to previous studies on variability [25], we neglect the intrinsic stochasticity of signaling cascades (cf. Introduction), and analyze deterministic models of kinase signaling using the framework of ordinary differential equations (ODEs). Experimental studies suggest that all signaling protein concentrations vary simultaneously due to noise in protein biosynthesis rates [6], [7], [9]. Extrinsic noise sources, in particular signaling protein concentration fluctuations, are thought to be the main source of non-genetic variability in yeast pheromone signaling [26] and in mammalian signaling pathways [6], [7], [27]–[29]. We applied two complementary strategies to understand how signaling protein expression noise gives rise to signaling heterogeneity. First, explicit cell-to-cell variability simulations were performed. All signaling protein concentrations were sampled from uncorrelated log-normal distributions, and the ODE system was solved for each set of sampled concentrations, yielding distributions in signaling pathway activity. Secondly, one-dimensional sensitivity analyses revealed the impact of individual signaling protein concentrations: The ODE system was solved for varying levels of each signaling protein (keeping all other components constant). Signaling protein concentrations that had a strong impact on signaling pathway activity could be identified as major determinants of cell-to-cell variability.
Our results show that generic five-step protein kinase cascades exhibit much stronger cell-to-cell variability than the yeast pheromone pathway, unless certain robustness requirements are fulfilled.
The term ultrasensitivity describes signaling cascades with steep, sigmoidal dose-response curves that allow for all-or-none decision making. Ultrasensitive behavior has been reported for the yeast pheromone pathway, although the steepness of the dose-response curve differs between literature reports [18,20,21]. Various molecular mechanisms establish ultrasensitivity in signaling cascades, e.g., double phosphorylation or competitive inhibition [3,38]. In this work, we neglect the mechanistic details underlying ultrasensitive regulation, and represent ultrasensitivity at one or more cascade levels by the Hill equation (see below). This modeling approach allows us to study the propagation of variability in ultrasensitive signaling cascades.
Two strategies exist to establish a very steep overall dose-response curve in a signaling cascade: Firstly, the all-or-none behavior may be primarily established at a single level, while the rest of the cascade shows gradual behavior (in isolation). Localized switching at the terminal cascade level has been reported for the yeast mating pathway [18]. Secondly, switching may be distributed over multiple steps, i.e., each cascade level exhibits mild ultrasensitivity in isolation and cascade amplification effects ensure that the overall dose-response curve is very steep. Such behavior has been observed for the MAPK cascade in Xenopus oocytes [39], and is likely to be relevant for other MAPK cascades like the yeast pheromone pathway. The following discussion of cell-to-cell variability will initially focus on the second mode of distributed ultrasensitive decision making, before turning to the case of focused switching at a single level.
A key question in biology is how cellular systems function robustly in face of internal and external fluctuations. We comprehensively characterized the determinants of cell-to-cell variability in protein kinase signaling cascades, and summarized our main findings in Table 1. Our work extends previous studies on cell-to-cell variability [6,42,43] and on variability reduction by negative feedback [9,33,44], bifunctional enzymes [45–47] or correlated protein concentration fluctuations [7,9,28,48]. We analyzed the steady state dose-response behavior of signaling systems, and showed that protein kinase cascades can be highly variable or inherently invariant, depending on the properties of individual reaction steps and their kinetic parameters. Our results may explain why the yeast pheromone pathway shows switch-like decision making with very little cell-to-cell variability.
In this paper, we made a central simplifying assumption to study the behavior of protein kinase cascades: it was assumed that the individual levels of a protein kinase cascade function as isolated modules. Based on this assumption, we described the local dose-response behavior of each cascade level by Michaelis-Menten or Hill equations (Eqs. 3 and 14), and studied their behavior in tandem to gain insights into the global dose-response behavior of the five-step cascade. Depending on the protein concentrations and kinetic parameters in the cascade, the modularity assumption may be violated, and explicit simulations of all enzyme-substrate binding and dissociation events in the cascade may be necessary: strong sequestration of upstream kinases by highly abundant downstream substrates affects the phosphorylation state of the upstream kinase, thereby leading to retroactivity in the cascade [49–51]. Retroactivity results in positive or negative feedback regulation [49–51], and may therefore increase or decrease the cell-to-cell variability of protein kinase signaling. Sequestration effects and retroactivity can give rise to complex dynamic phenomena such as bistability and oscillations in computational models of MAPK signaling without explicit feedback regulation [52–55]. The cell-to-cell variability of such complex protein kinase signaling systems cannot be understood by analytical approaches, and thus needs to be analyzed numerically using extensive parameter sampling strategies [54]. Throughout this paper, we assumed that the signaling activity at each cascade level scales with the total kinase concentration (Eqs. 2, 14 and 17). However, a nonlinear relationship between signaling activity and total protein concentration is possible for (de)phosphorylation cycles with tight enzyme-substrate binding and sequestration effects [56,57], implying that the cell-to-cell variability would be increased or decreased.
Negative feedback is known to suppress the variability of biological systems and to reduce the steepness of signaling dose-response curves [32,33]. Here, we define more precisely the role of negative feedback in the modulation of signal transduction variability. Negative feedback simultaneously reduces the variability of the maximal pathway activation and the signaling threshold, thereby resolving the robustness trade-off which we observed in non-feedback cascades. The topological organization of the feedback loop determines which dose-response features are primarily affected by negative feedback: A feedback that acts upstream in the cascade primarily promotes invariance of the pathway threshold, while a feedback acting downstream controls the variability of the maximal pathway activation. We further find that the time scale of negative feedback regulation may determine its functional role: Variability suppression in fast, post-translational loops comes at the cost of a very shallow dose-response curve, implying that switch-like decision making is not possible. This trade-off can be circumvented in slow transcriptional feedback loops because the time windows of variability suppression and switch-like decision making can be separated. Interestingly, our simulations reveal that negative feedback acting upstream in signaling cascade may increase the cell-to-cell variability: For low phosphatase activities in the cascade (), the maximal activation level of a feedback-less gradual cascade is determined by the terminal kinase concentration only and shows partial invariance (dashed orange line in Figure 2C). In contrast, the maximal activation level of a system with strong feedback is determined by multiple protein concentrations (Eq. 9), implying that negative feedback regulation increases the variability (solid orange line in Figure 2C).
In this paper, we analyzed the steady state behavior of negative feedback circuits, but did not focus on their dynamical properties such as sustained oscillations [58]. Interestingly, oscillations have been observed experimentally for yeast and mammalian MAPK cascades, and appear to be important for adequate cellular decision making [59,60]. We performed linear stability analyses to investigate whether sustained oscillations arise in our simple models of protein kinase signaling (Supplemental Text S1, Supplemental Figure S7 and Figure S8). As expected, oscillatory behavior was not possible in a simple multistep signaling cascade without negative feedback regulation (Eq. 1). Sustained oscillations were also not observed when the gradual kinase model was extended by a downstream negative feedback loop ( activates its own phosphatase; Eq. 12), because oscillations require a negative feedback with sufficient delay [58]. However, sustained oscillations can occur within a certain stimulus range in the model with upstream feedback (Eq. 8), provided that activates the phosphatase with high cooperativity ). Such high feedback cooperativity is required to overcome saturation effects in the kinase cascade which compromise the emergence of sustained oscillations [58]. Damped oscillations already occur at lower feedback cooperativities. Our cell-to-cell variability simulations for the upstream feedback system thus represent the steady state behavior reached without damped oscillations (low ), after damped oscillations (intermediate ) or the mean activity of a sustained oscillator (high ).
One way to reduce noise in biological signaling systems is to correlate the expression fluctuations of antagonizing enzymes [7,9,28,48], e.g., by co-regulation at the transcriptional and/or post-transcriptional levels [61,62]. Our results indicate that efficient variability reduction by a correlated fluctuation of only two enzyme concentrations can only be achieved in ultrasensitive signaling pathways (Figure 3C). Gradual signaling systems require correlated fluctuations in most if not all signaling protein concentrations. Single-cell studies indicate that protein concentrations in the cell may be globally correlated, possibly due to fluctuations in RNA polymerase and/or ribosome copy numbers [12,28]. In our cell-to-cell variability simulations, we made a conservative assumption and neglected these protein concentration correlations. It is straightforward to extend our analytical results to the case of correlated fluctuations in all enzymes. Interestingly, several growth factor signaling pathways are organized in so-called synexpression groups, where most positive and negative regulators of signaling show tight spatio-temporal co-regulation [62]. Functional organization in synexpression groups may reflect the need for correlated fluctuations in multiple kinase-phosphatase pairs to effectively reduce variability. Most synexpression groups are transcriptionally controlled by the activity of their own signaling pathway, and thus combine multiple positive and negative transcriptional feedback loops. We find that signaling cascades with synexpression of multiple feedback regulators show little cell-to-cell variability, much like systems with co-expression of non-feedback regulators (unpublished observation).
Gradual signaling systems transduce information quantitatively and faithfully report the stimulus concentration in the extracellular milieu. We therefore assumed that the maximal pathway activation and the pathway sensitivity of a gradual system should be invariant. However, recent experimental work revealed that the absolute signaling activities of a mammalian MAPK cascade pathway are highly variable, while the stimulus-induced fold-change in the signal is invariant between cells [11]. Our results indicate that robust fold-change encoding is possible in a gradual signaling cascade with low phosphatase activities : In this scenario, the pathway sensitivity is completely invariant (Eq. 4), and all cells show the same fold-change in response to a stimulus increase from one level to another. Future studies are required to investigate in more detail such alternative modes of robust signal transmission, especially in more complex models of protein kinase cascades.
All simulations were done in Python. Analytical solutions were obtained using the open-source python package SymPy (www.sympy.org). Numerical simulations were performed using the odeint function of the scipy.integrate package (www.scipy.org). The details of the model implementation process can be found in the figure captions and in the Supplemental Protocol S1. The model parameters are listed in Supplemental Table S1. Source codes are available upon request.
Cell-to-cell variability was introduced into deterministic ordinary differential equation models of protein kinase signaling by assuming fluctuations in initial protein concentrations. The total kinase and phosphatase concentrations (, , , , , , , , ) for each cell were sampled from independent log-normal distributions with a mean of 0 and a standard deviation of 0.35. The same kinetic parameter values were assumed for each cell of the population, and 1000 cells with different total protein concentrations were simulated for each model variant. The simulations of all model variants were performed with the same random number generator seed.
The steady state dose-response behavior of each cell was characterized by calculating the pathway sensitivity () and the maximal pathway activation upon strong stimulation (). was calculated as the stimulus level leading to half-maximal pathway activation by finding the zero of the dose-response curve having a negative offset of , where is the basal activation level in the absence of stimulation.
Various methods were employed to characterize the cell-to-cell variability of the signaling dose-response curves. In Figures 1B, 2B, 3A, 3C, 4A, 5B, and 6B, we explicitly show simulations of the dose-response behavior for specific parameter configurations, and highlighted cells with the highest and lowest or by the shaded areas (orange and blue, respectively). Since these population outliers could be subject to randomness, we additionally provide box plots at the top and the right to characterize the (normalized) and distributions, respectively (see Figures 1B, 2B, 3A, 3C, 4A, 5B, and 6B). The cell-to-cell variability for different parameter configurations was characterized using the interquartile (IQ) ratio (Figures 1C, 2C, 2D, 3D, 4B, 5C, and 6C), which was calculated as the ratio of the third (Q3) and first (Q1) quartile using the Python package NumPy (www.numpy.org). The IQ Ratio is a dimensionless number that reflects the fold difference between cells with high and low levels, while excluding (extreme) outliers. To further support our findings, we show in the Supplemental Figures S1, S2, S3, S4, S5, S6 that very similar results are obtained when using the coefficient of variation (CV = mean/standard deviation) as a measure of variability.
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10.1371/journal.ppat.1004183 | The PhoP-Dependent ncRNA Mcr7 Modulates the TAT Secretion System in Mycobacterium tuberculosis | The PhoPR two-component system is essential for virulence in Mycobacterium tuberculosis where it controls expression of approximately 2% of the genes, including those for the ESX-1 secretion apparatus, a major virulence determinant. Mutations in phoP lead to compromised production of pathogen-specific cell wall components and attenuation both ex vivo and in vivo. Using antibodies against the native protein in ChIP-seq experiments (chromatin immunoprecipitation followed by high-throughput sequencing) we demonstrated that PhoP binds to at least 35 loci on the M. tuberculosis genome. The PhoP regulon comprises several transcriptional regulators as well as genes for polyketide synthases and PE/PPE proteins. Integration of ChIP-seq results with high-resolution transcriptomic analysis (RNA-seq) revealed that PhoP controls 30 genes directly, whilst regulatory cascades are responsible for signal amplification and downstream effects through proteins like EspR, which controls Esx1 function, via regulation of the espACD operon. The most prominent site of PhoP regulation was located in the intergenic region between rv2395 and PE_PGRS41, where the mcr7 gene codes for a small non-coding RNA (ncRNA). Northern blot experiments confirmed the absence of Mcr7 in an M. tuberculosis phoP mutant as well as low-level expression of the ncRNA in M. tuberculosis complex members other than M. tuberculosis. By means of genetic and proteomic analyses we demonstrated that Mcr7 modulates translation of the tatC mRNA thereby impacting the activity of the Twin Arginine Translocation (Tat) protein secretion apparatus. As a result, secretion of the immunodominant Ag85 complex and the beta-lactamase BlaC is affected, among others. Mcr7, the first ncRNA of M. tuberculosis whose function has been established, therefore represents a missing link between the PhoPR two-component system and the downstream functions necessary for successful infection of the host.
| One of the best characterized two-component systems in Mycobacterium tuberculosis is represented by the PhoPR pair, with PhoR being the transmembrane sensor kinase and PhoP playing an essential part in controlling expression of virulence-associated genes, such as those encoding the ESX-1 secretion apparatus. Previous studies showed that mutations in phoP resulted in attenuation in the mouse model of infection, thus providing the basis for the development of a novel live attenuated Mycobacterium tuberculosis vaccine carrying a deletion in phoP which is today in clinical trials. To thoroughly investigate the role of PhoP in M. tuberculosis, we undertook a systems biology approach comprising ChIP-seq and RNA-seq technologies. We demonstrated binding of PhoP to at least 35 targets on the M. tuberculosis chromosome and direct impact on expression of 30 genes, while further amplification of the signal is provided by regulators acting downstream. The strongest binding site was located between rv2395 and PE_PGRS41, where transcription of the non-coding RNA (ncRNA) Mcr7 was demonstrated. Expression of Mcr7 was found to be restricted to M. tuberculosis species and totally silenced in a phoP mutant. Genetics and proteomics approaches proved that Mcr7 controls activity of the Twin Arginine (Tat) secretion system, thus modulating secretion of the immunodominant antigen Ag85 complex and the BlaC beta-lactamase.
| Mycobacterium tuberculosis, the etiologic agent of tuberculosis in humans, is arguably the world's most important intracellular pathogen. The bacterium disseminates via the aerosol route from open pulmonary lesions of an infectious case and on reaching the alveoli of a susceptible individual is phagocytosed by resident macrophages. After infection, the tubercle bacillus resides asymptomatically for years in 95% of the infected persons. This latent state can be life-long but disease develops when the immune system weakens as a consequence of HIV co-infection, aging or malnutrition [1]. M. tuberculosis encounters a variety of environmental conditions and has to adapt to both the extracellular milieu and to the intracellular niche in order to survive [2]. Improved understanding of how this pathogen fine-tunes gene expression to support active growth and non-replicating persistence, and of how it copes with stresses encountered within the host would not only shed light on bacterial pathogenesis and biology but also aid the design of novel intervention strategies.
Well-known adaptation mechanisms in bacteria include two-component signal transduction systems (TCSS). These consist of a sensor protein that, upon reception of specific signal(s), activates its cognate transcription factor resulting in transcriptional regulation of a defined set of genes. The number of TCSS in M. tuberculosis is lower than typically found in bacteria of similar genome size, possibly reflecting the evolution of the bacillus as a human pathogen adapted to a predominantly intracellular environment [3]. Of the 11 TCSS present in M. tuberculosis H37Rv, the PhoPR TCSS is essential for virulence [4], as demonstrated in ex vivo and in vivo infection models, where inactivation of phoP led to greatly impaired growth [4], [5]. Consistent with these observations, a single nucleotide polymorphism (S219L) in the DNA binding domain of PhoP, which affected the ability of the regulator to control gene expression of the ESX-1 secretion system, was responsible for the reduced virulence of the attenuated strain H37Ra [6], [7]. Biochemical analyses conducted on the phoP mutant revealed that the synthesis of the cell wall components diacyltrehaloses, polyacyltrehaloses and sulfolipids, specific to pathogenic mycobacterial species, was also diminished as compared to wild type M. tuberculosis, thus providing an additional mechanism for attenuation [8]. Loss of these phenotypes is the basis of candidate vaccine strains carrying deletions in phoP, such as the recently developed MTBVAC, which showed great promise upon preclinical evaluation [9].
The PhoP protein is part of the PhoB/OmpR subfamily of transcription factors, characterized by an N-terminal regulatory domain and a DNA binding domain at the C-terminus. The crystal structure revealed the dimeric nature of the regulator and predicted PhoP to bind to direct repeats [10], in agreement with in vitro investigations of the PhoP-DNA interactions at a few operator regions [11], [12], [13].
Microarray-based transcriptomic studies of wild type and phoP mutant strains have been performed to identify the regulon controlled by PhoP. Genes belonging to lipid and intermediary metabolism, to the PE, PPE and PE_PRGS families and to the transcriptional regulator categories were found to be deregulated in phoP-deficient bacteria [5], [14], while a very recent publication [15] identified the genes under direct control of PhoP by means of ChIP-seq experiments on a PhoP-overexpressing strain.
Despite this body of knowledge, several questions remain unanswered. These include defining the external stimulus sensed by the PhoPR TCSS, performing ChIP-seq experiments in physiological conditions, obtaining a single-nucleotide resolution transcriptomic map, and identifying transcriptional regulators acting downstream of PhoP.
In this study, we used a systems biology approach, combining ChIP-seq, RNA-seq and in-depth proteomics, to thoroughly investigate the role of PhoP in the biology of M. tuberculosis. We identified a ncRNA, encoded by the mcr7 gene, as a major target of PhoP and showed its involvement in controlling secretion of Twin Arginine Translocation (Tat) substrates.
The PhoP regulon has been characterized previously by transcription profiling using microarrays in both the H37Rv [5] and MT103 strains [14] of M. tuberculosis. Those studies indicated that approximately 2% of genes are regulated by PhoP at the transcriptional level. However, little is known about the biophysical interactions between PhoP and the promoter regions of the genes controlled with the exception of a few well-characterized promoters [13]. Here, we applied ChIP-seq (chromatin immunoprecipitation with anti-PhoP antibodies followed by ultra-high throughput DNA sequencing) to locate PhoP binding sites across the M. tuberculosis H37Rv [16] chromosome. To avoid false positive signals we included an isogenic phoP mutant [11], which served as a control and reference sample in all experiments.
ChIP-seq analysis of cultures grown to exponential phase led to the identification of 35 significantly enriched (p<0.0001, FDR 0.00%) regions in H37Rv compared to the phoP mutant (Table 1). Several of these peaks were localized between divergently transcribed open reading frames (ORF) or upstream of validated or predicted operons [17], [18], [19], thus increasing the number of genes potentially affected by PhoP binding directly. These targets were randomly distributed along the M. tuberculosis genome (Figure 1A) and predominantly located upstream of ORF (Figure 1B). However, we also observed PhoP binding sites in the 3′-end of ORF, as shown for the hddA-ldtA genes (Figure 1B). All of the functional categories in which the M. tuberculosis ORFs have been grouped were represented in the ChIP-seq results, although clear prevalence of regulatory proteins was observed (12% of the total number of signals as compared to 5% representation in the genome). Remarkably, of the 35 regions detected by ChIP-seq, a number of them had not been described previously [5] as being associated with PhoP-regulated genes (i.e. mcr7, PE27, PPE43, PE31, Rv3778c, lpdA).
The distance between the PhoP peak and the ORF start site was calculated for each gene and plotted as reported in Figure 1C. The majority (83%) of the PhoP peaks were between 0 and 200 bp upstream of the ORF start site, with 50% of them within the first 100 bp. Two binding sites were considerably further away from the closer ORF: these were the cases of lipF (>500 bp) and rv1535 (472 bp). Only one case was observed with the PhoP peak lying within the ORF: rv2137c, where the summit was located 97 bp downstream of the ATG start codon.
To gain insight into the interplay between PhoP and the transcriptional complex, we compared previous ChIP-seq data of RNA polymerase (RNApol) [17] with the PhoP profile obtained here. We observed that PhoP distribution mirrored that of RNApol at the putative promoter regions (Figure 1B). Closer examination indicated that PhoP binding sites precede those of RNApol. Additional confirmation came from calculation of the distance between the PhoP and RNApol signals, which was between 0 and 100 bp for most of the genes (Figure 1D). This might indicate a role of PhoP in positioning RNApol as a prerequisite for transcriptional control. Exceptionally, the PhoP binding region upstream of PE8 lies downstream of the RNApol binding site (Figure 1B). Curiously, we noticed that some strong PhoP peaks lacked a concomitant RNApol signal as illustrated by mihF (Figure 1B).
In order to confirm the ChIP-seq data independently, we quantified a selection of PhoP binding sites from the immunoprecipitated DNA of H37Rv, its phoP mutant and a control experiment performed without antibody, obtained from biological replicates. The results validated those obtained in ChIP-seq experiments (Figure S1).
We used the MEME suite to identify the PhoP consensus sequence from ChIP-seq signals. Two hundred bp surrounding the summit of the peaks were scanned and a motif was found in 83% of the instances (p-value between 5.88e-09 and 4.40e-05, Figure 1E and Worksheet 1 in Table S1). Additional, though more divergent, copies of the same consensus sequence were found in 11 peaks (31%) (Worksheet 1 in Table S1). Overall, six ChIP-seq signals (rv1535, PPE43, lpdA, rv3767, whiB1 and the region between yajC and gabT) were not found to be associated with the identified motif, suggesting either higher divergence of the sequences or indirect PhoP binding, i.e. mediated by other proteins. While the 5′ to 3′ orientation of the motif generally corresponded to the orientation of the gene associated with the ChIP-seq signal (22 out of 29 cases), we observed 7 exceptions, the most notable being the well-known PhoP-regulated gene pks3 [13], [14]. In this case the motif was localized on the opposite strand as compared to the direction of transcription of pks3.
To explore the relationship between binding of PhoP and transcriptional control on the target genes, we performed deep transcriptomic analysis by RNA-seq under the same in vitro conditions employed for ChIP-seq. We compared exponentially growing H37Rv wild type to the isogenic phoP mutant and quantified gene expression according to the functional categories in the TubercuList database (http://tuberculist.epfl.ch), generating results reported in Worksheet 1 in Table S2. An arbitrary 3-fold threshold was applied to the dataset for further analysis.
Integration of the ChIP-seq and RNA-seq data revealed that 19 PhoP binding sites were associated with altered expression of the flanking gene(s) (Table 1). Since some of these ORFs are part of predicted operons [17], [18], [19], the total number of genes under direct control of PhoP was found to be at least 30 in the experimental conditions tested (Table 1). The region showing the most remarkable affinity for PhoP (162-fold enrichment in ChIP-seq) lay between rv2395 and PE_PGRS41. This signal correlated with the expression of a small transcript (Mcr7) in the intergenic region that was severely affected by deletion of PhoP. This small transcript is further characterized later in this work. Other examples are represented by the operons composed of pks3-pks4-papA3-mmpL10 and rv2633c-rv2632c, which were more expressed in the wild type strain, whereas the PE8-PPE15 transcriptional unit was induced upon deletion of phoP, thus demonstrating the dual role of the regulator. On the contrary, 15 ChIP-seq peaks did not correlate with the presence of deregulated transcripts in their vicinity. The most striking signal in this group was the one upstream of mihF.
Deeper inspection of the RNA-seq results uncovered 140 transcripts whose expression underwent changes in the phoP mutant (Worksheet 1 in Table S2). Since 30 of these were part of the aforementioned operons, the remaining 110 were likely to be indirectly controlled by PhoP through regulatory cascades. It is worth recalling that PhoP binds upstream of genes encoding several transcriptional regulators (espR, whiB1, whiB3, whiB6) that may act downstream. Interestingly, an almost equal proportion of genes was found to be activated (68) or repressed (72) as a consequence of the mutation. Upon clustering these transcripts into functional categories, we observed significant enrichment for the “lipid metabolism” group among the up-regulated genes (p = 0.0016, Fisher's Exact test) and for the “PE/PPE” category among the down-regulated genes (p = 0.0024, Fisher's Exact test). Further discussion of the PhoP regulon will be presented elsewhere.
Independent validation of the RNA-seq data was obtained for a subset of genes by quantitative reverse transcription PCR (qRT-PCR), which confirmed the excellent correlation between high-throughput results and targeted quantification (Figure S1).
The most prominent PhoP binding site in the genome lay between genes rv2395 and PE_PGRS41 (Figure 2A) but, surprisingly, transcription of neither gene differed between strain H37Rv and its phoP mutant (Worksheet 1 in Table S2). However, in a previous study of ncRNA in M. bovis BCG, the mcr7 gene encoding a 350 nt transcript, was located within this region [20].
Northern blot analysis was performed on RNA extracted from wild type M. tuberculosis, phoP mutants and complemented strains in two different genetic backgrounds: the H37Rv laboratory strain and GC1237, a clinical isolate belonging to the Beijing family [21]. We detected an RNA of approximately 350 nt in length in wild type and complemented strains (Figure 2B), whose 5′-end could be mapped from the RNA-seq profile to coordinate 2,692,165 in the H37Rv genome. In contrast, we were unable to identify this RNA in the phoP mutants even when 10-times more RNA was used in Northern blot experiments (Figure S2). These results were also confirmed by qRT-PCR showing barely detectable levels of Mcr7 in the M. tuberculosis phoP mutant (Figure S2). The complete lack of expression of this ncRNA in the M. tuberculosis phoP mutant validates the findings obtained by ChIP-seq and RNA-seq and confirms the phoP mutant as an Mcr7-deficient strain as well. Next, we sought to establish whether Mcr7 is a primary transcript or conversely processed from a longer RNA. Detection of an mcr7 transcript of approximately 350 nt in length in the primary transcriptome of H37Rv (Figure 2B) ruled out the latter possibility and indicated that Mcr7 is a primary, unprocessed RNA.
We then investigated the presence of mcr7 in the Mycobacterium genus by bioinformatic analysis and found that it is predicted to be restricted to the M. tuberculosis complex. We therefore obtained expression data for mcr7 in five representative species. Surprisingly, the Mcr7 ncRNA is only weakly expressed in M. africanum, M. bovis, M. caprae and M. microti as compared to the high expression levels in M. tuberculosis (Figure 2C).
After demonstrating the strict PhoP regulation and the predominant expression of mcr7 in M. tuberculosis species, we tried to assign a biological role to this ncRNA. Most trans-acting ncRNA act by limited complementarity with their target mRNA, which results in post-transcriptional regulatory mechanisms [22]. A highly structured fold of Mcr7 with a 33-nt free loop (Figure S3) was predicted using the RNAfold server. A bioinformatic search for putative targets of Mcr7 resulted in 18 candidates with complementarity in their 5′-end portion (Figure S3). Given that some ncRNA exert their regulatory function through interaction with their loop structures [23], we focused on mRNAs that annealed with the 33-nt loop. As our previous unpublished results suggested that secretion of Tat-dependent substrates was affected in the phoP mutant strain, we focused on the predicted interaction between tatC and Mcr7. Interestingly, the 5′-end of the tatC mRNA is predicted to base pair with the major loop of Mcr7 (Figure 2D). The interacting region includes the putative ribosome binding site (RBS) and the first 6 codons of the tatC mRNA, suggesting that Mcr7 probably prevents ribosome loading and, consequently, translation of tatC mRNA. The tatC gene is essential for M. tuberculosis [24], and encodes a transmembrane protein that is part of the TatABC general secretory apparatus required for export of proteins with a twin arginine motif (RR) in their signal peptide [25] (Figure 2E). TatC recognizes the RR motif prior to protein translocation through the TatA channel (Figure 2E).
Our prediction suggested that Mcr7 might regulate tatC at the post-transcriptional level by occlusion of the RBS and the consequent translational down-regulation (Figure 2D). Consequently, we studied the secretome from exponentially grown cultures of strain H37Rv, its phoP mutant and a phoP complemented mutant by in-depth proteomics. The enrichment ratio for each protein in the secreted fraction was calculated as the log2 of normalized peptide abundance between the desired strains. Results are presented in Worksheet 1 in Table S3. Upon applying a cutoff based on the Significance B value (B<0.05), 37 proteins were found to be more secreted in the phoP mutant compared to the wild type strain. Sixteen of these (43.24%) exhibited an RR motif within the first 50 aminoacids. On the contrary, 6 out of 35 proteins, that were more abundant in the wild type displayed the RR motif (17.14%).
These encouraging findings prompted us to compare the abundance of previously predicted Tat substrates [24], [26], [27] in our secretome experiments. Results indicated that these were significantly more present in the secreted fraction of the phoP mutant relative to wild type and complemented strains (Figure 3A). In addition, we compared the relative secretion levels of EsxA (ESAT-6), EsxB (CFP-10), EspA and EspC since these proteins are well-known PhoP-dependent ESX-1 secretion substrates [7] and thus serve as controls. As expected, the secretome of the phoP mutant contained very low amounts of EsxA, EsxB, EspA and EspC, thus showing the opposite trend to Tat-dependent substrates (Figure 3A). Next, we validated these results by Western blot analysis of Ag85C [26] and Rv2525c [24] as known Tat-dependent substrates and EsxA as a PhoP-dependent ESX-1 substrate. The results corroborated the proteomic studies: the secreted fraction of the phoP mutant showed higher levels of Ag85C and Rv2525c proteins compared to the wild type and complemented mutant strains. On the contrary, EsxA secretion was undetectable in the phoP mutant compared to the strains harboring a wild type phoP allele (Figure 3B). Taken together, these results are consistent with a regulatory model involving PhoP, Mcr7 and tatC mRNA since the absence of Mcr7 in the phoP mutant would result in more efficient TatC translation and therefore increased secretion (Figure 3C).
In order to confirm that mcr7, but no other PhoP-dependent genes, influenced secretion of Tat substrates via post-transcriptional regulation of tatC mRNA, we restored Mcr7 production in the M. tuberculosis phoP mutant that we have previously demonstrated to be mcr7 deficient (Figure 2). The mcr7 gene was cloned downstream of the promoter for the 16S rRNA gene and the resultant construct was integrated into the chromosome of the H37Rv phoP mutant, thereby obtaining the mcr7-complemented strain. Northern blot experiments confirmed the authenticity and length of the mcr7 transcript (Figure 3D and Figure S4). Detection of TatC in whole-cell lysates of H37Rv, its phoP mutant, the phoP-complemented mutant and the mcr7-complemented strain demonstrated increased production of this protein in the phoP mutant in agreement with our proposed model (Figure 3E). Reintroduction of phoP complemented this phenotype as expected. Ectopic expression of mcr7 in the phoP mutant was sufficient to restore TatC levels to the wild type condition (Figure 3E), indicating that mcr7 per se was able to modulate expression of TatC, presumably by regulating translation of tatC mRNA.
To examine the phenotypic effect caused by reintroducing mcr7, we first showed by qRT-PCR that expression of the ncRNA from the surrogate promoter was only about 6-fold higher in the complemented mutant relative to the wild type strain (Figure 4A). Furthermore, we demonstrated that reintroduction of mcr7 in a phoP mutant did not influence the expression of the PhoP regulon that remained at undetectable levels in both the phoP mutant and mcr7-complemented strains (Figure 4A). Additionally, we proved that transcription of the tatC mRNA in the phoP mutant and in the mcr7-complemented strains showed no significant difference as compared to H37Rv (Figure 4A). Overall, these data ruled out a transcriptional impact of mcr7 on gene expression and supported the notion of a post-transcriptional effect exerted by the ncRNA on tatC.
We then investigated whether reintroduction of mcr7 in the phoP mutant restored secretion of Tat-dependent substrates to wild type levels. Western blot analysis of Ag85C in the whole-cell lysate and secreted fractions showed that while Ag85C is produced at very similar levels in all strains, secretion of this protein was more pronounced in the phoP mutant compared to the wild type and mcr7 complemented strains (Figure 4B). By contrast, inspection of ESX-1 substrates showed no detectable secretion of EsxA and EspD in either the phoP mutant or the mcr7-complemented mutant strains (Figure 4B). Therefore, the Mcr7 ncRNA did not impact the activity of the ESX-1 secretion apparatus whereas it did affect protein secretion through the Tat system.
Finally, we measured the enzymatic activity of a Tat-dependent substrate, the well-characterized BlaC [28] beta-lactamase using the chromogenic cephalosporin substrate nitrocefin. The results indicated faster reaction kinetics in the phoP mutant relative to wild type (Figure 4C), a finding correlated with the protein secretion levels observed in proteomic studies. Again, complementation with mcr7 was sufficient to successfully restore BlaC activity to wild type levels (Figure 4C). Since no deregulation of blaC transcription was observed in the phoP mutant (Worksheet 1 in Table S2) and in the mcr7-complemented strain (Figure 4A), we attributed this effect to post-transcriptional regulation of TatC by Mcr7.
High-resolution systems biology is helping greatly to unravel the complexities of the M. tuberculosis “regulome”. Recent works have uncovered a plethora of ncRNA [29] and reconstructed the hypoxia regulatory network [15] in this pathogen. In this study we integrated data from complementary high-throughput sequencing technologies and obtained extensive knowledge on PhoP-dependent transcriptional regulation in the tubercle bacillus. Specifically, ChIP-seq identified the PhoP binding sites along the M. tuberculosis chromosome (Figure 1), whereas strand-specific, single-nucleotide resolution transcriptomic analyses revealed previously unknown features of the PhoP regulatory network in vitro. Although good overlap was observed between RNA-seq data and published transcriptomic analyses ([5], see Worksheet 2 in Table S2 for comparison), major progress has been made as compared to traditional microarray-based approaches as indirect regulatory effects present in former studies [5], [14] have been unmasked. Importantly, we found many genes that were deregulated in the phoP mutant despite the absence of a PhoP binding signal in the respective promoter regions. In this regard, PhoP was found to control expression of several other regulatory proteins (e.g. EspR, WhiB1, WhiB3, WhiB6), which act in downstream regulatory cascades [30], [31]. Independent confirmation for this conclusion was presented recently in a regulatory model predicting production of acyltrehalose-derived lipids to be coordinated by a PhoP-WhiB3 network via regulation of pks2 and pks3 [15]. Additional proof was obtained upon comparing the transcriptome of the phoP mutant with the predicted binding sites of WhiB1, WhiB3 and WhiB6 (information available at http://www.tbdb.org/). Indeed, the overlap was found to include rv0996, rv1004c, rv1040c, rv2274c and rv3289c for WhiB1, rv1040c for WhiB3, and rv2396 for WhiB6. Concerning EspR, deregulation of the espACD operon was reported in an espR knockout strain [32], where a binding site for EspR was demonstrated [30]. Interestingly, PhoP was shown to bind upstream of lipF and of lppL, where EspR is also present [30], thereby increasing the complexity of the regulatory machinery at these loci. The small ncRNA Mcr7 can also be considered as an intermediate regulator in the PhoP global network, although it likely exerts its function at the post-transcriptional level. We will come back to Mcr7 later in the discussion.
Comparison of ChIP-seq and RNA-seq profiles uncovered several genes associated with a PhoP binding site but whose expression was not altered in a PhoP-deficient strain. We hypothesize that these genes may be subjected to additional layers of regulation or may respond to yet unexplored environmental conditions. This is exemplified by the mihF gene, which, despite its upstream PhoP binding site, was not found to be deregulated. Since the signal sensed and the downstream components of the PhoPR two-component system have not yet been completely elucidated, it is conceivable that signal transduction originating from PhoR may involve other factors than PhoP, thereby fine-tuning gene expression in M. tuberculosis in different conditions.
Galagan and colleagues recently mapped the binding sites of 50 transcription factors, including PhoP, in M. tuberculosis [15] by exploiting a tetracycline-inducible promoter system to overexpress the FLAG-tagged version of the protein of interest and using anti-FLAG antibodies in ChIP-seq experiments. Contrary to their approach, we worked in physiological conditions and performed immunoprecipitation assays using antibodies directed against native PhoP, thus avoiding artifacts due to abnormal expression levels or to biased protein-antibody interaction. In addition, use of the phoP mutant allowed false positive signals to be avoided. Interestingly, the number of peaks pinpointed in our work (35) was considerably smaller than that reported in Galagan et al. [15], where several signals were detected in intergenic as well as in intragenic regions. This could reflect the different methods employed, since artificial expression of PhoP may have increased binding to low affinity sites. Head-to-head comparison revealed that all but two of the peaks (yajC-gabT and lpdA-rv3304) identified here were also present in the other study (see Worksheet 2 in Table S1 and Worksheet 3 in Table S1 for detailed comparison).
The position of the PhoP peak with respect to the ORF start site merits discussion. We noticed that in the case of lipF and rv1535, the binding site was located >400 bp upstream of the translation start codon. This is consistent with previous results of footprinting assays for lipF [13] and with the presence of long 5′-UTRs, with presumptive regulatory roles, for lipF and rv1535 in the respective RNA-seq profiles. Interesting observations were made upon alignment of the PhoP and RNApol ChIP-seq profiles. PhoP was located upstream of the enzyme in most cases, suggesting a role as a positive regulator, later confirmed by RNA-seq data. On the contrary, PE8 was the only gene associated with a PhoP signal downstream of the RNApol peak, indicating potential steric hindrance and thus prevention of RNApol progression throughout the coding sequence. ChIP-seq analysis can therefore provide clues as to the role fulfilled by a transcription factor depending on the position of its binding site with respect to RNApol.
Inspection of the PhoP targets uncovered unusual binding sites in the 3′-end of ORF such as the one between hddA and ldtA. Since this peak is at the end of two convergent genes, it likely corresponds to an unmapped small RNA. Closer inspection of this intergenic region revealed the presence of a novel small transcript downregulated in the phoP mutant. Another case is represented by rv2137c, where the PhoP interacting region was mapped within the ORF, suggesting an alternative, PhoP-dependent start codon. Indeed, a polypeptide starting at the ATG codon at nucleotide 106, in frame with the currently annotated start site, shows more than 85% identity with the corresponding proteins in all other mycobacteria whose genomes have been sequenced.
The bipartite PhoP consensus sequence derived from ChIP-seq analysis is consistent with the crystal structure of the dimeric PhoP regulator that is predicted to bind to direct repeats [10]. It also agrees with previous footprinting experiments demonstrating binding of PhoP upstream of its own gene [11], [13] and in the promoter regions of lipF, fadD21, pks2 [13]. On the other hand, the divergent orientation of the PhoP binding motif relative to the pks3 gene can be subjected to different interpretations. It could be that the adjacent rv1179c is the gene directly controlled by PhoP while pks3 undergoes indirect regulation. Alternatively, transcriptional regulation in that locus might be independent of directional positioning of the transcription factor.
The last years have witnessed increased attention to ncRNA in prokaryotic organisms, including Salmonella enterica [33], Legionella pneumophila [34], Listeria monocytogenes [35] and M. tuberculosis [29]. These molecules have been predicted to exert their function at the post-transcriptional level by modulating translation of RNAs [36]. This process has important implications when bacteria face environmental stresses since it allows faster responses than classical transcriptional regulation. In this study we disclose the Mcr7 ncRNA encoded by the mcr7 gene, located between rv2395 and PE_PGRS41 (Figure 2). The latter was described as highly repressed in a phoP mutant from microarray experiments by Walters and co-workers [5]. Thanks to the increased resolution provided by RNA-seq, we can now identify the heavily deregulated gene as mcr7 rather than PE_PGRS41. The position of the probes in the microarray assay probably did not allow such precision. A similar observation can be made for a study that characterized the transcriptional differences between the avirulent strain H37Ra and H37Rv [6]. The gene encoding PE_PGRS41 (and likely the associated intergenic region carrying mcr7) was found to be the most highly deregulated. Importantly, H37Ra is a natural mutant in the phoP gene since it carries a polymorphism in the DNA binding domain [7], [11], thus indicating that reduced virulence is associated with lack of PhoP activity and impaired expression of the locus encoding mcr7. We confirmed this prediction by measuring the expression levels of mcr7 in H37Ra by qRT-PCR. The ncRNA was found to be poorly detectable as compared to H37Rv (Figure S5).
In the same genomic region of the CDC1551 strain, Abramovitch and colleagues postulated the existence of the aprABC locus with aprC corresponding to PE_PGRS41 and aprA and aprB corresponding to ORFs MT2466 and MT2467 [37]. These ORFs were not predicted in strain H37Rv as neither their codon usage nor positional base composition are typical of true protein coding sequences [16], [38]. The mcr7 gene completely overlaps the hypothetical aprA. A major PhoP binding site precedes the mcr7 gene but there is none immediately upstream of PE_PGRS41. Our results from Northern blot experiments clearly showed one prominent band of approximately 350 nt in length corresponding to Mcr7, that was first described in M. bovis BCG [20]. This genomic locus is restricted to species belonging to the M. tuberculosis complex, including M. canettii, and was not identified in M. kansasii and in M. marinum, although expression of mcr7 was found to be particularly prominent in M. tuberculosis only. The expression pattern of mcr7 tallies with the proposed evolutionary pathway of the tubercle bacilli [39]. Indeed, those lineages that evolved from a common M. tuberculosis-like ancestor by multiple deletions (M. africanum, M. microti, M. caprae and M. bovis) express low-levels of the ncRNA as compared to the M. tuberculosis strain. In light of our findings, it is tempting to speculate that modulation of the activity of the Tat secretion system by means of a small RNA has played a role in shaping the adaptation of tubercle bacilli and/or in restricting their host spectrum. We investigated the potential role played by Mcr7 in virulence by performing ex vivo and in vivo infections. Complementation of the phoP mutant with mcr7 alone did not restore the wild type virulence and the strain was more attenuated than the phoP mutant (Figure S6). This phenotype may be related to the ectopic overexpression of Mcr7, which was indeed associated with small colony size (data not shown).
The predicted folding model of Mcr7 revealed the presence of a 33-nt loop with the potential to anneal to three candidate mRNAs: rv2767c, rv2053c and tatC (Figure S3). Since our results provided convincing evidence for increased secretion of the Tat substrates, Ag85A and Ag85C, in phoP mutants, we prioritized the study of Tat-dependent secretion. However, we cannot exclude a post-transcriptional impact of Mcr7 on expression of the hypothetical membrane proteins Rv2767c and Rv2053c, although their role in M. tuberculosis physiology is questionable since previous proteomic experiments failed to detect them in the total proteome or in cellular subfractions [40], [41], [42], [43]). Proteomic analysis demonstrated that proteins secreted through the Tat system are more abundant in the extracellular fraction of the PhoP-deficient strain (Figure 3). A genetic approach relying on complementation of the phoP mutant with mcr7 proved the involvement of the ncRNA in the regulation of Tat-dependent secretion at the post-transcriptional level while no impact on the amount of mRNA was observed (Figures 3 and 4). This is the first report describing the function of a ncRNA in M. tuberculosis. Notably, M. tuberculosis phoP mutants display pleiotropic phenotypic effects including impaired secretion of ESX-1 substrates [7], compromised production of sulphatides (SL), diacyltrehaloses (DAT) and polyacyltrehaloses (PAT) [8] and reduced virulence in the macrophage and mouse models of infection [4], [5]. Mcr7 was found to be sufficient to re-establish the wild type phenotype with respect to secretion of Tat substrates whereas ESX-1 substrates were unaffected, thus evoking a specific regulatory cascade where Mcr7 acts downstream of PhoP.
Overall, this work refined the role played by PhoP in control of gene expression in M. tuberculosis. A previous study reported that PhoP is involved in the regulation of the ESX-1 secretion system [7] but no direct evidence had been provided so far. Here we uncovered the existence of a novel regulatory cascade composed of at least two regulatory factors, PhoP and EspR, that ultimately controls ESX-1 functions, such as secretion of EsxA, via regulation of the espACD locus [7], [30]. In addition, we demonstrated a role for the PhoP-dependent ncRNA Mcr7 in Tat-dependent secretion of well-known M. tuberculosis antigens, namely the immunodominant Ag85 complex. PhoP could therefore also mediate antigenicity and pathogenesis via the Ag85 complex itself and/or through trehalose 6,6-dimycolate, an abundant glycolipid in the mycobacterial cell wall whose biosynthesis is catalyzed by Ag85 proteins. The Ag85 complex is also involved in binding to human fibronectin, important for cell adhesion and invasion [44], [45]. Mcr7 could therefore represent the missing link between PhoP and the downstream processes required for successful infection of the host. Finally, our findings provided a new molecular basis to explain the better protection against tuberculosis conferred by the candidate vaccine strain MTBVAC, which carries a deletion in phoP [9]. While the reduced virulence results mainly from abrogation of the ESX-1 secretion system and possibly from lack of complex lipids, its efficacy may be ascribed to improved antigenicity properties following silencing of Mcr7 and the ensuing increase in secretion of Tat substrates such as the Ag85 proteins.
All animal work has been conducted according to the national and international guidelines. The protocols for animal handling were previously approved by University of Zaragoza Animal Ethics Committee (protocol number PI43/10).
Mycobacterium tuberculosis H37Rv [16], GC1237 [21] wild type strains and their isogenic phoP mutants were previously described [11]. The growth rate of the wild type and of the mutant strains were similar (Figure S7). M. bovis AF2122/97 [46], M. caprae M57 [39], M. microti 15496 and M. africanum MAF419 [47] were used as representative strains of the M. tuberculosis complex. Mycobacterial strains were grown at 37°C in 7H9 medium (Difco) supplemented with 0.05% Tween 80 and 10% albumin-dextrose-catalase (ADC, Middlebrook) or on 7H10 plates supplemented with 10% ADC. For M. tuberculosis complex strains different from M. tuberculosis, 40 mM sodium pyruvate was added to the medium. Escherichia coli DH5α used for cloning procedures was grown at 37°C in LB broth or on LB agar plates. Kanamycin (20 µg/ml) and hygromycin (20 µg/ml) were used as appropriate.
All chemicals were purchased from Sigma-Aldrich, unless otherwise stated. Immunoblotting was performed with mouse monoclonal anti-EsxA antibodies (Hyb 076-08, Abcam), mouse monoclonal anti-Ag85C antibodies (HYT27, Abcam), mouse monoclonal anti-GroEL2 antibodies (BDI578, Abcam), rabbit polyclonal anti-Rv2525c antibodies [24], rat polyclonal anti-EspD antibodies (kindly provided by Jeffrey Chen) and rabbit polyclonal anti-TatC (Eurogentec) antibodies. Polyclonal antibodies to the transcriptional regulator PhoP of M. tuberculosis were obtained from rabbits that received five doses of PhoP (0.5 mg), at weeks 0, 4, 8, 12 and 16, respectively. These anti-PhoP antibodies were validated by ELISA (ZEU-Immunotec Zaragoza, Spain). Sequences of the oligonucleotides used in this study will be provided upon request.
The pAZ31 plasmid was kindly provided by Ainhoa Arbues. The pWM222 plasmid was used for phoPR complementation in northern blot experiments and was constructed as follows. A 2.7 kbp region spanning the phoPR operon was amplified by PCR using primers (5′-ATACTAGTGGCATCACCCAACGCTTGTT-3′) and (5′-ATACTAGTGGTGAGCCAGCTGATCGG-3′). This PCR product was digested with SpeI and subsequently transferred into a pMV361 [48] derivative deleted from the phsp60 promoter. In this construct, the phoPR operon is expressed from its native promoter. Plasmid pLZ11 used for mcr7 expression was constructed by inserting a transcriptional fusion of the rrs (16S rRNA) promoter with the mcr7 transcript following a similar strategy to that described in [29]. This transcriptional fusion was accomplished using an overlapping two-step PCR strategy. Briefly, the rrs promoter was PCR amplified using primers rrsOV Fw: GACGTCCCGCAGCTGTCGAGCGCT and rrsOV Rv: GGGCCGCCGGCCCTGCCAGTCTAATACAAATCC. The mcr7 region was amplified using primers mcr7Ov Fw: GACTGGCAGGGCCGGCGGCCCGACACA and mcr7Ov Rv: AAGCTTCCACCTTCTCGTTACCCGCCTCTG. Both PCR products overlap in 21 bp (underlined nucleotides) and were used as self-templates in a PCR reaction. The entire transcriptional fusion was amplified by PCR using the flanking primers rrsOV Fw and mcr7Ov Rv, digested with HindIII and EcoRI and introduced between the HindIII and EcoRI sites of pMV361. The resulting construct was introduced in mycobacteria by electroporation and colonies carrying a chromosome-integrated vector were checked by PCR.
Chromatin immunoprecipitation experiments were performed as previously described [49] with the following modifications. We performed two independent ChIP-seq experiments with the wild type strain H37Rv and one experiment with the control phoP mutant. Briefly, M. tuberculosis cultures were grown to exponential phase (optical density at 600 nm of 0.4) and cross-linked with 1% formaldehyde for ten minutes at 37°C. Cross-linking was quenched by addition of glycine (125 mM). Cells were then washed twice with Tris-buffered saline (TBS, 20 mM Tris-HCl pH 7.5, 150 mM NaCl), resuspended in 4 ml immunoprecipitation (IP) buffer (50 mM Hepes-KOH pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, protease inhibitor cocktail from Roche) and sonicated to shear DNA using Bioruptor (Diagenode). Cell debris was removed by centrifugation and the supernatant used in IP experiments. Nucleo-protein extracts were incubated with 50 µl of rabbit polyclonal anti-PhoP antibodies at 4°C for 2 days on a rotating wheel. Complexes were subsequently precipitated with Dynabeads (Dynal, anti-rabbit, Invitrogen) for three hours at 4°C. Beads were washed twice with IP buffer, once with IP buffer plus 500 mM NaCl, once with buffer III (10 mM Tris-HCl pH 8, 250 mM LiCl, 1 mM EDTA, 0.5% Nonidet-P40, 0.5% sodium deoxycholate), once with Tris-EDTA buffer pH 7.5. Elution was performed in 50 mM Tris-HCl pH 7.5, 10 mM EDTA, 1% SDS for 40 minutes at 65°C. Samples were finally treated with RNAse A for one hour at 37°C and cross-links were reversed by incubation for two hours at 50°C and for eight hours at 65°C in 0.5× elution buffer with 50 µg Proteinase K (Eurogentec). DNA was purified by phenol-chloroform extraction and quantified by Nanodrop and Qubit fluorometer according to the manufacturer's recommendations (Invitrogen).
DNA fragments obtained from the immunoprecipitation procedure were used for library construction and sequencing with the ChIP-Seq Sample Preparation Kit (Illumina), according to the protocol provided by the manufacturer. One lane per library was sequenced on the Illumina Genome Analyzer IIx at the Lausanne Genomics Technologies Facility using the SR Cluster Generation Kit v2 and SBS 36 Cycle Kit v2. Data were processed with the Illumina Pipeline Software v1.40.
All analyses in this study were carried out using the M. tuberculosis H37Rv annotation from the TubercuList database (http://tuberculist.epfl.ch/), which includes 4019 protein coding sequences (CDS), 73 genes encoding for stable RNAs, small RNAs and tRNAs. In order to quantify protein occupancy and transcription across the entire genome, 3080 intergenic regions (regions flanked by two non-overlapping CDS) were included, resulting in a total of 7172 features.
ChIP-seq analysis was performed using the HTSstation pipeline at EPFL (http://htsstation.epfl.ch/). Briefly, the single-ended sequence reads generated from ChIP-seq experiments were aligned to the M. tuberculosis H37Rv genome (NCBI accession NC_000962.2) using Bowtie [50] with options “-l 28 -best -strata”. Peaks were analysed using MACS v.1.4 [51] with parameters “-bw 200 -m 10100”. Alignment files were converted to bigWig format for visualization in the UCSC genome browser Mycobacterium tuberculosis H37Rv 06/20/1998 Assembly [52]. To determine the level of ChIP-seq enrichment for each feature, an enrichment ratio (ER) was calculated by dividing the read count for the ChIP-seq sample in the wild type strain by the read count for the mutant (control) sample.
PhoP binding site motifs were searched using the MEME Suite (http://meme.nbcr.net/meme/) in sequence regions encompassing 100 bp upstream and 100 bp downstream of the predicted peak summit. Motif sequence logo was obtained using WebLogo3 (http://weblogo.threeplusone.com/).
Mycobacterial cultures (one for the wild type strain and one for the phoP mutant) were grown to exponential phase (OD600 = 0.5-0.6) and pelleted by centrifugation. To minimize RNA degradation bacteria were resuspended in 1 ml RNA Protect Bacteria Reagent (Qiagen), incubated for 5 min at room temperature and then centrifuged. Bacterial pellets were resuspended in 0.4 ml lysis buffer (0.5% SDS, 20 mM NaAc, 0.1 mM EDTA) and 1 ml phenol:chloroform (pH = 4.5) 1∶1. Suspensions were transferred to tubes containing glass beads (Qbiogene) and lysed using a ribolyser (Fast-prep instrument) with a three-cycle program (15 sec at speed 6.5 m) including cooling the samples on ice for 5 min between pulses. Samples were then centrifuged and the homogenate was removed from the beads and transferred to a tube containing chloroform:isoamylalcohol 24∶1. Tubes were inverted carefully before centrifugation and the upper (aqueous) phase was then transferred to a fresh tube containing 0.3 M Na-acetate (pH = 5.5) and isopropanol. Precipitated nucleic acids were collected by centrifugation. The pellets were rinsed with 70% ethanol and air dried before being re-dissolved in RNase-free water. DNA was removed from RNA samples using Turbo DNA free (Ambion) by incubation at 37°C for 1 h. RNA integrity was assessed by agarose gel electrophoresis and absence of contaminating DNA was checked by lack of amplification products after 30 PCR cycles.
Primary, unprocessed RNA from H37Rv was prepared as indicated in [53]. Briefly, 10 µg total RNA were treated with 10 U of Terminal 5′-phosphate dependent Exonuclease (Epicentre) for 24 h at 30°C followed by phenol extraction and isopropanol precipitation. Successful preparation of primary transcriptome was confirmed by lack of 23S/16S rRNA bands in agarose gels.
100 ng of total RNA were mixed with 5× Fragmentation buffer (Applied Biosystems), incubated for 4 minutes at 70°C and then transferred immediately on ice. RNA was purified using RNAClean XP beads (Beckman Coulter), according to the manufacturer's recommendations, and subsequently treated with Antarctic phosphatase (New England Biolabs). RNA was then re-phosphorylated at the 5′-end with polynucleotide kinase (New England Biolabs) and purified with Qiagen RNeasy MinElute columns. In order to ensure strand-specificity, v1.5 sRNA adapters (Illumina) were ligated at the 5′- and 3′-ends using RNA ligase. Reverse transcription was carried out using SuperScript III Reverse Transcriptase (Invitrogen) and SRA RT primer (Illumina). Twelve cycles of PCR amplification using Phusion DNA polymerase were then performed and the library was finally purified with AMPure beads (Beckman Coulter) as per the manufacturer's instructions. A small aliquot (2.5 µl) was analyzed on Invitrogen Qubit and Agilent Bioanalyzer prior to sequencing on Illumina HiSeq 2000 using the TruSeq SR Cluster Generation Kit v3 and TruSeq SBS Kit v3. Data were processed with the Illumina Pipeline Software v1.82.
The single-ended sequence reads generated from RNA-seq experiments were aligned to the M. tuberculosis H37Rv genome (NCBI accession NC_000962.2) using Bowtie2 with default parameters [54]. Read counts for all annotated features were obtained with htseq-count program (http://www-huber.embl.de/users/anders/HTSeq/doc/count.html). Regions where genes overlapped were excluded from counting. Reads spanning more than one feature were counted for each feature. Since the RNA library was strand-specific, the orientation of sequence reads had to correspond to the orientation of annotated features to be counted. Analysis of differential gene expression was carried out using the DESeq package [55].
One microgram of M. tuberculosis RNA was converted to cDNA using SuperScript III Reverse Transcriptase (Invitrogen) according to the manufacturer's recommendations. All PCR primers were designed using Primer Express software (Applied Biosystems). The 10 µl PCR reaction consisted of 1× Sybr Green PCR Master Mix (Applied Biosystems), 0.25 µM of each primer and 1 µl of 1∶10 diluted cDNA or IP DNA from immunoprecipitation reactions. Reactions were carried out in triplicate in an Applied Biosystems StepOnePlus Sequence Detection System (Applied Biosystems) according to the manufacturer's instructions. Melting curves were constructed to ensure that only one amplification product was obtained. In the case of qRT-PCR for RNA-seq data confirmation, normalization was obtained to the number of sigA molecules in each sample. Regarding the qPCR for ChIP-seq data validation, the number of target molecules was normalized to the mutant (control) sample, after subtraction of the background represented by the mock-IP (no antibody control).
Northern blot was performed using the DIG Northern starter kit (Roche) following the manufacturer's recommendations. Briefly, total RNA was separated using denaturing 1% agarose gels in 1× MOPS buffer containing 2% formaldehyde. RNA was transferred by capillary blotting to Hybond-N+ nylon membranes (Amersham) and UV-crosslinked prior to incubation with the desired probe. Digoxigenin (DIG)-labelled probes were synthesized to detect rrf (5S rRNA) and mcr7 transcripts using the primer pairs NB-5S-rRNA-fw (ttacggcggccacagcgg)/NB-T7-5S-rRNA-rv (taatacgactcactatagggtgtcctacttttccacccggagggg), NB-mcr7-fw (ccggcggcccgacacatg)/NB-T7-mcr7-rv (taatacgactcactatagggacccgctcaagcaggtcg) respectively. The T7 promoter used for in vitro transcription and labeling of RNA is underlined. RNA transcripts complementary to each probe were detected by Western-blot using an anti-DIG antibody conjugated to alkaline phosphatase and the chemiluminescent substrate CDP-Star.
The secondary structure fold of mcr7 was predicted using the RNAfold web server (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi). Prediction of mcr7 putative targets was performed using TargetRNA (http://cs.wellesley.edu/~btjaden/TargetRNA2/index.html) allowing antisense complementarity from -80 to +20 relative to ORF translation start sites of M. tuberculosis H37Rv. A minimum hybridization seed of 7 nt and a p-value threshold of 0.05 were required for target transcripts.
In order to avoid albumin contamination in the secreted protein fraction, cultures were grown in 7H9 (Difco) 0.05% Tween 80 supplemented with 0.2% dextrose, 0.085% NaCl. After 2-3 weeks incubation at 37°C, cultures were pelleted by centrifugation. The supernatant containing secreted proteins was incubated with 10% trichloroacetic acid (TCA) for one hour in ice and then centrifuged at 4°C for 30 min. Pelleted proteins were rinsed with cold acetone and then resuspended in 150 mM TrisHCl pH 8. Protein integrity and absence of albumin contamination was checked by SDS-PAGE and Coomassie staining. The pelleted fraction of bacterial cultures was used for extraction of whole-cell proteins. The pellet was resuspended in PBS containing 1% triton ×100 and a cocktail of protease inhibitors (Roche) and sonicated for 30 minutes at 4°C using a Bioruptor (Diagenode). Samples were then centrifuged and the upper phase containing whole-cell lysate was used in downstream experiments. To prepare whole-cell extracts for detection of TatC by Western blot, proteins were further solubilized with 9 M urea, 70 mM DTT and 2% Triton X-100 followed by TCA precipitation and final resuspension in 150 mM TrisHCl pH 8.
Each sample (8 µg) was reconstituted in 50 µl of 4 M Urea, 10% acetonitrile and buffered with Tris-HCl pH 8.5 to a final concentration of 30 mM. Proteins were reduced using 10 mM dithioerythritol (DTE) at 37°C for 60 min. Cooled samples were subsequently incubated in 40 mM iodoacetamide at 37°C for 45 min in a light-protected environment. Reaction was quenched by addition of DTE to a final concentration of 10 mM. A two-step digestion was performed using Lys-C (1∶50 enzyme: protein) for 2 hours at 37°C. The lysates were first diluted 5-fold and samples were again digested overnight at 37°C using Mass Spectrometry grade trypsin gold (1∶50 enzyme: protein) and 10 mM CaCl2. Reaction was stopped by addition of 2 µl of pure formic acid (FA) and peptides were concentrated by vacuum centrifugation to a final volume of 70 µl. Samples were dimethyl-labeled as previously described [56]. The sample H37Rv phoP- was labeled with light dimethyl reactants (CH2O + NaBH3CN), the sample H37Rv was labeled with medium reactants (CD2O + NaBH3CN) and the sample H37Rv phoP- complemented was labeled with heavy methyl reactants (13CD2O + NABD3CN). As a final step of labeling procedure, samples were mixed in a 1∶1∶1 [(Light: Medium: Heavy) ratio and extensively lyophilized. Technical replicates were obtained. SAX fractionation was performed as previously described [57]. The eluted fractions were dried by vacuum centrifugation and used for LC-MS analysis.
Each SAX fraction was resuspended in 2% acetonitrile, 0.1% FA for LC-MS/MS injections and then loaded on a homemade capillary pre-column (Magic AQ C18; 3 µm by 200 Å; 2 cm×100 µm ID) and separated on a C18 tip-capillary column (Nikkyo Technos Co; Magic AQ C18; 3 µm by 100 Å; 15 cm×75 µm). MS/MS data was acquired in data-dependent mode (over a 4 hr acetonitrile 2–42% gradient) on an Orbitrap Q exactive Mass spectrometer equipped with a Dionex Ultimate 3000 RSLC nano UPLC system and homemade nanoESI source. Acquired RAW files were processed using MaxQuant version 1.3.0.5 [58] and its internal search engine Andromeda [59]. MS/Ms spectra were searched against M. tuberculosis strain H37Rv database R23 (http://tuberculist.epfl.ch/) [60]. MaxQuant default identification settings were used in combination with dimethyl-labeling parameters. Search results were filtered with a false-discovery rate of 0.01. Known contaminants and reverse hits were removed before statistical analysis. Relative quantification within different conditions was obtained by calculating the significance B values for each of the identified proteins using Perseus [58].
Protein samples were quantified using the RC DC protein assay (BioRad) and equal amounts of protein preparations were loaded per well. Proteins were separated on SDS-PAGE 12–15% gels and transferred onto PVDF membranes using a semidry electrophoresis transfer apparatus (Bio-Rad). Membranes were incubated in TBS-T blocking buffer (25 mM Tris pH 7.5, 150 mM NaCl, 0.05% Tween 20) with 5% w/v skimmed milk powder for 30 min prior to overnight incubation with primary antibodies at the dilution indicated below. Membranes were washed in TBS-T three times, and then incubated with secondary antibodies for 1 h before washing. Antibodies were used at the following dilutions: 1∶2,000 for anti-EsxA, 1∶5,000 for anti-Ag85C, 1∶500 for anti-GroEL2, 1∶1,000 for anti-Rv2525, 1∶1,000 for anti-EspD and 1∶1,000 for anti-TatC. Horseradish peroxidase (HRP) conjugated IgG secondary antibodies (Sigma-Aldrich) were used at a 1∶20,000 dilution. Signals were detected using chemiluminescent substrates (GE Healthcare).
Bacterial cultures were grown to OD 600 nm 0.6–0.8 and pelleted. Nitrocefin was added to culture supernatants at 50 mM final concentration and absorbance was measured at 486 nm (Synergy HT BioTEK) every 10 minutes for 3h. Slope of linear range was measured and normalized against total CFUs of the culture.
Virulence of the different M. tuberculosis strains was evaluated in J774A.1 murine macrophages according to a previously published procedure [61], [62]. Briefly, cells were grown in DMEM medium containing 10% fetal bovine serum at 37°C under 5% CO2. 10,000 macrophages per well were seeded into a 384-well plate in a total volume of 45 µl and incubated at 37°C for 30 minutes before infection. Cells were infected at an MOI of 10 with titrated stocks of H37Rv, phoP mutant, phoP-complemented and mcr7-complemented strains. On day 3, macrophage survival was measured by exposing the infected cells to PrestoBlue Cell Viability Reagent (Life Technologies) for 1 hour. Fluorescence was read using a TECAN Infinite M200 microplate reader and statistical analysis was performed with the unpaired T-test method.
C57BL/6 mice were infected intranasally with an inoculum of 2.5×104 cfu/ml (6 mice per group). Four weeks post-infection mice were euthanized and lungs were plated on 7H11 plates supplemented with 0.5% glycerol, 10% albumin-dextrose-catalase (ADC, Middlebrook), polymixin B 50 U/ml, trimethoprim 0.02 mg/ml and amphotericin B 0.01 mg/ml. Unpaired T-test was used for statistical analysis. The protocols for animal handling were previously approved by University of Zaragoza Animal Ethics Committee (protocol number PI43/10).
The ChIP-seq and RNA-seq datasets have been deposited in NCBI's Gene Expression Omnibus [63] under accession number GSE54241.
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://www.proteomexchange.org) via the PRIDE (Proteomics Identification Database) partner repository [64] with the dataset identifier PXD000698.
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10.1371/journal.pntd.0004529 | Evaluation of PCR Approaches for Detection of Bartonella bacilliformis in Blood Samples | The lack of an effective diagnostic tool for Carrion’s disease leads to misdiagnosis, wrong treatments and perpetuation of asymptomatic carriers living in endemic areas. Conventional PCR approaches have been reported as a diagnostic technique. However, the detection limit of these techniques is not clear as well as if its usefulness in low bacteriemia cases. The aim of this study was to evaluate the detection limit of 3 PCR approaches.
We determined the detection limit of 3 different PCR approaches: Bartonella-specific 16S rRNA, fla and its genes. We also evaluated the viability of dry blood spots to be used as a sample transport system. Our results show that 16S rRNA PCR is the approach with a lowest detection limit, 5 CFU/μL, and thus, the best diagnostic PCR tool studied. Dry blood spots diminish the sensitivity of the assay.
From the tested PCRs, the 16S rRNA PCR-approach is the best to be used in the direct blood detection of acute cases of Carrion’s disease. However its use in samples from dry blood spots results in easier management of transport samples in rural areas, a slight decrease in the sensitivity was observed. The usefulness to detect by PCR the presence of low-bacteriemic or asymptomatic carriers is doubtful, showing the need to search for new more sensible techniques.
| Carrion’s disease is an endemic illness in the Andean valleys of Peru that achieves high mortality rates in the absence of antibiotic treatment. There are three clinical manifestations, febrile acute patients, chronic patients as well as asymptomatic carriers. No effective diagnostic tool exists nowadays leading to misdiagnosis and the perpetuation of the illness. The objective of this study was to determine the detection limit of three PCR approaches both from blood samples as well as from filter papers. Furthermore, the specificity was also accessed. We found that the best PCR approach studied was the amplification of the 16S rRNA from blood samples with a detection limit of 5 CFU/μL, the same when using dry blood in filter paper, although the obtained bands were not so evident. Present results highlight the need to develop more sensitive techniques able to be used both in rural areas and in the detection of asymptomatic carriers.
| Bartonella bacilliformis is the etiological agent of Carrion’s disease, an overlooked illness with a lethal febrile stage and a warty phase. Its endemicity is restricted to Peru, Ecuador and Colombia, with some cases having been described in Bolivia and Chile. The transmission is by a sandfly of the genera Luyzomyia, mostly Lutzomyia verrucarum [1]. The human is the only reservoir known, and in endemic areas about 40% of asymptomatic carriers have been described [2]. In addition, Carrion’s disease-like syndromes have been related to two other Bartonella species: Bartonella rochalimae and Bartonella ancashensis [3–5]. Although its relevance remains uncertain, these species may be an explanation for the Carrion’s disease cases sporadically reported in distant areas such as Guatemala or Thailand [1]. In fact, B. rochalimae has been isolated worldwide [6,7].
Although the warty phase is easy to diagnose by the clinic manifestations, the initial febrile stage as well as asymptomatic carriers, are often misdiagnosed or non-diagnosed leading to perpetuation of the illness. Correct diagnosis of both acute and asymptomatic carriers is extremely important and adequate treatment is imperative to save lives. In endemic areas the diagnosis is usually made by thin blood smear and/or by clinical data. Despite having a specificity of microscopy of 96%, a low sensitivity of 36% has been described [8]. Moreover, other diseases such as malaria, dengue or tuberculosis that are also present, should be taken into account, since the first symptoms are common and may lead to misdiagnosis and erroneous treatments. All these factors are of enormous relevance since the mortality rates of Carrion’s disease are of 40–85% without treatment [9]. Furthermore, even despite receiving correct treatment the mortality rate is of 10% [10]. A more reliable method is blood culture but this is cumbersome, time-consuming and contaminations have been described in the 7–20% of the cultures [11]. Serologic tests have also been described and show a higher specificity of about 85% for both IgM ELISA and indirect fluorescence antibody test, but are difficult for routine practice [1]. Molecular diagnosis by PCR is probably the easiest way to achieve a more accurate diagnosis in endemic areas, as the equipment required is not as sophisticated or expensive, may be installed in different Health Regional Centers which may provide diagnosis to more peripheral patients, and the personnel may be easily trained in technique management. Several PCR approaches have been described in the literature in the last years [12,13]. However, these studies do not generally involve a large number of samples and additionally, as occurs with the remaining diagnostic tools, they are hampered by the lack of a standard case definition. In any case, PCR approaches have been showed as more effective that optical microscopic [12], being able to diagnostic Carrion's disease patients in acute phase previously classified as negatives by thin blood smear. Nonetheless, a critical issue is the detection limit of these techniques, raising doubts about its usefulness in the detection of low-bacteraemia carriers.
Dried blood spot (DBS) is used for the diagnosis of several infectious diseases [14,15], and has been proposed for use as easy method to transfer blood samples from endemic areas to reference centers in order to carry out molecular techniques for the diagnosis of Carrion’s disease [13]. Therefore, since this illness principally affects children in rural areas, DBS may be an easy solution to both the transportation of samples and for small blood volume collection in the pediatric setting.
The aim of this study was to evaluate the detection limit of three PCR approaches designed to detect B. bacilliformis, both in blood and filter papers to test their potential use for transferring samples from endemic areas to reference centers.
We used a collection strain of B. bacilliformis from the Institute Pasteur, CIP 57.20 (NCTC 12136). The strain was grown on blood agar (BD, Germany) at 28°C and 5% CO2 until confluent growth.
To accurately quantify the amount of B. bacilliformis we used flow cytometry from the Citomics core facility of the Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS). For this, one grown agar plate was diluted in appropriate buffer and Perfect-count microspheres were used. Serial dilutions (106 CFU/mL—10 CFU/mL) were made in whole blood provided by the blood bank of the Hospital Clinic.
One-hundred μL of the above mentioned bacterial serial dilutions were transfered to Whatmann 903 filter papers and let dry at least one week at room temperature to mimic the sample transfer conditions in a real scenario.
DNA extraction was done from 100 μL blood and from dry blood spots with the Qiamp DNA Mini Kit (Qiagen, Germany), according to the manufacturer’s instructions except that the final elution volume was 100 μL.
Fragments of Bartonella-specific 16S rRNA, flagellin (fla) genes as well as the variable-intergenic region (its) were amplified. The primers used were 5’-CCTTCAGTTMGGCTGGATC-3’ and 5’-GCCYCCTTGCGGTTAGCACA-3’ for 16S rRNA [16], 5’-ATAGAAAGAGCCTGAATACC-3’ and 5`-TGATGAAGCATGACAGTAACAC-3’ for flagellin and 5’-AGATGATGATCCCAAGCCTTCTGG-3’ [17], and 5’-CTTCTCTTCACAATTTCAAT-3’ [18] for the amplification of variable-intergenic region. The PCRs were performed in a 25-μL total reaction volume with 500 nM forward primer, 500 nM reverse primer, 9,75 μL H2O and 5 μL of DNA following the conditions: 30 seconds at 94°C, 30 seconds at 55°C and 2 minutes at 72°C for 30 cycles. A 2% agarose gel stained with Sybr Safe was performed, and the results were visualized with an ImageQuant LAS4000 transiluminator (GE Healthcare Europe GmbH, Barcelona, Spain).
The detection limit was considered as the lowest dilution at which a positive result was obtained and considering the number of copies of each gene in the B. bacilliformis genome. All the above mentioned experiments were done in duplicate intra-assay and at two different times.
The specificity was tested by doing the same PCR approaches to other member of the Bartonella genus both in vitro: Bartonella elizabethae (strain 30455), Bartonella grahamii (strain 50771), Bartonella henselae, Bartonella koehlerae (strain 30773), Bartonella tamiae (Strain Th307), and Bartonella vinsonii subsp. vinsonii (strain 30453), and in silico for the remaining 25 recognized species plus B. ancashensis. In addition other plate-grown bacteremia microorganisms such as Escherichia coli, Pseudomonas spp., Shigella spp., Klebsiella spp., Haemophilus spp., Staphylococcus aureus and Streptococcus spp., as well as an intracellular microorganism such as Ricketsia spp. and Brucella melitensis were also tested.
When DNA was directly extracted from the blood, the detection limit was 5 CFU/μL for both the Bartonella-specific 16S rRNA and the fla genes. Meanwhile, a limit of 500 CFU/μL was obtained on amplification of the its region. In the case of DBS, the Bartonella-specific 16S rRNA PCR approach showed the lowest detection limit, which was also of 5 CFU/μL. Concerning dry blood, despite the detection limit being the same for 16S rRNA and its, the sensitivity decreased for fla when the detection limit dropped to 500 CFU/μL compared with 5 CFU/μL obtained directly from blood (Table 1). It was of note that fainter bands were always obtained with DBS.
Regarding specificity, the 16S rRNA gene amplifies for all Bartonella species (either in vivo or in silico) but a positive result was also obtained when tested B. melitensis. The its amplification assay was specific for Bartonella spp., and no other of the tested microorganisms had a positive PCR. Moreover, the its scheme might allow to distinguish between different Bartonella spp. by the different amplified size. The fla gene amplification was also specific for Bartonella species and differentiates between Bartonella spp. causing Carrion’s disease (B. bacilliformis, B. rochalimae and B. ancashensis) and the remaining Bartonella causing human disease (Table 2) once no amplification was obtained or predicted for the last ones.
Carrion’s disease is an overlooked and restricted disease that affects the poorest populations living in remote rural areas, which badly communicated, without equipped laboratories, and with many other illnesses with a common symptomatology [1]. Thus, correct diagnosis of Carrion’s disease is essential, particularly since misdiagnosis is frequent [12, 19]. PCR techniques rank among the most rapid techniques to diagnose B. bacilliformis. For this reason, the determination of the detection limit of these techniques is extremely important. For this study we have chosen three approaches, the amplification of 16S rRNA, the hypervariable intergenic transcribed spacer 16S-23S rRNA and the fla gene which codes for the flagelin protein of B. bacilliformis. The amplification of 16S rRNA has been proposed for Carrion’s disease diagnostic in Peru [12]. All tested Bartonella had an amplified product of 438 bp. Moreover, the in silico analysis showed that these primers are able to amplify all Bartonella spp. Then, this PCR approach may be also useful in other environments to detect and identify other Bartonella spp. either combining with sequencing or RFLP.
The its amplification permits to differentiate between B. bacilliformis and B. ancashensis from the main pathogenic Bartonella spp [20]. In fact the its region has been used in different studies of Bartonella spp [7].
Regarding fla, this gene permit to distinguish between the three Bartonella causing Carrion’s disease: B. bacilliformis (940 pb), B. rochalimae (974 bp) and B. ancashensis (937 bp) from the remaining Bartonella spp. with clinical interest. However, one exception is B. clarridgeiae (997 bp). Additionally, and in silico analysis showed that B. schoenbuchensis will also results in a positive fragment of 1008 bp.
Our results show that the Bartonella-specific 16S rRNA PCR seems to be the best of the techniques analyzed to detect the presence of B. bacilliformis in blood samples (5 CFU/μL) since the lowest detection limit was achieved on comparison with fla and its PCRs. These results are in accordance with Angkasekwinai et al. [21], who reported a detection limit of 1 and 10 copies/μL in a loop-mediated isothermal amplification when the detection limit was determined using bacterial genomic DNA alone or in the presence of human plasma respectively. This sensitivity might allow diagnosing the acute cases of Carrion’s disease, in which the mean percentage of infected RBCs is 61% (ranging from 2 to 100%) [22]. Nonetheless, the concomitant use of these PCR approaches will provide information about other Bartonella spp. infections.
Filter paper may be an alternative for easy transportation of samples from endemic areas to reference laboratories but the decreasing sensitivity of the results must been taken into account which may lead to the non-detection of cases with a low bacteremia. Although the same detection limit was obtained for 16S rRNA PCR both directly from blood and filter papers, the bands were fainter in the latter. It is true that 1 week delay in the sample processing could affect the PCR by increasing the detection limit. Nonetheless, in rural settings the transfer of samples to reference centers is associated with bad communications ways, resulting in some days from sample collection to molecular determinations.
None of the non-Bartonella microorganisms included in the study were positive when its or fla PCRs were performed. Nonetheless, when Brucella spp. was tested, amplification was obtained to 16S rRNA PCR. Although this is a limitation, it is need to take into account that a diagnostic should to be performed both in the adequate clinic context and in parallel with other diagnostic tools such as differential PCR for Brucella diagnostic when needed [23].
The prevalence of asymptomatic people in endemic areas has already been described by PCR being 0.5% [1]. However, the number of inhabitants previous exposed increases to around 40% when serologic techniques like ELISA or IFA are performed [1]. It is need to take into account that B. bacilliformis possess tropism for both erythrocytes and endothelial cells, being then present a non-blood circulating bacterial. In the chronic illness stage (verrucuous patients) the sensitivity of the microscopical techniques decreases from the 36% described in the acute phase to less than 10% [24], highlighting the lower blood bacterial carriage and a possible transient bacteremia. Those facts might results in false PCR-negative when the technique is applied in the detection of both verrucous patients and asymptomatic carriers.
It is important to remark that in the last years 2 more sensitive PCR techniques have been described in the literature: qPCR [13] in which 24.6% of DBS samples are positive, as well as a loop-mediated isothermal amplification [21] that achieves good results on analysing Lutzomyia samples. However, qPCR requires the expertise of trained personnel and is more expensive and difficult to be implemented. Meanwhile the usefulness of loop-mediated isothermal amplification remains to be validated to detect the presence of B. bacilliformis in human clinical samples. Enrichment of the sample before conventional PCR has been proposed to increase the positivity by 55% when compared with the original blood samples [25]. However, this enrichment technique results in a 14-days delay in sample processing thereby making it unaffordable for diagnostic purposes.
To conclude, here we show that 16S rRNA PCR have low cfu detection limit and should be used with special attention to test samples from individuals with clinical suspicion of Carrion’s disease since the applicability to detect healthy carriers is not clear. The use of DBS could facilitate the transfer of samples from rural endemic areas to health facilities, despite the possibility of a small decrease in positivity. It is critical to develop rapid, sensitive and specific techniques which may be applied in endemic rural areas to avoid misdiagnosis and to facilitate the detection of asymptomatic carriers and thereby the decrease the number of B. bacilliformis cases.
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10.1371/journal.ppat.1001278 | NS2 Protein of Hepatitis C Virus Interacts with Structural and Non-Structural Proteins towards Virus Assembly | Growing experimental evidence indicates that, in addition to the physical virion components, the non-structural proteins of hepatitis C virus (HCV) are intimately involved in orchestrating morphogenesis. Since it is dispensable for HCV RNA replication, the non-structural viral protein NS2 is suggested to play a central role in HCV particle assembly. However, despite genetic evidences, we have almost no understanding about NS2 protein-protein interactions and their role in the production of infectious particles. Here, we used co-immunoprecipitation and/or fluorescence resonance energy transfer with fluorescence lifetime imaging microscopy analyses to study the interactions between NS2 and the viroporin p7 and the HCV glycoprotein E2. In addition, we used alanine scanning insertion mutagenesis as well as other mutations in the context of an infectious virus to investigate the functional role of NS2 in HCV assembly. Finally, the subcellular localization of NS2 and several mutants was analyzed by confocal microscopy. Our data demonstrate molecular interactions between NS2 and p7 and E2. Furthermore, we show that, in the context of an infectious virus, NS2 accumulates over time in endoplasmic reticulum-derived dotted structures and colocalizes with both the envelope glycoproteins and components of the replication complex in close proximity to the HCV core protein and lipid droplets, a location that has been shown to be essential for virus assembly. We show that NS2 transmembrane region is crucial for both E2 interaction and subcellular localization. Moreover, specific mutations in core, envelope proteins, p7 and NS5A reported to abolish viral assembly changed the subcellular localization of NS2 protein. Together, these observations indicate that NS2 protein attracts the envelope proteins at the assembly site and it crosstalks with non-structural proteins for virus assembly.
| Hepatitis C virus (HCV) causes major health problems worldwide. Understanding the major steps of the life cycle of this virus is essential to developing new and more efficient antiviral molecules. Virus assembly is the least understood step of the HCV life cycle. Growing experimental evidence indicates that, in addition to the physical virion components, the HCV non-structural proteins are intimately involved in orchestrating morphogenesis. Since it is dispensable for HCV RNA replication, the non-structural viral protein NS2 is suggested to play a central role in HCV particle assembly. Molecular interactions between NS2 and other HCV proteins were demonstrated. Furthermore, NS2 was shown to accumulate over time in endoplasmic reticulum-derived structures and to colocalize with the viral envelope glycoproteins and viral components of the replication complex in close proximity to the HCV core protein and lipid droplets. Importantly, specific mutations within NS2 that affected HCV infectivity could also alter the subcellular localization of NS2 protein and its interactions, suggesting that this subcellular localization and its interactions are essential for HCV particle assembly. Altogether, these observations indicate that NS2 protein plays an important role in connecting different viral components that are essential for virus assembly.
| The hepatitis C virus (HCV) has a high propensity to establish a persistent infection in the human liver. Approximately 170 million people suffer from chronic hepatitis C and are at risk to develop cirrhosis and hepatocellular carcinoma [1]. Current antiviral therapy is based on the use of polyethylene glycol conjugated interferon alpha in combination with ribavirin. However, this treatment is expensive, relatively toxic and effective in only approximately half of the treated patients [2]. A better understanding of the HCV life cycle is therefore essential for the development of more efficacious and better tolerated anti-HCV treatments.
HCV is an enveloped virus that belongs to the Hepacivirus genus in the Flaviviridae family [3]. HCV has a positive strand RNA genome encoding a single polyprotein that is cleaved by cellular and viral proteases into 10 different proteins: core, E1, E2, p7, NS2, NS3, NS4A, NS4B, NS5A and NS5B [3]. The non-structural proteins NS3 to NS5B are involved in the replication of the viral genome, whereas the structural proteins (core, E1 and E2) are the components of the viral particle (reviewed in [4]). The remaining proteins, p7 and NS2, are dispensable for RNA replication and there is no evidence that they are part of the viral particle [5], [6].
For reasons still unknown, HCV clinical isolates do not propagate in cell culture. However, with the development of a cell culture system that enables a relatively efficient amplification of HCV (HCVcc) [7], [8], [9], all the steps of the HCV life cycle can be investigated. Due to the accumulation of HCV core protein around lipid droplets (LDs) (reviewed in [10]), a role of these lipid bodies in HCV assembly has been suspected for a long time. Moreover, it has recently been shown that viral non-structural proteins like NS5A and NS3 and double stranded viral RNA are also present around LDs [11], [12]. The association between core and LDs seems to play a role in the recruitment of the other viral proteins and for virus production [12], [13]. Furthermore, NS5A plays a double role in both replication and assembly processes as a potential switch between these two steps [14], [15], [16], [17].
Since it is dispensable for HCV RNA replication and it does not seem to be incorporated into viral particles [6], NS2 has been suspected to be involved in the assembly process of HCV particle. Recently, experimental evidence supporting this hypothesis has been obtained [18], [19], [20]. Although the role of NS2 in the assembly process remains elusive, some data suggest that NS2 might interact with viral partners involved in virion morphogenesis. Indeed, construction of chimeric viruses between different genotypes identified the C-terminus of the first transmembrane segment of NS2 as the optimum crossover point [21]. Thus, a genetic interaction was implied between the N-terminus of NS2 and the upstream structural proteins. In the context of a chimeric virus containing genotype 1a and 2a sequences, adaptive mutations in E1, p7, NS2 and NS3 were identified, also suggesting genetic interactions between these proteins [22]. Moreover, a detailed rescue mutant analysis recently showed genetic interactions between NS2, E1E2 and NS3-4A [23]. However, despite these genetic analyses we have almost no understanding about NS2 interactions with other viral proteins and the role of these interactions in the production of infectious particles. Here, we report molecular interactions between NS2 and p7 and E2 proteins. Using a functional HCVcc virus with a reporter epitope at the N-terminus of NS2, we found that NS2 accumulates in dotted structures derived from the endoplasmic reticulum (ER) and colocalizes with E1, E2, NS3 and NS5A in close proximity to the core protein and LDs. Mutations and deletions in the p7-NS2 region affecting the subcellular localization of NS2 and its physical protein-protein interactions abolished viral assembly. Moreover, mutations in other viral proteins reported to inhibit the assembly process induced consistent changes in NS2 subcellular localization. Together, these data suggest that p7, NS2 and E2 form a functional unit which drives the proteins in the proximity of the LDs where NS2 crosstalks with other viral proteins during the virion assembly process.
NS2 is a polytopic transmembrane protein containing 3 putative transmembrane segments [19](Figure 1). The p7 polypeptide and E1E2 heterodimer, which are putative partners of NS2, are also membrane proteins that contain transmembrane segments [24], [25]. Due to their respective topologies (Figure 1A), it is expected that interactions between these three proteins would involve helix-helix contacts in their transmembrane segments. Furthermore, helix-helix interactions between transmembrane segments of NS2 are also likely to take place.
To analyze the role of the transmembrane domain of NS2 in potential protein-protein interactions, we used a previously reported deletion mutant of NS2 (ΔTM12) which lacks the first two transmembrane segments [19]. For a more refined analysis, we also used alanine scanning insertion mutagenesis, a technique which has been shown to disrupt helix-helix interactions in a membrane environment [26], [27], [28], [29]. As illustrated in Figure 1C for the first transmembrane segment of NS2, this approach is based on the fact that insertion of a single amino acid into a transmembrane helix displaces the residues on the N-terminal side of the insertion by 110° relative to those on the C-terminal side of the insertion. The subsequent perturbation of the residue side-chain distribution could disrupt a potential helix-helix packing interface involving residues on both sides of the insertion. Such mutations are therefore expected to disrupt helix-helix interactions between the transmembrane segments of NS2 and/or between NS2 and other putative partners like p7 or E1E2.
Based on the NMR structure of the transmembrane segments of NS2 [19](Jirasko et al., 16th International Symposium on HCV and Related Viruses, Nice, October 3–7, 2009), we designed insertion mutations in the transmembrane domain of NS2. Three different mutants were designed by inserting alanine residues close to the middle of the transmembrane segments of NS2 (Figure 1, B–C). The positions for alanine insertions were carefully chosen within the putative helical segments to preserve the overall fold of the corresponding helices. According to previous reports [26], [27], [28], [29], alanine insertions near the center of putative transmembrane helices were expected to be the most efficient to disrupt inter-helices potential interactions.
Firstly, we wanted to evaluate the impact of our mutations on the viral life cycle. As negative controls for virus assembly, we used assembly-deficient viruses JFH-ΔE1E2-HA and JFH-Δp7-HA, containing a deletion in the regions encoding HCV envelope glycoproteins and the p7 polypeptide, respectively (Figure S1). As template for all our constructs, we used a full-length JFH1 plasmid containing adaptive mutations [30] in which the N-terminal sequence of E1 has been modified to reconstruct the A4 monoclonal antibody (Mab) epitope of the H77 isolate in order to facilitate the immunodetection of this envelope protein [31]. Moreover, we introduced an HA epitope in the N-terminus of NS2 to be used for protein detection (JFH-HA). Huh-7 cells were electroporated with different mutated viral genomes and the production of infectious virus was assessed at 72h by supernatant titration (Figure 2A). The insertion of the HA epitope did not affect the virus production as compared to the wild-type virus (JFH). As previously reported, deletion of the first two transmembrane segments prevented the production of infectious particles similar to the negative controls [19] (Figure 2A and S1). Furthermore, alanine insertions also drastically affected the viral production. While mutants A16 and A41 presented residual infectivity, A82 was not able to produce infectious particles (Figure 2A).
To identify the stage where the virus production was affected, we started with the evaluation of the replication capacity of our viruses. To this aim, we used Renilla luciferase reporter viruses (Figure S1A). As a negative control for replication, we used the replication-deficient construct JFHGND-Luc which contains a mutation in NS5B that prevents viral genome replication [7]. As the RNA input after electroporation is potentially variable, we evaluated the capacity of our viruses to replicate by determining the ratio between the luciferase activity at 72h and 4h post-electroporation when only the luciferase activity of the input RNA is present as previously shown [32]. As shown in Figure S1B, all the mutant viruses presented a similar replication capacity, which was comparable to the control viruses, whereas replication was abolished in the JFHGND-Luc mutant (Figure S1B-upper panel).
Since the replication was not affected, the effect of alanine insertion on NS2 protein stability was further investigated. As shown in Figure S1C, A82 insertion was detrimental for the protein integrity. For A16 mutant, the level of expression of NS2 was slightly lower as compared to the JFH-HA control. However, the level of expression of HCV proteins was higher for the JFH-HA control in this particular experiment. We therefore measured the NS2/E2 ratios which were similar for A16, A41 and JFH-HA, indicating that A16 and A41 insertions did not affect NS2 stability. Therefore, we focused our analysis on A16 and A41 mutants.
To test whether the lack of infectivity could be due to a defect in virus secretion or the release of non-infectious particles, we determined the level of core protein in the supernatants. The quantity of core protein in supernatants decreased drastically as compared to wild-type, paralleling the decrease in infectivity (Figure 2B). It has to be noted that for the mutants showing a residual infectivity (A16, A41 and RR/QQ), the release of core was at the same level as the dead mutants (ΔTM12, A82 and Δp7). This likely reflects a difference of sensitivity between the two assays. Then, we measured the viral RNA in the supernatants by qRT-PCR as previously described [17]. For all the alanine insertions, the release of viral RNA was close to the background level observed for the assembly-deficient control viruses (Figure S1B – lower panel), indicating a defect in particle secretion. These data suggest that either the process of assembly is affected at an early stage or the secretion of assembled infectious particles is impaired. To answer this question, we compared the intra and extracellular level of the core protein (Figure 2B). The ratio between intra and extracellular core was similar to JFH-Δp7-HA which was shown to be defective in early assembly steps. We also measured the intracellular infectivity of the alanine mutants in the Rluc reporter viruses context as previously reported [20]. As shown in Figure S1B (middle panel), there was no accumulation of intracellular infectivity for any of the mutants. These results suggest that mutations in the transmembrane domain of NS2 prevent the assembly process rather than the secretion of particles.
Considering the rationale of our mutagenesis, we investigated protein-protein interactions in E1E2-p7-NS2 region. Indeed, due to their position within the HCV polyprotein, it is reasonable to think that these proteins might potentially interact. Genetic and co-immunoprecipitation data suggest that NS2 interacts with E2 [23], [32], [33], [34]. Interaction assays between NS2 and E2 were performed in the context of the JFH-HA virus, which allows to immunoprecipitate NS2 with the HA tag (Figure 2C, JFH-HA). As shown in Figure 2C (JFH-HA), E2 and NS2 proteins migrated at the expected molecular mass, indicating that the polyprotein was correctly processed in these viruses. It has to be noted that in the absence of p7, E2 migrated slightly faster. This is likely due to the absence of E2-p7, which has a slightly slower migration profile in the band corresponding to E2.
The E2-NS2 interaction was then tested by co-immunoprecipitation with an anti-HA antibody followed by the detection of E2 by Western blotting. As shown in Figure 2C (JFH-HA), E2 co-precipitated with NS2, confirming that these proteins interact together in the context of the virus. In contrast, deletion of the first two transmembrane segments prevented the interaction between NS2 and E2 (Figure 2C, ΔTM12), whereas alanine insertions in the transmembrane region had different effects. While A16 (within TM1) did not affect the E2-NS2 interaction, A41 (within TM2) induced a consistent decrease in the amount of E2 co-precipitated by NS2 (Figure 2C, A16 and A41).
We further investigated the effect of p7 on the E2-NS2 interaction. To this aim, we used two mutants of p7, a deletion mutant (JFH-Δp7-HA) and a mutant having the two arginine residues in the cytosolic loop of p7 replaced by glutamine residues (JFH-RR/QQ-HA) (Figure 2C). This RR/QQ mutation is believed to abolish the ion channel activity of p7, and it induces a drastic decrease in virus production as previously reported [32], [35] and confirmed by us as shown in Figure 2A. As shown in Figure 2C (JFH-Δp7-HA and JFH-RR/QQ-HA), E2 and NS2 proteins migrated at the expected molecular mass, indicating that the polyprotein was correctly processed in these viruses. However, the two mutations had no significant effect on E2-NS2 interaction (Figure 2C JFH-Δp7-HA and JFH-RR/QQ-HA), suggesting that p7 does not modulate E2-NS2 interaction.
To obtain more insight into the assembly defects of our viral mutants, we decided to determine their potential effect on the subcellular localization of NS2. To this aim, we first characterized the subcellular localization of the wild-type NS2 in the context of an infectious virus, which has never been reported before. NS2 presented an ER-like reticulated pattern, and in some cells, accumulated in dotted structures (Figure 3A and data not shown). The number of these NS2-positive dot-like structures increased over time, suggesting a transition from an initial reticulate pattern to these dotted structures. At 72 hours post-electroporation, 34±15% of infected cells from 8 different electroporations displayed NS2 dots. The size of these structures was 0.84±0.38 µm (mean±SD, n = 402).
To further characterize these NS2 structures, we performed co-localization analyses with different cellular markers. As expected, the reticulate and perinuclear pattern of NS2 overlapped with calreticulin staining as well as other ER markers like calnexin and PDI (data not shown). Interestingly, NS2 dots also colocalized with ER markers (Figure 3B), but they did not colocalize with other organelle markers of the secretory pathway (data not shown). Importantly, NS2 dots were also found in close proximity of LDs (Figure 3B), suggesting that they might play a role in HCV assembly.
To better understand the potential role of NS2 dots in virus assembly, we analyzed the subcellular localization of NS2 in relationship with the other viral proteins. NS2 dots overlapped with HCV envelope glycoproteins E2 (Figure 3B) and E1 (Figure S2). NS5A and NS3 were also shown to colocalize with NS2 in its dotted pattern (Figures 3B and S2). Importantly, the structures containing both NS2 and NS5A were observed in close proximity to LDs (Figure 4A). This is illustrated by the presence of magenta dots (red NS2 and blue NS5A) in the proximity of LD (green). Finally, as observed with the LDs, NS2 dots were also found in close proximity to core protein (Figure S2), and as expected this association was observed in close proximity to LDs (Figure 4B). As shown in Figure 4B, core (blue) is tightly associated to LD (green) as the LD becomes cyan due to colocalization, NS2 (red) localizes in regions juxtaposed to the cyan LD (Figure 4B). Relying on the spatial proximity of NS2 dots, core, E1E2, NS3, NS5A proteins and LDs, we speculated that NS2 present in these structures is involved in the assembly process. Thus, we established some criteria of functionality for NS2 positive structures. They have to localize in the proximity of LDs and core protein and more importantly to colocalize with NS5A protein, which we used as a criteria for quantification purposes.
It has to be pointed out that a low number of cells contained NS2-positive structures with a different pattern of subcellular localization (Figure S3). This different pattern was indeed observed in approximately 1 to 3% of the cells at 72h post-infection or post-electroporation. These structures colocalized less with ER markers and they overlapped with ERGIC-53, a marker of the ER-to-Golgi intermediate compartment [36] (Figure S3). Furthermore, these NS2 dots were detected in close proximity to the ER exit sites, which were identified by markers of the COP II coatomer, Sec31 and p125 [37], [38] (Figure S3 and data not shown). However, cells containing these NS2 positive structures showed dramatic alterations of the secretory compartments as observed by immunofluorescence analysis of ER-to-Golgi intermediate compartment and Golgi morphology using ERGIC-53 and GM130 as markers (Figure S3). It is worth noting that NS2 did not colocalize with NS3 or NS5A and it was not found in the proximity of core and LDs in these cells (Figure S3). Due to these alterations, it is unlikely that these cells are involved in the production of infectious virus.
We asked further the relevance of NS2 dots for the production of infectious particles. After electroporation, we determined the titer of virus production at different time points (Figure 4C, upper panel). In parallel, we counted the number of cells which presented NS2/NS5A positive dots for reasons detailed above (Figure 4C, lower panel). The kinetics of virion production and the percentage of cells presenting NS2/NS5A positive dots showed a high correlation with a correlation coefficient of 0.9. To exclude the possibility that the NS2 phenotype depends on the cell culture adaptive mutations, we performed a similar experiment with a virus that does not contain the mutations. As for the adaptive mutant, virus production in the absence of mutation paralleled the NS2/NS5A dots formation with a significant correlation coefficient (data not shown). These data reinforce the idea that NS2/NS5A positive dots are involved in the virus production process.
To further investigate the NS2 localization, we performed immuno-electron microscopy with an anti-HA antibody on Huh7 cells which were electroporated with JFH-HA RNA and prepared by cryosubstitution. As shown in Figure 4D, we could identify clusters of gold particles in the proximity of LDs. Moreover, two of the clusters are lying on preserved ER bilayers. For one of the clusters of gold particles, a connection between the ER bilayer and the LDs could be observed (Figure 4D). Thus, the immuno-electron microscopy confirms the juxtaposition between NS2 dots and LDs in a 0.2µm range, which is consistent with the observations in confocal microscopy. It has to be noted that gold particles were detected on both sides of the membrane even if the HA epitope is supposed to be located in the ER lumen. This is compatible with the length of two antibodies (primary+secondary) since the gold particles were never further away than 30 nm from the membrane. However, we cannot exclude a double topology for NS2 as recently suggested [39].
The next obvious step was to determine the subcellular localization of NS2 for the different mutants defective in assembly. In the case of ΔTM12, NS2 localized in confined structures which did not colocalize with NS5A and they were not associated with the core protein or LD (Figure 5, panels A and C and data not shown). Moreover, there was no colocalization between the truncated NS2 and the E2 glycoprotein (Figure 5B), which correlates with the lack of interaction between the two proteins (Figure 2C). Then, we analyzed the subcellular localization of NS2 for the alanine insertion mutants. While A16 mutant presented NS2 dotted structures as wild-type, A41 mutation induced a drastic decrease in the percentage of cells with NS2 dots (Figure 5, panels A and C). These data suggest that NS2 transmembrane region is an important localization determinant.
A peculiarity of the Flaviviridae family is the involvement of both structural and non-structural proteins in the assembly process (reviewed in [40]). Thus, we wanted to investigate the NS2 subcellular localization in the context of assembly deficient viruses having mutations in different viral proteins.
Recruitment of core protein to LDs was reported to be essential for a productive assembly process [12], [13]. The proline residues 138 and 143 in domain D2 of the core protein are crucial for virus production and core recruitment to LDs [13], [41]. Furthermore, the mutation of these proline residues has been previously shown to prevent the core induced recruitment of NS5A to the LDs [12]. Therefore, we introduced these mutations in the context of JFH-HA virus (Figure 6, JFH-HA-PP). As previously reported [13], the mutation prevented the production of infectious virions (Figure 9A), and the core protein was not redistributed to LDs, which in turn remained spread in the cytoplasm rather than the perinuclear localization induced by a functional core protein (Figure S4). As shown in Figure 6, in the context of this mutation, NS2 protein maintained the capacity to accumulate in dotted structures that colocalized with NS5A. In contrast to the wild-type, NS2 dotted structures were not found in the vicinity of LDs in the context of the PP mutation, suggesting that NS2 does not have the signals to localize by itself around the LDs (Figure S4). Importantly, in this context, the number of cells presenting NS2 dotted structures increased tremendously in comparison to the wild-type (Figure 6B). These observations suggest that the PP mutation induces a block in the assembly process, which favors the accumulation of NS2 protein in the dotted structures.
As structural proteins, the envelope glycoproteins are involved in the assembly process [42]. The envelope proteins play a crucial role in the assembly of enveloped viruses, which can be due for some viruses to the capacity of the envelope proteins to establish lateral interactions and to generate a pushing force necessary for the budding process [43]. A deletion in the envelope region would therefore block the assembly process as shown by an in-frame deletion of 351 amino acids in the envelope proteins region [7]. In the context of our JFH-HA virus, this deletion mutant is also fully replicative, and it does not produce infectious particles (data not shown). To further understand the interplay between NS2 and HCV envelope glycoproteins, we analyzed the subcellular localization of NS2 protein in the context of the E1E2 deletion. In this context, NS2 localized in NS5A positive structures juxtaposed to the LDs and core protein (Figure 6 and S4). Interestingly, as for the JFH-HA-PP virus, the number of cells presenting NS2 dotted structures also increased in the case of JFH-ΔE1E2-HA, correlating also with a block in the assembly process favoring an accumulation of NS2 dotted structures (Figure 6B).
The deletion introduced in the envelope region is predicted to generate a chimeric protein comprising the N-terminus of E1 and the C-terminus of E2 protein. Since we introduced the A4 epitope in the N-terminus of E1, we were able to detect the subcellular localization of this small chimeric protein. It is worth mentioning that we also detected this truncated chimeric protein in NS2/NS5A dotted structures, suggesting that the E2 transmembrane domain is sufficient for the recruitment of HCV envelope proteins to NS2 dotted structures (Figure S4).
The p7 protein has been shown to be crucial for the assembly process [32], [35]. Moreover, the ion channel activity of p7 correlates with the virus assembly process since mutations predicted to abolish the ion channel activity have a strong inhibitory effect on the virus production [32], [35]. We therefore also analyzed the subcellular localization of NS2 in the context of p7 mutants corresponding to a complete deletion (JFH-Δp7-HA) or an amino acid substitution (JFH-RR/QQ-HA) previously reported to affect the assembly and release of the virus [32], [35]. The two constructs behaved as expected. While JFH-Δp7-HA produced no infectious particles, JFH-RR/QQ-HA presented a 2 log10 decrease in virus titers at 72h post-electroporation (Figure 2A). Importantly, the two mutants induced a drastic decrease in the number of cells presenting NS2/NS5A dotted structures (Figure 6B). Together, these data indicate that NS2 needs a functional p7 polypeptide to colocalize with NS5A in dotted structures.
Other partners than p7 are likely necessary for NS2 accumulation in dotted structures. Indeed, as shown in Figure 5, A41 and ΔTM12 mutants, which fail to interact with E2, also present a drastically reduced number of NS2 dotted structures. This suggests that NS2-E2 interaction might be crucial for NS2 subcellular localization. Thus, one additional determinant could be represented by the transmembrane domain of E2, which is most likely the interacting region with NS2 due to topological constraints. In order to check this hypothesis, we used both JFH-HA and JFH-ΔE1E2-HA constructs in which we replaced the transmembrane region of E2 with the autoprotease 2A from foot and mouth disease virus (FMDV) (JFH-ΔTME2-HA and JFH-ΔE1E2TME2-HA)(Figure S1). We first verified that a proper processing of the polyprotein mediated by FMDV 2A protease has occurred in these constructs. To check our constructs, we analyzed the molecular mass of E2 following deglycosylation with EndoH or PNGase endoglycosidases. As shown in Figure 6C, E2 from JFH-HA and JFH-ΔTME2-HA presented a similar molecular mass after deglycosylation with EndoH, suggesting that the FMDV 2A protease is functional since FMDV 2A and the transmembrane domain of E2 have similar sizes. Indeed, if FMDV 2A protease had not been functional, we would have observed a difference of 7kD corresponding to the molecular mass of unprocessed p7 (Figure 6C and data not shown). Interestingly, in contrast to the wild-type envelope protein, the truncated E2 did not interact with NS2 (Figure 6D). Furthermore, in contrast to what was observed for JFH-HA and JFH-ΔE1E2-HA (Figure 6A), NS2 protein of the ΔTME2 mutant presented an ER like pattern and the formation of NS2 dotted structures was prevented (Figure 6A and B). Similar data were also obtained with JFH-ΔE1E2TME2-HA construct (Figure 6B). Thus, it seems that p7-NS2 and the transmembrane domain of E2 form a functional unit that targets these proteins to NS5A positive structures.
The above data suggest a possible interaction between p7 and NS2. We therefore explored this putative interaction in a biochemical assay, by analyzing p7-NS2 association in a co-immunoprecipitation assay. Due to the difficulties in analyzing p7-NS2 interactions in the context of an infectious virus, we analyzed these interactions by co-transfecting cells with plasmids expressing these two proteins only. In this approach, the p7 polypeptide and NS2 were tagged with a Flag or a HA epitope, respectively (Figure 7A, p7-Flag and HA-NS2).
The p7-NS2 interaction was tested after co-expression of the tagged proteins in 293T cells. Co-immunoprecipitation experiments were performed with an anti-Flag antibody linked to agarose beads. The immunoprecipitates were separated by SDS-PAGE and probed with anti-HA antibodies by Western blotting. As shown in Figure 7B, NS2 of different genotypes coprecipitated with p7. Since only p7 of genotype 1a was used in these experiments, it suggests that p7 interacts with NS2 in a genotype independent manner. However, we cannot exclude that the system is not sensitive enough to discriminate between slight changes in affinities.
Further, we constructed two chimeric proteins, NS2 tagged with a green fluorescent protein (NS2-GFP) and NS2-GTM (Figure 7A). In NS2-GFP, the cytosolic domain of NS2 was replaced by GFP protein, whereas for NS2-GTM, we replaced the transmembrane domain of NS2 by the transmembrane domain of glycoprotein G of VSV. As shown in Figure 7B, the NS2-GFP could be precipitated by p7-FLAG, while NS2-GTM could not. This clearly shows that the transmembrane region is the main determinant of p7-NS2 interaction.
To confirm the p7-NS2 interaction with another approach, we used the FRET-FLIM technique. FRET-FLIM requires the presence of two fluorophores (a donor and an acceptor) fused in frame to the studied proteins. If the two proteins interact, an energy transfer occurs between the two fluorophores and the fluorescence life time of the donor (a parameter of the energy emitted by the donor) will decrease. To measure p7-NS2 interactions by FRET-FLIM, Cerulean fluorescent protein (CFP) and Venus yellow fluorescent protein (YFP) were fused to the N-terminus of p7 and NS2, respectively (Figure 8A).
As previously reported, CFP-p7 and YFP-NS2 showed a reticulate perinuclear distribution (Figure 8B), which is characteristic of ER proteins [34], [44]. Western blotting analyses indicated that CFP-p7 and YFP-NS2 migrate at the expected molecular mass with some degradation products of lower molecular weight (Figure 8C). The energy transfer in FRET-FLIM assay needs the integrity of the fluorophores and the correct positioning of the interacting partners. The degradation products could fall into two categories: either soluble fluorophores or membrane bound truncated chimeras. In either case the energy transfer is unlikely to occur with the cleavage products. Thus, the presence of degradation byproducts is unlikely to influence the accuracy of the FRET-FLIM acquisitions.
After biphoton laser excitation and data analysis, fluorescence life time maps were built. Interestingly, the regions showing interactions were located in distinct spots throughout the cells as illustrated in a fluorescence life time color-coded map (Figure 8D). A summary of FRET-FLIM analysis is presented in Table 1. The mean life time of fluorescence decreased from 2.69±0.12 ns (n = 10) in cells transfected with the donor only (CFP-p7) to 2.34±0.09 ns (n = 10) for double transfections (CFP-p7+YFP-NS2). The variation of the mean donor life time is characteristic for energy transfer between two fluorescent proteins in FRET-FLIM analyses as previously observed for other protein-protein interactions [45], [46]. As a positive control, we used the transmembrane domains of HCV glycoproteins E1 and E2 which are known to interact and to have the same subcellular localization as p7 and NS2 [25]. The positive control couple presented a comparable decrease in the mean lifetime of the donor to CFP-p7/YFP-NS2 couple. Indeed, the mean life time decreased from 2.56±0.03 ns (n = 14) in cells transfected with the donor only (CFP-E2) to 2.35±0.08 ns (n = 14) for double transfections (CFP-E2+YFP-E1). As a negative control, we used CFP fused to the transmembrane domain of yellow fever virus E protein (CFP-EYF), a donor protein with the same topology and localization as p7 [28], [47]. As shown in Table 1, the mean life time of the donor in monotransfection did not change in double transfections confirming the lack of interaction between CFP-EYF and YFP-NS2 (n = 11). The biphoton pictures for the positive and the negative control are shown in Figure S5. Thus, these data strongly suggest that p7 and NS2 proteins interact intracellularly.
Among the non-structural proteins, NS5A is the most characterized in terms of its role in the assembly process. NS5A is recruited through direct interaction by the core protein around LDs where its domain III is involved in the assembly process potentially by its phosphorylation [14], [16], [17]. By deletion mutagenesis, Tellinghuisen et al. identified a cluster of serine residues at positions 452, 454 and 457, which are crucial for virus production [17]. Furthermore, by alanine scanning mutagenesis, Masaki et al. reported that the same serine cluster is involved in the direct interaction between NS5A and core protein [16]. While Tellinghuisen et al. reported that serine 457 alone is essential for virus production, Masaki et al. showed that only double mutants had a significant impact on virus production [16], [17]. The apparent contradiction might be explained by the different viruses and time points for virus production assessment. While Tellinghuisen et al. used a chimeric virus consisting of the structural proteins of J6 strain up to NS2 protein, Masaki et al. used the wild type JFH strain [16], [17]. Thus, we constructed the two mutants in the context of our JFH-HA virus – JFH-S/A-HA and JFH-3BS/A-HA, respectively. We analyzed the phenotype of the mutants as well as the polyprotein processing. The results fitted the literature with JFH-S/A-HA virus infectivity moderately reduced at 72h and JFH-3BS/A-HA profoundly impaired in infectious virus production, while the replication and protein integrity were unaltered (Figure 9A, B, C). As reported, we showed that JFH-S/A-HA and JFH-3BS/A-HA present less hyperphosphorylated NS5A (Figure 9C). Surprisingly, for both mutants, NS2 localized mainly in an ER-like pattern and the number of cells with NS2/NS5A dots decreased dramatically (Figure 9D, E). The serine 457 may be replaced by an aspartate residue, which mimics a phosphoserine [17]. We therefore introduced the same mutation (JFH-S/D-HA) and as reported the virus titers were restored at wild-type levels (Figure 9A). Interestingly, this mutant partially recovered the NS2 subcellular localization both qualitatively and quantitatively (Figure 9D, E). Together, these results suggest that NS5A phosphorylation might stabilize the NS2 dotted structures in the assembly process.
Our understanding of the HCV morphogenesis process is still in its infancy. Different viral components were identified as players in the morphogenesis process. As expected, the structural proteins are essential in the virus makeup [7]. The scenario gets more complicated with the involvement of non-structural proteins in the assembly process. The p7 polypeptide, NS2, NS3, NS4B, NS5A were reported to be involved in viral assembly [14], [17], [20], [22], [32], [48]. However, the mechanism of the complex interplay between the structural and non-structural proteins towards the virion production is not understood. In this paper, we provide evidence for molecular interactions between NS2, p7 and E2, respectively. Furthermore, we show that NS2 accumulates over time in ER-derived dotted structures and colocalizes with the envelope glycoproteins and components of the replication complex in close proximity to the core protein and LDs. Characterized assembly deficient mutants in both structural and non-structural proteins present qualitative and quantitative modifications in NS2 subcellular localization. Indeed, specific mutations within NS2, p7 or E2 modify the subcellular localization of NS2 and impair virus production. Mutations in core, envelope proteins or NS5A affect the NS2 subcellular localization along with the virus titers. Altogether, these observations indicate that NS2 protein crosstalks with both structural and non-structural proteins during virus assembly.
Our data demonstrate a physical interaction between NS2 and p7. This interaction correlates with the previously reported genetic interactions present in the C-NS2 region [21], [22]. The HCV p7 polypeptide is a viroporin involved in viral assembly [20], [32], [49]. Viroporins represent a class of viral proteins that are involved in the viral morphogenesis process in different and largely unknown manners. Alphavirus 6K interacts with E1 and p62 envelope glycoproteins and is involved in optimal assembly and release of the virion by an unknown mechanism [50], [51]. The E protein of coronaviruses interacts with the M protein and is crucial for the assembly of virus-like particles and virions [52], [53], [54]. To our knowledge, the HCV p7 polypeptide is the first viroporin which interacts with a non-structural protein (NS2) and this might be a peculiarity of the members of the Flaviviridae family where the non-structural proteins are involved in particle assembly [40].
Topologically, the transmembrane domain of NS2 (NS2TM) would be the main region available for interactions with the upstream transmembrane proteins p7 and E1E2. Based on this assumption, we wanted to characterize NS2 interactions with p7 and E2 by deletion and chimeric mutants. Both NS2-E2 interaction and NS2-p7 interaction were mapped in the transmembrane region of NS2 as expected. However, since the lack of p7 does not affect the E2-NS2 interaction, we could imagine that NS2 uses separate domains to interact with p7 and the envelope proteins. Interestingly, when we deleted the transmembrane region of E2, the NS2-E2 interaction was impaired and the NS2 subcellular localization changed. Altogether, these observations suggest that p7, the transmembrane domain of NS2 and the transmembrane domain of E2 contain signals which act synergistically to direct the NS2 protein towards the NS5A positive membranes in the LD proximity.
The drastic effects of alanine insertion mutagenesis on HCV infectivity reflect more intrinsic effects on NS2 function in virus assembly. As shown for lactose permease, single alanine residue insertions into transmembrane helices of a polytopic membrane protein can be highly disruptive to protein structure and function due to their effects on intramolecular helix-helix interactions [26]. Furthermore, within the same protein, different transmembrane helices can have differential sensitivities to single residue insertions [26]. In the case of NS2, our data indicate that an insertion in the third putative transmembrane helix strongly reduces the stability of the protein, suggesting a drastic alteration of NS2 structure by this mutation. The decrease in infectivity for the mutations in the first two transmembrane helices also indicates a drastic alteration in NS2 function that can be linked to local alteration of its structure, as suggested by the change in subcellular localization of NS2 mutant A41. Finally, the drastic decrease in infectivity of mutant A16 in spite of NS2 localization in dotted structures indicates that the subcellular localization of NS2 in these structures is not sufficient by itself for infectivity. Rather, it likely needs to play additional function(s) at the site of virus assembly and such function(s) would be disrupted by the A16 mutation. One explanation could be that a weak E2-NS2 interaction as seen for A41 is not able to direct the p7-NS2 unit to the LDs and a potential strong interaction as for A16 does not allow the p7-NS2 unit to release the envelope proteins heterodimer to the assembly site and the subsequent recycling/dissociation of the unit.
Over time, a substantial part of NS2 accumulates in dotted structures localized in the ER in close proximity to the core protein which is associated to LDs. Since the LD/ER interface is considered as the potential particle assembly site [12], NS2 localization close to the LDs is expected to correlate with its function in a late step of the viral life cycle. Interestingly, NS2 accumulation in dotted structures parallels the colocalization of NS2 with NS5A, NS3 proteins and most likely the replication complex. Moreover, the NS2 dotted structures are juxtaposed to the core protein and colocalize with the envelope proteins E1 and E2. Thus the NS2 dots contain all the assembly players and are located in the microenvironment of the LDs, the proposed virus assembly site. It is worth mentioning that the formation of NS2 dotted structures is not due to a non-specific effect of the viral genome replication since some of our mutants showing the same replication rate had very different subcellular localizations of NS2 (e.g. A16 vs. A41). It seems rather that the formation of NS2 related dots represents a transition rather than an end state in a productive assembly process. Indeed, mutations in core (JFH-HA-PP) or in the envelope proteins (JFH-ΔE1E2-HA) induced an obvious increase in the number of cells presenting NS2 dots. This could mean that the p7-NS2-E1E2 complexes pre-exist in the NS5A positive subcompartment. In addition, the core-mediated redistribution of LDs could induce the recruitment of the assembly components to LDs followed by the envelope protein incorporation into the virion during the budding process. Finally, after budding, NS2 might relocate to another subcellular compartment or be degraded. This scenario is supported by the fact that NS2 dots accumulate around the LDs and core protein when the envelope proteins are deleted, which is expected to inhibit the lateral interactions between the envelope proteins [42], preventing the budding process to occur.
For the moment, the connection between NS2 and the replication complex (RC) is just inferred from genetic data and colocalization in immunofluorescence experiments [23], [34](this report). We show here that mutations in NS5A, which are reported to affect the phosphorylation status of the protein, abolish the accumulation of NS2 in dotted structures. Moreover, we could partially restore the phenotype by an aspartate mutant, which mimics phosphoserine. Thus, NS2 colocalization with NS5A is favored by the phosphorylation state of the latter. It is possible that NS5A charged state enhances a potential NS2-RC interaction, which translates in NS2 dot formation. Removing the charges would shift the interaction equilibrium and would affect the virus production kinetics in different extents depending on the number of charges and virus strain. Hence, if serine 457 is replaced, the virus titers are moderately reduced at 72h. In contrast, removing serine residues 452, 454, 457 determines a profound defect in infectious virus production. However, there is no NS2 accumulation for either of the two mutants. Thus, the NS2 dots may represent transition states in the assembly process. Indeed, some mutations may prevent the arrival of dots components to NS5A structures (JFH-ΔTM12-HA, JFH-A41-HA, JFH-Δp7-HA and JFH-ΔTME2-HA). Alternatively, other mutations may block the assembly and stabilize them (JFH-HA-PP, JFH-ΔE1E2-HA). If we combine a mutation from the former category (JFH-ΔTME2-HA) with one from the latter (JFH-ΔE1E2-HA) in the mutant JFH-ΔE1E2TME2-HA, we prevent the NS2 dots formation. This suggests that the formation of NS2 complexes and their arrival to the NS5A structures precede the accumulation of NS2 around the LDs during the assembly process. Furthermore, changes in the phosphorylation state of NS5A could regulate the stability of NS2 dots and virion production efficacy.
In our current view, the assembly process would involve several steps. Upon viral genome translation and polyprotein processing, formation of different complexes occurs: the E1E2 native heterodimer, the p7NS2 unit and the RC. The core protein and other viral proteins (e.g. NS4B) create the LD-ER microenvironment by redistribution of the LDs and intracellular membranes. The LDs surrounded by core protein are recruited to the RC. E1E2 complex interacts with p7NS2 unit and E1E2p7NS2 arrives to NS5A positive membranes in the proximity of LDs due to a combination of signals in p7, NS2 and E2 proteins. NS5A switches from the replication to assembly mode by phosphorylation, which stabilizes the presence of NS2 in dotted structures favoring the assembly process (Figure 10). Finally, our data indicate a crucial role played by NS2 in the assembly process and highlight the complexity of the mechanism of its action. In conclusion, NS2 emerges as an essential mediator between the structural and non-structural proteins in HCV assembly process.
293T human embryo kidney cells (HEK293T), U2OS human osteosarcoma cells (American Type Culture Collection) and Huh-7 human hepatoma cells [55] were grown in Dulbecco's modified essential medium (Invitrogen) supplemented with 10% fetal bovine serum.
Anti-HCV Mabs A4 (anti-E1) [56] and 3/11 (anti-E2; kindly provided by J.A. McKeating, University of Birmingham, UK) [57] were produced in vitro by using a MiniPerm apparatus (Heraeus) as recommended by the manufacturer. Anti E2 Mab AP33 was kindly provided by A. H. Patel, University of Glasgow, UK. Anti-capsid ACAP27 [58] and anti-NS3 (486D39) Mabs were kindly provided by JF Delagneau (Bio-Rad, France). The anti-NS5A Mab 9E10 [8] and polyclonal antibody were kindly provided by CM Rice (Rockefeller University, NY, USA) and M Harris (University of Leeds, UK), respectively. The anti-NS2 Mab 6H6 [18] and polyclonal antibody were kindly provided by CM Rice (Rockefeller University, NY, USA) and R Bartenschlager (University of Heidelberg, Germany), respectively. The anti-Sec31 antibody [38] was kindly provided by F Gorelick (Yale University School of Medicine, CT, USA). The anti-p125 Mab [37] was kindly provided by K Tani (University of Tokyo, Japan). The following antibodies were purchased: the anti-ERGIC-53 Mab (Alexis), the anti-actin (Santa Cruz Biotechnology), anti-calnexin polyclonal (Stressgen), anti-calreticulin polyclonal (Stressgen), anti-PDI (Stressgen), anti-GFP (Roche) and the anti-hemagglutinin (HA) Mab 3F10 (Roche) and Mab HA11 (Covance). Alexa 488, Alexa 555 and Alexa 633 conjugated goat anti–rabbit, anti-rat and anti-mouse immunoglobulin G (IgG) were purchased from Invitrogen.
All plasmids were constructed by standard molecular biology methods and the constructs were confirmed by sequencing. The following plasmids were assembled for FRET-FLIM analyses in the background of pCMV plasmid (Addgene): pCMV/YFP-E1TM, pCMV/CFP-E2TM, pCMV/CFP-p7, pCMV/CFP-EYF, pCMV/YFP-NS2. For these constructs, the plasmids encoding YFP and CFP were obtained from DW Piston (Vanderbilt University, USA) and A Miyawaki (Riken Institute, Japan), respectively. YFP-E1TM and CFP-E2TM are YFP and CFP in fusion with the transmembrane domain of E1 and E2 (H strain), respectively. In these two constructs, a two amino acid linker (serine and glycine) was inserted between the fluorescent protein and the transmembrane domains. CFP-p7 and CFP-EYF are CFP proteins fused to the N-terminus of p7 and the transmembrane domain of yellow fever virus envelope protein E (EYF), respectively. YFP-NS2 is a YFP protein fused to the N-terminus of NS2. In all the constructs, the calreticulin signal sequence was fused to the N-terminus of CFP and YFP for translocation in the ER lumen, allowing the study of the recombinant proteins in their native topology.
For p7-NS2 co-immunoprecipitation experiments, the following plasmids were constructed in the background of pTriex (Novagen) or pCI plasmids (Promega): pTriex/p7-Flag, pTriex/EYF-Flag, pCI/HA-NS2, pCI/HA-NS2-GFP and pCI/HA-NS2-GTM. p7-Flag and EYF-Flag are HCV p7 of genotype 1a (H strain) and YFE in fusion with the Flag epitope (DYKDDDDK). HA-NS2 has a HA epitope fused to the N-terminus of NS2. In HA-NS2-GFP, the cytosolic domain of NS2 was replaced by GFP protein, whereas for HA-NS2-GTM, we replaced the transmembrane domain of NS2 by the transmembrane domain of VSV-G protein.
In this work, we used a modified version of the plasmid encoding JFH1 genome (genotype 2a; GenBank access number AB237837), kindly provided by T. Wakita (National Institute of Infectious Diseases, Tokyo, Japan) [7]. Mutations were introduced in a JFH1 plasmid containing a Renilla Luciferase reporter gene [59] and mutations leading to amino acids changes F172C and P173S which have been shown to increase the viral titers [30]. Furthermore, the E1 sequence encoding residues 196TSSSYMVTNDC has been modified to reconstitute the A4 epitope (SSGLYHVTNDC) [56] as described [31]. Overlapping PCR was used to construct all the JFH1 mutants. The JFHGND-Luc construct was obtained by inserting the previously described GND mutation [7] in JFH-Luc plasmid. The JFH-HA construct was obtained by inserting the sequence of the HA epitope (YPYDVPDYA) followed by a GGG linker at the N-terminus of NS2. The JFH-Δp7-HA keeps the first 2 amino acids of p7 followed by the HA tag sequence and the SGG linker at the N-terminus of NS2. The JFH-ΔE1E2-Luc plasmid has been described previously [31]. It contains an in-frame deletion of amino acids 217–567. JFH-ΔTM12-HA has the first two transmembrane segments of NS2 deleted as described [19], in the context of our JFH-HA virus. JFH-HA-PP has proline residues 138 and 143 in domain D2 of the core protein replaced by alanine residues as described [13], [41]. JFH-S/A-HA has serine 457 of NS5A replaced by an alanine as described [17]. JFH-3BS/A-HA has serine residues at positions 452, 454 and 457 of NS5A replaced by alanine residues as described [16]. The JFH-S/D-HA has serine 457 of NS5A replaced by an aspartate residue as described [17]. JFH-ΔTME2-HA and JFH-ΔE1E2TME2-HA have the transmembrane region of E2 glycoprotein replaced by FMDV 2A autoprotease. JFH-ΔTME2-HA contains an in-frame deletion of amino acids 720–750, corresponding to the transmembrane domain of E2 of JFH1, whereas JFH-ΔE1E2TME2-HA contains in-frame deletions of amino acids 217–567 and 720–750. To construct the JFH-ΔTME2-HA, we replaced the C-terminal region of E2 (aa 720–750 of JFH1) by QLLNFDLLKLAGDVESNPGP FMDV 2A autoprotease peptide preceded by a GGG linker sequence. A similar strategy was used for the construction of JFH-ΔE1E2TME2-HA using as a backbone plasmid the JFH-ΔE1E2-HA. JFH-RR/QQ-HA has the arginines 33 and 35 of p7 replaced by glutamine residues. The primers and enzymes used for the constructs are presented in Table S1. Schematic representation of the constructs used in this study is presented in Figure S1.
Twenty-four hours before transfection, 293T cells were seeded in 100 mm tissue culture plates to reach a 70–80% confluency the next day. Cells were tranfected with 6 µg of DNA/plate at a ratio of 1∶4 with PEI transfection reagent (Eurogentec). In cotransfection experiments, equal quantities of each plasmid were used. At 24h post-transfection, cells were processed for co-immunoprecipitation analyses.
Twenty-four hours before transfection, U2OS cells were seeded in 6 well plates on 32mm slides to reach a confluency of 70–80% the next day. Cells were transfected with 1µg of CFP-expressing plasmid (donor) and 125 ng of YFP-expressing plasmid (acceptor) mixed with Fugene reagent (Roche) following the instructions of the manufacturer. For CFP-E1 and YFP-E2 co-transfection experiments, we used 300 ng of donor and 600 ng of acceptor plasmids, respectively.
Twenty-four hours after transfection, U2OS cells were selected for FRET-FLIM acquisition. We analyzed cells with similar expression levels of donor and acceptor fusion proteins. We also chose cells with normal reticulate ER morphology avoiding those where the overexpression of recombinant proteins was present.
In order to detect the FRET events, the Time Correlated Single Photon Counting FLIM system (TCSPC) was used [60], [61], [62]. The analyses were performed with a Leica SP5.X confocal Microscope (Leica Microsystem) with an internal FLIM detector. A dedicated photo-counting and timing electronic card (SPC 830 TCSPC card, Becker and Hickl) was coupled to the Leica internal detector and used to classify the photon emission in time to determine the lifetime of the donor protein. To excite the samples, Chameleon Ultra2 (Chorent Inc) biphoton was used at 830 nm at an average power of 0.13 mW/µm2 [60], [61], [62].
The fluorescence events of the donor protein result in a photon decay curve generated by the FLIM method. The decay curve was directly used to determine the donor's lifetime. The least square fitting method was used to describe the non linear responses commonly observed in FLIM analysis [60], [61], [62]. We used TITAN (“in the house” designed) and SPCImage (Becker and Hickl) software for advanced FLIM data analysis and curve fittings. In order to reduce the impact of background and improve the Signal to Noise Ratio (SNR), we excluded from our analysis the pixels located in the nuclear region or from ER-like regions where the donor protein was overexpressed. After setting these thresholds, we made a summation of all the pixels of interest to achieve a fitting statistically significant for the TITAN software. To compensate the possible large scattering of points in the curve, we used a Newton trust region algorithm [63] and an extraction of mean lifetimes was performed in order to determine the FRET events from the multi-exponential model [64].
Cell pellets were lysed in phosphate-buffered saline (PBS) lysis buffer (1% Triton 100-X, 20mM NEM, 2mM EDTA, protease inhibitors cocktail Roche) and they were precleared with 20 µl Prot G for 2h at 4C. The precleared lysates were incubated with anti-HA antibodies (HA11) or Sepharose beads covalently bound to HA11 antibody (Covance) overnight at 4h. The immunocomplexes were pulled down with 50 µl of Protein G and washed three times with lysis buffer.
For p7-NS2 interaction, cell lysates were incubated with 20 µl of agarose-anti-Flag beads over night at 4°C. The immunocomplexes were treated the same as above and the Western blots were revealed by an anti-HA antibody.
The endoglycosidase digestions were performed following the manufacture's instructions (NEB). Briefly, cell lysates containing 20 µg of protein were denatured in EndoH (PNGase) denaturing buffer (0.5% SDS, 1% 2-mercaptoethanol) for 10 min at 100°C. Then, the lysates were incubated or not with 1 µl of EndoH (PNgase) for 20h at 37°C.
After separation by SDS-PAGE, proteins were transferred to nitrocellulose membranes (Hybond-ECL, Amersham) by using a Trans-Blot apparatus (Biorad) and revealed with specific antibodies followed by secondary immunoglobulin conjugated to peroxidase. The proteins of interest were revealed by enhanced chemiluminescence detection (ECL, Amersham) as recommended by the manufacturer.
Plasmids encoding wild-type (WT) and mutated genomes were linearized at the 3′ end of the HCV cDNA with the restriction enzyme XbaI and treated with the Mung Bean Nuclease (New England Biolabs). In vitro transcripts were generated using the Megascript kit according to the manufacturer's protocol (Ambion). The in vitro reaction was set up and incubated at 37°C for 4 h and transcripts were precipitated by the addition of LiCl. Ten micrograms of RNA were delivered into Huh-7 cells by electroporation as described previously [30]. Replication was assessed at 72 h by measuring Renilla Luciferase activities in electroporated cells as indicated by the manufacturer (Promega). Supernatants containing HCVcc were harvested 72 h after electroporation and filtered through 0.45 µm pore-sized membrane for infectivity measurements. HCVcc were incubated for 3 h with Huh-7 cells seeded the day before in 24-well plates. At 72 h post-infection, Luciferase assays were performed on infected cells as indicated by the manufacturer (Promega). For supernatants titration, Huh7 electroporated cells were seeded in 6-well plates. 72h post-electroporation, naïve Huh-7 cells were inoculated with serial dilutions of the supernatant. 48h post-inoculation, the infected cells were fixed in ice-cold methanol, they were immunostained with anti-E1 antibody and the focus forming units (FFUs) were counted.
HCV Core was quantified by a fully automated chemiluminescent microparticle immunoassay according to manufacturer's instructions (Architect HCVAg, Abbott, Germany) [65], [66]. For the determination of intracellular core quantity, the electroporated cells were lysed in PBS lysis buffer (1% Triton 100-X, 20mM NEM, 2mM EDTA, protease inhibitors cocktail Roche) and the lysates were cleared for 20 min at 14,000g.
Mutated HCV genomes were delivered into Huh-7 cells. At day 3 post-electroporation, cells were trypsinized, washed once with fresh medium and reseeded into cell culture dishes. At day 5 post-electroporation, total RNA in cell lysates and HCV RNA in supernatants were extracted using the Qiagen RNeasy kit and Qiagen QiaAmp viral RNA mini kit, respectively. cDNA was synthesized using High Capacity cDNA Reverse Transcription kit as described by the manufacturer (Applied BioSystems) and titrated by quantitative real-time RT-PCR assay (RT-qPCR) using TaqMan and minor groove binding (MGB) probe detection. The primer pair and the probe were located in the 5′ HCV non-coding region [67].
Huh-7 cells transfected with HCV RNA were grown on 12-mm glass coverslips. At the indicated time points, cells were fixed with 3% paraformaldehyde and then permeabilized with 0.1% Triton X-100 in PBS. Both primary- and secondary-antibody incubations were carried out for 30 min at room temperature with PBS containing 10% goat serum. LDs were stained for 10 minutes in 300 ng/ml BODIPY 493/503 (Invitrogen). Nuclei were stained with 4,6′-diamidino-2-phenylindole (DAPI). The coverslips were mounted on slides by using Mowiol 4–88 (Calbiochem) containing mounting medium. Confocal microscopy was performed with an LSM710 laser-scanning confocal microscope (Zeiss) using a 63×/1.4 numerical aperture oil immersion objective. Signals were sequentially collected by using single fluorescence excitation and acquisition settings to avoid crossover. Images were processed using Adobe Photoshop software. Cells showing NS2/NS5A-positive dot-like structures were counted on images collected with a 40× oil immersion objective.
Huh-7 cells transfected with HCV RNA were grown on 75 cm2 flasks. At 72h post-electroporation, cells were fixed by incubation in a solution containing 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.2) for 20 h. The cells were collected by centrifugation and the cell pellet was then dehydrated in a graded series of ethanol solutions at −20°C, using an automatic freezing substitution system (AFS, Leica), and embedded in London Resin Gold (LR Gold, Electron Microscopy Science). The resin was allowed to polymerize at −25°C, under UV light, for 72 h. Ultrathin sections were cut and blocked by incubation with 3% fraction V bovine serum albumin (BSA, Sigma) in PBS. They were then incubated with anti-HA Mab (Covance) diluted 1∶50 in PBS supplemented with 1% BSA, washed and incubated with goat anti-mouse antibodies conjugated to 15 nm gold particles (British Biocell International, Cardiff, UK) diluted 1∶40 in PBS supplemented with 1% BSA. Ultrathin sections were cut, stained with 5% uranyl acetate, 5% lead citrate, and placed on electron microscopy grids coated with collodion. The sections were then observed with a Jeol 1230 transmission electron microscope (Tokyo, Japan) connected to a Gatan digital camera driven by Digital Micrograph software (Gatan, Pleasanton, CA) for image acquisition.
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10.1371/journal.pntd.0002646 | LmaPA2G4, a Homolog of Human Ebp1, Is an Essential Gene and Inhibits Cell Proliferation in L. major | We have identified LmaPA2G4, a homolog of the human proliferation-associated 2G4 protein (also termed Ebp1), in a phosphoproteomic screening. Multiple sequence alignment and cluster analysis revealed that LmaPA2G4 is a non-peptidase member of the M24 family of metallopeptidases. This pseudoenzyme is structurally related to methionine aminopeptidases. A null mutant system based on negative selection allowed us to demonstrate that LmaPA2G4 is an essential gene in Leishmania major. Over-expression of LmaPA2G4 did not alter cell morphology or the ability to differentiate into metacyclic and amastigote stages. Interestingly, the over-expression affected cell proliferation and virulence in mouse footpad analysis. LmaPA2G4 binds a synthetic double-stranded RNA polyriboinosinic polyribocytidylic acid [poly(I∶C)] as shown in an electrophoretic mobility shift assay (EMSA). Quantitative proteomics revealed that the over-expression of LmaPA2G4 led to accumulation of factors involved in translation initiation and elongation. Significantly, we found a strong reduction of de novo protein biosynthesis in transgenic parasites using a non-radioactive metabolic labeling assay. In conclusion, LmaPA2G4 is an essential gene and is potentially implicated in fundamental biological mechanisms, such as translation, making it an attractive target for therapeutic intervention.
| Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania. Its clinical manifestations are widespread, ranging from ulcerative skin lesions to life-threatening visceral infections. Approximately 1.5–2 million new cases of leishmaniasis are reported each year with an estimated 70,000 deaths. During the infectious cycle, Leishmania differentiates from the extracellular promastigote to the intracellular pathogenic amastigote form. Differentiation is triggered by environmental signals within the mammalian host, namely acidic pH and high temperature. Due to the absence of vaccination, chemotherapy, together with vector control, remains one of the most important elements in the control of leishmaniasis. Current anti-leishmanial drugs include pentavalent antimony, amphotericin B and miltefosine; most are toxic, expensive and risk becoming ineffective due to emerging resistance. Therefore, new drugs are urgently needed. LmaPA2G4 is a homolog of human proliferation-associated 2G4 protein (PA2G4, also termed Ebp1). We show that it is an essential gene in L. major and a gain-of-function approach allowed us to implicate LmaPA2G4 in translation and subsequent protein synthesis reduction, growth defects and virulence attenuation. This work highlights the essential role of LmaPA2G4 in the biology of the parasite and thus makes it an attractive target for drug development.
| Protozoan parasites of the genus Leishmania are the causative agents of leishmaniasis, a disease that is characterized by a spectrum of clinical manifestations ranging from ulcerative skin lesions to fatal visceral infections [1]. Leishmaniasis is a poverty-related disease and is associated with malnutrition, displacement, poor housing, illiteracy, gender discrimination, weakness of the immune system and lack of resources [2]. Leishmaniasis is further compromised by the emergence of co-infection with human immunodeficiency virus (HIV) in endemic areas [3]. Globally, there are an estimated 1.5–2 million new cases of leishmaniasis and 70,000 deaths each year, and 350 million people are at risk of infection and disease [4]. Due to the absence of vaccination, chemotherapy, together with vector control, remains one of the most important elements in the control of leishmaniasis. Current anti-leishmanial drugs include pentavalent antimony, amphotericin B and miltefosine; most are toxic and expensive. To date, no successful vaccine exists and the few anti-leishmanial drugs mentioned risk becoming ineffective due to emerging resistances [5], [6]. Therefore, new drugs are urgently needed [7].
During the infectious cycle, Leishmania differentiates from the extracellular promastigote to the intracellular amastigote form. Flagellated promastigotes develop in the midgut of sandflies, and following infection in humans, differentiate to intracellular amastigotes that multiply inside the macrophage lysosome [8]. This differentiation is triggered by environmental signals, mainly acidic pH and high temperature in the mammalian host [9].
Signal transduction pathways often relay these environmental stimuli through reversible phosphorylation, ultimately leading to changes in protein activity, interaction and expression profiles [10]. Mitogen-activated protein kinases (MAPKs) are conserved virtually across all eukaryotic organisms. To gain insight into the MAPK pathway in Leishmania we performed comparative phosphoproteomics of MPK7 [11] and WT parasites with the objective of characterizing putative substrates of this kinase. As part of the screening we identified LmaPA2G4, a homolog of human proliferation-associated 2G4 (PA2G4, also termed Ebp1) [12]. PA2G4 proteins are highly conserved in eukaryotes and are involved in the regulation of cell growth and differentiation. The human member of this family, ErbB3 binding protein 1 (Ebp1), is ubiquitously expressed and localizes in both the nucleus and cytoplasm [13]. The protein binds structured RNAs and was suggested to be involved in linking ribosome biosynthesis and cell proliferation [14]. Here we show that LmaPA2G4 is an essential gene in L. major. The over-expression of LmaPA2G4 results in accumulation of intermediates of translation initiation and ultimately leads to growth and virulence defects.
The University of Notre Dame is credited through the Animal Welfare Assurance (#A3093-01). All animal studies were conducted according to the Institutional Animal Care and Use Committee (IACUC) guidelines. The protocol for the infection of mice with Leishmania was approved by the University's IACUC (October 16, 2012, protocol #15-047).
Leishmania major strain Friedlin V1 (MHOM/JL/80/Friedlin) was cultured in M199 medium supplemented with 10% FBS at 26°C and pH 7.4 [15]. L. donovani strain 1S2D (MHOM/SD/62/1S-CL2D) was grown in M199 supplemented with 10% FBS and axenic amastigotes were differentiated as described previously [16]. For some experiments L. major metacyclic promastigotes were enriched by agglutination [17]. Briefly, cells were incubated for 30 min at RT with 50 µg/ml peanut agglutinin in M199 without serum, agglutinated parasites were removed by centrifugation and metacyclic parasites were recovered from the supernatant.
L. major CAJ07101 (gi|68126048) was used as an initial query for PSI-BLAST and after four cycles results with significant E-values (<10e−6) were selected. Sequences corresponding to putative aminopeptidase proteins from the sequenced genomes of H. sapiens, L. major, L. infantum, L. braziliensis, L. donovani, T. brucei, T. vivax, T. cruzi and T. congolense were retrieved using TriTrypDB and UniProt databases; (http://tritrypdb.org/tritrypdb/) and (www.uniprot.org). Sequences were aligned with Clustal X (v 2.0). Alignments were converted to MEGA compatible files and fed into the MEGA5.2 software package. A Neighbor-Joining tree was computed with 500 bootstrap replicates.
In order to generate null mutants, a 901 bp region in the 5′ untranslated region (UTR) upstream of LmaPA2G4 was amplified with the primers 5′-ACCGGTACCCAATCATGGCCCACCGAAGG- 3′ (KpnI) and 5′-CGCCCCGGG/CTCGAGTTTTTTTGGGTGGGTGGC-3′ (SmaI or XhoI). A 903 bp fragment in the 3′UTR was amplified with primers 5′-ACCCTCGAG/GGATCCACGGCCGTGGCATCCGTG-3′ (XhoI or BamHI) and 5′-CGCGGTACCCACGATGGGCAGAACGCC- 3′ (KpnI). Reactions were performed in a total volume of 50 µl containing LongAmp high fidelity Taq- DNA polymerase (New England Biolabs) following the manufacturer's recommendation. Products were cloned into pGEM-T and pGEM-T Easy vectors (Promega) to create pGEM-T-5′UTR-3′UTR. A 2.8 kb SmaI-XhoI fragment from pX63HYG containing the hygromycin B (HYG B) gene and a 2.5 kb XhoI-BamHI fragment from PX63PAC including the puromycin (PAC) were ligated between 5′UTR and 3′UTR to generate the two targeting constructs. Constructs were linearized with KpnI and dephosphorylated. The LmaPA2G4 homolog (CAJ07101) was PCR amplified from genomic DNA of L. major FVI using the primers 5′-ACCAGATCTATGTCAAAGAACGCTGAC- 3′(BglII) and 5-GCGAGATCTCTACTTCGCGCGCTTCTT- 3′(BglII). Purified PCR products were cloned into pGEM-T EasyVector (Promega). N-terminal GFP-PA2G4 fusion and pXNG-PA2G4 were obtained by inserting the 1.1 kb BglII fragment from pGEM-T into the respective site of pXG-GFP+2 [18] and pXNG [19]. F1, R1 primer pair 5′- CATCAATATTTCATGCGC-3′ and 5′-CGTGTCCTCCTCTTCTTC- 3′; F2, R1 5′- GGTAGTGTCGCGTGTTGG-3′; F1, R2 5′-CTGCATCAGGTCGGAGACGC-3′ and F1, R3 5′-GGGGTCAGGGGCGTGGGTCAG-3′ were used to corroborate the absence of LmaPA2G4 ORF in the null mutant lines. LmaPA2G4 was PCR amplified and cloned into episomal vector pLEXSY (Jena Bioscience), and parasites were selected in 75 µg/mL hygromycin B (Sigma). Parasites transfected with the empty vector, pXG-GFP+2 and pLEXSY were used as mock controls. Null mutants and episomal transfectants were established by electroporation as previously described [20], [21].
Total RNA was isolated from L. major and L. donovani WT and transgenic parasites with Trizol reagent (Life Technologies Inc., NY) using RNase-free plastic supplies. cDNA was amplified using M-MLV Reverse Transcriptase (Sigma) and oligo d(T)15 (Promega) following manufacturer's recommendations. PCR was carried out with LongAmp high fidelity Taq- DNA polymerase (New England Biolabs) using LmaPA2G4 specific primers 5′- CCACGTGGACGGCTACTGCGCCG-3′ and 5′- CTTCCTTTTCGAAGAGAATAGGG-3′ and GAPDH primers 5′- CGACGACGGCAAAGCAGAAG-3; and 5′- TCAGCGCCACACCGTTGAAG-3′. All RNA samples were treated with DNA-free (Ambion, Inc., TX) to remove contaminating genomic DNA. Each RT-PCR product was analyzed by gel electrophoresis using 1% agarose gels and band intensity analyzed with ImageQuant TL software (GE Healthcare).
Live L. major promastigotes over-expressing GFP-PA2G4 were immobilized on poly (L-lysine)-coated 35 mm glass bottom dishes (MatTek Corporation, USA) and counterstained with 1 µg/mL NucBlue Live Cell stain (Hoechst 33342) (Molecular Probes). Fluorescent imaging was performed using a spinning disk confocal Revolution (Andor Technology) and a 63× oil immersion objective. Image acquisition was done using AndorIQ software (Andor Technology) and images processed with ImageJ software (NIH, USA).
Virulence studies were performed as previously described [11]. Briefly, groups of five female BALB/c mice (Charles River) were injected in the footpad with 105 metacyclic promastigotes from GFP-PA2G4, cured GFP-PA2G4 and mock control. Lesions were followed weekly by measuring the thickness of footpads with a Vernier caliper.
Crude cell lysates were separated in 4–12% Bis-Tris NuPAGE gels (Life), and electro-blotted onto PVDF membranes (Pierce). Proteins were revealed using the following primary antibodies: mouse monoclonals anti-A2 (Abcam) and anti-GFP-HRP (Miltenyi Biotec), mouse monoclonal anti-tubulin (Sigma), and anti-rabbit or anti-mouse HRP-conjugated secondary antibodies (Pierce).
20 µL of immuno-complexes (GFPK7; transgenic parasites over-expressing an active MPK7) were incubated in a Thermomixer R (Eppendorff) for 30 min at 30°C and 1000 rpm in a 50 µL reaction mixture (Millipore) containing 5 µg myelin basic protein (MBP) substrate (positive control) or recombinant substrate and 10 µCi [γ-32P] ATP (3000 Ci/mmol). Reactions were terminated by heating the samples for 10 min at 98°C in NuPAGE sample buffer and reducing agent (Life). 30 µL of the reaction were separated by SDS-PAGE. The gel was Coomassie-stained, fixed, dried and analyzed by autoradiography.
Protein extracts from logarithmic L. major WT, GFP-PA2G4 and stationary GFPK7 promastigotes were differentially labelled with the spectrally resolvable Cy3 and Cy5 as previously described [21]. A pool of extracts was labelled with Cy2 for normalization purposes, following the manufacturer's recommendations (GE Healthcare). Phosphoproteins were enriched with affinity IMAC columns (Qiagen) as previously described [22]. Following labelling, proteins were precipitated using a 2-D Clean-Up kit (GE Healthcare), allowing for quantitative precipitation and removal of interfering substances, such as detergents, salts, lipids, phenolics, and nucleic acids.
Synthetic double stranded RNA Poly (I∶C) (Sigma) was labeled with Label IT Cy5 labeling kit (Mirus). Briefly, 5 µg of RNA was incubated at a 1∶1 (v∶w) ratio of Label IT Cy5 reagent to nucleic acid. 40 ng of labeled Poly(I∶C) was incubated with 20 ng GFP-PA2G4 fusion protein in binding buffer (50 mM Tris pH 7.4, 0.5 mM EDTA, and 150 mM NaCl) at room temperature for 45 min. GFP-PA2G4 was immunoprecipitated with anti-GFP magnetic beads as previously described [20]. An aliquot of gel loading buffer (0.25% bromophenol blue, 0.25% xylene cyanol, 50% glycerol) was added to the reaction mixture and resolved on 10% non-denaturing polyacrylamide gels in 1× TBE. Gels were scanned on a Typhoon FLA 9500 Imager (GE Healthcare) using 633/670 nm for Cy5 filter and 489/508 nm for GFP filter.
IEF of 100 or 120 µg of protein was carried out using an EttanIPGphor 3 System (GE Healthcare) at 20°C with 11 and 13 cm non-linear DryStrip (pH 4–7). Strips were passively rehydrated overnight at room temperature in rehydration solution (GE Healthcare) containing 0.5% IPG buffer 4–7 and the sample. The IEF maximum current setting was 50 µA/strip. The following conditions were programmed for IEF: 100 V gradient step for 5 h, 300 V gradient step for 5 h, 1000 V gradient step for 2 h, 6000 V gradient step for 8 h and 6000 V for 5 h (60550 Vh). Following IEF, strips were equilibrated in two different solutions for 15 min each (6 M urea, 75 mMTris/HCl pH 8.8, 29.3% glycerol, 4% SDS, 0.002% bromophenol blue) supplemented with 65 mM DTT and 13.5 mM iodoacetamide, respectively. The strips were transferred to SDS polyacrylamide gels and sealed with 0.5% agarose in 25 mMTris-base, 0.19 M glycine, 0.2% SDS, 0.01% bromophenol blue. Electrophoresis was carried out in an SE 600 Ruby cooled electrophoresis system (GE Healthcare) using 12.5% SDS-PAGE gels and two-step runs (1 W/gel for 15 min and 7 W/gel for 5 h).
After electrophoresis, gels were scanned on a Typhoon FLA 9500 Imager (GE Healthcare) using 488/520 nm for Cy2, 532/580 nm for Cy3, 633/670 nm for Cy5 and 100 µm as pixel size. Gel images were normalized by adjusting PMT voltage to obtain appropriate pixel value without any saturation. Images were analyzed with Decyder v. 6.5 (GE Healthcare) and Delta2D v.4.3 software (Decodon). Gels were matched or warped and spots detected across all images. A 2-fold difference in abundance, with p-values<0.05, was considered significant for the expression profiles. Polyacrylamide gels were then fixed in 50% methanol and 7% acetic acid and stained using SYPRO Ruby total protein gel stain (Life). Spots of interest were manually excised from gels using a blue-light transilluminator (Life).
The gel spots were subjected to reduction with 55 mM dithiothreitol (Sigma-Aldrich) in 25 mM ammonium bicarbonate (Fisher Scientific) at 56°C for 1 hour followed by alkylation with 100 mM iodoacetamide (Sigma-Aldrich) in 25 mM ammonium bicarbonate at room temperature in the dark for 45 min. The spots were washed with 25 mM ammonium bicarbonate for 10 min followed by two consecutive washes with 25 mM ammonium bicarbonate in 50/50 acetonitrile∶water for 5 min, each. The spots were placed in a vacuum concentrator to dry completely before the addition of 12.5 ng trypsin gold (Promega) to each gel spot. The spots were kept at 4°C for 30 min to swell and then were incubated at 37°C overnight. Following trypsin digestion, the supernatant was collected. Peptides were further extracted from the gel spots with two consecutive additions of 50% acetonitrile/45% water/5% formic acid to the spots followed by 30 min of vortexing. The two sets of extracts were combined with the supernatant from each gel spot and then vacuum concentrated to 10 µL. Each concentrated digest was desalted with a C18 Ziptip (EMD Millipore) according to the manufacturer instructions. The desalted digests were then dried down in a vacuum concentrator and reconstituted in 10 µL of 0.1% TFA in water. A 2 µL aliquot of each gel digest was injected onto a nanoAcquity UPLC (Waters Corporation) with a BEH300 C18 100 µm×100 mm column (Waters Corporation) with 1.7 µm particle size. A gradient of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) was performed starting with 2% B held for 6 min and then ramping to 40% B to 40 min and 90% B to 43 min. The column was washed with 90% B for 7 min and then re-equilibrated with 98% A: 2% B. The nanoAcquity was coupled to a LTQ Orbitrap Velos mass spectrometer (Thermo Corporation) for data dependent scans of the digested samples in which the top nine abundant ions in a scan were selected for CID fragmentation. The UPLC-MS/MS chromatograms and spectra were analyzed using Xcalibur software (Thermo), and the extracted data were searched against the L. major custom database via Mascot and/or Protein Pilot. Search criteria included a global modification of carbamidomethylation on the cysteines. Proteins identified had less than a 1% false discovery rate.
The metabolic labeling of de novo synthetized proteins was conducted using the non-radioactive assay Click-iT AHA kit and Click-iT Cell Reaction Buffer Kit (Life) following manufacturer's guidelines with minor modifications. Briefly, 2×108 mid-log phase WT and GFP-PA2G4 L. major and L. donovani promastigotes as well as GFP-PA2G4 L. donovani amastigotes were initially incubated for 30 min at 27°C in 2 mL methionine-free RPMI medium supplemented with 10% FBS in order to deplete methionine reserves. Metabolic labeling was performed for 2 h at 27°C in presence of 50 µM azidohomoalanine (AHA). A culture of L. major treated for 2 h with 100 µg/mL cycloheximide, an inhibitor of protein biosynthesis, was included as a positive control. After labeling, cells were harvested and lysed and 50 µL of each sample was employed to perform the Click reaction with TAMRA. Proteins were precipitated, resolubilized in 1D gel electrophoresis sample loading buffer and heated for 10 min at 70°C and subsequently resolved in a precast polyacrylamide gel (NuPAGE Novex 4–12% Bis-Tris gels, Life). Gel was visualized in a Typhoon FLA 9500 (GE Healthcare) and analyzed with ImageQuant TL software (GE Healthcare). After imaging the gel with TAMRA-labeled samples, the gel was fixed and stained with SYPRO Ruby in order to assess total protein content. Statistical comparisons were made using non parametric Mann–Whitney U-test.
We have previously shown that L. major MPK7 is implicated in parasite growth control, including the pathogenic amastigote stage [11]. The overexpression of an active MPK7 (GFPK7 transgenic parasites overexpressing MPK7) led to defects in cell cycle and, ultimately, attenuated virulence in a mouse model. In order to identify potential downstream targets of MPK7 we performed comparative 2D-DIGE of phosphoproteins isolated from four independent stationary cultures of L. major wild type (WT) and GFPK7 promastigotes. MPK7 shows increased activity at stationary phase [20]. Phosphoproteins were isolated by immobilized metal affinity chromatography (IMAC) and differentially labeled with CyDye fluors (GE Healthcare) as detailed in Material and Methods. Phosphoproteins were separated by 2DE on pH 4–7 IPG immobiline strips and SDS-PAGE. Images were analyzed with DeCyder 6.5 software (GE Healthcare). Up-regulation of spots more than 2-fold in GFPK7, with p-values<0.05 were considered significant. Spot ID 207 was over-represented in GFPK7 (2.97 fold change and p = 0.00056) and excised from the gel and analyzed by MS/MS (Fig. 1). LmjF19.0160 (Tritryp gene ID) is a putative aminopeptidase with a predicted MW of 43 kDa. Recombinant LmjF19.0160 was not phosphorylated in vitro by active GFPK7 using an in vitro kinase assay (Fig. S1). Phosphotransferase activity of recombinant GFPK7 was assessed by phosphorylation of MBP. Given the fact that LmjF19.0160 was enriched with an IMAC (phospho-specific) column, we are attempting to characterize the putative phosphorylation sites, with the objective of performing site-directed mutagenesis.
LmjF19.0160 belongs to the clan MG, family M24 of metallopeptidases according to the classification of MEROPS database [23]. Family M24 is further divided into subfamilies M24A and B. Typical members of subfamily M24A are methionyl aminopeptidases type I and II (METAP1 and 2). These peptidases are essential for the removal of the initiating methionine of many proteins [24]. We investigated the relationship between human and trypanosomatid members of the M24 family of metallopeptidases by multiple alignment and cluster analysis. LmjF19.0160 (protein ID gi|68126048) was used as an initial query for PSI-BLAST against the sequenced genomes of H. sapiens, L. major, L. infantum, L. braziliensis, L. donovani, T. brucei, T. cruzi, T. vivax and T. congolense. Homologs of human METAP1 and 2 are found in all trypanosomatids as shown by the clustering tree (Fig. 2). Bootstrap values support the existence of METAP1 and 2 subclasses among Leishmania and Trypanosoma. Interestingly, LmjF19.0160 clusters with homologs of the human proliferation-associated protein 2G4 (PA2G4) [25]. These are non-peptidase proteins which possess the “pita-bread” fold typical of methionyl aminopeptidases, however they lack metal cofactors and peptidase activity [26].
Although the genome of Leishmania seems to be constitutively expressed [27], between 6% and 9% of the genes display significant expression profiles. Therefore, we analyzed the transcript levels of LmaPA2G4 by semi-quantitative RT-PCR. Total RNA was isolated from L. major logarithmic and metacyclic parasites. Peanut agglutination was used to enrich metacyclics in stationary cultures [28]. For L. donovani we used host-free amastigotes as previously described [16]. GAPDH was used as a housekeeping gene for semi-quantification purposes. As judged by the LmaPA2G4/GAPDH ratio, there are not significant differences in the expression levels across different life stages (Fig. S2).
In order to gain insight into the putative function of PA2G4 in Leishmania we designed a loss-of-function strategy. Leishmania are diploid parasites and two rounds of targeted replacement with a drug-resistance marker are necessary. Unsuccessful attempts to replace the two PA2G4 alleles with resistance markers, to create a homozygous KO, suggested that LmaPA2G4 may be an essential gene. To demonstrate the essentiality of LmaPA2G4 we used a genetic method based on negative selection [19] to guard against the potential lethal phenotype. Both LmaPA2G4 alleles could be removed by homologous recombination in the presence of an episome expressing LmaPA2G4 (pXNG-PA2G4) (Fig. 3A). The loss of endogenous LmaPA2G4 in the null mutants was confirmed in two independent homozygous lines by PCR (Fig. 3B). As expected, only the episomal copy of LmaPA2G4 is present. The episome pXNG [19] carries a negative selectable thymidine kinase (TK), a fluorescent protein (GFP) and a resistance marker (SAT). TK renders the parasites susceptible to ganciclovir (GCV). PXNG-PA2G4/WT parasites were selected and grown in the presence of 250 µg/mL nourseothricin (SAT) and the GFP intensity was analyzed by flow cytometry. After negative selection with the addition to the culture of 50 µg/mL GCV during three passages, a dramatic shift in GFP fluorescence was observed (Fig. 3C, upper panel). However, after negative selection with GCV, mutant LmaPA2G4 parasites retained the ectopic copy of pXNG-PA2G4, as shown by the minimal reduction in GFP intensity (Fig. 3C, lower panel). These results suggest that LmaPA2G4 is an essential gene in L. major.
Since the essentiality of LmaP2G4 precluded further loss-of-function analysis, we followed a gain-of-function strategy to reveal the implication of LmaPA2G4 in the biology of Leishmania. We created parasites over-expressing an N-terminal GFP-PA2G4 fusion protein. LmaPA2G4 ORF was cloned into pXG-GFP2+ as previously described [20]. We confirmed the fusion by western blot analysis of L. major FVI wild-type (WT) and transgenic GFP-PA2G4 promastigotes using monoclonal anti-GFP and tubulin as a loading control (Fig. 4A). Fluorescence intensity of GFP-PA2G4 parasites was measured by flow cytometry (Fig. 4B). Live log-phase transgenic promastigotes were immobilized on poly(l)lysine-coated 35 mm glass bottom dishes and cells were analyzed using spinning disk confocal microscopy (Fig. 4C). Nuclei were counterstained with NucBlue Live Cell stain (Molecular Probes) (red). The ectopic expression of LmaPA2G4 is predominantly cytoplasmic and the overexpression had no effect on the morphology and viability of the parasites. Growth curves of WT, GFP-PA2G4 and GFP-mock control show that the overexpression of LmaPA2G4 results in a significant growth delay (Fig. 4D). LmaPA2G4 growth defect was reproduced by transgenic parasites expressing untagged protein (pLEXSY-PA2G4) and thus is independent from GFP expression. We also analyzed the infective metacyclic stage in control and transgenic lines. Similar numbers of metacyclic parasites were agglutinated in both lines (Fig. 4E), indicating that the over-expression of LmaPA2G4 does not affect metacyclogenesis.
We investigated the effects of LmaPA2G4 overexpression on parasite virulence using an established experimental mouse infection [29]. Mock, GFP-PA2G4 and cured GFP-PA2G4* parasites grown in G418-free medium were normalized for virulence through one passage in BALB/c mice [30]. 105 mock, GFP-PA2G4 and GFP-PA2G4* metacyclic parasites were inoculated into the hind footpad of groups of five female BALB/c mice. Lesion formation was followed by measuring the increase in footpad size with a Vernier caliper. Mock and GFP-PA2G4* parasites elicited a strong response ca. 30 days after inoculation and resulted in necrotic lesions (Fig. 5A). Interestingly GFP-PA2G4 are highly attenuated and lesions were only apparent after 40 days after inoculation. The cured line, grown in the absence of G418, elicited a response similar to GFP mock parasites, suggesting that the specific expression of LmaPA2G4 is responsible for the attenuated phenotype. At least two independent GFP-PA2G4 lines were used to rule out potential discrepancies due to clonal variations. To determine whether the overexpression of LmaPA2G4 affects the differentiation from pro- to amastigotes, we established L. donovani transgenic (GFP-PA2G4) lines that allow axenic amastigote differentiation. 2×105 promastigotes were inoculated in low pH medium and 37°C to trigger differentiation [9]. We monitored the axenic amastigotes 24 and 48 h after differentiation (Fig. 5B). As judged by the expression of the amastigote-specific A2 protein family [31], transgenic parasites carrying GFP-PA2G4 are bona fide amastigotes at 48 h and no differences are observed when compared with WT. This result suggests that the virulence attenuation is potentially due to defects in cell proliferation.
To gain a better insight into the function of LmaPA2G4, we quantitatively compared protein extracts from L. major GFP-PA2G4 and mock promastigotes of three independent biological repeats. Protein samples were differentially labelled with CyDye fluors (GE Healthcare) and separated by two-dimensional electrophoresis (2DE) on IPG strips and polyacrylamide gels as previously described [21]. A representative merged image of Cy5-labeled GFP-mock (red) and Cy3-labeled GFP-PA2G4 (green) is shown (Fig. 6A). Gels were scanned on a Typhoon FLA-9500 Imager and analyzed by Delta2D v 4.3 (Decodon) software package. Figure 6B shows a graphical representation of the expression profiles of mock (red) and GFP-PA2G4 (green) samples. Five spots with significant expression differences in GFP-PA2G4 (p-value<0.005) were selected. Gels were stained with the fluorescent stain SYPRO Ruby and the five spots of interest were excised and identified by MS/MS. Interestingly, the homologs of identified eukaryotic translation initiation factor 5 (LmjF25.0720, 6.1-fold change), 60S ribosomal protein (LmjF29.2460, 5.9-fold change) and 40S ribosomal protein (LmjF28.0960, 5.3-fold change) are implicated in translation initiation and elongation [32]. The chaperonin HSP60 (LmjF36.2030, 4.4-fold change) is involved in stress response and acts as a catalyst of folding proteins [33]. Raw data of protein identification and Mascot searches is presented in Table S1.
dsRNA-binding domains characterize an expanding family of proteins involved in different cellular processes, ranging from RNA editing and processing to translational control. Human homolog Ebp1 interacts with double stranded RNA [34] and thus it was tempting to study whether GFP-PA2G4 is able to bind a synthetic double stranded RNA Poly (I∶C). RNA molecules (Sigma) were labeled with Cy5 in order to normalize and visualize the reaction. 40 ng labeled Poly(I∶C) was incubated with 20 ng GFP-PA2G4 protein in binding buffer at room temperature for 45 min and resolved on 10% non-denaturing polyacrylamide gels in 1× TBE. Gel was scanned in a Typhoon imager (GE) with Cy5 filters. Poly (I∶C) is visible in lanes 1 and 3 (Fig. 7, left panel). Cy5 filters produce a non-specific background signal with xylene cyanol, which is part of the loading buffer. Taking advantage of the GFP fusion, the gel was re-scanned with a GFP filter, and arrows indicate the apparent mobility shift of GFP-PA2G4 in the presence of Poly (I∶C) (Fig. 7, right panel).
The results from last section suggest a defect in protein translation in transgenic parasites, potentially due to the non-physiological accumulation of intermediates of translation initiation and elongation. In order to confirm this phenotype, we measured de novo protein synthesis in the transgenic lines. Metabolic labeling of de novo synthetized proteins was conducted using a non-radioactive assay. Mid-log L. major WT and GFP-PA2G4 promastigotes as well as L. donovani WT and GFP-PA2G4 amastigotes were initially incubated in methionine-free medium. Metabolic labeling was performed for 2 h at 27°C in the presence of azidohomoalanine (AHA). A culture of L. major treated for 2 h with 100 µg/mL cycloheximide, an inhibitor of protein biosynthesis, was included as a positive control. After labeling, cells were harvested, lysed and subjected to the Click-iT (Life) reaction with TAMRA. Proteins were resolved in polyacrylamide gels and visualized in a Typhoon FLA 9500 imager and fluorescence measured with ImageQuant TL software (GE Healthcare). The gel was then fixed and stained with SYPRO Ruby in order to assess total protein content (Fig. 8). Percentage of de novo protein synthesis is shown normalized to the controls L. donovani and L. major WT parasites (Fig. 8, lower panel). These data suggest than indeed de novo protein synthesis is greatly reduced in pro- and amastigotes over-expressing LmaPA2G4. Defects in biosynthesis will likely impact cell proliferation and ultimately may be responsible for the phenotype observed.
LmaPA2G4 is a homolog of the proliferation-associated 2G4 protein [35] also termed Ebp1 [14]. The human counterparts are involved in the regulation of cell growth and differentiation [36]. Human homolog Ebp1 is a target for phosphorylation by PKC in vitro and in vivo, and its C-terminus has been suggested to harbor the phosphorylation site [37]. Furthermore, serine 363 (S363) of Ebp1 is phosphorylated in vivo and the S363A mutation significantly decreased the ability of Ebp1 to repress transcription and abrogated its ability to inhibit cell growth [38]. LmaPA2G4 was isolated through a phospho-enrichment procedure (IMAC), suggesting it may be a phosphoprotein. We are currently characterizing the potential phosphorylated residues in LmaPA2G4 with a combination of IMAC enrichment and 2D LC-MS/MS.
LmaPA2G4 is a metallopeptidase of the M24A family, clan MG (Fig. 2). Bio-informatics analysis and multiple alignment identified members of this family across all trypanosomatids, with a remarkable conservation. The crystal structure of the human PA2G4 has been determined at 1.6 Å resolution [13]. The structure revealed a pita-bread fold conserved in methionine aminopeptidases (MetAPs). In these enzymes, a divalent metal center within the catalytic site is involved in the cleavage of the appropriate substrate. However, the metal ions are not present in PA2G4 [13], and therefore no enzymatic activity can be performed. This group of enzymes are reflected in MEROPS database as non-peptidase members of the M24 family. LmaPA2G4 is well conserved among trypanosomatids, indicating an essential function and selection pressure despite having lost its catalytic site. Further substrate binding studies are necessary to conclusively label LmaPA2G4 as a pseudoenzyme. Inactive enzyme homologs are not simply debris and functional studies, for instance in the iRhom family of rhomboid proteases, have revealed important roles as biological regulators [39]. With the exception of pseudokinases, there is still a lack of functional information on the roles of inactive enzymes [40].
We have confirmed that LmaPA2G4 is an essential gene in L. major. Study of some promising candidate genes through loss-of-function is often hindered by lethal mutant phenotypes and our system (Fig. 3A) allows to test whether the guarding episome (pXNG) can be actively and quickly removed in the null mutant (Fig. 3C). Moreover, it indicates that the loss of LmaPA2G4 cannot be compensated by other related genes. Our findings may be applicable to other trypanosomatids, however viability of the LmaPA2G4 null mutant must be carefully examined in Trypanosoma and other Leishmania spp.
The overexpression of LmaPA2G4 did not impair the ability of L. major to differentiate into infective metacyclic promastigotes (Fig. 4D) and L. donovani promastigotes were able to fully differentiate into axenic amastigotes within 48 h (Fig. 5B). These data suggest that the attenuated virulence observed in the murine model (Fig. 5A) is likely due to a defect in proliferation. To better understand this phenotype, we performed quantitative proteomics that allowed us to study significant differences in protein expression as a result of LmaPA2G4 overexpression (Fig. 6A and B). 2D DIGE has proven to circumvent the limitation of traditional in-gel proteomics, especially when combined with bottom-up proteomics [41]. The most over-represented −6.1 fold change- spot corresponds to the eukaryotic elongation factor 5A. eIF5A is the only protein that contains the modified amino acid hypusine [42]. Hypusine is formed in eIF5A by post-translational modification of one of the lysyl residues in two consecutive steps through the action of deoxyhypusine synthase (DHS) and deoxyhypusine hydroxylase (DOHH) [43]. The hypusine pathway is conserved in trypanosomatids, and DHS and DOHH have been recently characterized in T. brucei [44] and L. donovani [45], respectively. In higher eukaryotes, eIF5A has an active role in translation elongation, however its precise requirement in protein synthesis remains elusive [46]. 60S and 40S ribosomal subunits showed a 5.9 and 5.3 fold change respectively in promastigotes over-expressing LmaPA2G4. In higher eukaryotes, translation initiation starts with the disassociation of the 80S ribosomal complex and the binding of eIF6 to the 60S ribosomal subunit and the binding of eIF3 and eIF1A to the 40S ribosomal subunit [47]. The HSP60 family of chaperonines −4.4 fold change in our analysis- are widely present in trypanosomatids and they have a potential role in folding of proteins imported into the mitochondria [48]. It is noteworthy that our proteomic approach does not allow us to confirm any potential interaction of LmaPA2G4 with the key transcription elements discussed above. The EMSA assay suggests that GFP-PA2G4 binds a generic and synthetic double stranded RNA (Fig. 7). It is tempting to speculate that further investigations on the RNA binding domains will allow us to gain insight on the biological relevance of binding on, for instance, translational control. The fact the de novo protein synthesis is significantly reduced in the transgenic lines (Fig. 8) and the new insights on transcriptional roles of the human counterpart [49] suggest a potential role of LmaPA2G4 in transcription in L. major. Altered transcription in lines over-expressing LmaPA2G4 lines leads to defects in cell growth, including the pathogenic amastigote stage. However, further investigation is required to dissect the molecular mechanisms in which LmaPA2G4 is involved. In conclusion, this work underscores its essential role in the biology of the parasite and opens new venues for potential therapeutic intervention.
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10.1371/journal.pntd.0005757 | Serum levels of interleukin-6 are linked to the severity of the disease caused by Andes Virus | Andes virus (ANDV) is the etiological agent of hantavirus cardiopulmonary syndrome in Chile. In this study, we evaluated the profile of the pro-inflammatory cytokines IL-1β, IL-12p70, IL-21, TNF-α, IFN-γ, IL-10 and IL-6 in serum samples of ANDV-infected patients at the time of hospitalization. The mean levels of circulating cytokines were determined by a Bead-Based Multiplex assay coupled with Luminex detection technology, in order to compare 43 serum samples of healthy controls and 43 samples of ANDV-infected patients that had been categorized according to the severity of disease. When compared to the controls, no significant differences in IL-1β concentration were observed in ANDV-infected patients (p = 0.9672), whereas levels of IL-12p70 and IL-21 were significantly lower in infected cases (p = <0.0001). Significantly elevated levels of TNF-α, IFN-γ, IL-10, and IL-6 were detected in ANDV-infected individuals (p = <0.0001, 0.0036, <0.0001, <0.0001, respectively). Notably, IL-6 levels were significantly higher (40-fold) in the 22 patients with severe symptoms compared to the 21 individuals with mild symptoms (p = <0.0001). Using multivariate regression models, we show that IL-6 levels has a crude OR of 14.4 (CI: 3.3–63.1). In conclusion, the serum level of IL-6 is a significant predictor of the severity of the clinical outcome of ANDV-induced disease.
| Andes virus (ANDV) causes hantavirus cardiopulmonary syndrome (HCPS) that is characterized by the development of vascular leakage syndrome, eventually leading to massive pulmonary edema, shock and, in many cases, death. To date, no FDA-approved immunotherapeutics, specific antivirals, or vaccines are available for use against HCPS. Patient survival rates hinge largely on early virus diagnosis, hospital admission and aggressive pulmonary and hemodynamic support in an intensive care unit. Individual host factors associated with the outcome of an ANDV infection are poorly known, and such knowledge could allow the disease progression of hospitalized patients to be predicted, resulting in individualized treatment. In this study, we show that serum levels of IL-6 at the time of hospitalization in ANDV-infected patients are associated with the severity of the clinical outcome of ANDV-induced disease. Therefore, these finding suggest that determining IL-6 levels at the time of admission to the hospital could be useful to predict the progression of ANDV-induced disease.
| Andes virus (ANDV) is a rodent-borne hantavirus member of the Bunyaviridae family, and is unique for its ability to be transmitted from person-to-person [1,2]. ANDV is endemic in Chile and according to the Chilean Ministry of Health (Department of Epidemiology, Chilean Ministry of Health; http://www.minsal.cl), 1087 cases of ANDV infection have been confirmed through May 2017 with a lethality rate of 35–40%. In humans, ANDV infection causes hantavirus cardiopulmonary syndrome (HCPS) [3,4]. The initial symptoms of HCPS are non-specific and include fever, headache, and myalgia among others. However, in later phases, symptoms progress from coughing to severe pulmonary edema requiring intubation and mechanical ventilation, whilst cardiogenic shock is the main cause of death [3,4]. Like other hantaviruses, the ANDV-induced illness is associated with the activation of the host's innate immune response, with cytokines playing a key role, rather than with direct cellular destruction induced by active virus replication [5]. Currently, no FDA approved drugs, immunotherapeutics, or vaccines are available for HCPS prevention or treatment [6]. Thus, patients’ survival rates hinge largely on early diagnosis, hospital admission and aggressive pulmonary and hemodynamic support in an intensive care unit [7,8]. Moreover, there are no blood biomarkers to predict the outcome of ANDV-induced HCPS. Interestingly, several reports show that the levels of the pro-inflammatory cytokines TNF-α, IL-1, IL-6, IL-10, and IFN-γ increase in patients infected with other hantaviruses such as Puumala Virus (PUUV), Dobrava Virus (DOBV), or the Sin Nombre Virus (SNV) [5,9,10,11,12,13,14,15]. Additionally, high levels of IL-6 and TNF-α in plasma of SNV- and PUUV-infected patients is associated with a severe or fatal disease outcome [5,10,13,14,16]. In the case of ANDV, elevated levels of cytokines including IL-6 were reported in ANDV-infected air-exposed organotypic human lung tissues [17]. Motivated by these findings, we designed a study to evaluate the cytokine profile (IL-1β, IL-12p70, IL-21, TNF-α, IFN-γ, IL-10, IL-6) in serum samples of Chilean ANDV-infected patients collected at the time of hospitalization with the aim of establishing if the levels of any of the selected cytokines are linked to the severity of ANDV-induced disease. Cytokine selection was based on reports published for ANDV and other hantaviruses [5,10,13,14,16]. Three cohorts were included; a group of healthy controls, a cohort of ANDV-infected patients with mild disease progression, and a cohort of ANDV-infected patients with a severe disease progression, many of whom subsequently died. Results show that on admission to hospital TNF-α, IFN-γ, IL-10, and IL-6 levels were elevated in serum of ANDV-infected patients compared to controls. Importantly, the serum levels of IL-6 in ANDV-infected patients at the time of hospitalization were associated with the severity of the clinical outcome of ANDV-induced disease.
A total of 43 non-heat inactivated serum samples from ANDV-infected patients were selected based on their availability. Samples were obtained from a collection generated between January 2006 and January 2014 and stored at the Instituto de Salud Pública (ISP) de Chile or at the Laboratorio de Infectología, Facultad de Medicina, Pontificia Universidad Católica de Chile. These samples were collected for ANDV diagnosis at the time the patient was admitted to hospital, equivalent to 2–11 days after suspected ANDV infection (prodromic stage of the clinical course of HCPS). After collection, samples were stored at -8°C and not thawed more than once before use. In a previous study, the samples used had been catalogued as coming from patients with mild or severe symptoms, according to the severity of the final outcome of the ANDV-induced disease [18]. Selected patients were non-related individuals and were considered to be representative of each group. Mild hantavirus infection was characterized by a febrile illness with unspecific symptoms such as headache, myalgias, chills, gastrointestinal symptoms, and no or minimal respiratory compromise [18]. Severe cases exhibited rapid and progressive impaired lung function with the need for an external oxygen supply and the use of vasoactive drugs, resulting in shock and/or death [18]. Based on this categorization, of the 43 infected samples used in this study, 21 were from mild casesand 22 were from severe cases. Fourteen individuals from the latter group subsequently died of HCPS, whilst there were no fatalities in the group showing mild symptoms. Approval for the use of the samples in this study was obtained from the Ethical Review Board of the Facultad de Medicina, Pontificia Universidad Católica de Chile (code 12–292 and 14–438). ANDV infection was confirmed by positive hantavirus immunoglobulin (IgM) serology or by ANDV genome detection by reverse transcription polymerase chain reaction (RT-PCR) [18,19,20]. Control samples correspond to 43 non-heat treated serum samples obtained from healthy donors, who tested negative for ANDV, the human immunodeficiency virus, and hepatitis B and C virus infection.
Cytokines IL-1β, IL-12p70, IL-21, TNF-α, IFN-γ, IL-10 and IL-6 were measured in serum samples using a custom made Milliplex magnetic bead panel (Merck KGaA, Darmstadt, Germany) following the manufacturer’s instructions. The lower limit of detection (pg/mL) for each cytokine in the assay was: IL-1β, 2.5; IL-12p70, 1.84; IL-21, 2.37; TNF- α, 1.23; IFN-γ, 4.95; IL-10, 4.5 and IL-6, 3.34.
Results were analyzed using Graph Prism V6.0 (La Jolla, CA, USA) and Statistical Package for the Social Sciences (SPSS) V10.1 (SPSS, Inc., Chicago, IL, USA) software. The significance between the clinical and laboratory findings variables, and the clinical outcome was calculated by a Fisher`s exact test. The relationship between the clinical outcome and the cytokine levels was determined by a Mann-Whitney test for continuous variables using only two variables in each analysis. The crude odd ratio (OR) values were calculated by a univariate logistic regression analysis followed by a multivariate stepwise forward and reverse logistic regression analysis using SPSS V10.1 software package. P values of <0.05 were considered as significant.
The general characteristics of the ANDV-infected patients included in the study are summarized in Table 1. The mean age of the patients was 33 ± 14 years and 60.5% were male (Table 1). The vast majority of patients (97.7%) were infected between 32° 02’ and 56° 30’ south latitude (in the center and south of Chile) in rural areas (92.5%), consistent with the geographical distribution of the known viral reservoir in the long-tailed pygmy rice rat (Oligoryzomys longicaudatus) [21]. The main clinical features and laboratory findings of the ANDV-infected patients, at the time of their admission in a hospital care unit are summarized in Table 2. In a previous study, samples of ANDV-infected patients used in this study had been grouped into mild or severe cases depending on the final clinical outcome of the disease [18] (Table 1). A univariante analysis revealed that respiratory distress and haematocrit levels at the time of sample collection were significantly higher in patients that ultimately exhibited a severe clinical outcome (Table 2).
The levels of pro-inflammatory cytokines in serum samples of healthy controls and ANDV-infected patients were determined using a custom designed Th17 based Bead-Based Multiplex assay coupled with a Luminex platform. The results obtained for IL-1β, IL-12p70, IL-21, TNF-α, IFN-γ, IL-10, and IL-6 in each group are shown in Figs 1 and 2, whilst the mean concentration (pg/mL) of each cytokine is shown in Table 3. When compared to the group of non-infected controls, no significant differences in IL-1β concentration were observed in ANDV-infected patients (mild plus severe patients; p = 0.9672), when comparing the control group with each individual sub-group (mild p: 0.5916; severe p = 0.6549), or between those suffering mild or severe symptoms (p = 0.4759) (Fig 1A and Table 3). The levels of IL-12p70 and IL-21 were significantly lower in ANDV-infected patients when compared to the controls (p = <0.0001), although no differences in the levels of IL-12p70 (p = 0.4119) and IL-21 (p = 0.9084) were detected between those showing mild and severe symptoms (Fig 1B and 1C, and Table 3). The expression of TNF-α (Fig 2A), IFN-γ (Fig 2B), IL-10 (Fig 2C), and IL-6 (Fig 2D) exhibited higher levels in ANDV-infected individuals than in the non-infected control group, and levels were not affected by disease severity in the case of TNF-α, IFN-γ, and IL-10 (Fig 2A–2C and Table 3). Notably however, the severe group of ANDV-infected patients displayed significantly more IL-6 compared to those with milder disease symptoms (2.1 log10, fold increase of 40.4; p <0.0001) (Fig 2D and Table 3). Due to the evident overlap in the levels of IL-6 when patients are categorized as mild or severe, it may be that reliance on IL-6 to determine a "severe" outcome for a patient is inadequate. To address this issue, the 43 patients were grouped according to their real final status, as either survivors or fatalities (Fig 3 and Table 4). For this, the eight severe patients who survived were grouped with the 21 mild individuals (none of whom died), and these 29 patients were then compared with the 14 severe and fatal cases of ANDV infection. The results show that the serum concentration of IL-6 was significantly higher in the fatal cases compared to the surviving (2.0 log10, fold increase of 28.3; p <0.0001) or the control group (3.2 log10, fold increase 150.8; p <0.0001) (Fig 3 and Table 4).
To control the potential confounding effects of the various risk factors identified by the univariate analysis, a multiple logistic regression model was constructed using the outcome and symptom severity of ANDV-infection, as the response variables. The stepwise forward and reverse logistic regression analysis included clinical variables (fever, gastrointestinal symptoms, headache, myalgia, respiratory distress, infiltrates on chest x-ray, blood shift, atypical lymphocytosis, thrombocytopenia, increased hematocrit) and the cytokine profiles. All cytokine values were dichotomized (above or under mean values). Table 5 summarizes the crude odd ratios (OR) for each variable processed by a univariate logistic regression model, and the OR obtained by a multivariate stepwise forward (model 1) and reverse (model 2) logistic regression analysis. In model 1, respiratory distress (p = 0.9999) and IL-6 (p = 0.0130) are the only variables that affect HCPS outcome. In model 2, respiratory distress (p = 0.9999), increased hematocrit (p = 0.0670), and IL-6 (p = 0.0150) impact on HCPS outcome. However, for both models, only the difference between the levels of IL-6 in mild and severe ANDV-infected patients is significant, with OR values greater than 1.
In summary, the results from this study show that IL-6 serum concentration at the time of patient hospitalization is linked to the severity of the disease induced by ANDV.
A growing number of reports suggest that the hosts’ immune response plays a key role in hantavirus induced disease [5,9,10,11,12,13,14,15,22]. Studies indicate that immune dysregulation rather than virus replication is responsible for the increasing changes in vascular permeability associated with HCPS [23,24]. Upon hantavirus infection, the pathogens-associated molecular patterns (PAMPs) are recognized by the extracellular or intracellular receptors of endothelial host cells, leading to the local production of pro-inflammatory cytokines and chemokines, such as IL-1β, TNF-α, and IL-6, by activated macrophages [10,25,26]. Additionally, Th1 cells produce IFN-γ and TGF-β, cytokines that are responsible for cell-mediated immunity, regulated by IL-12 [10,27]. The regulatory T cells that produce the immunosuppressive cytokines IL-10 and TGF-β play an important role in the regulation of the immune response and limit the immunopathology induced by hantavirus infection [10]. Thus, we were interested in determining whether the levels of serum cytokines in patients at the time of their admission to hospital were associated with the final clinical outcome of the ANDV-induced disease. To do so, we compared the levels of serum cytokines in three distinct groups of individuals: non-infected individuals (healthy controls), ANDV-infected patients with a mild disease progression and ANDV-infected patients with a severe disease progression.
When studying IL-1β we found that the serum levels of this cytokine remain unaltered in ANDV-infected patients when compared to that of the non-infected controls (Fig 1A). This finding confirms previous reports showing no increase in the levels of IL-1β in patients infected with PUUV or in patients with HCPS [5]. Nonetheless, other studies show that the cells producing IL-1β and other pro-inflammatory cytokines are concentrated in the lung tissue of individuals with HCPS [26,28]. Therefore, these observations suggest that production of local cytokines in the lungs of ANDV-infected patients could differ from circulating levels of cytokines.
Results regarding the levels of IL-21 in hantavirus-infected individuals are dissimilar between previous reports [5,29]. One study shows a high overexpression of IL-21 in patients with hemorrhagic fever with renal syndrome (HFRS) caused by hantavirus [29]. In that study, the authors establish an association between the levels of IL-21 and the severity of HFRS. However, another study reports that there is no change in the levels of IL-21 in the serum of HCPS patients [5]. In contrast to both previous studies [5,29], our results show that in the group of ANDV-infected patients, IL-21 levels are significantly lower when compared to non-infected controls (p = <0.0001) (Fig 1C). It is worth noting that the pathology induced by New-World hantaviruses, HCPS, shares many, although not all of the clinical features of the disease caused by Old-World hantaviruses, HFRS. Thus, results obtained in the context of ANDV infection are not expected to fully match those that have been described for Old-World hantaviruses.
An interesting, yet expected finding, is that in ANDV-infected patients the serum levels of IL-12p70 are significantly lower (p = <0.0001) when compared to the non-infected control group. This observation is in full agreement with a study showing a similar trend in Dobrava virus (DOBV) infected patients [15]. A plausible explanation is to consider that the overexpression of a repressor cytokine, such as IL-10, would down regulate the levels of IL-12p70 [30]. This possibility is strongly supported by our results, in which the level of IL-10 in serum of ANDV-infected patients is significantly higher (p = <0.0001) than in the healthy controls (Fig 2C; Table 3). It is well known that the overexpression of IL-10 is strongly favored by high levels of IL-6 and TNF-α [15]. Additionally, overexpression of TNF-α and IL-6 are associated with inflammatory systemic disease, an increase in vascular permeability and with the production of a negative inotropic effect [31,32]. In agreement with this positive loop of activation, our results show that the levels of IL-6 and TNF-α are elevated in ANDV-infected patients (Fig 2A and 2D). In fact, when compared to non-infected controls, significant increases in the levels of TNF-α, IFN-γ, IL-10 and IL-6 in ANDV-infected patient are observed (p = <0.0001; 0.0036; <0.0001; <0.0001, respectively) (Fig 2A–2D). These findings are in full agreement with most previous studies [5,10,14,15,33], although TNF-α expression is not always raised in patients with severe HCPS [10]. This discrepancy cannot be readily explained; however, one possibility is to consider the genetic differences between the subjects included in these two studies, one conducted in Brazil [10] and the other in Chile. Differences between both populations have been previously reported in other studies [18,34].
Additionally, we compared TNF-α, IL-10, and IL-6 levels between mild and severe cases of ANDV-infection. Even though no significant differences in TNF-α or IL-10 levels exist between these two groups (Fig 2A and 2C, and Table 3), the IL-6 levels in the serum of ANDV-infected patients with a mild disease progression increased by 2.49 fold (p = 0.0021) compared to the non-infected controls (Fig 2D), whilst those with severe symptoms displayed a 100.7 fold (p = <0.0001) increase in the levels of IL-6 compared to the control group (Table 3 and Fig 2D). Moreover, when categorizing patients into survivors and fatalities the IL-6 levels in fatal cases of ANDV infection were significantly higher compared to survivor or control patients (Fig 3 and Table 4). Finally, a multivariate stepwise forward and reverse logistic regression model was constructed, confirming the relevance of the IL-6 levels at the time of patient hospitalization in the prediction of the outcome of HCPS (Table 5). It is well documented that IL-6 levels are significantly elevated in the serum of patients with HCPS, epidemic nephropathy (EP), and HFRS [5,12,35]. Consistent with our findings, other reports show that IL-6 levels are even higher in severe-disease patients compared with mild-disease patients [13,35]. Thus, our findings establish a clear association between the Th1-type cytokine IL-6 and the severity of ANDV-induced HCPS, suggesting that the serum concentration of IL-6 at the time of hospitalization can potentially be used as a molecular marker to predict the clinical outcome in ANDV-infected patients, offering the promise of personalized intervention, right from the moment of hospital admission.
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10.1371/journal.pgen.1005451 | Strong Selective Sweeps on the X Chromosome in the Human-Chimpanzee Ancestor Explain Its Low Divergence | The human and chimpanzee X chromosomes are less divergent than expected based on autosomal divergence. We study incomplete lineage sorting patterns between humans, chimpanzees and gorillas to show that this low divergence can be entirely explained by megabase-sized regions comprising one-third of the X chromosome, where polymorphism in the human-chimpanzee ancestral species was severely reduced. We show that background selection can explain at most 10% of this reduction of diversity in the ancestor. Instead, we show that several strong selective sweeps in the ancestral species can explain it. We also report evidence of population specific sweeps in extant humans that overlap the regions of low diversity in the ancestral species. These regions further correspond to chromosomal sections shown to be devoid of Neanderthal introgression into modern humans. This suggests that the same X-linked regions that undergo selective sweeps are among the first to form reproductive barriers between diverging species. We hypothesize that meiotic drive is the underlying mechanism causing these two observations.
| Because the speciation events that led to human, chimpanzee and gorilla were close in time, the genetic relationship of these species varies along the genome. While human and chimpanzee are the closest related species, in 15% of the genome, human and gorilla are more closely related, and in another 15% of the genome the chimpanzee and gorilla are more closely related—a phenomenon called incomplete lineage sorting (ILS). The amount and distribution of ILS can be predicted using population genetics theory and is affected by demography and selection in the ancestral populations. It was previously reported that the X chromosome, in contrast to autosomes, has less than the expected level of ILS. Using a full genome alignment of the X chromosome, we show that this low level of ILS affects only one third of the chromosome. Regions with low level of ILS also show reduced diversity in the extant populations of human and great apes and coincide with regions devoid of Neanderthal introgression. We propose that these regions are targets of selection and that they played a role in the formation of reproductive barriers.
| Despite constituting only 5–6% of the human genome, the human X chromosome is important for elucidating evolutionary mechanisms. Because of its particular inheritance pattern and its cosegregation with the very different Y chromosome, evolutionary forces may act upon it in different ways than on the autosomes [1,2]. Thus contrasting the evolution of the X chromosome with that of the autosomes provides clues to the relative importance of different evolutionary forces.
Hemizygosity of males implies that there are fewer X chromosomes than autosomes in a population (3/4 for even sex ratios). Thus, genetic drift is expected to be relatively stronger on the X chromosome. New variants with recessive fitness effects will also be selected for or against more efficiently on the X chromosome, where they are always exposed in males, than on the autosomes, potentially overriding the increased genetic drift.
Empirical studies have shown that nucleotide diversity is more reduced around genes on the X chromosome than on the autosomes [3–5]. This has been interpreted as the result of more efficient selection on coding variants on the X chromosome, which affects linked positions around the genes. However, no distinction is made here between linked effects of positive selection (genetic hitchhiking [6]) and linked effect of selection against deleterious mutations (background selection [7]). For recessive variants, hitchhiking is expected to be more wide ranging for X chromosomes, whereas a different distribution of fitness effects of deleterious variants on the X is needed to cause stronger background selection on the X. Contrasting non-synonymous and synonymous substitutions with non-synonymous and synonymous polymorphisms, several recent studies have reported evidence for more positive selection on protein changes on the X chromosome in both primates and rodents [8–11]. Whether this is due to hemizygosity, different gene content of the X chromosome, antagonistic selection between sexes being more prevalent on the X chromosome, or some fourth reason is not known.
A separate observation is that the X chromosome in most investigated species is disproportionately involved with speciation, as it (i) contributes disproportionately to hybrid incompatibility (the large X effect) and (ii) together with the Y chromosome is responsible for stronger hybrid depression in males than in females (Haldane’s rule). We refer to Laurie (1997) [12] and Schilthuizen, Giesbers and Beukeboom (2011) [13] for several non-exclusive hypotheses for the underlying genetic mechanisms leading to Haldane’s rule.
Recent introgression from Neanderthals into modern humans was recently reported to be far less common on the X chromosome than on the autosomes. This can be interpreted as evidence for emerging incompatibilities between the two species preferentially residing on the X chromosome [14]. It has been suggested that incompatibilities can accrue due to genetic conflicts between the X and the Y [15–19] and some hybrid incompatibility factors in Drosophila do show evidence of causing meiotic drive [20].
We, and others, have previously reported that the X chromosome shows much less divergence between humans and chimpanzees than expected from autosomal divergence [21–23]. This observation is not based on the nucleotide divergence of the X chromosome versus the autosomes—which will be affected by a difference in mutation rate—but on estimating the effective population size of the ancestral species from the proportion of discordant gene trees.
Because the speciation event between human and chimpanzee and the speciation event between the human-chimpanzee ancestor and the gorilla occurred close in time, around 30% of the autosomal genome shows a gene tree different from the species tree—a phenomenon called incomplete lineage sorting (ILS). The expected amount of ILS depends on the difference between the two speciation times and the effective population size in the human-chimpanzee ancestor. For estimates of the two speciation times in question [24], and assuming that the effective population size of the X chromosome is three quarters of that of the autosomes, the X chromosome is expected to show 24% of ILS. The observed mean amount of ILS, however, is around 15%.
We recently reported that certain regions of the X chromosome in different great ape species often experience what looks like very strong selective sweeps [18]. Here we study the amount of incomplete lineage sorting between human, chimpanzee and gorilla along the X chromosome. We observe a striking pattern of mega-base sized regions with extremely low amounts of ILS, interspersed with regions with the amount of ILS expected from the effective population size of the X chromosome (that is, three quarters that of the autosomes). We show that the most plausible explanation is several strong selective sweeps in the ancestral species to humans and chimpanzees. The low-ILS regions overlap strongly with regions devoid of Neanderthal ancestry in the human genome, which suggests that selection in these regions may create reproductive barriers. We propose that the underlying mechanism is meiotic drive resulting from genetic conflict between the sex chromosomes, and that this is caused by testis expressed ampliconic genes found only on sex chromosomes and enriched in the regions where we find signatures of selective sweeps.
To explore the pattern of human-chimpanzee divergence across the full X chromosome we performed a detailed analysis of the aligned genomes of human, chimpanzee, gorilla and orangutan [21]. Using the coalescent hidden Markov model (CoalHMM) approach [25], we fitted a model of speciation by isolation, with constant but distinct ancestral effective population sizes for the human-chimpanzee (HC) and the human-chimpanzee-gorilla (HCG) ancestors. The parameters of the model are (i) two speciation times τHC and τHCG for human vs. chimpanzee and for HC vs. gorilla, respectively, (ii) two ancestral population sizes θHC and θHCG for the HC and HCG ancestral populations, respectively, as well as the recombination rate r assumed to be constant along both the alignment and phylogeny. An additional parameter is used to account for the divergence with the outgroup sequence. The speciation time, effective population size and recombination rate parameters are scaled according to 2.Ne.u.g, 2.Ne.u and u, respectively, where u is the mutation rate per generation, g the generation time and Ne the population size of a reference extant species [22,25]. Extant population sizes are not parameters of the model, and only serve for the purpose of scaling parameters. To account for putative variation of parameters along the genome alignment, we estimated demographic parameters in non-overlapping 1 Mb windows. We inferred the proportion of ILS using posterior decoding averaged over each of these 1Mb windows. The expected proportion of ILS in a 3-species alignment is given by the formula:
Pr(ILS)=23×exp(−Δτθ)
where Δτ is the difference in speciation times and θ is the ancestral effective population size of the two most closely related species [26,24] (see also [27]). Estimates of these parameters from the gorilla genome consortium are Δτ = 0.002468 and θ = 0.003232 [21]. From these parameters, the expected mean proportion of ILS is 31.06%. The observed distribution of ILS proportions on autosomes follows a negatively skewed normal distribution, with a mean of 30.58% (Figs 1A and S1 for individual chromosome distributions).
Assuming that the ancestral effective population size of the X chromosome, θX, is three quarters that of the ancestral effective population size of the autosomes, the expected amount of ILS on the X chromosome should be 24.08%. The distribution of ILS proportions on the X chromosome is bimodal (Fig 1B) and in stark contrast to the distribution on the autosomes (see also S1 Fig for a breakdown on individual autosomes). One mode represents 63% of the alignment, with a mean proportion of ILS of 21%, close to the expectation of 24% (the 99% confidence interval of the high ILS mode is [17.6%, 24.5%], estimated using parametric bootstrap). The second mode is estimated to represent 37% of the alignment and shows a mean proportion of ILS below 5%. The regions exhibiting low ILS form 8 major segments spread across the X chromosome (Table 1 and Fig 2A) and cover 29 Mb out of a total alignment length of 84 Mb. Region X5 is split in two by the centromeric region, where alignment data are missing. Regions with comparatively low amount of ILS have a higher frequency of genealogy where the human and chimpanzee coalesce within the HC ancestor, while in ILS genealogies, the human and chimpanzee lineages coalesce further back in time, within the HCG ancestor. As a result, low-ILS regions display a lower divergence compared to the rest of the genome. These results are two-fold: (i) they demonstrate that one third of the X chromosome explains the previously reported low divergence of the chromosome, as the remaining two thirds display a divergence compatible with the expectation under a simple model of divergence with an ancestral effective population size equal to three quarters that of the autosomes and (ii) that unique evolutionary forces have shaped the ancestral diversity in the low-ILS regions.
In Scally et al. [21], we independently estimated parameters in non-overlapping windows of 1 Mb, allowing for parameters to vary across the genome. To test whether inference of very low proportions of ILS could result from incorrect parameter estimation, we compared the inferred amount of ILS under alternative parameterizations with that inferred using fixed parameters (either fixing all parameters or fixing speciation time parameters only) along the genome. These alternative parameterizations result in very similar estimates of ILS (S2 Fig and corresponding UCSC genome browser tracks at http://bioweb.me/HCGILSsupp/UCSCTracks/).
We addressed the possibility that our observation is due to a lower power to detect ILS in the identified regions resulting from reduced mutation rate. We counted the number of informative sites supporting each of the three alternative topologies connecting humans, chimpanzees and gorillas in non-overlapping 100 kb windows along the alignment. If the reduction of ILS is due to a lower mutation rate in these regions, we expect to observe a reduction of the amount of parsimony-informative sites supporting all three topologies. While the total frequency of parsimony-informative sites is significantly lower in the low-ILS regions compared with the rest of the genome (0.00270 vs. 0.00276, Fisher's exact test p-value = 1.34e-05), there is a highly significant excess of sites supporting the species topology (0.00229 vs. 0.00210, Fisher's exact test p-value < 2.2e-16) and deficit of sites in these regions supporting ILS topologies (0.00042 vs. 0.00066, Fisher's exact test p-value < 2.2e-16, Fig 2B and 2C), which suggests that the observed reduction of ILS is not the result of a lower mutation rate.
We computed the ratio of human-chimpanzee divergence to human-gorilla divergence and human-orangutan divergence in 100 kb windows. Assuming a constant mutation rate across the phylogeny and constant ancestral effective population sizes along the genome, these ratios should be on average identical between regions from the genome. In regions with reduced ILS, however, this ratio is expected to be lower because of a more recent human-chimpanzee divergence. In agreement with this latter hypothesis, we observe a significant lower ratio of divergences in low-ILS regions (Fig 2D). A lower mutation rate in these regions would explain this pattern only if the reduction is restricted to the human-chimpanzee lineage.
Deleterious mutations are continuously pruned from the population through purifying selection, reducing the diversity of linked sequences. Such background selection potentially plays an important role in shaping genetic diversity across the genome [28]. The strength of background selection increases with the mutation rate, with density of functional sites, with decreasing selection coefficient against deleterious mutations, and with decreasing recombination rate [29]. Low-ILS regions display both a 0.6-fold lower recombination rate compared to the rest of the chromosome (1.01 cM/Mb versus 1.62 cM/Mb, Wilcoxon test p-value = 2.2e-07) as well as a two-fold higher gene density—a proxy for the proportion of functional sites (3.1% exonic sites versus 1.5% on average, Wilcoxon test p-value < 2.2e-16). Background selection is therefore both expected to be more common (by a factor of ~2.1 due to more functional sites) and to affect larger regions (by a factor of ~1.8 due to less recombination) in the low-ILS regions. To estimate extent to which this may explain our observations, we used standard analytical results that estimate the combined effect of multiple sites under purifying selection (see Material and Methods). Even if we assume that the proportions of functional sites in the candidate regions is two times higher than the observed number of exon base pairs, and that all mutations at these sites are deleterious with a selection coefficient that maximizes the effect of background selection, the expected proportion of ILS should only be reduced by approximately 10% relative to the level found on the remaining X chromosome (19% ILS compared to 21% ILS). To explain the observed reductions in ILS by background selection alone, unrealistic differences of functional site densities are required (e.g. 50% inside identified regions and 10% outside, see Figs 3 and S2). As a further line of evidence, we computed the maximal expected reduction of ILS based on the observed density of exonic sites and average recombination rate (see Methods). We find that only 79 of 252 analyzable windows (31%) could be explained by the action of background selection only, an observation incompatible with the hypothesis that background selection is the sole responsible for the widespread reduction of ILS along the X chromosome.
Finally, recombination rate is lower in males than in females. As X chromosomes spend 2/3 of their time in highly recombining females while autosomes spend only half, background selection is expected to be weaker on the X chromosome than on the autosomes. Consequently, in Drosophila where males do not recombine, X chromosomes display a higher than expected diversity [30]. The fact that we do not observe large regions devoid of ILS on the autosomes further argues against background selection as the major force creating the observed large regions with reduced ILS on the X chromosome.
Adaptive evolution may also remove linked variation during the process of fixing beneficial variants. In the human-chimpanzee ancestor, such selective sweeps will have abolished ILS at the locus under selection and reduced the proportion of ILS in a larger flanking region. Several sweeps in the same region can thus result in a strong reduction of ILS on a mega-base scale. We simulated selective sweeps in the human-chimpanzee ancestor using a rejection sampling method (see Material and Methods). A single sweep is only expected to reduce ILS to less than 5% on a mega-base wide region if selection coefficients are unrealistically high (s > 0.2), suggesting that several sweeps have contributed to the large-scale depletions of ILS (Figs 4 and S4).
If the low-ILS regions are indeed subject to recurrent sweeps, they are expected to also show reduced diversity in human populations. We therefore investigated the patterns of nucleotide diversity in the data of the 1000 Genomes Project [31]. We computed the nucleotide diversity in 100 kb non-overlapping windows along the X chromosome and compared windows within and outside low-ILS regions. Fig 5 summarizes the results for the CEU, JPT and YRI populations (results for all populations are shown in S5 Fig). We find that diversity is significantly reduced in all low-ILS regions compared with the chromosome average (Table 2), and this reduction is on average significantly greater in the Asian and European populations than in the African population (analysis of variance, see Material and Methods). This global difference in magnitude could be explained by phenomena such as sex-biased demography or generation time and population structure during the migration out of Africa [32]. We also compared the eight low-ILS regions separately, and reported differences between regions (Table 3). Plotting population specific diversity across the X chromosome revealed several cases of large-scale depletions of diversity in both Europeans and East Asians. While these depletions affect similar regions, their width differs between populations. This finding suggests that strong sweeps in these regions occurred independently in the European and East Asian population after their divergence less than 100,000 years ago.
Using a complete genome alignment of human, chimpanzee, gorilla and orangutan, we report that the human-chimpanzee divergence along the X chromosome is a mosaic of two types of regions: two thirds of the X chromosome display a divergence compatible with the expectation of an ancestral effective population size of the X equal to three quarters that of the autosome, while one third of the X chromosome shows an extremely reduced divergence, and is virtually devoid of incomplete lineage sorting. We have demonstrated that such diversity deserts cannot be accounted for by background selection alone, but must result from recurrent selective sweeps. We recently reported dramatic reductions in X chromosome diversity in other great ape species that almost exclusively affect areas of the low-ILS regions [18] (see S6 Fig).
If the low-ILS regions evolve rapidly through selective sweeps, they could be among the first to accumulate hybrid incompatibility between diverging populations. Recently, the X chromosome was reported to exhibit many more regions devoid of Neanderthal introgression into modern humans than the autosomes. This suggests an association of negative selection driven by hybrid incompatibility with these X-linked regions [14]. We find a striking correspondence between regions of low ILS and the regions devoid of Neanderthal introgression for European populations (p-value = 0.00021, permutation test) and a marginally significant association with the more introgressed Asian populations (p-value = 0.06721, Fig 5). Taken together, these findings show that the regions on the X chromosome that contributed to hybrid incompatibility in the secondary contact between humans and Neanderthals have been affected by recurrent, strong selective sweeps in humans and other great apes.
The occurrence of a secondary contact between initially diverged populations, one of which diverged into modern chimpanzees and the other admixed with the second to form the ancestral human lineage—the complex speciation scenario of Patterson et al. [23]–is also compatible with our observations: if these regions evolved to be incompatible, the lineages within the regions only came from the ancestral population related to chimpanzees while lineages outside the regions come from both ancestral populations, so that we would also expect to see reduced ILS within the regions and not outside the regions. However, such a complex speciation scenario does not explain the observed large-scale reductions of diversity in extant species. Conversely, a scenario consisting only of recurrent sweeps would explain both the divergence patterns along the human and chimpanzee X chromosomes and the reduction of extant diversity, without the need for secondary introgression.
To explain the occurrence of recurrent selective sweeps in the lineage of great apes, we propose a hypothesis that may account for the generality of our findings: Deserts of diversity may arise via meiotic drive, through which fixation of variants that cause preferential transmission of either the X or Y chromosome produces temporary sex ratio distortions [17]. When such distortions are established, mutations conferring a more even sex ratio will be under positive selection. Potential candidates involved in such meiotic drive are ampliconic regions, which contain multiple copies of genes that are specifically expressed in the testis. These genes are postmeiotically expressed in mice, and a recent report suggests that the Y chromosome harbors similar regions [33]. Fourteen of the regions identified in humans [34] are included in our alignment, 11 of which are located in low-ILS regions (Figs 2 and 5), representing a significant enrichment (p-value = 0.01427, permutation test), a result which is even more significant when regions in the centromeric region are included (p-value = 0.00642).
Whatever the underlying mechanism, our observations demonstrate that the evolution of X chromosomes in the human chimpanzee ancestor, and in great apes in general [18], is driven by strong selective forces. The striking overlap between the low-ILS regions we have identified and the Neanderthal introgression deserts identified by Sankararaman et al. [14] further hints that these forces could be driving speciation.
The Enredo/Pecan/Ortheus genome alignment of the five species human, chimpanzee, gorilla, orangutan and macaque from Scally et al. [21] was used as input. In order to remove badly sequenced and / or ambiguously alignment regions, we filtered the input 5-species alignments using the MafFilter program [35]. We sequentially applied several filters to remove regions with low sequence quality score and high density of gaps. Details on the filters used can be found in the supplementary material of Scally et al. [21]
The divergence of two genomes depends on both the mutation rate and underlying demographic scenario. With a constant mutation rate u and simple demography (constant sized panmictic population evolving neutrally), the time to the most recent common ancestor of two sequences sampled from different species is given by a constant species divergence, τ = T.u, and an ancestral coalescence time following an exponential distribution with mean θ = 2.NeA.u, where T is the number of generations since species divergence and NeA is the ancestral effective population size [22,36]. For species undergoing recombination, a single individual genome is a mosaic of segments with distinct histories, and therefore displays a range of divergence times [22,23,37]. When two speciation events separating three species follow shortly after each other, this variation of genealogy can lead to incomplete lineage sorting (ILS), where the topology of gene trees do not correspond to that of the species tree [22,26]. Reconstructing the distribution of divergence along the genome and the patterns of ILS allows inference of speciation times and ancestral population sizes. We used the CoalHMM framework to infer patterns of ILS along the X chromosome. Model fitting was performed as described in [21]. ILS was estimated using posterior decoding of the hidden Markov model as the proportions of sites in the alignment which supported one of the (HG),C or (CG),H topologies. All parameter estimates can be visualized in the UCSC genome browser using tracks available at http://bioweb.me/HCGILSsupp/.
For the autosomal distribution of ILS, we fitted a skewed normal distribution (R package 'sn' [38]) using the fitdistr function from the MASS package for R. For the X chromosome ILS distribution, we fitted a mixture of gamma and Gaussian distributions. The mixed distribution follows a normal density with probability p, and a gamma density with probability 1-p. In addition to p, the mixed distribution has four parameters: the mean and standard deviation of the Gaussian component, and the shape and rate of the gamma component. The L-BFGS-B optimization method was used to account for parameter constraints. Resulting parameter estimates are 0.209 for the mean of the Gaussian component, 0.066 for the standard deviation of the Gaussian component, 4.139 for the alpha parameter (shape) of the gamma component, 83.369 for the beta parameter (rate) of the gamma component, and p = 0.632. The mean of the gamma component is alpha / beta = 0.0497, that is, less than 5% ILS. We compared the resulting fit with a mixture of skewed normal distributions, which has two extra parameters compared to a Gamma-Gaussian mixture, and found that the skew of the higher mode is very close to zero, while the Gamma distribution offered a better fit of the lower mode. We used a parametric bootstrap approach to estimate the confidence interval of the proportion of ILS for the mean of the normal component of the mixed distribution. We generated a thousand pseudo-replicates by sampling from the estimated distribution, and we re-estimated all parameters from each replicate in order to obtain their distribution. Replicates where optimization failed were discarded (40 out of 1000).
In order to characterize the patterns of ILS at a finer scale, we computed ILS in 100 kb windows sliding by 20 kb along the posterior decoding of the alignment. To exhibit regions devoid of ILS, we selected contiguous windows with no more than 10% of ILS each. Eight of these regions were greater than 1 Mb in size, and their resulting amount of ILS is less than 5% on average (Table 1). The coordinates of these regions were then translated according to the human hg19 genome sequence. These data are available as a GFF file for visualization in the UCSC genome browser at http://bioweb.me/HCGILSsupp/.
Background selection reduces diversity by a process in which deleterious mutations are continuously pruned from the population. The strength of background selection in a genomic region is determined by the rate at which deleterious mutations occur, U, the recombination rate of the locus, R, and the strength of negative selection on mutants, s. We consider the diversity measure,π(the pairwise differences between genes) which in a randomly mating population is linearly related to the effective population size. If π0 denotes diversity in the absence of selection and π the diversity in a region subject to background selection, then the expected reduction in diversity is given by
ππ0=exp(−Us+R)
(1)
(see Durrett [39] equation (6.24))
The rates U and R are both functions of the locus length (U = uL and R = rL) where r denotes the per-nucleotide-pair recombination rate, u the per-nucleotide deleterious rate, and L the length of the locus. To investigate if background selection can explain the observed reductions in ILS we must compute the expected reduction in diversity in the low-ILS regions relative to the reduction in the remaining chromosome. A larger reduction in low-ILS regions may be caused by weaker negative selection, higher mutation rate, lower recombination rate, and larger proportion of functional sites at which mutation is deleterious. To model the variation of these parameters inside and outside low-ILS regions we simply add a factor to each relevant variable. The relative reduction can thus be expressed as:
πlow−ILSπgenome=exp(Us+R)exp(fu.Ufs.s+fR.R)
(2)
The recombination rate, R, and the factor, fR, can be obtained from the deCODE recombination map [40]. We computed the average deCODE recombination rate, as well as the proportion of sites in exons (as a measure of selective constraint) in non-overlapping 100 kb along the human X chromosome.
The recombination rate average outside the low ILS regions is 1.62 cM/Mb and the recombination rate inside the regions is 1.01 cM/Mb which gives us fR = 0.6. For the remaining parameters, s and U, we need to identify realistic values outside the low-ILS regions. Background selection is stronger when selection is weak, but the equation is not valid for very small selection values where selection is nearly neutral. Once s approaches 1/Ne, we do not expect any background selection. Most stimates of effective population sizes, Ne, in great apes are on the order 10,000–100,000 and this puts a lower limit on relevant values of s at 10−4–10−5. To conservatively estimate the largest possible effect of background selection we explore this range of selection coefficients: s = 10−4 and s = 10−5 and allow the selection inside the low ILS regions to be one tenth (fs = 0.1) of that outside. For U values outside low-ILS regions we assume the mean human mutation rate, estimated to be 1.2·10−8 per generation [41]. To obtain the rate of deleterious mutation we must multiply this with the proportion of sites subject to weak negative selection, d. Although this proportion is subject to much controversy it is generally believed to be between 3% and 10% [42]. However, as explained below we explore values up to 100% inside the low-ILS regions.
We assessed the relative diversity for combinations of s and d values (S3 Fig). Each cell represents a combination of parameter values for s, d, fU and fs. The reduction of diversity Δπ translates into reduction of ILS, ΔILS(Fig 3). Assuming the time between speciation events, the generation time and population size reported in Scally et al. [21] (ΔT = 2,250,000 years, g = 20) ILS is given by
ILS=23exp(−ΔT/g3/4×π)
(3)
and the relative ILS is given by
ILSILS0=exp(ΔT/g3/4(1π0−1π)).
(4)
For the most extreme parameter values, we see a relative reduction in ILS of nearly 100%. In these cases, however, 100% of the nucleotides within low-ILS regions are under selection. In the cases where 25% of the nucleotides in the low-ILS regions are under selection compared to 5% outside (fU = 5, d = 0.05), the regions retain more than half of the diversity seen outside the regions.
We further computed the expected reduction of ILS due to background selection in 100 kb windows located in low-ILS regions using (eq 4). For each window, we computed the frequency of sites in exons and the average deCODE recombination rate. We further assumed a selection coefficient s = 10−5 and allow the selection inside the low ILS regions to be one tenth (fs = 0.1). Out of 285 windows located in low-ILS regions, we could estimate the maximal reduction of ILS due to background selection in 252 windows for which a deCODE recombination estimate was available. In 79 of these windows only the expected reduction matched the observed one of 0.20.
To assess how hard and soft sweeps in the human-chimpanzee ancestor can have reduced the proportion of ILS we simulated sweeps for different combinations of selection coefficients, s, and frequencies of the selected variant at the onset of selection, f. Frequency trajectories of selected variants are obtained using rejection sampling to obtain trajectories that fix in the population. Trajectories used to simulate hard sweeps begin at one and proceed to fixation at 2N * 3/4 by repeated binomial sampling with probability parameter Nmut/(Nmut + (N − Nmut)(1-s)), where Nmut is the number of selected variants in the previous generation. We use a human-chimpanzee speciation time of 3.7 Myr, a human-gorilla speciation time of 5.95 Myr, a human-chimpanzee effective population size of 73,200 as reported in [21], assuming a mutation rate of 1e-9 and a generation time of 20 years. Trajectories used to simulate soft sweeps are constructed by joining two trajectories. If f is the frequency of the variant at the onset of selection F = f * 2N * 3/4 is the number of variants. We first sample a trajectory that represents the time before the onset of selection. This trajectory is required to reach F at least once before it fixes or is lost, and is truncated randomly at one of the points where it passes the value F. The truncated trajectory is then appended with a trajectory under selection that begins at F and proceeds to fixation.
In each simulation we consider a sample of two sequences that represent 10 cM. As the effect of the sweep is symmetric we only simulate one side of the sweep. We then simulate backwards in the Wright-Fisher process with recombination allowing at most one recombination event per generation per lineage but allowing mergers of multiple lineages expected to occur in strong sweeps. The simulation proceeds until all sequence segments have found a most recent common ancestor (TMRCA). For each combination of parameters s and f we perform 1,000 simulations and the mean TMRCA is computed in bins of 10 kb.
In each simulation individual sequence segments are called as ILS with probability 2/3 if the TMRCA exceeds the time between the speciation events. The width of the region showing less than 5% ILS is then computed for each simulation. In Figs 4 and S3 a recombination rate of 1 cM/Mb is assumed to translate to physical length.
We computed the nucleotide diversity in 100 kb non-overlapping windows along the X chromosome for the 14 populations from the 1,000 genomes project. The windows in each low-ILS region were compared to windows outside the regions using a Wilcoxon test with correction for multiple testing [43] (Table 2). We computed the relative nucleotide diversity in the 1,298 windows located in low-ILS regions by dividing by the average of the rest of the X chromosome. Each population was further categorized according to its origin, Africa, America, Asia or Europe [31]. A linear model was fitted after Box-Cox transformation:
BoxCox[RelativeDiversity] ~ (Region / Window) * (PopulationGroup / Population)
where Window is the position of the window on the X chromosome, and is therefore nested in the (low-ILS) Region factor. Analysis of variance reeals a highly significant effect of the factors Region and Window (p-values < 2e-16), PopulationGroup (p-value < 2e-16) and their interactions (p-value < 2e-16). The nested factor Population however was not significant, showing that the patterns of relative diversity within low-ILS regions are similar between populations within groups. A Tukey's Honest Significance Difference test (as implemented in the R package 'agricolae') was performed on the fitted model and further revealed that European and Asian diversity are not significantly different, while they are different from African and American diversity.
In order to test the association of low-ILS regions with other genomic features, we developed a Monte-Carlo simulation procedure. In such a test, we wanted to compare a set of "reference" intervals with a set of "query" intervals. The null hypothesis is that the query intervals are independent of the reference intervals. We use the size of the overlap of the two sets of intervals as a statistic. During the randomization procedure, the set of query intervals is shuffled, so that each interval is conserved in length, only the relative order and positions of intervals are changed. Intervals are not allowed to overlap, so that the size of the query set is constant through simulations and identical to the observed one. The distance between two intervals is however allowed to be zero. For each simulation, the size of the overlap with the reference set of intervals is computed. A p-value is calculated by counting the number of simulations with an overlap at least equal to the observed one. In order to randomize intervals, we developed the following procedure: 1) compute the total size S of the chromosome not included in any interval of the query set; 2) draw n breakpoints uniformly between 0 and S, where n in the number of intervals in the query set; 3) insert randomly one query interval at each breakpoint. This procedure has the advantage that it keeps the structure of the reference set, so that the putative auto-correlation of reference intervals along the genome is accounted for. The 'intervals' R package was used for handling intervals and computing their overlap, and 100,000 randomizations were performed for each test.
We applied the randomization test to the two sets of Neanderthal introgression free regions for European and Asian populations, as well as for the ampliconic regions. The coordinates of ampliconic regions tested in [34] were translated to hg19 using the liftOver utility from UCSC. Fourteen regions were included in our alignment. For all tests, the set of low-ILS regions was used as a query set. For ampliconic regions, we performed a second test where ampliconic regions located close to the centromere and not included in our alignment were discarded.
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10.1371/journal.ppat.1002492 | The Murine Coronavirus Hemagglutinin-esterase Receptor-binding Site: A Major Shift in Ligand Specificity through Modest Changes in Architecture | The hemagglutinin-esterases (HEs), envelope glycoproteins of corona-, toro- and orthomyxoviruses, mediate reversible virion attachment to O-acetylated sialic acids (O-Ac-Sias). They do so through concerted action of distinct receptor-binding (“lectin”) and receptor-destroying sialate O-acetylesterase (”esterase”) domains. Most HEs target 9-O-acetylated Sias. In one lineage of murine coronaviruses, however, HE esterase substrate and lectin ligand specificity changed dramatically as these viruses evolved to use 4-O-acetylated Sias instead. Here we present the crystal structure of the lectin domain of mouse hepatitis virus (MHV) strain S HE, resolved both in its native state and in complex with a receptor analogue. The data show that the shift from 9-O- to 4-O-Ac-Sia receptor usage primarily entailed a change in ligand binding topology and, surprisingly, only modest changes in receptor-binding site architecture. Our findings illustrate the ease with which viruses can change receptor-binding specificity with potential consequences for host-, organ and/or cell tropism, and for pathogenesis.
| Glycans cover the surface of every living cell. In vertebrates, these sugar trees commonly terminate with sialic acid (Sia) and, in consequence, Sias have become the attachment factors of choice for a multitude of pathogens: protozoa, bacteria and viruses alike. To ensure selectivity, viruses evolved to target distinct Sia species. Whether a particular type of Sia serves as receptor may depend -amongst others- on the absence or presence of specific Sia modifications. For example, most group A betacoronaviruses attach to 9-O-acetylated Sias. However, some murine coronaviruses have switched to using 4-O-acetylated Sias instead. In chemical/molecular terms this represents a momentous shift in receptor usage. We now have crystallized the hemagglutinin-esterase protein (HE) of a murine coronavirus and have solved the structure of its sugar-binding domain. Our findings reveal in exquisite detail the interactions between Sia binding site and cognate receptor. The data allow a reconstruction of how, during coronavirus evolution, the switch in receptor usage may have come about.
| To initiate infection viruses must bind to an appropriate host cell. Selectivity of binding is ensured by attachment proteins on the virion, tailored to recognize one -or at the most- a limited number of cell surface molecules. Remarkably, a large number of viruses, representative of at least 11 distinct families several of which of clinical and/or veterinary importance, use sialic acid (Sia) as receptor determinant. Owing to differential modification, Sia structural diversity exceeds that of any other monosaccharide [1]. The most common type of Sia substitution, O-acetylation at carbon atoms C4, C7, C8 and/or C9, occurs in a host-, organ- and even cell-specific fashion such that even individual cells of the same type and tissue may differ in their Sia expression profile [2]–[4]. Viruses have evolved to selectively use particular Sia variants and their attachment proteins are high-specificity sialolectins, the binding of which might depend on the identity of the penultimate residue in the sugar chain, the type of glycosidic linkage and/or the presence or absence of substitutions [5]–[9]. Ultimately, this preference in Sia receptor usage affects host-, organ-, and cell-tropism [10]–[14], the course and outcome of infection [15]–[18] as well as the efficacy of intra- and cross-species transmission [14], [19], all to extents not yet fully appreciated.
The hemagglutinin-esterases (HEs) are a class of Sia-binding envelope glycoproteins found in some negative-stranded RNA viruses, namely in influenza C and infectious salmon anemia virus (family Orthomyxoviridae; [5], [20]), but also in toro- and coronaviruses, positive-stranded RNA viruses in the order Nidovirales [21], [22]. From phylogenetic and comparative structural analyses it appears that toro- and coronaviruses acquired their HE proteins separately via horizontal gene transfer, with an (hemagglutinin-esterase-fusion) HEF-like protein as progenitor [22]–[25]. Like influenza C virus HEF, most nidovirus HEs bind to 9-O-acetylated (9-O-Ac) Sias and, correspondingly, display sialate-9-O-acetylesterase receptor-destroying enzyme activity [25]. Murine coronaviruses, however, occur in two closely related biotypes that differ in HE ligand/substrate preference. One of these -represented by mouse hepatitis virus (MHV) strain DVIM- displays the presumptive ancestral specificity and targets 9-O-Ac-Sias, while the other -represented by MHV strain S- appears to have evolved to use 4-O-Ac-Sias instead [6], [25]–[27] (for supplementary introduction see Text S1 and Figure S1). Given the stereochemical differences between these Sia variants (Figure 1) and the essentially different requirements for ligand and substrate recognition by the respective HEs, the question arises how this major shift in receptor usage was achieved and what changes must have occurred in the receptor-binding and O-acetylesterase domains to make this transition possible.
The crystal structures of a number of 9-O-Ac-Sia-specific nidovirus HEs have been solved [23], [24]. Unlike the receptor-binding site (RBS) of influenza C virus HEF [28], the RBSs of the corona- and torovirus HEs seem to be exceptionally plastic as they appear to have undergone significant changes and adaptations that altered their overall architecture in a relatively short evolutionary time span. Based on these observations, we anticipated and speculated [23] that this plasticity might have allowed for even more substantial adjustments in the RBS of the murine coronavirus HE as to produce an entirely novel binding site specific for 4-O-acetylated Sias.
We now present the crystal structure of the MHV-S HE receptor-binding domain, both in its native state and in complex with a receptor analogue. The data reveal in exquisite detail how the RBS changed to accommodate 4-O- instead of 9-O-acetylated Sias. Surprisingly, however, this shift in receptor usage seems to have involved primarily a change in ligand binding topology and relatively modest changes in RBS architecture.
We produced the ectodomain of MHV-S HE (residues 25-403) as an Fc-fusion protein, either in enzymatically active (HE-Fc) or inactive form (HE0-Fc), by transient transfection of HEK293 cells. MHV-S HE0-Fc bound to horse serum glycoproteins (HSG), which are decorated with 4-O-acetylated sialic acids (4-O-Ac-Sia), but carry little to no 9-O-Ac-Sias (Figure 2A; [29]). The receptor determinants in HSG could be destroyed by treatment with MHV-S HE-Fc, but not by treatment with BCoV-Mebus HE-Fc (a sialate-9-O-acetylesterase; Figure 2B). No binding of MHV-S HE0-Fc was observed to bovine submaxillary mucin (BSM), a glycoconjugate devoid of 4-O-Ac-Sias (Figure 2A; [30]). The MHV-S HE ectodomain, released from HE-Fc by thrombin-cleavage, retained proper sialate-4-O-acetylesterase activity when assayed for substrate specificity with a synthetic di-O-acetylated Sia (5-N-acetyl-4,9-di-O-acetylneuraminic acid α-methylglycoside, αNeu4,5,9Ac32Me; Figure 2C). In hemagglutination assays, MHV-S HE0 specifically bound to 4-O-acetylated Sias (Figure 2D). The combined findings show that the recombinant MHV-S HE proteins are biologically active, both as Fc fusion proteins (Figures 2A and B) and after the removal of the Fc tail by thrombin-cleavage (Figures 2C and D), which we take as an indication for proper folding and protein stability.
Crystals of free MHV-S HE and of a complex of HE0 with αNeu4,5Ac22Me diffracted to 2.1 and 2.5 Å resolution, respectively. The structures were solved by molecular replacement by using BCoV-Mebus HE (PDB ID 3CL5; [23]) as template (BCoV-Mebus and MHV-S HE share 59% sequence identity; for crystallographic details, see Table 1).
In overall structure, the HE of MHV-S closely resembles that of BCoV-Mebus. It assembles into homodimers and the monomers are composed of three modules: a small membrane-proximal (MP), a receptor-binding (R), and a central esterase (E) domain (Figures 3A–C; [23]). The MP domain is virtually identical to that of BCoV-Mebus HE with a root mean square difference (rmsd) on main chain Cα atoms of only 0.48 Å. Unfortunately, residues in the E domain, that form the catalytic site were disordered in both crystals. Hence, the molecular basis for the unusual substrate specificity of MHV-S HE remains unknown. The structure of the R domain, however, was resolved, and in the complex the ligand molecule is well-defined (Figure S2). The R domains of MHV-S and BCoV-Mebus HE are highly similar with an rmsd on main chain Cα atoms of 0.79 Å.
The receptor-binding sites of BCoV-Mebus and MHV-S HE are very much alike in architecture. This is particularly surprising given the considerable differences in ligand preference and in their requirements for binding (i.e. binding of 9-O-Ac-Sia in a 9-O-Ac-dependent fashion versus binding of 4-O-Ac-Sia in 4-O-Ac-dependent fashion, respectively; Figure 1). The MHV-S HE receptor-binding site (RBS), like that of BCoV-Mebus HE, is composed of 5 surface exposed loops, four of which extend from the conserved 8-stranded “Swiss role” core-structure (loops R1 through R4; Figures 3C and 4A) and one originating from the E-domain (E-loop). Whereas the R1-, R2- and E-loops of the BCoV-Mebus and MHV-S HE sites are almost identical, the R3- and R4-loops adopt different conformations in the two proteins as result of amino acid insertions in MHV HE (Figures 3C and 4A). Two other conspicuous elements of the MHV-S HE RBS are the RBS-hairpin and a conserved metal-binding site with a potassium ion that stabilizes the R3-loop and the RBS-hairpin exactly as in BCoV-Mebus HE (Figure 4B). The potassium ion is coordinated by main chain oxygen atoms of Ser231, Glu280 and Leu282 and side chain oxygen atoms of Asp230, Gln232 and Ser278. These residues are conserved in BCoV-Mebus HE and in all other coronavirus HEs with the exception of HCoV-HKU1 HE [23].
While the overall organization of the MHV-S RBS is similar to that of BCoV-Mebus HE, the orientation of the receptor analogue with respect to the RBS is strikingly different (Figures 4B, 5A and B). As compared to the ligand in the BCoV-Mebus HE binding site (Figures 5C and D), the αNeu4,5Ac22Me receptor analogue is rotated by about 90° and shifted by about 2.5 Å. Figures 5A and B show how residues from the four R-loops, the E-loop and the RBS-hairpin interact with the Sia receptor molecule. Two hydrogen bonds are formed between the nitrogen and oxygen main-chain atoms of Lys217 and the oxygen of the C4 acetyl group and the nitrogen of the 5-N-acetyl group, respectively. The Ser220 main chain nitrogen accepts an additional, weak hydrogen bond from the C8 hydroxyl group of the ligand (Figure 5B).
Most remarkably, the hydrophobic pocket that in BCoV-Mebus HE accommodates the 9-O-acetyl moiety of the receptor (comprised of Leu161, Tyr184 Leu266 and Leu267) -arguably the most crucial element of the BCoV HE RBS- is conserved in MHV-S HE (comprised of Ile166, Tyr189, Tyr281, and Leu282), but it now accepts the Sia 5-N-acetyl group, while the Sia glycerol side-chain is solvent exposed (Figure 5A). Moreover, the hydrophobic patch in the BCoV-Mebus HE RBS that interacts with the Sia 5-N-acetyl group (Figure 5C) apparently changed into a shallow pocket that accommodates the Sia 4-O-acetyl moiety (Figure 5A). The residues orthologous to BCoV-Mebus HE Thr114, Leu161, Phe211, and Leu266 were replaced by Leu119, Ile166, Ser216, and Tyr281, respectively, and Leu260 was recruited from the R4-loop, which in MHV-S HE is reoriented as compared to the one in BCoV-Mebus HE (Figure 4A). These residues, together with conserved Phe212, form the hydrophobic lining of the newly shaped pocket (Figures 5A and B). As the Sia-4-O-acetyl group is crucial for ligand recognition by MHV-S HE, this pocket must be key to receptor-binding. In accordance, single Ala substitutions of Leu119, Ile166, Phe212, Leu260, or Tyr281 all reduced receptor-binding activity (although that of Ile166 to lesser extent) as shown by hemagglutination assay (Figure 5E) and solid-phase lectin binding assay (Figure 5F).
The data reveal in minute detail not only the mode of interaction between MHV-S HE and its cognate receptor determinant, but also clarify how a CoV HE RBS for 9-O-Ac-Sia might have transformed into one that now specifically binds 4-O-Ac-Sia. The most striking observation is that this major shift in ligand specificity required only minimal changes in the protein and that the binding site architecture was essentially maintained. How this was possible can be explained from the mode of lectin-ligand interaction, based largely on the docking of the methyl groups of the Sia-acetyl moieties into hydrophobic pockets, and from the structures of the two types of ligands. The juxtaposition of the Sia 5-N- and 9-O-acetyl moieties is quasi-similar to that of the Sia 4-O- and 5-N-acetyl groups. The distance between the groups may be different (7.1 versus 5.7 Å as measured between the methyl carbon atoms, respectively), but for each combination the acetyl groups are located in roughly the same plane and at roughly similar angles (Figure S3). Thus, it can be envisaged that a pre-existing site for 9-O-Ac-Sia was converted to accommodate 4-O-Ac-Sia instead by (i) having the ligand rotate (with binding of the ligand in the novel orientation facilitated through hydrogen bonding with residues introduced by substitutions and/or insertions in the R3 loop) and (ii) by bringing the original 9-O-acetyl binding pocket and 5-N-acetyl binding patch more closely together so that they now can accept the 5-N- and 4-O-acetyl moieties, respectively (Figure S3). From attempts to fit αNeu5,9Ac22Me into the MHV-S RBS by in silico modelling, the the 9-O- and 5-N-acetyl groups would seem to be spaced too far apart to conveniently dock into the acetyl-binding pockets. Moreover, were the ligand to bind in this orientation, the Sia carboxylate would clash with the modified R3-loop. These findings thus provide an explanation for exclusion of the original ligand and for the specificity of MHV-S HE for 4-O-Ac-Sias (Figure S3 and Video S1).
The structure of the MHV-S HE-receptor complex allows guarded predictions only of how glycosidic linkage or additional Sia modifications might affect ligand binding. The C2-oxygen through which glycosidically-bound Sia would be linked to the penultimate residue of the glycan chain is exposed to the solvent and we would therefore expect the lectin to bind Sias in a linkage-independent fashion. Still, the R4- and/or E-loops, as they are proximal to Sia C2 (Figure 4A), might affect ligand binding such as to cause a preference for a particular linkage type. The pocket for the Sia 5-N-acetyl group would seem sufficiently wide to also accommodate the slightly larger 5-N-Gc substituent (Figure 5A); whether the lectin does accept 5-N-glycolylated Sias as ligands remains to be shown, however. Finally, from the topology and orientation of αNeu4,5Ac22Me in the RBS of MHV-S HE, ligand binding would seem to be tolerant to modifications at the Sia glycerol side chain (Figure 5A). Yet, as demonstrated by hemagglutination assay with native and sialate-9-O-acetylesterase-treated erythrocytes, MHV-S HE apparently prefers 4-mono-O- over 4,9-di-O-acetylated Sias [27].
The occurrence of two distinct MHV lineages –exemplified by strains S and DVIM– that through their HE proteins bind to widely different Sia subtypes poses an interesting conundrum. While the structure reported here provides clues to how an HE protein ancestral to that of MHV-S may have changed to bind to 4-O- rather than to 9-O-acetylated Sias, the conditions that selected for this shift in ligand specificity and the biological consequences thereof are unknown. The limited data available on the in vivo role of HE suggests that it promotes viral spread [31]. Entry of murine coronaviruses, however, is mediated not by HE, but by the S protein, a type I fusion protein that binds to the principal receptor CAECAM1a [32]–[34]. We propose that HE may act during the very early stages of the infectious cycle as a molecular timer for temporary virion attachment. Through the concerted actions of its lectin and sialate-O-acetylesterase domains, HE would allow virus particles to bind with high avidity and yet reversibly to sialylated surfaces. The time allowed for virions to remain attached would be a function of HE binding affinity/avidity, esterase activity and local Sia density. Virions by binding to the ubiquitous and highly accessible Sias in the glycocalix would buy time for the S protein to find and bind the main receptor at the cell's surface as an obligatory prelude to penetration. Such a strategy would be advantageous particularly under conditions of low receptor density or poor receptor accessibility. If within the allotted time, HE-mediated virion attachment would not progress to this next stage of entry (for example, because the particle attached not to a susceptible cell, but to decoy receptors on a non-cell-associated glycoconjugate), the default would be for the virus to elute and “take its business elsewhere”. In this model, , MHV HE would appreciably contribute to host cell selection, its ligand preference potentially affecting host-, organ- and cell tropism. Our findings pave the way to study the function of CoV HE and to assess the importance of ligand and substrate specificity through an approach of structure-guided mutagenesis, reverse genetics and animal experimentation in a natural infection model.
A synthetic DNA with human codon-optimized sequence for the HE ectodomain of MHV strain S (MHV-S; amino acid residues 25–403) was cloned in pCD5-Ig [23], [24], a derivative of expression plasmid S1-Ig [35]. The resulting construct, pCD5-MHV-S-HE-T-Fc, codes for a chimeric HE protein provided with an N-terminal CD5 signal peptide and, at its C-terminus, preceded by a thrombin cleavage site, the Fc domain of human IgG1 (HE-Fc). The QuikChange XL II site-directed mutagenesis kit (Stratagene) was used to construct pCD5-MHV-S-HE-T-Fc derivatives that code for an enzymatically inactive HE-Fc with the esterase catalytic residue Ser45 replaced by Ala (HE0-Fc), and for HE0-Fc mutants with Ala substitutions in the receptor-binding site. For analytical purposes, HE-Fc fusion proteins were produced by transient expression in HEK293T cells and then purified from the cell culture supernatants by protein A-affinity chromatography and low-pH elution (0.1M Citric-acid pH 3.0). The pH of the eluate was neutralized by adding Tris pH 8.0 to a final concentration of 0.2 M and the protein solution was dialyzed against phosphate-buffered saline (PBS). For crystallography, HE-Fc fusion-proteins were transiently expressed in HEK293 GnTI(-) cells [36] and the MHV-S ectodomain was purified by protein A-affinity chromatography and on-the-beads thrombin cleavage as described [23], [24].
Maxisorp 96-well plates (NUNC) were coated for 16 hrs at 4°C with horse serum glycoproteins (HSG; 10% v/v horse serum in PBS) or bovine submaxilary mucin (BSM; 10 mg/ml; Sigma) at 100 µl per well. The wells were washed with washing buffer (PBS, 0.05% Tween-20) and treated with blocking buffer (PBS, 0.05% Tween-20, 2% bovine serum albumin, BSA) for 1 hr at RT. Two-fold serial dilutions of HE0-Fc lectins were prepared in blocking buffer (starting concentration 100 µg/ml) and 100 µl samples of these dilutions were added to the glycoconjugate-coated wells. Incubation was continued for 60 min after which unbound lectin was removed by washing three times. Bound lectin was detected using an HRP-conjugated goat anti-human IgG antiserum (1∶10,000 in blocking buffer; Southern Biotech) and TMB Super Slow One Component HRP Microwell Substrate (BioFX) according to the instructions. The staining reaction was terminated by addition of 0.3 M phosphoric acid, the optical density was measured at 450 nm, and graphs were constructed using GraphPad software. To assess and compare the enzymatic activities of BCoV-Mebus and MHV-S HE-Fc towards 4-O-acetylated Sias, HSG coated in Maxisorp plates was treated with samples from two-fold serial dilutions of either enzyme (starting at 100 ng/ml in PBS, 100 µl/well) for 2 hrs at 37°C. The destruction of 4-O-Ac-Sia receptor determinants was determined by SLBA with MHV-S HE0-Fc (5 µg/ml in blocking buffer) as described above. Enzymatic de-O-acetylation of αNeu4,5,9Ac32Me was analyzed by gas-chromatography-electron impact mass-spectrometry (GC-MS) as described [24], [25], [37].
Hemagglutination assay was performed in V-shaped 96-well plates (Greiner Bio-One). Two-fold serial dilutions in 50 µl PBS, 0.1% BSA of HE0-Fc or of purified HE0 ectodomains (starting amounts indicated in the text) were mixed with 50 µl of a rat erythrocyte suspension (Rattus norvegicus strain Wistar; 0.5% in PBS) and incubated for 2 hours on ice.
Crystallization conditions were screened by the sitting-drop vapor diffusion method using a Honeybee 961 (Genomic Solutions). Drops were set up with 0.2 µl of HE protein solution in 10 mM Tris-HCl pH 8.0 and 0.2 µl reservoir solution. Crystals with space group P212121 were obtained from 0.2 M KH2PO4, 0.2 M sodium malonate, 15% (w/v) PEG3350 and 0-5% (w/v) glycerol at 18°C. Crystals for diffraction experiments were grown with the hanging drop vapor diffusion method set up by hand with reservoir and protein solution ratio 1∶1 (1.6 µl total) at 18°C, and grew to a final size of up to 0.25×0.20×0.20 mm within one week. For data collection, crystals were flash-frozen in liquid nitrogen using reservoir solution containing 20% (w/v) glycerol as the cryoprotectant. To determine the HE structure in complex with its receptor, crystals of HE0 were soaked by adding 2 µl of 10 mM αNeu4,5,9Ac32Me in cryoprotectant solution directly into the margin of the drop, resulting in a final substrate concentration of about 7 mM. Crystals were flash-frozen after 5 to 10 minutes.
Diffraction data of crystals of MHV-S HE and its complex (Table 1) were collected at ESRF station ID-14-1 and ID-14-3, respectively. Diffraction data of native and ligand-soaked HE crystals were processed using XDS [38] and scaled using SCALA from the CCP4 suite [39]. Molecular replacement was performed using PHASER with BCoV-Mebus HE as template (PDB ID: 3CL5; [23]). Models were built manually with Coot [40] and refinement was carried out using REFMAC [41]. Water molecules were added using ARP/WARP, graphics generated with PYMOL (http://pymol.sourceforge.net).
In the Ramanchandran plot three residues are found in disallowed regions. The electron density of these residues supports the modeled conformation. In both HE monomers present in the asymmetric unit of the crystal structure of free as well as ligand-bound HE, the active site region of the esterase domain is largely disordered. No electron density is observed for esterase domain residues A52-A59, B51-B59, A108-A114, A308-A314, A335-A347 and B338-B346, while residues 44-50, 60-72, 332-334, and 348-358 adopt different conformations in the two monomers. Modeling of chain A residues 397-401 and chain B residues 334-337 and 394-398 should be considered tentative. C-terminal residues 396-403 followed by the 7-residue thrombin recognition sequence of the cleavable Fc-fusion are stabilized by crystal packing interactions suggesting that the observed conformation is not physiologically relevant.
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10.1371/journal.pntd.0001344 | Canine Antibody Response to Phlebotomus perniciosus Bites Negatively Correlates with the Risk of Leishmania infantum Transmission | Phlebotomine sand flies are blood-sucking insects that can transmit Leishmania parasites. Hosts bitten by sand flies develop an immune response against sand fly salivary antigens. Specific anti-saliva IgG indicate the exposure to the vector and may also help to estimate the risk of Leishmania spp. transmission. In this study, we examined the canine antibody response against the saliva of Phlebotomus perniciosus, the main vector of Leishmania infantum in the Mediterranean Basin, and characterized salivary antigens of this sand fly species.
Sera of dogs bitten by P. perniciosus under experimental conditions and dogs naturally exposed to sand flies in a L. infantum focus were tested by ELISA for the presence of anti-P. perniciosus antibodies. Antibody levels positively correlated with the number of blood-fed P. perniciosus females. In naturally exposed dogs the increase of specific IgG, IgG1 and IgG2 was observed during sand fly season. Importantly, Leishmania-positive dogs revealed significantly lower anti-P. perniciosus IgG2 compared to Leishmania-negative ones. Major P. perniciosus antigens were identified by western blot and mass spectrometry as yellow proteins, apyrases and antigen 5-related proteins.
Results suggest that monitoring canine antibody response to sand fly saliva in endemic foci could estimate the risk of L. infantum transmission. It may also help to control canine leishmaniasis by evaluating the effectiveness of anti-vector campaigns. Data from the field study where dogs from the Italian focus of L. infantum were naturally exposed to P. perniciosus bites indicates that the levels of anti-P. perniciosus saliva IgG2 negatively correlate with the risk of Leishmania transmission. Thus, specific IgG2 response is suggested as a risk marker of L. infantum transmission for dogs.
| Leishmania infantum is the causative agent of zoonotic visceral leishmaniasis in the Mediterranean Basin and Phlebotomus perniciosus serve as the major vector. In the endemic foci, Leishmania parasites are transmitted mostly to dogs, the main reservoir host, and to humans. We studied the canine humoral immune response to Phlebotomus perniciosus saliva and its potential use as a marker of sand fly exposure and consequently as a risk marker for Leishmania transmission. We also characterized major salivary antigens of P. perniciosus. We demonstrated that under laboratory conditions, the levels of anti-P. perniciosus saliva antibodies positively correlated with the number of blood-fed sand flies and therefore, may be used to evaluate the need for, and the effectiveness of, anti-vector campaigns. In parallel, we studied sera of dogs naturally exposed to P. perniciosus in highly active focus of canine leishmaniasis in Southern Italy. Specific antibodies against P. perniciosus saliva were significantly increased according to the ongoing sand fly season. Moreover, the levels of anti-P. perniciosus antibodies in naturally bitten dogs negatively correlated with anti-Leishmania seropositivity. Thus, for dogs living in endemic areas, specific antibody response against saliva of the vector is an important marker for estimating the risk of Leishmania transmission.
| Leishmania infantum (syn. Leishmania chagasi) is a protozoan parasite that causes zoonotic leishmaniasis, including the life-threatening visceral form, occurring also in the Mediterranean Basin. Parasites are transmitted by the bite of infected phlebotomine sand flies to dogs, the major host and the main domestic reservoir for human visceral leishmaniasis, or to humans. The clinical forms of canine leishmaniasis range from asymptomatic to lethal (reviewed in [1], [2]). Nonetheless, all seropositive infected dogs, including those without any clinical signs, can serve as a source of infection for sand flies in endemic areas [3], [4]. The major vector of canine leishmaniases in Mediterranean countries, including Italy, is Phlebotomus perniciosus [5], [6]. Control programs for human visceral leishmaniasis caused by L. infantum are primarily aimed at preventing sand flies from feeding on dogs to reduce Leishmania transmission among dogs and humans (reviewed in [1], [2]).
Measuring the exposure of dogs to sand fly bites is important for estimating the risk of L. infantum transmission. Recently, it was demonstrated that experimental exposure of dogs to Lutzomyia longipalpis bites elicits the production of specific anti-saliva IgG which positively correlates with the number of blood-fed sand flies [7]. Therefore, monitoring canine IgG levels specific for sand fly saliva could indicate the intensity of exposure to sand fly bites. Such a monitoring technique would be useful for evaluating the need for, and effectiveness of, anti-vector campaigns [7], [8].
Exposure to sand fly bites as well as immunization with sand fly saliva or its compounds elicits in naive hosts protection against Leishmania infection under laboratory conditions (reviewed in [9]). It is widely accepted that the protective effect is mediated by CD4+ Th1 cellular response and characterized by increased production of IFN- γ, which activates macrophages to kill Leishmania parasites (reviewed in [10]). Recently, it was shown that protective effect elicited by inoculation of Lutzomyia longipalpis recombinant proteins in dogs was associated with production of IFN-γ by CD3+ CD4+ T cells and by dominance of IgG2 antibodies [11].
In this study we described the anti-saliva IgG response in dogs experimentally exposed to P. perniciosus under laboratory conditions and those naturally exposed in an endemic focus of L. infantum. We also tested the association between the anti-saliva IgG subclasses and the levels of IFN-γ in Leishmania infantum-seropositive and -seronegative dogs. Additionally, we characterized the major P. perniciosus salivary antigens recognized by sera of experimentally and naturally bitten dogs.
A colony of Phlebotomus perniciosus was reared under standard conditions as described in [12]. Salivary glands were dissected from 4–6 day old female sand flies, placed into 20 mM Tris buffer with 150 mM NaCl and stored at −20°C.
Twelve laboratory dogs, beagles, were housed and handled in the Bayer Animal Health GmbH animal facility (Leverkusen, Germany). Dogs were sedated and individually exposed to approximately 200 P. perniciosus females as described in [7], [13]. Twenty hours after exposure, sand flies were collected and microscopically examined to assess the ratio of blood-fed females. In two independent experiments, two groups of three dogs each were used. Dogs in groups 2 and 4 wore insecticide-impregnated collars that were administrated 8 days before the first sand fly exposure, for a reduction of sand fly bites. In comparison, dogs in groups 1 and 3 remained without any repellent or insecticide application during the whole study. Therefore, dogs in groups 1 and 3 are hereafter defined as high-exposed (HE) and the dogs in groups 2 and 4 as low-exposed (LE). Dogs were exposed to sand fly bites once a week for five consecutive weeks. For the detailed numbers of blood-fed females see Table 1. Blood samples were collected throughout the study according to the following schedule: before the first exposure (week 0, pre-immune serum), during the sand fly sensitization (weeks 1–5), and weekly after the last exposure for 5 weeks (weeks 6–10).
Twenty nine mixed-breed young dogs (from 90 to 145 days old) and eleven laboratory reared beagles (120 days old) were enrolled in the trial. All animals were housed in a private open-air shelter in Putignano (Bari province, Apulia, Italy), where P. perniciosus is the most abundant phlebotomine sand fly species [14]. All dogs were vaccinated against common dog pathogens and dewormed as described in [15]. The canine antibody response against P. perniciosus saliva was studied at the beginning (March 2008) and at the end (November 2008) of the sand fly season. In parallel, at four intervals (March, July, November 2008 and March 2009) dogs were tested for L. infantum infection status by serological, cytological and molecular methods. All dogs were L. infantum negative at the beginning of the trial (March 2008), which was proved by all three diagnostic methods used. Leishmania-positive dogs were defined by positive anti-L. infantum serology and, in a subset of seropositive dogs (4 out of 18), the infection was confirmed by PCR or cytology. For details on the diagnostic methods, see [15], [16]. Considering the long incubation period of canine leishmaniasis and the occurrence of sand flies exclusively during the summer season (from June to October) [14], dogs with anti-Leishmania seroconversion in March (2009) are presumed to have become infected during the previous season (2008). Dogs that were seronegative for L. infantum at all four screening intervals were included in the Leishmania-negative group.
Anti-P. perniciosus IgG, IgG1 and IgG2 were measured by enzyme-linked immunosorbent assay (ELISA) as described in [7] with some modification. Briefly, microtiter plates were incubated with 6% (w/v) low fat dry milk in PBS with 0.05% Tween 20 (PBS-Tw). Canine sera were diluted 1∶200 or 1∶500 in 2% (w/v) low fat dry milk/PBS-Tw. Secondary antibodies (anti-dog IgG, IgG1, or IgG2 from Bethyl laboratories) were diluted and incubated as previously described [7]. Absorbance was measured at 492 nm using a Tecan Infinite M200 microplate reader (Schoeller). The cut-off value (IgG = 0.145; IgG1 = 0.126; IgG2 = 0.165) was determined as less than two times the standard error of the mean of the absorbance of pre-immune serum.
Phlebotomus perniciosus salivary gland homogenate from 5-day-old sand fly females were separated by SDS-PAGE on a 10% gel under non-reducing conditions using the Mini-Protean III apparatus (BioRad). Separated proteins were blotted onto a nitrocellulose (NC) membrane by Semi-Phor equipment (Hoefer Scientific Instruments) and blocked with 5% (w/v) low fat dry milk in Tris-buffered saline with 0.05% Tween 20 (TBS-Tw). Strips of NC membrane were incubated with canine sera diluted 1∶50 (experimentally bitten dogs) or 1∶25 (naturally bitten dogs) in TBS-Tw for 1 hour. The strips were then washed three times with TBS-Tw and incubated with peroxidase-conjugated sheep anti-dog IgG (Bethyl Laboratories) diluted 1∶3000 in TBS-Tw. The chromogenic reaction was developed using a solution containing diaminobenzidine and H2O2.
For mass spectrometric analysis, salivary glands from 5-day-old P. perniciosus females were homogenized by 3 freeze-thaw cycles. Samples were dissolved in non-reducing sample buffer and electrophoretically separated in 10% polyacrylamide SDS gel. Proteins within the gels were visualized by staining with Coomassie Blue G-250 (Bio-Rad). The individual bands were cut and incubated with 10 mM dithiothreitol (DTT) and then treated with 55 mM iodoacetamid. Washed and dried bands were digested with trypsin (5 ng Promega). The alpha-cyano-4-hydroxycinnamic acid was used as a matrix. Samples were measured using a 4800 Plus MALDI TOF/TOF analyzer (AB SCIEX). Peak list from the MS spectra was generated by 4000 Series Explorer V 3.5.3 (AB SCIEX) without smoothing. Peaks with local signal to noise ratio greater than 5 were picked and searched by local Mascot v. 2.1 (Matrix Science) against a database of putative salivary protein sequences derived from a cDNA library [17]. Database search criteria were as follows – enzyme: trypsin, taxonomy: Phlebotomus, fixed modification: carbamidomethylation, variable modification: methionine oxidation, peptide mass tolerance: 80 ppm, one missed cleavage allowed. Only hits that scored as significant (p<0.05) are included.
The data from experimentally bitten dogs obtained by ELISA were subjected to GLM ANOVA and Scheffe's Multiple Comparison procedure to analyse differences in kinetics of antibody response between HE and LE dogs at all sampling points. The non-parametric Wilcoxon rank sum test for differences in medians was used for comparison of anti-P. perniciosus IgG, IgG1, IgG2 and IgG1/IgG2 ratios between Leishmania-seropositive and -seronegative dogs. The non-parametric Wilcoxon signed-rank test for differences in medians was used for comparison of antibody increases between March and November blood samples in naturally bitten dogs. For correlation tests we used the non-parametric Spearman rank correlation matrix. For all tests statistical significance was regarded as a p-value less than or equal to 0.05. All statistical analyses were performed using NCSS 6.0.21 software.
Relative risk (the probability of the developing the disease occurring in the group exposed to the risk factor versus a non-exposed group), attributive risk (absolute effect of exposure to the risk factor) and ODDS ratio (odds of an event occurring in the exposed group to the odds of it occurring in non-exposed group) were calculated for dogs from the field study to find out the relationship between the levels of anti-P. perniciosus saliva antibodies and leishmaniasis incidence as described in [18]. Low level of specific antibodies (lower than the cut-off value) was determined as the risk factor and the confidence interval for relative risk was calculated as described in [19].
Phlebotomus perniciosus: DQ153102; DQ154099; DQ150622; DQ150621; DQ192490; DQ192491; DQ153100; DQ153101; DQ153104; DQ150624; DQ150623; DQ150620; DQ153105.
Lutzomyia longipalpis: AF132518.
To investigate the kinetics of antibody response against anti-P. perniciosus saliva, two groups of experimentally bitten dogs, low-exposed (LE) and high-exposed (HE), were followed for 10 weeks. Five weekly experimental exposures to P. perniciosus bites led to increased levels of anti-saliva specific IgG, IgG1 and IgG2 in both LE and HE groups. No anti-saliva antibodies were detected in any pre-immune dog sera tested.
In HE dogs, anti-P. perniciosus antibody levels increased significantly (p<0.05) in comparison to the pre-immune sera after the second (IgG; IgG2) and third exposure (IgG1) (Figure 1A–C). Anti-saliva IgG and IgG2 developed with similar kinetics; rapidly increased after the third exposure, and gradual increase until week five (the last exposure), followed by a steady decrease to the end of the study. Anti-saliva IgG1 increased rapidly between weeks three and five and persisted at elevated levels until the end of the study.
In LE dogs, anti-P. perniciosus antibody levels increased significantly (p<0.05) in comparison to the pre-immune sera after the fourth (IgG; IgG2) and sixth exposure (IgG1) (Figure 1A–C). Similar to HE dogs, kinetics of anti-P. perniciosus IgG and IgG2 in LE dogs was detected at peak levels on week five followed by a rapid decrease. Conversely, IgG1 was measured at peak levels on week six and persisted at elevated quantities to the end of the study (Figure 1A–C).
All HE dogs produced significantly higher levels of anti-P. perniciosus IgG (p = 0.0001), IgG1 (p = 0.0032) and IgG2 (p = 0.0003) compared to LE dogs throughout the study (Figure 1A–C). A positive correlation was detected between number of blood-fed female sand flies and the levels of canine anti-P. perniciosus IgG (r = 0.75, p<0.0001), IgG1 (r = 0.74, p<0.0001) and IgG2 (r = 0.72, p<0.0001) (Figure 1D–F). Overall, sera of experimentally bitten dogs produced higher concentrations of specific IgG2 compared to specific IgG1 (data not shown).
To determine the anti-P. perniciosus saliva antibody levels and the seasonal changes in specific antibody response, canine sera were screened at the beginning and at the end of the sand fly season, March and November, respectively. Incidence of leishmaniasis in dogs naturally exposed to sand flies was high, 18 out of 40 (45%) were found anti-L. infantum seropositive (0/40 in March 2008; 0/40 in July 2008; 5/40 in November 2008; 13/40 in March 2009). In March, higher levels of anti-P. perniciosus IgG and IgG2 (compared to cut-off value) were detected in about 55% and 10% of dog sera, respectively, while IgG1 levels were comparable to pre-immune sera (Table 2). In November, elevated levels of specific IgG were found in 87.5%, IgG2 in 72.5% and IgG1 in 45% of the 40 enrolled dogs (Table 2). In both groups of dogs, Leishmania-positive and Leishmania-negative, specific IgG, IgG1 and IgG2 levels significantly increased during the sand fly season (Figure 2A–C).
Leishmania-positive and Leishmania-negative dogs did not statistically differ in IgG and IgG1 production (Figure 2A, B); however, a significant difference was found in IgG2 levels (Figure 2C). Indeed, Leishmania-positive dogs revealed significantly lower anti-P. perniciosus IgG2 at the beginning (p = 0.047) and at the end (p = 0.05) of sand fly season (Figure 2C). Negative correlation was found between the levels of anti-P. perniciosus saliva IgG2 and the risk of Leishmania transmission, supported well by epidemiological parameters: relative risk = 2.6 (95% confidence interval: 0.66; 10.63); attributive risk = 1.6; and ODDS ratio = 10. Sera of all naturally bitten dogs showed significantly higher levels of specific IgG2 compared to specific IgG1 (data not shown). Moreover, the IgG1/IgG2 ratio differed between Leishmania-positive and -negative dogs; Leishmania-positive dogs revealed higher IgG1/IgG2 ratio, although the difference was statistically significant only at the beginning of sand fly season (p = 0.039) (Table 2). Furthermore, higher levels of IFN-γ were detected in sera of Leishmania-negative dogs throughout the study but with no statistically significant difference (Figure S1).
Phlebotomus perniciosus salivary antigens were studied using sera of naturally and experimentally bitten dogs. Pre-immune sera of experimentally bitten dogs did not recognize any of the salivary proteins by Western blot analysis (Figure 3).
Sera of experimentally exposed dogs produced 11 bands on a salivary gland Western blot with approximate molecular weights of 75, 50, 42, 40, 38, 34, 33, 29, 27, 23 and 14 kDa (Figure 3). The molecular weights of salivary antigens recognized by canine sera were similar in all dogs tested with the exception of the 23 and 27 kDa protein bands (recognized only by some sera). The salivary gland antigens most intensely recognized by the sera of all experimentally bitten dogs had molecular weights of 42, 38, 33 and 29 kDa.
Sera of naturally bitten dogs with both negative and positive anti-L. infantum serology reacted with up to 9 protein bands of 50, 42, 38, 34, 33, 29, 27, 23 and 14 kDa. All naturally exposed dogs tested in both groups recognized similar salivary antigens and the most intensive reactions were detected with the 42 and 33 kDa salivary antigens.
Mass spectrometry revealed that the main antigens recognized by sera of bitten dogs were salivary endonuclease (50 kDa - DQ154099), yellow proteins (42 kDa - DQ150622; 40 kDa - DQ150621), apyrases (38 kDa - DQ192490; 38 kDa - DQ192491; 33 kDa - DQ192491), antigen-5 protein (29 kDa - DQ153101), D7 proteins (27 kDa - DQ153104; 23 kDa - DQ150624; 23 kDa - DQ150623, and proteins of the SP-15 like protein family (14 kDa - DQ150620; 14 kDa - DQ153105) (Table 3).
Canine antibody response against P. perniciosus saliva was studied in dogs bitten by sand flies under well-defined laboratory conditions as well as in dogs from an endemic focus of visceral leishmaniasis in Italy.
In experimentally bitten dogs we observed a significant increase in production of specific IgG, IgG1 and IgG2 in the course of 10 weeks and a positive correlation was found between the levels of specific antibodies and the number of blood-fed females P. perniciosus. Anti-saliva specific IgG and IgG2 developed with similar kinetics and correspond well with previous results [7] in dogs experimentally bitten by Lutzomyia longipalpis. While in sera of healthy dogs, IgG1 and IgG2 usually occur in comparable concentrations [20], IgG2 prevailed in sera of bitten dogs in our study as well as in dogs experimentally bitten by L. longipalpis [7], [11].
In our field trial, we detected the increase in number of anti-P. perniciosus saliva seropositive dogs as well as in the amount of specific antibodies in dog sera as the sand fly season progressed. Statistically significant increases in production of specific IgG, IgG1 and IgG2 were observed in both Leishmania-positive and Leishmania-negative dogs at the end of sand fly season. Interestingly, Leishmania-positive dogs revealed significantly lower anti-P. perniciosus saliva IgG2 compared to Leishmania-negative dogs and the IgG1/IgG2 ratio was significantly higher in Leishmania-positive dogs. These data may suggest either that dogs with low IgG2 levels were at the higher risk of becoming Leishmania-infected or that Leishmania infection decreases the production of IgG2 in bitten dogs. Considering the IFN-γ levels in canine sera, that were shown to positively correlate with the protective Th1 immune response [11], it seems that the first hypothesis is more feasible. Although, the difference in IFN- γ production between Leishmania-negative and Leishmania–positive dogs was not statistically significant.
Published data from field studies suggests that humoral immune responses against sand fly saliva vary between hosts with cutaneous and visceral forms of leishmaniases (reviewed in [9], [21]). In foci of cutaneous leishmaniases caused by L. tropica and L. braziliensis, the levels of specific anti-sand fly saliva antibodies in humans positively correlated with the risk of Leishmania transmission [22], [23]. In contrast, in foci of visceral leishmaniasis caused by L. infantum, levels of human anti-sand fly saliva antibodies positively correlated with anti-Leishmania DTH (delayed-type hypersensitivity) and thus with protection against potential infection [24], [25]. So far, those studies have been performed only in humans. In canids, several studies showed presence of anti-sand fly saliva antibodies in sera from endemic areas in Brazil [8], [26], [27], however our study is the first describing the association with canine leishmaniasis.
Canine sera recognized more than eleven P. perniciosus antigenic bands by Western blot and the most intense reaction was often observed against a 42 kDa band. Mass spectrometry identified the 42 kDa band as a single protein belonging to the Yellow protein family (DQ150622). Previously, another Yellow protein of 47.3 kDa (AF132518) was reported as the major antigen recognized by sera of dogs bitten by L. longipalpis in the field [26]. The recombinant L. longipalpis Yellow proteins (rLJM11 and rLJM17) prepared in mammalian expression system kept their antigenicity and were successfully used to screen dog sera from Brazil [27], predicting similar features for Yellow protein of P. perniciosus. All canine sera tested recognized additional three major antigens of the 38, 33 and 29 kDa; the 38 and 33 kDa proteins are apyrases and the 29 kDa antigen represents the antigen 5-related protein family. These four antigens (42, 38, 33 and 29 kDa) are promising candidates as markers of sand fly exposure.
In conclusion, we confirmed that levels of antibodies against sand fly saliva positively correlate with the number of blood-fed sand flies and therefore, monitoring canine antibody response to specific sand fly salivary proteins may evaluate the need for, and effectiveness of, anti-vector campaigns. Moreover, this is the first study demonstrating relationship between the anti-sand fly saliva antibodies and the status of L. infantum infection in dogs. The levels of anti-P. perniciosus IgG2 in dogs naturally bitten by this sand fly species negatively correlate with the anti-Leishmania seropositivity. Thus, for dogs living in endemic area specific IgG2 response against saliva of the vector is suggested as a risk marker of L. infantum transmission.
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10.1371/journal.ppat.1000706 | Three Dimensional Structure of the MqsR:MqsA Complex: A Novel TA Pair Comprised of a Toxin Homologous to RelE and an Antitoxin with Unique Properties | One mechanism by which bacteria survive environmental stress is through the formation of bacterial persisters, a sub-population of genetically identical quiescent cells that exhibit multidrug tolerance and are highly enriched in bacterial toxins. Recently, the Escherichia coli gene mqsR (b3022) was identified as the gene most highly upregulated in persisters. Here, we report multiple individual and complex three-dimensional structures of MqsR and its antitoxin MqsA (B3021), which reveal that MqsR:MqsA form a novel toxin:antitoxin (TA) pair. MqsR adopts an α/β fold that is homologous with the RelE/YoeB family of bacterial ribonuclease toxins. MqsA is an elongated dimer that neutralizes MqsR toxicity. As expected for a TA pair, MqsA binds its own promoter. Unexpectedly, it also binds the promoters of genes important for E. coli physiology (e.g., mcbR, spy). Unlike canonical antitoxins, MqsA is also structured throughout its entire sequence, binds zinc and coordinates DNA via its C- and not N-terminal domain. These studies reveal that TA systems, especially the antitoxins, are significantly more diverse than previously recognized and provide new insights into the role of toxins in maintaining the persister state.
| Most bacteria live in biofilms, microbial communities that cause more than 80% of human infections. Biofilms have a genetically identical sub-population of dormant cells, named persister cells, which are the well-recognized source of antibiotic resistance. Recently, it was demonstrated that toxins are highly upregulated in persisters and have therefore been postulated to play a role in the persister state. Using an inter-disciplinary approach, we reveal how mqsR, the gene most highly upregulated in persisters, together with mqsA, function: they are the founding members of a new family of toxin:antitoxin (TA) systems. Unexpectedly, the structure of MqsR reveals that it is a ribonuclease, a protein that controls the production of other essential proteins. Moreover, we identified multiple features of this TA system that are so unique that each is a starting point for drug development. Unlike other antitoxins, MqsA is structured throughout its entire sequence, its structure is unchanged between the free and toxin-bound states and it binds zinc. It also binds DNA via its C- and not N-terminal domain. Finally, MqsA binds both its own promoter and additional genes important for E. coli physiology. Taken together, our data provide fundamental new insights into the role of MqsR and MqsA in bacterial persistence and biofilms.
| The emergence of increasing numbers of bacteria that are resistant to antibiotics portends a major public health crisis. One well-recognized but poorly understood mechanism used by bacteria to survive environmental stress is through the formation of persisters, a subpopulation of cells that survive prolonged exposure to antibiotics [1] and exhibit multidrug tolerance [2]. Persisters are not antibiotic-resistant mutants. Instead, they are phenotypic variants that pre-exist in bacterial populations. The dormant, non-dividing persister cells [1]–[3] allow bacteria to survive until the environmental stress is relieved, after which the persisters spontaneously revert to the non-persistent state and repopulate the original culture. Critically, the detailed molecular events that lead to and propagate the persister phenotype are still elusive, as persisters typically represent only a small fraction of the bacterial population. In wild-type E. coli, the frequency of persisters in planktonic cultures is only about one in a million [4]. However, in biofilms, complex multicellular bacterial communities that are highly resistant to antibiotics and that are responsible for more than 80% of human infections, this frequency increases substantially, up to one in a hundred [5]. The increased incidence of persister cells in biofilms, and their role in human bacterial infections, has stimulated renewed efforts to understand the molecular mechanism(s) that underlies the persister phenotype.
Recent studies have demonstrated that the persister state is correlated with the increased expression of chromosomal toxins from toxin:antitoxin (TA) genes [2],[6]. TA pairs [7],[8], also known as plasmid addiction systems, are highly abundant on bacterial plasmids [9],[10] and chromosomes [11]–[15]. They are composed of two genes organized in an operon that encode an unstable antitoxin and a stable toxin, respectively. Critical to their function, the protein products of TA pairs have considerable differences in lifetimes [16], with the antitoxin being highly susceptible to degradation by cellular proteases and the toxin comparatively stable. Under normal conditions, the toxin and antitoxin associate to form a tight, non-toxic complex. However, under conditions of stress, the antitoxins are degraded by either the ATP-dependent protease (Lon [16],[17]) or the bacterial protease systems (ClpXP [18]; ClpAP [19]). This leads to a dramatic reduction of both translation and replication rates and, in turn, the cessation of cell growth due to the cellular effects of the toxin. Toxin activities are diverse, and include inhibiting replication by blocking DNA gyrase [20],[21], halting translation via mRNA cleavage [22],[23], or inactivating EF-Tu by phosphorylation [24], among others. TA complexes also typically function as transcriptional repressor:corepressors, where the antitoxin binds to the promoter DNA within the TA operon and the toxin enhances DNA binding [25]–[27].
To date, more than ten TA loci have been identified in E. coli [7], including relBE [12],[28], mazEF [11],[29], dinJ-yafQ [30], hipBA [24],[31], hicAB [32] and yefM-yoeB [17],[33]. Gene expression profiling experiments have shown that multiple toxins are highly upregulated in persister cells, especially relE, mazF and yoeB [2],[6]. The activities of these toxins lead to a rapid cessation of cell growth, and have been postulated to play a role in the persistence phenotype [6]. Unexpectedly, the gene most highly upregulated in persisters is mqsR (ygiU/b3022), a gene originally identified as one that encodes a regulator of motility, curli and quorum sensing and that influences biofilm development by mediating the response of the cell to autoinducer-2 [34],[35], but which had not, until recently, been shown to be a bacterial toxin. Because the sequence of MqsR is not similar to that of any other known toxin, its molecular function is unknown.
Deletion of mqsA (ygiT/b3021), the second gene in the two-gene mqsRA operon, is lethal [6],[36]. This led to the postulation that mqsRA constitutes a novel TA module [6],[15], with MqsR as the toxin and MqsA as its cognate antitoxin. However, mqsRA has many characteristics that differ from canonical TA systems. First, in the mqsRA operon, mqsR precedes, instead of follows, mqsA. This unusual genetic organization has only been observed in two other recently characterized TA systems, that of higBA [37] and hicAB [32]. Second, their isoelectric points are nearly identical (8.8, MqsR; 9.1, MqsA) rather than being basic and acidic for the toxin and antitoxin, respectively. Third, the MqsA protein is larger, instead of smaller, than MqsR; the only other TA system with an antitoxin larger than its cognate toxin is that of hicAB [32]. Finally, their sequences are not homologous to any member of a recognized TA system.
In this paper, we employed a combination of biochemical and structural studies to show that MqsR, along with MqsA, are a bona fide TA pair that, because of the unique features of MqsA, define a novel family of TA modules. We show that MqsR is toxic and forms a tight complex with its antitoxin, MqsA, an interaction that mitigates MqsR toxicity. MqsA and the MqsR:MqsA complex also bind the promoters of the mqsRA operon and, unexpectedly, genes critical for E. coli physiology, including mcbR and spy. To the best of our knowledge, this is the first time a TA pair has been shown to bind and regulate promoters other than its own. The structure of MqsR reveals that it is a member of the RelE/YoeB family of bacterial RNase toxins. Based on its similarity with RelE, MqsR likely functions as a ribosome-dependent RNase. This suggests that MqsR is important for bacterial persistence via its ability to inhibit translation and, in turn, cell growth. MqsA itself is a two-domain protein with a novel fold that, unlike every other antitoxin, is well-ordered throughout its entire sequence and whose structure does not change upon toxin binding. It is also the first antitoxin known that binds metal, in this case zinc. These studies reveal the molecular mechanisms by which MqsR and MqsA mediate the cessation of cell growth and provide novel targets for the development of a new class of antibiotics that target TA pairs.
The ability of MqsR to arrest cell growth was examined by measuring its effect on colony formation (CFU/ml) and cell viability. Expression of MqsR alone leads to cell growth arrest in multiple bacterial strains (BW25113 and MG1655), while co-expression of MqsR with full-length MqsA (referred to hereafter as MqsA-F) rescues the cell growth arrest phenotype (Figs. 1A, S1A-E and [6],[38]). In addition, MqsR and MqsA-F form a tight oligomeric complex, as MqsA-F (untagged) co-purifies with MqsR (his-tagged) and forms a dimer of dimers, composed of two copies of MqsR and two copies of MqsA-F (hereafter referred to as MqsR:MqsA2:MqsR), as determined using size exclusion chromatography (Fig. 1B) and confirmed using dynamic light scattering. Furthermore, deletion of mqsA is lethal [6],[36]; similar results have been found with other antitoxins, such as HigA of Vibrio cholerae [39]. MqsA-F is also sensitive to proteolysis (Fig. S2). Using electrophoretic mobility shift assays (EMSA), we demonstrate that both the MqsR:MqsA2:MqsR complex and MqsA-F bind specifically to the mqsR promoter (PmqsR; Figs. 1C, 1D). It was also recently shown that MqsA binds two distinct palindromic sequences within PmqsR and that MqsA binding is enhanced in the presence of MqsR [38]. Finally, MqsR and MqsA are conserved, both in sequence and in gene structure, throughout the gamma- delta- and epsilon proteobacterial classes (Fig. S3). Taken together, these results show that MqsR is a bona fide toxin and MqsA is the proteolytically sensitive antitoxin that blocks MqsR toxicity. Because MqsR:MqsA2:MqsR was also recently identified to regulate the expression of a number of E. coli genes including one that encodes the colonic acid regulator McbR [34],[40], we reasoned that this regulation may be mediated by the direct binding of MqsA-F and/or the MqsR:MqsA2:MqsR complex to the promoters of these genes. EMSA was used to show that MqsA-F binds specifically to the promoters of mcbR and spy (Figs. 1E, 1F, S1F, S1G).
The structure of full-length MqsA (MqsA-F; residues 1–131, the constructs used in this study are shown in Fig. S4) is shown in Figure 2A. MqsA-F was determined to a resolution of 2.15 Å by molecular replacement using the structures of the MqsA N-terminal domain (residues 1–76, referred to hereafter as MqsA-N; Fig. S5A) and the MqsA C-terminal domain (residues 62–131; referred to hereafter as MqsA-C; Fig. S5B), as search models. The space group of the MqsA-F crystal is P21, with two molecules in the asymmetric unit. The two monomers are identical in terms of the overall fold with well-ordered electron density throughout both chains. The final model contains 262 residues, and includes all 131 residues for each MqsA-F molecule.
The structure of the MqsR:MqsA-N complex (Fig. 2B) was solved to a resolution of 2.0 Å using the multiple wavelength anomalous dispersion method. The space group of the crystal is P41212, with two crystallographically independent complexes in the asymmetric unit. The two complexes are identical in terms of the overall fold of each protein, with well-ordered electron density observed throughout the first complex and throughout the majority of the second complex. The final model contains 70 residues (1–170) of the 76 residues for MqsA-N and 97 residues (1–97) of the 98 residues for MqsR in the first complex and 59 residues (1–20, 27–65) of MqsA-N and 90 residues (2–60, 64–95) for MqsR in the second complex. X-ray diffraction data quality and refinement statistics are reported in Table 1.
The structure of the MqsA-F dimer is shown in Figure 2A. The two monomers are related to one another by local two-fold symmetry (Fig. 2A, arrow). The MqsA monomer is composed of two structurally distinct domains connected by a flexible linker and resembles a human leg (Fig. 3A): the MqsA-N, composed of residues 1–67, is the ‘foot’ and ‘calf’ and the MqsA-C, composed of residues 69–131, is the ‘thigh’. The ‘knee’-like linker connecting the domains, centered on residue 68, is flexible, allowing the N- and C-terminal domains to rotate independently of one another as rigid bodies (superposition of the N- and C-terminal domains from chains A and B give RMSD values of 0.35 Å and 0.58 Å, respectively). Superposition of the C-terminal domains from both monomers shows that the corresponding N-terminal domains are rotated by ∼25° with respect to one another (Fig. S5C).
MqsA-N binds zinc and adopts a novel, elongated fold (Figs. 3A–3C). It is composed of one long five-turn α-helix, a twisted β-sheet, loops that connect the secondary structural elements and a coordinated zinc ion. The zinc, which serves a structural and not catalytic role [41],[42], is coordinated by four cysteines (Cys3, Cys6, Cys37, Cys40) with an average sulfur-zinc distance of 2.35 Å (Figs. 3B, S6A). In spite of the low sequence identity (12% identity, 18% similarity) in the MqsA-N domain among different species, these zinc-coordinated cysteines are perfectly conserved (Fig. S3B), suggesting that all bacterial MqsA proteins adopt a similar fold.
The interaction between the two, long twisted β-strands and the five turn α-helix of the MqsA zinc binding domain is stabilized by an extended hydrophobic core composed of 11 residues: Ile18, Tyr20, Phe22, Leu29, Ile32, Tyr36, Met45, Phe53, Val57, Phe60 and Val64 (Fig. 3C; residues in beige). Although none of these residues are identical among the MqsA bacterial homologs (Fig. S3B), they are highly similar. A second cluster of hydrophobic residues is found near the zinc binding pocket and includes Met1, Met11, Leu35 and Ile44 (Fig. 3C; residues in orange). The DALI server [43] was used to identify proteins with similar folds. Although more than 269 hits were obtained (Z-scores from 3.2 to 2.0), they aligned to only the two long β-strands and the five turn α-helix; none had the combination of strands, helix and the zinc binding pocket observed in MqsA. Thus, to the best of our knowledge, MqsA-N is a zinc binding domain with a novel fold, hereafter referred to as the MqsA fold.
MqsA-C, the helix-turn-helix (HTH) domain, is composed of five tightly packed α-helices (Figs. 3A, 3D-3E), which bury a central hydrophobic core. This core is composed of eight residues, including Val69, Val77, Leu83, Phe92, Phe99, Tyr102, Pro109, and Leu117. Of these, all but one (Val69) are perfectly conserved among the MqsA family (Fig. S3B). Structural similarity searches demonstrated that MqsA-C shows significant homology to the bacteriophage 434 Cro repressor, the P22 C2 repressor and HigA antitoxin, placing it in HTH-XRE family of DNA binding proteins [44].
MqsA dimerization is mediated by the MqsA-C HTH-XRE domain. Residues from α-helices 3, 5, and 6 participate in the dimerization interface (Fig. 3D), which buries 1600 Å2 of solvent accessible surface area (SASA; this constitutes 19.9% of the total SASA; Fig. 3E) and has a surface complementarity of 0.69, both of which are well within the ranges expected for biologically relevant protein:protein interactions. Six residues are buried upon dimer formation, including Ser112, Lys115, Leu116, Val119, Leu126 and Ile130 (Fig. 3F). Five of these six residues are either identical or highly similar among the MqsA family (Fig. S3B). Eight additional residues (Ile91, Phe92, Gly93, His110, Pro111, Arg118, His123 and Glu129) participate, but are not fully buried, in the dimerization interface. Residues from α-helix 5 (Pro111, Leu116, Val119 and the aliphatic portions of the Lys115 and Arg118 sidechains) form a long hydrophobic pocket down the center of the face of the monomer, which is bordered on one side by negatively charged residues and on the other by positively charged residues (Fig. 3F).
In order to determine which domain of MqsA (MqsA-N, MqsA-C or both) binds MqsR, we carried out two co-expression toxicity experiments using the same protocol as that used to produce the proteins for structural studies: 1) MqsR co-expressed with MqsA-N and 2) MqsR co-expressed with MqsA-C. When MqsR and MqsA-C are co-expressed, bacterial cell growth is arrested (Figs. 4A, S7A, black diamond). In contrast, when MqsR and MqsA-N are co-expressed, cell growth is robust and both MqsR and MqsA-N express to high levels (Figs. 4A, S7A, grey square). Moreover, following co-expression, the MqsR:MqsA-N complex is readily purified and forms a heterodimer (one copy of MqsR and one copy of MqsA-N), as determined using size exclusion chromatography (Fig. 4B), and confirmed using dynamic light scattering.
EMSA was used to determine which domain of MqsA binds DNA. As shown in Figures 4C–E, the MqsA C-terminal domain is necessary and sufficient for binding the mqsR promoter (PmqsR), as incubation of PmqsR with MqsA-C results in a shift in the electrophoretic mobility of the DNA (Fig. 4D). In contrast, no shift is observed when the DNA is incubated with either MqsA-N alone or the MqsR:MqsA-N complex (Figs. 4C, 4E). These results show that DNA binding is mediated exclusively by MqsA-C.
MqsR is a small, globular protein, consisting of a central six-stranded β-sheet (β1-β3-β4-β5-β6-β2) and three α-helices, with α-helix 2 adjacent to and α-helices 1 and 3 abutting the backside of the β-sheet (Fig. 5A, left; magenta). A three-dimensional structure alignment revealed that MqsR is most similar to the bacterial toxins YoeB [33] and RelE/aRelE [28],[45] (DALI Z-scores = 5.1 and 4.0/5.3 respectively [43]), both of which are ribonucleases (RNases) and adopt a microbial RNase fold, the RelE-like fold [46] (Fig. 5A; PDBIDs: RelE, 2KC8; YoeB, 2A6Q; RNase Sa, 1RSN). The sequence identity of MqsR with YoeB and RelE/aRelE is extremely low, only 11% and 13%, respectively, and demonstrates why structure determination was essential to identify MqsR as a member of this family. Critically, our finding that MqsR functions as a bacterial ribonuclease toxin was recently confirmed [38].
The RelE-like fold is characterized by a central, antiparallel β-sheet and adjacent α-helix, which is conserved among the MqsR, YoeB and RelE bacterial toxins (Fig. 5B; conserved secondary structural elements shaded in green). However, there are a few key differences. First, the β-sheet in MqsR is extended by one β-strand (β1) because MqsR has a longer N-terminus. Second, in contrast to YoeB, RelE and RNase Sa, which each have one α-helix that folds across the back of the central β-sheet at a 45° angle, MqsR has two α-helices, both of which are perpendicular to the β-strands. This allows the long loop in MqsR that connects β-strands 4 and 5 to extend towards and interact with α-helix 2, an interaction that is sterically prohibited in YoeB and RelE.
In order to test which residues play a role in MqsR-mediated toxicity, we used alanine-scanning mutagenesis of evolutionarily and structurally conserved residues. Toxicity was measured by monitoring MqsR-mediated growth arrest and protein expression levels. The MqsR mutants that exhibited the most robust growth (i.e., those with the least toxicity) were K56A, Q68A, Y81A and K96A (Figs. 6A, S7B). The mutant proteins also expressed and could be detected by Western Blot using an antibody directed to the MqsR his6-tag (expression of wildtype, WT, MqsR arrests cell growth so rapidly that free MqsR is not detectable, even by Western Blot; Fig. 6B). In contrast, growth curves for MqsR mutants Y55A, M58A and R72A were similar to WT and, like WT, did not express to detectable levels. This was also observed for MqsR mutants H7A, H64A and H88A (not shown). Thus, these results show that MqsR residues K56, Q68, Y81 and K96 play key roles in MqsR-mediated toxicity while residues H7, Y55, M58, H64, R72 and H88 do not.
MqsA recognition of MqsR occurs at two distinct interfaces. The primary interface buries 1537 Å2 of SASA while the secondary interface buries 479 Å2 of SASA. A total of 2016 Å2 of SASA is buried upon complex formation, or 17.7% of the total SASA, well within the range expected for biological interfaces. The primary interface is centered on MqsA β-strand 3 (Ser43-Met45), which interacts with MqsR β-strand 2 (Val22-Thr25) to form a single continuous β-sheet (β1R-β3R-β4R-β5R-β6R-β2R-β3A-β2A-β1A; Figs. 2B, 7A) throughout the complex. Sixteen residues from MqsA-N and 12 residues from MqsR contribute to the primary interface. The interaction is hydrophobic, with Pro4, Ser43, Ile44, Met45, Ser50, Phe53, and Met54 from MqsA (light blue sticks, Fig. 7A) and Thr24, Thr25, Arg26, Leu29, Phe39 and Ile92 from MqsR (light pink sticks, Fig. 7A) becoming completely buried upon complex formation. In addition, three key electrostatic interactions are located at the periphery of the interface: (1) Arg26 from MqsR forms hydrogen bonds with the hydroxyl sidechain of Ser43 and the carbonyl of Glu41 from MqsA-N, (2) Asp33 from MqsR forms a salt bridge with Arg61 from MqsA-N and (3) Asp40 from MqsR forms a salt bridge with Lys47 from MqsA-N. The secondary interface is much smaller, being formed by four residues from MqsA-N and five residues from MqsR (Figs. 7B, S6B). The interface is centered on His7 (MqsA-N), which interacts with Thr60, Ser62, Asp63 and Gln68 from MqsR. Additional interactions are observed between Lys2, Val5 and the carbonyl of Pro4 from MqsA-N and Asp63, Gln68 and Ser94 of MqsR. MqsR residues Asp63 and Gln68 are the only residues that become buried in the secondary interface (light pink, Fig. 7B). Finally, MqsA-N does not block the MqsR active site (Fig. 7C). Instead, nearly all of the residues predicted to be important for MqsR activity are accessible in the MqsR:MqsA-N complex. This accessibility of the active site has also been observed for the RelBE system [28]. The only exception is Gln68, which is part of the secondary interface. This suggests neutralization is achieved either through cellular localization (i.e., towards DNA via MqsA-C) or through steric occlusion by blocking the interaction of MqsR with other biomolecules, such as the ribosome.
Our biochemical and structural analysis of the E. coli MqsA antitoxin and the MqsR:MqsA-N complex provide a detailed 3-dimensional structural view of the free antitoxin and the TA complex. These structures combined with our biochemical data unequivocally demonstrate that MqsR, a protein previously shown to be critical for biofilm formation [34],[35] and the most highly upregulated gene in persister cells [6] in E. coli, and MqsA form a novel bacterial TA system. Our data show that, as expected for a TA system, the expression of the MqsR toxin leads to growth arrest, while co-expression with its antitoxin, MqsA, rescues the growth arrest phenotype. In addition, MqsR associates with MqsA to form a tight, non-toxic complex and both MqsA alone and the MqsR:MqsA2:MqsR complex bind and regulate the mqsR promoter. Finally, the structure of MqsR reveals that it is a member of the RelE/YoeB family of bacterial RNases, which are structurally and functionally characterized bacterial toxins.
Comparison of the microbial RNase active sites between MqsR, RelE, YoeB and RNase Sa demonstrates that MqsR is most similar to RelE (Fig. 8A). In microbial RNases, such as RNase Sa, RNA binding is mediated by polar (Q38) and aromatic (Y86) residues while RNA cleavage is catalyzed by a histidine (H85) and glutamic acid (E54) residue [47],[48]. While the catalytic histidine and glutamic acid are conserved in YoeB (H83, E46), which has intrinsic endoribonuclease activity [33], they are not found in RelE, which functions as a ribosome-dependent RNase [23],[45]. These catalytic residues are also not present at the MqsR functional site (Figs. 7C, 8B, 8C). This is further supported by our demonstration that single deletion mutants of the three histidines in MqsR (H7, H64, H88; none of which overlap with the positions of H83 in YoeB and/or H85 in RNase Sa) do not attenuate MqsR-mediated toxicity. Instead, the MqsR residues that play a role in MqsR-mediated toxicity (K56, Q68, Y81 and K96) overlap best with those that are important for RelE-mediated toxicity (see K52/K56, R81/K96, R83/K96, Y87/Y81 between RelE/MqsR, respectively; Fig. 8A) [45]. This strongly suggests that MqsR, like RelE, is a ribosome-dependent RNase. This structural and functional data contradict recent results that show MqsR cleaves mRNA in the absence of the ribosome [38] and demonstrates that the precise catalytic mechanism by which mRNA is cleaved by MqsR remains to be elucidated.
However, in sharp contrast to established TA pairs such as RelE:RelB, YoeB:YefM and MazF:MazE, the MqsR:MqsA TA pair has many unique characteristics that are not observed in canonical TA systems, and thus represents the founding member of a new family of TA systems. In typical TA pairs, the antitoxin gene precedes that of the toxin. In contrast, mqsR precedes mqsA. To date, this genetic organization has only been observed in two other recently characterized TA systems, that of higBA [37] and hicAB [32]. In addition, in canonical TA systems, the toxin is larger than the antitoxin (with the exception of HicB [32]) and the toxin is basic while the antitoxin is acidic. In the MqsR:MqsA TA system, MqsA is larger than MqsR, 14.7 kD and 11.2 kD, respectively, and both proteins are basic.
Critically, one of the most significant differences between MqsR:MqsA and typical TA systems is the nature of the MqsA antitoxin itself. All other canonical antitoxins whose structures are known, including HipB [24], RelB [28], YefM [33],[49] and MazE [29], have at least one dynamic, flexible domain and all of these, with the exception of HipB, either change conformation or become ordered upon toxin binding. In contrast, MqsA is well-ordered throughout its entire sequence and its structure does not change when bound to MqsR. This explains why MqsA must be larger than MqsR; MqsA binds MqsR via a longer, folded domain, while other antitoxins bind their corresponding toxins via shorter, unstructured peptides. In addition, all other functionally characterized antitoxins bind DNA via their N-terminal domains. MqsA is the first antitoxin that has been shown experimentally to bind DNA via its C-terminal domain. The only other antitoxin predicted to bind DNA via its C- and not N-terminal domain is HicB [32],[50]. MqsA is also the first antitoxin described that requires a metal, zinc, for structural stability. Moreover, unlike most other TA inhibition mechanisms, MqsA (like its RelB homolog [28]) does not occlude the toxin active site. Finally, in addition to binding its own promoter, MqsA and the MqsR:MqsA2:MqsR complex also bind and regulate the promoters of genes that play roles in E. coli physiology, including mcbR and spy. To the best of our knowledge, this is the first time a TA system has been shown to bind the promoters of genes other than its own.
The MqsR:MqsA2:MqsR complex is oligomeric, and forms a dimer of dimers (MqsR:MqsA2:MqsR). Since the structure of the MqsA-N is invariant among all structures determined (MqsA-F; MqsA-N and the MqsR:MqsA-N complex), we used the MqsA-F and MqsR:MqsA-N structures to generate an accurate model of the full MqsR:MqsA2:MqsR complex. As can be seen in Figure 9A, the MqsR:MqsA2:MqsR complex forms a highly extended structure with the MqsA-C domains located at the center of the complex (MqsA monomers in light blue and light orange; α-helix 4 in green) and the MqsR toxins (magenta) bound at the periphery. In overall shape, the MqsR:MqsA2:MqsR complex is most similar to the MazF:MazE complex [29], which is also highly extended, although the structures of the individual proteins and oligomerization states of the complexes between the two families differ dramatically.
We and others [38] used EMSA to show that MqsA-F, MqsA-C and the MqsR:MqsA2:MqsR complex bind directly to the mqsR promoter (Figs. 1C, 1D, 4D). Therefore, MqsA likely is a transcriptional regulator of its own promoter via direct binding interactions through MqsA-C. The electrostatic surface of the MqsR:MqsA2:MqsR complex is shown in Figure 9B. As can be readily observed, the bottom of the MqsA-C and by direct extension, the top of the MqsA-N are nearly exclusively positively charged, typical for a strong DNA binding site. As we showed experimentally, DNA binding is localized to MqsA-C. This enabled us to exploit the similarity of the MqsA-C HTH-XRE domain to other experimentally determined HTH-XRE protein structures to generate a model for DNA binding by the MqsR:MqsA2:MqsR complex. All HTH-XRE DNA binding proteins whose structures have been determined in complex with DNA and which are structurally homologous to MqsA (16 protein:DNA complexes with DALI Z-scores ranging from 6.0 to 2.5), bind DNA via the same helix, the HTH-XRE DNA binding helix. In MqsA, the HTH-XRE DNA binding helix corresponds to α-helix 4 (Fig. 9A, α-helix 4 highlighted in green). Importantly, in the MqsA-F dimer, this α-helix is accessible to solvent and thus, by extension, to DNA. It is also highly positively charged, decisively contributing to the positively charged surface of the MqsA-C HTH-XRE domain dimer. Moreover, MqsA α-helix 4 is the only stretch of residues that is perfectly conserved among all MqsA proteins (Fig. S3B). Taken together, these data provide strong evidence that MqsA binds DNA via α-helix 4.
We generated a model of the MqsR:MqsA2:MqsR:DNA complex using the coordinates of the P22 C2 repressor bound to DNA (PDBID 2R1J) [51], the HTH-XRE protein identified to be most similar to MqsA, and the MqsR:MqsA2:MqsR complex (Fig. 9C). As can be seen, the DNA binds between the MqsA-N domains and makes extensive interactions with the bottom surface of the MqsA-C dimer, with α-helix 4 of both monomers projecting into the major groove of DNA. In contrast, there is minimal interaction between the DNA and MqsA-N and no interaction between the DNA and MqsR. This is perfectly consistent with the EMSA results, which show that neither MqsA-N nor MqsR:MqsA-N complex bind the mqsR promoter (Figs. 4C, 4E). Notably, the MqsR:MqsA2:MqsR:DNA complex reveals that residues at the turn connecting β-strands 1 and 2 in the MqsA N-terminal domain may interact with DNA. In support of this hypothesis, these residues, 23RGRK26, are highly basic and thus are capable of forming ionic interactions with the phosphate backbone. Thus, although MqsA-N is not capable of binding DNA alone, it may contribute to DNA binding via the interactions of these residues. This also suggests that the ability of the MqsA N- and C-terminal domains to rotate independently of one another (Fig. S5C) may help facilitate DNA binding as it would enable the MqsA-N domains to reposition themselves so residues 23RGRK26 may optimally coordinate the phosphate backbone. Since the MqsR:MqsA2:MqsR complex has been shown to bind DNA more tightly than MqsA alone [38], the interaction of MqsR with MqsA-N may also affect this repositioning. Finally, the model also predicts that MqsA binding induces a 30°–45° bend in the DNA. Although α-helix 4 is connected to α-helix 3 by a glycine-rich turn (93GGG95), and thus may be able to adopt a slightly different conformation when bound to DNA, it is highly unlikely that it could shift sufficiently to permit binding to unbent DNA.
Taken together, the crystal structures of full-length MqsA and the MqsR:MqsA-N complex reveal that the MqsR:MqsA TA complex is the founding member of a novel family of TA pairs. It forms a dimer of dimers, in which DNA binding and MqsR recognition by MqsA are mediated via distinct, structured domains. Because MqsR is the gene most highly upregulated in E. coli persister cells [6] and because it also plays an essential role in biofilm regulation and cell signaling [34],[35], these structures provide fundamental new insights into how this novel TA system participates in bacterial persistence, biofilm formation and multidrug tolerance in the gamma- delta- and epsilon proteobacterial classes. This work has provided the first steps for understanding this novel, unique TA system at a molecular level and provides a basis for developing novel antibacterial therapies that target TA pairs.
The bacterial strains and plasmids used in this study are listed in Table S1. Growth experiments with E. coli strain BW25113 were conducted in LB medium at 37°C. Growth experiments were monitored using pBS(Kan)-based plasmids [52]. To construct pBS(Kan)-based plasmids for producing MqsR, MqsA-F, and MqsR-MqsA-F from a lac promoter, the fragments from genomic DNA were amplified by PCR (Table S2) and directionally cloned into pBS(Kan). The toxicity of selected proteins was investigated using pBS(Kan) plasmids with 1 mM IPTG added upon inoculation. Cell viability (CFU) measured by diluting cells from 102 to 107 via 10-fold serial dilution steps into 0.85% NaCl solution and applying them as 10 µL drops on LB agar with kanamycin or chloramphenicol [53].
Full-length MqsA (MqsA-F, residues 1–131), the MqsA N-terminal domain (MqsA-N, residues 1–76) and the MqsA C-terminal domain (MqsA-C, residues 62–131) were subcloned into a modified pET28a vector which contained an N-terminal his6-tag with a TEV cleavage site [54]. Proteins were expressed in One Shot BL21 (DE3) cells (Invitrogen) and purified by sequential his6-tag, TEV cleavage, subtraction his6-tag and size-exclusion chromatography (SEC). For the MqsR:MqsA-N complex, MqsR was subcloned into the pET28a vector (Novagen) which contains an N-terminal his6-tag with a thrombin cleavage site. MqsA-N was subcloned into the untagged pCA21a vector (Expression Technologies, Inc.). The complex was co-expressed and purified by sequential his6-tag, thrombin cleavage, second subtraction his6-tag and SEC.
Crystals of MqsA-F were obtained using the sitting drop vapor diffusion method at 4°C. Crystals were grown by mixing 0.2 µL of MqsA-F protein (3.5 mg/ml; 10 mM Tris pH 7.0, 50 mM NaCl, 0.5 mM TCEP, 5% (v/v) ethanol) with 0.2 µL of precipitant (75 mM Bis-Tris, pH 5.5, 150 mM MgCl2, and 19% (w/v) PEG3350). Crystals of MqsR:MqsA-N were obtained using the sitting drop vapor diffusion method at 4°C. Crystals were grown by mixing 0.2 µL MqsR:MqsA-N protein complex (14 mg/ml; 10 mM Tris pH 7.5, 100 mM NaCl, 0.5 mM TCEP) with 0.4 µL of precipitant (0.1 M Bis-Tris pH 5.5 and 25% (w/v) PEG3350). MqsA-F and MqsR:MqsA-N crystals were cryoprotected in precipitant containing 20% glycerol and frozen in liquid nitrogen for data collection.
Data for both MqsA-F and the MqsR:MqsA-N complex were collected at the National Synchrotron Light Source, beamline X6A at Brookhaven National Laboratory. Native data for MqsA-F was collected from a single crystal at a single wavelength. Anomalous data for the MqsR:MqsA-N complex was collected from a single crystal; a three-wavelength MAD experiment was performed collecting data at the experimentally determined Zn/K absorption edge, the inflection point and a high energy remote wavelength. Data were processed and scaled using HKL2000 [55].
MqsA-F: Molecular replacement (Phaser [56]) using the structures of the MqsA individual domains (MqsA-N and MqsA-C; structure determination of MqsA-N and MqsA-C described in Protocol S1, Figs. S5A, S5B and Tables S3, S4) as search models resulted in a single solution, with two molecules of each domain present in the asymmetric unit. The resulting electron density maps were readily interpretable. Model building was performed in Coot [57] followed by restrained refinement in REFMAC 5.2.0019 [58]. As observed for the structure of MqsA C-terminal domain alone (Fig. S3B), Gln108 is methylated at N5. MqsR:MqsA-N complex: SOLVE was used to determine the positions of the two expected zinc atoms. Initial phases to 2.0 Å were improved with density modification in RESOLVE. An initial model built by ARP/wARP served as the basis for subsequent manual model building and refinement. Iterative cycles of refinement in REFMAC against the high energy remote data were used to complete and refine the model. Data collection, model and refinement statistics for both structures are reported in Table 1. Structure validation and stereochemistry analysis was performed with Molprobity [59] and SFCHECK [60].
The targeted promoter regions (150–250 bp upstream of the start codon using primers reported in Table S2) were amplified, purified and labeled with biotin using the Biotin 3′-end DNA Labeling Kit (Pierce Biotechnology). After binding the proteins (200 ng; MqsA-F, MqsA-N, MqsA-C, MqsR:MqsA-N and MqsR:MqsA-F) with biotin-labeled target promoters (5 ng), electrophoresis was conducted at 100 V at 4°C using a 6% DNA retardation gel (Invitrogen). The binding mixtures were transferred to a nylon membrane (Roche Diagnostics GmbH) using a Mini Trans-Blot Electrophoretic Transfer Cell (Bio-Rad), and 3′-biotin-labeled DNA was detected with the Light-Shift Chemiluminescent EMSA kit (Pierce Biotechnology).
Mutagenesis of MqsR was carried out using the Quikchange mutagenesis kit (Stratagene) and sequence verified. Growth experiments were conducted in LB at 18°C and all mutants were tested in parallel. Briefly, 10 ml of an overnight culture was used to inoculate 1 L of LB and then incubated at 37°C with rigorous shaking. Once a mutant culture reached an OD600 of 0.4, it was cooled on ice. After all the mutants reached an OD600 of 0.4, the cultures were cooled for an additional hour, induced with 0.5 mM IPTG and incubated at 18°C with vigorous shaking. OD600 measurements were taken every hour for 20 hours. Growth assays for MqsR:MqsA-N and MqsR:MqsA-C were carried out similarly, except expression was induced at an OD600 of 0.7.
The P22 C2 repressor protein (PDBID 2R1J) [51] was identified as the closest structural homolog to the MqsA C-terminal domain (DALI Z-score = 6.0), whose DNA-bound structure was known. The MqsA:DNA complex was modeled by superimposing the conserved HTH-XRE helices from both proteins (MqsA residues 98–105; P22R residues 34–41) and mapping the rotated DNA coordinates onto one MqsA monomer. This MqsA monomer:DNA complex was then superimposed onto the symmetry-related MqsA monomer to obtain the rotated DNA coordinates for the second monomer.
The genes/proteins mentioned in the text include (UniProtKB/Swiss-prot ID unless stated otherwise): MqsR/YgiU (Q46865), MqsA/YgiT (Q46864), McbR (P76114), Spy (P77754), RelE (P0C077), RelB (P0C079), MazE (P0AE72), MazF (P0AE70), DinJ (Q8X7Q6), YaFQ (Q47149), HipA (P23874), HipB (P23873), HicA (P76106), HicB (P67697), YefM (P69346), YoeB (P69348), HigA (Q9KMG4/Q9KMA5), HigB (Q9KMG5/Q9KMA6). The PDB files mentioned in the text are RelE (PDBID 2KC8), YoeB (PDBID 2A6Q) and RNase Sa (PDBID 1RSN).
The structure factors and coordinates for MqsA-F and the MqsR:MqsA-N complex have been deposited with the Protein Databank with accession numbers 3GN5 and 3HI2, respectively.
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10.1371/journal.pbio.1000574 | Evidence of Activity-Specific, Radial Organization of Mitotic Chromosomes in Drosophila | The organization and the mechanisms of condensation of mitotic chromosomes remain unsolved despite many decades of efforts. The lack of resolution, tight compaction, and the absence of function-specific chromatin labels have been the key technical obstacles. The correlation between DNA sequence composition and its contribution to the chromosome-scale structure has been suggested before; it is unclear though if all DNA sequences equally participate in intra- or inter-chromatin or DNA-protein interactions that lead to formation of mitotic chromosomes and if their mitotic positions are reproduced radially. Using high-resolution fluorescence microscopy of live or minimally perturbed, fixed chromosomes in Drosophila embryonic cultures or tissues expressing MSL3-GFP fusion protein, we studied positioning of specific MSL3-binding sites. Actively transcribed, dosage compensated Drosophila genes are distributed along the euchromatic arm of the male X chromosome. Several novel features of mitotic chromosomes have been observed. MSL3-GFP is always found at the periphery of mitotic chromosomes, suggesting that active, dosage compensated genes are also found at the periphery of mitotic chromosomes. Furthermore, radial distribution of chromatin loci on mitotic chromosomes was found to be correlated with their functional activity as judged by core histone modifications. Histone modifications specific to active chromatin were found peripheral with respect to silent chromatin. MSL3-GFP-labeled chromatin loci become peripheral starting in late prophase. In early prophase, dosage compensated chromatin regions traverse the entire width of chromosomes. These findings suggest large-scale internal rearrangements within chromosomes during the prophase condensation step, arguing against consecutive coiling models. Our results suggest that the organization of mitotic chromosomes is reproducible not only longitudinally, as demonstrated by chromosome-specific banding patterns, but also radially. Specific MSL3-binding sites, the majority of which have been demonstrated earlier to be dosage compensated DNA sequences, located on the X chromosomes, and actively transcribed in interphase, are positioned at the periphery of mitotic chromosomes. This potentially describes a connection between the DNA/protein content of chromatin loci and their contribution to mitotic chromosome structure. Live high-resolution observations of consecutive condensation states in MSL3-GFP expressing cells could provide additional details regarding the condensation mechanisms.
| Mitotic chromosomes of eukaryotes are relatively large rod-like cellular organelles, about 1 µm in diameter and 10 µm long, of well-studied composition but unknown structure. The question of whether all DNA sequences equally contribute to the interactions leading to the formation of mitotic chromosomes has never been asked. To find an answer, we determined whether the radial positions of specific chromatin loci within mitotic chromosomes were reproduced at every cell cycle or were purely random. Based on fluorescence microscopy images of live or fixed chromosomes in cells from Drosophila embryos or Drosophila larval tissues expressing the MSL3-GFP fusion protein from a transgene, we report that the large-scale organization of mitotic chromosomes is reproduced not only longitudinally, as in the well-known chromosome banding phenomenon, but also radially. Actively transcribed, dosage-compensated genes of the Drosophila male X chromosome were always found at the periphery of mitotic chromosomes, starting from late prophase. Histone modifications specific to active chromatin were found to be more peripheral compared to silent chromatin that tended to be more central in the condensed chromosome. These findings are both exciting and significant for the field of cell and chromatin biology because they may help reconcile the old controversy between the existing models of chromosome structure that posit either radial loops of chromatin or consecutive coiling. In addition, we offer new insights into the mechanisms of mitotic condensation and suggest a link between structural and functional roles of different chromatin domains.
| Over the past decades mitotic chromosomes have been shown to have a high degree of organization. However, the exact configuration of the DNA molecule and its reproducibility within a chromosome are unknown. Consolidation of the results from diverse experimental approaches has not yet led to a thorough understanding of chromosome structure. Structural features of chromosomes are beyond the resolution of light microscopy, and tight compaction and lack of contrast in electron microscopy are among the main technical obstacles [1],[2]. Even though the correlation between DNA sequence composition and its contribution to the chromosome-scale structure has been suggested before [3],[4], it is unclear if any DNA sequence is equally able to participate in intra- or inter-chromatin or DNA-protein interactions, leading to formation of mitotic chromosomes. Alternatively, some regions may be suited for this purpose more than others. Distinct models of mitotic chromosomes concentrate on different aspects of their structure [5]. Complete or partial extraction of chromosomes known to modify the native chromosome morphology has lead to the “radial-loop” model [6],[7]. The original “radial-loop” model, with its later modifications based on biochemical and cytological experiments on fully condensed mitotic chromosomes [8], postulated the existence of specialized DNA sequences anchoring chromatin loops to non-histone proteins at the cores of chromosomes approximately every 100 kbp and indispensable for a variety of other biological functions besides mitotic condensation. Models of this class do not specify the organization of the “30 nm fiber” between the anchoring points. Alternative, “hierarchical-coiling” models, based on observations of bulk chromatin at different stages of mitotic condensation with light or electron microscopy, in part due to insufficient resolution, conceptually overlook the possibility of correlation between the DNA sequence/protein composition of a specific chromatin region and its contribution to chromosome structure [9]. These models concentrate on “large-scale” structural features ranging in size from several tens to several hundred nm and therefore detectable with microscopy. Additional models with features borrowed from both “radial-loop” and “hierarchical coiling” models also have been proposed [10],[11]. Despite identification of a number of proteins necessary for successful condensation and segregation of chromosomes in mitosis, key features of the structure, among which are banding patterns, reproducible chromosome geometry, and localization of topoisomerase II or condensin complexes within chromosomes, await their consolidation and explanation by a model. The question of whether all DNA sequences equally participate in the formation of chromosomes or whether the structural role is entrusted to a narrower class of specialized sequences remains unanswered.
Here we probed the connection between the function of chromatin loci in terms of transcriptional activity and their position on mitotic chromosomes. Dosage compensated genes on the X chromosome in fruit flies provide a functionally distinct subset of genes with a possibility of labeling for fluorescence microscopy. As demonstrated by both cytological and chromosome-wide mapping studies, the euchromatic arm of the X chromosome is specifically bound by MSL complex throughout the cell cycle, including mitosis [12]–[15], providing a convenient label for Drosophila melanogaster chromatin in its native state in live cells. As a source of mitotic chromosomes, we used diploid dividing cells from live fly tissues and freshly isolated primary cultures from cellularized embryos expressing GFP fused with MSL3, one of the components of the Drosophila dosage compensation complex (DCC), also called MSL complex (Male Specific Lethal) [16]. In live embryonic cultures or live 3rd instar larval tissues, specific MSL3-binding sites were detected through localization of MSL3-GFP, the feature characteristic of active genes on the male X chromosome.
We further explored the potential relationship between transcriptional activity and location of sequences within mitotic chromosomes by immunostaining for specific post-translationally modified histones [17],[18]. In a variety of organisms, both transcriptionally silent chromatin, characterized by relatively condensed DNA, and more decondensed transcriptionally active chromatin are marked by specific histone modifications [19]. Various histone marks may continuously stretch over regions of tens of kbp on the scale of gene clusters [20],[21] and remain stable over several cell cycles [22]–[24]. In Drosophila, methylation of histone H3 at lysine 4 is associated with actively transcribed sequences and found in interbands of polytene chromosomes. Monomethylation of lysine 27 in H3 (H3K27me1) is found at pericentric heterochromatin and in most euchromatic bands in polytene chromosomes [25]; monomethylated lysine 20 at H4 (H4K20me1) is known to associate with chromocenter heterochromatin and a high number of euchromatic bands [19].
Combining novel fluorescence microscopy techniques with improved spatial and temporal resolution [26] and labeling of specific chromatin loci on the genome scale, we were able to study distribution of native chromatin loci within intact mitotic chromosomes in cells isolated from Drosophila embryos. Our results reveal a higher than expected degree of organization, suggesting that the radial distribution of specific chromatin loci are non-uniform in fly mitotic chromosomes. Actively transcribed sequences were found to localize at the periphery of chromosomes during mitosis as labeled by specific histone modifications in fixed cells or by MSL3-GFP in vivo, while silent chromatin occupied more internal positions.
To visualize discreet, specific loci in live Drosophila cells, we took advantage of the observation that ∼80% of active X chromosome genes are clearly marked by DCC and only ∼1% of genes are free of DCC. DCC specifically binds a subset of genes on the euchromatic arm of the X chromosome in males and is necessary for about 2-fold up-regulation in expression levels through local modification of chromatin [27]. For live imaging of the DCC, a fly line was created carrying four copies of an MSL3-GFP fusion, marking specific MSL3-binding sites on the X chromosome with GFP, and two copies of a Drosophila H2A histone variant, His2AvD of the H2A.F/Z family, fused with mRFP [28] labeling total chromatin (Figure S1A). His2AvD is widely distributed in the genome. It is enriched in thousands of euchromatic bands and the heterochromatic chromocenter [29]. H2AvD, similar to H2A, associates less tightly with DNA in transcribed sequences. MSL3-GFP was fully functional as judged by transgenic rescue of msl3 mutant males. The transgene was expressed from the native msl3 promoter, and the presence of four copies did not cause ectopic staining as judged by the similarity to wild type MSL3 immunofluorescence staining patterns and the lack of cytoplasmic or nucleoplasmic background. To study the organization of the euchromatic arm of the X chromosome we focused on neuroblasts (NBs) [30]: diploid, dividing, and easily identifiable cells in dissected brains of 3rd instar larvae (Figure S1 B) and primary cultures isolated from 5–6-h-old embryos (Figure S1C). After isolation, primary cultures and tissues survived for up to 4 h of imaging without a change in medium, going through several divisions with normal cytokinesis and producing normal progeny. Embryonic cultures were isolated and deposited in a drop of Chan-Gehring medium on a cover slip glued to a microscope slide and sealed with enough air. The identity of NBs was confirmed by their size, 8–12 µm, the presence of smaller cells around them, and antibody staining against Dpn [31].
Fixed samples were imaged with wide-field deconvolution microscopy and the recently implemented structured illumination microscopy (SIM) technique. SIM has doubled resolution in X, Y, and Z, as compared to conventional wide-field microscopy. The excitation illumination dose was minimized through the use of optimized and dedicated emission filter sets: DAPI (460±25 nm), FITC (515±15 nm), and RHOD (590±15 nm). The 3D positions, orientations, and magnifications of signals imaged with FITC and RHOD emission channels varied systematically due to the differences in alignment of cameras and the chromatic aberration. In multi-color images, these channels were aligned using parameters calculated from multi-color micro-bead Z-stacks for wide-field microscopy or SIM (described in Supporting Methods in Text S1). The values of parameters were found as an optimization problem solution minimizing differences between the positions of the beads in different channels (Text S1 and Figure S2).
In live embryos, bright foci of MSL3-GFP binding scattered uniformly over the entire nucleus are first detected late in cell cycle 14, during interphase before the first asynchronous division in the cellularized embryo. Within a single cell cycle, scattered MSL3-marked foci relocate into a relatively compact nuclear sub-region, ∼10%–30% of the nucleus area in projection (Figure 1A). The signal retains its specificity and brightness during the entire cell cycle throughout development, making it possible to use embryonic and larval brain NBs for imaging of chromatin loci specific to male X chromosomes (Video S1).
To study localization of MSL3-GFP-marked specific sites on X chromosomes, live primary embryonic cultures isolated from the fly line expressing both MSL3-GFP and His2AvDmRFP1 were imaged with wide-field microscopy. Fast Z-stacks, 20–30 sections per second, were collected, followed by deconvolution with a measured point-spread function (PSF). In embryonic cultures, all interphase and late prophase through telophase cells showed peripheral localization of MSL3-GFP with respect to adjacent chromatin (Figure 1A–F). An example Z-stack of a fixed metaphase NB expressing MSL3-GFP and His2AvDmRFP1 is shown in Video S2. In early prophase, MSL3-GFP domains were often found sandwiched between bulk chromatin domains (Figures 1B and 2A) running the entire width of the chromosome. In live anaphase chromosomes of dissected 3rd instar larval brains imaged as Z-projections, MSL3-GFP was also peripheral in all observed cases. On all anaphase chromosomes, the MSL3-GFP pattern looked like two approximately parallel, ∼1–3 µm long, segments separated by ∼200–300 nm (Figure 1E). Figure 1F shows a cross-section of a live anaphase chromatid where the mRFP1-marked chromatin signal is clearly inside the peripheral GFP signal. The majority of foci of interphase MSL3-GFP, similarly to mitosis, localized to the periphery of chromatin domains marked by His2AvDmRFP1 with occasional partial overlap (Figure 1A). A similar organization of chromatin was observed in embryonic cultures fixed after isolation. This is very different from polytene chromosomes in which MSL3-GFP and His2AvDmRFP1 bands demonstrated, although not perfectly, a high degree of co-localization (1A). Immunostaining of fixed embryonic cultures for MSL2 (another DCC component) confirmed our finding that in X chromosomes, specific DCC-binding sites marked by MSL3-GFP target to the edges of compact chromatin domains in interphase or at the chromatid surface in mitosis (Figures S3 and S4 and Video S3).
In live or fixed interphase cells expressing MSL3-GFP and His2AvDmRFP1, the MSL3-GFP labeled X chromosome arm was a diffuse “cloud” of 20–30 foci of different intensity occupying 10%–30% of the nucleus area in projection, or 3–5 µm in linear dimensions. These foci were found at the periphery of condensed chromatin domains labeled with His2AvDmRFP1. In early prophase cells, the MSL3-GFP labeled condensing arm is a relatively compact structure, ∼4–6 µm long and ∼1 µm thick (Figure 2A). Interestingly, the MSL3-GFP signal was found not at the periphery of early prophase chromosomes but across their entire width between the bulk condensed chromatin regions (arrowheads in Figure 2A). The early prophase MSL3-GFP signal was complementary to the bulk chromatin domains marked by His2AvDmRFP1 but not peripheral. Transition from the internal to peripheral localization of MSL3-GFP occurred between early and late prophase (Figure 2A and B) before segregation of sister chromatids became apparent. After segregation at metaphase, MSL3-GFP stayed peripheral on both chromatids (Figure 2C). MSL3-GFP is found inside condensing early prophase chromosomes; however, starting from late prophase, MSL3-GFP signal is found only at the periphery of mitotic chromosomes in live or fixed cells.
DCC localization to the periphery of condensed chromosomes suggests that mitotic chromosome organization is correlated with its function. However, an alternative explanation could be that MSL complex reorganizes during mitosis to be released from condensed regions, and then re-binds after decondensation. Despite the small size of MSL3-GFP (∼100 kDa), the fully assembled MSL complex is thought to be at least 1 MDa. If the accessibility of chromatin targets inside condensed mitotic chromatin is limited, DCC complexes displaced during mitotic condensation would need to re-assemble at their proper targets during decondensation. We found no evidence for significant or noticeable redistribution or loss of MSL3-GFP during mitotic condensation in agreement with the extremely stable association of MSL2 with its targets both during interphase and mitosis [32]. The dynamics of the MSL3-GFP-labeled chromatin regions could be followed during mitotic condensation. In our Video S1, we show an example of progression from smaller faint speckles scattered over a large area to bright and compact foci on mitotic chromosomes through condensation and fusion. To further analyze possible redistribution of MSL complexes over the cell cycle, anaphase cells of embryonic cultures or larval brains were imaged for extended periods of time after cytokinesis to allow decondensation of chromatin and cell cycle-related redistribution of nuclear proteins. Dividing cells of embryonic cultures (Figure 3A) or in dissected 3rd instar larval brains (Figure 3B) were imaged with fast Z-stacks or Z-projections. During post-mitotic decondensation, MSL3-GFP-marked regions peripheral in anaphase moved outwards during decondensation, expanding 2–3-fold in the area over a period of 10–20 min, shown in time-series in Figure 3A and B and Video S4. The unlabeled regions of chromosomes, internal during anaphase, remain unlabeled for up to 30 min or more than a half cell cycle duration, when accessibility of the euchromatic X chromosome arm should no longer be an issue. For embryonic cultures expressing MSL3-GFP and His2AvDmRFP1, it takes 4 to 6 min for a dividing cultured NB to proceed from mid-anaphase to a state in G1 with a round nucleus, fully decondensed chromatin, and reformed nucleoli as judged by His2AvDmRFP1 staining. The increase in the area of the MSL3-GFP-marked region occurred exclusively through expansion of chromosome arm, not redistribution or de novo binding of MSL3-GFP. We conclude that the regions devoid of MSL3-GFP signal in mitotic chromosomes do not appear to be targets of MSL complex upon decondensation.
Another line of evidence for limited re-distribution comes from the low, little changing cytoplasmic MSL3-GFP background over the entire cell cycle. The background intensity of MSL3-GFP in mitotic cytoplasm is comparable to the uniform background intensity outside the cells created by out-of-focus and scattered light. For a metaphase cell, the outside background was 15–35 units (mean 24), the cytoplasm was 25–55 (mean 38), compared to the labeled X chromosome arm—125–525 (mean 305). In interphase, the nuclear and cytoplasmic background intensity of MSL3-GFP is comparable or higher than in mitosis. This suggests that in mitosis the majority of MSL3-GFP is divided between daughter cells by traveling on chromosomes and that there is minimal loss of MSL3-GFP from chromatin targets during mitotic condensation.
The MSL3-GFP labeled loci often surround an area of no or low GFP signal in live embryos of dissected tissues (Figure 3C and D and Figure S1C and B, respectively), despite their extremely dynamic behavior in interphase. In the majority of interphase nuclei, MSL3-GFP labeled regions demonstrate apparent reduction of GFP signal intensity in the middle of the labeled arm as shown in Figure 3C and D. These observations are consistent with the previous FISH studies of fixed tissue culture cells [33],[34].
The distribution of MSL3-GFP during mitosis or interphase is clearly distinct from the binding patterns of perichromosomal layer proteins, such as Ki-67 and nucleophosmin, which are associated with the outer surface of chromosomes in mitosis and populate the vicinity of chromosomes in the form of granules and fibrils [35]. MSL3-GFP and components of the DCC remain bound exclusively to chromatin locations of the euchromatic X chromosome arms at all stages of the cell cycle. In contrast, the proteins of the perichromosomal layer cover the periphery of all mitotic chromosomes over their entire length, except centromeres, from prophase to telophase. In interphase, the proteins of the perichromosomal layer are found in nucleoplasm and cytoplasm with preferential accumulation at nucleoli.
Dosage compensated genes represent a large subset of active male X chromosome genes with the transcription rate up-regulated about 2-fold as compared to non-compensated genes [27],[36]. Drosophila chromatin regions of different transcription states are known to be marked with specific histone modifications. Patterns of covalent core histone modifications are established in Drosophila embryos by cell cycle 15. We chose di- and trimethylation of lysine 4 on histone H3 (H3K4me2,3) as a marker for active euchromatin, and H3K27me1 and H4K20me1 as markers for silent, non-coding regions in euchromatic chromosomal arms for immunofluorescence. Double-antibody staining served two purposes: (a) to study the distribution of actively transcribed sequences over the entire X chromosome and autosomes, and (b) to study the distribution of histone modifications specific to different transcription states. In mitotic chromosomes, anti-H3K27me1 and anti-H4K20me1 antibodies were found to stain all chromosomes uniformly along their lengths, on both euchromatic and heterochromatic arms. In contrast, anti-H3K4me2,3 was limited to euchromatic arms of mitotic chromosomes, an indication of the specificity of the antibodies. We found that immunofluorescence signals from anti-H3K27me1 and anti-H4K20me1 were consistently found at more internal locations on chromosomes compared to the anti-H3K4me2,3, as summarized in Figure 4A–C for mitotic cells. Line profiles were used for better visualization of non-uniform distribution of different antibodies (Figure 4D). Anti-H3K4me2,3, as well as anti-MSL2, antibodies always stained the periphery of chromosomes, with a significant fraction of the signal found outside the visible DAPI signal. This is different from anti-H3K27me1 and anti-H4K20me1 signals, which were found mostly inside chromosomes with no or little signal extending into DAPI-free regions. In multiple examples, anti-H4K20me1 staining coincides with the DAPI signal uniformly staining chromosomes (Figure 4C). Anti-H3K27me1 signal stains, though not uniformly, the entire width of chromosomes or often with visible reduction of signal at chromosomal cores (Figure 4A and B). A more internal and uniform signal of anti-H4K20me1 antibody compared to anti-H3K27me1 is consistent with localization of H4K20me1 to relatively more condensed regions of genome (chromocenter and few euchromatic bands in polytene chromosomes). To support these observations, the widths and distributions of fluorescent labels after immunofluorescence staining with different antibodies or in live cells were measured (Table 1). The widths of chromosomes, immunostaining, and MSL3-GFP signals were measured as FWHM in averaged profiles. It is seen that anti-H3K4me2,3 and live MSL3-GFP signals localize to the periphery of mitotic chromosomes and are depleted at the cores of mitotic chromosomes relatively to the anti-H3K27me1 and anti-H4K20me1 signals, which show less depletion at the core (Figure S5). This is additional evidence that active coding sequences target to the periphery of chromosomes in mitosis. Hypothesis testing of the null hypothesis of equal means was done for each pair of data sets for different labeling methods and summarized in Table S1. In support of our observations and measurements, the null hypotheses of equal means were rejected for the following pairs: H3K4me2,3 and H4K20me1; H3K4me2,3 and H3K27me1; H4K20me1 and MSL3-GFP; MSL3-GFP and H3K27me1. Rejection of the null hypothesis of equal means is not supported for the following pairs: H3K4me2,3 and MSL3-GFP; H3K27me1 and H4K20me1. This is in agreement with similarity in the distributions of the signals and their widths. All primary antibodies used for immunofluorescence were of the monomeric IgG type, and secondary antibodies were F(ab')2 fragments labeled with a fluorophore.
In fixed interphase cells, actively transcribed sequences labeled with anti-MSL2 or anti-H3K4me2,3 (pseudo-colored magenta) are found outside chromatin regions labeled with DAPI and were complementary to them (Figure 5A and B, respectively). In contrast, anti-H3K27me1- and anti-H4K20me1-labeled silent chromatin was a subset of DAPI stained chromatin largely overlapping with it (Figure 5C and D, respectively).
To investigate the accessibility of mitotic chromatin to antibodies, we stained fixed mitotic cells with antibodies to Barren and to CID-GFP, both known to be buried inside mitotic chromosomes. Barren, a member of the kleisin family and an ortholog of human CAP-H, binds to the head domains of the SMC heterodimer in a complex with two other non-SMC subunits [37]. As a part of the condensin complex, Barren accumulates at the core regions of mitotic chromosomes starting in prophase through metaphase. Anti-barren [38] immunofluorescence signal was found at the central core regions of the DAPI stained chromosomes imaged with wide-field or SIM (Figure S6A and B, respectively), similar to live distributions (Y. Strukov, unpublished results). CID, a Drosophila ortholog of human CENP-A, is an H3-like protein that replaces canonical H3 in all eukaryotic centromeres [39]. It was proposed that CID is the epigenetic mark and a foundation for fly centromeres and localizes beneath the kinetochore with some overlap with the inner kinetochore [40]. Anti-GFP immunofluorescence of CID-GFP embryonic cultures gave a uniform labeling over all centromeres in mitotic cells (the same as in live cells), demonstrating accessibility of chromatin buried by the kinetochore structures to antibodies under our fixation conditions (Figure S6C and D). Centromeres of CID-GFP expressing cells had the same size and shapes irrespective of the imaging modality: live imaged with wide-field deconvolution, antibody-stained imaged with wide-field deconvolution, or antibody-stained imaged with SIM (Figure S6E), demonstrated by intensity profiles (Figure S6F).
A 5.5-kb BamHI genomic fragment containing the promoter and open reading frame of the msl3 gene [41] was subcloned from cosmid msl3-5-1 (AE003560.1 position 60298–92793) into the pBluescript II SK(−) vector [12]. A blunted NcoI/NotI fragment containing eGFP from the pEGFP-N1 vector (Clontech) was subcloned in-frame into the CelII blunted pBS-msl3 construct. The resulting MSL3-GFP BamHI fragment was subcloned into pCaSpeR3 to make the final MSL3-GFP–pCaSpeR3 construct. Several independent transgenic lines were produced by P-element-mediated transformation [42]. Two independent lines, one with the transgene on the 2nd and one with it on the 3rd chromosome, were crossed to generate a stock with homozygous MSL3-GFP on both chromosomes (4 copies). Fly line msl3-gfp, His2AvDmRFP1; msl3-gfp was created by recombination of msl3-gfp; msl3-gfp with a His2AvDmRFP1 transgenic line [28] for dual-color live experiments; CID-GFP line was generated in the lab of S. Henikoff (FHCRC).
Embryonic cultures were isolated according to a previously published protocol [30]. The fixation and immunostaining protocols were adapted from [43]. Antibodies were from Abcam (Cambridge, MA): rabbit polyclonal anti-H4K20me1 (ab9048), and anti-GFP (ab290), mouse monoclonal anti-H3K4me2,3 (ab6000), and Upstate Scientific: rabbit anti-H3K27me1 (07-448), all monomeric IgG. Rabbit anti-MSL2 antibody was generated in the Kuroda laboratory [44]. Goat anti-mouse or anti-rabbit secondary antibodies were from Molecular Probes: A-11017, A-11018, A-11070, A-11071.
Live, wide-field optical sectioning and SIM were done on a custom-made inverted microscope [26] supplied with a set of excitation lasers and cooled back-thinned CCD cameras (Andor). Deconvolution and SIM reconstruction were done using a measured PSF [45]. Image processing and computations were done using Priism, Python (align.py and simplex.py), and Octave scripts available upon request (contact YGS: [email protected]).
Our investigation has provided insights into mitotic chromatin organization and its connection to chromatin function. We observed several novel features of mitotic chromosomes in Drosophila cells. First, MSL3-binding sites, specific to the X chromosome, target to the periphery of mitotic chromosomes. Second, spatial distribution of chromatin loci within mitotic chromosomes was correlated with their functional properties judged by the core histone modifications. Third, during late prophase-to-anaphase condensation of chromatids, active sequences remain peripheral, suggesting rearrangement of chromatin within prophase chromatids and arguing against simple coiling of prophase chromatids during condensation. Although several investigations have been undertaken in the past, to our knowledge, ours is the first when native chromosomes in live cells have been studied for localization of specific DNA sequences at high resolution.
All imaging systems with multi-color capability are known to suffer from chromatic aberrations and offsets in translation, rotation, and magnifications between color channels. For interpretation of multi-color data sets, correct superimposition of different color channels was therefore critical. To exclude the influence of the chromatic aberration of the objective lens, variations in the CCD camera specifications, and differences in optics between different emission channels, control multi-color data sets were collected with multi-color micro-beads, separately for wide-field or SIM and compensated to a sub-pixel accuracy using custom software (Supporting Methods in Text S1 and Figure S2). Based on these measurements, we could conclude that peripheral localization of specific sequences marked by DCC or core histone modifications was not due to chromatic aberration or differences in the optics of individual channels.
MSL complex specifically marking dosage compensated genes in Drosophila provides a means for visualization of a functionally distinct fraction of the genome. The 22 Mbp X chromosome arm, predicted to contain about a thousand active genes, is ∼5% of the total DNA content of a diploid male Drosophila cell. There is evidence that ∼80% of active genes on the X chromosome are bound by DCC and less than 1% are clearly free of it, the rest of the genes are bound by intermediate amounts of MSL complex [46]. MSL3 binds preferentially to 3′ ends of transcribed regions of most active genes. In larval polytene chromosomes, MSL complex binds to gene-rich interbands, complementary to DAPI-stained bands, and co-localizes with H4 acetylated at lysine 16, a specific mark for open, transcriptionally active chromatin in general, both in vivo and in vitro [47]. Little is known about MSL binding dynamics, however it is possible that MSL localization is established at most active gene clusters early in development and maintained in a relatively static pattern throughout development [47]. However, blocking transcription can inhibit MSL complex binding at a transgenic target, suggesting that the MSL pattern is not completely static [48]. There is a possibility that MSL complex changes its localization in condensing chromosomes, but the fact that it looks similar to H3K4me2,3 (and different from H4K20me1) is strong evidence that it has stayed with the active regions. Our results support the possibility that little or no change in MSL targeting occurs during mitosis. We have shown that there is no appreciable dissociation and relocation of MSL3-GFP from chromosome targets to other cellular compartments at the beginning or during mitosis by measuring the cytoplasmic and nucleoplasmic backgrounds of MSL3-GFP. The entire population of MSL3-GFP remains bound to the X chromosome. No significant new binding of MSL3-GFP to the chromosome targets was observed during post-mitotic decondensation (Figure 3A and B). This is consistent with a number of earlier findings: MSL complex does not bind at significant levels to non-coding sequences and more than 90% of MSL complexes are found within genes [13],[14]; there has been found little [12],[49] or no [50] variation in the binding levels at specific loci during development. Redistribution of MSL3-GFP within the X chromosome by fast dissociation from the interphase set of targets to a new non-overlapping mitotic set would be hard to imagine for several reasons. First, most genes on the X chromosome in interphase are bound by MSL complexes [16] and there is no binding of MSL complex to non-coding DNA at the levels sufficient to accommodate the entire pool of DCC. This suggests a great deal of overlap between interphase and mitotic MSL complex binding sites. Second, available data on the dynamics of MSL binding to its targets show that the time-scale for MSL targeting is on the order of hours [50], which is not consistent with very fast cell mitoses and cycles of chromatin condensation-decondensation in developing Drosophila tissues. Testing mitotic distributions of MSL3 with ChIP at high resolution is currently unfeasible due to the technically challenging procedure of isolating sufficient quantities of purified mitotic cells from dissected tissues and embryonic cultures from Drosophila. At the resolution of fluorescence microscopy analysis we have demonstrated that binding of MSL3-GFP is not cell-cycle stage specific. We chose His2AvDmRFP1 as a contrasting fluorescent marker because it is found in the heterochromatic chromocenter, on transcribed and non-transcribed genes and in non-coding euchromatin [29].
The current concepts of nuclear organization suggest a relationship between the activity of chromatin loci and their positions within nuclei or interphase chromosomes [51]. However, whether this principle can be extended to also include live and fixed mitotic chromosomes has not been investigated. The question of whether antibodies faithfully represent the imaged features is always important. There are two aspects to this problem: first, uniform accessibility of chromosomal epitopes; and second, the finite size of the antibody complex with a potential to change the size and shape of sampled features. We argue that with our sample preparation and staining techniques mitotic chromatin was available for antibodies. Anti-barren staining produced a continuous axial pattern indicating that internal regions of chromosomes were uniformly accessible. Anti-GFP antibody staining of embryonic cultures isolated from CID-GFP expressing fly embryos gave the patterns of centromere labeling with similar shapes and sizes as in live cells. Consistent with our observations are previous studies of centromere and kinetochore organization where both structures were shown to be accessible after formaldehyde fixation to various antibodies raised against different centromeric or kinetochore proteins [52],[53]. It has been demonstrated that in live cells, proteins with molecular dimensions in the size range of components of the transcription machinery (several hundred kDa) can diffuse freely inside condensed chromatin domains [54]. The mass of individual IgG molecules is ∼150 kDa, and the size of the primary and secondary fluorophore-labeled antibody complex has been reported to be about 20 nm [55], which makes it small enough to faithfully reflect the features of imaged objects at a resolution of about 200 nm used for immunofluorescence experiments.
Using both immunofluorescence of fixed cells and live observations we showed that MSL3-GFP stays peripheral from late prophase to telophase in the same cell arguing against hierarchical coiling condensation [1]. If consecutive coiling is involved, the large-scale fibers have to have persistence length on the order of their thickness, and simultaneously, intra-fiber rearrangements have to be involved to keep the genes at the periphery of the X chromosome as mitotic condensation progresses. Our working model is shown in Figure 6: patches of chromatin carrying active genes, spanning the entire width of chromosomes at early prophase, become peripheral from late prophase through telophase as a result of large-scale rearrangements within condensing chromosomes.
An interesting explanation for why active DNA sequences are found at the periphery of mitotic chromosomes comes from biochemical studies of the “bookmarking” mechanism that helps cells remember which genes were active before mitosis. It was reported that active promoters/genes remain bound by a transcription factor TFIID in mitosis [56] and may escape the condensin complexes action through recruitment of the TBP-PP2A mitotic complex [57]. Transcribed sequences show more mitotic TBP binding than silent DNA. TBP interacts with the condensin I subunit CAP-G and condensin inhibitor phosphatase PP2A during mitosis at many chromosomal sites active before mitosis.
Our findings are consistent with the results of a number of earlier studies concentrated on the localization of specific DNA sequences or sequences of specific properties on mitotic chromosomes. However, our conclusions are based on observations of native chromatin loci in the context of unperturbed chromosomes. Specifically stained AT-rich DNA sequences in Munjac chromosomes formed a full-diameter coil at gene-poor regions and uncoiled in gene-rich regions staying at the core [7]. In accordance with the radially non-uniform organization, the AT-rich sequences were at the core regions of the gene-rich bands, while the rest of DNA in the bands was more peripheral. Radially different and reproducible positions of specific sequences after FISH of salt-extracted isolated chromosomes was observed in agreement with the radial-loop model, with no indication, however, of their positions in native chromosomes [6]. Preferentially external lateral positions of specific sequences on mitotic chromosomes in mitotic spreads have also been reported after FISH [58]. However, the conclusions were not as convincing due to limited resolution in the images and FISH procedure-induced disturbance of native morphology. The degree of reproducibility of radial positions of stably transfected and gene amplified lac op repeats varied in different tissue culture cell lines, probably due to position effects [4],[59]. Lac op repeats could be found either at the core regions of chromosomes or throughout the width of chromosomes.
Reproducibility of positioning of specific sequences might be related to functional contributions of diverse classes of loci to the structure. Actively transcribed sequences may be spared a structural function or cannot be involved because of their specific protein composition or kinetic restrictions due to delay in condensation compared to silent or non-coding DNA. Alternatively, there could be a difference in degree of condensation or in its temporal sequence between peripheral and more central regions of mitotic chromosomes. Together, our results suggest novel structural features of mitotic chromosomes that can contribute to the understanding of mitotic condensation, with important implications for understanding the connection between chromatin organization and its epigenetic regulation.
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10.1371/journal.pgen.1005681 | Convergent Evolution of Hemoglobin Function in High-Altitude Andean Waterfowl Involves Limited Parallelism at the Molecular Sequence Level | A fundamental question in evolutionary genetics concerns the extent to which adaptive phenotypic convergence is attributable to convergent or parallel changes at the molecular sequence level. Here we report a comparative analysis of hemoglobin (Hb) function in eight phylogenetically replicated pairs of high- and low-altitude waterfowl taxa to test for convergence in the oxygenation properties of Hb, and to assess the extent to which convergence in biochemical phenotype is attributable to repeated amino acid replacements. Functional experiments on native Hb variants and protein engineering experiments based on site-directed mutagenesis revealed the phenotypic effects of specific amino acid replacements that were responsible for convergent increases in Hb-O2 affinity in multiple high-altitude taxa. In six of the eight taxon pairs, high-altitude taxa evolved derived increases in Hb-O2 affinity that were caused by a combination of unique replacements, parallel replacements (involving identical-by-state variants with independent mutational origins in different lineages), and collateral replacements (involving shared, identical-by-descent variants derived via introgressive hybridization). In genome scans of nucleotide differentiation involving high- and low-altitude populations of three separate species, function-altering amino acid polymorphisms in the globin genes emerged as highly significant outliers, providing independent evidence for adaptive divergence in Hb function. The experimental results demonstrate that convergent changes in protein function can occur through multiple historical paths, and can involve multiple possible mutations. Most cases of convergence in Hb function did not involve parallel substitutions and most parallel substitutions did not affect Hb-O2 affinity, indicating that the repeatability of phenotypic evolution does not require parallelism at the molecular level.
| The convergent evolution of similar traits in different species could be due to repeated changes at the genetic level or different changes that produce the same phenotypic effect. To investigate the extent to which convergence in phenotype is caused by repeated mutations, we investigated the molecular basis of convergent changes in the oxygenation properties of hemoglobin (Hb) in eight pairs of high- and low-altitude waterfowl taxa from the Andes. The results revealed that convergent increases in Hb-O2 affinity in highland taxa involved a combination of unique and repeated amino acid replacements. However, convergent changes in Hb function generally did not involve parallel substitutions, indicating that repeatability in the evolution of protein function does not require repeatability at the sequence level.
| When multiple species evolve similar changes in phenotype in response to a shared environmental challenge, it suggests that the convergently evolved character state is adaptive under the changed conditions and that it evolved under the influence of directional selection. A key question in evolutionary genetics concerns the extent to which such cases of phenotypic convergence are caused by convergent or parallel substitutions in the underlying genes. This question has important implications for understanding the inherent repeatability of evolution at the molecular level [1–9].
In principle, the convergent evolution of a given phenotype may be attributable to (i) unique substitutions, (ii) parallel substitutions (where identical-by-state alleles with independent mutational origins fix independently in different lineages), or (iii) collateral substitutions (where shared, identical-by-descent alleles fix independently in different lineages)[8]. In the last case, allele-sharing between species may be due to the retention of ancestral polymorphism or a history of introgressive hybridization—either way, the function-altering alleles that contribute to phenotypic convergence do not have independent mutational origins.
One especially powerful means of assessing the pervasiveness of repeated evolution at the sequence level is to exploit natural experiments where phylogenetically replicated changes in protein function have evolved in multiple taxa as an adaptive response to a shared environmental challenge. For example, there are good reasons to expect that vertebrate species living at very high altitudes will have convergently evolved hemoglobins (Hbs) with increased O2-binding affinities [10,11]. Under severe hypoxia, an increased blood-O2 affinity can help ensure tissue O2 supply by safeguarding arterial O2 saturation while simultaneously maintaining the pressure gradient that drives O2 diffusion from the peripheral capillaries to the cells of respiring tissues [12–18]. Evolutionary adjustments in blood-O2 affinity often stem directly from structural changes in the tetrameric (α2β2) Hb protein. Genetically based changes in the oxygenation properties of Hb can be brought about by amino acid mutations that increase intrinsic Hb-O2 affinity and/or mutations that suppress the sensitivity of Hb to the inhibitory effects of allosteric co-factors in the red blood cell [19–22] (S1 Fig).
Derived increases in Hb-O2 affinity have been documented in some high-altitude birds and mammals [23–30], but other comparative studies have not revealed consistent trends [31–34]. Additional comparisons between conspecific populations and closely related species are needed to assess the validity of empirical generalizations about the relationship between Hb-O2 affinity and native elevation in vertebrates.
Previous surveys of sequence variation in the globin genes of Andean waterfowl documented repeated amino acid substitutions in the major Hb isoforms of multiple high-altitude taxa [35,36], but the functional effects of the substitutions were not assessed so it was not known whether the repeated changes contributed to convergent changes in the oxygenation properties of Hb. Here we report a comparative analysis of Hb function in eight phylogenetically replicated pairs of high- and low-altitude waterfowl taxa to test for convergent changes in biochemical phenotype, and to assess the extent to which convergent changes in phenotype are attributable to repeated amino acid substitutions. We measured the functional properties of native Hb variants in each population and species, and we used protein engineering experiments based on site-directed mutagenesis to measure the functional effects of repeated substitutions that were implicated in convergent increases in Hb-O2 affinity in high-altitude taxa. In six of the eight taxon pairs, the high-altitude taxa evolved derived increases in Hb-O2 affinity that were caused by a combination of unique, parallel, and collateral amino acid replacements. In comparisons involving high- and low-altitude populations of three different species, function-altering amino acid polymorphisms emerged as highly significant outliers in genome scans of nucleotide differentiation, with derived, affinity-enhancing mutations present at high frequency in the high-altitude populations. In combination with results of the functional experiments, the population genomic analyses provide an independent line of evidence that the observed changes in Hb function are attributable to positive directional selection.
We examined differences in the structural and functional properties of the two adult-expressed Hb isoforms (HbA and HbD) from eight pairs of high-and low-altitude sister taxa. Two of the taxon pairs include sister species with contrasting elevational ranges: Andean goose (Chloephaga melanoptera)/Orinoco goose (Neochen jubata) and Puna teal (Anas puna)/silver teal (A. versicolor). The remaining six taxon pairs include high- and low-altitude populations of the same species: ruddy ducks (Oxyura jamaicensis), torrent ducks (Merganetta armata), crested ducks (Lophonetta specularioides), cinnamon teal (Anas cyanoptera), yellow-billed pintails (Anas georgica), and speckled teal (Anas flavirostris). In addition to the eight high- and low-altitude taxon pairs from the Andes, we also examined Hb function in a pair of high- and low-altitude sister species from Africa: the Abyssinian blue-winged goose (Cyanochen cyanoptera), a high-altitude species endemic to the Ethiopian Plateau, and Hartlaub’s duck (Pteronetta hartlaubi), a strictly lowland species [37]. We included these species in the analysis because their Hbs are distinguished by two amino acid replacements that are shared with multiple Andean taxa [35], so experimental tests of Hb function provide an additional opportunity to measure the functional effects of repeated substitutions.
To characterize the red cell Hb isoform composition of each species, we analyzed blood samples from individual specimens using a combination of isoelectric focusing (IEF) and tandem mass spectrometry (MS/MS). Consistent with data from other anseriform birds [38,39], the waterfowl species that we examined expressed two distinct isoforms, HbA (pI = 8.0–8.2) and HbD (pI = 7.0–7.2) with the major HbA isoform comprising ~70–80% of total Hb (S1 Table). The major HbA isoform incorporates α-chain products of the αA-globin gene and the minor HbD isoform incorporates products of the tandemly linked αD-globin gene; both isoforms incorporate β-chain products of the same βA-globin gene [38,39]. Since avian HbD has a consistently higher O2-affinity than HbA in all avian taxa examined to date [28,30,32,39], upregulating HbD expression could be expected to provide an efficient means of increasing blood-O2 affinity in response to environmental hypoxia. However, it appears that high-altitude Andean waterfowl do not avail themselves of this option, as we observed no difference in relative isoform abundance between pairs of high- and low-altitude sister taxa (Wilcoxon signed-rank test, W = 12, N = 7 pairwise comparisons, P>0.05; S1 Table). MS/MS analysis confirmed that subunits of the two adult Hb isoforms represent products of the adult-expressed αA-, αD-, and βA-globin genes; products of the embryonic α- and β-type globin genes were not detected.
By combining αD-globin sequences with previously published αA- and βA-globin sequences for the same individual specimens, we identified all amino acid differences that distinguish the HbA and HbD isoforms of each pair of high- and low-altitude taxa (Fig 1). Full alignments of αA-, αD-, and βA-globin amino acid sequences are shown in S2 Fig, and the direction of changes in character state at all substituted sites are shown in S3–S5 Figs. Comparisons of the South American species revealed repeated amino acid replacements at five sites that distinguish the HbA isoforms of high- and low-altitude sister taxa, including repeated replacements at one site in the αA-globin gene (α77Ala→Thr in Andean goose, torrent duck, Puna teal, and speckled teal) and four sites in the βA-globin gene (β13Gly→Ser in ruddy ducks and speckled teal, β94Asp→Glu in crested duck and Puna teal, and both β116Ala→Ser and β133Leu→Met in yellow-billed pintail and speckled teal)(Fig 1).
The derived pair of βA-globin amino acid variants ‘116Ser-133Met’ that are shared between sympatric high-altitude populations of yellow-billed pintails and speckled teal are clearly identical-by-descent (S6 Fig). Independent evidence for hybridization between the two species [40,41] suggests that the ‘116Ser-133Met’ βA-globin allele in high-altitude yellow-billed pintails was derived via introgression from high-altitude speckled teals. The same is true for a shared β13(Gly/Ser) polymorphism, although the derived Ser variant is present at low-frequency in yellow-billed pintails. The repeated amino acid changes at βA-globin sites 13, 116 and 133 therefore represent collateral replacements, rather than true parallel replacements, as they do not have independent mutational origins in each species.
Three of the eight pairs of high- and low-altitude taxa had structurally distinct HbD isoforms due to 1–2 amino acid substitutions in the αD-globin gene (Fig 1). Repeated substitutions at αD96 occurred in Orinoco goose (Val→Ala) and silver teal (Ala→Val), but the direction of the change in character-state was different in each case (S5 Fig). In both interspecific comparisons (Andean goose vs. Orinoco goose, and Puna teal vs. silver teal), αD96Ala is associated with a higher HbD O2-affinity. However, the individual effects of amino acid replacements at αD96 could not be isolated in either comparison because of potentially confounding replacements in the β-chain (β86Ala→Ser in Andean goose, and β94Asp→Glu in Puna teal)(Fig 1).
We measured the O2-binding properties of purified HbA and HbD variants from each taxon and we estimated P50 (the PO2 at which Hb is half-saturated with O2) as an index of Hb-O2 affinity. We focus primarily on measures of Hb-O2 affinity in the presence of Cl- ions and IHP (P50(KCl+IHP)), as this is the experimental treatment that is most relevant to in vivo conditions in avian red blood cells. The experiments revealed that O2-affinities of HbD were consistently higher (P50 values were lower; S2 Table) than those of HbA, consistent with data from other birds [28,30,32,39].
Comparisons between high- and low-altitude sister taxa revealed appreciable differences in the O2-affinity of the major HbA isoform in six of eight cases, and in each of these six cases the HbA of the high-altitude taxon exhibited the higher O2-affinity (i.e., lower P50)(Fig 2A; S2 Table). The only two taxon pairs that did not exhibit appreciable differences in Hb-O2 affinity were those involving conspecific populations of ruddy ducks and torrent ducks (Fig 2A; S2 Table). In contrast to the altitudinal trend for HbA, O2-affinities of the minor HbD isoform were not consistently higher in high-altitude taxa (Fig 2B). However, there were three taxon pairs in which O2-affinities of HbA and HbD were both markedly higher in the high-altitude taxa than in the corresponding low-altitude taxa (crested duck, Puna teal, and speckled teal), a pattern that implicates causative mutations in the β-chain subunit, which is shared by both isoforms.
Comparisons involving purified Hb variants from birds with known genotypes provide a means of identifying the specific amino acid mutations that are responsible for evolved changes in Hb-O2 affinity. Below we describe the functional effects of unique and repeated replacements, and we report model-based inferences about the structural mechanisms responsible for the observed changes in Hb-O2 affinity.
The fact that high-altitude taxa exhibited higher Hb-O2 affinities than their lowland sister taxa in six of eight pairwise comparisons is an intriguing trend and is suggestive of adaptive convergence, but the overall pattern does not permit conclusive inferences about the adaptive significance of observed changes in Hb function in any particular high-altitude population or species. In principle, genome-wide analyses of nucleotide differentiation between individual pairs of high- and low-altitude populations can provide an independent means of assessing whether altitudinal differences in globin allele frequencies may be attributable to a history of spatially varying selection. Accordingly, we used restriction-site associated DNA sequencing (RAD-Seq) to survey genome-wide patterns of nucleotide differentiation between high- and low-altitude populations of three separate species: cinnamon teal, yellow-billed pintail, and speckled teal. In each of these three pairwise population comparisons, function-altering amino acid polymorphisms in the αA- and/or βA-globin genes emerged as highly significant outliers in the genome-wide distribution of site-specific FST values (Fig 7). Indirect inferences about the adaptive significance of these polymorphisms are corroborated by results of the functional experiments, which demonstrated that the derived variants at these sites contributed to increases in Hb-O2 affinity in high-altitude populations of all three species (αA9 in cinnamon teal and the two-site ‘116–133’ βA-globin haplotypes shared by yellow-billed pintail and speckled teal). Since the βA-globin allele of high-altitude yellow-billed pintail was derived via introgressive hybridization with high-altitude speckled teal, the combined results of our functional experiments and population genomic analyses provide strong evidence for positive selection on introgressed allelic variants. This finding contributes to a growing body of evidence that introgressive hybridization can provide an important source of adaptive genetic variation in animal populations [58–60].
Convergent increases in Hb-O2 affinity in high-altitude waterfowl taxa were caused by a combination of unique amino acid replacements (as in the case of cinnamon teal, where the causative mutation was not shared with other highland taxa), parallel replacements (as in the case of high-altitude crested ducks and Puna teal that shared independently derived β94Asp→Glu mutations), and collateral replacements (as in the case of yellow-billed pintail and speckled teal that shared identical-by-descent β-globin alleles due to a history of introgressive hybridization). Andean goose appears to represent another case where the evolution of a derived increase in Hb-O2 affinity is attributable to one or more unique substitutions, although additional experiments will be required to pinpoint the causative change(s). These results demonstrate that convergent changes in protein function can occur through multiple historical paths involving multiple possible mutations. Among the Andean waterfowl taxa that we examined, we identified only a single case where a convergent increase in Hb-O2 affinity was attributable to a true parallel amino acid substitution (β94Asp→Glu in high-altitude crested ducks and Puna teal).
The limited number of function-altering parallel substitutions in the Hbs of Andean waterfowl stands in contrast to patterns of functional evolution in vertebrate opsin proteins, where convergent changes in the wavelengths of maximum absorbance (spectral tuning) are very often attributable to parallel amino acid substitutions [61,62]. In vertebrate opsins, the more pervasive patterns of parallelism may reflect the fact that genetically based changes in spectral tuning can only be achieved via specific mutational replacements at a limited number of key residues in the active site [63]. Our findings are more consistent with results of experimental evolution studies in microbes and yeast where replicated changes in fitness involved little to no parallelism at the underlying sequence level [64,65].
Our comparative survey also identified numerous parallel substitutions that had no effect on the inherent oxygenation properties of Hb, although we cannot rule out the possibility that the derived variants contributed to changes in other structural or functional properties. Our results for waterfowl Hbs provide two important lessons about repeated evolution at the molecular level: (i) most cases of convergence in protein function did not involve true parallel substitutions (indicating that similar phenotypic outcomes can be produced by multiple possible mutations), and (ii) most parallel substitutions produced no change in Hb-O2 affinity (convergent or otherwise). These findings demonstrate that parallel substitutions cannot be interpreted as prima facie evidence for adaptive evolution [66,67], and that the functional significance (and, hence, adaptive significance) of specific substitutions needs to be experimentally tested in order to support conclusions about the molecular basis of phenotypic evolution.
Blood and tissue samples were obtained from Andean waterfowl at high- and low-altitude localities as described previously [35]. Samples from Orinoco geese, Abyssinian blue-winged geese, and Hartlaub’s ducks were obtained from Sylvan Heights Waterfowl Park (Scotland Neck, North Carolina). Animals were handled in accordance with protocols approved by the Institutional Animal Care and Use Committee of the University of Alaska (certification numbers 02-01-152985 and 05-05-152985).
We characterized Hb isoform composition in the mature erythrocytes of 106 wild-caught birds (median sample size = 14 individuals per species) (S1 Table). Native Hb components were separated by means of IEF using precast Phast gels (pH 3–9) (GE Healthcare; 17-0543-01). IEF gel bands were excised and digested with trypsin, and MS/MS was used to identify the resultant peptides, as described previously [26,28,32,68]. Database searches of the resultant MS/MS spectra were performed using Mascot (Matrix Science, v1.9.0, London, UK); peptide mass fingerprints were queried against a custom database of avian globin sequences, including the full complement of embryonic and adult α- and β-type globin genes that have been annotated in avian genome assemblies [38,69–73]. We identified all significant protein hits that matched more than one peptide with P<0.05. After separating the HbA and HbD isoforms by native gel IEF, the relative abundance of the two isoforms was quantified densitometrically using Image J [74].
The αA- and βA-globin genes were amplified and sequenced according to protocols described previously [35,36]. For all specimens used as subjects in the experimental analyses of Hb function, we extracted RNA from whole blood using the RNeasy kit (Qiagen,Valencia, CA), and we amplified full-length cDNAs of the αD-globin gene using a OneStep RT-PCR kit (Qiagen, Valencia, CA). We designed paralog-specific primers using 5’ and 3’ UTR sequences, as described by Opazo et al. [38]. We cloned reverse transcription (RT)-PCR products into pCR4-TOPO vector using the TOPO TA Cloning Kit (Invitrogen, Carlsbad, CA), and we sequenced at least five clones per sample in order to recover both alleles. This enabled us to determine full diploid genotypes for αD-globin in each specimen. The sequences were analyzed using Geneious Pro ver. 5.4.3. All new sequences were deposited in GenBank under accessions numbers KT988975-KT988992 and KU160516-KU160529.
For each amino acid difference between pairs of high- and low-altitude sister taxa, we identified ancestral and derived states by comparison with orthologous sites in a large number of other waterfowl species (n = 117 sequences for αA-globin, 96 for βA-globin, and 57 for αD-globin). Alignments of variable sites in the αA-, βA-, and αD-globin genes are shown in S3, S4 and S5 Figs, respectively. For each divergent site in each pair of sister taxa, unordered parsimony (using the trace character function in Mesquite [75]) yielded unambiguous inferences of character polarity.
One notable case of homoplasy in the βA-globin gene involved sites 116 and 133 in high-altitude yellow-billed pintails and speckled teal (S4 Fig), two species that are known to hybridize in nature [40,41]. To assess whether identical two-site ‘β116Ser-β133Met’ haplotypes from the two species were identical-by-descent, we reconstructed haplotype networks of βA-globin coding sequence using the median-joining algorithm [76], as implemented in the program Network 4.6 (Fluxus Technology, Suffolk, UK). We conducted the analysis on a sample of 257 βA-globin sequences (116 from yellow-billed pintails and 141 from speckled teal) obtained from sympatric high- and low-altitude populations of both species.
Sixty individuals representing three species of Andean ducks (speckled teal, cinnamon teal, and yellow-billed pintail) were selected for genome-wide surveys of nucleotide variation using single-digest RAD-Seq [77]. For each species, ten male specimens were selected from high-altitude (≥3,211 m above sea level), and ten were selected from low-altitude (≤ 914 m). Total genomic DNA was extracted from muscle tissue using a DNeasy Tissue Kit (Qiagen, Valencia, California, USA) and normalized using a Qubit Fluorometer (Invitrogen, Grand Island, New York, USA). DNA samples were submitted to Floragenex (Eugene, Oregon, USA) for single-digest RAD-Seq using SbfI, which recognizes an 8-nucleotide (CCTGCAGG) restriction site. Digested DNAs were ligated to barcodes and sequencing adaptors and then sequenced on the Illumina HiSeq 2000 with single-end 100 bp chemistry. Following Illumina sequencing, sequences were demultiplexed and trimmed to yield RAD sequences of 90 bp. Data analysis and bioinformatics pipelines were provided by Floragenex [77–79].
The Floragenex RAD unitag assembler and BSP pipelines v.2.0 were used to create a RAD-Seq ‘unitag’ assembly and Bowtie alignments of SAMtools pileup sequences to the reference assembly. Genotypes at each nucleotide site were determined using the VCF popgen v.4.0 pipeline to generate a customized VCF 4.1 (variant call format) database with parameters set as follows: minimum AF for genotyping = 0.075, minimum Phred score = 15, minimum depth of sequencing coverage = 10x, and allowing missing genotypes from up to 10% of individuals at each site.
To filter out base calls that were not useful due to low quality scores or insufficient coverage, genotypes at each nucleotide site were inferred using the Bayesian maximum likelihood algorithm described by Hohenlohe et al. [79]. This algorithm calculates the likelihood of each possible genotype at each site using a multinomial sampling distribution, which gives the probability of observing a set of read counts (n1, n2, n3, n4) for a particular genotype, where ni is the read count for each of the four possible nucleotides at each site, excluding ambiguous reads with low quality scores. The genotyping algorithm incorporates the site-specific sequencing error rate, and assigns the most likely diploid genotype to each site using a likelihood ratio test and significance level of α = 0.05.
A total of 372 million sequence reads were obtained with an average depth of 7.6 (±2.4 SD) million reads per sample for yellow-billed pintail and speckled teal and 3.3 (±1.4 SD) million reads per sample for cinnamon teal, corresponding to an average of 140,671 (±27,856) RAD loci. After filtering and genotyping, the RAD-Seq survey yielded 49,670 SNPs associated with 18,998 distinct loci in yellow-billed pintail, 47,731 SNPs associated with 19,433 distinct loci in speckled teal, and 18,145 SNPs associated with 9,300 distinct loci in cinnamon teal, respectively. The mean depth of coverage was 36.8 (±10.0 SD) reads per site with an average per site quality score of 166.2 (±31.3 SD) for yellow-billed pintail and speckled teal, and 39.8 (±24.4 SD) reads per site with an average per site quality score of 177.6 (±26.4 SD) for cinnamon teal. Illumina reads were submitted to the European Nucleotide Archive and can be accessed under the short read archive (SRA) accession number PRJEB11624.
Sequencing coverage and quality scores were summarized using the software VCFtools v.0.1.11 [80]. Custom perl scripts were first used to filter triploid or tetraploid sites and convert the Floragenex-generated VCF file to a biallelic, VCF v4.0 compatible format. We then calculated Weir and Cockerham’s [81] estimator of FST for each SNP in comparisons between high- and low-altitude population samples.
We purified HbA and HbD variants from hemolysates of 1–4 specimens per species, all of which had known αA-, αD-, and βA-globin genotypes. In the case of ruddy ducks and yellow-billed pintails, previous population surveys of sequence polymorphism in the αA- and βA-globin genes had revealed multiple amino acid haplotypes segregating within high- and/or low-altitude populations [35,36]. In each case we purified HbA and HbD variants from individuals that were homozygous for each of the alternative allelic variants. Hemolysates of each individual specimen were dialyzed overnight against 20 mM Tris buffer (pH 8.4). The two tetrameric HbA and HbD isoforms were then separated using a HiTrap Q-HP column (GE Healthcare; 1 ml 17-1153-01) and equilibrated with 20 mM Tris buffer (pH 8.4). HbD was eluted against a linear gradient of 0–200 mM NaCl. The samples were desalted by means of dialysis against 10 mM HEPES buffer (pH 7.4) at 4°C, and were then concentrated by using a 30 kDa centrifuge filter (Amicon, EMD Millipore).
We measured O2-equilibria of purified Hb solutions under standard conditions (37°C, pH 7.4, 0.3 mM heme) using a modified diffusion chamber where absorption at 436 nm was monitored during stepwise changes in equilibration gas mixtures generated by precision Wösthoff gas-mixing pumps [28,32,39,56,82,83]. In order to characterize intrinsic Hb-O2 affinities and mechanisms of allosteric regulatory control, we measured O2-equilibria in the presence of Cl- ions (0.1M KCl), in the presence of IHP (IHP/Hb tetramer ratio = 2.0), in the simultaneous presence of both effectors, and in the absence of both effectors (stripped). Free Cl- concentrations were measured with a model 926S Mark II chloride analyzer (Sherwood Scientific Ltd, Cambridge, UK). We estimated values of P50 and n50 (Hill’s cooperativity coefficient at half-saturation) by fitting the Hill equation Y = PO2n/(P50n + PO2n) to the experimental O2 saturation data by means of nonlinear regression (Y = fractional O2 saturation; n, cooperativity coefficient). The model-fitting was based on 5–8 equilibration steps between 30% and 70% oxygenation.
The αA- and βA-globin sequences of yellow-billed pintail were synthesized by Eurofins MWG Operon (Huntsville, AL, USA) after optimizing the nucleotide sequences in accordance with E. coli codon preferences. The synthesized αA-βA globin gene cassette was cloned into a custom pGM vector system along with the methionine aminopeptidase (MAP) gene, as described by Natarajan et al. [27,84]. We engineered each of the β-chain codon substitutions using the QuikChange II XL Site-Directed Mutagenesis kit from Stratagene (LaJolla, CA, USA). Each engineered codon change was verified by DNA sequencing.
Recombinant Hb expression was carried out in the JM109 (DE3) E. coli strain as described in Natarajan et al. [27,84]. To ensure the post-translational cleaving of N-terminal methionines from the nascent globin chains, we co-transformed a plasmid (pCO-MAP) containing an additional copy of the MAP gene. Both pGM and pCO-MAP plasmids were cotransformed and subject to dual selection in an LB agar plate containing ampicillin and kanamycin. The expression of each rHb mutant was carried out in 1.5 L of TB medium. Bacterial cells were grown in 37°C in an orbital shaker at 200 rpm until absorbance values reached 0.6–0.8 at 600 nm. The bacterial cultures were induced by 0.2 mM IPTG and were then supplemented with hemin (50 μg/ml) and glucose (20 g/L). The bacterial culture conditions and the protocol for preparing cell lysates were described previously [27–29,32,84].
The bacterial cells were resuspended in lysis buffer (50 mM Tris, 1 mM EDTA, 0.5 mM DTT, pH 7.6) with lysozyme (1 mg/g wet cells) and were incubated in the ice bath for 30 min. Following sonication of the cells, 0.5–1.0% polyethylenimine solution was added, and the crude lysate was then centrifuged at 15000 g for 45 min at 4°C. The rHbs were purified by two-step ion-exchange chromatography. Using high-performance liquid chromatography, the samples were passed through a prepacked anion-exchange column (Q-Sepharose) followed by passage through a cation-exchange column (SP-Sepharose). The clarified supernatant was subjected to overnight dialysis in CAPS buffer (20 mM CAPS with 0.5mM EDTA, pH 9.7) at 4°C. The samples were passed through the Q-column and the rHb solutions were eluted against a linear gradient of 0–1.0 M NaCl. The eluted samples were desalted by overnight dialysis with 20 mM HEPES pH 7.4 (4°C). Dialyzed samples were then passed through the SP-Sepharose column (HiTrap SPHP, 1 mL, 17-1151-01; GE Healthcare) equilibrated with 20 mM HEPES (pH 7.4). The rHb samples were eluted with a linear gradient of 20 mM HEPES (pH 9.2). Samples were concentrated and desalted by overnight dialysis against 10 mM HEPES buffer (pH 7.4) and were stored at -80°C prior to the measurement of O2-equilibrium curves.
The purified rHb samples were analyzed by means of sodium dodecyl sulphate (SDS)-polyacrylamide gel electrophoresis. After preparing rHb samples as oxyHb, deoxyHb, and carbonmonoxy derivatives, we measured absorbance at 450–600 nm to confirm that the absorbance maxima match those of the native HbA samples. Results of isoelectric focusing analyses indicated that each of the purified rHb mutants was present as a tetrameric assembly, and this was further confirmed by cooperativity coefficients (n50) >1.00 in the O2-equilibrium experiments. In vitro measurements of O2-binding properties were conducted in the same manner for rHbs and native Hb samples.
Homology-based structural modeling was performed with Modeller 9.15 [85] using human Hbs in different ligation states (PDB, 2hhb and 1hho) as templates. Models were evaluated on the SWISS-MODEL server [86]. All models had QMEAN values between 0.71 and 0.78. Structural mining was performed using PISA [87], PyMol (Schrödinger, New York, NY), and SPACE [88].
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10.1371/journal.pbio.1002247 | The Maternal Maverick/GDF15-like TGF-β Ligand Panda Directs Dorsal-Ventral Axis Formation by Restricting Nodal Expression in the Sea Urchin Embryo | Specification of the dorsal-ventral axis in the highly regulative sea urchin embryo critically relies on the zygotic expression of nodal, but whether maternal factors provide the initial spatial cue to orient this axis is not known. Although redox gradients have been proposed to entrain the dorsal-ventral axis by acting upstream of nodal, manipulating the activity of redox gradients only has modest consequences, suggesting that other factors are responsible for orienting nodal expression and defining the dorsal-ventral axis. Here we uncover the function of Panda, a maternally provided transforming growth factor beta (TGF-β) ligand that requires the activin receptor-like kinases (Alk) Alk3/6 and Alk1/2 receptors to break the radial symmetry of the embryo and orient the dorsal-ventral axis by restricting nodal expression. We found that the double inhibition of the bone morphogenetic protein (BMP) type I receptors Alk3/6 and Alk1/2 causes a phenotype dramatically more severe than the BMP2/4 loss-of-function phenotype, leading to extreme ventralization of the embryo through massive ectopic expression of nodal, suggesting that an unidentified signal acting through BMP type I receptors cooperates with BMP2/4 to restrict nodal expression. We identified this ligand as the product of maternal Panda mRNA. Double inactivation of panda and bmp2/4 led to extreme ventralization, mimicking the phenotype caused by inactivation of the two BMP receptors. Inhibition of maternal panda mRNA translation disrupted the early spatial restriction of nodal, leading to persistent massive ectopic expression of nodal on the dorsal side despite the presence of Lefty. Phylogenetic analysis indicates that Panda is not a prototypical BMP ligand but a member of a subfamily of TGF-β distantly related to Inhibins, Lefty, and TGF-β that includes Maverick from Drosophila and GDF15 from vertebrates. Indeed, overexpression of Panda does not appear to directly or strongly activate phosphoSmad1/5/8 signaling, suggesting that although this TGF-β may require Alk1/2 and/or Alk3/6 to antagonize nodal expression, it may do so by sequestering a factor essential for Nodal signaling, by activating a non-Smad pathway downstream of the type I receptors, or by activating extremely low levels of pSmad1/5/8. We provide evidence that, although panda mRNA is broadly distributed in the early embryo, local expression of panda mRNA efficiently orients the dorsal-ventral axis and that Panda activity is required locally in the early embryo to specify this axis. Taken together, these findings demonstrate that maternal panda mRNA is both necessary and sufficient to orient the dorsal-ventral axis. These results therefore provide evidence that in the highly regulative sea urchin embryo, the activity of spatially restricted maternal factors regulates patterning along the dorsal-ventral axis.
| A key event during development of bilaterians is specification of the anterior-posterior and dorsal-ventral axes of the embryo. In some species, such as the fly Drosophila, this process relies on the activity of maternal determinants localized into the egg during oogenesis. However, in other animals, such as mammals or echinoderms, which are renowned for the developmental plasticity of their embryos, there is presently no evidence for maternal determinants controlling axis formation, and how these embryonic axes emerge from radially symmetrical embryos remains unknown. In the sea urchin embryo, specification of the dorsal-ventral axis critically relies on the localized expression of the TGF-β ligand Nodal in the presumptive ventral territory, but what controls the spatially restricted expression of nodal is not known. We discovered that in the sea urchin embryo, the initial restriction of nodal expression is directed by another TGF-β ligand that is expressed maternally, which we named Panda. Panda is both necessary for the early spatial restriction of nodal and sufficient to orient the dorsal-ventral axis when misexpressed locally. Altogether, our findings suggest that Panda may act as a maternal signal that defines the orientation of the dorsal-ventral axis. Thus, an antagonism between Nodal and maternal Panda signaling drives dorsal-ventral axis formation in the sea urchin embryo.
| In bilaterians, specification of the dorsal-ventral (D/V) axis is a crucial event during embryogenesis to establish the correct body plan. In many species, this process relies on gene products translated from maternal mRNAs deposited in the egg. For example, in Drosophila, specification of the D/V axis of the embryo is initiated by the product of the gurken gene, which is active in the oocyte nucleus during oogenesis and encodes a member of the epidermal growth factor (EGF) superfamily that acts as a secreted dorsalizing signal [1–4]. Similarly, in Xenopus and zebrafish, although the D/V axis is not preformed in the unfertilized egg, dorsal determinants are localized to the vegetal pole of the egg [5–8]. Fertilization breaks the radial symmetry of the egg and triggers the asymmetric transport of these determinants from the vegetal pole to the future dorsal side where they activate the canonical Wnt pathway [9,10]. While maternal information is clearly important for specification of the D/V axis in a number of species, in contrast, there is very little evidence for the presence of maternal determinants of axis formation in the oocyte of mammals, consistent with the idea that the embryonic axes are specified entirely by cell interactions [11]. Accordingly, it has been argued that the regulative abilities of the first blastomeres of the mouse embryo rule out the possibility that maternal determinants influence axis specification [12] (reviewed in [13]).
The sea urchin embryo is well known for its extraordinary developmental plasticity [14]. In a now classical blastomere dissociation experiment, Driesch showed that dissociated blastomeres of the four-cell stage embryo have the potentiality to reestablish a D/V axis [15]. The outcome of this experiment not only demonstrated the impressive regulative ability of the early blastomeres of the sea urchin embryo but also strongly influenced ideas about how D/V patterning is established in this organism. By showing that the D/V axis is very easily respecified, it encouraged the view that there are no determinants for D/V axis formation in echinoderm embryos. On the other hand, egg bisection experiments performed by Hörstadius showed that differences in the fates of presumptive ventral and dorsal regions can be traced back to the egg, consistent with the idea that the oocyte already has a bilateral organization [16]. If there are maternal cues that influence D/V axis formation in this embryo, what could they be? There is a large body of evidence correlating formation of the D/V axis with the activity of redox gradients and with the asymmetric distribution of mitochondria in the unfertilized sea urchin egg. Classical experiments performed by Child, Pease, and Czihak in the thirties and sixties showed that it is possible to bias the D/V axis by treating embryos with steep gradients of respiratory inhibitors and that the activity of the mitochondrial enzyme cytochrome oxidase can predict the D/V axis as early as the eight-cell stage, with the presumptive ventral side being more oxidizing than the dorsal side [17–20]. This asymmetry of mitochondria activity is the first known manifestation of D/V polarity. Several recent studies by Coffman and colleagues addressed the question of causality between this early asymmetry and the orientation of the D/V axis [21–23]. Although these studies provided evidence that the D/V axis can be entrained by centrifugation, by microinjection of purified mitochondria, or by overexpressing a form of catalase targeted to the mitochondria, the correlations obtained remained modest, and in no case were these perturbations shown to efficiently orient the D/V axis [21–23]. Furthermore, perturbations that were expected to influence D/V axis formation, such as overexpression of a mitochondrially targeted form of superoxide dismutase, which generates the strong oxidizing component H2O2 and that would be predicted to efficiently orient the axis, did not show any effect on the orientation of the D/V axis. Therefore, the redox gradient model of D/V axis formation clearly needs further experimental validation, and the biological significance of the early asymmetry of mitochondria and of redox gradients and their relation to the D/V axis remains largely unclear.
At the molecular level, the earliest sign of specification of the D/V axis is the expression of the TGF-β nodal in the presumptive ectoderm at the 32-cell stage. nodal is the first known zygotic gene differentially expressed along the D/V axis, and Nodal signaling orchestrates patterning along the secondary axis first by specifying the ventral ectoderm and second by inducing the expression of BMP2/4, which acts as a relay to specify the dorsal ectoderm. nodal morphants completely lack D/V polarity, but expression of nodal into one blastomere is capable of completely rescuing D/V polarity in these embryos [24,25]. cis-regulatory studies further showed that nodal expression is driven by ubiquitously expressed maternal factors such as the transcription factor SoxB1 and that it requires maternal Wnt/beta catenin signaling as well as signaling by the Vg1/GDF1-related maternal factor Univin [26,27]. Intriguingly, nodal is initially expressed very broadly, almost ubiquitously, and then its expression is progressively restricted to a more discrete region of the ectoderm during cleavage. The progressive spatial restriction of nodal expression is thought to rely mostly, if not exclusively, on the ability of Nodal to promote its own expression through an intronic autoregulatory enhancer [26,27] and to induce the expression of the long-range Nodal antagonist Lefty. This regulatory mechanism based on the long-range diffusion of the Lefty antagonist fulfills the requirement for a reaction diffusion and is thought to be mainly responsible for amplifying an initial subtle asymmetry, possibly generated by redox gradients, into a robust spatially restricted expression of nodal [23,28].
Finally, nodal expression requires the integrity of the p38 pathway [22,23,26,29]. Inhibition of p38 signaling with pharmacological inhibitors abolishes nodal expression. Immunostaining experiments using an anti-phosho p38 and analysis of the spatial distribution of a p38-green fluorescent protein (GFP) fusion protein revealed that p38 is first activated ubiquitously and then selectively inactivated on the presumptive dorsal side of the embryo. The signals that regulate p38 in the early embryo are not known, but it has been proposed that redox gradients may be directly responsible for p38 activation [22,23,26,29]. However, direct evidence that p38 mediates the effects of redox gradients is presently lacking, and the transcription factors linking p38 to the machinery that regulates nodal expression are not known.
In this paper, we identify a maternal factor that plays a crucial role in D/V axis formation by directing the spatial restriction of nodal expression. First, we discovered that an unidentified TGF-β ligand cooperates with BMP2/4 to restrict nodal expression. We identified this ligand as the product of a maternally expressed TGF-β ligand related to Maverick from Drosophila and GDF15 from vertebrates that we named Panda. Inhibition of maternal panda mRNA translation blocked the early spatial restriction of nodal and caused persistent massive ectopic expression of nodal on the dorsal side despite the presence of Lefty. We further show that while blocking translation of bmp2/4 mRNA alone does not cause ectopic expression of nodal, the double knockdown of panda and bmp2/4 causes an extreme ventralization. We further provide evidence that the panda mRNA is broadly distributed in the early embryo, that local expression of panda mRNA efficiently orients the D/V axis, and that, although panda mRNA is broadly distributed, Panda activity is required locally in the early embryo. Taken together, these findings demonstrate that maternal panda mRNA is required early to restrict the spatial expression of nodal, that it is sufficient to orient the D/V axis when misexpressed, and therefore, that it fulfills the requirements for a maternal factor that specifies the D/V axis.
Our results suggest that, although specification of the D/V axis is established by the activity of Nodal in the zygote, maternally provided signaling molecules play crucial roles by antagonizing the activity of Nodal.
We showed previously that during D/V patterning in the sea urchin embryo, transduction of the BMP2/4 signals requires the activity of the type-I BMP receptor Alk3/6, the functional orthologue of Thickveins, which transduces Dpp signals in Drosophila. We noticed, however, that blocking Alk3/6 consistently produced a phenotype much less severe than the BMP2/4 loss-of-function phenotype. For example, while bmp2/4 morphants typically lack a population of immunocytes called pigment cells that requires BMP signaling, alk3/6 morphants always develop with numerous pigments cells (arrows in Fig 1A). This suggested that residual BMP signaling in alk3/6 morphants allows formation of pigment cells and/or that additional BMP type I receptors may contribute to transduction of BMP2/4 signals in the absence of Alk3/6. Indeed, in addition to alk3/6, the sea urchin genome contains a second gene encoding a BMP type I receptor named Alk1/2, which is mostly similar to Alk1 and Alk2 from vertebrates and to Saxophone from Drosophila. Like alk3/6, alk1/2 is expressed maternally and ubiquitously during the cleavage and blastula stages (S1 Fig). To evaluate the contribution of Alk1/2 in BMP2/4 signaling, we knocked it down with antisense morpholinos. Interestingly, blocking alk1/2 mRNA translation disrupted D/V axis formation and produced a phenotype stronger than that resulting from inhibition of Alk3/6 (Fig 1A). When the alk1/2 morpholino was injected at 1.2 mM, most alk1/2 morphants failed to develop their ventral arms and dorsal apex and appeared rounded. Alk1/2 morphants also lacked most pigment cells and developed with an ectopic ciliary band and ectopic spicules on the dorsal side, a phenotype largely identical to the bmp2/4 morphant phenotype. These phenotypes could be suppressed by coinjection of a modified wild-type alk1/2 mRNA immune against the morpholino (see S1 Fig). As shown previously in the case of Alk3/6 and of BMP2/4, blocking Alk1/2 caused a dramatic expansion of the ciliary band territory at the expense of the dorsal ectoderm, as evidenced by the massive ectopic expression of foxG and onecut on the presumptive dorsal side and the lack of expression of dorsal marker genes such as hox7 (Fig 1B). Unexpectedly, blocking Alk1/2 function, unlike blocking BMP2/4 or Alk3/6, caused a weak but consistent ventralization, as evidenced by the expression of chordin or foxA that extended to the dorsal side at the gastrula stage (black arrowheads in Fig 1B). Consistent with this ventralization, we found that at blastula stages, embryos injected with high doses of the alk1/2 morpholino displayed a massive ectopic expression of nodal similar to that observed in lefty morphants (Fig 1C). This phenotype, which is not observed in bmp2/4 or alk3/6 morphants, suggests that, in addition to BMP2/4, Alk1/2 may also be required to transduce an unidentified dorsalizing signal. Finally, consistent with the absence of expression of dorsal marker genes, inhibition of alk1/2 mRNA translation, like inhibition of bmp2/4 or alk3/6, drastically reduced phospho-Smad1/5/8 signaling in the dorsal ectoderm (Fig 1D). We conclude that Alk1/2 plays a pivotal role in transduction of BMP2/4 in the sea urchin and that the activities of Alk1/2 and Alk3/6 are nonredundant, both being functionally required during D/V patterning to transduce BMP2/4 signals and to activate Smad1/5/8 signaling in the dorsal ectoderm. Furthermore, these results suggest that in addition to BMP2/4, Alk1/2 may be required for transduction of (a) still unidentified signal(s) that regulate(s) D/V patterning.
To further characterize the requirements for Alk1/2 and Alk3/6 in D/V axis patterning, we performed a double knockdown. Our expectations were that the double knockdown of alk3/6 and alk1/2 would produce a phenotype roughly similar to the BMP2/4 loss-of-function phenotype. However, surprisingly, the morphology of the double knockdown embryos was very different from that of the bmp2/4 knockdown. The alk1/2 + alk3/6 morphants were completely radialized and developed with a prominent proboscis in the animal pole region and with an ectopic ciliary band surrounding the vegetal pole region (Fig 2A, white and black arrowheads, respectively). These features are typical of the strongly ventralized phenotype observed in nodal-overexpressing or nickel-treated embryos (Fig 2A). Indeed, molecular analysis revealed that the double inhibition of Alk3/6 and Alk1/2 caused a massive ectopic expression of nodal and of its downstream target genes chordin and foxA in the presumptive ectoderm at the blastula stage, whereas it abolished the expression of the dorsal marker gene hox7 (Fig 2B and 2C). Consistent with the extreme ventralization of the double alk1/2 + alk3/6 morphants, at the gastrula stage, expression of the ciliary band genes foxG and onecut was detected in a belt of cells surrounding the vegetal pole (black arrowheads in Fig 2B), a pattern typically observed in embryos ventralized by nodal overexpression (Fig 2C) [30]. Since these results suggest that signaling from these BMP receptors is required to restrict nodal expression, we tested if treatments with recombinant BMP2/4 can antagonize nodal expression. Indeed, treatments with increasing concentrations of recombinant BMP2/4 protein gradually antagonized nodal expression, with low concentrations first causing a typical Nodal loss-of-function phenotype and high concentrations resulting in dorsalization of the ectoderm (S2 Fig) [30].
Taken together, these results reveal that specification of the ventral territory is not independent of BMP signaling, as previously thought [25,30]. The results suggest instead that, in addition to specifying the dorsal region at the onset of gastrulation, signaling from the two BMP receptors Alk3/6 and Alk1/2 is critically required during or before blastula stages to restrict nodal expression to the ventral side. Importantly, the fact that the bmp2/4 morphant phenotype is considerably weaker than the double alk1/2 + alk3/6 morphant phenotype strongly suggests that an unidentified signal acting through these BMP type I receptors is critically required, in addition to BMP2/4, for the correct specification of the D/V axis and for the normal restriction of nodal expression.
We next attempted to identify the TGF-β ligand acting through Alk3/6 and Alk1/2 that cooperates with BMP2/4 and that restricts the early expression of nodal during D/V axis formation. In other species, BMP ligands of the BMP5/8 and anti-dorsalizing morphogenetic protein (ADMP) subfamilies have been shown to cooperate and to act redundantly with BMP2/4 factors during D/V patterning. For example, in Xenopus, while the single knockdown of either ADMP, BMP2, BMP4, or BMP7 resulted in partial central nervous system (CNS) expansion, the quadruple knockdown of BMP2,4,7 and ADMP caused full radialization and ubiquitous neural induction [31,32]. We therefore tested if in the sea urchin, like in Xenopus, members of the BMP5/8 and ADMP subfamilies of TGF-β ligands cooperate with BMP2/4 during D/V patterning.
The simple knockdown of BMP5/8 caused a phenotype much weaker than the phenotype caused by blocking BMP2/4. Surprisingly, the double knockdown of BMP2/4 and BMP5/8 only slightly increased the severity of the BMP2/4 morphant phenotype (Fig 3A and S3 Fig). Similarly, the double knockdown of BMP2/4 and ADMP did not cause a phenotype dramatically more severe than the BMP2/4 morphant phenotype. Even more surprising, the triple knockdown of BMP2/4, BMP5/8, and ADMP did not increase significantly the severity of the BMP2/4 morphant phenotype and did not result in ventralized embryos, suggesting that in the sea urchin, BMP5/8 and ADMP do not act redundantly with BMP2/4 to regulate the spatial restriction of Nodal (S3 Fig). We therefore extended our search for TGF-β ligands that would cooperate with BMP2/4 during D/V patterning to other members of the TGF-β superfamily. In addition to members of the BMP subfamily such as bmp2/4, bmp5/8, and admp, the sea urchin genome contains several genes encoding TGF-β ligands structurally related to Activins including TGF-β sensu stricto, Activin as well as SPU_018248, a less well-characterized gene related to Maverick from Drosophila that we renamed panda (paracentrotus anti-nodal dorsal activity) (see below) in this study [33]. Blocking Activin or TGF-β sensu stricto did not perturb establishment of the D/V axis, making unlikely the possibility that these factors cooperate with BMP2/4 to restrict nodal expression [34] (our unpublished results). In contrast, blocking translation of the TGF-β Panda strongly affected D/V polarity. While the triple knockdown of bmp2/4, bmp5/8, and admp1 did not increase the severity of the bmp2/4 morphant phenotype, in contrast, the double knockdown of bmp2/4 and panda produced a very strong phenotype, indistinguishable from that of the double alk1/2 + alk3/6 morphants. Strikingly, the ventralization induced by the double inactivation of panda and bmp2/4 was so strong that it frequently led to scission of the embryos in two parts by formation of a circular stomodeum and separation of the animal pole-derived proboscis from the vegetal part of the larva that contained the gut (Fig 3A). Indeed, starting at early stages, the double panda + bmp2/4 morphants displayed a massive ectopic expression of nodal, similar to that caused by the double inactivation of Alk1/2 and Alk3/6 (Fig 3B). The extent of this radialization was extremely pronounced, as evidenced by the radial expression of the other ventral markers chordin and foxA and of the ciliary band markers onecut and foxG as well as by the suppression of the dorsal marker hox7 both at the blastula and late gastrula stages. The summary diagram of Fig 3C shows that, while inactivation of bmp2/4 alone does not cause ventralization, in contrast, simultaneous inactivation of both panda and bmp2/4, like the double knockdown of alk1/2 and alk3/6, causes unrestricted expression of nodal leading to strong ventralization.
These observations strongly support the view that Panda is the elusive factor that, together with BMP2/4, is required to antagonize Nodal signaling during D/V patterning of the embryo. Taken together, these results also suggest that, in addition to Lefty, the normal restriction of nodal expression during D/V patterning in the sea urchin embryo requires the activities of Panda and BMP2/4 possibly signaling through the two BMP type-I receptors, Alk3/6 and Alk1/2.
In a previous study, we had suggested that the TGF-β encoded by SPU_018248 is related to Maverick sequences from insects and to GDF2 sequences from Crassostrea gigas; however, this analysis failed to identify any deuterostome orthologue of this gene, and the evolutionary origin of this TGF-β remained unclear [33]. To clarify the evolutionary relationships between SPU_018248 and other TGF-β family members and to identify orthologous sequences of this gene in deuterostomes, we performed a novel phylogenetic analysis using a comprehensive set of TGF-β sequences from protostomes, deuterostomes, and cnidarians and including in the analysis the Maverick sequence from Drosophila and the GDF2 sequence from Molluscs as well as a large set of BMP family members from different organisms (Fig 4 and S4 Fig). This analysis confirmed that the sea urchin Panda sequence is phylogenetically related to Drosophila Maverick and "GDF2-like" sequence from Crassostrea. However, it further revealed that Panda and Maverick/GDF15-like factors belong neither to the GDF2/BMP9 family nor to any known subclass of canonical BMP ligands. In addition, this analysis identified GDF15 from vertebrates as well as two genes from hemichordates and cephalochordates (called myostatin-like) as additional deuterostome orthologues of Panda (see also S6 Fig). Consistent with these conclusions, Panda, Maverick, and GDF15 share with Inhibins beta chains, TGF-β, and Myostatins a pattern of nine cysteines in the ligand domain, a pattern that is not shared by any prototypical BMP ligand (see S5 Fig and the alignment provided in the supplementary information). Therefore, Panda, Maverick, and GDF15-like sequences define a distinct subclass of TGF-β ligands within a larger branch of the TGF-β superfamily that comprises Inhibins beta chains, Lefty factors, Myostatins, and TGF-β sensu stricto (see also [35]).
Previous studies on sea urchin maverick/panda failed to detect expression of this gene by in situ hybridization [33], while by using an oligonucleotide microarray, a very weak expression was detected in 2 h zygotes and in 72 h pluteus larvae [36]. We reanalyzed the expression of panda by reverse transcription polymerase chain reaction (RT-PCR) and in situ hybridization and confirmed that transcripts of this gene are present predominantly in immature ovocytes, unfertilized eggs, and early embryos (Fig 5A and 5B and S9 Fig). Remarkably, a graded distribution of transcripts could be detected in immature ovocytes, in eggs, and during early stages, with one side of the embryo showing a slightly stronger staining than the other, reinforcing the idea that this factor plays an early role in D/V axis formation. Furthermore, double labeling with nodal revealed that the side with the highest concentration of mRNA was the dorsal side, opposite to the side of nodal expression, consistent with the idea that Panda is a factor that cooperates with BMP2/4 to restrict nodal expression (Fig 5B). Finally, starting at the prism stage, panda transcripts accumulated in the ciliary band territory, and strong expression was detected in individual cells within this territory.
To further characterize the role of panda during D/V axis formation, we injected two different antisense morpholino oligonucleotides targeting either the translation start site or the 5' UTR region of the transcript. Injecting these two different morpholinos gave rise to similar and remarkable phenotypes (Fig 6). While at the late gastrula stage, control embryos had started to flatten on the presumptive ventral side and had formed two PMC clusters on each side of the archenteron, the panda morphants were completely radialized, and the PMCs remained arranged into a ring around the archenteron (Fig 6A). Similarly at 48 hpf, when control embryos had developed into elongated pluteus larvae, panda morphants had conserved a radially symmetrical morphology and contained ectopic spicules rudiments (arrowheads in Fig 6A). Surprisingly, at 72 h, these embryos had partially recovered a D/V polarity as indicated by the bending of the archenteron towards the presumptive ventral ectoderm, the opening of the stomodeum, and the preferential elongation of spicules on the presumptive dorsal side (Fig 6A). Indeed, molecular analysis revealed that in most of the embryos (n > 300), knocking down panda with either the ATG morpholino (Fig 6B) or the UTR morpholino (Fig 6C) caused a strong ventralization accompanied with massive ectopic expression of nodal and chordin, which were expressed throughout most of the ectoderm at the mesenchyme blastula stage, and a concomitant loss of the dorsal marker gene hox7. At the late gastrula/prism stage (30 hpf), panda morphants remained ventralized, as evidenced by the expanded expression of ventral marker genes such as nodal, foxA, and foxG compared to control embryos, occupying about one-half of the embryo. However, consistent with the progressive recovery of D/V polarity observed in live embryos, the expression of hox7 in the dorsal region and of the ciliary band marker onecut at the late gastrula stage indicated that dorsal and ciliary band fates were allocated in panda morphants by the end of gastrulation (Fig 6B). Therefore, although the morphology of panda morphants is radially symmetrical at late gastrula stage, molecular analysis reveals that these embryos are nevertheless patterned along the D/V axis and that radialization is caused by a marked expansion of ventral cell fates. Taken together, these results suggest that Panda function is required early to restrict nodal expression. In the absence of Panda, ventral fates are expanded at the expense of dorsal fates, but this ventralization is most severe during the blastula and gastrula stages, the embryos progressively recovering, to some extent, a D/V polarity after 48 h (Fig 6D).
To determine when Panda, Alk1/2, and Alk3/6 functions are required to restrict nodal expression, we performed a time-course experiment. We compared nodal expression at successive developmental stages, from cleavage to mesenchyme blastula, in control embryos and in embryos injected with either the morpholino oligonucleotide targeting the ATG of panda mRNA or with a morpholino oligonucleotide targeting a splice junction of the panda gene or with a mixture of alk1/2 and alk3/6 morpholino (Fig 7A–7C). Strikingly, in embryos injected with the morpholino targeting the translation start site of panda mRNA, presumed to block both maternal and zygotic panda transcripts, or with a combination of the alk1/2 and alk3/6 morpholinos, nodal expression was never restricted and remained radialized at all stages analyzed (Fig 7B and 7C). In contrast, nodal expression was largely normal in embryos injected with the morpholino targeting the splice junction of panda (Fig 7B), and blocking zygotic panda function did not noticeably perturb development of the embryos (Fig 7A and S7 Fig). RT-PCR analysis indicated that this splice-blocking morpholino reduced the level of the mature panda transcript by more than 90% at the pluteus stage (S7 Fig). This analysis reveals that the function of maternal Panda, but not of zygotic Panda, and the activities of Alk1/2 and Alk3/6 are required very early to restrict nodal expression to the ventral side.
The results presented so far indicate that Panda is expressed in a broad D/V gradient and that, like Lefty, Panda is critically required for the correct spatial restriction of nodal to the ventral side during early stages. We then tested if overexpression of Panda, like overexpression of Lefty, efficiently blocks Nodal signaling. Surprisingly, overexpression of panda in the egg did not perturb establishment of the D/V axis, and the panda-overexpressing embryos developed into normal pluteus larvae (Fig 8A). This suggested that unlike Lefty, Panda alone is not capable of suppressing Nodal signaling when overexpressed. We then reasoned that rather than inhibiting Nodal signaling, the function of Panda may instead be to bias early Nodal signaling, perhaps by simply attenuating Nodal signaling on the dorsal side. If this were the case, then local overexpression of panda should efficiently orient the D/V axis. To test if local overexpression of panda is capable of biasing the orientation of the D/V axis, embryos at the two-cell stage were injected into one blastomere with panda mRNA together with a lineage tracer, and at the prism stage, the position of the clone of injected cells was recorded (Fig 8B). Strikingly, in almost 100% of the embryos injected with panda mRNA, the boundaries of the clone precisely coincided with the dorsal part of the embryo. Local overexpression of a constitutively active version of Alk3/6 (Alk3/6QD) or Alk1/2 (ALK1/2Q/D) mimicked the effects of local overexpression of panda, efficiently orienting the D/V axis in all the injected embryos (Fig 8B, Table 1).
Finally, we tested if overexpression of panda promotes expression of dorsal marker genes. We analyzed the expression of tbx2/3, the earliest zygotic expressed in all three germ layers in the presumptive dorsal region and that is thought to be induced by low levels of BMP signaling [24,25]. Overexpression of panda induced a moderate ectopic expression of tbx2/3 in all three germ layers, suggesting that panda, like bmp2/4, can activate BMP target genes requiring a low level of BMP signaling (Fig 8C) [24,25].
We next tested if removing the function of panda from part of the early embryo is also sufficient to orient the D/V axis (Fig 8B and Table 1). Indeed, injecting the panda morpholino randomly into one blastomere at the two-cell stage significantly biased the orientation of the D/V axis, most embryos (77.5%) showing a clone of fluorescently labeled cells in the ventral region. Similarly, injection of alk3/6 morpholino into one blastomere at the two-cell stage efficiently (70%) oriented the D/V axis, supporting the idea that Alk3/6 is involved in the early steps of D/V axis specification. Injection of the alk1/2 morpholino also significantly biased the orientation of the D/V axis, imposing a ventral identity to the clone in about 50% of the injected embryos (Fig 8B). In contrast, injection of the bmp2/4 morpholino into one blastomere did not significantly orient the D/V axis, 37% of the injected embryos displaying a clone of injected cells on the ventral side, further suggesting that bmp2/4 is not involved in the early steps of axis specification (Table 1).
In summary, these results show that while manipulating the levels of BMP2/4 does not appear to have a strong effect on the orientation of the D/V axis, in contrast, up-regulating or down-regulating the levels of Panda or Alk3/6, and to a lesser extent of Alk1/2, in part of the early embryo strongly impacts on the orientation of the D/V axis, partially mimicking manipulations of the levels of Nodal signaling.
In the course of our functional analysis of panda, we tried to rescue the defects of D/V patterning and the spatial restriction of nodal expression of panda morphants, by injecting a synthetic panda mRNA lacking the sequence targeted by the morpholino. Surprisingly, injection into the egg of a synthetic panda mRNA failed to rescue the severe defects of D/V polarity caused by injection of the panda morpholino (Fig 9). All the embryos derived from double injection of panda morpholino and panda mRNA at the one-cell stage developed with a phenotype indistinguishable from the panda loss-of-function phenotype. Since Panda is required to restrict nodal expression and since the endogenous panda mRNA is enriched on the presumptive dorsal side, we reasoned that maybe Panda activity had to be provided locally in order to mimic the distribution of endogenous panda mRNA and to rescue D/V polarity of panda morphants. Indeed, while injection of panda mRNA into the egg was inefficient to rescue the D/V axis, injection of panda mRNA into one blastomere completely rescued D/V polarity of embryos previously injected with the panda morpholino, all the embryos developing into perfectly normal pluteus larvae with the dorsal side corresponding to the panda-expressing clone (Fig 9). This experiment demonstrates that the activity of exogenous Panda has to be spatially restricted to rescue the lack of maternal Panda function, consistent with the idea that the activity of endogenous Panda is spatially restricted in the early embryo. Similarly, injection into one blastomere of alk3/6QD mRNA at low doses that do not dorsalize completely rescued D/V polarity of embryos previously injected with panda morpholino, consistent with previous results showing that local misexpression of an activated form of Alk3/6 is sufficient to antagonize nodal expression and to orient the D/V axis (Fig 8B).
The finding that knocking down Panda causes a phenotype similar to that caused by knocking down the two BMP type I receptors Alk1/2 and Alk3/6, leading to early ectopic expression of nodal, and the fact that local expression of alk3/6QD efficiently rescues D/V polarity in panda morphants indicated that Panda most likely uses Alk1/2 and Alk3/6 to signal. To further address the question of the specificity of the ligands regarding the receptors, we used an assay based on the double knockdown of Panda or BMP2/4 and Alk1/2 or Alk3/6 receptors (Fig 10).
Double inactivation of panda and alk1/2 or of panda and alk3/6 caused a strong ventralization similar to that caused by the double inactivation of panda and bmp2/4, consistent with the idea that the activities of Alk1/2 and Alk3/6 are both required to transduce BMP2/4 signals (Fig 10A). Similarly, the double inactivation of bmp2/4 and alk1/2 or of bmp2/4 and alk3/6 produced a strong ventralization, suggesting that the activities of Alk1/2 and Alk3/6 are both required to transduce Panda signals, although the phenotype was slightly less severe than that resulting from the double knockdown of panda and bmp2/4 (Fig 10A and 10B).
To test directly the hypothesis that Panda requires the BMP type I receptors to orient the D/V axis, we used the axis induction assay. We first injected the alk3/6 morpholino into the egg, and then, at the two-cell stage, we injected panda mRNA into one blastomere. While panda mRNA efficiently oriented the D/V axis when injected alone, previous injection of the alk3/6 morpholino into the egg abolished the ability of panda mRNA to orient the D/V axis, suggesting that the Alk3/6 receptor is required for the activity of Panda (Fig 10C).
Taken together, the results presented above strongly suggest that Panda requires the activity of the BMP type I receptors to orient the D/V axis; however, they do not answer the question of what signal transduction pathway is activated by this ligand. Since the axis-inducing activity of Panda requires the BMP type I receptor Alk3/6 and since manipulating the levels of this BMP type I receptor largely mimicked the effects of manipulating the levels of Panda, we expected that Panda, acting through Alk3/6 and Alk1/2, would activate canonical BMP signaling and Smad1/5/8 phosphorylation. However, intriguingly, previous studies failed to detect phospho-Smad1/5/8 before the hatching blastula stage using western blotting [24,25], suggesting that previous detection methods were not sensitive enough or that Panda may not activate canonical Smad signaling. We therefore attempted to detect endogenous phospho-Smad1/5/8 during the cleavage/early blastula period using an optimized western blotting assay. We were able to detect very strong phosphorylation of endogenous Smad1/5/8 at mesenchyme blastula stages (Fig 10D). However, endogenous phospho-Smad1/5/8 remained below the level of detection during cleavage stages, and overexpression of panda did not detectably increase the level of phosphorylated Smad1/5/8 at early stages. We also tried to detect phospho-Smad1/5/8 during cleavage/early blastula by using a sensitive immunostaining protocol. Fixed embryos were incubated with the anti-phospho-Smad1/5/8 antibody and then with a secondary antibody coupled to alkaline phosphatase (Fig 10E). While overexpression of bmp2/4 or of an activated form of alk3/6 or of alk1/2 induced robust and very strong phosphorylation of Smad1/5/8 starting during cleavage stages, overexpression of panda did not cause any detectable phosphorylation of Smad1/5/8 at these early stages (Fig 10E). However, intriguingly, at mesenchyme blastula, we consistently observed expanded phospho-Smad1/5/8 signals in most embryos overexpressing panda (Fig 10F), consistent with the observed ectopic expression of tbx2/3 (Fig 8C). However, pSmad1/5/8 signaling remained strongly polarized along the D/V axis, consistent with the apparent inability of panda to completely dorsalize embryos.
Taken together, these results suggest that panda may not directly activate phosphorylation of Smad1/5/8 and that the expanded pSmad1/5/8 signals may result from Panda antagonizing Nodal signaling and/or promoting BMP2/4 signaling [37].
In conclusion, the results presented in this study show that in addition to Lefty, the spatial restriction of nodal expression critically requires the activity of the maternal TGF-β ligand Panda. Panda is required very early and locally for the spatial restriction of nodal expression and is sufficient to orient the axis when locally overexpressed. Taken together, these properties strongly suggest that Panda may act as a maternal determinant of D/V axis formation in the sea urchin embryo.
An important and still largely unanswered question in developmental biology is how embryonic axes emerge in highly regulative and radially symmetrical embryos such as in mammals. Does formation of the primary and secondary axes depend entirely on cell interactions and reaction-diffusion mechanisms in the zygote, as suggested by the large developmental plasticity of the early blastomeres, or does it rely in part on maternal cues deposited in the egg? The process of D/V axis formation in the sea urchin embryo provides an interesting system to address this question. The D/V axis of the sea urchin embryo is thought to be specified largely in the absence of maternal determinants, as evidenced by the conspicuous developmental plasticity of the early blastomeres, and to rely instead on an asymmetry of the expression of the zygotic gene nodal established by a reaction-diffusion mechanism with its antagonist Lefty. Yet, are the concepts of maternal determination of axis formation and regulative development necessarily mutually exclusive?
In this study, we uncovered a very important and early function for a maternally expressed TGF-β ligand in the orientation of the D/V axis of the zygote through the spatial regulation of nodal expression. A key observation that was at the basis of this work was the finding that double inactivation of Alk1/2 and Alk3/6 produced an extreme radialization due to the unrestricted expression of nodal. Since this phenotype was much stronger than the bmp2/4 morphant phenotype, and since the effects of abolishing BMP signaling on nodal expression could be observed well before the onset of bmp2/4 expression, the inescapable conclusion was that another TGF-β ligand acting through these two TGF-β receptors was cooperating with BMP2/4 during D/V axis formation. Using a double morpholino injection assay, we identified this factor as Panda, a TGF-β ligand related to Inhibins, TGF-β and Lefty factors. These findings strongly impact on the current models of D/V axis formation since they reveal that the spatial restriction of nodal expression critically requires a maternally provided spatial cue. In addition, by showing that the graded localization of a maternal RNA provides a blueprint of the D/V axis in the highly regulative sea urchin embryo, they also provide additional support to the idea that the concept of maternal determination of axis specification and developmental plasticity are not necessarily exclusive. Finally, our findings that the orientation of nodal expression in the early embryo is negatively controlled by the spatially restricted activity of a TGF-β ligand that requires BMP type I receptors highlight the crucial role played by the antagonism between Nodal and BMP signaling in axis specification and suggest that this antagonism may represent an ancestral way to specify the axes during development.
The current prevailing model postulates that redox gradients generated by mitochondria asymmetrically distributed in the egg regulate the activity of redox-sensitive transcription factors that control the initial asymmetry of nodal expression [21–23,26]. However, although very attractive, the hypothesis that mitochondrial redox gradients drive nodal expression is not strongly supported by the extensive experimental work that has addressed this question.
Here we provided several lines of evidence demonstrating that the maternally expressed TGF-β ligand Panda acts as an early and central player in the establishment of the D/V axis. First, we showed that the function of Panda is required very early to restrict nodal expression. Second, we showed that panda mRNA is expressed in a broad gradient in the early embryo and that the activity of Panda is spatially restricted. Third, we showed that overexpression of panda promotes the overexpression of tbx2/3, the earliest zygotic dorsal gene marker. Finally, we showed that local misexpression of panda mRNA or local inhibition of panda very efficiently orients the D/V axis. The broad distribution of Panda mRNA raises the possibility that some other localized factor may be required in the normal embryo for imposing D/V polarity on Panda function. The fact that only local overexpression, and not global overexpression, of Panda rescues the D/V axis of Panda morphants strongly suggests that this is probably not the case, since if another localized factor was playing the role of a maternal determinant, then injection of panda mRNA into the egg would rescue the D/V axis of panda morphants. Therefore, Panda is, to our knowledge, the first signaling factor whose activity is spatially restricted in the embryo and that is both necessary and sufficient to efficiently orient the D/V axis upstream of nodal expression. How can we reconcile the roles of Panda as a maternal signal that orients the D/V axis with the wealth of data correlating redox gradients and the asymmetric distribution of mitochondria with the secondary axis? One possible mechanism that would reconcile the two bodies of evidence is that formation of the gradient of panda mRNA may be dependent on the activity or the distribution of mitochondria. Alternatively, redox gradients could differentially affect the stability/activity of Panda as shown recently in the case of another TGF-β ligand [38].
Similarly, these findings on Panda could be correlated to the role of p38 in promoting nodal expression during D/V axis formation. As shown by Bradham and colleagues, p38 activity is required for nodal expression, and after a period of ubiquitous activation, it is specifically down-regulated on the presumptive dorsal side [29]. Panda, acting through Alk1/2 and Alk3/6, may be responsible for this down-regulation of p38 activity on the dorsal side, which may in turn prevent Nodal autoregulation, a hypothesis that we are currently testing.
The sea urchin embryo is well known for its remarkable developmental plasticity, the best example of this flexibility being the ability of each blastomere of the four-cell stage to regulate and to develop into smaller but normally patterned pluteus larvae. The outcome of this experiment deeply influenced ideas about how the D/V axis may be specified in this embryo, leading to the commonly accepted view that D/V patterning of the sea urchin embryo relies on cell interactions in the zygote and not on asymmetrically distributed determinants. On the other hand, classical experiments of Horstadius using unfertilized eggs showed that artificially activated meridional halves frequently differentiate as left-right or D/V pairs. These observations led Horstadius to conclude that "there seems to be no doubt as to the existence of a ventral-dorsal axis in the unfertilized sea urchin egg" [14]. The finding that the spatially restricted activity of Panda directs D/V axis formation strongly supports this conclusion. However, the finding that the spatially restricted activity of maternal panda mRNA directs the orientation of the D/V axis may seem at odds with the results of the Driesch experiment. How can we reconcile the fact that the first blastomeres show an equivalent potential to reestablish a secondary axis with the graded activity of a maternal factor controlling formation of the D/V axis in the early embryo? Following dissociation, each blastomere is expected to inherit a portion of the gradient of activity of Panda. One possibility is therefore that the portion of the gradient of activity of Panda inherited by each blastomere following dissociation is sufficient to reestablish the secondary axis. Indeed, with a reduced gradient of activity of Panda, the reaction-diffusion mechanism between Nodal and Lefty may in some cases be sufficient to amplify an initial asymmetry of the expression of nodal or lefty, leading to the restriction of nodal expression and to reestablishment of the secondary axis.
Hörstadius repeated and extended the Driesch experiment by rearing each of the four blastomeres in a separate dish [39]. Interestingly, he noted that in some cases one or two embryos of the quartet differentiated and established a D/V axis faster than the others. This is exactly what would be expected if the blastomeres inherit different portions of the gradient of activity of Panda. The results of Hörstadius are therefore consistent with our finding that there is a maternal gradient of a dorsalizing activity in the early embryo.
Our finding that the activity of two type I BMP receptors is essential to restrict nodal expression in the sea urchin embryo adds further support to the previously suggested idea that an antagonism between Nodal and BMP signaling may be an ancestral mechanism to specify the axes [40]. Evidence is accumulating that both in chordates and in echinoderms, a correct balance between BMP signals and Nodal signals is required for normal D/V patterning [10,11,40–42]. Both in echinoderms and in vertebrates, inactivation of the BMP pathway promotes cell fates controlled by Nodal. The process of specification of the distal visceral endoderm in the mouse embryo offers a striking example of such an antagonism. Formation and positioning of the distal visceral endoderm is regulated by an antagonism between BMP-Smad1 and Activin/Nodal-Smad2 signaling, and activin receptor II (ACVRII) has been shown to act as a limiting factor in this process [43,44]. Similarly, there is accumulating evidence that during left-right axis specification, the opposing activities of Nodal and BMPs are required for proper patterning along this axis and that BMP signaling is required to spatially restrict nodal expression [45–47]. For example, mouse embryos mutant for smad1, smad5, spc4, or for the gene encoding the BMP type I receptor ACVR1 display bilateral expression of nodal [48–51]. Intriguingly, although this antagonism may be fundamental for axis specification, the underlying mechanism is not well understood, and how the two pathways interact is not known. This antagonism may result from a direct interaction at the level of the signaling components. For example, it has been suggested that a competition at the level of Smad4 may set a threshold on Nodal signaling [52]. Alternatively, this antagonism may result from an interplay at the level of the ligands and secreted antagonists produced downstream of each pathway or at the level of ACVRII, which acts as a common receptor for both pathways. Finally, an antagonism at the level of the transcription factors induced by Nodal or BMP may be responsible for the antagonism between the two signaling pathways. Interestingly, a recent study proposed that the gene encoding the homeobox repressor Hbox12, a member of the Hbox12/pmar1/micro1 family [53–59], is expressed early on the dorsal side of the embryo and that it represses nodal expression [60], raising the possibility that this gene may act downstream of Panda to repress nodal expression. However, preliminary experiments to test this idea did not provide evidence for a link between Panda and Hbox12 (S8 Fig).
Although we have detected expanded phospho-Smad signaling following misexpression of panda at the beginning of gastrulation, we failed to detect activation of Smad1/5/8 signaling during the early cleavage period, i.e., when panda is supposed to work, in embryos overexpressing panda. Therefore, Panda may not activate pSmad1/5/8 signaling directly, and the mechanism by which Panda antagonizes Nodal signaling during early stages remains presently unclear. We can envision several scenarios. Panda may antagonize Nodal signaling by heterodimerizing with Nodal and blocking its function. Such an activity has been reported in the case of BMP7 as well as in the case of Lefty [61,62]. Another possibility for the mechanism by which Panda may antagonize Nodal is that Panda may work like the Nodal antagonist Lefty, by sequestering a factor essential for Nodal signaling such as the co-receptor Cripto, ACVRII, or Alk4/5/7 [63]. The finding that blocking locally Nodal or ACVRII fully mimics the effects of overexpressing panda on the orientation of the D/V axis is consistent with this idea. However, these two hypotheses both predict that overexpression of Panda should strongly antagonize Nodal signaling, an activity that is not observed following overexpression of panda. One possibility to explain this result is that Panda may require another factor to act as a strong antagonist of Nodal signaling when overexpressed. Panda may therefore antagonize Nodal signaling in a way similar to that of Inhibin, which disrupts Activin signaling by acting through an intermediary factor and sequesters a factor required for Activin signaling [64]. A major difference between Panda and Inhibin is that while Inhibins have never been shown to require any type I receptor to function, our results indicate that Panda most likely requires the two BMP type I receptors Alk1/2 and Alk3/6 to antagonize Nodal. Therefore, if Panda acts by sequestering a factor required for Nodal signaling, this activity may also require functional Alk1/2 and Alk3/6, possibly in a complex with these two receptors. Finally, it remains also possible that Panda signals through these type I receptors and activates a noncanonical non-Smad pathway [65] that in turn may antagonize the Nodal pathway. In line with this conclusion, members of the Panda/Maverick/GDF15 subfamily lack a highly conserved leucine residue present in the so-called "wrist" domain of all BMP ligands (Leu 51 in human BMP2) that is critically required for binding of these factors to the type I BMP receptor. This suggests that members of the Panda/Maverick/GDF15 subfamily are low-affinity ligands for the BMP type I receptors or that the interaction between members of this subfamily and the BMP type I receptor may involve residues different from those involved in the interaction between canonical BMP ligands and the BMP type I receptors [66]. Also along these lines, it is intriguing to note that the mechanisms by which vertebrate GDF15 and Drosophila Maverick work remain also largely unknown. During Drosophila development, the maverick gene is broadly expressed during oogenesis and embryogenesis and throughout the larval stages [67]. Its function has long been enigmatic, but recent studies have uncovered a key role for Maverick during synaptogenesis at the neuromuscular junctions [68]. Maverick produced by glial cells was shown to promote expression of Glass bottom boat (Gbb), the fly ortholog of BMP7, in muscles. Similarly, the function of GDF15 in mice and humans is poorly understood. GDF15 is weakly expressed in most tissues, but its expression is induced in response to tissue injury, notably in the heart following myocardial infarction [69]. Neither Drosophila Maverick nor vertebrate GDF15 have been shown so far to activate any of the signaling pathways normally activated by BMP or Activin type ligands, and the mechanism by which these factors work remains unknown [69–71]. Our results showing that Panda antagonizes nodal expression by acting through type I BMP receptors and that overexpressed Panda induces tbx2/3 without detectably activating Smad1/5/8 signaling points to non-Smad signaling as a potential mechanism for the crosstalk between the Nodal and BMP pathway [37]. However, we cannot completely rule out the possibility that Panda may induce a level of Smad1/5/8 activation below the current limits of detection, a level that would be sufficient to mediate its effects. Finally, a combination of the different mechanisms mentioned above including antagonism between Smad1 and Smad2, sequestration of rate-limiting components, and antagonism between transcription factors induced downstream of Smads may underlie the antagonism between Nodal and BMP signaling in the sea urchin embryo. Biochemical and functional experiments will therefore be required to dissect the mechanism by which Panda antagonizes Nodal signaling in the sea urchin embryo.
An interesting parallel can be drawn between the identification of Panda as a maternal TGF-β ligand acting through BMP receptors that cooperates with the zygotic BMP2/4 and the finding that maternal Univin, a Vg1 related ligand, cooperates with the zygotic Nodal. In the case of Nodal and Univin, it has been suggested that Nodal may heterodimerize with Univin and increase its specific activity [72]. Indeed, while Nodal is a strong ventralizing factor, overexpression of Univin has very modest effects on D/V patterning. Similarly, BMP2/4 has an extremely strong dorsalizing activity, while Panda essentially lacks dorsalizing activity. Heterodimer formation is, however, probably not the mechanism by which Panda and BMP2/4 cooperate, since Panda and BMP2/4 act at different periods during D/V axis formation and the activities of these factors appear to be qualitatively different. Panda is required early, starting at cleavage stages, well before BMP2/4 starts to be expressed, for the spatial restriction of nodal expression, while BMP2/4 is required much later, starting after hatching. Furthermore, while the only known activity of Panda is to limit and orient nodal expression and to induce tbx2/3, BMP2/4 has a key role in activating a cohort of dorsally expressed transcription factors and signaling molecules. Finally, while BMP2/4 strongly activates phosphorylation and nuclear translocation of Smad1/5/8, Panda only appears capable of weakly activating pSmad signaling. Therefore, D/V axis specification in the sea urchin embryo requires two phases of signaling from the BMP receptors, but these two phases are temporally and qualitatively different. The first phase of signaling, which covers the period of cleavage up to hatching blastula, is the consequence of maternal Panda signaling through Alk3/6 and Alk1/2, either through very low canonical Smad signaling or possibly through noncanonical Smad signaling, while the second phase, which starts after hatching and continues late in gastrulation, is the result of zygotically produced BMP2/4 factors binding to the same receptors but activating canonical phospho-Smad signaling.
Despite the fact that Panda and Lefty are both expressed early and that both factors are required nonredundantly to restrict nodal expression, the function of Panda is also clearly different from that of Lefty. Panda is capable of orienting the D/V axis when expressed into one blastomere at the two-cell stage, but overexpression of Panda in the egg does not suppress Nodal signaling. Furthermore, Panda is not sufficient to restrict nodal expression in lefty morphants. Therefore, the function of Panda appears to be to break the radial symmetry and to create the asymmetry of nodal expression rather than to maintain the asymmetry of nodal expression. In support of this idea, in the absence of Panda, nodal remains radially expressed up to the beginning of gastrulation. Therefore, although the function of Lefty is normal in these embryos, its activity is not sufficient to restrict nodal expression in the absence of Panda. In other words, without Panda, Lefty is unable to create an asymmetry of nodal expression. The function of Lefty appears therefore important to maintain the asymmetry of nodal expression previously established by Panda rather than to create this asymmetry.
We have identified the maternal TGF-β ligand Panda as a novel and central player of the pathway controlling D/V axis formation. Although previous models placed Nodal as the first extracellular signal conveying spatial information for D/V axis formation, we can now place maternal Panda as the earliest known signal orienting the D/V axis upstream of nodal expression. A new model of D/V axis formation in the sea urchin embryo is the following (Fig 11). During oogenesis, maternal panda mRNA is deposited into the egg, possibly in a graded manner along the D/V axis, and following fertilization, this gradient of mRNA is translated into a shallow gradient of Panda protein. Starting at the 32/60-cell stage, ubiquitously expressed maternal transcription factors and maternal Wnt and Univin signaling activate nodal expression very broadly in the presumptive ectoderm. However, on the presumptive dorsal side, the increased activity of Panda weakly antagonizes nodal expression, introducing a first bias in nodal autoregulation that will initiate the spatial restriction of nodal expression to the presumptive ventral side. Then, starting at the early blastula stage, Nodal signaling induces expression of lefty, and the reaction-diffusion mechanism between Nodal and Lefty further contributes to maintain the spatial restriction of nodal expression. Finally, at the prehatching blastula stage, Nodal induces bmp2/4 and chordin expression. Chordin prevents BMP2/4 signaling on the ventral side while it shuttles BMP2/4 to the opposite dorsal side, where BMP2/4 activates the gene regulatory network responsible for specification of the dorsal side of the embryo.
In conclusion, although Nodal remains the pivotal factor that regulates D/V axis formation in the sea urchin embryo, we have shown that the reaction-diffusion mechanism between Nodal and Lefty is not sufficient to break the radial symmetry of the embryo. This process of symmetry breaking is accomplished by a maternal factor, Panda, whose activity is required early and locally in the embryo to restrict the spatial expression of nodal. This work therefore illustrates how in the highly regulative sea urchin embryo, the secondary axis is already "penciled in " by the graded maternal information deposited into the egg in the form of a gradient of panda mRNA. Since nodal plays a key role in specification of the proximal distal axis in mammals and in specification of the secondary and left-right axes in a number of species, this raises the question as to whether members of the Panda/Maverick/GDF15 also provide a blueprint of axial development in these embryos.
Adult sea urchins (Paracentrotus lividus) were collected in the bay of Villefranche. Embryos were cultured as described in Lepage and Gache (1989, 1990) [73,74].
For immunostaining and in situ hybridization at early stages, fertilization envelopes were removed by adding 2 mM 3-amino-1,2,4 triazole 1 min before insemination to prevent hardening of this envelope, followed by filtration through a 75 μm nylon net. Treatments with recombinant BMP2/4 or Nodal proteins were performed by adding the recombinant protein diluted from stocks in 1 mM HCl, in 24-well plates containing about 1,000 embryos in 2 ml of artificial sea water [25]. Treatments with NiCl2 were performed by exposing embryos to 0.2–0.3 mM of chemical. All treatments were carried out from 30 min to 48 h post fertilization.
A full-length panda cDNA was obtained by screening a cDNA library with conventional methods and sequencing the corresponding clones. A full-length alk1/2 cDNA was identified from a collection of P. lividus expressed sequence tags (ESTs) http://octopus.obs-vlfr.fr/). The complete sequence of this clone was determined.
The accessions numbers of panda and alk1/2 mRNA are KF498642 and KF498643.
To make pCS2 Alk1/2-Q225D, the CAG codon encoding Glutamine in position 225 of Alk1/2 was mutated to GAC by oligonucleotide-directed in vitro mutagenesis using the two following oligonucleotides:
Alk1/2-Q225D fw: 5ʹ-cgaacagtagcaagagacatcaaccttattcaac -3ʹ
Alk1/2-Q225D rev: 5ʹ- gttgaataaggttgatgtctcttgctactgttcg-3ʹ
TGF-β sequences from deuterostomes (vertebrates, cephalochordates, hemichordates, tunicates, and echinoderms), from protostomes (arthropods and molluscs), and from cnidarians were recovered from Genebank (http://www.ncbi.nlm.nih.gov/) using well-characterized orthologs of each TGF-β family member from human or mouse. The list of accession numbers of the 162 sequences is provided in the Supplementary Materials (S1 Text). Full-length sequences were aligned using ClustalOmega with default parameters (http://www.ebi.ac.uk/Tools/msa/clustalo/), and gap optimization and obvious alignment error corrections were made using Bioedit 7.0.5.3 (http://www.mbio.ncsu.edu/BioEdit/bioedit.html). The full complement of TGF-β sequences was recovered and used in the analysis in the case of human, mouse, sea urchin, Saccoglossus, Branchiostoma, and Drosophila. However, only a subset of sequences from Gallus, Xenopus, Danio, Ciona, Crassostrea, Platynereis, Hydra, and Nematostella was included in the analysis. Trees were built either using the maximum likelihood method based on the Whelan and Goldman model [75] or with Mr. Bayes3.2, using the mixed model with two independent runs of 3 million generations [76,77]. In the case of the maximum likelihood analysis, the tree was calculated using PhyML [3] with substitution model WAG (http://atgc.lirmm.fr/phyml/). A consensus tree with a 45% cutoff value was derived from 500 bootstrap analysis using Mega 3.1 (http://www.megasoftware.net/). For the Bayesian analysis, consensus trees and posterior probabilities were calculated once the stationary phase was reached (the average standard deviation of split frequencies was below 0.01). Numbers above branches represent posterior probabilities, calculated from this consensus.
The nodal, chordin, foxA, foxG, tbx2/3, hox7, and onecut probes have been described previously [24,25,34]. The panda probe was derived from a full-length cDNA cloned in Bluescript, while the alk1/2 probe was derived from a full-length cDNA cloned in pSport-Sfi. Probes derived from pBluescript vectors were synthesized with T7 RNA polymerase after linearization of the plasmids by NotI, while probes derived from pSport were synthesized with SP6 polymerase after linearization with XmaI. Control and experimental embryos were developed for the same time in the same experiments. Double in situ hybridizations were performed following the procedure of Thisse [78]. Detection of the lineage tracer was performed using an antifluorescein antibody coupled to alkaline phosphatase and using Fastred as substrate.
For the time-course analysis of panda expression, total RNA from staged embryos was extracted by the method of Chomczynski and Sacchi [79] and treated with DNase I. cDNA synthesis and PCR were performed using standard procedures using 32–35 cycles of PCR [80]. For the characterization of the splice-blocking morpholino, RNA was extracted at the pluteus stage from batches of 400 embryos injected with increasing doses of the morpholino. Following treatment with DNase-I and phenol-chloroform extraction, RNA samples were reverse transcribed using the QuantiTect reverse transcription kit from Quiagen and following the instructions provided by the manufacturer. The in vivo specificity and efficiency of this morpholino were monitored via semiquantitative RT-PCR using 40 cycles of PCR. PCR primers flanking intron 1 were used to amplify the cDNA products generated in the presence of this splice-blocking oligonucleotide. Both the Phusion DNA polymerase and the kit long-expand PCR from Roche that allows amplification of long DNA fragments were used following the recommendations of the manufacturers. Primer pairs for the panda and mkk3 transcripts were derived from the open reading frames (respectively 1,482 bp and 1,020 bp):
panda-fwd: 5ʹ-GGAAAATGGCTCGACGCACATTCC-3ʹ
panda-rev: 5ʹ-TGAGCAGCCGCAACTTTCTACGACCATATC-3ʹ
mkk3-fwd: 5ʹ-ATGGCGAGTAAAGGTAAAAAG-3ʹ
mkk3-rev: 5ʹ-TTAACTATTCTCCGGATCTCC-3ʹ
The antibody we used is an anti-phospho-Smad1/5/8 from Cell Signaling (Ref 9511) raised against a synthetic phosphopeptide corresponding to residues surrounding Ser463/465 contained in the motif SSVS of human Smad5. Embryos were fixed in paraformaldehyde 4% in microfiltrated sea water (MFSW) for 15 min and then briefly permeabilized with methanol. Embryos were rinsed once with Phosphate Buffered Saline Tween (PBST), four times with PBST–bovine serum albumine (BSA) 2%, and incubated overnight at +4°C with the primary antibody diluted 1/400 in PBST supplemented with 2% BSA. Embryos were washed six times with PBST-BSA 2%, and then the secondary antibody diluted in PBST-BSA 2% was added to the embryos. In all cases, the antibody was incubated overnight at +4°C. For immunofluorescence, the secondary antibody was washed six times with PBST. Two last rinses were made with PBST-Glycerol 25% and 50%, respectively. Embryos were mounted in a drop of the Citifluor antibleaching mounting medium and then observed under a conventional fluorescence microscope or with a confocal microscope. For Alkaline phosphatase revelation, two rinses were made with PBST following the secondary antibody incubation, and two with Tris Buffered Saline Tween (TBST). Embryos were washed twice with the alkaline phosphatase buffer supplemented with Tween 0.1%, and staining was performed with nitro blue tetrazolium (NBT) and 5-bromo-4-chloro-3-indolyl phosphate (BCIP) as substrates at the final concentration of 50 mM each. In both cases, staining was stopped by four rinses with PBST + EDTA 5 mM and then two rinses with PBST containing glycerol at 25% and 50%. Embryos were mounted and observed with a DIC microscope.
Protein samples (20 μg/lane) were separated by SDS-gel electrophoresis and electrophoretically transferred to 0.2 μm PVDF filters. After blocking for 2 h with 5% milk in TBST, blots were incubated overnight with the anti-phospho-Smad1/5/8 antibody (Ref 9511) diluted 1/1,000 in BSA 5% in TBST. After washing and incubation with the secondary antibody, bound antibodies were revealed by ECL immunodetection using the SuperSignal West Pico Chemiluminescent substrate (Pierce).
For overexpression studies, the coding sequence of the genes analyzed was amplified by PCR with a high-fidelity DNA polymerase using oligonucleotides containing restriction sites and cloned into pCS2. Capped mRNAs were synthesized from NotI-linearized templates using mMessage mMachine kit (Ambion). After synthesis, capped RNAs were purified on Sephadex G50 columns and quantitated by spectrophotometry. RNAs were mixed with Rhodamine Lysine-Fixable Dextran (RLDX) (10,000 MW) or Fluoresceinated Lysine-Fixable Dextran (FLDX) (70,000 MW) at 5 mg/ml and injected in the concentration range of 100–2,000 μg/ml.
Wild-type panda and mutated panda mRNAs were injected at 1,000 μg/ml. mRNAs encoding the activated form of alk3/6 and alk1/2, Alk3/6Q230D (Alk3/6QD), and Alk1/2Q225D (Alk1/2QD) [25] were injected at 200 μg/ml. bmp2/4 and nodal mRNAs were injected at 400 μg/ml. To make the Panda and Alk1/2 rescue constructs, oligonucleotides containing nine mismatches in the sequences recognized by the morpholinos were used to amplify the coding sequences. The sequences of these oligonucleotides are as follows:
Panda-rescue: 5ʹ-CCCATCGATACCATGGCGAGGCGTACGTTGCAGCGCTTGCAAGGGAGC-3ʹ
Alk1/2-rescue: 5ʹ-CCCGGATCCACCATGGCCACCCGTCGTCTTGAGTTTATTTTTATACTTTTGG-3ʹ (mismatches underlined).
Morpholinos oligonucleotides were dissolved in sterile water and injected at the one-cell stage together with Tetramethyl Rhodamine Dextran (10,000 MW) or Fluorescein dextran (70,000 MW) at 5 mg/ml. For each morpholino, a dose-response curve was obtained, and a concentration at which the oligomer did not elicit nonspecific defect was chosen. Approximately 2–4 pl of oligonucleotide solution was used in most of the experiments described here. The sequences for morpholino oligonucleotides used in this study are as follows:
Panda-Mo-ATG: 5ʹ-ATCTTTGGAATGTGCGTCGAGCCAT-3ʹ
Panda-Mo1-splice: 5ʹ-TACTAATTTGGCGAGCCTACCTGTA-3'
Panda-Mo2-splice: 5'-CGGAGGTCCATCTGAACGAAAGAAA-3'
Panda-Mo-5' UTR: 5'-TTTCCTCGTGCTTGTAGAAATCTCC-3'
Alk3/6-Mo: 5'- TAGTGTTACATCTGTCGCCATATTC-3'
Alk1/2-Mo: 5'-TAAATTCTAGTCGTCGCGTCGCCAT-3'
BMP5/8-Mo: 5'-CTTGGAGAGAAAATAAGCATATTCC-3'
BMP2/4-Mo: 5'-GACCCCAGTTTGAGGTGGTAACCAT-3'
ADMP-Mo: 5'-ACACGAAAATAATCTCCATTGTCTT-3'
ACVRII-Mo-ATG: 5’- GGATCTTTCCCAGCCATTTCGGATA-3’
The panda, alk3/6, and alk1/2 morpholinos were used at 1.2 mM, except the panda Mo1 splice, which was used at 2 mM. The bmp2/4 and bmp5/8 morpholinos were used at 0.3 mM. The acvrII and admp morpholinos were used at 1.5 and 0.8 mM, respectively.
All the injections were repeated multiple times, and for each experiment, >100 embryos were analyzed. Only representative phenotypes present in at least 80% of the injected embryos are presented.
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10.1371/journal.pntd.0005963 | Impact of Enterobius vermicularis infection and mebendazole treatment on intestinal microbiota and host immune response | Previous studies on the association of enterobiasis and chronic inflammatory diseases have revealed contradictory results. The interaction of Enterobius vermicularis infection in particular with gut microbiota and induced immune responses has never been thoroughly examined.
In order to answer the question of whether exposure to pinworm and mebendazole can shift the intestinal microbial composition and immune responses, we recruited 109 (30 pinworm-negative, 79 pinworm-infected) first and fourth grade primary school children in Taichung, Taiwan, for a gut microbiome study and an intestinal cytokine and SIgA analysis. In the pinworm-infected individuals, fecal samples were collected again at 2 weeks after administration of 100 mg mebendazole. Gut microbiota diversity increased after Enterobius infection, and it peaked after administration of mebendazole. At the phylum level, pinworm infection and mebendazole deworming were associated with a decreased relative abundance of Fusobacteria and an increased proportion of Actinobacteria. At the genus level, the relative abundance of the probiotic Bifidobacterium increased after enterobiasis and mebendazole treatment. The intestinal SIgA level was found to be lower in the pinworm-infected group, and was elevated in half of the mebendazole-treated group. A higher proportion of pre-treatment Salmonella spp. was associated with a non-increase in SIgA after mebendazole deworming treatment.
Childhood exposure to pinworm plus mebendazole is associated with increased bacterial diversity, an increased abundance of Actinobacteria including the probiotic Bifidobacterium, and a decreased proportion of Fusobacteria. The gut SIgA level was lower in the pinworm-infected group, and was increased in half of the individuals after mebendazole deworming treatment.
| Whether human pinworm infection plus mebendazole deworming treatment can shift intestinal microbiota to a composition that is beneficial to the host and influence their mucosal immune response is currently unclear. In a cohort of 109 primary school children, we discovered that Enterobius vermicularis infection is associated with increased intestinal microbial diversity, a lowered relative abundance of Fusobacteria and an enriched proportion of Actinobacteria, including the probiotic Bifidobacterium. Mebendazole deworming was found to be correlated with a further increase in bacterial diversity. However, lower gut SIgA levels were detected in the pinworm infected group, and they were increased in only half of the subjects after mebendazole treatment.
| The inverse epidemiology data of parasitosis and autoimmune diseases has led to the hypothesis that childhood exposure to parasites might have protective effects against the development of allergies and autoimmunity [1]. The immunomodulatory roles of helminth have been well studied, and several helminth-derived components might regulate the immune system [2, 3]. Enterobius vermicularis (human pinworm) is the most common parasite encountered in developed countries, and it has been suggested as a good candidate for testing the link between the “hygiene hypothesis” and disease [4]. In Taiwan, enterobiasis is found in about 0.6–3% of primary school children [5, 6]. A previous study using peri-anal tape tests and questionnaires with Taipei primary school children suggested a negative correlation between pinworm infection and allergic airway diseases [7]. Similarly, enterobiasis has been found to be associated with a decreased risk of allergic wheezing in Turkish school-aged children [8]. However, a large population cohort study collecting data of mebendazole prescriptions and chronic inflammatory diseases in Denmark showed that enterobiasis does not reduce the risk for asthma, type 1 diabetes (type 1 DM), arthritis, or inflammatory bowel disease (IBD) [9]. These abovementioned reports lacked mechanistic studies and did not examine the interaction between pinworm and intestinal microbiota.
The imbalance of pro-inflammatory and anti-inflammatory gut bacteria, or dysbiosis, is associated with autoimmune diseases such as type 1 DM, IBD, rheumatoid arthritis (RA), along with pro-inflammatory conditions, such as obesity, atherosclerosis and colon cancer [10–12]. Since the parasites and intestinal commensal bacteria reside in the same environment, interaction between these two micro-organisms is conceivable. In humans, it has been reported that helminth infections may increase intestinal bacterial diversity, and alter the composition of microbiota [13–15]. Gut microbiota is suspected as causing T helper type 1 (Th1) responses in Trichuris muris infections in mice, and Schistosoma mansoni has been suggested to cause Th1-mediated inflammation and granuloma formation via alteration of microbiota [16, 17]. Furthermore, the protective mucosal immune response against Toxoplasma gondii has been reported to be provided by gut microflora that stimulate dendritic cells [18]. Recently, Ramanan et al. reported that helminth infections may restore the number of goblet cells via suppression of an intestinal pro-inflammatory Bacteroides species, and thus protect genetically susceptible mice from the development of Crohn’s disease [19]. Therefore, the net immunomodulatory effect of pinworm on an individual may be dependent on its interaction with that individual’s intestinal microbiota.
In this study, we examined the impact of Enterobius exposure on the composition of gut microflora, and we investigated the interactions among pinworm, microbiota, and host immune responses in a prospective cohort of 109 primary school-aged children. Through our observations of differences in probiotic bacteria abundance and changes in gut levels of the protective secretory IgA (SIgA), we hypothesized the possible correlations of pinworm and mebendazole exposure with the inflammation status of the gut.
The study cohort consisted of 109 primary school children (1st and 4th grades) who had undergone pinworm screening in 2015, Taichung, Taiwan. The study was approved by the Research Ethics Committee of China Medical University Hospital (CMUH104-REC1-115). Written informed consent was obtained from parents.
A total of 30 children were tested negative for enterobiasis, while 79 were tested positive by anal tape screening. In children with positive pinworm anal tape results, additional stool samples were collected in tipped ova concentration tubes and were stained and fixed with freshly prepared merthiolate-iodine formaldehyde (MIF). After further mixing with ethyl acetate and centrifugation at 1500 rpm for 5 minutes, the sediments were examined carefully under light microscope to detect the presence of co-infected helminth eggs or protozoans as described previously [20]. Stool specimens were collected again from 65 pinworm-infected individuals 2 weeks following 100 mg mebendazole treatment, which underwent MIF-microscopic examination and 16s rRNA gene sequencing. We did not detect co-infection with other helminths or protozoans by MIF-concentration-sedimentation method in pinworm (+) samples before and after mebendazole treatment (Table 1). As shown in the flow diagram (S1 Fig), metagenomics analysis was performed on 30 pinworm (-), 65 paired pinworm (+) mebendazole (-) and pinworm (+) mebendazole (+) samples.
Stool specimens were collected at home and transported to our laboratory within 3 hours in ice, and were fixed in Transwab tubes (Sigma, Dorset, UK). DNA extraction was performed using the QIAamp DNA Stool Mini Kit (Qiagen, California, USA). PCR primers F515 (5’-GTGCCAGCMGCCGCGGTAA-3’) and R806 (5’-GGACTACHVGGGTWTCTAAT-3’), were designed to amplify the V4 domain of bacterial 16S ribosomal RNA gene as described previously [21]. The Nextera adapter sequence (Illumina, California, USA) was added to the 5’-end of the primer set for library preparation. PCR using 50~150 ng DNA was performed with 1 cycle of 98°C for 30 sec, 30 cycles of 98°C for 10 sec, 60°C for 30 sec, 72°C for 30 sec, and a final extension of 72°C for 5 min. Amplicons were purified using the AMPure XP Beads (Beckman Coulter, Indianapolis, USA), and quantified using Nanophotometer (IMPLEN, München, Germany). The Illumina Nextera Index Primer kit was used to create the library. The qualities and quantities of purified libraries were checked by 2% agarose gel electrophoresis, Qubit (Thermo Fisher Scientific, Massachusetts, USA) and qPCR methods. Finally, libraries were normalized to the same concentration and sequenced by Illumina Miseq sequencer.
FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit) was used to process the raw read data files. Sequence qualities were checked in 3 steps: (i) The minimal acceptable Phred quality score of sequences was 20 (having over 70% of the sequence bases ≥ 20). (ii) Following quality trimming from the sequence tail, the sequences over 100 bp and those with an acceptable Phred quality score of 20 were retained. (iii) Both forward and reverse sequencing reads which met the first and second requirements were retained for subsequent analyses.
UPARSE [22] was used to create operational taxonomic unit (OTU) clustering. Bowtie2 [23] was then used to align OTUs with 16S rRNA gene sequences of bacteria. These sequences were taken from the SILVA ribosomal RNA sequence database (version 115). Following sequence data collection, sequences were extracted by V4 forward primer and reverse primer. To prevent repetitive sequence assignments, V4 sequences from SILVA were then grouped into several clusters by 97% similarity using UCLUST. A standard of 97% similarity with the database was applied.
Fecal samples were weighed before adding equal amounts of sterile PBS together with Pierce proteinase inhibitor (Thermo Fisher Scientific). After thorough mixing and centrifugation at 10000 g for 10 minutes, the fecal supernatants were stored at -80°C until analysis. Stool secretory IgA (SIgA) was analyzed using Immundiagnostik ELISA kit (Bensheim, Germany), IL-1ß, and IL-4 levels were measured using Quantikine ELISA kits (R&D Systems, Minneapolis, USA) according to manufacturer’s instructions.
We further grouped the samples according to levels of SIgA, IL-1ß, and IL-4. Based on median levels detected, SIgA was considered high at >150 μg/ml, and low at <80 μg/ml; IL-1ß was high at >10 pg/ml, and low at <0.5 pg/ml; and IL-4 was high at >10 pg/ml, and low at <5 pg/ml. In paired samples (collected before and after mebendazole treatment), SIgA level was considered to be elevated given a greater than 1.1-fold increase.
A rarefaction process was applied to normalize the operational taxonomic unit (OTU) table following taxonomy assignment in the bioinformatic analyses. Alpha diversity (Shannon index, inverse Simpson index and richness) was calculated. Beta diversity using weighted UniFrac distance metrics [24], principal coordinate analysis (PCoA) and unsupervised clustering were performed. Multiple response permutation procedure (MRPP) in an R package “vegan” (https://cran.r-project.org/web/package=vegan) was performed to assess community difference in PCoA. Wilcoxon rank sum test were used to compare non-paired variables (e.g., P- v.s. P+M-), and Wilcoxon signed rank test were used to compare paired variables (e.g., P+M- v.s. P+M+). The differentially expressed bacteria were filtered by the following criteria: (i) P value < 0.05 (ii) Fold change > 1.40 or < 0.71 (iii) At least one group achieved an average relative abundance of 0.5%. Sex and pair factors were adjusted. For multiple group comparisons the false discovery rate (FDR) was controlled by using Benjamini-Hochberg (BH) FDR multiple test correction. Pathway enrichment analysis was performed using an R package 'Tax4Fun' [25]. ANOVA test was used to calculate the enrichment difference. Mann-Whitney U tests using GraphPad Prism version 5 were performed to compare fecal cytokine and SIgA levels between groups. Wilcoxon signed rank test was used to analyse the paired stool SIgA data before and after mebendazole treatment.
We analyzed the influence of pinworm infection on gut microbiome in a cohort of 109 children in the first or fourth grade of primary school. Grade and sex effects were both insignificant among the groups (p = 0.650, p = 0.403, respectively, Table 1). In the pinworm (+) group, additional stool specimens were collected to perform MIF concentration sedimentation procedure on to detect co-infection of other parasites. As shown in Table 1, no co-infection was detected. In the pinworm (-) group, possible confounding factors including a mostly-meat diet, recent (within these 2 months) gastroenteritis, recent respiratory tract infection with oral medication, and recent confirmed use of antibiotics were recorded, and diversity analysis of the gut microbiota showed that recent respiratory tract infection with oral medication might decrease the intestinal microbial diversity (Table 1). Further differential abundance analysis at the phylum level showed that children with recent respiratory tract infection and oral medication (8 of 10 had possible antibiotics usage) had a trend to correlate with relatively less abundance of Fusobacteria (0.01%± 0.18% vs. 1.65%±4.62%, p = 0.03, FDR = 0.17). Information about the above confounding factors was not available in the pinworm (+) group.
The alpha diversity of the pinworm (+) mebendazole (+) group was significantly higher than the pinworm (-) group (inverse Simpson index, p = 0.002), and the alpha diversity was only marginally higher when comparing the microbial composition between the pinworm (+) mebendazole (-) group and the pinworm (-) group (p = 0.061, Fig 1a). The principle coordinate analysis also showed a significant beta diversity difference among the 3 groups (pinworm (+) mebendazole (+) group vs. pinworm (-) group, p = 0.001, Fig 1b).
Analysis of the intestinal microbiome operational taxonomic units (OTUs) of our cohort revealed the following major bacterial phyla: Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, Verrucobacteria, and Fusobacteria. The phylum microbial distribution pattern differed significantly in the proportion of Actinobacteria (pinworm (+) mebendazole (+) vs. pinworm (-), 1.08% ± 1.15% vs. 4.23% ± 5.52%, fold = 2.56, P = 5.19 x 10−4, FDR = 0.012, Fig 1c) and Fusobacteria (pinworm (+) mebendazole (+) vs. pinworm (-), 0.04% ± 0.17% vs. 1.10% ± 3.82%, fold = 0.04, P = 3.30 x 10−3, FDR = 0.012, Fig 1c).
At the genus level, a trend of higher relative abundance of Alistipes (fold = 2.56, p = 0.008) and Faecalibacterium (fold = 1.64, p = 0.004), and a decreased proportion of Fusobacterium (fold = 0.18, P = 0.050), Veilonella (fold = 0.25, p = 0.042), Megasphaera (fold = 0.28, p = 0.021), and Acidaminococcus (fold = 0.56, p = 0.030) were found in the pinworm (+) mebendazole (-) group as compared with the pinworm (-) group (Fig 2a). However, the corrected p values (FDRs) for all differentially distributed taxa were all > 0.05. In the 65 pinworm (+) mebendazole (+) subjects, the intestinal bacterial diversity further increased and was correlated with significantly more abundant Collinsella (fold = 3.04, p = 1.58 x 10−4, FDR = 0.028), Streptococcus (fold = 2.94, p = 1.32 x 10−3, FDR = 0.043), Blautia (fold = 1.71, p = 1.22 x 10−3, FDR = 0.043), as well as a lower proportion of Suterrella (fold = 0.30, p = 1.32 x 10−3, FDR = 0.043), as compared with the microbial composition of pinworm (+) mebendazole (-) group, Fig 2b. The relative abundance of the probiotic Bifidobacterium increased after pinworm infection, and it became even higher in the mebendazole treated group (pinworm (+) mebendazole (+) vs. pinworm (-), 7.32% ± 9.28% vs. 2.86% ± 3.67%, p = 1.97 x 10–3, FDR = 0.100, Fig 2a and 2b).
At the species level, pinworm infection was associated with a trend of increased proportions of Faecalibacterium prausnitzii, Ruminococcus flavefaciens, Alistipes purtredinis, Bifidobacterium longum and uncultured Oscillospira sp. (Fig 3a, percentages and p values are shown in S1 Table), as well as a trend of decreased relative abundance of Acidaminococcus intestine, Megasphaera elsdenii, Veillonella dispar and Fusobacterium varium (Fig 3b and S1 Table). The relative abundance of Faecalibacterium prausnitzii and Ruminococcus flavefaciens were lower, while the proportion of Bifidobacterium longum and uncultured Oscillospira sp. were higher after mebendazole deworming (Fig 3a, S1 Table). Mebendazole deworming was not associated with an increase in the relative abundance of Acidaminococcus intestine, Megasphaera elsdenii, Veillonella dispar and Fusobacterium varium as compared with the pinworm (+) mebedazole (-) group (Fig 3b).
Furthermore, as shown in Fig 3c, the relative abundance of Collinsella aerofaciens and Streptococcus thermophilus did not change significantly after pinworm infection; however, an increase in the proportion of these 2 species was detected 2 weeks after mebendazole deworming treatment (Collinsella aerofaciens: pinworm (+) mebendazole (+) vs. pinworm (+) mebendazole (-), 3.07% ± 5.52% vs. 1.00% ± 2.00%, fold = 3.08, p = 9.18 x 10−5, FDR = 0.034; Streptococcus thermophiles: pinworm (+) mebendazole (+) vs. pinworm (+) mebendazole (-), 0.89% ± 2.04% vs. 0.31% ± 0.42%, fold = 2.93, p = 0.003, FDR = 0.158, Fig 3c).
Taxonomic annotation-based enrichment analysis showed that the abundance of Gram-positive and endospore-forming bacteria was increased in the pinworm (+) mebendazole (+) group, as compared with the pinworm (-) group (p = 0.0001 and p = 0.001, respectively, S2a and S2b Fig). Further pathway enrichment analysis suggested that an enriched gut microbiome involving fat absorption and digestion pathway (ko04975) was associated with pinworm infection (pinworm (+) mebendazole (-) vs. pinworm (-), fold = 2.49, p = 0.014, S2c Fig), and the statistical significance remained when comparing the microbiota of the mebendazole treated group with the pinworm uninfected group (fold = 2.54, p = 0.007, S2c Fig). In addition, exposure to pinworm and mebendazole was found to be correlated with the enrichment of the gut microbiome involving the fatty acid elongation pathway (ko00062, pinworm (+) mebendazole (+) vs. pinworm (-), fold = 2.01, p = 0.001, S2d Fig) and the caffeine metabolism pathway (ko00232, pinworm (+) mebendazole (+) vs. pinworm (-), fold = 2.04, p = 0.002, S2e Fig).
To analyze the impact of Enterobius exposure on a host’s intestinal immune response, stool samples from pinworm-uninfected, pinworm-infected and untreated, and pinworm-infected and treated groups were collected and measured for their SIgA, IL-1ß and IL-4 levels. Pinworm infection was found to be associated with a significant decrease in gut SIgA levels (median level of uninfected vs. pinworm (+) mebendazole (-) group: 125.59 μg/ml vs. 109.56 μg/ml, p<0.01, Fig 4a). The amount of fecal IL-1ß and IL-4 were similar before and after pinworm infection (Fig 4b and 4c).
Furthermore, the fecal levels of SIgA and cytokines were grouped into low, medium and high as described in the Methods Section. In the pinworm (-) group, possible confounding factors such as recent gastroenteritis and respiratory tract infection with oral medication were collected; and in this group, correlation studies of SIgA and cytokine levels with intestinal microbial taxa revealed an association of a higher Prevotella proportion with a decreased amount of gut SIgA (relative abundance of Prevotella in SIgA medium-high vs. SIgA low, 6.22% ± 2.97% vs. 26.46% ± 11.52%, p = 0.006 (corrected for respiratory and gastrointestinal infection factors), Fig 4d), and association of a higher Collinsella abundance with a decreased amount of gut IL-4 (relative abundance of Collinsella in IL-4 medium-high vs. IL-4 low, 0.36% ± 0.21% vs. 1.69% ± 0.84%, p = 0.043 (corrected for respiratory and gastrointestinal infection factors), Fig 4d).
After mebendazole deworming, the amount of intestinal SIgA only increased in half of the treated subjects (Fig 5a). We then investigated the fecal microbial composition of the mebendazole-treated samples with and without SIgA-restoration. Before mebendazole deworming, the SIgA-non-increased group was associated with a higher proportion of the gut pathogen Salmonella (SIgA-non-increased vs. SIgA-increased group, 1.40% ± 3.21% vs. 0.18% ± 0.45%, p = 0.012, Fig 5b), and a lower abundance of the commensal Klebsiella, as compared with the SIgA-increased group (0.00% ± 0.00% vs. 0.74% ± 2.84%, p = 0.010, Fig 5b). Furthermore, mebendazole deworming was associated with increased percentages of Bifidobacterium and Streptococcus in the SIgA-increased specimens (P+M+ vs. P+M-, Bifidobacterium: 9.96% ± 12.53% vs. 5.83% ± 8.47%, p = 0.037; Streptococcus: 1.58% ± 3.23% vs. 0.27% ± 0.35%, p = 0.004, Fig 6a), and a decreased relative abundance of Salmonella in the SIgA non-increased subjects (P+M+ vs. P+M-, 0.12% ± 0.48% vs. 1.40% ± 3.21%, p = 0.010, Fig 6b). A mebendazole-deworming associated increase in the proportions of Collinsella was observed in both the SIgA-increased and the SIgA-non-increased groups (P+M+ vs. P+M-, SIgA-increased group: 2.08% ± 3.43% vs. 0.43% ± 0.69%, p = 0.002; SIgA-non-increased group: 3.71% ± 5.95% vs. 1.38% ± 2.43%, p = 0.018, Fig 6a and 6b).
At the species level, a higher proportion of Salmonella enterica was noted in pre-treatment samples of the SIgA-non-increased group (fold = 7.81, p = 0.012), and it decreased after mebendazole deworming (fold = 0.08, p = 0.010) (S3a and S3b Fig). The association of the increased relative abundance of the probiotic bacteria Streptococcus thermophilus and Bifidobacterium longum with mebendazole deworming was detected only in the SIgA-increased group (Streptococcus thermophilus: fold = 5.84, p = 0.006; Bifidobacterium longum: fold = 1.67, p = 0.046, S3c Fig).
We examined the impact of Enterobius vermicularis infection and the effect of mebendazole deworming on intestinal microbial composition and mucosal immune responses in 109 primary school children. Both enterobiasis and mebendazole deworming were associated with altered intestinal microbiome.
Consistent with a previous study on helminth-infected microbiota [14], pinworm infection in our study was associated with increased intestinal bacterial diversity. Furthermore, it has been reported that hookworm infection in human subjects with celiac disease could not only increase gut microbial richness, but also regulate gluten-induced inflammation [26]. We did not conduct a pinworm infection trial on human with chronic inflammatory diseases. However, in our study we found that Enterobiasis was associated with an increased relative abundance of Faecalibacterium prausnitzii and Alistipes species. Faecalibacterium prausnitzii has been shown to be underrepresented in the gut of patients with IBD, type 2 DM, and obesity; while Alistipes spp. has been reported to be overrepresented in irritable bowel syndrome (IBS) patients reporting abdominal pain, and in depressive individuals, suggesting a possible role in disturbing the intestinal serotonergic system [10, 27–31]. The pro-inflammatory role of Alistipes spp. remains speculative. As for the bacterial taxa which showed lower percentages after pinworm infection, both Veillonella spp. and Fusobacterium spp. have been suggested to be correlated with pro-inflammatory conditions such as ulcerative colitis and colon cancer, and Veillonella dispar and Fusobacterium varium have been detected in colon carcinoma in adenoma [32–34]. Whether bacterial species altered by pinworm favors an anti-inflammatory profile requires further investigations.
Anthony et al. (2007) noted that the major immune response raised against helminth infection is the Th2-type response, consisting of an expansion of Th2 helper T cells, eosinophils, mast cells, basophils, elevated IgE, IL-4 and other cytokines, including IL-5 and IL-13 [2]. A previous study in children in central Greece suggested a Th2-type oriented response to pinworms based on elevated serum levels of IgE and eosinophil cationic protein (ECP) [35]. However, we found no differences in the fecal IL-4 levels among the pinworm (-), pinworm (+) mebendazole (-), and pinworm (+) mebendazole (+) groups. In our study, mebendazole deworming was found to be associated with an increased proportion of Collinsella. Interestingly, in the pinworm (-) group, after correction for possible confounding factors including recent gastroenteritis and respiratory infection with oral medication (and possible antibiotics usage), an inverse correlation between Collinsella abundance and gut IL-4 level was detected. Additional research on germ-free animals is needed to evaluate the effect of pinworm and mebendazole on gut Collinsella and IL-4 levels.
IL-1ß is another cytokine that could be altered by parasites, and its over-activation is associated with chronic inflammatory diseases [36]. To establish the chronicity of infection, the murine helminth Heligmosomoides polygyrus bakeri (Hp) has been observed to downregulate the host’s IL-4 response by promoting IL-1ß production [37]. In contrast, the parasite Fasciola hepatica has been seen to directly inhibit host IL-1ß secretion [38]. We found that pinworm infection alone did not significantly alter the fecal IL-1ß.
The principle immunoglobulin involved in combating intestinal microbial infection and maintaining mucosal homeostasis with commensal bacteria is SIgA, which mediates anti-inflammatory functions via multiple mechanisms [39]. A lack of SIgA can also cause inflammatory diseases [40, 41]. Bacteroides thetaiotaomicron colonization in mice has been reported to elevate SIgA levels via an influx of IgA-producing B cells and an increase of polymeric immunoglobulin receptor (pIgR) that mediates the transport of IgA across epithelia [42]. In this study, pinworm infection was found to be correlated with lower gut SIgA level. Furthermore, the amount of intestinal SIgA was found to be negatively associated with the relative abundance of Prevotella. Whether this pinworm—microbial interaction influences gut SIgA production is unclear. Of note, after mebendazole deworming treatment, SIgA levels increased in half of the pinworm-infected subjects. We observed that the intestinal pathogen Salmonella enterica was overrepresented in the SIgA non-increased group, when compared with the SIgA-increased group. Although the relative proportion of Salmonella enterica was lower after mebendazole deworming in the SIgA-non-increased samples, the increase of the probiotic species Streptococcus thermophilus and Bifidobacterium longum following mebendazole treatment was only observed in the SIgA-increased group. Our results suggest that the relative abundance of Salmonella might have a negative effect on the mebendazole deworming -associated increase in the amount of SIgA and probiotic species in the gut.
Mebendazole is a classic anti-helminth drug, which is well-tolerated [43], and is routinely given to pinworm-positive school aged children. A Danish study on pinworm infection and risk of chronic inflammatory diseases even used mebendazole treatment history as a surrogate for enterobiasis diagnosis [9]. The results of our study show that increased percentages of the known probiotic species, Streptococcus thermophilus, and another anti-inflammatory bacterium, Collinsella aerofaciens [44], could be associated with mebendazole deworming, but not with pinworm infection alone. Our study is limited in that we did not use anal tape, a more sensitive method for detection of pinworm eggs than MIF concentration sedimentation, to evaluate the efficacy of mebendazole deworming. However, Wang CC et al. reported in J Microbiol Immunol Infect. 2009 that the efficacy of mebendazole treatment on eradicating pinworm in primary school children in Taichung, Taiwan, was 96% [6]. Mebendazole was found to have anti-inflammatory, anti-angiogenesis and oncogene-suppressing activities in a mouse model of colon cancer initiation [45]. The direct effect of mebendazole on gut microbiota composition remains to be investigated. An enrichment pathway analysis of the microbiome in our study showed that the increase in the percentages of microbes involved in the metabolism of fatty acid elongation and caffeine after pinworm infection only became significant when a comparison was made between the pinworm (+) mebendazole (+) group and the pinworm (-) group. Further metabolomics studies are needed to evaluate if pinworm and mebendazole treatment could alter the metabolism of commensal bacteria and subsequently influence a host’s immune system.
In our study, the change in microbial composition detected two weeks after administration of mebendazole on pinworm-infected children could be confounded by late onset effects of enterobiasis. A larger prospective cohort study with a longer follow-up on gut microbiomes will help to determine more exactly the duration and dynamics of the change in microbiota and in SIgA levels after mebendazole deworming treatment. Host genetics and diet are confounding factors for long-term follow up. Variations in the human genome have been found to favor the colonization of different gut microbiota [46]. A high fat and low fiber diet has been shown to be associated with reduced beneficial microbes producing short chain fatty acids (SCFAs) and thus such a diet increases the risk of inflammatory and autoimmune diseases [47]. In addition, our results revealed that recent respiratory tract infection with oral medication usage in the study population may be inversely correlated with intestinal microbial diversity and a decreased relative abundance of Fusobacterium and Acidaminococcus, which might interfere with the effect of pinworm infection. Thus, differential host genetics, lifestyles, respiratory tract infection rates and medication usage can all contribute to the inconsistent association of enterobiasis and the risk of inflammatory diseases observed in pediatric cohorts of various countries.
In conclusion, Enterobius vermicularis infections are associated with increased intestinal microbial diversity, and decreased gut SIgA levels. Several bacterial taxa exhibited differential abundance in pinworm (-), pinworm (+) mebendazole (-), and pinworm (+) mebendazole (+) groups. Mebendazole deworming was correlated with increased intestinal SIgA level and a higher proportion of probiotic bacteria in half of the infected subjects. To better understand the causal relationships of pinworm infection and mebendazole treatment on gut microbial composition and hosts’ immune responses, more experiments including animal studies are needed.
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10.1371/journal.pntd.0005290 | Evaluation of a Smartphone Decision-Support Tool for Diarrheal Disease Management in a Resource-Limited Setting | The emergence of mobile technology offers new opportunities to improve clinical guideline adherence in resource-limited settings. We conducted a clinical pilot study in rural Bangladesh to evaluate the impact of a smartphone adaptation of the World Health Organization (WHO) diarrheal disease management guidelines, including a modality for age-based weight estimation. Software development was guided by end-user input and evaluated in a resource-limited district and sub-district hospital during the fall 2015 cholera season; both hospitals lacked scales which necessitated weight estimation. The study consisted of a 6 week pre-intervention and 6 week intervention period with a 10-day post-discharge follow-up. Standard of care was maintained throughout the study with the exception that admitting clinicians used the tool during the intervention. Inclusion criteria were patients two months of age and older with uncomplicated diarrheal disease. The primary outcome was adherence to guidelines for prescriptions of intravenous (IV) fluids, antibiotics and zinc. A total of 841 patients were enrolled (325 pre-intervention; 516 intervention). During the intervention, the proportion of prescriptions for IV fluids decreased at the district and sub-district hospitals (both p < 0.001) with risk ratios (RRs) of 0.5 and 0.2, respectively. However, when IV fluids were prescribed, the volume better adhered to recommendations. The proportion of prescriptions for the recommended antibiotic azithromycin increased (p < 0.001 district; p = 0.035 sub-district) with RRs of 6.9 (district) and 1.6 (sub-district) while prescriptions for other antibiotics decreased; zinc adherence increased. Limitations included an absence of a concurrent control group and no independent dehydration assessment during the pre-intervention. Despite limitations, opportunities were identified to improve clinical care, including better assessment, weight estimation, and fluid/ antibiotic selection. These findings demonstrate that a smartphone-based tool can improve guideline adherence. This study should serve as a catalyst for a randomized controlled trial to expand on the findings and address limitations.
| Diarrheal disease is responsible for one in ten deaths among children less than five years of age globally. Innovative interventions to address gaps in the clinical care of these patients are lacking, yet will likely reduce the morbidity and mortality from diarrheal diseases. Therefore, the objective of this pilot study was to take a technology-enabled approach to improve guideline adherence, including antibiotic selection for diarrheal disease management in a resource-limited setting. To do this we adapted WHO guidelines to a smartphone platform and evaluated the approach in Bangladesh at two rural hospitals. The platform was durable and demonstrated positive improvement in guideline adherence. The results suggest that the decision-support tool was associated with a decrease in intravenous fluid use while maintaining safety, an increase in use of the recommended antibiotic, and a decrease in use of medications not recommended. This study represents a critical first step towards technology-enabled decision-support tools for diarrheal disease in resource-limited settings.
| The provision of high-quality clinical care in resource-limited settings is challenged by logistical, educational, and temporal constraints that are exacerbated by a high volume of patients. This is especially true for the management of diarrheal diseases, which may overwhelm health facilities amid outbreaks or seasonal swells of disease. These diseases disproportionately burden poor communities and remain the second leading cause of death for children less than 5 years of age [1–3]. While outpatient decision-support tools like the paper-based World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) have improved community efforts despite limitations [4–8], inpatient references like the WHO Pocketbook of Hospital Care for Children are often scarce or anecdotally don’t meet providers needs in high-volume situations [9].
These collective challenges manifest in poor guideline adherence and exacerbate the ongoing struggle to improve care for patients with diarrheal disease, especially during large-scale outbreaks. For example, weight-based dosing is fundamental to pediatric care, however equations to estimate weight are rarely both accurate and user-friendly/accessible at the bedside [10–13]. Secondly, the inappropriate use of antibiotics is driving the proliferation of drug resistant pathogens; however prescription behavior change is challenging with complex unpredictable barriers, especially in developing countries [14]. Lastly, complications from basic interventions, such as fluid resuscitation, are more nuanced in resource-limited settings than previously thought [15, 16]. Relatively high rates of post-discharge mortality suggest that improved in-hospital efforts might decrease overall morbidity and mortality [17].
This pilot study aimed to address a subset of these challenges by adapting the WHO diarrhea management guidelines onto a smartphone platform and evaluating the approach in resource-limited hospitals. A simple ‘Rehydration Calculator’ was developed based on end-user feedback; functionality included weight-for-age estimation, a dehydration assessment guide, and treatment recommendations. The calculator was evaluated in a clinical study at two remote hospitals in Bangladesh to determine how technology-enabled decision-support impacts standard of care for the management of diarrheal disease.
This pilot study was approved by the Institutional Review Boards (IRB) at the IEDCR (IEDCR/IRB/2015/03) and Stanford University School of Medicine (6208). Written informed consent was obtained from all adult participants and guardians of minors (< 18 years); assent was obtained for children 11–17 years of age. The human experimentation guidelines of the US Department of Health and Human Services were followed during the conduct of this research.
Two software modalities were developed and utilized in this study (Fig 1A; S1 Movie; S1 Software). First: the WHO-guidelines were adapted onto a smartphone for decision-support and coined the ‘Rehydration Calculator’ (Fig 1B and 1C). Its development relied on end-user guidance (e.g. field clinical providers) to make it fast, desired, and durable in high-volume, low-resource settings. The calculator captures no personal health information and is not password protected. The calculator does, however, capture basic information to calculate the recommended treatment plan (aka age, gender, watery/bloody stool, five clinical signs of dehydration, allergies, and danger signs). On the back-end, these symptoms are encrypted, stamped with the time and GPS location, and can be aggregated for syndrome-based surveillance. Second: a wireless data collection and aggregation platform (aka the Outbreak Responder platform) was built for the research team (aka ‘response team’). It was developed based on end-user guidance (e.g. field research staff and epidemiologists) to be durable in settings with limited connectivity, yet capture essential epidemiologic data. The user interface is organized as a medical chart and collectively captures critical demographic, syndromic, laboratory, and outcomes information. The sections are ‘Patient Information’, ‘History of Present Illness,’ ‘Exam’, and ‘Results’ (Fig 1D). The Outbreak Responder captures personal health information and is therefore, password protected, encrypted, and built to industry standards for health information data security. Both technologies are Android-based, function on/off line, and are available upon request (S1 Software). Although the software was built and tested using a locally produced low-cost smartphone (Walton, Bangladesh; 80 USD), a Samsung Note 3 device (200–400 USD) was used in this study because of the improved battery life, ideal screen size (5 in.), and screen responsiveness.
A subset of stool samples (first and last patient per day) underwent targeted culture and sensitivity testing for Vibrio cholerae, Salmonella spp. and Shigella spp. [33] in the IEDCR and the International Centre for Diarrhoeal Diseases, Bangladesh (icddr,b) laboratories; stool swabs were placed in Cary Blair media, stored at four degrees Celsius, and transported from the remote field site on a bimonthly to monthly basis. Assays for additional pathogens (e.g. ETEC, cryptosporidium, norovirus, rotavirus) were not performed because the intent of the cultures was to identify antibiotic sensitivity patterns for pathogens that clinically warrant antibiotic treatment, not for comprehensive surveillance.
Basic secondary outcomes were collected from the discharge record (e.g. discharge type, mortality). At 10-days post-discharge, the research staff called the patient/guardian to collect secondary outcomes (e.g. readmission, mortality). Data were aggregated and reported by the research staff via the Outbreak Responder platform; the post-discharge calls were also placed from within the software.
Patient characteristics were described by percentages for dichotomous (yes/no) variables and by the median, 1st and 3rd quartiles (Q1, Q3) for continuous data. For dichotomous variables, significant differences were assessed by the two-sided Fisher’s Exact test at the 0.05 level; the risk ratio (RR) was calculated by dividing the proportion of individuals with the event during the intervention period by the proportion of individuals with the event during the pre-intervention period; thus, an RR < 1 indicates the event is less likely to occur in the intervention and an RR > 1 indicates the event is more likely to occur in the intervention. For continuous variables, significance at the 0.05 level was assessed by either the two-sided Wilcoxon signed rank test (comparison to a median) or the two-sided Wilcoxon rank sum test. When calculating the weight-adjusted IV fluid volume and the recommended IV fluid volume, measured weights were used where available, and otherwise (i.e., sub-district intervention), estimated weights were used. To calculate congruence during the intervention between IV fluid volume ordered and the recommended IV fluid volume, we used the Wilcoxon rank sum test to test if volume ordered was more than 30% different than what was recommended.
Standard of care at both study hospitals relies on a best guess weight estimation without a measured weight, however weight was independently measured to evaluate the weight estimation performed by the calculator. To assess how well estimated weights matched measured weights for children younger than 15 years of age, we calculated the difference between estimated and measured weight and used the Wilcoxon signed rank test to evaluate the differences; measured weights were not adjusted for dehydration status. For groups of patients aged 15 and older, we compared the distribution of measured weights to the gender-specific weight estimates with the Wilcoxon signed rank test. Measured weights during the intervention at the sub-district were unfortunately not obtained, and therefore, to assess how well the estimated weights of patients younger than 15 years of age matched the measured weights, we included district measurements and only sub-district pre-intervention measurements. For those aged 15 and older, we compared estimated weights to district intervention measurements only (since we used measured weights during pre-intervention to obtain estimated weights for these ages). After determining that the weight estimates for children less than 5 years of age were too high, we evaluated WHO percentiles and identified the percentile that best approximated weight for age in this cohort as the percentile that minimized the root mean square error (RMSE) between measured weights and gender-specific WHO percentile estimated weights. All summary statistics and statistical analyses were completed in the statistical software package R version 3.2.2 [34]. All data except for a few restricted items are provided in the supplementary materials (S3 Dataset).
A total of 841 patients were enrolled and their records analyzed (Table 1). In the pre-intervention period, we enrolled 325 patients (204 district and 121 sub-district), and in the intervention period, we enrolled 516 patients (430 district and 86 sub-district). Onboarding periods prior to both the pre-intervention and intervention periods were permitted to accommodate for unanticipated additional training and mitigate possible Hawthorne effects (see timeline section). During these periods, 113 additional patients were enrolled but were excluded from the analysis. No patients were excluded for co-morbidities because in practice, these patients were admitted directly to the non-diarrheal wards for more advanced care prior to study screening. Seven patients (0.8%) had less than 3 loose stools in 24 hours and were excluded from the analysis (see inclusion criteria). Comparing the pre-intervention and intervention periods in the district hospital, we observed significant differences (p < 0.001) in the proportions of children less than 5 years and adults 20 years and older (Table 1); therefore, we controlled for age in subsequent analyses. A total of 50 doctors participated in the study with participation generally spanning study periods; 35 doctors participated at the district hospital (27 pre-intervention period; 17 intervention) and 15 doctors participated at the sub-district hospital (13 pre-intervention; at least 3 intervention)
A total of 277 targeted cultures were performed and identified V. cholerae (N = 19; 7%); Aeromonas spp. (N = 19; 7%), Shigella spp (N = 5; 2%), and Salmonella spp (N = 3; 1%); note that the approach was targeted and did not test for viral pathogens and other common bacterial pathogens. V. cholerae strains were sensitive to azithromycin (19/19) and ciprofloxacin (19/19), had intermediate sensitivity to cotrimoxazole (16/19) and tetracycline (14/17), and were resistant to erythromycin (19/19). One cholera patient was co-infected with at least Aeromonas spp.
The objective of this clinical pilot study was to evaluate the impact of a smartphone adaptation of the World Health Organization (WHO) diarrheal disease management guidelines. The calculator was associated with significant and positive prescription change towards guideline adherence. The findings support our hypothesis that technology-enabled decision-support tools can promote evidenced-based practice in resource-limited settings.
With respect to basic medical care, all medical facilities should have a functional and suitable weight scale. However, this minimal standard is frequently untenable in resource-limited settings like those in this study, and therefore, clinicians are forced to estimate weight. We designed the calculator to estimate pediatric weight for age by -1 z-score during the study; however, post-study analysis showed measured weights approximated best to the 5th (females) and 3rd (males) WHO percentiles for children less than 5 years of age while estimates for older age groups did not differ significantly from measured weights. These low percentiles in children under 5 years are not likely to be due to the degree of dehydration at admission alone, but suggest that admitted patients in this age range may be a particularly vulnerable subset of the population. Future larger studies are required to expand on these findings. Additional anthropomorphic measurements (e.g. mid-upper-arm circumference length; MUAC) may also provide evidence that decision-support tools must be built to leverage locally available data in order to optimize weight and other anthropomorphic estimations. In the preparatory phase, we experienced surprising resistance to the use of MUAC bands for weight estimation or for malnutrition assessment [35, 36] because of past experience with the time required to take an accurate MUAC and difficulty maintaining MUAC strip supplies.
Rehydration Calculator deployment was associated with a reduction in overall IV fluid prescriptions but an increase in the volume ordered when IV fluids were prescribed. Previous studies have shown that transitioning low and medium risk patients towards oral rehydration solution reduces the risk of complications, provides cost-savings benefit to both patients and institutions, and remains effective [37]. The weight measurements enabled the calculator to provide weight-based dosing of fluids for the pediatric patients. The adult patients were often dosed with categorical fluid volumes (e.g. 1, 2, and 3 liters) which may be more practical for staff. In the intervention period, the fluid volumes ordered versus recommended for severely dehydrated patients during the intervention were congruent. In a setting like Netrokona District that treats 5000 diarrheal patients monthly, a safe decrease in IV fluid use represents significant overall cost-savings to the medical system while empowering clinicians with the ability to increase fluids volumes to the recommended dose when IV fluids are indicated. Therefore in a venue like Netrokona with low diarrhea-associated mortality, a safe reduction in IV fluid orders may be an important advantage of decision-support tools like the Rehydration Calculator. Future studies in areas with high morbidity and mortality are required to evaluate the physiologic benefits.
The Rehydration Calculator was associated with significant and positive antibiotic class-switching to the recommended agent azithromycin. Almost all patients were described as having acute watery diarrhea with moderate or severe dehydration. For these reasons, azithromycin was recommended for almost all patients out of concern for cholera. However, 7% of samples cultured were positive for V. cholerae [all tested strains were azithromycin-sensitive]. The presumed remaining 93% of patients with non-bloody (e.g. non-Shigellosis, non-Salmonellosis, non-Campylobacter) and non-cholera diarrhea likely did not require antibiotics. Although the calculator provided objective dehydration classification, we must also encourage clinicians to accurately report ‘acute watery diarrhea’ and deploy point-of-care diagnostic surveillance for V. cholerae to decrease the prescription of antibiotics. Although there was a small increase (1 day) in the duration of post-discharge days of diarrhea at the sub-district hospital, a larger study will be required to support this finding and determine if this finding has clinical significance. More importantly, this study should spark discussion on the utility of using the phrase “acute watery diarrhea” given that casual use may have contributed to an overuse of antibiotics. A reasonable alternative, despite some culture and physiologic nuance, would be ‘rice-water stool’ because this phrase implies that the clinician highly suspects cholera.
The first goal of this study was to improve adherence to WHO-derived guidelines that are evidence-based and considered the gold standard for safety and effectiveness. Although we were only reformatting these guidelines to a smartphone medium, we monitored secondary safety related outcomes, and found only minor differences in length of stay and type of discharge. At the district hospital during the intervention, there was one post-discharge death that occurred after the majority of the diarrheal symptoms had resolved (see hospital and post-discharge course). This event generated a case fatality rate (CFR) of 0.1% for the entire study (1 out of 954 total patients including patients in the onboarding periods) and 0.3% for the district intervention which are similar to expected CFRs at similar sites [17, 19]. These preliminary data suggest that the Rehydration Calculator is a safe tool to improve guideline adherence for diarrheal disease decision-support. Future studies that include more detailed physiologic assessments may provide further avenues to assess safety and support these initial findings.
A second goal was to develop the Rehydration Calculator to be scalable and desired by medical professionals in hospitals like those in Netrokona. Frameworks are being built to guide development and evaluation of smartphone decision-support tools [38]. Scalability of the Rehydration Calculator will likely depend on the use of personal smartphones. This is feasible because the Rehydration Calculator has minimal to no associated cost, captures no personal health information, and does not rely on connectivity. Venues for Rehydration Calculator deployment are broad. For example, Bangladesh and Pakistan both have considerable diarrheal disease, including cholera, and rank among the top ten globally for cell phone penetration per capita [39]. Professionals in these countries are rapidly transitioning to affordable Android smartphones. It also remains possible for institutions to embrace the Rehydration Calculator. This would require purchasing dedicated phones, training, and technical support to maintain the phones. Future studies will require rigorous qualitative and quantitative analysis to further these goals of scalability for both individual and/or institutional deployment.
Scalability also relies on desirability. Although an anonymous user acceptance survey was not performed, feedback was obtained in a group format to assess end-user experience. Clinicians expressed that the calculator expedited both clinical assessment and reduced the time required to generate a treatment plan (3–5 minutes). Additionally, use of the calculator conferred a level of professional prestige that was attractive. Physicians were allowed to deviate from the guidelines as needed. Clinicians vocalized that deviation was important when patients expected IV fluids or when emesis made oral rehydration inappropriate. These experiences likely manifested in IV fluid orders for patients with some or no dehydration (Fig 4A). Despite the lack of user acceptance surveys, the design of the Rehydration Calculator appeared to match clinician needs. Future studies are required to explore these preliminary observations.
The findings in this study should be viewed within the context of the limitations of the study design and available data. The study site is likely generalizable to remote rural settings with a significant burden of water-borne disease. However, the remoteness also made exhaustive oversight difficult and contributed to the limitations: (i) The first limitation was a suspected under-enrollment of patients at the district hospital during the pre-intervention period compared to intervention period. This suspected under-enrollment may have contributed to the age difference between the pre-intervention and intervention periods; we therefore accounted for age in the analysis. (ii) The second limitation occurred during the intervention period at the sub-district hospital where weight measurements were not obtained due to a work-flow issue. Thus, when evaluating the accuracy of weight estimates, we excluded sub-district patients enrolled during the intervention period. (iii) There was no independent assessment of dehydration during the pre-intervention phase, medications and fluids prescribed were not confirmed to have been administered, and objective physiologic outcome measures were not reliably obtained. The data collection method also relied heavily on the physician transcribing the assessment and plan to the paper chart, and then the researcher transcribing this information to the digital data collection platform. Future studies need a tractable method to validate accuracy through this chain of events; independent assessment would provide added quality control for these steps. (iv) The study was designed as a pilot study, and although the results from the two sites were similar, there was no concurrent control group; this puts the study at risk of temporal variation during the 12-week study. (v) Exclusion criteria were unbalanced because severely malnourished patients were excluded during the intervention because of limited clinical capacity, however, this limitation actually had no impact on the results because no patients were excluded for severe malnutrition alone. (vi) The study was not designed to perform sub-analysis at the level of individual clinicians because of logistical limitations and sensitivity concerns amongst stakeholders. Despite these limitations, the core results from this study were robust and can be viewed as a critical step towards improving the quality of care in remote regions inflicted with diarrheal disease. These findings warrant further investigation with a cluster randomized controlled trial (cRCT). One approach to address physician gestalt and make the cRCT more generalizable and standardized would be to randomize the control study arm to use a paper-based version of WHO guidelines via a laminated card and the intervention arm to the WHO guidelines on the Rehydration Calculator.
In conclusion, this pilot study highlights that significant improvement towards guideline adherence can be achieved when clinicians are provided a tool such as the Rehydration Calculator. The study also demonstrates how a simple tool can standardize the assessment of dehydration status, which is a critical first step to understanding physiologic determinants of severe disease and identifying opportunities to improve clinical acumen. Upon further investigation in a cRCT, we hope that tools like the Rehydration Calculator in the hands of frontline providers will contribute to the ongoing drop in morbidity and mortality from diarrheal disease as well as usher in a new era of clinical approach and scientific understanding.
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10.1371/journal.pntd.0000638 | Discovery of Markers of Exposure Specific to Bites of Lutzomyia longipalpis, the Vector of Leishmania infantum chagasi in Latin America | Sand flies deliver Leishmania parasites to a host alongside salivary molecules that affect infection outcomes. Though some proteins are immunogenic and have potential as markers of vector exposure, their identity and vector specificity remain elusive.
We screened human, dog, and fox sera from endemic areas of visceral leishmaniasis to identify potential markers of specific exposure to saliva of Lutzomyia longipalpis. Human and dog sera were further tested against additional sand fly species. Recombinant proteins of nine transcripts encoding secreted salivary molecules of Lu. longipalpis were produced, purified, and tested for antigenicity and specificity. Use of recombinant proteins corresponding to immunogenic molecules in Lu. longipalpis saliva identified LJM17 and LJM11 as potential markers of exposure. LJM17 was recognized by human, dog, and fox sera; LJM11 by humans and dogs. Notably, LJM17 and LJM11 were specifically recognized by humans exposed to Lu. longipalpis but not by individuals exposed to Lu. intermedia.
Salivary recombinant proteins are of value as markers of vector exposure. In humans, LJM17 and LJM11 emerged as potential markers of specific exposure to Lu. longipalpis, the vector of Leishmania infantum chagasi in Latin America. In dogs, LJM17, LJM11, LJL13, LJL23, and LJL143 emerged as potential markers of sand fly exposure. Testing these recombinant proteins in large scale studies will validate their usefulness as specific markers of Lu. longipalpis exposure in humans and of sand fly exposure in dogs.
| Leishmania parasites are transmitted by the bite of an infected vector sand fly that injects salivary molecules into the host skin during feeding. Certain salivary molecules can produce antibodies and can be used as an indicator of exposure to a vector sand fly and potentially the disease it transmits. Here we identified potential markers of specific exposure to the sand fly Lutzomyia longipalpis, the vector of visceral leishmaniasis in Latin America. Initially, we determined which of the salivary proteins produce antibodies in humans, dogs, and foxes from areas endemic for the disease. To identify potential specific markers of vector exposure, we produced nine different recombinant salivary proteins from Lu. longipalpis and tested for their recognition by individuals exposed to another human-biting sand fly, Lu. intermedia, that transmits cutaneous leishmaniasis and commonly occurs in the same endemic areas as Lu. longipalpis. Two of the nine salivary proteins were recognized only by humans exposed to Lu. longipalpis, suggesting they are immunogenic proteins and may be useful in epidemiological studies. The identification of specific salivary proteins as potential markers of exposure to vector sand flies will increase our understanding of vector–human interaction, bring new insights to vector control, and in some instances act as an indicator for risk of acquiring disease.
| Sand fly salivary proteins play a major role in blood feeding and Leishmania transmission [1]–[3]. Exposure to sand fly salivary proteins induces both cellular immunity and specific antibodies [3],[4]. A relationship between the level of specific antibodies to saliva, vector exposure and risk of contracting disease has been demonstrated for different vector-host models [5]–[9]. Production of antibodies against mosquito and tick saliva not only contributed to development of host allergic reactions but was strongly related to risk of disease development [5],[10]. Similarly, in an endemic area of Senegal, production of antibodies against Anopheles gambiae salivary proteins was identified as an indicator of the risk of malaria [10]. This correlation was also observed for tick exposure, where antibody production against tick saliva was associated with self-reported tick exposure and Lyme disease [11]. Recently, saliva of Triatoma infestans was shown to be a potential marker for vector infestation in domestic animals [12]. Therefore, the detection of antibodies against the saliva of hematophagous insect vectors could be used as an indicator of vector exposure and in some instances as an indicator for risk of contracting disease.
Previous work shows that humans and animals exposed to sand fly bites or immunized with saliva can develop antibodies that recognize specific salivary proteins [4], [7], [13]–[15]. In São Luis, an area of endemic visceral leishmaniasis (VL) in Maranhão, Brazil, the presence of anti-saliva antibodies in humans strongly correlated with protection and the development of anti-Leishmania delayed-type hypersensitivity response [7]. Furthermore, individuals that poorly recognized salivary proteins developed anti-Leishmania antibodies associated with disease progression [7]. In contrast, in areas endemic for cutaneous leishmaniasis (CL)—such as Canoa (Bahia, Brazil) and Sanliurfa (Turkey)—the presence of anti-saliva antibodies correlated with risk of contracting disease [16],[17].
The presence of antibodies to sand fly salivary proteins has also been demonstrated in animal reservoirs of leishmaniasis. In canines, two sand fly salivary proteins were recognized by sera of infected dogs from an endemic VL area in Brazil [18]. Hostomska et al. [14] reported the presence of anti-saliva antibodies to six different sand fly proteins in dogs experimentally exposed to Lutzomyia longipalpis bites. Importantly, foxes captured in Teresina, an endemic VL area in Brazil, also showed high levels of anti-saliva antibodies, particularly to a 44-kDa salivary protein from Lu. longipalpis, suggesting exposure to bites of this vector [19]. Hence, vector salivary proteins also represent a potential tool as markers of exposure to important reservoirs of disease.
Identification of the sand fly salivary proteins recognized by the mammalian host will not only increase our understanding of vector-host interactions but will also aid in developing new epidemiological tools to correlate host exposure to vector sand flies with immunity or susceptibility to leishmaniasis. It will also help identify potential reservoirs of Leishmania. Here we describe a practical functional transcriptomic approach for the identification of the Lu. longipalpis salivary proteins most recognized by humans and canids (dogs and foxes) using sera from São Luis and Teresina, endemic areas for VL in Brazil [15],[20].
Lu. longipalpis (Jacobina strain) were reared at LMVR, NIAID, USA; Lu. verrucarum (Peru strain) and Phlebotomus perniciosus (Italy strain) at WRAIR, USA; Lu. intermedia (Corte de Pedra strain) were obtained from CPqGM (FIOCRUZ, Bahia, Brazil). Females were used for dissection of salivary glands 5–8 days post-eclosion; SGH was prepared as described elsewhere [21]. Briefly, salivary glands were dissected and stored in sterile PBS (pH 7.4) at −70°C. To obtain the homogenate, salivary glands were disrupted by ultrasonication and the supernatant collected after centrifugation at 15,000g for 2 minutes.
A total of 14 human sera from from a VL-endemic region in São Luis (Maranhão, Brazil) [15] and 6 from a CL-endemic region in Canoa (Bahia, Brazil) [22] were used in this study. Informed written consent was obtained from parents or legal guardians of minors. The project was approved by the institutional review board from the Federal University of Bahia (1993) and the Federal University of Maranhao (1996). Dog and fox (Cerdocyon thous) sera (total of 8 and 11, respectively) were from animals captured in a VL-endemic area around Teresina (Piaui, Brazil) [19]. Fox and dog studies were approved in 2000 by the Brazilian agency for protection of the wildlife (IBAMA/PI) and in 2005 by the Federal University of Piaui. Sera were also obtained from dogs (total of 6) experimentally exposed to Lu. longipalpis [14]. Dog studies were approved by Bayer Health Care AG (Leverkusen, Germany) and handled in accordance with the European guidelines for animal husbandry.
DNA was amplified by polymerase chain reaction (PCR) using a forward primer deduced from the amino-terminus and a reverse primer encoding a hexhistidine motif. PCR amplification conditions were: one hold of 94°C 5 min, two cycles of 94°C 30 s, 48°C 1 min, 72°C 1 min, 23 cycles of 94°C 30 s, 58°C 1 min, 72°C 1 min, and one hold of 72°C 7 min. The PCR product was cloned into the VR2001-TOPO vector and sequenced [23].
A plasmid encoding a distinct salivary protein (1 µg/µl) was injected intradermally into female Swiss Webster mice in 10 µl, three times at two-week intervals to generate polyclonal antibodies for each of the nine selected candidates [23].
Recombinant proteins were produced by transfecting 293-F cells (Invitrogen) with plasmid following the manufacturer's recommendations. After 72 h, the supernatant was recovered, filtered and concentrated to 30 ml in an Amicon concentrator device (Millipore) in the presence of Buffer A (20 mM NaH2PO4, 20 mM Na2HPO4, pH 7.4, 500 mM NaCl). A HiTrap chelating HP column (GE Healthcare) was charged with 5 ml of 0.1M Ni2SO4. The concentrated protein was added to the HiTrap chelating HP column that was then connected to a Summit station HPLC system (Dionex, Sunnyvale, CA) consisting of a P680 HPLC pump and a PDA-100 detector. The column was equilibrated for 30 min with Buffer A at 1 ml/min. Elution conditions were: 0-5 min, 100% Buffer A; 5-15 min, a gradient of 0% to 100% Buffer B ( Buffer A+50 mM imidazole); 15-20 min, a gradient of 0% C (Buffer A+500 mM imidazole) to 10% C (90% B); 20-25 min, 90% B and 10% C; min 25-30, a gradient of 10% C to 20% C (80% B); 30-35 min, 80% B and 20% C; 35-40 min, a gradient of 20% C to 100%C; and 40-50 min, 100% C. Eluted proteins were detected at 280 nm and collected every minute on a 96-well microtiter plate using a Foxy 200 fraction collector (Teledyne ISCO).
Five-microliter aliquots of all fractions were blotted on nitrocellulose and blocked with TBS-tween 3% non-fat milk for 1 h and then incubated for 1 h with anti-saliva antibodies, washed, and incubated for 1 h with an anti-mouse IgG (H+L) alkaline phosphatase-conjugated secondary antibody (Promega). Positive fractions were developed with Western Blue stabilized substrate for alkaline phosphatase (Promega). Positive fractions were run on sodium dodecyl sulfate (SDS-PAGE) and silver stained using SilverQuest (Invitrogen). Imidazole was removed from positive fractions by dialysis overnight against PBS, pH 7.4.
Salivary glands (40 pairs approximately equivalent to 40 µg total protein) or soluble recombinant sand fly salivary proteins (20 µg) were run on a 4–20% Tris-glycine gel or on a 4–12% NuPAGE gel. After transfer to a nitrocellulose membrane using the iBlot device (Invitrogen), the membrane was blocked with 3% (w/v) nonfat dry milk in Tris-buffered saline (TBS)-0.05% Tween, pH 8.0, overnight at 4°C. After washing with TBS-T, pH 8.0, the membrane was placed on a mini-protean II multiscreen apparatus (Bio-Rad, Hercules, CA), and different lanes were incubated with various sera (1∶80 dilution, human and dog sera; 1∶50 dilution, fox sera) for 3 h at room temperature. After washing with TBS-T, pH 8.0, three times for 5 min, the membrane was incubated with either anti-dog IgG (H+L) alkaline phosphate-conjugated antibody (1∶10,000) (Jackson Immuno Research) for 1 h at room temperature for dog and fox sera or with anti-human IgG alkaline phosphate-conjugated antibody (1∶8,000) (Sigma) for human sera. Membranes were developed by addition of Western Blue stabilized substrate for alkaline phosphatase (Promega), and the reaction was stopped by washing the membrane with deionized water.
Lu. longipalpis salivary glands contain a large number of secreted proteins (figure 1A). Fourteen human sera from individuals living in São Luis, an area where Lu. longipalpis predominates, recognized a considerable number of these proteins, mainly between 15 and 65 kDa (figure 1B). Eight dog sera from Teresina recognized a large number of salivary proteins, many of the same size as human sera as well as several proteins of different sizes (figure 1B). As for foxes, 11 sera collected in the same endemic area as dogs recognized only a few salivary proteins and only one strongly of approximately 50 kDa (figure 1B).
To determine the specificity of human and dog sera for Lu. longipalpis salivary proteins, we tested the most reactive human (two) and dog (one) sera against salivary proteins from other sand fly species including Lu. intermedia which transmits CL in South America [24], Lu. verrucarum which transmits CL in Central and South America [25], and Phlebotomus perniciosus which transmits VL in Mediterranean countries [26]. Human sera recognized multiple bands of Lu. longipalpis saliva (figure 2A). One of the 2 human sera also recognized two salivary proteins from Lu. intermedia (figure 2A). We cannot exclude the possibility that this individual was weakly exposed to Lu. intermedia bites, as this species is also present, albeit rare, in São Luis [20] or to other non-abundant species in the area. All tested sera recognized proteins between 28 and 50 kDa from Lu. verrucarum saliva and a protein of approximately 40 kDa from P. perniciosus saliva (figure 2A); both species do not overlap with Lu. longipalpis in their geographical distribution.
Given that Lu. longipalpis and Lu. intermedia are sympatric in several areas of Brazil [27] we decided to further investigate the possibility of antibody cross-reactivity between these two species. To address this, we tested six human sera from Canoa (an area in Brazil endemic for CL where Lu. intermedia predominates) against Lu. longipalpis saliva. These sera did not recognize any of the salivary proteins of Lu. longipalpis but recognized those of Lu. intermedia (figure 2B). Together, these results suggest an overall low level of cross-reactivity between Lu. longipalpis and Lu. intermedia salivary proteins.
Because in an endemic area there is no control of the diversity and intensity of exposure of hosts to sand fly bites—both of which can influence antibody response [14]—we compared dogs from Teresina, where Lu. longipalpis is prevalent, with dogs experimentally exposed to Lu. longipalpis bites. Overall, the reactivity of sera from experimentally exposed dogs was considerably lower than that of dogs from Teresina. Both dogs from Teresina and experimentally exposed dogs recognized proteins between 15 to 65 kDa (figure 3). Both groups recognized multiple proteins in Lu. longipalpis saliva but also a few in the saliva of Lu. verrucarum and P. perniciosus. This, together with results from human sera, suggests that antibodies against these proteins may be cross reactive for these two species. Additionally, while proteins from 28 to 50 kDa from Lu. intermedia were recognized by sera of dogs from Teresina, only one protein was poorly recognized by sera from experimentally exposed dogs. Sera from foxes were also tested but showed no cross-reactivity with the other species (data not shown).
Nine abundant transcripts corresponding to the predicted molecular weight of the most antigenic salivary proteins recognized by human, dog, and fox sera within the range of 15 to 65 kDa (figure 1) were selected for expression (Table 1). Figure 4 shows a flow diagram of the approach used to express and purify the nine chosen recombinant salivary proteins. Notably, the same DNA plasmid is used for recombinant protein expression and antibody production. Nine different salivary proteins were expressed and a high level of purification was achieved by HPLC. Purification of recombinant salivary protein LJM17 resulted in a well separated peak eluting at 35–40 min (figure 4B). Aliquots of eluted fractions were recognized by sera of mice immunized with LJM17 DNA plasmid (figure 4C). SDS-PAGE of positive fractions shows a single band of approximately 50 kDa (figure 4D), the expected size predicted by the LJM17 transcript. Similar results were obtained with the other eight expressed proteins: LJM111, LJM11, LJL143, LJL13, LJL23, LJM04, LJL138, and LJL11 (data not shown).
To determine whether the nine expressed salivary recombinant proteins were recognized by sera from humans, dogs, and foxes, we chose those that recognized a considerable number of proteins (from total sand fly saliva) with some degree of variability for further analysis by western blot. Of the nine recombinant proteins tested, LJM17, a yellow-related protein of 45 kDa, was the only protein recognized by sera from the 3 different hosts (figure 5; data for foxes not shown). LJM11, a 43-kDa protein also of the yellow family of proteins, was recognized by human and dog sera, while a third yellow-related protein, LJM111 (43 kDa) was only recognized by human sera (figure 5). LJL23, LJL13, and LJM04 proteins were recognized only by dog sera; LJL143 was recognized by dog sera and weakly recognized by human sera (figure 5). LJL11 and LJL138 were not recognized by any of the sera tested (data not shown).
To confirm the specificity of LJM17 and LJM11 as potential markers of Lu. longipalpis exposure, we tested human sera from São Luis and Canoa where Lu. longipalpis and Lu. intermedia predominate, respectively. Both LJM17 and LJM11 were recognized specifically by human sera from São Luis but not from Canoa (figure 6).
Among parasitic diseases, leishmaniasis has one of the most complex epidemiologies. There are numerous Leishmania species and some cause a wide range of clinical manifestations and involve a large number of proven and potential reservoir hosts. In most cases, each form of leishmaniasis is transmitted by a sand fly species that acts as a principal vector; a few Leishmania species have multiple vector species [28]. In addition, endemic areas of leishmaniasis support several sand fly species other than the vector species responsible for Leishmania transmission. Finding tools to measure exposure of humans and reservoir hosts to specific vectors would provide valuable information regarding their contribution to parasite transmission and would be useful for assessing the risk of contracting disease.
Several studies have demonstrated that anti-saliva antibodies can be used to assess exposure of humans and other Leishmania hosts to sand fly bites and suggested that sand fly salivary proteins represent attractive targets for development of specific markers of vector exposure [4],[14],[16]. To date, none of the salivary proteins of sand flies have been characterized for their immunogenicity and specificity in mammalian hosts, an important prerequisite for their reliability as markers of exposure.
In the present work, we developed a robust method for producing and purifying recombinant salivary proteins. This approach proved highly successful in obtaining pure recombinant proteins that retain recognition epitopes. The purity of the produced recombinant salivary proteins is demonstrated by the presence of a single protein band following SDS-PAGE and silver staining; this level of purity was obtained for all recombinant salivary proteins tested. Soluble and pure recombinant proteins are likely to be properly folded and to better resemble native proteins. This should improve the sensitivity of the detection, enhancing recognition of such proteins by test sera and increasing the specificity of a test by decreasing the chances of false negatives often caused by impurities in the preparation. It is worth noting that the tested salivary recombinant proteins demonstrated specific responses, as two (LJL11 and LJL138) of the nine proteins were not recognized by any of the tested sera.
The seven immunogenic recombinant salivary proteins were differentially recognized by human, dog, and fox sera, the three host species investigated. Some proteins displayed host-specific recognition, reinforcing the importance of testing potential salivary markers for exposure against a variety of hosts to determine their range of applicability. Recombinant proteins LJM17 and LJM11 were strongly recognized by human sera from São Luis, a VL endemic area where Lu. longipalpis predominates, representing 66.4% of captured sand flies [29]. Notably, human sera obtained from Canoa, a CL area where Lu. intermedia represents 94% of the sand fly population [22], did not recognize LJM17 or LJM11. Lu. longipalpis and Lu. intermedia are sympatric species in many endemic areas of Brazil, often representing the two most abundant sand fly species [27]. As such, LJM17 and LJM11 can be considered as potential specific markers of exposure to Lu. longipalpis in areas where other man-biting sand fly species are present in negligible numbers [20],[24],[27]. LJM17 and LJM11 should be tested for specificity against other man-biting sand fly species that are relatively abundant in endemic areas—such as Lutzomyia whitmani [29]—to expand their utility as specific markers of exposure to Lu. longipalpis. Indeed, the faint bands recognized in Lu. intermedia saliva against one reactive human serum from São Luis (figure 2) may have been due to cross reactivity between salivary proteins of this species and those of Lu. whitmani, reported to constitute as much as 24% of the sand fly population in this area [29]. The absolute specificity of LJM17 and LJM11 for Lu. longipalpis exposure therefore requires further confirmation through studies that target more sand fly species. Serum samples from São Luis also recognized salivary proteins from P. perniciosus, a vector of VL in the Old World [30]. This is interesting, as sera from inhabitants of Sanliurfa, Turkey, where P. papatasi and P. sergenti—two established Old World CL vectors—are abundant, did not react with salivary proteins of Lu. longipalpis [16].
Both LJM17 and LJM11 were recognized by dog sera from Teresina, endemic for canine VL [19]. A recent survey of the sand fly population from this area showed that Lu. longipalpis represented 99.7% of the collection [20]. Different from work done previously, here we detected six different salivary proteins as potential specific markers for exposure to Lu. longipalpis by using recombinant proteins for dogs (figure 5). The dog-biting status of other Lutzomyia species needs to be established before the specificity of these proteins as markers of exposure to Lu. longipalpis can be validated. However, this does not detract from their usefulness as potential markers for sand fly exposure for the evaluation of intervention studies in dogs.
Although we did not test potential cross-reactivity of LJM17 and LJM11 with salivary proteins from other common vectors such as mosquitoes, kissing bugs, and black flies, extensive comparative transcriptomic analysis confirm that these two proteins are unique and distinct from those in the saliva of other arthropod vectors [31]–[35].
In conclusion, we have identified two salivary proteins from Lu. longipalpis, LJM17 and LJM11, that were specifically recognized by sera from humans living in an endemic area of VL. Once tested on a wider scale, these proteins could become an important tool for accurate surveillance of this important vector of VL in Latin America.
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10.1371/journal.pntd.0005723 | Implementation of a study to examine the persistence of Ebola virus in the body fluids of Ebola virus disease survivors in Sierra Leone: Methodology and lessons learned | The 2013–2016 West African Ebola virus disease epidemic was unprecedented in terms of the number of cases and survivors. Prior to this epidemic there was limited data available on the persistence of Ebola virus in survivors’ body fluids and the potential risk of transmission, including sexual transmission.
Given the urgent need to determine the persistence of Ebola virus in survivors’ body fluids, an observational cohort study was designed and implemented during the epidemic response operation in Sierra Leone. This publication describes study implementation methodology and the key lessons learned. Challenges encountered during implementation included unforeseen duration of follow-up, complexity of interpreting and communicating laboratory results to survivors, and the urgency of translating research findings into public health practice. Strong community engagement helped rapidly implement the study during the epidemic. The study was conducted in two phases. The first phase was initiated within five months of initial protocol discussions and assessed persistence of Ebola virus in semen of 100 adult men. The second phase assessed the persistence of virus in multiple body fluids (semen or vaginal fluid, menstrual blood, breast milk, and urine, rectal fluid, sweat, saliva, tears), of 120 men and 120 women.
Data from this study informed national and global guidelines in real time and demonstrated the need to implement semen testing programs among Ebola virus disease survivors. The lessons learned and study tools developed accelerated the implementation of such programs in Ebola virus disease affected countries, and also informed studies examining persistence of Zika virus. Research is a vital component of the public health response to an epidemic of a poorly characterized disease. Adequate resources should be rapidly made available to answer critical research questions, in order to better inform response efforts.
| The 2013–2016 West African Ebola virus disease epidemic was unprecedented in the numbers of cases, deaths and survivors. Prior to this epidemic, limited data were available about the persistence of Ebola virus in body fluids of Ebola virus disease survivors and the related risk of transmission, including sexual transmission through survivors’ semen. As more data were urgently needed, a study was implemented in Sierra Leone during the epidemic response operation. When establishing this study, many factors were unknown and the findings needed to be urgently translated into practice. This publication summarizes the methodology used for study implementation and the key lessons learned. Strong community engagement helped rapidly initiate this study despite the difficult circumstances of an overwhelming epidemic in a country with few experienced researchers. Implementation was in two phases, first investigating the persistence of Ebola virus in semen only (100 adult men), and secondly, assessing its persistence in multiple body fluids (semen or vaginal fluid, menstrual blood, breast milk; and urine, rectal fluid, sweat, saliva, tears) of an additional 120 men and 120 women. Data from this study immediately informed national and global guidelines and demonstrated the need for semen testing programs. The lessons learned and study tools developed, supported rapid program implementation in Liberia and Sierra Leone.
| The 2013–2016 West African Ebola virus disease (EVD) epidemic was unprecedented in terms of the number of cases, deaths [1] and socio-economic consequences across the three most affected countries: Guinea, Liberia and Sierra Leone [2, 3]. As of 10 June 2016, 28,616 confirmed, probable and suspected cases of EVD and 11,310 deaths were reported [1], with over 10,000 survivors documented [1].
Prior to this epidemic, data about the persistence of Ebola virus (EBOV) in survivors’ body fluids and its potential risks for transmission [4, 5] was limited. Based on the limited available evidence, both the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) interim guidelines on sexual practices for EVD survivors made a key recommendation of abstinence or correct and consistent condom use for three months post-discharge [6]. During the epidemic there was concern about the risk of Ebola virus transmission from survivors’ semen or other body fluids, especially after several clusters of cases occurred where sexual transmission from an EVD survivor appeared to be the most likely mode of infection [7–10]. Applicable international interim guidelines were immediately updated [11], with WHO recommending that survivors without access to semen testing abstain, or practice correct and consistent condom use for at least six months post-symptom onset. US-CDC recommended abstinence or safe sexual practices until additional evidence was available to update guidelines. EVD survivors were facing many challenges, including fear of possible virus transmission to their loved ones, short and long term health sequelae, as well as profound stigma from their communities. This stigma resulted in acute socio-economic consequences including loss of employment, loss of housing and rejection by family members [12–15]. These concerns led to a demand and sustained interest from the survivor community and the public health authorities, for better understanding of EBOV persistence after recovery from acute infection.
An observational cohort study rapidly commenced in Sierra Leone to investigate the presence of EBOV in semen and other body fluids of EVD survivors. When developing this study, many challenges were encountered, including the unknown duration of follow-up required, the potential infectiousness of specimens, the complexity of interpreting and communicating laboratory results, the urgency of translating the research findings into public health practice, and the community understanding and accepting such a study. Thus, given the scale and challenges of conducting the study during the West African EVD epidemic, we describe here the study development, implementation, lessons learned, and how we worked to communicate interim findings, which provided critical evidence to prioritize the development of semen testing programs. The results of the study will be published separately.
The study was approved by the Sierra Leone Ethics and Scientific Review Committee and the WHO Ethical Review Committee (No. RPC736).
The Sierra Leone Ministry of Health and Sanitation (MOHS) led the development and implementation of this study, with technical support from other government and international agencies. The WHO and the CDC were identified as study partner organizations to oversee the development of the study protocol. Steering and technical committees were established (S1 Box) and met regularly to guide the scientific objectives and the implementation of the study. The steering committee identified the Ministry of Defence as the implementing partner. As the CDC diagnostic laboratory in Sierra Leone closed before the study concluded, the Chinese Center for Disease Control and Prevention (China-CDC) joined as a laboratory partner.
International Conference on Harmonization of Good Clinical Practice guidelines were followed as much as possible to guarantee safe study implementation, as well as data integrity and validity of the study results. An independent research monitor conducted a monitoring visit within four months of initiating study enrolment and an external quality control auditor visited the China-CDC laboratory in Sierra Leone to confirm the validity of the real-time reverse transcriptase polymerase chain reaction (qRT-PCR) test results. Additionally, an Independent Data Monitoring Committee was established to review the data quality and guide the study analysis (see S1 Box).
Although the authors note the importance of acquiring information on the persistence of EBOV in various body fluids within the pediatric population, for ethical reasons this study was limited to adults. The observational cohort study was conducted in two phases. The first phase of the study was rapidly implemented to investigate the persistence of EBOV ribonucleic acid (RNA) in semen using qRT-PCR testing. It was performed in a convenience sample of 100 adult male EVD survivors. With the experience gained during the implementation of this first phase, a second cohort, using convenience sampling from 120 men and 120 women, was subsequently recruited. This was to extend the knowledge on EBOV persistence by assessing its presence in multiple body fluids (semen or vaginal fluid swab, menstrual blood swab, breast milk and urine, rectal fluid swab, sweat swab, saliva (oral swab), tears), using qRT-PCR testing. During the second phase of the study, an attempt was made to recruit EVD survivors living with human immunodeficiency virus (HIV). Venous blood was collected for the second phase of the study to correlate immune responses with persistence of EBOV. Both phases of the study provided an opportunity to assess sequelae among EVD survivors and any correlation with virus persistence. This was done by conducting health status questionnaires at the time of the study visit.
Participants were invited to a baseline visit and then for follow-up visits every two weeks to provide additional body fluid specimens. Follow-up visits continued until body fluids tested qRT-PCR negative for EBOV RNA on two consecutive visits. Upon initial study discharge, participants received durable, forge-proof certificates stating that ‘fragments of Ebola virus were not detected twice in a row’ for the body fluids analyzed.
Whilst the study was ongoing, there was a report of EVD meningitis in a UK-based survivor nine months into convalescence [16]. This raised questions about the frequency of EVD recurrence and its effect or relationship to EBOV persistence in body fluids. Thus, the study team introduced additional follow-up visits at three and six months after initial study discharge in order to investigate if EBOV could later be identified, after it was previously not detected on two consecutive visits. For the three- and six-month follow-up visits, study discharge criteria at each of these time points was one negative qRT-PCR result, rather than the two consecutive negatives required during the initial follow-up of the study. In the event of a positive qRT-PCR result during the additional three- or six-month follow-up visits, visits continued every two weeks thereafter until body fluids tested qRT-PCR negative at two consecutive visits.
The main criteria used to assess potential study sites were (1) the location and access for survivors, including the (2) size of the recent survivor population in the site’s catchment area, (3) the acceptability by survivors to participate in the study at a given location and (4) the proximity to EVD survivor care services. This was in addition to (5) the feasibility of rapid implementation, including the availability of physical space where infection prevention and control (IPC) standards and participant confidentiality could be maintained, and (6) the engagement and technical competence of the local site staff.
Two study sites were selected. The primary site for the first and second phase of the study was 34 Military Hospital (MH34), Freetown, Western Area. This is a 200-bed secondary referral hospital located in an urban area, run by the Ministry of Defence that included an active Ebola Treatment Unit (ETU) during the epidemic. Lungi Government Hospital (LGH), Kaffa Bullom, Port Loko district, was the second site used for the second phase of the study only. This is 77-bed secondary referral hospital run by the MOHS in a semi-rural area.
Due to the lack of adequate existing structures, temporary clinical research sites were constructed. Upon study completion the research sites will be recommissioned for clinical purposes by the respective hospitals. Plastic coated canvas Rubb Hall tents, (often used in humanitarian emergencies), with either wipe-clean tarpaulin or plastic flooring were erected over concrete foundations. At the LGH site, a corrugated metal sheet roof was built to protect the site from the sun and reduce the temperature inside the tents (Fig 1D (ii)). Both sites were designed to facilitate optimal IPC standards and maximize participant privacy (Fig 1A, 1B and 1C). One large tent was partitioned into consultation rooms, office space, and a waiting area, and a smaller tent was used for specimen collection (Fig 1C and 1D (i)). Study sites had separate toilets for staff and participants (the latter being compliant with IPC standards). All body fluids from EVD survivors were assumed to be potentially infectious. The specimen collection tent was divided into an anteroom (for blood drawing, donning of protective equipment, temporary storage of properly packaged specimens) and a specimen collection room (Fig 1A and 1B). The designated specimen collection room was used for all body fluid collections and was treated as a zone requiring personal protective equipment (PPE) for staff that entered the room.
Consultations with IPC experts were conducted in order to facilitate a study site design that would optimize safety and decrease the risk of possible exposure to viable virus. Inspections were performed on several occasions by IPC specialists external to the study. Specimen collection and processing procedures were developed to reflect the IPC requirements for EBOV, which included all items within the specimen collection tent to be (or modified to be) wipe-clean. Relevant staff (laboratory technicians, hygienists, and specimen collection nurses) were trained in IPC practices and all hygienists (who were responsible for decontamination procedures after each participant had completed specimen collection) had relevant and extensive ETU red zone experience prior to joining the study. IPC measures were monitored daily by designated study staff. All standard operating procedures (SOP) including those on IPC procedures, in addition to all study materials (listed in S1 Table) are available on request at [email protected].
The study staff at each site was comprised of around 20 trained personnel (S2 Table), preferably with previous ETU experience. All study staff were trained on site, on each of the specific components of the study (S3 Table), and there was a strong emphasis on sensitivity training and the reduction of stigma and discrimination. Refresher trainings were conducted as required over the course of the study. All national site staff completed International Conference on Harmonization Good Clinical Practice training and laboratory technicians received training on Good Laboratory Practice training and Shipping of Category A (Infectious Substance Affecting Humans) and Category B (Biological Substance) agents.
To help minimize the stigma faced by EVD survivors, recruitment processes were carefully planned. The study team led several informational meetings with key stakeholders, including members of the national EVD survivor advocacy group (Sierra Leone Association of Ebola Survivors; SLAES), to introduce the concept, importance and urgency of the study. SLAES supported hiring and training male and female EVD survivors as the study ‘community liaison officers’ (CLOs). The primary function of the CLOs were to engage the survivor community at the group level by conducting sensitization and recruitment meetings, and also at the individual level, by explaining the study to potential participants and facilitating enrollment and retention. CLOs were available on site during study visits to listen to any participants concerns and help answer their questions.
For the first phase of the study (semen only), male participants were recruited through meetings held in collaboration with SLAES and other survivor support groups. For the second phase of the study (multiple body fluids), both men and women were recruited through meetings held in two sessions. The first session addressed both men and women and discussed the study rationale, what to expect during participation, and the collection of non-intimate specimens. The second session was divided by sex, led by a same sex CLO, and discussed collection of intimate or sex-specific specimens (rectal fluid, semen or vaginal fluid, menstrual blood, breast milk). Additional recruitment efforts focused on liaising with active ETUs in order to recruit survivors upon discharge.
EVD survivors aged 18 years or older, who held an ETU discharge certificate and photo identification were eligible for recruitment. Preferentially recruited survivors were those either most recently discharged, or least recently discharged from an ETU (i.e. recent and long term, not mid-term survivors), as well as any pregnant or lactating women. For the targeted recruitment of people living with HIV, the Network for HIV Positives in Sierra Leone was engaged, through collaboration with UNAIDS.
The content of the different visits are summarized in Table 1.
During the second phase of the study, staff explained to participants how different body fluid specimens should be collected using illustrative posters and gave instructions on how to follow IPC procedures during collection. Whenever possible, the collection of body fluids followed an order pre-defined in a SOP. The order was based on the likelihood of contaminating another specimen and the intimacy of the specimen being collected. For example to avoid contamination with semen, participants provided their urine specimen first, self-collected in a designated, private, toilet which was compliant with IPC standards. All other body fluids were collected in the specimen collection tent, which was divided into ‘non-intimate’ and ‘intimate’ specimen collection areas using a privacy screen (Fig 1A and 1B). A staff member of the same sex as the participant assisted in the collection of saliva (oral swab), tears, and sweat swab. Then, intimate specimens (rectal fluid swab, vaginal fluid swab, menstrual blood swab, and breast milk) excluding semen, were either self-collected, or collected with assistance as desired. For semen collection, verbal instructions were given about self-collection and all staff left the vicinity during the collection process. The collection area (Fig 1A and 1B) was temperature controlled with the following provided: television and DVD player for viewing pornography if desired by the participant, water-based lubricant and a hospital bed. A radio turned to full volume in the anteroom was used to maintain audio privacy. See Fig 1A–1C for more details on IPC and participant privacy.
All female participants were offered a pregnancy test at each visit. If accepted, testing was conducted by the nurse in the specimen collection room during the body fluid collection stage of the visit, with the results relayed by the counsellor during the same visit. All pregnant participants were referred to antenatal care and all pregnant or lactating participants received nutritional support, including ready-to-use-therapeutic food. An SOP was in place in the event that a qRT-PCR test performed on a breast milk specimen detected the presence of EBOV RNA. In that scenario, the woman would have been immediately instructed to switch from breastfeeding to replacement feeding using ready-to-use infant formula[17], with ongoing counseling and support provided by MOHS Food and Nutrition Directorate staff or a trained study counselor. Ready-to-use infant formula was available onsite to be immediately provided free of charge.
As needed, participants were referred to available clinical services including clinical survivor services for post-recovery complications. Specific referral pathways were designed in collaboration with EVD survivor clinics and specialty medical services, including ophthalmic, mental health and HIV care provided by private and national healthcare pathways. At each visit, the participants received a fixed financial compensation; the amount was determined by the MOHS to cover the average cost of a meal and transport, plus additional money to compensate for loss of earnings due to the time spent at the study clinic.
Once specimen collection from an individual was completed for the visit, specimens were packaged in compliance with international requirements for Category A infectious agents and the room was decontaminated. Following appropriate bio-safety precautions, all specimens were refrigerated within 10 minutes of collection and transported periodically from the study site to the laboratory with cold chain maintained throughout. Specimens were processed within 24–72 hours of collection.
All specimens were tested for EBOV RNA by qRT-PCR in Sierra Leone at either the CDC laboratory (Bo, Bo District) [18–20] or China-CDC laboratory (Jui, Western Area) [21, 22]. At the laboratory, specimens were inventoried, aliquoted into three (if volume was sufficient) and stored in a liquid nitrogen dry shipper, or in a -80°C freezer as available. EBOV isolation was performed on qRT-PCR positive semen specimens from the first phase of the study (semen only), and on positive specimens other than semen from the second phase of the study (multiple body fluids). Unlike qRT-PCR, which was performed in Sierra Leone between 24–72 hours after collection, virus isolation was not available in-country. Therefore one aliquot of each of the specified qRT-PCR positive specimens were packed according to Category A biohazards protocols and transported to CDC in Atlanta, GA, USA, for virus isolation within a biosafety level-4 laboratory [5]. Sequencing of selected specimens from the first phase of the study was also performed in Atlanta, and at China-CDC laboratory (Jui, Western Area, Sierra Leone) for selected specimens from the second phase of the study. The third aliquot of each specimen remained in-country, under the custody of the MOHS.
Blood specimens collected during the second phase of the study, were tested in the China-CDC laboratory to assess both antibodies titers and cellular immune response via immunoglobulin (Ig) G (IgG) and IgM enzyme linked immunosorbent assays (ELISAs) and interferon gamma (IFN-γ) enzyme linked immunosorbent spot (ELISPOT)) assays [23]. Detailed laboratory methods will be published separately.
A data flow system was established between the study sites and the laboratory to reduce manual data entry. A paper and an electronic copy of the specimen list accompanied each specimen transport to the laboratory. Results of qRT-PCR assays were provided electronically within one week and imported into the database to ensure timely and accurate relay of results to participants at their follow-up visits. The results of EBOV isolation were often received months after specimen collection; when available, positive virus isolation results were reported back to participants with additional counselling.
Developing and implementing studies usually takes time. It is noteworthy that despite the extremely difficult circumstances faced during the EVD epidemic, the Sierra Leone Ebola Virus Persistence Study was able to commence study enrolment within five months of initial protocol discussions (May 2015), and publish baseline results [24] within 10 months of those discussions (October 2015). The time frame between study enrolment and symptom onset varied widely, with very few participants enrolled within a month of ETU discharge. However, results from this study were key to informing the overall epidemic response.
In the case of diseases for which there is a lack of knowledge, it is essential that research is part of the immediate response to emergency and epidemic situations. Research can be rapidly implemented by having the following key mechanisms in place: (1) funding to address key research questions; (2) ready-made generic protocols available, pre-approved by ethical committees from global institutions such as WHO, so that they can be adapted and submitted to different committees for fast tracked approval; and; (3) flexibility within the research protocol to adapt as new knowledge becomes available. During epidemics, it is important that ethics committees have the capacity to frequently and rapidly expedite clearance of protocol revisions to ensure their timely implementation. This would allow such studies to be implemented, the results obtained, and the response to be informed more rapidly.
The criteria of two consecutive negative test results to qualify for study discharge and the two week sampling scheme was a pragmatic decision. This was based on knowledge available at the start of the study on EBOV, and about intermittent shedding in semen of other viruses such as HIV and Hepatitis C virus [25–28]. As noted in the Methods section, the protocol was adapted to accommodate additional three and six month follow-up visits with specimen collection in response to the emergence of novel data [16]. This ability to modify the protocol and be flexible in a rapidly changing environment was an important lesson learned. It highlighted the significance of open communication and good partnerships to ensure timely exchange of new information between response and research. Remaining receptive to new scientific information becoming available and focusing on critical questions related to persistence of EBOV in body fluids allowed the study to provide better informed guidance in tandem to response activities.
In general, epidemic response efforts frequently encountered high turnover of staff and vigilant oversight was required to ensure high quality research work. In an emergency setting, it may not always be possible to maintain complete adherence to good management practices, but as a minimum, implementing written SOPs, procedure logs, verbal and written training sessions conducted periodically for all staff, and quality control procedures must be a high priority.
Many of the national staff were either EVD survivors or had ETU experience, and so were sensitive to EVD-related stigma. This was critical in facilitating community engagement and fine-tuning study tools, such as questionnaires and counselling scripts. For staff directly involved with specimen collection, prior ETU experience helped ensure they had the technical expertise and knowledge of IPC standards to conduct safe handling of biohazardous materials. It was essential that CLOs were EVD survivors so that they could relate to the participants. Referral to physicians with specific EVD-related clinical experience helped ensure that medical complications could be addressed efficiently. Of note, the physicians at the primary site (MH34) had treated many of the study participants either when they were admitted into the ETU, or at the EVD survivor clinic. These physicians were widely respected and trusted within the survivor community.
Consistent engagement with, and commitment to survivor communities proved to be critical to the study’s success and was achieved via regular meetings. Study results would be periodically presented to survivor communities and key stakeholders through meetings and/or communication workshops. These activities built relationships and trust between the immediate study staff, greater medical community, and survivor networks. This partnership contributed to achieving full recruitment goals with minimal participants lost to follow-up.
Effective community engagement by staff prior to study implementation was essential to conducting a successful study. For example, focus group discussion with potential female participants prior to the second phase of the study was key to gauging acceptability in this cohort and also aided in tailoring specific study tools. The acceptability by survivors to participate in the study at a particular location was given particularly careful consideration. Prior to finalizing the second site selection, consultation meetings were held with a broad demographic of local adult survivors and key stakeholders (including local chiefs and MOHS district officials), to ascertain if transportation to the existing site in Freetown was favorable to establishing a site in the district. Although many benefits of establishing a site in the district were evident to the local survivor community, concerns were expressed that sightings of small gatherings of survivors attending a local healthcare site may raise suspicion within the community and increase survivor stigma. Similar fears were expressed at establishing a site in the green zone of an existing ETU, in addition to associating ETUs with traumatic experiences and therefore these options were ruled out as potential sites. The Freetown site was a three hour journey away which would have guaranteed anonymity from the local community. However by emphasizing to potential participants the importance the study placed on confidentiality, ranging from the site design to the professional conduct of all staff, concerns were addressed.
With regards to study participation, from sensitization through to follow-up visits, there were several lessons learned and resulting recommendations. For example, during the sensitization and recruitment process for the second phase of the study, staff became aware of non-EVD survivors purchasing forged ETU discharge certificates in order to benefit from EVD survivor resources, including financial compensation for participation in research. Therefore it was recommended that establishing a system to verify EVD survivor status beyond checking ETU discharge certificates, should be considered (e.g. cross checking surveillance databases, laboratory databases and survivor registries or conducting serology testing).
In addition, there were requests from sexually active male survivors younger than 18 years to participate in the study. Therefore with due ethical respect, the authors recommend considering inclusion of minors in a semen testing program. Indeed, the Liberian and Sierra Leonean national semen testing programs were available to males aged 15 years and above.
In this largely Muslim country (77%) [29], the recruitment for the first phase of the study and some of the follow-up period, coincided with the holy month of Ramadan. During this time abstinence from any form of sexual activity was required during daylight hours for Muslim participants. Although recruitment and follow-up slowed significantly during this period, the study remained operational by adapting visit scheduling and successfully navigating cultural sensitivities of this time period. This demonstrated how important it was to engage with local religious leaders and community elders for advice on how to proceed.
Survivors frequently expressed a keen interest in the study because they wanted to know the status of their body fluids. This was to reassure themselves and those with whom they were in close physical contact. The study team had not originally planned to issue certificates to participants upon study completion. However in order to help combat stigma, there was a high demand from participants for certificates which demonstrated test negative status of body fluids. In light of this, certificates of study discharge were issued for which the wording (as noted in the Methods study design section) received very careful consideration from the study team. The wording was an intentionally simplistic factual statement, which could be understood by the community and did not provide any guarantee of “clearance”. Confirmation of twice consecutive test negative status was accompanied by the date of study discharge and the date of certificate issue.
Developing EVD risk reduction counseling messages was challenging due to limited data on the subject [4], the rapid evolution of knowledge as the study progressed, the need to adapt messages to multiple body fluids, and the complexity in explaining the significance of a positive qRT-PCR result [24]. For further details see [30] regarding the development and implementation of the Ebola Virus Persistence Risk Reduction Behavioral Counseling Protocol for this study.
In accordance with the original WHO interim guidance on sexual practices, MOHS recommended that all male and female EVD survivors should abstain from sex or use condoms for three months after ETU discharge [6]. These national guidelines were in effect at the start of the study, and most survivors were not aware that WHO guidance had been updated in May 2015, when the peak of the epidemic had passed in the three most widely effected countries [31]. This needed to be take into account in sensitization efforts and during recruitment. As knowledge on virus persistence was scarce [4, 5], the baseline qRT-PCR results from the first phase of the study (semen only), demonstrating qRT-PCR positive results in semen at least nine months post-symptom onset, were of immediate public health interest and were therefore disseminated rapidly [24]. However the detection of viral RNA by qRT-PCR did not necessarily indicate that infectious virus was present [32, 33] and there is limited data to conclude the correlation between qRT-PCR cycle-threshold (Ct) value and positive virus isolation in semen [32, 34]. Within the context of a public health emergency and the knowledge gaps related to virus persistence, the evolving messaging had to be clearly presented to the various relevant audiences (staff, the survivor community, the scientific community, and at the policy-making level), in a way which avoided further stigmatization of EVD survivors and their sexual partners. An important consideration was that although the interim data generated by the study was groundbreaking, data collection was still ongoing and the number of participants on which the data was based, was limited. The evolving data, combined with epidemiological investigations into potential sexual transmission cases, led to WHO interim guideline updates during the epidemic (January 2016). This included the recommendation that in survivors without access to semen testing, abstinence or correct and consistent condom use should be adopted for at least 12 months after the onset of symptoms [35]. The study team was committed to communicating the sensitive data in real-time, which sometimes proved challenging, when it appeared misaligned with international policy.
The authors recommend that future semen testing initiatives engage with local EVD survivor advocacy groups, communications and social mobilization experts, and key national stakeholders. Maintaining the involvement of these groups throughout the study via regular meetings is crucial to communicating study findings and policy updates, and to ensure consistent messaging is disseminated efficiently. It is critical that communication of study findings and the process of transforming these findings into policy be led by the Ministry of Health and local government agencies.
Participants commonly viewed participation in the study as receiving an important service. This, along with the initial findings of the study demonstrating a high proportion of qRT-PCR positive test results in semen [24], and the occurrence of new clusters of EVD cases within Sierra Leone potentially linked to transmission from sexual contact with male EVD survivors, were key elements that prompted the government to establish and accelerate the implementation of a semen testing program. WHO, CDC and other partners also engaged with the Ministries of Health in Liberia and Guinea to establish and implement National Semen Testing Programs with preventive behavioral counselling. The research study was also instrumental in accelerating the implementation of these programs by disseminating study SOPs and data capture tools, hosting study site visits, and facilitating training of program staff. Likewise, this was done for other EBOV research studies [e.g. the ‘Ebola Vaccine Ring Vaccination Trial in Guinea’ (Trial number PACTR201503001057193)] and studies on persistence of Zika virus (‘Zika Virus Persistence in Body Fluids of Patients with Zika Virus Infection in Puerto Rico (ZiPer Study)’].
The study sites were designed to be safe for specimen collection, with high standards of IPC maintained and no high risk occupational exposures reported. There were various challenges with collecting specimens for particular body fluids. Occasionally participants had difficulty providing tear or sweat specimens at a given visit, but more widely, there were challenges with providing semen or blood specimens.
With regards to semen collection, despite requests from some participants for home or assisted sampling (i.e. in presence of a female companion), it was a study policy to self-collect the specimen on-site. This was in order to (1) decrease the risk of exposing others to any viable EBOV, (2) decrease specimen contamination, (3) protect the viability of any EBOV by maintaining the cold chain, (4) and ensure the specimen was provided by that particular individual. Successful collection of semen specimens was enabled through the careful site design and professional conduct of the staff throughout the site. Some participants had difficulty producing a semen specimen on site for which additional counselling and/or referrals to additional medical care were offered.
A number of study participants were reluctant to have blood collected and shared feelings of mistrust concerning how the blood would be used. The following messages were communicated, which helped to ease fears and facilitate collection: (1) blood would not be sold; (2) all specimens would remain under the ownership of MOHS; (3) only a 4mL vial would be drawn, much less than was collected for other research studies or trials conducted in-country; (4) blood would not be tested to see if participants had EVD again, but used to examine immune response since they were sick with EVD.
During the course of study implementation, qRT-PCR testing switched from one laboratory group to another. The CDC laboratory performed qRT-PCR testing of all specimens collected throughout the first five months of study operations, at a temporary diagnostic field laboratory located within the grounds of an ETU in Bo District [18]. Prior to this epidemic there had been limited testing of semen for the presence of EBOV. However CDC was able to perform the initial work due to the availability of trained staff who had evaluated their qRT-PCR assay prior to this study to detect EBOV RNA in semen [34]. However, when the epidemic waned, the ETU was decommissioned and this laboratory closed in October 2015. The China-CDC laboratory, located in Western District, was a permanent, purpose-built category 3 laboratory that was constructed during the response effort. China-CDC joined the study team as they had the capacity and sustained presence to maintain qRT-PCR testing for the duration of study operations. A comparison between the qRT-PCR assays performed at these two laboratories was conducted leading up to the laboratory closure in Bo, and testing of study specimens transferred to the China-CDC lab. Details of qRT-PCR assay validation and laboratory data arising from this study will be published separately.
Rapidly implementing a high quality study during an EVD epidemic in a low-income country, within the context of an overwhelmed and weak health system and few experienced researchers was challenging. The process required balancing limited resources for research with competing response priorities in an ever-changing environment. However, such studies are feasible when led by Government ministries, national staff and community leaders, assisted by solid technical support, and driven by involvement of EVD survivors. This study was a collaborative, multi-organizational effort with the primary goal of supporting the national outbreak response.
Results from this research were crucial for rapidly informing the ongoing response, updating public health recommendations, and potentially preventing new cases. This study demonstrated that the time period between research and program implementation can be substantially reduced during a public health emergency. The data from this study informed national and global recommendations on risk reduction for EVD survivors and their sexual partners during the epidemic and demonstrated the need to implement semen testing services as part of an overall EVD survivor care program. Such semen testing programmes were implemented rapidly in both Sierra Leone and Liberia adapting the methods, mechanisms, and tools developed for the study.
Standardized approaches to studying key issues such as virus persistence during similar epidemics could reduce the time required to have ethical committee approved protocols. As part of the epidemic response planning and resources should be made available and rapidly allocated to address priority research questions. For example, expanding our understanding of virus persistence in the earliest convalescent period after symptom onset and including groups such as pregnant and lactating women, will allow a more accurate assessment of the residual risk of transmission. Commencing research early in an EVD epidemic, as part of the response, will facilitate a better understanding of sexual transmission as well as mother-to-child transmission and will better inform response efforts.
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10.1371/journal.pgen.1006011 | Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD | Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10−10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.
| Precision medicine is an emerging approach that takes into account variability in genes, gene and protein expression, environment and lifestyle. Recent advances in high-throughput genome-wide genotyping, genomics, and proteomics coupled with the creation of large, highly-phenotyped clinical cohorts now allows for integration of these molecular data sets at the individual level. Here we use genome-wide genotyping and blood measurements of 88 biomarkers in 1,340 subjects from two large NIH-supported clinical cohorts of smokers (SPIROMICS and COPDGene) to identify more than 300 novel DNA variants that influence measurement of blood protein levels (pQTLs). We find that many DNA variants explain a large portion of the variability of measured protein expression in blood. Furthermore, we show that integration of DNA variants with blood biomarker levels can improve the ability of predictive models to reflect the relationship between biomarker and disease features (e.g., emphysema) within chronic obstructive pulmonary disease (COPD).
| Implementing precision medicine will require extensive use of biomarkers and in-depth understanding of the contributions of genetic, epigenetic, and environmental variation to phenotypic diversity and disease progression. Genome-wide association studies (GWAS) linking disease phenotypes to single nucleotide polymorphic (SNP) markers have successfully identified genes and pathways involved in complex phenotypes [1, 2]. GWAS are complemented by efforts of functional studies, such as the Genotype-Tissue Expression (GTEx) program [3], which seek to identify expression quantitative trait loci (eQTLs) linking SNP markers with mRNA expression [4]. Such eQTLs can illuminate relationships between genetic variation and disease phenotypes. However, genetic variants can also affect protein levels by mechanisms not detectable by eQTL analyses by altering post-transcriptional processes involving stability, translation, secretion and/or detection of the gene product. Few studies have been focused on the impact of genetic variation on large numbers of protein biomarkers in chronic diseases. However, the recent work in Battle et al., [5] suggests that variants affecting gene expression and protein level may be distinct, so identifying the genetic features that affect protein variation [protein quantitative trait loci (pQTLs)] and gene expression for disease-relevant biomarkers will be important.
To investigate the role of genetic variation on blood biomarkers and their relationship to a chronic disease, we examined genotyping-biomarker-clinical phenotype relationships in two independent, large, well-characterized cohorts of subjects at risk for chronic obstructive lung disease (COPD): Sub-Populations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) [6] and COPDGene [7]. COPD is the third most common cause of death in developed countries [8] and has strong demographic (age, gender) and behavioral (e.g., smoking) risk factors, yet most smokers do not develop clinically important lung disease. Furthermore, COPD has several clinically important, but highly variable, phenotypes including extent and progression of airflow obstruction, loss of lung tissue (emphysema), frequent cough and sputum production (chronic bronchitis) and exacerbations. There have been many publications that have examined the relationship between blood biomarkers and these COPD phenotypes [9]. These biomarkers include both non-specific markers of inflammation (e.g., fibrinogen, C reactive protein, interleukin 6) as well as lung specific proteins (e.g., surfactant protein D, club cell 16) and other proteins [e.g., soluble receptor for advanced glycosylation endproducts (sRAGE), chemokine (C-C motif) ligand 18 (CCL18), and adiponectin]. Many of these biomarker studies have been replicated in independent cohorts and nearly all studies used antibody-based assays. The SPIROMICS and COPDGene biomarker efforts included many of these biomarkers as well as additional novel understudied biomarkers (S1 Table). Although some recent publications suggest that there may be important genetic associations for some blood protein measurements [10], there have been no studies that use multiple independent populations for large scale blood biomarkers, nor are there extensive evaluations on how the SNP-biomarker relationship influences prediction of disease phenotype. Because both SPIROMICS and COPDGene have complete genotyping data, some transcriptomic data, an identical panel of a large number of blood biomarkers, and extensive well-phenotyped clinical data, there is a unique opportunity to identify novel pQTLs and explore their influence on biomarker-disease relationships for COPD and its disease phenotypes.
Written informed consent was received from all subjects. Collection and use of subject information and samples was approved at each clinical center (see S1 File) with the main approval from the IRB at National Jewish Health (HS-1883a) and the IRB at the University of North Carolina at Chapel Hill (10–0048)
114 candidate blood biomarkers (S1 Table) were initially evaluated using custom 13-panel multiplex assays (Myriad-RBM, Austin, TX). The 13-panel multiplexes were primarily selected because they contained at least one biomarker with known or putative links to COPD pathophysiology [12, 13]. Any analytes measured in addition to the pre-selected biomarkers were intended to be utilized for discovery purposes. Although reports of general assay performance are beyond the scope of the present work, details of a pilot study using the SPIROMICS samples on these assays is available that describes the coefficient of variation and reliability estimates for a majority of the analytes measured [12]. Details of the ability of the panels to detect the analyte above background [the lower limit of quantification (LLOQ)] are provided for both studies (S1 Table). Assay performance across the two cohorts was highly similar. Reproducibility of the platform was assessed for selected biomarkers (S1 Fig) using a subset of COPDGene subjects: for sRAGE using Quantikine human RAGE ELISA kit (R&D Systems, Minneapolis, MN) as previously described [14]; CRP (Roche Diagnostics, Mannheim, Germany) and fibrinogen (K-ASSAY fibrinogen test, Kamiya Biomedical Co., Seattle, WA, USA) levels were measured using immunoturbidometric assays as previously described [15]; surfactant protein D using colorimetric sandwich immunoassay method (BioVendor, Heidelberg, Germany) as previously described [16]. Additionally, serum from 63 SPIROMICS subjects who were either GG (N = 27) or TT (N = 36) at rs7041 were analyzed using a monoclonal antibody assay from R&D (Quanitkine ELISA kit) at the Clinical Research Unit Core Laboratory at Johns Hopkins. Polyclonal vitamin D binding protein measurements (ALPCO, Salem, NH) were performed in the same SPIROMICS subjects.
pQTL features were characterized by: (1) Ensembl Variant Effect Predictor (VEP) [30]; (2) GWAS catalog [31]; and (3) comparison with gene expression QTLs (eQTLs) using subset of COPDGene blood microarrays [20, 32]. Details are provided below:
Demographic and clinical characteristics of subjects from the SPIROMICS (n = 750) and COPDGene (n = 590) cohorts, including disease phenotypes, are shown (Table 1; S3 Fig). These NHW subjects were representative of NHWs in the parent cohorts (S2 Table).
At a significance level of 8 X 10−10 we identified 290 pQTLs in the SPIROMICS cohort and 182 pQTLs in the COPDGene cohort (S3 Table). Many of the pQTLs SNPs were replicated between cohorts (Fig 1; S3 Table). Because of the similarity of the two studies in terms of sample size and subject characteristics as well as good replication of pQTLs between these two studies, we used a meta-analysis to increase power for finding pQTLs. Weighted meta-analysis identified 527 pQTL SNPs in 38 (44%) of the biomarkers (S4 Table) meeting genome-wide significance with Bonferroni correction for multiple testing of SNPs and biomarkers (P <8 X 10−10; Fig 2). The most significant independent pQTL SNP was rs7041 (P = 10−392) in GC (vitamin D binding protein—VDBP) on chromosome 4. Thirty-seven other biomarkers had significant pQTL SNPs (Table 2); corresponding Manhattan plots, Q-Q plots, and LocusZoom plots are shown for each individual analyte that had an associated pQTL (S4 Fig). Two or more independent pQTL SNPs were identified in 26 of 38 biomarkers using recursive conditioning (S5 Table).
To determine whether pQTLs SNPs were local (cis) or distant (trans), we examined proximity of each SNP to its assigned biomarker gene. The majority (76%) of pQTL SNPs were local (S5 Fig; S4 Table). However, distant pQTLs were observed for eleven biomarkers, and nine biomarkers had a distant pQTL SNP as their most significant pQTL (S2 Table). Five biomarkers had their most significant pQTL SNPs (either rs687289 or rs507666) in the ABO blood group locus on chromosome 9, which encodes alpha 1-3-N-acetylgalactosaminyltransferase, a major determinant of ABO blood type. This SNP is in the same genetic region as other QTLs and disease associations reported from a wide variety of a sources, including metabolites from the urine (Fig 3). An additional region on chromosome 19 contained distant pQTLs for more than one biomarker (S4 Table). The pQTLs represented SNPs with a broad range of minor allele frequencies (MAF) with distributions of MAFs of pQTL SNPs similar to all SNPs studied (S6 Fig).
Using VEP, we found intronic SNPs to be the most represented pQTL SNP category (43%), followed by intergenic variants (22%); however, missense variants showed the most significant enrichment (P<10−12) compared to all SNPs on the genotyping platform (Fig 4). Importantly, pQTLs were robust and concordant across the two source cohorts (S4 Table; S7 Fig).
Nine biomarkers had at least 10% of their variance explained by a single pQTL SNP in both SPIROMICS and COPDGene (Fig 5). For example, a single local pQTL SNP (rs8192284 SNP in IL6R) explained 45% of variance of plasma IL6R in SPIROMICS and 50% of this variance in COPDGene, and a single distant pQTL SNP (rs507666 SNP in ABO) explained 25% of variance of blood E-selectin (SELE) in SPIROMICS and 27% of variance in COPDGene (Fig 6). In many cases, pQTL SNPs explained more variance in the quantitative biomarker than did clinical covariates.
To assess the novelty of these pQTL SNPs, we cross-referenced the unique 478 pQTL SNPs we identified with SNPs associated with any published GWAS based on NHGRI GWAS catalog, including those related to COPD phenotypes or pulmonary function (n = 242). By these criteria, 90% of pQTL SNPs were novel (P < 10−34; S4 Table), even after removing SNPs in linkage disequilibrium [280 significant pQTL SNPs remained and, of those, 29 (10.4%) overlapped with at least one GWAS report (P < 10−20)].
We next evaluated whether pQTL SNPs were also eQTLs, by utilizing an overlapping dataset of peripheral blood mononuclear cell gene expression from COPDGene [32]. In this analysis, only COPDGene data were available, so results are limited to this dataset. Although there were more positive correlations between gene expression and protein levels than expected by chance (sign test P = 0.0009), the overall magnitudes of such correlations were low (S8 Fig), and there was little overlap between pQTL and eQTL SNPs (Fig 7; S6 Table). Furthermore, as previously shown, although both eQTL and pQTL SNPs were more likely to be intronic [20], among those that were not, pQTL SNPs were more likely to be in 5′ or 3′ untranslated region or to be missense SNPs, compared to eQTL SNPs (S9 Fig). Only one biomarker (haptoglobin, corresponding to gene HP) had pQTL SNPs that were also eQTL SNPs, and this is the only case where regression modeling suggested that measured biomarker levels are mediated by gene expression (S6 Table).
Given that QTLs may be dependent upon the cellular/tissue-specific expression [74], we examined whether the pQTLs would be significantly affected by the cellular composition of the blood by repeating the pQTL analysis adding cell counts (red blood cells, neutrophils, lymphocytes, basophils, monocytes, eosinophils, and platelets) as covariates in the models. For either all possible SNPs or only significant pQTL SNPs, the correlation between the p-values of the pQTLs with and without blood cell counts added as covariates was > 0.985, indicating that the pQTLs were not markedly dependent upon blood cell type composition (S10 Fig).
A recent report suggests that monoclonal antibodies for vitamin D binding protein may preferentially recognize a selected protein isoform [75] caused by the rs7041 pQTL, which is a missense mutation causing aspartic acid to glutamic acid change at position 432 (D432E). Therefore we used a polyclonal antibody to compare to measurements to the monoclonal assay used on the RBM platform in a subset of SPIROMICS subjects. Indeed, the measurements using the monoclonal antibody were significantly lower for the TT genotype compared to the GG genotype (P < 0.001), suggesting that measurements using the monoclonal antibody assay detected the D432E protein isoform less well compared to the polyclonal assay (S11 Fig).
With SNPs, biomarker levels, and disease phenotypes all available for both cohorts, statistical modeling could be used to infer the relationships among these three data types employing methods previously applied to eQTL-gene expression-phenotype relationships [22–27]. We chose four clinically important COPD phenotypes [airflow obstruction (FEV1% predicted), emphysema, chronic bronchitis, and a history of exacerbations] and applied regression models adjusted for covariates and PCs [22, 26]. We categorized the relationships of all 2,108 trios of SNP, biomarker, and disease phenotype (527 pQTL SNP/biomarker pairs and four disease phenotypes) into five categories, based on (conditional) dependence testing (Fig 8 and full results supporting Fig 8, including regression coefficients, are in S7 Table). Results for biomarker associations to disease phenotype for pQTL SNPs are also provided (S8 Table).
Significant evidence for inferred causal, complete, or collide relationships were found for emphysema and airflow obstruction for six biomarkers, with AGER represented by the same model in both phenotypes (Fig 8). In all of these cases, the direction of the regression coefficients were the same between SPIROMICS and COPDGene (S7 Table). By contrast, no significant relationships were found for chronic bronchitis or exacerbations. In the case of the collide model, the association between pQTL SNP and disease phenotype is strengthened given the biomarker, and thus inclusion of pQTL SNP information in biomarker-disease association testing will add predictive value. An example is AGER, which is classified as the “collide” model for the phenotype of emphysema. Including both AGER levels and its top pQTL SNP improved the explanation of variance (R2) for emphysema to 40%, compared to just 30% for the biomarker alone, and 22% when only clinical covariates were used.
In this study we identified hundreds of novel SNPs significantly associated with nearly 40% of blood biomarkers commonly used in both pulmonary and non-pulmonary clinical research. For many biomarkers, a single pQTL SNP accounted for a large percentage of measured variance. We demonstrated that pQTLs provide unique information compared to eQTLs and that inclusion of pQTL SNPs can improve explanation of variance when added to clinical covariates in statistical models, e.g., sRAGE and emphysema. Although the subjects in this study were recruited for COPD phenotypes, many of the pQTLs identified and the biomarkers studied have been associated with other diseases or traits, suggesting that the pQTL-biomarker relationships reported here are broadly relevant to human pathophysiology. Furthermore, the pQTL-biomarker-disease phenotype relationship is frequently not a simple SNP → gene expression → biomarker → disease phenotype association. These findings suggest that modeling with inclusion of measurements from multiple omics technologies may be needed to optimize precision medicine predictions.
A significant finding in this study is the number of distant pQTLs associated with the ABO locus (commonly associated with ABO blood group). PQTLs at the ABO locus were the strongest genetic association among six proteins encoded by genes on six different chromosomes. This ABO region, along with the FUT2 gene (galactoside 2-alpha-L-fucosyltransferase 2), which contained pQTLs for CDH1, was found to overlap with a growing number of previously reported QTLs for a variety of blood analytes, blood processes (such as clotting time), metabolites, lipids, and even urinary metabolites (Fig 3). The most likely explanation is these two loci affect enzymes that post-translationally modify multiple proteins leading to impaired protein function, half-life, or detection. Interestingly, older literature, prior to extensive genotyping and biomarker analysis, has reported association between ABO blood group and COPD [76] and has been associated with other diseases such as goiter [77] and hepatitis [78] in the candidate gene era. The extensive number of associations now reported at the ABO blood group from a wide variety of studies suggests that greater attention should be paid to ABO status for blood biomarker studies.
Much of the recent effort to identify genetic variants and genomic signatures associated with clinical disease has extensively used eQTLs to understand the function of loci identified in GWAS, including for COPD [4, 79–81]. We demonstrate a clear distinction between known eQTLs and pQTLs, which is consistent with previous work that compared variants associated with three different levels of gene regulation (transcription, translation and protein levels) in a study of 62 HapMap Yoruba (Ibadan, Nigeria) lymphoblastoid cell lines (LCLs) [5]. The authors used SILAC mass spectrometry to quantitate proteins and showed that only 35% of the pQTL variants overlapped with eQTLs using RNAseq. Some of the variance in protein expression was due to ribosomal occupation (ribosomal profiling); however, there were many pQTLs in which there was little variation in the mRNA or ribosomal profiling, suggesting that post-translational events may be responsible for differences in protein abundance. Similar to what we report, this is supported by the observation that the pQTLs are significantly enriched in protein coding (missense) and potential translational regulation (e.g., 3’ UTR) regions. They hypothesize this may be due to differences in protein degradation; however one cannot exclude that the peptide variants may be differentially measured with mass spectrometry, or that there may be altered biomarker stability, secretion rates, or processing/release from the cell surface. Another limitation of this study is that they only considered genetic variants within a 20-kb window around the corresponding gene; however, we found a significant number of pQTL SNPs mapped outside of this region. Another study of 441 transcription factors and signaling proteins in the Yoruban LCLs found that many pQTLs were not associated with gene expression and were also distant from the corresponding gene [82]. These studies highlight the general need to include protein expression in large-scale population variation studies such as GTEx to better understand the relationship between genome and protein in humans. Although such efforts are ongoing on a small scale (e.g. Chromosome-Centric Human Proteome Project [83]), our results imply these efforts can also be incorporated cost-effectively into large existing clinical cohorts.
These findings will be useful for GWAS and biomarker studies of other diseases. For instance, we identified novel pQTL SNPs explaining greater than 25% of variance in blood proteins such as interleukin 6 receptor, eotaxin-2, and E-selectin, which could be useful in studies of asthma and of non-pulmonary diseases. The sRAGE-emphysema example demonstrates that the application of causal modeling can provide new insights to the relationship between SNP, measured biomarker levels, and disease phenotypes. Additionally, this example demonstrates how predictive models of disease phenotype can be improved by adding pQTL information.
Furthermore, evaluating all possible statistical relationships among pQTL SNPs, biomarkers, and disease phenotypes suggests that many pQTL SNP effects may not be causally mediated directly through measured biomarkers. For instance, the minor allele rs2070600 SNP in AGER is associated with lower sRAGE in blood; COPD severity and emphysema extent have also been negatively associated with lower blood sRAGE concentrations in cross-sectional studies [13, 14]. Paradoxically, however, in large GWAS studies, the minor allele of rs2070600 is associated with reduced COPD severity and reduced emphysema [80, 81] suggesting potentially opposite effects of the SNP. Indeed, our evidence points to a “collide” relationship; however, given the previous published large scale genetic association studies have shown that rs2070600 is associated with COPD and emphysema, it is likely that this study is underpowered to distinguish between the “collide” and the “complete” model, which can be distinguished by a statistically significant association between the pQTL SNP and disease phenotype. Nevertheless, the association between pQTL SNP and disease phenotype becomes much stronger given the biomarker, which implies the collide relation. Regardless of whether rs2070600 is “collide” or “complete”, it is a missense SNP that causes a G82S amino acid change and thus illustrates the enrichment of coding SNPs in pQTL analysis. The mechanism by which rs2070600 causes disease is unknown, but the resultant amino acid substitution may block shedding of this cell surface receptor, reducing blood levels but at the same time improving sensing of damage-associated molecular pattern molecules, with a net protective effect [84]. However, once emphysema progresses, the source of sRAGE in the blood (the alveolar cells) is reduced, so that emphysema progression would be manifested by reduced sRAGE levels.
Several other relationships identified are also worth considering. For example, we identified evidence for the “collide” relationship for rs926144, an intergenic SNP in SERPINA1 (alpha-1-antitrypsin; AAT), a protein whose normal function is linked directly to the development of emphysema. Although we find strong pQTL SNPs for SERPINA1, and we see a relationship between COPD and SERPINA1 levels, we see no statistically significant evidence that pQTL SNPs associate directly with disease. This is similar to what authors of an GWAS of AAT serum levels have recently reported in this journal [85], in which they identified strong serum AAT pQTLs, but their association with lung function was driven by the rare disease variants (PiSZ and pZZ, who were excluded from SPIROMICS and COPDGene). Since SERPINA1 is produced by the liver and is well-known as marker of systemic inflammation, an established feature of COPD, this would support the finding that common SNPs may not be representative of the known disease-causing variants, which are rare, and that both non-disease causing variants and the disease itself may be associated with changes in biomarker levels.
We found that a “complete” model was suggested for the Complement Factor 3 (C3) pQTL SNP rs2230203. In a study of 111 subjects with COPD and 111 matched controls, blood C3 was noted to be lower in COPD subjects [86]. Similarly in a more recent study of 15 COPD subjects and 15 matched controls serum C3 was lower in COPD subjects [87]. Our findings confirm the relationship between C3 and COPD and emphysema and further suggest that it is partly mediated through C3 genetic variants. Although the rs2230203 variant is in the coding region of C3, it is a synonymous variant and was the only pQTL we identified for C3. The variant might affect protein levels though siRNA binding or other pre-translational mechanisms, but mechanistic studies will be necessary to confirm this.
As a final example, the “causal” relationship suggested for CDH1 (E-cadherin) for both emphysema and FEV1% predicted is also intriguing at a mechanistic level. The CDH1 pQTL SNPs are distant (trans) and are located in FUT2, which codes for a fucosyltransferase that, along with ABO, determines the expression of distinct blood group antigens. Evidence for a role of CDH1 and COPD is growing [13, 88, 89], yet the underlying mechanisms are not entirely clear. Our results suggest that future studies should focus on a direct role of CDH1 in the pathogenesis of disease.
Strengths of this study include the large number of subjects and the inclusion of validation cohorts. However, there are some limitations. Although it is one of the largest biomarker-GWAS studies reported, 1,340 subjects is still small compared to clinical GWAS studies, thus we are likely underpowered to detect some of the SNP-disease phenotype associations. Thus, we cannot say for certain, for example, that a causal or collide model might not actually be a complete model (e.g. for rs207060 in AGER with sRAGE). Second, because we identified distinct and independent pQTL SNPs for some biomarkers, there may be multiple mechanisms by which pQTL biomarkers mediate SNP-biomarker-disease phenotype interactions. Proving the validity of the causal inference models will require detailed mechanistic studies at both a genomic and proteomic level. Additionally, like nearly all biomarker assays, we used antibody based detection methods to measure biomarkers. Since antibodies recognize specific epitopes on proteins, it is possible that our pQTL may detect a specific isoforms of a protein rather than total protein. This has recently been suggested, but not proven, as an explanation for the strong genetic (racial) associations observed for vitamin D binding protein and the cis-SNP rs7041 (Asp432Glu). As we have and others have shown for vitamin D binding protein [75], assays that use polyclonal antibodies compared to the monoclonal sandwich immunoassay (R&D Systems) may overcome this limitation. Another example in the literature is a pQTL identified for TNF-alpha was not replicated when a different assay was applied to the same samples [10]. However, similar pQTLs for plasma proteins such as HP, SERPINA1, C3, APOE, and AHSG were identified using mass spectrometry [90] and for IL6R, F7, and others using aptamer-based detection [91], suggesting many pQTLs we identified were not platform specific. Thus, knowing that antibody used in biomarker measurement may preferentially detect a specific isoform of a protein does not discount its importance, particularly if the pQTL SNP has also been associated with the disease phenotype in genetic association studies, as is the case with vitamin D binding protein, sRAGE, and several other pQTL SNPs described in this study (see Table 2). Thus, investigators who conduct biomarker studies need to consider the possibility that genotype plays a role when measuring blood biomarkers.
An additional limitation of the study is using a candidate panel of 114 biomarkers that are overrepresented for inflammation and lung proteins. At the time, this was state of the art for large scale human studies; however, in the future there will be high-throughput, 1000+ biomarker panels that may be used such as SomaScan (Somalogic, Boulder, Colorado). Other limitations of this study include that it was limited to subjects over 45 years of age and only NHW subjects. Future studies should include other populations and the types of variants assessed, e.g., rare variants. Finally, due to the nature of the available data, evaluating quantitative change in biomarkers with disease progression was not conducted, but would be expected to enhance understanding of disease mechanisms in future studies.
In summary, this large scale, dual-cohort, combined GWAS and biomarker study represents a powerful approach to combine different omics data sets to better understand complex diseases such as COPD. We replicated some previously reported pQTL associations and discovered a large number of novel pQTLs, including distant pQTLs, which many studies are poorly powered to detect. Integration of pQTL genotypes with biomarker measurements will improve the precision of disease prediction for some clinically relevant phenotypes, and improve the mechanistic understanding of others, thus increasing the implementation of targeted clinical care.
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10.1371/journal.pbio.2003853 | Protein aggregates encode epigenetic memory of stressful encounters in individual Escherichia coli cells | Protein misfolding and aggregation are typically perceived as inevitable and detrimental processes tied to a stress- or age-associated decline in cellular proteostasis. A careful reassessment of this paradigm in the E. coli model bacterium revealed that the emergence of intracellular protein aggregates (PAs) was not related to cellular aging but closely linked to sublethal proteotoxic stresses such as exposure to heat, peroxide, and the antibiotic streptomycin. After removal of the proteotoxic stress and resumption of cellular proliferation, the polarly deposited PA was subjected to limited disaggregation and therefore became asymmetrically inherited for a large number of generations. Many generations after the original PA-inducing stress, the cells inheriting this ancestral PA displayed a significantly increased heat resistance compared to their isogenic, PA-free siblings. This PA-mediated inheritance of heat resistance could be reproduced with a conditionally expressed, intracellular PA consisting of an inert, aggregation-prone mutant protein, validating the role of PAs in increasing resistance and indicating that the resistance-conferring mechanism does not depend on the origin of the PA. Moreover, PAs were found to confer robustness to other proteotoxic stresses, as imposed by reactive oxygen species or streptomycin exposure, suggesting a broad protective effect. Our findings therefore reveal the potential of intracellular PAs to serve as long-term epigenetically inheritable and functional memory elements, physically referring to a previous cellular insult that occurred many generations ago and meanwhile improving robustness to a subsequent proteotoxic stress. The latter is presumably accomplished through the PA-mediated asymmetric inheritance of protein quality control components leading to their specific enrichment in PA-bearing cells.
| Since accurate protein folding is crucial for cellular viability, misfolded and aggregated proteins have typically been thought of as detrimental structures with potentially harmful physiological effects. In this report, we show that this general paradigm does not appear to hold in the model bacterium Escherichia coli. We find that the emergence of protein aggregates is mainly linked to sublethal, proteotoxic exposures (e.g., heat stress) and that asymmetric partitioning of these aggregates among daughter cells may have benefits beyond mere waste disposal. In fact, cells that inherited an ancestral protein aggregate (formed during stress exposure many generations before) were better able to cope with subsequent exposure to proteotoxic stress. Our observations therefore reveal the existence of stress-induced, long-term, and protein aggregate–mediated “memory” in prokaryotes and highlight the potential role of protein aggregation in aiding cellular survival and adaptation in fluctuating environments.
| Proper protein folding and maintenance of proteome integrity are essential for cell function and viability [1,2]. Nevertheless, the generation of nonnative protein conformations is inevitable to some extent because of the inherent stochastic nature of protein folding [3,4] and is often even aggravated by genetic (e.g., mutations [5]), physiological (e.g., cellular aging [6]), and environmental (e.g., heat [7] or antibiotics [8]) conditions. Such aberrant protein conformations typically expose hydrophobic residues and regions normally buried within their native structure, which drive nonfunctional intermolecular interactions leading to the formation of larger insoluble structures termed protein aggregates (PAs) [9].
In prokaryotes, the occurrence of PAs is regarded as a strictly adverse phenomenon. In the model bacterium E. coli, for example, intracellular PAs are perceived as naturally occurring, inevitable, and constantly accruing “garbage bins” [10–12]. This view is based on single cell–level observations of growing E. coli cells, in which the yellow fluorescent protein (YFP)-fused inclusion body binding protein (IbpA-YFP) was employed as a reporter for the presence of aggregated proteins [10,13]. Such PAs were observed to appear randomly during growth and to segregate asymmetrically upon cell division due to their nucleoid-enforced polar localization [10,14–16]. Moreover, PA accumulation in cells with older cell poles was shown to slow down their growth in a dosage-dependent manner, thereby giving rise to the previously observed pattern of aging and rejuvenation in growing microcolonies [10,17]. It was even theorized that this asymmetric damage segregation strategy was superior to damage repair strategies in most environmental conditions and could thus have evolved as an optimal damage riddance strategy [18–21].
This negative perception contrasts with emerging findings in eukaryotes that illustrate the potential functionality of stress-induced misfolded and aggregated proteins. The proteome-wide characterization of aggregation tendencies during thermal stress in the yeast Saccharomyces cerevisiae identified heat-induced aggregation and stress-granule formation as specific, reversible, and promoting cellular adaptation and survival [22,23]. Similarly, nutrient depletion of yeast cells leads to the formation of metabolite-specific, reversible protein assemblies that have been proposed to function as storage depots but at the same time might also enhance metabolic efficiency during nutrient stress [24–26]. Detailed characterization and quantification of the Caenorhabditis elegans proteome along its lifespan showed extensive proteome remodeling and suggested the sequestration of proteins in insoluble aggregates to be a protective strategy directed toward maintaining proteome integrity during aging [27]. In fact, the term quinary structures has been coined to describe such functional (stress-induced) large protein assemblies, as they lack the fixed stoichiometry specific to quaternary assemblies [28,29]. However, direct phenotypic evidence of their beneficial impact has remained limited. In addition, it remains unclear to what extent the formation of such structures affects and shapes future behavior, after alleviation of the stress conditions.
Given these emerging examples of functional protein aggregation in eukaryotes, we set forward to revisit the occurrence and potential impact of (stress-induced) protein aggregation in prokaryotes using E. coli as a model system. Using validated fluorescent PA reporters, we could demonstrate that intracellular PAs only emerge as a response to sublethal proteotoxic stresses, after which these structures become asymmetrically segregated and gradually disaggregated. Remarkably, rather than being fitness compromised, we found that cells asymmetrically inheriting an ancestral PA became significantly more resistant to a subsequent stress compared to their isogenic, PA-free siblings. Our results therefore reveal that PAs can serve as epigenetically inheritable memory elements that enable long-term cross-generational reminiscence of previous cellular insults.
The E. coli IbpA protein belongs to the conserved family of ATP-independent small heat shock proteins that readily associate with misfolded and aggregated proteins [30–32]. Previously, E. coli strains expressing an IbpA-YFP fusion protein have been used to microscopically visualize intracellular PAs, which appeared as punctate and polarly located fluorescent foci inside the cytoplasm of healthy, living cells [10,15,33]. However, given the potential of many commonly used fluorescent proteins to cause label-induced oligomerization and trivial foci formation, this IbpA-YFP reporter has been suggested to yield a biased view on intracellular PA dynamics [34,35]. In order to examine this bias more closely and look for a more reliable PA reporter, we created a set of nearly identical E. coli MG1655 strains only differing in the fluorescent reporter that was translationally fused to the 3′-end of the native chromosomal ibpA gene (Fig 1A, S1A Fig, and S1 Table). The observation that IbpA fusion proteins constructed with monomeric fluorescent protein derivatives (i.e., IbpA-mVenus, IbpA-mCherry, IbpA-monomeric cerulean [mCer], and IbpA-monomeric superfolder green fluorescent protein [msfGFP]) gave rise to lower fractions of punctate cellular fluorescence compared to reporters constructed with nonmonomeric forms (i.e., IbpA-YFP and IbpA-Venus) indeed confirms that the self-aggregating tendency of these fusion proteins can lead to an overestimation of the natural PAs present inside the cell (Fig 1A–1C). In the same vein, inclusion body binding protein B (IbpB)—the other small heat shock protein encoded in the ibp operon that associates with PAs through its interaction with IbpA [32]—displayed similar variability in localization when fused to different fluorescent proteins (S1D–S1F Fig), albeit to a lesser degree.
Interestingly, the IbpA-msfGFP reporter (i.e., the fusion equipped with the most monomeric fluorescent label; [35,36]) displayed a strictly diffuse cytosolic fluorescence in exponential phase (Fig 1A–1C) and only exhibited a punctate localization in some cells (12.6%) of a stationary phase population (S1A–S1C Fig). Furthermore, this reporter also displayed wild-type expression levels of the ibpA promoter (PibpA; which is highly sensitive toward perturbations of protein homeostasis [37–39]), while PibpA activity was markedly increased in the biased MG1655 IbpA-YFP reporter (Fig 1D). Nevertheless, exposure of the MG1655 IbpA-msfGFP reporter to a sublethal heat shock led to significantly increased ibpA expression (Fig 1E) and the emergence of multiple foci (mainly localized in the polar and mid-cell regions), confirming its ability to sense and report protein aggregation (Fig 1F–1H and S2A and S2B Fig). Moreover, sublethal treatment of this reporter strain with streptomycin—conditions known to induce mistranslation and subsequent protein misfolding [8,10]—induced a similar response in which emerging IbpA-msfGFP foci colocalized with the streptomycin-induced inclusion bodies (visible in the phase contrast images; S2C–S2E Fig). Inclusion body formation driven by the production of recombinant protein also instigated a similar up-regulation and colocalization phenotype (S2F–S2I Fig), further forwarding IbpA-msfGFP as a reliable reporter for monitoring native intracellular PA dynamics.
After validating IbpA-msfGFP as a truthful marker for the presence of natively occurring PAs, we set forward to further probe their occurrence and physiological impact. To this end, we exposed exponentially growing MG1655 ibpA-msfgfp cells to a range of sublethal heat shocks (temperatures higher than 50 °C led to the inactivation of a substantial fraction of cells and were subsequently not considered in this analysis) and monitored their response and subsequent outgrowth by time-lapse fluorescence microscopy (TLFM). While both IbpA up-regulation and IbpA-msfGFP foci formation originally increased with the severity of the heat shock, this coordinated behavior displayed remarkable alterations at higher sublethal temperatures. The number of visible PA foci per cell leveled off at temperatures higher than 45 °C, and IbpA expression reached a maximum at 47–48 °C (Fig 2A–2C). Given the observed foci account for most of the cellular fluorescence, this indicates that in between this temperature range (45–48 °C), larger but not more PAs were formed. After exposures to higher temperatures (49–50 °C), both the expression level and the number of PAs declined, and a significant delay in cellular growth resumption could also be observed (Fig 2D).
Within populations exposed to the same temperature, a large variability in the extent of protein aggregation (both in the number of visible PAs as in the amount of IbpA-msfGFP fluorescence) could be observed with cells containing 0 to 5 distinct IbpA-msfGFP foci at cell division after exposure to the sublethal heat treatment (Fig 2C). Populations exposed to higher sublethal temperatures (48–49 °C) displayed a remarkable bifurcation phenotype (Fig 2E and 2F). Although a significant fraction of cells behaved similarly as recorded for lower sublethal temperatures, displaying clear IbpA up-regulation (which persisted during initial growth resumption) and foci formation, others exhibited neither of these characteristics (Fig 2F). This fraction of cells did display belated IbpA up-regulation but not to the same extent as their PA-forming counterparts. Moreover, an initial “patchy” IbpA-msfGFP localization pattern could be observed in these cells, which appeared to be resolved concomitantly with IbpA up-regulation as cells resumed growth and division (Fig 2F). Interestingly, whereas the PA-forming fraction of cells readily resumed growth, this other non-PA-forming fraction displayed significantly longer resuscitation times (Fig 2E and 2F). Whereas only a limited number of cells (10.5%) displayed this behavior after exposure to 48 °C, this fraction, as well as their average resuscitation time, became significantly larger (22.5%) in populations exposed to 49 °C (Fig 2D and 2E). At higher temperatures (50 °C), this bifurcation disappeared, and the behavior of (surviving) cells shifted completely toward attenuated IbpA up-regulation without PA formation combined with longer resuscitation times (Fig 2D). Although the mechanisms underlying the observed bifurcation and phenotypic shift remain elusive, our observations highlight an apparent plasticity in the formation of PAs. This suggests that PA formation might be more than an inevitable artefact of exposures to elevated temperatures, in which case PAs would be observed in surviving cells after exposure to all temperatures above a given threshold.
To further examine the impact of PAs on cellular physiology, we exposed cells to a sublethal heat treatment leading to maximal PA formation while minimally compromising physiology and subsequent proliferation (47 °C, 15 min). We quantitatively characterized the growth of these cells over multiple generations in detail with TLFM. As the cells resumed growth, PAs typically remained intact, became localized in the cell poles, and segregated asymmetrically while cells grew out into microcolonies (Fig 3A–3D, S1 and S2 Movies). Although the existence of so-called cellular aging could be detected in these microcolonies (9.02% decrease in cellular growth rate of cells inheriting the oldest cell pole compared to the rest of the population; S2J Fig), stochastic partitioning of PAs during the first generation post heat treatment led to only 6 out of a total of 40 observed oldest cells containing a PA, suggesting both phenomena might not be associated with each other. Subsequent examination of aging in PA-free cells indeed confirmed this phenomenon to occur independently of protein aggregation (9.36% decrease in cellular growth rate; S2J Fig). Moreover, a permutation test revealed that the limited average fitness defect of PA-bearing cells does not differ from that of PA-free cells with a similar age structure (p-value = 0.56; Fig 3E–3G), indicating PAs themselves impart no significant growth defect on their individual host cells. This finding was further strengthened by the lack of correlation between cellular growth rate and average cellular GFP concentration for PA-bearing cells (Fig 3E).
To assure that the phenomenon of asymmetrically inherited PAs indeed represents native behavior and is not a trivial consequence of the fluorescent labeling of IbpA with a monomeric fluorescent protein, we equipped wild-type MG1655 cells (thus containing an unlabeled native copy of ibpA) with a vector (pBAD33-ibpA-msfgfp) in which expression of the IbpA-msfGFP fusion protein was placed under an arabinose-inducible promoter. These cells were grown in repressing conditions (0.2% glucose) to exponential phase, exposed to a sublethal heat treatment (47 °C, 15 min), and subsequently monitored on arabinose-containing (0.15%) agarose pads by TLFM (S3 Fig). As cells grew under these inducing conditions, a gradual increase in cellular fluorescence could be observed, followed by the appearance of one or multiple distinct foci that remained present during the rest of recording (S3A Fig). While a similar increase in cellular fluorescence could also be observed in control cells not subjected to a sublethal heat treatment, this increase was not accompanied by the manifestation and stable inheritance of distinct fluorescent foci (S3B Fig). Together, these findings indicate that the presence of fluorescently labeled IbpA is not a prerequisite for the formation of asymmetrically segregating PAs (which become apparent upon induction of the IbpA-msfGFP fusion protein), demonstrating that the latter is indeed a natively occurring phenomenon after exposure to a sublethal heat treatment. Remarkably, higher induction levels of the fusion protein led to the irregular formation (i.e., continuously, not only initially when cellular fluorescence increased) of multiple fluorescent foci in both heat-treated and control cells (S3C Fig). This suggests that increased expression of IbpA itself, in line with previous observations describing the emergence of PAs upon overproduction of IbpA [40,41], might contribute to the formation of these structures in proteotoxic stress conditions (as these are known to induce ibpA expression).
We noticed that the asymmetric segregation of PAs was accompanied by an IbpA-msfGFP concentration gradient stemming from the PA-bearing cell(s) and progressively diminishing in its closest relatives (Fig 3A–3D). To examine whether this concentration gradient could be the consequence of increased ibpA expression in PA-bearing cells, we performed an identical experiment with MG1655 ibpA-msfgfp pSG1 cells, which allow the concurrent monitoring of IbpA promoter activity, concentration, and localization (S4A Fig). Although these cells behaved similarly after exposure to a sublethal heat shock in terms of PA formation, localization, and asymmetric segregation (S4B Fig), no increased mCherry signal could be detected in PA-bearing cells (as compared to that of their PA-free counterparts; S4C Fig). This indicates that the observed concentration gradient is likely not the consequence of a transcriptional ibpA response to the presence of these PAs but presumably finds it origin in the gradual disaggregation of the existing PA. In agreement, a small fraction of intracellular PAs was even observed to disappear after a certain amount of time, presumably because of their complete disaggregation.
Intriguingly, the above described phenomenon was not only instigated by a sublethal heat treatment. Exposure to other sublethal environmental stressors impacting proteostasis, such as streptomycin or hydrogen peroxide, gave rise to similar PA-associated phenotypic behavior (S5 Fig). As such, the formation and subsequent asymmetric segregation of these structures appears to be a conserved phenomenon throughout a wide range of sublethal environmental conditions affecting cellular proteostasis.
Since PAs thus appear as asymmetrically segregating remnants of a previously encountered proteotoxic stress, we wondered to what extent the presence of these structures (and the resulting concentration gradient) could influence survival upon a subsequent stress exposure. To this end, we challenged a total of 38 lineages stemming from sublethally heat-shocked cells (47 °C, 15 min) to a subsequent semilethal heat shock (51 °C, 7 min; Fig 4A), an assay in which we have previously described cellular survival to behave as a stochastic trait free of predispositions [42]. The heterogeneity in PA formation gave rise to microcolonies harboring a variable number of PAs, with an average of 1.41 PA foci per microcolony at the moment of the second heat treatment. Average cellular survival within these microcolonies (i.e., 55.6%) did not significantly differ from that within microcolonies stemming from unstressed cells not exposed to a prior sublethal heat shock (Fig 4A–4C), indicating the previously mounted heat shock response had faded to levels unable to affect the overall survival frequency [43,44]. Interestingly, however, comparison of the average survival of PA-free cells to that of PA-bearing cells clearly revealed the latter to display a significantly higher survival frequency (Student t test, p-value = 1.33 × 10−3; Fig 4D). Moreover, by binning the frequency of survival by average cellular IbpA-msfGFP fluorescence before the second heat shock, it also became obvious that cells bearing the highest initial IbpA concentrations (i.e. typically containing a larger PA) tend to display significantly higher survival probabilities (from an approximately 1/2 to 4/5 chance to survive; Fig 4E). Even within sister-cell pairs consisting of a PA-free and PA-bearing cell, a similar differentiation could already be observed (survival frequencies of 58.3% and 74.4% for PA-free and PA-bearing cells, respectively), further underscoring the close link of this increased robustness to PA inheritance.
Another potential explanation for these observations could stem from a confounding factor underlying both increased robustness and PA inheritance. Given the asymmetric nature of the latter phenomenon, increased cell age could be such a biasing property. Although PA-bearing cells were, on average, indeed older than the remainder of the population (average cell age of 1.80 versus 3.44 generations for PA-free and PA-bearing cells, respectively), no overall tendency for older cells to display increased survival probabilities could be detected (S6A Fig). Moreover, by exploiting the stochastic partitioning of PAs during the first generation post sublethal heat treatment, which gave rise to PA-bearing and PA-free old pole cells, we could directly disentangle the effect of both phenomena on survival. In line with our previous findings in microcolony-level semilethal survival assays [42], we found no evidence for any age-related bias in survival frequencies of PA-free cells (S6B Fig), further strengthening the role of PAs as deterministic factors that increase robustness of individual cells in an otherwise stochastic assay.
In line with our previous observations with more severe heat shocks (Fig 2), no new, distinct PAs emerged in cells surviving the semilethal heat shock, although in cells already containing a PA, this structure itself mostly remained present (Fig 4A). Again, an initial “patchy” localization pattern and a belated up-regulation of IbpA together with its apparent dissolution could be observed (Fig 4A). A similar observation could be made in surviving cells of microcolonies consisting of unstressed cells not exposed to a prior heat shock (Fig 4B).
Taken together, these results indicate that heat-induced PAs are able to increase the survival probability of individual cells upon exposure to a subsequent heat shock. As such, PAs appear to function as asymmetrically segregating, epigenetic memory elements (i.e., reminiscing previous torments) that impose phenotypic heterogeneity in isogenic microcolonies by improving the robustness of the PA-inheriting siblings. Moreover, detailed lineage tracing after the semilethal heat shock revealed that the increase in robustness might not only manifest itself in terms of higher survival probabilities but also in terms of decreased resuscitation times. Surviving PA-bearing cells appeared to resume growth and division faster than other surviving cells, leading to an enrichment of their progeny in the emerging population (Fig 4F).
To independently confirm that intracellular PAs constitute an asymmetrically transmissible form of epigenetic memory that drives heat resistance, we set forward to develop a novel synthetic PA model system that alleviates the need for an external stress factor to induce PA formation. More specifically, we started with a fragment of the lambda prophage repressor protein (cI), a protein known for its potential to misfold and aggregate by the introduction of a limited number of mutations [45]. To ensure the inertness of the repressor within the E. coli cytoplasm, its N-terminal DNA-binding domain was first removed [46]. The obtained fragment, dubbed cI78WT (as it does not include the first 77 amino acids of the full-length repressor protein), was fused N-terminally to the mCerulean3 fluorescent protein (yielding mCer-cI78WT) and placed under the control of an isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible promoter on a pTrc99A expression vector (yielding pTrc99A-mCer-cI78WT). This construct was subsequently transformed into an MG1655 ΔlacY strain to allow the titratable expression of the fusion protein [47]. Upon induction, this fluorescent fusion protein displayed a diffuse cytosolic fluorescence, indicating the protein remained completely soluble (Fig 5A). To isolate constitutively aggregating mCer-cI78 variants, we screened for mutations in cI78, introduced by error-prone PCR, that resulted in an alteration of this diffuse fluorescent localization pattern. As such, we were able to identify 1 mutant (cI78EP8; harboring 1 synonymous and 3 nonsynonymous mutations) displaying strict punctate and polarly located fluorescence (Fig 5B–5F), characteristic of disrupted folding and subsequent aggregation.
To further validate this isolated mutant as an aggregating protein, we examined whether it displayed other typical properties of prokaryotic PAs. First, we investigated whether its polar localization was nucleoid-enforced by equipping our MG1655 ΔlacY pTrc99A-mCer-cI78EP8 strain with a hupA-Venus fusion, allowing the fluorescent visualization of the nucleoid [48], as well as by examining the behavior of mCer-cI78EP8 in the absence of a nucleoid (by employing a ΔrecA derivative of MG1655 ΔlacY pTrc99A-mCer-cI78EP8, which occasionally gives rise to anucleate cells [49]). In agreement with characteristic PA behavior, mCer-cI78EP8 foci localized to nucleoid-free regions of the cell and were retained in the cell poles by nucleoid occlusion (evidenced by foci freely roaming throughout the entire cytoplasm in anucleate cells; S7A–S7C Fig). Second, we equipped mCer-cI78WT- and mCer-cI78EP8-expressing strains with pSG1 and confirmed that ibpA expression under inducing conditions indeed increased in the latter (S7D Fig). In addition, we equipped MG1655 ibpA-msfgfp cells with a pTrc99A-mCherry-cI78EP8 construct (in which the mCerulean3 fluorescent protein was replaced by mCherry because of its spectral incompatibility with msfGFP) and examined whether IbpA was able to recognize the misfolding and aggregating cI78EP8 variant. From this, a clear colocalization pattern between the IbpA-msfGFP and mCherry-cI78EP8 foci could be observed, indicating the latter indeed represents a misfolded and aggregating protein species (recognized by IbpA; S7E and S7F Fig).
We subsequently investigated whether the production of the mCer-cI78EP8 aggregating protein, as is the case with other misfolding protein species [10,50], affected cellular fitness. Whereas no difference in fitness could be detected in uninduced conditions (without the addition of IPTG), cells actively producing the mCer-cI78EP8 variant grew significantly slower than the cells producing the mCer-cI78WT variant, with the fitness disadvantage attributable to PAs increasing with the amount of insoluble protein produced (S7G Fig). Moreover, in fully induced conditions (1 mM IPTG), a clear negative correlation between microcolony fluorescence (indicative of the amount of insoluble protein present) and microcolony growth rate could be observed for cells expressing the aggregating mCer-cI78EP8 variant (S7H Fig). A similar negative correlation could not be detected in cells expressing the soluble cI78WT variant (S7I Fig), indicating the fitness defect was indeed attributable to misfolding and aggregation of the mCer-cI78EP8 protein. Please note that the observed fitness defect under these conditions, in which the misfolding protein is actively being produced, is fundamentally different from the previously reported absence of a fitness defect for host cells of asymmetrically segregating PAs after exposure to a sublethal heat treatment (Fig 3G). Whereas the former represents cells that are continuously challenged by misfolding proteins, environmental proteotoxic stress conditions have been relieved in the latter, and the PAs only remain present as remnants of a previously encountered sublethal proteotoxic insult.
Using this newly developed and validated model system, we subsequently examined whether synthetic PAs (consisting of an aggregating, inert E. coli protein) indeed confer increased heat resistance. In a first step, MG1655 ΔlacY pTrc99A-mCer-cI78EP8 cells were grown to exponential phase in AB medium with 0.2% glycerol in the presence of 1 mM IPTG (to induce the production of mCer-cI78EP8), harvested, and washed into AB medium with 0.2% glucose (impeding further induction of mCer-cI78EP8 production), after which their growth was monitored by TLFM. Similarly as in MG1655 ibpA-msfgfp cells after a sublethal heat shock, the synthetic PAs not only segregated asymmetrically throughout the emerging microcolonies but were also accompanied by a mCer-cI78EP8 concentration gradient originating from the PA-bearing cells (Fig 6A and 6B and S3 Movie). In this case, given that production of the fluorescent protein has been halted, it is clear that the emerging concentration gradient is a consequence of protein disaggregation rather than a specific response to the presence of PAs. Although PA-bearing cells, on average, displayed a significant fitness defect (16.3%; Fig 6A–6D), this effect could not easily be discerned from cellular aging, as both phenomena were similar in magnitude (16.1% decrease in cellular growth rate of cells inheriting the oldest cell poles compared to the rest of the population; S2K Fig), and a larger fraction of old pole cells also contained a PA (13 out of 24 observed old pole cells). Nevertheless, a direct comparison of the growth rates of PA-bearing old pole cells with their PA-free counterparts yielded no significant difference (Student t test, p-value = 8.25 × 10−2), indicating that the contribution of PAs to the observed cellular aging was again negligible.
Upon exposure of these growing microcolonies to a heat shock killing approximately half of the population (52 °C, 7 min), the synthetic PAs were found to confer a similar protective effect as seen with the IbpA-msfGFP-labeled stress-induced PAs (Fig 6E–6I). While average cellular survival did not significantly differ between PA-harboring (MG1655 ΔlacY pTrc99A-mCer-cI78EP8) and PA-free (MG1655 ΔlacY pTrc99A-mCer-cI78WT) microcolonies (Fig 6G), PA-bearing cells displayed a significantly higher survival frequency within the former group (Fig 6H and 6I). Remarkably, this synthetic PA-mediated protection already manifested itself after one division (Fig 7A and 7B), underscoring the apparent speed with which this PA-mediated differentiation occurs. Upon further analysis, this experimental setup clearly revealed the dual effect of PA presence on stress management, as PA-bearing cells not only displayed a higher survival frequency, but surviving PA-bearing cells also displayed a significantly reduced resuscitation time as compared to their surviving PA-free counterparts (Fig 7C).
Synthetic PAs appeared as non-diffraction-limited fluorescent spots, which allowed the accurate determination of their size (instead of relying on indirect, and potentially biased, fluorescence intensity measurements, see Materials and methods; Fig 7D and 7E and S8 Fig). These measurements revealed that aggregate size did not appear to correlate with cellular survival, indicating that the amount of aggregated protein species is, at least within the sampled range, irrelevant for the observed phenotype (Fig 7F). Moreover, these measurements allowed us to estimate the copy number of the mCer-cI78EP8 molecules in each aggregate. Based on its amino acid sequence, the fusion protein has an approximate molecular weight of 45 kDa and is estimated to occupy a volume of 5.71 × 10−8 μm3 [51]. Assuming spherical aggregate shape and a pure mCer-cI78EP8 composition, individual synthetic aggregates are subsequently roughly estimated to be composed of around 4 × 105 protein molecules.
Cells actively producing misfolded and aggregating protein species became more susceptible to heat stress than those producing similar amounts of soluble protein (S9 Fig). This was illustrated by the decreased survival of MG1655 ΔlacY pTrc99A-mCer-cI78EP8 populations as compared to that of MG1655 ΔlacY pTrc99A-mCer-cI78WT populations after exposure to a semilethal heat treatment in inducing conditions (S9 Fig). The protective effect of PAs thus appears limited to cases in which the original conditions giving rise to their emergence have been relieved. Together with the previously described negative impact of PA production on cellular fitness (S7G–S7I Fig), this sensitization toward proteotoxic stress during PA production likely impedes PA formation from being an efficient way to increase average population-level robustness in benign conditions (without a previous sublethal proteotoxic exposure). As such, PAs likely function as true epigenetic memory elements, in which cells must have encountered an environmental condition licensing their emergence.
In order to further substantiate and characterize the observed PA-mediated robustness in a more versatile fashion, we used an alternative approach to determine survival frequencies of large numbers of PA-bearing and PA-free cells. In essence, after transient induction of synthetic (mCer-cI78EP8) PA production, the population continued growth and concomitant asymmetric PA segregation in liquid culture, after which the resulting PA-free and PA-bearing cells were stress-challenged and only then monitored on a single-cell level by TLFM (Fig 8A). By postponing the mounting and incubation of cells under the microscope, this setup allowed us to easily (i) increase the experimental throughput, (ii) vary the number of generations/segregations between PA production and stress challenge, (iii) expose the cells to other stresses than heat, and (iv) expose the cells more homogeneously to a stress as well.
First, we varied the intensity of the heat challenge and exposed clonal populations of PA-free and PA-bearing siblings to heat shocks ranging from 51 to 53.5 °C. As such, we found that PA-bearing cells consistently displayed higher survival frequencies than PA-free cells (Fig 8A), confirming the protective effect of PAs and indicating a wide protective range. Moreover, the relative increase in survival that could be attributed to PA presence generally increased with increasing heat intensity. For example, after an exposure to 53.5 °C (for 15 min), PA-bearing cells displayed an almost 6-fold higher survival frequency than PA-free cells (11.6% versus 2.0%). Even after the mildest exposure (51 °C for 15 min), PA-bearing survivors displayed significantly shorter resuscitation times than their PA-free counterparts (Fig 8B), again underscoring the dual protective effect of PAs.
Subsequently, we used the above-described approach to investigate PA-mediated robustness over longer timescales of asymmetric PA inheritance. While we previously employed timescales corresponding to around 4 generations or fewer (Figs 4, 6 and 7), we could now allow PA-containing populations to grow for longer periods of time before applying a heat challenge (52 °C for 15 min) and quantify the resulting survival of clonal PA-free and PA-bearing siblings. Although the ongoing asymmetric segregation of PAs during prolonged growth inevitably leads to a progressively decreasing fraction of PA-bearing cells, a sufficient number could still be observed to irrefutably demonstrate that the protective effect of PAs persists over longer timescales (Fig 8C). Even after 200 min of growth (corresponding to approximately 7–8 generations of growth), the presence of these structures was associated with a similar increase in survival frequency (Fig 8C).
Finally, this setup also enabled us to investigate whether the PA-mediated increase in cellular robustness extended to proteotoxic stresses other than heat. To this end, we exposed clonal populations of PA-free and PA-bearing siblings to semilethal concentrations of either hydrogen peroxide (Fig 8D) or the ribosome-targeting antibiotic streptomycin (Fig 8E). In line with the previous heat shock experiments, we found that cells harboring a PA consistently displayed higher survival frequencies for both challenges (Fig 8D and 8E), suggesting that PAs can confer robustness to a wide variety of proteotoxic stresses.
In a subsequent step, we sought to provide some insight into the molecular mechanisms underlying the observed increase in robustness of PA-bearing cells. Given the apparent absence of a direct link between the identity of the aggregated protein species and the memory encoded by them (as is the case for other examples of protein-based inheritance [52,53]), we chose to characterize and examine the potential role of protein quality control components, as previous work has demonstrated their physical association with (disaggregating) PAs [14,54–56].
In a first step, we constructed deletion strains of ibpA, ibpB, and the entire ibpAB operon and equipped these with our synthetic PA system. We subsequently probed for the existence of PA-mediated robustness in these strains by using a similar setup as in Fig 6, in which growing and PA-containing microcolonies were exposed to a semilethal heat shock (52 °C, 7 min). None of the deletions, however, appeared to affect the increase in heat resistance of PA-bearing cells (S10 Fig), suggesting these small heat shock proteins do not play a role in establishing PA-mediated asymmetry.
In contrast to the small heat shock proteins, deletion of many other protein quality components is known to severely compromise heat survival [57,58]. This is further exemplified by the lack of detectable survivors in a ΔclpB strain in our survival assay. (S10 Fig; ClpB is a heat shock protein 100 [Hsp100] AAA+ chaperone involved in protein disaggregation [59]). This reduction in survival frequency impedes direct interpretations concerning the potential role of ClpB (and other protein quality control components) in establishing PA-mediated robustness. To overcome this confounding issue and obtain some insight into the potential role of the proteostasis network, we resorted to the use of fluorescent fusion proteins. We constructed both transcriptional and translational msfGFP fusions to a variety of chaperones and proteases and equipped these strains with the mCherry version of our synthetic PA system (to ensure spectral compatibility with msfGFP). As we expected nonfunctional fusions to decrease heat survival frequency [57,58], we first validated the fusion proteins by exposing the corresponding strains to a heat treatment and comparing their inactivation levels to those of a control without any chaperone or protease fusions (S11A and S11B Fig). This analysis revealed that only the transcriptional PclpP-msfgfp fusion led to a significantly increased inactivation (S11A and S11B Fig) and thus likely exerts a polar effect that perturbs wild-type cellular physiology. We further validated the transcriptional fusions by exposing these strains to a sublethal heat shock and verifying the expected increase in promoter activity ([60]; S11C Fig). Whereas most transcriptional fusions indeed displayed a significant up-regulation directly after heat treatment (S11C Fig), the transcriptional PclpP-msfgfp fusion did not, further supporting the lack of functionality of this fusion. A similar increase in concentration after exposure to a sublethal heat shock could often not be detected on a protein level for these chaperones and proteases (S11D Fig), suggesting the potential existence of posttranscriptional regulation, slow protein folding and maturation, and/or high rates of protein turnover.
In line with the previously noted absence of a heat shock response to the presence of asymmetrically segregating PAs (S4 Fig), we found that none of the tested protein quality control components displayed a transcriptional up-regulation in the presence of PAs (S12A and S12B Fig). Their expression level (indirectly measured through the average cellular GFP concentration) often even appeared lower than that of PA-free cells, although this likely is a consequence of the impermeability of (synthetic) PAs to other cytosolic components (S12C Fig). The presence of PAs therefore appears to lead to an extra addition of cell volume containing no fluorescence, in turn leading to a lower apparent concentration.
On the protein level, however, specific protein quality control components (the Hsp70 chaperone DnaK, the Hsp40 chaperone DnaJ, the Hsp100 AAA+ chaperone ClpB, and the serine protease ClpP) displayed an increased concentration in PA-bearing cells (Fig 9A). TLFM revealed that the increased concentration was a direct consequence of a remarkable colocalization of these proteins with asymmetrically segregating PAs that persisted over multiple generations (Fig 9B). This observation appears to be in line with the similarly ongoing PA-disaggregation observed earlier (Figs 3A, 3C, 4A and 6A). Other protein quality components (the AAA+ protease subunit ClpX, the serine protease Lon, the AAA+ protease subunit HslU, and the Hsp90 chaperone HtpG) did not display any colocalization or an increased concentration (Fig 9A and S13 Fig). In fact, in similar fashion as for the transcriptional reporters and presumably because of the same PA-mediated addition of nonfluorescent cell volume, a lower apparent concentration was often measured in PA-bearing cells for these chaperones and proteases. Asymmetric segregation of PAs thus appears to drive the specific enrichment of protein quality control components in their host cells. This enrichment might in turn be responsible for the increased robustness of PA-bearing cells toward proteotoxic stresses.
In contrast to the governing view on prokaryotic PAs as inevitably debilitating structures [10–12,14], our findings in the E. coli model system reveal the potential of PAs to improve cellular robustness, both in terms of increased survival frequencies and decreased resuscitation times, independent of their origin. Moreover, because of their asymmetric segregation and limited disaggregation, these structures resist dilution during cytoplasmic inheritance and are therefore able to persist for many generations as a functional physical relic of an ancestral insult. As such, these observations reveal the existence of stress-induced, long-term epigenetic memory in prokaryotes.
More specifically, such cellular memory appears to be installed through (sublethal) proteotoxic exposures that lead to initial up-regulation of the heat shock response and colocalization of specific chaperones and proteases with the emerging PAs. Growth after relief of the proteotoxic exposure coincides with asymmetric segregation of this PA and its associated chaperones and proteases, while additional heat shock proteins do not seem to be induced upon the mere inheritance of this structure. Whereas the selective redistribution of quality control components to polar PAs in E. coli during and directly after proteotoxic stress has been observed before [8,10,13,14], our data indicate that this association persists over multiple generations and leads to their specific enrichment in PA-bearing cells. In fact, the slowly ongoing PA-disaggregation during outgrowth might be a consequence of a prolonged association of these components with the PA. Importantly, previous studies have reported that the association of several chaperones and proteases with PAs is dynamic [14], indicating that these proteins are not necessarily trapped within these intracellular structures and might become available as additional protein folding aids that increase cellular robustness in times of stress. Interestingly, this putative mechanism might even extrapolate to other (eukaryotic) microorganisms, since S. cerevisiae mother cells harboring an age-associated PA were previously reported to clear heat-induced aggregates faster than daughter cells without such an aggregate [61].
However, further research is required to establish a direct causal relation between PA-associated chaperones and proteases and the observed cellular robustness. While the use of individual deletion mutants does not seem a good option because of their severely compromised heat survival [57,58](S10 Fig), obtaining tunable amounts of different protein quality control components and subsequently assessing to what extent this gradually affects PA-mediated robustness might be a more fruitful strategy to establish causality. Furthermore, it will also be interesting to see whether or not PA-mediated protection is consequently limited to those stresses that require protein folding aids to be alleviated.
In essence, epigenetic memory refers to hysteretic behavior in which the physiological state of a cell is determined by its (or its ancestor’s) past experience rather than being dictated by genomic mutations or the current environment it resides in [62,63]. Nonmutational mechanisms allowing the long-term inheritance of a previously acquired physiological state are typically based on the passage of specific protein activities or interactions from one generation to the next. As such, E. coli cells already expressing the LacY permease in intermediate inducer concentrations will continue doing so for subsequent generations because their ability to import the inducer will lead to continued production of the permease [64,65]. Similarly, cells inheriting a self-propagating prion seed transmit this infectious protein conformation to their progeny, in which this process will repeat itself [66–69]. In contrast to these mechanisms, intracellular PAs essentially lack a self-proliferative effect (i.e., it is the original PA that is passed on from one cell to another) but nevertheless remain inheritable for many generations because the asymmetric segregation and limited disaggregation prevent their dilution. This relates to the mnemons concept posed by [52], in which the conditional self-aggregation of a specific protein (the Whi3 mRNA binding protein of S. cerevisiae) compromises its functionality and proper segregation to daughter cells. However, since the exact PA origin in our experiments appears to be irrelevant for its phenotypic consequences, prokaryotic PA-mediated memory seems to comprise a completely novel type of protein-based inheritance.
Our single cell–level observations also revealed other intriguing aspects of the process and impact of in vivo protein aggregation. PAs, for example, seem not to emerge by default after exposures to a proteotoxic stress. Instead, their formation appears limited to less severe, sublethal stressful encounters. Although we cannot currently provide a conclusive answer, we hypothesize that this observation could reflect different, nonmutually exclusive, cellular strategies. One possibility is that the dosage of misfolded proteins determines the choice between aggregation and refolding/degradation, given that a largely aggregated proteome would trivially lead to defects in cellular function. Another possibility is that the formation of these large intracellular structures requires a certain factor or multiple factors (in line with the observation in fission yeast that Hsp16 mediates PA fusion [70]), but this factor is allocated to repair or prevention of aggregation of other critical cellular components under more severe stress conditions. Alternatively, PA formation could pose too much of a risk under severe heat stress, as aggregation could potentially lead to coaggregation and trapping of other critical cellular components [71], themselves destabilized and misfolded under these more severe stress conditions.
Aside from their remarkable pattern of emergence, the slow and limited poststress disaggregation of PAs over multiple generations also represents cellular behavior that previously remained undetected using population-level determinations of aggregated protein fractions [8,54,72]. Although many of the molecular players and steps involved in the disaggregation process have already been identified and studied in vitro [54,56,73], our observations underscore that the in vivo implementation and kinetics of this process require further study. The resulting persistence and asymmetric inheritance of PAs over multiple generations gives rise to a progressively decreasing fraction of memory cells as population numbers increase. This heterogeneity is reminiscent of a bet-hedging strategy typically employed by microbial populations to mitigate costs or trade-offs associated with the installment of given phenotypes. However, PA-mediated fitness defects could not be detected in terms of cellular growth rate, in agreement with recent findings in fission yeast [74], suggesting such trade-offs either remain undetected in our setup (and are yet to be discovered) or are simply not present. In this light, the formal distinction between protein aggregation (believed to be deleterious) and quinary assembly formation (believed to be adaptive) [22,23] becomes more vague as well.
Importantly, microorganisms can encounter many forms of proteotoxic stress throughout their habitats, and our data indicate that sublethal exposures to streptomycin and hydrogen peroxide not only give rise to similar emergence and inheritance of PAs but that the PA-mediated robustness extends toward these other proteotoxic stresses as well. These stressors are of specific importance given their roles in curbing and fighting microbial infections in humans. Not only do many antibiotics specifically target protein homeostasis [8]; the mechanism behind the activity of bactericidal antibiotics has also been linked to the production of reactive oxygen species (ROS) [75,76]. Moreover, the generation of ROS has been implicated in microbial killing by phagocytes [77,78]. Consequently, our findings might have broader implications in the context of understanding how bacteria cope with and evade antimicrobial therapeutics and host defense mechanisms.
In conclusion, intracellular prokaryotic PAs appear to be more than inevitable and detrimental cellular garbage bins, as these structures encode epigenetic long-term memory of previous proteotoxic torments that becomes vertically transmitted for a number of generations while conferring an increased robustness to its carrier cell. As such, the formation and conservation of PAs in prokaryotes resembles an adaptive process that aids cellular survival and adaptation in fluctuating environments. Moreover, this paradigm further suggests that other types of cytoplasmically inheritable damaged biomolecules could likewise serve as functional “scar tissue” that improves cellular robustness over multiple generations.
Bacterial strains, plasmids, and primers used in this study are listed in S2, S3 and S4 Tables, respectively. For culturing of bacteria, mostly lysogeny broth (LB) medium was used as either a broth or solid medium after the addition of 2% agarose (for agarose pads intended for microscopy). In indicated cases, LB medium was replaced by AB medium, supplemented with 0.2% of a carbon source (glucose or glycerol), 0.2% casamino acids, 10 μg/ml thiamine, and 25 μg/ml uracil.
Stationary-phase cultures were obtained by growing E. coli overnight for approximately 15 h in LB broth at 37 °C under well-aerated conditions (200 rpm on an orbital shaker). Exponential phase cultures were in turn prepared by diluting stationary phase cultures 1/100 in fresh prewarmed broth and allowing further incubation at 37 °C until an OD600 of 0.2–0.6 was reached. When appropriate, the following chemicals (Applichem, Darmstadt, Germany, and Sigma-Aldrich) were added to the medium at the indicated final concentrations: kanamycin (50 μg/ml), ampicillin (100 μg/ml), chloramphenicol (30 μg/ml), 4′,6-diamidino-2-phenylindole (DAPI; 1 μg/ml), IPTG (1–1,000 μM), glucose (0.2%), L-arabinose (0.1%–0.2%), and glycerol (0.2%).
The construction of mutants in E. coli is greatly facilitated by lambda Red-mediated recombination. The protein products of the red genes (Gam, Exo, and Beta) enable the highly efficient recombination of PCR products (containing a selectable antibiotic cassette, optionally flanked by other genes of interest such as those encoding fluorescent proteins) flanked by short (50 bp) nucleotide sequences, homologous to the target sequence [79]. These antibiotic cassettes are usually flanked by frt sites, which allow the excision of the cassette by site-specific recombination between two frt sites.
All constructed mutants were initially confirmed by PCR with primer pairs attaching outside of the region where homologous recombination occurred. Correct deletion or integration of PCR products was further verified by sequencing (Macrogen, the Netherlands).
The different C-terminal translational fusions of IbpA and IbpB were constructed by recombineering PCR fragments obtained from plasmid pDHL1029 and its derivatives pDHL-venus, pDHL-mVenus, pDHL-mCherry, and pDHL-mCer (the original pDHL1029 plasmid contains a msfgfp-frt-nptI-frt cassette [36]; its derivatives were constructed during this work and contain sequences encoding other fluorescent proteins: Venus, mVenus [monomerized Venus by introducing V206K mutation], mCherry, mCerulean3) using primer pairs SG1-2 (IbpA) and SG3-4 (IbpB), in MG1655 creating a C-terminal fusion of IbpA and IbpB to the different fluorescent proteins. The resistance cassette was subsequently excised by transiently equipping this strain with plasmid pCP20 expressing the Flp site-specific recombinase [80], resulting in the desired strain containing a fluorescent IbpA/IbpB fusion protein.
C-terminal translational fusions of protein quality control components (DnaK/DnaJ/ClpB/ClpP/ClpX/HtpG/HslU/Lon) were constructed in similar fashion using primer pairs SG23-38. The transcriptional fusions probing their expression level were constructed by first obtaining a msfgfp-frt-nptI-frt amplicon from plasmid pDHL1029 using primer pairs SG39–SG54. The respective fluorescent transcriptional reporter strains were then created by recombineering this amplicon 5 bp after the stop codon of the gene of interest, creating an artificial operon and ensuring cotranscription. To maximize cotranslational activity, the gene encoding msfgfp was preceded by a strong synthetic ribosome binding site (BBa_B0034; sequence AAAGAGGAGAA [81]). The resistance cassette was subsequently excised by transiently equipping this strain with plasmid pCP20 expressing the Flp site-specific recombinase [80], resulting in the desired fluorescent transcriptional reporter strains.
The C-terminal translational fusion of HupA to Venus was constructed using primer pair SG5-6 and pGBKD-venus as a template.
Deletion strains were constructed using an amplicon prepared on pKD13 using the primers listed in the study by Baba and colleagues [79,82] for recombineering. This procedure replaced the genes of interest with an frt-flanked kanamycin resistance cassette, which could subsequently be excised by transiently equipping this strain with plasmid pCP20 [80], resulting in the desired deletion strain.
All constructed plasmids were verified by both PCR and sequencing (Macrogen, the Netherlands). Plasmids were introduced into their respective host strains by transformation and selection for antibiotic resistance encoded by the plasmid.
Plasmids pGBKD-mCherry and pGBKD-venus were constructed by integrating an mCherry/venus amplicon, generated from pDHL-mCherry and pDHL-venus with primer pairs SG7-8 (mCherry) and SG9-10 (venus), into pGBKDparSpMT1 [83] using EcoRI and BamHI restriction sites. In addition to adding the respective restriction sites to the end of the amplicon, these primer pairs also add a flexible linker (encoding GSGSGS; [84]), facilitating folding of fluorescent fusion proteins constructed with these sequences.
Plasmid pSG1 was constructed by first inserting an amplicon—obtained from pGBKD-mCherry using primer pair SG11-12—into MG1655, creating a transcriptional ibpA fusion in which the native ibpA gene was completely replaced by mCherry. From this, an amplicon (using primer pair SG55-56) was obtained containing the entire 5′-upstream region of ibpA (including its promoter and 5′-UTR) in front of mCherry, which was subsequently blunt-end ligated into a pACYC184 vector ([85]; opened by PCR amplifying the entire plasmid using primer pair SG13-14). The resulting plasmid, pSG1, was initially transformed to MG1655, in which it functioned as a transcriptional reporter for ibpA expression. The plasmid was validated by exposing MG1655 pSG1 cells to a sublethal heat treatment that, as expected, led to an increase in cellular mCherry fluorescence.
Plasmid pTrc99A-mCer-cI78WT was constructed by first making an mCer amplicon from pDHL-mCer with primer pair SG15-16. These primers amplified the mCer encoding gene without a start or a stop codon and added a NcoI and BamHI restriction site to the ends of the amplicon. Digestion of the amplicon and pTrc99A vector [86] with these restriction enzymes allowed the subsequent ligation of the amplicon in the expression vector. After being verified by both PCR and sequencing, the created construct was digested again with BamHI and SalI restriction enzymes. Another amplicon containing a fragment of the lambda prophage repressor protein was generated from an in-house E. coli strain harboring the lambda prophage, using primer pair SG17-18. These primers amplify a fragment of the lambda repressor protein named cI78 (as it does not contain the first 77 amino acids of the normal full-length protein) and added a BamHI and SalI restriction site as well as a flexible linker (coding for GSGS) to the end of the amplicon. Subsequent digestion of this amplicon allowed its ligation into the previously constructed and digested construct. The resulting plasmid, pTrc99A-mCer-cI78WT, expresses a C-terminal fusion of mCerulean3 to cI78 under control of an IPTG-inducible promoter.
Plasmid pTrc99A-mCer-cI78EP8 is a plasmid expressing a misfolding and aggregating cI78 mutant, identified in a screen specifically aimed at obtaining such mutants. To find mutations that contribute to misfolding, we first performed random error-prone PCR mutagenesis [87] on cI78 (dubbed cI78WT) and ligated the obtained mutant library behind mCer in pTrc99A (in similar fashion as cI78WT). We screened for misfolding cI78 variants by examining individual mutants under the microscope for alterations of the normally diffuse fluorescent localization pattern. From this, a cI78 mutant, cI78EP8 (the term “EP” stemming from the error-prone PCR method used for its construction, and 8 stemming from the number of the isolated mutant), was identified, which displayed strict punctate and polarly located fluorescence, characteristic of disrupted folding and subsequent aggregation. cI78EP8 harbors 1 synonymous (180C>T) and 3 nonsynonymous mutations (38A>T, 135T>A, and 343T>C). Plasmid pTrc99A-mCherry-cI78EP8 was created by replacing the mCer gene by mCherry.
Plasmid pBAD33-ibpA-msfgfp was constructed by first generating an ibpA-msfgfp amplicon from MG1655 ibpA-msfgfp cells using primer pair SG19-20. These primers amplify the entire gene fusion and add a strong synthetic ribosome binding site (BBa_B0034; sequence AAAGAGGAGAA [81]) preceding the ibpA start codon. The amplicon was subsequently blunt-end ligated into a pBAD33 vector ([88]; opened by PCR amplifying the entire plasmid, using primer pair SG21-22). The resulting plasmid was then transformed to MG1655, in which it was used to induce expression of the fusion protein to further probe native IbpA (i.e., unlabeled) behavior (of which the resulting strain thus also carries a copy).
For TLFM, cell suspensions were diluted appropriately, transferred to agarose pads (containing the appropriate medium), placed on a microscopy slide, and mounted with a cover glass. A Gene Frame (Thermo Scientific) was used to hold the cover glass on the microscopy slide. TLFM was performed with a temperature controlled (Okolab, Ottaviano, Italy; 37 °C) Ti-Eclipse inverted microscope (Nikon, Champigny-sur-Marne, France) equipped with a 60× objective, a TI-CT-E motorized condenser, a YFP filter (Ex 500/24, DM 520, Em 542/27), a CFP filter (Ex 438/24, DM 458, Em 483/32), a GFP filter (Ex 472/30, Dm 495, Em 520/35), an mCherry filter (Ex 562/40, Dm 593, Em 641/75), a DAPI filter (Ex 377/50, DM 409, Em 447/60), and a CoolSnap HQ2 FireWire CCD-camera. Images were acquired at user-chosen time intervals using NIS-elements software (Nikon). During the acquisition of TLFM recordings, care was taken to prevent potential photobleaching of fluorescent molecules (i.e., the photochemical alteration of a fluorophore molecule by, for example, the prolonged exposure light of excitation wavelengths, such that it permanently is unable to fluoresce) by minimizing excitation light intensity and enlarging time intervals in between acquisition of fluorescent images. The resulting images were further handled with open source software ImageJ.
Characteristics (e.g., length, area, fluorescence) of individual cells growing in/into microcolonies (and of the microcolonies themselves) were acquired using the MicrobeTracker software [89]. In order to obtain robust results, manual curation was necessary to improve automatic segmentation and tracking. The data generated by this analysis were fed into a relational database enabling its subsequent transformation (e.g., calculation of certain cellular characteristics [growth rate], establishment of genealogical relationships between cells) and mining. The distribution of fluorescent PA foci was obtained by detecting their relative localization along the cell axis using the SpotFinder tool of MicrobeTracker [89]. Heat maps displaying the distribution of intracellular PA localization were generated using MicrobeJ, an ImageJ plugin [90].
Given the relative constancy of cell width during cell cycle progression in a given environment, cell length was employed to quantify cellular growth of individual cells. Growth rates of individual cells were determined by exponential fits of cell length over time. To examine the effect of the presence of PAs on cellular growth, only growth rates of fourth- and fifth-generation cells were considered, as these cells have grown a sufficient number of generations after the removal of the corresponding PA-inducing agent (heat or 1 mM IPTG to induce expression of cI78EP8) but are not yet suffering from any (local) nutrient-depletion effects. To examine the effect of cI78EP8 production on cellular fitness, microcolony growth rates were determined in different induction regimes and compared to those of microcolonies producing cI78WT. Growth rates of microcolonies were determined by exponential fits of microcolony area over time (time interval: 1–3 h after beginning of recording).
The presence of cellular aging was examined by quantifying the fitness defect of the oldest cells within the fourth and fifth generation compared to all other cells of those generations. Cell age was inferred from old pole generations as introduced by Stewart and colleagues [17]. Consequently, each generation within a microcolony contained 2 oldest cells (i.e., the cells inheriting the cell original cell poles of the “founder” cell of the microcolony). Violin plots illustrating the extent of this effect were generated using a custom script in R.
Given that incorporation of fluorescent proteins into a PA potentially compromises their structure and fluorescence [91], we determined PA sizes directly using the ObjectDetection module within the Oufti software [92], which allows the detection of non-diffraction-limited fluorescent regions. The mCerulean3 fluorescent protein emits at 475 nm, leading to an Abbe diffraction limit of 475 / (2 * 1.4) = 169.6 nm (the value 1.4 corresponds to the numerical aperture of the microscope objective). The minimum size of circular spots that can subsequently be reliably detected is π * (169.6/2)2 = 22591.3 nm2 (or approximately 0.02 μm2), which is significantly smaller than the smallest measured size of PAs (0.037 μm2). From this, it is clear that PAs, produced by the synthetic mCer-cI78EP8 model system, occur as non-diffraction-limited spots within individual cells.
These measurements also allowed us to directly investigate the potential correlation between PA size and average cellular fluorescence (S8 Fig). Although an overall good correlation could be observed, this correlation appears nonlinear and noisy, especially for smaller and larger PAs.
The permutation test to investigate potential contributions of PAs to the observed fitness defect of their host cells (next to the age of these cells) was performed by randomly sampling PA-free cells of similar age structure (100,000 times/permutations) and comparing their average fitness to that of all other PA-free cells. A p-value was subsequently calculated as the proportion of sampled permutations in which the absolute difference in fitness was greater than or equal to the observed fitness defect of PA-bearing cells (as compared to all PA-free cells). For the synthetic PA system, a similar approach could unfortunately not be employed. The large fraction of old cells also bearing a PA (more than half the number of old cells) made it impossible to disentangle the potential individual contribution of both phenomena to cellular fitness defects using this approach.
Fifty μl of exponential phase cells was transferred aseptically to a sterile PCR tube and heat treated for 15 min at indicated temperatures in a thermocycler (Westburg, Leusden, the Netherlands). Control samples were also transferred to PCR tubes but were kept at room temperature for 15 min. After heat treatments, samples were aseptically retrieved from the PCR tubes and subjected to TLFM as described above.
To examine the effect of PAs on survival frequency in heat shock experiments, the same cells were microscopically examined before and after heat treatment. To accomplish this, cells were first mounted on a microscopy slide as described above and allowed to grow for an indicated number of generations while their spatial coordinates on the slide were noted. Subsequently, the slide as a whole was subjected to a heat shock (for indicated times at indicated temperatures; heat shock durations and intensities were chosen so to inactivate approximately half of the cellular population) by taping the slide to the lid of a thermocycler (Westburg, Leusden, the Netherlands), after which the spatial coordinates were used to trace back and microscopically follow up the same cells on the heat-treated slide.
For experiments in which cells were precultured and challenged in liquid cultures, and survival, together with potential presence of PAs, was examined microscopically, MG1655 ΔlacY pTrc99A-mCer-cI78EP8 cells were grown to exponential phase (OD600 = 0.2–0.6) in AB medium with 0.2% glycerol. PA production was subsequently induced by adding 1 mM IPTG to the medium for 1.5 h, after which the cells were harvested and washed into AB medium with 0.2% glucose. After washing, cells were incubated at 37 °C for 75 min (or longer times when indicated) and exposed to heat, peroxide, or streptomycin stress for the indicated period of time. The fractions of surviving PA-bearing and PA-free cells were subsequently determined at the single-cell level by TLFM (after washing away the peroxide or streptomycin in cases in which these stressors were employed). As PA production appeared to occasionally (10%–20%) give rise to likely anucleate, PA-bearing cells (observable as small cells with PAs filling almost their entire cytoplasm; S14 Fig), the fraction of surviving cells for both cellular classes (PA-bearing and PA-free) was compared to the fraction of outgrowing cells in unstressed control conditions to determine the relative fraction of surviving cells for each class.
To examine the effect of ongoing PA production on stress sensitivity, MG1655 ΔlacY pTrc99A-mCer-cI78WT and MG1655 ΔlacY pTrc99A-mCer-cI78EP8 cells were exposed to a semilethal heat shock (49 °C, 15 min) during mCer-CI78WT (soluble) and mCer-CI78EP8 (i.e., PA) production (AB medium with 0.2% of glycerol in the presence of 1 mM IPTG). The surviving fraction of cells was determined through spot-plating experiments in which the appropriate dilutions of a sample were prepared in PBS and subsequently spot-plated (5 μl) on LB agar. After 24 h of incubation at 37 °C, the plates were counted, and the number of survivors in CFU per ml was determined.
Streptomycin (10 μg/ml for 60 min or 15 μg/ml, final concentrations, for 30 min) and H2O2 (6 mM, final concentration, for 90 min) were directly added to exponentially growing MG1655 ibpA-msfgfp cultures. After treatments, streptomycin and H2O2 were washed away, and samples were diluted and prepared for microscopy as described above.
Cellular viability (i.e., the relative number of cells surviving the heat sock) was determined by TLFM. Cells that could be observed to grow and divide within an 8 h time frame after heat treatment were scored as surviving cells.
Cell meshes generated by the MicrobeTracker program were used to determine resuscitation times of individual cells, as described previously [15]. Since bacterial cells typically only elongate in the longitudinal direction, resuscitation times were measured by looking at the length increase of individual cells over time. First, an initial length was calculated as the mean of the first 3 measurements for each individual cell. The length of that cell in the subsequent frames was then compared to this initial length, and the resuscitation time was defined as the time corresponding to the frame in which cell length had increased over 10% compared to its initial length, plus the time between the end of the heat treatment and the beginning of microscopy recording (typically around 10 min). This 10% increase in initial length was taken as a threshold to prevent random measurement fluctuations from influencing the results and ensure that only resuscitation times of cells that had fully committed to growth were measured. In addition, only resuscitation times of surviving cells were measured, i.e., cells that subsequently committed to growth and division.
As we expected nonfunctional fusions to compromise cellular heat survival, the respective fusion proteins were validated by exposing PA-containing populations (MG1655 ΔlacY pTrc99A-mCherry-cI78EP8 cells, with the respective fusions) to a heat treatment and comparing inactivation levels to that of PA-containing populations of unlabeled cells (without any additional fluorescent fusions). For this, cells were first grown to exponential phase (OD600 = 0.2–0.3) in AB medium with 0.2% glycerol. PA production was subsequently induced by adding 1 mM IPTG to the medium for 1.5 h, after which the cells were harvested and washed into AB medium with 0.2% glucose. After washing, cells were exposed to a heat shock (52 °C, 15 min), and inactivation was determined through spot-plating experiments. In these experiments, the appropriate dilutions of a sample were prepared in 0.85% KCl and subsequently spot-plated (5 μl) on AB glucose agar. After 24 h of incubation at 37 °C, the number of survivors was scored, compared to that of untreated controls, and total inactivation was determined.
We further validated the transcriptional and translational fusions by exposing them to a sublethal heat shock (47 °C, 15 min), conditions known to activate the heat shock regulon of which the investigated proteins are a part of [93]. We quantified promoter activity and protein concentrations for the tested chaperones and proteases before and after heat treatment. For the transcriptional fusions, average cellular GFP fluorescence (corresponding to promoter activity) was determined directly after heat treatment. For the translational fusions, average cellular GFP fluorescence (corresponding to protein concentration) was determined after 30 min of incubation at 37 °C after heat treatment (allowing additional time for fusion protein folding and maturation).
Closer examination of the translational DnaK-msfGFP reporter strain revealed that this fusion protein occasionally displayed small and transient foci in unstressed PA-free cells (Fig 9B). Whether these foci reflect native, functionally relevant DnaK behavior or are artefactual (label-induced, but without severely compromising DnaK functioning) remains to be established.
To assess the variability in surviving cellular fractions, the original sample size was bootstrapped (sampled with replacement) 3,000 times, and the mean fraction of surviving cells was calculated for each of these samples. The bootstrapped estimation of the standard error of the mean fraction of surviving cells was subsequently calculated by taking the standard deviation of the bootstrapped means.
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10.1371/journal.pgen.1002853 | Mutations in a P-Type ATPase Gene Cause Axonal Degeneration | Neuronal loss and axonal degeneration are important pathological features of many neurodegenerative diseases. The molecular mechanisms underlying the majority of axonal degeneration conditions remain unknown. To better understand axonal degeneration, we studied a mouse mutant wabbler-lethal (wl). Wabbler-lethal (wl) mutant mice develop progressive ataxia with pronounced neurodegeneration in the central and peripheral nervous system. Previous studies have led to a debate as to whether myelinopathy or axonopathy is the primary cause of neurodegeneration observed in wl mice. Here we provide clear evidence that wabbler-lethal mutants develop an axonopathy, and that this axonopathy is modulated by Wlds and Bax mutations. In addition, we have identified the gene harboring the disease-causing mutations as Atp8a2. We studied three wl alleles and found that all result from mutations in the Atp8a2 gene. Our analysis shows that ATP8A2 possesses phosphatidylserine translocase activity and is involved in localization of phosphatidylserine to the inner leaflet of the plasma membrane. Atp8a2 is widely expressed in the brain, spinal cord, and retina. We assessed two of the mutant alleles of Atp8a2 and found they are both nonfunctional for the phosphatidylserine translocase activity. Thus, our data demonstrate for the first time that mutation of a mammalian phosphatidylserine translocase causes axon degeneration and neurodegenerative disease.
| Axonal degeneration is an important pathological feature of many neurodegenerative diseases, such as Alzheimer disease, Parkinson's disease, and amyotrophic lateral sclerosis. In most of these disease conditions, molecular mechanisms of axonal degeneration remain largely unknown. Spontaneous mouse mutants are important in human disease studies. Identification of a disease-causing gene in mice can lead to the identification of the human ortholog as the disease gene in humans. This approach has the power to identify unexpected genes and pathways involved in disease. Our study centered on wabbler lethal (wl) mutant mice, which display axonal degeneration in both the central and peripheral nervous systems. We identified the disease-causing gene in mice with different wl mutations. The mutations are in Atp8a2, a gene encoding a phosphatidylserine translocase. This protein functions to keep phosphatidylserine enriched to the inner leaflet of the plasma membrane. Our study demonstrates a new role for phospholipid asymmetry in maintaining axon health, and it also reveals a novel function for phosphatidyleserine translocase in neurodegenerative diseases.
| Neuronal loss and axonal degeneration are important pathological features of neurodegenerative diseases, such as Alzheimer disease, Parkinson's disease, amyotrophic lateral sclerosis and glaucoma. Axonopathies, conditions in which axon injury occurs first during disease progression, have been extensively studied, but the mechanism(s) underlying axon degeneration remain to be elucidated in most of these conditions. In order to rationally develop effective therapeutics for these conditions, it is critical to determine the molecular mechanisms underlying axonopathies.
Spontaneous mouse mutants have long been used to gain insight into human disease. Spontaneous mutants provide valuable platforms for identifying disease-associated pathways. Identifying the gene(s) that cause a certain phenotype in mice can lead to a greater understanding of the pathophysiology of a disease. Identification of a disease causing mutation in mice often precedes the identification of the orthologous gene as a cause of a corresponding disease in humans [1]–[10]. As with all forward genetic tools, the power of analyzing mutants is that it allows for the identification of pathways involved in disease that may not have been identified through experiments based on current knowledge. To understand axon pathophysiology better, we studied wabbler-lethal (wl) mutant mice that display axon degeneration. The autosomal recessive wl mutation arose spontaneously in a mouse colony at The Jackson Laboratory in 1952 [11]. Homozygous wl mice are characterized by severe neurological abnormalities that include ataxia and body tremors. Abnormalities are first apparent around twelve days of age and mutant mice generally die around 4 weeks of age [11]–[13]. Histopathology of the wl nervous system is consistent with wl being an axonopathy [12], [14], though it has also been suggested to be primarily a myelinopathy [11], [15]. The genetic defect that causes wl has been unknown.
Here, we performed an extensive analysis of wabbler lethal mice and show that they develop a progressive axonal degeneration in several different areas of the nervous system. The presence of prominent axon degeneration, without initial myelin damage, and the absence of obvious cell death point to an axonopathy. To gain further understanding of the molecular pathways that can underlie the axonopathy in wl mice, the autosomal recessive wl mutation was positionally cloned. Using a combination of genetic and biochemical approaches, we demonstrated that the pathological lesion of this mutation is due to loss of function mutations in the gene encoding the murine phosphatidylserine translocase (flippase) Atp8a2. Loss of phosphatidylserine flippase activity leads to decreased axonal transport as indicated by the accumulation of phosphorylated neurofilament in motor neurons and retinal ganglion cell bodies. These data establish a novel role for a phosphatidylserine flippase in maintaining axonal health in both the central and peripheral nervous systems.
Mice homozygous for the wl mutation (wl/wl; wl mutants) grow much slower than their littermate controls and are first phenotypically recognizable at about 12 days of age, due to their smaller body size (Figure 1A and B). Supplementation of dry food with a soft moist diet that was placed on the cage floor to allow easy access, allowed homozygous mutants to survive past the previously reported 30 days (Figure 1C) [11]. Even on this diet, however, twenty percent of wl/wl mice died by 65 days of age, and all died by 130 days. Homozygous mutant mice develop a body tremor, an abnormal gait (Figure 1D), and display an abnormal hind limb-clasping reflex indicative of a neurological deficit that is very obvious at two months of age (Figure 1E).
Central chromatolysis is regarded as a characteristic feature of axonopathies [16]. Here we documented chromatolysis in the lateral cerebellar nucleus (Figure 1F), medial cerebellar nucleus and lateral vestibular nucleus (Figure S1) in wl mutants but not controls. Affected neurons have pale staining and acentric nuclei in hematoxylin and eosin stained sections (Figure 1F, arrows). Importantly, despite cell bodies with obvious chromatolysis in the lateral cerebellar nucleus, intermediate nucleus, spinal cord and other regions of the cerebellum, no obvious cell loss was noted in any of these regions, and cleaved caspase 3 staining did not detect an increased number of apoptotic cells (Figure S2). Dystrophic axons were evident in the corticospinal tract, spinalcerebellar tract (Figure S3) and spinal white matter (Figure S4). These data are consistent with a primary axonopathy without cell loss.
Central chromatolysis was also observed in the spinal ventral horn at different spinal levels (Figure 2B and C), again without obvious cell loss. Spinal motor neurons are located in the ventral horns and their axons project into the ventral root and then the spinal nerves. Supporting an absence of cell loss, even at 3 months of age when the disease is very severe (see below), axon counts for the ventral root (close to the cell bodies) were indistinguishable between wl mutant (count 1090±14, n = 4) and control mice (count 1101±7, n = 4, P = 0.22).
To determine if axon injury was first visible closer (proximal) to the cell body, or farther away (distal) from the cell body, we analyzed axons in the proximal ventral root and the distal femoral motor branch, which primarily consists of long motor axons. At two months of age, no obvious axon loss or morphological changes were present in the L4 ventral root (Figure 2D, E and H). However, the same wl/wl mice had lost 20% of axons in their distal femoral motor branch (Figure 2F, G, and I) with many axons having an irregular shape and having darkly stained axoplasm (Figure 2G). The femoral motor branch in 8-day old mutant and control mice were similar in both the total axon number and axon morphology, indicating the axon damage is not developmental and occurs with aging (Figure S5). The axonal degeneration in the femoral nerve is thus initially prominent in the distal part of the nerve with no apparent loss of axons in the ventral root. These data suggest that distal axonal degeneration is the main cause of the disease in wl mice.
Analysis of the distal sciatic nerve of wl mice at two months of age revealed that large diameter axons were preferentially lost (Figure 2J–L; Figure S6C). In general, large diameter motor neurons are known to have thicker myelin than smaller diameter motor neurons. Consistent with loss of large axons, myelin thickness is reduced in two-month old mutant mice from a mean value of 0.99±0.02 µm to a mean value of 0.57±0.01 µm (p<0.01). Despite severe neurological abnormalities at two months of age, no obvious demyelination defects were observed by assessing the ratio of inner axon diameter (inside myelin) to the total axon diameter (G-ratio, not shown). Additionally the condition of myelin in wl mutants and controls was indistinguishable by transmission electron microscopy at 2 month of age (Figure S6). These data suggest that demyelination is not the primary cause of the observed phenotype.
The distal degeneration of motor axons in the wl mutants prompted us to examine phosphorylated neurofilament (pNF) as a marker of axonal transport in spinal motor neurons. The localization of intermediate to high molecular weight pNF has been widely used to assess axonal transport [17], [18]. Under normal conditions, pNF is rarely detected in the cell body as it is normally efficiently transported from the soma to the more distal axon. When axonal transport is disrupted, pNF accumulates in the cell body [17], [18]. Staining of lumbar motor neurons of one-month-old wild type mice with pNF antibody detected neurofilament in axons (Figure 3A and B), while accumulation in the somas was very rare (Figure 3E). In contrast, pNF was present in both the axons and somas of lumbar motor neurons in wl mutant mice (3C, D and E; 12±0.4 pNF positive soma in wl/wl mice; 0.2±0.2 pNF positive soma in +/+ control mice, p<0.01). Both motor neurons in the ventral horn and neurons in the dorsal gray column display accumulation of pNF in their cell bodies. In addition, pNF accumulation was observed in different regions of the brain, such as the medial cerebellar nucleus, intermediate reticular nucleus and raphe magnus nucleus (Figure S7), indicating a common defect in axonal transport of neurofilament.
The axons of retinal ganglion cells (RGCs) also degenerate in the optic nerves of wl mice [12]. By one month, RGCs from wl mutants showed disrupted axon transport as evidenced by pNF accumulation in their somas. This accumulation occurs mainly in the peripheral retina (Figure 3H, I and J; 364±7 pNF positive soma in wl/wl mice; 2±0.5 pNF positive soma in +/+ control mice, p<0.01). To determine if axon injury, evident as axon transport defects, occurs before or after morphological changes, the optic nerves of wl mutants were assessed for axon damage using a histochemical stain paraphenylenediamine (PPD). PPD stains all myelin sheaths but is very sensitive for detecting axon injury (Figure S8), as the axoplasm of injured axons stains darkly [19]–[23]. At 30 days of age, the optic nerves of wl mice are indistinguishable from those in wild type mice. Despite this normal appearance of their axons, the RGCs have axon transport defects as indicated by accumulation of pNF in their somas (Figure 3H–J). Together, the axon transport defects in neurons with structurally normal axons and myelin sheaths, the central chromatolysis and the distal axonal degeneration in the femoral and sciatic nerves are consistent with an axonopathy.
The Wallerian degeneration slow (Wlds) allele dominantly delays axonal degeneration after direct axonal trauma and in axonopathies [24]–[30]. To further examine the role of axonal injury in the wl mutant, the effect of the Wlds allele was tested by crossing the Wlds allele to wl mice. The Wlds mutation has a strong protective effect on the survival of femoral nerve axons (Figure 4A–C). At two months, wl mutant nerves displayed severe axon degeneration (Figure 4B and J). In comparison, the number of axons in wl Wlds nerves were nearly identical to the number of axons in controls (Figure 4C and J). To determine the effect of Wlds on early changes in motor neurons, we looked at pNF accumulation in lumbar spinal cord sections. Wlds prevented axon transport defects as assessed by pNF accumulation in wl mutants (for example, in the lumbar spinal cord; Figure 4D–I). This delay of axonopathy because of Wlds further argues that the wl phenotype is an axonopathy.
As a further functional evaluation of the protective effects of Wlds against spinal axonopathy, we assessed nerve conduction velocity (NCV) in the sciatic nerve. At two months of age NCV was decreased by 40% in the wl mice, falling from 30.7±3.1 m/s in control animals to 18.4±1.2 m/s (p<0.01) in wl mice. In contrast, wl Wlds mice retained normal conduction velocity (Figure 4K). These data indicate that Wlds delayed axon damage in wl mutants. However, at no point could wl Wlds mice be grossly distinguished from wl mice. Despite apparent healthy axon morphology, wl Wlds mice still displayed body tremor when walking. Their disease onset was still around 14 days after birth, they were smaller than wildtype controls, their locomotory performance did not improve, and they performed poorly in the wire hang test (Figure S9). In the latter, wl and wl Wlds mice were able to grip the cage top for an average of 12.2±3.4 and 13.2±2.9 seconds respectively (P>0.05), while wild type controls gripped for an average of 55.2±3 seconds (P = 0.01) compared to wl or wl Wlds mutants.
The Bax gene is best known for its role in somal apoptosis, but also has an independent and intra-axonal role in axon degeneration [31], [32]. Genetic ablation of Bax strikingly protects axons in the femoral motor branch in two-month old wl Bax−/− mutants. At this age, wl mutants had severe axon loss while wl Bax−/− mice had no axon loss (Figure 5C, D, E). As for Wlds, the Bax mutation had no effect on lifespan or the gross neurological phenotype.
Previous linkage analysis localized wl to chromosome 14 [33]. Analysis of 688 affected wl F2 mice narrowed the interval where the mutation resides to a 773 kb region on chromosome 14 between DLM14-10 (60.6 Mb) and DLM14-21 (61.4 Mb) (Figure 6A). This region is known to contain 10 protein-coding genes. The coding regions and splice sites of each of these genes were sequenced and a 21 bp deletion in exon 22 of Atp8a2 (Figure 6B and C) was identified. No mutations were found in the other 9 genes. This genomic deletion in Atp8a2 leads to the elimination of seven highly conserved amino acids (TAIEDRL) from the nucleotide-binding domain (N-domain) of ATP8A2 (Figure 6D, E). Two additional alleles of wl were available: wlvmd (vmd) and wl3J (3J). We found that the wlvmd mutation is a large 9167 bp genomic deletion that results in removal of the entire exon 32 of Atp8a2 (Figure 6C and Figure S10). This results in a 32 amino acid deletion in the ninth transmembrane domain of ATP8A2 (Figure 6D). wl3J mice were found to have a 641 bp deletion starting at the tenth base pair of exon 30 of Atp8a2, leading to the deletion of part of exon 30 and the whole exon 31. Furthermore, wl3J mice had a 10 bp duplication in exon 32 (Figure 6C and Figure S11). Genetic mapping and sequence analysis of these three alleles of wl clearly show that mutations in Atp8a2 cause the wl phenotype.
Atp8a2 is widely expressed in the central nervous system including the cerebrum, cerebellum, spinal cord, and retina (Figure 7A). Ectopic expression of ATP8A2 in HEK-293T or COS7 cells showed that ATP8A2 has the expected molecular weight of a 130 kDa (Figure 7B), as detected by a polyclonal antibody against mouse ATP8A2 developed in our laboratory (see methods). Western blot analysis of protein isolated from either cytosolic or membrane fractions showed that ATP8A2 is localized to the mouse brain membrane fraction (Figure 7C).
Based on sequence comparison, we hypothesized that ATP8A2 is likely a phospholipid translocase. To test this hypothesis, full-length Atp8a2 cDNA was expressed in UPS-1 cells. These cells are defective in non-endocytic uptake of 7-nitrobenz-2-oxa-1,3-diazol-4-yl phosphatidylserine (NBD-PS) analogs and are thus optimal for testing phospholipid translocase (flippase) activity with NBD substrates [34], [35]. Compared to control cells transfected with empty vector, Atp8a2 transfected UPS-1 cells displayed significant phosphatidylserine translocase activity (1500% of control, Figure 8A, B). This phospolipid flippase activity is specific to phosphatidylserine (PS), as translocation of only NBD-phosphatidylserine and not of NBD-phosphatidyletholamine (PE), NBD-phosphatidylcholine (PC) or NBD-phosphatidylglycerol (PG) was observed (Figure 8B). To ascertain that PS translocation was due to ATP8A2 activity, we generated a mutant version of ATP8A2 in which Asp388 in the highly conserved core sequence DKTGTLT was replaced with an Ala. This Asp residue is phosphorylated and dephosphorylated during the catalytic cycle and is critical for the activity of all P-type ATPases. The mutant protein encoded by Atp8a2D388A failed to translocate NBD-PS into the inner leaflet of the plasma membrane (Figure 8C). Similarly, the chemical inhibitor sodium vanadate greatly reduced the flippase activity of ATP8A2 (Figure 8C).
To examine the activity of the mutant protein encoded by the wl mutant alleles, we introduced the 21 bp deletion identified in the wl mutant, and the 108 bp deletion of the vmd mutant into the Atp8a2 cDNA by site-direct mutagenesis. Although these mutant proteins were expressed (Figure 8D), neither protein displayed any flippase activity (Figure 8C). These data clearly demonstrate that the mutant proteins encoded by wl and vmd have no flippase activity.
Axon degeneration and axonopathies are often observed in human neurodegenerative diseases, but their molecular causes are typically not known. To provide new insight into axon degeneration we studied the wabbler-lethal (wl) mouse. We show that wl mutant mice develop distal axon degeneration and neuronal chromatolysis in varying parts of the CNS and the PNS without cell death. Together with our finding that the Wlds mutation, which protects against axon injury, significantly delayed axonal degeneration [25]–[30], [36]–[39], this provides strong evidence that the wl mutation induces an axonopathy [11]–[15].
Although the Wlds mutation delays axonal degeneration in wl mice, it does not alter their gross phenotype or extend lifespan. Similar to the effect of Wlds in wl mice, Wlds inhibits axonal spheroid pathology in gracile axonal dystrophy (gad) mice with the Uchl1gad allele, but did not alleviate gad symptoms [38]. Compared to gad mice, both the gracile nucleus and cervical gracile fascicle contained fewer spheroids in Uchl1gad Wlds mice. However, similar to the previous observation that Wlds has a weaker effect on synapses than on axons, motor axon terminals at neuromuscular junctions continued to degenerate in Uchl1gad Wlds mice. This might contribute to the fact that Wlds did not alleviate gad symptoms. In contrast, in pmn mice, another motor neuron disease model, Wlds was able to delay axonal degeneration, extend life span, and improve motor performance [25]. The fact that Wlds did not appear to alter the gross behavior of wl mice suggests that there may be detrimental phenotypes in wl mice that are distinct from axonal degeneration.
Two recent reports demonstrated a requirement of Bax during axonal degeneration [31], [32], and Bax deficiency delayed axonal degeneration in a mouse model of glaucoma [21]. In wl mice, genetic ablation of Bax significantly delayed axonal degeneration, providing the first evidence for the role of Bax in an inherited mouse model of primary axonal degeneration. Our result indicates that Bax has a role in intrinsic axon degeneration, similar to a previous study using cultured sensory neurons [31]. As in cultured sensory neurons [31], it is possible that caspase 6 participates in this process. This will be the subject of future investigations.
While the wl phenotype has been investigated for over 60 years, the underlying mutation was not previously identified. We show that mutations in Atp8a2 underlie the phenotype for three independent wl alleles. Atp8a2 encodes a protein homologous to P4-type ATPases, putative phospholipid translocases that translocate aminophospholipids from the extracellular leaflet to the cytoplasmic leaflet of the plasma membrane bilayer [40]. They are important for the maintenance of phospholipid asymmetry in eukaryotic cell membranes, and play essential roles in many physiological conditions. Our cell-based PS translocation assay clearly showed that ATP8A2 is a phosphatidylserine translocase and both the wl and vmd mutant proteins do not retain PS flippase activity. These findings are in agreement with recent independent studies showing that ATP8A2 has PS flippase activity when reconstituted in liposomes [41], [42].
The importance of the P4-ATPase membrane protein family is increasingly evident through findings in which dysfunction of P4-ATPases is associated with developmental defect in animals and several human disorders [43]–[47]. In Caenorhabditis elegans loss of the P4-ATPase TAT-1 leads to the exposure of PS on the surface of germ cells and loss of certain neuronal cells [48]. In humans, mutations in the FIC1/ATP8B1 gene cause progressive familial cholestasis, a severe liver disease with defective bile secretion and hearing loss [43], [49]. Furthermore, Atp8b3 is exclusively expressed in the testis and has a role in sperm capacitation in mice [50]. Atp10a (also named Atp10c) is linked to diet-induced obesity and type II diabetes in mice [51]. Mutations in the murine Atp11c gene leads to cholestasis and a striking B cell differentiation defect [45]–[47]. Although Atp11c is ubiquitously expressed in different tissues, loss of ATP11C activity specifically affects adult B cell development, indicating cell and lineage-specific requirement of this transporter.
Based on our findings, we propose a number of possible mechanisms by which loss of ATP8A2 activity can lead to axonopathy. Like ATP8B1, ATP8A2 is thought to be important for maintaining phospholipid asymmetry of cell membranes by translocating phosphatidylserine from the outer leaflet to the inner leaflet of the membrane. PS asymmetry in the cell membrane has been shown to have an essential role in the mechanical stability of the red cell membrane [52]. In patients and mice with an ATP8B1/Atp8b1 deficiency, the canalicular membrane is not stable and extraction of lipid by the detergent action of bile salts leads to formation of granular bile and intrahepatic cholestasis. Similarly, loss of ATP8A2 activity may result in re-distribution of PS to the outer leaflet and loss of phospholipid asymmetry. Neuronal axon membranes may be unstable due to this abnormal PS distribution and thus become susceptible to degeneration. Alternately, loss of PS asymmetry might lead to a defect in intracellular sorting and transport of vesicular components similar to what was observed for the yeast homologue Drs2, which has a role in intracellular vesicular trafficking between the trans-Golgi network (TGN), the endosome and the plasma membrane [53].
Recent studies showed that disruption of phospholipid turnover and trafficking can lead to neurodegenerative diseases in both mice and people. For example, the spontaneous null mutation of mouse Fig4 in the pale tremor (plt) strain leads to neuronal loss, spongiform degeneration of the brain and loss of neurons from the dorsal root ganglia [54], [55]. Mutant animals had a severe peripheral neuropathy and a shorter life span. Fig4 encodes a phospholipid phosphatase with 5-phosphatase activity towards the 5-phosphate residue of PtdIns(3,5)P2 [56], [57]. Loss of the FIG4 phosphatase in humans leads to the autosomal recessive, demyelinating, Charcot-Marie-Tooth neuropathy (CMT), CMT type 4J (OMIM #611228) [54], [58]. The most common human mutation of FIG4 reduces the binding affinity of FIG4 for the PtdIns(3,5)P2 biosynthetic complex [59], while in mouse plt fibroblasts a significant decrease of PtdIns(3,5)P2 was observed [54]. Intracellular phosphoinositides (PIs) are essential regulators of membrane trafficking, including functions to promote recruitment and/or activation of spatially localized protein machinery on membranes. The production and turnover of PIs are tightly controlled by kinases and phosphatases [60], [61]. In the nervous system, neurons, especially neurons with long axons, depend on efficient membrane trafficking for maintenance of proper functions and health [62]. In humans and mice, autosomal recessive, demyelinating, Charcot-Marie-Tooth type B1 neuropathy [63]–[65] is also caused by an abnormality of phospholipid metabolism resulting from mutation of the myotubularin-related 2 gene (MTMR2). MTMR2 is a phospholipid phosphatase with 3-phosphatase activity towards the 3-phosphate residue of PtdIns(3,5)P2 and PtdIns3P [66], [67]. Therefore, Fig4, MTMR2 and Atp8a2 mutations may affect neurons by altering membrane protein trafficking. These mutant phenotypes highlight the importance phospholipid metabolism for neuronal health and the possible role of abnormal membrane trafficking in neurological diseases.
Loss of lipid asymmetry has an important impact on cell morphology, membrane protein activities, phagocytosis, apoptosis, endocytosis, and vesicle biogenesis [68]. In addition proper membrane lipid composition is also important for exocytosis and synaptic vesicle release in neuronal cells. PS content has been shown to affect PC12 cell exocytosis [69]. Altered PS distribution in wl neurons could affect release of synaptic vesicles and transduction of action potentials along nerve fibers.
Atp8a2 is specifically expressed in the nervous system and testis. In contrast, Atp8a1, a PS translocase and a close member of the P-type ATPase family, is expressed broadly in many tissues. Atp8a1 deficient mice have no grossly visible neurological phenotypes and have grossly normal brains ([70] and www.informatics.jax.org/external/ko/deltagen/1902.html). Recently, behavioral analysis of Atp8a1 deficient mice detected neurological abnormalities, including impaired hippocampus-dependent learning (Morris Water Maze test), hyperactivity, and poor maternal behavior [70]. Furthermore, deficiency of both Atp8a2 and Atp8a1 results in neonatal lethality (our unpublished data using the wl allele). Double mutant mice have labored breathing and die within a few hours after birth (unpublished observations). It is likely that ATP8A2 and ATP8A1 act redundantly in certain tissues to allow survival of single mutants by maintaining an adequate PS asymmetry. Consistent with this, no obvious neuronal death was detected in Atp8a2wl/wl mutants. Additionally, we could not detect any obvious disturbance of PS lipid asymmetry in cultured neurons and spermatogonia from these Atp8a2wl/wl mutants (not shown). Obvious loss of asymmetry has been well established to induce apoptosis [71]. Loss of both ATP8A2 and ATP8A1 leads to failure of tissue function and lethality. Conditional alleles of Atp8a2 and Atp8a1 will be helpful to pinpoint the temporal and tissue specific requirements of Atp8a2 and Atp8a1.
Interestingly, due to a de novo chromosome translocation, a patient with mental retardation and hypotonia was haploinsufficient for ATP8A2. No other genes were reported to be structurally altered by this genetic event. However, no gene expression studies for ATP8A2 or other genes flanking the translocation, whose expression may be affected by the translocation, were presented. Additionally, no mutations were found in thirty-eight other patients with similar phenotypes [72]. Thus, it is not yet clear if ATP8A2 plays a role in this disease. However, this patient together with the data presented in our current paper suggests that ATP8A2 should be further considered as a candidate gene for human neurological diseases.
In conclusion, normal ATP8A2 activity is indispensable for normal neuronal functions. To our knowledge, ATP8A2 is the first mammalian PS flippase identified to have a role in axon degeneration. Our experiments identify a new process that contributes to axonopathy and neurological disease.
The Association for Assessment and Accreditation of Laboratory Animal Care guidelines were followed for all animal procedures, and all procedures were approved by the Institutional Animal Care and Use Committee of The Jackson Laboratory. The original wabbler-lethal (wl) mutant arose in the pirouette strain of Mus musculus at The Jackson Laboratory [11]. The Vestibulo-Motor Degeneration (vmd) mutation arose in a C3H/H2SnJ colony at the Jackson Laboratory in 1987. Both wl and vmd mice were obtained from The Jackson Laboratory and backcrossed to C57BL/6J (B6) for at least ten generations. Before PCR based genotyping protocols were established, heterozygous mice of each strain (wl and vmd) were crossed and matings producing homozygous wl or vmd mutants were kept for strain production. As has become standard practice for these mutant mice, dry food was supplemented with a soft maintenance diet (DietGel 76A, ClearH2O). After identifying the mutations in Atp8a2 in the wl and vmd strains, PCR based protocols were established for genotyping. For wl, the primer pair wl-L 5′ - TGAACTGTCCCTTAACTGATGGTA - 3′ and wl-R, 5′ - TGGCTATGGTTTCTGGAACG - 3′ (Figure 6) were used. This primer pair spans the 21 base-pair deletion in exon 22 in wl mutant mice and produces a 108 bp amplicon in wild type controls, and an 87 bp amplicon in wl/wl mice. For the vmd genomic deletion, three primers flanking the region were used (Figure S10) to distinguish between the wild type and mutant alleles: vmdF, 5′ - CTAACTGTGGCTCACTTACCTCCT - 3′; vmdR1, 5′ - TCCTCCAGAACATTGAAGTGACTA - 3′; vmdR2, 5′ - TGCATCTTGATTTTTGCTTTGTAT - 3′. A 403 bp amplicon is produced in the presence of the wildtype allele using primer vmdF and vmdR1 and a 207 bp amplicon is produced in the presence of the mutant vmd allele using primers vmdF and vmdR2. wl3J arose in the CBA/J production colony at The Jackson Laboratory [73]. This strain is cryopreserved, and therefore genomic DNA of wl3J mutant and control animals was obtained from the DNA Resource at The Jackson Laboratory to determine the mutation.
To assess the effect of the Wlds allele on axonopathy in wl mice, the original Wlds allele (Wlds) [26]–[29], [74] was obtained from Harlan Sprague Dawley on a C57BL/10Hsd background. This allele is maintained by continuously backcrossing to C57BL/6J and was crossed to wl mice to generate the wl Wlds strain. All experiments assessing the effect of Wlds on axon degeneration were performed using mice hemizygous for Wlds. A Bax null allele (B6.129X2-Baxtm1Sjk/J, herein referred to as Bax−) was obtained from The Jackson Laboratory and crossed to wl mice to generate wl Bax−/− mice.
Anesthetized mice were fixed by transcardial perfusion with physiological saline (PBS), followed by freshly made 2% glutaraldehyde and 2% paraformaldehyde in 0.1 M cacodylate buffer (pH 7.4). Sciatic and femoral nerves were dissected under a dissecting microscope and post-fixed overnight in the same fixative. After post-fixing, nerves were rinsed twice with PBS and processed for plastic embedding, histological staining and transmission electron microscopy (TEM) by standard procedures [75]. Briefly, 0.5 µm semi-thin plastic sections were stained with toluidine blue and examined by light microscopy. The total number of myelinated axons in each nerve was counted using toluidine blue-stained plastic sections and a Leica DMRE microscope. For TEM, nerves were treated with uranyl acetate and standard embedding was done as previously described [75]. TEM images were collected on a Jeol 1230 microscope. Axon diameters of P30 and P60 mice were determined from six non-overlapping 5000× fields from each of three mutant and three littermate control samples. Axon diameters were measured using the associated software AMT Image Capture Engine. For axon counts, left and right nerves were taken, and counts obtained for both nerves were averaged. Thus each count sample represents the average count of the left and right nerve of one mouse.
For histological analysis of brain and spinal cord sections, anesthetized mice were fixed by transcardial perfusion with 2% glutaraldehyde and 2% paraformaldehyde in 0.1 M cacodylate buffer (pH 7.4). Tissues were dissected out, embedded in paraffin, serial sections obtained and stained using hematoxylin and eosin (H&E). To assess morphological structures of the retina, enucleated eyes were fixed overnight in 1.2% glutaraldehyde and 0.8% paraformaldehyde in 0.08 M phosphate buffer, embedded in Technovit resin, cut in 1.5 µm sections and stained with hematoxylin and eosin (H&E).
For analysis of the optic nerve sections with PPD staining, intracranial portions of optic nerves were processed and analyzed as previously described [19]–[23]. Briefly, optic nerves were fixed in situ in 1.2% glutaraldehyde and 0.8% paraformaldehyde in phosphate buffer for 48 hours, dissected free, processed, and embedded in plastic. One-micron-thick cross sections of optic nerve from behind the orbit were cut and stained with paraphenylenediamine (PPD). PPD darkly stains the myelin sheaths and axoplasm of sick or dying axons but not healthy axons.
For immunohistochemistry enucleated eyes or carefully dissected free spinal columns were fixed overnight in 4% paraformaldehyde in PBS, and then cryoprotected in sucrose. Tissues were embedded in optimal cutting temperature solution (OCT) and frozen on dry ice for sectioning. Neurofilament was stained with pNF antibody (Covance, 1∶500) and visualized with AlexaFluor488-conjugated anti-mouse IgG (Invitrogen). Nuclei were counterstained with DAPI (Sigma). Active caspase-3 (1∶200) was purchased from R&D Systems. Sections were analyzed and imaged on a Leica TCS SP5 II confocal microscope. Anti-Tubulin antibody was obtained from Sigma and used at a dilution of 1∶2000. Mouse anti-PSD95 was obtained from Neuromab (http://neuromab.ucdavis.edu/) and used at a dilution of 1∶1000.
The wl mutation had originally been mapped to chromosome 14, close to the hairless (hr) locus [33]. To further narrow the interval, wl/+ male mice originally obtained from The Jackson Laboratory on a mixed background were crossed to C57BL/6J wild type females to generate F1 progeny. F1 progeny were intercrossed to generate F2 animals. Offspring from these intercrosses were examined at 3 weeks of age to identify those with abnormal gait and thus homozygous for the wl allele. Tail tissue was obtained from a total of 688 affected animals for DNA preparation and analysis. To narrow the region, MIT markers able to distinguish between C57BL/6J and wl on chromosome 14 were used [i.e. D14Mit154 (58.6Mb), D14Mit113 (60.4 Mb), DLM14-10 (60.6 Mb), DLM14-21 (61.4 Mb), D14Mit3 (61.5 Mb), and D14Mit37 (63.0 Mb)]. Primer sequences are listed in Table S1.
Using the mapping strategy describe above, the region was narrowed to an interval of 773 Kb containing 10 genes. Primer pairs were designed to span the coding exons and splice sites for all 10 genes in the critical region. Those designed for Atp8a2 are given in Table S2. These primer pairs were utilized to obtain PCR products using Abgene 2X ReddyMix PCR Master Mix (1.5 mM MgCl2) with genomic DNA from wl/wl mutants as well as littermate controls as template. PCR products were purified using the QIAquick PCR Purification Kit (Qiagen) and subjected to sequencing using the BigDye Terminator Cycle Sequencing Kit (Applied Biosystems). Sequences were analyzed using the Sequencher 4.2 software, comparing publically available sequences for Atp8a2 with the sequences obtained for mutant and control mice. The same primer pairs and strategy were used to determine the mutation in wlvmd and wl3J mice.
Full-length cDNA of Atp8a2 (NM_015803.2) was synthesized using the de-novo DNA synthesis technique (GenScript Corporation, NJ, U.S.A.) and cloned into pUC57 with two flanking restriction enzyme sites EcoRV and SalI. The ORF itself is also flanked with AscI and MluI sites. The Atp8a2 ORF was sub-cloned into the pCMV6-AN-Flag (Origene) expression vector using the AscI and MluI restriction endonuclease sites to produce pCMV6-AN-Flag-ATP8A2. All sequences were confirmed using sequence analysis.
For the lipid translocation assays, the Atp8a2 ORF was amplified by PCR and sub-cloned into the pEntry1A dual selection vector (Invitrogen) at NotI and XhoI sites to generate pEntry1A-Atp8a2. PCR primers used were: Atp8a2-NotI: 5′-CACCAGTCCCGGGCCACGTCTGTTGGAGACC-3′; Atp8a2-XhoI: 5′-TTATTTCTTCCTTTCTCGAGTCTTTGGTGGTATCATAAGCGC-3′. The destination vector pcDNA6.2/EmGFP-Bsd/V5-DEST (Invitrogen Catalog no. V366-20) was used to generate the expression vector pcDNA6.2-Atp8a2 by performing an LR recombination reaction following the manufacture's instruction. pcDNA6.2/EmGFP-Bsd/V5-DEST contain the human cytomegalovirus immediate-early (CMV) promoter/enhancer for high-level expression of the gene of interest and the murine phosphoglycerate kinase-1 (PKG) promoter to drive expression of the Emerald GFP-Blasticidin (EmGFP-Bsd) fusion protein in mammalian cells. Transfected cells express both ATP8A2 and EmGFP-Bsd and can be detected by flow cytometry.
pcDNA6.2-Atp8a2wl was generated by introducing a 21 bp deletion into pcDNA6.2-Atp8a2 vector using the QuickChange II XL Site-Directed Mutagenesis Kit (Stratagene) according to manufacturer's instructions. Primers: wl-21bpdel-F, 5′-CTGTTACTTGGAG CTACAGCCGGCGTTCCAGAAACCATAGCCACTC-3′; wl-21bpdel-R, 5′-GAGTGGCTATGGTTTCTGGAACGGCTGTAGCTCCAAGTAACAG-3′. Similarly, pcDNA6.2-Atp8a2D388A was generated by the introduction of Asp→Ala (GAC→GCC) mutation into the pcDNA6.2-Atp8a2 vector. Primers: Atp8a2-changeD-F, 5′-GGGCAGGTAAAATACCTGTTTTCAGCCAAGACTGGAACTCTTACATGT -3′; Atp8a2-changeD-R, 5′-ACATGTAAGAGTTCCAGTCTTGGCTGAAAACAGGTATTTTACCTGCCC -3′. pcDNA6.2-Atp8a2vmd was generated by swapping the cDNA fragment (basepair 1489–3447th ) with the corresponding fragment amplified from vmd/vmd brain cDNA (HindIII and XhoI sites of pEntry1A-Atp8a2). Primers: vmdP1, 5′-AGCTAAGAAGCTTGGCTTTGTGTTTACCGGGAGG-3′; vmdP2, 5′-TTATTTCTTTGAATTCTCTTTGGTGGTATCATAAGCGC-3′. All mutant versions of Atp8a2 were confirmed by sequencing.
Tissue from 1–2 months old animals were dissected free and placed into RNAlater (Ambion) at room temperature. Total RNA was prepared from these tissues using TRIzol Reagent (Life Technologies) according to the manufacturer's instructions. RNA samples were treated with RNase-free DNaseI (Ambion) and RNA concentration determined using a NanoDrop (ND-1000) spectrophotometer. 10 µg RNA was reverse transcribed using random primers and the MessageSensor RT Kit (Ambion). The primers used for PCR were: Atp8a2, Atp8a2F1 = 5′-GGAGCAGATCCTGGAGATTGACT-3′; Atp8a2R1 = 5′-CGGAAGCACTCTC-3′; beta-Actin, ActinbF1 = 5′-AGCCATGTACGTAGC CATCC-3′; ActinbR1 = 5′-CGGCCAGCCAGGTCCAGAC-3′.
The ATP8A2 antibody was produced by Proteintech Group, Inc (Chicago, IL) using an ATP8A2-GST fusion protein designed in our laboratory. Briefly, a cDNA fragment encoding amino acid 536–840 of the ATP8A2 P-domain was amplified using the following primers: Atp8a2-PDF1, 5′-TTTTGGATCCATGTCTGTCATTGTCCGACTG-3′, Atp8a2-PDR1, 5′- TTTTCTCGAGTCAGTACAGGATACACTTGGTC-3′. The resulting PCR product was cloned in-frame with glutathione S-transferase (GST) in the pGEX-4T-1 vector (GE healthcare) using BamHI and XhoI restriction enzymes. The resulting plasmid was validated by sequencing and transformed into E. coli. BL21 was used for ATP8A2 fragment-GST fusion protein production. The fusion protein was purified with glutathione-Sepharose (GE Healthcare) using standard procedures. The purified fusion protein was used for immunization of rabbits. The ATP8A2 antibody was purified from serum by affinity purification using the GST-ATP8A2 fragment fusion protein.
HEK-293T cells (American Type Culture Collection (ATCC), Manassas, VA) were cultured in DMEM medium with high glucose (HyClone) supplemented with 10% fetal bovine serum and 1% (vol/vol) penicillin/streptomycin at 37°C in a 5% CO2 atmosphere. Cells were seeded in six-well plates (Corning, NY) and transfected at 50% confluency with 1 µg of pCMV6-AN-Flag-Atp8a2 or empty vector using Lipofectimine 2000 (Invitrogen) according to the manufacturer's instructions. Cells were harvested after 48 hours.
The CHO UPS-1 cell line is a mutant Chinese hamster ovary (CHO) cell line defective in the nonendocytic uptake of NBD-PS [34], and was kindly provided by Dr. Robert Pagano from Mayo Clinic (Rochester, MN). UPS-1 cells were grown in Ham's F12 medium supplemented with 10% fetal bovine serum and 1% glutamine (vol/vol), and 1% (vol/vol) penicillin/streptomycin at 37°C in a 5% CO2 atmosphere. Cells were seeded in 6-well plate and allowed to grow to 50% confluency. Transfection with empty vector or plasmid pcDNA62-Atp8a2 expressing ATP8A2 was carried out using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions.
The following labeled phospholipid analogs were purchased from Avanti Polar Lipids: 16:0–06:0 NBD-PS [1-palmitoyl-2-[6-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino] hexanoyl]-sn-glycero-3-phospho-L-serine (ammonium salt)]; 16:0–06:0 NBD-PE [1-palmitoyl-2-[6-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino] hexanoyl]-sn-glycero-3-phospho-L-etholamine (ammonium salt)]; 16:0–06:0 NBD-PC [1-palmitoyl-2-[6-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino] hexanoyl]-sn-glycero-3-phospho-choline (ammonium salt)]; and 16:0–06:0 NBD-PG [1-palmitoyl-2-[6-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino] hexanoyl]-sn-glycero (ammonium salt)]. NBD-lipid powder stocks were dissolved in 95% ethanol and diluted to 20 µM with Hank's balanced salt solution with 15 mM MgCl2 and without phenol red (HBSS-15 mM MgCl2; Gibco). The translocation of NBD-lipid was determined as described with some modification [35]. In short, transfected UPS-1 cells grown to confluency in 6-well plates (Corning, NY) were washed, equilibrated in pre-warmed HBSS-15 mM MgCl2 for 15 minutes at 20°C, and incubated with 20 µM NBD-lipid for 20 minutes at 20°C. Subsequently, the cells were washed with HBSS-15 mM MgCl2 on ice. To quantify NBD-lipid translocated into the inner leaflet of the plasma membrane, lipid from the outer leaflet was removed by back-extraction. This was done by adding ice-cold HBSS supplemented with 2% bovine serum albumin (Sigma) to the cells for 10 min on ice, and repeating the process three times. Finally, the cells were washed with cold HBSS, treated with 0.25% Trypsin and suspended in HBSS. One microliter of 5 mg/ml DAPI in PBS was added to 3×106 cells in 0.5 ml HBSS just before FACS analysis. Flow cytometry of NBD-lipid labeled UPS-1 cells were performed on a Becton Dickinson LSR II cytometer equipped with an argon laser using FACSDiVa software. Ten thousand GFP positive cells were analyzed during the acquisition. Dead cells were excluded from the analysis by blue fluorescence (DAPI positive). The data were analyzed with FlowJo software (Tree Star Inc., Ashland, OR). The NBD-lipid fluorescence intensity of living UPS1 cells was plotted on a histogram to calculate the median fluorescence intensity. Lipid translocation activity was calculated as a ratio to vector control samples.
Brain and spinal cord were collected from 1- to 2-month old mice and put directly into lysis buffer (10 mM Tris-HCL, pH 7.4, 100 mM NaCl, 1.5MgCl2, plus Complete Protease Inhibitor Cocktail (Roche)). Tissues were homogenized using a motor-driven Polytron homogenizer and the resulting lysates were centrifuged at 500 g to remove intact cells. To separate the cytosol and membrane fractions, the supernatant was subjected to centrifugation at 100,000 g for 45 min at 4°C using a Beckman SW40 rotor. The supernatant was collected as the cytosolic fraction and the pellet was resuspended in lysis buffer plus 0.1% Triton X-100 on ice for 20 min as membrane fraction. Total protein concentration was determined using the BCA Protein Assay Reagent (Pierce). Equal amounts of cytosol and membrane proteins were separated by electrophoresis using a 7.5% Mini-PROTEAN TGX Precast Gel (BioRad). Proteins were transferred to PVDF membranes (GE healthcare) and detected by immunoblotting using standard techniques.
Conduction velocity of sciatic axons was determined by measuring the latency of compound motor action potentials recorded in the muscle of the left rear paw [76]. Mice were anesthetized with 1%–1.5% isoflurane and placed on a thermostatically regulated heating pad to maintain normal body temperature. Action potentials were produced by subcutaneous stimulation at two separate sites: proximal stimulation at the sciatic notch and by a second pair of needle electrodes placed distally at the ankle. For recording, a needle electrode was inserted in the center of the paw (active) and a second electrode was placed in the skin between the first and second digits. Conduction velocity was calculated as [(proximal latency – distal latency)/conduction distance]. Four animals of each genotype were tested.
For the wire hang test each animal was placed on a wire cage top held approximately a foot above an empty cage. The wire cage top was then gradually inverted and the time each mouse was able to hang onto the cage top was recorded. If any mouse still gripped the wire top after 60 seconds, the animal was removed and the time was recorded as 60 seconds.
All data are presented as means ± SEM. Data were analyzed in JMP7.0 (SAS Campus Drive, Building T, Cary, NC). The differences between two groups was assessed by Student's t-test; p<0.05 was considered statistically significant.
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10.1371/journal.pntd.0007706 | Multiplex profiling of inflammation-related bioactive lipid mediators in Toxocara canis- and Toxocara cati-induced neurotoxocarosis | Somatic migration of Toxocara canis- and T. cati-larvae in humans may cause neurotoxocarosis (NT) when larvae accumulate and persist in the central nervous system (CNS). Host- or parasite-induced immunoregulatory processes contribute to the pathogenesis; however, detailed data on involvement of bioactive lipid mediators, e.g. oxylipins or eico-/docosanoids, which are involved in the complex molecular signalling network during infection and inflammation, are lacking.
To elucidate if T. canis- and T. cati-induced NT affects the homeostasis of oxylipins during the course of infection, a comprehensive lipidomic profiling in brains (cerebra and cerebella) of experimentally infected C57BL/6J mice was conducted at six different time points post infection (pi) by liquid-chromatography coupled to electrospray tandem mass spectrometry (LC-ESI-MS/MS). Only minor changes were detected regarding pro-inflammatory prostaglandins (cyclooxygenase pathway). In contrast, a significant increase of metabolites resulting from lipoxygenase pathways was observed for both infection groups and brain regions, implicating a predominantly anti-inflammatory driven immune response. This observation was supported by a significantly increased 13-hydroxyoctadecadienoic acid (HODE)/9-HODE ratio during the subacute phase of infection, indicating an anti-inflammatory response to neuroinfection. Except for the specialised pro-resolving mediator (SPM) neuroprotectin D1 (NPD1), which was detected in mice infected with both pathogens during the subacute phase of infection, no other SPMs were detected.
The obtained results demonstrate the influence of Toxocara spp. on oxylipins as part of the immune response of the paratenic hosts. Furthermore, this study shows differences in the alteration of the oxylipin composition between T. canis- and T. cati-brain infection. Results contribute to a further understanding of the largely unknown pathogenesis and mechanisms of host-parasite interactions during NT.
| Neurotoxocarosis (NT) is induced by larvae of the zoonotic roundworms Toxocara canis and T. cati migrating and persisting in the central nervous system of paratenic hosts, and may be accompanied by severe neurological symptoms. Toxocara spp. are known to modulate the hosts’ immune response, but data concerning involvement of signalling molecules are lacking. An important class of mediators participating in the complex molecular signalling network during infection and inflammation are bioactive regulatory lipids, derived from arachidonic acid and other polyunsaturated fatty acids. For a better understanding of inflammatory processes in the brain during an infection with Toxocara spp., a comprehensive analysis of regulatory lipids was conducted. The infection was predominantly characterised by only minor changes in the pattern of pro-inflammatory oxylipins, while anti-inflammatory metabolites, derived from lipoxygenase pathways, were significantly elevated in the subacute phase as well as in the beginning of the chronic phase of infection. This trend was also reflected in the 13-HODE/9-HODE ratio, a biomarker for the immunological status of an active infection. Obtained data provide a valuable insight in the host’s immune reaction as response against neuroinvasive Toxocara spp.-larvae, contributing to the characterisation of the mostly unknown pathogenesis of NT.
| Toxocara canis (Werner, 1782) and T. cati (Schrank, 1788) are globally distributed, intestinal helminth parasites with canids and felids as definitive hosts [1]. Humans and a wide range of other species can act as paratenic hosts after accidental ingestion of infective stages (L3) of Toxocara spp., resulting in persistence of the larvae in the body tissues [2, 3]. The infection of paratenic hosts comprises different stages, starting with larvae entering the cardiovascular system and reaching the liver and lungs during the first week post infection. This acute stage of toxocarosis is called the hepato-pulmonary phase. The myotropic-neurotropic phase indicates the beginning of the chronic stage and is characterised by migration and accumulation of larvae throughout somatic tissues [3].
Humans may get infected due to inadequate hygiene, geophagia or via foodborne transmission [4]. Even though toxocarosis is one of the most frequent helminthoses in humans, the global importance of this zoonosis is probably underestimated [2, 5].
Human toxocarosis may result in a variety of clinical symptoms. Depending on the somatic distribution of the larvae and the occurring symptoms, toxocarosis is currently classified into four syndromes: covert toxocarosis, visceral larva migrans (VLM), ocular larva migrans (OLM) and neurotoxocarosis (NT), whereby NT results from accumulation and persistence of Toxocara spp.-larvae in the CNS [2]. Neurotoxocarosis may lead to encephalitis, myelitis, cerebral vasculitis or optic neuritis [6, 7] and affected patients may suffer from headache, fever, oversensitivity to light, weakness, confusion, tiredness and visual impairment [6–9]. While the localization of Toxocara-larvae in the human brain has not been systematically investigated, it has been demonstrated that larvae exhibit a species-specific tropism in the murine brain. T. canis-larvae are mainly located in the cerebrum, while T. cati-larvae prefer the cerebellum but mainly accumulate in muscle tissue [10]. Consequently, T. canis- and T. cati-induced NT differs in the severity of structural brain damage as well as the severity of neurological symptoms and behavioural alterations [10–12]. However, host- or parasite-induced immunoregulatory processes contributing to pathogenesis as well as molecular pathogenic mechanisms are only marginally identified yet.
Bioactive regulatory lipids (also called oxylipins), such as octadecanoids, eicosanoids and docosanoids, constitute an important class of molecules involved in the complex molecular signalling network during infection and inflammation. Regulatory lipids comprise a plethora of structurally and stereochemically different bioactive mediators derived from arachidonic acid (ARA) and related ω-6-polyunsaturated fatty acids (PUFAs) like dihomo-γ-linolenic acid (DGLA) and linoleic acid (LA) as well as ω-3-PUFAs such as α-linolenic acid (ALA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Regulatory lipids are generated from the oxidation of different PUFAs by three major enzymatic pathways (an overview of these and selected regulatory lipids is given in Fig 1): The cyclooxygenase pathway (COX-1 and COX-2) results in different prostanoids like prostaglandins, prostacyclins and thromboxanes. Leukotrienes as well as several hydroxy fatty acids are derived from the lipoxygenase pathway (5-LOX, 12-LOX and 15-LOX). The murine 15-LOX (alox15) additionally acts as a 12-lipoxygenating enzyme, converting PUFAs to metabolites similar to those derived from 12-LOX [13]. Thus, hereinafter these metabolites are referred as 12/15-LOX-metabolites. The superfamily of cytochrome P450 (CYP 450) monooxygenase enzymes catalyses the epoxidation of ARA to epoxyeicosatrienoic acids (EpETrEs), which are hydrolysed to corresponding dihydroxy-derivatives (DiHETrEs) by soluble epoxide hydrolases (sEH). In addition, CYP 450 enzymes catalyse the ω-hydroxylation of PUFAs, forming terminal (ω and ω-n) hydroxylated fatty acids [14, 15]. Different regulatory lipids have been detected in cerebral tissues, playing important roles in a variety of physiological processes, such as the maintenance of homeostasis and neural functions including spatial learning and synaptic plasticity [16–19]. Under disease conditions, several oxylipins like COX-derived prostanoids and 5-LOX-derived leukotrienes are involved in inflammatory processes including fever, sensitivity to pain, oxidative stress, and neurodegeneration [20]. In contrast, several metabolites formed via the 12/15-LOX pathway, have been described to exhibit anti-inflammatory activities, e.g. by co-activating peroxisomal proliferator activating-receptors (PPAR), regulating cytokine generation and modulating expression of inflammation related genes [21, 22]. Furthermore, the 12/15-LOX-derived 13-HODE is an agonist for PPAR-γ and exhibit anti-inflammatory properties [23, 24]. In contrast, 9-HODE is mainly generated by non-enzymatic reactions and activates the G protein-coupled receptor G2A, which mediates intracellular calcium mobilization and JNK activation, promoting inflammatory processes [25, 26]. Both metabolites derive from linoleic acid and have been suggested as markers for lipid peroxidation in various chronic diseases [27]. Therefore, Tam et al. [28] proposed the ratio of 13-HODE to 9-HODE as useful biomarker to indicate the immunological status of an active infection. The involvement of regulatory lipids in inflammatory processes has been examined in numerous studies. Most of these studies focused primarily on selected oxylipins, and only a few studies have comprehensively examined quantitative changes during the course of bacterial [29, 30] and viral [28] infections. While these studies were conducted with tibiotarsal tissues [29], blood and exudates [30], and nasopharyngeal lavages [28] as sample material, nothing is known about the dynamic lipidomic profile in the brain during cerebral infections. Furthermore, information about a comprehensive description of these processes during parasitic infections is lacking. Therefore, this study aimed to characterise for the first time alterations in the brain pattern of bioactive regulatory lipids during acute, subacute and chronic NT in T. canis- and T. cati-infected mice as a model for paratenic hosts [3].
Animal experiments were performed in accordance with the German Animal Welfare act in addition to national and international guidelines for animal welfare. Experiments were permitted by the ethics commission of the Institutional Animal Care and Use Committee (IACUC) of the German Lower Saxony State Office for Consumer Protection and Food Safety (Niedersaechsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit) under reference numbers 33.9-42502-05-01A038 (experimental infection of dogs and cats), 33.12-42502-04-14/1520, 33.12-42502-04-15/1869 and 33.14-42502-04-12/0790 (experimental infection of mice).
Eggs of field isolates of Toxocara canis (field isolate HannoverTcanis2008) and T. cati (field isolate HannoverTcati2010), maintained at the Institute for Parasitology, University of Veterinary Medicine Hannover, were obtained from faeces of experimentally infected dogs and cats, respectively, by a combined sedimentation/flotation technique. Eggs were cultured in tap water for about 4 weeks in a controlled temperature chamber at 25±1 °C with oxygenation two times per week to allow development of third-stage larvae. Infective eggs were subsequently stored in tap water at 4 °C until use.
For the oxylipin analysis, 4-week-old female C57BL/6JRccHsd mice were purchased from Harlan Laboratories (The Netherlands) and were allowed to acclimatize for 14 days before the start of the experiment, while for microarray analysis, 5-week-old female C57BL/6JRccHsd were purchased and an acclimatisation time of one week was provided. Mice were housed in Makrolon cages in a 12/12 hours dark/light cycle receiving standard rodent diet (Altromin 1324, Germany) and water ad libitum. With regard to unsaturated fatty acids, the standard rodent diet contained 2,210 mg/kg α-linolenic acid and 16,152 mg/kg linoleic acid. At the age of 6 weeks, 45 animals each were infected orally with 2000 embryonated T. canis or T. cati eggs, respectively, administered at once in a volume of 0.5 ml tap water, whereas 45 control mice received the same volume of the vehicle (tap water) only. At each time point of investigation in the acute phase (day 7 post infectionem [pi]), the subacute phase (days 14 and 28 pi) and the chronic phase (days 42, 70 and 98 pi), five mice were sacrificed by cervical dislocation for the oxylipin analysis. Additionally, as of day 14 pi, three mice of each study group were euthanized for microarray analyses at each time point. For the oxylipin analyses, brains were removed, subdivided into cerebrum and cerebellum, and immediately snap frozen in liquid nitrogen. During further processing, specimens were homogenised in liquid nitrogen using a mortar and pestle, and 50±5 mg of homogenised tissue were weighed and stored individually at -150 °C until oxylipin extraction. For the microarray analysis, brains were removed and subdivided into left and right hemispheres as well as cerebrum and cerebellum. Right cerebrum and cerebellum hemispheres were stored individually in RNAlater RNA stabilization reagent (Qiagen, Hilden, Germany) at 4 °C overnight and afterwards at -80 °C until RNA isolation [31].
Extraction and analysis of oxylipins in brain tissue was carried out as described with modifications [32, 33]. Samples were thawed on ice and 300 μl acidified methanol (0.2% formic acid [Fisher Scientific, Germany] in LC-MS grade MeOH [Fisher Scientific, Germany]), 10 μl antioxidant solution (0.2 mg/mL EDTA [Sigma Aldrich; Germany], 0.2 mg/mL butylated hydroxytoluene [Sigma Aldrich; Germany], 100 μM indomethacin [Sigma Aldrich; Germany], 100 μM TUPS [34]) in MeOH/H2O (50/50, v/v) [35]) as well as 10 μl of internal standards (each 100 nM 2H4-6-keto-PGF1α, 2H4-PGE2, 2H4-PGD2, 2H4-TxB2, 2H4- LTB4, 2H4-9-HODE, 2H8-5-HETE, 2H8-12-HETE, 2H6-20-HETE, 2H11-14,15-DiHETrE, 2H11-14(15)-EpETrE, 2H4-9(10)-EpOME, 2H4-9(10)-DiHOME, 2H4-15-F2t-IsoP, 2H11-5(R,S)-5-F2t-IsoP, Cayman Chemicals (local distributor: Biomol, Germany) [32] were added. Subsequently, the samples were homogenised again using two 3 mm metal beads in a ball mill (Retsch, Germany) for 8 min at 15 Hz, followed by centrifugation at 20,000 x g for 10 min at 4 °C. The supernatant was diluted with 2700 μl of 1 M sodium acetate (pH 6.0; Carl Roth, Germany). Solid phase extraction was carried out on cartridges with an unpolar (C8)/anion exchange mixed mode material (Bond Elut Certify II, 200 mg; Agilent, Germany), preconditioned with one column volume of MeOH and one column volume of 0.1 M sodium acetate with 5% MeOH (pH 6.0). After sample loading, the cartridge was washed with one column volume of water and one column volume of MeOH/H2O (50/50, v/v). Cartridges were dried for 20 min by low vacuum (∼200 mbar). Oxylipins were eluted with n-hexane/ethyl acetate (25/75, v/v) (n-hexane: HPLC grade [Carl Roth Germany]; ethyl acetate [Sigma Aldrich; Germany]) with 1% acetic acid (Sigma Aldrich; Germany) in glass tubes containing 6 μl of 30% glycerol (Sigma Aldrich; Germany) in MeOH. The eluate was evaporated in a vacuum centrifuge (1 mbar, 30 °C, 40–60 min; Christ, Germany) until only the glycerol plug was left. Dried residues were immediately frozen at -80 °C for at least 30 min and reconstituted in 50 μl of MeOH containing a second internal standard allowing the evaluation of extraction efficacy afterwards. Samples were centrifuged (20,000 x g, 10 min, 4 °C). Oxylipins were quantified by liquid chromatography-mass spectrometry (LC–MS/MS) following negative electrospray ionization in scheduled SRM mode on a QTRAP6500 mass spectrometer (Sciex, Germany) injecting 5 μl as described by Rund et al. [32]. Authentic standard substances of oxylipins were purchased from Cayman Chemicals (local distributor: Biomol, Hamburg, Germany). As the standard for NPD1 is not commercially available it was synthesized as follows: The NPD1-methyl ester was synthesized as described for its C10-epimer [36] replacing the (S)-1,2,4-butanetriol by its (R)-enantiomer as starting material for the introduction of the E,E-iododiene. Methyl ester-NPD1 was than hydrolyzed with 1 M LiOH in MeOH/H2O (50/50, v/v) followed by acidification with McIlvains buffer (pH 5) producing NPD1 as a colorless oil in 97% yield. Peak integration and determination of oxylipin concentration was conducted using Multiquant (Sciex, Germany).
RNA isolation and whole genome microarray analysis was conducted as described by Janecek et al. [31]. Briefly, the total RNA content from three cerebra and cerebella from each group at time points 14, 28, 42, 70 and 90 pi was isolated using the RNeasy Lipid Tissue Mini kit (Qiagen, Germany). After further processing, quality control and Cy3-labelling of isolated RNA, labelled cRNA was hybridised to Agilent’s 4x44k Mouse V2 (Design ID:026655) for 17 h at 65 °C and scanned as described by Pommerenke et al. [37]. Obtained data for ptgs1 (COX-1; Probe A_51_P279100), ptgs2 (COX-2; Probe A_51_P254855), aloxe3 (A_55_P2023523), alox5 (A_51_P247249), alox5ap (A_51_P235687), alox8 (A_55_P2029957), alox12 (A_51_P520306), alox12b (A_55_P2121682), alox12e (A_51_P471659) and alox15 (A_51_P252565) were statistically analysed as described below.
Normality of distribution of all sample sets was analysed by the Kolmogorov-Smirnov test. For normally distributed variables the One-way ANOVA or for skewed distributions the Kruskal-Wallis test was used to reveal statistically significant differences between the infection and control group at each time point. To account for multiple comparisons, false discovery rate adjustment of P-values was carried out in R (version 3.1.2; [38]) and a Q-value of 0.1 was considered as statistically significant. For Q-values below 0.1, the following post-hoc tests were carried out: unpaired t-test for normally distributed datasets or Mann-Whitney U test (MWU) for skewed distributions, whereby P≤0.05 was considered as statistically significant. If a metabolite could not be detected in one of the study groups, the lower limit of quantification was used for statistics. Statistical analyses were conducted with GraphPad Prism™ software (version 6.03; GraphPad Software, California, USA). Due to normally distributed datasets for the ratio of 13-HODE to 9-HODE as well as the transcription rates of oxylipin-related genes, an unpaired t-test was used to reveal statistical differences between infected and uninfected groups (GraphPad Prism™ software [version 6.03; GraphPad Software, California, USA]).
Ratios of lipid mediators and transcriptional levels of mentioned genes between the uninfected control and infection groups over the course of infection were calculated by dividing each individual value of the T. canis-, T. cati- and uninfected control group by the mean value of the uninfected control group at the respective point in time. If a metabolite was not detected in the corresponding uninfected control group, the lower limit of quantification was used to calculate the fold change in infected mice. The fold changes were log2 transformed and the means of the log2 transformed fold-changes were presented as heatmaps to identify relative changes. Heat maps were visualised with MeV [39] (Version 4.9.0, TM4 Software suit [http://mev.tm4.org]).
Clinical assessment of mice as well as data on body weight, whole brain weight, cerebrum and cerebellum weight as well as brain to body mass ratio have been published previously by Waindok and Strube [40], using the same mice to investigate changes in brain cytokine and chemokine patterns during neurotoxocarosis. In short, infected mice showed varying degrees of neurobehavioural alterations as described by Janecek et al. [12], with T. canis-infected mice developing symptoms like ataxia to paresis and paraplegia or incoordination earlier and more severe than T. cati-infected mice. The brain/body mass ratio in comparison to the uninfected control group was significantly lower at day 14 pi in T. canis- and T. cati-infected mice (P = 0.0038 and P = 0.0024, respectively) and at day 28 pi in T. canis-infected mice (P = 0.0353). Similarly, the cerebrum/body mass-ratio was significantly lower at day 14 pi in T. canis- and T. cati-infected mice (P = 0.0005 and P = 0.0003, respectively) and at day 28 pi in T. canis-infected mice (P = 0.0396), but increased significantly at day 70 pi in the latter group (P = 0.0079). Regarding the cerebellum/body mass-ratio, statistically significant differences were not detectable between the infected and uninfected groups.
A total of 73 different oxylipins were successfully detected and quantified in brains of Toxocara spp.-infected mice and uninfected controls. To assess the composition of bioactive lipid metabolites over the course of infection, analysed metabolites of different PUFAs were summarised by their major formation routes, i.e. COX, LOXs, and CYP 450 as well as non-enzymatic autoxidation. The proportion of the respective pathways contributing to the analysed oxylipins in the brains of Toxocara spp.-infected and uninfected mice is illustrated in Fig 2. Concentrations and P-values regarding the comparison to uninfected control mice are provided in S1 Table. Absolute levels of COX-derived metabolites in the cerebra and cerebella did not differ significantly between infected and uninfected control mice with the exception of T. cati-infected cerebella at day 42 pi. Infection with T. canis and T. cati led to similar alterations of CYP 450-derived metabolites in the cerebra, namely a significant increase at days 14, 42 and 70 pi. In the cerebella, CYP 450-derived metabolites were significantly increased at days 14 and 42 pi in both infection groups. The total amount of 5-LOX-derived oxylipins was significantly higher in the cerebra of T. canis-infected mice at days 28 and 42 pi and of T. cati-infected mice at day 42 pi. In the cerebella, 5-LOX-metabolites were significantly increased at day 7 pi for T. canis- and at day 14 pi for T. cati-infected mice, as well as in both infection groups at day 42 pi. Metabolites derived by 8-LOX were significantly elevated in the cerebra of both infection groups at days 14, 28 and 42 pi. Regarding the cerebella, a significant increase was observed at days 14, 28 and 98 pi in T. canis-infected mice and at days 28 and 42 pi in T. cati-infected mice. In addition, levels of 12/15-LOX-derived oxylipins were significantly elevated at days 14, 28 and 42 pi in the cerebra of both infection groups. Significantly increased levels of 12/15-LOX metabolites were also observed in the cerebella of both infection groups at days 14 and 42 pi. The levels of non-enzymatically derived oxylipins did not differ significantly between the uninfected control group and the two infection groups in both brain parts.
While Fig 2 illustrates relative patterns of detected lipid mediators based on their major metabolic formation pathways during infection, Fig 3 displays alterations of individual bioactive lipid mediators and their fold change in infected compared to uninfected control mice. Oxylipin concentrations and P-values regarding the comparison to the control mice are given in S2 Table (cerebrum) and S3 Table (cerebellum). Metabolites are classified by their major biosynthetic pathways, however metabolite formation through other enzymes or non-enzymatic peroxidation cannot be excluded.
The ratio of 13-HODE to 9-HODE was shifted towards 13-HODE in the cerebra and cerebella of T. canis- as well as T. cati-infected mice during the course of infection (Fig 5). A significant increase of the 13-HODE/9-HODE ratio was already detected in the cerebra of T. canis-infected mice at days 7 and 14 pi (P = 0.0171 and P = 0.0001), reaching a maximum from days 28 and 42 pi (P≤0.0001 and P = 0.0011), while in T. cati-infected cerebra, the ratio peaked already at day 14 pi and remained significantly increased at days 28 and 42 pi (P = 0.0098, P = 0.0049 and P = 0.0461, respectively). In both infection groups, the 13-HODE/9-HODE ratio declined to homeostatic conditions in the later phase of infection at days 70 and 98 pi. Although the 13-HODE/9-HODE ratio in T. canis- and T. cati-infected cerebella showed a similar development as in the cerebrum, statistically significant alterations were only detected at days 7 and 28 pi (P = 0.0006 and P≤0.0001) in T. canis- and at days 14 and 28 pi (P = 0.0139 and P = 0.0067) in T. cati-infected mice.
Transcriptional alterations of different ptgs (encoding for COX enzymes) and alox (encoding for LOX enzymes)-genes are shown as fold changes in Fig 6. Transcription of ptgs1 was significantly increased in cerebra and cerebella of T. canis-infected mice during the whole study period (P≤0.0418). In T. cati-infected mice, ptgs1-transcription was significantly upregulated at day 28 pi (P = 0.0406) in the cerebra, and at days 28, 70 and 98 pi (P = 0.0056, P = 0.0453 and P = 0.0418) in the cerebella. By contrast, the transcription rate of ptgs2 was significantly downregulated in cerebra of T. canis-infected animals at day 28 pi (P = 0.026), cerebra of T. cati-infected mice were not affected. In the cerebella, a statistically significant increase was detected at day 98 pi in both infection groups (T. canis: P = 0.0039, T. cati: P = 0.0402). Furthermore, the transcription rate of alox5 and alox5ap (encoding for the 5-LOX activating protein FLAP) was significantly elevated as of day 14 pi (with the exception of alox5 in the cerebra at day 14 pi) in both brain parts of T. canis-infected mice (P≤0.0348). In brains of T. cati-infected mice, alox5 was significantly elevated at day 28 pi (P = 0.0266), while no significant alterations of alox5ap-transcription were detected. The transcription rate of different alox12-isoforms was mostly unaffected by NT, with a downregulation in both brain areas of T. canis-infected mice at days 14 (cerebra P = 0.0086; cerebella P = 0.0229) and 42 pi (cerebra P = 0.0061; cerebella P = 0.0398). In T. cati-infected mice, a significant downregulation occurred at days 14 and 28 pi (P = 0.0336 and P = 0.0229) in the cerebra and day 42 pi (P = 0.0268) in the cerebella. In contrast, the transcription of alox12e was elevated in the cerebella of T. canis-infected mice at days 28, 42 and 98 pi (P = 0.0300, P = 0.0021 and P = 0.0300), and at day 42 pi (P = 0.0017) of T. cati-infected mice. The transcription of alox15 was significantly upregulated at days 28 pi and 42 pi in cerebra of T. canis (P = 0.0037; P = 0.0037) as well as T. cati-infected mice (P = 0.0239; P = 0.0464). In the cerebella, T. canis-infection resulted in a significant upregulation of alox15 at days 14, 28 and 98 pi (P = 0.0191, P = 0.0252 and P = 0.0006), while T. cati-infected mice only showed an upregulation on days 28 and 98 pi (P = 0.0068, P = 0.0251).
The central nervous system exhibits inflammatory reactions in response to injury, infection or disease, comprising the activation of brain microglia, the rapid release of inflammatory mediators and invasion of immune cells, among others [41, 42]. Nevertheless, neuroinvasive larvae of Toxocara spp. are able to accumulate and persist in cerebral tissues [43] and even though the infection is characterised by neuroinflammatory hallmarks like hemorrhagic lesions, myelinophages, spheroids and activated microglia [10, 44, 45], larvae are not trapped by inflammatory reactions in cerebral tissues [44, 46]. Knowledge regarding the cerebral immune response during NT is scarce. In the present study, an overall shift to an anti-inflammatory oxylipin pattern during NT was found. In general, this trend was observed for T. canis- and T. cati-infected mice, even though minor differences, especially for pro-inflammatory regulatory lipids, between T. canis- and T. cati-induced NT were noted.
COX-derived prostaglandins are potent immunomodulatory mediators, triggering the expression of inflammatory enzymes [47–50], chemokines and cytokines [51]. It is commonly believed that ptgs1 (encoding for the enzyme COX-1) is expressed constitutively under homeostatic conditions, whereas the expression of ptgs2 (encoding for the enzyme COX-2) is induced in response to inflammatory stimuli [20]. However, recent data suggest that ptgs1 is a major player in neuroinflammatory processes, while ptgs2 activity mediates neurotoxicity or neuroprotection [52]. This hypothesis is supported by the transcriptomic analysis in the present study, which revealed significantly increased levels of ptgs1-transcription in both brain areas of T. canis-infected mice and at three time points in the cerebellum of T. cati-infected mice, whereas ptgs2-transcription remained largely unaffected. Nevertheless, elevated transcription rates of ptgs1 did not result in elevated concentrations of the corresponding oxylipins, which were only moderately altered. Similar observations have been made regarding cytokine secretion during NT. In a recent study, concentrations of pro-inflammatory cytokines were also not elevated during T. canis- and T. cati-induced NT [40], although a transcriptional upregulation of pro-inflammatory IFN-γ, IL-6 and TNF-α has been shown in brains of T. canis-infected mice [46, 53].
Metabolites derived by the 5-LOX enzyme (encoded by alox5) are considered to have pro-inflammatory properties. The transcription of alox5ap leading to FLAP, which is necessary to initiate the activation of 5-LOX, was increased throughout the study period in cerebra and cerebella of T. canis- as well as in cerebella of T. cati-infected animals. Alox5 transcription was also elevated at these time points in T. canis-, but not in T. cati-infected animals. An important 5-LOX-derived regulatory lipid is LTB4, which activates and recruits neutrophils during inflammatory processes [54, 55] and in interaction with pro-inflammatory cytokines, LTB4 induces the activation of NF-κB, a key regulator of neuroinflammation [56–58]. During the course of T. canis- as well as T. cati-induced NT, LTB4 levels in the cerebellum were mainly unaffected, while significantly increased levels were detected in the cerebrum during the subacute phase and the beginning of the chronic phase of infection. Recruited neutrophils were, besides eosinophils, shown to be present in perivascular lymphocytic cuffs of T. canis- and T. cati-infected mice brains. However, neutrophil infiltration may play only a subordinate role in the pathogenesis of NT as the pathological picture is dominated by eosinophilic meningitis, microglia activation and neurodegeneration, the latter especially in T. canis-infected mice [45].
The murine alox15 gene encodes for an enzyme that besides 15-lipoxygenation further acts as a 12-lipoxygenating enzyme, converting PUFAs to products also formed by 12-LOX [13] thus it is also referred to as 12/15-LOX. In the present study, alox12-transcription only showed minor alterations in both infection groups, whereas alox15-transcription was significantly upregulated at several time points pi. The highly increased levels of 12/15-LOX-metabolites at days 14, 28 and 42 pi are thus most likely due to elevated alox15-transcription rates. Most 12/15-LOX-metabolites exhibit anti-inflammatory properties. HETEs and HODEs are activators of the peroxisomal proliferator-activating receptor-γ (PPARγ), which plays an important role in the regulation of cell development and homeostasis [59]. Under neuroinflammatory conditions, 12/15-LOX-metabolites mediate effects of anti-inflammatory IL-4 on NF-κB trans-activation in glial cells and protect oligodendrocyte progenitors [60]. In case of NT, these anti-inflammatory and neuroprotective mechanisms may facilitate the persistence of Toxocara spp.-larvae in the brain and the survival of the paratenic host.
Multiple enzymatic conversions of PUFAs result in formation of specialised pro-resolving mediators (SPMs), including specific lipoxins, resolvins and protectins [51, 61, 62]. Besides their anti-inflammatory properties, SPMs promote the resolution of inflammation by blocking neutrophil recruitment and mediating phagocytosis and lymphatic clearance of apoptotic neutrophils [51]. Arachidonic acid-derived lipoxins, with LxA4 and LxB4 as the most prominent metabolites, are involved in the regulation of leukocyte trafficking [61, 63, 64]. Interestingly, even though LxA4 is involved in pro-resolving processes, LxA4 was not detected in any of the study cohorts. In contrast, LxA4 seems to play an important role during cerebral toxoplasmosis, with high levels of lipoxins suppressing the activation of dendritic cells and the secretion of IL-12 [65]. Furthermore, anti-inflammatory and pro-resolving functions are also partially mediated by resolvins, which can be separated in the EPA-derived E-series (RvEs) and the DHA-derived D-series (RvDs) [66, 67]. In the present study, neither RvEs nor RvD1 were present in detectable quantities in any of the study groups. However, the DHA-derived NPD1, a major bioactive effector in both anti-inflammation and neuroprotection [68, 69] was successfully detected in the brains of the Toxocara-infected groups, but not in the uninfected control group. In the T. canis- and T. cati-infected mice, NPD1 occurred primarily in the subacute phase of infection between day 14 and 42 pi. In the brain, NPD1 has far-ranging properties regarding neural cell survival, protection of cerebral tissues and inhibition of leukocyte infiltration as well as interleukin 1-β-induced NF-κB activation [68, 70]. According to Ariel et al. [71], NPD1 is produced by T-helper type 2-skewed peripheral blood mononuclear cells (Th2 PBMCs) in dependence of human ALOX-15 activity, but not in Th1 PBMCs. The “type 2” polarisation of CD4+ T-helper cells, as part of the adaptive immune response, is commonly induced by parasitic helminths [72, 73]. Del Prete et al. [74] demonstrated a stable Th2-like cytokine secretion of human CD4+ T cells derived from the peripheral blood (PB), stimulated with T. canis excretory/secretory (TES) antigen. Similarly to NPD1, 5,15-DiHETE and 8,15-DiHETE were detected in Toxocara-infected, but not in control mice. Both metabolites are secreted in large quantities from eosinophils, and eosinophilic meningitis is one of the most prominent pathologic features of NT in mice as well as in humans [6, 45].
The metabolites 9-HODE and 13-HODE as well as the ratio of both metabolites are proposed biomarkers for various chronic diseases and the immunological status of an active infection [27, 28]. The low 13-/9-HODE ratio in the acute phase of influenza-infected C57BL/6 mice implied a pro-inflammatory state of the immune response, which was shifted to an anti-inflammatory, pro-resolution state at later time points of the infection, indicated by a higher 13-/9-HODE ratio [28, 75]. This was also illustrated in the Lyme arthritis model of B. burgdorferi-infected C3H/HeJ and DBA/2J mice, with an elevated 13-/9-HODE ratio in the resistant mouse strain (DBA), compared to the susceptible strain (C3H) [29, 75]. In the present study, the 13-/9-HODE ratio in Toxocara-infected mice was clearly shifted towards an anti-inflammatory immune response in comparison to uninfected mice, particularly in the subacute phase of infection between days 14 and 42 pi. Thus, the ratio of 13-/9-HODE reflects the aforementioned trend of minor changes in the biosynthetic pathway of mostly pro-inflammatory prostaglandins (COX-pathway) in concert with a significant increase of potentially anti-inflammatory metabolites of the 12/15-LOX -pathway, observed for both infection groups and brain regions.
In general, T. canis is regarded as the main causative agent of toxocarosis while the cat roundworm T. cati is probably underestimated as a zoonotic pathogen [76]. In mice, T. cati-induced NT is characterised by a less severe pathogenesis in terms of clinical symptoms, histopathological alterations and behavioural changes compared to T. canis-infections [10, 11, 76]. Furthermore, T. canis-larvae exhibit higher neuroaffinity than T. cati-larvae and mainly accumulate in the cerebrum, whereas T. cati prefers the cerebellum but mainly accumulates in muscle tissue [10]. This species-specific tropism is partly reflected by alterations of the oxylipin pattern, as significant elevations of pro-inflammatory COX- and 5-LOX-metabolites were detected in the cerebellum rather than the cerebrum of T. cati-infected mice. Nevertheless, in essence, alterations in the cerebral oxylipin patterns during T. canis- and T. cati-induced NT were comparable. It remains unknown if the shift towards an anti-inflammatory oxylipin pattern is an immunogenic reaction of the host in response to neuroinvasive larvae to prevent excessive tissue damages or a parasite-induced immunomodulatory effect to evade the host’s immune regulation, facilitating persistence in cerebral tissues. Many parasitic helminths are known to manipulate the hosts’ immune response by secreting immunomodulatory components evoking for example the induction of a modified Th2 response [77] and the reduction of pro-inflammatory cytokines [78].
In conclusion, the present study revealed only minor changes in pro-inflammatory metabolites of the COX-pathway in T. canis- and T. cati-infected cerebra and cerebella. Generally, it should be kept in mind that NT is characterised by focally distributed lesions whereas entire cerebra and cerebella were processed for the current analysis. Thus, healthy brain tissue was overrepresented compared to damaged tissue, which may have masked local effects. Nevertheless, a statistically significant increase of metabolites of different LOX-pathways was demonstrated for both infection groups. Neuroprotective NPD1 was detected in T. canis- and T. cati-infected mice, especially in the subacute and at the beginning of the chronic phase of infection, but not in uninfected mice. Other anti-inflammatory and pro-resolving SPMs were not detected (LxA4, RvD1, RvEs). The ratio of 13-HODE to 9-HODE revealed a similar anti-inflammatory immune response in the cerebrum and cerebellum of each infection group. Even though T. canis infection resulted in more pronounced alterations in the pattern of lipid mediators, T. canis- and T. cati-induced NT were comparable in terms of alterations of cerebral oxylipins over the course of infection.
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10.1371/journal.pgen.1003163 | Increased Maternal Genome Dosage Bypasses the Requirement of the FIS Polycomb Repressive Complex 2 in Arabidopsis Seed Development | Seed development in flowering plants is initiated after a double fertilization event with two sperm cells fertilizing two female gametes, the egg cell and the central cell, leading to the formation of embryo and endosperm, respectively. In most species the endosperm is a polyploid tissue inheriting two maternal genomes and one paternal genome. As a consequence of this particular genomic configuration the endosperm is a dosage sensitive tissue, and changes in the ratio of maternal to paternal contributions strongly impact on endosperm development. The FERTILIZATION INDEPENDENT SEED (FIS) Polycomb Repressive Complex 2 (PRC2) is essential for endosperm development; however, the underlying forces that led to the evolution of the FIS-PRC2 remained unknown. Here, we show that the functional requirement of the FIS-PRC2 can be bypassed by increasing the ratio of maternal to paternal genomes in the endosperm, suggesting that the main functional requirement of the FIS-PRC2 is to balance parental genome contributions and to reduce genetic conflict. We furthermore reveal that the AGAMOUS LIKE (AGL) gene AGL62 acts as a dosage-sensitive seed size regulator and that reduced expression of AGL62 might be responsible for reduced size of seeds with increased maternal genome dosage.
| Flowering plants reproduce by forming seeds that contain an embryo surrounded by a nourishing endosperm tissue that, similar to the mammalian placenta, supports embryo growth. Normal endosperm development requires the FERTILIZATION INDEPENDENT SEED (FIS) Polycomb Repressive Complex2 (PRC2). In most flowering plants the endosperm is a polyploid tissue containing two maternal and one paternal genome copies. As a consequence of this particular genomic configuration the endosperm is a dosage sensitive tissue, and changes in the ratio of maternal and paternal genome copies have drastic effects on endosperm development. Here we investigated the consequences of increased maternal genome dosage on endosperm and seed development. We found that increased maternal genome dosage alleviates the need for the FIS-PRC2 in the endosperm. While in fis mutant seeds with normal maternal genome dosage the endosperm fails to cellularize and embryos arrest, in fis mutant seeds with increased maternal genome dosage the endosperm cellularizes and viable embryos develop. Our study suggests a functional role of the FIS-PRC2 in balancing parental genome dosage in the endosperm. We propose that the FIS-PRC2 evolved to reduce genetic conflict that arose as a consequence of unbalanced genome contributions in the endosperm.
| Seed development in flowering plants is initiated by double fertilization of the female gametophyte. Within the female gametophyte there are two distinct gametic cells that have divergent fates after fertilization. The haploid egg cell will give rise to the diploid embryo, while the homodiploid central cell will form the triploid endosperm [1]. The endosperm supports embryo growth by delivering nutrients acquired from the mother plant [2]. As most angiosperms, the endosperm of Arabidopsis thaliana follows the nuclear-type of development where an initial syncytial phase of free nuclear divisions without cytokinesis is followed by cellularization [3]. At the eighth mitotic cycle cellularization of the syncytial endosperm is initiated in the micropylar domain around the embryo, coinciding with the early heart stage of embryo development [4], [5]. The timing of endosperm cellularization correlates with final seed size. Precocious endosperm cellularization results in small seeds, while delayed endosperm cellularization causes the formation of enlarged seeds [6], [7]. Timing of endosperm cellularization can be manipulated by interploidy hybridizations, which have opposite effects on endosperm cellularization and seed size dependent on the direction of the increased parental genome contribution. Increased maternal genome contribution (4n×2n, corresponds to maternal excess hybridization) causes precocious endosperm cellularization and the formation of small seeds. Conversely, increased paternal genome dosage (2n×4n, corresponds to paternal excess hybridization) results in delayed or complete failure of endosperm cellularization, causing seed abortion with an accession-dependent frequency [6], [8], [9]. Developmental defects caused by interploidy hybridizations with increased paternal genome contribution are associated with deregulation of genes that are directly or indirectly controlled by the FERTILIZATION INDEPENDENT SEED (FIS) Polycomb Repressive Complex 2 (PRC2), implicating that developmental aberrations in response to interploidy crosses are largely caused by deregulated FIS-PRC2 target genes [9]. PRC2 is a chromatin-modifying complex that ensures mitotically stable repression of specific target genes by applying trimethylation marks at lysine 27 of histone H3 (H3K27me3) [10], [11]. In plants, several PRC2 subunits are encoded by small gene families that form specific complexes with distinct functions during plant development [10]. The FIS-PRC2 is comprised of the subunits MEDEA (MEA), FERTILIZATION INDEPENDENT SEED2 (FIS2), FERTILIZATION INDEPENDENT ENDOSPERM (FIE) and MULTICOPY SUPPRESSOR OF IRA1 (MSI1) [10]. The FIS-PRC2 complex plays a pivotal role in suppressing initiation of endosperm and seed development in the absence of fertilization [12]–[15]. After fertilization, loss of FIS function causes endosperm overproliferation and cellularization failure, ultimately leading to seed abortion [12], [16], [17]. The phenomenon of decreased seed size in response to maternal excess interploidy hybridizations is known since long [6]; however, the underlying molecular mechanism for this phenomenon remains unknown. A recent study revealed increased expression of the FIS-PRC2 subunit FIS2 in response to maternal excess hybridizations [18], possibly linking increased FIS-PRC2 activity with decreased seed size. Other recent work proposed increased levels of 24-nt small interfering RNAs (p4-siRNAs) to cause decreased seed size by decreasing expression of AGAMOUS-LIKE (AGL) MADS-box transcription factor encoding genes in the endosperm [19]. In this study we tested the role of FIS-PRC2 as well as p4-siRNAs in mediating maternal excess interploidy effects. Surprisingly, our study revealed that neither changed levels of FIS-PRC2 nor p4-siRNAs are likely to be involved in mediating effects caused by maternal excess interploidy hybridizations. Instead, our results strongly suggest that reduced AGL gene expression as a consequence of reduced paternal genome dosage causes decreased seed size and we reveal that AGL62 acts as a dosage sensitive seed size regulator. We furthermore show that FIS-PRC2 function can be bypassed in maternal excess triploid seeds. Loss of FIS-PRC2 causes the formation of enlarged viable triploid seeds containing a cellularized endosperm and a developed embryo, contrasting the strict requirement of FIS-PRC2 function in diploid seeds. Development of viable fis triploid seeds is connected with normalized expression of AGL genes, suggesting that reduced AGL gene expression as a consequence of reduced paternal genome dosage allows bypassing the need of the FIS-PRC2 complex.
To investigate the effect of increased maternal genome dosage on embryo and endosperm development, we made use of the meiotic omission of second division 1 (osd1) mutant that forms unreduced diploid male and female gametes at high frequency, whereas the ploidy of the parental plant remains unchanged [20]. Pollinating the osd1-1 mutant (introgressed into the Col accession) with wild-type pollen allowed us to mimic maternal excess interploidy crosses (4n×2n) without changing the ploidy of the maternal plant. Pollination of an osd1 plant with wild-type pollen resulted in 91.5% triploid seeds and 8.5% diploid seeds (n = 1921; Table S1), in close agreement with previously published results [20]. Triploid seeds derived from an osd1×2n cross were significantly smaller and lighter than diploid wild-type seeds (Figure 1A, 1B, p<0.001). Segregating diploid seeds from the osd1×2n cross were significantly bigger than wild-type seeds (p<0.001) (Figure 1A, 1B), maybe because the reduced seed size of the triploid seeds allows diploid sibling seeds to acquire more resources. Alternatively it is possible that loss of OSD1 affects seed size not only by altering ploidy of the endosperm but also by an unknown mechanism in the diploid maternal tissue. Tetraploid seeds derived from self-fertilized osd1 mutants were only slightly larger than wild-type seeds, contrasting the formation of considerably enlarged seeds by self-fertilized tetraploid plants (Col accession, Figure 1B; p<0.001). This finding suggests that increased size of seeds derived from tetraploid plants is largely caused by maternal sporophytic effects. Triploid seeds derived from 4n×2n interploidy crosses were smaller and lighter compared to tetraploid seeds (p<0.001), but of similar size and weight as wild-type diploid seeds (Figure 1B), contrasting previous data revealing decreased size of maternal excess triploid seeds in Landsberg erecta and C24 accession backgrounds [6]. Similar to triploid seeds derived from 4n×2n interploidy crosses [6], triploid seeds derived from osd1×2n crosses had a reduced endosperm proliferation rate (Figure 1C) and an early onset of endosperm cellularization (Figure 1D), correlating with decreased seed size compared to the self-fertilized maternal parents. Whereas endosperm cellularization in wild-type diploid and tetraploid seeds started at 6 DAP and was completed at 7–8 DAP, endosperm cellularization in triploid seeds derived from osd1×2n as well as 4n×2n crosses started two days earlier at 4 DAP and was completed at 5 DAP (Figure 1D, Figure S1). Precocious endosperm cellularization has previously been connected with small seed size [6], [7], [21], [22], implicating the early onset of endosperm cellularization in triploid seeds as the main cause for reduced seed size. We did not detect reproducible developmental differences of triploid embryos compared to diploid embryos, making it rather unlikely that alteration in triploid embryo development were causally responsible for decreased seed size.
Together, we conclude that increased maternal genome contribution inherited through female gametes is sufficient to cause reduced seed size, establishing the osd1 mutant as a suitable tool to investigate the effect of maternal excess interploidy hybridizations. While also the dyad mutant forms unreduced female gametes and small-sized triploid seeds [23], the very low frequency of viable seed formation (ranging from one to ten viable seeds per dyad plant) does not allow detailed investigations of the effect of increased maternal ploidy on seed development using this mutant.
Paternal excess interploidy hybridizations cause similar seed developmental defects as mutants lacking FIS-PRC2 function. This is reflected by strikingly similar sets of deregulated genes [9], suggesting failure of FIS-PRC2 function in response to increased paternal genome dosage. As maternal and paternal genome excess cause reciprocal phenotypes [6], we addressed the question whether also maternal excess hybridizations cause global deregulation of FIS-PRC2 target genes. Transcriptome profiling of seeds derived from osd1×2n crosses at 6 DAP identified 342 and 510 genes as significantly up- and down-regulated, respectively (Signal Log Ratio (SLR)>1, or SLR<−1, p<0.05; Figure S2, Tables S2 and S3) that significantly overlapped with previously identified genes deregulated in 4n×2n interploidy hybridizations [24] (Table S4). While the overlap of deregulated genes was significant, there was also a high number of non-overlapping genes that are likely a consequence of different tissue types and accession backgrounds used to generate both datasets. Whereas transcriptome data of 4n×2n hybridizations were generated from entire siliques of C24 plants, osd1×2n transcriptional profiles were specifically generated from seeds of Columbia plants. Both, up- and down-regulated genes are significantly enriched for the PRC2 hallmark H3K27me3 (Figure 2, Table S2), suggesting a role of FIS-PRC2 in response to maternal excess interploidy hybridizations. We tested whether the genes that are deregulated in interploidy crosses are deregulated also in seeds lacking FIS2 function. Down-regulated genes were significantly enriched for genes affected by loss of FIS2 function, whereas no enrichment was detected for up-regulated genes (Figure 2). Together, the enrichment for H3K27me3 and the overlap with FIS2-responsive genes suggests that the FIS-PRC2 might be involved in mediating the seed phenotype in response to maternal excess hybridizations.
We wished to further test the idea whether decreased expression of FIS2-responsive genes in triploid maternal excess seeds was mediated by increased FIS activity. FIS-PRC2 components FIS2 and MEA are regulated by genomic imprinting and exclusively expressed from the maternally inherited alleles [25]–[27]. Therefore, it was possible that increased maternal genome dosage could cause increased expression of FIS2 and MEA that might in turn be responsible for increased FIS activity. We tested this hypothesis by measuring mRNA levels of FIS2 and MEA in triploid osd1 seeds and triploid seeds derived from 4n×2n crosses. Levels of MEA mRNA were only increased in 4n×2n derived seeds compared to wild-type seeds at 3 DAP, whereas no increase but rather a decrease was detected in triploid osd1 seeds (Figure 3 and Figure S3). In contrast, FIS2 mRNA levels were strongly increased in triploid osd1 seeds and slightly increased in triploid seeds derived from 4n×2n crosses at 2 and 3 DAP (Figure 3 and Figure S3) in agreement with previously published data [18].
Increased FIS2 mRNA levels might cause increased FIS-PRC2 activity that in turn could be causally responsible for phenotypic abnormalities of triploid seeds. To test this hypothesis, we generated mea/MEA; osd1/osd1 and fis2/FIS2 osd1/osd1 double mutants (Figure S4). MEA and FIS2 are unlinked to the centromere, therefore, mutant and wild-type alleles of both genes will frequently recombine. Consequently, most central cells formed in theses double mutants will be duplex for the mea or fis2 mutation (mea/mea/MEA/MEA or fis2/fis2/FIS2/FIS2), with a small fraction of central cells being tetraplex for mea or fis2 (mea/mea/mea/mea or fis2/fis2/fis2/fis2). We pollinated these double mutants with wild-type pollen and analyzed the ploidy and genotype of the resulting progeny. Based on this analysis we infer that about 8% of central cells are nulliplex for MEA (tetraplex for mea), whereas 10% are nulliplex for FIS2 (tetraplex for fis2) (Table S5). If increased FIS activity was causally responsible for reduced triploid seed size, we expected that reducing the copy number of active MEA or FIS2 alleles should result in the formation of enlarged triploid seeds. More than 70% of seeds derived from pollination of mea/MEA; osd1/osd1 and fis2/FIS2; osd1/osd1 with wild-type pollen had only two active maternally inherited wild-type MEA or FIS2 alleles in the pentaploid endosperm, respectively (meaM/meaM/MEAM/MEAM/MEAP; fis2M/fis2M/FIS2M/FIS2M/FIS2P superscribed M and P correspond to maternal and paternal alleles, Table S5). These seeds were viable and the seed size distribution of the majority of seeds was almost identical to triploid osd1 seeds (Figure 4A and Figure S5). Based on these results we conclude that increased expression of FIS2 is unlikely to cause decreased size of triploid seeds.
We noted that a small fraction of seeds derived from pollination of mea/MEA; osd1/osd1 and fis2/FIS2; osd1/osd1 with wild-type pollen were strongly enlarged compared to triploid osd1 seeds (Figure 4A, Figure 5, Figure S6). Similarly, we detected about 10% and 5% of enlarged seeds in the progeny of the cross mea/MEA; osd1/osd1×mea/MEA and fis2/FIS2; osd1/osd1×fis2/FIS2, respectively (Figure 4B) that were not detected in the progeny of self-fertilized mea/MEA and fis2/FIS2 mutants (Figure 4B). About 4.3% of seeds derived from crosses mea/MEA; osd1/osd1×mea/MEA are expected to be diploid mea seeds (Tables S1 and S5), corresponding closely to the observed 5% of collapsed seeds that largely failed to germinate (only 1 out of 35 tested collapsed seeds germinated). We analyzed ploidy and genotype of enlarged seeds derived from crosses mea/MEA; osd1/osd1×mea/MEA. Genotyping revealed them being either duplex or triplex for the mea mutation (meaM/meaM/MEAP; meaM/meaM/meaP; Figure S7), revealing that triploid seeds can bypass the requirement of MEA function. Among collapsed fis2 seeds we found that 25% (n = 76) of those seeds were able to germinate. Similar to enlarged mea seeds, ploidy analysis and genotyping revealed that these seeds were triploid seeds duplex or triplex for the fis2 mutation (fis2M/fis2M/FIS2P; fis2M/fis2M/fis2P; Figure 4B, Figure S7), adding strong support to the view that triploid seeds can bypass the requirement of FIS-PRC2 function. In contrast, self-fertilized mea/MEA; osd1/osd1 and fis2/FIS2; osd1/osd1 mutants did not form enlarged seeds and none of the collapsed seeds was able to germinate, indicating that bypass of FIS function depends on an increased ratio of maternal to paternal genomes, rather than an absolute increase of maternal genome dosage.
Lethality of fis mutant seeds is associated with a failure of endosperm cellularization [28], [29]. We asked the question whether rescue of mea and fis2 mutant triploid seeds would be associated with a restoration of endosperm cellularization. Indeed, we found that in contrast to diploid mea and fis2 seeds, endosperm cellularization of triploid duplex or triplex mea and fis2 seeds was initiated and completed, albeit it occurred delayed compared to wild-type seeds. Whereas cellularization of wild-type seeds was largely progressed at 6 DAP, it was only initiated in triploid mea and fis2 seeds at this time and was completed only at 10 DAP (Figure 5 and Figure S6).
Together, we conclude that relative increase of maternal to paternal genome dosage allows bypass of FIS function and restores endosperm cellularization in fis mutant seeds.
Endosperm cellularization is negatively regulated by the MADS-box transcription factor AGL62, and complete loss of AGL62 causes precocious endosperm cellularization after few mitotic divisions [30]. We addressed the question whether precocious endosperm cellularization in triploid seeds correlates with decreased expression levels of AGL62. In agreement with this notion, expression of AGL62 was reduced in triploid osd1 seeds at timepoints before cellularization at 5 DAP (Figure 6), similar to decreased expression of AGL62 in 4n×2n interploidy hybridizations [19] (Figure S8). Yeast two-hybrid interaction studies revealed that AGL62 interacts directly with AGL transcription factors such as the PEG PHERES1 (PHE1), the MEG AGL36, and AGL90. AGL62 also interacts indirectly with the MEG AGL28 and AGL40 that both directly interact with PHE1 [31], [32]. We tested whether expression of the genes encoding direct and indirect interaction partners of AGL62 was altered in triploid seeds. Similar to the reduced expression of AGL62, expression of all tested AGL genes was strongly reduced in triploid osd1 seeds as well as triploid seeds derived from 4n×2n crosses (Figure 6 and Figure S8).
Reduced maternal genome dosage of AGL62 can suppress fis2 seed abortion [29] likely by initiating endosperm cellularization. We therefore addressed the question whether restoration of endosperm cellularization in triploid mea and fis2 seeds is accompanied by normalized expression of AGL genes. Because of the extreme size difference, triploid and diploid mea and fis2 seeds can easily be distinguished from other seeds at 8 DAP and manually isolated. Expression of six AGL genes implicated in endosperm cellularization was measured, and all tested genes had reduced transcript levels in triploid mea and fis2 seeds (Figure 7), correlating with the initiation of endosperm cellularization (Figure 5). Together, we conclude that decreased AGL gene expression in triploid maternal excess seeds alleviates the need for FIS-PRC2 function.
Previous work proposed a possible connection of increased levels of p4-siRNAs small interfering RNAs (siRNAs) in seeds derived from 4n×2n hybridizations and decreased expression levels of AGL genes [19]. Biosynthesis of p4-siRNAs is dependent on RNA polymerase IV (PolIV) encoded by NRPD1a [33], [34]. To test the requirement of p4- siRNAs for dampening expression of AGL genes, we generated nrpd1a/nrpd1a; osd1/osd1 double mutants and pollinated double homozygous mutants with wild-type pollen. In seeds resulting from this cross at 3 DAP expression of AGL62, PHE1, AGL28 and AGL40 was increased compared to wild-type triploid seeds, but remained significantly below wild-type expression levels (p<0.005; Figure 8A). In contrast, expression levels of AGL36 and AGL90 remained unchanged in triploid seeds lacking maternal NRPD1a function (Figure 8A). Maternal loss of NRPD1a in diploid seeds caused decreased expression levels of all tested AGL genes except for AGL40, which remained expressed at wild-type levels (Figure 8A). We also analyzed the size of seeds derived from osd1 nrpd1a×wild-type hybridizations and found that loss of NRPD1 was not connected with increased size of triploid maternal excess seeds (Figure 8B). Together, our results reveal that maternal loss of NRPD1a affects expression of only a subset of AGL genes and has no impact on seed size, strongly arguing against a causal role of p4-siRNAs in regulating triploid seed size.
Finally, we addressed the question whether AGL62 acts as a dosage dependent seed size regulator. Therefore, we generated agl62/AGL62; osd1/osd1 double mutants and fertilized them with wild-type pollen. The majority of triploid seeds was found to be simplex for the agl62 mutation and about 17% to be duplex, corresponding to allele frequencies in the pentaploid endosperm of agl62M/agl62M/AGL62M/AGL62M/AGL62P and agl62M/asgl62M/agl62M/agl62M/AGL62P respectively, Table S6). We analyzed the size of triploid seeds being simplex or duplex for the agl62 mutation and observed an AGL62 dosage-dependent decrease in size, with seeds having five functional AGL62 alleles in the endosperm being larger than seeds with only three or one functional AGL62 allele (Figure 9A, 9B). In contrast, heterozygous diploid agl62/AGL62 seeds were similar in size to wild-type seeds (Figure 9A), revealing a differential response of diploid and triploid seeds to reduced AGL62 dosage. If reduced dosage of AGL62 is responsible for decreased size of maternal excess triploid seeds, we expected to find an enrichment of MADS-box binding motifs in those genes that are down-regulated in triploid maternal excess seeds. We tested for the presence of CArG-box motifs of the SRE type (CC(A/T)6GG) as well as of the MEF2-type (CTA(A/T)4TAG), as both have been reported to be bound by plant MADS-box proteins [35], [36]. Among down-regulated genes both motifs were significantly enriched, with 54 (10.6%; Table S2) genes containing a MEF-type motif within 1000 bp upstream of the transcriptional start site, which was significantly higher compared to the genome-wide presence of this motif (6.9%, p<0.001). A similar number of genes contain a SRF-type motif in their promoter region (55 genes, 10.7%, Table S2), which is slightly, but significantly higher compared to the genome-wide frequency of 8.1% (p<0.01). Most strikingly, genes containing a MEF2-type motif in their promoter region were predominantly expressed in the chalazal region of the endosperm (17%; Figure S9), consistent with a preferential expression of AGL62 interacting AGLs in this region of the endosperm [37]. Together, we conclude that AGL62 is a dosage sensitive regulator of triploid seed size, strongly suggesting that reduced expression of AGL62 causes decreased size of triploid maternal excess seeds.
In this study we report the following new discoveries: (1) The functional requirement of the FIS-PRC2 can be bypassed by increasing the ratio of maternal to paternal genomes. (2) Bypass of FIS-PRC2 function is connected with decreased expression of AGL62 and interacting AGLs. (3) Decreased seed size of maternal excess triploid seeds is neither mediated by increased activity of FIS-PRC2 and nor by increased levels of p4-siRNAs. (4) Decreased size of maternal excess triploid seeds is likely a consequence of decreased expression of paternally expressed genes. (5) AGL62 is a dosage-sensitive seed size regulator.
The FIS-PRC2 complex is essential for viable seed development; however, the underlying forces that lead to the evolution of the FIS-PRC2 remained obscure. Our study demonstrates that maternal excess triploid seeds can bypass FIS-PRC2 function, suggesting that FIS-PRC2 is mainly required to balance expression of maternally and paternally expressed dosage sensitive genes in the endosperm. Consistent with previous work revealing that fis mutant seed abortion can be partially suppressed by maternal loss of AGL62 [29], we show that normalized fis triploid seed development is connected with normalized AGL62 gene expression. Based on yeast-two-hybrid data AGL62 interacts with several AGLs [31], with at least two of them, PHE1 and AGL36 are regulated by genomic imprinting [38], [39]. Therefore, altering the parental genome dosage is expected to impair the balance of AGL gene expression. This view is supported by the fact that increased paternal genome dosage causes strongly increased expression of PHE1, AGL62, and AGL36 [9], [24], [32], whereas increased maternal genome dosage exerts the converse effect [24] and data shown in this study). Type II MADS-box proteins are well known to form multimeric complexes [40] and changing the expression level of individual members of these complexes strongly impacts on plant development [41]–[43]. It can thus be assumed that unbalanced expression changes of AGL genes in the endosperm will similarly impair functional AGL complex formation and strongly affect endosperm development.
Our study furthermore revealed that decreased seed size in maternal excess interploidy seeds is neither connected with increased FIS-PRC2 function, nor with increased p4-siRNA levels. Instead, we propose that reduced expression of paternally expressed genes causes decreased seed size in maternal excess crosses and conversely, that increased expression of paternally expressed genes causes increased seed size in fis mutants. This model predicts that decreasing paternal genome dosage should reduce seed size in fis mutants and potentially even partially suppress seed abortion. This prediction is confirmed by the data presented in this study. FIS-PRC2 function can also be bypassed in fis seeds that form a sexual embryo and an asexual endosperm [44]. These surviving fis2 seeds with diploid endosperm remain smaller than wild-type seeds. Our results show that another class of surviving fis2 seeds, triploid maternal excess fis mutant seeds with a pentaploid endosperm, is larger than wild-type seeds. These two classes of surviving fis2 seeds have two major differences that could explain the very different seed size: First, fis2 seeds with pentaploid endosperm have four, whereas fis2 seeds with diploid endosperm have only two maternal genomes. Second, fis2 seeds with pentaploid endosperm but not those with diploid endosperm contain a paternal genome. Because increased maternal genome dosage usually causes decreased rather than increased seed size, the different maternal genome dosage in the two classes of surviving fis2 seeds is unlikely causing the observed differences in seed size. Instead, we conclude that the presence of a paternal genome is the main determinant for seed size in fis2 mutant seeds. Consequently, the FIS-PRC2 regulates paternally contributed seed size regulators that cause endosperm abnormalities and seed abortion upon overexpression. Our work revealed that AGL62 is a dosage-dependent seed size regulator, suggesting that either activation or function of AGL62 depends on a paternally contributed factor. AGL62 physically interacts with the paternally expressed PHE1 [31], raising the possibility that decreased expression of PHE1 and possibly other proteins reduces the number of functional AGL62 complexes.
Recent data implicate a link between increased levels of PolIV-dependent maternal p4-siRNAs and decreased size of maternal excess seeds [19]. It has been suggested that maternal p4-siRNAs target AGL genes and that increased siRNA levels in triploid maternal excess seeds causes decreased AGL transcript levels [19]. The results shown in this study reveal that maternal loss of NRPD1a in triploid seeds only affects expression of a subset of AGL genes with expression of none of the tested AGL genes being restored to wild-type levels. Maternal loss of NRPD1a in triploid seeds did neither affect size of triploid seeds. Therefore, the connection between maternal p4-siRNAs and regulation of AGL genes in response to interploidy hybridizations requires further investigations.
Many theoretical considerations argue that endosperms with higher levels of maternal ploidy and reduced levels of interparental genomic conflict have adaptive benefits and should be evolutionary favored [45]–[47]. In agreement with that view, transitions from triploid to higher endosperm ploidy occurred frequently by changing the mode of female gametophyte formation [47]. Based on the data presented in this study we propose that the FIS-PRC2 is needed to counteract excessive parental conflict. Therefore, reduced genetic conflict as a consequence of higher levels of maternal ploidy in the endosperm can bypass the need of the FIS-PRC2. We speculate that the FIS-PRC2 will be of less importance in species forming endosperms with higher maternal ploidy.
Plants were grown in a growth chamber at 60% humidity and daily cycles of 16 h light at 21°C and 8 h darkness at 18°C. Arabidopsis thaliana mutants mea-8 [48], fis2-5 [49], agl62-2 [30], and nrpd1a-3 [34] are in the Columbia accession. The osd1-1 mutant [20] was kindly provided by Raphael Mercier. The mutant was originally identified in the Nossen background and subsequently introgressed into Columbia by repeated backcrossing over five generations. Tetraploid Columbia plants were kindly provided by Ortrun Mittelsten Scheid. For crosses, designated female partners were emasculated, and the pistils hand-pollinated two day after emasculation.
For analysis of crosses in Figure 7, siliques were opened and a minimum of 50 seeds were harvested into RNA later (Sigma, Buchs, Switzerland). For analysis in all other Figures, three siliques were harvested for each timepoint and frozen in liquid nitrogen.
Glass beads (1.25–1.55 mm) were added, and the samples were ground in a Silamat S5 (IvoclarVivadent, Ellwangen, Germany). RNA was extracted using the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Residual DNA was removed using the Qiagen RNase-free DNase Set and cDNA was synthesized using the Fermentas First strand cDNA synthesis kit (Fermentas, Burlington, Canada) according to the manufacturer's instruction.
Quantitative RT-PCR was performed using an iQ5 Real-Time PCR Detection System (BioRad, Hercules, USA) and Maxima SYBR green qPCR master mix (Fermentas, Burlington, Canada) according to the manufacturer's instruction. Quantitative RT-PCR was performed with three replicates using primers as indicated in Table S7, and results were analyzed as described [50]. Expression of PP2A and ACTIN11 did not change in triploid versus diploid seeds (data not shown), and both genes were used as reference genes with similar results (expression normalized to PP2A is shown).
For quantification of agl62 alleles in triploid seeds, DNA was isolated from seedlings and the number of mutant alleles was determined by quantitative PCR using three different primer pairs (Table S7) for each mutant (one pair that specifically amplifies the T-DNA allele, one pair that amplifies only the wild-type allele and one pair that amplifies both the T-DNA and wild-type allele equally). To distinguish between simplex and duplex mutants, the ratio of T-DNA to wild-type alleles was calculated (Figure S10).
Tissue sections and clearing analysis were performed as previously described [51]. Pictures were taken using a Leica DMI 4000B microscope and Leica DFC360 FX camera and processed using Adobe Photoshop CS5.
Seeds were arranged on glass slides and pictures were taken using a Leica Z16apoA microscope. Images were converted to black and white using the “threshold” function in Adobe Photoshop CS5. Seed size was measured in ImageJ (http://rsbweb.nih.gov/ij/) using the “Analyze Particles” function. Seed size analysis shown in Figure 9A and Figure S5 was done from individual seeds that were later on germinated to determine genotype and ploidy.
Ploidy levels were measured by flow cytometry with a CyFlow Ploidy Analyzer (Partec, Münster, Germany). For seed ploidy analysis, seeds were allowed to germinate and 10 day old seedlings were analyzed. Plant tissue was chopped with a razor blade in CyStain extraction buffer (Partec), filtered through a 30-µm CellTrics filter (Partec) into a sample tube, and stained with CyStain Staining buffer (Partec).
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10.1371/journal.pgen.1000960 | GC-Biased Evolution Near Human Accelerated Regions | Regions of the genome that have been the target of positive selection specifically along the human lineage are of special importance in human biology. We used high throughput sequencing combined with methods to enrich human genomic samples for particular targets to obtain the sequence of 22 chromosomal samples at high depth in 40 kb neighborhoods of 49 previously identified 100–400 bp elements that show evidence for human accelerated evolution. In addition to selection, the pattern of nucleotide substitutions in several of these elements suggested an historical bias favoring the conversion of weak (A or T) alleles into strong (G or C) alleles. Here we found strong evidence in the derived allele frequency spectra of many of these 40 kb regions for ongoing weak-to-strong fixation bias. Comparison of the nucleotide composition at polymorphic loci to the composition at sites of fixed substitutions additionally reveals the signature of historical weak-to-strong fixation bias in a subset of these regions. Most of the regions with evidence for historical bias do not also have signatures of ongoing bias, suggesting that the evolutionary forces generating weak-to-strong bias are not constant over time. To investigate the role of selection in shaping these regions, we analyzed the spatial pattern of polymorphism in our samples. We found no significant evidence for selective sweeps, possibly because the signal of such sweeps has decayed beyond the power of our tests to detect them. Together, these results do not rule out functional roles for the observed changes in these regions—indeed there is good evidence that the first two are functional elements in humans—but they suggest that a fixation process (such as biased gene conversion) that is biased at the nucleotide level, but is otherwise selectively neutral, could be an important evolutionary force at play in them, both historically and at present.
| The search for functional regions in the human genome, beyond the protein-coding portion, often relies on signals of conservation across species. The Human Accelerated Regions (HARs) are strongly conserved elements, ranging in size from 100–400 bp, that show an unexpected number of human-specific changes. This pattern suggests that HARs may be functional elements that have significantly changed during human evolution. To analyze the evolutionary forces that led these changes, we studied 40 kb neighborhoods of the top 49 HARs. We took advantage of recently developed DNA sequencing technology, coupled with methods to isolate genomic DNA for our target regions only, to determine the genotypes in 22 chromosomal samples. This polymorphism data showed no significant evidence for adaptive selective sweeps in HAR regions. By contrast, we found strong evidence for a nucleotide bias in the fixation of mutations from A or T to G or C basepairs. Our work reveals that this bias in the HAR neighborhoods is not just an historic phenomenon, but is ongoing in the present day human population. This finding adds credence to the possibility that non-selective forces, such as biased gene conversion, could have contributed to the evolution of several of these regions.
| Understanding the forces that have shaped the evolution of the human genome is one of the most exciting problems in modern genomics. Two approaches to this problem are focused on identification and characterization of those genomic regions that have evolved the slowest and fastest along the human lineage [1]–[5]. The slowest evolving regions may contain elements that cannot be disturbed without disrupting essential function. The fastest evolving regions may harbor elements whose function is unique to our species lineage. To eliminate non-functional regions, both of these complementary approaches begin with a search for regions that are conserved throughout mammalian history or longer. The ultra conserved elements [1] maintain this conservation along the human lineage, and have been shown to be under purifying (negative) selection [6], strongly suggesting that they are functionally important to our species, although in ways that are still largely unknown.
By contrast, several groups have searched for positive selection along the human lineage by focusing on those previously slowly evolving regions of the genome that have evolved most quickly along the human lineage [4], [5], [7]. These regions, such as those in the set of Human Accelerated Regions (HARs) [7], may include some of the genetic changes that make our species biologically unique. Indeed, biological characterization of the topmost elements on this list of candidates has proven fruitful: HAR1 is part of a novel RNA gene (HAR1F) that is expressed during neocortical development [3]; HAR2 (or HACNS1) is a conserved non-coding sequence that has been shown to function as an enhancer in the developing limb bud with the human-specific sequence enhancing expression in the presumptive anterior wrist and proximal thumb [8].
Since the HARs were identified based on an excess of fixed differences between the human reference genome and sequences that are highly conserved among chimp, mouse and rat, such differences could have arisen at any time within the 5 million years that have elapsed since our common ancestor with the chimpanzee. As such it is important to recognize that even if such differences resulted from positive selection for advantageous mutations, they may have occurred so long ago that we have little power to find evidence for such selection using only the present day sequences available to us.
Furthermore, as previously noted [7], positive selection might not be the sole explanation for the rapid evolution that is evident in the HARs. Biased gene conversion (BGC) may also hasten the fixation of mutations in a local manner independent of any fitness benefits [9], [10]. BGC arises as a byproduct of recombination between homologous chromosomal regions. In this process DNA double stranded breaks are repaired and the alleles from one chromosome are copied to the other, with a bias for conversion of A or T (weak hydrogen bonding) alleles to G or C (strong hydrogen bonding) alleles [11]–[14]. A neutral locus can thus mimic the rapid evolution of loci under positive selection [9], [10], and furthermore, BGC may in fact drive fixation of deleterious alleles [15], the precise opposite of a positive, adaptive evolutionary effect.
One of the most powerful tools for identifying those regions that have been subjected to directional selection comes from examining the distribution of allele frequencies segregating within a species. For example, analysis of this distribution, known as the site frequency spectrum (SFS), allows for the identification of loci that have been involved in selective sweeps in the last few hundred thousand years. Analysis of the SFS has been used to identify targets of natural selection that may be responsible for genetic traits that are uniquely human, such as language [16] or cognition [17].
In the current work we investigate the top 49 HARs that were identified as having a 5% false discovery rate [3]. But rather than restricting our attention to the core elements, which are 100–400bp in length, we consider the polymorphism in a set of 22 chromosomal human samples in a 40kb neighborhood of each of these HAR elements, with an eye to capturing perturbations in the SFS at linked sites, and/or regionally biased patterns of allele fixation. Our samples are drawn from a single population, the Yoruba from Ibidan, Nigeria in order to avoid confounding issues of population admixture as well as to take advantage of a greater degree of variation in this population. We use an adaptation of several techniques previously developed [18]–[21] to enrich genomic DNA from our sample individuals for the target genomic neighborhoods. The enriched DNA is then subject to high throughput sequencing followed by genome-wide mapping of many overlapping sequences to determine genotypes at sites in the target regions, and hence derive the site frequency spectra.
With these spectra in hand, it is possible to test for the hallmarks of BGC. We employed an approach that compares the separate site frequency spectra for the weak-to-strong (i.e. A or T to G or C)(W2S) and strong-to-weak (S2W) mutations to determine if any shift towards high frequency, normally characteristic of a selective sweep, is biased towards one of the two sets of mutations. This signal would indicate an ongoing process in the current human population. Similarly, one can compare the proportion of W2S changes among already fixed substitutions on the human or chimp lineage to that among the still segregating sites. A W2S bias in fixed differences relative to polymorphisms would indicate that the regions have historically been subject to a BGC-like biased process.
On the other hand, the spectra may contain evidence of positive selection. Various techniques have emerged in recent years to search for signatures of positive selection using population genetic data [22]–[24], but many are based on the phenomenon of genetic hitchhiking [25], [26] in which fixation of beneficial mutations results in a skew in the site frequency spectrum. One such approach [27] is based on the composite likelihood of allele frequencies wherein the probability of the observed allele frequency at each polymorphic position is calculated based on its distance from a site under putative positive selection. This probability explicitly takes into account the strength of recombination and selection. A variant of this approach has been implemented [28] in the SweepFinder program that we use herein. It has been previously used [29] in a genome-wide search for sweeps at the scale of 500kb, since that study's data was restricted to loci with common polymorphisms. The power of that approach is probably limited to finding sweeps not much older than 200,000 years but has the attractive property that it is robust to demographic history [29]. Unlike other approaches that have been used [22], [23], [30], [31] it also does not require that the sweep be ongoing or differentially concluded in separate populations. Since we have discovered many novel polymorphisms by resequencing our samples, we use this method to take a more focused look at our 40kb HAR neighborhoods in search of adaptive evolutionary forces.
We enriched the genomic DNA from 11 individuals among the Yoruba from Ibidan, Nigeria (YRI samples) for 40kb neighborhoods of the top 49 Human Accelerated Regions [7] (HARs) (hereinafter “harseq1-49”), and 13 similar control regions not containing HAR elements (hereinafter “ctlreg50-62”) using a microarray hybridization technique (see Materials and Methods). Using ABI SOLiD [32] high throughput sequencing technology we obtained sufficient coverage in our target regions of the resulting short (35bp and 50bp) sequencing reads to restrict our analysis to genotype calls at positions where we had at least 35-fold coverage for an individual, to ensure accurate genotyping. We determined the frequency of the derived (non-ancestral) allele in our set of 22 chromosomal samples for all the resulting segregating sites (i.e. sites where not all samples have the same allele).
We analyzed these segregating site frequencies in several ways. First, we compared the frequency spectra of two subsets of our segregating sites: those comprising W2S mutations (where the ancestral allele is either A or T, and the derived allele is either G or C) versus the S2W mutations. Next, we compared the ratios of these different classes of mutations to the same ratios for fixed substitutions that have arisen on either the human or chimp lineages since our common ancestor 5 million years ago. For both of these analyses we tested the strength of our results across the full scale of our regions, and also performed the tests on the data pooled across our 49 genomic regions. Finally, we examined the pattern of spatial variation of derived allele frequencies in search of evidence for a selective sweep. Results for harseq regions and ctlreg regions were compared to each other. Since the number of control regions (limited by a tradeoff against target regions dictated by the enrichment technology) was small we also compared the results for the target regions to a set of 62 genic regions resequenced in the same 11 YRI individuals by the Seattle SNPs project (see Materials and Methods) [33].
When the HARs were first described, a strong W2S substitution bias was noted in the human-specific substitutions in these elements [3], [7]. This bias was extremely pronounced in HARs 1,2,3, and 5, but also noticeable as a general trend in the entire set 1–49. This evidence suggested that BGC could have had a historical role in the evolution of the HARs. Here we analyze our list of segregating sites in the 40kb HAR neighborhoods to determine if such bias is still ongoing in the human population. In each region, we separately computed the derived allele frequency spectra for the W2S mutations and the S2W mutations. We then tested for an offset in the spectra between the two categories with a two-sided Mann Whitney U (MWU) test (see Materials and Methods). This test has been shown to have good power to detect fixation bias [34].
We found a significant (p 0.05) difference in 11 out of 49 harseq regions (Table 1). In all 11 significant harseq regions, the offset was for the W2S mutations to be segregating at higher derived allele frequencies than S2W. This implies that regardless of the rate of introduction of W2S or S2W mutations, it is the W2S mutations that are more likely to reach high frequency and eventually fix in the human population. This is certainly consistent with a mechanism of gene conversion that favors selection of G or C alleles from a heterozygote or some other selective force generally favoring higher GC content. The novel features of this result are that it indicates that this process is ongoing and not confined to the core 100–400bp HAR elements.
This ongoing W2S fixation bias distinguishes the harseq regions from ctlreg regions and Seattle SNPs regions. Significant MWU tests were observed at none of ctrlreg50-62 (Supplementary Table 2 in Text S1) and five out of 62 Seattle SNPs regions (Supplementary Table 4 in Text S1), one of which has higher derived allele frequencies in S2W compared to W2S mutations. The distribution of the MWU test statistic in the test regions is also biased towards W2S mutations compared to the 62 Seattle SNPs regions (Supplementary Figure 6 in Text S1). Applying the MWU test to simulations of a neutral coalescent model (see Materials and Methods) showed that the p-values from this test accurately reflect the fraction expected by chance from a neutrally evolving locus (Supplementary Figure 2 in Text S1).
The two harseq regions with the greatest offset were harseq21 and harseq34 (Figure 1). We note that across these two 40kb regions, the ratio of W2S to S2W segregating sites is not extreme; it is the W2S shift towards higher frequency in the population that is significant. These ratios are consistent with the smoothed ratios reported in the top several thousand conserved candidate HAR elements [7] even though our 40kb regions do not comprise largely conserved regions.
It is natural to theorize that BGC will be a stronger driving force in areas of high recombination and studies have shown there to be a good correlation with the male recombination rate in particular [35]. We examined the recombination rates in the enclosing 1Mb windows as determined by the deCODE project [36]. Harseq21 is an outlier in that it is contained in a genomic region of extremely high recombination rate (male 4.29 cM/Mb, sex-averaged 3.43 cm/Mb, in contrast to genome-wide averages of 0.93 cM/Mb and 1.29 cm/Mb respectively). But this is not true of harseq34 (0 male and 1.42 cm/Mb sex-averaged). For the remainder of the regions with a significant p-value on the MWU test, the rates vary. An additional (not unrelated) factor that has been strongly correlated with biased substitutions is chromosomal position near telomeres [35]. With the exception of harseq1 this is not the case for the regions with significantly shifted W2S spectra (Table 1).
We also performed the MWU test for the shift in W2S sites toward higher frequencies after pooling all the segregating sites in our 49 harseq regions, and found a p-value . By contrast, the test was not significant (p = 0.26) for the pooled data in our 13 control regions, thereby controlling for possible systematic bias in our sequencing and genotyping techniques. We thus conclude that in many of the neighborhoods of the top 49 HARs there is an ongoing force driving W2S mutations to higher frequency in the human population.
Since the HARs were essentially defined based on fixed differences between the human and chimp reference genomes in otherwise strongly conserved elements [7], we compared such human/chimp fixed differences to the segregating sites in our human samples. Our set of fixed differences was based on high quality base calls from the reciprocal best alignments [37] of human and chimp genomes. We further restricted this list to the locations within our regions for which we had the above-mentioned 35-fold coverage for an individual in our sample. Finally, we removed from this initial fixed difference list those positions that we found to be segregating in our samples, or that appeared in the dbSNP129 database [38] (see Materials and Methods). The latter two filters removed 6.7% of the fixed differences at high coverage positions. Since we do not have information on sites that are segregating in the chimp population, we could not remove those, but would expect the number to be similarly small.
We separated the mutations in our segregating site set into the categories: W2S, S2W, and neither. We similarly divided the set of fixed differences, regardless of whether the substitution occurred on the chimp or human lineage. As in reference [35], we performed a variant of the McDonald-Kreitman (MK) test to compare the W2S∶S2W ratios in the sets of mutations and the sets of substitutions (see Materials and Methods).
We found a significant (p 0.05) difference between the substitution patterns of segregating versus fixed sites in 11 of the 49 harseq regions (Table 2). In all but one (harseq39) of these 11, the fixed substitutions had relatively more W2S mutations (compared to S2W) from the ancestral form than did the segregating sites. Four of the 11 fell in the 40kb neighborhoods of the top 11 HARs, and indeed the strongest result (p = 0.00015) was for harseq1. This is not surprising, since the HAR1 element has 18 fixed differences, all W2S [3]. By contrast, none of the ctlreg regions and nine out of 62 Seattle SNPs regions had a significant p-value on this test (Supplementary Tables 2 and 4 in Text S1). All of the nine significant Seattle SNPs regions had a higher W2S∶S2W ratio in fixed differences than in segregating sites. Thus, the harseq regions have a much stronger signal for historical fixation bias than our control regions and a somewhat stronger signal than the genic Seattle SNPs regions. Applying the MK test to simulations of a neutrally evolving primate phylogeny (see Materials and Methods) showed that the p-values from this test accurately reflect the fraction expected by chance from a neutrally evolving locus (Supplementary Figure 3 in Text S1).
We also recapitulate and expand the finding [7] that this bias towards W2S fixation is associated with telomeres, since 7 of the 11 significant regions under the MK test were found either in the karyotype band containing a telomere or the one immediately adjacent. (This count includes the two cases of harseq19 and harseq36 from chromosome 2 that fall adjacent to the ancestral telomeric fusion event at 2q14.1.) Although many of the 11 regions (including ones near telomeres) have elevated recombination rates, it is also worth noting that 5 of the 11 are in regions with much lower than average male recombination rates (including the two near the chromosome 2 fusion site, and harseq11 on chrX).
One noteworthy negative result from the MK test, (and the MWU test as well) is harseq2. It had been noted [7] that the core HAR2 element showed a strong bias towards W2S fixations, and that this extended to a region of 1kb. Here we find no significant signal for either the MK or MWU test in our 40kb neighborhood of that element (Supplementary Table 2 in Text S1). Another noteworthy negative result on these tests is the harseq6 region, which has an extremely elevated rate of mutation as estimated either by nucleotide diversity [39] or by the number of segregating sites [40] (Supplementary Table 1 in Text S1), but apparently no strong bias towards weak-to-strong fixation (Supplementary Table 2 in Text S1).
Although the MK test, like the MWU test, is consistent with the mechanism of BGC favoring fixation of G or C alleles, in fact only one of our regions (harseq1) had significant results in both tests. The complementary evidence from the MWU test (a total of 20 of our 49 test regions are W2S-significant on one test or the other) indicates that the ongoing bias in favor of W2S mutations has also probably led to human specific substitutions in otherwise conserved elements.
Because BGC is posited to operate on a scale much smaller than the 40kb of our target sequencing regions, perhaps operating at localized recombination hotspots, and because the (100–400bp) HAR elements at the core of our target regions were suspected of arising in part due to BGC [7], we wanted to test whether the MWU and MK signals depended on these core elements. We therefore performed the same tests after masking out the central 500bp, 1kb, 5kb or 10kb of each region.
We found that signals of both ongoing and historical fixation bias are fairly robust to removing sequences including and flanking the core HAR element. For the MWU test, all but two (harseq1, harseq18) of the 11 regions that were significant at the 5% level for the MWU test were still significant at that level with the central 5kb omitted. Seven of the 11 remained significant even with 10kb omitted (Table 1). For the MK test, the results were slightly more sensitive to masking. Considering the 11 regions with a significant result on that test (including the one favoring S2W fixation), 9(7) of these were still significant with the central 1kb(5kb) masked out (Table 2). We conclude that the evolutionary forces behind these results is not confined to the small HAR elements themselves, but rather that any bias in the substitutions found in the HARs is likely a byproduct of the forces acting at a larger scale.
To test whether there might be other localized elements within the 40kb regions driving these results we performed the tests under a set of fifteen overlapping 5kb masks (centered at a 2.5kb spacing along each region). Among the 11 MWU significant regions, 5 were still significant at 10% under this regime, while 3 completely lost significance (p 20%) for at least one such mask (Table 1). Of the 11 MK significant regions, 5 were still significant at 10% (including harseq1) under this regime, while 1 completely lost significance (p 20%) under at least one mask (Table 2). It is worth noting that the harseq1 result reflects the fact that of the 105 segregating sites we found in that 40kb neighborhood, 71 were S2W and only 19 W2S (Supplementary Table 2 in Text S1).
We conclude from this set of tests that the evolutionary forces behind W2S fixation bias are not necessarily highly local. If fixation bias relies on recombination hotspots and BGC, we have to posit that such hotspots extend over a long range of bases, or are somehow temporally and spatially variable (cf. [41]).
To determine if the results for the MWU and MK tests on the HAR neighborhoods are consistent with a model of GC-biased evolution, we performed simulations under a model of BGC (see Materials and Methods). Of 499 simulations, for the MK test 130 were significant (p ) with W2S bias (none significant with S2W bias), and for the MWU test 51 were significant with W2S bias (one significant with S2W bias). The union of the significant W2S simulations on the two tests comprised 169 cases while the intersection comprised 12 cases.
Compared to the simulations, the 49 HAR regions had significantly more than the expected number of MWU W2S cases (p = 0.003 for binomial probability of at least 11/49 cases using the simulation rate of 51/499), but the MK test does not (p = 0.77 binomially comparing 10/49 to 130/499). On the other hand, the small (one case) intersection of MWU and MK tests in the HAR regions is not unexpected based on the simulations. That is, using the fraction 12/499 ( = 0.024) of the MWU and MK intersection in the simulations as the expected rate, the small fraction 1/49 ( = 0.020) in the HAR regions is not statistically significant using either a binomial (p = 0.67) or Poisson (p = 0.67) test. Finally, for neither the simulations (Fisher's Exact test p = 0.74) nor the HAR regions (p = 0.42) is there a significantly greater correlation between the MWU and MK results than expected by chance.
Together, these analyses indicate that the MWU and MK results for the 49 HAR regions are consistent with a model of GC-biased evolution in terms of the overlap between the tests, although the number of MWU cases is enriched compared to the simulation model.
For each of the studied regions, we used the SFS to calculate two population genetic statistics that can sometimes indicate positive selection: Tajima's D [42], which is based on the folded SFS, and Fay and Wu's H [43]. Neither of these statistics exceeded the value of in any region (Supplementary Table 1 in Text S1). We next compared the distributions of these two statistics in the 49 harseq regions and the 13 ctlreg regions, to those for the same population (YRI) in 104 genic regions resequenced by Seattle SNPs (see Materials and Methods). We found the ctlreg regions to be indistinguishable from the Seattle SNPs for these statistics, while the harseq regions were only mildly more negative for Tajima's D (Wilcoxon rank sum p = 0.08) and not significantly different for H (Supplementary Figure 1 in Text S1). These results strongly suggest that the site frequency spectrum in harseq regions is indistinguishable from that found in either our control regions (ctlreg), or in the Seattle SNPs data set. Thus we have no reason to believe that harseqs represent some kind of genomic outlier with respect to recent selective events. Examination of these statistics calculated separately for the W2S and S2W segregating sites (Supplementary Figure 7 in Text S1) shows that the W2S subset in the harseq regions has a significantly more negative value of H than in the Seattle SNPs, which is consistent with the shift to higher derived allele frequencies for this subset noted above using the MWU test.
To test for evidence of a selective sweep, we analyzed the spatial variation of derived allele frequencies at the segregating sites from our 22 chromosomal samples in each of the target 40kb regions using the SweepFinder program. This software determines a composite likelihood ratio (CLR) statistic comparing the hypothesis of a complete selective sweep at the location to the null hypothesis of no sweep using Test 2 from [28]. We tested along a grid of 1000 points in each target region (see Materials and Methods). This test has been shown to be robust to demographic deviations from the standard neutral model in its ability to use an arbitrary background site frequency spectrum [29]. We tested with two such backgrounds: the first from the pooled set of all the data in the 49 harseq regions plus 13 ctlreg regions, the second from the same population (YRI) as our samples but with frequencies taken from the Seattle SNPs resequencing data [33] for a large set (104) of genic regions. It should be noted that using the SFS from our data as the background to define the neutral model should be particularly conservative in that we are testing any given region for deviations from that neutral model. To determine the significance of the maximum CLR values, we performed coalescent simulations of each target region and ran the SweepFinder program on each simulated set of segregating sites (see Materials and Methods). We report as a p-value the fraction of simulations of each target that had a CLR greater than or equal to the actual maximum CLR for that target (Supplementary Table 2 in Text S1)
The harseq regions with the most significant five SweepFinder p-values are listed in Table 3. These are nearly all at the 95% confidence level for either of the two background distributions used, but we note that none are individually significant after a conservative Bonferroni correction, given the 49 harseq regions that were tested. Since these may nevertheless harbor mutations that were selected for in the human lineage, here we briefly note some of their characteristics that can be seen in tracks from the UC Santa Cruz Genome Browser (Supplementary Figure 4 in Text S1).
Unlike the other four SweepFinder hits, which all contain introns or exons of coding genes, harseq25 is in a gene “desert”. The nearest known gene, approximately 1Mb away on chromosome 4, is ODZ3, which is a transmembrane signaling protein most highly expressed in brain. Note that harseq25 also has a significant result on the MWU test discussed above.
Evidence for a sweep in the harseq9 region is intriguing because it encompasses the 42-codon long, second exon of the PTPRT gene, a phosphatase with possible roles in the central nervous system. However, the human amino acid sequence of this exon matches the other primates chimp, gorilla, and orangutan, except where chimp has an obviously non-ancestral ThrAla substitution. The human sequence does have a single GA substitution near the 3′ splice site just upstream of this exon, but it falls in a position between the polypyrimidine (Py) tract and the AG acceptor site, for which the consensus sequence across many splice sites is evenly divided among the 4 nucleotides.
The location of harseq11 on chromosome X places its evidence for a sweep in the first intron of the 2.4Mb long dystrophin gene DMD. The evidence for a sweep in harseq16 is offset to one end of its region, about 20kb from an apparent pseudogene comprising a single coding exon with a 270-codon open reading frame (ORF) that is probably derived from the Poly-A binding protein PABPC1.
The harseq24 region encompasses the second through fourth exons of the SKAP2 gene with the strongest evidence for a sweep about 10kb from the closest exon, but closer to a LINE transposable element that is present also in chimp, orangutan, and rhesus macaque (but lost in gorilla).
Although the above evidence for selective sweeps is not statistically significant, and none of it seems to point directly to a mutation in a core HAR element based on the position of the SweepFinder peak CLR values, it is important to note that while having the advantage of robustness to demography and recombination rate, our tests would not likely have power to detect sweeps that occurred beyond the last 200,000 years [29]. Under an assumption that substitutions in the HAR elements occurred uniformly over the last 5 million years and that most of these substitutions were adaptive, we estimate (see Materials and Methods) that we would be able to detect fewer than 8 with our tests. Therefore this negative result should not be interpreted as ruling out a role for adaptive evolution in the HARs.
In the era of comparative genomics, strong signals of conservation across multiple species serve as signposts that can indicate regions where evolutionary forces may be preserving functional elements that are subject to purifying selection (e.g. [6]). By contrast, signals of positive selection pointing to adaptive changes in one lineage are harder to find, often employing sets of polymorphic sequences from multiple individuals of the same species. We exploited the two recently developed techniques of genomic enrichment and high throughput sequencing to characterize the polymorphism in a single human population across 40kb neighborhoods of the 49 HARs (harseq regions). We investigated the harseq regions because the HARs were defined based on a presumption that the human lineage specific fixed differences therein might have arisen due to adaptive evolutionary forces. On the other hand, it has been emphasized by some that the presumably evolutionarily neutral mechanism of BGC can influence the frequency spectra at polymorphic positions, or cause fixation of alleles in a way that partially mimics the action of adaptive evolution. Indeed, fixation bias was noted in connection with the limited set of human specific alleles for some of the HARs when they were first described [7].
With the extensive novel polymorphism in our samples, we were able to carefully characterize fixation bias — both historical and ongoing — in the harseq regions and to conduct tests for recent selective sweeps across these regions. Our deep resequencing data is noteworthy because it eliminates issues of SNP ascertainment bias that could have skewed previous investigations of polymorphism near HARs. We applied several established population genetic tests, as well as an application of the MWU test, to identify differences in the fixation patterns of W2S and S2W mutations.
Consistent with published reports [7], [9], [35], we find evidence of historical W2S fixation bias in harseq regions. Using a MK test, we compared the proportion of W2S mutations among already fixed substitutions on the human or chimp lineage to that among the still segregating sites in our samples. We found that 11 of our 49 regions show statistically significant evidence of historical bias in allele fixation, with all but one favoring W2S fixation. These results strengthen and expand previous findings by identifying signals for W2S bias in much larger regions flanking the core HAR regions in an ascertainment-free population sample.
This study goes beyond previous approaches by also looking at ongoing W2S fixation bias in the segregating site frequency spectrum. We performed a MWU test using only sites that are still segregating in the human population, separating out W2S from S2W mutations. This second test is designed to detect a phenomenon of bias that is currently driving W2S mutations to higher frequency in the population than S2W mutations. We found statistically significant evidence for this bias (and none in the opposite direction) in the regions flanking 11 of 49 HARs.
For both of our tests, we showed that the core HAR element is generally not the main source of the signal that we detected, since the signal usually remains strong even when we mask out the central 1kb or even 5kb of the region. This is not consistent with BGC due to a recombination hot spot that has remained in the same location for millions of years, because the length scale of the effect of BGC is set by the length of the heteroduplex tract formed during recombination that needs to be repaired, which is thought to be 500bp (e.g. [44]–[46]). However, it is consistent with a model in which the location of recombination hotspots drift fairly rapidly over evolutionary time scales, but may be denser in some regions [41], [47]–[50].
It is noteworthy that there was little overlap in the regions identified by these two tests, one for older W2S fixations and the other for present day forces toward fixation, with a total of 20 found in one or the other. Although this is consistent with the hypothesis that the regional focus of BGC, which may be recombination hot spots, drifts significantly on a time scale of many hundreds of thousands or millions of years, we also found from simulations of GC-biased evolution over these time scales that the relatively minimal overlap between the tests is not unexpected.
Another explanation for W2S fixation bias near HARs is selection for increased GC-content or individual fitness-improving GC alleles. To investigate these hypotheses and to attempt to disentangle the possible roles of BGC and positive selection in shaping the HARs, we applied a recently developed powerful method for detecting selective sweeps. Selection was previously investigated in much larger (500kb) regions using more sparse polymorphic loci [29]. That study found 101 regions with strong evidence for a selective sweep within 100kb of a known gene. Here, we found only 5 possible candidates for such sweeps among our 49 target regions (and none that were significant after correction for multiple hypothesis testing). Three of these candidates overlap regions with significant evidence of historical (2) or ongoing (1) W2S bias. As we are dealing with a lineage-specific evolutionary period of about 5 million years, and these tests can only see back a few hundred thousand years, it is quite possible that the original signal for selective sweeps in these regions has already decayed beyond our ability to recognize it in human population genetic data. That is, the lack of evidence for recent sweeps does not rule out the possibility that some of the excess substitutions in HARs were fixed by older selection. Similarly, the evidence for GC-biased evolution based on current population genetic data may not fully reflect patterns of polymorphism in the past.
Consistent with the idea that HAR regions may have experienced positive selection too long ago to be detected with population genetic methods, very few positively selected regions in the human lineage have been identified to date, despite the existence of numerous public databases. Selective sweeps that have been identified have typically been the product of very recent events in human history, such as dairy farming affecting the lactase gene [51] or climate differences influencing a salt sensitivity variant [52]. Such environmental or cultural changes result in differences in the genetic makeup of disparate human populations, and such differences can be exploited to find evidence of recent, possibly still ongoing, selective sweeps.
An alternative hypothesis that deserves consideration is that HARs may have an unusually high level of recent substitution due to a recent relaxation in purifying selection along the human lineage (e.g. [53]). Using previously described methods [7], we compared estimates of the rates of substitution in the 49 HAR elements to the neutral rate. We find that the human substitution rate exceeds the expected neutral rate in all 49 HARs, while this is true for the chimp substitution rate in only 10 HARs. Furthermore, in 33 HARs the human substitution rate significantly exceeds the neutral rate (Poisson p-value ) while none of the chimp substitution rates significantly exceed the neutral rate. This evidence argues against the hypothesis that these HAR elements are the product of relaxed selection.
We have focused in our study on 40kb neighborhoods of 49 HAR elements (and 13 similar control regions) because of their intrinsic interest but also because the scope of our study was appropriate to the state of the art of recently emerged enrichment and sequencing technologies. As larger data sets become available we will be able to apply our analysis on a genome-wide scale. Such analysis should give us insights into the properties associated with genomic regions that display this ongoing W2S fixation bias and their potential biological consequences.
Despite the evidence that the unusually high level of recent substitution in the more extreme HAR elements, such as HAR1 and HAR2, could be due to the process of BGC, there is ample evidence that these genomic elements remain functional, and thus the effect of BGC was to mutationally stress but not destroy these elements. HAR1 shows a very strong pattern of compensatory substitutions within its RNA helix structures, indicating a selective force to maintain these helix structures. The W2S substitutions all strengthen the RNA helices of HAR1, and in one case, a substitution appears to extend one of them. Human HAR1 and HAR2 both show evidence of specific function, the former by its highly specific expression pattern during neurodevelopment and the latter by its ability to enhance gene expression during limb development. Whether the human-specific evolutionary changes to these elements reflect a process that was essentially like swimming upstream against an onslaught of non-selective BGC just to keep in place on the fitness landscape, or whether the mutational stress pushed these elements into a configuration that enabled some positive selection for higher fitness in humans, remains to be seen.
Genomic DNA for our samples was obtained from the NHGRI Sample Repository for Human Genetic Research distributed by the Coriell Institute for Medical Research [Camden NJ] [54]. All of the 11 samples were chosen from the Yoruba from Ibidan Nigeria (YRI) HapMap population. In particular, the samples were chosen as a subset of the Seattle SNPs P2 panel [33]. The Seattle SNPs PGA-VDR (and Coriell repository numbers were): DY01(NA18502), DY03(NA19223), DY04(NA19201), DY17(NA19143), DY18(NA18517), DY19(NA18856), DY20(NA19239), DY21(NA18871), DY22(NA19209), DY23(NA19152), DY24(NA19210). Sample preparation as described below was similar for all samples, except that prior to processing, the DY01,DY03,DY04 samples were subject to whole genome amplification (WGA) using the Repli-G Kit (Qiagen N.V, The Netherlands) according to manufacturer's specifications. It was found that WGA caused a loss of coverage in some isolated target regions, most notably in the 28kb section of the harseq1 region with coordinates hg18:chr20:61,183,966–61,212,244, except for the core HAR1 element at chr20:61,203,919–61,204,081.
The primary target regions consisted of 20kb extensions in both directions from the 49 most statistically significant Human Accelerated Regions (HARs) identified as having a 5% false discovery rate [3]. Additional 40kb control regions were chosen in neighborhoods of 13 of the set of 34,498 vertebrate conserved elements that had extremely low LRT scores in the test used to define the HARs. The enrichment arrays were obtained from Nimblegen Systems (Madison WI) who designed the probes on their 385K array based on our specifications of the coordinates of the 62 target genomic regions (Supplementary Table 1 in Text S1). Probes were chosen to tile the target regions from both DNA strands. The design process avoided probes in highly repetitive sequences as described previously [19], [21]. The fraction of bases in the target regions covered by the probes ranged from 98% (harseq12) to 65% (harseq4) with a median of 88%. Details of probed bases are available upon request.
The library preparation of the samples for SOLiD (Applied Biosystems, Foster City, CA) sequencing generally followed the manufacturer's protocols for barcoded SOLiD System 2.0 Fragment Library Preparation (samples DY01,DY03,DY04) and SOLiD System 3.0 Barcoded Fragment Library Preparation (remaining samples), with changes as necessary for enrichment on the Nimblegen arrays as noted in the following: Samples were sheared to approximately 100bp using the Covaris S2 System Program B (Covaris Inc, Woburn, MA). End repair was performed with End-It DNA End-Repair Kit (Epicentre Biotechnologies, Madison, WI) per manufacturer's instructions. Single stranded oligos for the P1 and P2-barcoded SOLiD adaptors were ordered from Invitrogen Corporation and annealed per the SOLiD protocol to form double stranded adaptors, which were ligated to the end-repaired DNA fragments using the Quick Ligation Kit (New England BioLabs, Ipswich, MA) per manufacturer's directions, leaving a nick at the 3′ end of each genomic DNA strand where the 5′ end of the adaptor was not phosphorylated. Samples DY01,DY03,DY04 were size selected to 150250bp from a 6% polyacrylamide gel and purified via ethanol precipitation. This was followed by nick translation and ligation mediated PCR (LMPCR) amplification (6 cycles) in a combined reaction per the SOLiD protocols using Takara ExTaq polymerase (Takara Bio, Madison, WI). After dividing into 10 aliquots, an additional 10 cycles of ExTaq LMPCR amplification were performed on these samples in preparation for array hybridization. Samples DY17–DY20 were first nick translated without amplification using Pfu polymerase (Stratagene, La Jolla, CA). Samples DY21–DY24 were nick translated and ExTaq LMPCR amplified (6 cycles). Quantitation using a DNA 2100 BioAnalyzer (Agilent Technologies, Waldbronn, Germany) showed that the PCR associated with nick translation prior to size selection mainly served to amplify the nick translated adaptors. After the nick translation and any initial LMPCR, all of samples DY17–DY24 were size selected on an E-Gel SizeSelect 2% agarose gel (Invitrogen) per manufacturer's instructions. These size selected samples were then ExTaq LMPCR amplified (6, 9 or 10 cycles) in preparation for array hybridization.
Array hybridization to the Nimblegen arrays was performed on the Nimblegen Hybridization System 4 station under Mix Mode “B” for 64 to 70 hours using the Nimblegen Sequence Capture Kit per manufacturer's instructions. Prior to hybridization, samples DY21–24 were pooled to a total of 5.7g. All the other samples were hybridized individually, in amounts ranging from 1.9g to 8.0g per sample. For competitive hybridization to probes on the array that might nonselectively bind repetitive DNA, Human Cot-1 DNA (Invitrogen) that had been Covaris sheared to approximately 100bp was added to the hybridization mix in a 5∶1 ratio by weight. Additionally, to block the adaptor ends of the denatured, single stranded DNA fragments from binding to each other, a 10∶1 molar excess of adaptor oligos (P2 with an unused barcode sequence and P1) was also added. At the completion of hybridization, the slide was washed with the Nimblegen Sequence Capture Wash and Elution kit per manufacturer's directions, and the enriched DNA was eluted with 350L of C purified water using an affixed SA200 SecureSeal Hybridization Chamber (Grace Bio-Labs, Bend OR). A secondary elution with an additional 350L was also taken. Quantitative real-time PCR (qPCR) was performed on the eluted material to determine the rough fraction of target DNA and to compare the primary and secondary elutions. For this purpose qPCR amplicons within the target were compared to amplicons not in the target regions. It was generally found that the primary elution captured more than 95% of the target DNA (data not shown). By normalizing to pre-enrichment material, and taking into account the fact that the 2.1Mbp target region comprised approximately 0.1% of the entire human genome, it was estimated that more than 35% of the eluted material fell in the target regions (data not shown).
The eluted material was ExTaq LMPCR amplified (samples DY01 for 19 cycles, samples DY03–04 for 15 cycles, pooled samples DY21–24 for 10 cycles, samples DY17–20 for 12 cycles,) in preparation for the emulsion PCR step of SOLiD sequencing that was performed in the UC Santa Cruz Genome Sequencing Center. Samples DY01,DY03,DY04 were processed with the SOLiD Version 2 system, producing 35 bases of sequence information for each read. The remaining samples were processed with the SOLiD Version 3 system, producing 50 bases of sequencing information for each read.
The 35mer (DY01,DY03,DY04) or 50mer (remaining samples) sequencing reads were mapped to the whole human genome using the bwa program [55] which generates mappings and associated quality scores in the sam format [56] that can be processed with the samtools suite. The bwa program is aware of the colorspace nature of the SOLiD sequencing reads, and uses a dynamic programming algorithm to infer the best nucleotide sequence for the read [55]. All reads were also mapped to the DNA of the Epstein-Barr Virus, which was used to transform the Coriell cell lines from which the supplied genomic DNA was extracted. For the female samples, the Y chromosome was excluded from the mapping. For the male samples, the pseudo-autosomal region of the Y chromosome was excluded from the mapping. The set of mappings for each sample was then filtered to the regions covered by the probes on the Nimblegen enrichment array described above. To eliminate spurious pileups caused by overamplification of particular molecules in the library preparation process, the mapped reads were further filtered to select at most 4 reads from each strand at a given genomic starting position. Where there were more than 4, the 4 with the highest total read quality (not the best mapping quality, which would bias against reads containing non-reference alleles) from the SOLiD instrument were selected. Between 40% and 60% of the reads for a given sample were successfully mapped, and of those reads, between 33% and 48% mapped to bases covered by the probes on the enrichment array (Supplementary Table 3 in Text S1). For samples DY01,DY03,DY04 about 50% of the latter reads were lost in the “maximum 4 per strand” pileup elimination step, while only 11% to 23% were lost in this step in the remaining samples (Supplementary Table 3 in Text S1). This was likely due to the difference in LMPCR cycles used for the different samples as noted above.
To determine the coverage at each position in the target region and the consensus genotypes for each sample, the command “samtools pileup -v” was used with default parameters for its consensus calling model. Possible confounding of the genotypes due to contamination by paralogous sequences was avoided in two ways. First, as noted, only genotypes at positions delimited by the Nimblegen probes were used in the analysis and these probes were designed to avoid repetitive sequences. Second, the bwa mapping algorithm assigns low mapping quality to reads that are not genome-wide unique, and the samtools consensus caller requires high mapping quality. To filter SNPs from among the not homozygous reference genotypes, “samtools.pl varFilter” was run with default parameters, except that the maximum read depth was set to 425, because with up to 4 reads of length 50 on each strand, it was possible to get coverage of 400. This command filters out potential SNPs when more than 2 fall within a 10bp window, on the grounds that there might be an insertion/deletion event rather than separate SNPs, and also filters out reads with RMS mapping quality value less than 25. A similar quality filter was applied to the genotype calls that were homozygous reference. Because of stochastic variation in the composition of reads from the two chromosomes of each diploid individual, low coverage might cause an erroneous homozygous call in a true heterozygote. Therefore a further filter restricted the subsequent analysis to the SNP or homozygous reference calls made for a sample only at positions for which the coverage was 35 or greater. For each target region, the count of the union of such positions across all samples is listed in Supplementary Table 1 in Text S1. As shown in Supplementary Figure 5 in Text S1, the vast majority of the segregating sites that remain after the application of our 35× coverage filter are in Hardy-Weinberg Equilibrium, with only 0.3% having a p-value less than 0.05.
For purposes of all subsequent analysis, an ancestral allele at each position in the target regions was determined from the Enredo-Pecan-Ortheus (EPO) pipeline [57], [58] as published on the 1000 Genomes website [59]. This pipeline determines the common ancestor of human and chimp at a locus by considering alignments of the human, chimp, orangutan, and rhesus macaque genomes.
From the sets of filtered genotype calls in the 11 diploid samples as described above all the segregating sites were selected. A set of filters was applied to this list to produce the final set of segregating site derived (i.e. non-ancestral) allele frequencies (DAFs) for all downstream analyses. To avoid skewing the DAFs towards higher frequencies, segregating sites with less than 8 chromosomal samples were eliminated. Also eliminated were any positions with more than 2 alleles among the reference, ancestral, or sample alleles, or where the ancestral allele was not determined by the EPO pipeline. Lowercase values of the EPO ancestral allele, which result from various cases without complete evidence in all species were not eliminated.
The SweepFinder program [28] was applied to the allele frequencies for the final list of segregating sites to determine the composite likelihood ratio (CLR) of a selective sweep at each one of a grid of 1000 positions across each target region. The model used in this program requires a background derived allele frequency spectrum. Two such backgrounds were used. First, all of the DAFs from all filtered segregating sites in our sample were aggregated and used as input to the command “SweepFinder -f”, which accounts for missing data using a Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. We refer to this as the “harseq” background. A second, presumably more neutral background was obtained from the African-Derived (AD) YRI subset of 24 individuals in the Seattle SNPs P2 panel. The DAFs for all segregating sites in all 104 genes resequenced for this panel by Seattle SNPs [33] were included. As for the harseq background, the “seasnp” background was obtained with the command “SweepFinder -f” applied to these DAFs. The resulting “seasnp” frequency spectrum ran from 1 to 47 and was reduced to a spectrum running from 1 to 21, as needed by SweepFinder with our data, by hypergeometric weighting the relevant components of the input allele frequencies at each target allele frequency.(1)
Dividing by the sum of the in Eqn 1 produces a valid frequency spectrum that sums to 1. A similar hypergeometric weighting was also required to reduce the spectrum to a range from 1 to 19 for the harseq1,2 and ctlreg60 regions. In the latter regions missing data reduced the maximum number of samples at the segregating sites to 20.
P-values for our SweepFinder results were obtained via coalescent simulation conditional on the observed number of segregating sites in focal region, the observed coverage of this region, and the estimated recombination rate for a given region according to the pedigree data of [36] assuming a human effective population size of . The second point is important here in that our resequencing of both the harseq regions and the control regions was not perfectly complete, but instead was partial owing to an inability to design proper probes for our Nimblegen enrichment procedure (see above) in certain genomic segments. For each region examined we performed coalescent simulations under the standard neutral model which has been shown to be conservative for the SweepFinder procedure [29].
From the set of fixed differences between human and chimp in the 49 core HAR elements we use the EPO determined ancestral allele (see above) to count 206 as the total number of human lineage specific substitutions. Since a small number of substitutions are expected to occur by chance even in constrained elements, we used the number of substitutions on the chimp lineage as an estimate of the minimum number of non-adaptive substitutions in each HAR. These total 16 for all 49 HARs. So, we approximate that at most 190 ( = 206-16) substitutions were adaptive in humans. In reality some of the excess nucleotide changes for a given HAR were probably segregating at the same time on the same haplotype. So, 190 is most likely an overestimate of the number of adaptive events in HARs. But suppose there were indeed 190 separate adaptive substitutions and that these occurred uniformly over the last 5 million years. Further assume that any sweep from the last 200,000 years could be detected by SweepFinder. Then, 4% of the 190 adaptive substitutions (i.e., 7.6 sweeps) should be in the detectable time frame. Since the number 190 and the percentage 4% are both upper bounds, we conclude that at most 8 and probably much fewer than 8 sweeps would be detectable by our SweepFinder analysis even if all 49 HARs were shaped by adaptive evolution. Our finding of no significant sweeps after Bonferroni correction and 5 significant before correction is therefore consistent with expectations.
To determine if mutations from an ancestral weak (A or T) basepair to a strong (G or C) basepair (W2S mutations) are more likely to spread in the population represented by our samples, we compared W2S segregating sites to S2W segregating sites for each target region and for the aggregate set of segregating sites. We performed a Mann-Whitney U (MWU) test for a difference between the W2S and S2W derived allele frequency spectra. The test was performed in the R language with the command “wilcox.test (paired = FALSE, alternative = two.sided)”. The resulting “location” parameter was normalized to 22 samples and is positive if W2S mutations are segregating at higher frequencies than S2W. The resulting p-values are in Supplementary Table 2 in Text S1.
To determine if relatively more W2S mutations fixed along the human or chimp lineages than are segregating in the human population represented by our samples, we first determined the high mapping quality chimp reference bases that differ from the human reference using reciprocal best alignments of the chimp and human genomes [37]. This set was then restricted to the positions in our target regions for which we had a genotype call for at least one sample with read depth of coverage of 35 or greater as discussed above. From this set of fixed differences we removed any for which the EPO ancestral allele was not determined as discussed above, or for which we had a segregating site, or which appeared in dbSNP release 129 [38]. The remaining fixed differences as well as the segregating sites were divided into W2S or S2W (or other). A McDonald Kreitman-like (MK) test on the resulting 2×2 contingency table was performed in the R language with the command “fisher.test (alternative = two.sided)”. The resulting p-values are in Supplementary Table 2 in Text S1. For the significant cases it was easy to determine from the data in the contingency table if the fixed differences favored S2W mutations relatively more than the segregating sites (column “S2W” in Table 2).
For the target regions with significant p-values on either the MWU or MK tests, we tested whether the significance was due to a restricted locus within the region, by removing all segregating sites and fixed differences under a mask of a given size at a given position within the region and rerunning the test with the remaining data (Table 1 and Table 2).
We downloaded the data for the 104 genes resequenced with the Seattle SNPs P2 panel. The genotypes for our 11 YRI samples (which are a subset of the P2 panel) and the coordinates for the genotyped segregating sites were obtained from the global “prettybase” file mapped to UCSC hg18 coordinates. The total set of positions genotyped was obtained from the individual gene “genbank” files by excluding the features defined as “Region not scanned for variation” and aligning the remaining regions to the hg18 coordinates of the full extent of the genic region sequenced specified in the associated “ucscDataFile”. Given these coordinates and the genotypes at the segregating sites, the same techniques as described above for our resequencing data was applied to derive ancestral alleles and human/chimp fixed differences, and to perform the MWU and MK tests.
Our data was derived from (probed) regions of a relatively tight size distribution (Supplementary Table 1 in Text S1). By contrast, the sizes of Seattle SNPs variation-mapped genic regions varied widely. Some were rather small and contained few segregating sites. Therefore, we included only genic regions with a minimum of 10kb variation-mapped and a minimum of 40 segregating sites. Additionally, a small number of the genic regions were excluded because of data missing from the “prettybase” file or because there were no associated high quality reciprocal best human chimp differences as described above, possibly because of paralogous genes in one or the other lineage. The remaining set of results for the MWU and MK tests on 62 genic regions (Supplementary Table 4 in Text S1) were used for comparison to our 49 harseq regions.
To determine if the p-values for the MWU and MK tests were accurate, we also conducted the tests on sets of simulated data under a neutral model. For the MWU test, we performed coalescent simulations using Hudson's ms program [60]. For each simulation we generated 22 samples at 85 segregating sites (the average number of W2S plus S2W segregating sites in the 49 harseq target regions) and then randomly assigned the sites as either W2S or S2W in Bernoulli trials using the W2S∶S2W ratio from the 49 harseq regions of 2057∶2114. After calculating the MWU test p-value for each simulation, the fraction of simulations with p-value less than a given value was computed, as well as the subset of that fraction in which the W2S spectrum was offset towards higher derived allele frequencies (Supplementary Figure 2 in Text S1).
For the MK test, for each simulation we separately derived a set of human-chimp fixed differences and a set of segregating sites. The fixed differences were derived using the phyloBoot program from the PHAST package [61]. We used a phylogenetic model and substitution rate matrix derived from 4-fold degenerate amino-acid coding synonymous sites across the genome as an unbiased neutral model. The equilibrium GC-content of this model was adjusted to reflect the genome-wide average GC-content. From the primate sequences so generated, we extracted positions containing a human-chimp difference that could also be unambiguously assigned an ancestral allele based on the macaque allele at that position. Each simulation used 335 such sites, (the average number of fixed differences in the 49 harseq target regions) which were divided based on whether they were W2S or S2W (or other). The segregating sites for each simulation were derived from 101 (the average total number of segregating sites in the 49 harseq target regions) Bernoulli trials, randomly dividing them as W2S or S2W (or other) according the corresponding ratios in the fixed differences from all of the phyloBoot simulations. After calculating the MK test p-value for each simulation, the fraction of simulations with p-value less than a given value was computed, as well as the subset of that fraction for which the ratio of W2S∶S2W was higher for the simulated fixed differences than for the simulated segregating sites (Supplementary Figure 3 in Text S1).
Simulations of GC-biased evolution due to BGC were generated using a forward time Wright-Fisher model of a population. Simulations were run at a population size of 10,000, which is approximately comparable to the long term human effective population size. Details of the simulation method can be found in [62] and references therein. Briefly, we model BGC as a selection process in which each W2S (S2W) mutation adds (subtracts) some normal deviate fitness value to the haplotype on which it is found. This model is approximately equal to the normal shift model [63] if we were only to consider the W2S subset of mutations. Simulations were run for 40 * N generations as a burnin period to reach stationarity, at which point we modeled a vicariance event representing the human chimp divergence. After the population split we ran the two populations for 6.5 units of 4N generations, to approximate the divergence time between humans and chimps. We assume the strength of BGC acting was 4NB = 1.3 as recently estimated from human data [10], [64]. We also assumed a ratio of 4NB∶4Nu of 1. The MWU and MK tests were performed as above using a single sample from the chimp lineage and 50 samples from the human lineage. Association between MWU and MK tests on simulated (and HAR region) data was assessed using Fisher's Exact Test on the 2×2 contingency table defined by the counts of significant or not significant tests: {MWU, notMWU}×{MK, notMK}. The numbers of significant tests by either or both MWU and MK were compared between the simulated and HAR region data using binomial and Poisson tests.
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10.1371/journal.pbio.1002593 | Brain–Computer Interface–Based Communication in the Completely Locked-In State | Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain–computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)—two of them in permanent CLIS and two entering the CLIS without reliable means of communication—learned to answer personal questions with known answers and open questions all requiring a “yes” or “no” thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%. Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a “yes” or “no” response. However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS.
| Despite scientific and technological advances, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a condition that is called completely locked-in state. Brain–computer interfaces based on neuroelectrical technology (like an electroencephalogram) have failed at providing patients in a completely locked-in state with means to communicate. Therefore, here we explored if a brain–computer interface based on functional near infrared spectroscopy (fNIRS)—which measures brain hemodynamic responses associated with neuronal activity—could overcome this barrier. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS), two of them in permanent completely locked-in state and two entering the completely locked-in state without reliable means of communication, learned to answer personal questions with known answers and open questions requiring a “yes” or “no” by using frontocentral oxygenation changes measured with fNIRS. These results are, potentially, the first step towards abolition of completely locked-in states, at least for patients with ALS.
| Communication is the process of expressing and sharing feelings, thoughts, and intentions with one another by verbal and various nonverbal means. Communication skills appear automatic but can pose severe challenges to individuals suffering from motor neuron disorders. The most devastating of motor neuron diseases is amyotrophic lateral sclerosis (ALS) [1], which is progressive and renders an individual motionless, severely affecting his or her communication ability [2]. As the disorder progresses, it destroys the respiratory and bulbar functions, forcing the individual to make vital decisions. If they opt for life and accept artificial respiration, they can no longer communicate verbally, and assistive communication devices that rely on nonverbal signals such as finger movement and gaze fixation are then used for communication [3]. In ALS, the disorder progresses in most patients until the patient loses control of the last muscular response, usually the eye muscles, a condition known as completely locked-in state (CLIS) [4].
Brain–computer interface (BCI) represents a promising strategy to establish communication with paralyzed ALS patients [5–7], as it does not need motor control. BCI research includes invasive (implantable electrodes on or in the neocortex) [8–11] and noninvasive means, including electroencephalography [12,13], functional magnetic resonance imaging (fMRI) [14], and functional near-infrared spectroscopy (fNIRS) [15], to record brain activity for conveying the user’s intent to devices such as simple word-processing programs [12]. The first BCI for communication in ALS patients with intact eye muscles was demonstrated by Birbaumer et al. (1999) [12]. With at least intact eye muscles and the rest of the body paralyzed, the condition is known as locked-in state (LIS) [4]. Since then, several invasive and noninvasive BCIs have been developed for communication in ALS patients. Noninvasive methods, namely slow cortical potential (SCP)-BCI [12,16,17], sensory motor rhythm SMR-BCI [18–20], and P300-BCI [21–24], have been utilized more frequently than invasive methods [25–27] for communication in people with ALS [6,12,27–30]. Irrespective of the types of BCI, during the BCI session patients selected letters or words after learning self-regulation of the particular brain signal or by focusing their attention to the desired letter or a letter matrix [21,23], and the attention-related brain potential selects the desired letter.
In a meta-analysis of the scientific literature of all ALS patients in CLIS [13], it was found that none of the existing techniques such as the P300 event-related brain potential (ERP), SCP, frequency analyses of various frequency bands of the electroencephalogram (EEG), and invasive electrocorticogram (ECoG) recordings [31] allowed reliable and meaningful communication with BCI. All BCI procedures mentioned above were based on effortful and explicit (conscious) voluntary control of a neuroelectric brain response such as learning with feedback and reward, during which patients learned to increase or decrease amplitudes of the SCP [12] to produce event-related desynchronization (ERD) of the central alpha-rhythm [32] to focus attention on a visually or auditorily presented sequence of letters in order to select a desired letter with the brain response. P300 [21,22,24] is also used in a similar manner to visually select a desired letter. The required activation of explicit-voluntary (controlled) attention in these BCI tasks, none of them resulting in stable learning of brain-based communication, prompted us to propose the theoretical psychophysiological notion of “extinction of goal directed cognition and thought” [13] in complete paralysis with otherwise intact cognitive processing. This theoretical account—certainly highly speculative in light of the complete lack of data about cognition and inner speech and motivational processing in CLIS—we substantiated with the failure to replicate initial positive reports about instrumental (volitional) [33,34] learning of autonomic responses in the curarized (paralysed) rat. The persistent incapability to replicate these experiments suggests that intact or partially intact motor functions and somatic-motor system mediation of autonomic functions (i.e., subtle postural or muscle tension changes to affect the desired physiological changes) is a mandatory requirement for instrumental learning and control of physiological functions. Theoretical views of this problem, like the one proposed, are not new but were expressed already in Greek philosophy by Aristotle [35] and by the philosophers of volition, particularly Arthur Schopenhauer in his monumental account of “Die Welt als Wille und Vorstellung” [36] (The World as Volition and Imagery) and during twentieth century learning theory [37,38]. Conscious of the fact that it is problematic to justify a theory on negative facts (lack of instrumental learning in the curarized rat and missing BCI control of BCIs requiring controlled attention in CLIS), we argue that [27] classical reflexive conditioning and learning might circumvent volitional effort in instrumental control. Thus, an experimental procedure involving processing of overlearned (“automatic”) questions (i.e., “Berlin is the capital of France,” “You are in pain”) asking for automatic cognitive processing only may fulfill this criterion. Thinking but not voluntary imagining affirmative “yes” and negative “no” to overlearned questions occurs effortlessly, such as automatic nodding of the head in a conversation: the extensive literature (mostly Russian) on semantic classical conditioning [39] and implicit attention and memory [40] provides ample support of this notion. However, for the case of patients in CLIS, one is faced with the dilemma that we cannot expect a learning curve characteristic of skill learning (usually exponential) or classical conditioning in a BCI task asking for overlearned “yes” and “no” responses as used on the present BCI system [29,30]. Patients were confronted in their lifetime with these questions (“Berlin is the capital of France”) before entering (or on the verge of) CLIS, and we can assume with certain confidence that no further learning at the time of assessment with the BCI is necessary and thus no learning curve can be expected. The same holds true for personal questions (“Your husband’s name is Joachim”). Thus, at an experimental level, it remains difficult to prove the speculation of intact classical conditioning but lack of instrumental voluntary learning in CLIS patients involved in a BCI task after entering CLIS. Only one patient in the literature [31] using an electrocorticographic-based BCI before and after transitioning from LIS to CLIS was published. This patient was unable to communicate with the BCI after entering CLIS. However, observation of a single case cannot serve as strong evidence comparable to the animal experiments [33,34] using curarization for the creation of reversible paralysis. We are also aware of the fact that a single case of a cognitively intact CLIS patient or curarized organism learning to instrumentally drive a BCI disproves our hypothesis. Experimental descriptions of patients with ALS or subcortical stroke in locked-in state (LIS) or who are severely paralysed using spiking frequency changes of motor neurons to move a robotic arm [10,11,41] cannot disprove our account. Patients in those studies [10,11,41] still had intact motor control of eye movements and some remaining muscles and thus could use the remaining muscular forces (somatomotor mediation) for instrumental learning and BCI control.
Because none of the BCI techniques outlined above are able to provide viable means of communication [5], the patients in CLIS due to ALS, without any muscular control, are rendered communicationless. We are then faced with the dilemma of defining communication in CLIS. Does it only mean to express one’s feelings, thoughts, and intentions in a fluent, automatized manner? Or, alternatively, does it mean to convey one’s intent or one’s feelings and thoughts to questions? As mentioned, all the existing BCIs rely on two elements: first, the neuroelectric signal (EEG or ECoG) control and second at least an intact eye muscle; the neuroelectric signal–based BCI did not work so far in patients in CLIS, in which eye movement control is lost.
A single case report by Gallegos-Ayala et al. (2014) [42] used fNIRS to measure and classify cortical oxygenation and deoxygenation following the “yes” or “no” thinking of the CLIS patient in response to true or false questions, respectively. The report described a CLIS patient with ALS achieving BCI control and “yes” and “no” communication to simple questions with known positive answers or negative answers and some open questions over an extensive time period. Although it was not spontaneous and voluntary, controlled communication, it at least enabled the individual without any means of communication to transmit “yes” and “no” to questions framed by family members and/or caregivers. The result opened a venue to provide at least some means of communication to individuals in CLIS who are otherwise left communicationless. Hence, an extensive study was performed on four ALS patients in CLIS to train them to communicate “yes” and “no.” In the present study, which is the first of its kind, fNIRS-based BCI was used for binary communication in four ALS patients in CLIS. The fNIRS-based BCI was employed successfully to train patients to regulate their frontocentral brain regions in response to auditorily presented questions. After training a classifier separating “yes” from “no” answers for several days, the patients were given feedback of their affirmative or negative response to questions with known answers and open questions over weeks.
The relative change in oxygenated hemoglobin (O2Hb) during the “true/yes” and “false/no” sentences’ interstimuli interval (ISI)—which corresponds to patients’ response interval over the frontocentral brain region of patients F, G, B, and W—are shown in Fig 1.
Fig 1 illustrates the change in O2Hb during “true/yes” sentences’ ISI—which is significantly different from the “false/no” sentences’ ISI—as corroborated by the t-test performed between the averages of true and false sentences’ ISI using the relative change in O2Hb across the four patients (p < 0.05), shown in Table 1, row D. The same analysis performed using the EEG signals in the time domain across all the training sessions showed no significant differences (p > 0.05) between the true and false sentences’ ISI across each patient, as shown in Table 1, row E. The eye movements measured with an electrooculogram (EOG) (vertical or horizontal; patients were free to use any direction) for patients F, G, B, and W while they were performing the “ja” (German word for yes) or “nein” (German word for no) thinking task showed no significant difference in the eye movements between the true and false sentences’ ISI for all patients, confirmed by the t-test (all p > 0.05), as shown in Table 1, row F, and S1B Fig.
The support vector machine (SVM) classifier’s classification of true sentences as true, false sentences as false, true sentences as false, and false sentences as true was used to calculate the false positive rate (FPR) and true positive rate (TPR) for each training and feedback session and also for all the training sessions and feedback sessions separately for each patient. FPR and TPR was plotted to obtain the receiver operating characteristic (ROC) curve of the binary SVM classifier during training and feedback sessions for each patient, as shown in S2 Fig, S3 Fig, S4 Fig and S5 Fig for patients F, G, B, and W, respectively. The change in oxygenated hemoglobin (O2Hb), EOG, and EEG power spectrum in response to true and false questions, obtained from the frontocentral region of the brain, across all the sessions from each patient was used to determine the SVM classification accuracy of "true/yes" and "false/no" answers. Successively, the daywise CA (i.e., averaging CA of all sessions in a single day) of each patient was compared to the adjusted chance-level threshold (described in BCI effectiveness metric section), as shown in Figs 2, 3, 4 and 5 for patients F, G, B, and W, respectively, and Table 2. The offline CA is reported using O2Hb, EEG, and EOG signals for training sessions, while online CA is reported using only O2Hb for feedback and open question sessions because feedback was provided online only using the O2Hb signal. The answering concordance between semantically paired questions ("Paris is the capital of Germany," "Paris is the capital of France"), expressed as the percentage of concordant answers over pairs' repetition, was as follows: F, 68%; G, 67%; B, 67%; W, 70%. Thus, the semantic concordance rate (SCR) ranges from 67 to 78% (see S12 Table). Median values of SCR are significantly different from 50% (all p < 0.0001), in which 50% is the SCR expectation of a random classifier.
The results of ANOVA and post hoc t-test (see Table 1, row G, H, I, and J) further emphasize a significant difference between the classification accuracy of O2Hb versus EEG and O2Hb versus EOG, with no significant difference between EEG and EOG. There is one exception in the case of patient W, as no significant difference was found between the classification accuracy of O2Hb versus EOG. Patient W (23 y of age) suffering from juvenile ALS with an extremely rapid disease progression (2 y from diagnosis to CLIS) was not asked open questions because of the supposedly difficult emotional state at that early stage of BCI communication but continues to train the BCI at present.
The patients showed the following stable dominant frequencies: F, 6.75 Hz; G, 6.25 Hz; B, 7 Hz; and W, 8 Hz. Power spectrum density of electroencephalographic signals corresponding to "true/yes" and "false/no" sentences’ ISI acquired from channel FC6 is shown in S1 Fig. The middle-frequency bands’ (high-theta, low-alpha, and high-alpha) mean power comparison between "true/yes" and "false/no" sentences’ ISI revealed no main effects of conditions and channels in any patient (all p > 0.05). The middle-frequency bands’ (high-theta, low-alpha, and high-alpha) spectral features comparison between sentence presentation interval and sentences’ ISI revealed some main effects of the intervals factor, as reported in S1 Table, section A. In two (G and B) out of four patients, a smaller low-alpha band “power variability” in the sentences’ ISI compared to the sentence presentation interval was found (p < 0.05). In patient W, a higher high-theta, low-alpha, and high-alpha bands’ mean power in the sentences’ ISI compared to the sentence presentation interval was found (ISI was synchronized compared to sentence presentation). Patient F did not show any significant difference in the middle-frequency bands’ mean power and “power variability” (all p > 0.05). See Results section of S1 Text for details.
The correlation analysis between fNIRS classification accuracy and low-frequency bands’ (those more related to vigilance) mean power revealed some interesting results (see S1 Table, section B). In three (G, B, and W) out of four patients, the median of the negative averaged correlation between low-theta band mean power and fNIRS classification accuracy was significantly different from zero (patient G: r = –0.365; patient B: r = –0.264; patient W: r = –0.386; all p < 0.05). However, in patient F, who had the longest time period in CLIS, the median of the positive averaged correlation between delta and high-theta band mean power and fNIRS classification accuracy was significantly different from zero (delta: r = 0.233; high-theta: r = 0.213; all p < 0.05). The low-frequency bands’ mean power distribution medians of successful and unsuccessful days (i.e., days with classification accuracy above chance-level threshold were considered successful) was further investigated for each patient to ascertain the difference, if any. In patient G, the low-theta band mean power of successful days was significantly smaller than that of unsuccessful days (p < 0.05). In patient B, the high-theta band mean power of successful days was significantly smaller than that of unsuccessful days (p < 0.05). This strengthens the above results of lower-frequency bands’ dominance for more unsuccessful performance. Additional details are provided in the Results section of S1 Text and in S1 Table.
Four patients in CLIS communicated with frontocentral cortical oxygenation-based BCI with an above-chance-level correct response rate over 70% during a period of several weeks. The performance of the binary SVM classifier across all the patients, except a few training sessions of patient B, was above chance level. None of the sessions were eliminated in the analysis, and only very few sessions had to be interrupted because of life-saving measures such as sucking saliva; thus, no bias for selecting “successful” sessions incriminates the results. Correct response rate for feedback and open questions sessions, as judged by the criteria mentioned in the Material and Methods section (Experimental Procedures), exceeded 75% in three out of four patients (F: 78.6%; G: 78.8%; B: 75.8%). Patients F, G, and B answered open questions containing quality of life estimation repeatedly with a “yes” response, indicating a positive attitude towards the present situation and towards life in general, as reported in larger samples of ALS patients [43,44]. Repeated presentation of an open question is necessary to ascertain the validity of the answer. From the ROC curve of each patient, it can be deduced that if the patient answers a question seven out of ten times with the same answer, then we can be sufficiently certain of the answer if the questioning is repeated over a long time period as done here. Correct classification of “yes” and “no” answers given mentally through fNIRS exceeded classification of EEG oscillations from 0–30 Hz and vertical and horizontal EOG classification. However, despite the absence of reliable eye communication in all patients as the inclusion criteria in the study and by the definition of CLIS condition, EOG classification was at some sessions, albeit rarely, above chance, mainly in patient W. Nonetheless, the inability of the social environment to perceive them and their instability and the eye tracker’s failure to use them for communication [45] prevents the use of this physiological signal.
The unreliable discrimination between "true/yes" and "false/no" sentences’ ISI by means of EEG signals (see Results section of S1 Text for details) is consistent with the results of a very similar auditory paradigm used for discriminating delayed, conditioned brain responses and tested in fourteen healthy participants [46]. However, with the exception of patient F, the comparison of middle-frequency bands’ spectral features (averaged across days) between sentence presentation interval and sentences’ ISI confirms the fact that two different states of arousal were present during sentence presentation and sentences’ ISI (see Results section of S1 Text for details). We cannot infer about the “qualia” of the two specific brain states occurring during listening to “yes–no” questions and executing the answers mentally, but at least we can state that the two mental states were different, meaning that differential cognitive processing occurred during the BCI task [47,48]. We do not have a physiologically plausible explanation as to why fNIRS responses to “yes” and “no” are different, as they are in three patients showing oxygenation increase during the answering interval (ISI) for “yes” responses with negligible topographical differences and oxygenation decrease during negative answers, again without topographical differences throughout the frontal cortical area. Only the very young patient W, with an extremely rapid course of the disease and a strong genetic variant, shows a variable fNIRS pattern with different slow oscillatory oxygenation changes between “yes” and “no” answering periods. In an animal study with nonhuman primates [49], we identified oxygenation increase as highly correlated with nearby recorded multiunit activity. Assuming a similar situation in the human brain, a “yes” answer may indicate a more coherent and more active brain state, probably supporting cellular associative binding [50–52] more readily than negative answering states. However, such a generalization remains highly speculative.
Three patients (G, B, and W) showed a negative averaged correlation between low-theta band mean power and fNIRS classification accuracy [53], meaning the smaller the low-theta band mean power, the higher the performance, except in patient F, who has been in CLIS for more than 4 y. The correlations analysis between daywise classified BCI performance and low-frequency bands across all intervals and all electrodes for each patient separately gave consistent and highly significant results (see Results section of S1 Text for details). The binary communication performance worsened with lower frequencies in two patients (G and B) as predicted. The number of days for patient W was limited (i.e., 6 d), thus it is quite unlikely to expect a significant difference in low-frequency bands’ mean power for successful and unsuccessful days (see S1 Table, section B). Decrease in vigilance reflected in slow frequencies impedes BCI performance and communication. Patient F, who had an extremely long history of CLIS without any communication over the years, showed a positive correlation in the delta and high-theta band with performance. She was the patient with very slow dominant frequency during rest, and it may be speculated that in such a deprived brain, superposition of delta and high-theta frequency represent a sign of increased attention and focus. For instance, low-theta band mean power can be used in future BCIs to stop a BCI session or to avoid the presentation of the sentences and/or questions during decreased vigilance.
For a robust validation of the BCI binary communication system in CLIS, two main unsolved questions remain: (i) the physiological identification of the cognitive processes underlying the listening to “yes–no” questions and the answerer’s mental state and (ii) the online identification of decreased vigilance states that are detrimental (lowering performance) for BCI binary communication purposes, such as decreased alertness, drowsiness, and sleeping.
Multielectrode EEG recordings used simultaneously with the fNIRS system and quantitative source analysis of the different frequency bands at different sites are necessary to clarify these questions. For the study reported here, only portable devices and a few EEG channels could be used in the interest of the bedside, home-based strategy selected. Thus, our interpretation of the EEG frequency bands’ variations remains speculative.
In patients completely motionless over a period of years with restricted vision because of eye muscle paralysis and compromised vision because of drying and reduced or absent afferent input from the sensorimotor system, reduced vigilance measured with EEG and an irregular sleep–wake cycle was documented by Ramos et al. (2011) [31] and Soekadar et al. (2013) [54]. De Massari et al. (2013) [45] have shown that reduction of P300 amplitude across the BCI paradigm presentation predicted negative performance, again suggesting excessive loss and excessive variation of wakefulness and attention as a major limiting factor for BCI applications in such severely compromised patients. Thus, we modified the existing fNIRS–BCI–system in a hybrid EEG–fNIRS–BCI, with the EEG allowing online corrections of excessive reduction of vigilance indicated by appearance of delta and low-theta periods. This new hybrid system should allow further improvement of communication in CLIS.
The results on four CLIS patients reported here allow the following conclusions:
The Internal Review Board of the medical faculty of the University of Tubingen approved the experiment reported in this study, and the patients’ legal representative gave informed consent for the study with permission to publish the results and show the face of patients in the publication. The study was in full compliance with the ethical practice of medical faculty of the University of Tubingen. The clinical trial registration number is ClinicalTrials.gov Identifier: NCT02980380. At the time of this study, prospective clinical trial registration was not mandatory for nonpharmacological studies; it was therefore registered retrospectively.
A continuous wave (CW)-based fNIRS system, NIRSPORT (NIRX), which performs dual-wavelength (760 nm and 850 nm) CW near-infrared spectroscopic measurement at a sampling rate of 6.25 Hz, as shown in in Fig 6A, was used. The NIRS optodes were placed on the frontocentral regions as shown in Fig 6B. During the BCI sessions, the EEG was also recorded with a multichannel EEG amplifier (Brain Amp DC, Brain Products, Germany) from ten Ag/AgCl passive electrodes mounted on the head cap. Six electrodes (FC5, FC1, FC6, CP5, CP1, and CP6) were used to acquire EEG signals and four electrodes were used to acquire the vertical and horizontal EOGs. The signals were bandpass filtered using a finite impulse response filter with a bandpass of 0.5–30 Hz. The EOG was filtered with different bandpass filters (0.5–3.5 Hz, 0.5–10 Hz, and 0.5–30 Hz), but none of these filters led to significant differences of neurophysiological patterns related to the ocular activity. Question- or response-related eye movements were not detected in any of the patients over the whole time period of many weeks. Each EEG channel was referenced to an electrode on the right mastoid and grounded to the electrode placed at Fz location of the scalp. Electrode impedances were kept below 10 kΩ and the EEG signal was sampled at 500 Hz. During all BCI sessions, the spontaneous EEG was visually controlled by one of the authors (NB or BX) to avoid longer periods of slow-wave sleep during the BCI evaluation. A BCI session was initiated only if the EEG was free of high-amplitude slow activity below 4 Hz.
Patient F (female, 68 y old, completely locked-in state) was diagnosed with bulbar sporadic ALS in May 2007, was diagnosed as locked-in in 2009, and was diagnosed as completely locked-in May 2010, based on the diagnoses of experienced neurologists. She has been artificially ventilated since September 2007, fed through a percutaneous endoscopic gastrostomy tube since October 2007, and is in home care. No communication with eye movements, other muscles, or assistive communication devices was possible since 2010. Further details of this patient are described in Gallegos-Ayala et al. (2014) [30].
Patient G (female, 76 y old, CLIS) was diagnosed with bulbar ALS in 2010. She lost speech and capability to walk by 2011. She has been fed through a percutaneous endoscopic gastrostomy tube since September 2011, artificially ventilated since March 2012, and is in home care. She started using assistive communication devices employing one finger for communication in February 2013. Later, she was diagnosed with degeneration of vision because of cornea defects in September 2013. After the failure of the finger-communication device, an attempt was made to communicate using eye tracking in early 2014. She stopped communicating with the eye in August 2014, before the BCI was introduced, and an attempt was made to communicate with the subtle twitch of an eye lid, which was not reliable. The husband and caretakers declared no communication with her since August 2014.
Patient B (male, 61 y old, CLIS) was diagnosed with nonbulbar ALS in May 2011. He has been artificially ventilated since August 2011, fed through a percutaneous endoscopic gastrostomy tube since October 2011, and is in home care. He started communicating with a speech device in his throat from December 2011, which ultimately failed, and he started using the MyTobii eye-tracking device in April 2012. He was able to communicate with MyTobii until December 2013, after which the family members attempted to communicate by training him to move his eyes to the right to answer “yes” and to the left to answer “no,” but the response was variable. No communication was possible since August 2014.
Patient W (female, 24 y old, locked-in state on the verge of CLIS) was diagnosed with juvenile ALS in December 2012. She was completely paralyzed within half a year after diagnosis and has been artificially ventilated since March 2013, fed through a percutaneous endoscopic gastrostomy tube since April 2013, and is in home care. She was able to communicate with eye tracking from early 2013 to August 2014 but was unable to use the eye-tracking device after the loss of eye control in August 2014. After August 2014, family members were able to communicate with her by training her to move her eyes to the right to answer “yes” and to the left to answer “no” questions until December 2014. In January 2015, eye control was completely lost, she tried to answer yes by twitching the right corner of her mouth, that too varied considerably, and parents lost reliable communication contact.
All four patients reported in this manuscript were enrolled consecutively. Patients’ family approached us to get enrolled in the study because of the past work and public appearance of the corresponding author. Patients were never screened and excluded for this study. The only criterion for the inclusion in this study was that the patient should be in completely locked-in state (CLIS) or on the verge of CLIS, and family members could not communicate with eye movements or any other response with the patient. The CLIS state was then verified with confirmation of the attending neurologist, EOG recordings, and video recordings of the families’ failures to achieve contact with the patient.
The schematic depicting the experimental procedure, acquisition, and analysis of fNIRS and EEG data during BCI sessions is shown in Fig 6.
An auditory paradigm was employed to (a) train patients on questions with known answers, termed as training sessions; (b) give feedback on questions with known answers, termed as feedback sessions (i.e., “Your husband’s name is Joachim,” and after classification during ISI: “your answer was recognized as ‘yes’/‘no’); and (c) answer open questions, termed as open question sessions (“You have back pain”). Known questions are personal questions based on patient’s biography. For every known question with a clear “yes” answer, a semantically related question with a clear “no” answer was constructed and vice versa; for example, “You were born in Berlin” and “You were born in Paris.” Patients were asked to think “yes” or “no” answers and, if possible, also to use their previously successful eye movements. They were explicitly instructed not to imagine the answer or visually or auditorily imagine the word (i.e., as a visual or sound form) “yes” or “no”. Open questions are general questions related to quality of life and questions of caretakers whose answers can only be known by the patient. A total of at least 200 known questions and 40 open questions were constructed for each patient with family members before the initiation of the BCI study. Each patient was visited for 4 to 5 d in a month, except patient W. Three to four sessions were performed each day depending upon the health condition reported by the caretakers of the patient. Every session lasted for 9 min, and a session in progress was terminated extremely rarely (i.e., if removal of saliva became urgent). In such a rare event, the session was started again. Since each session lasted for 9 min, the caretaker or the family member was always instructed to take care of the needs of the patient before the start of the session, and the session was always started with the permission of the caretaker or the family member. A session, once in progress, was never terminated for patients F, G, and W. For patient B, a session was terminated while in progress three times because of removal of saliva, and the data were not included in any kind of analysis.
Acoustically presented instructions about the procedure were given repetitively before each training, feedback, and open questions sessions, allowing patients to recall and consolidate the required task to listen and answer mentally. Each BCI session started with training sessions, during which the patients were instructed to listen to 20 personal questions (with known answers) consisting of 10 true and 10 semantically equivalent false sentences. The sentences were presented randomly in such a way that two semantically related questions never played one after another. Family members were always present throughout the BCI session, and they never prompted the patient to answer the question. Complete pin-drop silence was maintained during the session, and only the recorded sentences were presented via audio presentation software connected to sound box with the voice of a family member or caregiver. Patients were asked to think “ja, ja…” (German for “yes”) and “nein, nein…” (German for “no”) for 15 s during the ISI until they heard the next sentence after an interval of 5 s of rest, as shown in Fig 6. After the end of each session, the fNIRS feature necessary to differentiate between “yes” and “no” answers during ISI was extracted and classified. Only training sessions were performed during the first few days, and upon several successful training sessions (as described below in BCI effectiveness metric section), the online feedback session was performed. During training sessions, both the patient and the algorithm were trained. Patients learned to mentally answer the question, and the algorithm learned to classify the “yes” and “no” fNIRS pattern of a particular patient. This kind of “mutual learning” seems important to optimize the “yes” versus “no” classification outcome and to customize and/or adapt the BCI system to each individual patient. At the end of each training session with 20 sentences (questions), patients were told the average classification accuracy of the session (calculated using the SVM classifier) to motivate and help patients in learning. In the course of an online feedback session, patients were presented the known questions as described above, but now at the end of the 15 s ISI they were given auditory feedback of accuracy, during which the computer said, “Your answer was recognized as yes” or “Your answer was recognized as no” depending upon the question (all sessions were videotaped and are available on request). Feedback to strengthen the conditioned response was provided only if the classification accuracy was greater than the chance-level upper limit to guide the conditioned learning toward meaningful answers and to avoid frustration by negative feedback already at the beginning of a daily session. Feedback was driven by the fNIRS classifier, calculated using the data acquired during the training sessions. After successful training and feedback sessions, the patients were presented with open questions, during which they were always given the auditory feedback of their answer.
The validity of answers to open questions can only be estimated by (a) face validity (i.e., questions of pain in the presence of an open wound); (b) stability over time; (c) external validity, estimated by family members and caretakers; and (d) internal validity between questions (i.e., the concordance between the answer to “I love to live” with the answer to “I rarely feel sad” [presented to all patients—except W—regularly]). Table 1, rows A, B, and C enumerate the total number of training, feedback, and open questions sessions performed by each patient, respectively. Patient W received no open questions because of low classification accuracy, which we and the parents attributed to her emotional state distracting her from concentrating on the responses because of the short time period of adaptation to the CLIS.
The binary BCI system effectiveness and robustness depends on its capability of correctly classifying the neurophysiological correlates of “yes” and “no” answers to true and false questions. The proposed true and false questions have two possible outcomes only, which are equally distributed with a probability of 0.5. To ensure that the classification of “yes” and “no” answers is not at chance-level, a reliable metric has to be used. Based on binomial distribution theoretical background, Müller-Putz et al. [59] defined a metric for experimental procedures with a binary outcome and multiple repetitions to determine the chance-level threshold above which the classification accuracy results can be considered as not resulting from chance. Because type and number of questions (personal questions with known answers and open questions) are partly different over days (i.e., the experimental conditions were different) the chance-level threshold was calculated on a daily basis. The daily-based chance level was computed using the formulas described in Müller-Putz et al. [59] and by taking into account the number of true and false sentences presented in a single day to each patient.
The fNIRS data was acquired online throughout all the sessions, namely training, online feedback, and open question sessions. The fNIRS data acquired online was normalized, filtered using different bandpass filters (0.0016–0.3), (0.01–0.3) and (0.02–0.3) and processed using modified Beer–Lambert law [60,61] to calculate the relative change in concentration of oxyhemoglobin (O2Hb) and deoxyhemoglobin (RHb). The choice of bandpass filter had no effect on the waveforms of signal. The relative change in O2Hb computed online during each session was used to train a SVM classifier model. The mean of relative change in O2Hb across each channel was used as a feature to train the SVM model through a 5-fold cross-validation procedure. In this study, only the relative change in O2Hb was used, as after the end of sessions with known answers it was observed that O2Hb provided stable and higher cross-validation classification accuracy than RHb. In an invasive animal study with nonhuman primates, we have also measured a superior covariation of oxygenation changes compared to deoxygenation, with intracortically recorded neural activity [49] supporting this clinical observation. Since the classification accuracy achieved was higher for O2Hb, the SVM model generated using O2Hb was used to provide online feedback for known as well as open questions sessions. If the classification accuracies for at least three consecutive “training” sessions with questions with known answers were greater than the chance-level threshold, a new model was generated using the relative change in O2Hb across three training sessions to give online feedback. During an online feedback session, fNIRS data acquired online corresponding to each ISI was processed to obtain the relative change in O2Hb, as described above, across all the channels. The mean of the relative change in O2Hb across all the channels was used as test feature to map onto model space. Upon mapping of this test feature onto the model space, the SVM predicted (called predict label) the side of the hyperplane the test feature fell on. Depending on the value of the predict label, appropriate feedback was provided to the patient: if the predict label was 0, the patient was given feedback that his or her answer was recognized as “no,” and if the predict label was 1, the patient was given feedback that his or her answer was recognized as “yes.”
fNIRS provides three different signals: oxyhemoglobin (O2Hb), deoxyhemoglobin (RHb) and total hemoglobin (THb) [60,61]. As mentioned in the section Online data analysis, since the classification accuracy achieved was higher for O2Hb, only the results from the offline processing of O2Hb data will be shown along with the EEG and EOG data. The relative change in O2Hb, EEG, and EOG data were processed offline to determine:
To ascertain the difference between the averaged ISI of true and false sentences, t-tests were performed. t-test was performed separately for O2Hb, EEG, and EOG signals acquired from all the sessions and across all the channels in a session, averaged over many sessions varying slightly between patients. Furthermore, t-tests were also performed for each session between the ISI of all the ten true sentences and all the ten false sentences (“Berlin is the capital of France,” “Berlin is the capital of Germany”) across different channels in a session.
For the EEG, frequencies between 0 and 30 Hz, estimated by Welch’s method [62], were used for classification and statistical testing. ANOVA and post hoc t-test were used.
Frequency bands’ mean power and their “variability” were estimated using Welch’s method [44,58]. For each patient, middle-frequency bands’ (i.e., high-theta, low-alpha, and high-alpha) features of “true/yes” and “false/no” sentences’ ISI were compared, as well as middle-frequency bands’ features of sentence presentation and interstimulus intervals. Successively, the averaged correlation between each low-frequency band (i.e., delta, low-theta, and high-theta) mean power and fNIRS classification accuracy was computed to find relevant relationships of low EEG rhythms with the BCI experimental procedure outcome. Details are provided in the Methods section of S1 Text.
The performance of the binary SVM classifier was ascertained by plotting the ROC curve. The ROC curve was created by plotting the TPR against the FPR (obtained from the contingency table created for each session) and the average of all the sessions, separately for each patient, using the four possible outcomes of a binary SVM classifier. The formation of contingency table for training and feedback sessions for each participant is described in the Receiver operating characteristic curve section of S2 Text. Further chi-square test was performed to determine the statistical significance of the observed outcomes in the contingency table, also described in the Receiver operating characteristic curve section of S2 Text.
Semantic concordance rate (SCR) was calculated to ascertain the consistency and/or concordance of the answers between semantically equivalent but contrasting true and false sentences requiring “yes” and “no” answers, respectively. SCR (i.e., the percentage of concordant answers over pairs’ repetition) was calculated for all semantically related sentences presented to each patient. The method employed to calculate the semantic concordance rate is described in the Semantic concordance rate (SCR) section of S2 Text. This measure also provides indirect information about the intact cognitive processing of the presented sentences in a CLIS patient.
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10.1371/journal.pcbi.1002058 | Impact of Microscopic Motility on the Swimming Behavior of Parasites: Straighter Trypanosomes are More Directional | Microorganisms, particularly parasites, have developed sophisticated swimming mechanisms to cope with a varied range of environments. African Trypanosomes, causative agents of fatal illness in humans and animals, use an insect vector (the Tsetse fly) to infect mammals, involving many developmental changes in which cell motility is of prime importance. Our studies reveal that differences in cell body shape are correlated with a diverse range of cell behaviors contributing to the directional motion of the cell. Straighter cells swim more directionally while cells that exhibit little net displacement appear to be more bent. Initiation of cell division, beginning with the emergence of a second flagellum at the base, correlates to directional persistence. Cell trajectory and rapid body fluctuation correlation analysis uncovers two characteristic relaxation times: a short relaxation time due to strong body distortions in the range of 20 to 80 ms and a longer time associated with the persistence in average swimming direction in the order of 15 seconds. Different motility modes, possibly resulting from varying body stiffness, could be of consequence for host invasion during distinct infective stages.
| Single cell motility is essential for many physiological functions. We seek to further our understanding of the swimming mechanism of trypanosomes - parasites responsible for deadly disease in humans and cattle. Trypanosomes are found both in the blood stream and invading tissue spaces. The swimming mechanisms used by microorganisms, such as bacteria and parasites, are very different from those used in our macro-scale environment. The trypanosome's ability to swim, mediated through a flexible rod like structure called a flagellum, attached to the length of the body, is essential for its survival. We found that cell shape (or elongation) correlates to directionality in cell movement. Additionally, using high-speed microscopy we uncovered extremely fast dynamics of the flagellum tip, up to 50 times faster than the whole cell swimming speed. Together our results imply that physical parameters such as cell stiffness may influence a trypanosome's ability to invade tissue.
| Eukaryotic cell motility plays a key role in many physiological functions including cell division, development, survival, as well as disease pathogenesis [1]–[4]. Studies of single cell motility have not only uncovered different aspects of these functions, but also provide great biophysical insight into life at low Reynolds numbers [5]–[8]. Trypanosomes, found across Africa and South America, are causative agents of diseases that are endemic in low income areas [9]. Recent work demonstrating that cell surface hydrodynamic drag is used by trypanosomes to sweep antibodies to the flagellar pocket, the ‘cell mouth’, for host immune evasion, has piqued interest in trypanosome motility [10], [11]. In this study, we examine the microscopic swimming behavior of the model organism Trypanosoma brucei brucei, the pathogen responsible for animal African trypanosomiasis.
As in other motile cells such as E.coli and spermatozoa, T. brucei brucei motility is mediated by a flagellum. However, unlike sperm and E.coli, the flagellum of the trypanosome emerges from the flagellar pocket near the base of the cell and runs along the length of the entire body as illustrated in Fig. 1 . Until very recently it was believed that the flagellar beat originates at the tip and is carried to the base of the cell resulting in a corkscrew like forward movement [12]–[14]. Branche et al. showed that base to tip wave propagation resulted in reorientation of the whole cell without any significant backward movement [14]. Work by Rodriguez et al. suggests that both bloodstream form and procyclic form cells move by anti-chiral helices separated by a kink traveling along the length of the body during wave propagation [15]. RNAi-based ablation of flagellar proteins in bloodstream-form parasites has resulted in loss of cell viability demonstrating the importance of cell motility and in particular the flagellum in cell survival [3], [16]. The discovery of social motility in trypanosomes [17] further motivates a deeper look into their motility.
While there have been many efforts to uncover the molecular biology of motility in trypanosomes and other microorganisms, a quantitative understanding of parasite motility is still lacking. However, recent temporal correlation analysis of experimentally derived trajectories suggests the presence of two characteristic relaxation times in trypanosome motility which could be used to construct a model of Langevin equations to describe trypanosome motility. A short relaxation time is attributed to the strong body distortions that the cell undergoes during swimming, while a significantly longer relaxation time is associated with the persistence in average swimming direction (submitted, Zaburdaev V. et al.). All studies of trypanosome motility have focused exclusively on cells swimming directionally [12], [15]. However, it is known that more than half of a trypanosome population is dividing under normal culture conditions underscoring the importance of carrying out population-wide analysis for quantitative characterization.
In this work, we aim not only to characterize differences seen in swimming behavior within a single population, but also to link these behaviors to the microscopic physical processes that may explain these observations. To this end, we examined motility of single trypanosomes in a quasi two-dimensional environment. A Pearson random walk model [8], [18], [19] is used to describe the swimming trajectories of trypanosomes. We further suggest that the observed diversity in swimming trajectories may be correlated to varying cell stiffness.
Recordings of swimming cells in culture medium were taken at 7 Hz and processed to obtain single cell trajectories which follow the center of mass of the cell. Characteristic time-lapse trajectories are given in Fig. 1 b and corresponding movies are given in the Supplementary Information (Video S1 and Video S2). The cells are in a homogeneous environment with no chemical gradients, therefore specific chemo-attraction may be ruled out as the basis for cell locomotion in this study. It is worth mentioning that although chemotaxis has not been demonstrated in trypanosomes, it is likely that they are capable of it [20].
As seen in the example trajectories of Fig. 1 c, trypanosomes from the same population taken from the same cell culture which are exposed to identical environmental conditions evidently do not follow a single motility mode.
About a quarter of the trypanosomes ‘tumble’ with no persistence in direction (shown in red in Fig. 1 c). These cells, referred to as tumbling walkers (TW), do not seem to have a well defined orientation, hence the most frequently described flagellum end-led swimming is not discernible.
Secondly, close to half of the swimmers are highly directional (persistent walkers - PW) with a complete absence of tumbling motion within the observation interval. Cell orientation of these swimmers remains constant, with the flagellum tip leading in the swimming direction.
The remaining population is comprised of cells that swim directionally with constant cell orientation but occasionally stop, tumble and reorient themselves and then move directionally again. These trajectories resemble those of E.coli in which steady forward motion is interrupted by tumbling [8]. We refer to these cells as intermediate walkers (IW).
Cell swimming was characterized using mean squared displacement (MSD) given by , where is the MSD, r is position, and is the time interval - bound only by the time resolution of the experiment - see Fig. 2 a. The scaling exponent , with where is the motility coefficient [21], [22], gives a measure of the degree of directional correlation. Tumblers are characterized by an average scaling exponent near , indicating uncorrelated motion. The intermediate walkers, exhibit more correlation and for persistent walkers, tends to increase well above unity, indicating longer term correlations in swimming direction [21], [23]. See Table 1 for a summary.
Interestingly, as shown in the distribution of Fig. 2 b all three types of swimmers have virtually the same average swimming speed, demonstrating that these motility modes arise primarily due to directional motion. Note that here speed is defined by the distance covered by the center of mass from frame to frame of experimental recordings. Thus, a tumbling cell also exhibits a speed, even if there is no net displacement. The differentiation characteristic of the motility modes is the persistent motion.
The time scale at which directional persistence is lost is characterized by the cosine correlation function,(1)
where v is the velocity vector, is the angle between the two adjacent vectors separated by the time interval , and denotes an ensemble average. In the case of TWs, the correlation decays rapidly and remains close to zero as shown in Fig. 2 c. For both persistent walkers and intermediate walkers a rapid decay in correlation is also seen, followed by a second slower decay. Sharp correlation drops for small time lags likely arise from the strong body distortions mentioned previously. An exponential fit to the second slower decay (shown in green in Fig. 2 c) of the average cosine correlation function for persistent walkers reveals a persistence time of . For cells that alternate between tumbling and directional swimming, we find the average perlsistence time significantly smaller, . The mean tumbing interval is - much longer than the tumble time of 0.1 s seen in E.coli[8]. Here a ‘tumbling interval’ was derived by the maximal time for which the distance traveled stays virtually constant with respect to time. Interestingly, all three correlations appear to remain above zero in the observation interval pointing to yet another longer term correlation.
Tumbling walkers for which all turn angles have equal likelihood are characterized by a flat turn angle distribution, while individuals that are directionally persistent draw from a rather narrow turn angle distribution. We use the spread of the turn angle distributions - related to , of each trajectory to systematically categorize individuals into their respective swimming modes.
Table 1 summarizes the quantitative differences between the motility modes - highlighting the striking diversity in motility in a single trypanosome population.
Fig. 3a illustrates typical displacement calculated between every 100 frames of recordings for each motility mode. It clearly demonstrates that while TWs and PWs maintain low and high displacement values respectively throughout the observation interval, the IWs alternate or switch between the two ‘states’ thus warranting the classification of trypanosome motility into three distinct modes. The physical differences correlated to these motility modes are discussed below.
We describe a simulation model with which we are able to reconstruct the motion of the three motility modes. A Pearson random walk model [8], [18], [19] is used where step size and turn angle are determined by two independent distributions, and respectively. The Pearson random walk is one of the simplest conceivable models which accounts for stochastic but directional motion. It has been used as a paradigmatic model for locomotion in animals widely used in microorganisms but also in butterflies, for instance. It is therefore a suitable minimal model for the locomotion in trypanosomes. Fortunately, we found that this minimal model describes the locomotion in trypanosomes to a very satisfactory extent. The turn angle is determined by the previous displacement direction and a randomly drawn turn angle. Step sizes are drawn from an exponential distribution with a characteristic displacement [18] (equivalent for each motility mode) - see Supplementary Information Text S1 for details. The model captures the motion of the trypanosomes, in particular their differences in persistence of translation motion see Fig. 1 d and table 1. However, it does not capture the smaller scale ‘jaggedness’ seen in the trajectories. This jaggedness arises from the rapid bending and twisting of the trypanosome cell body during swimming. To further characterize the dynamics of trypanosome motility at these smaller time scales we utilized high speed microscopy.
In order to investigate the physical mechanisms for the observed motility behaviors, we examine trypanosomes at higher magnifications and at a much higher sampling rate of (See Supplementary Information Video S3 and Video S4). Very recent work with high speed microscopy has demonstrated surprisingly fast flagellum tip velocities up to 25 times faster than the average cell swimming speeds [15] further highlighting the need for high temporal resolution towards gaining a physical understanding of this parasite's motility.
The flagellum runs along the cell body resulting in complex body deformations during swimming, and while the quantitative descriptions of flagellar movements have been done for other species [24], little has been been done for T. brucei brucei.
We chose a straightforward approach and examined the variations in the distance between the two ends of the cell (referred to as end to end distance here on) over time. Trypanosomes were tracked to ascertain their motility mode, and then followed by high speed recordings of their movement at a higher magnification. Recorded images were processed as described in the methods section. A skeleton line through the center of the cell body was obtained as illustrated in Fig. 4 a. The end to end distance, indicated by the yellow arrow in Fig. 4 a was calculated and normalized by the cell length itself (represented by the length of the single pixel line in red). Thus resulting in a time series of the normalized end-to-end distance which allows for comparison of motility modes as shown in Fig. 4 b (see Supplementary Information Videos S3 and S4). Fig. 4 b shows that the persistent cell is consistently more stretched or elongated than the the tumbler.
Histograms of the end-to-end distances [25], [26] show clear differences between the swimming behaviors ( Fig. 4 c). A higher mean end-to-end distance for the PWs, indicates that directional persistence is correlated to an elongated cell shape. Snapshots of the high speed movies of individuals demonstrate, as shown in Fig. 4 d, that cells swimming with directional persistence appear to take a more stretched body shape. Indeed the mean end-to-end distance of persistent cells is 0.6 - more than 1.5 times that of tumblers for which it is 0.38.
We ascribe the shape of the trypanosome to a worm-like chain [25], [27]. The mean squared end-to-end distance is given by [27], [28]. Where is the length of the chain, and is a dimensionless variable dependent on flexural rigidity, , and energy utilization for motility, . Assuming equal energy utilization for self propelling motion in both motility modes, that is , the ratio of the end-to-end distances of the two motility modes is given by(2)
Based on the above assumption of equal energy utilization, we find that persistent cells have three times more flexural rigidity than tumbling walker cells. The directional cells may be stiffer due to reorganization of motor proteins and crosslinking within the microtubules found both in the cell body and the flagellum. Cell division is associated with the growth of a second flagellum alongside the old one which may also contribute to differences in cell stiffness (see below). The assumption of equal energy utilization for motility from one cell to another is supported but not confirmed by the fact that the velocity distribution for all motility modes is the same. It is not unlikely that these observations are due to an interplay of differences in flexural rigidity and also energy utilization for cell motion which may depend on cell length [29]. The precise differences in flexural rigidity and energy utilization among the motility modes remain to be confirmed through direct measures in further studies.
Nevertheless, these results are in qualitative agreement with theoretical work by Wada and Netz [30] on motility of the bacterium Spiroplasma in which softer cells were shown to flex more significantly due to random thermal fluctuations and thus were less efficient in directional motion. Spiroplasma has previously been reported to swim using a kinking helical mechanism [31] similar to the one recently suggested by Rodriguez et al. [15] for trypanosome swimming. Further theoretical work suggests that changes in flexural rigidity of the cell have a direct effect on the pitch of the helical movement [32].
Cell division for blood stream form trypanosomes begins with the growth of a new flagellum. The cell body begins to expand while the kinetoplast, attached to the basal body, is replicated. Mitosis of the nucleus begins while the second flagellum continues to grow. Cell width may be used an indicator for the expansion associated with cell division. For this study, while only single cells that appeared to have a single flagellum were selected the exact stage of the cell cycle can only be assessed by measuring the cell width in this experiment. In Fig. 3 b the distribution of cell width (measured at the widest point) for cells in each motility mode is shown. On average, persistent walkers are wider than the other two motility modes, indicating that they are already involved in cell division and the growth of a second flagellum. How cell motility is affected once cytokinesis progresses and daugther cells begin to resolve requires further investigation.
Aside from giving us clues about the microscopic origin of the observed motility modes, this data also allows us to study the dynamics of cell movement to further our understanding of the swimming mechanism of the trypanosomes. We track the movement of the base and flagellum tip with respect to the center of mass of the cell, allowing us to isolate the whole body movement from the movement of the cell ends alone. Typical trajectories shown in Fig. 5 indicate that the flagellum tip appears to move faster than the base of the cell.
Velocity distributions of cell ends, extracted from the trajectories of cell ends are shown in Fig. 6 a and b. We obtain mean flagellum tip velocities of and for the tumbling walker and persistent cells respectively while base-end velocities are much lower for both TWs and PWs and respectively) - all in fair agreement with [15]. This finding points to a ‘velocity gradient’ along the cell body, which is lowest at the base of the cell and increases toward the flagellum end. The gradient, which appears to be steeper in persistent walkers, in turn may stem from a gradual increase in elasticity due to the tapering body shape and from the bias in the hydrodynamic center of mass toward the base of the cell. Changes in overall cell stiffness would hence not only affect flagellar velocities, but also the directionality in cell-end movement, thus determining the motility mode of the cell.
Notably, we find extremely fast cell body dynamics; the flagellum tip of the persistent walker moves over 40 times faster than the average reported whole cell swimming speed of . For the tumbling walker, there is a 20-fold difference between the tip and the whole cell speed. Together these results support the notion that the motility modes are a consequence of cell elongation and could be correlated to cell stiffness. The single pixel line to represent the cell body provides a straightforward method for further investigations into the movements of a cell and may shed light on the kinking mechanism recently suggested for trypanosome movement [15].
Finally, using a velocity autocorrelation function [33] for each cell end we are able to extract the fast time scales relevant to the cell movement. The correlation function is given by(3)
where velocity , is the temporal resolution, is the profile time or time lag, is the mean and is the velocity standard deviation. Eq. (3) reveals how fast the correlation in velocity is lost (temporal decay) and uncovers any underlying periodicity in velocity. In Fig. 6 c, a typical velocity autocorrelation function is shown for the base and tip of a cell.
An exponential fit to the correlation function reveals that the decay time is and for the base and tip respectively. These decay times do not appear to vary significantly across the three motility modes and point to surprisingly fast dynamics in cellular motility due to the fast body distortions. Note that the first fast decay is attributed mainly to resolution effects and therefore ignored in the fitting. Further a longer periodicity of is seen in the autocorrelation functions ( Fig. 6 c inset). This periodicity is likely to arise from the repeated ‘bend and release’ motions of the cell body which can clearly be seen in the trajectory shown in Fig. 5 middle panel.
Although trypanosome run and tumble motion has been reported previously [14], [34], different motility modes in T. brucei brucei had never been characterized in detail. Directionality may confer the ability to invade into tissue - the last stage of sleeping sickness is characterized by parasite invasion of the blood brain barrier [35], or it may be an effective nutrient search strategy[36]. On the other hand, cell swimming has been shown to be essential for host antibody removal and one of these motility modes may increase local hydrodynamic drag on surface proteins [10]. Our results indicate that macroscopic motility modes could be correlated to varying cell stiffness. Direct measurements of cell stiffness are required to confirm this hypothesis. The analysis of cell end-to-end distance provides a rapid screen for identification of differences in microscopic properties of cells. Finally our analysis points to remarkably fast cell motility dynamics influencing both the intrinsic rotational and translational cellular motion. Extending the present experiments to include flow for further investigations into the biological relevance of the motility modes may thus help identify the importance of cell swimming in various infective stages.
A mixed population of monomorphic wildtype bloodstream form Trypanosoma brucei brucei, strain Lister 427, clone 221a (MITat 1.2) grown in HMI9 complete medium were cultured at , 37°C and harvested at a cell density of cells/mL. Cells were resuspended in fresh medium before every tracking experiment.
An Olympus BX61 microscope with 20x and 60x oil objectives equipped with either a PCO SensiCamQE camera, or a Phantom Miro (Vision Research) camera for higher temporal resolution movies were used. During recording the image is taken very slightly out of focus enhancing image contrast. High speed movies were recorded at 1000 frames per second the 60x oil objective, 1.25 aperture, for a minimum of 7 seconds.
Each movie frame is processed using Matlab's (2009a, The MathWorks) image processing toolbox. The images were processed with gaussian filters and then processed to a black and white image using the appropriate threshold. The in-built skeletonization feature of Matlab was used to obtain a single pixel line representing the center line running through cell body. The end points of the skeleton line are detected and distance between these two points is recorded. Thus, a time series of normalized end-to-end distances is obtained.
Cell swimming was observed between two microscope slides at room temperature in HMI-9 complete medium in a 2-dimensional setting between a microscope slide and a coverslip, cleaned by sonication in isopropanol and dried with high pressure. Cell solution was sufficiently dilute to allow for tracking of isolated individual cells without any contact with neighboring cells. Recording were made within 30 min of removal from the 37°C incubator. Cell trajectories are derived from movies collected at 7 Hz using transmission light with a 20x oil objective using the track object feature of the Image-Pro Plus software from Media Cybernetics.
All statistical analysis was done using MATLAB. All fitting was done using Origin (2008, Origin Lab Corporation) with an exponential decay, .
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10.1371/journal.pcbi.1006143 | Latent environment allocation of microbial community data | As data for microbial community structures found in various environments has increased, studies have examined the relationship between environmental labels given to retrieved microbial samples and their community structures. However, because environments continuously change over time and space, mixed states of some environments and its effects on community formation should be considered, instead of evaluating effects of discrete environmental categories. Here we applied a hierarchical Bayesian model to paired datasets containing more than 30,000 samples of microbial community structures and sample description documents. From the training results, we extracted latent environmental topics that associate co-occurring microbes with co-occurring word sets among samples. Topics are the core elements of environmental mixtures and the visualization of topic-based samples clarifies the connections of various environments. Based on the model training results, we developed a web application, LEA (Latent Environment Allocation), which provides the way to evaluate typicality and heterogeneity of microbial communities in newly obtained samples without confining environmental categories to be compared. Because topics link words and microbes, LEA also enables to search samples semantically related to the query out of 30,000 microbiome samples.
| In the past decade, microbiomes from various natural and human symbiotic environments have been thoroughly studied. However, our knowledge is limited as to what types of environments affect the structure of a microbial community. In the first place, how can we define “environments”, in particular, the environmental entities that are often continuously varying and difficult to discretely categorize? We assumed that environments could be represented from microbiome data because the structure of microbial communities reflect the state of the environment. We applied a probabilistic topic model to a dataset containing taxonomic composition data and natural language sample descriptions of >30,000 microbiome samples and extracted “latent environments” of the microbial communities, which are core elements of environmental mixtures. Integrating the training results of the model, we developed a web application to explore the microbiome universe and to place new metagenomic data on this universe like a global positioning system. Our tool shows what kinds of the environment naturally exist and are similar to each other on the perspective of the structural patterns of microbiome, and provides the way to evaluate the commonality and the heterogeneity of users’ microbiome samples.
| Microbial communities are present worldwide in almost all possible environments. Because the composition (structure) of a microbial community and its surrounding environment are closely related to each other, it is important to understand what kinds of structural patterns are possible and how environmental factors affect community formations. Over the past decade, the structures of tens of thousands of microbial samples derived from various natural environments, including those in symbiosis with humans, have been analyzed. Using these datasets, global patterns of microbial diversity have been characterized that show that community structures constitute distinct clusters among at least certain environments[1–4]. In addition, the community structure of each examined environment has been evaluated using a clearly defined environmental ontology[5,6].
However, the granularities (i.e., the levels of detail) of human-classified environmental categories do not necessarily coincide with structural patterns of microbial communities, and this unavoidable arbitrariness in the granularities of environmental labels may bias the interpretation of results of comparative analysis, such as an enrichment analysis of environments among communities[1]. There are three types of incongruences between environmental labels and community structures. First, there are different subtypes in the microbial community structures associated with certain environments, e.g., enterotypes in the human gut and vaginal community types[7–9]. Second, in contrast to the first case, a nearly identical community structure may be observed across different environmental labels. For example, microbial communities of the surface of the home environment and their inhabitants show highly similar structural patterns[10]. Third, because an environment varies continuously over time and space, it is impossible to define it using a strict segmentation or hierarchical structure. For example, the brackish water of an estuary can have various mixtures of fresh water and seawater, for which the relative proportion continuously shifts[11]. Although an environment is difficult to definitively define owing to uncharacterized factors, these factors can potentially be defined indirectly using microbial community data because microbes respond quickly to environmental changes[12,13], and their community structure reflects the state of the environment[14–16].
Microbial community structures have been analyzed by various data clustering methods. Most of these approaches are based on the evaluation of data densities on high-dimensional feature space in which microbiome samples are distributed. Microbial community structures are complex as they are described by a large number of features (taxa), although not all the features necessarily vary independently. There are groups of taxa that show co-occurrence patterns in samples[17–19]. Herein, we refer to such co-occurrence relationships of microbes as “sub-communities”. Summarizing community structure data as mixing ratios of sub-communities makes it easier to interpret community dynamics according to environmental changes[20]. To extract such partial structures from mixed data, the machine-learning technique denoted a topic model has been extensively studied in recent years since introduction of the Latent Dirichlet Allocation (LDA) model[21]. The LDA model is a probabilistic generative modeling approach, mainly used in natural language processing, for discovering the latent (unobserved) structures of the dataset. The LDA model and its extended models have been used to analyze microbiomes[20,22,23], however, it is often difficult to interpret extracted sub-communities of microbial taxa. Sub-communities have been characterized by evaluating their relationships with occurrences of sample metadata (i.e., data describing information about the samples, such as body sites and gender) after modeling[24], or by explicitly modeling associations between metadata and sub-communities[20]. These methods cannot be applied unless all samples have standardized metadata with a uniform granularity, and thus tend not to be practical for comprehensive analyses of microbial samples from various environments.
All metagenomic data registered in public databases have such metadata, that is, natural language data described by the researchers who registered the samples. The paired dataset of community structures and description documents can be used for modeling the conditional relationship between them. However, natural language descriptions in databases are not always sufficiently described as their content for many samples is often incomplete and has widely variable resolution. For example, in a sample, various information on the host such as race and gender, experimental conditions, the purpose of the research project, etc. are described, but in another sample, it is described only as “human gut metagenome” and is needed to be treated as a sample with missing values. Therefore, for robust modeling, it is necessary to assume stochastic-generating processes not only for community structures but also for documents within the framework of the probabilistic generative model. For such purpose, the Correspondence-LDA (Corr-LDA)[25,26] model can be applied. Corr-LDA is a probabilistic modeling approach that is used to extract correspondence between various types of elements occurring in the same dataset, for example, the correspondence between sub-regions of pictures and their captions[25], between topics of blog documents and their annotation tags[26], or between brain regions and their cognitive functions[27][28].
In this research, we attempted to find relationships between patterns of microbial community structures and patterns of “environments” that the human recognizes and describes. To this end, we applied the Corr-LDA model to pairs of taxonomic compositions and natural language sample descriptions for tens of thousands of sequenced 16S rRNA gene amplicon samples reanalyzed by the unified analysis pipeline. Using this dataset, “topics” extracted by the Corr-LDA model would represent the core elements of environmental mixtures. By integrating training results, we developed an interactive web application denoted LEA (Latent-Environment Allocation), which is freely available at http://leamicrobe.jp. The extracted connections between microbial sub-communities and subsets of English words via topics are applicable to various analyses. LEA enables researchers to do the following: 1) clarify the relationship between environments and patterns of microbial community structures. 2) predict the “latent environments” of new samples from, for example, the ocean, a diseased gut, or another unexpected environment, and quickly compare new samples with tens of thousands of existing samples based on their environmental similarity, which makes it easy to detect dysbiosis of the microbiome in the human gut or contaminants in natural environments. 3) search for samples in the >30,000-sample dataset based on an ecological perspective, without depending on exact word matching of queries and sample descriptions. In this paper, we show the patterns found in the human gut and vaginal communities as an example of separations and connections of extracted “environments”, and show how the LEA global map and a semantic search method on the map make it easy to explore patterns in microbial community structures. In addition, as an example of environmental predictions for newly acquired microbiome samples, we show the LEA mapping results for the datasets of the human gut microbiome and the microbiome derived from various natural environments.
We collected sequenced 16S rRNA gene amplicon samples from the MicrobeDB.jp database and performed a phylogenetic analysis using VITCOMIC2[29], which is the metagenomic analysis pipeline improved from VITCOMIC[30], on all samples. This resulted in a dataset containing 30,718 samples with genus level information on their taxonomic composition linked to a document containing sample description information. For this dataset, model inference runs were performed by Corr-LDA with a varying number of topics. The perplexity, which is the performance evaluation index of the model (smaller values indicate a better performance), was sufficiently small for the model with 80 topics (S1 Fig). In the following sections, we discuss the results for the model inferred with 80 topics.
The inferred word subsets and microbial sub-communities for each topic are shown in S2 and S3 Figs. Each topic has a unique subset of words and a microbial sub-community. The structure of a microbial community sample that has a large proportion of a certain topic is likely to contain microbes in the sub-community of that topic, and the description of the sample is likely to contain words in the word subset of that topic. For most topics, the word subset associated with the topic represents a single natural environment or a symbiotic environment with humans.
Based on the topic composition of each sample, the similarities among the samples were visualized using parametric t-SNE[31]. In Fig 1, the dots represent the 30,718 samples and the pictures represent 80 topics. A sample that is mapped near a picture indicates that the sample has a large proportion of that topic. On the map in Fig 1C, the sample (SRS425923), which is located approximately midway between the two pictures, has the two topics (topic #37 and #52) mixed in similar amounts.
The 80 topics can be regarded as latent environments that affect the formation of microbial community structures. Topics form several clusters with dense connections formed by many samples but with sparse or no connections between clusters. Topics can be roughly divided into gut, skin, vagina, oral cavity, ocean, and soil. In addition, there are several isolated topics, which include a coral reef, a mosquito, phyllosphere, etc.
The gut microbiome in healthy adult humans are reported to consist of three[7] or four[24] community types. However, whether truly discrete clusters exist as individual gut microbial communities remains in doubt[32]. For the gut community types (enterotypes), the key genera characterizing each community type have been identified—Bacteroides, Prevotella, and Ruminococcus[7]—although the abundance of key genera varies between samples instead of being discretely clustered[32,33]. Therefore, unlike discrete clusters, e.g., blood types, the compositions of microbial communities are continuously shifting, perhaps as a result of environmental factors. Such continuous variation of the structure of a microbial community can be discerned by our method. 22 topics are related to the gut according to the word subsets associated with each of the topics (S8 Fig), including those with a large proportion of Bacteroides, Ruminococcus, and Prevotella (topics #79, #51, and #24. Fig 1B). However, most of the samples do not reside near a single topic but instead occupy an intermediate position between multiple topics, meaning that the samples are a mixture of several topics. Because many samples with intermediate properties owing to multiple topics exist, there is variability across a limited area of the gut microbiome. Bacteroides is often found in the guts of people who eat diets rich in protein and fat, and Prevotella is often found in the guts of vegetarians[12,34]. In fact, words denoting herbivores, e.g., “pig”, “swine”, “horse”, “bovine”, and/or “rumen”, are frequently found in the Prevotella-rich topic and in topics that are peripherally connected to the Prevotella-rich topic (Fig 1B).
Regarding the vaginal flora, three related topics were found (Fig 1C): the Lactobacillus-rich topic (#37); the topic including Gardnerella, Sneathia, and Atopobium (#52); and the Shuttleworthia-rich topic (#43). Vaginal community types (Community State Type; CST) have previously been examined in detail with five CSTs recognized to date: four (CSTs I, II, III, and V) in which Lactobacillus species dominates and one (CST IV) with various obligate or facultative anaerobes and very few Lactobacillus[9,35]. The two topics detected in our model are consistent with the above results (Fig 1C). Because we used community structure data found at the genus level, we cannot distinguish between CSTs I, II, III, and V, so these communities were identified as a single topic dominated by Lactobacillus. For the second topic corresponding to CST IV in which Gardnerella and Atopobium dominate, the associated samples likely were obtained from African-American women (as estimated by the word subset of topic #52). Interestingly, samples in which the Lactobacillus-rich and Gardnerella-rich topics are mixed in various proportions are frequently found, as indicated by the many dots that connect these two pictures (Fig 1C). Vaginal bacterial communities are known to be stable throughout pregnancy and to be relatively stable throughout the menstrual cycle although changes in the Lactobacillus spp. populations have been observed[36,37]. Therefore, an environmental gradient of unidentified factors may exist in the vagina, which would cause a community structure to exist as an intermediate state between two topics. Another topic related to the vaginal environment is a topic dominated by Shuttleworthia. The presence of Shuttleworthia may be related to bacterial vaginosis[38] or to squamous intraepithelial cervical lesions[39], but its ecology is not well understood. Interestingly, the continuous transition of samples to the Shuttleworthia-rich topic links only with the Gardnerella-rich topic (Fig 1C).
LEA can predict latent environmental topics of newly acquired samples using the Bayesian prediction method with the identified 80 topics (see Methods). By examining the word subsets associated with the mixed topics, the environment in which new samples are found can be estimated. In addition, by training the dimension-reduction function of t-SNE in our system using a neural network procedure, it is possible to arrange the locations of new samples on the global map (Fig 1A) according to their topic compositions without changing the coordinates of previously mapped samples. The topic prediction of a new sample and its placement on the map are implemented by the LEA web application. By uploading the taxonomic assignment file of VITCOMIC2, the placement of the sample on the map can be viewed in a few seconds.
As examples of LEA mapping results, we have analyzed the dataset of a time-series human gut microbiome analysis[40], which consists of fecal samples obtained every day from two male subjects from the US (subjects A and B). The results are shown in S4 Fig. The LEA visualization reproduces the results of David et al.[40], such as the stability of the gut microbiome of subject A over the course of the experiment with the exception of his time in Southeast Asia, and the change in the gut microbiome of subject B caused by an infection. Such results can be easily obtained using the LEA web application.
In a meta-analysis of a large-scale dataset, the existence of systematic bias due to the difference in methods across studies often becomes a problem. We tried to address this problem by processing all samples with a unified information analysis pipeline, but there is a possibility that a further upstream, sample preparation protocol could be a confounding factor. In particular, a bias due to differences in DNA extraction methods often becomes a problem[41]. To assess the impact of different DNA extraction methods of the human gut microbiome analysis on the locations on LEA global map, we conducted LEA environment predictions for the Microbiome Quality Control (MBQC) dataset[42]. This dataset contains 16S amplicon sequencing data from human stool samples, chemostats, and artificial microbial communities. For the same biological sample, there are multiple sequencing data analyzed with different wet laboratories or different DNA extraction methods. The results are shown in S10 Fig. First, most of the samples derived from human feces in the MBQC dataset were properly mapped to the “gut” area of the LEA global map (S10A Fig). As a whole, there was no tendency for samples processed with a specific DNA extraction kit to be mapped only to a specific topic. Therefore, separation of topics on LEA is not necessarily influenced by differences in experimental protocols. However, considering samples that have a same biological origin, some samples were mapped to the nearly same position on the map, and the others were mapped on the location biased by the DNA extraction kits (S10B–S10S Fig). The direction of biases probably differs depending on the position of the true taxonomy composition. Therefore, topics may partially contain systematic bias due to differences in studies, and caution is necessary for interpretation.
As an example of LEA involving a natural environment, Fig 2 shows the topic predictions for 38 samples of microbial communities obtained over a short period of time and at a high density from various points along the Tamagawa river in Japan. The upstream region of the river begins in a deep mountainous region; the middle region flows through a densely-populated zone where there is water from sewage treatment plants and from tributaries that joins the river; and the downstream-most region flows into Tokyo Bay. On May 26 and 27, 2015, we sampled the surface water of the river at 38 points (S5 Fig, S1 Table) and identified the microbes contained in the samples by VITCOMIC2 after sequencing of their PCR-amplified 16S rRNA genes. The genus level taxonomic compositions are shown in Fig 2A. Limnohabitans is the major genus found in the samples from the river. The microbial community structure of the river continuously shifted as it flowed to the estuary, but sample 200 had a greatly different structure. Sample 200 was obtained just under the sewage treatment facility, and its composition probably reflects the microbiome of the treated water. The community structures of samples 10, 20, and 30, which were obtained from brackish water in the estuary, also differed greatly from those collected elsewhere along the river.
The topic predictions for the river samples are shown in Fig 2B. After performing LEA, two topics related to “river” were found: one was topic #3, which frequently occurs together with words such as “Baltic sea,” “lake,” and “river,” and the second was topic #53, which is associated with the words, “river,” “wastewater,” and “urban.” The aforementioned words belong to the dominant topics in Fig 2B. Topic #3 is primarily associated with the upstream region of the river and topic #53 with all areas of the river. The relative proportions of these two topics gradually change as the river flows downstream. Given the word subsets associated with the two topics, topic #3 represents freshwater ecosystems, such as lakes and rivers, and topic #53 represents river areas adjacent to cities. Samples 10 and 20 (from the estuary) are largely associated with topics #11 and #63, which represent the ocean, and, along with sample 30, are associated with topic #56, which represents activated sludge. For the Tamagawa river, about half its water that flows into the estuary is treated water[43]. Thus, our results suggest that the mixing of the river water with seawater greatly changes the community structure and that the river’s ecosystem is greatly affected by it interaction with the urban area.
Topic #45 is associated with the upstream region of the Tamagawa river (sample 360 to 250); the words associated with this topic include “pet” and an indoor environment (Fig 2B). Many of the 16S rRNA sequences associated with topic #45 belong to Blastomonas, a genus associated with domestic wastewater, which is found in tap water, faucets, and shower hoses and is resistant to disinfection[44–46]. Advanced sewage treatment facilities are not found in this region of the Tamagawa river, suggesting that untreated household wastewater is being dumped into the river.
Most of the Tamagawa river samples were mapped near “freshwater” topics #53 and #3 on the global map (Fig 2C), although the topics of sample 200, taken near the sewage treatment plant, and samples 10, 20, and 30, taken from the estuary, diverged, to some extent, from the freshwater topic. Specifically, sample 10 were mapped within “ocean” topics. In this way, the LEA web application can place a new sample appropriately on the global map of existing samples and enables visual and intuitive operation to evaluate its characteristics, e.g., deviations from the expected environments.
For further testing of LEA using external dataset, we conducted environmental predictions of microbiome data derived from a highly diverse environment produced by the Earth Microbiome Project (EMP) [47]. One of the good points about this dataset is that every sample is given an environmental label based on a controlled vocabulary, the EMP Ontology (EMPO). EMPO is a hierarchical framework that captures the major axis of the microbial community diversity and is used to assign samples of EMP to its habitat[47]. Therefore, by comparing the result of LEA mapping with each EMPO label, we can estimate the accuracy of environmental prediction by LEA. For each of the lowest layer label (level 3: most specific habitat name) of EMPO, we examined the location of the samples given that label on the LEA map. The results are shown in S11 Fig. Environmental prediction results have well captured the influence of salinity known as the main axis that determines the community structure[2]. For most samples of water, sediments, biofilms, and soils, saline samples were mapped around the ocean area, and non-saline samples were mapped to freshwater or soil area (S11A–S11H Fig). Regarding the samples derived from the host-associated environments, it was observed that the mapping pattern varied depending on the host species even with the same EMPO label. Also, the EMPO label “Plant surface” intuitively evokes leaf surface of land plants, but most of the EMP samples labeled with “Plant surface” mapped to “ocean” area on LEA. This is because most of the EMP samples used in this study with the label “Plant surface” are derived from the kelp as the host (S11J Fig). In such a case, environmental prediction by LEA gives interpretable results (microbial communities on the kelp surface reflects the oceanic community structure pattern, etc.). When the environmental ontology and the community structure pattern seem to conflict, LEA can be used to infer the reason from the mapping results.
The topic-model approach can be used to semantically search documents related to a user’s query[48,49]. By using the trained model parameters in LEA, existing samples can be searched using natural language such as “forest soil”, or “hot spring”. Instead of needing to search for samples by exactly matching the queried words and the description information associated with samples, we can find the sample using latent environmental topics, using the probability of each sample to generate the query sentence as the score of the sample.
As an example, Table 1 shows the top five scoring samples obtained by querying “What kind of microorganisms are in an oil sands tailings pond?” Oil sands tailings ponds are slag ponds accompanying oil sand development and are highly toxic environments as they contain heavy metals, naphtha, bitumen, and other toxic chemicals. Tailing ponds have heterogeneous environments, being aerobic at their surfaces and anaerobic at their bottoms. Many members of the class Methanomicrobia, including Methanoculleus, Methanolinea, Methanosaeta, Methanobrevibacter, and Methanocorpusculum, which are methanogenic archaea found in the sediment of tailing ponds, contribute to the decomposition of hydrocarbons[50,51]. Given the query, “What kind of microorganisms are in oil sands tailings ponds?”, LEA returned the samples derived from oil sand tailing ponds and oil-water mixtures (Table 1). In addition, LEA returned the sample derived from ocean sediments, although the description of this sample did not contain words such as “oil,” “sand,” or “pond.” Although the microbial community structures of these samples varied in terms of their taxonomy, almost all were composed of methanogenic archaea. Within the machine-learning process, these members of Methanomicrobia are considered simply as variables in the microbial community structure data and their shared characteristics are not recognized (although humans would recognize properties common to all of them given that “Methano-” is at the beginning of each of their names). All high-scoring samples were associated with a large proportion of topic #8 related to methanogenesis (S2 Fig). Therefore, the fact that samples containing many methanogenic archaea were retrieved after querying for “oil sands tailing ponds” indicates that LEA can automatically extract the following two linkages: 1) the association between words such as “hydrocarbon,” “oil,” “tailing,” and “methane,” and the latent environmental topic that represent “methanogenesis,” and 2) the association between methanogenic archaea of various genera and the latent environmental topic that represent “methanogenesis”.
For this study, we applied a correspondence topic model to more than 30,000 samples of microbial community structure data and extracted the latent environments of each sample as topics. By doing so, we obtained microbial sub-communities that can be regarded as “base variables” to describe an entire dataset and associated word subsets that characterize the environments corresponding to the base variables. By visualizing each sample, which is expressed as a linear combination of these base variables, in two-dimensional space, LEA clarifies continuous variation of the microbial community structures linking two or more environments. The difference between continuously connected environments and an isolated environment might mean that only a few samples have been characterized that bridge the isolated environments. Such environments currently include wastewater, the phyllosphere, and environments related to insect symbiosis. Conversely, human-related environments have been vigorously sampled, and therefore we believe that the visualization reported in this manuscript represents a nearly complete picture of those environments related to healthy human adults.
Using the extracted environmental topics, LEA can infer what mixture of core environments influenced the taxonomic compositions of newly acquired samples. In the river microbiome analysis, we showed that samples taken from the brackish water area of the Tamagawa river can be expressed as a mixture of a “freshwater” topic, a “seawater” topic and a “wastewater” topic. Environmental prediction of new samples is performed by a Bayesian approach similar to that used in a microbial source tracking algorithm[52], but using topic sub-communities extracted from a large-scale dataset as source communities, instead of using the samples pre-specified by a user as sources. This allows to compare new samples virtually with tens of thousands of samples related to diverse natural environments and human body sites. Environmental prediction of new samples may be done by fixing the granularities of environmental labels to be used and comparing with samples to which those labels are added in advance[53]. In such a method, however, it is difficult to set the level of granularities, especially when there are multiple structural patterns of microbial communities in a single environment. When analyzing the dynamics of community structures in a single environment, for example, the time series analysis of human gut microbiome or the spatial distribution of river microbiome, it is more useful to use fine-grained environmental labels than to use simple labels such as “river” or “human gut”. Our method clarifies the structural patterns naturally existing in various environments and provides the way to evaluate how new samples transit among them.
By using a neural network algorithm that maps the data to a two-dimensional space, LEA can position new samples onto the existing global map. This mapping system can be regarded as a microbial global positioning system[54] used to specify the position of a new sample based on the positions of existing sample and allows a user to intuitively evaluate the properties of new samples. Dysbiosis, a deviation from the ordinary distribution of a microbial community structure that exists in symbiosis with humans, has been discussed in relation to diseases, especially those of the gut[55]. Because a newly acquired sample, such as one from an ill patient, can be located anywhere on the map, identifying the ideal end-point from a clinical perspective and defining its vector may be useful information when choosing a specific treatment that can transition its community structure to another state[54].
To perform comparative metagenomics based on environmental information, a huge amount of environmentally labeled data ordered as a dataset is required. However, manually labeling such data is nearly impossible, as the amount of available data is increasing too rapidly at present. In addition, as microbial community structures from new environments are characterized, much work will be needed to design the ontologies of the corresponding environmental labels at the appropriate granularities while incorporating all new environments. Furthermore, because binary environmental labels (presence or absence of an environmental property) are often used to characterize the samples, it is not possible to manually and appropriately evaluate samples that have intermediate properties associated with several environments. Our method automatically extracts the relationship between microbes and their environments by assuming that the microbial community structure and the natural language description for a given sample are both generated from a state in which several environments are mixed. The accuracy of the model should increase as more training data are incorporated.
Prior to extending our method for future work, several problems must be solved. First is how many topics are needed to model microbiomes in highly diverse environments. The number of topics in this study, 80, is an arbitrarily determined value in a sense. In fact, the prediction accuracy of the model for the validation set shows that 80 topics are still inadequate and that a more accurate model can be constructed by setting the more number of topics (perplexity, S1 Fig). However, increasing the number of topics may lead to overfitting, and too large a number of topics may make the map difficult to visualize and interpret. Therefore, we aimed to explain the data with as few topics as possible while keeping the overall prediction accuracy. We are not claiming that microbial communities can be explained by a combination of 80 patterns. The model used in this study is a practical choice to facilitate the interpretation of the whole picture of the microbial community structures and to provide a tool to explore interesting patterns. In the future, as the number of samples acquired from various environments increases, it will be necessary to set a larger number of topics. Nevertheless, from the results of experiments with a large number of topics, the characteristics of the environments considered in this paper (i.e., the gut and vagina) are robust, and an increase in the number of topics would mainly lead to the generation of a topic related to a single research study (e.g., “whale skin” as part of a new topic separated from topic #14, which contains “coral reef”). Second is a sampling bias among environments. Depending on the environment, the number of samples studied so far varies greatly. Since LEA determines topics based on the prediction accuracy of the validation set samples, LEA tends to express an environment with a large number of samples in high resolution by placing many topics in it, while environments with a small number of samples tend to be compressed into a limited number of topics. Currently, the environment related to humans, especially to the intestinal tract, have high resolution, but the environment related to freshwater or hot springs has relatively few samples and the separation of topics can be insufficient (S9 Fig). Therefore, attention should be paid to interpretation concerning LEA predictions for such natural environments in which relatively a few number of samples were studied. By incorporating large-scale data of microbiomes sampled from the natural environment, such as data from the Earth Microbiome Project[47], high-resolution topics will be obtained for those environments in the future. The third problem is, though common to any meta-analysis, a bias due to the difference in experimental methods. In the environmental prediction results of the MBQC dataset by LEA, it was hardly the case that the difference in experimental methods yields a large difference in coordinates on the LEA global map. Nonetheless, depending on the area on the map, such a bias may have the effect of placing the sample close to a particular topic. Ideally, there should be as much data as possible processed in a unified experimental protocol.
We do not know if all topics used for our research specifically express a set of related environmental parameters, except for topics such as those representing “methanogenesis.” In the future, the environmental parameters that determine the microbial community structure will need clarification that can be accomplished by examining the relevance of various metadata and topic compositions and transitions.
Genus-level taxonomic composition data for 48,873 sequenced 16S rRNA gene amplicon samples were obtained from MicrobeDB.jp (http://microbedb.jp/MDB/), which is an integrated, publically available database for microbes in which all metagenomic data registered in the International Nucleotide Sequence Database Collaboration Sequence Read Archive (SRA) prior to August 2014 are stored. MicrobeDB.jp includes reanalyzed taxonomic compositions of all samples using the unified analysis pipeline. Briefly, for all original sequence data registered in the SRA, we trimmed adapter sequences and filtered out low-quality sequences, sequences derived from the PhiX genome, and sequences derived from the human genome. For all high-quality sequences in each sample, we searched for similar sequences in the VITCOMIC2[29] database (http://vitcomic.org) using CLAST[56] and then performed taxonomic assignments on these sequences. Finally, the number of sequences assigned to a specific genus were summed for each sample. As a result, we obtained a dataset for which each SRS ID (identifier associated with a sample in SRA) had a genus-level taxonomic composition. All samples with <1,000 sequences with an assigned genus were discarded, and samples with >10,000 assigned sequences were subsampled as 10,000 sequences.
To obtain metadata for the samples, XML files for the SRS IDs registered prior to August 2014 were downloaded from the SRA database. The XML file for each SRS ID contains descriptions of the properties of the corresponding sample, such as pH values and a description of the environment from which the sample was obtained. We extracted the text sandwiched between all tags (e.g., research titles, scientific names, geographical locations, and sample descriptions) in those XML files, lemmatized all words, and organized them according to a bag-of-words model (with counts of the number of times each word appears). We removed all English stop words from the extracted text. Additionally, all words corresponding to any of the following conditions were removed: 1) words that contain numbers or symbols that are not alphabetic (in many cases these were the project-specific sample identifiers); 2) words in which the letters “A”, “T”, “C”, or “G” occupied ≥70% of the word length (often tag sequences or primer sequences); 3) words generally used in many samples, e.g., genome and metagenome (S2 Table); 4) words used only for a single research study; and 5) words that appeared <20 times or in >30% of the samples in the dataset. Samples with all words removed according to the aforementioned conditions were themselves discarded.
Finally, we integrated the taxonomic composition and sample description datasets to construct a dataset consisting of only samples containing both features. This dataset contains 30,718 samples, each of which has a taxonomic composition (a count of each genus in the sample) and a description of the properties of the sample (bag-of-words). The number of genera in the final dataset (vocabulary of taxonomic composition data; S3 Table) is 1675, and the number of unique words in the dataset (vocabulary of sample description data; S4 Table) is 764. The final dataset is publicly available at http://palaeo.nig.ac.jp/Resources/lea2018/.
We assumed that each sample had a multinomial distribution of latent environments and that both the observed genera and the sample description document were generated according to the mixing ratio of those environments or topics. To infer topics, we applied Corr-LDA[25,26] to the dataset.
In our LEA system, Corr-LDA model was applied to our dataset as described below. Each sample d (d = 1 …D) has taxonomic composition data wd = {wdn} (n = 1…Nd) and description data td = {tdm} (m = 1…Md). Here, Nd is the number of sequences with an assigned taxonomy in sample d, wdn is the taxonomy assigned to the nth sequence in sample d, Md is the number of words in the description given to sample d, and tdm is the mth word in sample d. In a topic model, it is assumed that all elements in the data have a latent topic. The latent topic of the nth sequence in sample d is defined zdn ∈ {1 …K}, and K is the number of latent topics that have been pre-specified. The latent topic of the mth word in sample d is defined cdm ∈ {1 …K}, with K again representing the number of latent topics, which is the same for both sequences and words. Topics assigned to each sequence and each word is undefined prior to analysis and inferred using the entire dataset. For the entire dataset, the joint probability distribution for the taxonomic composition data W, the description data T, the latent topics Z for sequences, and the latent topics C for words was written as follows:
P(W,T,Z,C|α,β,γ)=P(Z|α)P(W|Z,β)P(C|Z)P(T|C,γ)
where α, β, and γ are hyper-parameters of prior distributions for topics, taxa, and words, respectively.
We assumed that latent topics Z for genera appearing in each sample d is generated according to the multinomial distribution θd and that the prior of θd is the asymmetric Dirichlet distribution having αz (z = 1 …K) as hyper-parameters. θd can be integrated out and P(Z|α) can be written as follows:
P(Z|α)=∏d=1D∫P(Zd|θd)P(θd|α)dθd=(Γ(∑z=1Kαz)∏z=1KΓ(αz))D∏d=1D∏z=1KΓ(Nzd+αz)Γ(Nd+∑z=1Kαz)
where Nzd is the number of sequences that are assigned to a topic z in sample d and Г is the gamma function.
The genera appearing in a sample is generated according to a multinomial distribution φz when its latent topic is z. φz can be interpreted as a sub-community of genera associated with latent topic z. We assumed the symmetric Dirichlet prior for φz. φz can also be integrated out and P(W|Z, β) can be written as follows:
P(W|Z,β)=∏z=1K∫P(W|z,φz)P(φz|β)dφz=(Γ(βV)Γ(β)V)K∏z=1K∏w=1VΓ(Nzw+β)Γ(Nz+βV)
where Nzw is the number of sequences assigned to genus w with a topic assigned to z, Nz is the number of sequences assigned to a topic z, and V is the number of unique genera in the dataset.
Topics for words in sample description data were generated according to the following:
cdm∼Multinomial({NzdNd}z=1z=K)
where Nd is the number of genus-assigned sequences in sample d. Thus, topics for words are conditional on topics assigned for genera.
A word appearing in a sample is generated according to the multinomial distribution ψc when its latent topic is c. ψc can be interpreted as a subset of words representing latent topic c, and we assumed the symmetric Dirichlet prior for ψc. As in the case of P(W|Z, β), P(T|C,γ) can be written as follows:
P(T|C,γ)=∏z=1K∫P(T|c,ψc)P(ψc|γ)dψc=(Γ(γT)Γ(γ)T)K∏z=1K∏t=1SΓ(Mzt+γ)Γ(Mz+γS)
where Mzt is the number of words t with a topic assigned to z, Mz is the total number of words assigned to a topic z, and S is the number of unique words in the dataset.
We assumed the asymmetric Dirichlet prior only for the topic multinomial distribution, and the symmetric Dirichlet prior for the genera and word multinomial distributions because samples for which their microbial community structure had been analyzed previously are more likely to have been acquired from human symbiotic environments, suggesting that a bias might also exist for the topic occurrence probabilities. It was reported that setting an asymmetric Dirichlet prior for a topic distribution is effective for robust inference of a topic model[57].
The posterior distributions of the latent topic Z for genera and the latent topic C for words were approximated by the collapsed Gibbs sampling method[26,58]. Hyper-parameters (α, β, and γ) were updated in each step during the Gibbs sampling by the fixed-point iteration method[59].
To determine the number of topics K, we randomly divided the dataset into 25,718 samples as a training set and a group of 5,000 samples as a test set, and then the test set perplexity was calculated using the results from the training set with varying numbers of topics. Perplexity is an index for measuring the predictive performance of a held-out test set, and the smaller the value, the better the performance. We ran five Markov chains using different initial values and inferred the model parameters. Next, using the model parameters inferred from the training set, the topic composition of each sample in the test set was estimated using 50% of the sequences in each sample, and the generation probability of genera assigned to remaining 50% of the sequences was calculated. S1 Fig shows the average perplexities and standard deviations obtained by inference with five Markov chains for 5 to 300 topics. Setting too large a number of topics reduces the interpretability of the training results and might cause overfitting for existing samples, so we fixed the number of topics as 80.
Finally, we ran a Markov chain using 30,718 samples with the number of topics set at 80 and acquired the topic composition θ for each sample, the genera probability φ for each topic (microbial sub-community in each topic), and the word probability ψ for each topic (word subset corresponding to each topic) when the joint likelihood converged after a sufficient number of Gibbs iterations. The implementation of Corr-LDA used in this study is available at https://github.com/khigashi1987/CorrLDA. Parameters used were -I 1000 -K 80 (1000 Gibbs iterations and 80 topics).
Word subsets and microbial sub-communities associated with each topic are listed in S2 and S3 Figs. All word-cloud images were generated using the word-cloud generator in Python (https://github.com/amueller/word_cloud).
The structure of sub-communities differs greatly between most topic pairs (S6A Fig). The topic pair with the most similar sub-communities is formed by the soil-environment topics #19 and #54, although the structures of these topics are still greatly different (S6B Fig). Similar comparison on word subsets of topics shows that there are many more similar topic pairs with respect to word probabilities (S6C Fig). The topic pair with the most similar word subsets is formed by the vagina-associated topics #43 and #52 (S6D Fig). In both topics, two words, “female” and “vaginal”, dominate greatly in their probabilities and reflect similar environmental concepts (“vagina”). However, in a topic #52, two words, “african” and “american”, have relatively large probabilities, whereas in a topic #43 the probabilities of those words are very small (S6D Fig). Unlike microbial sub-communities of topics, it is natural that there are multiple similar topics for word-subsets. That is because there are some environments which have different patterns of the community structures but are difficult for the human to distinguish and describe those, as with the case of the human gut environment (enterotypes[7]). Nonetheless, like these vagina topics, some topics may reflect slight differences that appear in the sample descriptions.
As for what kinds of environment the topics show, it is strongly influenced by the sampling bias by the previous research. The vigorously studied environment can be modeled at the high resolution, resulting in a large number of topics associated with the environment. We investigated the number of topics related to a specific environment by calculating the generation probability of specific words for each topic. If the topic had a probability of generating the word “gut” above 5%, we considered that topic to be related to the gut-associated environment. Similarly, we calculated the sum of the generation probabilities the words “oral”, “cavity”, “dental”, “plaque”, “gingiva”, “tonsil” and “saliva” for the oral cavity-associated environment, the word “skin” for the skin-associated environment, the words “vaginal” and “vagina” for the vagina-associated environment, the words “marine”, “sea”, “ocean”, “seawater” and “saline” for the ocean-associated environment, the words “freshwater”, “lake” and “river” for the freshwater-associated environment, the words “soil”, “agricultural” and “field” for the soil-associated environment, and the words “hot” and “spring” for the hot spring-associated environment for each topic. As a result, there were many topics associated with the gut environment (22 topics), and the number of topics related to the natural environment tended to be small (S8 Fig). When examining the number of samples with more than 50% of each “environment-associated topics”, similar trends were observed (S9 Fig).
After the above process, all samples in the dataset were expressed as 80-dimensional, real-valued vectors showing topic compositions. In general, for visualization of 80-dimensional vectors, it is effective to arrange sample points in two or three-dimensional space by dimensionality reduction, and various dimensionality reduction methods, such as principal component analysis or the multidimensional scaling method, can be applied. For this approach, we used t-SNE (t-distributed Stochastic Neighbor Embedding)[60], which embeds sample points in a low-dimensional space while maintaining the local structures between sample points in the original high-dimensional space. However, simple t-SNE method is inadequate for newly acquired samples. When predicting the topic composition of a new sample and comparing it with existing samples, the distance calculation and the cost minimization for the entire dataset must be performed again with the new sample, which might cause a random change of the coordinates in the low-dimensional space each time a new sample is added. Therefore, we adopted the parametric t-SNE method[31], which trains a function with the same behavior as t-SNE using a neural network. This neural network inputs an 80-dimensional vector and outputs two-dimensional coordinates. Weights of the neural network are trained with the same loss function as for the normal t-SNE. We constructed a four-layer, feed-forward neural network (the number of nodes is 80, 160, 160, 640, and 2). We used the Rectified Linear Unit as the activation function of the nodes in all layers except the last one and the linear activation function in the last layer and trained the weights using the mini-batch stochastic gradient descent method. We implemented parametric t-SNE by Theano[61] and Keras (https://github.com/fchollet/keras) and used Adam[62] as the optimizer. After a sufficient number of epochs, we obtained the coordinates of all samples by inputting topic compositions of samples into the neural network and then visualized samples in a scatter plot. At the same time, we obtained the coordinates of topics in two-dimensional space by inputting one-hot vectors (80-dimensional vectors with a single 1 value and 0 for all other values) into the neural network. On those coordinates in a scatter plot, we mapped pictures corresponding to the word subset of each topic. Thus, the sample plotted in a position close to a given picture means that the mixing ratio of that topic is very high for the sample. All pictures used in this study are in the public domain.
The topic composition of a new sample was estimated using the taxonomic composition of the new sample, the hyper-parameter α, and the genera generation probability φ, learned from the training samples. First, the DNA sequence data from a new sample was analyzed by VITCOMIC2 and converted into taxonomic composition data. The estimation of the topic composition in a Bayesian approach requires consideration of all possible assignments of sequences to topics, but direct estimation of the posterior distribution of the topic composition is intractable[52]. In LEA, the posterior distribution of the topic composition of a new sample is approximated by performing Gibbs sampling with randomly initializing topics assigned to sequences in the new sample. For the genus w assigned to the nth sequence of a new sample d, the latent topic z was sampled according to the following conditional distribution:
P(zdn=k|W,Z∖dn,φ,α)∝φkwNkd∖dn+αkNd∖dn+∑z=1Kαz
where φkw is the generative probability of genus w when zdn is k, αk is the hyper-parameter of the topic probability; Nkd\dn is the number of sequences assigned to topic k, except for the nth sequence in sample d; and Nd\dn is the total number of sequences in sample d minus one. This distribution is similar to the sampling formula used in SourceTracker[52], but LEA uses microbial sub-communities of 80 topics as source communities, instead of using a sample set specified by the user as the source in SourceTracker. LEA estimates the topic composition of the new sample using only sequence information because we assume that the primary use of LEA is to predict the environment of the sample for which description information is not available, or, more importantly, to predict contamination of the unexpected environment. After a sufficient number of Gibbs-sampling iterations, the topic composition of the new sample was expressed as an 80-dimensional vector and converted to the coordinates on a two-dimensional map by the feed-forward neural network learned with training samples. By arranging the transformed coordinates of the new sample on the same two-dimensional map as the existing samples, we could compare the features of the new sample with those of all samples in our database.
We provide REST (representational state transfer)-style APIs (application programming interfaces) to access all the model parameters of LEA and the environmental prediction function of the user sample at http://snail.nig.ac.jp/leaapi/. With the HTTP POST method, environmental predictions for the new samples can be calculated from the command line interface without going through the web application. There is also an API that provides information on the instability of the posterior distribution calculation of topic proportions for the new samples. The function computes topic proportions by drawing 100 samples from 100 independent Gibbs sampling chains with different initial settings and by averaging 100 samples. This also provides standard deviations of each topic proportion estimates. These values can be regarded as “goodness of fit” of the new samples to the LEA model. In the calculation results on the Tamagawa river microbiome, the topic proportions shown in Fig 2 was stable at any point (S7 Fig).
The training results from the 30,718 samples and their visualization are available as an interactive web application at http://leamicrobe.jp. The back end of the application is written in C language and Python, and the front end is written in JavaScript. Users can examine the taxonomic and topic compositions of each sample, and the sub-community and word subset of each topic on the global map. By uploading the taxonomic composition data obtained via VITCOMIC2, it is possible to place user samples on the global map after several seconds of calculation for estimating topic compositions. Because LEA accepts only taxonomic composition data generated by VITCOMIC2, VITCOMIC2 must be used to obtain taxonomic compositions from raw sequence data.
Furthermore, with the use of the training results in our model, it is possible to perform a sample search that does not depend on exact matching between the query text and sample metadata. The sample-retrieval process uses the word generation probability ψ in each topic and the topic composition θ in each sample as learned with the existing samples. The search query consists of free words, i.e., several English words or English sentences. First, the search query is divided into words. If the search query contains words that do not exist in the vocabulary of LEA (764 words), those words are simply discarded. LEA also carries out preprocessing of query words (removal of English stop words and lemmatization) same as the training step of LEA model. After that, the score of sample d is calculated using the following equation:
Score(d)=P(q|d)=∏n=1N∑z=1KP(qn|z)P(z|d)=∏n=1N∑z=1Kψzqnθdz
where q = {qn} (n = 1 to N) is a search-word set and N is the number of valid words in the search-word set. This means that we use the probability that the sample d generates the search query q as the score of the sample d. LEA calculates scores of all samples in LEA dataset (30,718 samples) and sorts them in descending order. By entering free words in the search window of the web application, it is possible to highlight samples with a high score, meaning that those samples are semantically related to the search query. If the taxonomy name of a microbe (or its synonym(s)) is included in the search query, the score is scaled according to the abundance of that microbe in each sample. If the search query contains the name of higher taxa than a genus level, the score is scaled according to the sum of abundances of microbes below the input taxa (taxonomy based on NCBI Taxonomy). This makes it possible to search samples by queries such as “Gemmatimonadetes in the ocean”.
As examples that show how LEA mapping can be used, we acquired four microbiome datasets. 1) Intestinal microbiota data from two subjects over the course of a year[40]. 2) Intestinal microbiota data obtained by various DNA extraction methods from Microbiome Quality Control Project[42]. 3) Microbiota data from various natural environments from Earth Microbiome Project[47]. 4) Microbiota data from the upper region to the estuary of the river Tamagawa.
The intestinal microbiota data are those reported by David et al. (PRJEB6518)[40]. In that study, every day for all or most of a year, the microbial 16S rRNA genes from the feces of two healthy adult men (subjects A and B) were PCR amplified and sequenced. The sequence data were downloaded from the European Nucleotide Archive (ENA), and the taxonomic compositions of the samples were analyzed by VITCOMIC2. LEA was used to predict the topic compositions from the taxonomic composition data and input the topic compositions into the neural network to obtain coordinates of the samples on the global map. For the topic composition prediction, samples with <1,000 sequences assigned to genera were discarded, and samples with >10,000 sequences were sub-sampled to 10,000 sequences. For comparison, the dataset of Japanese gut microbiome samples by Nishijima et al. (PRJDB3601)[63] was also downloaded, their taxonomic compositions were estimated by VITCOMIC2, their topic compositions were estimated by LEA, and the samples were mapped on the global map. The number of gut samples was 314 for subject A, 184 for subject B, and 265 for the Japanese population.
To assess the impact of different DNA extraction methods of the human gut microbiome analysis on the locations on LEA global map, we used the Microbiome Quality Control (MBQC) dataset reported by Sinha et al. (PRJNA260846)[42]. This dataset contains 16S amplicon sequencing data from human stool samples, chemostats, and artificial microbial communities. For the same biological sample, there are multiple sequencing data analyzed with different wet laboratories or different DNA extraction methods. The sequence data were downloaded from the SRA, and the taxonomic compositions of the samples were analyzed by VITCOMIC2. LEA was used to predict the topic compositions and obtain coordinates of the samples on the global map. For the topic composition prediction, samples with <1,000 sequences assigned to genera were discarded, and samples with >10,000 sequences were sub-sampled to 10,000 sequences. The number of samples was 1558 for human stool samples, 228 for chemostats, 133 for fecal artificial communities, and 130 for oral artificial communities.
For the microbiome data from the highly diverse natural environments, we used the subset of Earth Microbiome Project (EMP) dataset[47]. The original dataset of EMP contains more than 27,000 samples of 97 studies, but they also provide the subset of the sample list which gives as equal as possible representation across EMP Ontology (EMPO)-level 3 sample types and across studies within those sample types. We downloaded the table of sample identifiers for the EMP subset of 2,000 samples (emp_qiime_mapping_subset_2k.tsv) from the FTP site of the EMP project (ftp://ftp.microbio.me/emp/release1). Raw sequence data of each sample were downloaded from the ENA, and the taxonomic compositions of the samples were analyzed by VITCOMIC2. LEA was used to predict the topic compositions and obtain coordinates of the samples on the global map. For the topic composition prediction, samples with <1,000 sequences assigned to genera were discarded, and samples with >10,000 sequences were sub-sampled to 10,000 sequences. The number of samples used in the LEA mapping was 1760.
For the spatial microbiome distribution, we sampled the Tamagawa river in Japan. Its source is at the peak of Mt. Kasatori, and it flows through the Yamanashi, Tokyo, and Kanagawa prefectures. The river’s basin area is 1,240 km2, its total length is 138 km, and the altitude of its source is 1,953 m. We sampled the river’s water on May 26 and 27, 2015 at 38 points from its upper region to its estuary (S5 Fig, S1 Table). Sampling was carried out during sunny weather and no precipitation had occurred for the 4 days before sampling. Samples were collected from the river’s surface water (defined as flowing water no deeper than ~30 cm from the river surface) using a ladle at the riverbank without disturbing the sediment. At least 500 ml of water was sampled at each site and then immediately injected into a sterile polypropylene bottle. Samples were transported in an insulated box containing a refrigerant and were moved to a refrigerator at 4°C within the day. The water of each sample was filtered through a 0.2-μm membrane within 5 days of sampling. Each filter was quickly placed in a sterile tube and stored frozen at –20°C. DNA was extracted from the material on each filter using the PowerWater DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA) with bead homogenization on a Micro Smash MS-100R (TOMY, Tokyo) at 3,000 rpm for 30 s. PCR amplification was performed using the primers 342F and 806R, which are V3–V4 region universal primers for prokaryotic 16S rRNA genes[64], and Ex Taq DNA polymerase (Takara, Shiga, Japan) with a denaturation step at 98°C for 2 min, followed by 30 cycles at 98°C for 10 s, 50°C for 30 s, and 72°C for 40 s, and a final extension step at 72°C for 10 min. PCR products were purified with the Agencourt AMPure XP system (Beckman Coulter) and sequenced using an Illumina MiSeq sequencer (Illumina, San Diego, CA, USA). The taxonomic composition of each sample was analyzed by VITCOMIC2, and topic compositions for the samples were predicted by LEA. The Tamagawa river microbiome data has been deposited in DDBJ BioProject database; accession number: PRJDB5936.
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10.1371/journal.pgen.1002640 | Population Structure of Hispanics in the United States: The Multi-Ethnic Study of Atherosclerosis | Using ∼60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population.
| Using genotype data from about 60,000 distinct genetic markers, we examined population structure in 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By comparing genetic ancestry of MESA Hispanic participants to reference samples representing worldwide diversity, we show major differences in ancestry of MESA Hispanics reflecting their Caucasian, African, and Native American origins, with finer differences corresponding to North-South geographic origins that separate MESA Mexican versus Central/South American samples. Based on our analysis, we define four subgroups of the MESA Hispanic cohort that show close agreement with the following self-identified regions of origin: Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. We examine association of triglycerides with selected genetic markers, and we further demonstrate the importance of considering differences in genetic ancestry (or factors associated with genetic ancestry) when performing genetic studies of the United States Hispanic population.
| Although epidemiologic studies often regard Hispanics in the United States as a homogenous group, U.S. Hispanics have a complex population structure comprised of many overlapping subgroups, and also vary markedly in environmental and cultural factors linked to country of origin and history of immigration to the United States. A widely recognized distinction from genetic analysis has been between Hispanics carrying primarily Caucasian and African ancestry, versus those having predominantly Caucasian and Native American ancestry [1], [2], [3], with little admixture observed between individuals of predominantly African versus Native American ancestry. In the MESA Hispanic cohort, previous work using 199 ancestry informative markers (AIMs) to estimate proportions of ancestry in a subset of 705 individuals identified strong differences in proportions of European, Native American, and African ancestry by self-identified country/region of origin, with Mexican/Central Americans having the highest proportions of Native American ancestry, Puerto Ricans having the highest European ancestry, and Dominicans the highest African ancestry [3]. Recent studies have also documented diversity and population substructure within the Native American founder populations [4].
The Multi-Ethnic Study of Atherosclerosis (MESA) provides one of the largest and most thoroughly-characterized samples of Hispanic individuals to date. MESA has 1,374 unrelated Hispanic individuals and a total of 2,174 subjects of self-reported Hispanic ethnicity, including pedigrees. Most self-reported Hispanic participants also reported more detailed self-identification corresponding to Central America, Cuba, the Dominican Republic, Mexico, Puerto Rico or South American origin (Table S1). As MESA participants, each of these individuals was assessed for subclinical cardiovascular disease and risk factors that predict progression to clinically overt cardiovascular disease. In addition, genome-wide genotyping of >800,000 SNPs was performed for each of these individuals through the NHLBI SHARe program (MESA SHARe). These valuable phenotypic and genotypic data provide opportunities to perform Genome-Wide Association (GWA) studies for many cardiovascular phenotypes. Proper GWA analysis of the MESA Hispanic cohort requires a clear understanding of the population structure of Hispanics in the United States.
Using the recently available genome-wide genotype data, we perform population structure analysis of an unrelated subset of 1,374 individuals from the MESA Hispanic cohort. By Principal Component Analysis (PCA) [5], [6] and model-based cluster analysis [7], [8], we identify clear patterns of diversity across the MESA Hispanic cohort. We further draw on samples from the HapMap phase III [9] and Human Genome Diversity Panel (HGDP) [10], [11], representing worldwide genetic diversity including European, African, and Native American samples, to inform our population structure analysis. By combining dense genotype data from MESA SHARe with the available worldwide reference panels, we achieve greater resolution in examining intra-continental diversity, particularly among Native American ancestral populations.
We perform cluster analysis on the first four principal components (PCs) of ancestry to identify four distinct subgroups of the MESA Hispanic cohort. Based on participant self-identification, we find these subgroups represent primarily Central/South America, the Dominican Republic and Cuba, Mexico, and Puerto Rico. To demonstrate a principled approach to genetic association analysis taking into account genetic diversity in the MESA Hispanic cohort, we perform analysis of SNPs in the lipoprotein lipase (LPL) and tribbles homolog 1 (TRIB1) gene regions with triglycerides in the full MESA Hispanic cohort, as well is in stratified analyses to assess evidence for association within each of the four Hispanic subgroups. Our genetic analysis indicates pooled analysis provides the best power when there is only modest heterogeneity in genetic effects, while stratified analysis offers better resolution to detect genetic loci in which SNP effects are limited to or much stronger within a single subgroup of Hispanics.
Principal components (PCs) of ancestry were computed for 1,374 unrelated individuals from the MESA Hispanic cohort using the program SMARTPCA, which is distributed with the software package EIGENSTRAT [5], [6]. The individuals included in the analysis represented six major countries/regions of origin: Central America, Cuba, the Dominican Republic, Mexico, Puerto Rico, and South America, with the exact counts detailed in Table S1. The principal component analysis was performed using 64,199 autosomal SNPs typed through MESA SHARe, with SNPs selected for minimal linkage disequilibrium (LD) among MESA Hispanics, and availability of genotypes in the HapMap phase III and HGDP reference panels.
The resulting PCs were projected to HapMap phase III and HGDP samples, and the first four principal components of ancestry are displayed for an unrelated set of MESA Hispanic subjects and key reference populations in Figure 1. Among the many diverse populations in these reference panels, the HapMap phase III includes a sample of 30 unrelated individuals of Mexican ancestry from Los Angeles, California (MXL), while the HGDP includes 29 unrelated Native American individuals, further classified as either Colombian, Karitiana, Maya, Pima, or Surui. A geographic representation [10] of the sampling locations of the HGDP Native American individuals indicates they span Northern Mexico (Pima), Southern Mexico (Maya), the region of Colombia near the border with Brazil (Colombian), and Southwestern Brazil (Karitiana and Surui). These Native American samples provide a valuable resource to inform potential differences in Native American ancestry across the MESA Hispanic cohort. That said, there are notable gaps in coverage provided by the HGDP with, for example, no representation of Taino Arawaks, widely noted as a major source of Native American ancestry for present day Caribbean Hispanics [12]. Indeed, there is a practical limitation to obtaining genetic samples from Taino Arawaks (as well as other Native American founder populations) because few or no individuals survived past the period of European colonization.
The first two PCs of ancestry display strong population stratification across the Hispanic cohort. The three predominant sources of ancestry correspond to Caucasian, Native American and African founder populations, with the vast majority of MESA Hispanic individuals lying along two edges of a triangle, corresponding to two major clusters broadly representing individuals reporting Mexican versus Caribbean (Puerto Rican, Dominican or Cuban) origin. Projection of these principal components to all four MESA ethnic groups (Figure S1) as well as the worldwide diversity panels comprised of HapMap phase III and HGDP samples (Figure S2), we find the Mexican cluster predominantly represents admixture of Caucasian and Native American ancestry, while the Caribbean cluster reflects admixture of Caucasian and African ancestry. Although these two clusters are remarkably well separated from one another, evidence for Native American ancestry among Caribbean Hispanics is reflected in the plot of PC2 versus PC1. This evidence emerges forth when the PCs of Hispanics are viewed together with those of African Americans (Figures S1 and S2) who populate a more extreme (i.e. less admixed) position on the plot.
The plot of the third and fourth PCs reveals additional structure, separating Puerto Rican and Central/South American subjects into two distinct groups that are further separated from the rest of the MESA Hispanic cohort. Interestingly, population structure shown in the plot of PC4 versus PC3 is specific to MESA Hispanic and HGDP Native American samples, with little separation of other worldwide populations (Figures S1 and S2). A linear axis defined by PC3 and PC4 aligns with South-to-North geography of HGDP Native American subgroups (Colombian, Karitiana, Maya, Pima and Surui) with the South American Colombian, Karitiana and Surui at one end and the North American Pima at the other. The same axis corresponds closely with Mexican versus Central/South American origin, building on previous evidence that geographic and genetic distance show good correlation among Native Americans [13], and supporting the natural hypothesis that diverse Native American founder populations contributed to present day Hispanic populations in these regions. None of the available reference panels aligned with the Caribbean (Puerto Rican, Dominican or Cuban) samples along the third and fourth principal components of ancestry, a reasonable result given none of the known Native American populations of the Caribbean region, such as Taino Arawaks [12], were included in the available reference panels [10]. These data suggest Native American founders contributing to present day Caribbean populations are genetically distinguishable from those in Mexico and Central/South American.
We did not identify any clear patterns of population substructure in the MESA Hispanic cohort in plots of the higher order PCs (Figures S1 and S2). We further examined the proportion of variance explained by the strongest PCs of ancestry. The first four PCs of ancestry explained 1.90%. 0.85%, 0.141% and 0.125% of variance, respectively, compared to 0.093%–0.109% of variance explained by each of the remaining PCs corresponding to the largest 100 eigenvalues from the PCA. Based on this combination of evidence from the scatter plots and eigenvalues from PCA, we determined it was sufficient to focus subsequent genetic analyses on the first four PCs of ancestry.
Using the same set of 1,374 unrelated individuals from the MESA Hispanic cohort and the same 64,199 autosomal SNPs as used for PCA, we performed model-based cluster analysis using the software ADMIXTURE [7]. We performed analysis for K = 2 to K = 7 distinct ancestral populations. Keeping in mind that the model-based cluster analysis does not make use of the self-identified country/region of origin information available through MESA, we see remarkable structure in the results plotted by region (Figure 2, Figure S3). For K = 3, the putative Caucasian ancestral population accounts for a considerable proportion of ancestry across all countries/region of origin, ranging from 37% in Central Americans to 73% among Cubans, while the putative African ancestral population accounts for as much as 43% of ancestry overall in Dominicans, and as little as 4% of overall ancestry among Mexicans. For K = 3, a third group corresponds to the Native American ancestry population, accounting for only 6% of ancestry overall in Cubans and Dominicans, and as much as 45 and 48% in Central Americans and Mexicans, respectively (Table 1). We also note considerable diversity within each country/region of origin with, for example, 34% of Cubans having greater than 90% Caucasian ancestry, while another 15% of Cubans have less than 50% Caucasian ancestry.
For K = 4 and K = 5, the first two groups correspond to Caucasian and African ancestral populations as seen for K = 3, while additional ancestral populations appear to account for regional differences in Native American ancestry (Table 1, Figure 2). Comparing results from K = 3 and K = 4, we see remarkable agreement in the relative proportions of Caucasian, African and Native American ancestry across all Hispanic countries/regions of origin. However, K = 4 shows a very clear separation in assignment of Native American ancestry to distinct groups for individuals of self-identified Mexican versus Puerto Rican origin, with Central/South Americans demonstrating a mixture of these two Native American ancestral populations. Results from K = 5 suggest further separation in the Native American ancestral populations, with one group represented predominantly among Mexicans, one group predominantly among Puerto Ricans, and a third group represented primarily in Central/South Americans. Due to the relatively lower proportion of Native American ancestry among individuals of Cuban and Dominican origin, it is difficult to comment definitively on their sources of Native American ancestry.
We performed k-means clustering using the first four principal components of ancestry, to define four major groups within the Hispanic cohort. The resulting clusters of ancestry showed notably good agreement with self-identified country/region of origin, and were accordingly identified with Central/South America (abbreviated “CSAmer”), the Dominican Republic and Cuba, Mexico, and Puerto Rico (Table 2).
Each of the clusters was labeled as such because it carried the vast majority of individuals self-identifying with the corresponding region, i.e. the Mexican cluster contained 658 of 708 unrelated individuals with Mexico as their self-identified country of origin. In most cases, it was also true that a given cluster carried very few individuals self-identifying with a different country/region of origin, with the Dominican/Cuban cluster being the one notable exception. The Dominican/Cuban cluster is labeled as such because it contains 199 of 203 self-identified Dominican individuals and 49 out of 50 self-identified Cuban individuals from the unrelated subset of individuals reported in Table 2. However, this cluster also includes fourteen to thirty unrelated individuals self-identifying with each of the following: Central America, Mexico, Puerto Rico, and South America. This result reflects the fact that the Dominican/Cuban cluster tends to capture individuals carrying relatively little Native American ancestry, with varying proportions of Caucasian and African ancestry. While this genetic profile is characteristic of individuals self-identifying as Dominican or Cuban in the MESA Hispanic cohort, such individuals are also found throughout Latin America.
Multiple studies have reported association between SNPs in the lipoprotein lipase (LPL) and tribbles homolog 1 (TRIB1) gene regions with triglyceride levels in GWAS of Caucasians [14], [15], [16], [17], yet it remains unclear whether the same gene regions show association in Hispanics [18]. A recent paper probed association in samples of Mexican individuals for SNPs reported in these gene regions in GWAS of Caucasians, identifying suggestive, but not statistically significant evidence of association [18]. Here, we perform a more comprehensive study looking at an expanded set of SNPs across the more diverse set of individuals included in the MESA Hispanic cohort.
We selected SNPs rs10096633 and rs12678919 reported in previous studies [14], [15], [16], [17], [18], and examined association between 33 SNPs in the MESA Hispanic cohort (8 genotyped and 25 imputed) that exhibited strong linkage disequilibrium (LD) with the LPL index SNPs in Caucasians. To assess association, we performed pooled analysis of MESA Hispanics (N = 1779), as well as stratified analysis within the PCA-based clusters corresponding to Central and South America (N = 204), the Dominican Republic (N = 472), Mexico (N = 913) and Puerto Rico (N = 181).
In pooled analysis of the selected 33 LPL SNPs in MESA Hispanics, we saw statistically significant association of 18 SNPs with triglyceride outcomes (even after conservative Bonferroni correction for multiple testing using the cutoff 0.05/33 = 0.0015), with the strongest association observed for rs325, P = 8.86E-6, and rs328 (Ser474Stop), P = 8.88E-6 (Figure 3A, Table S2). Given the ancestral variability across Hispanic subgroups included in the pooled analysis, we further examined estimated effects of the functional SNP rs328 within each of our four PCA-based subgroups (Figure 3B). In stratified analysis, the Dominican/Cuban and Mexican subgroups had comparable estimated effects of −0.224 (SE = 0.063, coded allele freq. 0.073) and −0.182 (SE = 0.047, coded allele freq. 0.069) on log triglycerides (log mg/dL) per copy of the coded G allele, respectively. These estimated effect sizes are comparable to the value of −0.123 (SE = 0.025) previously reported as the estimate effect for the minor allele of the most strongly associated LPL region SNP rs10096633 in a GWAS of Caucasians [16]. In contrast, the estimated effects for Central/South American and Puerto Rican subgroups were closer to zero, with values −0.012 (SE = 0.091, coded allele freq. 0.077) and −0.034 (SE = 0.095, coded allele freq. 0.091), respectively.
To quantify evidence for heterogeneity in genetic effects of rs328 observed across the four Hispanic subgroups, we performed a test of genetic heterogeneity using the meta-analysis software METAL [20]. We do not find statistically significant evidence of heterogeneity (P = 0.13, heterogeneity I2 = 11.4), perhaps reflecting the fact that rs328 is a nonsense mutation, and is quite possibly a causal variant underlying the observed association. Still, we keep in mind the test of heterogeneity may be somewhat underpowered given the Central/South American and Puerto Rican subgroups have only ∼200 individuals each.
We went on to examine strength of association with each of the selected 33 SNPs in the LPL region, in stratified analyses of each of the four Hispanic subgroups (Figure 3C–3F; Tables S3, S4, S5, S6). We found statistically significant evidence of association for 17 SNPs in analysis of the Mexican subgroup, and for 4 SNPs in analysis of the Dominican/Cuban subgroup, but nothing close to suggestive for the Central/South American and Puerto Rican subgroups (Tables S3 and S6). Our genetic analysis of the LPL gene region demonstrates that when there is only modest genetic heterogeneity across the Hispanic cohort for a given locus, pooled analysis will tend to provide a stronger signal than any subgroup alone.
We performed genetic association analyses stratified by self-reported country/region of origin to provide a direct comparison with our stratified analyses based on PCA-based clusters (Figure S4). The two sets of stratified analyses were qualitatively similar overall. In particular, we observed very similar profiles of statistical significance for the Mexican PCA-based cluster as compared to the self-reported group of Mexican origin. This is not surprising because there was strong correspondence between individuals classified as Mexican by PCA-based cluster versus self-report. In analysis of individuals with self-reported origin in the Dominican Republic, we also see a suggestion of association in the vicinity of the index SNP rs325, but no SNPs reach the Bonferroni threshold for statistical significance. We did not observe any other statistically significant or suggestive signals of genetic association in stratified analysis of those with country/region of origin self-reported as Central America, Cuba, Puerto Rico or South America.
We selected 45 SNPs (17 genotyped and 28 imputed) that exhibited modest to strong linkage disequilibrium (LD) with the TRIB1 index SNPs in Caucasians. We then performed genetic association analysis for these 45 SNPs, both pooled across the entire MESA Hispanic cohort and stratified by PCA-based Hispanic subgroup.
In pooled analysis of the MESA Hispanic cohort, rs4351435 (P = 1.09E-3) is the only SNP that reaches the Bonferroni cutoff (0.05/45 SNPs = 1.11E-3) (Figure 4A, Table S7). In stratified analysis of the most strongly associated SNP rs4351435, we find the Dominican/Cuban subgroup has the strongest estimated effect of 0.163 (SE = 0.041, coded allele freq. 0.213) on log triglycerides (log mg/dL) per copy of the coded G allele, followed by the Puerto Rican subgroup with an estimated effect of 0.111 (SE = 0.061, coded allele freq. 0.203). Estimated effects for the Central/South American and Mexican subgroups are considerably closer to zero, at 0.025 (SE = 0.059, coded allele freq. 0.203) and 0.030 (SE = 0.029, coded allele freq. 0.235), respectively (Figure 4B), and a test of heterogeneity in genetic effects across the four subgroups is statistically significant (P = 0.044, heterogeneity I2 = 38.1).
The observed differences in genetic effects across the four Hispanic subgroups suggest the strength of genetic association increases with the proportion of African ancestry, seen in higher proportions for the Dominican/Cuban and Puerto Rican subgroups compared to the Central/South American and Mexican subgroups. To quantify this relationship, we added an interaction between the SNP rs4351435 and PC1 in the linear model used to assess genetic association in the pooled Hispanic cohort. The rs4351435-PC1 interaction term is statistically significant (P = 0.019), suggesting heterogeneity in effects of rs4351435 on triglycerides is attributable in part to the proportion of African versus Native American or Caucasian ancestry (as quantified by PC1) within the MESA Hispanic cohort. In validation, we observed statistically significant association of the rs4351435 SNP with triglycerides in analysis 2,067 individuals from the MESA African American cohort (P = 0.037). Interestingly, the index SNP rs2954029 originally identified in studies of Caucasians was neither statistically significant in association analysis of the pooled MESA Hispanic cohort (P = 0.134) nor in analysis of the MESA African American cohort (P = 0.748). These results suggest that while the TRIB1 gene plays a role in determining triglycerides in Caucasians as well as African American and Dominican/Cuban individuals, the variants underlying this association vary by genetic ancestry. Another possibility is that the SNP effects interact with an environmental or dietary factor that is correlated with proportion of African ancestry within the MESA Hispanic cohort.
We went on to examine genetic association in stratified analysis of the four Hispanic subgroups for the full set of 45 TRIB1 SNPs (Figure 4C–4F; Tables S8, S9, S10, S11). While there was only one statistically significant SNP reaching the Bonferroni threshold in pooled analysis of the full MESA Hispanic cohort, we observe 11 SNPs reaching statistical significance in stratified analysis of the Dominican and Cuban subgroup. Further, the p-value of the most strongly associated SNP rs4351435 is more than ten times stronger in stratified analysis of the Dominican and Cuban subgroup (P = 8.67E-5) as compared to pooled analysis (P = 1.09E-3). We do not observe any SNPs reaching the threshold for statistical significance in analysis of the Central/South American, Mexican, or Puerto Rican subgroups. These results indicate that when there is considerable heterogeneity in genetic effects observed across the full Hispanic cohort, stratified analysis may provide better resolution to uncover genetic association signals that exhibit stronger effects within a single subgroup of the Hispanic cohort.
As we did for the genetic association analysis of LPL, we performed genetic association analyses of TRIB1 stratified by self-reported country/region of origin to compare with results of analyses stratified by PCA-based clusters (Figure S5). As we saw for LPL, the results of genetic association analysis were similar for the two sets of analyses. Stratification based on self-reported country/region of origin did produce generally weaker profiles of statistical significance, partially due to grouping by stratum with fewer individuals. Thus, genetic association analysis among those with self-reported country/region of origin in the Dominican Republic (Figure S5C) produces suggestive evidence of association, but does not reach the statistically significant result seen in analysis of the Dominican/Cuban PCA-based cluster (Figure 4D).
Our detailed population structure analysis of 1,374 unrelated individuals from the MESA Hispanic cohort, with reference to HapMap phase III and HGDP samples, provides a comprehensive view of the complex population structure inherent to the MESA Hispanic cohort. Our analyses document contributions of Caucasian, African and Native American ancestry to present day U.S. Hispanic populations. Our results are consistent with historical records and with previous studies [1], including an analysis of 705 Hispanic individuals from the MESA cohort using 199 AIMs [3]. Drawing on the resolution of the genome-wide genotype data recently available for the full MESA cohort through MESA SHARe (including 1,374 unrelated individuals and 2,174 Hispanic individuals in total), as well as geographic diversity of the MESA cohort with regard to Hispanic country/region of origin, we demonstrate diversity among the Native American ancestral populations contributing to present day Hispanic populations, consistent with Latin American historical records. In particular, we find the third and fourth principal components (PCs) of ancestry bring out a striking South-to-North axis in the available Native American samples that clearly separates Mexican versus Central/South American samples in MESA. Further, we find the fourth PC of ancestry separates Puerto Ricans from all other Hispanic groups in MESA, although there are no appropriate Native American samples available to verify this axis aligns with genetic differences in the corresponding Native American founders. To our knowledge, this is the first time diversity in underlying sources of Native American ancestry has been documented at this level of resolution, and in a sample reflecting the broad diversity of Hispanic origins represented among U.S. Hispanics.
Our population structure analysis and subsequent cluster analysis identified at least four distinct groups within the surveyed Hispanic cohort. Although self-identified country/region of origin was not used to inform the cluster analysis, the resulting groups showed remarkably close agreement with self-identification data, allowing us to identify the resulting PCA-based clusters roughly with the following four regions: Central/South America, the Dominican Republic and Cuba, Mexico, and Puerto Rico. We emphasize that the labels we have assigned to these clusters should be regarded loosely, provided as an aid to interpretation of results, but not intended as a vast generalization of individuals from the said regions. Indeed, we recognize there is great diversity in genetic ancestry within each of these regions, and this diversity is documented extensively in our population structure analysis. Taken as a whole, our thorough population structure analysis and genetic analysis brings forth the important message that the “Hispanic” population is in fact highly heterogeneous and genetically diverse. Our thorough genetic population structure analysis reveals genetic subgroups that correspond with groups of Hispanics with shared culture and history.
One notable difference between our study and previous reports of population structure in Hispanic groups (e.g. Bryc et al. [1]) lies in how we incorporate information from external reference panels. While previous studies performed population structure analysis on pooled data sets including Hispanic samples and relevant individuals from the HapMap, HGDP or other reference panels [1], [2], [4], we compute principal components in MESA Hispanic samples alone, leveraging information from the reference panels by projecting these principal components across samples. For the purpose of understanding the Hispanic population, we find it is more informative to focus the analysis in this way, particularly for characterizing finer structure within Native American ancestral groups. Our focused approach to population structure analysis is feasible mainly because our sample size is considerably larger than that available to previous studies of Hispanic population structure.
To demonstrate the differences described above, we performed principal component analysis for unrelated individuals from the MESA Hispanic cohort pooled with samples from the HapMap and HGDP (Figures S6 and S7). For the first two PCs, the results of the our pooled PCA as well as that of Bryc et al. [1] agree largely with those seen in our PCA computed for MESA Hispanic samples only. For higher order PCs, we do see qualitative differences in pooled versus focused PCA. Notably, there is a clear separation of Puerto Rican samples from Central and South American samples in the plot of PC4 versus PC3 from analysis of the MESA Hispanic cohort alone (Figure 1), but this separation is not observed in higher order PCs from our pooled PCA (Figure S7) nor is it apparent in higher order PCs from pooled analysis presented in Bryc et al. [1]. This comparison further indicates the finer differences we detected among Hispanic and Native American groups of distinct geographic origins were possible due to our focused approach of computing principal components using genotype data from the MESA Hispanic cohort only.
There are several immediate applications of our work for genetic analysis of Hispanic cohorts. We have defined at least four distinct clusters of genetic ancestry within the MESA Hispanic cohort, and we suggest future genetic analyses of MESA Hispanics should be stratified across these clusters. Of course, stratified analysis will introduce problems of multiple testing, and reduced sample sizes within strata. When the number of individuals with phenotypes available does not allow stratification, a reasonable approach will be to perform pooled analysis of the entire Hispanic cohort, with adjustment for the strongest principal components of ancestry. An intermediate approach will be to stratify using just two clusters inferred from the first two principal components of ancestry.
To demonstrate our recommendations for genetic association analysis taking into account our documented genetic diversity in the MESA Hispanic cohort, we performed association analysis of triglycerides with SNPs in the LPL and TRIB1 gene regions, previously implicated in GWAS of Caucasians but unconfirmed in Hispanics. We began with pooled analysis, in which we found SNPs reaching the Bonferroni thresholds for statistical significance in each of the two gene regions. Follow-up by stratified analysis in each of four subgroups of the MESA Hispanic cohort revealed a suggestion of heterogeneity in the strongest functional LPL variant rs328 (Ser474Stop) and statistically significant evidence for genetic heterogeneity at the most strongly associated TRIB1 SNP rs4351435. Furthermore, evidence for the TRIB1 SNP rs4351435 was substantially stronger in stratified analysis of the Dominican and Cuban subgroup alone, as compared to pooled analysis of the full MESA Hispanic cohort. Our genetic association analyses indicate pooled analysis provides good power to detect variants exhibiting little heterogeneity in genetic effects, while stratified analysis will provide an advantage in detecting SNPs with heterogeneity in which the genetic effect is strong for one subgroup and close to zero in other subgroups of the Hispanic cohort.
In practice, whether a formal test of heterogeneity is statistically significant or not, examining heterogeneity by effect plots or other tools will be an important step toward identifying the most promising samples for follow-up and replication studies. We do not expect genetic diversity will be the sole cause of heterogeneity in SNP effects. Evidence both from our current work and from previous studies [13] indicates that genetic distance correlates with geography, that geography correlates to a certain extent with environmental exposures as well as with social and cultural factors [21], and that these factors, in turn, may serve as independent predictors of cardiovascular outcomes of interest or modifiers of genetic effects [21], [22], [23], [24]. Given the strong correspondence between our inferred genetic clusters and self-identified country/region of origin, stratified analysis will serve as a general strategy to examine differences across subgroups of the Hispanic cohort, which differ not only in genetic origins but also in terms of lifestyle factors (e,g, diet) as well as other social and cultural factors associated with diverse regions of origin and diverse histories of migration to the United States.
Toward generalizing our results to the United States Hispanic population as a whole, it is important to keep in mind the demographics of our cohort. The MESA Hispanic cohort represent primarily recent immigrants to the United States, with 65% born outside the United States, and another 28% having at least one parent born outside the United States [25]. Our reported population structure analyses may be biased in part by this distribution of immigration to the United States, and it is possible that self-reported Hispanics whose families have been in the United States for multiple generations may exhibit different patterns of ancestry, including a greater degree of admixture across the Hispanic countries/regions of origin represented in this study, as well as admixture with other racial/ethnic groups living in the United States (e.g. Caucasian, Asian, or African American).
Individuals in the MESA Hispanic cohort were recruited primarily from three sites in the United States, namely New York City, Minneapolis, and Los Angeles. Based on this geographic distribution, the MESA Hispanic cohort cannot be regarded as a fully representative sample of Hispanics from across the United States. Examining the self-identification data for an unrelated subset of 1,374 individuals from the MESA Hispanic cohort (51.5% Mexican, 14.0% Puerto Rican, 3.6% Cuban, 14.8% Central/South American, 14.8% Dominican, and 1.2% other/not specified) compared to the U.S. Hispanic population (63.0% Mexican, 9.2% Puerto Rican, 3.5% Cuban, 13.4% Central/South American, 2.8% Dominican, and 8.1% other, based on data from the United States 2010 Census [26]), we find generally good agreement between countries/regions of origin in the MESA Hispanic cohort compared to the U.S. Hispanic population. The notably higher representation of individuals with Dominican origin in the MESA Hispanic cohort reflects the fact that New York City, an area with one of the highest concentrations of Dominicans in the United States, was one of the main recruitment sites for MESA Hispanic participants (Table S12). Overall, these data suggest the MESA Hispanic cohort does provide good representation of the major countries/regions of Hispanic origin found in the U.S. Hispanic population. Thus, the population structure analysis of the MESA Hispanic cohort will provide a valuable resource toward understanding genetic diversity in the broader U.S. Hispanic population. However, given the possibility of migration bias due in part to socioeconomic or cultural factors, we caution against drawing on our results to interpret genetic diversity of Hispanics living outside the United States.
All MESA participants gave written informed consent, including consent to participate in genetic studies. This MESA study was conducted under Institutional Review Board approval at all study sites, including the Cedars-Sinai Medical Center and the University of Virginia.
The Multi-Ethnic Study of Atherosclerosis (MESA) is a longitudinal study of subclinical cardiovascular disease and risk factors that predict progression to clinically overt cardiovascular disease or progression of the subclinical disease [27]. The first clinic visits occurred in 2000 in 6,814 participants recruited from six field centers across the United States. Approximately 38% of the recruited participants are White, 28% African-American, 22% Hispanic, and 12% Asian, predominantly of Chinese descent. Genome-wide genotyping was performed in 2009 using the Affymetrix Human SNP array 6.0. SNPs were filtered for SNP level call rate <95% and individual level call rate <95%, and monomorphic SNPs were removed. Examining the distribution of heterozygosity rates across all genotyped SNPs, we observed a generally uniform distribution between 0–53%, with less than 0.01% of SNPs having heterozygosity >53%. Thus, we removed all SNPs with heterozygosity >53%. The cleaned genotypic data was deposited with MESA phenotypic data into dbGaP as the MESA SHARe project (study accession phs000209, http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000209.v4.p1) for 8,227 individuals (2,686 Caucasian, 777 Chinese, 2,590 non-Hispanic African-American, and 2,174 Hispanic) with 897,981 SNPs passing study specific quality control (QC). Due to differences in allele frequencies across the MESA ethnic groups, there was no filter of minor allele frequency prior to release of the genotype data on dbGaP. Thus, we applied a filter on minor allele frequency at the stage of genetic association analysis (see “Selection of SNPs for genetic association analysis” below).
The country or region of Hispanic origin was coded for individuals in the MESA Hispanic cohort using the following categories: Mexican, Dominican, Puerto Rican, Cuban, Central American, South American or other Hispanic subgroup. Participant self-identification was available for 83% of individuals in the MESA Hispanic cohort. For the remaining 17% where this self-identification was not provided, the data were obtained from the place of birth of the most recent generation (among the participant, parents, and grandparents) outside of the 50 United States as follows:
To make use of the HapMap phase III release 3 genotypes as a reference panel for our analysis of MESA samples, we began with 1,397 individuals from the following 11 HapMap populations: ASW, CEU, CHB, CHD, GIH, JPT, LWK, MEX, MKK, TSI, and YRI. Genotype data were obtained by the HapMap 3 Consortium using the Affymetrix Human SNP array 6.0 and the Illumina Human1M-single beadchip. Following data merging and cleaning [9], there were 1,457,897 SNPs in the publicly available data downloaded from http://hapmap.ncbi.nlm.nih.gov/downloads/ in PLINK [28] format.
Publicly available genotype data for the Human Genome Diversity Project (HGDP) were downloaded from http://hagsc.org/hgdp/files.html for 1,043 individuals on 660,918 SNPs [11]. These data included genotypes generated on Illumina 650Y arrays, with a GenCall Score cutoff of 0.25. The publicly available genotypes were filtered on overall SNP level call rate <98.5%, with no additional filtering of SNPs.
To allow common analysis across the three sources of genotype data (MESA, HapMap and HGDP), the first step was to merge the genotype files aligned on a common set of alleles. To avoid any ambiguity in strand alignment, we merged the genotype data files using an approach that does not rely on a priori knowledge of strand direction in annotation files. Briefly, SNPs with alleles A/G or C/T could be merged across genotype files, unambiguously flipping alleles (A→T, G→C) as necessary. The small proportion of SNPs with alleles A/T or G/C could not be merged using this method, due to ambiguities in strand-flipping, as so were removed. Although this strand-flipping procedure forces us to remove the small proportion of ambiguous SNPs, the resulting set of retained SNPs is less error prone (in terms of called alleles) than if we had relied on strand direction reported in annotation files alone. The allele flipping procedure described as run as currently implemented in the software package KING [29].
After file merging and allele flipping, we filtered on SNP level call rate <95% across 12,058 genotyped individuals from the three genotype data sets (MESA, HapMap and HGDP), resulting in 144,564 autosomal SNPs common to all data sets. We then filtered on individual level call rate <95%, resulting in a combined set of 10,666 individuals across the three genotype data sets with overall genotyping rate 0.998 across the 144,564 autosomal SNPs.
To identify an unrelated set of MESA individuals for population structure analysis, we performed relationship inference using a method we developed recently for accurate relationship up to the 3rd-degree using genotypes from genome-wide association data, implemented in the freely available software package KING [29]. Because precision of the method increases with the number of typed SNPs available for any pair of individuals, relationship inference was performed using the full set of SNPs, prior to filter for SNPs common to all three genotype data sets. Individuals were clustered into connected groups (i.e. families) using the KING option “cluster –3”, which defines clusters such that any pair of individuals with inferred relationship as distant as 3rd-degree are grouped together.
A list of unrelated individuals from MESA was constructed by selecting one individual from each known family (common family ID reported in the downloaded data), and further thinning to include no more than one individual from each cluster inferred in the KING clustering of individuals with inferred relationships up to the 3rd degree. While we recognize that some inferred relationships of the 3rd degree may be false positives, we used this stringent criterion to ensure we had a clean set of individuals for population structure analysis. The final list of unrelated individuals generated by this procedure included 6,496 MESA participants. From this group of individuals, we further removed 5 Hispanic individuals identified as outliers according to principal components of ancestry, as computed in SMARTPCA (see “Principal component analysis” below).
Because the HapMap samples were collected with systematic relatedness including father-mother-child trios [9], we implemented an algorithm using the software KING [29] to extract multiple unrelated individuals from a pedigree, when available. The algorithm, available using the KING option “–unrelated” proceeds as follows. Related individuals (defined by existing pedigree or estimated kinship coefficient <0.088) are first clustered into connected groups (i.e., families). Within each family cluster, individuals are ranked according to the count of unrelated family members (having estimated kinship coefficient <0.022). To construct a set of unrelated individuals, we first select the individual with the largest count of unrelated individuals within the family cluster. We then proceed to choose the individual with the next highest rank (number of unrelated family members) within the family cluster, only if that individual is not related to any of the previously selected individuals in the list of unrelated individuals. We applied this algorithm to construct a set of 1,096 individuals from the HapMap and 922 individuals from the HGDP, with no 1st- or 2nd-degree relatives in the unrelated set.
Details of the data set after SNP QC, individual-level genotype QC, by cohort representation and status of inclusion in the final set of unrelated individuals, are provided in Table S1.
Prior to population structure analysis, we first constructed a subset of typed SNPs, thinned for linkage disequilibrium (LD) among MESA samples self-identified as Hispanic. Based on the assumption that Hispanics have a considerable proportion of Caucasian ancestry, we first removed from consideration SNPs in regions of known long-range linkage disequilibrium among Caucasians [30], including the HLA region (Chr 6: 24.5–34.5 Mb), a chromosome 8 inversion (Chr 8: 113–116 Mb), and a region on chromosome 11 (Chr 11: 45–58 Mb). We then thinned for local LD within an unrelated subset of the MESA Hispanic cohort using the PLINK [28] option “–indep-pairwise” to create a subset of typed SNPs thinned for pairwise R-squared no more than 0.2 in a 100 SNP window, moving the windows 25 SNPs at a time. This LD pruning procedure resulted in a set of 64,199 autosomal SNPs of minimal LD among the MESA Hispanic samples that are the focus of the current study. The resulting SNP set provides good resolution for global ancestry inference by both unsupervised (principal component analysis) and supervised (model-based clustering, as in the program STRUCTURE [8]) analysis.
Using our LD-thinned subset of 64,199 SNPs, we performed Principal Component Analysis (PCA) as implemented in the program SMARTPCA [5], [6] from the software package EIGENSTRAT to compute principal components (PCs) of ancestry for an unrelated subset of 1,374 self-reported Hispanic individuals from MESA. In computing the PCs, we performed additional LD correction by using results of regression on the previous 5 SNPs as input to the PCA (SMARTPCA option “nsnpldregress”), and performed 5 iterations of outlier removal in which we removed individuals with computed values more than 10 standard deviations from mean along along the first 6 PCs of ancestry. Based on this procedure, five outliers were removed from an initial set of 1,379 unrelated MESA Hispanic individuals, prior to computation of the final set of PCs with 1,374 unrelated individuals.
Following computation of the PCs using the unrelated subset of MESA Hispanic individuals, we used SMARTPCA to project these components to all MESA samples that were not included in the PCA (including non-Hispanic samples), as well as the HapMap and HGDP samples, to assist in interpretation of the strongest PCs (corresponding to the largest eigenvalues).
As principal component analysis is known to be sensitive to outliers and undetected family structure, both of which can produce spurious PCs, we undertook a series of QC steps to assess properties of the components reported by SMARTPCA. First, we constructed histograms and QQ-plots to assess symmetry and normality of the distribution of loadings for each principal component. We found the distributions of loadings for the first four PCs closely matched the ideal symmetric, standard normal distribution, with no coefficients more extreme than 4.6. For higher PCs, we observed loadings as extreme as 6.4 (the loadings should follow a standard normal distribution). We also performed genome-wide association of each principal component as a quantitative trait among MESA Hispanics, using a method that accounts for familial correlation [31] as implemented in the software GDT [32], to assess the extent to which the component serves as a marker of genome-wide population stratification, versus strong correlation with smaller chromosomal regions, as would occur if the component was produced as a result of long-range LD. Individuals with principal component values greater than three standard deviations from the mean were removed prior to analysis of each principal component. We did not observe any principal components that appeared to reflect influence of long-range LD.
For comparison with our PCA of the MESA Hispanic cohort with projection to relevant reference panels, we performed PCA for unrelated individuals from the MESA Hispanic cohort pooled with samples from the HapMap and HGDP, using the same set of 64,199 SNPs selected for population structure analysis performed on the MESA Hispanic cohort. The resulting principal components from pooled analysis were subject to the same QC steps as applied for the MESA Hispanic-specific analysis, including examination of the distributions of PC loadings, and genome-wide association analysis of each PC as a quantitative trait to assess potential effects of long range LD.
We performed model-based cluster analysis using our LD-thinned subset of 64,199 SNPs using the package ADMIXTURE [7]. This software package implements the same clustering method as in STRUCTURE [8], using a block relaxation approach implemented with a novel quasi-Newton acceleration method that makes the method computationally feasible for much larger data sets [7], both in terms of the number of individuals and the number of SNPs.
In order to obtain a clear characterization of the MESA Hispanic cohort, we first performed the ADMIXTURE analysis for the same unrelated subset of 1,374 MESA Hispanic individuals that we used to perform PCA. We ran this analysis for K values 1, …, 10, assessing results for each of these runs in terms of cross validation error, as well as with graphic displays of proportions of ancestry across self-identified Hispanic country/region of origin.
Visual inspection of the first four PCs of ancestry (Figure 1) suggested at least four distinct groups of individuals defined by these PCs, roughly divided along PC1 and PC4, with PC2 and PC3 reflecting variation within those groups. We have not ruled out the possibility of further substructure beyond these four visually discernable clusters. However, we choose to limit the number of clusters to four, based on the practical consideration that a larger number of clusters would lead to within-cluster sample sizes too small to allow subsequent genetic association analyses to be stratified by cluster.
We performed k-means clustering in the statistical software R [33], using the first four principal components of ancestry to define four major groups (k = 4) within the MESA Hispanic cohort. Starting values for cluster centers were assigned based on means observed within the upper and lower strata of values for PC1 and PC4. Based on overall correspondence between the cluster assignments and self-identified country/region of origin (Table 2), we labeled the four subgroups resulting from k-means clustering as Central/South American, Dominican, Mexican and Puerto Rican. Cluster assignments were made for all N = 2,169 individuals from the MESA Hispanic cohort with computed PCs available (5 individuals were excluded because they were removed as outliers during computation of PCs).
To assess association of SNPs in the LPL gene region with triglycerides, we began by selecting SNPs of interest in the region, with a focus on the index SNPs rs12678919 and rs10096633 reported in previous GWAS of Caucasians [14], [15], [16], [17], [18]. Due to patterns of linkage disequilibrium, the index SNPs identified in previous studies of Caucasians are not necessarily causal in determining genetic association with the phenotype of interest. However, we do expect the index SNPs are in linkage disequilibrium with the putative causal variant(s) underlying the genetic association. To improve our chance of capturing the causal variant(s) in our association analysis, we expanded our SNP set to include any SNPs exhibiting strong pairwise LD with our two initial SNPs (R-squared >0.7 in the HapMap II+III CEU samples, using release 28, NCBI Build 36 (dbSNP b126)). Of these 33 SNPs, 8 were genotyped on Affy 6.0 and passed genotype QC. IMPUTE version 2.1.0 was used to perform imputation for the MESA SHARe Hispanic participants (chromosomes 1–22) using HapMap Phase I and II - CEU+YRI+CHB+JPT as the reference panel (release #22 - NCBI Build 36 (dbSNP b126)), and another 25 could be imputed with quality >0.8, based on the observed versus expected variance quality metric [34]. We verified the minor allele frequency of all these SNPs was greater than 0.01 in the pooled MESA Hispanic cohort, as well as in all stratified analyses. In this way, we identified 33 SNPs to be included in a more comprehensive analysis of the LPL gene region.
For selection of SNPs to be included in association analysis of the TRIB1 gene region, we used a strategy similar to that for LPL. We began by targeting the SNP rs2954029 previously reported in GWAS of Caucasians [14], [15], [18]. We further selected 19 SNPs exhibiting strong pairwise LD with our initial index SNP (R-squared >0.7 in the HapMap II+III CEU samples, using release 28, NCBI Build 36 (dbSNP b126)). Association analysis of these 19 SNPs did not reveal any results approaching statistical significance, so we expanded the association analysis to 45 SNPs having modest to strong LD with the rs2954029 index SNP (R-squared >0.3 in the HapMap II+III CEU samples). Of these 45 SNPs, 17 were genotyped on Affy 6.0 and passed genotype QC, and the other 28 could be imputed with quality >0.8, based on the observed versus expected variance quality metric [34].
Fasting triglycerides were measured in plasma using a glycerol blanked enzymatic method (Trig/GB, Roche Diagnostics, Indianapolis, Indiana). To select individuals to be included in this analysis, we began with the full set of N = 2,169 individuals with data available from principal component analysis. We then restricted the data set to individuals with triglyceride phenotypes available (N = 2,151) and no known use of any lipid lowering medication (N = 1,788). To allow study site to be included as a covariate in genetic association analysis, we restricted the data set to individuals from study sites with data available for at least 20 individuals (N = 1,782). Outliers were defined as individuals with log triglyceride values more then 3.5 SD from the mean, with the mean and SD calculated separately for each of the five analyses performed (pooled analysis of all MESA Hispanics, and stratified analysis of the four subgroups).
Based on these criteria, we performed pooled analysis of all MESA Hispanics (N = 1,779), as well as stratified analysis within the PCA-based clusters corresponding to Central and South America (N = 204), the Dominican Republic and Cuba (N = 472), Mexico (N = 913) and Puerto Rico (N = 181). For comparison, we also performed stratified analysis by self-reported country/region of origin for the following groups: Central America (N = 109), Cuba (N = 34), the Dominican Republic (N = 315), Mexico (N = 961), Puerto Rico (N = 202), and South America (N = 123). Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships as implemented in the package R/GWAF [19]. We used a basic model including the covariates gender, age, study site, and the first four PCs of ancestry, using principal components computed for the full Hispanic cohort. PC2 was not included in stratified analyses of Dominican/Cuban, Mexican and Puerto Rican PCA-based clusters, for which we observed the correlation between PC1 and PC2 was −0.95, 0.90, and −0.89, respectively. Similarly, PC2 was omitted from stratified analyses with country/region of origin self-reported as Cuba, the Dominican Republic, Mexico, and Puerto Rico, for which we observed the correlation between PC1 and PC2 was −0.94, −0.98, 0.89, and −0.89, respectively.
To assess genetic heterogeneity seen in stratified analysis of the four Hispanic subgroups, we performed a test of heterogeneity using Cochran's Q and also examined the inconsistency metric I2 which quantifies the proportion of total variation across studies due to heterogeneity rather than chance [35].
To validate results seen for SNPs exhibiting the strongest association in the Hispanic cohort, we performed genetic association analysis in the MESA African American cohort. We began with the full set of N = 2,588 consenting individuals from MESA or MESA Family self-identified as African American. There were N = 2,580 individual remaining after removing outliers from principal component analysis. We then restricted the data set to individuals with triglyceride phenotypes (N = 2,552) available and no known use of any lipid lowering medication (N = 2,071). All study sites had data available for at least 20 African American individuals. After removing outliers were defined as individuals with log triglyceride values more then 3.5 SD from the mean, we performed genetic association analysis of N = 2,067 individuals, using a linear mixed-effects model to account for familial relationships [19] and a basic model including the covariates gender, age, study site, and the first principal component of ancestry.
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10.1371/journal.pbio.1002580 | ADAMTS5 Is a Critical Regulator of Virus-Specific T Cell Immunity | The extracellular matrix (ECM) provides physical scaffolding for cellular constituents and initiates biochemical and biomechanical cues that are required for physiological activity of living tissues. The ECM enzyme ADAMTS5, a member of the ADAMTS (A Disintegrin-like and Metalloproteinase with Thrombospondin-1 motifs) protein family, cleaves large proteoglycans such as aggrecan, leading to the destruction of cartilage and osteoarthritis. However, its contribution to viral pathogenesis and immunity is currently undefined. Here, we use a combination of in vitro and in vivo models to show that ADAMTS5 enzymatic activity plays a key role in the development of influenza-specific immunity. Influenza virus infection of Adamts5-/- mice resulted in delayed virus clearance, compromised T cell migration and immunity and accumulation of versican, an ADAMTS5 proteoglycan substrate. Our research emphasises the importance of ADAMTS5 expression in the control of influenza virus infection and highlights the potential for development of ADAMTS5-based therapeutic strategies to reduce morbidity and mortality.
| Movement of immune cells is critical for effective clearance of pathogens. The response to influenza virus infection requires immune cell trafficking between the lung, mediastinal lymph node and other peripheral lymphoid organs such as the spleen. We set out to assess the contribution of a specific extracellular matrix enzyme, ADAMTS5, to migration of lymphocytes and overall pathogenesis following infection. In our studies, we demonstrate that mice lacking Adamts5 have fewer influenza-specific lymphocytes in the lung and spleen following infection. These observations correlated with an accumulation of influenza-specific lymphocytes in the mediastinal lymph node and increased virus titres. This work suggests that ADAMTS5 is necessary for immune cell migration to the periphery, where lymphocyte function is required to fight infection.
| Influenza A virus infection is responsible for substantial global morbidity and mortality (>500,000 deaths each year [1]) and largely afflicts high-risk groups, including the very young and elderly. There are currently two countermeasures employed to control influenza virus infection: vaccines and antivirals. Although generally effective, the imperfect proofreading capacity of the RNA-dependent RNA polymerase drives constant genetic drift. Moreover, a segmented genome facilitates rapid genetic shift, resulting in the need for reformulation of seasonal vaccines or the emergence of resistance following administration of antivirals, leading to suboptimal prophylactic or therapeutic intervention [2].
T cells are a vital component of the adaptive immune response following influenza virus infection. Critically, trafficking of activated influenza-specific T cells from draining lymph nodes (including the mediastinal lymph node [MLN]) to the site of primary infection in the lung requires direct contact and interaction with the extracellular matrix (ECM) [3]. The ECM provides adhesive substrates, such as proteoglycans and collagen, to encourage and facilitate lymphocyte trafficking [4]. Expression and remodelling of ECM components is strictly regulated to control movement of immune cells. Therefore, it is not surprising that perturbations in substrate availability and ECM remodelling significantly impact granulocyte and lymphocyte migration in a number of model systems [5–7].
The A Disintegrin-like and Metalloproteinase with Thrombospondin-1 motifs (ADAMTS) family are a group of secreted metalloproteinases found within the zinc-dependent metzincin super-family that also consists of matrix metalloproteinases (MMPs) and ADAMs [8]. The ADAMTS family comprises 19 mammalian ADAMTs enzymes [9]. ADAMTS5 is one of the most highly characterised and well-known proteinases in this family and has been shown to cleave the hyalectan class of chondroitin sulphate proteoglycans (CSPGs), including aggrecan, brevican, neurocan, and versican [10–13]. Hyalectans/CSPGs are large aggregating macromolecules that hydrate tissue and confer rigidity to the extracellular space. ADAMTS5 has become a major drug target for arthritis therapy as ADAMTS5 knockout mice (Adamts5-/- mice) are resistant to aggrecan cleavage in articular cartilage and are thus protected from experimentally induced arthritis [14,15]. Aside from the documented role in arthritis, ADAMTS5 has been shown to play a role in embryonic development, including limb and cardiac morphogenesis, and skeletal muscle development through its versican remodelling properties [11,16,17]. Importantly, its role in viral immunity is currently undefined.
Versican, a substrate of ADAMTS5, is a widely expressed tissue proteoglycan involved in cell adhesion, proliferation, and migration [4]. The two predominant splice-variants of versican that harbour ADAMTS cleavage sites in their shared glycosaminoglycan (GAG)-β domain are V0 and V1 [18]. GAG chains provide interactive points for antigen recognition receptors (Toll-like receptor 2 and 4), chemokines (MCP-1, MCP-2, CCL5), and cell surface markers (CD62L, CD44), some of which are directly linked to immune cell migration [19–21]. Furthermore, in vitro studies have shown that Poly I:C induced versican expression can restrict CD4+ T cell migration by preventing ECM adhesion [22]. Given the fact that ADAMTS5 is a versicanase [11], we hypothesised that it would play a key role in viral immunity. Our data demonstrates that host expression of ADAMTS5 is required to help ameliorate disease following influenza virus infection. Adamts5-/- mice clearly show increased weight loss and higher viral titres throughout the course of influenza virus infection along with impaired CD8+ T cell migration and immunity.
ADAMTS enzymes are widely distributed in human adult tissues and play a key role in normal cellular function. Although Adamts5-/- mice are viable and phenotypically normal, based on gross analysis of histological samples [14,15], detailed characterisation has revealed decreased interdigital web regression leading to fused digits [11], cardiac valve maturation [17], and abnormal formation of multinucleated myotubes required for skeletal muscle development [16]. In contrast, the homeostatic immune cell composition of naïve Adamts5-/- mice has yet to be determined. Antibody staining and flow cytometric analysis of immune cell populations performed prior to infection suggested that the proportion of dendritic cells (CD11c+MHCII+), alveolar macrophages (CD11c+F480+), and interstitial macrophages (CD11b+F480+) were comparable in the lungs of C57.BL/6 (wild-type [WT] control mice) and Adamts5-/- mice (Fig 1A–1C), as were the number of CD4+ and CD8+ T lymphocytes and B cells in the spleen (Fig 1D–1F). Furthermore, we analysed immune cell populations (dendritic cells, macrophages, NK, T and B cells) in the lung (S1 Fig), spleen (S2 Fig), and thymus (S3 Fig) and found no observable differences between Adamts5-/- mice and C57.BL/6 controls. As such, we concluded that Adamts5-/- mice were immunologically “normal” prior to infection.
We also carefully analysed the gene expression level of related ADAMTS family members that have “versicanase” activity to determine if compensation of enzymatic activity was evident. Quantitative reverse-transcriptase polymerase chain reaction (QRT-PCR) data demonstrated similar gene expression levels of Adamts1, 4, 8, 15, and 20 in the lungs of WT C57.BL/6 and Adamts5-/- mice (Fig 1G). However, increased expression of the Adamts9 versicanase was observed in Adamts5-/- mice when compared to WT controls, although this was not statistically significant (p = 0.067).
To investigate the role of ADAMTS5 in influenza virus infection, we initially examined in vivo weight loss and lung virus replication kinetics following infection. Influenza virus titres normally peak 3 d post infection (p.i.), and virus is cleared by day 7–10 p.i. [23]. We intranasally infected Adamts5-/- mice and C57.BL/6 controls with 104 pfu/mouse-adapted X31 (H3N2) influenza virus, and observed enhanced weight loss (p < 0.05 on day 8 p.i.) in Adamts5-/- mice across the experimental infection period when compared to C57.BL/6 controls (Fig 2A). At the peak of viremia (day 3 p.i.), Adamts5-/- mice showed higher virus titres in the lung when compared to C57.BL/6 controls by both plaque assay (Fig 2B) and QRT-PCR analysis of influenza virus Matrix-1 gene expression (Fig 2C). Similar observations were recorded at 7 d p.i. (Fig 2D and 2E), suggesting Adamts5-/- mice do not clear influenza virus as effectively as C57.BL/6 controls. Additionally, a qPCR time-course analysis of ADAMTS enzyme expression in the lungs of influenza-infected WT and Adamts5-/- mice was also performed and shown in S4 Fig for general reference.
As there was evidence of delayed viral clearance in Adamts5-/- knockout mice, we set out to determine if this observation was associated with perturbations in cellular immunity. We initially enumerated total CD4+ and CD8+ T cell numbers in the lungs and spleens of Adamts5-/- and C57.BL/6 control mice to determine if the delay in viral clearance observed in Fig 2 correlated with functional differences in T cell populations. We observed decreased numbers of total CD4+ and CD8+ T cells in the spleen and lung of Adamts5-/- mice at days 7 and 10 following infection (Figs 3A, 3B, 4A and 4B). In the C57.BL/6 mouse model of influenza virus infection, influenza-specific CD8+ T cell immunity is first detected 4–5 d p.i. and peaks at day 10 p.i. [23,24]. In our study, influenza-specific CD8+ T cells were enumerated using tetrameric complexes that recognised the immunodominant DbNP366-372 (ASNENMETM) or DbPA224-233 (SSLENFRAYV) CD8+ T cell epitopes [25]. Fewer DbNP366-372 and DbPA224-233 CD8+ T cells were detected in the spleen and lung of Adamts5-/- mice at both day 7 and 10 p.i. when compared to C57.BL/6 controls (Figs 3C, 3D, 4C and 4D). The intracellular cytokine staining (ICS) assay was then used to assess the functionality of the CD8+ T cell response in the spleen and lung at multiple time points following infection. Adamts5-/- mice had fewer DbNP366-372+IFNγ+CD8+ and DbPA224-233+IFNγ+CD8+ T cells in the spleen and lung at days 7 and 10 p.i. (Figs 3E, 3F, 4E and 4F). The lack of influenza-specific CD8+ T cells in the periphery of Adamts5-/- mice suggested possible accumulation of cells in the draining lymph nodes of the lung, such as the MLN. Careful analysis revealed increased numbers of total CD4+ and CD8+ T cells and DbNP366-372+ and DbPA224-233+ tetramer+ CD8+ T cells in the pooled MLN of Adamts5 -/- mice when compared to controls 7 and 10 d p.i. (Fig 5A–5D). These observations were further validated using ICS of pooled MLN (Fig 5E and 5F) and suggested that the ECM remodelling by ADAMTS5 contributes to migration of effector T cells from the MLN to peripheral tissue.
ADAMTS5 is an important enzyme involved in the remodelling of the ECM, and its actions are thought to contribute to the trafficking of key immune cell populations, such as macrophages [26]. While the hyalectans (aggrecan, brevican, and neurocan) are tissue specific, V0/V1 versican isoforms are widely expressed throughout the body [4,27]. We therefore reasoned that the lack of ADAMTS5 enzymatic activity in the MLN of Adamts5-/- mice would result in an accumulation of the versican substrate. It is also important to note here that the presence of versican has previously been associated with inhibition of lymphocyte migration [22,28] and may result in T cell accumulation in the MLN (Fig 5A). Moreover, versican upregulation has also been associated with inflammatory stimuli [4,29]. As such, versican and versican cleavage fragment (versikine) expression in the MLN of influenza virus infected Adamts5-/- mice was assessed by immunohistochemistry. MLN tissue from infected animals was paraffin embedded, sectioned, and stained with anti-GAGβ (V0/V1 versican side chains) and anti-DPEAAE (versikine) antibodies to define expression. Confocal microscopy of sections revealed increased levels of versican in the MLN of Adamts5-/- mice when compared to C57.BL/6 controls (Fig 6A and 6B). Our data also suggested that versican was expressed within the T cell areas of the lymph node following immunohistochemical staining as T cells and versican co-localised in the MLN (S5 Fig). In support of this data, decreased versikine was observed in the MLN of Adamts5-/- when compared to C57.BL/6 controls (Fig 6C and 6D). Adamts5-/- mice used in these studies were generated via the insertion of a Lac-Z allele into the catalytic site of the ADAMTS5 gene, and so Lac-Z expression can be used as a surrogate reporter for ADAMTS5 expression. X-gal staining of the MLN of Adamts5-/- mice showed expression throughout the organ (Fig 6E). Additionally, we stained influenza-infected lung with antibodies specific for versican and versikine. Bronchioles (S6A Fig) and arteries (S6B Fig) in the lung were stained with DAPI and anti-Gagβ (versican) to compare versican expression and cleavage to that found in the MLN. No differences were observed in staining between C57.BL/6 and Adamts5-/- mice (S6 Fig). This suggests that the absence of ADAMTS5 in the MLN prevents efficient cleavage of versican and results in accumulation of T cells in the draining lymph node. In contrast, the absence of ADAMTS5 in the lung does not influence versican cleavage.
Given the accumulation of versican in the MLN of Adamts5-/- influenza virus infected mice (Fig 6), we examined if the absence of ECM remodelling was linked to impaired CD8+ T cell migration. Ex vivo transwell assays were employed to assess migration of CD8+ T cells as previously described [30,31]. The surface of the upper transwell chamber was initially coated with versican-enriched conditioned media from transfected HEK293T cells [16,32], prior to the addition of a T cell chemoattractant (CXCL12) to the lower transwell chamber to encourage T cell migration. Enriched CD8+ T cells from influenza virus infected Adamts5-/- or C57.BL/6 mice were then added to the upper chamber of the transwell and migration assessed. The data clearly demonstrates (p < 0.05) that CD8+ T cells isolated from Adamts5-/- influenza virus infected mice show impaired migratory capacity through a versican-overlay transwell system when compared to C57.BL/6 controls expressing functional ADAMTS5 enzyme (Fig 7A). Furthermore, the introduction of exogenous versicanase (ADAMTS5/ADAMTS15 conditioned media from HEK293T cells) resulted in improved migration of CD8+ T cells through the versican overlay (Fig 7A). We also assessed if we could replicate these observations using human T cells. In these assays, we assessed the migration of a human immortalised CD4+ T cell line (JURKAT cells) following inhibition of ADAMTS5 with antibody. The data demonstrates that ADAMTS5-inhibited JURKAT cells do not migrate as efficiently as their uninhibited counterparts (S7C Fig).
We also determined that CD8+ T cells from C57.BL/6 mice express ADAMTS5 using qRT-PCR (Fig 7B). The expression of other versicanases (ADAMTS1, 4, 9, 15) in CD8+ T cells extracted from C57.BL/6 and Adamts5-/- mice was also assessed and showed increased expression of ADAMTS4, 9, and 15 following influenza virus infection (S8 Fig). We also confirmed the expression of ADAMTS versicanases in JURKAT cells and peripheral blood lymphocytes (S7A and S7B Fig) and found that ADAMTS5 and 15 are highly expressed in both cell types.
Additionally, we assessed the ability of CD8+ T cells from C57.BL/6 and Adamts5-/- mice to cleave versican. Our data indicates that versican was not cleaved as effectively by CD8+ T cells from Adamts5-/- mice (S9 Fig). These results indicate that ADAMTS5 is indeed expressed by CD8+ T cells and establishes that ADAMTS5-mediated cleavage of versican is necessary for T cell migration.
Given the accumulation of versican observed in the MLN of Adamts5-/- mice (Fig 6B) and inhibition of CD8+ T cell migration in transwell assays (Fig 7A), we wanted to assess if reducing versican expression would result in a rescue of T cell function. To achieve reduced versican levels, we crossed Adamts5-/- mice with versican reduced mice (Vcan+/hdf). It should be noted that disruption of both versican alleles is embryonic lethal [33]. We infected C57.BL/6, Adamts5-/-Vcan+/hdf (versican reduced), and Adamts5-/-Vcan+/+ mice with 104 pfu X31 (H3N2) influenza virus and assessed CD8+ T cell immunity in the spleen and MLN at day 10 p.i. Adamts5-/-Vcan+/hdf (versican reduced) mice showed increased numbers of total CD8+ T cells in the spleen at day 10 following infection when compared to the Adamts5-/-Vcan+/+ control group (Fig 8A). Increased influenza-specific DbNP366-372 and DbPA224-233 CD8+ T cell numbers were also detected by tetramer staining in the spleen of Adamts5-/-Vcan+/hdf versican reduced) mice at day 10 p.i. when compared to Adamts5-/-Vcan+/+ controls (Fig 8B). This was also reflected in functional assays where Adamts5-/-Vcan+/hdf (versican reduced) mice showed improved IFNγ production for both T cell specificities (Fig 8C). We also assessed CD8+ T cell numbers in MLN of influenza-infected Adamts5-/-Vcan+/hdf (versican reduced) mice to determine resumption of egress. Careful analysis revealed comparable numbers of total CD8+ T cells and influenza-specific CD8+NP366-372+ and DbPA224-233+ (by tetramer and ICS) in the MLN of Adamts5-/-Vcan+/hdf (versican reduced) and C57.BL6 control mice (Fig 8D–8F). We also assessed influenza-specific immunity in influenza virus-infected Vcan+/hdf and C57.BL/6 mice and found that NP366-372-specific CD8+ T cells were increased in the lungs of Vcan+/hdf mice (S10 Fig). Concurrently, NP366-372-specific CD8+ T cell numbers were decreased in the MLN of Vcan+/hdf mice when compared to C57.BL/6 controls (S10 Fig). These important and highly novel findings highlight the importance of the ADAMTS5 enzyme-versican substrate interaction as a key process in the regulation of virus-specific immunity.
Increasing evidence in the literature highlights the importance of zinc-dependent metzincins in the regulation of immune responses. MMPs and ADAMs have been strongly associated with neutrophil, macrophage, dendritic cell, and lymphocyte migration [6,34–36]. Here, we show for the first time that ADAMTS5, a member of the ADAMTS family, has a key role in influenza virus-specific immunity through a mechanism that involves ECM remodelling. Adamts5-/- mice had higher peak viremias and showed signs of delayed influenza virus clearance when compared to C57.BL/6 controls (Fig 2). The defect contributed to fewer total CD4+ and CD8+ T cells in the periphery and an accumulation of these cells in the MLN (Figs 3–5). Results from our transwell migration assays and Adamts5-/-Vcan+/hdf mouse studies further support our hypothesis that the absence of ADAMTS5 reduces ECM proteoglycan cleavage and impedes (but does not entirely block) the movement of influenza-specific lymphocytes to effector sites, such as the lung or to the periphery (Figs 7 and 8).
Migration of CD8+ T cells from draining lymph nodes to the periphery is critically important for the establishment of full effector function and eventual clearance of pathogens, such as influenza virus. Our research suggests that the lack of ADAMTS5 enzymatic activity in influenza virus-infected Adamts5-/- mice results in accumulation of the large extracellular proteoglycan V0/V1 versican (Fig 6A). Increased V0/V1 versican expression has also been noted in the developing limb [11] and heart valves [17] of Adamts5-/- mice. We believe that the accumulation in the MLN shown in this current study prevents lymphocyte trafficking and results in the exacerbation of disease following influenza virus infection. Furthermore, corroborating evidence by others in the field demonstrates that an epitope in the N-terminal globular domain of versican promoted CD4+ T cell migration and lymphocyte rolling [22]. Additionally, versican overexpression was associated with decreased infiltration of CD8+ T cells in stromal compartments of cervical cancer [28].
Further studies have suggested that the related zinc-dependent metzincins, the MMPs, are essential for immune cell trafficking. Like ADAMTs enzymes, MMPs contain a catalytic domain that utilises a conserved zinc binding sequence (HEXXHXXXGXX) for catalysing reactions [8] and have a broad range of cleavage substrates. This is in contrast to the highly specific cleavage moieties associated with ADAMTS enzyme activity. It is therefore not surprising that the MMPs have been identified in a vast number of physiological processes [37]. For example, MMP9, a highly characterised extracellular metalloproteinase associated with immune cell trafficking, has been detected in neutrophils, macrophages, dendritic cells, and T cells [31,38]. MMP9 and related MMPs (MMP2, 7, 10, 14) have been shown to degrade ECM roadblocks associated with immune cell trafficking in a similar fashion to what we have proposed in our study. Specifically, MMP9 and MMP2 expressing Th1 T cells demonstrate increased motility through collagen in a transwell migration assay [39]. Supporting in vivo data has suggested that a blockade of the MMP9 and MMP2 signalling pathway (Wnt) is associated with impaired T cell extravasation in an experimentally induced skin inflammation model [6]. Moreover, lipopolysaccharide-stimulated macrophages isolated from Mmp10-/- mice fail to migrate efficiently in transwell studies when compared to C57.BL/6 control macrophages [5]. In these studies, ECM components, such as collagen and elastin, inhibited immune cell migration. Collagen and elastin form key ECM components of basement membranes, and so dampened MMP activity would, in turn, lead to accumulation of these components and inhibition of immune cell migration and tissue infiltration. In contrast to the abovementioned studies, versican, a key ADAMTS5 substrate, is widely expressed in tissues and is not predominately associated with the basement membrane (as are MMP substrates).
ECM components, such as versican, provide a “sticky” surface for T cell adherence. Versican GAG chains interact directly or indirectly with molecules on the T cell surface, such as CD62L and CD44 [20,21,40], both of which are known to contribute to T cell trafficking. Increased levels of versican, such as those observed in Adamts5-/- mice, may therefore prevent T cell interaction with the ECM, leading to perturbations in T cell migration. Thus, we propose that cleavage and removal of versican blockades via the action of proteoglycanases, such as ADAMTS5, is required for efficient T cell interaction with the ECM to encourage migration to effector sites in the periphery and for the subsequent resolution of infection (Fig 8G). Our hypothesis is further strengthened by data demonstrating that reduction of versican restores normal T cell function in Adamts5-/-Vcan+/hdf mice (Fig 8). It is important to note that the migration of influenza-specific CD8+ T cells was not fully impeded in our experimental model. ADAMTS5 may therefore be working in concert with other metalloproteinases to facilitate T cell migration. The proteoglycanases, ADAMTS1, 4, 8, 9, 15, and 20, as well as MMP1, 2, 3, 7, and 9, are capable of producing versican fragments in a similar fashion to ADAMTS5 [9,16,32,41,42]. It is reasonable to suspect that there is redundancy built into the trafficking system, as related family members, such as ADAMTS9 (Fig 1F), may be providing some compensatory function in the absence of ADAMTS5, allowing some T cell migration (although highly restricted) to occur into the periphery (Figs 3 and 4). The cooperative requirement of versican cleavage by ADAMTS9 with ADAMTS5 has been observed in embryogenesis, and so its presence in regulation of immune cell migration cannot be discounted [11,43–45]. Furthermore, the transwell migration assay indicated that multiple ADAMTS enzymes can mediate T cell migration (Fig 7A). Further studies with related family members are required to ascertain their specific contribution to influenza-specific immunity.
Our findings would suggest that overexpression of ADAMTS5 or reduced versican expression could restore and improve immunity. Evidence from MMP9-related influenza studies suggests that a more circumspect approach may be required. MMP9 has been shown to be involved in the repair of lung tissue following influenza virus infection and can prevent bleomycin-mediated lung fibrosis by remodelling the ECM and degrading cytokines [46]. However, MMP9 overactivity in MMP9 transgenic mice has been associated with excessive neutrophil infiltration following influenza virus infection, leading to poor survival [38]. An inhibitor targeting ADAMTS5 has already undergone clinical trial as an osteoarthritic therapeutic (https://clinicaltrials.gov/show/NCT00454298). Administration of ADAMTS5 inhibitors for osteoarthritis may therefore be contraindicated in elderly patients, as they are more susceptible to influenza infection. Careful dissection and characterisation of metalloproteinase expression may therefore be required to determine the contribution of these enzymes to overall tissue repair and immunity.
In summary, our data show that the ADAMTS5 ECM enzyme activity is critically important for lymphocyte trafficking following influenza virus infection (especially CD8+ T cell immunity). In conclusion, interventions that facilitate increased ADAMTS5 expression used in conjunction with current approved antivirals and/or vaccines offer a new approach for combating unexpected emerging influenza virus pandemic threats.
All animal experiments were approved by the Deakin University Animal Ethics Committee (under G38-2013 and G34-2015) and were conducted in compliance with the guidelines of the National Health and Medical Research Council (NHMRC) of Australia on the care and use of animals for scientific purposes.
Six-to-twelve-week old Adamts5 (B6.129P2-Adamts5tm1Dgen/J) and Vcan (Vcan+/hdf) male and female mice (Jackson Laboratory), backcrossed eight times on a C57.BL/6 background, were bred at the School of Medicine, Deakin University [11,47]. Adamts5-/- mice and Vcan+/hdf mice were crossed for three generations to generate Adamts5-/-Vcan+/hdf and Adamts5-/-Vcan+/+ mice. The animals were housed at 20°C on a 12-h day/night cycle in sterilised cages (Techniplast) and provided food and water ad libitum. Mice were sex and age matched for experiments. C57.BL/6 (WT; wildtype) mice were purchased from the Animal Resource Centre, Perth, Australia.
Eight–to-ten-week old male or female naïve C57.BL/6, Adamts5-/-, Adamts5-/-Vcan+/hdf and Vcanhdf/+ mice were anaesthetized by isoflurane inhalation and infected intranasally (i.n.) with 104 plaque forming units (pfu) X-31 (H3N2) in a 30 μl volume, diluted in PBS. All mice were weighed throughout the course of infection and euthanized at days 3, 7 or 10p.i. We also detail experiments using Adamts5-/- and WT littermate controls in S11 Fig.
Spleen, lung, and MLN samples were aseptically removed from mice at various time-points following influenza virus infection. Lungs were digested with collagenase (Sigma), whilst spleens and MLNs were disrupted with glass microscope slides to generate single-cell suspensions. Spleen cell suspensions were enriched for T cells following B cell panning on plates coated with goat anti-mouse IgG and IgM antibody (Jackson ImmunoResearch, West Grove, PA) for 1 h at 37°C.
Lungs from influenza virus-infected mice were removed and homogenised in RPMI medium 1640 (Life Technologies), containing 40 μg/ml gentamycin and 10,000 μg/ml penicillin/streptomycin. Viral titres (pfu/ml) were determined by plaque assay on Madin-Darby Canine Kidney (MDCK) cell monolayers, as previously described [48].
Spleen, lung, and MLN single cell suspensions from naïve and infected mice were stained with conjugated monoclonal antibodies targeting murine CD3, CD8, CD4, CD314, CD11c, CD11b, MHCII, F4/80, and B220 for 30 min at 4°C and analysed on a BD-LSRII (BD-USA). The following antibodies were purchased from BD Pharmingen: CD8α-PERCP and CD8α-FITC (53–6.7), CD4α-FITC (RM4-4), CD3-APC (145-2C11), F4.80-PE (T45-2342) CD314-PE (CX5), CD11c-APC (HL3), MHCII-PERCP (M5/114), CD11b-FITC (M1/70), and CD45r-PERCP (RAB3-6B2). Results were analysed using Flowjo software version 7 (Flowjo; Ashland, USA).
CD8+ T lymphocyte populations from the spleen, lung, and MLN were enumerated following staining with fluorescently labelled tetrameric complexes directed against the two immunodominant influenza-specific CD8+ T cells epitopes (DbNP366-372-PE or DbPA224-233-PE) for 1 h at room temperatures in 0.1% BSA/ 0.02% sodium azide in PBS, as previously described [49,50]. Cells were then washed and stained with anti-CD8α-FITC and analysed on a BD-LSRII. DbNP366-372 and DbPA224-233 CD8+ T function was then assessed using ICS. Briefly, cells were cultured for 5 h in 96-well round bottom plates with influenza NP366-372 (ASNENMETM) or PA224-233 (SSLENFRAYV) peptide in the presence of Golgi-plug (BD, USA) and IL-2, permeabilised and stained for the presence of CD8α and IFNγ (BD, USA) as previously described [51]. Data was acquired on a BD-LSRII and analysed using Flowjo software.
RNA was extracted from CD8+ T cells in the lung and spleen of C57.BL/6 and Adamts5-/- mice, immortalised CD4+ T cells (JURKAT cells), and human peripheral blood lymphocytes as per the manufacturer’s instructions using an RNeasy kit (Qiagen). One microgram of total RNA was reverse transcribed using the Superscript III cDNA synthesis kit (LifeTech). QRT-PCR was undertaken on cDNA using iQ SYBR Green Super mix (Bio-Rad) and oligonucleotide primers for ADAMTS proteoglycanases 1, 4, 5, 8, 9, 15, and 20 with the following qRT-PCR parameters: 94°C for 2 min followed by 40 cycles of 94°C for 15 s and 58°C for 1 min. Influenza M1 cDNA levels were measured using probes as previously described (Life Technologies) [52]. The quant-iT OliGreen ssDNA Assay Kit (Invitrogen) was used to quantitate total cDNA input following manufacturer’s instructions. Changes in mRNA levels in lungs were calculated using 2-ΔΔCt method [16].
Human embryonic kidney (HEK) 293T cells (ATCC, Manassas, VA) were grown in DMEM (Gibco) containing 10% FCS in 5% CO2 at 37°C. Cells were transfected using Lipofectamine 2000 (Invitrogen) with pcDNA3.1MycHisA+ (Invitrogen) constructs encoding mouse full length V1 versican (kindly provided by Professor Dieter Zimmerman), full-length ADAMTS5, catalytically inactive ADAMTS5 (ADAMTS5EA), full-length ADAMTS15 and catalytically inactive ADAMTS15 (ADAMTS15EA), and empty vector control according to the manufacturer’s instructions. Serum-free conditioned medium was collected at 48 h post transfection as previously described, and expression was detected by western blotting using an anti-GAG antibody (Merck Millipore) and anti-myc (Merck Millipore) antibody for transfection with ADAMTS constructs.
CD8+ T cells from influenza virus infected Adamts5-/- and C57.BL/6 mice were incubated with HEK293T conditioned media containing full-length V1 versican and IL2 (20 U/mL) for 16 h at 37°C. Cleavage was detected by western blotting using an anti-GAG (versican) antibody (Merck Millipore) and anti-DPEAAE (versikine) antibody (Abcam). Immunoblots were analysed using ImageJ software.
Migration assays were performed in 12-well chamber inserts (5 μM) (Corning Inc.) as previously described [39]. Inserts were coated with V1 versican conditioned media [16,32], and recombinant mouse CXCL12 (10ng/ml) (R&D) was added to the lower chamber of the transwell to promote migration. Adamts5-/- or C57.BL/6 magnetically-enriched (Stemcell) CD8+ T spleen cells (105) were loaded to the upper chamber in migration media containing ADAMTS5FL, ADAMTS5EA, ADAMTS15FL, or ADAMTS15EA conditioned media from tramsfected HEK293T cells or serum-free migration media. Additionally, ADAMTS5 antibody (1000 ng/mL) was added to the upper chamber of JURKAT cells (105) in the versican transwell chamber and migration assessed. These cells were allowed to migrate for 4 h at 37°C at 5% CO2. Following removal of non-migrating cells in the upper chamber, the transwell membrane was stained with haematoxylin (Sigma-Aldrich) to determine the number of migrating cells.
MLNs from infected C57.BL/6 and Adamts5-/- mice were removed at day 10p.i. and fixed in 4% paraformaldehyde prepared in β-gal wash buffer (0.1 M phosphate buffer pH 7.4, 2 mM MgCl2, 0.02% NP-40, 0. 01% Na deoxycholate). MLNs were then washed in this buffer and incubated overnight in β-gal staining solution (5 mM potassium ferricyanide, 5 mM potassium ferrocyanide, 1 mg/ml X-gal in DMSO) at 37°C. The next day, MLNs were rinsed in wash buffer and fixed in 4% paraformaldehyde (prepared in wash buffer) at 4°C overnight. After a brief rinse in wash buffer, tissues were imaged then embedded in paraffin for sectioning and eosin staining.
MLNs from infected C57.BL/6 and Adamts5-/- mice were fixed in 4% paraformaldehyde at 4°C overnight and then paraffin-embedded and sectioned. Seven micrometre sections were stained with anti-versican (Merck-Millipore) or anti-DPEEAE (versican cleavage fragment) (Thermo-Fischer) antibodies at 4°C overnight. The following day, tissues were washed in 10% Triton-X (Astral) to remove excess antibody and then incubated with Alexa-fluor594 goat-anti-mouse antibody (Life-technologies). Sections were then washed in 10% X Triton-X (3 x 10 min) and stained with DAPI (Thermo-Fischer). Slides were then viewed under a Leica SP5 confocal microscope at 400 x magnification.
As data were normally distributed, they are presented as grouped data expressed as mean ± standard deviation (SD); n represents the number of mice. Statistical differences between two groups were analysed by Student's t test. Statistical differences between more than two groups were determined by two-way analysis of variance (ANOVA), followed by a Bonferroni multiple-comparison test. All statistical analyses were performed using GraphPad Prism 5 for Windows. In all cases, probability levels less than 0.05 (*p < 0.05) were indicative of statistical significance.
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10.1371/journal.pcbi.1000883 | Computational Models of HIV-1 Resistance to Gene Therapy Elucidate Therapy Design Principles | Gene therapy is an emerging alternative to conventional anti-HIV-1 drugs, and can potentially control the virus while alleviating major limitations of current approaches. Yet, HIV-1's ability to rapidly acquire mutations and escape therapy presents a critical challenge to any novel treatment paradigm. Viral escape is thus a key consideration in the design of any gene-based technique. We develop a computational model of HIV's evolutionary dynamics in vivo in the presence of a genetic therapy to explore the impact of therapy parameters and strategies on the development of resistance. Our model is generic and captures the properties of a broad class of gene-based agents that inhibit early stages of the viral life cycle. We highlight the differences in viral resistance dynamics between gene and standard antiretroviral therapies, and identify key factors that impact long-term viral suppression. In particular, we underscore the importance of mutationally-induced viral fitness losses in cells that are not genetically modified, as these can severely constrain the replication of resistant virus. We also propose and investigate a novel treatment strategy that leverages upon gene therapy's unique capacity to deliver different genes to distinct cell populations, and we find that such a strategy can dramatically improve efficacy when used judiciously within a certain parametric regime. Finally, we revisit a previously-suggested idea of improving clinical outcomes by boosting the proliferation of the genetically-modified cells, but we find that such an approach has mixed effects on resistance dynamics. Our results provide insights into the short- and long-term effects of gene therapy and the role of its key properties in the evolution of resistance, which can serve as guidelines for the choice and optimization of effective therapeutic agents.
| A primary obstacle to the success of any anti-HIV treatment is HIV's ability to rapidly resist it by generating new viral strains whose vulnerability to the treatment is reduced. Gene therapies represent a novel class of treatments for HIV infection that may supplement or replace present therapies, as they alleviate some of their major shortcomings. The design of gene therapeutic agents that effectively reduce viral resistance can be aided by a quantitative elucidation of the processes by which resistance is acquired following therapy initiation. We developed a computational model that describes a patient's response to therapy and used it to quantify the influence of therapy parameters and strategies on the development of viral resistance. We find that gene therapy induces different clinical conditions and a much slower viral response than present therapies. These dictate different design principles such as a greater significance to the virus' competence in the absence of therapy. We also show that one can effectively delay emergence of resistance by delivering distinct therapeutic genes into separate cell populations. Our results highlight the differences between traditional and gene therapies and provide a basic understanding of how key controllable parameters and strategies affect resistance development.
| With no HIV-1 vaccine or cure in sight, treating and controlling the virus continues to be a major global health concern [1], [2]. The advent of highly active antiretroviral therapy (HAART) has remarkably prolonged patients' survival, but has failed to eradicate the virus or to control the epidemic. In particular, HAART is a lifelong treatment, and as such presents major obstacles, including cumulative toxicities, severe side effects, a strict and complicated regimen, and problematic economics. Its major problem, however, is HIV-1's ability to escape it by developing drug-resistant mutants, which is further worsened by poor patient compliance [3]. Currently, the pace of development for new therapies lags behind HIV's rapid evolution of drug resistance, and alternative approaches are sought to either complement or replace HAART.
Gene therapy is an emerging and promising approach to treating HIV-1 infection, whereby engineered genes are delivered ex vivo, and potentially ultimately in vivo, into a patient's cells. They then act within these cells to disrupt the viral life cycle. Gene therapy offers the potential to attain sustained viral suppression and a restored immune system, with the added advantage of a simplified regimen, very few medical interventions, and reduced toxicities. To date, a plethora of potent gene-based inhibitors have been developed in the lab and some have undergone early-phase clinical trials (reviewed in [4]). While the trials demonstrated safety and feasibility, the infused gene-modified cells did not accumulate with time and consequently could not exert meaningful clinical effects [5], [6], [7]. Achieving therapeutic proportions of gene-modified cells in vivo is thus a necessary preliminary step for gene therapy's success. Ultimately, however, this approach must prove efficacious in the presence of viral resistance in order to qualify as a feasible therapeutic option. Indeed, as with HAART, viral escape is presently a major concern in the design of any gene-based technique [8], [9], [10], [11], and combinatorial gene cassettes are commonly developed as a means of limiting escape [12], [13], [14]. While the qualitative relations between key design parameters and viral escape are generally understood, a more rigorous quantitative investigation is essential to better understand the parameters' long-term effects under clinically-relevant conditions. The focus of this work is on a computational modeling approach to illustrate the contribution of therapy parameters and strategies to delaying the emergence of resistant virus in a patient.
Modeling HIV dynamics is by now a well-accepted tool for elucidating mechanisms of interest and for understanding viral evolution [15], [16], [17], [18]. A great deal of work has been published with regards to HAART, and has had much success largely due to its clinical validation against patient data. For novel treatments like gene therapy, however, substantial clinical data is not yet available. One must then resort to theoretical investigation as a much-needed step in therapy design. However, very few models have explored viral dynamics under gene therapy, and these have focused primarily on the response of virus that is sensitive or not resistant to the therapy [19], [20], [21]. Interestingly, this work revealed major deviations from HAART-like dynamics, thus underscoring a need for a dedicated model of viral resistance under gene therapy conditions.
Leonard et al. [22] developed a stochastic in vitro model that elucidates HIV's escape from RNA interference (RNAi) gene therapy. While powerful for studying escape in vitro [23], the model has several features that limit its relevance to in vivo scenarios. First, it focuses on RNAi therapies that degrade viral transcripts, an intervention that occurs after a cell has been infected and may thus not facilitate sufficient outgrowth of the gene-modified cells in vivo, as was later suggested in [21]. Conferring the modified cells with substantial outgrowth capacity is essential in any practical setting due to severe limitations on the fraction of cells that can be genetically modified [24], [25]. Other properties that diverge from in vivo conditions include simulations that often predict complete viral eradication [19], [21], and small population sizes that might under-represent minority viral strains [26], [27]. Since sustained viral replication and pre-existing mutants both play a crucial role in fueling resistance, they should be included in an in vivo model. Recently, von Laer et al.'s study [21] suggested that genes which inhibit early stages in the viral life cycle (by preventing cell binding, membrane fusion, reverse transcription, or integration) have the capacity to propel major cell expansion and therapeutic benefit. A variety of suitable gene-based techniques can be used, including RNAi- [28], [29], ribozyme- [13], zinc-finger nuclease- [30], and antibody-mediated [31] disruption of the CCR5 co-receptor, expression of fusion-inhibitory and binding-inhibitory peptides and of single-chain antibodies [32], and interference with capsid uncoating [33].
In this study, we developed a hybrid stochastic-deterministic approach for describing the evolution of HIV's resistance to early-stage gene-based inhibitors in vivo. We extended prior modeling work [19], [21] to incorporate a diverse viral population entailing varying degrees of sensitivity to therapy, and to account for the random effects that dominate early phases of resistance development. Our aim is to provide a general model that captures the commonalities of a broad range of technologies and that can be further adapted to faithfully describe any specific treatment. We apply the model to elucidate the general principles that govern resistance evolution and present extensive simulation results that quantify the tradeoffs between controllable therapy parameters. We show that the fundamentally different dynamics under gene therapy suggest different design guidelines from HAART's. Specifically, unlike HAART, in which drugs provide nearly-homogeneous protection to most cells, protected (gene-modified) and unprotected (untreated) cells co-exist under gene therapy. We find that this property can be harnessed to impede escape, provided that the mutations are associated with non-negligible fitness losses in non-modified cells.
We also investigate a novel delivery strategy to combat resistance, whereby different genes that target different viral functionalities are delivered into separate cell populations. Model simulations indicate that under some conditions, this idea, which is uniquely applicable to gene therapy and has not been analyzed previously, can dramatically prolong viral suppression and decrease the likelihood of escape. Finally, we study the development of resistance when the gene-modified cells have a proliferative advantage over untreated cells. Simulations demonstrate mixed implications on viral escape, namely, that it is less frequent but that when it does occur, it occurs more rapidly. The presented work provides a basic and general understanding of the key characteristics of gene therapy and their role in the evolution of resistance. Model predictions thus offer guidelines to optimizing therapy for long-term suppression of HIV-1 in patients.
Gene therapy is still a nascent technology; however, there have been a number of studies that serve to motivate our model. Here, we briefly outline the methodology and findings of several studies and discuss how our modeling work was inspired by them.
The first study is a phase I trial in which CD4+ T cells were harvested from five HIV-positive patients, transduced ex vivo with a lentiviral vector expressing an antisense RNA targeting HIV, amplified, and then infused back to the patients [5]. The patients were followed for several years, throughout which their immunological function and the persistence of the gene-modified cells were assessed. This trial not only demonstrated long-term survival of these cells in vivo, but also showed sustained and statistically significant reductions in the viral load in several patients. However, the modified cells declined in number following the infusion, and persisted at frequencies lower than 1% for most of the trial duration. These findings suggest that the cells are imposing some sort of selective pressure on the virus, although their mechanism of action is currently unclear as gene modification frequencies were too low to account for the observed changes.
As we mentioned earlier, current transduction efficiencies are low, implying that the modified cells must accumulate in vivo to reach therapeutic numbers. Such trend has not yet been observed in early-phase anti-HIV trials [24], indicating that the selective advantage of these cells in vivo is not sufficiently high. This may be because the engineered genes or their products lose their activity in vivo, and/or because the cells' proliferative capacity was impaired during their ex vivo manipulation. Current attempts to tackle these issues focus on increasing the vector-copy numbers per cell, and on intensive development of culture systems that better enrich and maintain T cell subsets which display extensive replicative capacity (reviewed in [34], [35]). Encouraging results from two recent studies are also worth noting. In one study, cells modified with zinc-finger nucleases expanded to therapeutic levels and induced substantial clinical effects in a mouse model of HIV infection, thus demonstrating their efficacy and selective advantage [30]. A clinical trial to test this approach in humans is currently underway. In a second study, the long-term (i.e., two years) expression of therapeutic genes in human blood cells in vivo was confirmed [36]. Clearly, the enabling technology is yet to mature, but once these barriers are overcome and gene therapy enters the clinic, viral resistance is to become the major concern. This is the starting point to our study, and one of our goals is to understand the implications of a potent gene therapy on viral evolution and what may be done to prolong its therapeutic effects in the presence of a rapidly mutating virus.
Another noteworthy phase I trial is a very recent one, in which CD34+ hematopoietic progenitor cells of four patients were transduced with a lentiviral vector expressing a combination of three unique gene therapies [36]. While combination therapy similar to HAART has been promoted in the gene therapy field as a method for combating viral escape, this is the first trial to put this idea into practice. In this trial, all three therapies were expressed from the same vector, a technique that provides the highest levels of protection in each cell, but also requires significant optimization and is subject to constraints. Given that more combination therapies will likely be developed, we asked how effective such combinations are in maintaining long-term viral suppression. Furthermore, gene therapy opens up a unique opportunity to split such combinations across cells, such that some cells express one therapy and others express another therapy. Such approach may offer an appealing and less technically demanding alternative to current combinatorial approaches. Here, we aim to explore its potential to provide significant improvements in preventing escape.
The model consists of two types of susceptible CD4+ T cells: transduced cells that are infused to the patient (protected (P) cells), and naturally occurring cells that were not manipulated (unprotected (U) cells). The overall CD4+ T cell pool is maintained by homeostatic proliferation, which saturates according to Michaelis-Menten kinetics (Figure 1A) [37]. Both cell types are regulated by the homeostatic mechanism in the same manner, and contribute equally to saturation, thus equally competing for presence in the pool. The renewal of susceptible cells is assumed to rely mainly on self-proliferation, with additional minor contribution from the bone marrow, modeled as a constant export of mature cells from the thymus. In this work, we focus on delivery of T cells, as opposed to stem cells, and hence the bone marrow engenders only U cells. HIV infection dynamics follow standard HIV models [16], [17], [38], with the therapy effects manifested as an inhibition of viral infectivity of the P cells.
The model considers a viral population consisting of a sensitive wild-type (WT) strain as well as other strains to which WT may evolve through a series of mutations. There are n genomic sites that confer resistance to therapy, and each mutant strain can have any combination of them mutated away from their WT form. Resistance is assumed to gradually intensify with increasing numbers of mutated sites, and it manifests as an improved ability to infect P cells, but is at the same time associated with a fitness cost when infecting U cells (Figure 1B). The model captures all interactions via ordinary differential equations (ODE), but uses a stochastic routine to treat populations at low densities, such as those of newly-emerging species (see the Methods section for details).
We start by exploring a restricted baseline model, similar to the model of Lund et al. [19], in which resistant strains are absent. We use it to demonstrate the inherent differences between gene therapy and HAART and to quantify the effects of different parameters on the achievable viral suppression levels.
Figure 2A shows in vivo dynamics following a P cell infusion, as simulated with default parameter values (see the Methods section). The P cells are shown to expand, thereby impeding the supply of U cells and thwarting viral replication. The establishment of a P cell reservoir brings the virus to a new reduced set point. The P cells' expansion is driven by a selective advantage they possess over U cells, which derives from a reduced susceptibility to infection. The expansion thus slows down as the viral load declines.
In comparison to post-HAART behavior [39], [40], three substantial differences are clear: the heterogeneity of the target-cell population, the slow nature of the response, and the limited reduction in viral load. In particular, HAART blocks viral replication on nearly the entire target-cell population, resulting in a rapid viral decline. This is in contrast to our model, where the U cells experience only a slight decline, and continue to facilitate viral replication, albeit at lower volumes due to their reduced turnover rates. In addition, HAART takes effect promptly and comprehensively, whereas gene therapy induces a gradual depletion of the U-cell supply, fueled by the inherently slow process of P cell accumulation. To summarize, the two approaches exhibit different mechanisms of action: brute-force disruption of viral activity by blocking access to existing resources (HAART) versus draining the supply of those resources in favor of more robust ones (gene therapy). Since gene therapy profoundly re-structures the blood system to render it less susceptible to HIV, it is slow to exert its effects. Yet, only innate changes of this kind can facilitate sustained virus control with limited medical intervention. HAART, on the other hand, is quick to act, but once interrupted, results in immediate viral rebound and thus necessitates a strict lifelong adherence to an elaborate drug regimen.
The integration of resistance into our model gives rise to an additional therapy characteristic, namely, a genetic barrier , which we define as the number of mutations that the virus must accumulate in order to completely overcome inhibition by P cells (Figure 1B). To preserve the simplicity and tractability of the model, we assumed that a mutant's phenotype is determined by the number of accumulated mutations, but not by their actual identity or location (see the Methods section). In other words, strains that possess different combinations of m mutations are phenotypically indistinguishable. Our model limits the mutational effects to modified infection rates (i.e., varying attenuations of WT's infection rate) for two reasons. First, this is the most likely route to escape [10]. Second, it is the most effective one, since once the P cells expand and the increased infection rates allow the virus to infect significant portions of them, only then virion production can exert meaningful effects. As gene therapy involves two cell populations, the model accounts for contradicting effects on both cell types, as follows. On one hand, resistance confers the virus an improved ability to infect P cells, while on the other hand, the accumulation of mutations in key regions of the virus targeted by the therapy can also be associated with a reduction in fitness in the absence of therapy, that is, in U cells [23], [49], [50], [51], [52]. The model assumes that both effects gradually escalate with each additional mutation [10], [50], [52], [53], [54]. Since our model attempts to broadly apply to several techniques, it perceives the genetic barrier as a general property and disregards its specific origins. A large may thus correspond to several inhibitors acting concomitantly, or to a single inhibitor whose interaction with the virus spans a large domain [10]. A similar interpretation applies to the inhibition factor .
We performed stochastic simulations with our model and aggregated the mutants' densities according to number of mutations, such that all strains with mutations () count as one species. Figure 3 shows the outcome of a typical simulation of the stochastic trajectory when . It demonstrates an accumulation of increasingly fitter mutants at the expense of less competent pre-existing (e.g., ) and early-appearing mutants (e.g., ). Mutants that possess higher numbers of mutations (e.g., ) are increasingly more competent, and eventually reach levels that give rise to even fitter strains (e.g., ), which soon outgrow them. In the presence of highly resistant strains, the P cells lose their advantage and decline (not shown). Note that the decline of pre-existing mutants with one or two mutations is a consequence of both the drop in the WT viral population, which continuously feeds the mutants populations, and of their susceptibility to therapy. However, the decline is slow, thereby constituting a major obstacle for gene therapy, as it fuels progressive mutation accumulation and expedites the emergence of non-existing resistant strains. In contrast, under HAART, pre-existing strains swiftly drop to very low levels, and while still present, their mutation into more resistant strains is severely confined by their small absolute numbers.
The stochasticity that arises from the random effects that dominate at small population sizes can be seen for existing mutants with three mutations and for newly emerging mutants. New advantageous mutations occur at random and may drift away before reaching critical levels, thereby delaying emergence in comparison to fully-deterministic trajectories [18]. As delays build up along the evolutionary process, larger 's are progressively associated with increasingly varying fixation times of highly resistant strains (see Figure S1). As a result of this stochasticity, it is striking that two “identical” patients (i.e., infected with the “same” virus) can experience remarkably different clinical outcomes.
As a measure of the treatment's efficacy, we computed the fixation time, defined as the time required for the resistant strains to reach 50% of the viral population. Treatment was considered successful if fixation had not occurred within four years of its initiation. Figure 4A–4C provides an overview of the three figures of merit by which we evaluate a treatment. Each point in the plots summarizes the results of 500 simulations with default parameter values (see the Methods section). The blue curves correspond to a standard application of gene therapy, as discussed above, whereas the red curves correspond to a more advanced strategy and will be discussed in a later subsection. At this point, we limit discussion to the blue curves. Figure 4A shows how the fraction of successful treatments, called the success rate, varies with the genetic barrier n. Interestingly, success rates exhibit a threshold-like behavior, which was found to be typical with many other parameter choices (not shown). Such an effect becomes important when one considers combining several gene-based inhibitors within a P cell as a means of increasing . If a therapy is near the threshold, the addition of an inhibitor can make a dramatic difference in its efficacy. Figure 4B shows average fixation times for all 's for which therapy success rates were below 0.9 (see Figure 4A). As expected, larger 's result, on average, in prolonged viral suppression. Figure 4C shows the average of the corresponding viral load reductions (i.e., the inverses of the suppression gain) obtained at the fixation time. One can see that the average viral reduction keeps declining until , reflecting the fact that for , resistance emerged before therapy has reached its steady state in the absence of resistance. This implies that under our default parameter choices, even considerable genetic barriers (e.g., ) do not suffice to allow therapy to reach its full suppression potential before resistance emerges.
Finally, we wish to stress that the inhibition factor is assumed to be independent of and thus was kept fixed throughout simulations. Specifically, a larger does not imply a stronger inhibition, even when obtained through insertion of additional genes. The dynamics under gradually increasing inhibition can be readily obtained using similar simulations.
The notion of replication fitness has long been used for understanding HIV's evolution under HAART [55], [56], [57], [58]. It is a model-derived measure of a strain's ability to expand over time in a given environment, and provides a tool for understanding and predicting clinical outcomes. This measure, however, was derived from HAART models and does not apply to gene therapy, whereby HIV is faced with a mixed cell composition and may display different replication traits within each cell subpopulation. We used our model to extend this notion to the case of a heterogeneous target-cell environment (see the Methods section and Text S1 for derivation). The replication fitness (F) of a strain is captured by the following expression:(1)where U and P are the two cell-type densities, is a generalized infection rate constant (see the Methods section), is the infected cells' death rate, is a replicative fitness cost associated with mutating, and is the infectivity-attenuation factor. The parameters and summarize the cost and benefit involved in viral escape, and are determined by the number of mutations in strain , as well as by the therapy's potency () and genetic barrier () (see the Methods section and Figure 1B). The parameters and , in contrast, are unaffected by viral evolution, and so distinct viral strains feature different and , which, when weighted through Eq. (1), yield an overall replication fitness. Importantly, U and P are time dependent, and so F varies as therapy progresses. Yet, changes to U are minor in our model, and P transiently increases until it saturates.
Eq. (1) encapsulates the key determinants of viral resistance and provides insights into therapeutic design tradeoffs. We illustrate this point with a simple example. Consider a potent therapy that inhibits WT's infectivity with (Figure 1A), and suppose that the U and P cell densities both equal cells/µL. The WT virus' fitness is then given by . Consider two strategies to weaken the virus: applying a tenfold-stronger infectivity inhibition () or interrupting its normal function such that it experiences a modest fitness loss of 5% () but the same inhibition as before (). The corresponding changes to the replication fitness amount to in the first case, compared to a more favorable in the latter case. Even starting with and applying a 100-fold decrease (yielding the same ) attains only a decrease of in the first case, which is somewhat more comparable to the losses from the moderate 5% replicative cost. Clearly, realistic tradeoffs depend on the U and P densities as well as on simultaneous changes in all parameter values, but this example stresses the different impact of and on controlling viral replication. It shows that viral fitness in the U cell pool plays an important role in restraining replication, provided that mutations are associated with a replicative cost, and that powerful protection in P cells has a modest contribution in comparison. Next, we use simulations to show that modest fitness penalties can impede resistance as effectively as major increases in potency.
To further explore the roles of mutational fitness cost and inhibition potency in controlling resistance, we simulated the model over a wide range of parameters. We first varied the average fitness cost that the virus incurs with each additional mutation (), while fixing all other parameters at their default values. Figure 5A–5B shows success-rate and fixation-time graphs, spanning a range of fitness costs. As expected, greater fitness penalties hamper escape and shift the “success threshold” towards lower 's. Increased penalties also delay therapy failure (fixation time), allowing for greater viral reductions (not shown). One can also see that it is essential for gene therapy to target viral functionalities for which resistant mutations incur non-negligible costs ().
The effects of altering the inhibition factor are shown in Figure 5C–5D for two fitness costs. Here, the inhibition was amplified tenfold each time, resulting in meaningful improvements. It can be seen that when mutational fitness costs are minor (), highly potent inhibition is needed to effectively hamper resistance, as this is the only way to restrain the virus. The tradeoffs between the two factors are exemplified by the two sets of nearly overlapping curves (shown in dashed lines), which correspond to a tenfold increase in inhibition coupled with a slight decrease in fitness cost.
While mutational fitness cost for viral replication in the U cell pool appears to dominate resistance dynamics, our simulations also indicate that powerful inhibition can in fact control the virus, and constitutes an important design criterion. It takes effect by weakening early mutants, such that for a given , more mutations are needed to reach a sufficiently fit virus. One means to achieve increased potency may be through combination therapy, provided that the individual effects are multiplicative. Nonetheless, to the best of our knowledge, the incremental contributions of single therapies within an ensemble have not been determined to date.
Combination therapy has traditionally been the treatment of choice against the rapidly mutating HIV-1. Simultaneous targeting of several functional domains slows down resistance by concurrently increasing the genetic barrier and strongly suppressing replication. Gene therapy adopted this principle [12], [13], [41], but more importantly, opened the door to a new combination strategy that has hitherto been infeasible, that is, of combining targets across cell populations as opposed to within individual cells.
Ex vivo gene transduction provides the clinician with control over the destination of delivered drugs, which, in turn, enables the infusion and in vivo expansion of distinct P cell pools, each containing different inhibitors that target distinct viral functionalities. In this setting, a strain that resists one inhibitor is confined to replicate only on a fraction of the P cell population, and is still suppressed within the rest of it until it acquires additional mutations. The idea, then, is to limit the resources available to the evolving virus by forming sub-populations of P cells.
We illustrate this principle and explore its potential using a computational model (see the Methods section and Text S1). Since this strategy can be used on top of any gene therapy technique, it represents an additional mechanism to combat resistance. We thus seek to quantify its added value compared to using homogeneous protection. Our model considers two gene therapies, P1 and P2, targeting distinct viral functions such that cross-resistance between them is excluded. This may apply, for example, to binding and/or fusion inhibitors (P1) combined with integrase and/or reverse-transcriptase inhibitors (P2). Both therapies are modeled as equally powerful - they display the same and same , and each may correspond to one or to several concurrent inhibitors. When introduced into two cell populations, they divide the P cells into two smaller sub-populations and give rise to a complex quasispecies environment with viral strains that display a range of resistance levels to one or both therapies. The baseline case, as captured by the previously discussed model, corresponds to use of just one of these therapies.
The resulting average fixation times and success rates were compared to their single-therapy counterparts (Figure 4A–4B). We found that fixation times improved gradually (but modestly) throughout the entire range, until a dramatic increase in favor of therapy combination took place at (Figure 4B). The same effect is manifested as a meaningful shift in the success-rate curve in favor of combination therapy - it suffices to use two therapies with as opposed to , which is required for a successful stand-alone approach (Figure 4A). Figure 4 pertains to simulations performed with default parameter values, but we observed the same trend for a wide range of other parameter values, with some variation in the position () of the dramatic shift (data not shown). Simulations with three distinct therapies showed further improved gains at all levels, as expected (data not shown). We conclude from our findings that this approach is powerful when used judiciously within the “right” range.
Another interesting question is how the added value of splitting therapies across cells compares with the added value of increasing the genetic barrier. This question is of significance to gene therapy, where large 's are necessary to guarantee long-term suppression (Figure 4). Large barriers will likely be accomplished through combinatorial approaches (within each cell), which in turn, are associated with technical and physiological challenges [59], [60] that may consequently limit the achievable barriers. This is especially relevant to RNAi therapy, which is characterized by low individual barriers and strongly relies on combinatorial approaches, but at the same time, faces serious obstacles associated with multiple payloads [61]. In light of these challenges, splitting therapies may offer an alternative to pursuing large multi-component payloads.
We used our model to answer the following question: is it more beneficial to use two therapies whose genetic barrier is or to invest the effort to design one therapy with an enhanced barrier ? We show that there is not a definitive answer to this question, as illustrated in Figure 6A for . Average fixation times under both strategies are depicted for a range of inhibition factors , where each case resulted in a different answer. The “splitting strategy” appears to perform slightly better in the presence of a relatively weak therapy (), but loses its advantage under more potent regimens. Similar situations were observed for other values (data not shown).
We interpret our ambiguous results by examining the viral replication fitness (Eq. (1)) under both conditions. Consider a resistant strain M whose replication fitness equals . When two therapies are split across cells, one can think of it as downsizing the pool of P cells susceptible to M by a factor of two, corresponding to a replication fitness of . On the other hand, an enhanced genetic barrier typically renders the mutants less resistant than before the enhancement, as they now need to overcome additional inhibitory mechanisms. In our model, this is reflected in a smaller inhibition factor , which should be compared to . An important point here is that in practice, the extent of decrease from to is case-dependent. Since no pertaining data are currently available, we determine based on simple functional relations between the inhibition factor and the number of mutations (see the Methods section). Our results are therefore model-specific, yet they support our main point that the answer is nontrivial and that strategies should be compared on a case basis.
We further linked our findings to the suggested interpretation by computing the ratios when . In our model, resistance is a function of the number of mutations, giving rise to five distinct ratios for each case considered, as depicted in Figure 6B. The considered cases differ in their initial inhibition potency (), which determines all intermediate inhibition factors. The correlation between the ratios and the advantageous strategy can be readily observed – the more potent a therapy is (i.e., smaller ), the sooner the ratio curve crosses one, at which point the single-therapy strategy imposes stronger inhibition. For example, in the weakest therapy case (), the curve is entirely below one, which means that the “splitting strategy” presents the virus with harsher conditions throughout its escape route. In the other extreme case (), a single therapy exerts stronger inhibition fairly early in the evolutionary process, even on pre-existing mutants. The middle case shows mixed effects, which balance out to yield similar fixation times.
In our analysis, we assumed that increasing has no implications for therapy potency, or in other words, that is independent of . However, when an increase in is achieved by the insertion of additional genes into each P cell, it may be reasonable to assume that concomitantly becomes smaller. If we further assume that decreased tenfold during the transition to a larger , then our simulations indicate that fixation times are consistently longer for the single therapy regimen (Figure 6A). As we already emphasized, this might be a model-specific prediction that may not hold true under other conditions, but we can certainly state that a simultaneous decrease in renders the single-therapy strategy more powerful than before. Finally, we stress that such scenario-specific results stand in contrast to the comparison we made earlier, where we found that splitting equally-powerful therapies across cell populations is always advantageous over using one of them alone (Figure 4).
As we showed earlier, the potential reductions in viral load under gene therapy are limited in comparison to HAART (Figure 2C). Methods to enhance the selective advantage of P cells by extending their proliferation capacity are being explored, as a means of boosting their expansion, such that lower viral set points could be attained. Proposed techniques include expression of stimulatory interleukins, microRNAs, and telomerase reverse transcriptase (hTERT) [34], [62], [63], [64], [65]. While each technique carries the risk of uncheck proliferation that could result in cancer [66], engineering additional safety controls could eventually solve this problem [34]. We chose to consider the idea of proliferation enhancement even though the underlying technology is not fully developed yet, so that we can better understand its impact on the emergence of resistance.
The potential gains from proliferation enhancement were previously illustrated by von Laer et al.'s modeling study [21], and our baseline model displays similar trends of reduction in the viral load. Since latently infected cells are precluded from the model, a sufficiently powerful enhancement can in fact eradicate the virus. It also expedites P cell accumulation, thereby intensifying the selective pressure on the virus soon after therapy begins, but at the same time accelerating viral decline (see Figure S2). Figure 7A–7C shows the outcomes of simulations of viral evolution under this strategy, with a range of improved proliferative abilities and with default parameter values. We modeled enhanced proliferation as a constant percentage increase in the proliferation rate of P cells with respect to U cells, represented by a factor (see the Methods section). We found that enhancing proliferation consistently expedited the fixation of resistant strains and, as explained below, seemingly paradoxically improved the success rates. Overall, the improvements are modest, and gains diminish with increasing . The major therapeutic benefit of this approach is in improved viral reduction before resistance emerged, despite the faster emergence (Figure 7C). In particular, improvements become major for large 's, amounting to one to two logs. Note that under such conditions, early-appearing mutants that are weakly resistant to therapy may reach 50% of the viral population at very low densities sometime during WT's decline to extinction, albeit without expanding much further. We therefore modified the definition of fixation to exclude cases where the entire viral population continues to circulate well below detectable levels (e.g., 0.01/µL).
The shortened fixation times are not surprising and reflect a combined influence of acceleration in both WT's decline and P-cell expansion. As P cells accumulate, the selective pressure on the virus builds up and fuels the expansion of resistant strains. P cell levels are also higher, which further assists those strains. In light of these factors, it is rather surprising that success rates gradually improved with further enhancement (Figure 7A). We attribute this result to a strong counteractive effect of the drastic viral decline and extinction, which cuts down the de novo generation of highly resistant strains. It is also worth noting that we further explored resistance dynamics when proliferation is impaired (), as that may reflect current conditions in gene therapy trials. Therapy effects on the entire viral population are weakened and slowed down in this case, and so, as expected, lesser gains from therapy are associated with weaker and delayed resistance (data not shown).
The effects of proliferation enhancement resemble those of the intensification of traditional anti-HIV drugs [16], and, in fact, the similarity to post-HAART dynamics manifests itself in another way – it allows the P cells to “take over” the immune system. This is because sufficiently intense proliferation renders them advantageous even in the absence of HIV, allowing them to populate the system at the expense of a dwindling U cell reservoir (Figure S2). Fitter P cells occupy bigger portions of the immune system and bring it closer to HAART's homogenous conditions. To summarize, in the absence of resistance, proliferation enhancement constitutes an important step towards achieving HAART-like performance. However, when resistance is accounted for, this strategy may be associated with adverse effects and may prove beneficial under limited conditions.
Table 1 summarizes each of the above-discussed strategies along with their impact on treatment outcomes and their parametric tradeoffs. The first three columns pertain to the qualitative changes in the figures of merit introduced earlier (Figure 4), where the success threshold (first column) stands for the minimal for which success rates exceed 0.9. The fourth column quantifies the changes in the success threshold that correspond to the specified parameter deviations from the default case.
We developed a computational model of HIV-1's evolutionary dynamics in the presence of gene therapy to study its long-term efficacy against the virus. We used the model to quantify the contributions of key therapy-design parameters and different treatment strategies to the suppression of viral resistance. We find that when judiciously designed, gene therapy has the potential to provide long-term suppression of HIV-1, but meeting this goal requires highly powerful and robust antiviral genes, which may not be currently available. In particular, large barriers, ranging from 5 to 9 (depending on therapy parameters), are needed in order to guarantee that viral suppression below a few percent of pre-therapy loads lasts at least four years. In addition, gene therapy is characterized by very slow dynamics in comparison to HAART, as a consequence of its inherently different mechanism of action. In particular, it can take about two years to reach a new viral set point (Figure 2A). This, in turn, makes a long-lasting resistance control even more critical, if one aims to fully realize the therapy's clinical benefits. We find that delaying resistance emergence merely to the point of reaching a new steady-state viral set point still requires fairly large genetic barriers, which are only slightly smaller than those required for a four-year viral control.
We find that two controllable parameters play a key role in delaying emergence of resistance – the replicative fitness cost associated with mutation () and the therapy's inhibition factor (). Our results demonstrate that intensifying both factors can severely impair the competence of mutants, and thereby offset the effects of a significant mutational influx of newly emerging strains (Figure 5). Our analysis stressed the difference between the ways by which the two factors and take effect, where the mutational fitness cost acts primarily by hindering viral replication in U cells (Figure 1B) and the inhibition potency serves to hinder the virus as it replicates in P cells. This brings into attention a major difference between gene and traditional therapies, namely that gene therapy leaves a major fraction of the target cells unprotected. As it turns out, one can harness this apparent weakness to improve virus control. Our model predictions support this by showing that small relative decreases in the average fitness cost per mutation dramatically improve success rates and delay resistance emergence (Figure 5). The fitness of mutants in the U cell reservoir, thus, plays an important role here, and targeting highly conserved regions may prove to be particularly beneficial for gene therapy. These results also stress the importance of assessing the fitness of resistant mutants on cultures of unprotected cells as part of a therapy's evaluation.
Our model predicts that sufficiently potent inhibition also provides a powerful means for preventing the development of resistance. However, major amplifications in potency are required in order to achieve benefits that are comparable to those attainable by minor relative changes in fitness cost (Figure 5). For example, we find that 's as low as 10−4 can successfully hamper resistance when combined with a barrier . Whether such 's are practical or not is yet to be determined. Combination therapy, possibly involving different gene-based techniques, may provide a viable approach to achieving this goal, and may simultaneously feature large 's [4], [14]. Nonetheless, its promise depends on the synergistic interaction between multiple concurrent agents. For example, potent inhibition may be achieved when their joint potency () amounts to the product of their individual 's, rather than to their sum. Importantly, such cumulative effects have not yet been quantified or validated in the context of gene therapy. Note also that the importance of highly potent inhibition in preventing resistance stands in contrast to its negligible contribution in reducing the sensitive virus load (Figure 2B).
The methods presented above are conventional approaches to enhancing an antiretroviral treatment's efficacy, as applied to gene therapy. However, there are supplementary, less obvious, ways to boost gene therapy's performance, which are not possible with HAART. One such method is to combine multiple therapies across cells such that the P cells form several sub-populations, where each population is susceptible to different viral strains. We illustrated this novel principle through simulations of an abstracted model, which considers several distinct and equally powerful therapies. Our results demonstrate that dramatic improvement in success rates as well as in fixation times can be obtained in this manner (Figure 4). However, these occur only within a certain window of genetic barriers. When applied outside this window, improvements are modest, albeit increasing with increasing number of therapies. One must therefore reliably characterize the individual therapies before attempting such a strategy.
It is also interesting to compare the improvements obtained by splitting therapies to those obtained by increasing the genetic barrier of a single therapy. We find that the “winning” strategy varies, depending on the therapy parameters and on the resistance spectrum of intermediate mutants (Figure 6). The comparison result particularly depends on , and on whether it decreases while increases. It is reasonable to expect that would decrease when is extended by an insertion of additional therapeutic genes, as long as they exert their effects via distinct independent pathways [13], [14], [41]. In this case, our model predicts that a single improved therapy will perform at least as well as a two-therapy approach. If this is not the case, it is difficult to predict the overall effect on and which strategy performs better. An example of the latter case is a combination of RNAi targets, which is constrained by a maximum tolerable level of siRNA molecules so as to avoid toxicity [67] and competition for cellular resources [68]. It is possible that overall inhibition power may not decrease or may even increase under such constraints, especially when numerous targets are involved. Although combination gene therapy has been extensively studied via experiments over the past few years, we are unaware of any quantitative results that can be used within our model. Nonetheless, our analysis points out the key parameters which need to be quantified in order to compare the two options, namely, the therapies' entire spectrum of inhibition potencies.
Gene therapy pressures the virus indirectly by way of a slow P cell expansion, and its limitations derive from the persistence of U cells. It has been argued that it could be improved by boosting proliferation of P cells, such that they outcompete U cells [42]. We explored the implications of such strategy on resistance dynamics and observed mixed effects on clinical outcomes: the likelihood of viral escape is decreased for a narrow range of genetic barriers, but when escape does occur, it occurs more rapidly than without enhancement (Figure 7). Overall, both effects are moderate, even for increases of 30%–40% in proliferative activity. In light of our results, it is worth noting that this technique is appealing for other reasons. It induces rapid viral decline, powerfully suppresses WT virus, and facilitates an extensive re-population of the immune system with P cells. These major improvements may be associated with further clinical advantages that are not captured by our model, such as the recovery of the lymphocyte homeostasis mechanism, which is believed to be impaired in the presence of high HIV load [47], [69], [70].
It is also worthwhile to consider alternative ways for gene therapy to overcome its limitations. Resorting to hematopoietic stem cells may not suffice, as simulations we conducted show that when the infused fraction is small, both approaches are confined by the same factors and exhibit similar dynamics and efficacies (data not shown). Only when large fractions are infused, can meaningful improvements over T-cell-based methods be obtained (see Figure S3). This is concerning in light of results of recent clinical trials which indicate that current delivery efficiencies reach several percent at best [4], [41], [71]. A potentially promising way to overcome this limitation is by including a selectable genetic marker in the antiviral payload that renders the P cells resistant to a chemotherapeutic agent, to which stem cells are normally sensitive. It thereby allows for their chemotherapeutic selection and expansion in vivo. Recent progress in this direction is encouraging, as substantial selective expansions were observed in large animal models that were treated with such drugs [72], [73], [74], suggesting that these may be used with humans [36], [74]. Another option is to ablate the immune system to make space for the P cells, and despite its associated risks, is it currently applied in a number of ongoing clinical trials [75]. From a modeling perspective, these approaches can be captured by increasing the fraction of infused stem cells. Our simulations then suggest that the effects of increased fractions on resistance dynamics are qualitatively similar to those of proliferation enhancement, although they are more modest (see Figure S4). At any rate, a more elaborate modeling study of stem-cell delivered therapy is required to map the conditions that facilitate considerable responses. This exceeds the scope of this work but the ideas and models presented here are readily applicable to this case.
Gene therapy could also potentially be combined with HAART, so as to benefit from HAART-induced suppression. The problem with this approach is that HIV is the driving force behind the expansion of P cells, and their selective advantage over U cells becomes small with decreasing viral loads [42]. It thus appears that methods for stimulating P cell proliferation and/or selection must be developed, such that the P cells can accumulate independently of the infection, and can then be effectively combined with HAART. Moreover, these methods can then leverage on a broad range of agents, including a variety of RNA-based inhibitors (reviewed in [4]), which may not propel sufficient cell expansion, but do provide powerful viral suppression (see Text S1).
A number of simplifying assumptions were incorporated into our model to retain its generality and tractability. First, we assumed a fixed incremental effect of each subsequent mutation on a strain's phenotype. In reality, individual effects depend on the actual mutational composition and on order of appearance, and not all mutational routes yield a viable virus. We therefore neglected epistatic interactions between mutations and parameterized only the average incremental contribution. Second, we assumed that fitness in P cells is monotonically increasing with a mutant's order, and thus mutants of sufficiently high order always thrive under therapy conditions, and will likely emerge if generated. Other choices of fitness functions, such as ones that are non-monotonic or monotonic but assigning lower fitnesses to all strains, can render such high-order mutants less competent. Such choices would result in quantitatively different (more optimistic) emergence statistics, and would mostly depend on therapy effects on pre-existing mutants. Clearly, once data for specific therapies become available, the model should be revised to generate more precise predictions, but at this point, we focus on capturing the key features involved in the development of viral resistance, namely, the tradeoffs between the effects of and . These qualitative findings do not depend on the monotonicity assumption since the same factors determine the ability of pre-existing strains to outgrow in the mixed-cell environment. The ideas and analysis pertaining to combinatorial approaches also apply regardless of the mutants order and are not function-specific. However, the effects of proliferation enhancement may differ under non-monotonic fitness assignments and should be revisited once relevant data are available.
We also assigned equal mutational fitness costs to replication in both cell types, which may not always apply. This implies that a strain is never fitter in P cells than in U cells, and matches recent experimental findings [23] (see the Methods section). However, if highly resistant strains replicate better in P cells, our predictions are over-optimistic - they provide upper bounds on therapy efficacy and overestimate 's impact on escape. Another simplification entails neglecting escape routes that are unlikely under HAART but may be selected in mixed cell compositions. Consider, for example, a strain with several mutations that partially resists therapy and is less fit than WT in U cells. Suppose that an additional mutation then partially rescues its fitness in U cells and possibly boosts its resistance as well. If such compensatory routes exist, they may be selected in environments where both fitness traits are valuable, and our assumption excluded such possibilities from consideration. Future work may further investigate the consequences of such complex fitness landscapes.
We extended our model to account for the contribution of recombination to the formation and loss of resistant strains. To maintain reasonable model complexity and size (especially for large 's), we included only simple recombination patterns, whereby mutations from distinct strains are adjoined, and mutations are lost during recombination with WT. The complete details are given in Text S1. We observed very little quantitative effects on the average treatment outcome, even under very high recombination rates. Therefore, in the interest of clarity and simplicity, we omitted recombination from the model. However, a more detailed treatment of recombination, particularly in conjunction with epistatic interactions, may reveal other outcomes, as it was previously shown that recombination effects are pronounced only in the presence of epistasis [76], [77]. A detailed analysis of this type requires a large dedicated model (see e.g. the work in [77]) and is not practical when studying a wide range of genetic barriers as was done here. Future work may incorporate such detailed mechanisms for special cases of interest. An additional extension we considered entailed a homeostasis control of thymus input rates as opposed to constant rates, but that did not qualitatively affect our results either (data not shown).
Many of our assumptions can be partially corroborated by in vitro assays. For example, it is common practice to characterize the spectrum of potentially resistant mutants through serial passage experiments. In our case, one can assess the attenuated infection rates from single-round infection assays [10], [44] and reconstruct an approximate “resistance profile”. Similar tests should be conducted with U cells to quantify both fitness traits. Assays with a mixed cell population can serve to probe the fitness landscape and identify mutants that trade their resistance to therapy for recovered fitness in U cells, as these may be selected in vivo. Experiments can also help determine the incremental contributions of single agents in combinatorial settings and whether they interact multiplicatively, additively, or in any other way. The real challenge is in extrapolating in vitro findings to clinical settings, since these do not fully reflect their in vivo counterparts, but yet are indicative of relative competence among strains. Clinical data are invaluable to validating our model, but to the best of our knowledge, sustained P cell expansion has not yet been reported in any clinical trial. Thus, experimental data from animal models [41], [78] can provide a starting point for better characterizing P cell expansion and viral suppression, by fitting variations of our model to clinical measurements.
To summarize, we use a generalized model of HIV gene therapy to identify both the most effective routes to control the virus and the critical properties of the antiviral genes that must be experimentally assessed during the design of such therapies.
We assumed the availability of two distinct protective-gene cassettes, which target different viral components, and thereby give rise to two non-overlapping sets of resistance-conferring sites, and . We further assumed that both cassettes, hereafter called Therapy 1 and Therapy 2, are equally powerful, that is, they have the same starting inhibition factor , same n, and same intermediate inhibition factors. When used jointly, there is no cross-resistance between them. That means that the inhibition exerted by Therapy 1 (2) on a given strain depends solely on the number of mutated sites in (), and is independent of the mutated sites in (). For a given n, there are now resistance-conferring sites, contained in and . A strain that shares m mutated sites with and l mutated sites with will encounter inhibition factors of and by Therapies 1 and 2, respectively. The fitness cost factor is determined by the total number of mutated sites, and equals .
We used the following parameter values based on prior studies [70], [83], [84], [86], [88], [89], [90]: the death rate of healthy CD4+ cells, 1/day; the death rate of infected cells, 1/day; the viral clearance rate, 1/day; the mutation rate, per base per replication. The virion production and infection rates are not well characterized and were set to virions/[cell*day] and µL/[virion*day], respectively, to match observed viral dynamics in untreated infections. We set the T-cell renewal parameters to yield realistic T cell densities in the absence of infection (1,000–1,300 cells/µL) and realistic pre-therapy viral loads (102–103 virions/µL) [16], [38], [90]. We also assumed that the thymus regenerates 1% of the total cell loss, implying the following parameter values: cells/[µL*day], cells/[µL*day], and cells/µL. We also tested numerous other parameter values and did not observe qualitatively different results. A species was considered extinct if its density fell below per µL, corresponding to one entity per body, based on 5 liters of blood which contain 1%–2% of the entire susceptible cell population [86]. Its dynamics were treated stochastically as long as its density did not exceed per µL. The default values of therapy-related parameters were and [44]. The fraction of infused P cells was 1% of the entire U cell population at the time of infusion (day 0) [5], [21]. The system was first brought to steady state, where simulations over a 400-day period ensured convergence to a HIV set point. Our parameter choices result in pre-existing strains with one, two, and three resistance mutations. Clinical experience suggests that double-point mutations are likely to exist in sufficient levels in untreated HIV patients, but it is not known if triple-point mutations are sufficiently abundant as well. Our model may thus be too “pessimistic”, and under less conservative parameter choices will result in less stringent requirements for a successful gene therapy.
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10.1371/journal.pntd.0004843 | Rickettsial Disease in the Peruvian Amazon Basin | Using a large, passive, clinic-based surveillance program in Iquitos, Peru, we characterized the prevalence of rickettsial infections among undifferentiated febrile cases and obtained evidence of pathogen transmission in potential domestic reservoir contacts and their ectoparasites. Blood specimens from humans and animals were assayed for spotted fever group rickettsiae (SFGR) and typhus group rickettsiae (TGR) by ELISA and/or PCR; ectoparasites were screened by PCR. Logistic regression was used to determine associations between patient history, demographic characteristics of participants and symptoms, clinical findings and outcome of rickettsial infection. Of the 2,054 enrolled participants, almost 2% showed evidence of seroconversion or a 4-fold rise in antibody titers specific for rickettsiae between acute and convalescent blood samples. Of 190 fleas (Ctenocephalides felis) and 60 ticks (Rhipicephalus sanguineus) tested, 185 (97.4%) and 3 (5%), respectively, were positive for Rickettsia spp. Candidatus Rickettsia asemboensis was identified in 100% and 33% of the fleas and ticks tested, respectively. Collectively, our serologic data indicates that human pathogenic SFGR are present in the Peruvian Amazon and pose a significant risk of infection to individuals exposed to wild, domestic and peri-domestic animals and their ectoparasites.
| Rickettsial infection remains relatively unexplored in South America compared to other regions of the world. For most regions of Peru (including the Amazon Basin), nothing more than broad serological characterization is available about circulating rickettsiae. Even less is known about the animal reservoirs and insect vectors involved in disease transmission. With this study we aimed to better characterize the circulating species of Rickettsia in humans in the Amazon Basin, as well as investigate their domestic animal reservoir and arthropod vectors. Out of 2054 fever patients enrolled we identified 38 individuals with serologic evidence for acute rickettsial infection. Their homes were visited in order to draw blood samples and collect ectoparasites from their domestic animals. Serology and molecular methods were used to test the animal blood samples as well as the ectoparasites. The information collected contributes to the understanding of the transmission dynamics of rickettsial diseases in Iquitos and leads to a better understanding of the exposure risk to rickettsial infection and it will guide approaches for prevention.
| Rickettsiae and rickettsia-like organisms are a diverse group of obligate intracellular bacteria within the order Rickettsiales that include members of the genera Rickettsia, Orientia, Ehrlichia, Anaplasma, Neorickettsia and Wolbachia. Based on whole-genome analysis, species within the genus Rickettsia are divided into four geno-groups: 1) spotted fever group rickettsiae (SFGR), which comprises R. rickettsii, R. conorii and others; 2) typhus group rickettsiae (TGR) with R. prowazekii and R. typhi; 3) an ancestral group with the non-pathogenic members R. bellii and R. canadensis; and 4) a transitional group that harbours the disparate species R. akari, R. australis and R. felis. This latter group is often still included within the SFGR due to antigenic relatedness. Not all of the known Rickettsia species are pathogenic to humans.
Outbreaks caused by some Rickettsia species are occasionally associated with enzootic vectors, such as mosquitoes, fleas, mites and ticks [1–7]. Rickettsial diseases are neglected, potentially severe, but easily treatable and preventable. Due to overlapping symptoms, it is often not possible to distinguish them from dengue (and other arbovirus infections), leptospirosis, typhoid fever, malaria, or enterovirus infection. Overall, the distribution of rickettsial pathogens is poorly characterized in Peru with the exception of R. parkeri, a pathogen identified in Amblyomma maculatum ticks from northwestern Peru [8] and Candidatus Rickettsia andeanea [9], a novel rickettsial species detected during a febrile disease outbreak investigation in the town of Sapillica in northern Peru. Previous data from the Amazon Basin of Peru suggested that rickettsial agents represent potential causes of fever [10] and that seroprevalence in domestic animals was high [11]; however, there remains a paucity of information on risk factors, vectors and circulating rickettsial species.
Rickettsial infections remain underrecognized and underreported due to a lack of awareness and limited access to diagnostics, many of which are often suboptimal. Current research efforts are directed both towards defining the epidemiology of these diseases and development of improved diagnostics.
The objectives of our study were: (1) to determine the proportion of human febrile illnesses that were associated with rickettsial infections and (2) to identify vectors and reservoirs of rickettsial pathogens in the Peruvian Amazon.
This study was conducted in Iquitos, which is located in the Amazon forest (73.2’W longitude, 3.7°S latitude, 120 m above sea level) in the Department of Loreto, in the northeastern region of Peru. The city is populated by approximately 400,000 people and is accessible only by air or river [12]. Participants included in the study lived in neighborhoods in urban, peri-urban or rural Iquitos.
This project was nested in an ongoing surveillance study (protocol # NMRCD.2010.0010), which was approved by the Naval Medical Research Unit, No 6 (NAMRU-6) Institutional Review Board (Lima, Peru) in compliance with all U.S. federal regulations governing the protection of human subjects. In addition, the study protocol was reviewed and approved by health authorities in Peru (Instituto Nacional de Salud (INS), Direccion General de Epidemiologia (DGE) and Dirección Regional de Salud Loreto (DIRESA-LORETO)). Written informed consent was obtained from patients 18 years of age and older. For patients younger than 18 years, written informed consent was obtained from a parent or legal guardian. Additionally, written assent was obtained from patients between 8 and 17 years of age.
Animal handling and ectoparasite collection was approved and performed in accordance with the NAMRU-6 Animal Care and Use Committee (NAMRU-6 Protocol number 13–5). The experiments reported herein were conducted in compliance with with the Animal Welfare Act and in accordance with the principles set forth in the “Guide for the Care and Use of Laboratory Animals,” Institute of Laboratory Animals Resources, National Research Council, National Academy Press, 1996. Written informed consent was obtained from all animal owners before specimen collection.
The proportion of human febrile illness associated with rickettsial infection was determined by testing human samples obtained through an ongoing clinic-based passive febrile surveillance study. The febrile surveillance study offered enrollment if the individual presented to one of 12 health facilities (3 hospitals and 9 outpatient clinics) distributed across 4 districts of Iquitos; 2 were military health facilities and the rest were Ministry of Health hospitals and clinics. Febrile patients fulfilled the inclusion criteria of the surveillance study if their fever (axillary temperature ≥ 37.5°C) duration was ≤ 5 days and they were 5 years or older. A serum sample was collected at the time of enrollment (acute) and a convalescent serum sample was obtained 10–30 days later. These samples have already been used to investigate other pathogens (mainly dengue virus and other arboviruses); the results and testing methods are described in detail elsewhere [13,14].
The objective of identifying vectors and reservoirs of rickettsial pathogens was approached by a prospective sentinel-case driven, case-control design that was nested in the ongoing surveillance study for human febrile disease described above. We visited households of human participants whose laboratory results indicated recent rickettsial infection. We asked residents if they had contact with domestic animals such as dogs, cats, pet birds, and livestock (pigs, poultry and guinea pigs). If so, they were invited to participate in the study after a brief explanation of the study objectives and procedures. After obtaining permission from the owner of the domestic animals, we obtained blood samples and removed ectoparasites including (but not limited to) fleas, ticks and lice. The location, name and description of the animal and owner’s address were collected for each animal. In order to obtain a control-group of animals and ectoparasites, we randomly selected another household at a distance of greater than 500 m but under 2 km away from the sentinel household. Controls were processed as described for households with confirmed rickettsial disease. The ratio of rickettsial and non-rickettsial households was 1:1. Additionally, all military bases in and around Iquitos were visited and the same procedure was repeated there.
Depending on the size of the animal, we collected 1–3 ml of blood using EDTA tubes. Tubes were immediately stored on ice and transported to NAMRU-6‘s Iquitos field laboratory within 3 hours. Samples were centrifuged and plasma was separated and stored at -80°C until further testing. The residual blood cells were placed in a separate vial and immediately stored at -80°C.
Ectoparasites were collected from domestic animals using combs and tweezers and placed in vials that were dry, plastic, and covered. Fleas, ticks and lice from each animal were placed in separate vials (maximum 30 specimens per vial). The vials were stored on ice until transport to the Iquitos field laboratory, where fleas, ticks and lice were taxonomically identified and stored at -80°C until shipment on dry ice to Lima, Peru, for DNA extraction and molecular analysis. Fleas, ticks and lice were identified according to the entomological keys of Aragao and Fonseca (1961), Graham and Price (1997), and Johnson (1957) [15–17]. Ticks, fleas and lice were pooled by species and individual host animal. Prior to laboratory testing, ectoparasites were rinsed using distillated water, placed on a sterile petri dish and divided into 2 pieces using sterile surgical blades. A new blade was used for each ectoparasite. Ticks and lice were divided longitudinally, whereas fleas were cut horizontally, dividing upper from lower body part. Half of each ectoparasite was immediately frozen and stored, and the other half was used for DNA extraction.
DNA was extracted from human and animal whole blood samples using QIAmp DNA mini kits (QIAGEN, Valencia, CA) following the manufacturer’s instructions into a final elution volume of 100 μl and stored at -80°C. Ectoparasite halves were extracted individually by mechanical disruption using 100 μl of PrepMan Ultra Sample Preparation Reagent (Applied Biosystems, Waltham, MA) and a Kontes Pellet Pestle (Thermo Fisher Scientific, Waltham, MA). After grinding, individual samples were heated to 95°C for 10 minutes using a heat block. To clarify them, samples were centrifuged at room temperature, for 5 minutes at top speed (13,000 rpm) using a table top Eppendorf centrifuge (Hamburg, Germany). Cleared supernatants were transferred to clean tubes and stored at -20°C until further processing.
Human and animal whole blood samples were individually screened using a qPCR assay targeting the Rickettsia genus-specific 17-kD gene (Rick17b) that has been previously described [24]. Positive (plasmid) and negative (no template control—water) controls were used in all the assays. To optimize the efficiency of workflows for ectoparasites, individually extracted samples were pooled by host in groups of 5 samples or less per pool. Pools were initially screened using Rick17b [24]. Samples in positive pools were further tested individually using the same approach. Individual positive samples were then tested using a nested PCR assay that also targeted the 17-kD gene but that can differentiate between SFGR and TGR [3]. Positives from this screen were then tested using two additional qPCR assays that targeted variable regions of the ompB gene: R. felis group (RfelG) and the Ca. Rickettsia asemboensis genotype (Rasemb) [3].
Statistical analysis was performed using Stata 12 (StatCorp., College Station, TX, USA). Data were double entered and crosschecked. Means with corresponding standard deviations (SD) or medians and interquartile ranges are presented for normally and non-normally distributed variables, respectively, to account for the sampling design.
Comparisons across groups for categorical variables were done with Chi-square test or Fisher's exact test if an expected cell count was less than five. Continuous variables were analyzed with Student’s t-test. The association between seroconversion (recent active infection) and independent variables was determined using logistic regression. To evaluate strength of association, odds ratios and their 95% confidence intervals were calculated. Multivariate logistic regression was performed with seroconversion as the outcome, using substantive knowledge to guide variable selection. All variables that were associated with an outcome at a significance level of p<0.10 in the univariate analysis were included in the initial model. The significance level for removal from the model was set at p = 0.06 and that for addition to the model at p = 0.05. Strength of association was determined by estimating the odds ratio and the 95% confidence intervals (CI).
Logistic regression models were constructed with the dichotomous dependent variable SFGR or TGR seroconversion (recent active infection) to evaluate risk factors for infection. For those with evidence of active infection, symptoms and clinical findings as originally reported in the participant questionnaires were evaluated. We tested: age (three age categories), sex, occupation (4 categories: students, home-based, high-risk exposure, other), and potential animal contact (Table 1). The occupation groups were formed using the information on the participant questionnaire when available. “Home-based occupation” contains housewives, retired and unemployed individuals. The “High-risk exposure occupation” group contains active military duty (majority), local law enforcement and occupations outside of town such as hunting, fishing, farming and logging. The group of “others” contains various job activities, such as self-employed individuals, occupation in health establishments, office, construction or merchandising. The following independent variables were evaluated additionally as risk factors for acquiring an infection: trip outside of the city during 15 days prior to presentation at health care facility and contact with febrile individual 15 days prior to presentation. Symptoms, clinical findings and information on the course and outcome of disease (hospitalization, length of stay, duration of illness, disease evolution at follow-up visit) were also analyzed. The different bleeding manifestations (epistaxis, oral mucosal bleeding, hematochezia, hematuria and hematemesis) were collapsed to “any form of bleeding” due to the small sample size among seroconverters. The group of participants with evidence of co-infection with rickettsial agents and another pathogen were further analyzed separately (S1 Table).
Between January 2013 and February 2014, 2,562 participants were enrolled in the main study. Of them, 2,054 had paired (acute and convalescent) samples and therefore they became our study population. The median age of this population was 23 years and ranged from 5 to 82 years. The population was evenly distributed by gender, and nearly 20% were military. Of the 2,054 participants tested, 786 (37.4%) were infected with dengue virus when tested by PCR and/or isolation confirmed by indirect immunofluoresence assay. Other arboviruses were detected by PCR and/or isolation in 27 of cases (1.3%) (3 Mayaro virus, 1 Group C orthobunyavirus (only isolation), 1 Maguari virus, 22 Venezuelan equine encephalitis virus).
When tested for TGR IgG no seroconversions or 4-fold rise of titer were detected. Thirty-eight (1.85%) of all febrile participants seroconverted or had a 4-fold or greater rise in titer of SFGR IgG between their acute and convalescent samples. These were classified as active rickettsial infections at the time of acute sample collection. Of these, 13 (34.2%) were identified in a military hospital, 12 (31.6%) were on active military duty, with 9 living permanently in camp. The active rickettsial infection cases were identified from 11 out of 12 study sites across and around the city.
Univariate logistic regression analysis indicated that people 21–35 years of age, being a student, and having a high risk occupation were risk factors for acute rickettsial infection (Table 1). Median age and sex did not differ between groups. Univariate logistic regression analysis of symptoms, clinical findings and course of febrile illness suggested that SFGR infection was associated with longer duration and persistence of illness at the follow-up visit (Tables 2 and 3). None of the analyzed symptoms and clinical findings were clearly associated with rickettsial infection. Only photophobia showed an association in univariate analysis, which did not remain statistically significant in multivariate analysis. In the multivariate logistic regression model, home-based occupations as well as high-risk occupations were associated with SFGR seroconversion (Table 4). The longer duration of illness remained significantly associated in multivariate analysis.
Of the 38 participants with serologic evidence of active rickettsial infection, 14 (36.8%) and 2 (5.2%) of the participants were co-infected with dengue virus and Venezuelan Equine Encephalitis virus, both diagnosed by PCR (S1 Table). The remaining 22 did not have serologic or molecular evidence of co-infection with an arbovirus. We also tested the 38 acute samples of the seroconverters by PCR targeting the 17-kD antigen, but none tested positive.
Domestic animals were sampled from 15 households of the 38 human participants with evidence of active rickettsial infection, that were eligible for a household visit. The remaining 23 participants and their households were not included for the following reasons: inaccessibility of the dwelling (10.5%), no animal contact (30.4%), participant had been transferred away from military camp (39.1%), refused participation (4.3%), could not be located (4.3%), and pet had died since febrile episode (4.3%). Overall, we visited 30 households (15 sentinel and 15 control households) and 5 military camps. During these visits, a total of 51 dogs, 9 cats and 14 other animals (1 pet bird, 1 duck, 4 goats and 8 chickens) were sampled.
We identified anti-SFGR IgG antibodies in only 3 animal samples: 1 cat (titer of 400) and 1 chicken (titer of 1600) from separate military camps; and 1 chicken from a sentinel household (titer of 400). All 51 animals tested were negative for the 17-kD antigen gene targeted by PCR.
Of a total of 284 ectoparasites collected (Table 5): 190 were fleas from dogs and cats (all of which belonged to the species Ctenocephalides felis); 34 were lice from poultry and dogs (3 belonged to the species Goniocotes gallinae, 1 to Menopon gallinae and the rest to Menacanthus stramineus); and 60 were ticks from dogs (all of which belonged to the species Rhipicephalus sanguineus). All samples were tested for the presence of rickettsia using a variety of PCR assays. Initially, all 284 samples were tested using Rick17b qPCR assay. Of these, 188 (185 fleas and 3 ticks) were positive and 96 were negative. This result was confirmed using an additional 17KDa nested PCR assay that distinguished between the SFGR and TGR. With this assay, we determined that all 188 positives belonged to the SFGR. Further testing (RfelG R. felis-genogroup specific qPCR assay and ompB fragment (2484-bp) sequencing) allowed us to determine that the 188 positives contained Rickettsia felis-like organisms, but none were positive for Rickettsia felis. Out of these 188 positive samples 79 (76 fleas and 3 ticks) were further tested with the recently described Ca. Rickettsia asemboensis species-specific Rasemb qPCR assay and all 76 fleas as well as 1 of 3 ticks were positively identified as Ca. R. asemboensis. The two ticks negative for the Rasemb qPCR assay contained R. felis-like organisms not R. felis or Ca. R. asemboensis.
We demonstrated that almost 2% of individuals presenting with a febrile episode in Iquitos had evidence of recent active SFGR infection when serologic testing was performed during a 14-month period. TGR infection did not seem to play an important role in causing human infection in this area though 1.0% of febrile patients had evidence of a previous infection with TGR. This proportion of febrile cases due to SFGR infection is very similar to previous studies [25]. However, in that previous study the sample size was much smaller. In particular, our study is the first that systematically analyzed febrile samples for rickettsial infection in the area of Iquitos, Peru.
We showed that among study participants with evidence of rickettsial infection, the presentation of disease was non-specific and symptoms or clinical findings did not help guide diagnosis. This is in agreement with previous reports where rickettsial infection presented as acute febrile illness with poor clinical predictors [26,27]. Further detailed comparison of febrile patients with SFGR infection to those without SFGR did not reveal any helpful differences [28,29] and diagnosis was mostly made retrospectively. This underlines the inability to rely on clinical presentation and rapid reliable diagnostic methods to identify cases. In our study we defined severe disease by respiratory distress, circulatory collapse, multiorgan failure, loss of consciousness, fluid accumulation or shock; having any of these manifestations was rare among febrile patients (3 patients) and was not identified among patients with rickettsial infection. But 21% of patients with rickettsial infection were hospitalized with a mean duration of 3.1 days (range 1 to 8 days). During this study we did not record if the patients received antibiotic treatment during the hospitalization or if a rickettsial infection was suspected in this context by the treating physician. But it is not uncommon in this setting for the patients to receive doxycycline as other diseases considered in the differential diagnosis such as leptospirosis are also endemic in this study area. During the study follow-up convalescent visit, significantly more patients with rickettsial disease reported persistent symptoms than those with another febrile disease. This impact of morbidity was also demonstrated by significantly longer duration of illness reported for rickettsial infection compared to the other febrile patients.
According to the analysis of patient data, we demonstrated that acute rickettsial infection was strongly associated with home-based occupations (including housewives, retired and unemployed individuals) but also with rural and out of town (high-risk) exposure occupations, while other professions were not associated with the disease. The majority of those working in high-risk exposure professions were military personnel, and most of them lived permanently on base and were men. All military individuals affected in our study worked extensively outdoors. The protective effect of being a student in the univariate analysis is probably confounded by age and thus appropriately fell out of our final multivariate model.
Sixteen of our participants with evidence of active rickettsial infection presented with a co-infection with an arbovirus. This is an important finding, as rickettsial infection can be treated with antibiotics (i.e., doxycycline); however, it would not be uncommon for a physician in a dengue endemic area to stop looking for additional infections in someone with presumed or confirmed dengue. The high percentage of co-infections among those with rickettsial infection could raise concerns about the specificity of our in house ELISA assay; however, the observation that out of 786 dengue virus positive samples (by PCR and/or isolation), only 14 showed seroconversion to SFG argues against this being a non-specific reaction. Also, co-infection causing rickettsial disease plus another illness (such as dengue, malaria or other bacterial diseases) has been well-demonstrated [29,30]. Unfortunately, our study could not determine which pathogen—arbovirus (mainly dengue virus), rickettsial agent, or both—was responsible for the observed clinical symptoms. In a separate analysis of the co-infection subgroup, we observed statistically significant features distinguishing co-infections from either mono-infected arboviral or monoinfected rickettsial infections (S1 Table), but the very low number of co-infections precludes drawing definitive conclusions. The substantial proportion of co-infections may represent the similarity of epidemiological risk factors for the different infections.
We were unable to confirm the diagnosis of rickettsial infection in our study with specific molecular diagnostic assays. This is not surprising considering the low sensitivity when performed in blood samples [22]. PCR, culture or immunohistochemical identification using biopsies of skin lesions (rash or eschar) would be desirable and is reported to have much higher sensitivity [22,31,32] but is not available in many limited-resource settings and was not part of our study procedure. This underlines the fact that empiric anti-rickettsial treatment should often be based on the clinicoepidemiologic diagnosis due to the retrospective nature of the currently available serologic tools.
Surprisingly, reported contact with animals was not associated with active rickettsial infection. In accordance, our evaluation of domestic animals did not reveal that they played a major role as hosts for rickettsial pathogens of the SFGR. This finding contrasts with the results of a previous study in this area where almost 60% of all dogs tested were found to be positive for SFGR antibodies [11]. Besides differences in sample size and location of animals tested, the different serological methods used could explain some of the differences observed. Unfortunately, we could not detect rickettsial pathogens by PCR in any of the animal samples. This could possibly support the conclusion that domestic animals do not seem to be an important reservoir for rickettsiae; however, sensitivity with animal blood samples is known to be low and varies with the molecular methods used [33], as is the case with human specimens [22]. Another limitation of this study was the fact that we did not sample rodents. In our data, home-based occupations were a clear risk factor for contracting the infection, which indicates exposure to a reservoir in and around the house. At the same time, reporting a high-risk profession, which mainly represents living on a military camp was also associated with rickettsial infection. Both occupation groups could have exposure to rodents.
By analyzing the ectoparasites collected from domestic animals, we demonstrated that almost all of the fleas were positive for Ca. R. asemboensis, which belongs to the SFGR. However, the known rickettsial flea-borne pathogens, R. typhi and R. felis were not detected. Ca. R. asemboensis was originally isolated from fleas from Kenya [34] and has recently been reported in Ecuador [35]. The high prevalence of Ca. R. asemboensis in fleas known to bite humans and to transmit agents that cause human illness gives rise to suspect this agent could be transmitted to humans. However, in a similar situation in Kisumu, Kenya, where high prevalence of Ca. R. asemboensis was found, only R. felis DNA was found in febrile patients blood [20]. At this time, this agent’s involvement as a cause of human disease cannot be ruled out. The involved flea species (C. felis) is known to bite a variety of animals, including rodents. Collecting rodent samples in and around human housing would be an important next step in order to investigate the presence of both the SFGR and TGR infections.
In conclusion, almost 2% of all undifferentiated febrile illnesses in Iquitos, the major Peruvian city in the Amazon basin, had an active rickettsial infection based on serology. Similar to other past reports, we could not identify features that would distinguish rickettsial diseases from other endemic diseases, and thus permit implementation of appropriate treatment. We demonstrated that home-based occupations, as well as high-risk occupations outside of town, were risk factors for rickettsial infection. This implies that exposure to domestic, as well as non-domestic animals could be important. In those individuals with undifferentiated fever, clinicians should be aware of the possibility of co-infection with rickettsial pathogens, even in confirmed arboviral cases as our study demonstrated. This has direct consequences for management and treatment and can potentially impact morbidity and time missed from work. Although hospitalization rate and severity of disease did not differ between rickettsial disease and other febrile illnesses, the association with prolonged duration of illness can have an important impact on morbidity and health care cost. Evaluation of specimens from domestic animals and their ectoparasites revealed a high percentage of fleas infected with Ca. R. asemboensis. Human pathogenicity of Ca R. asemboensis and its main reservoir remains to be determined.
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10.1371/journal.pbio.0050179 | Rb-Mediated Neuronal Differentiation through Cell-Cycle–Independent Regulation of E2f3a | It has long been known that loss of the retinoblastoma protein (Rb) perturbs neural differentiation, but the underlying mechanism has never been solved. Rb absence impairs cell cycle exit and triggers death of some neurons, so differentiation defects may well be indirect. Indeed, we show that abnormalities in both differentiation and light-evoked electrophysiological responses in Rb-deficient retinal cells are rescued when ectopic division and apoptosis are blocked specifically by deleting E2f transcription factor (E2f) 1. However, comprehensive cell-type analysis of the rescued double-null retina exposed cell-cycle–independent differentiation defects specifically in starburst amacrine cells (SACs), cholinergic interneurons critical in direction selectivity and developmentally important rhythmic bursts. Typically, Rb is thought to block division by repressing E2fs, but to promote differentiation by potentiating tissue-specific factors. Remarkably, however, Rb promotes SAC differentiation by inhibiting E2f3 activity. Two E2f3 isoforms exist, and we find both in the developing retina, although intriguingly they show distinct subcellular distribution. E2f3b is thought to mediate Rb function in quiescent cells. However, in what is to our knowledge the first work to dissect E2f isoform function in vivo we show that Rb promotes SAC differentiation through E2f3a. These data reveal a mechanism through which Rb regulates neural differentiation directly, and, unexpectedly, it involves inhibition of E2f3a, not potentiation of tissue-specific factors.
| The retinoblastoma protein (Rb), an important tumor suppressor, blocks division and death by inhibiting the E2f transcription factor family. In contrast, Rb is thought to promote differentiation by potentiating tissue-specific transcription factors, although differentiation defects in Rb null cells could be an indirect consequence of E2f-driven division and death. Here, we resolve different mechanisms by which Rb controls division, death, and differentiation in the retina. Removing E2f1 rescues aberrant division of differentiating Rb-deficient retinal neurons, as well as death in cells prone to apoptosis, and restores both normal differentiation and function of major cell types, such as photoreceptors. However, Rb-deficient starburst amacrine neurons differentiate abnormally even when E2f1 is removed, providing an unequivocal example of a direct role for Rb in neuronal differentiation. Rather than potentiating a cell-specific factor, Rb promotes starburst cell differentiation by inhibiting another E2f, E2f3a. This cell-cycle–independent activity broadens the importance of the Rb–E2f pathway, and suggests we should reassess its role in the differentiation of other cell types.
| The simplicity of the retina makes it an ideal tissue to study neurogenesis. Its development proceeds through three overlapping steps starting with retinal progenitor cell (RPC) proliferation, followed by birth of post-mitotic retinal transition cells (RTCs, also referred to as precursors), and ending with terminal differentiation of seven major cell types (Figure 1A) [1]. RPCs are multipotent and exit the cell cycle to generate different RTCs at specific time periods in development [1]. This process of RTC “birth” requires coupling of differentiation and cell cycle exit. Once born, post-mitotic RTCs migrate and form different retinal layers. Rods and cones make up the outer nuclear layer (ONL); horizontal, bipolar, and amacrine cells, as well as Müller glia cell bodies, reside in the inner nuclear layer (INL); and ganglion and displaced amacrine cells form the ganglion cell layer (GCL) (Figure 1A). The outer plexiform layer (OPL) and inner plexiform layer (IPL) house synaptic connections separating the ONL/INL and INL/GCL, respectively.
The retinoblastoma protein (Rb) is critical for cell cycle exit during retinal transition cell birth. Rb knockout (KO) RTCs continue to proliferate inappropriately and some (rod, ganglion, and bipolar cells) die by apoptosis [2,3]. Rb controls the cell cycle by binding and inhibiting E2f transcription factors (E2fs) (Figure 1B), first defined as transcription factors that bind adenoviral E2 regulatory elements and subsequently shown to be critical cell cycle regulators [4,5]. E2fs bind to DNA as heterodimers with proteins of the related Tfdp family. E2f1, E2f2, and E2f3a are “activating E2fs” that are required for fibroblast division. They are strong transcriptional activators that can drive G0 fibroblasts into cycle, and are inhibited when bound to Rb [4,5]. Ectopic division in Rb KO embryos can be rescued to various extents in different tissues by knocking out E2f1, E2f2, or E2f3 [6–9], but which member(s) drive division in Rb KO RTCs is unknown. Other members of the family, such as E2f4 and E2f5, are known as “repressive E2fs” because they are weak activators and appear to be primarily involved in gene silencing in quiescent or differentiated cells.
Activating E2fs may also promote apoptosis in the Rb KO retina (Figure 1B). Originally, E2f1 was considered the primary pro-apoptotic member of the family [10]. However, this view was reevaluated when it was shown that either E2f1 or E2f3 deletion rescues apoptosis in the developing central nervous system (CNS) of Rb KO embryos [6,11]. Subsequently, CNS apoptosis was shown to be an indirect result of placental defects and probable hypoxia [12–14]. Indeed, E2f3-induced apoptosis in fibroblasts has recently been shown to require E2f1 [15]. Thus, it is controversial whether E2f3 is required for apoptosis of any Rb KO cell type. Determining which activating E2fs promote death in distinct Rb KO tissues requires conditional rather than germ line models of Rb deletion to avoid secondary indirect effects (such as hypoxia).
E2f family diversity is expanded by E2f3 isoforms. Alternative promoters generate two forms (a and b) that are identical except for distinct first exons [16]. E2f3a is a strong activator, and, like other activating E2fs, its expression is induced when quiescent cells are stimulated to divide [16]. E2f3b, like repressive E2fs, is present in both quiescent and dividing cells, and in quiescent fibroblasts it associates primarily with Rb, suggesting that it mediates repression [16–18]. Indeed, silencing the Cdkn2d (p19Arf) locus in unstressed cells relies on E2f3b [19]. Other E2fs may also exist in isoforms since at least two mRNA species have been detected for E2f1 and E2f2 [16]. The roles of E2f isoforms in vivo are unknown.
E2fs are also regulated by subcellular localization. Although this feature has been best characterized for repressive E2fs [20–22], it also affects activating E2fs [23–25]. The distribution of E2f isoforms has never been assessed.
It has been known for many years that Rb loss perturbs neuronal differentiation [26–29]. However, prior work could not exclude the possibility that differentiation defects are simply an indirect consequence of abnormal division and death. If Rb does regulate differentiation directly it is unclear whether it does so in all or a subset of neurons. Moreover, the mechanism has never been solved. In other cell types where Rb may promote differentiation directly, such as muscle and bone, it seems to do so through E2f-independent means by potentiating tissue-specific transcription factors (Figure 1B) [30–33]. In the retina, others have noted abnormally shaped Rb KO rods and have suggested Rb may directly promote their morphogenesis by activating retina-specific factors [29]. However, differentiation defects in any Rb KO neuron could be an indirect effect of ectopic division and/or apoptosis (Figure 1B). Thus, it is critical to study differentiation of Rb KO cells in the absence of ectopic proliferation and death.
Here, we establish that Rb suppresses RTC division and death by inhibiting E2f1, not E2f2 or E2f3. When these defects were rescued, most retinal neurons, including rods, survived, differentiated, and functioned normally. Thus, unexpectedly, retina-specific differentiation factors function independently of Rb. However, comprehensive assessment of the Rb/E2f1 double-null rescued retina revealed a differentiation defect in cholinergic starburst amacrine cells (SACs). Recent breakthroughs have revealed that these interneurons are critical for direction selectivity and developmentally important rhythmic bursts [34–36]. However, their differentiation is poorly understood. Contrary to the prevailing view that Rb promotes differentiation through E2f-independent tissue-specific transcription factors, we show that Rb facilitates SAC development through E2f3. Defects in Rb null SACs correlated with specific E2f3 expression in these cells, and E2f3 expression was absent in neurons that differentiated without Rb. E2f3 is also present in a specific subset of other CNS neurons, implying that this may be a general mechanism by which Rb facilitates neurogenesis. To define the mechanism in even more detail, we determined which E2f3 isoform Rb targets to control SAC differentiation. E2f3b mediates Rb function in quiescent fibroblasts [19], yet no prior studies to our knowledge have dissected E2f3a or E2f3b functions in vivo. Using an isoform-specific null mouse we show that Rb drives SAC differentiation through E2f3a. Thus, independent of E2f1-mediated effects on division and death, Rb does regulate neuronal differentiation, but only in specific neurons and, unexpectedly, through E2f3a, not tissue-specific differentiation factors.
We used the α-Cre transgene to delete floxed Rb exon 19 at embryonic day (E) 10 in peripheral retina [2]. RbloxP/loxP;α-Cre mice were bred with strains lacking E2f1 or E2f2 in the germ line, or a strain carrying a floxed E2f3 allele [5]. RbloxP/loxP;E2f1+/− and RbloxP/loxP;E2f1+/−;α-Cre mice were bred to produce RbloxP/loxP;E2f1−/−;α-Cre mice at a frequency of 1/8 and littermate controls at the same or higher (1/4) frequency. For simplicity we will refer to the RbloxP/loxP;E2f1−/−;α-Cre peripheral retina as the Rb/E2f1 double knockout (DKO) retina. Similar strategies were employed in the case of E2f2 or E2f3. Cre-mediated excision of Rb and E2f3 alleles in the retina was confirmed by PCR as described previously [2,5].
To measure ectopic cell division, mice were pulse-labelled with bromodeoxyuridine (BrdU) 2 h before sacrifice and the peripheral retina analyzed for BrdU incorporation by immunofluorescence. As reported before [2,3], Rb KO retinas exhibited both spatial and temporal ectopic DNA synthesis (Figures 1C and S1A). This is easily detected at E14, E16, and postnatal day (P) 0 in the inner retina where abnormal BrdU+ ganglion and amacrine RTCs are located, or on the outermost region of the P0 retina, where BrdU+ photoreceptor RTCs reside (Figures S1A and S2, arrows) [2]. Ectopic RTC division in Rb KO retinas is even more obvious at P8 or P18, when division is completed in wild-type (WT) retina (Figures 1C and S1A). Strikingly, the ectopically positioned S-phase cells at E14, E16, and P0 and all the abnormal division at P8 and P18 were completely suppressed in the Rb/E2f1 DKO retina (Figures 1C, 1E, 1F, S1A, and S2). In contrast, deletion of E2f2 or E2f3 had no effect at any stage of development. Analysis of mitotic cells with anti–phosphohistone 3 (PH3)–specific antibodies confirmed that loss of E2f1, but not E2f2 or E2f3, suppressed ectopic division (Figure S3). Deleting one E2f1 allele partially suppressed ectopic S-phase and mitosis in Rb KO RTCs (Figures 1C, 1E, 1F, S1A, S2, and S3), suggesting that E2f1 drives ectopic division in Rb KO RTCs in a dose-dependent fashion. These data contrast with previous findings in the lens and CNS of Rb KO embryos, where deletion of any activating E2f suppresses ectopic division to some extent [6–9].
Loss of Rb in the retina results in considerable RTC apoptosis, eliminating most bipolar and ganglion cells as well as many rods (Figure 2A–2D) [2,3]. The loss of Rb KO rods is evident from the thinner ONL, and the death of these cells as well as bipolar and ganglion neurons can be detected directly by double labelling for apoptotic and cell-type-specific markers [2] (M. P. and R. B., unpublished data). Loss of peripheral Rb KO ganglion cells is also evident from thinning of the optic nerve (D. C. and R. B., unpublished data). Deleting E2f1, but not E2f2 or E2f3, blocked this ectopic cell death in a dose-dependent fashion (Figures 1D, 1G, and S1B).
To investigate the molecular mechanism that underlies the unique role of E2f1, we assessed the expression of known E2f targets as well as other genes that regulate the cell cycle and apoptosis. Numerous positive and negative cell cycle and apoptotic regulators were up-regulated in the Rb KO retina (Figure 1H). Among the E2f family, E2f1, E2f2, E2f3a, and E2f7 were induced following Rb loss, but E2f3b, E2f4, and E2f5 were unaffected. Consistent with the BrdU and terminal dUTP nick-end labelling (TUNEL) analyses, E2f1 deletion specifically reversed all these molecular defects, but E2f3 deletion had no effect (Figure 1H).
Because E2f1 deletion blocks abnormal division and death in the Rb KO retina, the Rb/E2f1 DKO retina provided a unique opportunity to evaluate whether Rb controls differentiation independent of cell cycle effects. The Rb/E2f1 DKO retina had many Sag+ (S-antigen/rod arrestin) photoreceptors, Pou4f2+ (Brn3b) ganglion cells, and numerous Prkca+/Cabp5+ bipolar neurons (Figure 2A–2D). In contrast, there was no such rescue of cell types in Rb/E2f2 or Rb/E2f3 DKO retinas (Figure S4). Analysis with general neuronal markers Mtap2 (MAP2) and Snap25, as well as other markers expressed in bipolar cells (Chx10, Rcvrn, Vsx1, Tacr3, and Atp2b1) and rod photoreceptors (Rho and Rcvrn) confirmed rescue of the Rb/E2f1 DKO retina (Table S1). Moreover, neurons exhibited the same complex morphology as in WT retina. Bipolar cell bodies were located in the INL and had ascending and descending processes ending in the OPL and IPL, respectively (Figure 2A). In addition, the Rb/E2f1 DKO retina had a healthy appearing ONL consisting of morphologically normal rods with descending processes ending in the OPL and ascending processes that terminated in inner and outer segments (Figure 2A). It was suggested that Rb might regulate photoreceptor differentiation, possibly through rod-specific transcription factors (Figure 1B) [29]. However, if Rb does regulate photoreceptor differentiation, it does so by inhibiting E2f1, not by potentiating rod differentiation factors, such as Otx2, Crx, or Nrl. It is impossible to tell whether E2f1 perturbs differentiation directly, by affecting the expression of genes that modulate maturation, and/or indirectly through its effects on proliferation and survival (Figure 1B).
As with ectopic division and apoptosis (Figure 1C and 1D), the rescue of Rb KO retinal bipolar, ganglion, and rod cells was dependent on E2f1 dose (Figure 2A–2D). Separate from its role in driving ectopic division of Rb KO RTCs, E2f1 also promotes normal RPC division since in its absence RPC proliferation drops ~2-fold (D. C. and R. B., unpublished data). This modest reduction of RPC numbers in the absence of E2f1 accounts for the slight reduction in the number of ganglion cells at P0, in the number of bipolar cells at P18 or P30, and in the thickness of the ONL at P18 or P30 in the E2f1 KO and Rb/E2f1 DKO retina (Figure 2B–2D). The morphology of E2f1 KO neurons was WT (Figure 2A). Despite a slight drop in absolute cell numbers, the proportion of Rb/E2f1 DKO and E2f1 KO bipolar cells was the same as WT (data not shown). Slightly reduced cell numbers were not due to residual RTC death since we have not observed ectopic apoptosis at any embryonic or postnatal stage in the developing Rb/E2f1 DKO retina (Figures 1D, 1G, and S2). Moreover, deleting Ccnd1, which acts upstream of Rb proteins, also reduces RPC number, but does not suppress any defect in the Rb KO retina (D. C. and R. B., unpublished data). Thus, slightly reduced RPC division and dramatic rescue of severe defects in Rb KO RTCs are distinct effects stemming from the deletion of E2f1.
The discovery that E2f1 loss rescues even the morphology of Rb KO neurons is surprising because Rb is thought to regulate differentiation primarily through E2f-independent pathways [30–33]. However, normal morphology may not equate to completely normal differentiation. Thus, we compared the electroretinograms (ERGs) of adult WT (α-Cre), E2f1−/−, α-Cre;RbloxP/loxP, and α-Cre;RbloxP/loxP;E2f1−/− mice. ERGs functionally assess visual signalling in the mammalian retina from photoreceptors to amacrine cells (but usually not gangion cells), and are dominated by rod and cone bipolar cells. Typically, an ERG signal begins with a negative deflection initiated by the photoreceptors (the a-wave), which is terminated by a large positive deflection due to the activation of ON bipolar cells (the b-wave).
Responses to dim light in dark-adapted (scotopic) conditions specifically assess the rod system, and were defective in the Rb KO retina (Figure 2E). The substantial reduction of both a- and b-waves is consistent with rod and bipolar cell apoptosis [2]. The sensitivity of the residual response appeared unchanged, suggesting it arose from the Cre-negative portions of the retina. Responses were about the same in the WT and E2f1 KO retina, and, most importantly, also the Rb/E2f1 DKO response median lay at the lower end of the normal range for most intensities (Figure 2F). Thus, E2f1 deletion almost completely rescued the rod system in the Rb KO retina.
Light-adapted (photopic) recordings to specifically assess the cone system yielded comparable results. Cones represent only 3% of photoreceptors and, unlike rods, develop without Rb, but they require rods for survival, and in the Rb KO retina, they have abnormal morphology and their synaptic targets, bipolar cells, are much depleted [2]. The photopic response, a product of cone and mainly bipolar activity, was much reduced by Rb loss, but was rescued considerably in the Rb/E2f1 DKO retina (Figure S5). Again, the median amplitude lay at the lower end of the E2f1 KO range. The photopic response in E2f1 KO mice was slightly reduced relative to WT (Figure S5B), possibly because E2f1 is required for maximal expansion of embryonic RPCs, and the E2f1 KO retina has, as noted earlier, slightly fewer cells than the WT retina, although cell type proportions are unaffected (D. C. and R. B., unpublished data). Thus, marginally subnormal photopic responses in the Rb/E2f1 DKO retina can be attributed to both a reduction of cone numbers in E2f1 KO mice alone, and a “genuine” slight reduction in cone function attributable to Rb loss relative to WT. This slight effect may relate to a true differentiation defect in a subset of amacrine cells discussed below. This discussion should not obscure the major outcome that E2f1 deletion recovers most of the ERG response. Thus, E2f1 deletion not only rescues morphology but also both rod and cone system function in the Rb KO retina.
ERGs primarily assess photoreceptor and bipolar cell function, but may miss differentiation defects in other cells. To test for subtle differences we stained the Rb/E2f1 DKO retina with 43 markers (Table S1). Thirty-two proteins displayed identical patterns in WT, E2f1 KO, and Rb/E2f1 DKO retina (Table S1). The other 11 markers revealed a cell-cycle– and apoptosis-independent differentiation defect in SACs. We first studied Calb2 (calretinin), which marks a subset of amacrine and ganglion cell bodies as well as three tracks corresponding to their processes in the IPL (Figure 3A). Normal Calb2 staining was seen in the E2f1 KO IPL (data not shown). However, only one Calb2+ track was evident in the Rb KO IPL, and this defect was not rescued in the Rb/E2f1 DKO retina (Figure 3A). We quantified Calb2+ cell bodies in the Rb KO INL (corresponding to amacrine cell staining only) and observed a reduction from P8 onwards (Figures 3C and S6).
Of the three Calb2+ tracks in the IPL, the two outer tracks are from SACs, named after their extensive dendritic-tree-like morphology [37]. SACs are cholinergic, represent ~5.2% of amacrine neurons [38], and are critical for both direction selectivity [34,35] and spontaneous rhythmic activity that occurs during normal retinal development [36]. SACs in the INL synapse in the OFF layer of the IPL that responds to decreasing light, while displaced SACs in the GCL have processes that synapse in the nearby ON layer of the IPL that responds to increasing light (reviewed in [39]). Mature SAC processes stain specifically for Slc18a3 (vesicular acetyl choline transporter, VAChT) [37], and, significantly, this marker was absent in the peripheral Rb KO or Rb/E2f1 DKO P18 retina (Figures 3A and S7B). Chat, expressed from the same locus, is also SAC specific, but marks both cell bodies and processes of mature SACs [37]. Chat was seen in fewer cells in the mature Rb KO retina, and was present in the soma but absent from processes (Figure 3B). We obtained similar results for Sv2c, a synaptic vesicle protein found in SACs [40]; Kcnc1b and Kcnc2, potassium channels expressed on SAC soma and dendrites as well as a very small number of ganglion cells [41]; gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter present in about half of amacrine cells including SACs, as well as horizontal and some bipolar neurons [37]; and Calb1 (calbindin), which is expressed in many amacrine cells and labels SAC process faintly (Figure S7A and S7B; Table S1; and data not shown). Finally, we also examined the effect of Rb deletion on SAC differentiation using a Chx10-Cre transgene that is expressed in a mosaic pattern across the retina, generating patches of Cre-expressing cells [42]. Consistent with the mosaic deletion pattern, we observed markedly reduced Chat/Slc18a3 staining in the IPL of Chx10-Cre;RbloxP/loxP retina compared to WT (Figure S7C). Together, these results suggest a role for Rb in SAC differentiation.
The above findings could indicate a defect in SAC specification, SAC survival, or the levels and/or transport of the markers described above. Camk2a marks both SACs and ganglion cells [37], but because ganglion cells are eliminated in the Rb KO retina, Camk2a is a specific SAC marker in this context. Importantly, Camk2a+ tracks and dendrites were present in both the WT and Rb KO retina (Figure 3B), and the number of Camk2a+ soma was similar in WT and Rb KO retina at P30 and beyond, although fewer cells stained in Rb KO retina at P18, suggesting a delay in its appearance (Figures 3C and S6B). Thus, Rb is not required for SAC survival or for process outgrowth, but rather it seems to regulate the expression and/or stability of Calb2, Calb1, Chat, Slc18a3, Sv2c, Kcnc1b, Kcnc2, and GABA in SACs, but leaves Camk2a expression virtually unaffected. The presence of Chat in some cell bodies but never in processes (Figure 3B) also suggests a transport defect. The developmental pattern of Slc18a3 expression also supported this notion. In mature WT SACs Slc18a3 is only in processes, but in early postnatal SACs, it is found in the cell body, and moves into emerging processes at approximately P4–P6. As noted above, Slc18a3 was absent at P18 in the Rb KO retina (Figure 3A); at P4 or P5 it was in cell bodies, yet was rarely present in Rb KO processes (Figures 4A and S6). Slc18a3 became virtually undetectable in Rb KO SACs by P8 (Figures 3C and S6C). These data suggest that Rb affects both the synthesis/stability and transport of SAC markers.
Rb binds more than 100 proteins [43] and in some non-neuronal cells, such as skeletal muscle, adipocytes, and bone, Rb is thought to bind and potentiate tissue-specific transcription factors that promote differentiation [31–33]. Thus, we expected that Rb might interact with retina-specific factors to facilitate SAC differentiation. A direct role for E2f in mediating Rb-dependent differentiation defects (independent of cell cycle or death defects) has to our knowledge not been described, but because E2f can regulate some differentiation genes [44–48], we first tested whether E2f2 or E2f3 might perturb Rb KO SAC maturation. At multiple time points, E2f1 deletion suppressed ectopic mitosis (PH3+ cells), but did not reverse the SAC defect, and E2f2 deletion had no effect on either defect (Figure 4A). Remarkably, although E2f3 deletion did not reverse ectopic mitosis, it rescued Calb2, Slc18a3, Chat, GABA, Kcnc1b, Kcnc2, and Sv2c staining at multiple times (Figure 4A and data not shown). Rb/E2f3 DKO SAC tracks were slightly more disordered than in WT retina, most likely because of the absence of synaptic partner cells, which are killed by E2f1. Indeed, this minor defect was rescued in the Rb/E2f1/E2f3 triple knockout retina, where bipolar and ganglion cell death was rescued and SAC differentiation was restored (Figure 4A). E2f3 deletion alone did not affect SAC differentiation (Figure 4A); thus, it is unleashed E2f3 activity that is detrimental, and the critical role for Rb is to inhibit E2f3.
We quantified the fraction of Camk2a+ SACs in different genotypes and found that 60% of WT P30 Camk2a+ cells expressed Chat and Slc18a3, which dropped to only 5.6% in the Rb KO retina, and remained low at 3.7% in the Rb/E2f1 DKO retina, but rose to 91% in the Rb/E2f3 DKO retina (Figure 4B). The latter fraction is higher than WT because ganglion cells, which normally make up ~40% of Camk2a+ cells, are killed by apoptosis.
To quantify the effect of different E2fs on ectopic division specifically in SACs, we exploited Isl1 (Islet1). This marker is expressed in both SACs and ganglion cells, thus Isl1+ cells in the INL are exclusively SACs [49]. We found that 98.2% ± 1.8% of Isl1+ cells in the forming inner INL at P5 were also Slc18a3+, confirming that Isl1 is an excellent SAC marker (Figure 4C). Moreover, Isl1, unlike Slc18a3, is nuclear, which facilitates scoring of Isl1+/Mki67+ cells. It is also expressed earlier than Slc18a3, permitting analysis of SACs soon after their birth at ~E15; thus, we could study retina at P0, a time when ectopic division is high in the inner retina and prior to Rb-independent cell cycle exit associated with terminal differentiation [2]. At P0, no WT Isl1+ cells in the inner neuroblastic layer (NBL) (which is the future INL) were dividing, but 57 ± 14 Isl1+/Mki67+ cells were detected in the Rb KO inner NBL (Figure 4D). Indeed, about one-third of all Isl1+ cells in the entire inner NBL were dividing in the Rb KO retina, or ~50% in the periphery where Cre is expressed (Figure 4E and data not shown). This defect was suppressed in the Rb/E2f1 DKO retina, where we detected only 1 ± 1 dividing SAC, but not the Rb/E2f3 DKO retina, where there were 53 ± 8 dividing SACs (Figure 4D and 4E). We observed similar effects at P0 with Calb2, which marks newborn SACs and other amacrine cells (data not shown). Thus, in Rb KO SACs, E2f1 deletion suppresses ectopic division but not aberrant differentiation, whereas E2f3 deletion suppresses aberrant differentiation but not ectopic division.
The unique effect of E2f3 in disrupting the differentiation of SACs but not other retinal neurons might be due to cell-type-specific expression or cell-type-specific activity of E2f3. Determining between these two possibilities is not easy, as E2f immunostaining in mouse tissues is problematic. We did not solve this issue for E2f1 or E2f2, but used a modified protocol [50] to successfully track E2f3 expression (Figure 5). At P0, E2f3 was detected in RPCs, consistent with a putative role in normal proliferation (Figure 5A). The signal was specific as it was absent in the E2f3 KO peripheral retina (Figure 5A). As the retina differentiated and RPC division diminished, the number of E2f3+ cells also dropped, and by P8, when division is virtually over, only a subset of post-mitotic cells in the inner retina expressed E2f3 (Figure 5A). By P18, E2f3 was also detected in two tracks in the IPL (Figure 5A and 5B), reminiscent of SAC markers such as Chat and Slc18a3 (c.f. Figures 3 and 4). This cytoplasmic E2f3 staining was also specific, as it was absent in the E2f3 KO peripheral retina of α-Cre;E2f3loxP/loxP mice (Figure 5A). Indeed, double labelling with E2f3 (red) and Chat plus Slc18a3 (green) confirmed that E2f3 is present in both SAC soma and dendrites (Figure 5B). Rb protein was also detected in the inner retina (Figure 5A), and showed a similar distribution as E2f3 in SACs (Figure 5B), and was also present in mature ganglion cells and Müller cells as reported [51]. Rb staining in SAC processes was specific as it was absent in the peripheral retina of αCre;RbloxP/loxP mice (Figure 5A). These data suggest that Rb and E2f3 colocalize in SACs and that E2f3 triggers defects in SAC differentiation because it is specifically expressed in these retinal neurons.
We also found that E2f3 is present in a specific subset of mature neurons in various brain regions (data not shown). For example, in the P20 amygdala, E2f3 colocalized with the general neuronal markers Mtap2 and Mecp2 [52], but not with Calb2, which marks a subset of neurons, or with the glial marker Gfap (data not shown). Unlike in retinal SACs, E2f3 was not coexpressed in Chat+ or Slc18a3+ cholinergic neurons located in various regions of the brain and spinal cord (data not shown). In agreement, we could not detect defects in cholinergic Rb KO neurons in the developing forebrain, but other Rb KO neurons in this region showed differentiation defects that were rescued by deleting E2f3 [53]. Together, these results suggest that the common mechanism by which Rb promotes neural differentiation is through E2f3 inhibition.
As noted above, E2f3 and Rb staining in SACs was both nuclear and cytoplasmic (Figure 5A and 5B). The antibody that worked in immunostaining recognizes a C-terminal region and thus, does not distinguish a/b isoforms. To our knowledge, the subcellular location of E2f3 isoforms has not been determined in any cell type. To verify the dual locations of E2f3 and to determine which isoforms were present in retina, we analyzed nuclear and cytoplasmic fractions by Western blot at different times during development. Analysis with the pan-E2f3 antibody (sc-878, Santa Cruz Biotechnology) detected a 55-kD E2f3a species and a 40-kD E2f3b polypeptide (Figure 6). To confirm that the upper species in our retinal lysates was E2f3a, we exploited novel mice that lack E2f3 exon 1a and thus express E2f3b exclusively (R. O. and G. L., unpublished data). The genotyping strategy is discussed in detail later and is outlined in Figure 7A. Western analysis confirmed that the upper band was absent in E2f3a−/− mice (Figures 6 and S8). Consistent with the drop in E2f3-expressing cells during WT retinal maturation (Figure 5A), the total amount of E2f3a was less at P18 compared to P0 (Figure 6). E2f3b was present in similar amounts at both time points. At P0 and P18, E2f3a was present in both nuclear and cytoplasmic fractions, but in marked contrast, E2f3b was exclusively nuclear at both times (Figure 6). Two closely migrating E2f3a bands were detected, more clearly evident at P18, of which the faster migrating species was dominant in nuclear and the slower species was dominant in cytoplasm (Figure 6). The identity of both as E2f3a species was confirmed by their absence in the P18 E2f3a KO retina (Figure S8). Analysis of Pou4f2, a nuclear transcription factor expressed in ganglion cells, showed that nuclear proteins had not contaminated the cytoplasmic fraction, and analysis of Slc18a3, a cytoplasmic SAC marker, confirmed that the reverse had also not occurred (Figure 6). These data show, to our knowledge for the first time, that E2f3a and E2f3b exhibit distinct patterns of subcellular distribution, and raise the possibility that E2f3a localization may be regulated by as yet unknown post-translational modifications.
We also examined the distribution of other cell cycle regulators during retinal development. Like E2f3a, Rb was present in both the WT cytoplasm and nucleus at P0, but at P18, when the levels of Rb had increased, it was primarily nuclear (Figure 6). A very faint cytoplasmic Rb signal was evident at P18, which is consistent with Rb staining of SAC processes (Figure 5B), and with the very small proportion of SACs in the retina [38]. E2f1 was also detected in both nuclear and cytoplasmic fractions, although unlike E2f3a it was predominantly nuclear both at P0 and P18 (Figure 6). The E2f dimerization partner, Tfdp1, which lacks a nuclear localization signal [54], was primarily cytoplasmic at both P0 and P18, and the Cdk inhibitors Cdkn1a and Cdkn1b showed a similar pattern of distribution (Figure 6). Thus, among the cell cycle regulators we examined, most showed bivalent distribution, and E2f3b was unusual in its solely nuclear compartmentalization.
To test which E2f3 isoform is responsible for aberrant Rb KO SAC differentiation we exploited E2f3a−/− mice (Figure 7A). The genotyping strategy outlined in Figure 7A was used to distinguish the E2f3a, WT, and null alleles. Reverse transcriptase PCR (RT-PCR) confirmed the presence of both E2f3a and E2f3b RNA species in the developing WT retina, and the specific absence of E2f3a RNA in the E2f3a−/− retina (Figure 7B). E2f3a protein was absent in E2f3a−/− retinal lysate (Figures 6 and S8). Importantly, the levels of E2f3b message were similar in the Rb KO and Rb/E2f3a DKO retina, ruling out the possibility that any effects of E2f3a deletion we might observe were due to down-regulation of E2f3b (Figure 7C). Also, the levels of other E2fs were the same in the Rb KO, Rb/E2f3 DKO, and Rb/E2f3a DKO retina, ruling out any cross-regulatory effects (Figure 7C) [55]. E2f3a can trigger cell cycle induction, but because SAC defects are not linked to cell cycle perturbation (Figures 3A and 4), and in view of the predominant association between E2f3b and Rb in quiescent cells [16,19], we suspected that E2f3b may perturb differentiation in Rb KO SACs. Unexpectedly, however, E2f3a deletion suppressed the Rb KO SAC defect (Figure 7D). Thus, separate from its role in cell cycle control, Rb regulation of E2f3a is critical to ensure proper neuronal differentiation.
Work in the early 1990s showed that Rb loss triggers defects in neuronal cell cycle exit, survival, and differentiation [26–28]. Much of the death is an indirect consequence of probable hypoxia linked to placental defects [12–14]. However, targeted KO and chimeric studies reveal that Rb autonomously promotes cell cycle exit in newborn neurons, and is required for survival of a subset of neurons, particularly in the retina [2,3,13,14,56–59]. However, whether Rb also regulates differentiation is obscured by potentially indirect effects of ectopic division and death. Moreover, a mechanism though which Rb may regulate neuronal maturation has not been elucidated.
Here, deleting E2f1 specifically rescued ectopic division and death in the Rb KO retina. Importantly, major Rb/E2f1 DKO neurons differentiated normally, and ERGs revealed the rescue of rod- and cone-mediated function, implicating a regular signal flow from photoreceptors to bipolar and amacrine cells. Division and death genes were induced in Rb KO cells, and deleting E2f1, but not E2f2 or E2f3, reversed these molecular events. E2f1 may also regulate differentiation targets, but whether this contributes to defects in retinal cell maturation is impossible to separate from potentially indirect consequences of deregulated division and death. In any case, it is clear that in most retinal cells, including photoreceptors [29], transcription factors that promote differentiation function independently of Rb.
We have also found that E2f1 deletion rescues cell-autonomous ectopic division, death, and differentiation defects in sporadic Rb KO clones generated using a Cre retrovirus vector (M. P. and R. B., unpublished data). These data are consistent with the observation that E2f1 overexpression in newborn photoreceptors drives ectopic division and apoptosis [60], and add to the growing evidence indicating that E2f1 is the major, and perhaps only, member of the three activating E2fs required to induce apoptosis in Rb KO cells [10,15]. Thus, deregulated E2f1 activity in the retina, whether resulting from the inactivation of Rb or from overexpression, promotes unscheduled cell division and triggers apoptosis in susceptible RTCs. E2f1, rather than other E2fs, may be a potential target for novel therapeutics to prevent retinoblastoma in RB1+/− humans.
Our ERG studies revealed rescue of the Rb KO rod–bipolar system, and almost complete restoration of the cone–bipolar system following E2f1 deletion. There was a slightly lower response in the Rb/E2f1 DKO retina relative to the E2f1 KO control retina. This difference might reflect a role for Rb in the development of cones, bipolar cells, or other cells that may contribute to the photopic ERG, including potentially SACs, which do have a serious defect in the Rb/E2f1 DKO retina.
Comprehensive marker analysis revealed that, in striking contrast to other retinal neurons, E2f1 deletion did not suppress defects in Rb KO cholinergic SACs. Instead, we observed E2f1-independent defects in the synthesis and transport of a large cohort of SAC proteins. These data expand insight into the development of these important interneurons, but more critically, provide to our knowledge the first unambiguous evidence that Rb regulates neurogenesis beyond terminal mitosis. Rb binds more than 100 factors [43], and in several non-neuronal cells, such as skeletal muscle, adipocytes, and bone, it binds and potentiates tissue-specific transcription factors that promote differentiation [31–33]. The idea that Rb promotes muscle differentiation by potentiating Myod1 activity was contested [61], and other mechanisms proposed [62,63], but not involving E2f repression. Strikingly, however, we discovered that Rb promotes SAC differentiation through E2f3 (Figure 8).
Rb regulation of SAC differentiation through E2f3 was independent of its role in controlling division or death: E2f3 deletion rescued Rb KO SAC defects but did not suppress aberrant proliferation or death, whereas E2f1 deletion reversed abnormal proliferation and death but did not rescue SAC differentiation. Double labelling confirmed that E2f1 but not E2f3 deletion reversed Rb KO SAC division. Moreover, deleting E2f1, but not E2f3, reversed deregulated expression of cell cycle and apoptotic genes in the Rb KO retina. E2f3 is expressed in a subset of CNS neurons (this work) and drives specific cell-cycle–independent defects in Rb KO forebrain neurons [53]. Thus, E2f3 inhibition is the first, and may be the only, mechanism by which Rb participates directly in neuronal differentiation.
To further dissect the mechanism of action of Rb in SACs we determined the E2f3 isoform it targets to promote differentiation. E2f3b was the primary candidate, since Rb and E2f3b collaborate to repress targets in quiescent cells in vitro [19]. However, in the first work to our knowledge to examine the function of any E2f protein isoform in vivo, we made the surprising observation that Rb regulates SAC differentiation through E2f3a (Figure 8). Formally, we cannot exclude the possibility that deleting E2f3b might also rescue SAC differentiation, but definitive proof will require analysis of E2f3b null mice. Nevertheless, our data prove that Rb definitely regulates SAC differentiation through the activating E2f3 isoform.
The subcellular location of E2f isoforms has not to our knowledge been addressed before. E2f3a and E2f3b share 110 C-terminal amino acids that encode the NLS, DNA-binding, marked box, transactivation, and Rb-binding domains [16], yet they exhibit different subcellular distribution in developing retinal cells. E2f3a is both nuclear and cytoplasmic, but E2f3b is always nuclear. The unique 121- and six-residue N-termini of E2f3a and E2f3b, respectively, likely mediate this difference. This region in E2f1, E2f2, and E2f3a binds Ccna2, establishing a negative regulatory loop that deactivates E2fs in mid-late S-phase [64,65]. However, even E2f3b, which lacks this domain, binds and is regulated by Ccna2 [18], so the domain difference may not explain the unique distributions we observed. Rb family and Tfdp proteins can also determine E2f localization [20–22], and we found that a portion of both Rb and Tfdp1 proteins are cytoplasmic in retinal cells. Indeed, immunostaining revealed that Rb and E2f3 colocalize to SAC processes.
The nuclear localization of E2f3b contrasts with that of other repressive E2fs in differentiating muscle, where E2f5 switches from the nucleus to cytoplasm, while E2f4 remains in both compartments [23]. The distinct compartmentalization of E2f3a and E2f3b in the retina suggests temporally and functionally distinct activities. Rb distribution matches that of E2f3a, consistent with its critical role in supporting SAC differentiation through E2f3a.
Rb is critical to ensure that many types of terminally differentiating cells leave the cell cycle (e.g., neurons, gut and skin epithelia, muscle, and lens fibres) (reviewed in [66]). Early overexpression studies in vitro suggested Rb might temper expansion of cycling cells, but KO studies in vivo indicate that its major role is to block division in terminally differentiating cells. In its absence, many (but clearly not all) aspects of differentiation go ahead relatively unperturbed. In the retina, differentiating transition cells are born in the absence of Rb, migrate to the correct layer, and express appropriate markers ([2] and this work). In brain, Rb KO neurons migrate away from the ventricular zone and switch on Tubb3 (βIII-tubulin), but continue to incorporate BrdU [13], and in gut epithelia, differentiated enterocytes migrate up the villi and activate expression of serotonin, yet continue to incorporate BrdU [67]. In the case of SACs, the differentiation defects we observed (e.g., loss of Slc18a3 and Chat) were not due to aberrant division, but it is possible there are other problems with these cells that are caused by ectopic division. Nevertheless, it is clear that many aspects of differentiation in multiple cell types are compatible with ectopic division. However, division of terminally differentiating cells is dangerous, since it may facilitate transformation, as is the case in retinoblastoma (reviewed in [66]).
E2f3a could disrupt SAC differentiation through its well known role as a transcriptional activator, or, in view of the discovery that it is partially cytoplasmic, E2f3a may affect processes other than gene regulation. Both scenarios are feasible since E2fs regulate differentiation genes [44–48], and cell cycle regulators, such as Cdkn1b, have cytoplasmic activities that influence differentiation [68,69]. Many transcription factors shuttle between nucleus and cytoplasm during neurogenesis (e.g., [70] and references therein). It may be difficult to identify E2f3a-specific target genes or cytoplasmic proteins in SACs since these neurons are a small proportion (<1%) of the total retina and only ~5.2% of amacrine neurons [38].
Others have suggested that Rb promotes differentiation in non-neuronal cells through E2f-independent means [31–33]. However, these studies did not assess whether these cell types differentiate normally if Rb is deleted along with one or more E2f family members. One study reported that Rb mutants that do not bind E2f still induce differentiation [30]. However, the binding assays were performed in solution, and we have found that several of these mutants do bind E2f, albeit weakly, on chromatin (T. Yu and R. B., unpublished data). It is possible that Rb-mediated potentiation of tissue-specific transcription factors may, at least in some cases, be a redundant activity, and that the only critical Rb function is to inhibit E2f. Our study is the first to our knowledge to assess comprehensively whether Rb KO cells can differentiate in the absence of different E2fs. In light of our findings, it will be important to reassess differentiation defects in other Rb KO tissues in the absence of individual and combined activating E2f family members.
Mice were treated according to institutional and national guidelines. α-Cre mice (P. Gruss), Chx10-Cre mice (C. Cepko), RbloxP/loxP mice (A. Berns), E2f1–/– mice, E2f2–/– mice, E2f3loxP/loxP mice, and E2f3a−/− mice were maintained on a mixed (NMRI × C57/Bl × FVB/N × 129sv) background. A detailed description of E2f3a−/− mice will be published elsewhere. Mice of different genotypes were compared within the same litter and across a minimum of three litters. We have not noted any phenotypic differences in separate litters. Genotyping was performed as before [2,5], and the primers used for genotyping E2f3a−/− mice were E2f3a KL (5′-CTCCAGACCCCCGATTATTT-3′), E2f3a KR1 (5′-TCCAGTGCACTACTCCCTCC-3′), and E2f3a KM (5′-GCTAGCAGTGCCCTTTTGTC-3′).
Eyeballs were fixed in 4% paraformaldehyde for 1 h at 4 °C, embedded in OCT (TissueTek 4583, Sakura, http://www.sakuraeu.com), frozen on dry ice, and cut into 12-μm sections on Superfrost plus slides (VWR, http://www.vwr.com). For S-phase analysis, BrdU (100 μg/g of body weight) was injected intraperitoneally 2 h prior to sacrifice. BrdU+ cells were detected using a biotin-conjugated sheep polyclonal antibody (1:500, Maine Biotechnology Services, http://www.mainebiotechnology.com). All other antibodies are described in Table S1. For E2f3, Mki67, and Rb staining, antigen retrieval was performed by boiling sections in citric acid solution for 15 min according to Ino [50], except on frozen sections. TUNEL was performed as described [13]. Briefly, sections were incubated for 1 h at 37 °C with 75 μl of mixture solution consisting of 0.5 μl of terminal deoxynucleotide transferase, 1 μl of biotin-16-dUTP, 7.5 μl of CoCl2, 15 μl of 5× terminal deoxynucleotide transferase buffer, and 51 μl of distilled water. After three washes in 4× SSC buffer, sections were incubated with Alexa 488– or Alexa 568−streptavidin (1:1,000; Molecular Probes, http://probes.invitrogen.com) for 1 h at room temperature. Primary antibodies or labelled cells were visualized using donkey anti-mouse Alexa 488 or Alexa 568, donkey anti-rabbit Alexa 488 or Alexa 568, donkey anti-goat Alexa 488 or Alexa 568, and streptavidin Alexa 488 or Alexa 568 (1:1,000; Molecular Probes). Nuclei were counter-stained with 4,6-diamidino-2-phenyindole (DAPI; Sigma, http://www.sigmaaldrich.com). Labelled cells were visualized using a Zeiss (http://www.zeiss.com) Axioplan-2 microscope with Plan Neofluar objectives and images captured with a Zeiss AxionCam camera. For double-labelled samples, confocal images were obtained with a Zeiss LSM 5.0 laser scanning microscope.
The retina was separated into three bins by dividing the ventricular edge of the retina into equal parts and extending a line to the vitreal edge [2]. Bin 1 contains only cells that expressed Cre as progenitors; bin 3 is at central retina and contains cells derived from progenitors that did not express Cre. For cell counts or thickness measurement we used a region 0–100 μm peripheral to the boundary separating bins 1 and 2. Measurements were performed on an Axioplan-2 microscope using Axiovison software. Quantification of S-phase, M-phase, and apoptotic cells was performed on horizontal sections that included the optic nerve. Quantification of differentiated cell types was performed using horizontal sections at equal distances from the optic nerve. A minimum of three sections per eye and three eyes from different litters were counted.
Total RNA was isolated from dissected peripheral retina using TRIzol reagent (Invitrogen, http://www.invitrogen.com) followed by digestion with RNase-free DNase (DNA-free, Ambion, http://www.ambion.com) to remove DNA contamination. First-strand cDNA was synthesized from 0.2–0.5 μg of total RNA using the SuperScript II first-strand synthesis system (Invitrogen). PCR primers are listed in Table S2. Real-time quantitative PCR was performed using an Applied Biosystems (http://appliedbiosystems.com) PRISM 7900HT. Tests were run in duplicate on three separate biological samples with SYBR Green PCR Master Mix (Applied Biosystems) exactly as we described previously [71]. Briefly, master stocks were prepared such that each 10-μl reaction contained 5 μl of SYBR Green PCR Master Mix, 0.1 μl of each forward and reverse primer (stock 50 μM), 0.8 μl of blue H2O (0.73% Blue Food Colour; McCormick, http://www.mccormick.com), 2 μl of diluted cDNA template, and 2 μl of yellow H2O (0.73% Yellow Food Colour). PCR consisted of 40 cycles of denaturation at 95 °C for 15 s and annealing and extension at 55 °C for 30 s. An additional cycle (95 °C, 15 s, 60 °C) generated a dissociation curve to confirm a single product. The cycle quantity required to reach a threshold in the linear range was determined and compared to a standard curve for each primer set generated by five 3-fold dilutions of genomic DNA or cDNA samples of known concentration. Values obtained for test RNAs were normalized to Hprt1 mRNA levels.
Mouse retinas were homogenized by passing them through a 30-gauge BD 9 http://www.bd.com) needle 5–10 times in 1× PBS solution. Nuclear and cytoplasmic proteins were extracted using the NE-PER Nuclear and Cytoplasmic Extraction Kit (Product# 78833, Pierce Biotechnology, http://www.piercenet.com). Proteins were separated by 10% SDS-PAGE and transferred to nitrocellulose. After blocking overnight at 4 °C in 5% skim milk, membranes were incubated in the primary antibody for 2 h at room temperature. After three 10-min washes in TPBS (100 mM Na2HPO4, 100 mM NaH2PO4, 0.5 N NaCl, 0.1% Tween-20), membranes were incubated for 30 min at room temperature in the secondary horseradish peroxidase-conjugated antibody (Jackson ImmunoResearch Laboratories, http://www.jacksonimmuno.com). Blots were developed using the ECL-Plus chemiluminescent detection system (Amersham Pharmacia Biotech, http://www.pharmacia.ca), according to the manufacturer's instructions.
The following primary antibodies were used: E2f-1 (SC-193), E2f-3 (SC-878), Cdkn1a (p21, SC-471), Cdkn1b (p27, SC-528), Pou4f2 (Brn3b, SC-6062), and Tfdp1 (Dp1, SC-610) from Santa Cruz Biotechnology (http://www.scbt.com), pRB (554136) from BD Science-Pharmingen (http://www.bdbiosciences.com), and Slc18a3 (VAChT, G448A) from Promega (http://www.promega.com).
ERGs were recorded from dark-adapted mice as described [72]. Briefly, mice were dark-adapted overnight and anaesthetized by subcutaneous injection of ketamine (66.7 mg/kg body weight) and xylazine (11.7 mg/kg body weight). The pupils were dilated and single-flash ERG recordings were obtained under dark-adapted (scotopic) and light-adapted (photopic) conditions. Light adaptation was accomplished with a background illumination of 30 candela (cd) per square meter starting 10 min before recording. Single white-flash stimulation ranged from 10−4 to 25 cd·s/m2, divided into ten steps of 0.5 and 1 log cd·s/m2. Ten responses were averaged with an inter-stimulus interval of either 5 s (for 10−4, 10−3, 10−2, 3 × 10−2, 10−1, and 3 × 10−1 cd·s/m2) or 17 s (for 1, 3, 10, and 25 cd·s/m2). Band-pass filter cut-off frequencies were 0.1 and 3,000 Hz.
Different genotypes were evaluated using analysis of variance (ANOVA) followed by the Tukey honestly significant difference (HSD) test or Fisher test (XLSTAT program, http://www.xlstat.com).
The GenBank (http://www.ncbi.nlm.nih.gov/genbank) accession numbers for the major genes and gene products discussed in this paper are Camk2a (NM_009792), Chat (NM_009891), E2f1 (NM_007891), E2f2 (NM_177733), E2f3 (NM_010093), Rb (NM_009029), and Slc18a3 (NM_021712).
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10.1371/journal.ppat.1007871 | The lectin-specific activity of Toxoplasma gondii microneme proteins 1 and 4 binds Toll-like receptor 2 and 4 N-glycans to regulate innate immune priming | Infection of host cells by Toxoplasma gondii is an active process, which is regulated by secretion of microneme (MICs) and rhoptry proteins (ROPs and RONs) from specialized organelles in the apical pole of the parasite. MIC1, MIC4 and MIC6 assemble into an adhesin complex secreted on the parasite surface that functions to promote infection competency. MIC1 and MIC4 are known to bind terminal sialic acid residues and galactose residues, respectively and to induce IL-12 production from splenocytes. Here we show that rMIC1- and rMIC4-stimulated dendritic cells and macrophages produce proinflammatory cytokines, and they do so by engaging TLR2 and TLR4. This process depends on sugar recognition, since point mutations in the carbohydrate-recognition domains (CRD) of rMIC1 and rMIC4 inhibit innate immune cells activation. HEK cells transfected with TLR2 glycomutants were selectively unresponsive to MICs. Following in vitro infection, parasites lacking MIC1 or MIC4, as well as expressing MIC proteins with point mutations in their CRD, failed to induce wild-type (WT) levels of IL-12 secretion by innate immune cells. However, only MIC1 was shown to impact systemic levels of IL-12 and IFN-γ in vivo. Together, our data show that MIC1 and MIC4 interact physically with TLR2 and TLR4 N-glycans to trigger IL-12 responses, and MIC1 is playing a significant role in vivo by altering T. gondii infection competency and murine pathogenesis.
| Toxoplasmosis is caused by the protozoan Toxoplasma gondii, belonging to the Apicomplexa phylum. This phylum comprises important parasites able to infect a broad diversity of animals, including humans. A particularity of T. gondii is its ability to invade virtually any nucleated cell of all warm-blooded animals through an active process, which depends on the secretion of adhesin proteins. These proteins are discharged by specialized organelles localized in the parasite apical region, and termed micronemes and rhoptries. We show in this study that two microneme proteins from T. gondii utilize their adhesion activity to stimulate innate immunity. These microneme proteins, denoted MIC1 and MIC4, recognize specific sugars on receptors expressed on the surface of mammalian immune cells. This binding activates these innate immune cells to secrete cytokines, which promotes efficient host defense mechanisms against the parasite and regulate their pathogenesis. This activity promotes a chronic infection by controlling parasite replication during acute infection.
| Toxoplasma gondii is a coccidian parasite belonging to the phylum Apicomplexa and is the causative agent of toxoplasmosis. This protozoan parasite infects a variety of vertebrate hosts, including humans with about one-third of the global population being chronically infected [1]. Toxoplasmosis can be fatal in immunocompromised individuals or when contracted congenitally [1], and is considered the second leading cause of death from foodborne illnesses in the United States [2].
T. gondii invades host cells through an active process that relies on the parasite actinomyosin system, concomitantly with the release of microneme proteins (MICs) and rhoptry neck proteins (RONs) from specialized organelles in the apical pole of the parasite [3]. These proteins are secreted by tachyzoites [4, 5] and form complexes composed of soluble and transmembrane proteins. Some of the MICs act as adhesins, interacting tightly with host cell-membrane glycoproteins and receptors, and are involved in the formation of the moving junction [6]. This sequence of events ensures tachyzoite gliding motility, migration through host cells, invasion and egress from infected cells [4, 7]. Among the released proteins, MIC1, MIC4, and MIC6 form a complex that, together with other T. gondii proteins, plays a role in the adhesion and invasion of host cells [8, 9], contributing to the virulence of the parasite [10, 11].
Several studies have shown that host-cell invasion by apicomplexan parasites such as T. gondii involves carbohydrate recognition [12–15]. Interestingly, MIC1 and MIC4 have lectin domains [11, 16–18] that recognize oligosaccharides with sialic acid and D-galactose in the terminal position, respectively. Importantly, the parasite’s Lac+ subcomplex, consisting of MIC1 and MIC4, induces adherent spleen cells to release IL-12 [17], a cytokine critical for the protective response of the host to T. gondii infection [19]. In addition, immunization with this native subcomplex, or with recombinant MIC1 (rMIC1) and MIC4 (rMIC4), protects mice against experimental toxoplasmosis [20, 21]. The induction of IL-12 is typically due to detection of the pathogen by innate immunity receptors, including members of the Toll-like receptor (TLR) family, whose stimulation involves MyD88 activation and priming of Th1 responses, which protects the host against T. gondii [19, 22]. It is also known that dysregulated expression of IL-12 and IFN-γ during acute toxoplasmosis can drive a lethal immune response, in which mice succumb to infection by severe immunopathology, the result of insufficient levels of IL-10 and/or a collapse in the regulatory CD4+Foxp3+ T cell population [23, 24].
Interestingly, regarding the innate immune receptors associated with IL-12 response during several infections, the extracellular leucine-rich repeat domains of TLR2 and TLR4 contain four and nine N-glycans, respectively [25]. Therefore, we hypothesized that MIC1 and MIC4 bind TLR2 and TLR4 N-glycans on antigen-presenting cells (APCs) and, through this interaction, trigger immune cell activation and IL-12 production. To investigate this possibility, we assayed the ability of rMIC1 and rMIC4 to bind and activate TLR2 and TLR4. Using several strategies, we demonstrated that TLR2 and TLR4 are indeed critical targets for both MIC1 and MIC4. These parasite and host cell structures establish lectin-carbohydrate interactions that contribute to the induction of IL-12 production by innate immune cells, and we show here that the MIC1 lectin promotes T. gondii infection competency and regulates parasite virulence during in vivo infection.
The native MIC1/4 subcomplex purified from soluble T. gondii antigens has lectin properties, so we investigated whether their recombinant counterparts retained the sugar-binding specificity. The glycoarray analysis revealed the interactions of: i) the Lac+ subcomplex with glycans containing terminal α(2–3)-sialyl and β(1–4)- or β(1–3)-galactose; ii), rMIC1 with α(2–3)-sialyl residues linked to β-galactosides; and iii) of rMIC4 with oligosaccharides with terminal β(1–4)- or β(1–3)-galactose (Fig 1A). The combined specificities of the individual recombinant proteins correspond to the dual sugar specificity of the Lac+ fraction, demonstrating that the sugar-recognition properties of the recombinant proteins are consistent with those of the native ones.
Based on the sugar recognition selectivity of rMIC1 and rMIC4, we tested two oligosaccharides (α(2–3)-sialyllactose and lacto-N-biose) for their ability to inhibit the interaction of the MICs with the glycoproteins fetuin and asialofetuin [26]. Sialyllactose inhibited the binding of rMIC1 to fetuin, and lacto-N-biose inhibited the binding of rMIC4 to asialofetuin (Fig 1B). To ratify the carbohydrate recognition activity of rMIC1 and rMIC4, we generated point mutations into the carbohydrate recognition domains (CRDs) of the rMICs to abolish their lectin properties [11, 18, 27]. These mutated forms, i.e. rMIC1-T126A/T220A and rMIC4-K469M, lost their capacity to bind to fetuin and asialofetuin, respectively (Fig 1B), having absorbance as low as that in the presence of the specific sugars. Thus, our results indicate that rMIC1 and rMIC4 maintained their lectin properties, and that the CRD function can be blocked either by competition with specific sugars or by targeted mutations.
We have previously demonstrated that the native Lac+ subcomplex stimulates murine adherent spleen cells to produce proinflammatory cytokines [20]. We evaluated whether recombinant MIC1 and MIC4 retained this property and exerted it on BMDCs and BMDMs. BMDCs (Fig 2A–2D) and BMDMs (Fig 2E–2H) produced high levels of the proinflammatory cytokines IL-12 (Fig 2A and 2E), TNF-α (Fig 2B and 2F), and IL-6 (Fig 2C and 2G). This was not attributable to residual LPS contamination as the recombinant protein assays were done in the presence of polymyxin B, and LPS levels were less than 0.5ng/ml [see Materials and Methods section]. Although conventional CD4+ Th1 cells are known to be the major producers of IL-10 during murine T. gondii infection [28], we also found that rMIC1 and rMIC4 induced the production of this cytokine by BMDCs (Fig 2D) and BMDMs (Fig 2H). We verified that the two recombinant proteins induced the production of similar levels of IL-12, TNF-α, and IL-6 by both BMDCs (Fig 2A–2C) and BMDMs (Fig 2E–2G). Both MICs induced the production of similar levels of IL-10 in BMDCs (Fig 2D); however, BMDMs produced significantly higher levels of IL-10 when stimulated with rMIC1 than when stimulated with rMIC4 (Fig 2H). These cytokine levels were similar to those induced by the TLR4 agonist LPS. Thus, recombinant MIC1 and MIC4 induce a proinflammatory response in innate immune cells, which is consistent with the results obtained for the native Lac+ subcomplex [20].
To investigate the mechanisms through which T. gondii MIC1 and MIC4 stimulate innate immune cells to produce cytokines, we assessed whether these MICs can activate specific TLRs. To this end, BMDMs from WT, MyD88-/-, TRIF-/-, TLR2-/-, TLR4-/-, or TLR2/4 DKO mice, as well as HEK293T cells transfected with TLR2 or TLR4, were cultured in the presence or absence of rMIC1 and rMIC4 for 48 hours. The production of IL-12 by BMDMs (Fig 3A–3I) and IL-8 by HEK cells (Fig 3J and 3K) were used as an indicator of cell activation. IL-12 production by BMDMs from MyD88-/-, TRIF-/-, TLR2-/-, and TLR4-/- mice was lower than that of BMDMs from WT mice (Fig 3A–3D); no IL-12 was detected in cultures of TLR2/4 DKO mice cells stimulated with either rMIC1 or rMIC4 (Fig 3E). These results show that TLR2 and TLR4 are both relevant for the activation of macrophages induced by rMIC1 and rMIC4. The residual cytokine production observed in macrophages from TLR2-/- or MyD88-/- mice may be the result of activation of TLR4 (Fig 3A and 3C), and vice versa; e.g., the residual IL-12 levels produced by macrophages from TLR4-/- mice may be the result of TLR2 activation. The finding that MICs fail to induce IL-12 production in DKO mice BMDMs suggests that cell activation triggered by T. gondii MIC1 or MIC4 does not require the participation of other innate immunity receptors beyond TLR2 and TLR4. Nevertheless, because parasite components such as DNA or profilin engage TLR9, TLR11, and TLR12 to produce IL-12 in macrophages [19, 22, 29], we investigated the involvement of these receptors, as well as TLR3 and TLR5, in the response to rMIC1 or rMIC4. BMDMs from TLR3-/-, TLR5-/-, TLR9-/-, and TLR11/12 DKO mice stimulated with rMIC1 or rMIC4 produced similar levels of IL-12 as cells from WT (Fig 3F–3I), indicating that the activation triggered by rMIC1 or rMIC4 does not depend on these receptors. Additionally, stimulation of HEK cells transfected with human TLR2 (Fig 3J) or TLR4 (Fig 3K) with optimal concentrations of rMIC1 (S1A and S1C Fig) and rMIC4 (S1B and S1D Fig) induced IL-8 production at levels that were higher than those detected in the absence of stimuli (medium), and similar to those induced by the positive controls. Finally, by means of a pull-down experiment, we demonstrated a physical interaction between rMIC1 and TLR2 or TLR4 and between rMIC4 and TLR2 or TLR4 (Fig 3L).
We hypothesized that in order to trigger cell activation, rMIC1 and rMIC4 CRDs target oligosaccharides of the ectodomains of TLR2 (four N-linked glycans) [25] and TLR4 (nine N-linked glycans) [30]. This hypothesis was tested by stimulating BMDCs (Fig 4A) and BMDMs (Fig 4B) from WT mice with intact rMIC1 and rMIC4 or with the mutated forms of these microneme proteins, namely rMIC1-T126A/T220A and rMIC4-K469M, which lack carbohydrate binding activity [11, 18, 27]. IL-12 levels in culture supernatants were lower upon stimulation with rMIC1-T126A/T220A or rMIC4-K469M, showing that WT induction of cell activation requires intact rMIC1 and rMIC4 CRDs. The same microneme proteins were used to stimulate TLR2-transfected HEK293T cells (Fig 4C), and similarly, lower IL-8 production was obtained in response to mutated rMIC1 or rMIC4 compared to that seen in response to intact proteins. These observations demonstrated that rMIC1 and rMIC4 CRDs are also necessary for inducing HEK cell activation.
We used an additional strategy to examine the ability of rMIC1 and rMIC4 to bind to TLR2 N-glycans. In this approach, HEK cells transfected with the fully N-glycosylated TLR2 ectodomain or with the TLR2 glycomutants [25] were stimulated with a control agonist (FSL-1) or with rMIC1 or rMIC4. HEK cells transfected with any TLR2 form, except those expressing totally unglycosylated TLR2 (mutant Δ1,2,3,4), were able to respond to FSL-1 (Fig 4D), a finding that is consistent with the previous report that the Δ1,2,3,4 mutant is not secreted by HEK293T cells [25]. Cells transfected with TLR2 lacking only the first or the third N-glycan (mutant Δ1; Δ3) responded to all stimuli. The response to the rMIC1 stimulus was significantly reduced in cells transfected with five different TLR2 mutants, lacking some combination of the second, third, and fourth N-glycans (Fig 4D). Moreover, rMIC4 stimulated IL-8 production was significantly reduced in cells transfected with the mutants lacking some combination of the third and fourth N-glycans (Fig 4D).
These results indicate that T. gondii MIC1 and MIC4 use their CRDs to induce TLR2- and TLR4-mediated cell activation. Among the TLR2 N-glycans, the rMIC1 CRD likely targets the second, third, and fourth glycan, whereas the rMIC4 CRD targets only the third and fourth. Additionally, our findings suggested that TLR2 and TLR4 activation is required to enhance the production of IL-12 by APCs following rMIC stimulation.
Because IL-12 production is induced by rMICs that engage TLR2 and TLR4 N-glycans expressed on innate immune cells, we investigated whether such production is impaired when APCs are infected with T. gondii lacking MIC1 and/or MIC4 proteins, as well as complemented strains expressing mutant versions of these proteins that fail to bind TLR2 or TLR4 carbohydrates. We generated Δmic1 and Δmic4 strains in an RH strain expressing GFP and Luciferase using CRISPR/Cas9 to replace the endogenous MIC gene with the drug-selectable marker HPT (HXGPRT–hypoxanthine-xanthine-guanine phosphoribosyl transferase) (Fig 5A and 5B). We then complemented MIC deficient parasites with mutated versions expressing an HA-tag, thus generating the Δmic1::MIC1-T126A/T220AHA or Δmic4::MIC4-K469MHA strains (Fig 5A) that expressed endogenous levels of MIC1 and MIC4 as confirmed by Western Blotting (Fig 5C).
IL-12 secretion by BMDCs and BMDMs infected with WT, Δmic1, Δmic1::MIC1-T126A/T220A, Δmic4 and Δmic4::K469M parasites was assessed at 24 hours post infection. All mutant strains (Δmic1, Δmic1::MIC1-T126A/T220A, Δmic4 and Δmic4::K469M) induced lower IL-12 secretion by BMDCs (Fig 5D) and BMDMs (Fig 5E) compared to that induced by WT parasites, indicating that engagement of TLR2 and TLR4 cell surface receptors by the MIC lectin-specific activity led to an early release of IL-12.
Using flow cytometry, we confirmed that parasites deficient in MIC1or MIC4, or mutated in their carbohydrate recognition domain resulted in lower intracellular IL-12 production than WT infected BMDCs (Fig 5F–5H). Interestingly, the Toxo+ BMDCs presented the same level of intracellular IL-12, independent of the T. gondii strain infected (Fig 5F and 5H). Whereas the Toxo- BMDCs produced less IL-12 when they were infected with knockout or CRD-mutated T. gondii compared to WT-infected cells (Fig 5G and 5H). Taken altogether, these results indicate that MIC1 and MIC4 induce IL-12 production in innate immune cells during in vitro T. gondii infection. It is known that other parasite factors act as IL-12 inducers, such as profilin, which is a TLR11 and TLR12 agonist [29, 31], or GRA7 [32], GRA15 [33], and GRA24 [34], which directly trigger intracellular signalling pathways in a TLR-independent manner, and these likely account for the majority of IL-12 released after 24 hours of intracellular infection.
Given the importance of MIC1 and MIC4 as lectins that engage TLR2 and TLR4 N-glycans to induce increased levels of IL-12 release during T. gondii in vitro infection, we investigated the biological relevance of these proteins during in vivo infection. Mice were injected with 50 tachyzoites of RH WT, Δmic1, Δmic1::MIC1-T126A/T220A, Δmic4 and Δmic4::MIC4-K469M strains into the peritoneum of CD-1 outbred mice, a lethal dose that causes acute mortality. The survival curve showed that parasites deficient in MIC1 (Δmic1 group) or mutated to remove MIC1 lectin binding activity (Δmic1::MIC1-T126A/T220A group) were less virulent, resulting in a slight, but significant (p = 0.0017) increase in mouse survival (12 days post-infection) compared to WT infected mice that all succumbed to infection by day 10 (Fig 6A). This was not the result of a difference in parasite load, which was equivalent across all T. gondii-infected mice at Day 5 (Fig 6D and 6I). Whereas, the absence of the MIC4 gene or MIC4 lectin activity did not change the survival curve (Fig 6E) indicating that MIC4 is less relevant than MIC1 during in vivo infection.
Acute mortality in CD-1 mice infected with Type I T. gondii is related to the induction of a cytokine storm, mediated by high levels of IFN-γ production. Thus, we measured systemic levels of IFN-γ and IL-12 in mice infected with WT, Δmic1, Δmic1::MIC1-T126A/T220A, Δmic4 and Δmic4::MIC4-K469M strains. According to Kugler et al. (2013), the peak of systemic IL-12p40 and IFN-γ during ME49-T. gondii infection is between days 5–6 post-infection, therefore, we measured these cytokines in the serum of CD-1-infected mice at day 5. Mice infected with Δmic1 or Δmic1::MIC1-T126A/T220A strains had 3–5 fold lower systemic levels of IL-12 (Fig 6B; p = 0.016) and IFN-γ (Fig 6C; p ≤0.0002) than WT infected mice. In contrast, mice infected with parasites lacking the MIC4 gene, or those expressing the mutant version of MIC4 showed no difference in IL-12 (Fig 6F) or IFN-γ (Fig 6G) compared to WT infected mice. Hence, only MIC1 altered systemic levels of key cytokines induced during T. gondii in vivo infection, and mice survived longer with lower systemic levels of cytokines typically associated with acute mortality.
To formally show that MIC1 alters systemic levels of pro-inflammatory cytokines associated with acute mortality, we complemented Δmic1 parasites at the endogenous locus with a Type I allele of MIC1 expressing an HA tag (MIC1HA). Western blotting for either MIC1 or HA expression showed WT levels of MIC1 expression in the complemented parasites Δmic1::MIC1HA (Fig 7A). The complemented strain restored WT virulence kinetics during in vivo infection and all mice died acutely, in contrast to Δmic1 or Δmic1::MIC1-T126A/T220A parasites, that had a slight, but significant delay in their acute mortality kinetics (Fig 7B; p = 0.0082). Systemic levels of IFN-γ (Fig 7C) and parasite load (Fig 7D and 7E) from mice infected with the complemented strain were indistinguishable from WT. To better resolve the apparent difference in acute mortality, parasites were injected into the right footpad to monitor mouse weight loss and survival kinetics [35]. Mice infected locally in the footpad with Δmic1 survived significantly longer, or did not die (Fig 7G; p = 0.0002), and lost less weight during acute infection (Fig 7F) than those infected with WT or Δmic1::MIC1 complemented parasites. Further, mice infected with Δmic1::MIC1-T126A/T220A parasites that fail to bind TLR2 and TLR4 N-glycans in vivo also lost less weight and survived significantly longer than WT or Δmic1::MIC1 complemented parasites (Fig 7F and 7G). In conclusion, our results suggest that MIC1 operates in two distinct ways; as an adhesin protein that promotes parasite infection competency, and as a lectin that engages TLR N-glycans to induce a stronger proinflammatory immune response, one that is unregulated and results in acute mortality upon RH infection of CD-1 mice.
In this study, we report a new function for MIC1 and MIC4, two T. gondii microneme proteins involved in the host-parasite relationship. We show that rMIC1 and rMIC4, by interacting directly with N-glycans of TLR2 and TLR4, trigger a noncanonical carbohydrate recognition-dependent activation of innate immune cells. This results in IL-12 secretion and the production of IFN-γ, a pivotal cytokine that mediates parasite clearance and the development of a protective T cell response [19, 22], but in some cases promotes a dysregulated cytokine storm and acute mortality, as seen during RH infection of CD-1 mice [36]. This MIC-TLR activation event explains, at least in part, the resistance conferred by rMIC1 and rMIC4 administration against experimental toxoplasmosis [20, 21].
T. gondii tachyzoites express microneme proteins either on their surface or secrete them in their soluble form. These proteins may form complexes, such as those of MIC1, MIC4, and MIC6 (MIC1/4/6), in which MIC6 is a transmembrane protein that anchors the two soluble molecules MIC1 and MIC4 [8]. Genetic disruption of each one of these three genes does not interfere with parasite survival [8] nor its interaction with, and attachment to, host cells [10]; however, MIC1 has been shown to play a role in invasion and contributes to virulence in mice [10]. We previously isolated soluble MIC1/4, a lactose-binding complex from soluble T. gondii antigens (STAg) [17], and its lectin activity was confirmed by the ability of MIC1 to bind sialic acid [9] and MIC4 to β-galactose [18]. We also reported that MIC1/4 stimulates adherent splenic murine cells to produce IL-12 at levels as high as those induced by STAg [20]. Recently, it was also demonstrated that MIC1, MIC4 and MIC6 are capable of inducing IFN-γ production from memory T cells in mice chronically infected with T. gondii [37]. Our data herein shows that MIC1/4 binds to and activates TLRs via a novel lectin-carbohydrate interaction, rather than by its cognate receptor-ligand binding groove, establishing precisely how the interactions of microneme protein(s) with defined glycosylated receptor(s) expressed on the host cell surface are capable of altering innate priming of the immune system.
To formally demonstrate the MIC1/MIC4 binding to glycosylated TLR cell surface receptors we generated recombinant forms of MIC1 and MIC4, which retained their specific sialic acid- and β-galactose-binding properties as indicated by the results of their binding to fetuin and asialofetuin as well as the glycoarray assay. Both recombinant MIC1 and MIC4 triggered the production of proinflammatory and anti-inflammatory cytokines in DCs and macrophages via their specific recognition of TLR2 and TLR4 N-glycans, as well as by signaling through MyD88 and, partially, TRIF. Importantly, our results establish how binding of rMIC1 and rMIC4 to specific N-glycans present on TLR2 and TLR4 induces cell activation through this novel lectin-carbohydrate interaction. The ligands for MIC1 and MIC4, α2-3-sialyllactosamine and β1-3- or β1-4-galactosamine, respectively, are terminal N-glycan residues found on a wide-spectrum of mammalian cell surface-associated glycoconjugates. Thus, it is possible that additional lectin-carbohydrate interactions may exist between MIC1/4 and other cell surface receptors beyond TLR2 and TLR4. Such interactions likely evolved to facilitate adhesion and promote the infection competency of a wide-variety of host cells infected by T. gondii, further underscoring how these proteins exist as important virulence factors [10] beyond immune priming. However, it is the immunostimulatory capacity of rMIC1 and rMIC4 to target N-glycans on the ectodomains of TLR2 and TLR4 that likely rationalizes how these microneme proteins function as a double-edged sword during T. gondii infection. Mice infected by Type I strains die acutely due to a failure to regulate the cytokine storm induced by high levels of IL-12 and IFN-γ[38, 39]. In this study, T. gondii Type I strains engineered to be deficient in MIC1 or defective in binding TLR2/4 N-glycans lost less weight, survived significantly longer, and produced less IL-12 and IFN-γ. Future studies that test whether the immunostimulatory effect of MIC1/4 alters the pathogenesis and cyst burden of Type II strains of T. gondii should be pursued to formally demonstrate that Type II parasites rely on MIC1/4 induction of Th1-biased cytokines in order to limit tachyzoite proliferation and induce a life-long persistent bradyzoite infection.
Several pathogens are known to synthesize lectins, which are most frequently reported to interact with glycoconjugates on host cells to promote adherence, invasion, and colonization of tissues [40–43]. Nonetheless, there are currently only a few examples of lectins from pathogens that recognize sugar moieties present in TLRs and induce IL-12 production by innate immune cells. Paracoccin, a GlcNAc-binding lectin from the human pathogen Paracoccidioides brasiliensis, induces macrophage polarization towards the M1 phenotype [44] and the production of inflammatory cytokines through its interaction with TLR2 N-glycans [45]. Furthermore, the galactose-adherence lectin from Entamoeba histolytica activates TLR2 and induces IL-12 production [46]. In addition, the mammalian soluble lectin SP-A, found in lung alveoli, interacts with the TLR2 ectodomain [47]. The occurrence of cell activation and IL-12 production as a consequence of the recognition of TLR N-glycans has also been demonstrated using plant lectins with different sugar-binding specificities [48, 49].
The binding of MIC1 and MIC4, as well as the lectins above, to TLR2 and TLR4 may be associated with the position of the specific sugar residue present on the receptor’s N-glycan structure. Since the N-glycan structures of TLR2 and TLR4 are still unknown, we assume that the targeted MIC1 and MIC4 residues, e.g. sialic acid α2-3-linked to galactose β1-3- and β1-4-galactosamines, are appropriately placed in the receptors’ oligosaccharides to allow the recognition phenomenon and trigger the activation of innate immune responses.
Several T. gondii proteins have previously been shown to activate innate immune cells in a TLR-dependent manner, but independent of sugar recognition. This is the case for profilin (TgPRF), which is essential for the parasite’s gliding motility based on actin polymerization; it is recognized by TLR11 [29] and TLR12 [31, 50]. In addition, T. gondii-derived glycosylphosphatidylinositol anchors activate TLR2 and TLR4 [51], and parasite RNA and DNA are ligands for TLR7 and TLR9, respectively [19, 22, 50]. The stimulation of all of these TLRs culminate in MyD88 activation which results in IL-12 production [19, 22]. Several other T. gondii secreted effector proteins regulate the production of proinflammatory cytokines such as IL-12, independent of TLRs. For example, the dense granule protein 7 (GRA7) induces MyD88-dependent NF-kB activation, which facilitates IL-12, TNF-α, and IL-6 production [32]. MIC3 is reported to induce TNF-α secretion and macrophage M1 polarization [52], whereas GRA15 expressed by Type II strains activates NF-kB, promoting the release of IL-12 [33], and GRA24 triggers the autophosphorylation of p38 MAP kinase and proinflammatory cytokine and chemokine secretion [34]. In contrast, TgIST interferes with IFN-γ induction by actively inhibiting STAT1-dependent proinflammatory gene expression indicating that the parasite is capable of both activating as well as inhibiting effector arms of the host immune response to impact its pathogenesis in vivo [53]. Thus, multiple secretory effector proteins of T. gondii, including MIC1 and MIC4, appear to work in tandem to ultimately promote protective immunity by either inducing or dampening the production of proinflammatory cytokines, the timing of which is central to controlling both the parasite’s proliferation during the acute phase of infection and the induction of an effective immune response capable of establishing a chronic infection [19].
Our results regarding soluble MIC1 and MIC4 confirmed our hypothesis that these two effector proteins induce the innate immune response against T. gondii through TLR2- and TLR4-dependent pathways. This is consistent with previous studies that highlight the importance of TLR signaling, as well as the MyD88 adapter molecule, as essential for conferring resistance to T. gondii infection [29, 51, 54, 55]. In addition, we show that both MIC1 and MIC4 on the parasite surface contribute to the secretion of IL-12 by macrophages and DCs during in vitro infection, but only MIC1 plays a significant role during in vivo infection, demonstrated by its ability to promote a dysregulated induction of systemic levels of IFN-γ and a proinflammatory cytokine storm that leads to acute mortality during murine infection.
All experiments were conducted in accordance to the Brazilian Federal Law 11,794/2008 establishing procedures for the scientific use of animals, and State Law establishing the Animal Protection Code of the State of Sao Paulo. All efforts were made to minimize suffering, and the animal experiments were approved by the Ethics Committee on Animal Experimentation (Comissão de Ética em Experimentação Animal—CETEA) of the Ribeirao Preto Medical School, University of Sao Paulo (protocol number 065/2012), following the guidelines of the National Council for Control of Animal Experimentation (Conselho Nacional de Controle de Experimentação Animal—CONCEA).
The lactose-eluted (Lac+) fraction was obtained as previously reported [17, 21]. Briefly, the total soluble tachyzoite antigen (STAg) fraction was loaded into a lactose column (Sigma-Aldrich, St. Louis, MO) and equilibrated with PBS containing 0.5 M NaCl. The material adsorbed to the resin was eluted with 0.1 M lactose in equilibrating buffer and dialyzed against ultrapure water. The obtained fraction was denoted as Lac+ and confirmed to contain MIC1 and MIC4. For the recombinant proteins, rMIC1 and rMIC4 sequences were amplified from cDNA of the T. gondii strain ME49 with a 6-histidine tag added on the N-terminal, cloned into pDEST17 vector (Gateway Cloning, Thermo Fisher Scientific Inc., Grand Island, NY), and used to transform DH5α E. coli chemically competent cells for ampicillin expression selection, as described before [21]. The plasmids with rMIC1-T126A/T220A and rMIC4-K469M were synthesized by GenScript (New Jersey, US) using a pET28a vector, and the MIC sequences carrying the mutations were cloned between the NdeI and BamH I sites. All plasmids extracted from DH5α E. coli were transformed in E. coli BL21-DE3 chemically competent cells to produce recombinant proteins that were then purified from inclusion bodies and refolded by gradient dialysis, as described previously for rMIC1 and rMIC4 wild type forms [21]. Endotoxin concentrations were measured in all protein samples using the Limulus Amebocyte Lysate Kit–QCL-1000 (Lonza, Basel, Switzerland). The rMIC1, rMIC1-T126A/T220A, rMIC4 and rMIC4-K469M contained 7.2, 3.2, 3.5 and 1.1 EU endotoxin/μg of protein, respectively. Endotoxin was removed by passing over two polymyxin-B columns (Affi-Prep Polymyxin Resin; Bio-Rad, Hercules, CA). Additionally, prior to all in vitro cell-stimulation assays, the proteins samples were incubated with 50 μg/mL of polymyxin B sulphate salt (Sigma-Aldrich, St. Louis, MO) for 30 min at 37°C to remove possible residual LPS.
The carbohydrate-binding profile of microneme proteins was determined by Core H (Consortium for Functional Glycomics, Emory University, Atlanta, GA), using a printed glycan microarray, as described previously [56]. Briefly, rMIC1-Fc, rMIC4-Fc, and Lac+-Fc in binding buffer (1% BSA, 150 mM NaCl, 2 mM CaCl2, 2 mM MgCl2, 0.05% (w/v) Tween 20, and 20 mM Tris-HCl, pH 7.4) were applied onto a covalently printed glycan array and incubated for 1 hour at 25°C, followed by incubation with Alexa Fluor 488-conjugate (Invitrogen, Thermo Fisher Scientific Inc., Grand Island, NY). Slides were scanned, and the average signal intensity was calculated. The common features of glycans with stronger binding are depicted in Fig 1A. The average signal intensity detected for all of the glycans was calculated and set as the baseline.
Ninety-six-well microplates were coated with 1 μg/well of fetuin or asialofetuin, glycoproteins diluted in 50 μL of carbonate buffer (pH 9.6) per well, followed by overnight incubation at 4°C. Recombinant MIC1 or MIC4 proteins (both wild type (WT) and mutated forms), previously incubated or not with their corresponding sugars, i.e. α(2–3)-sialyllactose for MIC1 and lacto-N-biose for MIC4 (V-lab, Dextra, LA, UK), were added into coated wells and incubated for 2 h at 25°C. After washing with PBS, T. gondii-infected mouse serum (1:50) was used as the source of the primary antibody. The assay was then developed with anti-mouse peroxidase-conjugated secondary antibody, and the absorbance was measured at 450 nm in a microplate-scanning spectrophotometer (Power Wave-X; BioTek Instruments, Inc., Winooski, VT).
Female C57BL/6 (WT), MyD88-/-, TRIF-/-, TLR2-/-, TLR3-/-, TLR4-/-, double knockout (DKO) TLR2-/-/TLR4-/-, TLR5-/-, and TLR9-/- mice (all from the C57BL/6 background), 8 to 12 weeks of age, were acquired from the University of Sao Paulo—Ribeirao Preto campus animal facility, Ribeirao Preto, Sao Paulo, Brazil, and housed in the animal facility of the Department of Cell and Molecular Biology—Ribeirão Preto Medical School, under specific pathogen-free conditions. The TLR11-/-/TLR12-/- DKO mice were maintained at American Association of Laboratory Animal Care-accredited animal facilities at NIAID/NIH. For the in vivo infections, female CD-1 outbred mice, 6 weeks of age were acquired from Charles River Laboratories, Germantown, MD, USA.
A clonal isolate of the T. gondii RH-Δku80/Δhpt strain was used to generate the GFP/Luciferase strain, which was the recipient strain to generate the single-knockout parasites. The GFP/Luc sequence was inserted into the UPRT locus of Toxoplasma by double crossover homologous recombination using CRISPR/Cas-based genome editing and selected for FUDR resistance to facilitate the targeted GFP/Luc gene cassette knock-in. The MIC1 and MIC4 genes were replaced by the drug-selectable marker hpt (hxgprt—hypoxanthine-xanthine-guanine phosphoribosyl transferase) flanked by LoxP sites. For all gene deletions, 30 μg of guide RNA was transfected along with 15 μg of a repair oligo. Parasites were transfected and selected as previously described [57, 58]. For the MIC gene complementation, the sequence was amplified from RH genomic DNA with the addition of one copy of HA-tag sequence (TACCCATACGATGTTCCAGATTACGCT) before the stop codon, and cloned into pCR2.1-TOPO vector, followed by site-directed mutagenesis using the Q-5 kit (New England Biolabs) in order to generate point mutations into MIC1 (MIC1-T126A/T220A) and MIC4 (MIC4-K469M) sequences. For transfections, 30 μg of guide RNA was transfected along with 20 μg of linearized pTOPO vector containing the MIC mutated sequences.
Strains were maintained in human foreskin fibroblast (HFF) cells grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated foetal bovine serum (FBS), 0.25 mM gentamicin, 10 U/mL penicillin, and 10 μg/mL streptomycin (Gibco, Thermo Fisher Scientific Inc., Grand Island, NY).
Bone marrows of WT, MyD88-/-, TRIF-/-, TLR2-/-, TLR3-/-, TLR4-/-, DKO TLR2-/-/TLR4-/-, TLR5-/-, TLR9-/-, and DKO TLR11-/-/TLR12-/- mice were harvested from femurs and hind leg bones. Cells were washed with RPMI medium and resuspended in RPMI medium with 10% FBS, 10 U/mL penicillin, and 10 μg/mL streptomycin (Gibco). For dendritic cell (DC) differentiation, we added 10 ng/mL of recombinant murine GM-CSF (Prepotech, Rocky Hill, NJ), and 10 ng/mL murine recombinant IL-4 (eBioscience, San Diego, CA); for macrophage differentiation, 30% of L929 conditioned medium was added to RPMI medium with 10% FBS. The cells were cultured in 100 × 20 mm dish plates (Costar; Corning Inc., Corning, NY), supplemented with respective conditioned media at days 3 and 6 for DCs, and at day 4 for macrophages. DCs were incubated for 8–9 days and macrophages for 7 days; the cells were then harvested and plated into 24-well plates at 5 × 105 cells/well for protein stimulations or T. gondii infections, followed by ELISA. Cell purity was analyzed by flow cytometry. Eighty-five percent of differentiated dentritic cells were CD11b+/CD11c+, while 94% of differentiated macrophages were CD11b+.
Human embryonic kidney 293T (HEK293T) cells, originally acquired from American Tissue Culture Collection (ATCC, Rockville, MD), were used as an expression tool [59] for TLR2 and TLR4 [45, 60]. The cells grown in DMEM supplemented with 10% FBS (Gibco), and were seeded at 3.5 × 105 cells/mL in 96-well plates (3.5 × 104 cells/well) 24 h before transfection. Then, HEK293T cells were transiently transfected (70–80% confluence) with human TLR2 plasmids as described previously [25] or with CD14, CD36, MD-2 and TLR4 [61] using Lipofectamine 2000 (Invitrogen) with 60 ng of NF-κB Luc, an NF-κB reporter plasmid, and 0.5 ng of Renilla luciferase plasmid, together with 60 ng of each gene of single and multiple glycosylation mutants and of TLR2 WT genes [25]. After 24 h of transfection, the cells were stimulated overnight with positive controls: P3C (Pam3CSK4; EMC Microcollections, Tübingen, Germany), fibroblast stimulating ligand-1 (FSL-1; EMC Microcollections), or LPS Ultrapure (standard LPS, E. coli 0111:B4; Sigma-Aldrich); or with the negative control for cell stimulation (the medium). Cells transfected with empty vectors, incubated either with the medium or with agonists (FSL-1 or P3C), were also assayed; negative results were required for each system included in the study. IL-8 was detected in the culture supernatants. The absence of Mycoplasma contamination in the cell culture was certified by indirect fluorescence staining as described previously [62].
The quantification of human IL-8 and mouse IL-12p40, IL-6, TNF-α, and IL-10 in the supernatant of the cultures was performed by ELISA, following the manufacturer’s instructions (OptEIA set; BD Biosciences, San Jose, CA). Human and murine recombinant cytokines were used to generate standard curves and determine cytokine concentrations. The absorbance was read at 450 nm using the Power Wave-X spectrophotometer (BioTek Instruments).
The pcDNA4/TO-FLAG plasmid was kindly provided by Dr. Dario Simões Zamboni. The pcDNA4-FLAG-TLR2 and pcDNA4-FLAG-TLR4 plasmids were constructed as follows. RNA from a P388D1 cell line (ATCC, Rockville, MD) was extracted and converted to cDNA with Maxima H Minus Reverse Transcriptase (Thermo-Fisher Scientific, Waltham, MA USA) and oligo(dT). TLR2 and TLR4 were amplified from total cDNA from murine macrophages by using Phusion High-Fidelity DNA Polymerase and the phosphorylated primers TLR2_F: ATGCTACGAGCTCTTTGGCTCTTCTGG, TLR2_R: CTAGGACTTTATTGCAGTTCTCAGATTTACCCAAAAC, TLR4_F: TGCTTAGGATCCATGATGCCTCCCTGGCTCCTG and TLR4_R: TGCTTAGCGGCCGCTCAGGTCCAAGTTGCCGTTTCTTG. The fragments were isolated from 1% agarose/Tris-acetate-ethylenediaminetetraacetic acid gel, purified with GeneJET Gel Extraction Kit (Thermo-Fisher Scientific), and inserted into the pcDNA4/TO-FLAG vector by using the restriction enzymes sites for NotI and XbaI (Thermo-Fisher Scientific) for TLR2, and BamHI and NotI (Thermo-Fisher Scientific) for TLR4. Ligation reactions were performed by using a 3:1 insert/vector ratio with T4 DNA Ligase (Thermo-Fisher Scientific) and transformed into chemically competent Escherichia coli DH5α cells. Proper transformants were isolated from LB agar medium plates under ampicillin selection (100 μg/mL) and analyzed by PCR, restriction fragment analysis, and DNA sequencing. All reactions were performed according to the manufacturer’s instructions.
We used the lysate of HEK293T cells transfected (70–80% confluence) with plasmids containing TLR2-FLAG or TLR4-FLAG. After 24 h of transfection, the HEK cells were lysed with a non-denaturing lysis buffer (20 mM Tris, pH 8.0, 137 mM NaCl, and 2 mM EDTA) supplemented with a protease inhibitor (Roche, Basel, Switzerland). After 10 min of incubation on ice, the lysate was subjected to centrifugation (16,000 g, at 4°C for 5 min). The protein content in the supernatant was quantified by the BCA method, aliquoted, and stored at -80°C. For the pull-down assay, 100 μg of the lysate from TLR2-FLAG- or TLR4-FLAG-transfected HEK cells were incubated with 10 μg of rMIC1 or rMIC4 overnight at 4°C. Since these proteins had a histidine tag, the samples were purified on nickel-affinity resin (Ni Sepharose High Performance; GE Healthcare, Little Chalfont, UK) after incubation for 30 min at 25°C and centrifugation of the fraction bound to nickel to pull down the TgMIC-His that physically interacted with TLR-FLAG (16,000 g, 4°C, 5 min). After washing with PBS, the samples were resuspended in 100 μL of SDS loading dye with 5 μL of 2-mercaptoethanol, heated for 5 min at 95°C, and 25 μL of total volume was run on 10% SDS-PAGE. After transferring to a nitrocellulose membrane (Millipore, Billerica, MA), immunoblotting was performed by following the manufacturer’s protocol. First, the membrane was incubated with anti-FLAG monoclonal antibodies (1:2,000) (Clone G10, ab45766, Sigma-Aldrich) to detect the presence of TLR2 or TLR4. The same membrane was then subjected to secondary probing and was developed with anti-TgMIC1 (IgY; 1:20,000) or anti-TgMIC4 (IgY; 1:8,000) polyclonal antibodies and followed by incubation with secondary polyclonal anti-chicken IgY-HRP (1:4,000) (A9046, Sigma-Aldrich) to confirm the presence of rMIC1 and rMIC4.
Bone marrow-derived dendritic cells (BMDCs) and bone marrow-derived macrophages (BMDMs) were infected with WT (Δku80/Δhpt), Δmic1, Δmic1::MIC1-T126A/T220A, Δmic4 and Δmic4::K469M strains (Type I, RH background) recovered from T25 flasks with HFF cell cultures. The T25 flasks were washed with RPMI medium to completely remove parasites, and the collected material was centrifuged for 5 min at 50 g to remove HFF cell debris. The resulting pellet was discarded, and the supernatant containing the parasites was centrifuged for 10 min at 1,000 g and resuspended in RPMI medium for counting and concentration adjustments. BMDCs and BMDMs were dispensed in 24-well plates at 5 × 105 cells/well (in RPMI medium supplemented with 10% FBS), followed by infection with 3 parasites per cell (multiplicity of infection, MOI 3). Then, the plate was centrifuged for 3 min at 200 g to synchronize the contact between cells and parasites and incubated at 37°C. The supernatants were collected at 24 hous after infection for quantification of IL-12p40.
Six-week-old female CD-1 outbred mice were infected by intraperitoneal injection with 50 tachyzoites of RH engineered strains diluted in 500 μl of phosphate-buffered saline. The mice were weighed daily and survival was evaluated.
Bioluminescent detection of firefly luciferase activity was performed at day 5 post-infection using an IVIS BLI system from Xenogen to monitor parasite burden. Mice were injected with 3 milligrams (200 μl) of D-luciferin (PerkinElmer) substrate, and after 5 minutes the mice were imaged for 300 seconds to detect the photons emitted.
The data were plotted and analysed using GraphPad Prism 7.0 software (GraphPad, La Jolla, CA). Statistical significance of the obtained results was calculated using analysis of variance (One-way ANOVA) followed by Bonferroni's multiple comparisons test. Differences were considered significant when the P value was <0.05.
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10.1371/journal.pntd.0002819 | A Thrombomodulin Mutation that Impairs Active Protein C Generation Is Detrimental in Severe Pneumonia-Derived Gram-Negative Sepsis (Melioidosis) | During severe (pneumo)sepsis inflammatory and coagulation pathways become activated as part of the host immune response. Thrombomodulin (TM) is involved in a range of host defense mechanisms during infection and plays a pivotal role in activation of protein C (PC) into active protein C (APC). APC has both anticoagulant and anti-inflammatory properties. In this study we investigated the effects of impaired TM-mediated APC generation during melioidosis, a common form of community-acquired Gram-negative (pneumo)sepsis in South-East Asia caused by Burkholderia (B.) pseudomallei.
(WT) mice and mice with an impaired capacity to activate protein C due to a point mutation in their Thbd gene (TMpro/pro mice) were intranasally infected with B. pseudomallei and sacrificed after 24, 48 or 72 hours for analyses. Additionally, survival studies were performed. When compared to WT mice, TMpro/pro mice displayed a worse survival upon infection with B. pseudomallei, accompanied by increased coagulation activation, enhanced lung neutrophil influx and bronchoalveolar inflammation at late time points, together with increased hepatocellular injury. The TMpro/pro mutation had limited if any impact on bacterial growth and dissemination.
TM-mediated protein C activation contributes to protective immunity after infection with B. pseudomallei. These results add to a better understanding of the regulation of the inflammatory and procoagulant response during severe Gram-negative (pneumo)sepsis.
| Pneumonia and sepsis are conditions in which a procoagulant state is observed, with activation of coagulation and downregulation of anticoagulant pathways, both closely interrelated with inflammation. The protein C (PC) system is an important anticoagulant pathway implicated in the pathogenesis of sepsis. After binding to thrombomodulin (TM), PC is converted into active protein C (APC), mediated via high-affinity binding of thrombin to thrombomodulin (TM) and further augmented via association of the endothelial protein C receptor (EPCR) to the TM-thrombin complex. We studied the role of TM-associated PC-activation during the host response during pneumonia-derived sepsis caused by Burkholderia (B.) pseudomallei, the causative agent of melioidosis, a common form of community-acquired Gram-negative (pneumo)sepsis in South-East Asia and a serious potential bioterrorism threat agent. Mice with an impaired capacity to activate protein C displayed a worse survival upon infection with B. pseudomallei, accompanied by increased coagulation activation, enhanced lung neutrophil influx and bronchoalveolar inflammation at late time points, together with increased hepatocellular injury. These data further expand the knowledge about the role of the protein C system during melioidosis and may be of value in the development of therapeutic strategies against this dangerous pathogen.
| Thrombomodulin (TM, CD141) is a multifunctional transmembrane glycoprotein receptor expressed on the surface of all vascular cells and various hematopoietic cells involved in activation of various parameters of inflammation and coagulation including protein C (PC), thrombin-activatable fibrinolysis inhibitor (TAFI), complement factors and in high mobility group box-1 (HMGB1) [1], [2]. TM plays a pivotal role in the regulation of coagulation via its capacity to activate PC into active protein C (APC), mediated by high-affinity binding of thrombin to TM [3], [4] and further augmented via association of the endothelial protein C receptor (EPCR) to the TM-thrombin complex [3], [4]. Once dissociated from EPCR, APC serves as an anticoagulant by inactivating coagulation factors Va and VIIIa, together with its cofactor protein S [3], [4]. On the other hand, APC has anti-inflammatory, cytoprotective and anti-apoptotic properties through signaling via G-coupled protease activated receptors-1 (PAR-1) [4]. Futhermore, APC may exert anti-inflammatory effects via PAR-3 [5] and involvement of α3β1, α5β1, and αVβ3 integrins [6], mechanisms that are in part EPCR-independent.
Ample evidence has shown that severe (pneumo)sepsis is accompanied by both activation of a strong proinflammatory response and increased coagulation activation, inadequate anticoagulation and suppression of fibrinolysis [7], [8]. The interplay between inflammation and blood coagulation is considered to be an essential part of host defense against pathogenic bacteria. Indeed, patients with severe sepsis displayed low levels of PC and APC, which correlated with organ dysfunction and an adverse outcome [9], [10]. Preclinical studies investigated the role of endogenous PC during inflammation and sepsis. Mice with decreased PC levels, due heterozygous deficiency for PC, had more severe disseminated intravascular coagulation, increased fibrin depositions and higher levels of proinflammatory cytokines upon intraperitoneal injection with lipopolysaccharide (LPS) [11], while reduced PC levels in mice with genetically modified (low) PC expression strongly correlated with a survival disadvantage after LPS challenge [12]. Furthermore, inhibition of endogenous PC increased the procoagulant response during Escherichia coli peritonitis [13] and H1N1 influenza in mice [14].
Melioidosis is an infectious disease common in Southeast-Asia and Northern-Australia and an important cause of community-acquired pneumonia and sepsis in these areas with mortalities up to 40% despite appropriate antibiotic therapy [15]–[17]. Once a patient is infected by the causative pathogen Burkholderia (B.) pseudomallei, this bacterium spreads rapidly throughout the body resulting in many possible disease manifestations, septic shock being the most severe [15], [16]. Additionally, B. pseudomallei was recently classified as a ‘Tier 1’ disease agent considered to be an exceptional threat to security [18]. Previous research has demonstrated pronounced coagulation activation in patients with culture-proven septic melioidosis together with downregulation of anticoagulant pathways [10], [19]. In particular, PC levels were markedly decreased in these patients [10], [19], correlating with a worse disease outcome [10]. In the present study, we sought to determine the role of TM and in particular its function in endogenous APC generation, in the host defense during pneumosepsis caused by B. pseudomallei.
Pathogen-free 10-week old male WT C57BL/6 mice were purchased from Charles River (Maastricht, The Netherlands). TMpro/pro mice were generated as described [20] and backcrossed eight times on a C57BL/6 background. Homozygous mutant TMpro/pro mice, due to a single amino acid substitution (Glu404Pro) in the Thbd gene, exhibit a decrease of approximately 1000-fold with respect to PC activation and approximately 100-fold with respect to binding of thrombin at physiologic levels of the enzyme [20]. In addition, TMpro/pro mice produce less than 4% of APC in their alveolar space upon intratracheal administration of PC and thrombin [21]. Mice were maintained at the animal care facility of the Academic Medical Center (University of Amsterdam), according to national guidelines with free access to food and water. The Committee on Use and Care of Animals of the University of Amsterdam approved all experiments.
Mice studies were carried out under the guidance of the Animal Research Institute of the Academical Medical Center in Amsterdam (ARIA). All animals were maintained at the animal care facility of the Academic Medical Center (University of Amsterdam), with free access to food and water, according to National Guidelines for the Care and Use of Laboratory Animals, which are based on the National Experiments on Animals Act (Wet op de Dierproeven (WOD)) and the Experiments on Animals Decree (Dierproevenbesluit), under the jurisdiction of the Ministry of Public Health, Welfare and Sports, the Netherlands. The Committee of Animal Care and Use (Dier Experimenten Commissie, DEC) of the University of Amsterdam approved all experiments (Permit number DIX100121-101700)
Experimental melioidosis was induced by intranasal inoculation with B. pseudomallei strain 1026b (750 colony forming units (CFU)/50 µL 0.9% NaCl) as previously described [22]–[25]. The number of mice per group used in each experiment is provided in the Figure Legend. For each experiment all mice were infected at the same time point to avoid variance in the bacterial inoculum. For survival experiments mice were checked every 4–6 hours until death occurred for a maximum of 15 days. Sample harvesting and processing and determination of bacterial growth were done as described [22]–[25].
Bronchoalveolar lavage fluid (BALF) was obtained as described [24]. Total counts of paraformaldehyde (4%)-fixed BALF cells were measured using a Coulter Counter (Beckman Coulter Inc. Brea, CA). Differential counts were determined by FACS (FACSCalibur, Becton Dickson, San Jose, CA) using directly labeled antibodies against Gr-1 (Gr-1 FITC; BD Pharmingen, San Diego, CA) and F4/80 (F4/80 APC; AbD Serotec, Oxford, UK). Neutrophilic granulocytes were defined according to their scatter pattern and Gr-1 positivity. All antibodies were used in concentrations recommended by the manufacturer.
Interleukin (IL)-6, IL-10, IL-12p70, interferon (IFN)-γ, monocyte-chemoattractant protein-1 (MCP-1) and tumor necrosis factor-α (TNF-α) were measured by cytometric bead array (CBA) multiplex assay (BD Biosciences, San Jose, CA) in accordance with the manufacturers' recommendations. Thrombin-antithrombin complexes (TATc; Siemens Healthcare Diagnostics, Marburg, Germany) and D-dimer (Asserachrom D-dimer, Roche Woerden, the Netherlands) were measured with commercially available ELISA kits. Protein levels in BALF were measured using a Bradford-based protein assay (Bio-Rad Laboratories, Hercules, CA). Aspartate aminotranspherase (ASAT) and alanine aminotranspherase (ALAT) were determined with commercial available kits (Sigma-Aldrich, St. Louis, MO), using a Hitachi analyzer (Boehringer Mannheim, Mannheim, Germany) according to the manufacturers' instructions.
Paraffin-embedded 4 µm tissue sections were stained with haematoxylin and eosin (H&E) and analyzed for inflammation and tissue damage as described [22]–[25]. Briefly, all slides were coded and scored by a pathologist blinded for the experimental groups. Lung tissues were scored for the following parameters: interstitial inflammation, necrosis, endothelialitis, bronchitis, edema, pleuritis, presence of thrombi and percentage of lung surface with pneumonia. All parameters were rated separately from 0 (condition absent) to 4 (most severe condition). The total histopathological score was expressed as the sum of the scores of the individual parameters, with a maximum of 24. Granulocyte stainings, using fluorescein isothiocyanate-labeled rat-anti-mouse Ly-6G mAb (BD Pharmingen, San Diego, CA) were done as described previously [23]–[25]. Slides were counterstained with methylgreen (Sigma-Aldrich, St. Louis, MO). The total tissue area of the Ly-6G-stained slides was scanned with a slide scanner (Olympus dotSlide, Tokyo, Japan) and the obtained scans were exported in TIFF format for digital image analysis. The digital images were analyzed with ImageJ (version 2006.02.01, National Institutes of Health, Bethesda, MD) and the immunopositive (Ly6G+) area was expressed as the percentage of the total lung surface area.
Data are expressed as box and whisker plots showing the smallest observation, lower quartile, median, upper quartile and largest observation or as medians with interquartile ranges. Comparisons between groups were tested using the Mann-Whitney U test. For survival studies Kaplan-Meier analyses followed by Log-rank (Mantel-Cox) test were performed. All analyses were done using GraphPad Prism version 5.01 (GraphPad Software, San Diego, CA). P-values<0.05 were considered statistically significant.
To explore whether a decreased capacity to generate APC impacts on survival during severe Gram-negative (pneumo)sepsis caused by B. pseudomallei we infected TMpro/pro and WT mice with 750 CFU of this bacterium and followed them for 15 days (Figure 1). TMpro/pro mice had an accelerated mortality when compared to WT mice: after 3.8 days already 7 out of 16 TMpro/pro mice (44%) had died, whereas the first WT mice did not die until 3.9 days. After the total observation period, 16 out of 18 WT mice had died (89%), while all TMpro/pro mice had passed away (100%) (P<0.05; Figure 1). These results indicate that a reduced capacity to generate APC renders mice more vulnerable for death during Gram-negative (pneumo)sepsis caused by B. pseudomallei.
We have previously shown that in our model of murine melioidosis severe inflammation is associated with marked coagulation activation, which is most prominent at later time points [22]–[25]. To determine whether the increased mortality of TMpro/pro mice was accompanied by alterations in local and systemic coagulation activation of B. pseudomallei, we measured levels of TATc, a well-known marker for coagulation activation, in the lungs and systemically in TMpro/pro and WT mice 24, 48 and 72 hours after infection. In accordance with their detrimental phenotype in the survival study, TMpro/pro demonstrated increased coagulation activation, as reflected by elevated pulmonary and plasma levels of TATc at 24 and 72 hours after infection with 750 CFU B. pseudomallei intranasally (P<0.05 for the differences between WT and TMpro/pro mice, Figure 2A and B). Moreover, when compared to WT mice, TMpro/pro mice had increased lung levels of D-dimer at these time points (P<0.01, Figure 2C). These data show that a point mutation in the TM-gene associated with a decreased capacity to generate APC leads to enhanced coagulation activation during Gram-negative (pneumo)sepsis (melioidosis).
Our model of murine melioidosis is associated with marked bacterial growth locally in lungs with subsequent spreading to distant organs [22]–[25]. To determine whether the increased mortality of TMpro/pro mice was accompanied by alterations in the local and systemic growth of B. pseudomallei, we examined bacterial loads in the lungs (the primary site of infection), liver, spleen and blood (to evaluate the extent of bacterial dissemination) harvested from TMpro/pro and WT mice 24, 48 and 72 hours after infection with 750 CFU of B. pseudomallei. At 48 hours modestly increased bacterial loads were counted in lungs of TMpro/pro mice when compared to WT mice (P<0.05, Figure 3A). However, after 72 hours pulmonary bacterial loads of WT and TMpro/pro mice were similar. Furthermore, no differences in bacterial dissemination could be detected: WT and TMpro/pro mice had similar bacterial loads in spleen (Figure 3B), liver (Figure 3C) and blood (Figure 3D) at all time points. These data demonstrate that TM-mediated APC-generation has a modest and temporary effect on local antibacterial defense during severe Gram-negative (pneumo)sepsis.
Our murine model of melioidosis is associated with severe lung inflammation and damage [22]–[25]. To analyze whether impaired TM-mediated APC generation would impact hereon, we determined histopathological scores of lungs after infection with B. pseudomallei. All mice infected with B. pseudomallei had inflammatory lung infiltrates characterized by interstitial inflammation together with necrosis, endothelialitis, bronchitis, edema, thrombi and pleuritis (Figure 4A–C). Twenty-four hours after infection of 750 CFU of B. pseudomallei the lung histopathology score (as detailed in the Methods section) was significantly increased in TMpro/pro mice when compared to WT mice (P<0.05; Figure 4A–C), while at later time points no differences were seen between both mouse strains. Additionally, we analysed neutrophil recruitment to lung tissue, as it is known that neutrophils play an important role in the host response during melioidosis [16], [17], [26]. For this lung tissues were stained for Ly-6G. Clear neutrophilic infiltrates were seen in both WT and TMpro/pro mice, increasing over time during the course of the experiment. Seventy-two hours after infection, lung tissue of TMpro/pro mice contained significantly more neutrophils than that of WT mice (P<0.01, Figure 4D–F). These data suggest that TM-mediated APC generation reduces neutrophil recruitment and lung pathology during severe Gram-negative (pneumo)sepsis.
Since cytokines and chemokines are important regulators of the inflammatory response to B. pseudomallei [16], [17], [27] we measured pulmonary and plasma levels of TNF-α, IL-6, IL-10, IL-12p70, IFN-γ and MCP-1 (Table 1). Interestingly, early (24 hours) after infection of 750 CFU of B. pseudomallei, TMpro/pro mice showed reduced IFN-γ levels in both lungs and plasma and decreased IL-12p70 levels in lung homogenates, relative to WT mice. In plasma, these differences remained present at 48 hours after infection. During the late phase of the infection (72 hours) most mediator levels were higher in TMpro/pro mice when compared with WT mice, significantly so for lung IL-12p70 and IL-6 concentrations.
Many studies have demonstrated that severe pneumonia may lead to alveolar damage and subsequent alveolar leakage and release of pro-inflammatory parameters [28], [29]. To determine the impact of impaired APC generation on this extra-vascular, intrabronchial compartment, we determined CFU, protein leakage and parameters of inflammation in BALF 72 hours after inoculation of 750 CFU of B. pseudomallei, i.e. shortly before the first deaths occurred and at a time point when lung injury is expected to be at its peak. No differences in bacterial growth (Figure 5A) or total protein content, a marker for alveolar damage (Figure 5B), could be detected in BALF of WT and TMpro/pro mice, nor were there any differences in total cell influx in BALF (Figure 5C). The percentage of neutrophils in BALF of TMpro/pro mice, however, was significantly higher than in WT mice (P<0.01; Figure 5D), which is in accordance with the increased neutrophil influx visualized by Ly6-staining of lung tissue. Moreover, BALF levels of the proinflammatory cytokines IL-6 (Figure 5E) and TNF- α (Figure 5F) we significantly increased in TMpro/pro mice when compared to WT mice (P<0.001 for both cytokines). These results indicate, that during severe Gram-negative (pneumosepsis) intact TM-mediated APC generation limits the proinflammatory response in the alveolar compartment.
Our model of experimental melioidosis is associated with hepatocellular injury as reflected by elevated plasma levels of transaminases [23], [25]. To obtain insight in the possible role of TM-mediated APC generation herein, we measured ASAT and ALAT in plasma of WT and TMpro/pro mice 24, 48 and 72 hours after infection with 750 CFU of B. pseudomallei. Indeed, when compared to WT mice, TMpro/pro mice showed modestly increased levels of plasma ASAT (P<0.01 at 24 and 72 hours; Figure 6A) and ALAT (P<0.05 at 72 hours post-infection; Figure 6B). Taken together, intact TM-mediated APC generation seems to protect against hepatocellular injury during experimental melioidosis.
In the present study we sought to investigate the role of TM and in particular its function in endogenous PC activation during melioidosis, a Gram-negative infection often associated with severe pneumonia and sepsis [15], [16]. Melioidosis, as we have demonstrated by our established mouse model, is characterized by gradual growth of bacteria from the lung followed by dissemination to distant body sites, activation of coagulation and inflammation, tissue injury and death, thereby mimicking the clinical scenario of severe (pneumo)sepsis [22]–[25]. Our data show that impaired TM-dependent conversion of PC into APC is associated with enhanced lethality during experimental melioidosis, accompanied by increased coagulation activation, bronchoalveolar inflammation and hepatocellular damage. These data indicate that the capacity to properly activate endogenous PC contributes to protective immunity during experimental melioidosis.
TM is known to play important roles in coagulation and inflammation, that are largely based on its distinct structural domains, including the lectin-like domain, EGF-like repeats, transmembrane domain and short cytoplasmic tail [1], [2]. The EGF-like repeats play a pivotal role in the PC-system via binding of thrombin, thereby increasing the capacity to generate APC a 100-fold [1], [20]. During sepsis, the expression of TM on endothelial cells is downregulated [30], causing impaired APC-generation that may then affect parameters of coagulation and inflammation important for the host response of the infected individual. To answer our research questions, we used genetically modified mice, TMpro/pro mice. In contrast to Thbd gene-deficient mice, which die in the embryonic stage [31], TMpro/pro mice develop to term and possess normal reproductive performance [20], but have a decreased endogenous APC synthesis ability when compared to WT mice, as was demonstrated both in the circulation [20] and in the alveolar space [21]. Our data showing increased coagulation activation in TMpro/pro mice, as reflected by increased levels of TATc and D-dimer, are fully in accordance with this. Interestingly, previous studies examining the impact of the TMpro/pro mutation on coagulopathy during experimental (pneumo)sepsis induced by the Gram-positive pathogen Streptococcus (S.) pneumoniae or the Gram-negative bacterium Klebsiella (K.) pneumoniae or after intranasal administration of E. coli LPS failed to show differences in TATc in plasma or BALF between TMpro/pro and WT mice [21]. Similarly, in a model of experimental tuberculosis no differences in lung and plasma TATc were detected between WT and TMpro/pro mice [32]. During systemic endotoxemia TMpro/pro mice were reported to have enhanced fibrin deposition in lungs and kidneys in the presence of unaltered plasma D-dimer concentrations [33]. Clearly, the influence of the TMpro/pro mutation on the procoagulant response depends on the type and extent of the inflammatory stimulus.
Besides its anticoagulant properties TM-activated PC also influences the host immune response during sepsis: APC may exert anti-inflammatory, anti-apoptotic and cell-protective effects by proteolytic cleavage of PAR-1 [3], [4]. Indeed, our data demonstrate that impaired APC generation due to a mutation in the Thbd gene resulted in pro-inflammatory effects, as indicated by increased lung pathology at early time points and exaggerated bronchoalveolar inflammation and hepatocellular injury at later time points in TMpro/pro mice. Of interest, the neutrophilic infiltrates measured by Ly-6G staining seem to decrease at 72 hours in WT mice, as opposed to TMpro/pro mice. Remarkably, our results are in contrast with murine models of airway inflammation induced by S. pneumoniae, K. pneumoniae or LPS, in which no differences in the abovementioned parameters for inflammation were seen between WT and TMpro/pro mice [21]. On the other hand, TMpro/pro mice displayed enhanced diabetic nephropathy, in a model of streptozotocin-induced diabetes mellitus, accompanied by glomerular apoptosis, pointing to a detrimental phenotype when endogenous PC activation is impaired [34], while after induction of lung tuberculosis by Mycobacterium (M.) tuberculosis a pro-inflammatory phenotype in TMpro/pro mice was seen [32], comparative to our findings. An obvious explanation for the differences in inflammatory responses observed between these different pathogens and disease conditions is lacking. In contrast to K. pneumoniae, both M. tuberculosis and B. pseudomallei are intracellular organisms, both using the cytosol for survival and escape from anti-bacterial host defense mechanisms. It could be hypothesized that this also influences both generation and inflammatory effects of endogenous APC. Obviously this is area for further research. The coagulation system and APC in particular are of major importance in the host defense against melioidosis, in which both the anti-coagulant and anti-inflammatory function of APC play a major role [24], [35]. It could be hypothesized that during infections with other pathogens such as K. pneumonia or S. pneumonia the relative contribution of the coagulation system to the host response is of less importance than during hypervirulent B. pseudomallei infection in which containment of bacteria at the original site of infection is of utmost importance. In this case it is likely, that deficiency of endogenous APC, as in TMpro/pro mice, has more impact during melioidosis than during infection with other pathogens, which might explain the differences in observed phenotypes between the various pathogens.
In TMpro/pro mice the capacity to generate APC from its precursor protein C is disabled due to a point mutation in the thrombomodulin gene. Therefore, there would be a clear rationale to study exogenously administered APC in our model. However, we have reported very recently that overexpression of APC is detrimental during experimental melioidosis [35]. This is in line with more recent studies in which APC was proven to be ineffective in patients with severe sepsis [36]. Moreover, exogenous recombinant APC has a very short half life, which requires continuous intravenous administration in order to maintain adequate APC levels and to mimic the human situation as much as possible. Obviously, continuous intravenous administration of medication is hard to achieve in freely moving mice.
At late stage infection, shortly before the first deaths occurred, TMpro/pro mice displayed increased local and systemic coagulation activation, increased neutrophil influx and cytokine levels in the lungs, high bacterial loads and -most likely as a consequence thereof- increased end organ damage, as reflected by for example elevated plasma levels of transaminases indicating hepatocellular injury. We therefore hypothesize that the accelerated mortality observed in the TMpro/pro mice is the consequence of a combination of these factors resulting in end-stage multi-organ failure (MOF). In addition, MOF might have induced diffuse intravascular coagulation (DIC) as well, as is often seen in humans with severe sepsis [8]. Indeed, formation of thrombi was observed on histological examination of lung, liver and spleen tissues (data not shown). However, differences between WT and TMpro/pro mice were too small to display any significant differences between groups. Together these data suggest that endogenous APC protects mice against melioidosis induced death by limiting coagulation activation, lung inflammation and MOF.
An important component of the host response to B. pseudomallei is the release of proinflammatory cytokines [17], [27], [37]. Clinical studies in melioidosis patients showed elevated serum levels of TNF-α, IL-6 and IFN-γ [27], [37]. The pro-inflammatory cytokine IFN-γ, produced by cytotoxic T-cells and natural killer cells, has an important protective role in early resistance against B. pseudomallei infection [38]: administration of a neutralizing monoclonal antibody against IFN-γ was associated with marked increases in bacterial loads in the liver and spleen, together with enhanced lethality [38]. Similarly, inhibition of the production of IL-12, one of the predominant inducers of IFN-γ, resulted in increased mortality in the same model [38]. Interestingly, we found decreased levels of IFN-γ and IL12p70 in TMpro/pro mice early after infection. Although a clear explanation for this observation is lacking, it may in part explain the modestly higher bacterial loads in the lungs of TMpro/pro mice at 48 hours post-infection. While we observed marked differences in pro-inflammatory cytokines between WT mice and TMpro/pro mice, no differences in the anti-inflammatory cytokine IL-10 could be observed. Of note, IL-10 concentrations were very low both in WT mice and TMpro/pro mice during murine septic melioidosis which is in line with earlier reports [39], [40].
Our study also has limitations. It should be noted that there is no consensus in the literature over which mouse strain best models the pathology seen in human melioidosis and both BALB/c and C57BL/6 mice have been used [41]–[43]. BALB/c mice have been thought to be more susceptible for B. pseudomallei than C57BL/6 mice, although we and others demonstrated that even after inoculation of a fairly low dose of bacteria (300–750 CFU) C57BL/6 mice develop an acute and severe infection which is lethal in most cases and perfectly mimics acute melioidosis [22], [39], [40], [44]–[46]. The reason for the hypersusceptibility of the BALB/c strain is not known, but Watanabe et al. have reported that BALB/c macrophages express lower beta-glucuronidase, in response to levels of the lysosomal enzyme, macrophage-activating lipopeptide-2 (a synthetic TLR2 ligand) and to E. coli lipopolysaccharide when compared to C57BL/6 macrophages [47]. In humans, beta-glucuronidase deficiency manifests as ‘Sly syndrome’ or mucopolysaccharidosis type VII. The potential association of the BALB/c mouse with an inherited human disease should prompt caution in the interpretation of experiments conducted using this strain.
The current study identifies TM-mediated APC generation as part of the protective host response during melioidosis and is in accordance with recent evidence from our laboratory showing that inhibition of endogenous PC by specific anti-PC antibodies converts a non-lethal model of experimental melioidosis into a lethal model, associated with increased coagulation activation, severe tissue injury and a strongly increased proinflammatory response [24]. Together these data emphasize the importance of adequate APC levels during melioidosis. As such, administration of recombinant human APC hypothetically could be a promising therapeutic agent in melioidosis. However, in 2012 this drug was withdrawn from the market after negative results from the PROWESS SHOCK trial in sepsis patients [36]. Recombinant soluble TM currently undergoes clinical evaluation as an anticoagulant and anti-inflammatory agent in patients with sepsis [48], [49]. It would be of interest to test the effects of soluble TM in experimental (and clinical) melioidosis.
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10.1371/journal.ppat.1003362 | IL-21 Restricts Virus-driven Treg Cell Expansion in Chronic LCMV Infection | Foxp3+ regulatory T (Treg) cells are essential for the maintenance of immune homeostasis and tolerance. During viral infections, Treg cells can limit the immunopathology resulting from excessive inflammation, yet potentially inhibit effective antiviral T cell responses and promote virus persistence. We report here that the fast-replicating LCMV strain Docile triggers a massive expansion of the Treg population that directly correlates with the size of the virus inoculum and its tendency to establish a chronic, persistent infection. This Treg cell proliferation was greatly enhanced in IL-21R−/− mice and depletion of Treg cells partially rescued defective CD8+ T cell cytokine responses and improved viral clearance in some but not all organs. Notably, IL-21 inhibited Treg cell expansion in a cell intrinsic manner. Moreover, experimental augmentation of Treg cells driven by injection of IL-2/anti-IL-2 immune complexes drastically impaired the functionality of the antiviral T cell response and impeded virus clearance. As a consequence, mice became highly susceptible to chronic infection following exposure to low virus doses. These findings reveal virus-driven Treg cell proliferation as potential evasion strategy that facilitates T cell exhaustion and virus persistence. Furthermore, they suggest that besides its primary function as a direct survival signal for antiviral CD8+ T cells during chronic infections, IL-21 may also indirectly promote CD8+ T cell poly-functionality by restricting the suppressive activity of infection-induced Treg cells.
| T cell exhaustion represents a state of T cell dysfunction associated with clinically relevant diseases, such as persistent viral infections or cancer. Although the molecular signature of exhausted T cells has been characterized in detail at the functional and transcriptional level, the immunological mechanisms that lead to T cell exhaustion during chronic infections remain poorly understood. Our present study reports two major findings that illustrate a pathway that contributes to T cell exhaustion during viral infection, and indicate its modulation by both, the pathogen and the host. First, we show that a persistence-inducing virus triggers the massive proliferation of Foxp3+ regulatory T (Treg) cells and demonstrate the potential of Treg cells to promote T cell exhaustion and chronic infection. Second, we identify IL-21 as a crucial host factor that antagonizes this virus-driven expansion of the Treg population in a cell intrinsic manner independent of IL-2. Thus, in addition to its known pre-dominant direct positive effects on antiviral T cells, IL-21 can also alleviate the suppressive activity of Treg cells. Together, these results suggest enhanced Treg cell responses as a mechanism of immune evasion that could be therapeutically targeted with IL-21.
| The immune system has to efficiently eliminate pathogens but simultaneously needs to avoid the potential self-damage and immunopathology caused by excessive immune activation. Therefore, a tight regulation of immune responses is critical for host survival. The subset of CD4+CD25+ regulatory T (Treg) cells exerts key negative regulatory mechanisms of the immune system that prevent autoimmunity and T cell mediated inflammatory disease [1]. Treg cells are best defined by expression of the signature transcription factor forkhead box P3, Foxp3 [2], [3], [4], [5], [6], [7]. Their fundamental role in the maintenance of immune homeostasis and tolerance is well established [8], [9], [10] and unambiguously demonstrated by the severe multi-organ autoimmune disease, allergy and inflammatory bowel disease that develops in Foxp3-deficient mice or patients with immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome [3], [11], [12], [13]. However, the relevance of Treg cell responses for shaping adaptive immunity against pathogens, in particular in the context of chronic infections, refigmains much less understood.
Treg cells potentially have both beneficial and adverse effects on disease outcomes during viral infections. By dampening effector immune responses, Treg cell responses mitigate immunopathology resulting from exaggerated inflammation and tissue destruction during acute [14], [15], [16], [17], or protracted infections [18], [19], [20], [21], [22]. In addition, Treg cells have been shown to support antiviral immunity by modulating T cell migration to the site of infection [15], [23]. Conversely, Treg cells were shown to suppress CD8+ T cell responses in some infections [21], [24], which may prevent immunopathology, but hampers effective pathogen control and ultimately promotes persistent infection [18], [21], [25], [26]. Thus, while Treg cells favorably influence pathogen clearance in many acute infections [14], [15], [16], [23], they seem to negatively regulate CD8+ T cell responses during chronic infections [18], [19], [20], [24], [26]. Furthermore, elevated numbers of Treg cells have also been associated with persistent viral infections in humans [27], [28], [29]. However, to date little is known as to whether Treg cell activation represents a mechanism of immune evasion that facilitates persistence, or about host factors that might regulate Treg cell responses during chronic viral infection.
Many characteristics of persistent viral infections in humans, such as HIV, HCV, or HBV, are also observed during chronic infection of mice with the arenavirus lymphocytic choriomeningitis virus (LCMV). Accordingly, murine models of chronic LCMV infection have been extensively studied and have provided key insights into the molecular mechanisms leading to virus persistence and the associated modulation of adaptive immunity in face of persistent infection. For example, infection with a high dose of the fast-replicating strain LCMV Docile (or Armstrong clone 13) results in virus persistence that is accompanied by a progressive functional impairment and – for some specificities – even leads to deletion of the virus-specific CD8+ T cell response, termed T cell “exhaustion” [30]. Exhausted antiviral T cells gradually lose their capacity to produce antiviral cytokines and to proliferate ex vivo [31], [32], [33]. Similar states of T cell exhaustion have been demonstrated in patients with chronic HIV or HCV infections [34]. CD8+ T cell exhaustion directly correlates with the antigen load, i.e. is enhanced during prolonged or high viral replication. Furthermore, CD8+ T cell exhaustion is also influenced by the sustained expression of several inhibitory receptors [35] and immunomodulatory cytokines, such as IL-10 [36], [37] and TGFβ [38]. Moreover, it can be aggravated by the loss of help or essential cytokines provided by CD4+ T cells. In particular, we and others [39], [40], [41] have recently identified the pro-inflammatory cytokine IL-21 as an essential CD4+ T cell-produced factor that prevents CD8+ T cell exhaustion during chronic viral infection.
Here we have investigated the role of Treg cell responses in chronic LCMV infection and found that the persistence-prone strain Docile (DOC) induces a substantial dose-dependent expansion of the Treg cell population that directly correlated with the magnitude of the virus inoculum. The expanded Treg cell population massively impaired the functionality of antiviral CD8+ T cells, interfered with virus clearance, and thereby promoted chronic viral infection. Strikingly, enforced expansion of the Treg cell population with IL-2/anti-IL-2 immune complexes (IL-2ic; [42]) permitted the persistence of LCMV-DOC already at much lower infection doses and even predisposed to chronic infection with an LCMV-strain that generally fails to establish persistence in immunocompetent mice. Importantly, this infection-triggered expansion of the Treg cell population was greatly inhibited by IL-21R signaling in Treg cells, thus defining a novel role for IL-21 in preventing T cell exhaustion and viral persistence via limiting virus-driven Treg cell proliferation.
We examined the role of Foxp3+ Treg cell responses in chronic viral infections in mice infected with the fast-replicating strain of LCMV-DOC. LCMV-DOC is characterized by its potential to establish a chronic, persistent infection depending on the size of the virus inoculum. Although low doses of LCMV-DOC (2×102–2×103 PFU) induce potent CD8+ T cell-mediated immunity and are cleared in immunocompetent hosts, infection with intermediate and high doses (2×104–2×106 PFU) results in virus persistence due to the exhaustion of the virus-specific CD8+ T cell response [30], [31]. By choosing different virus inocula, we determined dynamics of the Treg cell population during an acute, resolving infection and a chronic infection. After an initial decline of Treg cell numbers between days 0–7 that was independent of the dose of infection and the LCMV strain used (i.e. DOC or WE) (Figure 1A and data not shown), we observed a striking dose-dependent expansion and recovery of the Treg cell population in LCMV-DOC infected mice (Figure 1B, C) that directly correlated with the ability of the virus to establish persistence (Figure 1D). Compared to animals infected with a low dose of LCMV-DOC (200 PFU), mice infected with intermediate (2×104 PFU) or high (2×106 PFU) virus doses exhibited markedly increased proportions of Treg cells in spleen and peripheral organs that amounted up to 20% of all CD4+ T cells at 15 days post infection (Figure 1B, C). At this time, infectious virus had been cleared from the blood and organs of all mice infected with 200 PFU and some of the animals infected with 2×103 PFU of LCMV-DOC (Figure 1D and data not shown); while mice infected with persistence-inducing doses (2×104–2×106 PFU) of LCMV-DOC still exhibited high viral titers in blood, spleen, liver and kidney, and subsequently failed to control the infection (Figure 1D and data not shown). As expected, we also detected a dose-dependent reduction in the frequency of gp33-specific CD8+ T cells, which was indicative of the progressing exhaustion of the T cell response, particularly in the spleen (Figure 1E), and the resulting inability to resolve the infection (Figure 1D).
Consumption and bioavailability of IL-2 by Treg cells has been suggested to restrict IL-2-dependent effector T cell differentiation and expansion [43], [44], [45], [46]. Conversely, IL-2-driven expansion of CD8+ T cell expansion during an immune response can occur at the expense of Treg proliferation/survival [47], [48]. Moreover, IL-21 has been suggested to interfere with Treg-mediated suppression by inhibition of IL-2 [49]. To monitor IL-2 and IL-21 production during acute and chronic infection, we took advantage of Il2-emGFP-Il21-mCherry dual reporter transgenic mice [50] that were infected with low dose (i.e. 200 PFU) LCMV-WE or high dose (i.e. 2×105 PFU) LCMV-DOC. Both Il2 (GFP) and Il21 (mCherry) were predominantly expressed by CD4+ compared to CD8+ T cells and frequencies increased from days 7–15 post infection with low dose LCMV-WE (Figure 1F–H). Interestingly, chronic infection with high dose LCMV-DOC potently suppressed Il2-GFP expression, but did not affect Il21-mCherry expression by CD4+ T cells (Figure 1F–H).
Together, these data demonstrate a direct correlation between the size of the virus inoculum, CD8+ T cell dysfunctionality, virus persistence and the expansion of Treg cells, thus indicating a potential contribution of Treg cells to the impaired T cell function and the induction of viral persistence.
IL-21 receptor (IL-21R) signaling is essential for the maintenance and sustained functionality of antiviral T cell responses in chronic infections [39], [40], [41]. As a consequence, IL-21R−/− animals have an impaired control of LCMV-DOC, and exhibit much higher virus loads [40]. In LCMV-DOC infected WT mice, the main Treg cell expansion was observed between days 10 to 20 post infection, peaking at 15 days before returning to almost naïve levels by five weeks post infection (Figure 2A, B). Strikingly, this virus-driven Treg cell expansion was much more pronounced and longer lasting in mice lacking IL-21R expression, which suggested that the pro-inflammatory cytokine IL-21 restricted the proliferation of Treg cells in viral infections (Figure 2A, B). Indeed, the Treg cells of naïve and LCMV-DOC infected WT mice expressed the IL-21R as assessed by flow cytometry (Figure 2C). We thus investigated whether the increased Treg expansion observed in IL-21R−/− mice represented a direct inhibitory effect of IL-21 on Treg cells or rather was related to the increased viral replication in these mice. To address this issue, we generated mixed bone marrow (BM) chimeras by reconstituting lethally irradiated WT (CD45.1+) mice with a 1∶1 ratio of WT (CD45.1+) and IL-21R−/− (CD45.2+) BM, and evaluated their Treg cell responses to infection with LCMV-DOC. Analysis of naïve BM chimeras confirmed similar reconstitution efficacy of the CD8+ and CD4+ T cell populations including Treg cells from both WT and IL-21R−/− donor BMs at 8 weeks after BM transfer (Figure 2D, and data not shown). However, upon infection the population of IL-21R−/− Treg cells expanded 3-fold over that of WT Treg cells to represent 30% versus 10% of all CD4+ T cells, respectively (Figure 2E–G). Since this augmented proliferation of IL-21R−/− Treg cells was detected side by side to WT Treg cells in the same animals and identical viral loads, it clearly established the direct inhibitory effect of IL-21 on Treg cells in vivo. Nevertheless, we considered that the higher Treg cell numbers in infected IL-21R−/− mice could not only result from the absence of inhibitory IL-21R signaling but might also indicate a compensatory proliferation to overcome a potential functional deficit of the IL-21R−/− Treg cells. To exclude the latter possibility, we isolated CD4+CD25+ GFP+ Treg cells from WT DEREG and IL-21R−/− DEREG mice by FACS-sorting 15 days post infection with 2×106 PFU LCMV-DOC, and compared their suppressive activity in a classical T cell inhibition assay. As shown in Figure 2H, both WT and IL-21R−/− Treg cells comparably inhibited the proliferation of anti-CD3/CD28–stimulated naïve CD25negCD4+ T cells in vitro, suggesting a normal function of IL-21R−/− Treg cells. Thus, the increased expansion of IL-21R−/− Treg cells in LCMV-DOC infected mice highlighted an important inhibitory role of IL-21 in restraining Treg cell expansion during chronic LCMV infection. Except for a slightly reduced CD25 expression, IL-21R−/− Treg cells were comparable to WT Treg cells with respect to the expression or characteristic Treg cell surface markers (Figure 2I). We did not detect any IL-10 producing or gp61-specific Treg cells in infected mice, suggesting that the suppressive activity of Treg cells in LCMV-DOC infection involved neither IL-10–mediated suppression nor virus-specific Treg cells (Figure 2J and data not shown).
IL-6 has been suggested to regulate the balance between Treg and pro-inflammatory Th17 cell responses [51], [52] similar to IL-21 [53]. While we have previously shown that IL-17–producing CD4+ T (Th17) cells were barely detectable in LCMV-DOC infection [40], it remains possible that IL-21 inhibits Treg cell expansion by regulation of IL-6. However, comparing IL-6−/− and WT mice we found no differences in Treg cell expansion (Figure 2K), antiviral CD8+ and CD4+ T cell responses (Figure 2L, M), and virus titers (Figure 2N) up to day 30 post infection with 2×104 PFU LCMV-DOC. Together with the Treg cell-intrinsic negative IL-21R−/− signaling observed in WT:IL-21R−/− mixed BM chimeras, these data suggest that IL-21 restricted the virus-driven Treg cell expansion in LCMV-DOC infection independently of IL-6 signaling.
To directly evaluate the impact of Treg cells on viral persistence, we next sought to analyze antiviral T cell responses and viral clearance in the absence of Treg cells. We therefore studied LCMV-DOC infection using the DEREG mouse model, in which Treg cells can be ablated by diphtheria toxin (DT) treatment due to transgenic expression of a high affinity DT receptor under control of the Foxp3 promoter [54]. DEREG and nontransgenic WT control mice were treated with DT and infected with 2×105 PFU LCMV-DOC on day 0, and the DT treatment was continued throughout the experiment (Figure 3A). Since a single DT injection depleted Treg cells in naïve mice for 3 days, we injected DT every 3 days to achieve complete Treg cell ablation. While this treatment effectively depleted all Foxp3+ Treg cells in naïve DEREG mice ([54], and data not shown), we consistently observed in LCMV-DOC infected DEREG mice the emergence of a residual GFPnegFoxp3+ Treg cell population not depleted by DT, presumably due to lacking expression of the GFP-DTR fusion protein (Figure 3B). Compared to DT-treated WT controls, Treg cell-depleted DEREG mice exhibited a greatly enhanced morbidity in response to high dose LCMV-DOC infection, as indicated by an increased weight loss (Figure 3C). As a result, a significant number of DT-treated, LCMV-DOC–infected DEREG mice had to be prematurely removed from the experiment and euthanized, whereas no equivalent morbidity was observed in DT-treated WT mice (Figure 3D). Thus, Treg cell depletion – even if not absolute – substantially aggravated the disease severity in LCMV-DOC–infected DEREG mice. Depletion of Treg cells did not affect the numbers of virus-specific CD8+ T cells, as indicated by the percentage of gp33-specific CD8+ T cells in spleens and livers (Figure 3E). However, Treg cell-depletion to some extent restored the functionality of the antiviral CTL and significantly increased the frequencies of gp33-specific and overall splenic CD8+ T cells producing IFN-γ upon restimulation in vitro (Figure 3F). In comparison, Treg cell-depletion did not enhance frequencies of IFN-γ–producing virus-specific CD4+ T cells (Figure 3G). In spite of partly restoring CD8+ T cell cytokine responses, Treg cell depletion did not influence virus control, and LCMV-DOC replicated to comparably high levels in the blood and organs of DT-treated WT and DEREG mice at 15 days post infection (Figure 3H). Thus, Treg cells appeared to inhibit the functionality rather than the expansion of antiviral T cells. Consistent with the observed onset of immunopathology, the effect of Treg cell depletion on the antiviral T cell response was more pronounced at 10 days post infection (Figure 3I–M). Depletion of Treg cells resulted in higher percentages of gp33-specific CD8+ T cells (Figure 3J) and increased percentages of cytokine-producing antiviral CD8+ and CD4+ T cells (Figure 3K, L), yet did not affect viral titers (Figure 3M).
We next assessed the impact of increased Treg cell numbers in IL-21R−/− mice and treated both IL-21R−/− and WT mice with DT between days 8 to 15 post infection with 2000 PFU LCMV-DOC (Fig. 4A). Although Treg cells were considerably (but not entirely) depleted, frequencies of gp33-specific CD8+ T cells remained unchanged (Figure 4B, C) similar to the results obtained by depletion through the entire course of infection (Figure 3B, E). However, Treg cell depletion partially restored frequencies of IFN-γ-producing gp33-specific cells in IL-21R−/− mice to levels found in WT mice (Figure 4D). Furthermore, Treg cell depletion lowered virus titers significantly in liver and lung of IL-21R−/− mice, although viral loads in spleen and kidney remained unaffected (Figure 4F).
Whether the differences in antiviral CD8+ T cells were too small to result in better virus control or whether the failure to detect differences in viral titers (Figure 3H, M) has to be attributed to the suppressive activity of the residual Treg cells that resisted DT depletion (Figures 3B and 4B) remains to be clarified. Regardless, our results establish a link between the functional impairment of the CD8+ T cell response and the elevated Treg cell levels observed during chronic infection in absence of IL-21.
We next examined the potential of Treg cells to limit the antiviral immune response and to promote virus persistence in a gain of function approach. For this purpose, we injected immune complexes (ic) comprised of recombinant IL-2 and the anti-IL-2 antibody JES6-1 [42], to selectively expand the subset of Foxp3+CD4+ Treg cells in vivo (Figure 5A). In naïve mice, three injections of IL-2ic drastically expanded the Treg population to represent 40–50% of all CD4+ T cells in blood, spleen and liver within 5 days after the first injection (Figure S1 and Figure 5B). Similarly, IL-2ic treatment triggered a pronounced expansion of Treg cells in mice infected with 2×103 PFU LCMV-DOC, established stably elevated levels of Treg cells in blood for at least 30 days (Figure 5B), and thus appeared comparable to that observed during high dose LCMV-DOC infection. The Treg cell population of infected, IL-2ic-treated mice was fully comparable to that of untreated, infected mice, with respect to cell surface expression of FR4, GITR, CD103 and CD25 (Figure 5C) as well as TCR–Vβ profiles (data not shown). The IL-2ic-stimulated expansion of the Foxp3+ Treg cell population profoundly interfered with generation and maintenance of gp33-specific CD8+ T cells, CD62L downregulation, and their capacity to produce IFN-γ and TNF-α as measured in spleen and liver at days 15, 30, and 65 post infection (Figure 5D–G, and Figure S2), which is reminiscent of the state of exhaustion that usually coincides with viral persistence in high dose LCMV-DOC infection. Accordingly, this long lasting impairment of antiviral T cells in presence of the enhanced Treg cell expansion prevented IL-2ic–treated animals from controlling a low dose LCMV-DOC infection. While infectious virus was readily cleared from the blood and most organs in control mice within 15 days, IL-2ic-induced Treg cell expansion resulted in a failure to clear virus in spleens, livers, kidneys and lungs for more than 2 months (Figure 5H–J). Notably, IL-2ic expanded Treg cells also impaired antiviral CD8+ T cell effector responses and viral clearance of low dose LCMV-WE infection, which is otherwise rapidly cleared irrespectively of the viral inoculate (Figure S3).
Taken together, the experimental expansion of Treg cells recapitulated both the long lasting functional impairment of the antiviral T cell response and the viral persistence that characterize high-dose LCMV-DOC infection in the setting of a low dose inoculum, and thus emphasized the remarkable potency of Treg cells to facilitate persistent viral infections. While Sprent and colleagues have clearly shown that IL-2:IL-2mAb (JES6-1) primarily target high affinity IL-2R+ Treg cells and have minimal effects on low affinity IL-2R+ naïve and memory CD8+ T cells [42], we cannot completely rule out the possibility that the IL-2:IL-2mAb also target high affinity antiviral effector CD8+ T cells resulting in terminal differentiation and exhaustion. However, this scenario appears unlikely considering the time of treatment with the IL-2:IL-2mAb complexes (i.e. days 0–2), their short half-life (i.e. 4 h) [42], and the normal expansion of antiviral CD8+ T cells until day 7.
The above data demonstrate the differential regulation of the Treg cell population during viral infection by IL-2 and IL-21. To better understand counter-regulation of Treg cells by IL-2 and IL-21 in the absence of confounding virus dynamics, we delivered the IL-21 gene by hydrodynamic injection to IL-2ic treated mice in the absence of viral infection (Figure 6). Indeed, IL-21 significantly inhibited IL-2ic driven expansion of Treg cells. Similar results were obtained by co-injection of an engineered IL-21-Fc fusion protein together with IL-2ic. Together, these data indicate that IL-21 interferes with IL-2 driven expansion of Treg cells to optimize antiviral effector T cell responses.
T cell exhaustion represents a state of T cell dysfunction associated with clinically relevant diseases, such as persistent viral infections or cancer. Even though the molecular signature of exhausted T cells has been characterized in detail at the functional and transcriptional level [32], [34], we are only beginning to understand the immunological mechanisms that support or counteract the development of T cell exhaustion during chronic infections [35]. In this study we report two major findings that establish a pathway of T cell exhaustion mediated by Treg cells during viral infection, and indicate its modulation by both, the pathogen and the host. First, we show that a persistence-inducing virus triggers the massive proliferation of Treg cells between days 9–20 post infection and demonstrate the potential of Treg cells to promote T cell exhaustion and chronic infection. Second, we identify IL-21 as a crucial host factor that antagonizes this virus-driven expansion of the Treg cell population. Together, these results suggest enhanced Treg cell responses as a mechanism of immune evasion that could be therapeutically targeted with IL-21.
Treg cells are essential for immune homeostasis and T cell tolerance [8], [9], yet their contribution to anti-infectious immune responses is poorly defined. In the setting of persistent viral infections that was investigated here, Treg cells appear to down-regulate antiviral T cell responses, and thus prevent the potentially lethal immunopathology caused by prolonged immune activation in presence of highly replicating virus (Figure 3). Supporting this notion, clinical studies have associated high numbers of Treg cells to chronic infection with HIV [29], HCV [28], [55], or HBV [56], whereas low Treg cell numbers have been reported for elite HIV controllers [57], which collectively suggests that Treg cells modulate the equilibrium between host immune response and persisting virus [27][34]. Our study has now recapitulated these observations in a murine model of persistent viral infection and applied experimental manipulation of the Treg cell response to define the direct link between virus-induced Treg cell expansion and T cell exhaustion. Our initial observation that titrated doses of LCMV-DOC induce graded degrees of Treg cell proliferation and T cell exhaustion allowed us to assess the impact of such virus-induced Treg cell expansion under “non-persisting conditions” using the identical LCMV strain. Although the expansion of Treg cells with IL-2ic achieved very high Treg cell numbers, it is important to note that this level of Treg cell expansion was fully comparable to that induced with LCMV-DOC at high doses or in IL-21R−/− mice. The Treg cell responses triggered by LCMV-DOC in presence or absence of IL-2ic treatment did not phenotypically differ with respect to their cell surface marker expression and exhibited TCRβ profiles similar to that described for LCMV clone 13 infection [58]. Thus, the IL-2ic-elicited Treg cell proliferation appeared to truly mimic the physiological Treg cell expansion during chronic LCMV-DOC infection.
These experiments exposed two facets of the antiviral Treg cell response in LCMV-DOC infected mice that are central for our understanding of the role and potential of Treg cells within antiviral and anti-tumoral responses. First, the increased morbidity in Treg-depleted DEREG mice infected with LCMV-DOC clearly revealed the critical role of Treg cells for preventing lethal immunopathology caused by potent immune responses to persisting antigen. Second, both the depletion as well as the gain of function approach demonstrated that Treg cells primarily modulate the functionality (e.g. cytokine response, antiviral activity) of antiviral T cells rather than influencing their priming consistent with an earlier report showing compromised cytolysis but no defect in priming, proliferation and motility of regulated CTLs [59]. This finding is especially promising, since it implies that the primed but exhausted antiviral T cells present in chronically infected subjects could be therapeutically rescued by removal of Treg cell-mediated suppression.
Dynamics of regulatory and effector T cell populations in homeostasis and during an immune response are very sensitive to the availability of IL-2. Competition for IL-2 between effector and regulatory T cells has been suggested to control tolerance and immunity or the outcome of infectious disease. Associated with the rapid expansion of virus-specific CD8+ T cells in the early phase of infection between days 0 to 8, we observed a remarkable drop in Treg cells below naïve levels irrespective of the LCMV inocula and strain used. This has also been observed in other infections and suggested to be due to consumption of IL-2 by expanding CD8+ T cells and required for efficient clearance of the invader [47], [48]. While this appears feasible, it should be noted that expansion of CD8+ T cells in the acute phase of LCMV infection is independent of IL-2 [60], [61] and that hyper-proliferation of Treg cells between days 10–20 in chronic LCMV infection was associated with potent suppression of IL-2 production by CD8+ T cells (Figure 1F, G) arguing that IL-2 availability does not sufficiently explain cross-regulation of effector CD8+ T cells and Treg cell proliferation in acute and chronic LCMV infection. In vitro experiments suggested that IL-21 could inhibit Treg cells by suppression of IL-2 production in CD4+ effector T cells [49]. However, our results in the WT:IL-21R−/− mixed BM chimeric mice demonstrate that IL-21 inhibits Treg cell expansion directly in a cell intrinsic manner (Figure 2E–G). The finding that expansion of Treg cells induced by IL-2ic treatment was impaired by simultaneous (hydrodynamic) overexpression of the IL-21 gene (Figure 6) further supports this conclusion. Thus, IL-2 and IL-21 exert opposing activities on Treg cells, while they cooperate in driving effector and memory T cell responses, which adds another level of complexity to theoretical and experimental models addressing the dynamics of Treg cells and effector T cells [45].
Amongst the known Treg cell effector molecules, IL-10 has been shown to support the functional impairment of T cell responses during chronic infection with LCMV clone 13 [36], [37]. However, we were unable to detect any IL-10-producing Treg cells in LCMV-DOC infected mice (Figure 2J). Furthermore, IL-10 blocking antibodies or genetic IL-10-deficiency did not prevent T cell exhaustion and viral clearance in response to LCMV-DOC, in contrast to LCMV clone 13 infection [62]. The exhausted T cells in chronically LCMV infected mice have been shown to upregulate expression of several co-inhibitory receptors, e.g. PD-1 and Tim3, which contribute to T cell exhaustion in the LCMV model [63], [64] and in human HIV patients [65], [66]. It will thus be important to test whether these pathways are involved in the Treg cell-induced T cell exhaustion described in our study. Though a detailed characterization is beyond the scope of the current analysis, the experiments described in this manuscript will provide the framework for further mechanistic studies.
IL-6 and IL-21 have similar activities and interact in the cross-regulation of inducible Treg and Th17 cell development in vitro and in vivo depending on the experimental model [51], [52], [53], [67], [68]. Interestingly, while IL-6 has recently also been shown to be essential for viral control by enhancing follicular T helper cell responses at late stages of chronic infection [69], virus titers and Treg numbers were comparable in LCMV-DOC infected IL-6−/− and WT mice up to day 30 post infection (Figure 2K–N) [69]. Therefore, during chronic LCMV-DOC infection, the inhibition of virus-induced Treg cell expansion is a distinct function of IL-21, which is not accompanied by elevated IL-17 production of CD4+ T cells [40].
The importance of cell-intrinsic IL-21R signaling for the maintenance of CD8+ T cell functionality has been well documented [39], [40], [41] and is considered as the primary effect of IL-21 promoting the immune control of chronic viral infections. However, the present report clearly demonstrates that besides this direct function on CD8+ T cells, IL-21 also efficiently restricts the virus-induced expansion of the Treg population in a cell intrinsic manner. It remains to be clarified to which extent the direct effects of IL-21 on CD8+ T cells and its indirect effects on CD8+ T cells via the inhibition of Treg cells differentially contribute to the overall protective function of IL-21 in chronic viral infections considering that we only achieved limited recovery of T cell functionality (e.g. regain of cytokine production) and improved viral clearance only in lung and liver but not spleen and kidney of IL-21R−/− mice by Treg depletion. It should be noted, however, that, for reasons unknown, a fraction of (GFPnegFoxp3+) Treg cells resisted depletion by diphtheria toxin during LCMV infection in both IL-21R−/− and WT mice, although Treg depletion in naive mice was almost complete (>98%). It remains to be investigated whether the undeletable GFPnegFoxp3+ cells can compensate for the deleted Treg cells or represent a subpopulation of Treg cells that is responsible for maintenance of regulatory activity in LCMV-infected DEREG mice.
Regardless, our findings suggest a dual importance of IL-21 for preventing T cell exhaustion during chronic viral infections, and demonstrate that IL-21 in addition to its known direct effects on antiviral T cells [39], [40], [41] also partially alleviates the suppressive activity of Treg cells. Notably, in a model of acute lung infection, it was recently demonstrated that IL-21R−/− mice are protected from fatal lung immunopathology induced by pneumonia virus [70]. It is tempting to speculate that IL-21 might aggravate immunopathology by suppression of Treg cells in this infection model.
In summary, our data support the concept of virus-induced Treg cell expansion as an active immune evasion strategy, and thus highlight a novel pathway by which viruses exploit regulatory mechanisms of the immune system to establish persistent infection.
In view of the relevance to human disease these results have direct therapeutic implications and suggest strategies that boost IL-21 signaling in T cells as novel treatment options for chronic viral infections and cancer.
The LCMV strains WE and DOC were originally provided by Rolf Zinkernagel (University of Zurich, Switzerland) and were propagated on L929 or MDCK cells, respectively. C57BL/6 WT and IL-6−/− mice [71] were from Charles River Inc. SMARTA-2 mice (expressing a transgenic TCR specific for LCMV-GP 61–80; [72] and IL-21R−/− mice [73] were bred locally. DEREG-mice [54] were kindly provided by Tim Sparwasser (TWINCORE, Hannover, Germany) and crossed with the IL-21R−/− strain at our facility. Il21-mCherry/Il2-emGFP dual-reporter transgenic mice [50] were kindly provided by Warren Leonard, National Institutes of Health, Bethesda, MD, USA. Mice were housed in individually ventilated cages under specific pathogen free conditions at BioSupport AG (Zurich, Switzerland). For the generation of BM chimeras, recipient mice were lethally irradiated (9.5 Gy, using a cesium source) one day before reconstitution with 1×107 CD4/CD8-depleted (Miltenyi Biotec) BM cells. Mice were infected i.v. with the indicated virus doses.
Ethics statement: All animal experiments were approved by the local animal ethics committee (Kantonales Veterinärsamt Zürich, licenses 217/2008 and 113/2012), and performed according to local guidelines (TschV, Zurich) and the Swiss animal protection law (TschG).
All cell lines were originally obtained from the American Tissue Culture Collection (ATCC). Chemicals were purchased from Sigma-Aldrich except were otherwise stated. PE- and APC-conjugated peptide-MHC class I tetramers (H-2Db/gp33-41) were generated as described [74] or kindly provided the NIH tetramer core facility. The LCMV-GP peptides gp33-41 (KAVYNFATM) and gp61-80 (GLNGPDIYKGVYQFKSVEFD) were bought from Mimotopes. The following antibodies (all eBioscience unless otherwise stated; clone names given in parentheses) were used for flow cytometry: FITC-labeled anti-CD4 (L3T4), anti-CD62L (MEL-14); PE-labeled anti-CD4 (GK1.5; conjugated in our laboratory), anti-CD8α (53-6.7; BioLegend), anti-CD25 (PC61), anti-GITR (DTA-1; BioLegend), anti-CD103 (2E7), anti-FR4 (12A5; BioLegend), anti-IL21R-biotin (4A9) – Streptavidin-RPE (BioLegend) and anti-TNF-α (MP6-XT22); PerCP-labeled anti-CD4 (RM4-5; BioLegend), anti-CD45.1 (A20; BioLegend), anti-CD8 (53-6.7; BD); APC-labeled anti-CD4 (GK1.5), anti-CD127 (SB/199; Biolegend), anti-CD8 (53-6.7; BioLegend), anti-CD45.2 (104), anti-IL21R (4A9; BioLegend), anti-Foxp3 (FJK-16S), anti-IFN-γ (XMG1.2; BioLegend), anti-IL-2 (JES6-5H4), anti-IL-10 (JES5-16E3).
In depletion experiments, DEREG and WT control mice were i.p. injected with DT (Merck) diluted in PBS. After an initial dose of 200 ng DT, mice were treated with 100 ng DT every third day unless otherwise indicated. To boost Treg cells, mice received 3 daily i.p. injections of IL-2ic generated from carrier-free recombinant mouse IL-2 and anti-IL-2 mAb (JES6-1A12; both from eBioscience) as described [42].
Tetramer and antibody staining was performed on blood cells and single cell suspensions prepared from organs. Spleens and kidneys were passed through a 70 µM cell strainer to obtain single cell suspensions. Livers were first dissected into small pieces, and then passed through a cell strainer before lymphocytes were purified by Lympholyte M gradient centrifugation (Cedarlane Laboratories Ltd.). Blood samples were pretreated with red blood lysis buffer (155 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA, pH 7) for 10 min at RT. Cells were incubated with anti-CD16/CD32 mAb (2.4G2) to block FcγR. For surface staining, cells were incubated at RT with peptide MHC I tetramers in FACS buffer (FB; PBS containing 0.5% BSA) for 15 min followed by addition of the relevant surface antibodies and incubation for additional 20 min at 4°C. Cytokine-production by T cells was assessed using intracellular cytokine staining of single cell suspensions that had been stimulated in presence of 2 µg/ml Monensin with either 1 µM specific peptide or 100 ng/ml PMA and 1 µg/ml Ionomycin for 4 hours in vitro. The cells were surface-stained, fixed with 4% Formaldehyde in PBS and permeabilized with permeabilization buffer (FB containing 1% Saponin). Intracellular staining was then performed in permeabilization buffer at 4°C for 20 minutes. After 2 washes with permeabilization buffer, cells were resuspended in FB. All samples were acquired on a FACSCalibur with CellQuest software (both BD Biosciences) and analyzed using the FlowJo software (Tree Star Inc.).
Blood samples were obtained from LCMV-infected mice at indicated times, diluted 5-fold in MEM (5% FCS) containing 50 U.I. of Liquemin (Drossapharm) and frozen. Organs were collected in 1 ml MEM (5% FCS) and smashed with a Tissue Lyser (Qiagen). Samples were stored at −80°C until further analysis by plaque forming assay [75].
Responder CD4+ T cells were purified from naïve spleens by positive MACS separation (Miltenyi Biotec) and labeled with 25 µM CFSE (Molecular Probes, C-1157) at a density of 106 cells/ml in PBS containing 0.5% BSA for 7 min at RT. The labeling reaction was stopped with pure FCS and cells were washed twice with IMDM containing 10% FCS. As suppressor cells, CD25+ Treg cells were FACS-sorted from MACS-purified CD4+ T cells isolated from LCMV-DOC infected DEREG and IL-21R−/− DEREG mice. CFSE-labeled responder CD4+ T cells (1×105/well) and sorted Treg cells were then incubated at defined responder/suppressor ratios (1∶1, 2∶1, 4∶1) in RPMI (10% FCS, 50 µM β-ME and 100 U/ml IL-2) for 6 days in the presence of 5×106 anti-CD3/CD28-coated (both eBioscience) latex beads.
Mouse IL-21 coding sequence was amplified by PCR and linked with hIgG1 Fc domain and cloned into pLIVE in vivo expression vector (Mirus Bio). Endotoxin-free plasmid DNA (100 µg) was injected i.v. in PBS in a volume equal to 10% body weight (0.1 ml/g) within 5 s. As a control, a hIgG1 expression vector was injected. To supplement IL21, mice received i.p. injections of 2 µg recombinant IL-21-hIgG1 fusion protein (kindly provided by Daniel Christ, Garvan Institute for Medical Research, Sydney, Australia) or PBS two times daily.
Data are shown as average ±SEM. Statistical analysis was performed with the unpaired two-tailed t–test (except for Fig. 1B–E) using the Prism 4.0 software (GraphPad Software). Differences were considered significant for p<0.05 and were denoted as *, p<0.05; **, p<0.01; ***, p<0.001.
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10.1371/journal.ppat.1006226 | The transcriptome of HIV-1 infected intestinal CD4+ T cells exposed to enteric bacteria | Global transcriptome studies can help pinpoint key cellular pathways exploited by viruses to replicate and cause pathogenesis. Previous data showed that laboratory-adapted HIV-1 triggers significant gene expression changes in CD4+ T cell lines and mitogen-activated CD4+ T cells from peripheral blood. However, HIV-1 primarily targets mucosal compartments during acute infection in vivo. Moreover, early HIV-1 infection causes extensive depletion of CD4+ T cells in the gastrointestinal tract that herald persistent inflammation due to the translocation of enteric microbes to the systemic circulation. Here, we profiled the transcriptome of primary intestinal CD4+ T cells infected ex vivo with transmitted/founder (TF) HIV-1. Infections were performed in the presence or absence of Prevotella stercorea, a gut microbe enriched in the mucosa of HIV-1-infected individuals that enhanced both TF HIV-1 replication and CD4+ T cell death ex vivo. In the absence of bacteria, HIV-1 triggered a cellular shutdown response involving the downregulation of HIV-1 reactome genes, while perturbing genes linked to OX40, PPAR and FOXO3 signaling. However, in the presence of bacteria, HIV-1 did not perturb these gene sets or pathways. Instead, HIV-1 enhanced granzyme expression and Th17 cell function, inhibited G1/S cell cycle checkpoint genes and triggered downstream cell death pathways in microbe-exposed gut CD4+ T cells. To gain insights on these differential effects, we profiled the gene expression landscape of HIV-1-uninfected gut CD4+ T cells exposed to bacteria. Microbial exposure upregulated genes involved in cellular proliferation, MAPK activation, Th17 cell differentiation and type I interferon signaling. Our findings reveal that microbial exposure influenced how HIV-1 altered the gut CD4+ T cell transcriptome, with potential consequences for HIV-1 susceptibility, cell survival and inflammation. The HIV-1- and microbe-altered pathways unraveled here may serve as a molecular blueprint to gain basic insights in mucosal HIV-1 pathogenesis.
| The gastrointestinal (GI) tract is a major site of early HIV-1 replication and death of CD4+ T cells. As HIV-1 replicates in the gut, the protective epithelial barrier gets disrupted, causing the entry of bacteria into the underlying tissue and the bloodstream, leading to inflammation and clinical complications even in HIV-1-infected patients taking antiviral drugs. Counteracting these pathogenic processes may require in-depth understanding of the molecular pathways that HIV-1 and microbes utilize to infect, functionally alter and/or kill CD4+ T cells. However, to date, the nature of the genes altered by transmitted/founder HIV-1 strains and gut bacteria in intestinal CD4+ T cells remains unclear. Here, we obtained the first gene expression profiles of primary gut CD4+ T cells infected in cell culture with HIV-1 in the context of microbes found in the GI tract of HIV-1 infected patients. Our findings reveal common and distinct signaling pathways altered by HIV-1 depending on the presence of microbes that may shed light on infection, inflammation and CD4+ T cell depletion in HIV-1 infected individuals. More detailed studies of these molecular programs may inform potential ways to counteract pathogenic outcomes initiated and/or sustained by HIV-1 infection in the GI tract.
| CD4+ T cells are the major targets of HIV-1 infection, and their preferential depletion during the course of infection is the hallmark feature of progression to AIDS [1]. Thus, major efforts have been made to understand the molecular events that occur following HIV-1 infection of CD4+ T cells. Insights on the cellular pathways inhibited and/or hijacked by HIV-1 have been obtained through global transcriptome profiling. Microarray studies reported that infection of CD4+ T cells with laboratory-adapted, CXCR4-tropic HIV-1 altered pathways associated with DNA repair, T cell activation, cell cycle control, subcellular trafficking, programmed cell death, RNA processing and nucleic acid metabolism (reviewed in [2]). However, those studies used either CD4+ T cell lines or mitogen-activated CD4+ T cells from peripheral blood, which are resistant to killing by primary, CCR5-tropic HIV-1 strains in vitro [3]. To date, the CD4+ T cell-intrinsic pathways altered by transmitted/founder (TF) HIV-1, which best approximate the initial strains, i.e. those identified to have established clinical infection in vivo [4, 5], remain unknown.
Regardless of the route of transmission, acute HIV-1 infection is characterized by high levels of replication and CD4+ T cell depletion in the gastrointestinal (GI) tract [6–8]. The GI tract harbors large numbers of activated memory CD4+ T cells expressing CCR5 [9], the coreceptor used by nearly all TF HIV-1 strains [10]. Within the first year of HIV-1 infection, preferential depletion of gut CD4+ T cell subsets that produce IL17 (Th17) and IL22 (Th22) were documented [11, 12]. Th17 and Th22 cells protect the integrity of the epithelial barrier, and their selective depletion has been linked to gut barrier disruption and the translocation of enteric commensal microbes to the systemic circulation [13–15]. This phenomenon, referred to as ‘microbial translocation’, is now widely accepted as a fundamental mechanism driving HIV-1-associated chronic immune activation. Notably, a microarray study using intestinal mucosal biopsies from patients 4 to 8 weeks following HIV-1 infection revealed the upregulation of interferon (IFN), immune activation, inflammation, chemotaxis, cell cycle and apoptotic pathways compared to HIV-1 uninfected patients [16]. These findings revealed that early HIV-1 infection altered host gene expression in the GI tract in vivo. However, it remains unclear which triggers (viral or bacterial) and cell types [e.g., T cells, dendritic cells (DCs), epithelial cells] in the bulk tissues were driving these gene expression changes.
To model how the interactions between intestinal lamina propria mononuclear cells (LPMCs), CCR5-tropic HIV-1 and the microbiota influence CD4+ T cell death, we developed the Lamina Propria Aggregate Culture (LPAC) model [17, 18]. In contrast to HIV-1 infection of PBMCs, the LPAC model does not require prior exogenous mitogen stimulation to obtain reproducible CCR5-tropic HIV-1 infection and CD4+ T cell death. Using the LPAC model, we previously showed that exposure of LPMCs to gut microbes enriched in the intestinal mucosa of HIV-1-infected patients [19, 20] enhanced HIV-1 infection and CD4+ T cell death [20]. Thus, the LPAC model provides a unique opportunity to catalogue the gene profile of a gut CD4+ T cell infected with TF HIV-1 in the presence or absence of enteric microbes. These altered gene signatures and pathways may in turn provide novel avenues for gaining basic insights on mucosal HIV-1 pathogenesis.
Modeling early events in primary HIV-1 infection and depletion in the gut ex vivo may require the use of relevant HIV-1 strains. In previous studies with the LPAC model, we utilized a laboratory adapted R5-tropic HIV-1 strain, Ba-L [17, 18, 20]. To determine the nature of HIV-1 strains that initiated and established clinical infection in patients, TF HIV-1 sequences were inferred using a phylogenetic model of acute HIV-1 infection sequences [5, 10]. To investigate if TF HIV-1 strains caused LP CD4+ T cell death, we spinoculated LPMCs (n = 9–11 donors) with normalized levels of TF HIV-1 strains CH058.c, CH470 and CH040.c (Fig 1A). At 6 days post infection (dpi), absolute CD4+ T cell counts were calculated by flow cytometry and automated cell counting relative to mock controls (Fig 1B). Infection with CH040.c resulted in detectable CD4+ T cell depletion relative to mock-infected cells in 90% of LPMC donors, whereas CH058.c and CH470 depleted CD4+ T cells in 60% of LPMC donors by 6 dpi (Fig 1C). The differences in CD4+ T cell killing potential between TF HIV-1 strains suggest that viral factors may contribute to CD4+ T cell death in the LPAC model. Due to consistent depletion at 6 dpi, we chose HIV-1 CH040.c TF strain for subsequent global transcriptome profiling.
Transcriptome data on HIV-1 infection remains limited to CD4+ T cell lines, mitogen-activated CD4+ T cells and bulk tissue samples from HIV-1-infected individuals [2]. CD4+ T cells productively infected with HIV-1 can be evaluated by intracellular HIV-1 p24 flow cytometry, but this method requires a membrane permeabilization step that could compromise transcript levels. To capture live, intact CD4+ T cells productively infected with TF HIV-1, we constructed an HIV-1 CH040.c infectious molecular clone encoding eGFP, referred to herein as CH040.c-eGFP. The eGFP insert utilized the start site of nef, followed by a T2A self-cleaving peptide, then nef. As expected, high proportions of CD4+ T cells expressing eGFP (>80%) were positive for intracellular HIV-1 p24 by flow cytometry (S1 Fig).
LPMCs from 4 donors were spinoculated with CH040.c-eGFP for 2 h. As controls, untransfected 293T cell supernatants were used for mock infection (Fig 1A). Since HIV-1 downregulates CD4, viable, infected CD4+ T cells were sorted as CD3+CD8-GFP+ at 4 dpi using the gating strategy shown (S2 Fig). On average, ~1% of CD3+CD8- cells were GFP+ after 4 days of CH040.c-eGFP infection of LPMCs. Thus, large numbers of LPMCs (15 million) had to be infected to ensure that sufficient numbers of HIV-1 CH40 GFP+ cells were sorted for microarray analyses. We successfully sorted 5,660 to 26,171 viable GFP+ cells, yielding 72 to 360 pg of RNA, which was sufficient for microarray analyses but not enough for multiple quantitative PCR analyses. Viable CD3+CD8- cells were also sorted from mock infected LPMCs. Total RNA isolated from sorted cells was amplified and labeled using the WT Pico kit (Qiagen) for transcriptome profiling using Affymetrix Human Gene 2.0 arrays. These arrays cover >30,000 coding transcripts and >11,000 long intergenic non-coding transcripts.
Principal component analysis of the full gene expression data revealed strong donor dependence (S3A Fig), consistent with the heterogeneity of LPMCs from diverse donors. To identify genes consistently altered in the 4 LPMC donors, we utilized two criteria: a p-value of <0.05 using paired t-statistics and a fold-change cut-off of 1.25 (S3B Fig). Using these criteria, we detected 1,207 genes that were altered in HIV-1 infected (GFP+) versus uninfected (mock) LP CD4+ T cells (Fig 2A). To determine if these genes were novel, we compared our dataset of differentially regulated genes to a recent microarray study using peripheral blood CD4+ T cells [21]. In this report, X4-tropic HIV-1 genetically linked to heat-stable antigen (HSA) was used to infect phytohemagglutinin-activated blood CD4 T cells that were co-cultured with IL-2. HSA+ cells were enriched by magnetic bead selection and analyzed by microarray. The authors found 267 differentially-regulated genes using a 1.7-fold cut-off. Notably, we found that only 3% of differentially expressed genes from our dataset overlapped with the Imbeault et al study [21] (Fig 2B), even though we utilized a lower fold-change cut-off value. These genes include ATF3, FOSL2, JUN, FOS and FOSB from the FOS/JUN gene family (Fig 2B; S1 Table). Thus, the majority of differentially-regulated genes due to HIV-1 infection in gut CD4+ T cells were novel relative to the Imbeault et al study. Fig 2C, S4 Fig and S2 Table lists the top-ranked and full list of upregulated and downregulated genes due to HIV-1 infection. Many of the genes were previously reported to be involved in HIV-1 replication. Our data suggests that HIV-1 induces a distinct transcriptome profile in primary gut CD4+ T cells compared to mitogen-activated blood CD4+ T cells.
To determine enriched biological themes in HIV GFP+ versus mock-infected cells, we conducted gene set enrichment analysis (GSEA). GSEA is a computational method that determines whether a previously defined gene set shows statistically significant differences between two biological states [22]. Gene lists pre-ranked according to t-statistics allows comparisons across array platforms through calculation of enrichment scores, which reflects the degree to which a gene set is overrepresented at the top or bottom of a ranked list of genes.
We pre-ranked our entire gene probe list according to t-statistics and then loaded the ranked gene list into GSEA (http://www.broad.mit.edu/gsea/). Positive enrichment scores suggest upregulation of the gene set whereas negative enrichment scores suggest downregulation of the gene set. To gain an overall view of the total number of gene sets enriched, we first investigated canonical pathways. Out of 3,709 canonical pathways, GSEA identified 193 upregulated and 412 downregulated gene sets (normalized or NOM p-value <1%) in HIV-eGFP+ cells versus mock. Thus, there was a disproportionate downregulation of canonical pathways in HIV-1-infected cells. Fig 3A outlines some of these gene sets. Notably, the top downregulated gene sets include 97/186 (52%) genes previously associated with HIV-1 infection, which we refer to here as the HIV-1 ‘reactome’ (Fig 3B). A substantial fraction (23/97; 24%) of these HIV-1 reactome genes are associated with mRNA processing, but genes linked to the proteasome, transcription, vesicle transport and TCR-MHC signaling were also downregulated (Fig 3C). Thus, productive HIV-1 infection appears to induce a host cellular ‘shutdown’ response that involves extensive downregulation of host-encoded HIV-1 co-factors.
GSEA is a valuable tool for evaluating the enrichment of a defined gene set but is not designed to predict pathway activation or inhibition. By contrast, Ingenuity Pathway Analysis (IPA) combines differential gene expression data with the Ingenuity Pathways Knowledge Base to determine altered canonical pathways, upstream regulators and predicted downstream disease/functions (www.ingenuity.com) [23]. To determine which canonical pathways were activated or inhibited by HIV-1 in gut CD4+ T cells, we performed IPA using a p-value cut-off of 0.05 and a fold change cut-off of ±1.25. Positive Z-scores indicate pathway activation whereas negative Z-score suggest pathway inhibition.
The top canonical pathway induced by HIV-1 in gut CD4+ T cells was OX40 signaling (Z = 2.236; p = 0.00018) (Fig 4A). OX40 (CD134) is a member of the TNFR superfamily and is involved in CD4+ T cell regulation [24]. To further delineate the OX40 signaling components altered by HIV-1 infection, we utilized the Molecular Activity Predictor tool in IPA. The OX40 signaling pathway genes altered in HIV-1-infected LP CD4+ T cells include CD4 (-1.46×), CD3G (-1.93×), HLA-B (-1.26×), JUN (3.06×), MAPK12 (1.34×) and NFKB1A (-2.38×) and the anti-apoptotic factor Bcl-xL (1.25×; p = 0.058). IPA further predicted that activation of the OX40 signaling by HIV-1 would result in T cell expansion (Fig 4B). To examine if OX40 signaling was altered at the protein level, we evaluated OX40 expression on HIV-1-infected gut CD4+ T cells by flow cytometry. Even though increased OX40 mRNA was not detected in the microarray, in 4 of 5 LPMC donors, the percentage of OX40+ cells was higher in HIV-1-infected versus mock-infected gut CD4+ T cells (Fig 4C). Altogether, these data suggest that HIV-1 infection may have triggered a pro-survival, OX40-linked pathway in gut CD4+ T cells.
In terms of downregulated pathways, HIV-1 inhibited peroxisome proliferator-activated receptor (PPAR) signaling (Z = -1.90; p = 0.0057) in LP CD4+ T cells (Fig 4A). PPARs are nuclear-hormone receptors that in the context of T cell signaling are anti-inflammatory usually through repression of NFkB [25]. The PPAR signaling genes that were altered in HIV-1-infected cells include CREBBP (-1.42×), FOS (2.49×), HRAS (1.37×), IL18RAP (2.04×), JUN (3.06×), NFKB1A (-2.38×), NR1H3 (1.37×), NR1P1 (-1.57×), SHC1 (-1.33×) and TNF (2.22×) (Fig 4D). The upregulation of IL18RAP and TNF in HIV-1-infected gut CD4+ T cells was consistent with an augmented inflammatory signature.
The induction of OX40 signaling at 4 dpi is expected to promote cell survival (Fig 4B and 4C). However, TF HIV-1 also causes CD4+ T cell death by 6 dpi (Fig 1C). Previously, we showed that HIV-1 Ba-L induced CD4+ T cell death in ~50% of LPMC donors at 4 dpi [18]. These data suggested that some gut CD4+ T cells may be poised to undergo death at 4 dpi. To determine if pathways associated with programmed cell death were detectable, we further scanned the IPA results. Indeed, death receptor signaling (p = 0.04) was upregulated (Fig 4A). Moreover, DNA damage response pathways such as ‘Radiation (UVC) induced MAPK signaling’ (p = 0.008) and ‘Ataxia telangiectasia mutated (ATM) signaling’ (p = 0.04) were induced (Fig 4A). ATM signaling is induced in response to chromatin damage due to DNA double strand breaks, a proposed inducer of CD4 T cell death via DNA-PK [26].
To identify upstream master regulators that may promote HIV-1-mediated LP CD4+ T cell death, we utilized IPA's ‘Upstream Regulator Analysis’ tool (Fig 4E). HIV-1 activated MAPK pathways (specifically, MAPK1, MAPK3, MAPK14 and MAP3K1; Z = 2.3 to 3.2; p<0.01). MAPK pathways are involved in cell activation, proliferation, differentiation, survival/apoptosis and cytokine production in response to cellular stress response such as DNA damage and viral infection [27]. The HIV-1-altered genes that can be linked to MAPK1 activation include upregulated FOS, JUN, TNF, ATF3 and FOSB as well as downregulated HLA-B and interferon-stimulated genes (ISGs) IFI16, IFIT2, ISG20 and OAS3. Consistent with the downregulation of these ISGs, IPA also predicted the inhibition of upstream regulators IFN and IRF7 (Fig 4D). Interestingly, we identified FOXO3 (Z = 2.760 p = 0.002) as an upstream regulator of differentially regulated genes in HIV-1 infected LP CD4+ T cells. FOXO3 is a tumor suppressor gene that can regulate DNA damage responses, as well as CD4+ T cell survival and differentiation [28]. Ten genes were consistent with FOXO3 activation. Of these genes, TNF (2.22×), PPP1R15A (1.54×), EGR2 (1.38×) and EGR1 (4.51×) are pro-apoptotic. Thus, FOXO3 could be functioning as an upstream regulator controlling HIV-1 induced LP CD4+ T cell death.
Gut barrier dysfunction during acute HIV-1 infection results in microbial translocation that has been associated with immune activation [29]. Our group and others discovered that bacteria from the Prevotella genus were enriched in mucosal tissues of HIV-1-infected individuals [19, 30–34]. Further, Prevotella abundance was linked to mucosal mDC and T cell activation [19, 35]. Using the LPAC model, we showed that two representative species, Prevotella stercorea and Prevotella copri, enhanced HIV-1 Ba-L replication and depletion in intestinal CD4+ T cells [20]. To determine if this phenomenon extends to TF HIV-1 strains, LPMCs were spinoculated with HIV-1 CH040.c, CH058.c and CH470, then incubated in the presence or absence of Prevotella stercorea. At 6 dpi, infection levels and CD4+ T cell counts were assessed relative to microbe-exposed mock controls (Fig 1A). Microbial exposure significantly enhanced both HIV-1 CH040.c infection (Fig 5A) and CD4+ T cell killing (Fig 5B). Similar results were also obtained with the CH058.c and CH470 (S5 Fig). CH040.c-eGFP infection was also enhanced by microbial exposure (S1 Fig). These data demonstrate that microbial exposure promotes TF HIV-1 infection and CD4+ T cell depletion in the LPAC model.
We next sought to identify transcriptome signals that may explain the increased HIV-1 susceptibility of microbe-exposed LP CD4+ T cells. HIV-1 uninfected LP CD4+ T cells sorted from microbe-untreated and microbe-treated LPMCs (n = 4 donors each) were subjected to microarray analyses. We identified 525 differentially altered genes (Fig 6A). Fig 6B, S6 Fig and S3 Table highlight the top 30 and full list of upregulated and downregulated genes in microbe- versus mock-treated LP CD4+ T cells. Microbial exposure upregulated Ki-67 (2.2×), a marker of cellular proliferation, as well as several histones and DNA topoisomerase. Interestingly, ATG5, a critical component of the autophagy pathway, and multiple zinc finger proteins of unknown function were downregulated (Fig 6B). We next utilized IPA to determine significantly altered pathways; of these, p38 MAPK signaling was significantly induced (Z = 0.707; p = 0.00024) (Fig 6C; S6 Fig). This pathway was previously shown to enhance HIV-1 replication in mitogen-activated peripheral blood CD4+ T cells [36, 37].
CD4+ T cells are classified into different subsets depending on the predominant cytokines they produce. Our group and others reported that IL17-producing CD4+ T cells (Th17), and in particular, those producing IFNγ and IL17 (Th1/17), were highly susceptible to HIV-1 infection in vitro [18, 38–40]. Th1/17 cells appeared to have a higher state of cellular activation and lower antiviral properties based on transcriptional profiling [41]. To extend these findings to TF HIV-1, we evaluated the relative susceptibility of gut Th1 versus Th17 cells by p24 flow cytometry (Fig 7A). Regardless of microbial exposure, we observed higher percentages of TF HIV-1-infected Th17 versus Th1 cells (Fig 7B). A recent microarray study revealed 38 differentially altered genes in mitogen-activated, peripheral blood CD4+ T cells grown under Th17 polarizing conditions (S4 Table) [42]. The authors linked the downregulation of RNASE2, RNASE3 and RNASE6 to enhanced susceptibility of Th17 cells HIV-1 infection [42]. We tested if gene sets linked to ‘Th17 susceptibility’ were similarly altered in microbe-exposed gut CD4+ T cells. Indeed, using GSEA, upregulated Th17 co-factor genes were similarly altered in microbe-exposed LP CD4+ T cells (Fig 7C). However, RNASE2, RNASE3 and RNASE6 expression were not inhibited in microbe-exposed CD4+ T cells (Fig 7D). Overall, our findings suggest that the increased susceptibility of microbe-exposed LP CD4+ T cells to HIV-1 may be due to a combination of factors that include increased cellular proliferation, p38 MAPK activation, induction of host co-factors required for viral DNA replication and enhanced Th17 cell differentiation.
HIV-1 can be inhibited by restriction factors, a collective term for host-encoded proteins with direct antiretroviral activity. Thus, we hypothesized that in addition to the upregulation of host co-factors, the increased susceptibility of microbe-exposed LP CD4+ T cells to HIV-1 may be due to the downregulation of restriction factors. Most antiviral restriction factors are induced by type I interferons (IFN). Surprisingly, GSEA found that 6 of the 8 top gene sets enriched in microbe-exposed LP CD4+ T cells were Type I interferon-responsive genes. Fig 8A highlights the extensive upregulation of GSEA-compiled ISGs (n = 62) in microbe-exposed gut CD4+ T cells. This included several known retroviral restriction factors such as MX2, SAMHD1, Tetherin (BST2), Viperin (RSAD2), ISG15 and IFITMs [43] (S5 Table). Furthermore, using IPA, the upstream regulators of differentially-expressed genes included IRF7 (p = 0.00025), IFNβ1 (p = 0.0046), IFNα2 (p = 0.00007) and IFNα (p = 0.00081), as well as sole type II IFN, IFNG (p = 0.000052) and a type III IFN, IFNλ1 (p = 0.000069) (Fig 8B). ISG induction at the protein level was confirmed by analyzing tetherin/BST2 expression on microbe-exposed gut CD4+ T cells from 4 LPMC donors (Fig 8C). Thus, microbial exposure stimulated with a strong IFN gene signature in gut CD4+ T cells.
We showed in Figs 3 and 4 that HIV-1 infection induced OX40 and FOXO3 signaling while downregulating HIV-1-reactome and PPAR signaling in LP CD4+ T cells. Since microbial exposure triggered significant transcriptome changes, we next investigated if HIV-1 infection altered the same gene sets/pathways in microbe-exposed versus non-microbe exposed LP CD4+ T cells. LPMCs were either mock- or HIV-1-infected, then incubated with P. stercorea. At 4 dpi, HIV-1-GFP+CD3+CD8- cells and counterpart microbe-exposed mock controls were sorted for microarray analyses. Notably, only a modest overlap (15%, Fig 9A) was observed between the genes that were differentially-expressed due to HIV-1 infection in the presence or absence of microbial exposure. The genes altered by HIV-1 independent of microbial exposure included EGR1, JUN, FOSB, APOBEC3H and FAM102A (Fig 9A), which were also reported in HIV-1 infected blood CD4+ T cells (Fig 1B) [21]. In contrast, HLA-DMB, MIR4725, SPINK2 and YME1L1 were altered in HIV-1-infected gut CD4+ T cells regardless of bacterial exposure, but these genes were not altered by HIV-1 in blood CD4+ T cells [21]. Of note, HLA-DMB is a peptide exchange factor involved in MHC class II antigen presentation, an operational process in human CD4+ T cells [44].
IPA provided insights on the microbe-independent pathways altered by HIV-1 infection. HIV-1 activated Cdc42 and ATM signaling (Fig 9B), as well as upstream regulators MAPK3K1, IGF1, IRF8, PDGFBB and FOXL2 with or without microbial exposure (Fig 9C). By contrast, HIV-1 failed to downregulate HIV-1-reactome genes based on GSEA (NOM p>0.05) and failed to perturb OX40, PPAR, MAPK1/3/14 and FOXO3 signaling based on IPA in microbe-exposed LP CD4+ T cells (Fig 9B and 9C). Microbial exposure also differentially altered the impact of HIV-1 on upstream IFN regulators (Fig 9C). As shown earlier, microbial exposure alone already induced multiple ISGs (Fig 8; S7 Fig and S5 Table). HIV-1 further induced 17% of these ISGs in microbe-exposed CD4+ T cells including BST2, IFI44L, IFIT3, ISG15, PARP9 and RSAD2, as well as 32 additional ISGs (S5 Table). This contrasts from our data showing HIV-1-mediated downregulation of ISGs in the absence of microbial exposure (Fig 4E). Thus, microbial exposure significantly influenced how HIV-1 altered the transcriptome gut CD4+ T cells.
We next inspected the top-ranked genes altered by HIV-1 in microbe-exposed CD4+ T cells (Fig 9D, S8 Fig and S6 Table). Several microRNAs were inhibited, possibly linked to RNA polymerase III (POLR3B) downregulation (Fig 9D). Interestingly, HIV-1 infection triggered a significant increase in granzyme A, B and H expression in LP CD4+ T cells, which we confirmed at the protein level by flow cytometry (Fig 9E). Granzymes are typically produced by cytotoxic CD8+ T cells and natural killer cells, where they function as apoptosis-inducing serine esterases [45]. Only a small subset of CD4+ T cells express granzymes, and these CD4+ T cells exhibit cytotoxic properties [46]. These findings suggested that HIV-1 infection may have conferred cytotoxic properties to microbe-exposed LP CD4+ T cells. Furthermore, 2 genes linked to Th17 function were significantly induced. HIV-1 infection of microbe-exposed LP CD4+ T cells stimulated 3.4-fold higher levels of IL26, a Th17-derived cytokine that has antibacterial properties [47], and 3.0-fold higher levels of the IL23R, which acts as the receptor of IL23 and helps maintain Th17 cell function [48]. Thus, HIV-1 infection may have enhanced the antibacterial properties of microbe-exposed LP CD4+ T cells.
Intriguingly, the IPA results suggested that HIV-1 may have perturbed cell cycle processes in microbe-exposed CD4+ T cells. The 14-3-3 pathway is activated; this pathway is critical for cell cycle progression (Fig 9B) [49]. Moreover, genes associated to the G1/S checkpoint regulation, particularly MYC, were inhibited in HIV-1-infected LP CD4+ T cells exposed to microbes (Fig 9D, S8 Fig) [50]. Downregulation of c-Myc can in turn inhibit CDK4/6 and cyclin D activation (S9 Fig) [51]. As noted earlier, HIV-1 infection further enhanced the type I IFN response (Fig 9C, S5 Table). Type I IFNs can block the cell cycle at the G1 phase [52]. These findings suggest that productive HIV-1 infection may cause cell cycle arrest at the G1/S phase in microbe-exposed CD4+ T cells.
In the absence of microbial exposure, we detected apoptosis signals in HIV-1-infected LP CD4+ T cells (FOXO3), but these were counterbalanced by survival signals (OX40). To predict potential outcomes from gene expression data, ‘downstream analysis’ can be performed through IPA. Using IPA's knowledge base of differential gene expression in varying disease and functional states, a p-value for overlap can be calculated by Fisher’s exact test. An activation Z-score determines if there was a significant pattern match between the predicted and observed up/downregulation for each disease/function analyzed.
Downstream analysis revealed that the gene expression profile of HIV-1-infected, microbe-exposed LP CD4+ T cells predicted a strong infection, cell cycle perturbation and cell death outcome (Fig 9F). IPA predicted cell death of immune cells, cell death of T lymphocytes, apoptosis and necrosis based on specific genes that were altered (S7 Table). By contrast, these disease/function states were not significantly predicted in LP CD4+ T cells infected with HIV-1 in the absence of microbial exposure (Fig 9F). Thus, microbial exposure appears to have increased the potential of HIV-1 to directly cause cell death.
Acute HIV-1 infection causes profound CD4+ T cell loss in the GI tract, and the sequelae of dysbiosis, barrier dysfunction, microbial translocation and chronic inflammation likely contributes to various renal, cardiac, liver, vascular and pulmonary co-morbidities that does not resolve with antiretroviral therapy [14, 53]. Documenting the transcriptome of gut CD4+ T cells infected with HIV-1 may thus provide insights on mucosal HIV-1 pathogenesis. Previous studies showed that HIV-1 altered the transcriptome of CD4+ T cell lines and mitogen-activated peripheral blood CD4+ T cells [2, 21, 54]. Given that HIV-1 infection in culture is quite low (<5% GFP+ cells), cell lines and blood CD4+ T cells are advantageous because large numbers of these cells can be infected. By contrast, obtaining large numbers of CD4+ T cells from tissues for sorting productively infected, HIV-1-GFP+ cells is logistically difficult. Here, we obtained large numbers of LPMCs from 4 donors that allowed us to profile the transcriptome of TF HIV-1-infected gut CD4+ T cells for the first time. The limited RNA yields, particularly for HIV-1 GFP+ cells, were just enough to perform microarray analyses. Microarrays have limited dynamic range and may underestimate fold-differences in gene expression, compared to more sensitive techniques such as real-time PCR [55]. To circumvent this limitation, rather than solely highlighting individual gene changes, we focused our analysis on the coordinate regulation of multiple genes and/or gene sets linked to a certain pathway or phenotype.
We initially compared our set of differentially altered genes to published microarray results on HIV-1-infected CD4+ T cell lines and mitogen-activated peripheral blood CD4+ T cells. We found at least 2 results in common with HIV-1-infected gut CD4+ T cells. First, HIV-1 induced JUN and FOS gene expression [21, 56, 57]. JUN and FOS form the AP1 transcription factor [58]. Multiple AP1 binding sites are found in the HIV-1 LTR, which can drive HIV-1 viral transcription [59]. Thus, AP1 may be critical in driving productive HIV-1 replication in gut CD4+ T cells. Second, HIV-1 infection of gut CD4+ T cells downregulated HIV-1-reactome (cofactor) genes at 4 dpi, mirroring data on HIV-1 infection of SupT1 cell lines at 1 dpi [54]. These findings suggest a sustained host cellular shutdown response to infection that may impose blocks for HIV-1 superinfection. Efforts are ongoing to genetically inactivate individual viral genes in TF HIV-1 to determine the viral mechanisms driving the HIV-1-induced host cellular shutdown response in primary gut CD4+ T cells.
We should emphasize that the similarities between blood versus gut gene expression profiles were rare. Differences in the Th cell subset composition in the blood (naïve/central memory) versus the gut (effector memory) may explain the lack of overlap between differentially expressed genes, but naïve and memory CD4+ T cells share 95% of their transcriptomes [60]. We expected that if genes shared between blood and gut CD4+ T cells were essential for replication, these genes should have been similarly altered by HIV-1. Instead, >97% of the genes that HIV-1 altered in gut CD4+ T cells were not detected in mitogen-activated peripheral CD4+ T cells. One explanation is that the selection criteria used to identify altered genes in HIV-1-infected gut CD4+ T cells (this study) was different from that of the blood [21] to accommodate the heterogeneity of LPMC donors. However, despite our less stringent fold-change criteria, we still did not capture a substantial fraction of HIV-1-altered genes reported in blood CD4+ T cells.
We postulate that several features of the LPAC model that distinguish it from standard HIV-1 infection of PBMCs may have contributed to the differences in gene expression between blood and gut CD4+ T cells. Standard HIV-1 infection of PBMCs requires exogenous mitogens, which maximize HIV-1 infection and minimize donor-to-donor variation in HIV-1 replication. While this method has technical advantages particularly for in vitro HIV-1 propagation, exogenous mitogen activation of PBMCs may mask subtle effects of HIV-1 on CD4+ T cell activation and function. By contrast, LPMCs do not require exogenous mitogens for HIV-1 infection. LPMCs also contain myeloid DCs (mDCs) that have distinct phenotypic properties compared to those found in blood [61]. The presence of mDCs may explain the strong induction of OX40 signaling in HIV-1-infected gut CD4+ T cells, as OX40 induction requires its ligand in antigen-presenting cells, OX40L. Of note, OX40-OX40L signaling promoted the survival of memory CD4+ T cells within the gut lamina propria [62], and enhanced productive HIV-1 infection in activated blood CD4+ T cells [63]. Our data implied the existence of viral mechanisms that promote the survival of the infected cell, presumably to enhance virus production.
In the LPAC model, TF HIV-1 depleted gut CD4+ T cells starting at 6 dpi. Thus, the induction of a pro-survival pathway (OX40) at 4 dpi (the time point we used for microarray analyses) may be followed by the induction of cell death pathways. Multiple MAP kinases and FOXO3, a transcription factor involved in apoptosis induction in part through the induction of DNA damage responses [64, 65], were predicted as upstream regulators in infected gut CD4+ T cells at 4 dpi. These data suggest that there may be a subset of gut CD4+ T cells at 4 dpi that were poised to undergo apoptotic cell death. The mRNA used for transcriptomic profiling came from bulk CD4+ T cells, making it difficult to determine if survival and death mechanisms were simultaneously operating in an HIV-1-infected cell. Single-cell transcriptomics should provide more in-depth information on the transition from survival to death during the course of HIV-1 infection in the LPAC model. In addition to the activation of survival and death pathways in HIV-1-infected gut CD4+ T cells, PPAR signaling, an anti-inflammatory pathway [25], was inhibited. Two genes inhibited by PPAR signaling, TNFα and IL18RAP, were induced. TNFα is a proinflammatory cytokine that can induce apoptosis [66]. IL18RAP is part of the heterodimeric receptor for the proinflammatory cytokine IL18, which can also induce apoptosis. Interestingly, IL18RAP polymorphisms have been associated with inflammatory bowel disease [67]. Further studies would be required to track the kinetics of OX40, FOXO3, MAPK and PPAR signaling in mediating CD4+ T cell survival, death and inflammation.
Gut barrier disruption occurs a few weeks after HIV-1 infection, resulting in microbial translocation. Bacteria from the Prevotella genus may be important, as these bacteria were abundant in the mucosal tissues of HIV-1-infected individuals, either as a result of HIV-1 infection itself or sexual practices [68]. Moreover, mucosa-associated Prevotella abundances correlated with mucosal T cell and DC activation in vivo [19, 35]. Using the LPAC model, we found that these enteric microbes promoted HIV-1 replication and gut CD4+ T cell depletion. Enhanced HIV-1 replication was linked to increased CCR5 expression, activation and proliferation of gut CD4+ T cells [20], but the underlying molecular pathways that regulate these processes remain unclear. Thus, a major goal of the current study is to determine how microbial exposure changes the CD4+ T cell transcriptome. In addition to gene signatures indicative of proliferation (Ki67) and activation (p38 MAPK), we detected an increase in HIV-1 ‘dependency factors’ that were recently linked to Th17 polarization [42]. Previously, we showed that HIV-1 infection of LPMCs ex vivo is skewed towards Th17, Th1 and particularly Th1/17 cells [17, 18]. Here we extended these data, showing that gut Th17 cells supported higher TF HIV-1 infection levels than Th1 cells. Of note, co-incubation of LPMCs with E. coli enhanced Th17 proliferation [17]. Our data are consistent with an increase in more HIV-1 susceptible Th17 cells in microbe-exposed LPMCs.
The enhanced TF HIV-1 susceptibility of microbe-exposed gut CD4+ T cells may also be a consequence of downregulated antiviral genes. Surprisingly, we observed a strong type I IFN signature in microbe-exposed gut CD4+ T cells. ISGs encoding potent antiretroviral restriction factors such as tetherin/BST-2 were induced following microbial exposure. Despite the induction of multiple antiviral genes, microbial exposure enhanced TF HIV-1 replication. Microbial exposure also induced genes associated with T cell proliferation and activation, suggesting that microbial exposure may have raised the threshold for innate antiviral factors to confer protection. In other words, the induction of restriction factors was overcome by the enhanced expression of HIV-1 susceptibility genes in microbe-exposed gut CD4+ T cells. Of note, early HIV-1 infection in the gut was associated with increased type I IFN responses [16]. Our data suggest that the gut CD4+ T cell response to microbes may contribute to the enhanced type I IFN responses in mucosal tissues of individuals with early HIV-1 infection.
One important finding from the current study is that microbial exposure influenced how HIV-1 altered the gut CD4+ T cell transcriptome. HIV-1 did not perturb the OX40, PPAR, FOXO3 and HIV-1-reactome genes in microbe-exposed CD4+ T cells, in contrast to what we observed in the absence of microbial exposure. Instead, HIV-1 appears to have augmented the antibacterial properties of microbe-exposed gut CD4+ T cells, based on the upregulation of granzymes, IL26 and IL23R. We speculate that these phenotypic changes may perpetuate and exacerbate inflammation in the GI tract. For example, the release of granzymes by cytotoxic CD4+ T cells, especially in the context of potentially increased MHC class II presentation by HIV-1-infected CD4+ T cells, may further disrupt the epithelial barrier. Notably, the gene expression profile in HIV-1-infected gut CD4+ T cells strongly predicted apoptotic cell death. This prediction is consistent with our previous report showing enhanced HIV-1-mediated apoptotic CD4+ T cell death with microbial exposure [18]. Disruptions in the cell cycle, particularly at the G1/S phase, were hinted by the gene expression data. These findings are in agreement with the strong type I IFN signature of microbe-exposed CD4+ T cells, which was enhanced even further by HIV-1 infection. Type I IFN signaling can suppress cellular growth and promote apoptosis through the inhibition of CDK4/6, cyclins and c-Myc, which were observed in our study. Our findings suggest a model in which CD4+ T cell-intrinsic type I IFN signaling due to microbial exposure may potentiate gut CD4+ T cells for accelerated HIV-1 mediated death.
In conclusion, we provide the first transcriptome study of TF HIV-1-infected gut CD4+ T cells in the presence or absence of enteric bacteria ex vivo. Our findings confirm and/or identify novel HIV-1- and microbe-induced molecular pathways that may regulate HIV-1 infection, CD4+ T cell depletion, and immune dysregulation inside and outside the context of microbial translocation in the intestinal mucosa. Further validation and in-depth mechanistic studies on these pathways in the LPAC model, and in vivo through human clinical studies and the SIV/rhesus macaque model, should provide more basic insight on mucosal HIV-1 pathogenesis. Such investigations may unravel novel therapeutic targets to counteract HIV-1 infection and inflammation in the GI tract.
Jejunum samples that would otherwise be discarded were obtained from HIV-1 uninfected patients undergoing elective surgery at the University of Colorado Hospital. These patients signed a release form for the unrestricted use of the tissues for research purposes following de-identification to laboratory personnel. The procedures were approved and given exempt status by the Colorado Multiple Institutional Research Board at the University of Colorado.
We previously reported on the generation of Transmitted/Founder (TF) HIV-1 infectious molecular clones (IMCs) CH040.c and CH058.c [4]. TF IMC CH470 was generously provided by Beatrice Hahn (U. Pennsylvania) [69]. pCH040.c-GFP.T2A/K3223 (referred to herein as CH040.c-eGFP) was constructed from pCH040.c (GenBank #JN944939.1) by inserting eGFP using the nef ATG, followed by a self-cleaving T2A peptide cassette and nef in frame, analogous to a previous approach [70]. pCH470, pCH040.c and pCH058.c plasmids were prepared using Stbl3 cells (Invitrogen, Carlsbad, CA) and used to produce viral stocks in 293T cells as previously described [71]. Virus stocks were titered using an HIV-1 Gag p24 ELISA kit (Perkin Elmer, Waltham, Massachutsetts). Prevotella stercorea (DSM #18206, DSMZ, Braunschweig, Germany) stocks were prepared as previously detailed [35], frozen at -80°C in DPBS in single use aliquots and enumerated using CountBright counting beads and a LSRII flow cytometer (BD Bioscience, San Jose, CA).
Human jejunal tissue samples were obtained from the patients undergoing elective surgery and disaggregated as previously described [17, 18, 72] and cryopreserved until use. Lamina propria mononuclear cells (LPMCs) were thawed using a standard protocol as detailed previously [18] and resuspended in RPMI, 10% human AB serum (Gemini Bioproducts, West Sacramento, CA), 1% penicillin/streptomycin/glutamine (Life Technologies, Grand Island, NY), 500 μg/ml Zosyn (piperacillin/tazobactium; Wyeth, Madison, NY) (cRPMI). 500 ng p24 of TF IMCs CH040.c, CH058.c or CH470 or the GFP reporter TF IMC, CH040.c-eGFP, were spinoculated into 2.5 × 106 LPMCs. For microarray analyses, CH040.c.-eGFP infections were performed on six replicate tubes containing 2.5 × 106 LPMCs (total of 15 million cells). LPMCs were mock infected in parallel. Infection by spinoculation was performed at 1500 × g for 2 h at room temperature. After 2 h of spinoculation, supernatant containing the free virus was discarded and the LPMCs were washed with cRPMI. LPMCs were resuspended at 106 LPMCs/ml in cRPMI and plated into a 96-well V-bottom culture dish and live P. stercorea was added (2.5 P. stercorea per 1 LPMC) where appropriate. LPMCs were cultured for 4 or 6 days at 37°C, 5% CO2 and 95% humidity.
LPMCs were infected with normalized input of TF HIV-1 stocks as described above (‘LPAC culture’) and input virus was removed after 2 h. Infectious virus production from supernatants harvested at 6 dpi from LPMCs was measured in TZM-bl reporter cells [73] (NIH AIDS Reagent Program #8129) as previously described [71]. For this, TZM-bl cells were plated in an opaque flat bottom 96-well plate; HIV-1 containing supernatants were added in 10-fold dilutions, mixed and incubated for 48 hours at 37°C. After 48 hours, cells were lysed using Britelite luciferase reagent (Perkin Elmer, Waltham, MA) and Relative Light Units (RLU) of luminescence were determined on a VictorX5 plate reader (Perkin Elmer, Waltham, MA). To quantify depletion, the difference in the number of viable T cells in HIV-1-infected cultures was compared to the number of viable T cells in matched mock-infected cultures. This ratio was reported as the percentage of T cells depleted from the HIV-1-infected wells as previously described [18]. All conditions were run in triplicate wells with the average depletion for the 3 wells reported.
To determine percentages of p24+ cytokine+ cells, LPMC were collected at 4dpi and stimulated with 250 ng/ml PMA (Sigma-Aldrich) and 1 μg/ml ionomycin (Sigma-Aldrich) in the presence of 1 μg/ml brefeldin A (Golgi Plug; BD Biosciences, San Jose, CA) for 4 h at 37°C, 5% CO2. Percentages of IFNγ and IL17-producing CD4 T cells expressing p24 were determined by intracellular flow cytometry as previously described [17, 18].
To validate certain altered genes/pathways from the microarray analyses, LPMCs (from donors different from those used in the microarray) were infected with CH040.c-eGFP or mock and stimulated with or without P. stercorea as detailed above. At 4 dpi, total LPMCs were collected and stained with PerCP-Cy5.5 α-CD45 (2D1, eBioscience, San Diego, CA), Zombie Aqua Fixable Viability dye (Biolegend, San Diego, CA), PE α-CD3 (OKT3, Tonbo Biosciences, San Diego, CA), PE-Cy7 α-CD8 (RPA-T8, Tonbo Biosciences), APC-Cy7 α-OX40 (CD134; Ber-ACT35, Biolegend), APC α-tetherin (CD317; RS38E, Biolegend) following standard flow cytometry protocols [17, 18, 20]. Cells were fixed (Medium A; Life Technologies), permeabilized (Medium B; Life Technologies) and stained with Pacific Blue α-Granzyme B (GB11; Biolegend) following standard intracellular staining flow cytometry protocols [17, 18, 20]. Manufacturer recommend isotype controls were used to establish OX40, tetherin and Granzyme B-specific staining. Data acquisition was performed using an LSRII flow cytometer (BD Biosciences) and analysis performed using FlowJo Software (V10).
After 4 d in culture, cells were surface-stained with PerCP-Cy5.5 α-CD3 (OKT3, Tonbo Biosciences), APC α-CD8 (OKT8, Tonbo Biosciences), and Zombie Viability Dye (Biolegend). HIV-1 productively infected cells were sorted using a Moflo Astrios EQ cell sorter based on live CD3+CD8- GFP+ staining. Mock infected, P. stercorea exposed and non-productively HIV-1 infected cells were sorted based on live CD3+CD8-GFP- staining.
Total RNA from sorted fresh cell pellets was isolated using the RNAeasy micro kit according to the manufacturers instructions (QIAGEN, Valencia, CA). The integrity of extracted RNA from sorted cells was analyzed using an Agilent Tapestation 2200 and concentrations were determined using qubit 2.0 fluorometer (Thermo Fisher Scientific) instrument. Transcriptional analysis was done at the University of Colorado Denver Genomics and Microarray Core facility. Following the manufacturer’s protocol, starting total RNA was converted to cDNA with the GeneChip WT Pico Kit (Affymetrix) and amplified for 13 PCR cycles. In vitro transcription was performed using the same kit to generate cRNA. The cRNA was converted into single-strand cDNA in a second cycle. The cDNA was fragmented and end-labeled with biotin. After standard labeling, each sample was hybridized to a GeneChip Human Gene 2.0 ST array, followed by examination of fluorescence intensity of the probes with an Affymetrix GeneChip Scanner 3000 7G.
Statistical analyses were performed using the open-source R (version 2.11) statistical software with supporting statistical libraries from the Bioconductor consortium (www.bioconductor.org) [74]. Raw data were transformed using the Robust Multi-chip Average (RMA) normalization method for background correction [75]. Principal Component Analyses (PCA) were plotted in 3D using the R library rgl (https://cran.r-project.org/web/packages/rgl/vignettes/rgl.html). Given the heterogeneity of LPMC samples by PCA (S3A Fig), false discovery rates [76] were >1%. Differentially-expressed genes consistent between the 4 LPMC donors were identified based on paired Student’s t-test with p<0.05 and a fold-change cut-off of 1.25 (S3B Fig).
Gene set enrichment analysis (GSEA) was performed using an open-source software package (version 2.0, Broad Institute http://www.broad.mit.edu/gsea) [22]. First, a ranked list was obtained by performing paired-t test between groupings of interest. The ranked list was ordered based on the paired-t statistics ranging from the most positive to the most negative. Then the association between a given gene set and the group was measured by the non-parametric running sum statistic termed the enrichment score (ES). To estimate the statistical significance of the ES, a nominal (NOM) p value was calculated by permuting the genes 1,000 times. To adjust for multiple hypotheses testing, the maximum ES was normalized to account for the gene set size (NES) and the false discovery rate (FDR). The gene sets used were from Molecular Signatures Database (MsigDB), catalog C2 (version 3.0) functional sets, subcatalog canonical pathways, which include 880 gene sets from pathway databases. These gene sets were collected from online databases such as Bio-Carta, Reactome, and KEGG (Kyoto Encyclopedia of Genes and Genomes).
Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, www.ingenuity.com) was used to identify canonical signaling pathways, upstream regulators and downstream disease/function pathways associated with the expression profiles of HIV-1 GFP+ vs. Mock, P. stercorea vs. Mock, P. stercorea HIV-1 GFP+ vs. P. stercorea. Differentially expressed Affymetrix Probe IDs were imported into the Ingenuity software and mapped to the Gene Symbol from the Ingenuity knowledge database. A p-value cutoff of <0.05 and fold change cutoff of ± 1.25 was used to filter the differentially expressed gene list into IPA. The significance of the association between the dataset and the canonical pathway, upstream regulator, downstream disease/function was determined by over-representation. Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway, upstream regulator or downstream disease/functions could be explained by chance alone. A p-value <0.05 was used as the cut-off for identifying significant altered canonical pathways, upstream regulators or downstream disease/functions. Z-scores were calculated by IPA to infer predicted canonical pathways, upstream regulators, disease/function activation or inhibition. Briefly, the basis for inferences are edges (relationships) in the molecular network that represent experimentally observed gene expression or transcription events, and are associated with a literature‐derived regulation direction which can be either “activating” or “inhibiting” [23]. Positive Z-scores indicate activation whereas negative Z-scores indicate inhibition.
Statistical analyses were performed using GraphPad Prism version 6 for Windows (GraphPad Software, San Diego, CA). Data were analyzed using a 2-tailed Student’s t test (either paired or unpaired). For multiple comparisons, 1-way ANOVA followed by a Dunnett’s posthoc test was used. P values <0.05 were considered significant.
Microarray data from this study was deposited in the NCBI Gene Expression Omnibus (GEO) Database Accession GSE86404.
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10.1371/journal.pntd.0006684 | Ecological niche modelling and predicted geographic distribution of Lutzomyia cruzi, vector of Leishmania infantum in South America | In some transmission foci of Leishmania infantum in Brazil, Lutzomyia cruzi could be considered the main vector of this pathogen. In addition, L. cruzi is a permissive vector of L. amazonensis. Its geographical distribution seems to be restricted and limited to Cerrado and Pantanal biomes, which includes some areas in Brazil and Bolivia. Considering that predicting the distribution of the species involved in transmission cycles is an effective approach for assessing human disease risk, this study aims to predict the spatial distribution of L. cruzi using a multiscale ecological niche model based in both climate and habitat variables. Ecological niche modelling was used to identify areas in South America that are environmentally suitable for this particular vector species, but its presence is not recorded. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Bioclimatic variables, altitude, and land use and cover were used as predictors in five ecological niche model algorithms: BIOCLIM, generalised linear model (logistic regression), maximum entropy, random forests, and support vector machines. The vector occurs in areas where annual mean temperature values range from 21.76°C to 26.58°C, and annual total precipitation varies from 1005 mm and 2048 mm. Urban areas were most present around capture locations. The potential distribution area of L. cruzi according to the final ecological niche model spans Brazil and Bolivia in patches of suitable habitats inside a larger climatically favourable area. The bigger portion of this suitable area is located at Brazilian States of Mato Grosso do Sul and Mato Grosso. Our findings identified environmentally suitable areas for L. cruzi in regions without its known occurrence, so further field sampling of sand flies is recommended, especially in southern Goiás State, Mato Grosso do Sul (borders with Mato Grosso, São Paulo and Minas Gerais); and in Bolivian departments Santa Cruz and El Beni.
| Leishmaniases are vector-borne diseases caused by Leishmania parasites which are transmitted to humans by the bites of infected female sand flies. The sand fly Lutzomyia cruzi is the vector of Leishmania infantum, the causative agent of visceral leishmaniasis (VL), in some specific areas of Brazil. The transmission of Leishmania species is climate-sensitive and involves complex ecological interactions between parasites, vectors and hosts. Considering that the vectors are strongly sensitive to climatic and environmental conditions, studies of their geographical distribution are important for understanding the eco-epidemiology of VL, as well as for the planning of disease control actions. The ecological niche of a species is a critical determinant of its distribution. Therefore, we conducted a study to evaluate and model the ecological niche of L. cruzi and predict susceptible areas to its occurrence in South America. The potential distribution area of L. cruzi according to the final ecological niche model spans Brazil and Bolivia in patches of suitable habitats inside climatically favourable areas. Cerrado and Pantanal biomes comprise the biggest portion of this suitable area which includes three Brazilians states, and some areas in Bolivia. Our findings reinforce the importance of conducting more ecological studies on sand fly fauna.
| World Health Organization data show that vector-borne diseases represent more than 17% of the global burden of all infectious diseases, causing more than 1 million deaths per year [1]. The dynamics and intensity of transmission of pathogens exhibit significant spatial and temporal heterogeneity, especially in vector-borne diseases [2,3]. Part of this lies in the fact that vector-borne diseases are climate-sensitive, because the species involved in their complex cycles of transmission are highly dependent on climatic variables [4–6]. In addition, there is evidence that ongoing climate change is affecting, and will continue to affect the distributions and burdens of these infections [4].
Predicting the distribution of the species involved in transmission cycles is an effective approach for assessing human disease risk. The spatial distribution of a species is a reflection of its ecology and evolutionary history, influenced by specific factors depending on the spatial scale [7–9]. Species distributions are hierarchically structured in space, with climatic variables limiting distributions at coarse scales, habitat variables gaining importance as the scale narrows, and biotic interactions affecting distributions at microscales [9,10].
Leishmaniases are climate-sensitive diseases transmitted to humans by the bites of female sand flies (Diptera: Psychodidae) infected with Leishmania parasites. The distribution and behaviour of the species involved in the transmission cycle, especially of the sand fly vectors, are strongly affected by climatic variables, such as precipitation, temperature and humidity [11,12]. In Latin America, Lutzomyia longipalpis is the main vector of Leishmania infantum, the causative agent of visceral leishmaniasis (VL) [13,14]. Due to its great epidemiological importance and wide distribution, L. longipalpis has been the object of different studies on the effects of environmental variables and anthropogenic environmental changes on its ecology [15–20]. Some of these studies have used ecological niche modelling to estimate the geographic distribution of this vector and predict its expansion or contraction under climate change scenarios [18–20].
However, in some transmission foci of L. infantum in Brazil, the sand fly L. cruzi may be acting as the main vector of this protozoan due to absence of L. longipalpis [21–25]. Although there were suspicions that L. cruzi was the vector responsible for the transmission of L. infantum since the 1980s [21,22], only recently this phlebotomine sand fly was confirmed as a proven vector of L. infantum [25], based on the Killick-Kendrick criteria [26], and as a permissive vector of L. amazonensis [25]. Lutzomyia cruzi can also act as an alternative vector in the location where both sand flies occur in sympatry [19]. In Brazil, the geographical distribution of L. cruzi seems to be restricted and limited to Cerrado and Pantanal biomes [23,24,27–29]. There are also reports of the presence of L. cruzi in Bolivia [30]. Recent evidences suggest introgressive hybridization between L. cruzi and L. longipalpis based on molecular analyses [31,32], reinforcing the idea that they are sibling species.
Even though L. cruzi has medical and epidemiological relevance, until now there are few published reports focused on the ecology and effects of environmental variables on the distribution and abundance of this sand fly [19,21,27,28,33,34]. A recent study applied ecological niche models to predict the distributions of L. longipalpis and L. cruzi in Brazil, but models were based on both species together, thus making it impossible to evaluate their distributions separately [19]. A further assessment of the potential distribution of L. cruzi is needed, especially for those areas where L. longipalpis does not occur.
Considering that ecological niche modelling represents a tool for monitoring disease trends in natural ecosystems and identify opportunities to mitigate the impacts of climate-driven disease emergence [35], this report aims to predict the spatial distribution of L. cruzi using a multiscale ecological niche model based in both climate and habitat variables. Besides contributing to the study of the ecological niche of L. cruzi, our goal includes the identification of specific areas in Brazil and neighbour countries that are environmentally suitable for this particular vector species, but its presence is not recorded.
We conducted a literature review to compile records of the presence of L. cruzi. On July 2016, the online databases PubMed, ISI, Scopus and SciElo were searched for relevant studies using the terms ‘Psychodidae’ and ‘Lutzomyia’. After removal of duplicate references, the papers were scanned for mention of L. cruzi captures, and all records compiled in a Microsoft Excel database with the available description of the capture sites (country, state/province/department, district/municipality, and locality). Additionally, the sand fly distribution lists compiled by Martins et al. [36], Young & Duncan [37], Aguiar & Medeiros [38] and Galati [39] were also consulted to ensure known presence records were not missed. As females of L. cruzi and L. longipalpis are morphologically indistinguishable [37,39], only the records with species identification based on captured males were considered as valid.
The main sand fly collections in Brazil were physically visited to search for additional unpublished records of the species. These included Centro de Pesquisas René Rachou (FIOCRUZ, Belo Horizonte, assisted by Dr J. D. Andrade-Filho), Instituto Butantan (IBUT, São Paulo, assisted by Dr R. Moraes), Instituto Evandro Chagas (IEC, Belém, assisted by Dr T. Vasconcelos dos Santos), Instituto Oswaldo Cruz (FIOCRUZ, Rio de Janeiro, assisted by Dr J. M. Costa), Instituto de Pesquisas da Amazônia (INPA, Manaus, assisted by Dr R. Freitas and Dr M. L. Oliveira), Universidade de São Paulo/Faculdade de Saúde Pública (USP, São Paulo, assisted by Prof. M. A. Sallum), and Universidade de São Paulo/Museu de Zoologia (USP, São Paulo, data provided by Dr A. J. Andrade). The online databases SpeciesLink (http://splink.cria.org.br/) and GBIF (https://www.gbif.org/) were also searched for presence records on February 2018.
All presence records were associated with geographical coordinates (latitude and longitude) and classified in three levels according to their spatial precision: High level: coordinates of the capture site were available in the original source of the record; Medium level: coordinates were obtained at Google Earth (https://earth.google.com/) by visually searching for the capture site when its description was available in the source of the record; Low level: coordinates of the municipality/district centre were obtained at Google Earth when the source of the record had no information on the capture locality, but only at this administrative level. We excluded from the database those records with information only at state/province/department or country levels.
The occurrence database thus contained the following information for each record: country, state/province/department, municipality/district, locality, year of capture, longitude, latitude, spatial precision, reference (S1 Table). The year of capture and spatial precision were used to split the records in separate sets for model training and validation, in accordance with the spatial and temporal precision of the variables used in the ecological niche models.
As some modelling algorithms require presence/absence data, we randomly sampled pseudo-absences in the space outside the environmental domain favourable for the species [40] but restricted to a maximum distance of 1000 km from the presence records. This environmental domain was estimated using the bioclimatic envelope model BIOCLIM [41]. The number of pseudo-absences was 10 times the number of presence records for each model run. We ran the pseudo-absence sampling procedure once for each modelling step (climate and habitat models). These procedures were performed in R platform [42], using the packages raster [43] and dismo [44].
We obtained historical (1970–2000) climate data for South America at WorldClim (version 2), an online database of 19 bioclimatic variables derived from monthly averages of temperature and precipitation [45]. For the climate model, we obtained the variables at the spatial resolution of 2.5 minutes (approximately 5x5km per pixel), which is an adequate coarse resolution where climate influences species distributions [9]. We selected a subset of the original 19 variables by running a Pearson correlation matrix and retaining only the six less correlated ones (r < 0.6). The final set of climate variables used to run the climate model consisted of annual mean temperature (BIO1), mean diurnal range of temperature (BIO2), temperature seasonality (BIO4), annual precipitation (BIO12), precipitation seasonality (BIO15) and precipitation of warmest quarter (BIO18) [45].
Remote sensing variables representing vegetation and topography were used as potential habitat indicators of L. cruzi. The Enhanced Vegetation Index (EVI), a product of the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor was obtained at NASA’s EarthExplorer website (https://earthexplorer.usgs.gov/) and processed with the MODIS Reproject Tool (https://lpdaac.usgs.gov). Monthly EVI data for 2000–2015 was obtained for the study area at the spatial resolution of 1 km. A Principal Component Analysis (PCA) was performed in order to reduce collinearity in the dataset. We retained the first five components, because they represented 99% of the cumulative variance in the monthly EVI dataset. Altitude, aspect and slope variables were derived from a digital elevation model from SRTM (Shuttle Radar Topographic Mission) and obtained at AMBDATA, an online database of environmental layers maintained by INPE (Instituto Nacional de Pesquisas Espaciais, http://www.dpi.inpe.br/Ambdata/). The eight habitat variables were resampled to 1 km2 resolution by bilinear interpolation and cropped at the extension of the study area, which was determined by the results of the climate model. All variable processing was done using the R packages raster and RSToolbox [46].
To describe the ecological niche of L. cruzi, the values of the main bioclimatic variables and altitude in the location of each presence record were extracted. We also assessed the types of land use and cover where the vector occurs using data from MapBiomas (http://mapbiomas.org/), a high-resolution database of annual land use and cover for Brazil. Each presence record was associated with the land use and cover data of the same year of capture. We excluded the records with low spatial precision at this step, because they do not match the native resolution of the MapBiomas data layers (30x30m). The percentage of each land cover type was extracted in a 500 m buffer created on each presence record. Analyses were performed in R package raster.
There are several algorithms available for developing ecological niche models, which produce different results and predictive maps even when running with the exact same input data [47–49]. There is not a consensus on the literature about one single best algorithm, thus researchers are encouraged to apply different methods to overcome this methodological uncertainty in their model predictions [50,51]. Therefore, we applied the same five modelling algorithms as McIntyre et al. [52], which had satisfactory results in niche models of Brazilian sand flies: BIOCLIM, Generalised Linear Models (GLM, logistic regression), Maximum Entropy (MaxEnt), Random Forests (RANFOR), and Support Vector Machines (SVM). For a short description of the five algorithms, see McIntyre et al. [52].
To reduce spatial auto-correlation, we randomly selected a subset of species occurrences which were at least 10 km apart from the nearest record. We ran all models with their default settings on the dismo package of R platform. In order to use the whole set of unique presence/pseudo-absence records in model training, we used 10-fold cross-validation, with 10% of the records retained for internal model testing. For internal evaluation, we used the True Skill Statistic (TSS), which ranges from -1 to +1, with +1 indicating complete agreement between predicted and observed records, and values close to and below 0 representing models no better than random predictions [53]. Model outputs with TSS scores lower than 0.6 were discarded. Outputs with the highest TSS scores from each algorithm were overlaid and consensus areas extracted by the majority ensemble rule [54]. Final maps were produced based on the consensus between the five modelling algorithms. Uncertainty was mapped by calculating the standard deviation of pixel values from model outputs produced by each of the five algorithms.
Because of the great difference in spatial precision of the species records, we ran two models with adequate settings for each spatial scale (Table 1). On a first step, we ran a climatic suitability model at the coarse spatial resolution of the climatic variables (2.5 minutes). For this model we used the set of L. cruzi records captured between 1970 and 2000 with the six bioclimatic variables. Model calibration area was restricted to a hypothesised accessible area of 1000 km around all known species records [55]. As we were aiming for a more conservative output for this first model, we chose the “zero omission” threshold rule [56] to convert model outputs into binary predictions. With this threshold rule, all presence records are retained inside the predicted area of occurrence, thus maximizing sensitivity (the proportion of correctly predicted presences), but sacrificing specificity (the proportion of correctly predicted absences).
The resulting binary map of climatic suitability was then used to limit the calibration area of the habitat suitability model, which was based on the vegetation and topography variables at higher scale (Table 1). As we narrowed the spatial resolution, at this second stage we only used the presence records classified as precision levels high and medium, with capture years matching the variables (2004–2013). The same model settings were applied, except for the threshold rule to produce binary predictions. For the final models, we chose threshold values that maximised both sensitivity and specificity [56]. With this, the final outputs become more objective, minimising both false positives and false negatives.
External validation of both models was done with independent records, separated from model training (Table 1). Model significance was evaluated by binomial probabilities calculated over binary outputs, and model performance was evaluated by sensitivity (number of correctly predicted presences divided by total number of records). Resulting model outputs were exported to QGIS software version 3.0.1 [57] for preparation of final maps.
The compiled database included 116 presence records of L. cruzi with associated geographical coordinates (S1 Table). Most records of the vector are in Mato Grosso and Mato Grosso do Sul Brazilian states, with a single record in State of Goiás and one in Bolivia, in Santa Cruz Department (Fig 1). Most of the records have low spatial precision (68%), followed by records with medium (25%) and high (7%) precision levels (Fig 1).
The vector occurs in areas where annual mean temperature values range from 21.76°C to 26.58°C, and annual total precipitation varies from 1005 mm and 2048 mm (Table 2). In these areas, temperatures in the coldest month of the year reach 11.3°C and the warmest month can reach as high as 34.3°C (Table 2). Extremes of monthly precipitation range from 1 mm to 157 mm (Table 2). In terms of elevation, most records of L. cruzi occur around 270 m above sea level, with a minimum of 86 m and up to 741 m (Table 2).
Nine different types of land use and cover were detected around records of L. cruzi (Fig 2). Urban areas were most present around capture locations (64%), followed by open forests (10%), dense forests (5%), pasture areas (4%), and open fields (3%). The remaining land use and cover types were identified only eventually and are presented in Fig 2.
The TSS scores of the climatic suitability models ranged from 0.48 to 1 (8% were discarded with TSS < 0.6); and in the final models, from 0 to 1 (22% with TSS < 0.6). Outputs produced by different algorithms varied considerably (S1 Fig), but consensus areas showed less uncertainty (S2 Fig). The climatic suitability model performed significantly better than random predictions (binary probabilities, p = 0.00498) and had sensitivity of 0.92; while the final ecological niche model was also significant (binary probabilities, p < 0.001) with a sensitivity of 0.72.
The coarse resolution model predicted an area of climatic suitability for L. cruzi that occupies the Central-West region of Brazil, extending westwards into Bolivia (blue and green areas in Fig 3). However, when considering the habitat variables at high resolution, the results of the final ecological niche model show that the area with suitable climate and habitat conditions for L. cruzi is much smaller, occupying 38.7% of the climatically suitable regions (only green areas in Fig 3).
The potential distribution area of L. cruzi according to the final ecological niche model spans Brazil and Bolivia in patches of suitable habitats inside climatically favourable areas. The bigger portion of this suitable area is located at Brazilian States of Mato Grosso do Sul and Mato Grosso, where most known records of the species are located (Fig 4). Four known records of the vector fell out of the predicted area: one in Bolivia (El Carmen), and three in Mato Grosso State (Nova Canaã do Norte, Colíder and Rondolândia) (see arrows in Fig 4). Suitable areas without known occurrence of the vector are located in Bolivian departments Santa Cruz and El Beni; southern State of Goiás in Brazil, as well as northern Mato Grosso do Sul and in border areas with São Paulo and Minas Gerais States (see circles in Fig 4).
This study represents the first report of the predicted spatial distribution of L. cruzi using a multiscale ecological niche model based on both climate and habitat variables, applying different algorithms for the same data. The final ecological niche model comprises mainly areas of the Central-West region of Brazil and some parts of East Bolivia.
The low number of occurrence records and their low spatial precision were limitations of the modelling process, being the most probable reason for the low TSS scores of a minority of model outputs. We reduced these limitations by discarding outputs with TSS < 0.6 in the final models and subsampling the records by spatial precision, thus running models at appropriate spatial scales. Models produced by different algorithms had great spatial variability, as expected [47–51]. Uncertainty mapping provided more confidence to the areas predicted as environmentally suitable by most algorithms.
Our results describe the ecological niche of L. cruzi in terms of climate, altitude and vegetation/land cover where the species occurs. The climatic values recorded at capture locations of L. cruzi are in accordance with the Köppen’s climate classification for most parts of the Central-West region of Brazil: tropical zone with monsoon period (Am) and with dry winter (Aw) [58]. Ecological studies that evaluated the linear relationship between L. cruzi abundance and climatic variables showed no significant statistical association [24,27,59]. However, it was observed that the species occurs throughout the year, with population peaks in the months with high temperature [21,24,27,59]. These previous studies considered both male and female specimens of L. cruzi and reported data from regions where L. longipalpis has not been detected, except in Corumbá city [60]. However, L. longipalpis was reported in Corumbá only once [60]. Successive sand fly surveys performed by different research groups were unable to confirm the presence of L. longipalpis in this area [21,27,28,61,62]. It should be noted that the occurrence sites of this vector have annual mean temperature relatively constant and annual precipitation ranging from moderate to high (Table 1).
Altitude data show that most records of L. cruzi occur in the Central and Southern plateau and in the Pantanal plains of Brazil. This observation allows us to hypothesize that the distribution of L. cruzi may be limited, among other factors, by altitude, since there is no record of the species in coastal regions. Cerrado and Pantanal are the biomes where L. cruzi mostly occurs, with few observations in southern Amazon. Our results of the percentage of land use and cover types demonstrate that L. cruzi is present predominantly in urban areas. However, this does not necessarily mean that L. cruzi prefers urban areas, because most of the sand fly samplings where performed in these areas or in peri-urban localities. Nevertheless, considering that L. cruzi and L. longipalpis are sibling species [31,32], the probable preference of L. cruzi for urbanized environments would not be surprising. As an example, data from the city of Corumbá, State of Mato Grosso do Sul, showed that in the 1980s the greatest abundance of L. cruzi was in native forest areas with low human interference [21]. Almost 30 years later a lower abundance was observed in the city’s peripheral forests, while in the urban area, the vector increased its abundance [27,28]. Similar situation was found in the city of Camapuã (Fernandes et al., 2017), also located in Mato Grosso do Sul State. No significant association was found between the absolute frequencies of L. cruzi and percentage of vegetal coverage and three spectral indices (normalized difference vegetation index, NDVI; normalized difference water index, NDWI; impervious surface areas, ISA) [27].
The predicted area of occurrence from our models corroborates a previously published distribution model of L. cruzi that was restricted to the Central-West region of Brazil [34]. The predicted area of occurrence of L. cruzi cannot be determined in Andrade-Filho et al. [19], but the general distribution of the species records used in the models is similar. Neither of the two studies give information on the spatial precision of the presence records. Positional uncertainty in species occurrence records have direct effects on ecological niche model predictions [63] and must be considered especially when developing models from secondary data. The vast majority of information available on species occurrence databases from Brazil is restricted to the municipal level. This can lead to serious bias in model predictions, as municipalities in Brazil have widely different areas, ranging from approximately 3 km2 to 160,000 km2 [64]. It is crucial that the spatial precision of species records match the spatial resolution of the models [65]. With our multiscale approach, we were able to develop models that incorporated the records with low spatial precision, thus reducing positional bias in our predictions. In addition, the spatial thinning process reduced the spatial auto-correlation bias. The four records that were not successfully predicted by the final models had low spatial precision, so it is not possible to determine the exact location of the species occurrence.
Our models predict occurrence areas of L. cruzi in Bolivia, where the vector was found in chicken coops and pigsties in the town of El Carmen, Santa Cruz District [30]. This is the only published record from the country, and according to our predictions, L. cruzi is probably present, but so far undetected in many Bolivian regions. Both visceral and cutaneous leishmaniases are endemic in Bolivia with occurrence of L. infantum, L. braziliensis and L. amazonensis [66–69]. However, there are few reports of ecological studies of phlebotomine fauna in this country, so further field sampling of sand flies is recommended.
According to the Brazilian Ministry of Health [70], except for the southwest Minas Gerais State, in the confluence region between the Grande river and the Paranaiba river (boundary with the states of São Paulo, Goiás and Mato Grosso do Sul), there are autochthonous human cases of VL reported in almost all the predicted suitable areas for L. cruzi. However, in many regions there are also the presence of L. longipalpis and/or L. cruzi [19]. A particular region, predicted as favorable to the vector, deserves to be highlighted due to the presence of autochthonous VL cases [70] and absence of L. longipalpis records according to Andrade et al. [19]: Brazil-Bolivia border in the extreme southwest Rondônia State, in the area adjacent to the municipality of Pimenteiras do Oeste. In Bolivia, few VL human cases have been reported and the disease appears to be restricted to Yungas region in the Beni department [71].
In Brazil, although the vector’s occurrence is widely known in State of Mato Grosso, some municipalities in Mato Grosso do Sul and the southern region of Goiás remain to be investigated. The border region between the states of Minas Gerais, São Paulo and Mato Grosso do Sul is also a predicted area of occurrence according to our models, but without known records of L. cruzi. This region, where the Paraná river basin divides the states, has many records of L. longipalpis, especially on the east side of the river [19]. To our knowledge, there is not a published study on the ecological interactions between L. cruzi and L. longipalpis that could justify their separation in space. Further studies on the phylogeography of both species might investigate if the Paraná river basin could have been a relevant dispersion barrier for their speciation.
In conclusion, our results contribute to the study of the ecology and distribution of an important vector of VL. The disease is being increasingly reported in urban and peri-urban areas of Brazil, especially because of the geographical expansion of its main vector, L. longipalpis [72]. Given the genetic proximity of this vector with L. cruzi [31,32] and its absence in specific VL foci, our predictive maps also indicate potential risk areas of this disease associated with L. cruzi. It is crucial that entomological surveillance activities are performed in these areas, especially where the vector has not been detected so far.
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10.1371/journal.ppat.1000171 | B Cell Recognition of the Conserved HIV-1 Co-Receptor Binding Site Is Altered by Endogenous Primate CD4 | The surface HIV-1 exterior envelope glycoprotein, gp120, binds to CD4 on the target cell surface to induce the co-receptor binding site on gp120 as the initial step in the entry process. The binding site is comprised of a highly conserved region on the gp120 core, as well as elements of the third variable region (V3). Antibodies against the co-receptor binding site are abundantly elicited during natural infection of humans, but the mechanism of elicitation has remained undefined. In this study, we investigate the requirements for elicitation of co-receptor binding site antibodies by inoculating rabbits, monkeys and human-CD4 transgenic (huCD4) rabbits with envelope glycoprotein (Env) trimers possessing high affinity for primate CD4. A cross-species comparison of the antibody responses showed that similar HIV-1 neutralization breadth was elicited by Env trimers in monkeys relative to wild-type (WT) rabbits. In contrast, antibodies against the co-receptor site on gp120 were elicited only in monkeys and huCD4 rabbits, but not in the WT rabbits. This was supported by the detection of high-titer co-receptor antibodies in all sera from a set derived from human volunteers inoculated with recombinant gp120. These findings strongly suggest that complexes between Env and (high-affinity) primate CD4 formed in vivo are responsible for the elicitation of the co-receptor-site-directed antibodies. They also imply that the naïve B cell receptor repertoire does not recognize the gp120 co-receptor site in the absence of CD4 and illustrate that conformational stabilization, imparted by primary receptor interaction, can alter the immunogenicity of a type 1 viral membrane protein.
| A major goal of HIV-1 vaccine research is to design novel candidates capable of neutralizing the vast array of viruses circulating in the human population. One approach is to base the vaccine upon the HIV-1 outer surface envelope glycoproteins to generate antibodies. However, during persistent infection in humans, the HIV-1 envelope glycoproteins have evolved structural features that limit the elicitation of broadly neutralizing antibodies. These immune “decoys” divert the antibody response resulting in virus subpopulations that can escape the host response. A potential means by which the virus elicits these decoy responses comes as a by-product of the entry process. Binding of the HIV-1 envelope glycoproteins to the primary receptor, human CD4, induces the formation of a second co-receptor binding site on the envelope glycoproteins, which then binds to another protein required for viral entry. Antibodies to the co-receptor binding site are generally ineffective at neutralizing HIV-1 patient isolates. Here, we demonstrate the mechanism by which antibodies to the HIV-1 co-receptor binding site are elicited in animals and humans injected with HIV-1 envelope glycoproteins and describe the implications of their formation regarding natural HIV-1 infection and vaccine design.
| The human immunodeficiency virus (HIV-1) exterior envelope glycoprotein, gp120, and the transmembrane glycoprotein, gp41, are non-covalently associated to comprise the trimeric, functional viral spike. These glycoproteins mediate entry and represent the sole virally encoded targets for neutralizing antibodies (nAbs) on the surface of the virus. The HIV-1 envelope glycoproteins, and those from related immunodeficiency viruses, are somewhat unusual in that they mediate target-to-membrane fusion by receptor-triggered conformational changes rather than by low pH-mediated fusion events typified by the influenza virus type 1 viral membrane protein, hemagglutinin (HA) [1]. The interaction of gp120 with the primary receptor, CD4, induces formation or exposure of a bridging sheet mini-domain that is, along with elements of the gp120 third variable region (V3), involved with binding to the co-receptor, CCR5 [2],[3],[4].
As was previously shown, antibodies against this induced co-receptor binding site are abundantly generated during natural HIV infection [5] and may be in part elicited due to the unique ability of gp120 to undergo receptor-induced conformations required for the sequential entry process. The co-receptor site antibodies are termed CD4-induced (CD4i) because following CD4 binding to gp120 (which functionally induces the co-receptor binding), these antibodies bind with enhanced affinity to gp120. The prototype for the co-receptor-directed, CD4i antibodies is 17b. However, it is less well appreciated that several full-length gp120 proteins actually are recognized by CD4i antibodies like 17b with high affinity (or avidity) even in the absence of the primary receptor [6]. The co-receptor-directed antibodies do not generally neutralize most circulating isolates [7]. However, these antibodies have attracted considerable interest due to the remarkable post-translational sulfation of a subset of these antibodies that mimics the functionally important sulfation of the CCR5 co-receptor N-terminus and their selective VH gene usage [8],[9]. Viral evasion of the CD4i antibodies likely occurs due to the in vivo selection for viruses that occlude or do not form this highly conserved region until the virus interacts with the primary receptor, CD4 [7],[10]. Once formed, the conserved site interacts with the largely invariant HIV co-receptor, CCR5. In contrast to the ability of affinity-matured CD4i antibodies, which can recognize the co-receptor site in the absence of CD4 with high functional affinity, the requirements for the naïve B cell receptor to recognize the same site is not presently understood and may differ from that of a mature CD4i antibody. Therefore, one aim of this study was to determine if previously described soluble envelope glycoprotein trimeric immunogens [11] might elicit CD4i antibodies in primates that possess a CD4 that is capable of a high-affinity interaction with the viral spike.
As an immunogen, monomeric gp120 does not elicit broadly nAbs [12] and has failed as a vaccine in a large clinical trial [13]. Therefore, much of the field has moved toward the design of soluble trimeric Env immunogens that more closely mimic the functional spike [11],[14],[15],[16],[17],[18]. The gp140 trimers which we have studied are derived from a neutralization resistant primary isolate, YU2, and are stabilized by heterologous trimerization domains (foldon) and somewhat improve the elicitation of neutralizing antibodies when inoculated into small animals possessing CD4 molecules that do not interact with gp120 [19],[20]. However, these stabilized trimeric immunogens have not been extensively tested in primates, which possess CD4 molecules capable of high affinity interaction with HIV-1 Env. Here, we demonstrate directly that the elicitation of CD4i antibodies by Env trimers is dependent upon the in vivo presence of high-affinity CD4 found in primates, but not present in the wild-type (WT) rabbits. We definitively demonstrate that the presence of endogenous primate CD4 is sufficient to generate CD4i antibodies following inoculation of these same trimers into rabbits rendered transgenic for human CD4. Consistent with these data, we also show the presence of co-receptor-directed antibodies in sera from a subset of patients who participated in the non-efficacious VaxGen phase III clinical trial using monomeric gp120 as a candidate vaccine. Our findings provide clear evidence that binding of a type-I viral membrane protein to its primary receptor can lead to its in vivo altered immunogenicity. It also illustrates that, in contrast to antibodies that have the undergone affinity maturation, the naïve B cell receptor repertoire does not recognize the co-receptor binding site with sufficient affinity to elicit antibodies against this region in the absence of primate CD4.
The highly glycosylated gp140-F trimers derived from the primary isolate YU2; previously referred to as YU2gp140(-)/FT [11] were purified from the supernatant of transiently transfected mammalian cells by lentil lectin affinity chromatography followed by chelation chromatography. In most cases, size exclusion chromatography was used to isolate the predominant trimeric peak fraction (Fig. S1). To confirm binding of the trimers to sCD4 independent of avidity effects, a solution-based binding assay was developed. To begin, 1 to 137 nM of the gp140-F molecules were co-incubated with 2, 4 or 9 nM soluble human 4-domain CD4 (sCD4) in solution. Next, non-Env-bound sCD4 was captured by RPA-T4 and detected in an ELISA format to evaluate the relative binding (Fig. 1A). The gp140-F trimers bind to sCD4 in a concentration dependent manner with half-maximal binding at approximately 7, 13 and 26 nM respectively. To confirm the specificity of the binding, we introduced a mutation at position 368 of gp140 such that 368 Asp was changed to Arg. This mutation (368D/R) was previously shown to specifically reduce or abrogate CD4 binding of monomeric gp120 [21] and as expected in this soluble CD4 reporter assay, the gp140 368D/R trimers did not bind sCD4 at any concentration tested. Since in vivo, abundant cell-surface CD4 is a potential source for high-affinity binding of Env, we sought to confirm that the YU2 trimers could bind to CD4-positive cells derived from non-human primates before initiating immunogenicity studies. We co-incubated primate peripheral blood mononuclear cells (PBMCs) with 2 µg/ml, 10 µg/ml or 20 µg/ml gp140-F trimers and stained the cells for CD3, CD4, CD8 and a marker for dead cells. Trimer binding to cell-surface-expressed CD4 was detected with the V3-directed antibody 447-52D on live, CD3+/CD4+/CD8− cells by flow cytometry (Fig. 1B; for staining and gating strategy, see Fig. S2). Similar to the results obtained in the CD4 solution assay, the gp140-F trimers bound to the CD4+ T cells in a concentration- dependent manner. Trimer binding to the CD4+ cells could be fully abrogated by pre-incubation of 20 µg/ml of the gp140-F molecules with an excess of sCD4. Further, no binding could be detected after incubation of the PBMCs with the gp140 368D/R CD4 binding-defective trimers. Together, the data confirmed that the YU2 gp140-F trimers used in this study bind both soluble, and importantly, cell-surface CD4 in a dose-dependent and specific manner.
To confirm that the highly purified trimers used in this study were competent for recognition by 17b, as well as competent for induction of the CD4i epitope by CD4, we performed both ELISA-based and surface plasmon resonance (SPR) binding assays. First, we incubated 3.2 ng/ml to 10 µg/ml of the YU2 gp140-F trimers in solution, without or with an excess of sCD4, after which gp140-F was captured by 17b on a plate (Fig. 2A). Consistent with previous data with monomeric YU2 gp120 [6],[22],[23], the trimers were well recognized by 17b in the absence of CD4. However, under the conditions of this assay, the relative binding increases approximately 2 to 5-fold in the presence of 1 ug/ml or 20 ug/ml sCD4, confirming that sCD4 induces better exposure, by formation or stabilization, of the CD4i site on the gp120 moieties present in the soluble trimeric context.
We next determined the recognition of the trimers by the protypic CD4i antibody 17b by Biacore SPR in two formats (Fig 3). In the first format, the gp140-F trimers were flowed over 17b immobilized on the surface of chip as the analyte. Because the trimers are oligomeric, this binding analysis detects avidity rather than strict affinity. However, this would be the case if the trimers were in solution and recognized by the bivalent BCR in multi-valent array on the surface of a B cell or if the trimers were displayed on the surface of a CD4+ lymphocyte. By this means, we determined that the avidity of the trimers for 17b was nanomolar to subnanomolar regardless if the trimers were in complex or not with CD4 (see Fig 3A). To better approximate the actual affinity of interaction, the 17b antibody was flowed over the gp140-F trimers immobilized on the chip and the binding was analyzed by bivalent curve fitting. This analysis also confirmed that recognition of the trimers by 17b in the absence of CD4 was a high-affinity interaction in the low nanomolar range (Fig 3B).
To assess if in vivo interaction of primate CD4 with HIV-1 Env is a requirement for elicitation of CD4i antibodies, we utilized the previously published observations that WT rabbit CD4 is unable to bind HIV-1 Env [24]. We immunized cynomolgus macaques and rabbits four times with the YU2 gp140-F trimers formulated in the GSK Adjuvant System AS01B and confirmed that the ELISA titers saturated by three inoculations and were roughly equivalent (data not shown). For neutralization, we first analyzed the serum samples from individual animals for their ability to inhibit viral entry against a panel of selected HIV-1 isolates. The rationale for this analysis was to assess the over-all neutralization capacity of responses elicited in monkeys versus the rabbits against HIV-1 to make a comparison of other responses more meaningful (i.e. CD4i-directed HIV-2 neutralizing antibodies, see below). The sera were analyzed at a 1 to 5 dilution against a panel of nine HIV-1 Env pseudotyped viruses in a standardized neutralization assay using TZM-bl target cells [25],[26] (Fig. 4A). Sera derived from animals of both species potently neutralized the three sensitive viruses, the lab-adapted HxBc2 (clade B), SF162 (clade B) and MW.965 (clade C) with values between 90 and 100%. Overall, the potency and breadth of neutralization for this panel of viruses were very similar between the monkey and rabbit sera. Subtle cross-species differences in neutralization were observed, but these were not statistically significant. For example, sera from the immunized rabbits tended to display more consistent animal-to-animal neutralization capacity against the homologous YU2 strain. In contrast, the sera derived from the monkeys displayed a trend of greater potency against BaL and the tier 2 isolate SS1196 (clade B). Sera derived from both species of animals inoculated with the YU2 trimers sporadically neutralized the DJ293 isolate (clade A), but poorly neutralized JRFL (clade B), as well as TRO1.1 (clade B; not shown, done only with monkey sera), demarking the limits of the neutralization activity elicited by the current immunogen design. Perhaps the subtle differences observed in the neutralization potency against some of the isolates between sera derived from the monkeys versus the rabbits is due to slight differentials in the elicited antibody repertoires, however, in general, the data highlights the overall similarities in the elicited neutralization capacity.
To detect if there is a species-difference in specific antibody elicitation against the co-receptor site of gp140-F trimers, we analyzed the sera in the same assay format as above but against virus pseudotyped with Env from an HIV-2 isolate, 7312 (containing a V434M amino acid change). While this virus is relatively insensitive to antibodies raised against HIV-1 Env it becomes highly sensitive to anti-HIV-1 CD4i-antibodies in the presence of sub-inhibitory concentrations of sCD4, facilitating the specific detection of such antibodies [5]. Consistent with data from HIV-1 infected individuals [5],[27] and gp140-inoculated humans (GMS, unpublished observations), CD4i-antibodies detected by this assay were abundant in sera from all five monkeys (ID50 titers: 55, 91, 268, 479 and 2618; Fig 4B). We could detect low-level cross-neutralization of HIV-27312/V434M in sera from four of the five monkeys, consistent with what had been observed previously from some HIV-1 infected humans [5]. Following these results, we analyzed three cynomolgus macaques that had been inoculated with the YU2 gp140-F trimers in Ribi adjuvant 2 times and at a similar dose, and detected CD4i antibodies in these animals with ID 50 values of 21, 27 and 198 (Fig S3). The lower levels of CD4i antibodies relative to those elicited by trimers formulated in AS01B, correlated with similarly lower potency of neutralization against the HIV-1 isolate, MN (Fig S3). We also detected CD4i antibodies in 5 out of 5 cynomolgus monkeys primed with Semliki Forest virus (SFV) particles expressing the YU2 gp140-F trimers and boosted with trimeric protein with ID50 values of 57, 1619, 44 and 335 and in 3 out of 3 baboons immunized with YU2 gp140 molecules rendered trimeric with a modified GCN4 motif [16] in Ribi adjuvant (data not shown). Taken together, these data clearly demonstrate that elicitation of CD4i antibodies by Env trimers in non-human primates is a highly reproducible and commonly elicited response and can occur at low levels when Env is expressed from a viral vector (SFV; not shown).
In stark contrast, CD4i-antibodies could not be detected in the serum from any of the WT rabbits (Fig 4B), suggesting that the elicitation of CD4i-antibodies is dependent on the in-vivo presence of CD4 with affinity for the YU2 trimers in the non-human primates. Alternatively, it might be that rabbits lack B cell receptors (BCR) in their naïve repertoire with the ability to recognize the HIV-1 co-receptor binding site while the monkeys possess such a capacity.
While the HIV-2 assay detects antibodies specific for the co-receptor binding site, we wanted to confirm these results by performing an ELISA based assay where serum from immunized animals were tested for their ability to compete with a biotinylated 17b antibody for binding to gp120. It is known that antibodies not directly directed against the co-receptor site are capable of competing with 17b for binding to gp120 [28]. To minimize such indirect effects, we analyzed the ability of sera to compete for biotinylated 17b binding to an HXBc2 gp120 core protein capable of binding 17b with high affinity (Dey et al, manuscript in preparation). In this assay format serum samples from monkeys were able to inhibit 17b binding at an approximately 10-fold higher dilution than that of serum samples from the rabbits (Fig 4C). The weak, but detectable, level of antibodies capable of competing with 17b in serum from rabbit serum may be due to antibodies recognizing the co-receptor binding elements in the pre-CD4 induced state or other antibodies that can inhibit 17b binding, such as CD4 binding site antibodies [28].
Direct interaction of the trimers with primate CD4 might be expected to expose as well V3, the other element of gp120 involved in co-receptor interaction [4]. To address this issue, we performed a binding assay to determine the proportion of V3-specific antibodies compared to antibodies against intact gp120 in sera derived from the 3 types of test animals. We observed a slight 1.6-fold average higher proportion of antibodies against V3 in serum samples from primates than in WT rabbits that was not statistically significant (Fig S3). The huCD4 rabbits, although possessing lower gp120 and V3 binding titers compared to WT animals, also displayed a slightly greater proportion of V3-specific antibodies. The lack of a significant increase in V3-directed antibodies may be due to maximal exposure of V3 on gp140 as we could see no enhancement of binding of a V3-specific antibody to the trimers following addition of CD4 (not shown) consistent with a previous study [28].
To address if the presence of primate CD4 was required for the elicitation of CD4i antibodies from the gp140-F trimeric immunogens, we used rabbits previously engineered to be transgenic for human CD4 (huCD4) [24]. These rabbits were generated from the New Zealand White (NZW) background, and are relatively similar in their genetic background to the out-bred NZW WT rabbits used for the initial immunogenicty analysis above. The huCD4 transgenic rabbits allowed us to perform controlled immunogenicity experiments to determine if the in vivo presence of primate CD4 allows for BCR recognition of the YU2 gp140-F co-receptor site and subsequent elicitation of CD4-induced antibodies in rabbits. Before initiating the immunogenicity experiment, we confirmed that the huCD4 transgenic animals, ranging from 2 to 5 years of age, still expressed human CD4 on their PBMCs as follows. Incubation of 20 µg/ml gp140-F trimers with PBMCs from WT and huCD4 rabbits and analysis by flow cytometry using species-specific cellular makers (for staining and gating strategy, see Fig. S5) confirmed that only PBMCs from the transgenic animals can bind the gp140-F, and that binding occurred only on cells co-expressing human and rabbit CD4 (rCD4; Fig. 5A). These results are consistent with the initial design of the transgenic rabbits to restrict expression of huCD4 only to cells also co-expressing rCD4 by use of a cell-type-specific promoter [24].
Five huCD4 rabbits were inoculated with the YU2 gp140-F trimers formulated in AS01B adjuvant by an identical regimen as the WT rabbits and monitored for the appearance of CD4-induced antibodies by the in vitro HIV-2 assay. CD4-induced antibodies could be detected in the sera from four out of five huCD4 rabbits after three immunizations with gp140-F trimers (Fig 5B). The ID50 titers detected were 84, 127, 190 and 4507, which is comparable with the levels detected in serum samples derived from immunized monkeys. These data demonstrate that rabbits have the capacity to induce antibodies against the HIV-1 co-receptor site, but that the in vivo presence of primate CD4 is required for the elicitation of these antibodies. This most likely occurs by a direct interaction with primate CD4 and induction of the co-receptor binding site, consistent with a recent study that detects CD4i antibodies following inoculation of monkeys with a CD4-gp120 fusion protein [29]. We also monitored for elicitation of neutralizing antibodies against HIV-1 pseudotyped virus in serum samples of these huCD4 rabbits and observed that the titers were not as consistent and potent for as for the WT rabbits (data not shown). These results might be due to the relatively advanced age of the huCD4 rabbits or as a consequence of unanticipated immune-related effects of the huCD4 transgene. However, as a slightly diminished immune response in these animals would only bias the results against the elicitation of CD4i antibodies, this remains a stringent model to assess the dependence upon primate CD4 for elicitation of these antibodies.
The elicitation of CD4i antibodies in monkeys and huCD4 rabbits after immunization with YU2 trimers suggests that the co-receptor site of gp120 is accessible for BCR recognition: likely on the surface of CD4+ PBMCs. Therefore, we investigated if the prototypic, co-receptor-site-directed mAb, 17b, could bind gp140-F trimers once they were bound cell-surface CD4. We sought to confirm that there was adequate accessibility of the CD4-induced co-receptor binding site on the trimers, once they were removed from the context of the virus. In the viral spike context, the induced co-receptor site is apparently not accessible to most CD4-induced antibodies due to steric constraints. To approximate the in vivo scenario in which trimers formulated in adjuvant would likely drain to proximal lymph nodes to encounter abundant CD4+ cells, we incubated cynomolgus macaques PBMCs with 20 µg/ml of the gp140-F trimers and detected CD4-specific binding to live CD3+/CD4+/CD8− cells by the V3-directed antibody 447-52D or 17b using flow cytometry (Fig. 6A). Binding of the gp140-F trimers could be detected with both antibodies. Recognition of the trimers by 447-52D verifies that the YU2 gp140-F molecules bind to the CD4+ cells, while cell-surface recognition of the trimers by 17b confirms that the co-receptor site is accessible after trimer binding to membrane-bound CD4. Similar results were obtained when the cell-surface binding assay was repeated using human PBMC targets as shown in Fig 4B. Monomeric gp120 bound to the human CD4+ cells was used as a control and displayed the 17b epitope at levels higher than that of the gp140-F trimers (Fig 6B).
Sera from humans immunized with gp120 possess CD4i antibodies. Following the observation that the gp120 monomers bound to cell-surface CD4 displayed the CD4i epitope, we obtained serum samples from the VaxGen Inc phase III clinical trial now licensed to the Global Solutions for Infectious Diseases. Twenty randomly selected sera from trial volunteers that had been inoculated four times with recombinant gp120 (MN/GNE8 mixture) formulated with alum were assessed for gp120 binding antibodies by ELISA. All sera exhibited detectable binding titers to the unmatched YU2 gp120 ranging from 5000 to 100,000 endpoint titers (not shown). We assessed the ability of the sera to inhibit the entry of MN and, in the presence of CD4, the HIV-2 virus 7312. As shown in Table 1, all sera neutralized not only the homologous virus, MN, but displayed detectable, relatively high-titer, cross-neutralizing, co-receptor-directed antibodies against the CD4-triggered HIV-2 isolate.
In this study, we demonstrate that the elicitation of co-receptor site directed antibodies by the YU2 gp140-F trimers requires the presence of primate CD4. We show that the relatively homogenous, soluble, stable YU2 trimers bind to human CD4 with high affinity in a solution-based assay that, by design, should be independent of oligomeric influences on functional affinity by avidity-dependent interactions. Analysis of the interaction between the prototypic co-receptor antibody, 17b, and the trimers demonstrated that high-affinity and high-avidity binding is detectable even in the absence of CD4. Binding of the trimers to primate cell-surface CD4, but not to cell-surface rabbit CD4 was also shown. WT rabbits and monkeys inoculated with the CD4-binding YU2 Env trimers formulated in the same adjuvant system elicited an overall similar pattern of HIV-1 in vitro neutralization against the viruses tested. However, cross neutralization of HIV-2 in presence of sCD4, an assay that is diagnostic for the detection of CD4i antibodies, was observed initially in sera derived from monkeys inoculated with the YU2 trimers but not in WT rabbits. Taken together, these data strongly suggest that Env-CD4 complexes generated in vivo upon inoculation are the source for elicitation of the CD4-induced antibodies following vaccination. This observation was confirmed by the inoculation of Env trimers into rabbits transgenic for human CD4 and the detection of CD4i antibodies in the sera of these animals, in contrast to WT rabbits, revealing conclusively the mechanism of their generation (see schematic Fig 7). Consistent with this observation, high levels of co-receptor-directed, HIV-2 (+CD4) cross neutralizing antibodies were detected in 20 of 20 human serum samples from the VaxGen phase III clinical trial using monomeric gp120 [13], suggesting that during natural infection shed, soluble gp120 can elicit these antibodies [30].
The induction of co-receptor site directed antibodies in non-human primates and humans is consistent with previous reports that detected the presence of CD4i, co-receptor directed antibodies in gp140-immunized humans (GMS, unpublished observations), as well as in naturally infected humans [5],[31] and following SHIV challenge of naïve non-human primates [29]. However, the mechanistic basis for the elicitation of CD4i antibodies was not previously addressed in a direct manner. In this study, we present a controlled experiment, which demonstrates that the elicitation of the co-receptor binding site antibodies by Env alone requires the presence of, and likely direct interaction with, primate CD4. This requirement has not previously been defined, despite numerous Env trimer immunogenicity experiments performed to date in both monkeys and non-primate species [17],[18],[20],[32],[33]. This is in part, because the HIV-2-based neutralization assay diagnostic for CD4i antibodies was a relatively recent development and is more definitive for neutralizing capacity directed at the co-receptor binding site then are binding assays employed by us and others previously [23],[34],[35]. Elicitation of CD4i antibodies in primates by Env trimers also nicely illustrates another potential mechanism of immune escape by HIV-1. Not only does Env binding to CD4 obscure a conserved surface that, if it was highly immunogenic, might elicit antibodies capable neutralizing a broad array of isolates (essentially antibodies mimicking the soluble primary receptor), but the binding event induces a second conserved and apparently immunogenic region: the co-receptor binding site. The CD4i antibodies directed against this region are not generally able to neutralize primary isolates in vitro [7]. This is likely due to a commonly elicited selection pressure that renders the co-receptor binding inaccessible to most antibodies of this type [10]. The inability of the CD4i antibodies to control HIV-1 infection is supported by data from a recent study where elicitation of CD4i antibodies can be detected prior to the detection of autologous virus neutralization capacity in sera derived from HIV-1 infected patients [27], as well as the data here, which indicates that they were elicited in the phase III Vaxgen clinical trial where no protection was observed. However, a recent study in non-human primates suggests that the presence of CD4i antibodies (as determined in vitro by the same HIV-2 detection assay as used here) is associated with more rapid viral clearance following SHIV162P3 challenge [29], illuminating that this is an area worthy of further investigation.
In the present study, the most likely in vivo source for presentation of the CD4i region to the humoral immune system is by direct interaction of the trimers with cell-surface CD4 displayed on CD4-expressing T cells or other CD4-positive cells of the hematopoetic lineage. It is also possible that low levels of CD4 are shed from CD4+ cells into interstitial spaces and soluble complexes are formed. Detection of low levels of sCD4 has been previously reported in humans [36],[37], although in the assay used here we could not detect soluble CD4 in the sera of animals examined. It is also possible that trimer binding to cell-surface CD4 induces shedding of complexes, but we could find no such evidence for soluble Env-CD4 complexes in the sera.
The implications of inducing the co-receptor binding site has been discussed extensively at the level of entry, but less so at the level of antibody elicitation. That the CD4i antibodies are not elicited by trimers in the absence of CD4, even though the gp140-F molecules are well recognized by 17b, and that CD4 induction of the epitope in the trimer context is not a requirement for 17b binding, may seem to be a bit of a paradox. However, we interpret these data to indicate that the conformational fixation imparted by CD4 binding to gp120 is a critical requirement for the naïve, germline B cell repertoire to efficiently recognize the site as opposed to the affinity-matured 17b antibody, which can likely induce the fit of its epitope in the absence of CD4 (see Schematic Fig 7). This is more likely to be an affinity limitation of the non-affinity matured BCR repertoire, although it is possible that it is somehow related to binding limitations of Ig molecules on the surface of B cells and epitope accessibility issues.
Another possible interpretation of the data is that elements of the pre-CD4 co-receptor binding site are immunogenic, but do not elicit antibodies that cross react efficiently with the fully formed site induced by CD4. However, the 17b blocking assay, using a form of the gp120 core with the potential to be recognized by any antibodies to the co-receptor site indicated that the pre-CD4 state of the trimers did not elicit many antibodies directed toward this region compared to those elicited in the presence of primate CD4 (Fig 4C). Also, we cannot rule out that array of gp140 or gp120 on the surface of CD4+ cells might also enhance the elicitation of CD4i antibodies in a manner independent of conformational fixation. However, the study by DeVico et al [29] clearly demonstrates that covalent gp120-CD4 complexes incapable of binding cell-surface CD4 efficiently elicit CD4i antibodies, so CD4-dependent cell-surface array cannot be the only explanation for their elicitation in the presences of primate CD4.
The implications of the data presented here are also an important consideration for vaccine candidates designed to elicit neutralizing antibodies against the conserved gp120 CD4 binding site. The Env CD4bs likely remains fully accessible in animals without human or primate CD4, however the elicitation of the CD4i antibodies in animals with primate CD4 indicates that this is likely not the case in species harboring CD4 molecules with a high affinity to Env. These results suggest that a fraction of the population of a CD4-binding-competent immunogen will interact with primate CD4 and thereby occlude the CD4 binding region on this protein subset. It is possible that the subtle differences detected in the neutralization profile between WT rabbits and monkeys occur as a result of such an interaction, partially altering the spectrum of antibodies that are elicited. However, the fractional component of the inoculate which binds to CD4 as yet remains to be determined, and may not be absolute as the overall HIV-1 neutralization profile elicited by the trimers used in this study was similar between the rabbits and the non-human primates.
It was also previously shown that HIV Env-CD4 interaction resulted in altered CD4+ T cell function in vitro [38] and it was suggested that elimination of Env interaction with CD4 in the context of vaccination might be beneficial to better elicit functional T cell help and more potent neutralizing antibody responses. From that study and the data presented here it will be interesting to assess if Env variants that do not bind CD4, but still retain the ability to bind CD4 binding site antibodies might make better immunogens than do unmodified YU2 gp140-F proteins. Alternatively, redirecting the immunogen more efficiently to B cell and antigen presenting cells might also overcome any potential detrimental effects of Env-based immunogens interacting with primate CD4. Follow up immunogen trimer design, characterization and immunogenicity studies are warranted to clarify these issues further in the near future.
Proteins were produced by transient transfection of adherent 293F cells or 293Freestyle suspension cells. The highly glycosylated and His-tag containing YU2gp140-F trimers were captured and purified from the serum-free media in a three-step process. First, the protein was captured via glycans over with lentil-lectin affinity chromatography (GE Healthcare, Uppsala, Sweden). After extensive washing with PBS the protein was eluted and captured in the second step via the His-tag by nickel-chelation chromatography. (GE Healthcare) then washed and eluted with a 300 mM Imidazole containing PBS buffer. In some cases the YU2gp140-F trimers were separated from lower molecular weight forms by the third step of gel filtration chromatography using a superdex200 26/60 prep grade column by the ÄKTA Fast protein liquid chromatography system (GE Healthcare). In contrast, the YU2gp120 and HXBc2 gp120 core proteins were purified by capturing the molecules on an IgG17b affinity column. After extensive washing with PBS, the proteins were eluted from the column with 100 mM glycine/Tris HCl/150 mM NaCl. pH 2.8 and immediately neutralized with Tris base, pH 8.5.
Env protein was co-incubated at concentrations of 0.4 to 46 nM in PBS with 2, 4 or 9 nM sCD4 at room temperature for 1 h, allowing for CD4-Env trimer complexes to form. Non-Env bound sCD4 was captured on a plate pre-adsorbed with 200 ng/well of the anti-CD4 antibody RPA-T4 (Ebioscience, San Diego, CA). RPA-T4 binds to domain 1 of CD4 and competes with HIV-1 gp120 for binding. RPA-T4 bound sCD4 was probed with a biotin-conjugated, non-competitive anti-CD4 antibody, OKT-4 (Ebioscience). Horseradish peroxidase (HRP) conjugated streptavidin (Sigma) followed by the colorimetric peroxide enzyme immunoassay substrate (3,3′,5,5′-tetramethylbenzidine; Bio-Rad) was added to induce a colorimetric change and the reaction was stopped by adding 1 M H2SO4. OD was read at 450 nm.
High-protein-binding MaxiSorp plates (Nunc) plates were coated with 200 ng/well of mAb 17b in 100 µl of PBS at 4°C overnight after which the wells were blocked for 2 h at room temperature (RT) with PBS-2% fat-free milk. The gp140-F trimers, at concentrations between 3.2 ng/ml to 10 µg/ml, were pre-incubated with 20 µg/ml sCD4 for 1 h at RT and then added to the wells. The wells were then probed for 17b bound gp140-F trimer with rabbit anti-gp140-F polyclonal sera. Addition of HRP conjugated anti-rabbit-Ig (Fc region) (Jackson Laboratories, Bar Harbor, MN) followed by the colorimetric peroxide enzyme immunoassay substrate (3,3′,5,5′-tetramethylbenzidine; Bio-Rad) was used to induce a colorimetric change and the reaction was stopped by adding 1 M H2SO4. OD was read at 450 nm. The 17b binding competition assay was performed by coating the ELISA plate with 200 ng/well of lectin from Galanthus nivalis (Sigma), followed by 200 ng/well of HXBc2 core protein. After blocking with 2% fat-free milk, serum was incubated for 45 minutes at dilutions between 25 and 6400 in a total volume of 100 ul after which 25 ul of biotin conjugated 17b antibody was added to a final dilution of 2500 and incubated for an additional 45 minutes. The plate was probed with HRP conjugated streptavidin and developed as above.
To determine the kinetic constants of YU2gp140-F interaction with 17b IgG, we performed binding analysis were in two different formats on a Biacore3000 surface plasmon resonance spectrometer. In one format (Fig 3A and B), YU2gp140-F (without or with pre-binding to 20-fold molar excess of D1D2 sCD4 for 1 h) was passed over the 17b IgG surface. Because of the potential oligomeric interaction of the trimers with 17b IgG presented on the surface of the chip, this analysis likely measured avidity rather than simple affinity. However, since the curves approximated single binding kinetics and we did not know how many monomeric subunits within the trimers were capable of 17b interaction, curve fitting was done assuming a 1∶1 interaction. In the reverse format (Fig. 3C), the 17b IgG was passed over YU2gp140-F surface and functional (or apparent) affinity was calculated using the bivalent analyte program (Biacore) that derived affinity from the potential bivalent interaction of the 17b with trimers immobilized on the chip surface.
To prepare binding surfaces, ligands (7 µg/ml in 10 mM NaOAc, pH 5.5 buffer) were immobilized on CM5 chip by the amine coupling method following manufacturer's protocol. One flow cell receiving only NaOAc buffer was used as reference control for correction of background binding. For binding experiments, analytes were serially diluted at concentrations ranging from 4.6 nM to 600 nM in the HEPES-EP reaction buffer. To determine the rate of association, each analyte was allowed to flow over the activated surfaces at a rate of 30 µl/min for 3 minutes. Dissociation was determined by washing off bound analyte for the next 5 min. Likely due to avidity of the oligomeric analytes, especially in Fig 3A, the rate of dissociation was difficult to determine and likely represents and over estimate of the actual 1∶1 binding kinetics. The surface was regenerated by removing any unbound analyte with two injections (60 sec each) of 10 mM Glycine, pH 3.0. All procedures were performed at RT.
Monkey, human and rabbit PBMCs were analyzed by flow cytometry using a modified LSR I system (BD Biosciences). Data analysis was performed using FlowJo software (Tree Star, San Carlos, CA). Staining and gating strategies to detect YU2 trimer binding to live, CD3+/CD4+/CD8− cells from primates is described in Fig. S2. Staining and gating strategy for trimer binding to rabbit cells is described in Fig. S4. The mAb 447-52D was a kind gift from Susan Zolla-Pazner (New York University School of Medicine).
Five female cynomolgus macaques (Macaca fascicularis) of Chinese origin, 5–6 years old, were housed in the Astrid Fagraeus laboratory at the Swedish Institute for Infectious Disease Control. Housing and care procedures were in compliance with the provisions and general guidelines of the Swedish Animal Welfare Agency. The animals were housed in pairs in 4 m3 cages, enriched to give them possibility to express their physiological and behavioural needs. They were habituated to the housing conditions for more than 6 weeks before the start of the experiment, and subjected to positive reinforcement training in order to reduce the stress associated with experimental procedures. All immunizations and blood sampling were performed under sedation with ketamine 10 mg/kg intramuscularly (i.m.). (Ketaminol 100 mg/ml, Intervet, Sweden) The macaques were weighed and examined for swelling of lymphnodes and spleen at each immunization or sampling occasion. Before entering the study, all monkeys were confirmed negative for simian immunodeficiency virus (SIV), simian T-cell lymphotropic virus and simian retrovirus type D. Female New Zealand White Rabbits and male huCD4 New Zealand White transgenic rabbits were housed at BioCon, Inc (Rockville, MD) or at an animal facility at the National Institutes of Health according to current regulations. Cynomolgus macaques were injected once with 200 ug followed by three injections with 100 µg YU2gp140-F trimer. Rabbits were injected four times with 50 ug YU2 trimer. All proteins were formulated in the GSK-AS01B adjuvant system (GlaxoSmithKline, Rixensart, Belgium) prior to injection unless otherwise stated and all injections were administered i.m. at an interval of one month. Sera were collected before the first injection as well as two weeks after each injection. All procedures were approved by the Local Ethical Committee on Animal Experiments.
Analysis for HIV-1 and HIV-2 neutralization in serum samples were performed as previously described [25],[26]. Briefly, Env pseudoviruses were prepared by co-transfecting 293T cells with an Env expression plasmid containing a full gp160 env gene and an env-deficient HIV-1 backbone vector (pSG3 Env). For screening, a single dilution of sera or plasma was used and the percent neutralization was calculated compared to controls with no sera added. To determine the dilution of the sera that resulted in a 50% reduction in RLU against selected viruses, serial dilution assays were performed and the neutralization dose-response curves were fit by non-linear regression using a 4-paremeter hill slope equation programmed into JMP statistical software (JMP 5.1, SAS Institute Inc., Cary, NC). The results are reported as the serum neutralization ID50, which is the reciprocal value of the serum dilution resulting in a 50% reduction in viral entry. Dana Gabuzda (Dana Farber Cancer Institute) provided the Env plasmid for YU2. Env plasmids for SF162 and JRFL were provided by Leonidas Stamatatos (Seattle Biomedical Research Institute) and James Binley (Torrey Pines Institute), respectively. The Clade A Env-pseudovirus DJ263.8 was cloned from the original PBMC derived virus provided by Francine McCutchan and Vicky Polonis (U.S. Military HIV Research Program). Env plasmids BaL.01 was recently described by our laboratory [26] and the Env used to generate the pseudovirus SS1196.1 was previously described [39]. The HIV-2 Env-pseudovirus 7312 containing the V343M mutation have previously been described [5]. The remaining functional Env plasmids were obtained from the NIH ARRRP.
Twenty randomly chosen serum samples were obtained via an MTA with the Global Solutions for Infectious Diseases. These sera were derived from volunteers from the VaxGen Inc phase III clinical trial. At the time of sampling (month 12.5) the participants had received four injections (month 0, 1, 6 and 12) with the AIDSVAX B/B vaccine containing 300 ug each of recombinant HIV-1MN or HIV-1GNE8 derived gp120 in Alum adjuvant [13].
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10.1371/journal.pntd.0006616 | Dengue virus serotype distribution based on serological evidence in pediatric urban population in Indonesia | Dengue is a febrile illness transmitted by mosquitoes, causing disease across the tropical and sub-tropical world. Antibody prevalence data and serotype distributions describe population-level risk and inform public health decision-making.
In this cross-sectional study we used data from a pediatric dengue seroprevalence study to describe historical dengue serotype circulation, according to age and geographic location. A sub-sample of 780 dengue IgG-positive sera, collected from 30 sites across urban Indonesia in 2014, were tested by the plaque reduction neutralization test (PRNT) to measure the prevalence and concentration of serotype-specific neutralizing antibodies according to subject age and geography. PRNT results were obtained from 776 subjects with mean age of 9.6 years. 765 (98.6%) neutralized one or more dengue serotype at a threshold of >10 (1/dil). Multitypic profiles were observed in 50.9% of the samples; a proportion which increased to 63.1% in subjects aged 15–18 years. Amongst monotypic samples, the highest proportion was reactive against DENV-2, followed by DENV-1, and DENV-3, with some variation across the country. DENV-4 was the least common serotype. The highest anti-dengue antibody titers were recorded against DENV-2, and increased with age to a geometric mean of 516.5 [1/dil] in the oldest age group.
We found that all four dengue serotypes have been widely circulating in most of urban Indonesia, and more than half of children had already been exposed to >1 dengue serotype, demonstrating intense transmission often associated with more severe clinical episodes. These data will help inform policymakers and highlight the importance of dengue surveillance, prevention and control.
| Dengue is a febrile illness transmitted by mosquitoes, causing disease across the tropical and sub-tropical world. Antibody prevalence data and serotype distribution describe population-level risk and inform public health decision-making. We present data from a dengue seroprevalence study in children in Indonesia; circulation of the four dengue serotypes (DENV-1, -2, -3, -4) was assessed, by age and location. Samples collected from 30 urban Indonesian sites were tested using the plaque reduction neutralization test (PRNT), which enabled us to measure prevalence and concentration of antibodies specific to dengue virus serotypes. Results were obtained from 776 subjects (mean age: 9.6 years). 765 (98.6%) neutralized ≥1 dengue serotype; the highest proportion was reactive against DENV-2, followed by DENV-1, and DENV-3, with some variation across the country. Reaction to multiple serotypes was observed in 50.9% of samples. The highest anti-dengue antibody titers were recorded against DENV-2, and increased with age. The fact that all four dengue serotypes have been widely circulating in urban Indonesia, and more than half of children had been exposed to >1 dengue serotype, shows intense transmission, often associated with more severe clinical episodes. These data will help inform policymakers and highlight the importance of dengue surveillance, prevention and control.
| Dengue is a febrile illness caused by dengue virus (DENV) infection. The clinical manifestations of dengue occur on a spectrum, ranging from asymptomatic or a mild flu-like syndrome known as classic dengue fever (DF), to a more severe form known as dengue hemorrhagic fever (DHF) and the potentially fatal dengue shock syndrome (DSS) [1]. DENV, which belongs to the family Flaviviridae, is transmitted by mosquitoes of the genus Aedes; predominantly Aedes aegypti. There are four evolutionarily distinct, antigenically related DENV serotypes; DENV-1, -2, -3, and -4 causing disease across the tropical and sub-tropical world [2, 3].
Neutralizing antibodies (NAbs) against the four serotypes are considered a critical component of the protective immune response which is achieved when adequate, specific antibody titers circulate [4]. Accordingly, plaque reduction neutralization tests (PRNT), which quantify serum concentrations required to neutralize live viruses, are the most specific assays for detecting flavivirus exposure history [5]. The dengue PRNT is able to target individual viral serotypes, and therefore can infer serotype-exposure history, however, interpretation of heterotypic responses is complicated for reasons including original antigenic sin [6, 7].
Indonesia is the largest archipelago country in the world with over 17,000 islands, inhabited by around 240 million people. Dengue was first reported in 1968, and has been expanding ever since, in both incidence and geography, with an annual burden of >750,000 cases [8]. The disease is likely hyperendemic across most islands [9, 10]. Reporting of DHF in Indonesia is mandatory within 72 hours of diagnosis, health centers and public/private hospitals use the World Health Organization’s (WHO) 1997 case definitions [11] and only DHF/DSS cases are reported. Laboratory confirmation of dengue is rare, especially in health services with limited facilities although dengue IgG/IgM and NS1 rapid tests are increasingly used in hospitals and health clinics. Indonesia does not conduct nationally-representative dengue serotype surveillance. Genotypic and serological surveillance has been undertaken by some Indonesian institutions, on a project basis which confirmed the dengue serotypes in symptomatic individuals [12–14]. Those studies include in Makassar, South Sulawesi from 2007–2010, where dengue infection was confirmed in >100 patients, many of whom were aged 11–20 years old. Serotyping revealed that DENV-1 was the most common form (41%) followed by DENV-2 (31%), DENV-3 (20%), and DENV-4 (7%) [15]. In Surabaya, East Java, in 2012, dengue RNA was isolated from 79 of 148 suspected dengue patients (53%), with DENV-1 as the predominant serotype (73%), followed by DENV-2 (8%), DENV-4 (8%), and DENV-3 (6%), while 5% were found to have mixed serotypes [16]. In Semarang, Central Java in 2012, 66 of 120 suspected cases (55%) were serologically confirmed and viral RNA was detected in 31 samples [12]. DENV-1 was the predominant serotype, followed by DENV-2, DENV-3, and DENV-4. DENV-1 predominance has also been reported from other studies and cities in Indonesia, including Surabaya [17] and Makassar [15]. Finally, from urban and rural areas of Bali (Denpasar and Gianyar), in 2015, 205 adult patients with suspected dengue were recruited in a prospective cross-sectional study. Of these, 161 patients had virologically-confirmed dengue; DENV-3 was predominant (48%), followed by DENV-1 (28%), DENV-2 (17%), and DENV-4 (4%). Five samples (3%) were detected which contained two different serotypes, and it was noted that the proportions varied in urban and rural areas [18].
Understanding antibody prevalence is an important consideration in the interpretation of epidemiological data, especially when reviewing interactions with other flaviviruses or considering vaccine introduction. The co-circulation of multiple dengue serotypes is a population-level risk factor for severe dengue disease because of the increased likelihood of a second or subsequent infection, and also due to the fact that sequential infections are associated with increased severity [19]. Serotype distribution may be predictive of future epidemiology and is important information for dynamic transmission models. The objective of this study was to use data from a dengue seroprevalence survey to describe the historical serotype (DENV-1, 2, 3, 4) circulation based on the prevalence of serotype-specific anti-DENV antibodies, according to age and geographic location, in a pediatric population in Indonesia.
In this cross-sectional study, serum samples and data from a national-level pediatric dengue seroprevalence study were used to describe historical dengue serotype circulation, according to age and geographic location. Dengue IgG-positive sera, collected from 30 sites across urban Indonesia, were tested by the PRNT to measure the prevalence and concentration of serotype-specific dengue neutralizing antibodies according to subject age and geography.
Surveillance and sample collection methods were previously described [20]. Ethical approval was obtained from the Health Research Ethics Committee of Faculty of Medicine of University of Indonesia (No. 462/H2.F1/ETIK/2014). Briefly, between 30 October 2014–27 November 2014, blood samples were collected from 3,210 children aged 1–18 years in 30 urban Indonesian subdistricts, randomly selected from west to east based on the probability proportional to population size. The blood samples were to be tested for dengue IgG by enzyme-linked immunosorbent assay (ELISA). A sub-sample of 780 dengue IgG positive sera was used to estimate the prevalence of serotype-specific neutralizing antibodies by PRNT. The sample size was estimated to provide 95% confidence and a margin error of 5%; this is accounting for the 30 clusters with a design effect of two and assuming the “worst case” of 50% exposure to any one serotype. The sample was not strictly representative of the dengue IgG positive population as the samples were selected equally from each of the four age groups, i.e. 195 samples per age group, and, to provide geographical representativeness, from clusters in proportion to dengue IgG seroprevalence rates. This method over-sampled from younger subjects to; 1) increase the number of samples tested from children recently infected with dengue, to provide a record of recent dengue circulation; 2) reduce the number of PRNTs performed on samples from older children, likely to have been infected with many serotypes, which may therefore be impossible to meaningfully interpret.
The PRNT method was performed based on optimized and validated PRNT50 assay for the detection of neutralizing antibodies to four serotypes of DENV [21]. Each serum sample was heat inactivated at 56°C and assayed in four separate PRNT runs, which corresponded to four different DENV serotype challenge viruses. Vero cells (CCL-81) were obtained from American Type Culture Collection (ATCC). Cells were grown and maintained in Minimum Essential Medium (MEM) (Gibco-Thermo Fisher Scientific, CA, USA), supplemented with 5% heat-inactivated Fetal Bovine Serum (FBS), 2 mM of L-glutamine, and 1% of antibiotic/antimycotic (Gibco-Thermo Fisher Scientific, CA, USA) at 37°C in an atmosphere of 5% CO2. Working banks of Vero cells were prepared in-house, qualified, and confirmed to be free of any microbial, mycoplasma, and viral contaminants. Purified mouse monoclonal antibodies (MAbs) specific to the DENV serotype envelope protein were used as the primary antibodies for virus detection according to the corresponding serotype: anti-DENV-1 (D2-1F1-3), anti-DENV-2 (3H5-1-12), anti-DENV-3 (8A1-2F12), and anti-DENV-4 (1H10-6-7) (Biotem, Le Rivier d’Apprieu, France). Alkaline phosphatase-conjugated goat anti-mouse IgG (Jackson Immunoresearch Laboratories, West Grove, PA, USA) was used as the secondary antibody. The parental DENVs of the recombinant CYD vaccine viruses, i.e., DENV-1 strain PUO-359, DENV-2 strain PUO-218, DENV-3 strain PaH881/88, and DENV-4 strain 1228, were used as challenge viruses in the PRNT. The initial source, and the suitability of these four DENV serotypes to be used in dengue neutralization assay have been described elsewhere [21–23]. Dengue-antibody positive and negative human serum sample controls were obtained from healthy adult donors from Indonesia. The serum controls were used in each assay run, and served to monitor its performance and validity.
The neutralization titer (PRNT50) of the test serum sample was defined as the reciprocal of the highest test serum dilution for which the virus infectivity was reduced by 50% when compared with the average plaque count of the challenge virus control, calculated using a four-point linear regression method. Since the lowest starting dilution of serum in the assay was 1:5, the theoretical lower limit of quantitation of the assay was a titer of 10 (reciprocal dilution).
This is a descriptive analysis, no hypotheses were tested. The study population mean age was calculated and geographic distribution described. Dengue serotype specific PRNT profiles were defined according to the following algorithm; categorizing samples as naïve (no previous dengue infection), monotypic (infection with one serotype), or multitypic (>1 serotype)[24]:
PRNT profile distribution by age and geography were described. PRNT profile prevalence and their 95% confidence intervals (95% CI) were calculated, the clusters results were aggregated at province level and a map was generated using QGIS 2.16.2 “Nødebo”.
The mean PRNT titer, GMT (Geometric Mean Titer), and the 95% CI for each age group and dengue serotype was calculated for all samples based on their DENV PRNT results. To calculate the GMT, samples with an antibody titer T <10 (1/dil) were given the value 5 and the mean titer was calculated using the equation:
GMT^jh=10^∑1nlog10Tin
Where GMT^jh is the mean titer for the dengue serotype h of the age group j, Ti is the PRNT titer of the subjects i and n the number of subjects with a PRNT titer in the age group j for the serotype h.
All statistical analyses were performed using Excel 2013.
Blood samples were collected from 3,210 children aged 1–18 years in 30 urban Indonesian subdistricts, randomly selected from west to east. From a sub-sample of 780 dengue IgG positive sera, PRNT50 results were obtained from 776 participants, equally sampled from each age group (1–4, 5–9, 10–14 and 15–18 years old). In the youngest, 1–4 years old group, four serum samples were of insufficient quantity to be tested. The mean age was 9.6 years old (95% CI [9.3–10.0]. The 30 clusters were represented with 14–39 samples per cluster. Of these, 765 (98.6%) neutralized one or more dengue serotypes at a threshold of >10 (1/dil), a proportion which varied by age: 95.3% in the 1–4 years old, 99.5% in the 5–9 years old, 99.5% in the 10–14 years old and 100% in the 15–18 years old.
Samples were categorized according to PRNT50 profile. Multitypic profiles were observed in 50.9% of the subjects, with 28.3% in those aged 1–4 years old, 48.2% in the 5–9 years old, 63.6% in the 10–14 years old and 63.1% in those aged 15–18 (Fig 1). The proportion of monotypic profiles decreased with increasing age, representing 67.0% of those aged 1–4 years, 51.3% of the 5–9 year old group, 35.9% of the 10–14 years old group, 36.9% of the 15–18 years old and 47.7% of the overall sample. There were no naïve subjects in the 15–18 years old group whereas 4.7% of the 1–4 years old group; 0.5% of the 1–9 and 10–14 years old groups, and 1.4% of the overall sample had no detectable neutralizing dengue antibodies at the 10 (1/dil) threshold. Amongst monotypic samples, the highest proportion of samples were reactive against DENV-2, followed by DENV-1, and DENV-3, a trend which was also observed in the two youngest age groups, while the three serotypes were more evenly distributed amongst the 10–14 and 15–18 years old age groups (Fig 1).
The clusters were aggregated within 14 provinces, resulting in samples per province ranging from 15 to 183 serum samples. In seven provinces multitypic profiles were the most common (from 52.2% to 69.4% of samples). In seven provinces the monotypic profile was more prevalent (from 49.7% to 68.8%). DENV-4 was dominant in one province, in the 13 other provinces DENV-1, DENV-2, DENV-3 or a combination of these serotypes were dominant, with DENV-2 dominance being more common (Fig 2). The four monotypic serotypes were identified in every province, with the exception of DENV-2 in Nanggroe Aceh Darussalam and DENV-4 in Sulawesi Tenggara and Sulawesi Selatan.
GMTs increased with age. DENV-2 had the highest GMT overall (406.5 [1/dil]) and for three of the four age groups with titers of 208.8, 502.2, 497.4 and 516.5 [1/dil], respectively (Fig 3). DENV-4 had the lowest GMT for each age group (51.2, 98.9, 138.1 and 128.2 [1/dil]) and overall (97.6 [1/dil]). In the oldest subjects, titers against DENV-1 were highest (593.08 [1/dil]) followed by DENV-3 (550.2 [1/dil]) and DENV-2.
We conducted a dengue seroprevalence study which identified serological evidence for the circulation of all four dengue serotypes across urban areas of Indonesia, in children who were exposed to infection from 1996 to 2013. The proportion of children with exposure to >1 serotype increased with age, and children were more likely to have been infected with DENV-2, DENV-1 and DENV-3 than DENV-4. Nonetheless, these results show that all four serotypes have been widely circulating in most of Indonesia, as is common in hyper-endemic countries. This study generated data on serotype-specific prevalence in areas where little or no data were previously available, with the exception of historical data from Yogyakarta, Java island [32].
Available dengue serotype data collated from 1994 to 2012 (n = 596) [25] and recent publications from all over Indonesia confirm the concomitant presence of the four DENV serotypes [10, 12, 15–18, 26–28]. Samples were collected from suspected cases and therefore suffer a potential selection bias towards serotypes associated with more symptomatic/severe cases. The serological data we report here indicate a consistent pattern of distribution of serotypes, a finding which may indicate that the cases captured within these surveillance studies is broadly reflective of the DENV serotype circulation in the country.
PRNT enables the interrogation of samples according to their exposure history. In this study, it was remarkable to observe that in this pediatric population more than half (50.9%) had already been exposed to >1 dengue serotype, a proportion which increased with age. This rate is important because it demonstrates early and intense transmission in Indonesia; and we know that second infections have been described as more likely to be symptomatic, severe and hemorrhagic [29]. Individuals of an age likely to have received one natural exposure, but before their second, may represent an attractive target for dengue vaccination programs [30]. The observed GMT increase with age is most likely explained by continuous re-exposure to DENVs over time, further boosting antibody levels. These profiles imply that existing vector control activities in urban areas are largely insufficient at preventing infection; and that investments in novel methods may be warranted. The prevalence of multitypic profiles further reinforces the requirement for development of a safe and effective, quadrivalent dengue vaccine which could be used in children at highest risk of developing symptomatic and severe disease episodes. Additionally, these data can be useful for the calibration of dengue transmission models which may help to understand disease dynamics and the likely effects of dengue control interventions.
There are several limitations to our study. Sera collected during the convalescent phase represent infection history in the population, but are limited by the sensitivity and specificity of the serological methods used to quantify antibodies. We had the benefit of analyzing samples in this study by PRNT; however interpretation of data can be confused by heterotypic cross-neutralization between serotypes. For this reason, we did not interpret the serotype distributions of multitypic infections. Only samples positive for dengue IgG in ELISA screening assay were selected to undergo PRNT, therefore these may not be fully representative of dengue positive sera. We also observed discrepancies between IgG ELISA and PRNT data in which some samples that were positive by IgG ELISA were negative in PRNT (1.4%). This may be a consequence of the well-documented serological cross-reactivity across the flavivirus group [31] Our sample collection was also limited to urban areas and subjects consenting to the study which may have introduced additional bias.
In conclusion, this study confirmed the distribution of multiple dengue serotypes across urban Indonesia. Many children were infected with multiple serotypes, and the accompanying risk of severe disease, from an early age. DENV-1, DENV-2 and DENV-3 may play a more significant epidemiological role than DENV-4. It is hoped that these data influence policymakers to afford increased attention to dengue surveillance, prevention and control.
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10.1371/journal.ppat.1004816 | Prospective Large-Scale Field Study Generates Predictive Model Identifying Major Contributors to Colony Losses | Over the last decade, unusually high losses of colonies have been reported by beekeepers across the USA. Multiple factors such as Varroa destructor, bee viruses, Nosema ceranae, weather, beekeeping practices, nutrition, and pesticides have been shown to contribute to colony losses. Here we describe a large-scale controlled trial, in which different bee pathogens, bee population, and weather conditions across winter were monitored at three locations across the USA. In order to minimize influence of various known contributing factors and their interaction, the hives in the study were not treated with antibiotics or miticides. Additionally, the hives were kept at one location and were not exposed to potential stress factors associated with migration. Our results show that a linear association between load of viruses (DWV or IAPV) in Varroa and bees is present at high Varroa infestation levels (>3 mites per 100 bees). The collection of comprehensive data allowed us to draw a predictive model of colony losses and to show that Varroa destructor, along with bee viruses, mainly DWV replication, contributes to approximately 70% of colony losses. This correlation further supports the claim that insufficient control of the virus-vectoring Varroa mite would result in increased hive loss. The predictive model also indicates that a single factor may not be sufficient to trigger colony losses, whereas a combination of stressors appears to impact hive health.
| Roughly one third of the food supply relies on pollinating insects. The number of colony losses of the domesticated Honey Bee (Apis mellifera) has grown significantly in the past eight years, endangering pollination of crops like almonds. Recent research indicates that colony losses are influenced by a combination of several factors. We conducted an extensive and controlled study that allowed us to look at the contribution of different factors to colony loss. Results helped us build a predictive model showing that a single factor is often insufficient to trigger colony loss. Combination of stressors has shown to have greater impact on hive health; replication of the Deformed Wing Virus, stressful weather conditions, and Varroa destructor comprise the primary identified causes.
| Pollination by wild or managed species of pollinators is essential to agricultural productivity. Honey Bees (Apis mellifera) play an essential role in this process by pollinating many important crops such as apples, almonds, and alfalfa [1,2]. In the United States (USA), almond-bearing acres have grown by 130% since 1982 and now rely on 1.6 million colonies (65%-70% of all USA colonies) to pollinate 740 thousand acres of almond trees [3].
In years prior to 2007, winter losses of hives averaged 10%–15%, represented by general decline in hive health, brood density, and total honeybee number. In 2007 beekeepers began to report unusually high losses of hives ranging from 30% to 90% [4]. The losses were associated with an unusual phenomenon of sudden disappearance of bees, with very few dead bees located near the colony. This phenomenon was designated Colony Collapse Disorder (CCD) [5,6]. Metagenomics analysis performed in 2007 identified the Israeli Acute Paralysis Virus (IAPV) as a potential cause of CCD [7,8], but further research showed that IAPV was present in the USA before the CCD epidemic [9]. Other research on CCD hives failed to show an association with IAPV [4]. Since 2007, colony losses have been monitored across the USA and found to average around 30% [10].
Recent research indicates that the decline of managed hives during winter months is influenced by a combination of several factors, including pests, parasites, bacteria, fungi, viruses, pesticides, nutrition, management practices, and environmental factors [4,11,12]. There is no consensus, however, regarding the relative importance of these factors, singly or in combination, in causing CCD [11].
Several studies have been performed in the USA and other world regions to identify the most significant factors associated with hive decline [4,7,13–18]. Most have focused on hive pathogens such as bee viruses, Nosema, and the Varroa mite. Cornman et al. performed a survey of pathogens in CCD and non-CCD hives, showing an increase of pathogens in collapsed hives; no association was determined for other factors such as weather, pests, or nutrition [17]. Runckel et al. performed a 10-month pathogen investigation using hives under migration stress and antibiotic treatment, and were able to show a correlation of hive collapse to various bee viruses and Nosema [16]. The ectoparasitic Varroa destructor mite, in combination with various bee viruses, is also associated with colony losses [7,19–26].
The Varroa mite is currently considered to be the most serious threat to honey bee populations worldwide [27]. Varroa has adapted to the developmental stages of the honey bee, entering uncapped brood cells to reproduce and feeding on the larval hemolymph after capping, causing nutrient depletion and weakening the larvae. Several bee viruses have been reported to be transmitted by and replicated in Varroa mites who act as an alternative host. These include Deformed Wing Virus (DWV), Kashmir Bar Virus (KBV), Sacbrood Virus (SBV), Acute Bee Paralysis Virus (ABPV), and Israeli Acute Paralysis Virus (IAPV) [24,28–32]. Surveys monitoring virus and Varroa mite levels have been supplemented by modeling approaches that are found to predict and understand the dynamics of the Varroa-virus interaction in the hive and their effect on hive health [33–35].
Beekeepers monitor Varroa mite levels extensively and use several acaricides to maintain low Varroa levels. However, determining presence of pathogenic bee viruses and whether they replicate is complex and not always available to beekeepers. Furthermore, not all bees exhibit a phenotype when virally infected, thus complicating any diagnosis and making prediction of viral outbreaks even more difficult.
To further dissect factors that influence hive health, we conducted large-scale controlled winter trials at three locations across the USA. Each site contained approximately 60 monitored hives that were not treated with antibiotics or acaricides in order to better understand the effect of Varroa destructor, bee viruses, and Nosema on hive health. The trials were conducted without exposing hives to migratory stress. A weather station was placed at each location for daily monitoring of temperature, humidity, and precipitation. The hives were assessed four times during a period of 7 months. The assessment included Varroa levels, prevalence and replication of 8 bee viruses, Nosema ceranae levels, hive strength, measured by the Almond Grower Method (AGM), and total adult bee number.
The trial was carried out in 3 different locations across the US to represent 3 different climate and geographical conditions: mountain area of Northern California (site 1), costal area of Florida (site 2) and Southeast Texas (site 3) (Fig 1). Hives were internally fed monthly with sugar syrup and were assessed four times for hive strength, virus levels, Nosema ceranae, and Varroa mite counts (Fig 1). Two methods were used to assess hive strength: the Almond Grower Method (AGM), used by beekeepers to assess hive strength as number of bee covered frames before almond pollination; and imaging software, counting bees from frame images [36]. The number of bees in the hive provides a reliable proxy to the comparative strength of the hive. Fig 2A shows different results between the two methods; while the AGM method showed equal hive strength at start point in all three sites, the frame imaging method indicates that Site 3 had significantly (P<0.05) fewer bees than the other two sites.
To allow for consistency between sites, different inspectors and beekeepers, we defined in the trial protocol a collapsed hive as a hive with AGM assessment of less than or equal to 1 bee frame coverage. In our study the probability of overwinter survival for a hive, based on bee population, is estimated to be 50% when bee number drops below 2500 and 90% when at least 4000 bees are present (Fig 2B).
Eight bee viruses were assessed for prevalence (defined as percentage of hives where the virus was detected) throughout the trial using QuantiGene Plex 2.0 platform. Bee virus prevalence reported here is a snapshot of the prevalence for those hives that were classified as live at the sampling time. As the study progressed, the number of sampled hives decreased due to hive loss. The temporal patterns in virus prevalence were similar for the subset of hives that had measurements at every sampling period and those reported here for all live hives at each sampling period (S1 Table). KBV, ABPV, and IAPV exhibited similar patterns, starting with relatively low levels at the beginning of the trial (October time point Fig 3A, 3B, and 3E) and increasing by trial end to >65% across sites. DWV was found at high prevalence (75%-95%) throughout the trial with no significant difference among sites. BQCV and CBPV ranged between 25%-95% prevalence across sites throughout the trial. Lake Sinai Virus (LSV) was present in >90% of the hives across sites and time points. One striking difference was found at Site 1, where prevalence of paralysis viruses (KBV, ABPV, BQCV, CBPV, IAPV) dropped dramatically in the February assessment, while DWV and LSV prevalence remained high.
Nosema ceranae was analyzed by QuantiGene Plex 2.0 platform using two different probes to verify consistency of results. Nosema prevalence averaged 60–85% across sampling times (Table 1), but differences in prevalence were noted among sites at different sampling times.
Significant increases in Varroa counts per 100 bees were observed across time at sites 2 and 3 while a significant decrease was found at Site 1 (Fig 4).
The relationship between DWV and IAPV virus levels in bees and Varroa infestation, defined as the number of phoretic mites per 100 bees, is depicted in Fig 5A and 5B. Results show a significant linear association of virus levels of the two viruses in bees and Varroa at high Varroa infestation (>3 Varroa mites per 100 bees). At low Varroa infestation (≤3 Varroa mites per 100 bees), there were insufficient numbers of colonies with non-zero levels of DWV and IAPV in bees to determine a correlation. DWV can be found at high virus levels in Varroa, but not in bees at low Varroa infestation (Fig 5A).
Two sites exhibited a positive linear relationship between Varroa mite infestation and levels of at least one virus. Varroa infestation was positively associated with DWV (Sites 1 and 2), IAPV (Site 2), and VDV (Site 2) (Table 2). No significant association between Varroa infestation and viral load was found at Site 3. The variation in Varroa mite number was narrower at Site 3 than Sites 1 and 2, which may have limited the ability to detect significant relationships. Positive linear relationships were found between Nosema ceranae load and levels of the Dicistroviridae family of viruses (ABPV, BQCV, CBPV, IAPV, KBV) and LSV at all sites. Associations with DWV or VDV-1 were less consistent (Table 2).
Replication of DWV, but not IAPV or LSV, increased significantly with an increase in Varroa infestation at two sites (Table 3). Increase in Nosema was significantly associated with increase in replicating IAPV at 3 sites, with DWV at 2 sites and with LSV at 1 site (Table 3). Data were not sufficient to determine association between replication of ABPV or KBV with Varroa mite increase or Nosema ceranae.
Table 4 shows the colony loss pattern throughout the trial. During the January and February assessments, all sites exhibited similar losses of about ≃22%; while in April there was an increase in colony losses at site 2 and 3 compared to previous months, and in comparison to site 1(30 colonies at Site 2 and 13 colonies at Site 3). Temperature loggers at each site indicated that, while at Site 1 minimum temperatures remained below 6°C throughout the trial, Sites 2 and 3 showed average temperature increases to above 7°C in April (Table 5).
At site 1 replicating DWV was greater in collapsed hives in the October measurement but not in January or February (S2 Table). At Site 2, DWV load was consistently greater in collapsed hives and significantly so (P<0.05) at the February collection period after which the greatest loss in hives occurred (Fig 6A). IAPV and replication of DWV were also significantly greater at the February collection (Fig 6B and 6C). Although not significant, we found that the replication of IAPV was higher in collapsed hives (P<0.15 Fig 6D). At Site 3, DWV and IAPV loads were significantly greater in collapsed hives in January (S2 Table). Replication of IAPV, however, was found to be consistently greater in survived hives and significantly greater at the February time period (S2 Table).
Varroa mite counts increased throughout the trial at Site 2 and differed significantly between collapsed and survived hives in February (Fig 6E), where levels of Varroa mites in collapsed hives averaged 15 mites per 100 bees. A similar pattern of increasing Varroa infestation over time with greater levels in collapsed hives and significantly so at the February collection was noted at Site 3 but not at Site 1.
The predictor variables in the final logistic model were DWV replication, VDV load, average 10-day minimum temperature, average maximum temperature, IAPV replication, and location. A consistent linear association was found between collapsed hives and DWV replication. At DWV replication levels of >32 QG units, 80% of the hives collapsed. In the predictive model, increased DWV replication, VDV load, sustained cold temperatures, and sustained warm temperatures were found to increase the probability of colony losses. A counter-intuitive association between high IAPV replication and hive survival was found. Further examination of this factor revealed 10 surviving hives at Site 3 with high levels of IAPV replication and DWV replication. Replicating IAPV helped to correctly predict survival of those hives. At the other two sites, high levels of IAPV replication were associated with colony losses. Nosema ceranae was not chosen as a predictive marker by the model. Varroa also was not selected as a predictor due to lack of a linear association across all levels of Varroa infestation. However, at loads ≥8 Varroa/100 bees, 87% of the hives collapsed. The relative contribution of each factor to colony losses is depicted in Fig 7. Although a linear relationship between collapse and Varroa load was not found, Varroa is included in the figure to account for collapse in hives with high infestation. Collapse was attributed to Varroa at ≥8 mites/100 bees, to DWV replication at ≥32 QG units and to VDV at ≥300 QG units. Collapse of hives that were correctly predicted as collapsed but did not meet any individual threshold level was attributed to a combination of factors at lower threshold levels. Cold weather in combination with at least one other factor, but not by itself, was included as contributing to collapse. Warm weather was associated with increased Varroa, DWV replication or VDV levels and was not considered as a direct factor in collapse. Varroa infestation, DWV replication, VDV loads, and cold weather accounted for 69% of the collapsed hives. The reason for collapse in the remaining 31% is unknown. The same criteria, when applied to the surviving hives, predicted 19% should have collapsed.
In the last seven years, mean annual colony losses across the USA increased to approximately 30%. Extensive research has been performed in the previous decade to characterize reasons for colony losses [4,6,7,13,17,37–39].
Our study, a comprehensive, controlled experiment with a large number of monitored hives (179 hives in three locations), examines hive losses as a multi-factorial event. Evaluated factors include Varroa mite, bee viruses, Nosema ceranae, weather, and location, but do not account for factors such as migration, treatment of antibiotics and mitocides, nutrition or exposure to pesticides.
Several pathogens, such as bee viruses, Nosema ceranae and Varroa destructor have been proposed as contributing to increased winter losses of bees [15,37,40–43].
In 2012, the National Honey Bee Pests and Diseases Survey Report published the prevalence of bee viruses Nosema and Varroa destructor across the USA between the years 2009–2011 [18]. Similar levels of bee viruses, with slight differences among sites, are reported in this study (Fig 3). The 2012 Survey Report found that IAPV prevalence varies from year to year and increases between January and April. A similar increase from January to April, with higher levels of IAPV, KBV, ABPV, and BQCV, was reported here. LSV was surveyed by Runckel at al. showing a peak in prevalence in January, whereas here we show high virus prevalence throughout the trial [16]. Difference in detection methods (QuantiGene Plex 2.0 platform vs. QPCR), year of sampling, or the fact that the hives in this study were not treated could account for differences noted between the two studies. Francis et al. compared IAPV, KBV, and ABPV viral levels in their study in mitocide-treated and untreated hives and showed an increase in viral load in the untreated groups [43]. This increase could be correlated to an increase in Varroa mite infestation reported in our study (Fig 4), as the mite was found to transmit these viruses to bees [21,29–31,43]. Prevalence of these viruses exhibited reduction at the February time point at Site 1, potentially caused by virus-carrying bee mortality between the January and February time points. Unlike Cornman et al., in our study, the overall picture of virus prevalence was similar across all sites and no difference was found between the different regions in the USA [17].
The microsporidia Nosema ceranae is associated with colony losses, especially in Europe [44,45]. Botias et al. showed presence of Nosema spores in honey and honey bee samples taken as early as 2000, and an increase in prevalence from 30% in 2002 to 47% in 2007 [46]. Our data from 2012/2013 show the prevalence of Nosema to be 60–80%, depending on the site and month of sampling (Table 1). Similar data was reported by others [17,18]. Runckel et al. reported lower levels of Nosema, but in their study, hives were treated with fumagillin [16].
Varroa destructor is considered to be one of the main causes of hive decline; therefore, we monitored its levels (expressed as number of mites per 100 bees) throughout the trial. Rennich et al. reported Varroa mite levels of 2–8 per 100 bees depending on the season and year of sample [18]. The average mite number in their survey is only from hives where Varroa was found, whereas we show an average count of all hives (Fig 4). Sites 2 and 3 started with very low levels of Varroa, as the participating beekeepers treated against Varroa prior to start point. Mite levels at these sites increased during the trial, averaging as high as 15 mites per 100 bees at Site 2. Unlike Sites 1 and 3, the temperature at Site 2 (Table 5) was high throughout the trial. Bee number of survived hives (S3 Table) at Site 2 did not decrease for the first 5 monitored months, indicating the presence of brood throughout the trial, hence the opportunity for Varroa mite to propagate. Sites 1 and 3 experienced a roughly 50% drop in the bee population during the first 5 months of our study (S3 Table), most likely due to low winter temperatures (Table 4), thus preventing bee brood development as well as Varroa reproduction.
Previous studies have demonstrated the correlation between Varroa mite infestation and viruses, especially DWV [21,24,28–31], the association between viral replication in mites and development of crippled wings [19,24,47,48], and the correlation between DWV viral copies in bees and mite infestation [43].
This study supported a direct association between 3 parameters: viral load (DWV or IAPV) in bees, viral load in mites, and mite infestation level. Given the observational nature of this study, the reported associations do not imply causation. The DWV viral load in bees can remain low in the presence of a high DWV copy number in Varroa as long as Varroa infection is low (≤3 mites/100 bees). At mite levels >3 mites/100 bees, a linear association can be found between the DWV and IAPV loads in the mite and in the bees. Martin et al. showed in their survey that, once Varroa penetrated the Hawaiian islands, DWV increased both in prevalence and in copy number [24]. Similarly, Mondet et al. showed that presence of DWV or KBV in bees correlates to their presence in the mites [49]. The penetration of Varroa mite to New Zealand also increased the number of different bee viruses in infested hives as compared to hives from Varroa-free areas. These results support the theory of direct transmission of bee viruses between Varroa to bees.
To further characterize the relationship between Varroa infestation and different bee viruses, the association between Varroa mite infestation and viral load and replication was tested at each site. At Site 1, only DWV was positively correlated with mite infestation, while at Site 2, DWV, VDV-1, and IAPV were positively associated. At site 3, non-significant negative correlations were found between mite infestation, KBV and ABPV. Similar negative correlation was reported by Mondet et al. once Varroa penetrated New Zealand [49]. They also showed that different viruses can be found in the bees in correlation to the number of years Varroa infested the island. The differences between sites could also result from different viruses being carried by Varroa at different locations as reported by others [24,30,31]. DWV replication increased proportionately to mite infestation, but the replication of IAPV and LSV did not associate with mite infestation. Sumpter et al. have used a mathematical model to investigate the relationship between mite load and viral epidemic potential within a colony, demonstrating that, because the paralysis viruses (IAPV/ABPV/KBV) are harmful to bees at low levels, a pupa infected in a brood cell by a mite with APV will die before reaching adulthood [35]. Pupal mortality indicates that the virus will not spread in the hive unless transmitted by the Varroa to adult bees. DWV is less virulent in its effects and infected pupae most probably will emerge with the virus allowing the epidemiological correlation of replicating virus with Varroa infestation [35].
Our study shows that increase in Nosema ceranae is associated with an increase of all tested paralysis viruses, while having low correlation, if any, with DWV, VDV-1, or LSV. These results are supported by data obtained by Martin at al., in which no correlation was found between Nosema ceranae and DWV viral load [50]. The association between the microsporidia to the paralysis viruses has been demonstrated; Toplak et al. showed potential synergistic effects when co-infecting bees with CBPV and Nosema ceranae, yet this association does not necessarily imply direct causation and could result from effects of high Nosema or viral load on the immune system of the bee [51]. Antunez et al. showed that infection of Nosema ceranae leads to suppression of bee immune response, which could lead to an increase in bee pathogens. [52].
Suggested reasons for hive decline have been numerous: Varroa mite, bee viruses, Nosema, nutrition, extreme cold, beekeeping practices, and pesticides. This study addressed colony loss as a multi-factorial problem and identified DWV replication, Varroa infestation and VDV loads as influential in colony losses. Weather could not be considered as a stand alone factor for collapse since all hives at each location were exposed to the same weather patterns. There were, however, notable differences in weather patterns among sites and the predictive model found both sustained cold and sustained warm weather to increase the probability of colony collapse. A sustained cold period at site 1 was predicted to influence the February colony loss. The sustained warm weather at site 2 was accompanied by increased levels of at least one other risk factor. Late colony collapse at this site was attributed to high levels of Varroa infestation, DWV replication or VDV which may have increased under mild winter weather conditions.
Factors contributing to colony losses were Varroa infestation (≥8/100 bees), DWV replication (≥32 QG units), VDV-1 load (≥300 QG units), combinations of Varroa infestation, DWV replication and VDV-1 at lesser threshold levels, and cold weather in combination with at least one other contributing factor (Fig 7). The model suggests that non-treated hives with increased mite populations are more likely to decline due to mite infestation, as reported by others [37,42]. Based on the factors measured in this study, DWV replication has the greatest impact on colony loss in treated hives where mite population is controlled. Interestingly, the model predicted that a portion of the surviving hives had a greater than 50% probability of collapse. These hives were largely subject to high levels of single risk factors; further suggesting collapse is enhanced by the presence of multiple factors.
Our study supports the hypothesis that combinations of factors contribute to colony losses. With no available treatment against Varroa, its levels can exceed 8 mites per 100 bees, causing hives to collapse. At lower mite infestation rates, replication of bee viruses takes an active role in the collapse. According to our model, approximately 70% of hive collapse is caused by Varroa and bee viruses. Control of mite and viral levels may mitigate colony loss, resulting in levels more acceptable to the apiary industry.
Three commercial beekeepers participated in the trial. At each site hives were re-queened with queens of the same age and genetic background, and equalized to have 7 frames covered with bees and 3–4 frames covered with capped brood. Queens were purchased from Kona Queen Hawaii, Inc.; at study initiation, queen acceptance was verified.
Bees intended for Quantigene Plex 2.0 assay and Nosema counts were collected from the outer frame in a 50mL tube. Immediately following collection, samples were placed on dry ice and were kept at -80°C until analyzed. For Varroa counts, half a cup of bees was sampled from the inner frame into Wide-Mouth HDPE Packaging Bottles with PP closure (Thermo Scientific cat 03-313-15D), which contained 70% alcohol solution.
To collect Varroa mites for virus analysis, the sugar shake method, used by beekeepers to monitor mite level or as treatment against Varroa, was used. A cup of powdered sugar was placed on top of the frames and spread using a hive tool or a paint brush, allowing adult bees to be covered with sugar powder. The sugar powder causes the majority of mites to dislodge from their host and fall down onto the bottom board. A white paper was placed on a bottom board of each hive. After five minutes the bottom board was removed and live Varroa mites were collected into 50mL tubes using a paint brush. The mites were immediately placed on dry ice and kept at -80°C until analyzed.
Almond grower assessment method (AGM) was performed by the beekeepers or the assigned study monitor that was trained to perform the assessment. Hives were opened and graded by the number of covered bee frames assessed after looking at the top and bottom of each hive.
A weather collection station monitoring temperature, humidity, and precipitation was placed at each site (Phytech, ILS). The data was transmitted in real-time over a cellular network and collected in our computers.
Frames with bees were slowly removed to avoid disruption and placed on a frame holder. Photos were taken from both sides of the frame. Total number of bees on each frame was determined using image recognition software (IndiCounter, WSC Regexperts).
Quantigene, a quantitative, non-amplification-based nucleic acid detection analysis, was performed on total lysate from frozen honey bees or Varroa mite samples. The oligonucleotide probes used for the QuantiGene Plex 2.0 assay were designed and supplied by Affymetrix, using the sense strand of bee virus sequences as template or negative strand for replicating virus. The probe, designed to detect the sense strand, reflects the presence of virus (viral load) and probe designed to detect the anti-sense strand reflects level of viral replication. Housekeeping gene probes were designed from sequences of Apis mellifera mellifera Actin, Ribosomal protein subunit 5 (RPS5), and Ribosomal protein 49 (RP49). For Varroa mites, actin and α tubulin were used as housekeeping gene references.
The QuantiGene assay was performed according to the manufacturer’s instructions (Affymetrix, Inc., User Manual, 2010) with the addition of a heat denaturation step prior to hybridization of the sample with the oligonucleotide probes. Samples in a 20 μL volume were mixed with 5 μl of the supplied probe set in the well of a PCR microplate, followed by heating for 5 minutes at 95°C using a thermocycler. Heat-treated samples were maintained at 46°C until use. The 25 μl samples were transferred to an Affymetrix hybridization plate for overnight hybridization. Before removing the plate from the thermocycler, 75 μl of the hybridization buffer containing the remaining components were added to each sample well. The PCR microplate was then removed from the thermocycler; the content of each well (~100 μl) was then transferred to the corresponding well of a Hybridization Plate (Affymetrix) for overnight hybridization. After signal amplification, median fluorescence intensity (MFI) for each sample was captured on a Luminex 200 machine (Luminex Corporation).
Bees were collected in 70% alcohol solution and shaken for 10 minutes on a Burrel (Model 75) Wrist Action Shaker. Bottles were emptied onto a VWR 1/8 inch US Standard Testing Sieve (Cat # 57334–242) to collect the Varroa shaken off the bees. Washed Varroa fell through the sieve onto a weigh boat, and the sieve, with the bees on top, was shaken by hand to collect any Varroa mites that had not washed off immediately. The bottle was checked for any Varroa that had not poured out onto the sieve. Varroa mites were then counted. To determine the number of bees in each bottle, 10 bees from each bottle were weighed and average bee weight was calculated. Weight of all bees was then divided by the average bee weight to calculate number of bees. The Varroa count was divided by number of bees and multiplied by 100 to determine number of Varroa/100 bees.
Differences among sites for bee number at study initiation was tested using one-way analysis of variance with site as the sole fixed effect. Mean separation was performed using Fisher’s Protected LSD. A logistic regression was used to predict the probability of hive survival (AGM score >1) given bee counts. The number of bees associated with a collapsed hive at the first observation of collapse was considered as the bee count for collapsed hives. For non-collapsed hives, the minimum bee count across the 4 measurement periods was used as the bee count for the hive. The logistic regression modeled the binomial response of collapsed or live hives as a function of bee number, site, and the interaction between site and bee number. The relationship was not found to differ by site and the across site model is reported here.
Viruses were considered present if the Quantigene Plex 2.0 value was above the background level. Percent prevalence was calculated as the number of hives with the virus over the number of hives that were sampled. As the study progressed, the number of sampled hives decreased due to hive loss.
Pearson’s Product-Moment Correlation analysis was performed to test for linear relationships between viral loads in Varroa and bees for DWV and IAPV. A logarithmic transformation was applied to the Quantigene units data before conducting the correlation analysis.
The relationship between Varroa number and viral load was examined using a repeated measures analysis of covariance. The viral load (log transformed) was fitted as a function of Varroa number and collection time, while modeling the repeated measures on hives across time with an autoregressive covariance structure of order 1. The analysis was conducted by location and virus. The same analysis was performed for the relationship between viral load and Nosema load.
Hives that had an AGM score of ≤1 at any time during the study were considered to be collapsed. Data from the first 3 collection periods was used in a repeated measures analysis that fitted individual viral loads or Varroa load as a function of the binary variable of collapsed or live hives, collection time, and the interaction between hive status and collection time. The data from the April collection period was not used since it was unknown if the hives collapsed at a subsequent time period and viral data from collapsed hives was limited at the final collection period. The analysis was conducted by location and virus.
A multi-factor model to predict colony losses was developed by first selecting a subset of predictor variables from the profile of nine viruses, Varroa mite counts, Nosema load, 3-, 7-, 10-, and 14-day moving averages for minimum and maximum temperatures (defined as extreme weather conditions), and average minimum and average maximum temperature. For collapsed hives, the virus profile, Nosema load, and Varroa counts at the time of collapse or at the previous collection period were used in the model and the weather variables for the time period between the time of collapse and the previous time period were used. For live hives, the profile at the February collection period and the weather variables between the January and February collection periods were used. The February profiles were selected for live hives as they represented the time period when most colony losses occurred. The subset of variables included in the final modeling process was selected by consensus of variable importance ranking by Random Forest [53] and LASSO regression techniques [54]. Stepwise logistic regression was applied to the subset of variables to develop a final model.
Results are considered significant at P<0.05. The Random Forest and LASSO regressions were performed in R [55,56]. All other statistical analyses were conducted using SAS/STAT software.
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10.1371/journal.pntd.0004254 | The Impact of Trachomatous Trichiasis on Quality of Life: A Case Control Study | Trachomatous trichiasis is thought to have a profound effect on quality of life (QoL), however, there is little research in this area. We measured vision and health-related QoL in a case-control study in Amhara Region, Ethiopia.
We recruited 1000 adult trichiasis cases and 200 trichiasis-free controls, matched to every fifth trichiasis case on age (+/- two years), sex and location. Vision-related quality of life (VRQoL) and health-related quality of life (HRQoL) were measured using the WHO/PBD-VF20 and WHOQOL-BREF questionnaires. Comparisons were made using linear regression adjusted for age, sex and socioeconomic status. Trichiasis cases had substantially lower VRQoL than controls on all subscales (overall eyesight, visual symptom, general functioning and psychosocial, p<0.0001), even in the sub-group with normal vision (p<0.0001). Lower VRQoL scores in cases were associated with longer trichiasis duration, central corneal opacity, visual impairment and poor contrast sensitivity. Trichiasis cases had lower HRQoL in all domains (Physical-health, Psychological, Social, Environment, p<0.0001), lower overall QoL (mean, 34.5 v 64.6; p<0.0001) and overall health satisfaction (mean, 38.2 v 71.7; p<0.0001). This association persisted in a sub-group analysis of cases and controls with normal vision. Not having a marriage partner (p<0.0001), visual impairment (p = 0.0068), daily labouring (p<0.0001), presence of other health problems (p = 0.0018) and low self-rated wealth (p<0.0001) were independently associated with lower overall QoL scores in cases. Among cases, trichiasis caused 596 (59%) to feel embarrassed, 913 (91.3%) to worry they may lose their remaining eyesight and 681 (68.1%) to have sleep disturbance.
Trachomatous trichiasis substantially reduces vision and health related QoL and is disabling, even without visual impairment. Prompt trichiasis intervention is needed both to prevent vision loss and to alleviate physical and psychological suffering, social exclusion and improve overall well-being. Implementation of the full SAFE strategy is needed to prevent the development of trachomatous trichiasis.
| There is clear evidence that visual impairment generally reduces quality of life. However, relatively little is known about the impact that trachomatous trichiasis (TT) has on the lives of affected people with and without the presence of visual impairment. We measured the impact of TT on vision and health-related quality of life in 1000 people with TT using standard WHO quantitative tools and compared these with 200 trichiasis-free controls, matched to every fifth trichiasis case on age, sex and location. We found TT cases had lower vision and health related quality of life than controls regardless of visual impairment and other health problems suggesting the burden of TT goes beyond visual loss. The results provide solid data for advocacy and encourage programme leaders and funders to secure resources to promote trichiasis intervention. Trichiasis causes considerable physical and psychosocial trauma including sleep disturbance, low self-esteem and possibly a less stable marriage regardless of visual impairment. These suggest that, timely treatment is needed not only to prevent visual loss but also alleviate physical and psychological suffering and social exclusion of TT patients, thereby improving their physical and psychological health, general functioning and social relations.
| Trachoma is the leading infectious cause of blindness worldwide [1]. About 229 million people live in trachoma endemic areas [2]. The disease starts in early childhood with repeated episodes of Chlamydia trachomatis infection. This triggers conjunctival inflammation of the upper eyelid, which leads to scarring. The scarring causes the eyelid to turn in (entropion) and the eyelashes to scratch the eye, which is known as trachomatous trichiasis (TT). Visual impairment and blindness develop when the cornea is damaged directly or indirectly by the trichiasis and ocular surface dysfunction, leading to corneal opacification (CO). Approximately 7.3 million people have un-treated trichiasis [3]. It is estimated that 2.4 million people are visually impaired from trachoma worldwide, among which between 439,000 and 1.2 million are estimated to be irreversibly blind [2,4].
Clinical examination provides little insight into the impact a condition has on the overall functioning and life of an affected individual and their family. Trichiasis can cause ocular pain and impaired vision but it can also have a profound effect on broader aspects of general health and well-being [5]. However, there is very limited data on the effect of trichiasis and its associated visual impairment on quality of life (QoL). QoL is a broad concept that refers to an “individual’s perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” [6]. It can be measured quantitatively by a variety of tools including health-related quality of life (HRQoL) tools and tools measuring broader concepts to evaluate the overall experience of life [7]. HRQoL tools can be further divided into those measuring disease-specific quality of life (e.g. vision related QoL) and generic HRQoL [7].
A comprehensive vision related quality of life (VRQoL) measure has been developed by the World Health Organization (WHO): WHO/PBD-VF20 (World Health Organisation/ Prevention of Blindness and Deafness—Visual Functioning 20 item questionnaire) [8]. This tool was designed to explore the eyesight, ocular pain and discomfort, general functioning and psychosocial factors related to vision. WHO/PBD-VF20 has been evaluated and showed good psychometric properties in studies of people with visual impairment from cataract in Kenya, Bangladesh and the Philippines [9–11]. However, it has not been used to measure VRQoL in people living with trichiasis.
Generic HRQoL tools assess a range of health related issues and can be used irrespective of disease entity [7,12]. The WHOQOL-BREF is one such tool, which has been developed and validated across 20 countries in Africa, Asia and Latin America [12–16]. A hospital-based study in India used the WHOQOL-BREF to compare the QoL of 60 “trachomatous entropion” patients with age, sex and socio-economic status matched hospital patients without entropion or trichiasis [5]. However, about two-thirds of the cases had entropion without trichiasis, which precludes drawing conclusions about the QoL of trichiasis patients and the controls were not necessarily representative of the population.
Relatively little is known about the impact that trichiasis has on the lives of affected people. In this case-control study we measured the impact on vision and health-related QoL in Ethiopia, using standard WHO quantitative tools.
This study was reviewed and approved by the Food, Medicine and Healthcare Administration and Control Authority of Ethiopia, the National Health Research Ethics Review Committee of the Ethiopian Ministry of Science and Technology, Amhara Regional Health Bureau Research Ethics Review Board Committee, the London School of Hygiene and Tropical Medicine (LSHTM) Ethics Committee, and Emory University Institutional Review Board. Written informed consent in Amharic was obtained prior to enrolment from participants. If the participant was unable to read and write, the information sheet and consent form were read to them and their consent recorded by thumbprint.
This case-control study was nested within a clinical trial of two alternative surgical treatments for trichiasis. For the trial 1000 trichiasis cases were recruited, and these were also enrolled into this QoL study. Cases were defined as individuals with one or more eyelashes touching the eyeball or with evidence of epilation in either or both eyes in association with tarsal conjunctival scarring. People with trichiasis from other causes, recurrent trichiasis and those <18 years of age were excluded. Trichiasis cases were identified mainly through community-based screening. Trichiasis screeners and counsellors (Eye Ambassadors) visited every household in their target village, identified and referred trichiasis cases to health facilities where surgical services were provided. Some cases self-presented or were referred by local health workers. Recruitment was done mainly from three districts of West Gojam Zone, Amhara Region, Ethiopia between February and May 2014. This area has one of the highest burdens of trachoma worldwide [17].
We recruited 200 matched controls to every fifth consecutive trichiasis case. Controls were individuals without clinical evidence or a history of trichiasis (including epilation), and who came from households without a family member with trichiasis or a history of trichiasis. Controls were individually matched with every fifth trichiasis case by location, sex and age (+/- two years). The research team visited the sub-village (30–50 households) of the trichiasis case that required a matched control. A list of all potentially eligible people living in the sub-village of the case was compiled with the help of the sub-village administrator. One person was randomly selected from this list using a lottery method, given details of the study and invited to participate if eligible. If a selected individual refused or was ineligible, another was randomly selected from the list. When eligible controls were not identified within the sub-village of the index case, recruitment was done in the nearest neighbouring sub-village, using the same procedures.
The VRQoL and HRQoL were administered orally by six trained Amharic speaking interviewers, because of the low literacy rate amongst participants. Data from trichiasis cases were collected at health facilities at the time of enrolment into the clinical trial, prior to surgery. Data from the controls were collected at their homes.
Data were also collected on general health problems and self-rated socioeconomic status (SES). For the self-rated socio-economic status, participants were asked to rate the wealth of their households in relation to other households in their village by choosing one of the following options: (1) very poor, (2) poor, (3) average, (4) wealthy or (5) very wealthy. In addition, data were collected on social relations, marriage and sleeping, through semi-structured questions including: “Do you feel ashamed or embarrassed due to the trichiasis?”, “Do you worry that you may lose your remaining eyesight due to the trichiasis?”, “Do you have a sleeping problem?” and “If yes, do you think your sleeping problem is related with the trichiasis?”
Presenting LogMAR (Logarithm of the Minimum Angle of Resolution) visual acuity at two metres was measured using “PeekAcuity” software on a Smartphone in a dark room [21]. We assessed contrast sensitivity with a prototype smartphone based test that presents the individual with calibrated grey scale spots against a white background, which they have to identify by touching the screen (www.peekvision.org). Unlike visual acuity, which is measured with high contrast, contrast sensitivity perhaps more accurately reflects the person’s everyday visual experience in varying conditions. Patients with normal visual acuity may have profound contrast sensitivity impairment. Therefore it is useful to measure contrast sensitivity while investigating VRQoL, as impairment could lead to decreased functioning and quality of life [22]. The ophthalmic examination was conducted using a 2.5x binocular magnifying loupe and a bright torch. Clinical signs were graded using the Detailed WHO FPC Grading System [23].
The sample size was calculated with the aim of detecting a three point difference in mean QoL score between trichiasis cases and controls [5]. The sample size of 1000 cases and 200 controls has 90% power to detect even minimal effect of trichiasis on QoL with an effect size of about 0.27 (effect size = QoL score difference (3)/SD (11)) with a Type I error of 5%.
Data were double-entered into Access (Microsoft), cleaned in Epidata 3.1 and transferred to Stata 11 (StataCorp) for analysis. Data were analysed as follows:
Cases and controls were adequately matched for age (Table 1) and for geographical distribution across the 20 administrative units where recruitment was conducted. There were proportionately slightly more females among the 200 controls (83%) than the 1000 cases (76%). This occurred by chance as the 200 trichiasis cases used to determine the control matching characteristics had proportionately more females than in the full group of 1000 trichiasis cases. The trichiasis cases were more likely to be widowed or divorced, be from poorer households, and report other health problem in the past month than controls. For the analysis of socio-economic data we combined the “very poor” with the “poor” group and the “very wealthy” with “wealthy” group because of small numbers at both ends of the SES distribution. Trichiasis cases had substantially lower visual acuity scores (median LogMAR, 0.35; IQR, 0.15 to 0.6) than the controls (median LogMAR, -0.05; IQR, -0.1 to 0.1; Wilcoxon ranksum test, p<0.0001). Among trichiasis cases vision in the better eye was 6/18 or better in 63%, and 50% had minor trichiasis. The median duration of trichiasis among the cases was 5 years (IQR, 2–10).
The trichiasis cases had substantially lower VRQoL scores than the controls in all four subscales (p<0.0001) (Table 2). The largest differences between cases and controls were found for visual symptoms (mean difference, 51.5; 95%CI, 48.5–54.5) and the smallest in general functioning (mean difference, 24.1; 95%CI, 21.1–27.1). In a sub-group analysis, trichiasis cases with normal vision had significantly lower VRQoL in all subscales than controls with normal vision (Table 3).
The relationship between VRQoL and various demographic and clinical characteristics among individuals with trichiasis are presented in Table 4. VRQoL scores were lower in all domains with increasing age, severity and duration of trichiasis and with decreasing visual acuity and contrast sensitivity scores. Scores were also lower in females, illiterate individuals and in cases with central corneal opacity.
In multivariable analysis, lower overall VRQoL scores were found in those with longer trichiasis duration, central corneal opacity, visual impairment and poor contrast sensitivity (Table 4). In addition to these, older age and female gender were associated with lower VRQoL score in the general functioning subscale. In the model, people with Major Trichiasis had lower VRQoL score in the visual symptom domain after controlling for potential confounders (p = 0.011), however, this is confounded by duration of trichiasis and was therefore dropped from the final model (Table 4).
The trichiasis cases had substantially lower overall HRQoL (p<0.0001) and overall self rated health (p<0.0001) scores than the controls (Table 2). Strikingly, 55.4% of trichiasis cases rated their overall QoL “poor” or “very poor” compared to only 9.5% of controls (p<0.0001) and 5.6% of cases rated their overall QoL “good” or “very good” compared to 59.5% of controls (p<0.0001). Across all four domains there were substantial differences in the QoL scores of cases and controls (all p<0.0001, Table 2). The largest difference was seen in the physical health domain (mean difference, 32.4; 95%CI, and 30.1–34.7). Trichiasis cases with normal vision had significantly lower HRQoL in all subscales than controls with normal vision (Table 3).
Among trichiasis cases, overall HRQoL and the four domain scores decreased with increasing age (except for environment domain), decreasing self-rated wealth, visual acuity and contrast sensitivity scores (Table 5). They were also lower in divorced/widowed, illiterate individuals, females and those with other health problems in the past month. Daily labourers, the unemployed and those with central corneal opacity had lower overall QoL and domain scores except for the environment domain. Participants with longer duration trichiasis had lower physical, psychological and social domain scores.
Multivariable analyses identified predictors of HRQoL among trichiasis cases (Table 5). Lower self-rated wealth was associated with lower QoL scores in all domains. Poorer overall QoL was related to not having a marriage partner, visual impairment, being a daily labourer and presence of other health problems. Older participants, females, the unemployed, those with visual impairment, poor contrast sensitivity score and other health problems were associated with lower physical domain scores. Daily labouring, not having a marriage partner and presence of other health problems were associated with lower psychological domain scores.
Among the 200 controls, 198 (99%) reported no ocular pain or discomfort. In contrast, among the 1000 trichiasis cases, 143 (14.3%), 281 (28.1%) and 562 (56.2%) reported mild, moderate and severe ocular pain or discomfort, respectively (p<0.0001). The cases reported the following effects of trichiasis: 596 (59%) felt ashamed or embarrassed; 913 (91.3%) worried that they might lose their remaining eyesight; 70 (7.0%) had been troubled in their marriage and ignored by their marriage partner; 681 (68.1%) reported sleeping problems, largely due to pain (675/681 (67.5%)) from the trichiasis.
Satisfying the known-groups difference criteria, the trichiasis cases had significantly lower VRQoL and HRQoL scores in all domains (p<0.0001) than the controls (Table 2). With respect to convergence validity, worsening visual acuity and contrast sensitivity scores, trichiasis duration and central corneal opacity were significantly associated with lower scores in all four domains of VRQoL (Table 4). The VRQoL data were reliable after being assessed for internal consistency with a Cronbach’s alpha: coefficients of >0.80. The overall QoL data showed very high internal consistency with a Cronbach’s alpha of 0.90. The physical health, psychological, social and environment domains demonstrated internal consistency with Cronbach’s alpha of 0.87, 0.65, 0.47, and 0.64, respectively.
Trachomatous trichiasis results in considerable morbidity even before the development of irreversible visual impairment or blindness from corneal opacification. The eyelashes constantly rub the cornea causing irritation and pain [26]. However, despite these important consequences there are surprisingly limited data on the impact of trichiasis on quality of life. Moreover, few studies have investigated the resulting functional physical impairment [27,28]. The psychological and social effects of trichiasis have usually been overlooked. Little information has previously been collected using validated tools. In response, this study was conducted to address these gaps, using standard WHO QoL instruments, and found that trachomatous trichiasis has a very profound impact on both VRQoL and HRQoL, even prior to the development of visual impairment.
Overall, the VRQoL of trichiasis cases was substantially lower in all domains compared to controls. When we restricted the analysis to people with a visual acuity of 6/18 or better, the difference in VRQoL was of a similar magnitude and was highly significant. This is an important observation, which demonstrates that trichiasis reduces VRQoL even before impairment of visual acuity develops.
We found that, of the four sub-scales, the one with the largest difference was the visual symptom subscale. This is a composite of questions about visual functioning, pain/discomfort, glare and light/dark adaption. The general functioning subscale showed the smallest difference compared to controls. This may be because approximately two-thirds of trichiasis cases had normal vision, and this subscale includes items on vision difficulties and role limitation.
Consistent with our study, several other studies have demonstrated the effect trichiasis has on physical functioning [26–28]. A population-based study in Tanzania found trichiasis without visual impairment results in limitation of physical functioning in women that was comparable to limitation associated with visual impairment from other causes [27]. A qualitative study of 23 women with trichiasis in Niger (without a control group for comparison) reported trichiasis had marked effects on the general well-being of these individuals and was linked to physical disability and inability to work and earn an income [26]. In a study conducted in southern Ethiopia, 61% of trichiasis cases reported difficulty in physical functioning including walking, recognizing faces and performing day-to-day farming activities [28].
Among trichiasis cases, VRQoL was significantly lower with central corneal opacity, increasing trichiasis duration and decreasing visual acuity and contrast sensitivity scores. This suggests that trichiasis cases had significantly lower contrast sensitivity than the controls. Other studies have found conditions such as dry eyes and reduced tear break-up time, resulting from progressive conjunctival scarring and ocular epithelial tissues damage, are associated with reduced contrast sensitivity score [29,30]. Impaired contrast sensitivity score can greatly affect the person’s ability to recognise objects and perform daily activities under different conditions. A recent study conducted on glaucoma patients revealed that contrast sensitivity plays a major role in daily functioning and VRQoL; and the contrast sensitivity score was correlated with VRQoL indicators such as facial recognition, finding objects, motion detection and general vision [22]. Poor contrast sensitivity has been associated with physical injuries from accidents, suggesting that it would greatly hamper overall well being [31,32]. The association between trichiasis severity and the visual symptom subscale was weakened after including trichiasis duration in the multivariable regression model. Severity and duration are not independent of each other. The general functioning subscale of the VRQoL and physical health domain in HRQoL were lower in females than males. Although the reason is not apparent, a similar finding has been reported in a Tanzanian study: women without visual impairment were more likely to have functional limitation than their male counterparts [27].
Trichiasis cases had a poorer HRQoL than controls in all domains, again even without visual impairment. Strikingly, the physical domain in the WHOQOL-BREF, which includes questions on pain and discomfort, had the highest mean score difference between cases and controls, emphasising the suffering trichiasis causes. Among the QoL domains, the environmental domain had the lowest score in both trichiasis cases and controls. This domain is built from items such as satisfaction with financial resources, access to health service, transport, information and leisure activities, which generally have low availability in the communities where this study was conducted. Hence, participants would be anticipated to have a lower rate of satisfaction for these items.
The WHOQOL-BREF has been used to assess HRQoL of entropion patients (with and without trichiasis) [5]. Apart from the environment domain scores of the trichiasis cases, the average QoL scores of trichiasis cases and controls in all domains in the Indian study were generally lower than those we recorded in Ethiopia [5]. This difference could be attributed to two things. Firstly, perceptions towards QoL in Indian and Ethiopian communities could be different. Secondly, in the Indian study, all participants were recruited from hospital, compared to community-based recruitment in our study. Hospital participants might be more likely to report poor QoL than people in the community [14]. In contrast to trichiasis, there is an extensive literature about the impact of cataract on QoL [7,9–11,33]. However, there are fundamental differences between cataract and trichiasis in the nature of visual loss, pain and other symptoms they cause.
These tools have previously been reported to be valid and reliable in studies conducted in similar settings to this study [9–11,14,18–20]. In this study, both the VRQoL and HRQoL data measured what they were intended to measure (construct validity) by demonstrating significant differences in the scores between groups known to be different; cases and controls had lower and higher scores respectively. The VRQoL data also showed that sub-scales correlate well with measures of similar constructs (convergent validity) such as visual acuity and contras sensitivity where worsening in these measures is associated with lower VRQoL subscale scores. There was evidence of higher homogeneity among the items in each VRQoL subscale (internal consistency) than the generally accepted criteria of >0.70. In the HRQoL data, the overall QoL and the physical health domain items were internally consistent and reliable in measuring the same construct, while the psychological, social relations and environment domains had less internal consistency. Similar psychometric properties have been reported in the field trial results of this tool in other countries [14]. The lower alpha score for the social relationship domain is anticipated as its analysis is based on three items instead of the generally recommended minimum four for evaluating internal consistency [6]. Hence, this domain’s results should be interpreted in caution, as there was insufficient evidence that the items in this domain are always measuring the same construct.
This is a large case-control study examining the impact of trichiasis on QoL. We used tools validated in settings similar to this study setting. The VRQoL tool has been validated in Kenya while the HRQoL tool has been tested and used in other studies in Ethiopia. The study has some limitations. Perfect matching was not achieved in terms of gender, resulting in more females in the controls than the trichiasis cases. However, all comparisons between cases and controls were adjusted for gender, age and self-rated wealth. A community-based screening method was employed to identify trichiasis cases. Although we think that this was an efficient and comprehensive approach to finding cases, it is possible that some cases might have been missed, particularly those with mild disease who may be less likely to come forward, which could lead to an overestimation of the QoL scores for trichiasis cases in general. Trichiasis is strongly associated with a poorer QoL, However, the cross-sectional nature of this study precludes us from drawing definite conclusions about causality. This question is being investigated by reassessing this group of people one year after surgery.
In this Ethiopian population, we found that trichiasis cases have significantly lower VRQoL and HRQoL than controls regardless of visual impairment. The results provide solid data for advocacy and encourage programme leaders and funders to secure resources to promote trichiasis intervention. Trichiasis inflicts considerable physical and psychosocial trauma including sleep disturbance, low self-esteem and possibly a less stable marriage. The burden of trichiasis goes beyond visual loss. Timely treatment is needed not only to prevent visual loss but also alleviate physical and psychological suffering and social exclusion of trichiasis patients, thereby improving their physical and psychological health, general functioning and social relations. The comprehensive SAFE strategy is needed to prevent the development of trichiasis. The long-term effect of trichiasis surgery on VRQoL and HRQoL in trichiasis patients needs to be measured in longitudinal studies.
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10.1371/journal.ppat.1005323 | Dual Requirement of Cytokine and Activation Receptor Triggering for Cytotoxic Control of Murine Cytomegalovirus by NK Cells | Natural killer (NK) cells play a critical role in controlling murine cytomegalovirus (MCMV) and can mediate both cytokine production and direct cytotoxicity. The NK cell activation receptor, Ly49H, is responsible for genetic resistance to MCMV in C57BL/6 mice. Recognition of the viral m157 protein by Ly49H is sufficient for effective control of MCMV infection. Additionally, during the host response to infection, distinct immune and non-immune cells elaborate a variety of pleiotropic cytokines which have the potential to impact viral pathogenesis, NK cells, and other immune functions, both directly and indirectly. While the effects of various immune deficiencies have been examined for general antiviral phenotypes, their direct effects on Ly49H-dependent MCMV control are poorly understood. To specifically interrogate Ly49H-dependent functions, herein we employed an in vivo viral competition approach to show Ly49H-dependent MCMV control is specifically mediated through cytotoxicity but not IFNγ production. Whereas m157 induced Ly49H-dependent degranulation, efficient cytotoxicity also required either IL-12 or type I interferon (IFN-I) which acted directly on NK cells to produce granzyme B. These studies demonstrate that both of these distinct NK cell-intrinsic mechanisms are integrated for optimal viral control by NK cells.
| Natural killer (NK) cells play a crucial role in the protection of the host against viruses and in particular herpesvirus infections. Through their activation receptors which recognize surface ligands on target cells, NK cells can mediate direct killing (cytotoxicity) of virus-infected cells and produce their signature cytokine IFNγ, but it is unclear to what extent these effector arms contribute to clearance of murine cytomegalovirus (MCMV) infections. Additionally, NK cells are activated through their cytokine receptors but the interplay between the activation and cytokine receptor pathways has not been elucidated. Herein we devised a viral competition assay that allowed direct evaluation of the requirements for NK cell mediated MCMV control. We found that cytotoxicity is the main effector mechanism by which NK cells control virus infection through activation receptors. Complemented by in vitro assays, we delineated the requirements for NK cell cytotoxicity and identified a 2-step mechanism for NK-mediated cytotoxicity. Firstly, NK cells require cytokine signals for the accumulation of cytotolytic proteins. Secondly, direct target cell recognition results in release of the cytolytic cargo and lysis of virus-infected cells. Our study demonstrates the integration of NK activation and cytokine receptor signals are required for effective viral control.
| Natural killer (NK) cells are a critical component of the innate immune system. They play essential roles in controlling viral infections as illustrated in patients with selective NK cell defects who are susceptible to recurrent herpesvirus infections [1]. These clinical responses are recapitulated in animal studies, particularly with murine cytomegalovirus (MCMV), a natural mouse pathogen of the β-herpesvirus family, thus allowing further mechanistic insight. In the C57BL/6 (B6) mouse strain, NK cell-mediated control of MCMV infection is dependent upon the Ly49H activation receptor which is responsible for genetic resistance and is absent in susceptible strains such as BALB/c [2–4]. Ly49H specifically recognizes the MCMV-encoded ligand, m157, triggering NK cell activation and subsequent control of MCMV [5, 6]. Ly49H associates with the DAP12 adaptor molecule required for Ly49H surface expression and signaling. DAP12 has cytoplasmic immunoreceptor tyrosine-based activation motifs (ITAM) and directly mediates Ly49H signaling [5–7]. While the requirement of the related adapter molecule DAP10 is controversial [8, 9], Ly49H-dependent antiviral control is also illustrated by selection pressure in T cell-deficient hosts in which escape viral clones deficient in m157 expression emerge after several weeks following infection [10]. Unlike with the wild-type (WT) virus, these escape MCMV clones cannot be controlled by NK cells, even in Ly49H-sufficient mouse strains [10, 11]. Recently, infection with multiple, purified wild isolates of MCMV confirmed that Ly49H+ NK cells could only control m157-sufficient virus, resulting in an apparent outgrowth of m157-deficient strains [12]. Thus, Ly49H-m157 interactions are critical for MCMV control.
As with other NK cell activation receptors, Ly49H recognition of m157 in vitro can trigger two major effector functions: target-cell lysis (cytotoxicity) and cytokine production [5, 13]. Indeed, NK cell activation receptor ligands expressed on insect cells are sufficient to trigger NK cell degranulation as measured by cell-surface CD107a (LAMP1) staining [14]. Stimulation of NK cells with plate-bound anti-activation receptor antibodies, such as anti-Ly49H, causes similar NK cell activation and target killing [13, 15]. In addition, Ly49H-dependent stimulation in vitro leads to release of the signature NK cell cytokine, interferon gamma (IFNγ) [5], which has direct antiviral activity and can modulate subsequent immune responses [16, 17]. Indeed, prior to identification of the role of Ly49H, NK cell-dependent control of MCMV in B6 mice was reported to be dependent on both cytotoxicity and IFNγ [18, 19]. A more recent report also supports a role for IFNγ in NK cell control of MCMV but cytotoxicity was not examined [20]. Moreover, NK cells also release chemokines upon Ly49H stimulation [21]. Thus, it is still unclear which NK cell effector mechanisms, i.e. cytotoxicity versus cytokine/chemokine production, contribute specifically to Ly49H-dependent clearance of MCMV.
In addition to stimulation through their activation receptors, NK cells can be non-specifically activated through cytokine receptors to produce other cytokines [22–24]. During MCMV infections, other immune and non-immune cells produce an array of pro-inflammatory cytokines including IFNα/β [25] (IFN-I), IL-12 and IL-18 [26]. Many of these cytokines are induced by pattern receptors, such as TLRs, which are required for MCMV control, even in Ly49H-sufficient mice [27]. Importantly, these cytokines can directly or indirectly stimulate NK cells to produce IFNγ and this early production of IFNγ by NK cells can influence antiviral responses. However, the pleiotropic nature of the antiviral cytokines, such as IFN-I, IL-12, and IL-18, makes it difficult to dissect their broader effects on viral replication from their role in stimulating NK cell cytokine production [16].
Indeed, several cytokines are also capable of potentiating NK cell cytotoxic function [24]. Pretreatment of mice with poly(I:C) enhances both in vivo and ex vivo cytotoxic responses against various NK cell targets [28]. This effect is due to indirect stimulation of NK cells via accessory cells responding to poly(I:C) through the nucleic acid sensors, subsequently inducing the production of IL-12 and IFN-I [29]. These cytokines also induce DCs to produce IL-15, which has been linked to “priming” of NK cells thereby enhancing killing of target cells as well as increasing production of IFNγ and granzyme B (GzmB) [30]. In addition, resting murine NK cells express abundant transcripts for their cytotoxic proteins; cytokines such as IL-2 and IL-15 induce production of GzmB and perforin protein, which also correlates with increased target cell lysis [31]. It has been postulated that the elevated protein levels of these effector molecules facilitate NK cytotoxicity mediated through activation receptors, such as Ly49H, but direct evidence is not available and the roles of other cytokines have not been explored.
Therefore, it has been challenging to determine the precise NK cell effector mechanism mediated by Ly49H-dependent activation of NK cells in controlling MCMV as well as the role of non-specific cytokine stimulation of NK cells in this control. Herein we employed an in vivo viral competition approach which, together with in vitro and other in vivo studies, strongly suggests that NK cells require triggering of the Ly49H activation receptor for degranulation and that certain cytokine signals are also required to promote sufficient GzmB protein levels for effective cytotoxic elimination of MCMV.
To address the relative impact of NK cells and Ly49H versus cytokine signaling on MCMV infection, we evaluated the splenic titers of mice three days after challenge with a low dose (5000 PFU) of a wild type (WT) MCMV clone (Fig 1A). Importantly, our studies allowed a direct comparison of different genetically deficient mice on the C57BL/6 (B6) background. We found the expected marked susceptibility in B6 mice depleted of NK1.1-expressing cells or that were genetically deficient in Ly49H expression (B6.BxD8 strain). Compared to B6 mice, we also noted either no effect of IL-12 cytokine deficiency (p40 subunit) or relatively minor effects of IL-12Rβ2 (three-fold) and IFNγ (two-fold) deficiency on titers. Moreover, interferon α/β receptor 1 deficiency (IFNAR1-/-) increased titers over 30-fold but not to the levels seen with NK cell or Ly49H deficiency. The loss of perforin (Prf1-/-) resulted in elevated viral titers similar to both Ly49H and NK cell deficiency, suggesting that cytotoxic effector function is a key mechanism in early splenic control of MCMV, but the role of cytotoxic T cells could not be excluded. On the other hand, when we challenged a cohort of mice for five days with a larger dose (20000 PFU) of MCMV, we observed somewhat different effects (Fig 1B). While we again observed the expected susceptibility in mice depleted of NK1.1-expressing cells and also a minor effect of IL-12Rβ2 and IFNγ deficiency, IFNAR1-/- mice demonstrated markedly increased titers (over 1000-fold) approaching levels seen with NK cell depletion (Fig 1B). Thus, overall viral titers suggest dominant roles for perforin and IFN-I in control of MCMV with contributions from IL-12 and IFNγ, but their relationship to Ly49H-dependent responses remained unclear as their effects can be dependent on the viral dose.
Since it was challenging to attribute functional defects specifically to NK cells and Ly49H when genetically deficient mice were infected with WT MCMV and viral titers or survival were assessed, we designed and validated an in vivo dual-infection, viral competition assay. To specifically determine the role of Ly49H-dependent and independent control of MCMV in vivo, we inoculated mice with a mixture of MCMV lacking m157 expression (Δm157) due to a single point mutation, and WT MCMV at a defined frequency. We hypothesized that Ly49H-dependent control of WT MCMV should result in a proportional increase in Δm157 virions, as measured by the splenic frequency of Δm157 relative to WT MCMV (Fig 1C). By contrast, absence of Ly49H-dependent control should not lead to selection of Δm157.
To validate this approach, we first studied B6 mice individually infected with WT MCMV or Δm157 and treated with either isotype control antibody or anti-NK1.1 (Fig 1D). The dose of viral inoculum was chosen to reflect the frequencies to be used in the dual-infection assay (Fig 1C). We found that depletion of NK cells led to markedly elevated WT MCMV levels with no additional effect on Δm157 titers which were already elevated in control antibody-treated mice despite a lower inoculum (Fig 1D). We next characterized the splenic frequency of Δm157 virus in NK cell-depleted and control mice after inoculation with a mixture of 90% WT MCMV and 10% Δm157 viruses (Fig 1E). Indeed, in B6 mice treated with the control antibody, the Δm157 viral frequency increased from input level (10%) to the limit of the assay (75%), indicating selective outgrowth of Δm157. However, in mice depleted of NK cells, the Δm157 MCMV frequency was similar to the input inoculum (Fig 1E), even though overall viral titers were significantly elevated (S1A Fig). These results demonstrate that without the selective pressure contributed by NK cells, both WT and m157-deficient viruses replicate to similar levels in vivo.
The degree of viral burden might contribute to differences in the ability of NK cells to clear WT MCMV during the viral competition assay. Consistent with this, we have seen that splenic selection of Δm157 in B6 mice can be overwhelmed with a larger viral inoculum (S1B and S1C Fig). However, 1e6 PFU per mouse (50-fold higher than in the experiments performed within the rest of this study) were required to observe such an effect. Therefore, we utilized a low inoculum (2e4 PFU per mouse) that allowed for optimal discrimination of Ly49H-dependent clearance without overwhelming NK cell-dependent viral control.
When we infected the Ly49H-deficient B6.BxD8 strain, we observed no Δm157 selection, demonstrating that the co-infection reflected Ly49H-dependent antiviral function (Fig 1F). There was also impaired Δm157 selection in DAP12–/–and DAP10–/–DAP12–/–mice. As genetic deficiency of DAP12 prevents surface expression of Ly49H, we also analyzed DAP12 knock-in (KI) mice having a mutation in the ITAM that allows normal Ly49H surface expression with defective signaling [7]. The DAP12 KI mice also showed no selection. Although DAP10 can associate with Ly49H, the single DAP10–/–animals allowed Δm157 to accumulate, indicating that DAP10 is not required as previously reported [9] while contrasting with other reports [8]. Consistent with previous data (Figs 1E and S1A), viral titers were elevated in all dual-infected mice in which selection was impaired (S1D Fig). Thus, Ly49H and DAP12 are required for Δm157 selection, consistent with previous studies on Ly49H-DAP12-dependent mechanism for WT MCMV control [5, 7] and emergence of Δm157 in T cell-deficient mice [10].
When inoculated with a mixed MCMV inoculum, mice with TLR signaling deficiency due to absence of both TLR signaling adapters (myeloid differentiation factor 88, MyD88; and Toll/IL-1R domain-containing adapter inducing IFN-β, TRIF) demonstrated enhanced susceptibility (Fig 2A). Though consistent with prior reports demonstrating that TLR deficiency leads to enhanced susceptibility to MCMV [27], MyD88–/–TRIF–/–mice showed no defect in Δm157 selection (Fig 2B), suggesting that the TLR signaling-dependent results were not due to effects on Ly49H-dependent control. Similarly, we observed that deficiency of IFNAR1 markedly affected infection with a mixed MCMV inoculum (Fig 2A) but did not markedly affect Δm157 selection (Fig 2B). Notably, in both cases, there was Δm157 selection even though overall viral titers were elevated in the absence of TLR or IFN-I responses (Fig 2A), indicating that the viral competition assay could reveal Ly49H-dependent effects even during marked viremia.
We next assessed the role of two other cytokines, IL-18 and IL-12, previously demonstrated to enhance NK cell function both in vivo and in vitro [32]. However, deficiency of IL-18 or IL-12p40 also did not result in loss of viral selection (Fig 2C). Furthermore, Batf3-dependent conventional DC subsets that present IL-15 to NK cells were also not required to control WT MCMV (Fig 2C). Consistent with the lack of Δm157 outgrowth, viral titers were also similar to B6 mice (S1E Fig). Taken together, these findings demonstrate that Δm157 selection remains intact in the face of individual deficiencies of TLR signaling, IFN-I, IL-18, or IL-12, as well as Batf3 deficiency. Since overall viral titers were higher in the absence of TLR signaling and IFN-I deficiency, our findings suggest that Ly49H-dependent control of WT MCMV could occur normally despite global deficits of innate antiviral immunity which affect Ly49H-independent control of both WT MCMV and Δm157.
Since we found no major effect on Δm157 selection in mice with single immune deficiencies, we next assessed their potential redundant roles with IFN-I. The combination of anti-IFNAR1 blockade with MyD88 deficiency markedly impaired Δm157 selection (Fig 2D). Notably, this loss of selection was not seen in B6 mice treated with anti-IFNAR1 or MyD88–/–mice treated with an isotype control, indicating that both MyD88-dependent and IFN I-dependent pathways are critical for Δm157 selection by Ly49H+ NK cells. Since MyD88–/–mice fail to elaborate both IL-12 [27] and IL-18 [33], we next combined IFNAR1 blockade with IL-12 or IL-18 deficiency. While IFNAR1 blockade in IL-18–/–mice had no effect on Δm157 selection, IFNAR1 blockade plus either IL-12p40 or IL-12Rβ2 deficiency led to impaired selection, similar to IFNAR1 blockade in MyD88-deficient mice (Fig 2D). To further substantiate these findings, we assessed IFNAR1–/–mice genetically deficient in either IL-12 cytokine or receptor (Fig 2E). Both of these compound cytokine genetic deficiencies compromised Δm157 selection and resulted in elevated viral titers (S1F Fig). Taken together, these findings support a redundant relationship between IL-12 and IFN-I in promoting Ly49H-dependent control of WT MCMV.
To better understand how IL-12 and IFN-I affect Ly49H+ NK cell activities, we studied the classical NK cell effector functions, cytotoxicity and IFNγ production. Perforin was critical for promoting Δm157 selection (Fig 2F). Similarly, Jinx [34] and Lyst [35] mice, both of which have distinct defects in cytotoxicity, recapitulated the impaired Δm157 selection observed in Prf1–/–mice. Surprisingly, however, Δm157 selection was observed in IFNγ-/- and IFNγR-/- mice; both were similar to B6. Viral titers supported our observations in these strains (S1G Fig). However, a prior publication has demonstrated that cytotoxic control of MCMV during acute infection occurs primarily within the spleen and IFNγ-dependent control is more prominent in the liver [19] while a more recent study suggests IFNγ affects both spleen and liver control [18]. To address whether IFNγ contributes to m157-dependent control in the liver, we evaluated the consequences of hepatic MCMV selection in B6, IFNγ-/-, and Prf1-/- mice (S1H Fig). We found that Δm157 selection in the liver was incomplete in the B6 background, confirming that Ly49H-dependent mechanisms are not as prominent in the liver compared to the spleen. As observed in the spleen (Fig 2F), Prf1-/- mice were not able to select for Δm157 in the liver (S1H Fig), demonstrating that the primary mechanism responsible for selection in these organs requires perforin-mediated cytotoxicity. We observed only partial hepatic Δm157 selection in IFNγ-/- mice, suggesting that in contrast to the spleen, IFNγ exerts a mild influence on MCMV control in the liver. The hepatic titers were mildly elevated (about 10-fold) in the IFNγ-/- strain and markedly higher (1000-fold) in Prf1-/- mice when compared to B6 mice (S1I Fig), similar with what we observed in the spleen (S1G Fig). In summary, these results suggest that Ly49H-dependent control of WT MCMV in the spleen and liver depends on perforin-mediated cytotoxicity and IFNγ partially contributes to Δm157 selection in the liver.
To further evaluate the role of IL-12 and IFN-I in Ly49H-dependent MCMV control, we performed in vitro assays. As cytotoxicity is required for Ly49H-dependent MCMV control, we first studied NK cell degranulation. NK cells did not degranulate in response to cytokines alone (Fig 3A). Instead, stimulation with transgenic m157-expressing (m157-Tg) splenocytes [36] but not WT splenocytes induced degranulation in Ly49H+ cells. Almost all NK cells that expressed lower levels of cell-surface Ly49H (Ly49Hdim) were CD107a+, suggesting that virtually all Ly49H+ NK cells that encountered m157 during the course of the experiment subsequently degranulated. The CD107a-specific response to m157-Tg stimulators was abrogated in Jinx and B6.BxD8 mice, confirming that Ly49H-signaling specifically induced degranulation (Fig 3B). Jinx NK cells displayed the Ly49Hdim population after m157-Tg exposure, indicating that these NK cells were indeed triggered but did not degranulate. Stimulation of DAP12-deficient NK cells did not induce any specific degranulation whereas stimulation of DAP10-deficient NK cells resulted in degranulation similar to WT controls (Fig 3C), indicating that DAP12, but not DAP10 is involved in Ly49H-mediated degranulation and consistent with our in vivo data on Δm157 selection (Fig 1F). Interestingly, m157-Tg stimulation did not induce the Ly49Hdim population in DAP12-deficient NK cells, indicating that signaling through Ly49H and DAP12 is required for the emergence of this population. These data also show that DAP12 is cell-intrinsically required on NK cells, which was also confirmed by co-culture of purified NK cells with m157-Tg murine embryonic fibroblasts (MEF) (Fig 3D). Finally, combining m157-Tg stimulation with cytokines did not further enhance the percentage or MFI of the CD107a+ NK cells (Fig 3A–3D). Together, these in vitro data show that activation receptor engagement by ligand alone is sufficient for degranulation, and suggest that IL-12 and IFN-I play another role to enhance NK cell cytotoxicity.
Since control of MCMV via cytotoxic NK cell effector function is critically dependent upon the pro-apoptotic activity of GzmB following degranulation [31], and we demonstrated that degranulation, per se, was not influenced by IFN-I or IL-12, we next assessed the ability of these cytokines to affect GzmB protein expression. Indeed, addition of either IFNβ or IL-12 to NK cells in vitro induced GzmB production, without affecting Ly49H levels (Fig 4A). The increase in GzmB MFI likely corresponds to increased protein levels of GzmB rather than a conformational change in GzmB such that it is more readily detectable as the NK cells show more cytotoxic function. However, it cannot be distinguished if the increased GzmB MFI reflects greater GzmB content per granule, greater number of GzmB+ granules, or a combination of both. IFNβ was more potent than IL-12 in inducing GzmB. Signaling through Ly49H itself with m157-Tg targets did not induce GzmB and did not substantially enhance GzmB levels induced by cytokines. Stimulation of GzmB production by cytokines is consistent with prior studies [31], but the role of IFN-I had not been extensively evaluated. Since IFNα and IFNβ could differentially affect GzmB production, we compared them directly. Both IFN-I subtypes induced GzmB in a dose-dependent fashion, but IFNβ was >30 times more potent in inducing GzmB than IFNα4 (Fig 4B). Simultaneous addition of IFNβ and IL-12 resulted in a synergistic effect on GzmB production and reached levels similar to those induced by IL-2 and IL-15 (Fig 4C). To determine if the requirements for IFNAR1 or IL-12Rβ2 were NK cell-intrinsic for GzmB production, splenocytes deficient in either of these receptors were stimulated with cytokine and WT stimulator cells. In terms of GzmB protein production, NK cells deficient in IL-12Rβ2 did not respond to IL-12, but still responded to IFNβ whereas IFNAR1-/- NK cells did not respond to IFNβ, but responded to IL-12 (Fig 4D). This NK cell-intrinsic response was confirmed by stimulating purified NK cells with MEFs and IFNβ or IL-12 (Fig 4E). Intriguingly, the IFNβ-induced GzmB accumulation was greater in the Ly49H+ compared to the Ly49H- NK cells, whereas in response to IL-12, GzmB accumulation was more pronounced in the presence of m157 (S2A Fig). Taken together, these data suggest that both Ly49H and either IFNβ or IL-12 are intrinsically necessary for efficient cytotoxicity in vivo.
Interestingly, at three days following MCMV infection, we found that levels of GzmB and Prf1 in splenic NK cells examined ex vivo were significantly elevated in mice with deficiency in either IFNAR1 or IL-12p40 (Fig 5A and 5B). Upregulation of GzmB appeared to be equivalent in both Ly49H+ and Ly49H- cells and did not require the presence of m157-expressing virus at 40 hours after infection (Fig 5C). IFNAR1–/–mice demonstrated a less robust increase in the expression of GzmB, as compared to WT and IL-12p40-deficient mice which displayed equivalent levels. Prf1 production was increased in IFNAR1–/–mice while IL-12p40–/–mice displayed decreased levels of Prf1 compared to WT, suggesting that GzmB and Prf1 are differentially regulated. Importantly, double cytokine-deficient mice did not display enhanced GzmB nor Prf1 protein levels following infection (Fig 5A and 5B). Indeed, the GzmB levels were even below those observed in uninfected WT mice, suggesting that both IL-12 and IFN-I contribute to production of small amounts of GzmB in naïve mice. For Prf1 this effect was not clearly observable, which may be due to the less robust Prf1 changes or detection. WT MCMV induced elevated levels of GzmB in Ly49H+ NK cells compared to their Ly49H- counterparts, similar to what was observed in vitro (S2 Fig). Importantly, this effect was also observed for Δm157 MCMV (S2B Fig), further indicating that signaling through Ly49H is not required for enhanced GzmB production in Ly49H+ NK cells during MCMV infection. Regardless, both IL-12 and IFN-I redundantly contribute to enhanced GzmB and Prf1 protein expression during MCMV infection.
To confirm that NK cell-intrinsic signaling by cytokines was required, we adoptively transferred WT or IFNAR1-/-xIL-12Rβ2-/- splenocytes into congenic WT hosts that were simultaneously infected with MCMV. We observed no increase in GzmB levels in the double-deficient donor NK cells whereas WT donor control and WT host control NK cells produced the expected increased levels of GzmB (Fig 5D). These results indicate that the enhancement of GzmB protein expression by IFN-I or IL-12 signaling is NK cell-intrinsic during MCMV-infection.
To directly determine the requirement for both Ly49H and IFN-I activation for cytotoxicity in vivo, we determined the in vivo cytotoxic elimination of m157-expressing cells (Fig 6A). Following injection of equal numbers of congenic m157-Tg and WT splenocytes differentially labeled with CFSE into control-treated mice, we found there were low levels (15–35%) of m157-specific elimination (Fig 6A and 6B), consistent with previous findings on m157-specific rejection under steady-state conditions [37, 38]. Anti-NK1.1 depletion abrogated m157-specific elimination of target cells, confirming NK cell dependence. However, mice pretreated with poly(I:C) exhibited 3-fold higher levels of m157-specific rejection as compared to control-treated mice (Fig 6A and 6B). Blockade of Ly49H prevented m157-expressing target elimination in poly(I:C)-treated mice, confirming the role of Ly49H (Fig 6C). Similar results were obtained in B6.BxD8 mice specifically lacking the Ly49H receptor. Specific elimination was also abrogated in Jinx mice (Fig 6D), indicating that m157-expressing target elimination in vivo in poly(I:C)-treated mice is due to cytotoxicity. The m157-specific killing was 3-fold lower in IFNAR1-/- mice treated with poly(I:C) as compared to WT control mice, showing IFN-I signaling affects m157-specific rejection (Fig 6D). This defective cytotoxic capacity in IFNAR1-/- mice correlated with a lack of increased GzmB production in response to poly(I:C) (Fig 6E). Conversely, Jinx mice failed to eliminate m157-expressing targets (Fig 6D) even though GzmB levels were similar to WT mice (Fig 6E), highlighting the requirement of both degranulation and cytokine activation for optimal Ly49H-dependent cytotoxicity in vivo.
To directly assess the role of cytotoxicity during MCMV infections, we performed rejection assays in infected mice. During the first day post-infection, in vivo cytotoxicity of m157-Tg targets was similar to untreated controls while it increased on day 2 and plateaued at days 3 and 4 (Fig 7A). At day 2 after infection, GzmB and perforin responses in the NK cells peaked (S3A Fig and [31]), but the cytotoxic capacity was only modestly but not significantly increased. However, at this time the number of detectable NK cells in the spleen dramatically drops to approximately 30% of steady-state levels [39], which may explain the discrepancy of enhanced GzmB and perforin production in NK cells with only modestly increased in vivo cytotoxic capacity. At day 3–4 after infection, GzmB levels decreased (S3A Fig) but the number of detectable NK cells increased and the net result was more potent m157-specific target clearance. Despite a requirement for m157 expression on the target, there was otherwise no overt role for m157 in potentiating NK cell cytotoxicity during MCMV infection, as m157-specific rejection of targets was similar at day 3 in mice infected with Δm157 or WT MCMV (Fig 7B). These data show that NK cell cytotoxicity increases during the first days after MCMV and plateaus at day 3, which reflects the capacity of NK cell mediated control of MCMV during this time.
Finally, we found mice deficient in IFNAR1 were substantially hampered in their cytolytic capacity during MCMV infection (Fig 7C). These mice showed a 40% reduction in m157-specific killing, but the remaining cytotoxic capacity was still sufficient for Δm157 selection, though somewhat incomplete (Fig 2B). Interestingly, IL-12 alone played no detectable role in cytotoxicity in vivo as IL-12Rβ2-/- mice showed similar levels of m157-specific rejection. However in mice deficient in both IFNAR1 and IL-12Rβ2, m157-specific killing was reduced to below baseline WT levels (Fig 7C). Moreover, uninfected IFNAR1-/-xIL-12Rβ2-/- mice showed reduced baseline m157-specific target rejection despite normal maturation and functional capacity of the IFNAR1-/-xIL-12Rβ2-/- NK cells (S3B–S3E Fig), showing a role for cytokine activation even under steady-state conditions. Together, these results closely mirror the results on m157-dependent MCMV selection as well as GzmB production (Figs 2E, 3A and 4A), indicating that both IFN-I and IL-12 contribute to NK cell cytotoxicity during MCMV infections.
Herein we used an in vivo viral competition assay to specifically demonstrate that Ly49H-mediated MCMV control is predominately due to cytotoxicity that is enhanced by cytokine signaling, independent of IFNγ. All strains of mice tested here with defects in cytotoxicity, including granule formation (Lyst), exocytosis (Jinx), or perforin, showed the same effect, i.e., an inability of Ly49H+ NK cells to control MCMV, as evidenced by an absence of Δm157 MCMV selection. Our findings are independent of overall viral titers, providing unequivocal evidence for cytotoxicity despite potential confounding effects.
A recent report described that both Ly49H-m157 engagement and IFNγ production were important in NK-mediated control of MCMV [20]. Interestingly, mortality did not correlate well with splenic titers in prior studies that have examined mice globally deficient in IFNγ, suggesting that the cytokine likely potentiates other critical aspects of the innate immune response. For example, Fodil et al demonstrated a marked effect on survival with only a mild (1-log) increase in splenic titers of WT MCMV if Ly49H was transgenically expressed in a mouse strain deficient for IFNγ production [20]. We have confirmed these overall WT MCMV titer differences in our studies within both the splenic and hepatic compartments of IFNγ-/- mice. However, our data suggest that global defects can affect Ly49H-independent viral control, suggesting that the role of IFNγ in antiviral control by Ly49H+ NK cells can be difficult to resolve when studying mice globally lacking immune molecules and assessing overall viral titers [18, 19].
Interestingly, Ly49H-dependent degranulation occurred in naïve mouse NK cells in vitro, reminiscent of activation receptor-induced granule exocytosis of human NK cells in an LFA-1-dependent manner in an in vitro system that limited the number of receptor and ligand pairs. The combination of engagement of Fc receptor and LFA-1 by ICAM on insect cells facilitated human NK cell degranulation [14] and triggering of multiple activation receptors was shown to further increase human NK cell degranulation [40]. Of note is that our in vitro assays utilized syngeneic splenocytes as stimulators differing from control splenocytes only in the transgenic expression of m157. The stimulator cells therefore expressed the steady state repertoire of the relevant adhesion molecules as well as activation and inhibitory ligands, which together contributed to degranulation. Nonetheless, our studies indicate that degranulation was insufficient for effective target killing and provide a cautionary note that assessment of degranulation by CD107a may not be a good correlate for cytotoxicity under certain conditions.
Our studies indicate that effective NK cell cytotoxicity requires other signals, in addition to Ly49H triggering. The in vivo viral competition assay demonstrated a requirement for either IL-12 or IFN-I acting directly on NK cells to promote activation receptor-mediated killing of MCMV-infected cells. In the presence of either IL-12 or IFN-I alone, Ly49H-dependent control appeared to occur, despite overall increases in viral titers, likely due to the pleiotropic effects of these cytokines on the immune response. Interestingly, the effect of IFN-I was much greater than that of IL-12, but it was only when both were genetically absent and/or neutralized, that Ly49H-dependent control was markedly affected, as shown by significant effects on Δm157 MCMV selection. Regardless, both cytokines can independently contribute to Ly49H-mediated cytotoxicity via a mechanism involving upregulation of GzmB protein expression, markedly expanding prior observations that IL-15 can also induce this effect [30, 31].
Our results showing an important role for IFN-I and IL-12 on Ly49H-dependent viral control was unexpected as IL-15 is a potent stimulator of GzmB protein production and IL-15 is produced during MCMV infections [31, 41]. Prior studies suggested a role for cross-presentation of IL-15 by dendritic cells as a critical component in NK cell responses during viral infections [30]. In the current study, we did not examine the role of IL-15 because mice lacking IL-15 or any component of the IL-15 receptor lack NK cells [42]. However, here we indirectly addressed the role of IL-15 priming of NK cells by DCs by utilizing Batf3-deficient mice [43] that fail to develop CD8α DC subsets. We observed that Batf3-dependent DCs were not required for the Ly49H-dependent cytotoxicity observed during MCMV infection. Although IL-15 should act directly on NK cells and is thought to be downstream of IL-12 and IFN-I stimulation of DCs, we found that the IL-12 and IFN-I effects studied here were due to direct effects of these cytokines on the NK cells themselves. Therefore, our current studies showed that IL-12 and IFN-I had profound effects directly on NK cells, suggesting that IL-15 could account for the residual IL-12- and IFN-I-independent effects on Ly49H-mediated MCMV control, or that IL-15 acts together with IL-12 and IFN-I in a more complex manner.
Our findings here also markedly extend prior studies on the role of both IFN-I and IL-12 in affecting NK cell responses during MCMV infection. Recently, both IFN-I and IL-12 were shown to induce the high-affinity IL-2 receptor (CD25) on NK cells via a STAT4-dependent mechanism [44]. However, the consequence of these cytokines on CD25 expression was studied only for NK cell proliferation during MCMV infection. The role of proliferation, per se, in MCMV control is unclear because Ly49H-specific proliferation is detectable after Ly49H-dependent MCMV control had already occurred [39]. By contrast, we report here that both cytokines are required for Ly49H activation receptor-dependent killing and are critical for MCMV control.
The roles of IL-12 and IFN-I during MCMV infection also have been extensively studied by others [23]. Initially, it was reported that NK cell-dependent IFNγ production required an IL-12-mediated pathway while cytotoxicity was enhanced through IFN-I signaling during MCMV infection. Another role of IFN-I is to promote chemotaxis of NK cells to virally infected livers [45] but Ly49H-mediated effects are generally not observed in the liver [3]. More recent characterization of both IL-12 and IFN-I dependent pathways intrinsic to NK cells during MCMV infection has demonstrated non-redundant effects on cytotoxicity, IFNγ production, and survival [41]. IFN-I signaling clearly promoted NK cell cytotoxicity but it was not previously clear if these effects were Ly49H-dependent or Ly49H-independent. Here we report that IL-12 and IFN-I pathways act redundantly to enhance Ly49H-dependent cytotoxicity.
IFNα was previously reported to only modestly induce GzmB in NK cells [31]. However, IFNα and IFNβ are both induced during acute MCMV infection with similar kinetics [46]. Here we show IFNβ is much more potent than IFNα in inducing GzmB in NK cells, consistent with reported increased immune responses to IFNβ as compared to IFNα [47]. IFNβ only requires the IFNAR1 subunit, whereas IFNα requires both the IFNAR1 and IFNAR2 subunits [48], which may provide a molecular explanation to the observed differences between both IFN-I molecules. Regardless, our studies suggest that IFNβ may be the primary IFN-I in mediating NK cell mediated viral control during acute MCMV infection, but due to the large number of IFN-I molecules, additional work will be needed to determine if a specific IFN-I molecule is critical for Ly49H-dependent control.
Finally, there are clear parallels between our work and cytokine-involvement in T cell responses, as cytokines have been described to act as a “third” signal, in addition to T-cell receptor and co-stimulatory molecule engagement during T-cell priming as well as proliferation and survival [49]. This third signal has been found to be essential for priming of CD8+ T cells as well as enhancing their effector functions [50]. Analogous to the effect of cytokines on T-cell expansion, IL-12 and IL-15 have been described to play a central role in proliferation and survival of NK cells [51, 52]. Our data now show that other cytokines provide crucial signals to NK cells for activation receptor-mediated cytolytic functions, beyond that of NK cell proliferation and survival.
In conclusion and based on results reported here, we propose the following model, where NK cells require two signals for efficient cytolytic effector function. Signaling through a cytokine receptor on the NK cell itself, which is provided by IL-12 or IFN-I in the case of MCMV infection, results in the accumulation of cytotoxic effector molecules, including GzmB. Activation receptor recognition of target cells through ligands, such as m157, signals NK cell release of cytotoxic cargo, ultimately leading to killing of the (virus-infected) target cell and MCMV control. In the absence of cytokine stimulation, Ly49H activation may occur, resulting in granule exocytosis, but the NK cells will be impotent killers because cytotoxic effector molecules, such as GzmB, are poorly expressed. Conversely, absence of activation receptor stimulation leads to poor NK cell control. Thus, NK cells require two intrinsic signals to mediate effective cytotoxic control of viral infection.
B6 (and congenic Ly5.2 B6) mice were purchased from either the National Cancer Institute (Fredrick, MD) or Charles River Laboratories (Wilmington, MA). The following strains were purchased from Jackson Laboratory: IL-12Rβ2-/- (003248), IL-12p40-/- (002693), Prf1-/- (002407), IFNγ-/- (002287), IFNγR-/- (003288), IL-18-/- (004130), Lyst (000629), and RAG1-/- (002216). Jinx mice, harboring a mutation in UNC13D, and TRIF-/- mice, were both provided by B. Beutler (Scripps Research Institute, La Jolla, CA). MyD88-/- mice were provided by S. Akira (Osaka University, Osaka, Japan) or purchased through the Jackson Laboratory (009088). DAP12 KI mice were provided by E. Vivier (CNRS-INSERM-Universite de la Mediterranee, France). DAP12-/- and DAP10-/-DAP12-/- [53] mice were provided by T. Takai. DAP10-/- [54] mice were provided by M. Colonna. Batf3-/- [43] mice were provided by K. Murphy. IFNAR1-/- [55], m157-Tg [36] and Ly49H-deficient B6.BxD8 [56] mice were generated and maintained in our laboratory. The Speed Congenics Facility of the Rheumatic Diseases Core Center at Washington University assisted in backcrossing mice to the B6 background using a marker-assisted approach. Standard breeding strategies were used to generate double deficient MyD88-/-TRIF-/- mice, IL-12p40-/-IFNαβR-/- mice, and IL-12Rβ2-/-IFNαβR-/- mice. All mice used were generated on or backcrossed to the C57BL/6 (B6) genetic background. All mice were used in accordance with institutional guidelines for animal experimentation.
The salivary gland propagated MCMV stocks were generated from purified and sequenced clones. Virus was inoculated via the intraperitoneal (IP) route in a total volume of 200μl PBS. The complete genomic sequence for the strain designated WT1 was previously published [57]. To generate an escape virus deficient in m157 expression, WT1 MCMV was passaged in a T cell-deficient host (TCRbd-/-; Jackson Laboratory, 002122) for 3 weeks, followed by plaque purification of spleen-derived virus and subsequent assessment of Ly49H-engagement as described previously [10]. The entire genome of the Δm157-MCMV was sequenced (HiSeq, Illumina) and a single G to T nucleotide substitution at position 502 of m157 (numbering relative to translational start codon) was identified as the only deviation in the 230kb sequence compared with WT1, consistent with our prior studies indicating the stability of MCMV and the role of Ly49H in specifically selecting mutations in m157 [10, 57]. The base substitution results in a premature stop codon (GAA to TAA) within the m157 ORF. An in vitro LacZ-based Ly49H-reporter assay [5] confirmed that the Δm157-MCMV lacked m157 expression and was unable to engage the Ly49H receptor. A multi-step growth curve [58] was performed on NIH 3T12 (ATCC CCL-164) fibroblasts (multiplicity of infection [MOI] = 0.1). Viral genome copies quantified from cell lysates and culture supernatants demonstrated that replication of Δm157-MCMV was equivalent to WT MCMV (S1J Fig). To generate an inoculum containing 10% Δm157-MCMV, 2000 PFU of Δm157-MCMV was mixed with 18000 PFU of WT1 virus, both of salivary gland origin.
Genomic DNA was prepared from mouse tissues, cell lysates, and culture supernatants using the Puregene extraction kit (Qiagen) following manufacturer recommendations. DNA was adjusted to achieve a concentration between 20–50ng/μl. Each qPCR reaction consisted of 5μl of TaqMan Universal PCR Master Mix, No AmpErase UNG (Life Technologies, #4324018), 2μl of organ DNA, 0.5μl of a 20X primer/probe mixture (PrimeTime qPCR Assay, IDT) with sequences as described in S1 Table, and 2.5μl water. This is done for both IE1 and actin in separate reactions. Amplification was performed on the AppliedBiosystems StepOne Plus qPCR instrument (Life Technologies). Each reaction was run in duplicate and quantified against a standard curve to ensure linearity, sensitivity, and enable comparison between independent runs. The standard curve efficiencies were consistently near 100%. Data were analyzed by first generating a ratio of MCMV IE DNA copies/murine actin DNA copies. As actin appears to be present in about a 1000-fold excess over IE1, multiplying the resulting ratio by 1000 closely reflects PFU values obtained by standard plaque assays.
To calculate the frequency of escape virus (Δm157-MCMV) following infection, genomic DNA was prepared as for viral genome quantification. The primer/probe mixtures (S1 Table) contained Taqman probes for both WT and m157-deficient alleles in a single tube, differentiated via the dye conjugate (Custom TaqMan SNP Genotyping Assay, Life Technologies, #4332075). To validate the specificity and dynamic range of the Taqman-based genotyping assay, we initially determined the ratio of SNP-specific product to WT product using predetermined mixtures of 1%, 5%, 10%, 25%, 50%, 75%, and 100% Δm157-MCMV. The ΔΔCT (cycle threshold) was calculated as a difference between the CT of the 100% Δm157-MCMV sample and the individual reference samples. A second-order polynomial (quadratic) function to describe the slope of the line through these points allowed accurate determination of Δm157-MCMV frequencies between 5% and 75% (S1K Fig). In validation assays, the percent of Δm157 that was greater than 75% could not be quantified due to cross-reactivity with the Taqman probe for WT MCMV. Therefore 75% was considered the limit of quantification (L).
Purified 3D10 (α-Ly49H), PK136 (α-NK1.1), and MAR (isotype controls) were obtained from hybridomas purified by the Rheumatic Diseases Core Center Protein Purification and Production Facility. MAR1-5A3 (α-IFNAR1) [59] and GIR-208 (IFNAR1 isotype control, [60] were previously described. Antibodies was injected IP at a dose of 200μg per mouse 24hrs (α-Ly49H) or 48 hrs (α-NK1.1) prior to infection, or 2mg (α-IFNAR1) per mouse 24 hrs prior to infection.
Fluorescent-labeled antibodies used were, anti-NK1.1 (clone PK136), anti-NKp46 (29A1.4), anti-CD3 (145-2C11), anti-CD19 (eBio1D3), anti-CD45.1 (A20), anti-CD45.2(104), anti-CD107a (eBio1D4B), anti-perforin (eBioOMAK-D) were purchased from Affymetrix, anti-granzyme B (GB12) and isotype control were purchased from Life Technologies, and biotinylated anti-Ly49H (clone 3D10) was produced as described earlier [36]. IFNα4 and IFNβ were purchased from PBL Assay Science, IL-6, IL-12, IL-15 from Peprotech, IL-18 from MBL, and IL-2 was produced in-house. NK cells were purified from RAG1-/- splenocytes to a purity >95% using CD49b microbeads (Miltenyi Biotec) according to manufacturer’s instructions. m157-Tg murine embryonic fibroblasts were isolated from day 11.5–13.5 embryos, WT embryos from the same pregnant female served as in utero controls.
All cells were first stained with fixable viability dye (Affymetrix), followed by surface staining with directly conjugated antibodies or in two steps using streptavidin-PE (Becton Dickinson) in 2.4G2 hybridoma supernatant to block Fc receptors and washed with PBS with 0.5% BSA and 0.02% Sodium Azide (PBA). Subsequently, samples were fixed and stained intracellularly using Cytofix/Cytoperm kit according to manufacturer’s protocol (BD Biosciences). Samples were analyzed by flow cytometry using FACSCanto (BD Biosciences) and FACScan with DxP6 upgrade (Cytek). Data was analyzed using FlowJo software (Tree Star). NK cells were defined as Viability-NK1.1+CD3-CD19-.
Freshly isolated splenocytes were co-cultured with congenic CD45.1/2 or CFSE-labeled m157-Tg or littermate control splenocytes at a 1:1 ratio in the presence or absence of cytokine at the concentration indicated. For degranulation assays anti-CD107a and monensin (Affymetrix) was added after 1 hour and the reaction was stopped 5–8 hours later by the addition of PBA. For GzmB staining, samples were stained after 16–20 hours after initiation of co-culture. For stimulation assays with pure NK cells, NK cells were co-cultured with MEF at a 1:1 ratio.
Target splenocytes isolated from Ly5.1xLy5.2 m157-Tg animals were labeled with 1μM CFSE (Life technologies) and Ly5.1xLy5.2 WT littermate controls were labeled with 5μM CFSE. CFSElow and CFSEhigh target cells were mixed at a 1:1 ratio and 2-3x106 target cells were injected i.v. into naïve or treated mice as described. After 3 hours challenge splenocytes were harvested and stained. The ratio of CFSElow and CFSEhigh viable CD19+ cells was determined by flow cytometry. Target cell rejection was calculated using the formula [(1−(Ratio(CFSElow:CFSEhigh)sample/Ratio(CFSElow:CFSEhigh)control)) × 100]. Average of 2–3 NK1.1-depleted mice and input mixture served as control.
Statistical analysis was performed using Prism (GraphPad software). Unpaired, two-tailed Student's t-tests were used to determine statistically significant differences between experimental groups. Error bars in figures represent the SEM.
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Animal Studies Committee at Washington University School of Medicine under animal protocol 20130049A2.
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10.1371/journal.pcbi.1005285 | Dynamic Maternal Gradients Control Timing and Shift-Rates for Drosophila Gap Gene Expression | Pattern formation during development is a highly dynamic process. In spite of this, few experimental and modelling approaches take into account the explicit time-dependence of the rules governing regulatory systems. We address this problem by studying dynamic morphogen interpretation by the gap gene network in Drosophila melanogaster. Gap genes are involved in segment determination during early embryogenesis. They are activated by maternal morphogen gradients encoded by bicoid (bcd) and caudal (cad). These gradients decay at the same time-scale as the establishment of the antero-posterior gap gene pattern. We use a reverse-engineering approach, based on data-driven regulatory models called gene circuits, to isolate and characterise the explicitly time-dependent effects of changing morphogen concentrations on gap gene regulation. To achieve this, we simulate the system in the presence and absence of dynamic gradient decay. Comparison between these simulations reveals that maternal morphogen decay controls the timing and limits the rate of gap gene expression. In the anterior of the embyro, it affects peak expression and leads to the establishment of smooth spatial boundaries between gap domains. In the posterior of the embryo, it causes a progressive slow-down in the rate of gap domain shifts, which is necessary to correctly position domain boundaries and to stabilise the spatial gap gene expression pattern. We use a newly developed method for the analysis of transient dynamics in non-autonomous (time-variable) systems to understand the regulatory causes of these effects. By providing a rigorous mechanistic explanation for the role of maternal gradient decay in gap gene regulation, our study demonstrates that such analyses are feasible and reveal important aspects of dynamic gene regulation which would have been missed by a traditional steady-state approach. More generally, it highlights the importance of transient dynamics for understanding complex regulatory processes in development.
| Animal development is a highly dynamic process. Biochemical or environmental signals can cause the rules that shape it to change over time. We know little about the effects of such changes. For the sake of simplicity, we usually leave them out of our models and experimental assays. Here, we do exactly the opposite. We characterise precisely those aspects of pattern formation caused by changing signalling inputs to a gene regulatory network, the gap gene system of Drosophila melanogaster. Gap genes are involved in determining the body segments of flies and other insects during early development. Gradients of maternal morphogens activate the expression of the gap genes. These gradients are highly dynamic themselves, as they decay while being read out. We show that this decay controls the peak concentration of gap gene products, produces smooth boundaries of gene expression, and slows down the observed positional shifts of gap domains in the posterior of the embryo, thereby stabilising the spatial pattern. Our analysis demonstrates that the dynamics of gene regulation not only affect the timing, but also the positioning of gene expression. This suggests that we must pay closer attention to transient dynamic aspects of development than is currently the case.
| Biological systems depend on time. Like everything else that persists for more than an instant, there is a temporal dimension to their existence. This much is obvious. What is less obvious, however, is the active role that time plays in altering the rules governing biological processes. For instance, fluctuating environmental conditions modify the selective pressures that drive adaptive evolutionary change [1, 3–5], time-dependent inductive signals or environmental cues trigger and remodel developmental pathways [6, 7], and dynamic morphogen gradients influence patterning, not only across space but also through time [8–16]. In spite of this, many current attempts at understanding biological processes neglect important aspects of this temporal dimension [17]. For practical reasons, experimental studies often glance over the detailed dynamics of a process, and focus on its end product or output pattern instead. Similarly, modelling studies frequently restrict themselves to a small-enough time window allowing them to ignore temporal changes in the rules governing the system. Accuracy is sacrificed and the scope of the investigation limited for the sake of simplicity and tractability. Although reasonable, and often even necessary, such simplifications can lead us to miss important aspects of biological regulatory dynamics.
We set out to tackle explicitly time-dependent aspects of morphogen interpretation for pattern formation during animal development. As a case study, we use the gap gene network, which is involved in segment determination during the blastoderm stage of early development in the vinegar fly Drosophila melanogaster [18]. Activated by long-range gradients of maternal morphogens Bicoid (Bcd) and Caudal (Cad), the trunk gap genes hunchback (hb), Krüppel (Kr), giant (gt), and knirps (kni) become expressed in broad overlapping domains along the antero-posterior (A–P) axis of the embryo (Fig 1). The establishment of these domains is fast and dynamic. Subsequently, gap gene domain boundaries sharpen and domains in the posterior region of the embryo shift anteriorly over time (Fig 1). Towards the end of the blastoderm stage, gap gene production rates drop and domain shifts slow down. The blastoderm stage ends with the onset of gastrulation.
The gap gene system is one of the most thoroughly studied developmental gene regulatory networks today. For our particular purposes, we take advantage of the fact that it has been extensively reverse-engineered using data-driven modelling. This approach is based on fitting dynamical models of gap gene regulation, called gene circuits, to quantitative spatio-temporal gene expression data [19–27, 29, 34, 35].
Dynamical models capture how a given regulatory process unfolds over time. They are frequently formulated in terms of ordinary differential equations (ODEs) with parameter values that remain constant over time. Such equations represent an autonomous dynamical system. Central to the analysis of such dynamical systems is the concept of phase space and its associated features (S1A Fig). Phase (or state) space is an abstract space that contains all possible states of a system. Its axes are defined by the state variables, which in our case represent the concentrations of transcription factors encoded by the gap genes. Trajectories through phase space describe how a system’s state changes as time progresses. The trajectories of a gap gene circuit describe how transcription factor concentrations change over time. All trajectories taken together constitute the flow of the system. This flow is shaped by the regulatory structure of the underlying network—the type (activation/repression) and strength of interactions between the constituent factors—which is given by the system’s parameters. Since these parameters are constant over time in an autonomous system, the trajectories are fully determined given a specific set of initial conditions. Once the system’s variables no longer change, it has reached a steady state. Steady states can be stable—such as attractors with converging trajectories from all directions defining a basin of attraction—or unstable—such as saddles; where trajectories converge only along certain directions and diverge along others. The type and arrangement of steady states, and their associated basins of attraction define the phase portrait of the system (S1A Fig). There exist powerful analytical tools to analyse and understand the phase portrait and the range of dynamic behaviours determined by it. Geometrical analysis of the phase portrait enables us to build up a rich qualitative understanding of the dynamics of non-linear autonomous systems without solving the underlying equations analytically [36].
The application of dynamical systems concepts and phase space analysis to the study of cellular and developmental processes has a long history (see [37–39] for recent reviews). In particular, it has been successfully applied to the study of the gap gene system. Manu and colleagues [22, 23, 40] examined the dynamics and robustness of gap gene regulation in D. melanogaster using diffusion-less gene circuits fit to quantitative expression data. These models have a four-dimensional phase space, where the axes represent the concentrations of transcription factors encoded by the trunk gap genes hb, Kr, gt, and kni. The analysis of these phase portraits yields a rigorous understanding of the patterning capabilities of the system.
The analysis by Manu et al. [23] corroborated and expanded upon earlier genetic evidence [41] indicating that the regulatory dynamics responsible for domain boundary placement in the anterior versus the posterior of the embryo are very different. In the anterior, spatial boundaries of gap gene expression domains are positioned statically, meaning that they remain in place over time [42]. Stationary boundaries are regulated in two distinct ways [23]. (1) In the case of the posterior boundary of the anterior gt domain, different nuclei along the A–P axis have equivalent attractors positioned at different locations in phase space (shift in attractor position); (2) in the case of the posterior boundary of the anterior hb domain, system trajectories fall into different basins of attraction (attractor selection) (Fig 2A). In both of these cases, patterning is largely governed by the position of attractors in a multi-stable phase space.
In contrast, gap domain boundaries in the posterior of the embryo shift anteriorly over time [25, 42]. In this region, the system always remains far from steady state, and the dynamics of gene expression are transient. Therefore, trajectories here are fairly independent of precise attractor positions. The model by Manu et al. [23] shows that posterior gap gene expression is governed by an unstable manifold (Fig 2A). An unstable manifold is the trajectory connecting a saddle to an attractor (S1A Fig). The authors demonstrate that this manifold has canalising properties since it compresses many incoming neighbouring trajectories into an increasingly smaller sub-volume of phase space over time [23]. This explains the observed robustness of posterior patterning. Moreover, the geometry of the unstable manifold provides an explanation for the ordered succession of gap genes that become expressed in each nucleus of the posterior region. Such an ordered temporal sequence of gene expression, if arranged appropriately along the A–P axis, creates the observed kinematic anterior shifts of gap domains over time (Fig 2A).
Despite its explanatory power, the analysis by Manu et al. [23] is limited in an important way. In order to simplify phase space analysis, the authors implement simplified dynamics of maternal morphogens Bcd and Cad in their model (Fig 2A). They use a time-invariant exponential approximation to simulate the Bcd gradient and Cad is assumed to reach a steady-state profile about 20–30 minutes before gastrulation [22, 23]. This steady-state profile is used for model analysis. (Based on this, we will refer to this formulation as the static-Bcd gene circuit model in what follows). Although reasonable, these simplifications affect the accuracy of the model, since Bcd and Cad have their own expression dynamics on a similar time scale as gap proteins. The Bcd gradient decays and Cad clears from much of the posterior trunk region towards the end of the blastoderm stage (Fig 2B) [42]. This means that the autonomous analysis of the static-Bcd model is not well suited to investigate the dynamic interpretation of morphogen gradients. In particular, assuming autonomy makes it impossible to isolate and study the explicitly time-dependent effects of changing gradient concentrations on gap gene regulation and pattern formation.
For this reason, we consider the dynamics of maternal morphogens explicitly in our model. We have obtained gap gene circuits that incorporate realistic time-variable maternal gradients of Bcd and Cad (Fig 2B) [26]. These gradients are implemented as external inputs to gap gene regulation (see Models and Methods section). They are not influenced by any of the state variables and, thus, are parameters of the system. This means that our gap gene circuits become fully non-autonomous [54], since certain parameter values now change over time. While non-autonomous equations are not significantly more difficult to formulate or simulate than autonomous ones, phase space analysis is far from trivial. As model parameters change, so does the geometry of the phase portrait, and consequently system trajectories are actively shaped by this time-dependence. Separatrices and attractors can change their position (geometrical change), and steady states can be created and annihilated through bifurcation events (topological change) (S1B Fig). In autonomous systems, bifurcations can only occur along the spatial axis of the model. In non-autonomous systems, they also occur in time, implying that trajectories can switch from one basin of attraction to another during a simulation run. We can think of time-variable phase portraits as embedded in parameter space. We call the combination of phase and parameter space the configuration space of the system. The configuration space on non-autonomous models hence encodes a much richer repertoire of dynamical mechanisms of pattern formation than autonomous phase space alone. This can complicate analysis and interpretation of the system considerably.
Using a simple model of a genetic toggle switch, we have established a methodology for the characterisation of transient dynamics in non-autonomous systems (S1B Fig), based on the analysis of instantaneous phase portraits [43, 45]. Such portraits are generated by fixing the values of system parameters starting at a given point in time, and then determining the geometrical arrangement of saddles, attractors, and their basins under these “frozen” conditions. The overall non-autonomous trajectory of the system is given by a series of instantaneous phase portraits over time. With sufficiently high temporal resolution, this method yields an accurate picture of the non-autonomous mechanisms of pattern formation implemented by the system. These mechanisms can be classified into four broad categories [43]: (1) transitions of the system from one steady state to another, (2) pursuit of a moving attractor within a basin of attraction, (3) geometrical capture, where a trajectory crosses a separatrix, and (4) topological capture, where a trajectory suddenly falls into a new basin of attraction due to a preceding bifurcation event (S1B Fig). This classification scheme can be used to characterise the dynamical repertoire of non-autonomous models in a way analogous to phase space analysis in autonomous dynamical systems.
In this paper, we present a detailed analysis of a non-autonomous gap gene circuit. Specifically, we use the model to address the effect of non-autonomy, i. e. the effect of time-variable maternal gradient concentrations, on gap gene regulation (Fig 2). To isolate explicitly time-dependent regulatory aspects, we simulate gap gene expression in the presence and absence of maternal gradient decay. Using phase space analysis, we then identify and characterise the dynamic regulatory mechanisms responsible for the observed differences between the two simulations. Our analysis reveals that maternal gradient decay limits the levels of gap gene expression and controls the dynamical positioning of posterior domains by regulating the rate and timing of domain shifts in the posterior of the embryo.
Non-autonomous gene circuit models are based on the connectionist formalism introduced by Mjolsness et al. [21], modified to include time-variable external regulatory inputs as previously described [26, 34]. Gene circuits are hybrid models with discrete cell divisions and continuous gene regulatory dynamics. The basic objects of the model consist of nuclei arranged in a one-dimensional row along the A–P axis of the embryo, covering the trunk region between 35 and 92% A–P position (where 0% is the anterior pole). Models include the last two cleavage cycles of the blastoderm stage (C13 and C14A) and end with the onset of gastrulation; C14A is further subdivided into eight time classes of equal duration (T1–T8). At the end of C13, division occurs and the number of nuclei doubles.
The state variables of the system consist of the concentration levels of proteins produced by the trunk gap genes hb, Kr, gt, and kni. We denote the concentration of gap protein a in nucleus i at time t by g i a ( t ). Change in protein concentration over time is given by the following set of ODEs:
d d t g i a ( t ) = R a ϕ ( u i a ( t ) ) + D a ( n ) g i - 1 a ( t ) + g i + 1 a ( t ) - 2 g i a ( t ) - λ a g i a ( t ) (1)
where Ra, Da and λa are rates of protein production, diffusion, and decay, respectively. Diffusion depends on the distance between neighbouring nuclei, which halves at nuclear division; thus, Da depends on the number of preceding divisions n. ϕ is a sigmoid regulation-expression function representing coarse-grained kinetics of transcriptional regulation. It is defined as follows:
ϕ ( u i a ( t ) ) = 1 2 u i a ( t ) ( u i a ( t ) ) 2 + 1 + 1 (2)
where
u i a ( t ) = ∑ b ∈ G W b a g i a ( t ) + ∑ m ∈ M E m a g i m ( t ) + h a (3)
with the set of trunk gap genes G = {hb, Kr, gt, kni}, and the set of external regulatory inputs M = {Bcd, Cad, Tll, Hkb}. External regulator concentrations g i m are interpolated from quantified spatio-temporal protein expression profiles [26, 42, 46]. The dynamic nature of these profiles renders the parameter term representing external regulatory inputs ∑ m ∈ M E m a g i m ( t ) time-dependent; explicit time-dependence of parameters implies non-autonomy of the dynamical system (see Introduction and [54]).
Interconnectivity matrices W and E define interactions among gap genes, as well as regulatory inputs from external inputs, respectively. The elements of these matrices, wba and ema, are called regulatory weights. They encode the effect of regulator b or m on target gene a. These weights may be positive (representing an activating regulatory input), negative (representing repression), or near zero (representing the absence of a regulatory interaction). ha is a threshold parameter that represents the activation state of target gene a in the absence of any spatially and temporally specific regulatory input. This term incorporates the regualtory influence of factors that are not expressed in a spatially specific manner (for example, the pioneer factor Zelda [31]). Eq (1) determines regulatory dynamics during interphase. In order to accurately implement the non-instantaneous duration of the nuclear division between C13 and C14A, the production rate Ra is set to zero during a mitotic phase, which immediately precedes the instantaneous nuclear division. Mitotic schedule as in [26].
We determine the values for parameters Ra, λa, W, E, and ha using a reverse-engineering approach [19, 25, 26, 34]. For this purpose, we numerically solve gene circuit Eq (1) across the region between 35 and 92% A–P position using a Runge-Kutta Cash-Karp adaptive step-size solver [26]. Models are fit to a previously published quantitative data set of spatio-temporal gap protein expression [26, 42, 46] (see Fig 1 for gap gene expression patterns, and Fig 2B for dynamic Bcd and Cad profiles). Model fitting was performed using a global optimization algorithm called parallel Lam Simulated Annealing (pLSA) [47]. We use a weighted least squares cost function as previously described [26].
To enable comparison of our results to the static-Bcd gene circuit analysis by Manu et al. [23], we keep model formalism and fitting procedure as similar as possible to this earlier study. Manu and colleagues fitted gene circuits including a diffusion term, but analysed the model with diffusion rates Da set to zero [23]. This diffusion-less approach reduces the phase space of the model from hundreds of dimensions to 4 by spatially uncoupling the equations and considering each nucleus independently from its neighbours. Dimensionality reduction is essential for geometrical analysis of phase space. Unfortunately, setting diffusion to zero in our best 3 (of a total of 100) non-autonomous gene circuits fitted to data with non-zero diffusion terms leads to severe patterning defects (see S2 Fig for common patterning defects). This is likely due to numerical, not biological issues, since we do find circuit solutions that correctly reproduce gap gene patterns both in the presence and absence of diffusion using an alternative fitting approach that fixes diffusion parameters Da to zero during optimization (see below). To further facilitate comparison with the static-Bcd model, we constrained the signs of regulatory weights to those reported in Manu et al. [23]. In previoius work, we have verified this network structure extensively against experimental data [18, 25, 26, 34]. Optimization was performed on the Mare Nostrum supercomputer at the Barcelona Supercomputing Centre (http://www.bsc.es). One optimization run took approximately 35 min on 64 cores.
The purpose of our reverse-engineering approach is not to sample parameter space systematically, but instead to discover whether there are specific model-fitting solutions that are consistent with the biological evidence and reproduce the dynamics of gap gene expression correctly. Global optimization algorithms are stochastic heuristics without guaranteed convergence, which means that for complex non-linear problems many optimization runs will fail or end up at sub-optimal solutions (see also discussions in [24, 26, 33]). In order to find the best-fitting solution, we therefore select solutions from 200 initial fitting runs as follows: (1) we discard numerically unstable circuits; (2) we only consider solutions with a root-mean-square (RMS) score less than 20.0 as most circuits with scores above this threshold show gross patterning defects; (3) we use visual inspection to detect remaining gross patterning defects among selected circuits (missing or bimodal domains, and disconnected boundaries. See S2 Fig) as previously described [34]. Out of the resulting 7 highest scoring circuits, only 3 recover the shifting dynamics of posterior gap domains. In order to rule out diffusion as a pattern-generating mechanism in these circuits, we compared their performance in the presence and absence of diffusion (see above). For this purpose, we used values of diffusion rates Da obtained by fitting our non-autonomous models with diffusion. All three circuits produce satisfactory gap gene patterns (including anteriorly shifting posterior trunk domains) whether diffusion is present or not. The best fit among these was selected for detailed analysis (see S1 Table, for parameter values).
The residual error of our best-fitting diffusion-less circuit (RMS = 10.73) lies at the lower end of the range of residual errors for fully-non-autonomous circuits with diffusion, which range from RMS scores of 10.43 to 13.32 [26]. This lends further support to the notion that diffusion is not essential for gap gene patterning. Moreover, our previous work also shows that circuits which were fit without weighting the data show somewhat lower RMS scores of 8.71 to 10.11 despite exhibiting more patterning defects at late stages [26]. The RMS score of the static-Bcd model (fit without weights) is higher, at 10.76 [22]. Taken together, this implies a slightly better quality-of-fit of our fully non-autonomous diffusion-less model compared to the static-Bcd diffusion-less circuits of Manu et al. [22].
We characterise the time-variable geometry and topology of phase space in our fully non-autonomous gap gene circuit for every nucleus in a sub-range of the fitted model between 35 and 71% A–P position. This restricted spatial range allows us to simplify the analysis by excluding the influence of terminal gap genes tll and hkb on patterning (similar to the approach in [22]). We aim to identify those features of configuration space that govern the placement of domain boundaries, and thus the patterning capability of the gap gene system. We achieve this by generating instantaneous phase portraits for the model [43, 45] at 10 successive points in time (C13, C14A-T1–8, and gastrulation time). To generate an instantaneous phase portrait, all time-dependent parameter values—i. e. those corresponding to the profiles of external regulators—are frozen at every given time point. This yields an autonomous system for each point in time, for which we can calculate the position of steady states in phase space using the Newton-Raphson method [48, 49] as implemented by Manu et al. [23]. We classify steady states according to their stability, which is determined by the corresponding eigenvalues (see S1A Fig).
Nuclei express a maximum of three trunk gap genes over developmental time, and only two at any given time point. Therefore, we project four-dimensional phase portraits into lower-dimensional representations to visualise them more easily. This yields a graphical time-series of instantaneous phase portraits for each nucleus, which allow us to track the movement, creation, and annihilation of steady states (typically attractors and saddles) by bifurcations. The transient geometry of phase space governs the non-autonomous trajectories of the system. We classify the dynamic behaviours exhibited by these trajectories into transitions, pursuits, and captures according to our previously established methodology (see Introduction and S1B Fig) [43].
Previously published non-autonomous gap gene circuits suggest a specific regulatory structure for the gap gene network in D. melanogaster (Fig 3A) [26]. This structure is consistent with the network predicted by the static-Bcd model of Manu et al. [23], and with the extensive genetic and molecular evidence available in the published literature on gap gene regulation [18]. Unfortunately, it is difficult to derive insights about dynamic regulatory mechanisms from a static network diagram. Computer simulations help us understand which network interactions are involved in positioning specific expression domain boundaries across space and time [24–26, 34]. Although powerful, this simulation-based approach has its limitations. It cannot tell us how expression dynamics are brought about: for instance, why some gap domain boundaries remain stationary while others shift position over time. To gain a deeper understanding of the underlying regulatory dynamics, we analyse the configuration space of a fully non-autonomous gene circuit through instantaneous phase portraits (S1B Fig) [43], analogous to the autonomous phase-space analysis presented by Manu and colleagues [23] (Fig 2). This type of analysis requires diffusion-less gap gene circuits to keep the dimensionality of phase space at a manageable level.
We obtained fully non-autonomous gap gene circuits that lack diffusion through model fitting with diffusion parameters Da fixed to zero and interaction signs constrained to those of previous works (as described in “Models and Methods”). This resulted in a set of three selected, well-fitted circuits. The network topology of these gene circuit models correspond to that shown in Fig 3A. The following analysis is based on the best-fitting model with a root mean square (RMS) residual error of 10.73, which constitutes a slight overall improvement in quality-of-fit compared to static-Bcd models (see “Models and Methods” and [22, 26]). Its regulatory parameter values are listed in S1 Table.
This diffusion-less non-autonomous gene circuit accurately reproduces gap gene expression (Fig 3B). In particular, it exhibits correct timing and relative positioning of domain boundaries. Together with the fact that it fits the data equally well as equivalent circuits with diffusion (see “Models and Methods”, and [26]), this confirms earlier indications that gap gene product diffusion is not essential for pattern formation by the gap gene system [23, 25]. Interestingly, previously published diffusion-less static-Bcd circuits show rugged patterns with abrupt “on/off” transitions in expression levels between neighbouring nuclei [23]. In contrast, diffusion-less fully non-autonomous circuits produce smooth spatial expression patterns with a graded increase or decrease in concentration levels across domain boundaries. This is because non-autonomy, with its associated movement of attractors and separatrices over time, provides increased flexibility for fine-tuning expression dynamics over time compared to models with constant phase-space geometry (see below). In biological terms, it suggests that the expression of smooth domain boundaries does not strictly require diffusion. Although diffusion undoubtedly contributes to this process in the embryo, its role may be less prominent than previously thought [23, 25].
We used our non-autonomous gap gene circuit to assess the effect of maternal gradient decay on gap gene regulation. One way to isolate this effect is to compare the output of the fully non-autonomous model—with decaying maternal gradients—to simulations using the same model parameters, but keeping maternal gradients fixed to their concentration levels early during the blastoderm stage (time class C12). As shown in Fig 4, the relative order and positioning of gap domains remain unaffected when comparing models with fixed versus time-variable gradient concentrations. This indicates that maternal gradient decay is not strictly required for correct pattern formation by gap genes.
We do observe, however, that maternal gradient dynamics significantly affect the levels of gap gene expression throughout the trunk region of the embryo (Fig 4, shaded areas). While early expression dynamics are very similar in both models (time classes C12–T2), they begin to diverge at later stages. The fully non-autonomous model reaches peak expression at T2/T4, but the autonomous model without maternal gradient decay overshoots observed expression levels in the data between T4 and T8. This indicates that maternal gradient decay leads to decreasing activation rates at the late blastoderm stage, thereby regulating the timing and level of peak gap gene expression. Such a limiting regulatory effect of maternal gradients has been proposed before [25, 42], but has never been tested explicitly.
Interestingly, the overshoot occurs in different ways in the anterior and the posterior of the embryo. In the anterior, maximum concentrations of Hb and Kr across each domain remain unchanged, but levels of expression keep increasing around the Kr/Gt interface, rendering the domain boundaries steeper and less smooth in the simulation without maternal gradient decay (Fig 4, asterisk). In the posterior, we observe increased levels of Kni and Gt across large parts of their respective expression domains (Fig 4, arrows). These effects are asymmetric: both posterior Kni and Gt domains exhibit an anterior expansion, while the posterior boundary of the Kni domain is not affected. Considering that both of these domains shift towards the anterior over time (Fig 1) [25, 42], we interpret this as follows: maternal gradient decay not only decreases the rate of expression at late stages in the posterior region, but also leads to a slow-down of gap domain shifts, thereby limiting the extent of the shift. In the autonomous simulation without maternal gradient decay, both Kni and Gt domains keep on moving, which explains the observed expansion and increase of expression levels towards the anterior part of the domain.
We asked whether the differing effects of maternal gradient decay in the anterior and the posterior of the embryo depend on the presence of different regulatory mechanisms in these regions [23]. To validate this hypothesis, we need to understand and characterise the dynamic mechanisms underlying gene regulation in our non-autonomous model. We achieve this through analysis of the time-variable phase spaces of nuclei across the trunk region of the embryo using the methodological framework presented in the Introduction (S1B Fig; see [43] for details). To briefly reiterate, this analysis is based on the characterization of the changing phase space geometry that shapes the trajectories of the system. The shape of a trajectory indicates typical dynamical behaviors, that can be classified into four distinct categories—transitions, pursuits, as well as geometrical and topological captures—each showing particular dynamic characteristics. These categories provide mechanistic explanations for the dynamic behavior of the system. For every nucleus, we then compare these non-autonomous mechanisms to the autonomous mechanisms of pattern formation found in the static-Bcd model [23]. This direct comparison allows us to identify the causes underlying the observed effects of maternal gradient decay on the temporal dynamics of gap gene expression.
In agreement with Manu et al. [23], we find different patterning modes anterior and posterior to 52% A–P position. Just like in static-Bcd models, anterior expression dynamics are governed by convergence of the system towards attractors in a multi-stable regime. In contrast, our model differs from that of Manu et al. [23] concerning posterior gap gene regulation. We find that a monostable spiral sink drives gap domain shifts in the posterior of the embryo; this differs markedly from the unstable manifold observed in static-Bcd gap gene circuits [23]. An in-depth analysis and biological discussion of spatial pattern formation driven by this mechanism goes beyond the scope of this study. It is provided elsewhere [44]. Here, we focus on temporal aspects of gene regulation and pattern formation, namely the regulation of the velocity of gap domain shifts by maternal gradient dynamics in the posterior of the embryo.
In this paper, we have examined the explicitly time-dependent aspects of morphogen gradient interpretation by a gene regulatory network; the gap gene system of the vinegar fly D. melanogaster. Using a fully non-autonomous gap gene circuit, we compared the dynamics of gene expression in the presence and absence of maternal gradient decay. We find that dynamic changes in the concentration of maternal morphogens Bcd and Cad affect the timing and rate of gap gene expression. The precise nature of these effects differs between the anterior and the posterior region of the embryo. In the anterior, gradient decay creates smooth domain borders by preventing the excessive accumulation of gene products across boundary interfaces between neighbouring gap domains. In the posterior, gradient decay limits the rate of gap gene expression, and therefore the extent of gap domain shifts, towards the end of the blastoderm stage. A temporal effect on gene expression rates is translated into slowing rates of domain shifts, which in turn alter the spatial positioning of expression boundaries. As a consequence, gradient decay stabilises spatial gap gene patterns before the onset of gastrulation. An effect of maternal gradient decay on gap gene expression rates has been suggested before—based on the analysis of quantitative expression data [25, 42]. However, only mechanistic dynamical models—such as the non-autonomous gap gene circuits presented here—can provide specific mechanisms and quantitative causal evidence for this aspect of gap gene regulation.
Our analysis suggests that maternal gradient decay—specifically, the disappearance of Cad from the abdominal region of the embryo—has an important role in regulating the timing of gap gene expression as well as limiting the rate and extent of gap domain shifts in the posterior of the embryo. This result is consistent with experimental data indicating that Cad affects gap domain shifts. Mutants lacking maternal cad, which show a reduced level of Cad protein throughout the blastoderm stage [28], show a delay in the shift of the posterior domains of kni and gt [32, 44]. However, Cad does not seem to act exclusively. An indirect role of Bcd in regulating gap domain shifts through altering gap-gap interactions was suggested by a modelling study [30]. It remains unclear whether Cad is also involved in mediating this effect. Finally, a recent study of Bcd-dependent regulation of hb postulated an additional mechanism for gap gene down-regulation that acts before maternal gradient decay occurs [2]. This could have an indirect effect on the timing of late (Bcd-independent) hb regulation, which may mediate the direct effect of Bcd decay on late hb expression we are observing in our models.
To better understand the mechanistic basis for the observed differences in patterning between the anterior and the posterior, we analysed the time-variable phase portraits in our non-autonomous model [43]. In agreement with a previous study based on autonomous phase space analysis of static-Bcd gap gene circuits [23], we find that two distinct dynamical regimes govern gap gene expression anterior and posterior to 52% A–P position (Fig 8). Stationary domain boundaries in the anterior are governed by regulatory mechanisms that are equivalent in static-Bcd and fully non-autonomous models (our work and [23]): they take place in a multi-stable dynamical regime where the posterior boundary of the anterior Gt domain is set by the movement of an attractor in phase space, and the posterior boundary of the anterior Hb domain is set by attractor selection (i. e. the capture of transient trajectories in the non-autonomous case) (Fig 8, left). Attractor movement in fully non-autonomous models leads to smooth expression boundaries, which are absent in the static-Bcd case. In contrast, static-Bcd and non-autonomous models suggest different mechanisms for gap domain shifts in the posterior of the embryo. While these shifts are controlled by an unstable manifold in the static-Bcd gene circuit model [23], we find a pursuit mechanism featuring a monostable spiral sink to govern their behaviour in our fully non-autonomous analysis (Fig 8). The spiralling geometry of transient trajectories imposes temporal order on the progression of gap genes being expressed. If arranged appropriately across nuclei in the posterior of the embryo, this temporal progression from Kr to kni to gt to hb leads to the emergence of the observed kinematic domain shifts [44].
It is important to note that similar regulatory principles can be found in all three solutions of our fully non-autonomous model that reproduce gap-gene patterning correctly both in the presence and absence of diffusion. We have chosen the most structurally stable solution for detailed analysis. The other two circuits show more variability of regulatory features both across space and time. Still, both of these models consistently exhibit multi-stability in the anterior, and spiral sinks as well as transiently appearing and disappearing limit cycles in the region posterior to 52% A–P position. This indicates that the two main dynamical regimes described here—stationary boundaries through attractor selection in the anterior vs. shifting gap domain boundaries through spiralling trajectories in the posterior—are reproducible across model solutions.
It is important to note that non-autonomy of the model is not strictly required for the spiral sink mechanism to pattern the posterior of the embryo. Simulations with fixed maternal gradients demonstrate that domain shifts can occur in an autonomous version of our gap gene circuit (see Figs 4 and 7). The reason why earlier models [22, 23] do not feature spiral sinks remains unknown although one possibility is that fitting in the absence of diffusion somehow benefits characterisations of posterior pattern formation in terms of oscillatory behaviours. In spite of this, there are two reasons to consider the mechanism proposed here an important advance over the unstable manifold proposed by Manu et al. [23]. The first reason is technical: non-autonomous gap gene circuits—implementing correct maternal gradient dynamics—are more accurate and stay closer to the data than the previous static-Bcd model. The fact that the quality of a reverse-engineered model usually depends on the quality of its fit to data implies that our model provides more accurate and rigorous predictions than previous efforts. The second reason is conceptual: although it is difficult to interpret an unstable manifold in an intuitive way, it is straightforward to understand the spiral sink as a damped oscillator patterning the posterior of the embryo. The presence of an oscillatory mechanism in a long-germband insect such as D. melanogaster has important functional and evolutionary implications, which are discussed elsewhere [44].
Analysis of an accurate, non-autonomous model is required to isolate and study the explicitly time-dependent aspects of morphogen interpretation by the gap gene system. Here, we have shown that such an analysis is feasible and leads to relevant and specific new insights into gene regulation. Other modelling-based studies have used non-autonomous models before (see, for example, [16, 26, 34, 50–53]). However, none of them have directly addressed the proposed role of non-autonomy in pattern formation [17]. Our analysis provides a first step towards a more general effort to transcend this limitation in our current understanding of the dynamic regulatory mechanisms underlying pattern formation during animal development.
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10.1371/journal.ppat.1000579 | The Chlamydia Type III Secretion System C-ring Engages a Chaperone-Effector Protein Complex | In Gram-negative bacterial pathogens, specialized chaperones bind to secreted effector proteins and maintain them in a partially unfolded form competent for translocation by type III secretion systems/injectisomes. How diverse sets of effector-chaperone complexes are recognized by injectisomes is unclear. Here we describe a new mechanism of effector-chaperone recognition by the Chlamydia injectisome, a unique and ancestral line of these evolutionarily conserved secretion systems. By yeast two-hybrid analysis we identified networks of Chlamydia-specific proteins that interacted with the basal structure of the injectisome, including two hubs of protein-protein interactions that linked known secreted effector proteins to CdsQ, the putative cytoplasmic C-ring component of the secretion apparatus. One of these protein-interaction hubs is defined by Ct260/Mcsc (Multiple cargo secretion chaperone). Mcsc binds to and stabilizes at least two secreted hydrophobic proteins, Cap1 and Ct618, that localize to the membrane of the pathogenic vacuole (“inclusion”). The resulting complexes bind to CdsQ, suggesting that in Chlamydia, the C-ring of the injectisome mediates the recognition of a subset of inclusion membrane proteins in complex with their chaperone. The selective recognition of inclusion membrane proteins by chaperones may provide a mechanism to co-ordinate the translocation of subsets of inclusion membrane proteins at different stages in infection.
| The obligate intracellular bacteria Chlamydia trachomatis is a common sexually transmitted pathogen and the leading cause of preventable blindness worldwide. Chlamydia co-opts host cells by secreting virulence factors directly into target cells through a multi-protein complex termed a type III secretion system or “injectisome”. The lack of a system for molecular genetic manipulation in these pathogens has hindered our understanding of how the Chlamydia injectisome is assembled and how secreted factors are recognized and translocated. In this study, a yeast two-hybrid approach was used to identify networks of Chlamydia proteins that interact with components of the secretion apparatus. CdsQ, a conserved structural component predicted to be at the base of the injectisome, interacted with multiple proteins, including a new chaperone that binds to and stabilizes secretory cargo destined for the membrane of the pathogenic vacuole. These results suggest that the base of the secretion apparatus serves as a docking site for a chaperone and a subset of chaperone-cargo complexes. Because the chlamydial injectisome represents a unique and ancestral lineage of these virulence-associated secretion systems, findings made in Chlamydia should provide unique insights as to how effector proteins are recognized and stabilized, and how a hierarchy of virulence protein secretion may be established by Gram-negative bacterial pathogens.
| The obligate, intracellular bacterium Chlamydia trachomatis infects the epithelium of the genital tract and conjunctivae, causing a wide range of ailments including the blinding disease trachoma, conjunctivitis, salpingitis, pelvic inflammatory disease and infertility [1]. Chlamydiae display an elaborate life cycle beginning with the attachment of an elementary body (EB), the infectious form of the bacteria, to the surface of epithelial cells [2]. Shortly after invasion, EBs differentiate into reticulate bodies (RBs). The RB-containing vacuole is rapidly segregated from normal endosomal maturation pathways to generate a membrane-bound “inclusion” [3]. As the inclusion expands, chlamydial replication becomes asynchronous to yield both RBs and EBs. Eventually, most of the cytoplasmic space of the host cell is occupied by the inclusion and EBs exit the host cell to infect adjacent cells [4].
All Chlamydiae code for the core components of a type III secretion (T3S) apparatus, a protein transport system used by Gram-negative bacteria to translocate effector proteins directly into host cells [5]. T3S systems are macromolecular structures composed of 20–35 proteins that are often referred to as “injectisomes” due to their resemblance to an injection needle [6]. Chlamydia injectisome components are present at all stages of infection and needle-like structures have been observed on the surface of EBs and at the sites of RB attachment to inclusion membranes [7],[8],[9], suggesting that this secretion system is functional. Putative chlamydial targets of T3S have been identified by their ability to be secreted by Shigella, Yersinia or Salmonella injectisomes [10],[11],[12],[13]. These T3S substrates include >25 soluble proteins and a large family of ∼40–50 integral membrane proteins of unknown function that localize to the inclusion membrane (Incs) [8],[10],[11],[12],[13]. At least one of these effectors, Tarp, is translocated during EB invasion of epithelial cells [10]. T3S substrates are likely translocated in a hierarchical fashion to manipulate specific cellular functions at distinct stages of infection [14].
Injectisomes belong to at least seven distinct families [15] of which three (Ysc, SPI-1 and SPI-2) are predominantly found in free-living pathogens of animals and two are more common in plant pathogens (Hrp1 and Hrp2). The remaining injectisome families are limited to the Chlamydiae phylum and the Rhizobiale order [6]. In the Chlamydiales, the genes encoding the T3S apparatus are scattered in small genomic islands [5],[16]. Remarkably, the content and synteny of these gene clusters is largely conserved among the Chlamydiaceae, suggesting that even though members of this phylum diverged over 700 million years ago [17], the genetic blueprint for the assembly of this translocation system has remained largely intact. These findings, combined with the lack of evidence for robust lateral gene transfer of T3S genes in Chlamydiae, support the hypothesis that the chlamydial T3S system is the closest to the primordial injectisome [18].
A transcriptional analysis of genes encoding chlamydial injectisome components revealed 10 operons containing 37 genes [16]. Recent work in C. pneumoniae indicated that interactions among components of the basal structure of the secretion apparatus are evolutionarily conserved [19]. For example, the putative C-ring component, CdsQ, interacts with the cytoplasmic component CdsL, mimicking similar interactions between YscL and YscQ in Yersinia [19],[20]. Although many components of the chlamydial injectisome basal structure are conserved, the needle and needle tip components are not [21]. The identification of needle component, CdsF, and its chaperones CdsE and CdsG, required more sophisticated bioinformatic approaches [22],[23]. Similarly, two sets of translocation pore components, CopB/CopD and CopB2/CopD2 (CT578/CT579 and CT861/CT860, respectively) were identified based on their close linkage to genes encoding secretion chaperones and their similarity in protein topology to the Yersinia translocators YopB and YopD [24],[25],[26].
The lack of a system for genetic manipulation in Chlamydia and the observation that chlamydial proteins cannot functionally substitute for orthologous T3S components in other bacterial systems [24] has limited a functional analysis of this ancestral secretion system. As a result, little is known as to how the Chlamydia injectisome is assembled, regulated or how the translocation of effector proteins is temporally and spatially controlled. Here, we applied yeast two-hybrid (Y2H) technology [27] to identify new core components, regulators and secretory cargo of the chlamydial injectisome. In this manner, we identified two protein-protein interaction nodes linking multiple effector proteins to CdsQ, the putative C-ring component at the base of the injectisome. We provide evidence that one of these protein interaction hubs is a chaperone for a subset of proteins destined for transport to the inclusion membrane and that CdsQ interacts with this chaperone alone and in complex with effector proteins. We propose that the C-ring of the chlamydial injectisome acts as a platform for the recognition and engagement of chaperones complexed to secretory cargo.
The core components of the T3S apparatus are defined by 10 operons dispersed among five different loci in the chromosome [16] (Fig. 1A). In addition, two unlinked operons encode proteins of the T3S-related flagellar export machinery. Based on protein-protein interactions defined among homologous components (Table 1) in other bacterial pathogens, the chlamydial injectisome has been proposed to have an architecture as outlined in Fig. 1B [5],[6].
To identify additional injectisome components, regulators, secretion chaperones and their respective cargo, we screened chlamydial proteins for their ability to interact with core components of the injectisome by Y2H analysis. We amplified 208 Chlamydia-specific, conserved hypothetical ORFs, and genes encoding putative effectors and injectisome components by PCR and cloned them into Y2H vectors to generate carboxyl terminal fusions to the DNA-binding domain (DBD) or Activator Domain (AD) of the yeast transcription factor Gal4, respectively (Table S1). For proteins containing large hydrophobic regions which tend to be toxic when expressed in yeast (e.g. inclusion membrane proteins) [28], only the putative cytoplasmic regions were expressed. MATα and MATa yeast strains expressing chlamydial proteins fused to Gal4AD or Gal4DBD were crossed to generate diploid strains. Positive interactions among these fusion proteins were identified by their ability to transcribe Gal4-dependent reporter genes (GAL1UAS-GAL1TATA-HIS3, GAL2UAS-GAL2TATA-ADE2) and restore growth in histidine and adenine deficient media. We identified 49 proteins that displayed various levels of homotypic and heterotypic interactions (Table S2). Most of these interactions were among proteins predicted to reside at the inner membrane and the cytoplasmic side of the basal T3S apparatus, although homotypic and heterotypic interactions were also observed among the predicted cytoplasmic domains of Inc proteins, including IncA, Ct565, Ct229 and Ct223.
We predicted that secretory cargo proteins engaged by known T3S chaperones could be identified based on protein interactions revealed by Y2H analysis. There are three classes of T3S chaperones: class III chaperones prevent the premature polymerization of needle components in the bacterial cytoplasm [23],[29]. An interaction between the needle component CdsF and its chaperone CdsG [22] was detected in our Y2H analysis as well as an interaction between CdsG and its co-chaperone CdsE (Fig. 1C). Class II chaperones, which in Chlamydia include and Ct274 (LcrH/SycD-like) and the tetratricopeptide repeat (TPR) containing Scc2 and Scc3 [24], bind to hydrophobic translocator proteins [30]. We determined that Scc2 binds to both CopB and CopD while Scc3 binds to CopN (Fig. 1B). The interactions between Scc2/CopB and Scc3/CopN are in agreement with previous findings [24],[31]. In addition, we detected heterotypic interactions between Ct274 and a small (24 kDa), acidic (pI 4.5) protein Ct668. Ct274 also interacted with Ct161, a homologue of the secreted protein Lda2 [32] (Fig. 1C).
Class I chaperones are small (∼15 kDa), acidic (pI<5) proteins that assemble mostly as homodimers to bind effector proteins [25]. These chaperones have been further subclassified depending on whether they associate with one (class Ia) or several (class Ib) effectors [33]. By amino acid sequence analysis, C. trachomatis encodes at least three putative T3S chaperones: Ct043, Ssc1 and Ct663 (SycE/CesT-like). Unlike their counterparts in other pathogenic bacteria, we did not detect many homotypic interactions among chlamydial class I T3S chaperones. Instead, we found evidence of potential heterodimeric complexes, including an Scc1-Ct663 interaction.
Overall, these results suggest that chlamydial homologues of Class 1 T3S chaperones may form heterodimeric complexes and thus limit the usefulness of a binary Y2H screen to identify binding partners. However, given that ∼10% of the chlamydial coding potential may be devoted to T3S substrates,[12],[34],[35],[36] it is apparent that the number of effector proteins is in excess of potential chaperones. This indicates that the secretion of many chlamydial T3S substrates is either chaperone-independent or that many secretion chaperones remain to be identified.
CdsQ (Ct672), a homologue to the C-ring component FliN of the Salmonella flagellar apparatus [37] and the Shigella Spa33 protein [38], represented a central node of Chlamydia protein-protein interactions (Fig. 1D). CdsQ interacted with CdsL, an interaction that is conserved among related components in Yersinia, Shigella, and enteropathogenic E. coli (EPEC) injectisomes [20],[38],[39]. CdsS and CdsT were also observed to interact with CdsQ (Fig. 1D). These proteins are predicted to reside at the inner membrane and likely interact as components of the inner membrane spanning ring in other bacterial pathogens [40],[41]. An interaction between CdsQ, CdsL and CdsD has been recently reported in C. pneumoniae [19]. Unfortunately, we were unable to detect CdsD interacting proteins because this protein was self-activating in our Y2H reporter system. Additional components of the basal T3S apparatus may include the CdsQ-interacting proteins Ct560 and Ct567, an ORF immediately adjacent to this operon (Fig 1A). CdsQ did not interact with CdsV (Ct090), a conserved injectisome inner membrane protein, but did interact with the CdsV/FlhA homologue Ct060 (Fig. 1D). This raises the possibility that the Chlamydia injectisomes may be formed by a mixture of flagellar and T3S components. A combinatorial assembly of injectisomes may enhance the functionality of the secretion apparatus by adding assembly control checkpoints or by expanding the repertoire of cargo proteins that can be secreted.
We did not detect several of the predicted interactions among integral membrane components of the injectisome or the secretin CdsC. The inability to detect such interactions is likely due to the limitations of the classical Y2H system in identifying interactions among integral membrane proteins and proteins destined to fold outside of cytoplasmic compartments [42]. Nonetheless, we detected interactions between the needle component CdsF and its chaperones, CdsG and CdsE [22], and Ct584, a conserved chlamydial ORF (Fig. 1C). We postulate that these interactions were detected because they most likely occur in the bacterial cytoplasm.
Despite the known limitations of classical Y2H analysis, our ability to identify previously reported interactions validate the utility of this approach. Importantly, novel interactions identified by Y2H suggest that CdsQ may play a central role in organizing networks of proteins at the base of the injectisome, including secreted effectors.
T3S effectors require secretion chaperones for efficient translocation by injectisomes [43],[44]. ATPases in Yersinia and Salmonella, YscN and InvC respectively, recognize chaperone-effector complexes and provide the energy for their dissociation, thus facilitating effector protein export [45],[46]. Similarly, the inner membrane component YscU in Yersinia recognizes translocators as T3S export substrates [47]. Indeed, it makes intuitive sense that components at the base of the injectisome provide a platform for the recognition of secretory cargo.
Based on these observations, two chlamydial protein-protein interaction hubs linking known secreted effector proteins to CdsQ were of particular interest. In the first interaction node, Ct700, via its TPR repeat domain could act as a scaffold to dock effectors directly (e.g. Ct223 and Ct226) [34],[35] or indirectly (IncA and Lda2) [32],[48]. These protein interaction networks are linked to CdsQ via the putative protease, Ct824 (Fig. 1D). The relevance of these interactions in vivo remains to be determined. The other protein-protein interaction hub has Ct260 at its center and links CdsQ to the inclusion membrane proteins Cap1 [49], Ct618 [28] and Ct225 [35] (Fig. 1D). Cap1 has been postulated to be a secretion target of the chlamydial injectisome because fusion of the first 15 amino acids of Cap1 to adenylate cyclase is sufficient to impart T3S-dependent export of the fusion protein in Shigella [12].
To validate the novel interactions identified by Y2H, we decided to characterize the putative bindings of Ct260 to CdsQ and the secreted effectors Ct618 and Cap1 (Fig. 2A). We generated specific antisera to Ct260, and CdsQ, and determined by immunoblot analysis that these proteins are expressed in infected cells and in density gradient purified EBs and RBs (Fig. 2B&C). Next, we determined the subcellular localization of Ct260 and CdsQ in EBs by assessing their fractionation properties upon extraction from EBs with Triton-X114 or Sarcosyl [50],[51]. CdsQ and Ct260 phase partitioned with Triton-X114 indicating an association with hydrophobic proteins (Fig. 2D), although a significant portion of CdsQ was also present in the aqueous, non-hydrophobic phase. Any association of Ct260 with membranes is unlikely to include chlamydial outer membrane complexes (COMC) since the protein was readily extracted from EBs with Sarcosyl (Fig. 2E). Given its association with CdsQ and translocated effectors, Ct260 could be a core T3S component or a secreted effector. We tested the ability of Ct260 to be secreted from EBs by adapting a recently described in vitro secretion system. In this system, EB effectors are released into the culture supernatant by treatment with calcium chelators and bovine serum albumin at 37°C [52]. Under these conditions Ct260 was not released into the extracellular media, while Tarp, a known secreted effector, was efficiently secreted (Fig. 2F), suggesting that at least in EBs, Ct260 is not an efficient target of T3S.
We performed a detailed analysis of Ct260, CdsQ, Cap1 and Ct618 localization at 12 and 24 h post infection by immunofluorescence microscopy. Consistent with the fractionation experiments (Fig. 2D&E), CdsQ and Ct260 associated exclusively with bacteria, while Ct618 and Cap1 were found at the inclusion membrane, even at early time points (Fig. 2G). Overall, these results indicated that at steady state, Ct260 and CdsQ reside within the confines of bacterial cells, while Ct618 and Cap1 are largely exported. Therefore, any interaction between Ct260 and C618 or Cap1 is likely to be transitory and occur in the bacterial cytoplasm or in association with bacterial cytoplasmic membranes.
PSI-BLAST-based database searches of Ct260 did not reveal homology to any known proteins. However, given that Ct260 is a small (18.8 kDa), acidic (pI 4.6) protein that interacted with itself and T3S effectors by Y2H analysis (Fig. 2A), we hypothesized that it was a secretion chaperone. In support of this, the predicted secondary structure of Ct260 has a similar arrangement of α-helices and β-strands as T3S chaperones (Fig. S1). We propose that Ct260 is a new T3S secretion chaperone, and thus refer to it as Multiple cargo secretion chaperone (Mcsc).
We first tested if recombinant Mcsc formed homotypic complexes by treating recombinant protein with low levels of the chemical crosslinker DSP on ice and assessing the formation of higher molecular weight complexes by SDS PAGE and immunoblots. Mcsc was readily cross-linked into a major 36 kDa molecular weight species consistent with the formation of a dimer (Fig. 3A). This dimeric form of Mcsc was seen even in the absence of chemical crosslinkers (Fig. 3A) and its relative abundance was sensitive to heat and reducing agents (not shown). Higher order complexes consisting of potential trimers and tetramers were also observed at the highest concentration of crosslinker. The relevance of these higher order complexes is not clear since we have observed a cold-induced aggregation of purified Mcsc (not shown). Next, we determined if Mcsc associated with the effectors Cap1 and Ct618 by co-expressing untagged Mcsc and a hexahistidine-tagged versions of full-length Cap1 (aa 1–298) and the amino-terminal domain of Ct618 (aa1–189) from bicistronic vectors. Mcsc efficiently co-purified with 6xHis-tagged Ct618 or Cap1 on Ni2+ NTA resin, suggesting a stable interaction between these inclusion membrane proteins and the putative chaperone (Fig. 3B). We sized these complexes by gel filtration chromatography and determined that when expressed alone, Mcsc eluted as a ∼35 kDa complex, a size consistent with that of the dimeric forms identified in cross-linking experiments (Fig. 3C). In contrast, when Mcsc was co-expressed with Cap1 or Ct618, it fractionated as a protein complex of 66–78 kDa and 54–65 kDa respectively (Fig. 3C). The size of these complexes is consistent with that of two Mcsc subunits bound to one effector protein.
Next, we tested if Mcsc could bind to full-length Cap1 from infected cell lysates. We incubated NP40 solubilized membranes from infected cells with recombinant 6xHis-tagged Mcsc dimers, and assessed the ability of endogenous Cap1 to be purified on Ni2+NTA agarose. Cap1, but not the inclusion membrane proteins IncA, specifically bound to Ni2+-beads in the presence of Mcsc (Fig. 3D).
We mapped the regions of Ct618 and Cap1 that interact with Mcsc by Y2H analysis. Truncated forms of Ct618 and Cap1 were fused to the GAL4DBD and co-expressed in yeast with GAL4AD-Mcsc. Positive interactions were assessed by growth in histidine or adenine deficient media as described above. A region encompassing the central region of both Ct618 and Cap1 was sufficient to mediate an interaction with Mcsc (Fig. 3E). The Mcsc binding site on its secretory cargo (Ct618 and Cap1) is within the range of what has been observed in other chaperone-effector protein complexes [44].
Our initial attempts at purifying recombinant Cap1 and Ct618 indicated that these proteins were not stable when expressed at high levels in E.coli. Given our finding that Mcsc may constitute a chaperone for these effectors, we compared the effect of expressing Cap1 and Ct618 with or without Mcsc from mono and bicistronic E. coli expression vectors under the control of a T7 promoter (Fig. 4A). Consistent with our earlier observations, the detected levels of Cap1 and Ct618 protein were significantly lower in the absence of Mcsc (Fig. 4B). Interestingly, Cap1 and Ct618 migrated at a higher apparent molecular weight in the absence of Mcsc (Fig. 4B), suggesting that in the presence of the chaperone, these inclusion membrane proteins may be subjected to conformational changes that expose susceptible regions to E. coli proteases. Although this processing may be specific to E. coli, it is clear that the expression and stability of Cap1 and 618 is dependent on Mcsc, thus establishing its role as a chaperone.
Based on the crystal structures of various class I secretion chaperones, conserved hydrophobic amino acids in the first α-helix and β-strand are proposed to be important for the recognition of secretory cargo [44]. We identified the corresponding amino acids in Mcsc (Fig. 4C & S1) and tested if these residues played a role in the binding of Mcsc to Ct618 and Cap1. First, we generated point mutation in Mcsc's α1-helix (L15A) and β1-strand (I31A, I33A V35A (“3A”) and I31G, I33G V35G (“3G”)) and tested the ability of these mutants to interact with effectors by Y2H. Consistent with their proposed role in binding substrates, all three mutations in Mcsc significantly impaired interactions with Cap1 and Ct618 (Fig. 4D). Next, we introduced these mutations into Mcsc-Cap1 and Mcsc-Ct618 bi-cistronic E. coli expression vectors (Fig. 4A). The 3A and 3G mutations did not affect the expression of Mcsc (Fig. 4E) or its ability to form dimers (not shown). However, co-expression of 3A and 3G Mcsc mutants led to significantly lower levels of Ct618 and Cap1 (Fig. 4E) as assessed by immunoblot analysis of total protein lysates. In contrast, co-expression with the L15A Mcsc mutant had little effect on the total amounts of Cap1observed but Ct618 was not detectable (Fig. 4D). We tested if these mutants were still capable of binding either hexahistidine tagged Cap1 or Ct618 by co-isolating effector complexes on Ni-NTA agarose beads. As shown in Fig. 4D, Mcsc and Mcsc mutants co-purified with Cap1 in a manner proportional to the levels of Cap1 present in the total lysates. Not surprisingly, Mcsc mutants did not co-purify with Ct618, since the effector was not detectable in the total samples. It is worth noting that Mcsc 3A and 3G mutants had a much greater impact on Cap1 and Ct618 expression levels than the absence of Mcsc, suggesting that Ct618 and Cap1 levels are directly affected by their interaction with Mcsc (Fig. 4B & E). We postulate that 3A and 3G Mcsc mutants, like the wild type counterpart, still provides a platform for the binding of Cap1 and Ct618, but the binding interaction is not strong enough to prevent their dissociation from Mcsc and eventual degradation.
T3S chaperones are proposed to play a role in targeting secretory cargo to the injectisome, either by providing new targeting information [53], or facilitating the exposure of the short amino terminal export signal [6]. Based on these findings we were intrigued by the significance of Mcsc-CdsQ interactions identified by Y2H. First, we confirmed these interactions by demonstrating that purified Mcsc dimers efficiently bound to GST-CdsQ, indicating that the presence of the effector is not required for Mcsc to engage the C-ring (Fig. 5A). Next, we tested if CdsQ can bind to Mcsc-effector complexes by incubating GST-CdsQ with Mcsc-Cap1 complexes isolated by gel filtration chromatography. A complex of Mcsc and Cap1 co-eluted from glutathione sepharose beads when incubated with GST-CdsQ but not GST alone (Fig. 5B). Based on CdsQ's homology to the C-ring component Spa33, which localizes to the base of Shigella injectisome [38], we postulate that CdsQ associates with the base of the chlamydial secretion needle apparatus and provides a platform for the recognition of Mcsc and Mcsc-effector protein complexes.
Greater than 10% of the Chlamydia genome is predicted to encode for substrates of T3S [14],[54]. This arsenal of effectors is required for efficient cell invasion, establishment of the inclusion, acquisition of nutrients, and avoidance of innate immune responses. How these highly adapted pathogens coordinate the secretion of effector proteins is largely unknown. In one model, a hierarchy of effector secretion is established by the timing of their synthesis. In this manner, temporal gene expression over the early, mid and late cycles dictates effector delivery [14],[55]. One caveat with this model is that early cycle genes continue to be expressed throughout infection [55]; therefore, all effectors are theoretically present by mid-late cycle (∼20 h for urogenital C. trachomatis serovars). The presence of early, mid and late effectors competing to engage a common injectisome would argue that additional components are involved to ensure an orderly translocation of effectors. How do mid-late effectors compete for efficient transport given that all these effectors must engage a common injectisome?
Most effector proteins contain a T3S targeting signal at their extreme amino terminus that is broadly recognized by divergent injectisomes. Additional targeting information contained within approximately the first 200 amino acid residues of the effector provides binding sites for secretion chaperones [44],[56],[57]. These chaperones are multi-functional: they target effectors to the proper injectisome, stabilize pre-formed effector proteins, mask membrane targeting domains prone to aggregation and possibly impart a translocation hierarchy [43],[58],[59]. T3S chaperones are small, acidic proteins that display a conserved α−β−α fold and form stable homo- and heterodimers [60]. These dimers bind to partially unfolded effector proteins via a hydrophobic patch formed by residues on the α1, β1 and β4-5 strands [44].
Despite the limited homology among T3S chaperones, six potential C. trachomatis chaperones can be identified based on primary amino acid sequences: Ct043, Scc1 (Ct088), Scc2 (Ct568), Scc3 (Ct860), Ct274 and Ct663 [61]. To identify their potential effector cargo, we screened for interacting chlamydial proteins by Y2H analysis. We confirmed previously reported interactions between Scc2 and CopB, and Scc3 and CopN [24],[31] (Fig. 1C). In addition, we identified a novel interaction between Scc2 and CopD and determined that Ct274 may interact with two small acidic proteins, Ct161 and Ct668 (Fig. 1C). Ct668, encoded within an operon of structural injectisome components, was previously identified in a screen for proteins exported from the inclusion [28]. PSI-BLAST analysis indicates that Ct668 is related to a large family of hypothetical DNA-binding proteins, raising the intriguing possibility that Ct668 is a transcriptional regulator. Whether Ct668 is a chaperone, a core T3S component, or a regulatory factor remains to be determined.
Given the large number of effectors and the dearth of T3S chaperone interacting partners identified by bioinformatics, we hypothesized that many secretion accessory factors remained to be found. We speculated that these factors may act as adaptors between effector proteins and components of the injectisome. In a subgenomic interactome map of the T3S apparatus, CdsQ emerged as a central node of protein-protein interactions (Fig. 1D). Based on its homology to FliN and Spa33, CdsQ is predicted to form the C-ring, a large protein scaffold at the base of the injectisomes. Interestingly, many of the CdsQ interacting factors we identified are conserved among injectisomes [18], suggesting small deviations in the basic architecture of this secretion apparatus despite the large evolutionary distance between Chlamydiales and other Gram-negative bacteria [17]. We identified two novel hubs of protein-protein interactions (Ct260 and Ct700) that linked multiple inclusion membrane proteins to the secretion apparatus (Fig. 1D). Ct260/Mcsc was of particular interest because, in addition to its direct interaction with CdsQ and many secretory cargo proteins (Cap1, Ct225 and Ct618) [35],[49],[54], it had predicted secondary structural similarity to other T3S chaperones (Fig. S1). Mcsc is expressed in both developmental forms of Chlamydia (Fig. 2C) and forms stable dimers and complexes with at least two inclusion membrane proteins (Fig. 3A–D). In E. coli, significantly lower levels of Ct618 and Cap1 were observed in the absence of Mcsc or when Mcsc was mutated to impair effector protein binding (Fig. 4B, D, E). These findings suggest that one of the functions of Mcsc is to stabilize these inclusion membrane proteins. While the data presented here implies that Mcsc acts as a bona fide Class I T3S chaperone, at present we cannot assign a role for Mcsc in directing the secretion of Cap1 or Ct618. Proof of such a role will require the development of a proper system for genetic manipulation in Chlamydia or the reconstitution of chaperone-dependent secretion in a heterologous T3S system.
In S. typhimurium, the SptP-SicP effector chaperone complex binds to the conserved ATPase InvC [45]. InvC then dissociates this complex and provides the energy required for the translocation of the SptP across the injectisome. A similar role for the ATPase in substrate selection by chaperone binding has been proposed for the EPEC T3S system and flagellar export [62]. By analogy to these findings, we propose that CdsQ recruits Mcsc-effector complexes directly to the base of the injectisome (Fig. 6). However, because Mcsc can bind to the C-ring component CdsQ in the absence of its bound substrate (Fig. 5A), it is also possible that Mcsc is pre-docked on the C-ring of the injectisome. This latter possibility is consistent with the observation that Mcsc partitions with inner membranes in EBs (Fig. 2D) in the absence of its cognate cargo, which are not synthesized until after 1 h (Cap1) and 8 h (Ct618) post invasion [55]. Because the stability of effectors require binding by Mcsc, either Mcsc dimers detach from the injectisome and bind to newly synthesized effectors at distal sites or the translating ribosome itself is recruited to the C-ring. In support of a localized translation model, the chlamydial GTPase (HflX) that binds to the 50S ribosome subunit, localizes to the bacterial inner membrane [63], suggesting that some ribosomes may be pre-docked at secretion sites. Furthermore, one of the CdsQ-interacting proteins identified by Y2H is Ct677, a putative ribosome recycling factor (Fig. 1D). These findings raise the intriguing possibility that the injectisome may interact with the bacterial translational machinery. As an obligate intracellular pathogen, it would not be surprising if the chlamydial injectisome, the main portal for communication with the host, is hard wired to interface with the bacterial and transcriptional and translational machinery. Such a system would allow for rapid regulation of effector protein translocation in response to intracellular (pathogen's metabolic status, developmental cues) and extracellular (host responses, inclusion lumen environment) signals.
The prominence of CdsQ as a hub of protein-protein interactions, including secretion chaperones, suggests a central role in regulating the recognition of effector proteins. In the flagellar system, the CdsQ-related C-ring component FliM binds to the flagellar general chaperone FliJ [64]. Similarly, the Shigella CdsQ homologue, Spa33, can be co-isolated with effector proteins indicating that recognition of chaperone-effector complexes by C-ring components may be evolutionarily conserved [38]. Based on these observations and our own findings with the Chlamydia injectisome, we hypothesize that proteins at the base of the translocon integrate multiple intracellular signals to regulate the production and secretion of virulence factors. Interestingly, in the flagellar systems, the C-ring protein FliG was recently shown to bind to fumarate reductase to control the direction of flagellar rotation in response to fumarate [65]. These findings suggest that in addition to the recognition of secretion substrates, the C-ring may provide a mechanism for the integration of intracellular cues by binding to “sensor” proteins not typically thought to be associated with T3S (e.g. envelope assembly, intermediary metabolism, stress). Global screens for proteins that interact with components at the base of the injectisome, such as those described in this study, should help elucidate the intricacies of T3S function and regulation.
Bacteria, yeast strains and plasmids are listed in Supplementary Table S3. C. trachomatis ORFs were amplified from purified C. trachomatis serovar D genomic DNA using the primers listed in Table S3 and the Expand High Fidelity PCR system (Roche). PCR products were subcloned into pET24d, pET15b (Novagen) and/or pGEX-4T1 (Invitrogen). Mutation sites, L15→A, 3A (I31, I33, V35→A) and 3G (I31, I33, V35→G), were chosen based on sequence alignments of several T3S chaperones (Fig. 4C & S1) and site directed mutagenesis was performed with QuickChange Kit (Stratagene) as instructed by the manufacturer. Mcsc/Cap1-6xHis, Mcsc/Ct618-6xHis and Mcsc(L15A, 3A or 3G)/Ct618-6xHis were vo-expressed as a bicistronic construct in pET-24d(+). Affinity-tagged recombinant proteins were expressed in E. coli BL21 DE3 (Stratagene) with 0.5 mM IPTG.
The Matchmaker Two-Hybrid System (Clontech) was used in this screen. C. trachomatis genes (CT) were amplified from a previously established chlamydial ORF library [28] using the pGAD and pGBT9 primers or directly from genomic DNA using ORF specific primers (Table S3) with Expand High Fidelity polymerase. PCR products were transformed into both Y2H reporter yeast strains PJ69-4a (MATα) or AH109 (MATa) along with digested Y2H vectors pGAD424 or pGBT9 by the lithium acetate method [66]. MATα yeast strains containing C. trachomatis ORFs inserted into pGAD424 were arrayed in 96 well plates. This ordered array of yeast strains was mated against individual MATa yeast strains containing pGBT9-CT ORFs. The resulting diploids were selected in synthetic complete (SC) medium lacking leucine and tryptophan. Yeast matings were performed semi-automatically with a RoboMEK-FX (Beckman-Coulter) liquid handler. Positive interactions were assessed by monitoring growth on SC media lacking histidine (His) or adenine (Ade) over 4 days.
HeLa cell monolayers were grown in Dulbecco's minimal essential medium (DMEM)(Gibco) supplemented with 10% fetal bovine serum (Mediatech) at 37°C/5% CO2. C. trachomatis LGV (serotype L2) EBs were purified by density gradient centrifugation as previously described [51]. Infections were initiated by adding EBs to HeLa monolayers (multiplicity of infection (MOI) ∼1) followed by centrifugation at 3,500×g for 25 min at 10°C.
GST-Mcsc, GST-CdsQ and GST-Ct618 (aa1–96) were expressed in E. coli and purified with glutathione coated Sepharose 4-Fast Flow beads (GE Healthcare) at 4°C for 2 hr. Recombinant GST-Mcsc was eluted with 20 mM reduced glutathione in PBS (pH 8.0) and used to immunize female White New Zealand rabbits (5∼6 lbs) (Robinson Services, Inc.). Antisera was depleted of anti-GST antibodies and affinity purified over a GST-Mcsc or GTS-CdsQ column. Bound antibodies were eluted with 0.2 M Glycine (pH 2.5) and neutralized with 1 M K2HPO4. The resulting antibodies was then dialyzed in PBS and stored in −80°C. For immunoblot analysis, protein samples were separated by SDS-PAGE, transferred to 0.45 µm nitrocellulose membranes and blocked in 2% non-fat powder milk in TBST (50 mM Tris-Base, 150 mM NaCl, 0.2% Tween pH 7.4). Membranes were incubated with primary antibody diluted in 1% milk-TBST, followed by incubation with secondary antibody conjugated to horseradish peroxidase and detection by chemiluminescence (Pierce). Primary antibodies include anti-Tarp (T. Hackstadt, RML), Hsp60, MOMP and CdsJ (K. Fields, U. of Miami), Cap1 (A. Subtil, Pasteur Institute), RpoD (M. Tang, UC Irvine), actin (Sigma-Aldrich), α-tubulin (Sigma-Aldrich), hexahistidine (Rockland Inc.) and IncA (generated in our laboratory).
For indirect immunofluorescence detection, HeLa cells were seeded on glass coverslips and infected at MOI∼1 for the indicated times. Infected cells were fixed in methanol, blocked with 2% bovine serum albumin (BSA) in PBS and incubated with polyclonal anti-Mcsc, CdsQ, Ct618 or Cap1 antibodies (1∶100 in PBS/2% BSA) and mouse monoclonal anti-MOMP antibodies (RDI) (1∶300 in PBS/2% BSA) for 1 hr at 4°C. Immunoreactive material was detected with Alexafluor conjugated secondary antibodies (Invitrogen, CA). Images were acquired with a Leica TCS SL confocal microscope and processed with Leica imaging software.
Freshly purified GST or GST-CdsQ coupled to glutathione sepharose beads was incubated with 6xHis-Mcsc dimers or Mcsc/Cap-6xHis complexes purified by gel filtration and incubated overnight at 4°C in binding buffer (50 mM Tris-HCl, 150 mM NaCl, 2 mM EDTA and 0.5% TritonX-100) After extensive washing, bound proteins were eluted with 25 mM glutathione and subjected to SDS PAGE followed by staining with Sypro orange (Molecular Probes) and detection with a Typhoon 9410 Phosphoimager, (GE Healthcare). Parallel samples were analyzed by immunoblot.
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10.1371/journal.pntd.0004000 | Short-term Forecasting of the Prevalence of Trachoma: Expert Opinion, Statistical Regression, versus Transmission Models | Trachoma programs rely on guidelines made in large part using expert opinion of what will happen with and without intervention. Large community-randomized trials offer an opportunity to actually compare forecasting methods in a masked fashion.
The Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Given antibiotic coverage and biannual assessments from baseline through 30 months, forecasts of the prevalence of infection in each of the 24 communities at 36 months were made by three methods: the sum of 15 experts’ opinion, statistical regression of the square-root-transformed prevalence, and a stochastic hidden Markov model of infection transmission (Susceptible-Infectious-Susceptible, or SIS model). All forecasters were masked to the 36-month results and to the other forecasts. Forecasts of the 24 communities were scored by the likelihood of the observed results and compared using Wilcoxon’s signed-rank statistic.
Regression and SIS hidden Markov models had significantly better likelihood than community expert opinion (p = 0.004 and p = 0.01, respectively). All forecasts scored better when perturbed to decrease Fisher’s information. Each individual expert’s forecast was poorer than the sum of experts.
Regression and SIS models performed significantly better than expert opinion, although all forecasts were overly confident. Further model refinements may score better, although would need to be tested and compared in new masked studies. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models.
| Forecasts of infectious diseases are rarely made in a falsifiable manner. Trachoma trials offer an opportunity to actually compare forecasting methods in a masked fashion. The World Health Organization recommends at least three annual antibiotic mass drug administrations where the prevalence of trachoma is greater than 10% in children aged 1–9 years, with coverage at least at 80%. The Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Here, we compared forecasts of the prevalence of infection in each of the 24 communities at 36 months (given antibiotic coverage and biannual assessments from baseline through 30 months, and masked to the 36-month assessments) made by experts, statistical regression, and a transmission model. The transmission model was better than regression, with both far better than experts’ opinion. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models.
Clinicaltrials.gov NCT00792922
| The World Health Organization (WHO), the International Trachoma Initiative, Ministries of Health, and their partners aim to control blinding trachoma by 2020, implementing surgical campaigns, antibiotic distributions, hygiene initiatives, and environmental improvements [1]. Trachoma control is a massive undertaking: 50 million doses of antibiotics are now distributed annually, in 30 countries [2]. The Global Trachoma Mapping Project alone will complete population-based surveys in more than 1400 districts worldwide by the end of 2015 [3, 4]. Surveys and treatment histories are now available for the vast majority of trachoma-endemic districts worldwide [5]. However, we do not know where WHO goals will likely be and not be achieved. Decisions on when to start and stop treatments are still based on guidelines dependent in large part on expert opinion [6].
Mathematical models have provided insight into the transmission of infectious diseases including trachoma [7–15]. However, they have rarely been used to make falsifiable predictions. As a candidate for prediction, trachoma may have some advantages over other infectious diseases. Trachoma has no nonhuman reservoirs, no long-lasting latent stage, and as yet no clinically important drug resistance, simplifying modeling greatly compared to diseases such as cholera, onchocerciasis, and tuberculosis [1]. With many infectious diseases such as SARS and Ebola [16, 17], epidemics occur sporadically in time and place; forecasting can be made in a predictable time frame with post-treatment trachoma. Community-randomized trials have provided longitudinal assessment of multiple communities after mass antibiotic distributions. In a sense, after each mass treatment has brought infection to a low level, infection returns in a synchronized manner in a number of communities, offering results somewhat analogous to a repeated experiment.
Accurate forecasts could inform stakeholders of realistic goals, define trouble spots to focus resources, and suggest areas headed towards control even in the absence of intervention. Prediction has scientific value as well. The ability to predict the prevalence of an infectious disease is a test of our understanding of the epidemiology. Here, we use recent clinical trial data to forecast the prevalence of ocular chlamydial infection in children in 24 endemic communities in Niger. We compare model forecasts to expert opinions, and to a statistical regression that uses no special knowledge of the infectious process.
Forty-eight communities were followed as part of the Niger arm of the Partnership for the Rapid Elimination of Trachoma (PRET) study. Communities were randomized to either mass antibiotics of the entire community, or antibiotics targeted just to children 12 years and younger. The 24 communities included in this study received annual antibiotic treatment of all ages. Communities were assessed at baseline and then biannually for 3 years. All individuals were offered antibiotic treatment annually, within two weeks of the assessment: children under 6 months, those allergic to macrolides, and pregnant women were offered topical tetracycline, and all others were offered a single dose of oral azithromycin (20 mg/kg for children and 1 gram for adults).
A random sample of 100 children 0–5 years old were selected from each community. If a community had less than 100 0–5 year-old children, then all were offered assessment. Each participating child had their upper right tarsal conjunctiva swabbed, and processed for PCR as previously described [18].
This study of de-identified data received ethical approval from the Committee on Human Research of the University of California San Francisco and was carried out in accordance with the Declaration of Helsinki. All adult subjects provided informed consent, and a parent or guardian of any child participant provided informed consent on their behalf. The informed consent given was oral: (a) we chose verbal consent because of the low literacy rates in the study area, (b) the IRB (10.00812) approved the use of oral consent, and (c) oral consent was documented on the registration form for each study participant prior to examination in the field.
The WHO NTD-STAG Monitoring and Evaluation Working Group had a sub-group meeting to discuss trachoma surveillance on September 11–12, 2014 in Atlanta, GA, USA at the Task Force for Global Health, co-sponsored by WHO and the NTD Support Center. Fifteen trachoma experts were asked to forecast the 36 month prevalence of infection in the 24 communities of the PRET-Niger study described above, and were provided, for each community, the biannual prevalence estimates from 0–30 months, the antibiotic coverage at 0, 12, and 24 months, the estimated population of 0–5 years olds at baseline, and the number of children sampled at baseline (Table 1). For each of the 24 communities, the experts were asked to provide their median estimate at 36 months, as well as the lower and upper bounds of their centralized 95% credible interval (the 2.5th, 50th, and 97.5th percentile of their belief). The community expert opinion was constructed by estimating each of the 15 individual’s distribution for each village (see Scoring below) and then taking the arithmetic average assuming equal weights, and was used as the primary survey forecast, although each individual’s forecast was also scored separately. The number of trachoma publications by each of the 15 experts was assessed by a PubMED (National Library of Medicine) search on December 1, 2014 (expert name as author AND “trachoma” as keyword).
Linear mixed effects regression was used to model the prevalence at 12 months and 24 months based on observations at 6 months and at 18 months, respectively. A random intercept was used for each village. To improve normality and homoskedasticity, the square root transform was applied to the prevalence fractions. The fitted model was then used to predict the prevalence at time 36 based on observations at 30 months. Standard errors were obtained using clustered bootstrap. All calculations were conducted using R (R Foundation for Statistical Computing, Vienna, Austria, v.3.1 for Macintosh, package lme4). While the primary regression was of square-root transformed regression with a community-level random effect, we also included a linear regression model without a community-level random effect.
We constructed a stochastic transmission model of transmission of Chlamydia trachomatis infection over time. For village j (j = 1, … 24), we assumed a population of size Nj, taken from the number of children aged 0–5 years found in the census at the time of treatment k (k = 1, 2, 3 corresponding to baseline, 12 and 24 months). We assumed a classical SIS (susceptible-infectious-susceptible) model structure, assuming that the force of infection is proportional to the prevalence of infection in the population of children aged 0–5 years with proportionality constant β, and a constant per-capita recovery rate γ [19]. Between periods of treatment, we assumed that the probability pi,j(k)(t) that there are i infectives in village j at time t after treatment time point k obeys the following equations [20, 21]:
dp0,j(k)dt=γp1,j(k)
dpi,j(k)dt=β(i−1)(Nj−i+1)Njpi−1,j(k)+γ(i+1)pi+1,j(k)−βi(Nj−i)Njpi,j(k)−γipi,j(k),for1≤i≤Nj−1
(1)
dpNj,j(k)dt=βNj−1NjpNj−1,j(k)−γNjpNj,j(k)
To model treatment, we assumed that each child aged 0–5 years in village j has probability cj(k) of receiving treatment with the antibiotic efficacy ek for treatment period k. We modeled each treatment according to pi,j(k)(t=0)=∑i′=iNjpi′,j(k,pre)(i′i)(1−cj(k))i(cj(k)ek)i′−i, where i′ is the number of infected individuals of children aged 0–5 years eligible for treatment, pi′,j(k,pre) is the probability of i′ infected individuals of children aged 0–5 years before treatment time point k, and i is the number of infected individuals of children aged 0–5 years after treatment. Let Sj(l) and Mj(l) be the observed number of PCR-positive individuals of children aged 0–5 years and the sample size at each observation time point l (l = 0, 1, 2, 3, 4, and 5 corresponding to baseline, 6, 12, 18, 24 and 30 months, respectively) for village j, and Sj be the possible number (ranging from 0 to Mj(l)) of positive individuals of children aged 0–5 years detected in the sample at observation time point l. From village j with population (children aged 0–5 years) size Nj of which the number Yj of infectives equals i, the probability P(Sj = s|Yj = i) that s positives are observed from a sample of size Mj is given by the hypergeometric distribution: (is)(Nj−iMj(l)−s)/(NjMj(l)). We assumed a standard beta-binomial prior (the binomial distribution in which the probability of success at each trial follows the beta distribution) P(Yj=y)=(Njy)B(y+μ,Nj−y+ρ)B(μ,ρ) (where the shape parameters μ and ρ for each treatment were computed from the observed distribution of infection of 24 villages at baseline, 12 and 24 months, B(z1, z2) is the beta function) [22]. The pre-treatment prevalence distribution was then computed for each village by applying Bayes’ theorem:
pi,j(k,pre)=P(Yj=i|Sj=s)=P(Sj=s|Yj=i)P(Yj=i)∑i=0NjP(Sj=s|Yj=i)P(Yj=i).
(2)
For each village j, the initial condition is determined from Eq (2), and the system numerically integrated for six or twelve months according to Eq (1). Specifically, for each village j, the pre-treatment distributions of kth treatment is pi,j(k,pre)=P(Yj=i|Sj=Sj(2k−2)). Given the number i of infected individuals of children aged 0–5 years, we computed the probability of the observed data of treatment k in village j according to P(Sj=s)=∑i=sNjpi,j(k)(τ)(is)(Nj−iMj−s)/(NjMj) (where Mj here denotes the sample size at one of the observation time points in the period k, and τ (6 or 12 months) is the interval between treatment time point and observation time point). We assumed independent villages, so that the total loglikelihood at time τ months after each treatment k may be computed by summing over all 24 villages ∑j=124log(∑i=0Njpi,j(k)(τ)(is)(Nj−iMj−s)/(NjMj)).
The transmission coefficient and antibiotic efficacy in the model were optimized by using the Metropolis algorithm with the total likelihood of three treatment periods to fit the model to the observed numbers of PCR-positive individuals of children aged 0–5 years in each village at 6, 12, 18, 24 and 30 months [23]. Forecasting the distribution of the observed number of PCR-positive individuals of children aged 0–5 years in a village at 36 months, conditionally on the observed numbers of PCR-positive individuals of children aged 0–5 years at baseline, 6, 12, 18, 24 and 30 months from the same village, was done by using a hidden Markov model according to the equation of forecast distribution [24].
The primary modeling forecast was pre-specified as the SIS process model with a random effect, although the SIS model without a random effect was included as a sensitivity analysis. In addition, the forecast of each model as a distribution over 101 discrete units was included as a comparison to the distribution estimated by minimizing the Fisher’s information (which allows a symmetric credible interval to approach a normal distribution, as well as the flexibility of asymmetric credible intervals to represent skewed distributions). Sensitivity analyses included changing the fixed mean infection duration assumed in the model to be 6 months, to 3 months or to 12 months.
To ensure a fair comparison, all forecasts were scored from the proposed median and 95% CrI. Given the denominator of the sample for each village at 36 months, the discrete distribution which minimized the Fisher’s information while constrained to that expert’s median and 95% CrI was estimated (Mathematica 10.0). As a sensitivity analysis, the SIS model forecasts were also presented as a distribution from 0 to 100%, with the score compared to the score derived from the median and 95% CrI. The modeler, statistician, and each of the 15 experts surveyed were all masked to the 36-month results, as well as to the forecasts made by others. Different forecasts were pairwise compared using Wilcoxon’s signed-rank test (Mathematica 10.0), using the Holm–Bonferroni multiple comparison correction, assuming 3 tests.
As a sensitivity analyses, we assessed whether the likelihood of the observed data would be greater (or lesser) had each forecast been more (or less) certain. Specifically, we perturbed each forecast by taking the density at each possible prevalence to the 1+ϵ power, normalizing, and determining the likelihood of the observed data. Note that this maintains the support of a forecast, maintains the ordering of the outcomes, and increases the Fisher’s information proportionally by ϵ (or decreases information proportionally for ϵ<0).
At the baseline census, communities had a mean of 146 children (95% CI 137 to 155) aged 0 to 5 years. The mean antibiotic coverage of children was 92.3% at baseline, 89.0% at 12 months, and 89.8% at 24 months. At baseline, the estimated prevalence of infection in the 24 communities ranged from 2% to 58% with a mean prevalence of 21.1% (95% CI 19.8% to 22.5%) [18]. The community prevalence of infection at each biannual visit is displayed in Table 1. The observed prevalence of infection at 36 month which was to be forecasted ranged from ranged from 0% to 22.5% with a mean prevalence of 5.8% (95% CI 5.2% to 6.4%).
The 15 experts provided forecasts for each of the 24 communities, with the mean taken as the community forecast (Fig 1). Fig 2 shows the forecast distributions for the community of experts, regression, and the SIS model, and Table 2 ranks the likelihood of the observed 36-month prevalence for each (S1 Fig and S1 Table in Supporting Information show the difference between observed and forecast prevalence). The estimated parameters of the SIS model with random effect are shown in Table 3. The SIS model and the square root-transformed regression had significantly better likelihood than the experts (p = 0.004 and p = 0.01, respectively), and than the linear regression (p = 0.01 and p = 0.02, respectively). All forecasts were positively biased, on average estimating a greater prevalence than was observed. All forecasts had a lower (worse) likelihood if their Fisher’s information was marginally increased. No individual expert forecast was better than the community forecast (the mean of the 15 experts).
A priori, the SIS model assumed a mean duration of infection of 6 months, obtaining a loglikelihood of the observed 36 month data of -41.03. Had we assumed the mean duration of infection was 3 months or 12 months, the loglikelihood would have been -41.47 or -39.91, respectively. If we had assumed the 6 month duration of infection, but did not use a community-level random effect, the likelihood score would have been -41.57. To fairly compare the different methods, the distribution of each forecast was estimated by minimizing the Fisher’s information given the estimated median and 95% CrI. For the SIS model, we also expressed each full distribution, obtaining a loglikelihood score of -40.90, or nearly the same as the -41.03 obtained from minimizing the Fisher’s information.
The mean number of trachoma citations on PubMED by the experts was 42 (range 0 to 133). The likelihood score and number of publications was actually inversely correlated (Spearman’s correlation -0.33, p = 0.24), thus we were unable to demonstrate that this measure of expertise was associated with better forecasting.
We performed logistic regression, assuming the individual PCR results most likely to have obtained the observed pooled results, but this performed no better than linear regression of the square-root transformed regression.
An SIS hidden Markov model and a regression model both produced forecasts with significantly higher likelihood of the observed data than a community of experts. The SIS model, which attempted to utilize an understanding of the infectious process and mass treatment, performed significantly better than linear regression, but only slightly (and not significantly) better than regression of the square root-transformed prevalence.
In general, more uncertainty resulted in better scoring forecasts. For every forecast a mathematical perturbation which reduced the Fisher’s information resulted in a higher likelihood of the observed data. The inclusion of a community-level random effect in the SIS hidden Markov model improved forecasting, perhaps by increasing uncertainty. The composite survey contained less information than any individual survey, and did better than any single individual forecast. The benefit of adding uncertainty could suggest that forecasts are inherently over-confident, or that additional variance components of the data were not considered by any of the methods.
Even though the SIS hidden Markov model and regression model had significantly higher likelihood than the community experts, the forecasted distributions of prevalence (as shown in Fig 2) by all models were very similar and did not show which model was significantly better than other models. With more available data, models could improve forecasting. The SIS hidden Markov model did not include infection from outside the population of children aged 0–5 years in each community. Our previous model [13] used a simple constant exogenous infection rate to represent infection from older children or adults to children aged 0–5 years within the same community, and did not find significant differences between the estimated transmission coefficients with and without the exogenous infection rate for different durations of infection. Of course, such models could be further refined to reflect age structured transmission dynamics. In this setting, the other age groups (older children and adults) were being treated as well, and other studies have shown consistently higher prevalence in small children than in other age groups (e.g. [25]).
The prevalence of infection in different communities is clearly correlated visit-to-visit, with visits 6-months apart having a higher correlation than visits further apart. However, there may be a fundamental limit to the predictability at the community level, simply due to the vagaries of who infects whom and when they do so. Mathematical models and cross-sectional empirical studies have suggested that as disease is disappearing, the prevalence of infection should form an exponential distribution (or its discrete analog, a geometric distribution), whether the disappearance is due to mass antibiotics, environmental improvements, or a secular trend [26, 27]. This exponential distribution has a much heavier tail than, for example, the normal distribution, so outliers are to be expected even when all communities are assumed to have identical transmission characteristics. Six-months is a relatively short period in trachoma control—programs typically reassess endemicity every 1–5 years. If predictability decreases as time increases between visits, than we would expect that apparent hotspots at one visit may not be the most affected areas at a subsequent visit. This has been termed chasing ghosts by trachoma programs (personal communication, PME).
Forecasts, whether made by experts, statistics, or mathematical transmission models, are rarely done in a falsifiable manner. Here, all participants were presented with identical information and masked to the results and to the other forecasters. Forecasts described the distribution of all possible outcomes, not a prediction of the single most favorable, and were scored in a pre-specified manner. The availability of results from 24 communities allowed a statistical comparison between forecasts, reducing the chance that the overall score would be dependent on a single fortunate guess.
Current WHO guidelines for starting mass drug administration are based on the district prevalence of the clinical signs of disease rather than infection, and future studies could assess forecasting at that level. In this study, we forecasted the community-level prevalence of ocular chlamydial infection. WHO guidelines currently include sub-district level intervention, at least for hypo-endemic districts with 5–10% prevalence of clinical activity in children. Individual community-level forecasting may become important for surveillance after mass antibiotic administrations have been discontinued.
Programs currently make decisions based on recommendations offered by the WHO [1]. Guidelines have relied heavily on extrapolation of existing evidence and expert opinion, since not all scenarios have been, or likely will ever be, tested in community-randomized trials. Forecasting at the individual community level has not been particularly successful. While forecasting at the district level may be more feasible than forecasting at the individual community level, statistical and transmission model forecasts should be evaluated. If proven more effective, as they were in this setting, then it may be reasonable for programmatic decisions to be based on statistical or modeling forecasts rather than just expert opinion.
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10.1371/journal.pgen.1001247 | A Quantitative Systems Approach Reveals Dynamic Control of tRNA Modifications during Cellular Stress | Decades of study have revealed more than 100 ribonucleoside structures incorporated as post-transcriptional modifications mainly in tRNA and rRNA, yet the larger functional dynamics of this conserved system are unclear. To this end, we developed a highly precise mass spectrometric method to quantify tRNA modifications in Saccharomyces cerevisiae. Our approach revealed several novel biosynthetic pathways for RNA modifications and led to the discovery of signature changes in the spectrum of tRNA modifications in the damage response to mechanistically different toxicants. This is illustrated with the RNA modifications Cm, m5C, and m22G, which increase following hydrogen peroxide exposure but decrease or are unaffected by exposure to methylmethane sulfonate, arsenite, and hypochlorite. Cytotoxic hypersensitivity to hydrogen peroxide is conferred by loss of enzymes catalyzing the formation of Cm, m5C, and m22G, which demonstrates that tRNA modifications are critical features of the cellular stress response. The results of our study support a general model of dynamic control of tRNA modifications in cellular response pathways and add to the growing repertoire of mechanisms controlling translational responses in cells.
| While the genetic code in DNA is read from four nucleobase structures, there are more than 100 ribonucleoside structures incorporated as post-transcriptional modifications mainly in tRNA and rRNA. These structures and their biosynthetic machinery are highly conserved, with 20–30 present in any one organism, yet the larger biological function of the modifications has eluded understanding. To this end, we developed a sensitive and precise mass spectrometric method to quantify 23 of the 25 ribonucleosides in the model eukaryotic yeast, Saccharomyces cerevisiae. We discovered that the spectrum of ribonucleosides shifts predictably when the cells are exposed to different toxic chemical stimulants, with these signature changes in the spectrum serving as part of the cellular survival response to these exposures. The method also revealed novel enzymatic pathways for the synthesis of several modified ribonucleosides. These results suggest a dynamic reprogramming of the tRNA and rRNA modifications during cellular responses to stimuli, with corresponding modifications working as part of a larger mechanism of translational control during the cellular stress response.
| The complexity of the transfer RNA (tRNA) system confers great potential for its use in cellular regulatory programs. There are hundreds of tRNA-encoding genes in S. cerevisiae and human genomes, with extensive post-transcriptional processing that includes enzyme-mediated ribonucleoside modifications [1]. Considering both tRNA and ribosomal RNA (rRNA), there are more than 100 known ribonucleoside modifications across all organisms in addition to the canonical adenosine, guanosine, cytidine and uridine [2], [3]. In general, tRNA modifications enhance ribosome binding affinity, reduce misreading and modulate frame-shifting, all of which affect the rate and fidelity of translation [4]–[7]. However, information about the higher-level biological function of ribonucleoside modifications has only recently begun to emerge. We have approached this problem with a systems-level analysis of changes in the spectrum of ribonucleosides in tRNA as a function of cell stress, which has revealed novel insights into the biosynthesis of tRNA modifications and their role in cellular responses.
Emerging evidence points to a critical role for tRNA and rRNA modifications in cellular responses to stimuli, with evidence for a role in tRNA stability [8], [9], cellular stress responses [10]–[12] and cell growth [13]. We recently used high-throughput screens and targeted studies to show that the tRNA methyltransferase 9 (Trm9) modulates the toxicity of methylmethanesulfonate (MMS) in S. cerevisiae [11], [14]. This is similar to the observed role of Trm9 in modulating the toxicity of ionizing radiation [15] and of Trm4 in promoting viability after methylation damage [14], [16]. Trm9 catalyzes the methyl esterification of the uracil-based cm5U and cm5s2U to mcm5U and mcm5s2U, respectively, at the wobble bases of tRNAUCU-ARG and tRNACCU-GLU, among others [17]. These wobble base modifications in the tRNA enhance binding of the anticodon with specific codons in mixed codon boxes [18]. Codon-specific reporter assays and genome-wide searches revealed that Trm9-catalyzed tRNA modifications enhanced the translation of AGA- and GAA-rich transcripts that functionally mapped to processes associated with protein synthesis, metabolism and stress signalling [11]. The resulting model proposes that specific codons will be more efficiently translated by anticodons containing the Trm9-modified nucleoside and that tRNA modifications can dynamically change in response to stress.
To assess the dynamic nature of tRNA modifications proposed by this model, we developed a systems-oriented approach using liquid chromatography-coupled, tandem quadrupole mass spectrometry (LC-MS/MS) to quantify the full set of tRNA modifications in an organism. Mass spectrometry-based methods have recently emerged as powerful tools for identifying and quantifying RNA modifications [19], [20]. We applied such an approach to quantify changes in the spectrum of tRNA modifications in yeast exposed to four mechanistically dissimilar toxicants. Multivariate statistical analysis of the data reveals dynamic shifts in the population of RNA modifications as part of the response to damage, with signature changes for each agent and dose. Further, analysis of yeast mutants lacking specific modification enzymes revealed novel biosynthetic pathways and compensatory or cooperative shifts in the levels of other modifications.
As shown in Figure 1, we developed an LC-MS/MS method capable of quantifying 23 of the ∼25 known ribonucleoside modifications in cytoplasmic tRNA in S. cerevisiae [2], [3]. The method begins with isolation of small RNA species (<200 nt) and quantification of the tRNA content (∼80–90% of small RNA species). Individual ribonucleosides in enzymatic hydrolysates of tRNA were resolved by HPLC and identified by high mass accuracy mass spectrometry, by fragmentation patterns with collision-induced dissociation (CID) and by comparison to chemical standards. Each ribonucleoside was subsequently quantified by pre-determined molecular transitions during CID in the LC-MS/MS system. We were able to quantify 23 of the 25 tRNA modifications in yeast, with 2′-O-ribosyladenosine phosphate (Ar(p)) not detected in positive ion mode, possibly due to the negatively charged phosphate, and only tentative identification of ncm5Um by CID due to weak signal intensities.
A critical feature of our approach is quantitative rigor given the need for highly precise measurement of even small changes in the relative quantities of ribonucleosides. To this end, we used an Agilent Bioanalyzer (microfluidics-based sizing and quantification against an internal standard) for quantification of total tRNA species in the mixture of small RNA (85±5%, N = 39) and an internal standard ([15N5]-2′-deoxyriboadenosine) to minimize variation in the levels of the individual ribonucleosides. One caveat here is low-level contamination (a few percent) with 5S rRNA that also contains ribonucleoside modifications. We were able to obtain highly reproducible data for the signal intensity associated with each ribonucleoside (see Figure S1 for linearity of signal intensity for the 23 ribonucleosides). Multiple reaction monitoring (MRM) mode yielded no detectable background signal in the absence of tRNA hydrolysates except for i6A (9±2%). The method proved to be highly precise: 3±1% intra-day variance in average signal intensity and 12±10% inter-day variance in average fold-change values for each ribonucleoside in treated and untreated cells (294 analyses in three biological replicates over several weeks).
Analysis of tRNA from wild type cells revealed a three-log range of signal intensity, with I and ac4C producing the highest intensity and ncm5Um the lowest (Figure 1). In general, modifications can be categorized in high (I, ac4C, m1A, m22G, Am, Y), medium (Cm, m5C, Gm, m1G, t6A, m7G, m2G, m3C, i6A) and low signal intensities (m1I, D, m5U, ncm5Um, mcm5U, mcm5s2U, Um, yW, ncm5U), with signal intensity reflecting both the abundance and mass spectrometric sensitivity for each ribonucleoside.
To quantify the dynamics of tRNA modifications in cellular responses, we selected four well studied chemicals that possess distinct mechanisms of toxicity: MMS, hydrogen peroxide (H2O2), sodium arsenite (NaAsO2), and sodium hypochlorite (NaOCl, pKa 7.5; ref. [21]). The behavior of yeast upon exposure to MMS, NaAsO2 and H2O2 has been extensively studied in terms of transcriptional response and cytotoxicity phenotyping [11], [22], [23]. We also chose NaOCl since it produces an oxidative stress distinct from that of H2O2 and could thus affect the tRNA modification spectrum differently. We then performed cytotoxicity dose-response studies in S. cerevisiae exposed to agents (Figure S2), choosing concentrations (Figure 2) that produced ∼20%, 50% and 80% cytotoxicity to ensure a common phenotypic endpoint for comparison.
One important issue with the methylating agent, MMS, was the possibility that changes in methyl-based modifications in tRNA could be due to both enzymatic methylation and direct chemical methylation. Literature precedent indicates that MMS reacts with DNA to form adducts mainly at guanine N7 (68%), adenine N1 (18%) and cytosine N3 (10%) [24], [25]. To address the extent of direct methylation of RNA by MMS, control studies were performed and revealed that direct alkylation by MMS contributes <25% to the cellular burden of m7G in small RNA, with the bulk of m7G arising by enzymatic methylation of tRNA (Figure S3). No other agent affected tRNA modifications in this manner, with changes in the relative quantities of the modifications resulting from alterations in biosynthesis, tRNA gene transcription or tRNA degradation.
With exposure and analytical parameters established, we tested the hypothesis that the spectrum of tRNA modifications would dynamically change as a function of the S. cerevisiae stress response. In addition, we predicted that these changes would serve as biomarkers of each exposure. Cells were exposed to three concentrations of each chemical and 23 tRNA modifications were quantified by LC-MS/MS, with the results shown in Tables S1 and S2, the latter as the ratio of treated to control signal intensities. A crude analysis of the data shows fold-changes ranging from 0.2 to 4, with 25% and 36% of the exposure data significantly different from control values by Student's t-test at p<0.05 and p<0.1, respectively (Table S2). These results point to the non-random and regulated nature of the exposure-induced changes in the levels of the tRNA modifications.
Multivariate statistical analyses revealed important patterns or signatures in the toxicant-induced changes in tRNA modifications. As shown in Figure 2, hierarchical clustering distinguished both agent- and dose-specific changes in the modification spectra, with unique patterns of increase and decrease apparent in all cases. H2O2 consistently increased the levels of m5C, Cm and m22G and, at the highest concentration, t6A, with dose-dependent decreases in m5U, m1G, m2G, mcm5s2U, i6A, yW and m1A. MMS consistently increased the level of m7G, and decreased Am, m5C, Cm, mcm5s2U, i6A, and yW. NaAsO2 caused only decreases in modification levels at the highest concentration, most notably for mcm5U, m3C, m7G, mcm5s2U, i6A, yW, m5C, and Cm. Interestingly, the dose-response for NaOCl showed an inverse correlation between concentration and increased levels of Am and Um and decreased levels of m5C. Given the reproducibility of the data, the changes in tRNA modification spectra can be considered signature biomarkers of exposure for these four classes of chemical stressor.
Principal component analysis (PCA) creates a model that reduces the complexity of a data set by identifying hidden correlations (the principal components) comprised of weighted, linear combinations of the original variables, with the first principal component (P1) accounting for the largest portion of the variation of the data and so on. The results of PCA of the dataset of nucleoside fold-change values (Table S2) are shown in Figure 3. With 88% of the variability expressed in the first 3 principal components (56%, 22% and 10%, respectively), individual agents contributed variance to each as shown in Table S3, with H2O2 contributing 74% in P1, MMS and NaOCl each contributing >40% in P2 and NaAsO2 contributing 53% in P3. The scores plots (Figure 3A, 3C) clearly distinguish the four agents, with H2O2-induced changes as the major determinant of P1 and with MMS, NaOCl and NaAsO2 distinguished best in P2. While H2O2 and NaOCl are negatively correlated in P1, they are more closely grouped in P2 and P3, which suggests that the changes in tRNA modifications reflect both common and unique facets of the toxic mechanism of each agent. For example, H2O2 and NaOCl are both oxidizing agents, but H2O2 generates hydroxyl radicals by Fenton chemistry while the protonated form of NaOCl yields hydroxyl radicals, chloramines and singlet oxygen [26]–[29]. Similarly, MMS and NaAsO2 are negatively correlated in P3 and more positively correlated in P2, with the latter consistent with recent evidence for alkylation-like adduction of arsenic to DNA and proteins following its metabolism [30], [31]. This would also explain the negative correlation of NaAsO2 and H2O2 in P1, while the recognized oxidative stress caused by arsenite [32] is consistent with a positive correlation between NaAsO2 and H2O2 in P2.
Both PCA (Figure 3B, 3D) and cluster analysis (Figure 2) revealed that m5C, m22G, Cm and t6A are major features of the H2O2 response, while m1A, m3C and m7G were associated with MMS. Increases in Gm, Um, I and Am were responsible for the variance induced by NaOCl, which is consistent with the inversely related doses and levels for Am and Um observed in cluster analysis. NaAsO2 was poorly distinguished in P2, with only m2G accounting for variance only at the highest concentrations (Figure 2).
The observation of toxicant- and dose-dependent changes in the levels of the 23 tRNA modifications is consistent with a model in which cells respond to toxicant exposure by modifying tRNA structure to enhance the synthesis of proteins critical to cell survival, as has been proposed in our earlier work with yeast exposure to MMS [11]. In this case, the conversion of cm5U to mcm5U by Trm9 was found to be critical for surviving MMS exposure [11]. To define the roles of specific tRNA modifications in the toxicant response, cytotoxicity phenotypic analyses were performed with yeast mutants lacking each of 13 trm tRNA methyltransferase genes and 3 other types of RNA modification biosynthetic genes. As shown in Figure 4, heightened sensitivity to H2O2 was observed in mutants lacking Trm4 and Trm7, which catalyze formation of two modifications elevated by H2O2 exposure: m5C and Cm, respectively [33], [34]. The simple explanation is that the increase in a specific tRNA modification is needed to promote an efficient stress response. However, m22G was also elevated by H2O2 (Figure 2, Figure 3), yet loss of an enzyme involved in its biosynthesis, Trm1 [35], [36], did not confer H2O2 sensitivity (Figure 4). This behavior draws a comparison to mRNA, as it has been reported that many of the transcripts induced in response to a stress are not essential for viability during a challenge from that stress [37], [38]. MMS sensitivity was identified in trm1, trm4 and trm9 mutants, the latter as shown previously [11], whose corresponding proteins synthesize m22G, m5C and mcm5U/mcm5s2U, respectively. However, these modifications were not strongly associated with MMS exposure in PCA (Figure 2, Figure 3). Somewhat surprisingly, loss of Trm1, Trm4, Trm7 and Trm9 conferred NaAsO2 sensitivity. These methyltransferases are responsible for m22G, m5C, m1G (position 37) and mcm5u/mcm5s2U, respectively, of which only m2G was found to vary significantly in PCA (Figure 3). For NaOCl, only trm4 was sensitive to exposure and the m5C product of Trm4 was not associated with NaOCl exposure (Figure 3). Again, this behavior parallels that of mRNA transcripts the levels of which do not change after exposure but that encode proteins important for viability after exposure [37], [38].
These results reveal a complex and dynamic control of tRNA modifications in cellular survival responses and suggest models for homeostasis of the modifications. One example involves modifications for which the biosynthetic mutant is sensitive to exposure but the modification level does not change in wild type cells following exposure (e.g., MMS exposure and trm1/m22G, trm4/m5C, trm9/mcm5U or mcm5s2U; Figure 2, Figure 3, Figure 4). The simplest explanation here is that the modification change occurs in a single tRNA species and the change is masked by an inverse change in the level of the modification in the larger population of tRNA molecules. As noted in Table S4, both m22g and m5C occur in multiple tRNAs. A second explanation parallels the idea of both pre-existing mRNA and stressor-induced transcription during a stress response. We have observed stress-induced increases in the levels of several modifications required for the survival response (Figure 2, Figure 3; ref. [11]). However, other modifications may already exist on tRNA molecules involved in selective translation of stress response messages. In both cases, the modifications are absolutely required for survival, but some are already present in unstressed cells and others are induced. Finally, it is possible that a modification, though its level may not change, is required for the subsequent synthesis of other modifications that are critical to the survival response. Such “cooperativity” is suggested by data from mod5-deficient cells, in which i6A decreases by ∼75-fold while D is reduced by ∼2-fold. The presence of i6A may signal downstream biosynthetic events, with deficiencies promoting a general reprogramming of tRNA. Similarly, cells deficient in Trm82, a subunit of m7G methyltransferase, had a ∼7-fold reduction in m7G and a >1.5-fold increase in m3C, mcm5U, m1G, m2G, t6A, mcm5s2U and m22G (Figure 5), which raises the possibility that Trm82 itself or m7G inhibits other tRNA modifying enzymes. With the caveat of possible increases in tRNA copy number, the ∼50% increase in these modifications suggests a pool of unmodified tRNA molecules, an observation supported by increases in m3C after exposure to MMS, mcm5U after exposure to NaOCl, and both t6A and m22G after exposure to H2O2 (Figure 2, Figure 3).
Cooperativity could also explain the case in which the level of a modification changes significantly following exposure yet the mutant strain is not sensitive to the exposure. For example, loss of trm1 did not confer sensitivity to H2O2 but its product, m22G, rose significantly with H2O2 exposure (Figure 2, Figure 3, Figure 4). The stress-induced change in m22G may be a response to a change occurring with another modification for which the mutant strain might be sensitive to the exposure. In support of this argument, m5C modifications increase along with m22G after H2O2 exposure and deficiencies in the m5C-producing methyltransferase Trm4 confer sensitivity to H2O2. Wohlgamuth-Benedum et al. have also demonstrated such cooperativity among RNA modifications in their observation of the negative regulation of wobble position C-to-U editing by thiolation of a U at position 33 outside the anticodon in T. brucei [39].
Finally, there is the case in which a modification decreases with exposure to a stressor and a deficiency in the enzyme responsible for that modification confers sensitivity, as in the case of m5C, trm4 and NaOCl (Figure 2, Figure 3, Figure 4). The population level of m5C may decrease with NaOCl exposure in spite of a protective increase in the level of m5C at some critical tRNA location. This may reflect a decrease in the transcription of tRNA substrates of Trm4 or the targeted degradation of specific tRNA species. It is important to note that biosynthetic redundancy, as in the case of Gm with Trm3 and Trm7, could mask any major changes in tRNA modification levels that are associated with mutational loss of one enzyme (Figure 5), yet loss of one of the redundant enzymes can induce sensitivity, such as the case of H2O2 and trm7 (Figure 2, Figure 3, Figure 4). These observations lead to many questions that obviously require more mechanistic study to define the precise role of tRNA modifications in cellular responses to stress.
One consistent feature that arose from our studies of modifications affected by or protecting against toxicant exposure was the frequent involvement of the wobble position, 34 (Tables S4, S6). The correlation between the wobble modification and the importance of a corresponding enzyme after toxicant exposure is not surprising in light of recent observations of the critical role played by these modifications and anticodon loop ribonucleosides in translational fidelity and efficiency [4]. Controlled alteration of ribonucleoside structure at position 34, and that at the conserved purine at position 37, is proposed to allow reading of degenerate codons by modulating the structure of the anticodon domain to facilitate correct codon binding [4]. As the most frequently modified ribonucleosides, positions 34 and 37 also have the largest variety of modifications [40], [41], so it is reasonable that they would be extensively involved in translational control of the survival response. This is also consistent with our previous observation that mcm5U at the wobble position was critical to the translation of key protein synthesis and DNA damage response genes [11].
Perhaps more interesting is a potential role for putative non-anticodon loop ribonucleoside modifications in the survival response. For example, Trm44 is the 2′-O-methyltransferase in yeast responsible for formation of 2′-O-methyl-U (Um), which occurs only at position 44 in yeast tRNA [42], [43]. Loss of Trm44 conferred sensitivity to NaAsO2 exposure. This observation suggests three possibilities: (1) that Trm44 synthesizes or influences the synthesis of modifications at other positions in tRNA; (2) that Um occurs in positions other than 44 (e.g., anticodon loop); or (3) that Um(44) plays a role in modulating translation in response to NaAsO2 exposure. Another example involves Trm1 and m22G at position 26. Current evidence suggests that m22G occurs only at position 26 in yeast tRNA [43] and that Trm1 is the methyltransferase responsible for its formation [44]. The fact that loss of Trm1 conferred sensitivity to MMS and NaAsO2 exposure and that H2O2 exposure increased the level of m22G again suggest the three possibilities analogous to those for Trm44 and Um. Similar arguments can be made for Trm3 and Gm at position 18 with NaOCl exposure, for Trm11 and m2G at position 10 with NaOCl and NaAsO2 exposure, and for Trm8/82 and m7G at position 46 with MMS exposure.
All of these observations point to participation of wobble and non-wobble RNA modifications in a complex and dynamic network of translational mechanisms in cellular responses. This expands the repertoire of translational control mechanisms, which includes recent discoveries about the effect of ribonucleoside modifications on tRNA stability [8], [9]. In this model, cell stress leads to rapid degradation of specific tRNAs and subsequent effects on translational efficiency. Another similar stress response involves cleavage of cytoplasmic transfer RNAs by ribonucleases released during the stress [10]. One consequence of these degradation pathways would be to decrease the amount of modified ribonucleoside detected in our assay, which may explain some of our observations with the toxicant stresses. Our approach to quantifying tRNA modifications provides information only about population-level changes, so the observed changes could result from modification of existing tRNA molecules or changes in the number of tRNA copies. Of particular importance here is the observation by Phizicky and coworkers that loss of m7G at position 46 leads to degradation of specific tRNAs [9], which suggests that our observation of changes in the levels of RNA modifications could be amplified by both reduction in the activity of modifying enzymes and by tRNA degradation. On the other hand, one argument against large increases in tRNA copy number arises from recent observations of repressed tRNA transcription during S-phase and, of direct relevance to the present studies, during replication stress induced by MMS, hydroxyurea and likely other toxicants [45]. Finally, our findings may also parallel recent work on tRNA charging. Reactive oxygen species have been implicated as a methionine misacylation trigger and modification status could help promote these programmed changes to the genetic code [12]. As we are beginning to appreciate the precision and coordinated nature by which cells mount a regulated stress-response, it is most likely the observed changes in tRNA modification levels promote multiple biological responses.
As recognized by several groups [19], [20], the LC-MS/MS platform facilitates definition of biosynthetic pathways for RNA modifications. This is illustrated in Table S5, which contains ratios of the basal levels of tRNA modifications in yeast mutants lacking various tRNA modification enzymes compared to wild type yeast, and in a heat map visual depiction of these ratios in Figure 5. These data corroborate known substrate/enzyme pairs [43] and further demonstrate the highly quantitative nature of our approach. For example, the level of m1I drops to nearly undetectable levels with loss of Tad1, the adenosine deaminase producing the inosine precursor to m1I [46]. That a diploid heterozygous mutant of trm5, the product of which catalyzes N-methylation of I [47], caused a ∼40% reduction in total m1I attests to the accuracy of our assay and demonstrate that gene dosage effects alter the level of tRNA modification. A similar ∼50% reduction in yW occurred in the trm5 mutant due to the absence of the m1G(37) precursor to yW [47], while complete loss of Trm12, which methylates the 4-demethylwyosine precursor of yW, made yW undetectable. Other pathways critical to yW are apparent in the smaller decreases in yW (0.3– to 0.5-fold) occurred in cells deficient in other enzymes (Trm8, Trm82, Tad1, Mod5, Tan1, Trm11, Trm5; Figure 5, Table S5).
The data in Figure 5 also reveal several novel observations. Pintard et al. observed that Trm7 catalyzes 2′-O-methylation of G and C nucleosides at positions 32 and 34, but they could not detect the ncm5Um product of 2′-O-methylation of ncm5U [34]. While we could only tentatively identify ncm5Um, we observed a quantifiable signal for a species with the correct molecular transition for ncm5Um and observed that loss of Trm7 led to a lowering of putative ncm5Um to undetectable levels (Figure 5, Table S5). This supports their prediction that Trm7 catalyzes formation of ncm5Um in yeast.
Another example involves the formation of Um. While Trm44 catalyzes synthesis of Um at position 44 in tRNA(ser) [42], analysis of trm mutants in Figure 5 and Table S5 suggests a redundancy in methyltransferase activity capable of 2′-O-methylation of U(44), including Trm7, which methylates U at positions 32 and 34 [34], and Trm13 methylation of C and A at position 4 in several yeast tRNAs. Cells lacking Trm44, Trm7 or Trm13 have 53%, 50% and 76% of wild type levels of Um, respectively. More striking evidence for this redundancy arises in correlation analysis that revealed a strong covariance in the levels of tRNA modifications in cells lacking either Trm 44 or Trm 13 (Table S7; C = 0.87). This correlation ranks second highest in our analysis behind the two subunits of the m7G methyltransferase (Trm8 and Trm82; C = 0.95), which suggests possible functional redundancy for Trm44 and Trm13, with broader substrate specificities for either or both enzymes.
In summary, a quantitative bioanalytical approach to the study of tRNA modifications has revealed several novel biosynthetic pathways for RNA modifications and has led to the discovery of signature changes in the spectrum of tRNA modifications in the damage response to different toxicant exposures. The results support a general model of dynamic control of tRNA modifications in cellular response pathways and add to the growing repertoire of mechanisms controlling translational responses in cells [8]–[10], [13]. Further, these cellular response mechanisms almost certainly involve parallel changes in spectrum of ribonucleoside modifications in rRNA and perhaps other RNA species.
All chemicals and reagents were of the highest purity available and were used without further purification. 2′-O-Methyluridine (Um), pseudouridine (Y), N1-methyladenosine (m1A), N2,N2-dimethylguanosine (m22G), and 2′-O-methylguanosine (Gm) were purchased from Berry and Associates (Dexter, MI). N6-Threonylcarbamoyladenosine (t6A) was purchased from Biolog (Bremen, Germany). N6-Isopentenyladenosine (i6A) was purchased from International Laboratory LLC (San Bruno, CA). 2′-O-Methyladenosine (Am), N4-acetylcytidine (ac4C), 5-methyluridine (m5U), inosine (I), 2-methylguanosine (m2G), N7-methylguanosine (m7G), 2′-O-methylcytidine (Cm), 3-methylcytidine (m3C), 5-methylcytidine (m5C), alkaline phosphatase, lyticase, RNase A, ammonium acetate, geneticine and desferrioxamine were purchased from Sigma Chemical Co. (St. Louis, MO). Nuclease P1 was purchased from Roche Diagnostic Corp. (Indianapolis, IN). Phosphodiesterase I was purchased from USB (Cleveland, OH). PureLink miRNA Isolation Kits were purchased from Invitrogen (Carlsbad, CA). Acetonitrile and HPLC-grade water were purchased from Mallinckrodt Baker (Phillipsburg, NJ). All strains of S. cerevisiae BY4741 were purchased from American Type Culture Collections (Manassas, VA).
Cultures of S. cerevisiae BY4741 were grown to mid-log phase followed by addition of toxicants to the noted final concentrations (cytotoxicity of ∼20%, 50% and 80%): H2O2, 2, 5 or 12 mM; MMS, 6, 12 or 24 mM; NaAsO2, 20, 40 or 60 mM; NaOCl, 3.2, 4.0 or 4.8 mM. The sensitivity of the following mutant strains to toxicant exposure was also determined (doses producing ∼80% cytotoxicity in wild-type: 12 mM H2O2, 24 mM MMS, 60 mM NaAsO2, or 4.8 mM NaOCl): trm1, trm2, trm3, trm4, trm7, trm8, trm9, trm10, trm11, trm12, trm13, trm44, trm82, tad1, mod5, and tan1. Since trm5 is essential, a diploid strain (GBY1) lacking one copy of trm5 was used. After a 1 h, cells were collected and viability determined by plating.
Following lyticase treatment (50 units) in the presence of deaminase inhibitors (5 µg/ml coformycin, 50 µg/ml tetrahydrouridine) and antioxidants (0.1 mM desferrioxamine, 0.1 mM butylated hydroxytoluene), tRNA-containing small RNA species were isolated (Invitrogen PureLink miRNA kit) and the tRNA quantified (Agilent Series 2100 Bioanalyzer).
Following addition of deaminase inhibitors, antioxidants and [15N]5-2-deoxyadenosine internal standard (6 pmol), tRNA (6 µg) in 30 mM sodium acetate and 2 mM ZnCl2 (pH 6.8) was hydrolyzed with nuclease P1 (1 U) and RNase A (5 U) for 3 h at 37°C and dephosphorylated with alkaline phosphatase (10 U) and phosphodiesterase I (0.5 U) for 1 h at 37°C following addition of acetate buffer to 30 mM, pH 7.8. Proteins were removed by filtration (Microcon YM-10). Ribonucleosides were resolved with a Thermo Scientific Hypersil GOLD aQ reverse-phase column (150×2.1 mm, 3 µm particle size) eluted with the following gradient of acetonitrile in 8 mM ammonium acetate at a flow rate of 0.3 ml/min and 36°C: 0–18 min, 1–2%; 18–23 min, 2%; 23–28 min, 2–7%; 28–30 min, 7%; 30–31 min, 7–100%; 31–41 min, 100%. The HPLC column was coupled to an Agilent 6410 Triple Quadrupole LC/MS mass spectrometer with an electrospray ionization source where it was operated in positive ion mode with the following parameters for voltages and source gas: gas temperature, 350°C; gas flow, 10 l/min; nebulizer, 20 psi; and capillary voltage, 3500 V. The first and third quadrupoles (Q1 and Q3) were fixed to unit resolution and the modifications were quantified by pre-determined molecular transitions. Q1 was set to transmit the parent ribonucleoside ions and Q3 was set to monitor the deglycosylated product ions, except for Y for which the stable C-C glycosidic bond led to fragmentation of the ribose ring; we used the m/z 125 ion for quantification [48], [49]. The dwell time for each ribonucleoside was 200 ms. The retention time, m/z of the transmitted parent ion, m/z of the monitored product ion, fragmentor voltage, and collision energy of each modified nucleoside and 15N-labeled internal standard are as follow: D, 1.9 min, m/z 247→115, 80 V, 5 V; Y, 2.5 min, m/z 245→125, 80 V, 10 V; m5C, 3.3 min, m/z 258→126, 80 V, 8 V; Cm, 3.6 min, m/z 258→112, 80 V, 8 V; m5U, 4.2 min, m/z 259→127, 80 V, 7 V; ncm5U, 4.3 min, m/z 302→170, 90 V, 7 V; ac4C, 4.4 min, m/z 286→154, 80 V, 6 V; m3C, 4.4 min, m/z 258→126, 80 V, 8 V; ncm5Um, 5.5 min, m/z 316→170, 90 V, 7 V; Um, 5.1 min, m/z 259→113, 80 V, 7 V; m7G, 5.1 min, m/z 298→166, 90 V, 10 V; m1A, 5.7 min, m/z 282→150, 100 V, 16 V; mcm5U, 6.4 min, m/z 317→185, 90 V, 7 V; m1I, 7.3 min, m/z 283→151, 80 V, 10 V; Gm, 8.0 min, m/z 298→152, 80 V, 7 V; m1G, 8.3 min, m/z 298→166, 90 V, 10 V; m2G, 9.4 min, m/z 298→166, 90 V, 10 V; I, 10.9 min, m/z 269→137, 80 V, 10 V; mcm5s2U, 14.2 min, m/z 333→201, 90 V, 7 V; [15N]5-dA, 14.4 min, m/z 257→141, 90 V, 10 V; m22G, 15.9 min, m/z 312→180, 100 V, 8 V; t6A, 17.2 min, m/z 413→281, 100 V, 8 V; Am, 19 min, m/z 282→136, 100 V, 15 V; yW, 34.2 min, m/z 509→377, 80 V, 5 V, and i6A, 34.4 min, m/z 336→204, 100 V, 17 V. The mass spectrometer monitored ions with the molecular transitions of D, Y, m5C, and Cm from 1 to 4 min; molecular transitions of m5U, ncm5U, ac4C, m3C, ncm5Um, Um, m7G, m1A, and mcm5U from 4 to 7 min; molecular transitions of m1I, Gm, m1G, and m2G from 7 to 10 min; molecular transitions of I, mcm5s2U, [15N]5-dA, m22G, t6A, and Am from 10 to 30 min; molecular transitions of yW and i6A from 30 to 40 min. The identities of individual ribonucleosides were established by comparison to commercially available synthetic standards, high mass accuracy mass spectrometry, fragmentation patterns generated by collision-induced dissociation (CID) in a quadrupole time-of-flight mass spectrometer (QTOF) or MSn analysis by ion trap mass spectrometry, with comparison to literature data (e.g., ref. [48]).
To assess the direct and indirect effects of MMS on levels of methylated ribonucleosides, the absolute levels of m7G were quantified in small RNA hydrolysates isolated from MMS-exposed and unexposed mutant and wild type strains of yeast by the LC-MS/MS method described above. Calibration curves were generated by mixing variable amounts of m7G (final concentrations of 0, 5, 50, 300, 600, 1000, and 2000 nM) with a fixed concentration of [15N]5-dA (40 nM). A volume of 10 µl of each solution was analyzed with the LC-MS/MS system described earlier.
Differences in the levels of ribonucleosides in exposed versus unexposed and in mutant versus wild-type yeast were analyzed by Student's t-test. Hierarchical clustering analyses were performed using Cluster 3.0. Data were transformed to log2 ratios of modification levels in treated cells relative to unexposed controls. Clustering was carried out using the centroid linkage algorithm based on the distance between each dataset measured using the Pearson correlation, with heat map representations produced using Java Treeview. Principal component analysis was performed using XLStat (Addinsoft SARL, Paris, France), with a Pearson correlation matrix consisting of data that were mean-centered and normalized to the standard deviation. Correlation analysis was used to assess the degree of covariance among the various sets of fold-change values for each mutant (Table S5), with correlation coefficients calculated using Excel (Microsoft).
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10.1371/journal.pgen.1007723 | Capitalizing on the heterogeneous effects of CFTR nonsense and frameshift variants to inform therapeutic strategy for cystic fibrosis | CFTR modulators have revolutionized the treatment of individuals with cystic fibrosis (CF) by improving the function of existing protein. Unfortunately, almost half of the disease-causing variants in CFTR are predicted to introduce premature termination codons (PTC) thereby causing absence of full-length CFTR protein. We hypothesized that a subset of nonsense and frameshift variants in CFTR allow expression of truncated protein that might respond to FDA-approved CFTR modulators. To address this concept, we selected 26 PTC-generating variants from four regions of CFTR and determined their consequences on CFTR mRNA, protein and function using intron-containing minigenes expressed in 3 cell lines (HEK293, MDCK and CFBE41o-) and patient-derived conditionally reprogrammed primary nasal epithelial cells. The PTC-generating variants fell into five groups based on RNA and protein effects. Group A (reduced mRNA, immature (core glycosylated) protein, function <1% (n = 5)) and Group B (normal mRNA, immature protein, function <1% (n = 10)) variants were unresponsive to modulator treatment. However, Group C (normal mRNA, mature (fully glycosylated) protein, function >1% (n = 5)), Group D (reduced mRNA, mature protein, function >1% (n = 5)) and Group E (aberrant RNA splicing, mature protein, function > 1% (n = 1)) variants responded to modulators. Increasing mRNA level by inhibition of NMD led to a significant amplification of modulator effect upon a Group D variant while response of a Group A variant was unaltered. Our work shows that PTC-generating variants should not be generalized as genetic ‘nulls’ as some may allow generation of protein that can be targeted to achieve clinical benefit.
| The development of variant specific modulators that correct dysfunctional cystic fibrosis transmembrane conductance regulator (CFTR) protein is an excellent example of precision medicine. Currently there is no molecular treatment available for individuals with cystic fibrosis (CF) carrying nonsense or frameshift variants because such variants introduce a premature termination codon (PTC), and are not expected to produce CFTR protein. We have performed a systematic study of nonsense and frameshift variants located in four regions of CFTR that we postulated should have varying effects on mRNA stability, protein production, and/or function. Using primary nasal cells and three different cell line models stably expressing CFTR expression mini-genes (EMGs), we report molecular consequences of 26 PTC-generating variants in CFTR, and identify which variants allow generation of CFTR protein that is responsive to currently available modulator therapies and which require alternative therapeutic approaches.
| The development of variant-specific modulators that correct dysfunctional cystic fibrosis transmembrane conductance regulator (CFTR) protein is an excellent model for precision medicine [1–4]. Cystic fibrosis (CF) is a progressive, multi-organ, life-threating, autosomal recessive disease caused by variants in CFTR gene leading to reduced or no protein function in approximately 70,000 individuals worldwide [5–7]. Two classes of compounds have been approved by the US Food and Drug Administration (FDA). Ivacaftor (VX-770; Kalydeco) potentiates function by increasing the probability of channel opening to enhance chloride ion conductance of CFTR gating variants [8, 9]. Lumacaftor (VX-809) corrects the processing and trafficking of the most common CF-causing variant (F508del) to increase the quantity of CFTR channels at the cell surface [10]. A potentiator-corrector combination (ivacaftor and lumacaftor; Orkambi) has been approved for individuals with CF who carry two copies of the F508del variant [11]. More recently, a new CFTR corrector, tezacaftor (VX-661) in combination with ivacaftor (Symdeko) has demonstrated clinical efficacy in individuals who carry two copies of F508del, or one copy of F508del and a variant from a select set of ‘residual function variants’ [12–14]. While these break-through treatments dramatically alter outcomes in CF, they require the presence of targetable CFTR protein. However, approximately 28% of individuals with CF carry one or two variants that introduce premature termination codons (PTCs) resulting in loss of CFTR protein (https://cftr2.org).
The challenge of treating variants that cause premature termination is not unique to CF. It has been estimated that one-third of inherited and acquired human diseases are caused by nonsense, frameshift or splice-site variants that lead to generation of PTCs [15]. RNA transcripts bearing a PTC are generally targeted for elimination by a cellular quality-control mechanism called nonsense-mediated mRNA decay (NMD) [16]. Our current mechanistic understanding of NMD leads to the prediction that transcripts containing PTCs greater than 50 nucleotides upstream of the last exon-exon junction should undergo NMD [17]. In cells derived from individuals carrying PTC-generating variants, NMD reduces the level of mRNA transcripts to 5–25% of normal (i.e. PTC-free) level and substantially reduces synthesis of the encoded truncated protein [18]. Moreover, truncated proteins that derive from any residual nonsense transcripts typically lack function. However, there are circumstances where PTC-generating variants could produce transcript resulting in functional protein. Nonsense or frameshift variants within the last exon generally do not activate NMD thereby allowing synthesis of C-terminally truncated polypeptides [17, 19]. Furthermore, the efficiency of NMD can vary among cell types and individuals [20, 21]. Consequently, transcripts containing PTCs, even those that are targeted by NMD, can be maintained at low steady-state levels that may allow production of truncated protein [22–26]. Under both scenarios, protein may be present in cells that can be targeted with small molecules to generate sufficient levels of function to ameliorate disease. However, for many genes, including CFTR, it is unknown which, if any, PTC-generating variants permit production of protein that is targetable.
At least one intron, and pre-mRNA splicing are required for NMD of mammalian mRNAs that harbor or acquire PTCs [27, 28]. Inclusion of at least 200 bp intron sequences each from the 5' and 3' splice sites ensures that majority of the regulatory signals necessary for constitutive and alternative splicing are present [29, 30]. We and others have demonstrated that expression minigenes (EMGs) containing at least 200 bp of flanking intron splices heteronuclear pre-mRNA in precisely the same fashion, and with the same fidelity as observed in primary cells [31, 32]. Furthermore, we have shown that disease-associated variants alter splicing patterns of EMGs that replicate patterns found in primary nasal cells of affected individuals [33].
Here, we have performed a systematic study of nonsense and frameshift variants located in four regions of CFTR that were postulated to have varying effects on mRNA stability, protein production, and/or function. Using primary nasal cells and three different cell line models stably expressing CFTR-EMGs, we report molecular consequences of 26 PTC-generating variants in CFTR, and identify which variants allow generation of CFTR responsive to currently available modulator therapies and those that require alternative therapeutic approaches.
Variants that introduce premature termination codons (PTCs) located in the last exon or less than 50–55 nucleotides upstream of the 3’-most exon-exon junction (E-EJ) generally do not elicit nonsense-mediated mRNA decay (NMD) [17]. Consequently, individuals carrying such variants have stable RNA transcripts that can synthesize C-terminal truncated protein [34]. As we know that certain 3’ nonsense variants in CFTR generate stable protein [35–37], we wanted to determine if the function of these truncated proteins could be augmented by CFTR modulators [13]. To test this concept, we used expression mini-genes (EMG) to evaluate the transcriptional and translational effects of eleven variants (nine nonsense and two frameshift variants) that introduce a PTC into the most distal 3’ region of CFTR (Fig 1A, top). EMGs contain full-length CFTR cDNA and introns flanking the variants under study, and they faithfully reproduce splicing patterns observed in affected tissues (S1 Fig) [32, 33]. A key advantage of EMGs is the inclusion of intron sequences which allow formation of E-EJs upon splicing that are required to engage NMD (Fig 1A, bottom)[27]. EMGs containing each of the 11 variants and wild-type (WT) were individually integrated into a single genomic site in Human Embryonic Kidney (HEK) 293-Flpin cells. Six nonsense variants (E1401X to Q1476X), and two frameshift variants (p.E1418RfsX14 and p.S1435Gfs14X) predicted to evade NMD based on their location (Fig 1A) demonstrated no significant difference in mRNA levels compared to WT CFTR (Fig 1B) [38]. Conversely, the three variants located in regions predicted to be subjected to NMD (E1371X, Q1382X and Q1390X) had significantly reduced levels of CFTR mRNA transcript consistent with degradation. The presence of detectable amounts of PTC-bearing transcript is expected as NMD is not completely effective in cell line models that utilize potent constitutive promoters (as employed here). Other investigators have reported that ~5–25% of PTC bearing mRNA can escape NMD under these circumstances [18, 39].
EMGs offer the advantage that protein synthesis can be studied simultaneously with RNA synthesis and splicing [32]. The WT-CFTR-EMG produced abundant mature, complex-glycosylated CFTR protein (band C) and minor amounts of immature, core-glycosylated CFTR protein (band B) as determined by immunoblot (IB) analysis (Fig 1C). Variants at or between codons 1371 and 1412 generated only immature truncated protein (Fig 1C, S2 Fig). In contrast, variants located more 3’ including nonsense (E1418X), and two frameshifts (E1418Rfs14X and S1435Gfs14X) that truncate protein at residue 1442 and 1459 respectively generated minimal to moderate amounts of mature truncated protein. The final two variants S1455X and Q1476X, showed no apparent effect on the steady-state amounts of the immature or mature truncated forms of CFTR when compared with WT, as shown previously (Fig 1C, S2 Fig) [40, 41]. The nonsense and frameshift variants in the 3’region fell into three groups (A-C) based on effects on RNA and protein levels (Fig 1D). We used this molecular characterization to select variants for testing with FDA-approved CFTR corrector compounds. Variant Q1390X was chosen to determine if any protein synthesized from the severely reduced levels of RNA transcript could be stabilized. However, treatment of cells expressing the Q1390X-CFTR-EMG with correctors either alone or in combination (lumacaftor/tezacaftor or both) did not result in the appearance of CFTR protein (left panel, lanes 3–5 vs lane 2) (Fig 1E). Conversely, the correctors (lumacaftor/tezacaftor or both) increased the abundance of mature and immature CFTR in the cell line expressing E1418X-EMG (right panel, lanes 3–5 vs lane 2; Fig 1E).
To evaluate the function of C-terminal truncated forms of CFTR, EMGs were integrated into the genomes of CF bronchial epithelial cells (CFBE41o-) and/or Madin-Darby Canine Kidney (MDCK) cells. These two cell lines retain the ability to polarize and each has been used previously to assess CFTR chloride channel function [42–50]. Chloride channel activity of CFTR was measured in Ussing chambers by activation with forskolin followed by inhibition with the CFTR-specific compound Inh-172. A representative tracing from CFBE cells expressing E1418X-EMG (Fig 1F, dashed blue line) from group C demonstrates that CFTR chloride channel function is present (ΔIsc = 4.1 ± 0.6 μA/cm2 representing ~ 3.5% of the chloride channel current generated in cell lines expressing WT-CFTR, Fig 1G). Acute exposure to the potentiator (ivacaftor) resulted in a minimal increase in function (Fig 1F, blue solid line). However, CFTR correctors (lumacaftor, tezacaftor or both) in combination dramatically increased E1418X-CFTR current by ~ 4.6 fold equivalent to ~ 16% of WT-CFTR function, consistent with the increased steady state levels of mature protein upon treatment with these correctors (Fig 1E; green, red, and purple lines). Addition of ivacaftor to the corrector-treated cells further increased current achieving about 23% of the function of cells expressing WT-CFTR (Fig 1F and 1G). Likewise, two frameshift variants in group C (E1418RfsX14 and E1435GfsX14) exhibited improvement in CFTR function upon modulator treatments (~ 2.3% to ~ 13.4% and ~ 10.4% to ~ 30.3% of WT-CFTR respectively) (Fig 1G). A variant in group B (Q1412X) generated minimal CFTR function, and exhibited no improvement with modulators (Fig 1G). Similarly, Q1390X from group A displayed negligible function and no response to modulators, as expected for a variant that induced NMD leading to severe reduction of RNA and no mature CFTR protein (Fig 1G). To address whether cell-type specific factors affected processing, function or response of truncated forms of CFTR, we created MDCK cells expressing 9 of the 11 variants. Group C variants E1418X and E1418Rfs14X exhibited residual channel activity (Isc = 8.8 ± 0.6 μA/cm2 and 5.5 ± 0.3 μA/cm2 representing ~ 9% and ~ 6% of WT-CFTR function respectively) (Fig 1H). This level of function is consistent with that reported from studies of primary nasal cells bearing E1418X variant (~ 10% of WT) [51] and is also consistent with minimal amount of mature CFTR protein generated by this variant (Fig 1C). Lumacaftor, tezacaftor or both followed by acute treatment with ivacaftor increased CFTR current in the MDCK cell lines expressing E1418X and E1418Rfs14X to ~ 35% and ~ 18% of WT-CFTR function, respectively. Likewise, modulator treatments increased CFTR function to near WT levels in the cells expressing three downstream variants from group C (E1435Gfs14X, S1455X and Q1476X) (Fig 1H). As expected, three upstream nonsense variants in group A (Q1382X, Q1390X) and group B (E1401X and Q1412X) displayed negligible function (<1% of WT-CFTR), and no improvement with modulators. These results indicate that Group C nonsense and frameshift variants downstream of codon 1417 allow synthesis of stable truncated CFTR that responds to CFTR modulators.
Prior studies of synthetically truncated forms of CFTR revealed that protein sequence following intracellular loop (ICL6) is not required for conformational maturation of CFTR [52, 53]. To test whether naturally occurring CF-causing nonsense variants in exon 22 encoding ICL6 (codons 1150 to 1218) allow production of stable and potentially drug-targetable forms of truncated CFTR, we utilized EMG-i21-i22 that contains abridged introns 21 and 22 incorporated into full-length CFTR cDNA to evaluate variant effects (Fig 2A, top, S3 Fig). Each of the seven nonsense variants are predicted to engage NMD because they are located >50 nt from the 3’-most E-EJ within EMG-i21-i22 (Fig 2A, bottom). Indeed, each of the seven nonsense variants produced lower steady state levels of CFTR mRNA compared to WT (Fig 2B). Transient expression in HEK293 cells was utilized to determine if any translated products were processed to mature forms of CFTR; conditions that could lead to therapeutic benefit if NMD could be counteracted. IB revealed two apparent patterns: core glycosylated truncated CFTR only (R1158X and R1162X) or complex glycosylated and core glycosylated truncated forms of CFTR (7 variants; Fig 2C). To evaluate glycosylation status, proteins were subjected to endoglycosidase H which removes sugar moieties from immature core glycosylated protein (see EMG-WT, lane 2; Fig 2D) and PNGase F that removes sugars from mature complex and immature core glycosylated protein (EMG-WT, lane 3; Fig 2D). As expected, endoglycosidase endo H and PNGase F digestion altered truncated protein generated by EMGs bearing R1158X or R1162X (Fig 2D). Thus, only core glycosylated truncated protein was generated by these two variants. Conversely, susceptibility to digestion by PNGase F but not by endo H confirmed that the higher molecular mass CFTR protein generated by S1196X was complex glycosylated (Fig 2D). Complex glycosylated truncated CFTR generated by the remaining nonsense variants in this cluster showed the same susceptibility to PNGase F but not Endo H (S4 Fig). These results indicate that the nine naturally occurring nonsense variants in exon 22 fall into the previously described group A or group D based on mRNA abundance, and whether they permit synthesis of mature truncated CFTR (Fig 2E).
We next tested whether the mature truncated CFTR protein generated by group D nonsense variants were functional. CFBE cells stably expressing EMG-S1196X generated baseline chloride channel activity upon application of forskolin that was inhibited by inh-172 (Fig 2F; dashed blue line; Isc = 1.03 ± 0.1 μA/cm2 representing ~1.3% of the chloride current generated by WT-CFTR in the same cell line). Acute exposure to ivacaftor generated a 4.5 fold increase in CFTR function from baseline levels (Fig 2F, blue solid line). There was only a marginal increase in forskolin stimulated CFTR function upon treatment of cells with correctors (lumacaftor, tezacaftor or both; Fig 2F; green, red, and purple lines). However, application of ivacaftor substantially increased CFTR function (~11 fold compared to baseline levels; Fig 2F and 2G). The level of combined modulator response of EMG-S1196X exceeded 10% of the chloride currents generated by cells expressing WT-CFTR. A similar profile of response was observed for cells expressing additional group D variants (W1204X and S1206X); whereas group A variants R1158X and R1162X failed to generate current in response to forskolin or after treatment with any of the CFTR modulators (Fig 2G), as observed for the Group A variants in the 3’ region.
To verify studies in CFBE cell lines, MDCK cell lines were created that stably expressed CFTR-EMGs bearing 6 of the exon 22 nonsense variants. EMG-S1196X generated functional CFTR protein with activity similar to that observed in CFBE cells (1.8 ± 0.3 μA/cm2 representing 1.7% of current observed in MDCK cells expressing WT-CFTR; Fig 2G and 2H). Furthermore, ivacaftor increased S1196X-CFTR function (~ 6.8 fold representing ~ 7.8% WT-CFTR); and application of correctors (lumacaftor, tezacaftor or both) resulted in dramatic increases in function (~ 12.5 fold representing ~ 21.5% WT-CFTR function; Fig 2H). Interestingly, group D variants W1204X and S1206X also exhibited similar robust responses when ivacaftor was combined with correctors (lumacaftor, tezacaftor or both; Fig 2H). Notably, Y1182X variant showed even greater response to the modulators when compared to other variants in group D (Fig 2H). Finally, as noted in CFBE cell lines, MDCK cells bearing EMGs with group A variants (R1158X and R1162X) did not generate forskolin-activated CFTR current or respond to any of the CFTR modulators (Fig 2H). Together, these results indicate that corrector and potentiator treatment, especially in combination, elicits substantial CFTR function for exon 22 nonsense variants that generate mature truncated CFTR.
Antagonism of NMD caused by nonsense variants that produce modulator responsive CFTR could provide substantial therapeutic benefit. To address this issue, we evaluated whether NMD inhibition increases the function of exon 22 nonsense variants expressed in primary nasal epithelial cells. As predicted by EMGs, CFTR transcript bearing two exon 22 nonsense variants (R1158X and S1196X) was reduced in the primary cells. Quantification by pyrosequencing revealed that CFTR transcript bearing R1158X was much less abundant (8.5%) compared to CFTR transcript with the F508del variant (Fig 3A, left bar graph). F508del transcript is expressed at approximately 83% of WT- CFTR transcript [31, 54], suggesting that R1158X levels were ~ 7.0% of WT level. Likewise, the level of CFTR transcript with S1196X was significantly lower (22.2%) compared to transcript bearing G85E (Fig 3A, right bar graph). Expression levels of S1196X relative to WT could not be drawn since expression of G85E relative to WT has not been established so far. To evaluate the time scale of NMD inhibition, a mRNA stability assay was performed on the HEK293 cell lines stably expressing R1158X and S1196X. While WT CFTR transcript level was stable over 120 minutes, transcripts bearing either nonsense variant were degraded to ~50% of WT levels by 30 min (R1158X) or 90 min (S1196X) (Fig 3B). To verify that the reduction in transcript abundance was due to NMD, we used siRNA mediated knockdown of UPF1, a gene that mediates nonsense transcript degradation [55]. Western blot analysis showed efficient siRNA mediated downregulation of UPF1 expression in HEK293 cells stably expressing R1158X (~29.4%), and S1196X (~29.8%) (Fig 3C). Transfection of S1196X expressing cells with Non-Targeted (NT) and GAPDH-targeted siRNA had no effect on UPF1 level (Fig 3C). UPF1 knockdown resulted in significant increases in CFTR transcript abundance in HEK293 cells stably expressing R1158X (1.9±0.5 fold) and S1196X (2.1±0.7 fold) compared to untreated cells or cells transfected with non-target (NT) or GAPDH siRNA (Fig 3D). We next determined whether inhibition of NMD augments modulator treatment of CFTR bearing exon 22 nonsense variants. Cells transfected with UPF1 siRNA exhibited significant potentiation of S1196X-CFTR function by ivacaftor (red solid line, 13.81 ± 0.6 μA/cm2 representing ~ 18% WT-CFTR function Fig 3E and 3F); that was further increased upon corrector (lumacaftor) treatment (green solid line, 23.9 ± 1.1 μA/cm2 representing 30% WT-CFTR function Fig 3E and 3F). However, UPF1 inhibition could not increase the function of R1158X-CFTR consistent with prior evidence that this form of CFTR is severely misfolded and non-responsive to modulators, even after improvement of transcript abundance by NMD inhibition (Fig 3F). Finally, transfection with GAPDH siRNAs did not alter the effect of ivacaftor alone or ivacaftor/lumacaftor combination compared to non-targeted (NT) siRNA transfected cells (Fig 3E and 3F). Collectively, these results indicate that suppression of NMD should be able to amplify modulator response of CFTR bearing exon 22 nonsense variants that generate complex glycosylated truncated CFTR.
Nonsense variant E831X is caused by a change in the first nucleotide of exon 15 of CFTR. This change alters the 3’ splice site of intron 14 leading to the generation of aberrantly spliced CFTR transcripts in primary airway epithelial cells [56]. Of the resulting three proteins, only CFTR missing glutamate at codon 831 (CFTR-del831) generates mature glycosylated protein that functions similarly to WT [56]. However, response to CFTR modulators in cell-based system has not been reported for this ‘nonsense’ variant. To this end, we introduced E831X into an EMG containing flanking sequences from introns 14 to 18 (EMG-i14-18) (Fig 4A, top). WT-EMG-i14-i18 was found to splice normally in HEK293 stable cells (S5 Fig), as previously shown [32]. HEK293 stable cells expressing E831X-EMG generated 3 CFTR splice isoforms: Isoform 1 (CFTR-E831X i.e. truncation at 831), Isoform 2 (CFTR-del831-873 i.e. in frame deletion of exon 15), and Isoform 3 (CFTRdelE831, single amino acid deletion), (Fig 4A, bottom, and S6 Fig), as previously reported [56]. Furthermore, IB analysis identified three CFTR specific protein bands, each generated from their respective splice isoform (Fig 4B). Thus, the effect of E831X on mRNA splicing and protein production constitutes fifth group, E. To assess modulator response, CFBE cells stably expressing E831X-EMG were created to measure CFTR function. CFTR function was evident upon addition of forskolin and inhibition using inh-172 (14.7 ± 0.65 μA/cm2 representing ~8% of current in CFBE cells expressing WT-EMG) (Fig 4C, graph). Acute treatment with ivacaftor alone did not result in significant improvement of E831X-EMG function. However, correctors (lumacaftor, tezacaftor or both) increased E831X-CFTR function (~13.6% WT-CFTR) and subsequent acute addition of ivacaftor increased function further (~15.0% of WT-CFTR) (Fig 4C, graph). Furthermore, primary nasal epithelial cells harvested from an individual with CF harboring E831X (in trans with F508del) exhibited residual CFTR function (Fig 4D, black tracing) that was increased by ivacaftor and further augmented by correctors (lumacaftor and tezacaftor) (Fig 4D, green and red tracings). CFTR specific current that drops below baseline after addition of Inh-172 is due to the constitutive activation of CFTR in primary nasal cell culture. Of note, modulator responses were greater in F508del/E831X primary cells compared to F508del/Indel (2184InsA, 2183delAA>G, and 3659delC) primary cells (Fig 4D, graph). Since the indel variant does not generate functional CFTR, the increased CFTR function in the E831X/F508del cells compared to the indel/F508del cells can be attributed to CFTR generated by the E831X variant. Thus, modulator combinations demonstrate consistent evidence of functional improvement in primary and CFBE cells expressing E831X-CFTR.
Lastly, we determined what treatment options are appropriate for nonsense variants occurring in the 5’ region of CFTR. This area was reasonable to study as it has been reported that variants that introduce PTCs in the 5’ of processed mRNA may evade NMD [57]. Under these circumstances, translation may initiate at downstream Met codons leading to the synthesis of N-terminal truncated protein. To establish whether 5’ nonsense variants in CFTR evade NMD, we quantified RNA transcripts from primary nasal epithelial cells of a CF individual harboring L88X and the F508del variant. Three methods established that CFTR transcript bearing the L88X variant was stable and at quantities similar to RNA transcripts containing F508del (Fig 5A and 5B). Sanger sequencing revealed that transcript bearing the G nucleotide at nt 263 (corresponding to L88X) was almost as abundant as T nucleotide present in transcripts bearing F508del (Fig 5A, left panel). Fragment size analysis capitalizing on the 3bp deletion caused by the F508del variant revealed that 233 bp fragments amplified from F508del transcript were of near equal abundance to 236 bp fragments derived from L88X transcript (F508del (52%) and L88X (48%); Fig 5A, right panel). As the prior two methods use PCR that may not linearly amplify transcript [58], we performed RNA sequencing of the L88X bearing primary cells. Sequencing depth distribution of all transcribed genes were similar in the F508del/L88X and healthy control nasal cells (Fig 5B, left graph). Expression levels of the target gene (CFTR), three housekeeping genes (TBP, GAPDH, and B2M), and NMD regulator genes in F508del/L88X sample were in the same range as in healthy control, indicating that NMD machinery was not compromised in the affected individual (S7A Fig). The counts of L88X transcripts (n = 7) sequenced from L88X/F508del primary cells were similar to F508del (n = 8; Fig 5B, right graph). Exon skipping was not observed in the nasal cells of the individual harboring L88X/F508del (S7B and S7C Fig). Since F508del transcript is found at 83% of WT levels [31, 54], we conclude that L88X does not elicit NMD. Absence of NMD was also detected in primary nasal cells of CF individual harboring another 5’ nonsense variant G27X in trans with F508del (S8 Fig).
Next, we investigated whether other nonsense variants in the 5’ region of CFTR evade NMD. Seven different naturally occurring nonsense variants including G27X and L88X were introduced into WT-CFTR EMG i1-i5 (Fig 5C). WT-EMG i1-i5 resulted in normal splicing when expressed in HEK293 cells (S9 Fig). Each EMG including WT was stably integrated into HEK293 cells and CFTR mRNA abundance was quantified using qRT-PCR. The levels of CFTR RNA transcripts in the cell line bearing the G27X and L88X EMGs were not different from cells with the WT EMG (Fig 5D). This result suggested that G27X and L88X transcripts expressed from the CFTR EMG in the HEK293 cells evaded NMD, as observed in the primary cells. Furthermore, CFTR transcript levels in HEK293 stable cell lines expressing the five other 5’ nonsense variants were no different than WT-EMG. The ability of the N-terminus nonsense variants to bypass NMD is likely due to re-initiation of translation downstream start codon(s) [57, 59]. Methionines at codon positions 150,152 and 265 in exons 3, 4, and 7 of CFTR have been shown to be able to operate as alternative start sites in CFTR [60, 61]. IB analysis showed that each of the seven cell lines expressing 5 ‘nonsense variants (lanes 2–6, Fig 5E, and S10A Fig) generated two CFTR-specific products ((~135 kDa and ~130 kDa; indicated with stars) consistent with downstream translation initiation. Deglycosylation assay revealed that shortened protein fragments generated from 5’ nonsense variant, e.g. G27X, are immature core glycosylated (S10B Fig). Of note, these proteins are distinct from the molecular mass of immature core-glycosylated protein generated from wild-type-CFTR-EMG (lane 1), Phe508del cDNA (lane 7), and wild-type-CFTR cDNA (lane 9, Fig 5E). Thus, 5’ nonsense variants were classified into group B based on CFTR mRNA and protein characteristics. Similar sized molecular mass bands were previously reported in association with nonsense variant Y122X, and 5’ PTC caused by a frameshift variant (c.120del23) when expressed using intronless constructs (i.e. cDNA) [61, 62].
Since mRNA transcripts bearing 5’ nonsense variants were stable, we evaluated the feasibility of readthrough therapy. CFBE stable cells expressing CFTR EMGi1-i5 with L88X were created to test whether readthrough compound (G418) is effective in improving CFTR function. L88X-CFTR generated minimal chloride current (1.18 ± 0.1 μA/cm2; Fig 5F). Ivacaftor either alone or in combination with lumacaftor was not effective in restoring L88X-CFTR function (Fig 5F). Additionally, treatment with G418 at low (5 μM) and high (25 μM) concentrations followed by acute treatment with ivacaftor did not improve L88X-CFTR function. However, G418 in combination with lumacaftor increased activity of L88X-CFTR by ~ 4 fold (3.5% of WT-CFTR function) at 5 μM and by ~ 9 fold (8.5% of WT-CFTR function) at 25 μM concentration (Fig 5F, graph).
Genetic variants that generate premature termination codons (PTCs) usually cause severe reduction in protein quantity, either due to nonsense mediated RNA decay (NMD) and/or degradation of any truncated protein that is synthesized [15, 63–65]. We show here that exceptions exist to both paradigms which leads to a reconsideration of variants that might be amenable to protein-targeted therapies. Systematic analysis of variants clustered in four regions of CFTR provides compelling evidence that a fraction of PTC-generating variants allow production of protein which can be processed to a stable mature glycosylated form. Importantly, chloride channel function of these mature forms of CFTR can be augmented by FDA approved modulators. Additionally, our results inform where evolving therapeutic approaches might be most effectively employed. For example, compounds that modestly inhibit NMD could be utilized at non-toxic doses to increase the amount of CFTR truncated beyond ICL6 and modulators could be used to achieve therapeutic level of chloride transport. Conversely, CFTR transcripts bearing 5’ PTC-generating variants that allow normal levels of RNA transcript would be ideal targets for read-through strategies. Our studies emphasize that the consequences of PTC-generating variants upon RNA and protein can be assembled into groups (Table 1), each necessitating different strategies to achieve optimal precision therapy.
Our rationale for the selection of nonsense variants from the C-terminal and ICL-6 regions was based on prior evidence that CFTR is stable after truncation in these regions [52, 53]. However, these studies employed complementary DNA (cDNA) constructs that lacked introns and did not undergo pre-mRNA splicing, a requirement for engagement of NMD [27, 28, 65]. Consequently, the cDNA-based studies did not model the in vivo effects of the nonsense variants in each region. Assessing the clinical consequences of the PTC-generating variants necessitated that the mRNA be derived from intron-containing constructs. The EMG system employed here faithfully replicates in vivo splicing events and engages NMD [33]. A key additional advantage of the EMGs is that variant effect upon mRNA stability and protein synthesis can be evaluated simultaneously [31, 32]. Furthermore, use of the CMV, a potent constitutively active promoter, enables detection and characterization of proteins that are present at low levels in vivo. Such studies can provide justification for therapeutic strategies to augment the level and function of truncated proteins generated from genes bearing PTC-generating variants. Furthermore, EMGs provide a viable alternative to primary cell analysis when affected tissues are difficult to procure. This includes variants that are carried by small numbers of geographically dispersed individuals or cell types that cannot be easily accessed (i.e. lung or pancreas). EMGs also allow interrogation of the effects of variants upon CFTR processing and function individually, rather than in a primary cell context where contributions of variants in both CFTR genes usually have to be taken into account. Finally, EMGs can be expressed in different cell lines to address the issue of cell-type specific factors. In this study, we employed three cell lines, of which two were of human origin. CFBE cells provide a native human context for CFTR expression [47] whereas MDCK cells are of mammalian non-human origin but retain epithelial cell machinery for protein trafficking and polarization [42–48]. HEK293 stable cell lines are useful for rapid evaluation of mRNA stability and protein processing in a human-derived cell model system that does not polarize [66, 67]. Although heterologous expression systems may not capture features of airway cells from primary nasal or bronchial cultures such as assessment of nasal mucociliary clearance [68], they have been deemed sufficient to approve clinical expansion of drug labels by the FDA [69, 70]. Consistent results among the different cell lines verified by observations in available primary cells increase confidence that the observed functional effects are due to heterologous expression of mutant forms of CFTR.
Investigation of nonsense or frameshift variants located in the 3’ region provided an excellent opportunity to test the feasibility of targeting truncated CFTR, as PTC-generating variants located either in the last exon or < 50 nt from the EJC in the penultimate exon are not subject to NMD [17, 34, 65]. As expected, CFTR transcripts bearing 8 PTC (6 nonsense and 2 frameshift) variants located in the aforementioned regions were stable, whereas those bearing 3 nonsense variants located elsewhere (Q1390X, Q1382X, and E1371X) were unstable. Synthetic truncations of CFTR established that the C-terminal domain modulates the biogenesis and maturation of CFTR and suggested that variants that introduced PTCs upstream of codon 1390 or downstream of codon 1440 may result in truncated but stable forms of CFTR protein [36, 71]. However, by using EMGs we show that nonsense variants at or upstream of codon 1390 do not generate stable truncated protein due to NMD of mRNA transcript. Consequently, therapeutic targeting of PTC-generating variants upstream of codon 1390 should focus on abrogation of NMD and targeting of truncated CFTR. Conversely, we found that truncations caused by naturally-occurring PTC-generating variants up to codon 1418 were associated with low to wild-type amounts of mature truncated CFTR protein that were partially functional. Consistent with our observation, CFTR bearing E1418X has been reported to be functional in primary nasal epithelial cells derived from an individual with CF [72]. CFTR bearing each of the 5 naturally-occurring PTC variants from 1418 to 1476 were responsive to lumacaftor, tezacaftor, or both in combination, consistent with our observation that correctors increase the level of mature truncated forms of CFTR. Together, our results indicate that PTC-generating variants in the C-terminus of proteins should be carefully evaluated as they may allow generation of stable mRNA and mature truncated protein with residual function. In the case of CF, individuals carrying PTC-generating variants at or downstream of codon 1418 may benefit from protein modulator treatments.
A cluster of 7 naturally occurring CF-causing nonsense variants within exon 22 encoding the ICL6 region were studied as it has been shown that CFTR missing the second nucleotide binding domain (NBD2) and thereafter matures well to form a functional chloride channel at the cell surface [52, 53]. These studies demonstrated that CFTR truncated at codon 1218 (just prior to NBD2) generated protein kinase A (PKA) stimulated halide conductance when expressed in in BHK cells and chloride conductance by single channel recording when embedded in planar lipid bilayers [52, 53]. Our studies revealed that CFTR truncated up to codon 1182 is glycosylated and partially functional whereas truncation 20 residues further upstream (to codon 1162) results in immature protein with no CFTR function. More recently, truncations studied in ATP-binding cassette transporter, Ste6, of yeast demonstrated distinct metabolic stabilities [73]. L1240X and R1268X truncations exhibited similar stabilities as the wild-type protein. In contrast, truncations in between these two locations destabilized the protein emphasizing the fidelity with which Endoplasmic-Reticulum-associated degradation substrates are selected [73].
The truncated forms of CFTR that retained residual chloride channel function, especially Y1182X, were remarkably responsive to ivacaftor when combined with a corrector. We emphasize that modulator mediated improvements in CFTR function observed here in stable cell lines expressing nonsense variants cannot be extrapolated to improvements expected in individuals harboring nonsense variants unless measures are taken to antagonize NMD [74]. Indeed, primary cells demonstrated that CFTR mRNA bearing R1158X and S1196X undergo NMD, and it is reasonable to predict that transcripts in vivo bearing each of the remaining 5 nonsense variants in this region would be similarly degraded. We show in cell lines that increasing the stability of transcripts bearing S1196X expressed in CFBE cells by NMD disruption resulted in higher forskolin-activated CFTR chloride currents that were substantially augmented by modulators. However, it remains to be determined whether NMD can be effectively inhibited in vivo to stabilize disease-causing PTC transcripts with minimal deleterious impact on the normal transcriptome. In addition to its role in RNA surveillance, NMD is a post-transcriptional regulatory pathway that keeps transcriptome under control from ‘noisy’ expression of faulty transcripts across various mammalian species [75–78]. Therefore, therapeutic strategies based on interference of this pathway are limited. Recently, antisense oligonucleotide mediated reduction of NMD factors have been proposed to be effective and safe to stabilize nonsense transcripts [79]. From a therapeutic perspective, the risks inherent in antagonizing NMD may be better justified when the target RNA transcript encodes a protein that is partially functional and responsive to modulators (e.g. exon 22 nonsense variants).
The importance of understanding pathologic mechanism for treatment of individuals bearing PTC variants is further illustrated by the E831X variant. Hinzpeter and colleagues demonstrated that the nucleotide change that predicted a nonsense variant at codon 831 actually altered RNA splicing, leading to production of minimally functional CFTR missing a single amino acid at codon 831 (del831) [80]. We now show that ivacaftor and correctors augmented current generated by a CFBE cell line stably expressing the E831X-EMG. The most likely target for the modulators is the CFTR isoform missing the amino acid at 831 as it achieves mature glycosylation and is partially functional [80]. We also show that correctors augment potentiator response in primary nasal cells with genotype E831X/F508del. Based on drug response data from stable cells that had hemizygous expression of F508del-CFTR, we inferred that increased response of modulators in primary nasal cells was due to their action on E831X allele rather than F508del alone. In support of this supposition, a recent study has confirmed that corrector-potentiator combination therapy is beneficial to CF individuals with the F508del/E831X genotype [13] and the FDA has now approved use of ivacaftor for E831X based on analysis of in vitro data [81].
CFTR mRNA transcripts bearing nonsense variants in the 5’ exons did not elicit NMD thereby providing an opportunity for protein synthesis. Our observation is consistent with studies of other genes where mRNAs with nonsense and frameshift variants in the first exon do not engage NMD due to internal methionine usage [57, 61, 82–85], even though mechanistic models predict that variants should activate NMD [17, 86]. It is postulated that translation from downstream Met codons removes complexes that occur at exon-exon junctions from pioneer transcripts and in doing so, eliminates the trigger for NMD [59]. Here, we show in both primary and stable cells that five disease-causing nonsense variants located up to exon 4 of CFTR resist NMD. We propose that NMD was not engaged due to removal of complexes since each of the five nonsense variants in 5’ exons produced protein of a molecular weight consistent with translation initiation from internal methionine at codon 254. RNA sequencing provided evidence that NMD machinery was not compromised in the primary nasal cells of an individual with one of the 5’ nonsense variants. The proteins generated from internal methionines had residual CFTR chloride channel activity, as previously reported [60], but were poorly responsive to CFTR modulators.
From a treatment perspective, 5’ nonsense variants that do not affect mRNA stability due to internal translation initiation are attractive targets for readthrough therapeutics. However, incorporation of a foreign amino acid at a premature-termination codon may not be sufficient to restore protein function, if the intended residue at this position is critical [87]. Therefore, readthrough therapeutics in combination with treatment aimed to increase protein stability and function, e.g. CFTR directed modulators (correctors and/or potentiators), should be efficacious for treating PTCs with stable mRNA. G418, a neomycin analog, is the most widely used compound for readthrough of PTCs [88–90]. Our detailed studies of L88X in its native CFTR context in primary cells and in CFTR expression minigene in CFBE cells showed that G418 in combination with lumacaftor significantly increased CFTR function of L88X. Similarly, other investigators have shown improvement in CFTR function by G418-CFTR corrector and potentiator co-treatment in intestinal organoids from CF individuals harboring E60X (exon 3) [91]; and Fisher Rat Thyroid (FRT) or HEK293 cells expressing Y122X (exon 4) [62, 92]. Although the former study did not measure RNA levels in intestinal organoids, and the latter studies utilized hybrid minigenes or cDNA constructs that could not evoke NMD, it can be predicted from our EMG results that E60X and Y122X transcripts are stable in vivo and therefore likely to be responsive to readthrough agents. Interestingly, there is mounting evidence that efficacy of PTC suppression by readthrough compounds is affected by the sequence context of nonsense variants [93, 94]. Of note, the three 5’ nonsense variants (E60X, L88X, and Y122X) that show improvement in CFTR function by G418-modulator co-therapy have different sequences coding for nonsense variants (UAG, UGA, and UAA), different flanking amino acids (ArgXGlu, PheXTyr, and IleXLeu), and different nucleotide sequences at -1 (A,U, and U), and +4 positions (C,U, and C) [62, 91]. There could be numerous factors that affect efficiency of readthrough, but data presented here and by others [62, 91, 92, 94] show that favorable clinical outcome is possible using readthrough and protein modulator co-therapies for those 5’- nonsense variants that unequivocally produce normal mRNA levels.
In summary, our systematic approach reveals that PTC-generating variants have a variety of consequences that can be exploited for therapeutic purposes. We show that the location of PTC-generating variants can help predict whether NMD may be engaged but that effects on protein stability and residual function are less obvious. Three scenarios are evident that necessitate different strategies. First, individuals harboring disease-causing PTC-generating variants that produce stable RNA and mature protein (e.g. 3’ end) are eligible for currently available protein modulators without a need for NMD inhibitor although translational read-through drugs might be beneficial. Second, individuals harboring variants generating unstable mRNA but mature protein (e.g. exon22/ICL6) should be considered for NMD inhibitor and protein modulator therapy. Finally, individuals with nonsense variants (e.g. 5’ end) where mRNA abundance is not affected due to use of alternative start sites should be amenable to read-through and protein modulator treatment without the need for NMD inhibitor. These results show that nonsense and frameshift variants that introduce PTCs can have markedly different effects on CFTR protein synthesis and eligibility for modulator treatments.
This study was approved by the Institutional Review Boards at Johns Hopkins Medicine, Baltimore, and Case Western Reserve University/University Hospitals Case Medical Center, Cleveland (approval numbers IRB00116966 and UHCMC#10-14-14). Written informed consent was obtained from all subjects.
The purpose of this study was to systematically evaluate mRNA stability, protein production, and/or function of PTC-generating variants in CFTR to identify which variants allow generation of CFTR responsive to currently available modulator therapies and those that require alternative therapeutic approaches. Twenty six PTC generating variants were selected. To explore mRNA and protein using a single platform we generated four WT-EMGs. Each variant EMG was created by site directed mutagenesis of WT-EMG. Variant and WT-EMGs were expressed in three different stable cell lines. Primary nasal cells obtained from CF individuals harboring PTC generating variants were conditionally reprogrammed. CFTR mRNA abundances, mRNA stability, and splicing were assessed by qRT-PCR, Sanger sequencing, fragment analysis, pyrosequencing, and RNA sequencing. CFTR protein processing was evaluated by immunoblotting and glycosidase digestion. CFTR function was determined by short-circuit current (Isc) measurements on Ussing chambers. To evaluate whether modulators were effective in improving CFTR function of cells expressing PTC generating variants, following FDA approved small molecules were selected; (i) Correctors (lumacaftor and tezacaftor), and (ii) Potentiator (ivacaftor). CFTR specific function in the cells was calculated as change in Isc (ΔIsc) defined as the difference between the sustained phase of the current response after stimulation with forskolin and the baseline achieved after adding Inh-172. UPF1 siRNA was used to determine effect of NMD inhibtion on CFTR function in cells expressing EMG harboring nonsense variants that produced unstable mRNA. G418 was used to evaluate whether translational readthough resulted in improvement of CFTR function in cells expressing EMG harboring nonsense variants that produced stable mRNA. Each experiment was repeated at least 3 times.
Four EMGs were created as described previously [7, 32, 33]. CFTR-EMG-i1-i5 contained: abridged intron 1 (216 bp of 5' and 212 bp of 3'), abridged intron 2 (311 bp of 5' and 264 bp of 3'), abridged intron 3 (374 bp of 5' and 456 bp of 3'), abridged intron 4 (307 bp of 5' and 333 bp of 3'), and full-length intron 5 (882 bp). CFTR-EMG-i14-i18 contained: full-length intron 14 (2272 bp), abridged intron 15 (259 bp of 5' and 359 bp of 3'), full-length intron 16 (668 bp), abridged intron 17 (330 bp of 5' and 302 bp of 3'), and abridged intron 18 (333 bp of 5' and 339 bp of 3'). CFTR-EMG-i21-i22 contained abridged intron 21 (227 bp of 5' and 222 bp of 3'), and abridged intron 22 (191 bp of 5' and 256 bp of 3'). CFTR-EMG-i25-i26 contained full-length intron 25 (598 bp) and full-length intron 26 (1343 bp). A single nucleotide alteration c.3519T>G (p.Gly1173Gly) was introduced to avoid missplicing of EMG-i21-22.
Human embryonic Kidney (HEK293), CF bronchial epithelial (CFBE41o-), and Madin Darby Canine Kidney (MDCK II) cells each containing a Flp Recombinase Target (FRT) integration site, which facilitates site-specific recombination, were used to create stable cell lines expressing WT-CFTR-EMG or variant CFTR-EMG, as described previously [32, 46, 47, 49, 50].
Nasal cells were collected from CF and healthy individuals following IRB protocols at Johns Hopkins University, Baltimore (IRB# 00116966) and Case Western Reserve University, Cleveland (IRB# UHCMC#10-14-14). An experienced physician performed endoscopic procedures to harvest nasal cells from individuals after informed consent was obtained. Nasal epithelial cells were collected from the mid-part of the inferior turbinate of healthy/CF individuals by brushing with interdental brushes, after spraying a topical anesthetic on the nasal mucosa.
Primary human nasal epithelial (HNE) cells were harvested from CF individuals and healthy volunteers. Expansion and culture of nasal epithelia were performed as previously described [95, 96]. Briefly, nasal cells were expanded by culturing in DMEM/F-12 media in the presence of 10 μM Y-27632, a ROCK inhibitor, and irradiated fibroblast feeder cells. After 2 passages of expansion, cells were seeded (5x105 cells/cm2) onto snap-well inserts (Costar #3801). On confluence (day 5–7), propagation media was replaced with differentiation media containing Ultroser G serum substitute (Pall; Port Washington, NY) without reagent Y. The following day, cells were maintained at an air-liquid interface (ALI) by removing media from the apical compartment and providing media to the basal compartment only. The apical surface was washed with phosphate-buffered saline (PBS) to remove any mucus accumulation, and the medium was replaced in the basal compartment every 48 h. Cells were maintained at 37°C and 5% CO2.
CFTR mRNA abundance in stable cells was determined by real-time, quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Briefly, cDNAs were synthesized using iscript cDNA synthesis kit (Biorad#170–8890). PCRs for target gene (CFTR) and housekeeping gene (B2M) were performed using SsoAdanced Universal SYBR Green mix (Biorad#172–5271) Sequence of CFTR primer pair was: CFTR, forward 5’-TGACCTTCTGCCTCTTACCA-3’, reverse 5’-CACTATCACTGGCACTGTTGC-3’. B2M primer pairs are commercially available from Biorad (#qHsaCID0015347). Real time qRT-PCR data were obtained on CFX connect Real time system (BioRad). Expression levels were calculated by subtracting housekeeping control (B2M) cycle threshold (Ct) values from target (CFTR) Ct values to normalize for total input, resulting in ΔCt levels. Relative transcript abundance was computed as 2^−ΔCt. Each sample was run in triplicate.
Since CF individuals harboring nonsense variant were in compound heterozygosity with a different CFTR variant, we were able to quantify relative abundance of each allele. Reverse transcription (RT) was carried out using 50–250 ng total RNA using i-Script cDNA synthesis kit (BioRad#170–8890). The reaction mix was incubated for 5 min at 25°C, 30 min at 42°C and 5 min at 85°C. Undiluted cDNA product was used to perform following assays.
Statistical analysis was performed, and graphs were generated using GraphPad Prism6 (GraphPad Software Inc.). Results are presented as means ± SEM, with the number of experiments indicated. One-way ANOVA followed by Dunnett's multiple comparisons test was performed. P values ≤ 0.05 were considered significant. Individual-level data underlying each graph and exact P values are provided in S1–S5 Data.
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10.1371/journal.pcbi.1003323 | Linking Transcriptional Changes over Time in Stimulated Dendritic Cells to Identify Gene Networks Activated during the Innate Immune Response | The innate immune response is primarily mediated by the Toll-like receptors functioning through the MyD88-dependent and TRIF-dependent pathways. Despite being widely studied, it is not yet completely understood and systems-level analyses have been lacking. In this study, we identified a high-probability network of genes activated during the innate immune response using a novel approach to analyze time-course gene expression profiles of activated immune cells in combination with a large gene regulatory and protein-protein interaction network. We classified the immune response into three consecutive time-dependent stages and identified the most probable paths between genes showing a significant change in expression at each stage. The resultant network contained several novel and known regulators of the innate immune response, many of which did not show any observable change in expression at the sampled time points. The response network shows the dominance of genes from specific functional classes during different stages of the immune response. It also suggests a role for the protein phosphatase 2a catalytic subunit α in the regulation of the immunoproteasome during the late phase of the response. In order to clarify the differences between the MyD88-dependent and TRIF-dependent pathways in the innate immune response, time-course gene expression profiles from MyD88-knockout and TRIF-knockout dendritic cells were analyzed. Their response networks suggest the dominance of the MyD88-dependent pathway in the innate immune response, and an association of the circadian regulators and immunoproteasomal degradation with the TRIF-dependent pathway. The response network presented here provides the most probable associations between genes expressed in the early and the late phases of the innate immune response, while taking into account the intermediate regulators. We propose that the method described here can also be used in the identification of time-dependent gene sub-networks in other biological systems.
| The innate immune response is the first level of protection in organisms against invading pathogens. It consists of a large number of proteins functioning in signaling cascades triggered by the binding of fragments from microbes to specific cellular receptors. Disruptions in these pathways can lead to numerous diseases. As such, the innate immune system has been the subject of a large number of studies. However, due to its complexity and the interplay of a large number of pathways, it is not yet completely understood. In this study, we measured transcriptional changes in activated immune cells and used this information in the context of a large network of protein-protein and protein-DNA interactions to identify a smaller network of response genes. We did this by identifying the most probable network paths connecting genes showing large changes in their expression patterns at successive stages of the response. Analysis of this activated gene network revealed the associations between various temporal regulators of the innate immune response. We also identified response networks for immune cells lacking important mediators, MyD88 and TRIF, to clarify the distinct functional modules affected by their associated pathways in the innate immune response.
| The innate immune system is the primary host response to invading pathogens. The innate immune response is characterized by germline-encoded pattern-recognition receptors (PRRs) that detect and bind to specific microbial components, also known as pathogen-associated molecular patterns (PAMPs). Toll-like receptors (TLRs) are a family of PRRs that are conserved from worm to mammals and expressed on different types of immune cells, such as macrophages, dendritic cells (DCs) and B cells, as well as non-immune cells, such as fibroblasts and epithelial cells. 10 and 13 TLRs have been identified in human and mouse, respectively, each with distinct microbial ligands. The binding of these ligands to their specific receptors triggers downstream signaling cascades causing the expression of pro-inflammatory cytokines, ultimately leading to systemic inflammation. TLRs primarily function through two pathways – the MyD88-dependent pathway which leads to the expression of proinflammatory cytokines, and the TIR-domain–containing adaptor protein-inducing IFN-β (TRIF)-dependent pathway which produces the type I interferons (IFNs) [1], [2].
Though much is known about the pathways activated during the innate immune response, recent perturbation studies have identified previously unknown regulators and transcription factors, highlighting the complexity of the innate immune system and the incompleteness of our current knowledge [3]–[5]. While these studies provide important information about the genes affected on perturbation of a causal gene, they do not explain the cause of the observed expression changes. Additionally, these studies are inherently limited to genes which show changes in expression at the time of observation thus providing an incomplete representation of the activated pathways. The complexity of the innate immune system, the ease of monitoring transcriptional changes, and the availability of large amounts of regulatory and interaction information, all facilitate its analysis using computational methods. An initial computational study mapped all the known interactions associated with the immune response from literature [6]. This study provided a high confidence signaling network and identified the “bow-tie” structure of the immune response. However, it was limited in size and coverage. Li et al. used this signaling map to identify 10 distinct input-output pathways [7]. The resultant modules were further used by Richard et al. to identify a minimum set of genes whose deletion affects the fidelity of the TLR signaling pathways [8]. Though these methods used novel approaches to analyze the TLR signaling pathways, they did not take the temporal changes of the immune response into account. Using a different approach, Seok et al. studied the regulatory networks of 10 transcription factors and their targets using the Network Component Analysis approach [9]. While this study considered the dynamic nature of the immune response through the use of time-course gene expression profiles, it was limited to only 10 transcription factors. Thus, the computational analyses so far performed to study the innate immune response have either been limited by the size of the molecular network used, or by the lack of time-course gene expression profiles. In this study, we perform a comprehensive computational analysis of the dynamic aspects of the innate immune response in the context of a large-scale molecular network.
Several methods using condition-specific genetic, transcriptional and epigenomic data in the context of large protein-protein interaction (PPI) and protein-DNA interaction (PDI) networks have been developed, and have led to the identification of novel regulators and pathways in several cellular systems [10], [11]. These include Network Component Analysis (NCA) [12], DREM [13] and its recent update SDREM [14], ResponseNet [15] and SteinerNet [16], [17]. Data from time-course gene expression profiles is particularly informative in this context since it can capture chronological events in the cellular system. However, some of the methods listed above, like ResponseNet and SteinerNet, are insensitive to the temporal aspect of gene expression, while others like NCA and SDREM use the temporal gene expression information only to identify transcription factors activated at various time points but not to predict active networks. Others have used time-course gene expression profiles either to identify time-specific protein-modules in PPI networks [18]–[21], or to infer transcription regulatory networks activated over time [12], [13],[22]. Though all the methods described so far are relatively successful in identifying network components and modules activated at specific time points, no attempt has been made to identify paths connecting genes expressed at different time points. Such temporal paths can show potential connections between genes expressed at different stages of a response thus providing information about intermediate, transiently expressed regulators that would otherwise have been overlooked.
In this work, we studied the innate immune response in dendritic cells (DCs) stimulated by lipopolysaccharide (LPS). LPS is a component of the outer membrane of Gram-negative bacteria and specifically binds to the TLR4 receptor, triggering both the downstream MyD88 and TRIF-dependent pathways. We used time-course gene expression profiles collected at 8 time points after LPS stimulation in the context of a high-confidence PPI, PDI and post-translational modifications (PTM) network. We grouped the gene expression profiles into three groups – the initial response genes (greatest fold-change in expression between 0.5–1 hour after stimulation), the intermediate regulators (greatest fold-change in expression between 2–4 hours after stimulation) and the late effectors (greatest fold-change in expression between 6–8 hours after stimulation). We then attempted to identify the most probable paths connecting the initial response genes to the late effectors in the interaction network, while taking into account the intermediate regulators. In order to do this, we used a network flow optimization approach allowing the flow to follow a time-dependent path within the molecular network. Using this method, we were able to identify an optimal gene sub-network for activated DCs. Based on this sub-network, we identified several known core components of the innate immune response, novel down-stream participants and pathways connecting these core components. We were able to identify genes playing an important role in the innate immune response but showing no observable change in expression. We also analyzed time-course gene expression profiles of MyD88-knockout cells and TRIF-knockout cells, and compared their gene sub-networks to that obtained for wild-type DCs in order to identify the components that are independently activated in each pathway. Finally, we identified the distinct functional classes of genes expressed during different stages of the immune response and how their patterns of expression change in MyD88 and TRIF-knockout DCs compared to those in wild-type DCs.
We used a minimum cost flow optimization approach to identify important components of the innate immune response over time on LPS stimulation. A network of PPI and regulatory interactions, including transcription factor-target gene, phosphorylation, dephosphorylation and ubiquitination relationships, was prepared. Network edges were scored based on interaction reliability as obtained from the protein-protein interaction database, HitPredict [23]. Time-course gene expression levels were obtained using RNA-seq from DCs before LPS stimulation and up to 8 hours after LPS stimulation. The genes with significant changes in expression after LPS stimulation were divided into 3 groups based on the time of their greatest change in expression:
In order to identify potential paths through the molecular network connecting the genes within the three groups, we formulated the problem as a minimum cost flow optimization problem incorporating the gene expression levels in three stages. Figure 1 shows a schematic representation of the proposed method. We set our source nodes as the initial response genes. The target nodes of the network were the late effector genes. Edges of the network were assigned costs that were inversely proportional to their interaction reliability. Edges were also given a flow capacity proportional to the observed change in expression of the adjacent genes. A constraint was added to the flow optimization problem to force the flow to go through at least one intermediate regulator. We solved the optimization problem to identify the path of minimum cost for the flow to pass through the network using linear programming techniques (see Materials and methods for the problem formulation). The method found the most probable paths in the network between genes expressed in the initial response and those expressed at a later time while taking into account the genes expressed during the intermediate stage. Each edge of the optimal sub-network was assigned a flow signifying its importance. This resulted in a weighted gene sub-network where the edges were scored according to their importance. Flows were calculated for nodes, or genes, as the sum of the flows of their incoming edges. Genes with high flows were considered important due to their connection to high-flow edges. The reliability of the optimal solution was confirmed and statistical significance was calculated for each gene in the optimal sub-network by randomizing the source and target nodes (see Materials and methods). The flow assigned to a gene within the sub-network shared an inverse relationship with its statistical significance, demonstrating that a high flow was a good indicator of reliability (Figure S1). The genes with the highest flows – Socs3, Nfκb1, Jak2, Jun, Fos, Cxcl10 and Stat1 are well-known components of the innate immune response. Table 1 shows 20 genes with the highest flows in the optimal sub-network for activated wild-type DCs (See Table S1 for the list of all predicted genes and their statistical significance). As shown by the results, the method not only predicted essential genes expressed within each of the 3 groups, but also genes for which no significant change in expression was detected but were connected to others with significant changes in expression over time.
In order to evaluate the reliability of the gene network resulting from the paths identified by solving the flow optimization problem, we compared the genes in the optimal sub-network with the experimentally identified regulators of the innate immune response from previous perturbation experiments [3], [4]. Of the 125 regulators identified by Amit et al. [3], our sub-network contained 62 (49.6%), all of which had a flow greater than 1 (Table S2). In a similar study by Chevrier et al. [4], our sub-network contained 30 of the 43 known or novel regulators identified (69.8%), and 56 of the 102 (54.9%) TLR target genes affected by the perturbation of these regulators (Table S3). The sub-network also contained the gene, Polo-like kinase 2 (Plk2), which activates a distinct signaling cascade. Thus, our sub-network contained a significant number of the regulators of the innate immune response that were recently experimentally identified.
We further confirmed the quality of the predicted gene network through Gene Ontology (GO) and KEGG pathway enrichment analysis. The genes having flows greater than 1 in the sub-network, were enriched for the Toll-like receptor signaling pathway (p = 5.10e-41), Jak-STAT signaling pathway (p = 4.88e-45), pathways in cancer (p = 2.50e-41) and chemokine signaling pathway (p = 5.16e-40) among others (See Table S4 for full list). The association of the predicted genes with the innate immune response is further confirmed by the GO Biological Process terms enriched for these genes. Protein amino acid phosphorylation (p = 7.80e-36), immune response (p = 1.35e-32) and regulation of programmed cell death (p = 1.72e-29) were some of the most enriched terms (See Table S5 for full list). 49.7% of the genes identified in the optimal sub-network did not show significant change in their expression levels on LPS stimulation. In order to confirm that these genes contribute to the enrichment of functional terms associated with the innate immune response, we compared the enrichment of the KEGG pathways and the GO terms in all predicted genes with those that showed differential expression after LPS stimulation (Table S6, S7). Including predicted genes lacking differential expression significantly improved the enrichment of the KEGG pathways and the GO terms associated with the innate immune response over that observed for differentially expressed genes only. This further confirmed the association of the genes predicted in optimal sub-network with the innate immune response.
Additional analysis of GO term enrichment of genes identified in the sub-network at each time point showed the distinct processes active during different stages of the immune response. Table 2 shows the most significant GO Molecular Function and Cellular Component terms enriched in genes identified at each time point. The most significant term enriched for genes expressed between 0.5–1 hour is “transcription regulator activity” (p = 1.18e-09) for 20% of the genes indicating an upregulation of transcription factors during the first hour of the immune response. On the other hand, genes predicted at 2–4 hours are enriched for “nucleotide binding” (p = 9.33e-04, 28.5% genes) and “protein kinase activity” (p = 1.27e-03, 13% genes) suggesting a role for signal transducers. Finally, the terms enriched for genes predicted between 6–8 hours are “proteasome complex” (p = 2.98e-11, 7%) and “peptidase activity” (p = 5.2e-08, 13%) highlighting the activity of the immunoproteasome during this phase of the innate immune response. Finally, genes that were identified in the optimal sub-network but which did not show change in expression during the sampled time points were enriched for GO terms such “protein kinase activity” (p = 7.52e-31, 16%), “cytokine binding” (p = 5.9e-26, 6%) and “transcription factor activity” (p = 1.18e-07, 12%) (Table 3).
To check the quality of the network paths predicted by the method, we identified all the possible paths predicted in the optimal sub-network that matched a directed path of the same length in a KEGG pathway. Our method was able to predict directed paths of 3 edges or more in 13 KEGG pathways, including the Jak-STAT signaling pathway, the Chemokine signaling pathway, the Toll-like receptor pathway and the MAPK signaling pathway (Table 4, Table S8). The longest predicted directed path contained 7 edges and was part of the Jak-STAT signaling pathway. Thus, the method was able to partially recover known pathways in the form of short paths connecting genes expressed at consecutive time points. We also identified all shortest paths up to 3 edges (i.e. containing 4 nodes at most) between genes expressed at different stages of the immune response and checked how well they were represented in the same KEGG pathway. We found that 84.9% of the predicted paths have at least 2 genes in the same KEGG pathway, while 11.6% of the paths have all genes in the same KEGG pathway (Figure S2). Taken together, these results confirm the reliability of the optimal gene sub-network identified for activated wild-type DCs.
To demonstrate the utility of our algorithm, we compared the optimal sub-network identified by our method to that identified using a non-temporal minimum cost flow optimization method, ResponseNet [15]. Using minimum cost flow optimization through our initial network, ResponseNet identified paths from the initial response genes to the late effectors without taking the intermediate regulators into account (Table S9). Table 5 shows the results of the comparison between the optimal sub-networks predicted by our method and ResponseNet. ResponseNet identified fewer genes and interactions in the predicted sub-network. More significantly, since there was no constraint for the flow to pass through the intermediate regulators, it identified only 49 of these as compared to the 154 by the current method. Our method also identified significantly higher number of known regulators in the innate immune response in addition to longer paths in associated pathways. On the other hand, ResponseNet failed to identify a directed path of 3 or more edges within any KEGG pathway associated with the innate immune response. These results clearly demonstrate that including the intermediate regulators into the problem formulation, as we propose here, improves the ability of the method to predict candidate genes and associated networks using time-course gene expression profiles.
The gene predicted with the highest flow in the optimal sub-network was Suppressor of cytokine signaling 3 (Socs3) followed by Nuclear factor κb1 (Nfκb1). Both genes were significantly upregulated between 2–4 hours and are well-known regulators of the innate immune response. Socs3, along with Socs1 and Socs2, is an inhibitor of cytokine signaling pathways. It is a key regulator of interleukins 6 and 10 (Il6 and Il10) [24]. In the identified sub-network, Socs3 is induced by the primary regulators of the immune response such as Nfκb1 and inhibits a large number of proteins, specifically interleukin receptors (Figure 2a).
Nfκb1 is induced both in the early and late phase of the innate immune response and is primarily responsible for the expression of inflammatory cytokines. Other genes identified with high flows were the Janus kinase 2 (Jak2), Rous sarcoma oncogene (Src) and phosphoinositide-3-kinase, regulatory subunit 5 (Pik3r5), all of which have been implicated in the TLR response pathway. Src, a protein tyrosine kinase that modulates a large number of signaling pathways during the innate immune response, was upregulated between 2–4 hours. Along with Src, other tyrosine kinases from the Src family, such as Hck and Lyn, were also identified (Figure 2b). Syk, another protein tyrosine kinase of the Syk-ZAP70 family that is found in innate immune cell types, was also identified as part of the network though no significant change in gene expression levels was detected at the tested time points (Figure 2c). Several other components of the Src signaling pathways like Card9, Cblb, Fcerγ and various integrins were also identified within the gene sub-network.
Among other known regulators, the induction of Ralgds by Ras proteins, and the further upregulation of the Rac genes, was also detected (Figure 2d). Gadd45b, an anti-apoptotic inhibitor induced by Nfκb [25] was also part of the sub-network. Gadd45b was significantly upregulated between 2–4 hours and was predicted to inhibit the cyclins B2, B3 and CDK (Figure 2e). Another anti-apoptotic inhibitor, the X-linked inhibitor of apoptosis (Xiap) was also identified. Xiap is regulated by Nfκb and in turn inhibits Casp3 and Casp7 thus controlling apoptosis (Figure 2f) [25].
Another class of proteins identified, were the Akt serine-threonine protein kinases Akt1, Akt2 and Akt3, which are downstream effectors of the PI3K pathway (Figure 2g). Expression level change was only observed for Akt1 which was down-regulated at 0.5–1 hours followed by an up-regulation at 3 hours. Other predicted components include the Dual specificity phosphatases (DUSP proteins) which were significantly upregulated between 0.5–1 hour, except Dusp6. The Dusp proteins regulate the immune response by dephosphorylating the Map kinases and repressing the LPS-induced inflammatory response (Figure 2h). Interestingly, the network indicated that the Dusp genes were expressed within the early stages of the innate immune response suggesting that control of inflammation begins soon after its induction.
Many of the genes identified in the network do not show any significant change in expression after activation of the DCs, but are known to be essential for the response. An example is the protein phosphatase 2a catalytic subunit α (ppp2ca) which has a high flow in the sub-network. A serine threonine phosphatase required for the dephosphorylation of the 20S proteasome subunits, ppp2ca is known to affect the ability of the proteasome to degrade substrates, along with protein kinase A (PKA) [26]. Ppp2ca has also been recently shown to play an important role in the regulation of endotoxin tolerance through the regulation of MyD88 activity [27]. The identified gene sub-network indicated extensive interactions between ppp2ca and the subunits of the immunoproteasome, suggesting a role of ppp2ca in the regulation of the immunoproteasome (Figure 3). The immunoproteasome is induced by interferons and is central to the regulation of the immune response and in the prevention of auto-inflammatory diseases through its ability to degrade toxic protein aggregates during cytokine-induced oxidative stress [28].
We applied the method described above to time-course gene expression profiles obtained from DCs of MyD88 and TRIF-knockout mice in the context of the comprehensive molecular interaction network. MyD88 and TRIF are essential components of the innate immune response and trigger distinct pathways that result in the activation of early and late phase Nfκb, respectively.
Previous studies have shown that Nfκb and Mapk8 (JNK) are activated in a delayed manner in MyD88-knockout cells. However, inflammatory cytokines like IL12 or TNFα are not produced [29]. In order to identify the MyD88-independent response network, we used gene expression levels from MyD88-knockout DCs to assign edge capacities, and removed the MyD88 gene and its links within the network prior to solving the minimum cost flow optimization problem (See Table S10 for identified genes and edges). We performed a similar analysis on the data from TRIF-knockout DCs by removing TRIF and its links from the network and predicting a MyD88-dependent response network on LPS stimulation (See Table S11 for identified genes and edges).
A comparison of the genes and their flows in the identified sub-networks suggests that the response pathways active in the wild-type and TRIF-knockout sample are similar (Figure 4a). The active sub-networks identified for both these samples are enriched in the KEGG pathways “Cytokine-cytokine receptor interaction” (p = 1.13e-29), “Jak-STAT signaling pathway” (p = 2.34e-15) and “Toll-like receptor signaling pathway” (p = 6.07e-11). These findings suggest the dominance of the MyD88 pathway in the wild-type response. Indeed, this dominance has been previously observed during pulmonary infection [30]. On the other hand, the most enriched pathways in the genes exclusively identified in the MyD88-knockout network are the “Circadian rhythm” (p = 6.29e-5) and “Ubiquitin mediated proteolysis” (p = 3.2e-4) suggesting an association between these pathways and the MyD88-independent, TRIF-dependent pathway (Table 6, Tables S12 and S13).
In order to identify the dominant changes in the immune response over time, we classified the genes from the optimal sub-networks obtained for the wild-type, MyD88-knockout and TRIF-knockout DCs into functional classes. Global changes in the expression patterns of genes identified as part of the optimal sub-network at each of the 3 stages showed a dominance of functionally distinct groups at different times during the immune response (Figure 4b). In wild-type DCs, transcription factors and enzyme modulators were predominantly expressed during 0.5–1 hour after LPS stimulation. On the other hand, kinases and signaling molecules were abundant between 2–4 hours after stimulation. Finally, proteases and defence/immunity proteins along with receptors showed the greatest changes in expression in the late phase of the immune response between 6–8 hours. TRIF-knockout DCs showed similar changes in the expression patterns of genes. However, these patterns were significantly different in the MyD88-knockout DCs. Transcription factors were not as significantly upregulated in the early phase, but more so in the late phase, when the expression of proteases and defence/immunity genes was significantly reduced. Thus, the identified sub-networks suggest a pattern in the global change in gene expression during the different stages of the immune response. The similarity of the patterns of gene expression in the TRIF-knockout DCs and wild-type DCs further support the dominant role of the MyD88-dependent pathway in the innate immune response. An analysis of the functional distribution of the genes predicted in the network, but not showing significant differential expression on activation, illustrates their similarity to the intermediate regulators in the wild-type as well as knockout DCs.
Several important components of the innate immune response were identified in both knockout sub-networks, however, with significantly different flows. Nfκb1, Jak2 and Socs1 were genes with the highest flows (>40) in the TRIF-knockout network. These genes were also identified in the MyD88-knockout network, but with flows just above 1. This disparity in the flows possibly indicates their changing levels of expression and significance within the two sub-networks. The sub-network associated with MyD88-knockout DCs had different genes with high flows – Akt3, Casp8 and Stat2. Interestingly, the kinase Pik3r5 had similar levels of predicted flow in both knockout networks. It was upregulated in both instances but much more so in the MyD88-knockout DCs.
Git1 and Cry1 were two of the important candidates identified only in the MyD88-knockout gene network. Git1 (G-protein coupled receptor kinase interacting protein 1) acts in the formation of a scaffold to bring together molecules to form signaling modules and increase the speed of cell migration. Its role in the innate immune response is currently not known. However, it was significantly upregulated in the MyD88-knockout sample and found to interact with Pxn, Arhgef6 and Arhgef7 (Figure 5a).
The other important gene identified, Cry1, is a key component of the circadian core oscillator complex. The role of Cry1 in the negative regulation of the activation of Nfκb and further induction of proinflammatory cytokines has been recently elucidated [31]. Cry1 was significantly upregulated in the MyD88-knockout DCs between 6–8 hours after stimulation and could potentially be regulating the activation of Nfκb signaling. Though Cry1 was part of the gene network associated with the activation of wild-type DCs, it was not identified in the optimal gene sub-network associated with TRIF-knockout DCs, suggesting that the upregulation of Cry1 and its role might be controlled by the MyD88-independent, TRIF-dependent pathway (Figure 5b).
The MyD88-knockout associated gene network also contained a number of genes from the E2 and E3 ubiquitin-conjugating enzyme families, including several members of the Trim family, which are known for their role in suppressing the immune response by increasing the ubiquitination and subsequent degradation of regulatory genes [32]. The selective prediction of these ligases in the MyD88-knockout response network suggests that proteolytic degradation might also be predominantly affected by the TRIF-dependent pathway.
The response network identified for the TRIF-knockout sample highlights the wild-type MyD88 pathway wherein MyD88 triggers the activation of Nfκb which in turn induces the inflammatory cytokines, further inducing the Jaks and Stats and finally upregulating the Socs genes which repress the inflammatory response (Figure 5c).
We used a method based on minimum cost flow optimization to identify paths connecting genes expressed during 3 major stages of the innate immune response within a large molecular interaction network. This method was able to identify a sub-network active during the innate immune response, with genes and interactions associated with flows corresponding to their importance in the network, while taking their time of expression into account. A large number of genes were identified in spite of their lack of significant change in expression, but based on how well they were connected to genes that showed significant changes in expression over time.
The optimal sub-network identified in this study is based on gene expression profiles obtained from LPS stimulated DCs and represents a pathogen-specific response of the innate immune system against infection by Gram-negative bacteria. A significant number of previously known components of the innate immune response were identified along with important pathways triggered immediately after LPS stimulation in DCs. One of the genes identified was the protein phosphatase 2 catalytic subunit α (pppc2a), recently found to be an important player in the immune response [27]. Based on the interactions of this protein in the optimal sub-network, we propose an additional role for this protein in the regulation of protein degradation by the immunoproteasome. The differences between the MyD88 and TRIF-dependent pathways are difficult to predict based on the wild-type response network alone due to the large overlap between these two pathways. Both pathways result in the activation of Nfκb and its downstream effectors. However, the analyses of the activated MyD88 and TRIF-knockout DCs performed here helped clarify their difference. The results indicate the dominance of the MyD88-dependent pathway during the innate immune response and the association of the TRIF-dependent pathway with the circadian genes and those involved in immunoproteasomal degradation. Both these findings need to be investigated further.
The wild-type response sub-network also shows the distinct functions of genes expressed during the three stages of the innate immune response. The enrichment of transcription factors during the early stage highlights the induction of the immune response. This is followed by significant changes in the expression of kinases and signaling molecules activating the signaling cascades during the intermediate stage. These in turn lead to the expression of defense/immunity proteins, such as cytokines in the late phase of the immune response. The late phase is also characterized by an increase in the expression of proteases signifying the start of suppression of the immune response through the degradation of proteins promoting inflammation.
There are currently very few methods available that allow the use of time-course gene expression profiles for the prediction of active gene sub-networks. Two such methods, NCA and SDREM, use the temporal gene expression information only to identify transcription factors activated at various time points but not to predict the gene sub-networks. Additionally, SDREM requires source genes to be defined based on prior knowledge of the pathway and is very slow. Our method allows the use of time-course gene expression profiles and attempts to identify optimal paths between genes expressed at subsequent stages of a cellular response over time. Due to the use of connectivity as additional evidence, the method proposed here has a limited dependence on gene expression levels, thus identifying several components lacking significant changes in expression on LPS stimulation. Additionally, important regulators were identified from the genes showing changes in expression levels based on their connections within the network, thus limiting the effect of erroneous experimental observations. The approach proposed was used to identify time-dependent gene sub-networks in activated immune cells. However, this method is independent of the system studied and can be used in any other biological system that changes over time, such as embryonic development or cellular response to stress.
The method proposed here is based on minimum-cost flow optimization approach through a large interaction network. A variation of the method, ResponseNet, has been previously used in yeast to identify optimal paths within a yeast molecular network leading from genetic hits to differentially expressed genes without accounting for transcriptional changes over time [15]. Our method differs from ResponseNet in its ability to analyze time-course gene expression profiles. The source nodes and targets of the flow optimization problem are both differentially expressed genes. Most importantly, our method has an additional constraint that forces the predicted flow through genes showing significant differential expression at intermediate time points. This constraint greatly improves the prediction performance of the minimum cost flow optimization by identifying a greater number of known regulators and associated pathways. Additionally, the reliability scores used to weight the network edges are derived from the genomic features and functional annotations of the interacting proteins rather than the characteristics of the experiments in which they were identified [33]. One of the advantages of the original method was the identification of genes whose change in transcriptional activity cannot be detected in expression detection experiments. In addition to this, our method also has the ability to identify intermediate regulators acting between different stages of the response. Further, the method proposed here succeeds in capturing sections of KEGG pathways and several known candidate genes associated with the innate immune response.
The method described in this study requires that time-course gene expression profiles from a biological system be partitioned into three stages – early, intermediate and late. While this partitioning works reasonably well for the innate immune system, it may not necessarily be possible for other biological systems. Additionally, it is likely that grouping of time points potentially hides certain relationships between genes resulting in a network that is not completely representative of cellular processes. Extending the method to include additional time points would improve the quality of the sub-network predicted. The inclusion of more interactions and pathway information would further increase the probability of identifying novel candidate genes.
A problem common to all such methods that attempt to predict pathways using gene expression data is the difficulty in completely reconstituting existing pathways on the basis of changes in gene expression levels alone. This is because genes are not necessarily expressed in the order of their known role in a pathway (Figure S3, S4, S5, S6, S7, S8). This problem can be partially addressed by including data about protein levels and post-translational modification events. An associated problem is the dependence of the prediction accuracy on the frequency at which gene expression levels are monitored. The currently prevalent time intervals of 30 minutes and 1–2 hours after stimulation do not accurately represent the time-scale of cellular events which take place on the scale of seconds to minutes [34]. This is illustrated by the fact that the most important regulatory gene, Nfκb1, showed high levels of expression at the first time point – 0.5 hours, indicating that the expression data used here does not include a significant number of events that occur between 0 and 0.5 hours. The emergence of Socs3 as a more important component of the optimal sub-network than Nfκb1 might also be a result of the experiment focusing not on the TLR pathway, but events that follow after the first effectors have already been expressed i.e. Nfκb signaling pathway, chemokine-chemokine signaling pathway, etc. Thus, experiments that monitor gene expression levels starting immediately after activation and at frequent time intervals would help improve the accuracy of the predicted network.
Despite these drawbacks, our results clearly demonstrate that the method described here is capable of predicting active gene sub-networks from time-course gene expression profiles with reasonable accuracy.
The innate immune response is complex and occurs through multiple pathways. The interplay within the activated pathways makes the identification of novel components and their associations difficult. In this study, we addressed this issue by using time-course gene expression profiles of activated dendritic cells in combination with a comprehensive molecular interaction network. We developed a method based on minimum cost flow optimization in a large interaction network to identify paths between genes expressed at different time points of the immune response. Using this method, we identified an optimal gene sub-network activated during the innate immune response. We confirmed the role of several known and novel components in the identified network and suggest a role for the protein ppp2ca in the regulation of the immunoproteasome. A flow value was assigned to each identified gene and interaction within the network indicative of its importance. We also compared the response of the wild-type DCs with DCs from MyD88-knockout mice and TRIF-knockout mice and identified the global changes in expression patterns of genes in distinct functional classes. Our results are consistent with previous studies suggesting the dominant role of the MyD88-dependent pathway. We further showed that genes related to proteasomal degradation and circadian rhythms are primarily associated with the MyD88-independent, TRIF-dependent pathway. The method proposed here is independent of the biological system and can be used to identify time-dependent gene sub-networks with the help of time-course gene expression profiles related to any other cellular conditions. Future work in this area will be aimed at developing methods to accurately predict longer pathways while incorporating time-course gene expression profiles from multiple time points without the necessity of grouping them.
GM-CSF-induced bone marrow-derived dendritic cells (GM-DCs) were prepared from C57BL6/J mice (purchased from Japan Clea Inc.) as described previously [3]. The cells were stimulated with LPS from Salmonella minessota Re-595 (purchased from Sigma) at a concentration of 100 ng/ml. Stimulated cells were harvested at 0, 0.5, 1, 2, 3, 4, 6, 8, 16, 24 hours after stimulation. Total RNA was extracted from the cells using TRIzol (Invitrogen) according to the manufacturer's instruction. The RNA was subjected to RNA-seq as described in a previous study [35]. Mice deficient in MyD88 or TRIF were prepared as described in an earlier study [36], [37]. The RNA-seq data is available in the Sequence Read Archive under the accession number DRA001131.
The RNA-Seq data for the wild type, MyD88-knockout and TRIF-knockout DCs at 10 time points, from 0 to 24 hours after LPS stimulation, were obtained in the form of 35 bp single-end reads. The reads were mapped to the RefSeq mm9 mouse reference genome [38] using Bowtie [39]. Exon-exon junctions were found using TopHat [40] with each read having at most 2 mismatches and 20 mappings to the reference genome, and a minimum intron length of 70 bp. For each read, the mapping with the highest alignment score was selected. The mapping statistics are shown in Table S14. Transcript abundances for all three samples at 10 time points were estimated using Cufflinks and Cuffdiff [41] using the –T option to treat the samples as a time-series. The data from the last two time points, 16 hours and 24 hours, was not used in this study because we were concerned about the effect of their large separation from the prior time points on the quality of the sub-network predicted. Maximum absolute log fold change in expression was calculated for each gene over all time points, as follows:(1)Where = maximum absolute log fold change for gene i over time j where j = {0.5,1,2,3,4,6,8},
Genes with at least 2 fpkm in 50% of the experiments, at least 10 fpkm for 2 or more time points and an absolute fold change greater than 2 for at least one time point in each sample were considered for further analysis. Each selected gene was assigned to one of the following groups depending on the time at which it showed the highest absolute fold change (Figure 1A):
The genes and their expression levels are shown in Tables S15, S16, S17.
A network of regulatory and physical interactions from mouse was prepared by combining the following datasets:
Table S18 shows the counts of the different interaction types included in the network. PPIs were considered as bi-directional edges whereas all other associations (transcription factor–target gene, functional association, expression regulation, post-translational modification and inhibition) were considered uni-directional. Genes and their corresponding proteins were represented by a single node in the network.
The edges of the network were weighted according to their reliability. Reliability scores provided by HitPredict and TRANSFAC were used. Innatedb core PPIs and interactions from KEGG pathways were uniformly assigned a high reliability score of 999 since these were manually curated. All scores were scaled to values between 0 and 0.8 as shown in Table S19.(2)Where = scaling functionThe complete network of 103218 interactions among 12856 proteins, or protein complexes, along with the data source, reliability scores and edge weights is given in Table S20.
The network was denoted by a graph G = (V, E) with E edges and V nodes (including the auxiliary source S and the auxiliary sink T). The auxiliary source, S, was connected to the set of initial response genes (GT1), while the auxiliary sink, T, was connected to the late effector genes (GT3). Direct edges between GT1 and GT3 were excluded. The intermediate regulators (GT2) were also a part of the network but not connected to the S or T nodes. All edges, E, were assigned a capacity and a cost (See Figure 1B).
The genes identified as part of the optimal gene sub-network were assigned a statistical significance. This was done by solving the minimum cost flow optimization problem 5000 times using the original molecular interaction network but with randomly selected genes in the GT1, GT2 and GT3 sets i.e. initial response genes, intermediate regulators and late effectors in numbers equal to those from the real sample. The p-value was calculated as the fraction of solutions in which a gene was identified with an equal or higher flow than that in the optimal sub-network and with at least all the connecting edges in the optimal sub-network. We observed that the predicted flow in the final network increased with decreasing p-value (Figure S1) suggesting that a high flow was a good indicator of high statistical significance and hence greater reliability.
The ResponseNet algorithm was implemented as a non-temporal minimum cost flow optimization method. The problem formulation was changed to remove the constraint in equation (12) thus allowing the flow to go from the initial response genes (source nodes) to the late effectors (target nodes) without being constrained to pass the intermediate regulators. Additionally, the term involving was also removed from the optimization problem. The algorithm was run on the same network as our method with identical edge costs. The capacities of edges GT1 – S and GT3 – T were set as described in equations (3) and (6). The capacity of all other edges was set to 1. The optimal solution was calculated for and the identified genes were compared to known regulators and KEGG pathways as described in the Results.
All possible paths within the optimal sub-network from the initial response genes (GT1) to the late effectors (GT3) were identified and compared to all KEGG pathways to determine their overlap.
Paths were predicted between genes in the groups GT1, GT2 and GT3 by finding the weighted shortest paths [47] of up to 3 edges in the optimal sub-network. The edges were weighted as per the formula suggested by Opshal et al. [48]:(13)where = flow assigned to edge (i, j),
The shortest weighted paths identified were then compared to all KEGG pathways.
An optimal gene sub-network was identified by solving the flow optimization problem using the time-course genes expression profiles from MyD88 and TRIF-knockout DCs in a manner similar to that described above for the wild-type DCs. The MyD88 gene and its interactions were removed from the starting network when the MyD88-knockout gene expression levels were considered. Similarly, during the analysis of the TRIF-knockout sample, TRIF (Ticam1) and its interactions were removed from the network.
Enriched Gene Ontology terms and KEGG pathways were obtained using DAVID [49] with all mouse genes used as the background. Networks were prepared and formatted using Cytoscape2.7 [50]. Protein functional classes were identified using PANTHER [51].
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10.1371/journal.pmed.1002476 | Life course socioeconomic position, alcohol drinking patterns in midlife, and cardiovascular mortality: Analysis of Norwegian population-based health surveys | Socioeconomically disadvantaged groups tend to experience more harm from the same level of exposure to alcohol as advantaged groups. Alcohol has multiple biological effects on the cardiovascular system, both potentially harmful and protective. We investigated whether the diverging relationships between alcohol drinking patterns and cardiovascular disease (CVD) mortality differed by life course socioeconomic position (SEP).
From 3 cohorts (the Counties Studies, the Cohort of Norway, and the Age 40 Program, 1987–2003) containing data from population-based cardiovascular health surveys in Norway, we included participants with self-reported information on alcohol consumption frequency (n = 207,394) and binge drinking episodes (≥5 units per occasion, n = 32,616). We also used data from national registries obtained by linkage. Hazard ratio (HR) with 95% confidence intervals (CIs) for CVD mortality was estimated using Cox models, including alcohol, life course SEP, age, gender, smoking, physical activity, body mass index (BMI), systolic blood pressure, heart rate, triglycerides, diabetes, history of CVD, and family history of coronary heart disease (CHD). Analyses were performed in the overall sample and stratified by high, middle, and low strata of life course SEP. A total of 8,435 CVD deaths occurred during the mean 17 years of follow-up. Compared to infrequent consumption (<once/month), moderately frequent consumption (2–3 times per week) was associated with a lower risk of CVD mortality (HR = 0.78, 95% CI 0.72, 0.84) overall. HRs for the high, middle, and low strata of SEP were 0.66 (95% CI 0.58, 0.76), 0.87 (95% CI 0.78, 0.97), and 0.79 (95% CI 0.64, 0.98), respectively, compared with infrequent users in each stratum. HRs for effect modification were 1.30 (95% CI 1.10, 1.54, p = 0.002; middle versus high), 1.23 (95% CI 0.96, 1.58, p = 0.10; low versus high), and 0.96 (95% CI 0.76, 1.21, p = 0.73; low versus middle). In the group with data on binge drinking, 2,284 deaths (15 years) from CVDs occurred. In comparison to consumers who did not binge during the past year, HRs among frequent bingers (≥1 time per week) were 1.58 (95% CI 1.31, 1.91) overall, and 1.22 (95% CI 0.84, 1.76), 1.71 (95% CI 1.31, 2.23), and 1.85 (95% CI 1.16, 2.94) in the strata, respectively. HRs for effect modification were 1.36 (95% CI 0.87, 2.13, p = 0.18; middle versus high), 1.63 (95% CI 0.92, 2.91, p = 0.10; low versus high), and 1.32 (95% CI 0.79, 2.20, p = 0.29; low versus middle). A limitation of this study was the use of a single measurement to reflect lifetime alcohol consumption.
Moderately frequent consumers had a lower risk of CVD mortality compared with infrequent consumers, and we observed that this association was more pronounced among participants with higher SEP throughout their life course. Frequent binge drinking was associated with a higher risk of CVD mortality, but it was more uncertain whether the risk differed by life course SEP. It is unclear if these findings reflect differential confounding of alcohol consumption with health-protective or damaging exposures, or differing effects of alcohol on health across socioeconomic groups.
| Individuals with low socioeconomic position tend to consume alcohol less frequently than individuals with middle or high socioeconomic position but experience more alcohol-related hospitalisations and deaths.
The study was performed to assess whether the relation between drinking patterns and cardiovascular disease differs by socioeconomic position.
We obtained information on socioeconomic factors throughout the life course of Norwegian adults and categorised them into low, middle, or high position.
We found that moderately frequent alcohol consumers had a lower risk of dying from cardiovascular disease than infrequent consumers, and that this was more pronounced among those with high position. Very frequent consumption was associated with increased risk of CVD mortality, but only among those with low socioeconomic position.
We also found that weekly binge drinkers had higher risk of dying from cardiovascular disease than current drinkers who did not binge drink the past year, but we could not elucidate whether the risk differed by life course socioeconomic position.
The study observed socioeconomic differences in risk estimates of CVD mortality associated with given alcohol consumption levels. It is unclear if this reflects differential confounding of alcohol consumption with health-protective or damaging exposures, or differing effects of alcohol on health across socioeconomic groups.
The heterogeneity between groups in the population needs to be assessed when making population recommendations regarding alcohol consumption.
| Socioeconomic position (SEP) is relevant to behaviours, exposures, and susceptibilities that may influence health [1], such as social support, financial resources, or the knowledge, awareness, and determination required to actively follow a healthy lifestyle or consult a physician if needed. There is an inverse socioeconomic gradient in the exposure to risk factors for cardiovascular diseases (CVDs) [2], which translates into a gradient in the risk of clinical CVD events [3,4]. The majority of heart attacks and strokes occur in late adulthood, but atherosclerosis development starts in childhood [5]. Socioeconomic disadvantage at different stages throughout the life course could therefore be relevant to risk factor exposure, atherosclerosis development, and the long-term risk of clinical cardiovascular events [6–10].
In contrast to tobacco smoking, which is more frequent among socioeconomically disadvantaged individuals and has a clear detrimental effect on health, the relationship between SEP, alcohol, and health is less clear. Disadvantaged groups tend to report less frequent alcohol consumption but experience more harm from a given level of alcohol exposure [11–14]. This is sometimes referred to as the alcohol harm paradox [15]. In terms of CVDs, associations between alcohol drinking patterns and CVD risk further complicate the situation. A drinking pattern characterised by more frequent consumption of low to moderate volumes is associated with a reduced risk in comparison to infrequent drinking or abstainers, while episodic heavy drinking, also called binge drinking, is associated with an increased risk [16]. Alcohol has multiple biological effects on the cardiovascular system, both harmful and potentially protective [17–20], and it has been suggested that differing dose-response relationships of these mechanisms may explain the overall J-shaped risk curve.
Biological effects of alcohol should not differ by SEP, but the noncausal associations could do so if the lifestyles that accompany a drinking pattern differ according to SEP [21]. When consuming alcohol, for example, disadvantaged individuals may more frequently co-consume junk food or smoke cigarettes, while advantaged individuals may be more prone to combine drinking with advantageous health-related behaviours and characteristics [21]. These potential differences may be profound and captured by the measurement of important risk factors but may also be subtle and difficult to measure individually. The assessment of SEP throughout the life course could be an approach that encapsulates the effect of these potentially subtle differences over time. In this study, we investigated whether the diverging relationships between alcohol drinking patterns and CVD mortality differed by life course SEP.
The Counties Studies [22], the Cohort of Norway [23], and the Age 40 Program [24] are three partly overlapping cohorts containing data from Norwegian population-based health surveys (1974–2003). Participants were recruited to the surveys through their personal identification number (PIN), which is unique to each inhabitant of Norway. The surveys assessed CVD risk factors, and a subset (1987–2003, n = 330,745) assessed the frequency of alcohol consumption. A further subsample also assessed the frequency of binge drinking episodes. The number of participants and age distribution in the surveys are provided (S1 Table).
We linked data from the cohorts and national registries (the National Registry, the National Educational Database, and the Cause of Death Registry) by the use of PINs and a trusted third party (Statistics Norway). Data were sent from each source to the third party, which substituted the PIN with dummy numbers and sent the de-identified data to the authors. The authors then used the dummy numbers to link the data.
This study is part of a larger research project. The data linkage and the research project was approved by the Regional Ethics Committee South-East (11/1676). The Ethics Committee also gave exemption regarding consent in older surveys in which consent was not obtained. The project protocol (S1 Protocol) as well as a description of differences from the protocol and the study performed (S1 Text) is included. This study is reported as per RECORD guidelines (S1 Checklist).
Participants were eligible for the study if they were born before October 15, 1960, to 2 Norwegian parents, did not emigrate or die until after September 20, 1990, and if they completed the mandatory censuses in Norway from 1960 through 1990. These criteria were used to provide a sample that could be analysed with respect to life course SEP. Because of cohort overlap and individuals taking part in more than one survey, some participants were represented by multiple observations in the linked data. To optimise sample size, we selected 1 observation per participant, conditional on whether the observation had data on alcohol consumption frequency and placing priority on cohorts with longer follow-up. Eligible participants who had missing values on alcohol consumption frequency, CVD risk factors or indicators of SEP, or inconsistent follow-up data were excluded. The resulting sample was included in statistical analyses using the exposure variable alcohol consumption frequency. A subgroup of this sample was included in analyses of binge drinking episodes, which were available from some surveys.
The assessment of alcohol exposure differed between the source surveys, and we harmonised the data for use in the current study (S2 Table and S3 Table). Data identifying current and lifetime abstainers were harmonised into current abstainers for the main statistical analyses. Among current drinkers, alcohol consumption frequency was categorised into ‘Infrequent’, ‘Once per month to once per week’, ‘2–3 times per week’, and ‘4–7 times per week’. In surveys in which beer, wine, and liquor consumption were assessed separately, we first recoded the reported ordinal frequency categories into days of alcohol consumption per month, then summed the days to reflect total alcohol consumption, and finally recoded the sum back into the ordinal categories for harmonisation. This approach assumes that each beverage type was consumed on different days of the month. Participants reporting to be an abstainer on one question and reporting drinking on another question were defined as drinkers.
We defined a standard unit as 12.8 grams of pure alcohol, corresponding to a small bottle of beer (33.3 cl, 4.5%, 11.8 g), a glass of wine (15 cl, 12%, 14g), or a small glass or shot of liquor (4 cl, 40%, 12.6g). The frequency of heavy drinking episodes (5+ units or 60+ g of pure alcohol on a single occasion), which reflects the intake of high volumes, was categorised into ‘Not last year’, ‘A few times’, ‘1–3 times per month’, and ‘≥1 time per week’. The average amount of alcohol (g/day) could be assessed and harmonised for a subsample. Three calculations were applied, depending on which questions were available in each survey. Two calculations combined the average number of units consumed per occasion (0–20; higher values were truncated to 20) with the drinking frequency reported either per month (0–30) or in ordinal categories (4–7/week = 286/year, 2–3/week = 130/year, once/week = 52/year, 2–3/month = 30/year, once/month = 12/year, infrequent = 6/year). The third calculation was based on the total number of units consumed of beer, wine, and liquor in the course of 2 weeks.
CVDs tend to develop throughout the life course and manifest clinically in late adulthood. The manner in which risk factors and protective factors influence disease development may not be in unison; for example, there could be critical or sensitive time periods. A life course approach to epidemiology is one that takes this notion of time into account by acknowledging that measuring risk factors only once could be inadequate in order to assess the full impact they may have through the life course [25,26]. Previous studies have observed that CVD mortality is related to the number of occasions individuals have been exposed to socioeconomic disadvantage, measured by adding multiple indicators from different periods in the life course together in a cumulative manner [6,27].
We obtained a cumulative measure of life course SEP by combining indicators on household conditions from mandatory population and household censuses in 1960, 1970, and 1980 (type of dwelling, apartment block, row or detached house, ownership status, rooms per household capita, telephone ownership, access to water closet, and bath inside the dwelling), household income from the census in 1990, and the highest level of obtained education ever recorded until 2011 (National Educational Database). In contrast to the 1960 and 1970 censuses, which obtained almost complete population and household data, the census in 1980 did not pursue missing household data to the same extent. The household indicators have previously been observed to be independently associated with cause-specific mortality, as well as when combined into cumulative indexes [27,28]. A more detailed description of the role of the use of household indicators may be found here [1,25]. The household conditions, household income, and education provided a total of 20 indicators, which were scored (0 or 1) and given equal weight by summing the scores to construct the cumulative index (range 0–20). A high score indicated disadvantage and low life course SEP.
The health surveys provided self-reported data on current smoking, physical activity, diabetes, previous CVD (myocardial infarction, stroke, or angina pectoris), family history of coronary heart disease (CHD), objective measurements of blood pressure and heart rate, anthropometry, and biochemical nonfasting measurements (mmol/l) of serum triglycerides, total cholesterol, and high-density lipoprotein cholesterol (HDL-C). The Norwegian Cause of Death Registry provided outcome data on causes of death using the ninth and tenth revision of the International Classification of Diseases (ICD). The primary outcome was CVD mortality (1990–1995: ICD-9 390–459; 1996–2014: ICD-10: I00–I99). Three secondary outcomes were added in response to peer review, including death from ischemic heart disease (IHD) (1990–1995: ICD-9 410–414; 1996–2014: ICD–10: I20–I25), death from cerebrovascular diseases (1990–1995: ICD-9 430–438; 1996–2014: ICD-10: I60–I69), and all-cause mortality. The registry is almost exclusively based on certificates filled out by on-site medical doctors, and in the few cases in which autopsies are performed, 32% of deaths are reclassified over major ICD-10 chapters [29].
We described the study population according to categories of life course SEP (index score 0–5 = high; 6–9 = middle; ≥10 = low) as well as according to alcohol consumption frequency within categories of SEP. Continuous variables were presented as mean (SD) and categorical variables as counts (%). Analysis of variance and chi-squared tests assessed differences between the groups. In survival analyses, we followed participants prospectively until emigration (December 31, 2012), death from any cause, or December 31, 2014. Cox Proportional Hazard Models estimated hazard ratios (HRs) and confidence intervals (CIs). Visual inspection of scaled Schoenfeld residuals against time did not indicate strong deviation from the assumption of proportional hazard. All analyses were conducted in R statistical software using R studio 1.0.44 [30], with additional use of the packages survival [31] and mice [32]. We did not impose a p-value cutoff to define statistical significance [33] nor apply survey weights.
To evaluate whether the SEP index was relevant to the outcome, we estimated the risk of CVD mortality in a model with the SEP index, age, and gender. Potential mediators, such as smoking, were not included, in order to assess the total effect. The index was first modelled using a smoothed penalised spline, allowing for a visual presentation of the functional relationship with the outcome, and then as a continuous and categorical variable for a formal assessment.
The aim was to assess if the relation between alcohol drinking patterns and the risk of CVD mortality differed by life course SEP. The hypothesis we tested statistically was whether SEP modified the effect of alcohol drinking patterns on the risk of CVD. We present HRs with 95% CI for alcohol consumption frequency and for the frequency of binge drinking episodes overall, in strata of SEP, and measures of effect modification on a multiplicative scale as HRs with 95% CI and p-values. Both exposures were modelled as ordinal categorical variables, with infrequent consumers and those who did not binge drink the last year as reference categories, respectively. Current abstainers were modelled separately as a dichotomous variable, with infrequent consumers as the reference category. Confounders of the relation between alcohol and CVD that were adjusted for included age, gender, current smoking, physical activity, body mass index (BMI), systolic blood pressure, heart rate, triglycerides, diabetes, history of CVD, and family history of CHD. In the subgroup with data on binge drinking, we adjusted the risk of CVD mortality according to alcohol consumption frequency for episodes of heavy drinking, and vice versa. Analyses were performed separately for total CVD, IHD, stroke, and all-cause mortality. Missing values were handled by list-wise deletion and totalled to 18.4%. We also performed missing value imputations of CVD risk factors and census data by chained equations among 245,336 eligible individuals with data on alcohol consumption (n = 38,284 with data on binge drinking). This reduced the amount of missing values to 3.5%. Alcohol exposure variables, CVD risk factors, census data, outcome data, and the SEP index were included in the imputation model and 10 data sets were generated. We then reanalysed the relationships with total CVD in each data set and report pooled HRs with 95% CIs.
We performed 2 sensitivity analyses in response to peer review. In the subgroup with data on binge drinking, we reanalysed the relation between alcohol consumption frequency and the risk of CVD while excluding binge drinkers (‘≥1 time per month’). In the subgroup with data on lifetime abstaining, we compared the risk of CVD when using lifetime abstainers and infrequent consumers as reference categories.
We added analyses while performing planned statistical analyses, which were elaborated during peer review. Short-term experimental studies show a dose-response relationship between alcohol intake and levels of HDL-C [17]. Using ordinary least squares regression and models adjusted for age and sex, we regressed HDL-C on a continuous variable of drinking frequency (4 categories of current drinkers). In a subsample, we also regressed HDL-C on a continuous variable of the average amount of alcohol consumed per day (g/day). Changes in HDL-C were compared to the dose-response relationship in a meta-analysis of experimental studies [17] to indicate if the main study variable was consistent with an increase in total alcohol consumption as judged by HDL-C and to indicate if the self-reported data were underreported. We also reanalysed the relationship between HDL-C and drinking frequency in strata of SEP to indicate if SEP could influence the ability to report consistently [34]. A formal test for a difference in slope was performed by including an interaction term between drinking frequency and SEP.
From 330,700 potentially eligible observations, we selected (Fig 1) 1 observation per participant (n = 317,171). Participants with an immigration history (n = 24,198), who were born after October 15, 1960 (n = 30,176), or died before September 20,1990 (n = 250), or who did not attend one or more of the censuses (n = 8,370) were considered not eligible. We further excluded 18.4% of the 254,177 eligible participants for inconsistent data at follow-up (n = 3) or for missing values on alcohol consumption frequency (n = 8,841), CVD risk factors (n = 11,940), household data from the censuses (n = 25,656), and for education (n = 343). The final sample (n = 207,394) was included in complete case analyses using alcohol consumption frequency, of which 188,603 were current drinkers and 18,791 current abstainers. From this sample we also selected subgroups with data available on binge drinking episodes (n = 32,616) and data on lifetime abstaining (n = 30,455).
Individuals with missing values on household conditions were not different from those eligible and not different from individuals in the final sample. Individuals with missing alcohol data, and especially those with missing CVD risk factors, were older, more often female, had lower education, and experienced more CVD deaths during follow-up (S1 Table).
Baseline characteristics differed according to life course SEP for all included variables (Table 1). Participants with low SEP (n = 29,998) were on average older, more often female, had a higher prevalence of CVD risk factors, more previous diseases, and were more often a current abstainer or an infrequent consumer of alcohol. Participants with high SEP (n = 64,412) had the lowest prevalence of CVD risk factors, were more often frequent consumers of alcohol, and were more likely to binge drink within the subgroup for which these data were available. Estimates for middle SEP participants (n = 112,984) were mostly between the other strata.
The distribution of covariates over categories of alcohol consumption frequency followed comparable patterns within the strata of life course SEP, but with different magnitudes (S4 Table). Notably, frequent consumers of alcohol were consistently more often also frequent bingers, but the percentage among the most frequent consumers who were also weekly bingers was 32.8% in the low, 19.1% in the middle, and 16.9% in the high SEP strata.
The mean (SD) follow-up time in the study population was 16.6 (4.0) years. In total, 25,950 participants died—8,435 (4.1%) from CVDs, including 3,837 from IHD and 1,972 from stroke. In the subgroup of current drinkers with additional data on heavy drinking episodes, 7,274 died during 15.4 (6.2) years of follow-up, 2,284 (7.0%) from CVDs, including 1,028 from IHD and 553 from stroke. In the subgroup with additional data on lifetime abstaining, the number of CVD deaths in the course of 12.5 (3.0) years was 2,166 (7.1%).
Fig 2 depicts the distribution of study participants according to the index of life course SEP and the dose-response relationship of the risk of CVD mortality with the index. The HR (and 95% CI) for risk of CVD mortality with each incremental increase of life course SEP index (range 0–20) was 1.06 (1.05, 1.07). In comparison to individuals with high SEP, HRs for risk of CVD mortality were 1.16 (1.11, 1.22) and 1.50 (1.41 1.59) among individuals with middle and low SEP, respectively.
The risk of CVD mortality was lower among frequent drinkers than among infrequent consumers in imputed (Table 2) and complete case analyses (S5 Table), with even lower estimates and stronger associations when excluding or adjusting for binge drinking (S6 Table). There was no difference in risk between infrequent consumers and lifetime abstainers when they were used as reference categories in a subgroup (S7 Table). Compared with infrequent drinkers, HRs among moderately frequent drinkers (2–3/week) were 0.78 (95% CI 0.72, 0.84) for CVD mortality (Table 2), 0.70 (95% CI 0.61, 0.79) for IHD mortality (S8 Table), 0.77 (95% CI 0.64, 0.93) for stroke mortality (S9 Table), and 0.91 (95% CI 0.87, 0.95) for all-cause mortality (S10 Table). Stratified analyses and tests for effect modification indicated differences in risk by life course SEP, in which HRs for CVD mortality, IHD mortality, and all-cause mortality were even lower among moderately frequent drinkers with high SEP than among moderately frequent drinkers with middle and low SEP. In the high, middle, and low strata of SEP respectively, HRs were 0.66 (95% CI 0.58, 0.76), 0.87 (95% CI 0.78, 0.97), and 0.79 (95% CI 0.64, 0.98) for CVD mortality, 0.53 (95% CI 0.42, 0.67), 0.83 (95% CI 0.70, 0.99), and 0.68 (95% CI 0.48, 0.97) for IHD mortality, and 0.86 (95% CI 0.79, 0.93), 0.94 (95% CI 0.88, 1.00), and 0.91 (95% CI 0.80, 1.03) for all-cause mortality. The analyses also indicated differences in risk by SEP for very frequent consumption (4–7/week). While HRs for very frequent consumption compared to infrequent consumption in the high and middle strata of SEP were 0.75 (95% CI 0.63, 0.90) and 0.77 (95% CI 0.64, 0.92) for CVD mortality, 1.08 (95% CI 0.76, 1.52) and 1.03 (95% CI 0.71, 1.48) for stroke mortality, and 0.93 (95% CI 0.84, 1.04) and 0.96 (95% CI 0.86, 1.06) for all-cause mortality, HRs for this level of drinking among participants with low SEP were 1.42 (95% CI 1.06, 1.90) for CVD mortality, 1.70 (95% CI 0.82, 3.51) for stroke mortality, and 1.49 (95% CI 1.24, 1.80) for all-cause mortality, respectively. HRs for IHD mortality in the high, middle, and low strata were 0.56 (95% CI 0.41, 0.77), 0.61 (95% CI 0.45, 0.84), and 0.87 (95% CI 0.50, 1.53), respectively.
Binge drinking (≥1 time/week) was associated with a higher risk of CVD mortality in imputed (Table 3) and complete case analysis (S11 Table) as well as a higher risk of IHD (S12 Table), and all-cause mortality (S14 Table) compared with no binge drinking the last year. HRs were 1.58 (95% CI 1.31, 1.91) for CVD mortality, 1.62 (95% CI 1.20, 2.17) for IHD mortality, 1.39 (95% CI 0.88, 2.20) for stroke mortality (S13 Table), and 1.36 (95% CI 1.20, 1.53) for all-cause mortality. HRs for less frequent binge drinking (1-3 times/month) were 1.12 (95% CI 0.98, 1.28) for CVD mortality, 1.09 (95% CI 0.88, 1.34) for IHD mortality, 1.15 (95% CI 0.86, 1.53) for stroke mortality, and 1.08 (95% CI 1.00, 1.17) for all-cause mortality, compared to no binge drinking the last year. Stratified analyses and tests for effect modification did not indicate large differences in risk by life course SEP. Estimates tended to be more robust and consistent in the larger middle stratum of SEP and less consistent in the low and high SEP strata.
The crude distributions of HDL-C according to categories of alcohol consumption frequency and life course SEP are presented (S4 Table). When adjusted for age and sex, the increase in HDL-C per increase in drinking frequency (4 frequency categories) was 0.093 (0.090, 0.095) mmol/l and corresponded to an estimated mean increase of approximately 26.6 g ethanol/day when we compared it to the estimated dose-response relationship between alcohol and HDL-C in a meta-analysis of experimental studies [17]. The increases in the high, middle, and low strata of SEP were 0.095 (0.090, 0.099), 0.090 (0.086, 0.093), and 0.086 (0.079, 0.093) mmol/l, respectively (P-interaction term = 0.715). In a subsample, the change in HDL-C per increase in the amount of alcohol consumed per day (grams/day) was 0.009 (0.009, 0.010) mmol/l. This corresponded to a 0.113 mmol/l increase per unit of alcohol, which is in the upper range when compared to experimental studies in which 1–2 drinks/day corresponded to an estimated increase in HDL-C of 0.072 mmol/l (0.024, 0.119) [17].
Among adult participants in Norwegian health surveys (1987–2003), alcohol consumption and episodic heavy drinking were more frequent among individuals with high SEP throughout their life course. Participants with low SEP were more likely to currently abstain or drink less frequently, but apart from that, they were more exposed to all other CVD risk factors. Moderately frequent drinking was associated with a lower risk of CVD, IHD, and all-cause mortality than infrequent drinking, and we observed that this association was more pronounced among participants with high SEP. Very frequent drinking was associated with a higher risk of CVD and all-cause mortality compared with infrequent drinking, but only among participants with low SEP. Frequent binge drinking among current drinkers was associated with a higher risk of CVD, IHD, and all-cause mortality compared with no binge drinking during the last year, but it was not possible to determine whether the risk differed by life course SEP. Because of few events, it was also difficult to make firm inferences regarding stroke mortality.
The higher prevalence of current abstainers and infrequent consumers among those with low life course SEP is consistent with studies in other countries [35]. Alcohol taxes are particularly high in Norway, and differences in financial resources to purchase alcoholic beverages could contribute to this difference [36]. Interestingly, episodes of heavy drinking were somewhat more common among individuals with high SEP, illustrating the widespread acceptance of this behaviour in Norwegian society, even in the most health-conscious segment of the population. Another interesting observation was that individuals with low SEP were overrepresented in the most heavy drinking category (32.8%) when the frequencies of alcohol consumption and binge drinking were combined, a tendency that has also been observed previously in Europe [15]. The comparability to other populations in this regard strengthens external validity.
We observed lower risk of CVD, IHD, stroke, and all-cause mortality among moderately frequent drinkers in the study population compared with infrequent drinkers, which is in agreement with the majority of similar studies [16,37]. However, evidence from Mendelian randomisation studies thus far do not support a protective effect of alcohol on CVD nor provide support for a causal effect of factors that were considered to mediate a protective effect of alcohol—in particular, HDL-C and fibrinogen—on CVD [19,38–42]. It is therefore important to consider whether our findings may have been influenced by unmeasured confounders or misclassification of previous heavy drinkers [43]. In our study, we addressed the issue of reverse causality by choosing infrequent consumers over current abstainers as a reference category. We considered that small differences in alcohol consumption could not account for the difference in risk observed between these groups, unless moderate drinkers were misclassified as infrequent consumers because of underreporting [34,44]. Although we did not have data on lifetime abstainers for all participants, their risk of CVD did not differ from that of infrequent consumers in a subgroup analysis. We addressed the issue of confounding by adjusting for the uneven distribution of measured confounders, which did not strongly influence the associations. However, as measured confounders and thus, likely also unmeasured confounders, were distributed unevenly over categories of alcohol consumption within each stratum, there could clearly be residual confounding in the within-strata analyses as well. It is therefore unclear whether the findings reflect differential confounding of alcohol consumption with other exposures or differing effects of alcohol on health across socioeconomic groups.
The stratified analyses and tests for effect modification suggested that the relationship between alcohol consumption frequency and the risk of CVD mortality differed by life course SEP. The association between moderately frequent consumption and lower risk of CVD was more pronounced for participants with high SEP than among participants with low and middle SEP. Very frequent drinkers in the middle and high strata of SEP had either lower or comparable risk of CVD, IHD, stroke, and all-cause mortality in comparison to infrequent consumers, while very frequent drinkers with low SEP had a higher risk of CVD and all-cause mortality. Alcohol is subjected to first-pass metabolism in the gastrointestinal system, and when alcohol is co-ingested with foods, metabolism by enzymes in the stomach is extended [45]. This reduces bioavailability of ingested alcohol overall and also delays and reduces peak blood alcohol concentration, which may attenuate the systemic toxic effects of alcohol [46]. If drinking were more often accompanied by meals in one stratum of SEP, such as those with high SEP, it could account for a lower risk among binge drinkers compared to those not binge drinking, but not when considering alcohol as a protective factor. Another possibility, as indicated previously, is that the differences in risk by life course SEP could have arisen or been influenced by confounders having different effects in each stratum, such as if alcohol consumption is accompanied by a different set of behaviours in each stratum.
We observed higher CVD, IHD, and all-cause mortality among current drinkers who were frequent binge drinkers compared with current drinkers who did not binge drink during the last year, possibly mediated by increased blood pressure [19,47], cardiomyopathy, cardiac arrhythmias, and disturbances in blood electrolyte status [48,49]. A previous meta-analysis found binge drinking not to be associated with a higher risk of IHD in comparison to lifetime abstainers [50], and in that sense this finding sticks out. Findings were strong and consistent in the large middle stratum but less clear and less consistent in the smaller low and high SEP strata, which likely resulted in some inaccurate effect estimates and reduced precision when testing for effect modification. In light of the sample size and number of events, and the heterogeneity in the most extreme drinking categories, it is difficult to conclude with confidence that there is no socioeconomic difference in the relationship between binge drinking and CVD among adult Norwegians.
Multiple measurements of alcohol exposure over time is the best approach to account for variation in consumption [51], but this study was limited to a single self-reported measurement. Previous studies found a test–retest correlation for men and women of approximately 0.6 when using data on repeated measurements from the source population [52,53]. Furthermore, 52% of the men and 62% of the women reported consistently in a follow-up postal survey 10 years later, of which abstainers were the most consistent (68% and 75%, respectively). Abstainers and heavy drinkers, however, appeared to be more prone to dropout than infrequent and light consumers. The large sample size also accounts for random variation.
We used HDL-C as a biomarker of a change in the magnitude of total alcohol consumption, and after adjusting for differences in age and sex, we observed an increase in HDL-C of about 0.093 mmol/l for each categorical increase in alcohol consumption frequency. This value corresponds to an estimated mean increase of approximately 26.6 g ethanol/day if we compare it to the estimated dose-response relationship between alcohol and HDL-C from a meta-analysis of clinical trials [17] and substantiated that increasing consumption frequency accompanied increased amount of ethanol consumed. The increase in amount of HDL-C was comparable within all strata of SEP; thus, it seems unlikely that differential exposure misclassification can explain the differing relationships between alcohol consumption and CVD mortality in the different strata of life course SEP. The dose-response relationship of cardiovascular mortality with alcohol consumption seemed to nadir at a frequency of 2–3 times per week, or 40 g ethanol/day higher intake on average than infrequent consumers, which is comparable to overall estimates from previous studies when men and women are combined [37]. The increase in HDL-C per increase in the amount of alcohol consumed was higher in the current study than in short-term experimental studies. Although the dose-response relationship between alcohol intake and HDL-C might differ for short-term and long-term intakes, the comparison suggests that alcohol consumption may be underreported to some extent in the health surveys.
Without information on previous alcohol intake or the cause of alcohol abstaining, we were unable to identify and exclude previous heavy drinkers. Only a few surveys distinguished between lifetime and current abstainers, which we combined with current abstainers in order to harmonise the data. As a result, findings involving abstainers have low generalisability. This also precluded the combination of infrequent drinkers and lifetime abstainers into a single comparison group, which has been suggested as the best alternative [51]. However, the sensitivity analysis comparing the use of lifetime abstainers and infrequent consumers did not indicate differences between these groups, which we consider a strength of our chosen reference category. Combined information on consumption frequency and volume were available for the subgroup with additional data on current episodes of heavy drinking. Apart from the overrepresentation of individuals with low SEP at the more extreme end of intake levels, there was a consistent increase in episodes of heavy drinking with increasing alcohol consumption frequency in all strata of life course SEP, suggesting that the main exposure variable, alcohol consumption frequency, differentiated individuals according to average alcohol intake.
To reflect life course SEP, we used an index constructed from multiple indicators that, with the exception of education, we derived from census surveys performed decennially between 1960 and 1990. In order for all participants to have the possibility to obtain a full score, we imposed selection criteria regarding immigration, birth date, time of death, and census participation. This resulted in a clearly defined and homogenous sample, but at the expense of sample size.
Previous studies have assessed the relationship between SEP and the risk of alcohol-related outcomes [54], and to various degrees, the mediating role of alcohol consumption [55]. Our study appears novel in the sense that it assesses the relationship between alcohol consumption and CVD mortality within strata of life course SEP, which appears to be very sparse or nonexistent in the current literature, regardless of how SEP is measured [55]. A possible reason could be the extensive sample size required to test for differences (effect modification) between groups and the registry linkages required to measure life course SEP, which highlights the major strengths of this study. For this reason and because of variation in alcohol consumption patterns, alcohol taxes, and socioeconomic inequalities between countries, it may be difficult to repeat the study in detail. The overall findings, however, should be available for replication in another population using similar study design, albeit with variation in the measurement of SEP or alcohol consumption.
In this observational study of Norwegian adults, we observed lower CVD risk among frequent consumers of alcohol compared with infrequent consumers and higher CVD risk among current drinkers who reported frequent episodes of binge drinking in comparison to current drinkers who did not binge drink during the past year. The lower risk of CVD mortality associated with frequent consumption appeared to be more profound among those with high SEP throughout their life course than among those with middle and low SEP. We also observed higher CVD risk among very frequent consumers compared with infrequent consumers, but only among participants with low SEP. It was more uncertain whether the association between binge drinking and CVD mortality differed by life course SEP.
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10.1371/journal.pntd.0002524 | Identification of Strain-Specific B-cell Epitopes in Trypanosoma cruzi Using Genome-Scale Epitope Prediction and High-Throughput Immunoscreening with Peptide Arrays | The factors influencing variation in the clinical forms of Chagas disease have not been elucidated; however, it is likely that the genetics of both the host and the parasite are involved. Several studies have attempted to correlate the T. cruzi strains involved in infection with the clinical forms of the disease by using hemoculture and/or PCR-based genotyping of parasites from infected human tissues. However, both techniques have limitations that hamper the analysis of large numbers of samples. The goal of this work was to identify conserved and polymorphic linear B-cell epitopes of T. cruzi that could be used for serodiagnosis and serotyping of Chagas disease using ELISA.
By performing B-cell epitope prediction on proteins derived from pair of alleles of the hybrid CL Brener genome, we have identified conserved and polymorphic epitopes in the two CL Brener haplotypes. The rationale underlying this strategy is that, because CL Brener is a recent hybrid between the TcII and TcIII DTUs (discrete typing units), it is likely that polymorphic epitopes in pairs of alleles could also be polymorphic in the parental genotypes. We excluded sequences that are also present in the Leishmania major, L. infantum, L. braziliensis and T. brucei genomes to minimize the chance of cross-reactivity. A peptide array containing 150 peptides was covalently linked to a cellulose membrane, and the reactivity of the peptides was tested using sera from C57BL/6 mice chronically infected with the Colombiana (TcI) and CL Brener (TcVI) clones and Y (TcII) strain.
A total of 36 peptides were considered reactive, and the cross-reactivity among the strains is in agreement with the evolutionary origin of the different T. cruzi DTUs. Four peptides were tested against a panel of chagasic patients using ELISA. A conserved peptide showed 95.8% sensitivity, 88.5% specificity, and 92.7% accuracy for the identification of T. cruzi in patients infected with different strains of the parasite. Therefore, this peptide, in association with other T. cruzi antigens, may improve Chagas disease serodiagnosis. Together, three polymorphic epitopes were able to discriminate between the three parasite strains used in this study and are thus potential targets for Chagas disease serotyping.
| Serological tests are preferentially used for the diagnosis of Chagas disease during the chronic phase because of the low parasitemia and high anti-T. cruzi antibody titers. However, contradictory or inconclusive results, mainly related to the characteristics of the antigens used, are often observed. Additionally, the factors influencing variation in the clinical forms of Chagas disease have not been elucidated, although it is likely that host and parasite genetics are involved. Several studies attempting to correlate the parasite strain with the clinical forms have used hemoculture and/or PCR-based genotyping. However, both techniques have limitations. Hemoculture requires the isolation of parasites from patient blood and the growth of these parasites in animals or in vitro culture, thereby possibly selecting certain subpopulations. Moreover, the level of parasitemia in the chronic phase is very low, hindering the detection of parasites. Additionally, direct genotyping of parasites from infected tissues is an invasive procedure that requires medical care and hinders studies with a large number of samples. The goal of this work was to identify conserved and polymorphic linear B-cell epitopes of T. cruzi on a genome-wide scale for use in the serodiagnosis and serotyping of Chagas disease using ELISA. Development of a serotyping method based on the detection of strain-specific antibodies may help to understand the relationship between the infecting strain and disease evolution.
| Chagas disease, a zoonosis caused by the protozoan parasite Trypanosoma cruzi, affects approximately 10 million people in the Americas. Approximately 14,000 deaths occur annually, and 50,000–200,000 new cases are diagnosed each year [1]. During the acute phase of infection, diagnosis is based on parasitological methods [2]; however, in the chronic phase, such parasitological approaches have a low sensitivity, between 50–65%, because of low levels of parasitemia [3], [4]. The chronic phase is also characterized by a strong and persistent humoral immune response, thus the measurement of IgG antibodies specific for parasite antigens should be performed for diagnosis [5]. However, serological methods from different laboratories have been observed to be inconclusive or contradictory [6]–[8]. These discrepancies are mainly related to technical errors and antigen composition because crude or semi-purified protein extracts of epimastigotes, a parasite stage not found in the mammalian host, are generally used [6], [9]. Moreover, false-positive results are frequently observed because of the cross-reactivity of crude preparations of T. cruzi antigens with sera from individuals infected with Leishmania sp. and T. rangeli [10]–[12]. The use of recombinant antigens and synthetic peptides as a substitute for parasite lysates has increased reproducibility and, in addition, does not require the maintenance and processing of live parasites [13], [14]. Despite recent advances in Chagas disease diagnostics, the methods available still have limitations related to low specificity and sensitivity [15], [16]. Among the factors that compromise the performance of diagnostic tests, the genetic variability of the parasite is known to contribute to false-negative results in Chagas disease serodiagnosis [17].
Epidemiological, biochemical, and molecular studies have demonstrated that the T. cruzi taxon is extremely polymorphic [18]–[21]. Recently, T. cruzi strains were reclassified into six DTUs (discrete typing units) called TcI to TcVI [22], and there is much speculation regarding whether this parasite variability could be associated with different disease prognoses. Although T. cruzi infection results in a broad spectrum of clinical forms as indeterminate, cardiac, and digestive forms, the determinant factors involved in the development of each clinical form have not been elucidated, though it is likely that genetic factors of the host and parasite are involved [23]. However, no study to date has found an unequivocal association between the infecting parasite DTU and the clinical forms of the disease. Nevertheless, this hypothesis has not been discarded because correlations between the geographic distribution profiles of different T. cruzi DTUs and a higher frequency of specific clinical forms have been reported [21]. Indeed, digestive manifestations are more common in the central region of Brazil and the southern part of South America, where infection by TcII, TcV, and TcVI predominates; in contrast, such manifestations are rare in the northern part of South America and in Central America, where infection caused by TcI is more common [24].
Correlation studies between the parasite DTU and clinical forms of Chagas disease are challenging because most of the techniques require parasite isolation from patient blood or parasite genotyping directly from infected tissues. Because many T. cruzi populations are polyclonal, hemoculture may select sub-populations of parasites more adapted to in vitro growth conditions [25]. Moreover, because of different tissue tropisms of some T. cruzi strains [26], in infections caused by polyclonal populations and/or co-infections, the clones circulating in the patient blood may not be the same as those found in tissue lesions. The current methodologies to genotype the parasite from tissue biopsies are laborious and expensive, thus limiting the number of samples that can be analyzed. Within this context, a parasite typing method based on the detection of strain-specific antibodies from patient sera could resolve many of these problems. Thus far, there is only one study that proposes the use of an antigen to discriminate among T. cruzi DTUs [17]. This study is based on an antigen named TSSA (trypomastigote small surface antigen), belonging to the TcMUC III protein family, which can differentiate between humans infected with TcI, TcIII, and TcIV and those infected with TcII, TcV, and TcVI.
In the present study, we performed a genomic screen to identify polymorphic and conserved linear B-cell epitopes in the predicted proteome of the CL Brener T. cruzi strain in an attempt to identify targets for the serotyping and serodiagnosis, respectively, of T. cruzi-infected patients. The results were validated using sera from experimentally infected mice and chagasic patients.
The design and methodology of all experiments involving mice were in accordance with the guidelines of COBEA (Brazilian College of Animal Experimentation), strictly followed the Brazilian law for “Procedures for the Scientific Use of Animals” (11.794/2008), and were approved by the animal-care ethics committee of the Federal University of Minas Gerais (protocol number 143/2009).
The study protocol involving human samples from Bolivia was approved by the ethics committees of the study hospital, A.B. PRISMA, Johns Hopkins University and the U.S. Centers for Disease Control and Prevention. All subjects provided written informed consent before blood was collected. As for the Brazilian patients, written informed consent was obtained from the participants and was approved by the Ethics Committee of the Federal University of Minas Gerais (UFMG), under protocol number No. 312/06.
Each experimental group was composed of six 2–4-week-old C57BL/6 male mice. The mice were infected with 50 Colombiana or 500 Y trypomastigotes. For the CL Brener clone, we used three mouse groups infected with 50, 100, or 500 trypomastigotes. Infection was confirmed by the observation of trypomastigote forms in blood collected from the tail at seven days after intraperitoneal inoculation. One additional group was infected with 1×105 T. rangeli trypomastigotes, and the infection was confirmed by PCR [27]. Six un-infected mice were used as the control group. The chronic phase of infection was confirmed after approximately 3 months by negative parasitemia and the presence of anti-parasite IgG (as tested against T. cruzi and T. rangeli crude antigens) by ELISA [28]. Mouse blood was then obtained by cardiac puncture; coagulation was performed at room temperature for 30 minutes, and the serum was obtained after centrifugation at 4000×g for 15 minutes.
Blood samples from chagasic patients from Bolivia were collected in a public hospital in Santa Cruz de la Sierra. DNA was extracted from patient blood samples and parasite genotyping was performed as previously described [29]. Infection by TcI parasite lineage was confirmed for six samples (Supplementary Figure S1). Samples from 10 chagasic patients previously characterized to be infected with TcII [30] and 56 samples from chagasic patients infected with untyped parasites collected from Rio Grande do Norte State, Brazil, were also used. Samples from 14 patients infected with L. braziliensis and 14 patients with visceral leishmaniasis both known to be un-infected with T. cruzi and the sera from 24 un-infected humans were used as specificity and negative controls, respectively.
Epimastigotes of the Colombiana and CL Brener clones, and Y strain of T. cruzi and T. rangeli SC-58 were maintained in a logarithmic growth phase at 28°C in liver infusion tryptose (LIT) medium supplemented with 10% fetal bovine serum, 100 µg/mL streptomycin, and 100 units/mL penicillin [31]. A total of 1×106 T. cruzi epimastigotes/mL were incubated in triatomine artificial urine (TAU) medium for 2 hours at 28°C. L-proline (10 mM) was added to the medium, and the metacyclic forms were obtained after 72 hours at 28°C [32]. Trypomastigotes and amastigotes were obtained from rhesus-monkey epithelial LLC-MK2 cells infected with metacyclic forms cultured in RPMI medium supplemented with 2% fetal bovine serum at 37°C and 5% CO2 [31]. Differentiation of T. rangeli epimastigotes to trypomastigotes was induced with 106 parasites/mL in DMEM medium (pH 8) for 6 days at 28°C [33].
Linear B-cell epitopes were predicted for all the proteins of the CL Brener genome release 4.1 [34] using the Bepipred 1.0 program with a cutoff of 1.3 [35]. The BepiPred program assigns a score to each individual amino acid in a sequence, therefore only amino acids with prediction Bepipred score ≥1.3 were considered for the downstream analysis. Proteins encoded by the pair of Esmo and Non-Esmo alleles were aligned using the CLUSTALW program [36], and each pair of amino acids aligned received a polymorphism score according to the following scale: 0 for identical amino acids; 1 for different amino acids with similar physical-chemical properties; 2 for a mismatch involving amino acids with dissimilar physical-chemical properties; and 3 for a gap position. A perl script based on a sliding window approach that uses a fixed window size of 15 amino acids and an increment of one amino acid identified all 15-mer subsequences in which each individual amino acid has a bepipred score ≥1.3. Those peptides with a polymorphism score above 6 (sum of the individual amino acid polymorphism scores) and a mean BepiPred score ≥1.3 were classified as polymorphic epitopes; those peptides identical between the Esmo and Non-Esmo haplotypes and with a mean BepiPred prediction score ≥1.3 were classified as conserved epitopes. The selected peptides were compared with the predicted proteins from the genomes of L. infantum, L. major, L. braziliensis, and T. brucei (release 4.1) [37] using the BLASTp algorithm [38]. Peptides with at least 70% similarity along 70% of the length were discarded. After elimination of peptides with potential cross-reactivity with Leishmania and T. brucei, 50 Esmo-like peptides, 50 Non-esmo-like peptides and 50 peptides conserved with the highest mean Bepipred score were selected.
Peptides were synthesized on pre-activated cellulose membranes according to the SPOT synthesis technique [39]. Briefly, Fmoc-amino acids were activated with 0.05 mM HOBt and 0.1 mM DIC and automatically spotted onto pre-activated cellulose membranes using the MultiPep SPOT synthesizer (Intavis AG). The non-binding sites of the membrane were blocked with 10% acetic anhydride, and the Fmoc groups were removed with 25% 4-methyl piperidine. These processes were repeated until peptide chain formation was complete. After synthesis, side-chain deprotection was performed by adding a 25∶25∶1.5∶1 solution of trifluoroacetic acid, dichloromethane, triisopropylsilane, and water. The amino acid coupling and side-chain deprotection were monitored by staining the membrane with 2% bromophenol blue. The immunoblotting methodologies followed a previously described protocol [39]. First, the membrane containing peptides was blocked with 5% BSA and 4% sucrose in PBS overnight and incubated with infected and control mouse sera diluted 1∶5,000 in blocking solution for 1 hour. After washing three times with PBS-T (PBS; 0.1% Tween 20), the membrane was incubated with the secondary HRP-conjugated anti-mouse IgG antibody (Sigma-Aldrich) diluted 1∶10,000 in blocking solution for 1 hour. After a third wash, detection was performed using ECL Plus Western blotting (GE Healthcare), following the manufacturer's instructions, with the Gel Logic 1500 Imaging System (Kodak). The densitometry measurements and analysis of each peptide were performed using Image Master Platinum (GE), and the relative intensity ratio (RI) cutoff for positivity was determined at 2.0.
The soluble peptides were synthesized in solid phase on a 30-µmol scale using N-9-fluorenylmethoxycarbonyl [40] with PSSM8 equipment (Shimadzu). Briefly, Fmoc-amino acids were activated with a 1∶2 solution of HOBt and DIC. The active amino acids were incorporated into Rink amide resin with a substitution degree of 0.61. Fmoc deprotection was then performed using 25% 4-methylpiperidine. These steps were repeated until the synthesis of each peptide was complete. The peptides were deprotected and released form the resin by treatment with a solution of 9.4% trifluoroacetic acid, 2.4% water, and 0.1% triisopropylsilane. The peptides were precipitated with cold diisopropyl ether and purified by high-performance liquid chromatography (HPLC) on a C18 reverse-phase column using a gradient program of 0 to 25% acetonitrile. The peptides were obtained with 90% purity, as confirmed by mass spectrometry using Autoflex Speed MALDI/TOF equipment.
Each well of flexible ELISA polyvinylchloride plates (BD Falcon) was coated with 2 µg of soluble peptide. After blocking with 5% BSA in PBS for 1 hour at 37°C, followed by three washing steps with PBS containing 0.05% Tween 20 (PBS-T), the plates were incubated with human or mouse serum (dilution 1∶100). The plates were washed three times with PBS-T, and secondary HRP-conjugated anti-human or anti-mouse IgG antibody was added for 1 hour at 37°C, followed by four washes. A solution containing 0.1 M citric acid, 0.2 M Na2PO4, 0.05% OPD, and 0.1% H2O2 at pH 5.0 was used for detection; the reaction was stopped with 4 N H2SO4, and the absorbance was measured at 492 nm. The mean optical density value at 492 nm plus three times the standard deviation of the negative serum was used as the cutoff value. For affinity ELISA, 6 M urea was added for 5 min at 37°C after incubation with the primary antibodies; the remainder of the protocol was the same [41]. The results are shown as an affinity index (AI) determined as the ratio between the absorbance values of the samples treated and not treated with urea. An AI value lower than 40% represented low-affinity antibodies, between 41 and 70% was classified as intermediate affinity and higher than 70% as high affinity.
Genomic DNA extraction was performed using the GFXTM Genomic Blood DNA Purification kit (GE Healthcare) following the manufacturer's instructions. The DNA samples were quantified using a NanoDrop Spectrophotometer ND-1000 (Thermo Scientific). The PCR products amplified with the primers listed in Supplementary Table S1 were subjected to sequencing at both ends using the ABI Prism 3730×l DNA Analyzer (Applied Biosystems) by Macrogen Inc (Korea).
Total RNA was isolated from 108 epimastigotes, 106 trypomastigotes, and 108 LLC-MK2 cells infected with approximately 105 intracellular amastigotes of the Colombiana, Y, and CL Brener strains using the NucleoSpin RNA II RNA extraction kit (Macherey-Nagel) following the procedures described by the manufacturer. RNA from 108 LLC-MK2 cells was also extracted and used as a negative control. The concentration and purity of the RNA samples were measured with a NanoDrop Spectrophotometer ND-1000 (Thermo Scientific). cDNA was synthesized using 10 ng of total RNA and the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) using random hexamer primers according to the manufacturer's instructions.
Specific primers for each Esmo and Non-Esmo allele were designed, and the primer specificity was verified by electronic PCR using the entire parasite genome as a template. The primers used are listed in the Supplementary Table S2. Real-time PCR reactions were performed in an ABI 7500 sequence detection system (Applied Biosystems). The reactions were prepared in triplicate and contained 1 mM forward and reverse primers, SYBR Green Master Mix (Applied Biosystems), and 20 ng of cDNA. Standard curves were prepared for each experiment for each pair of primers using serially diluted T. cruzi CL Brener genomic DNA to calculate the relative quantity (Rq) values for each sample. qRT-PCRs for the constitutively expressed GAPDH gene were performed to normalize the expression of the specific alleles.
All statistical analyses were performed using Graph Prism 5.0 software. First, the normal distribution of data was evaluated by the Kolmogorov-Smirnov test; because all they showed a Gaussian profile, an unpaired t test was used for the comparative analysis between the two sets of data, and an ANOVA was used for three or more experimental groups. P-values lower than 0.05 were considered statistically significant. The sensitivity, specificity, and accuracy of the peptides were also calculated for the human samples. The sensitivity is represented by Se = TP/(TP+FN), where TP (true positive) is the number of sera from individuals infected with T. cruzi above the cutoff value and FN (false negative) is the number of sera from infected individuals below the cutoff for the conserved peptide. For the polymorphic peptides, TP was defined as the number of sera from individuals infected with a specific strain above the cutoff value, and FN is the number of these sera below the cutoff for polymorphic peptides. The specificity is represented by Sp = TN/(TN+FP), where TN (true negative) is the number of sera from individuals infected with L. braziliensis or un-infected individuals below the cutoff and FP (false positive) is the number of sera from these samples with reactivity the for conserved peptide. For the polymorphic peptides, TN was defined as the number of sera from individuals infected with a non-specific strain or L. braziliensis and uninfected individuals below the cutoff, and FP is the number of sera from these samples with reactivity. The accuracy is calculated as Ac = (TP+TN)/(TP+TN+FP+FN).
We performed B-cell epitope prediction for 3,983 proteins derived from pair of alleles of CL Brener genome. We decided to restrict our analysis to this dataset because the CL Brener clone is a recent hybrid between the TcII and TcIII DTUs and evidence suggests that the latter is an ancient hybrid between TcI and TcII [42]. Therefore, it is likely that polymorphic epitopes in the pairs of alleles of CL Brener could also be polymorphic for its parental genotypes and other T. cruzi strains. In the CL Brener hybrid diploid genome, it is possible to identify two haplotypes: “Esmo”, which is more similar to TcII; and “Non-Esmo”, which is more similar to TcIII [34]. A total of 1,488 predicted epitopes were classified as conserved between the two haplotypes, and 428 were classified as polymorphic. We next excluded epitopes also present in Leishmania major, L. infantum, L. braziliensis and T. brucei to minimize the chance of cross-reactivity, because these parasites share many antigens with T. cruzi [10], [12], [16], thus reducing the number of conserved and polymorphic epitopes to 1,086 and 242, respectively.
A total of 50 conserved, 50 polymorphic Esmo-specific, and 50 polymorphic Non-Esmo-specific peptides with high epitope prediction scores were selected for the construction of peptide arrays. The reactivity of the peptides was tested using a pool of sera from six C57BL/6 mice chronically infected with Colombiana (TcI), Y (TcII), or CL Brener (TcVI) strains and un-infected mice as the control group (Figure 1). The quantification of the reactivity was performed by densitometric analysis (Supplementary Table S3). A peptide was considered reactive and antigenically conserved if its intensity signal with all T. cruzi strains was two times higher than its signal with the sera from un-infected mice. A peptide was considered reactive and antigenically polymorphic if its intensity signal with a specific strain was two times higher than the values with the other two strains and the un-infected mice. A total of 36 peptides were considered reactive with at least one strain (Figure 1E). A conserved peptide with the highest reactivity with all T. cruzi strains (C6_30_cons) and three polymorphic peptides specific for Colombiana (A6_30_col), Y (B2_30_y), and CL Brener (B9_30_cl) were selected for soluble synthesis, and their reactivity was validated by ELISA.
Because immunoblotting assays are semi-quantitative techniques, we validated the results with quantitative ELISA and affinity ELISA assays using individual sera from six C57BL/6 mice chronically infected with the Colombiana (TcI), Y (TcII), or CL Brener (TcVI) strains and the un-infected mice as a control group. For the conserved peptide C6_30_cons, no significant difference in the reactivity among the sera from animals infected with different T. cruzi strains was observed (Figure 2A). The sera from mice infected with the Colombiana strain had a higher antibody titer against the A6_30_col peptide compared to the sera from mice infected with the Y strain. More importantly, the affinity antibodies discriminated Colombiana infection from those caused by the other two strains (Figure 2B). An expected recognition profile was also observed for the peptide B2_30_y (Figure 2C): sera from mice infected with the Y strain had a significantly higher antibody titer than those from mice infected with Colombiana, and the highest affinity antibodies generated by the Y strain discriminated its infection from those caused by Colombiana and CL Brener. With regard to peptide B9_30_cl, conventional ELISA was able to discriminate CL Brener infection from that caused by Y and Colombiana (Figure 2D).
Because the infection caused by different T. cruzi strains has specific evolution and mortality rates in a mouse model [43], we infected mice with a distinct parasite inoculum for each strain to reach the chronic phase when the sera were collected. Thus, to evaluate whether the differences in reactivity observed in the ELISA experiments were dependent on the inoculum, we tested the reactivity of sera from mice infected with 50, 100, or 500 CL Brener trypomastigotes (Supplementary Figure S2). There was no significant variation among the different CL Brener inocula, suggesting that the antigenic variability among the parasite strains is the main factor responsible for the distinct recognition profile of the peptides tested in the ELISA experiments. The evaluation of cross-reactivity with sera from mice infected with T. rangeli and from Leishmaniasis patients demonstrated that the peptides are T. cruzi specific (Supplementary Figures S3 and S4).
We next analyzed the polymorphisms of the epitopes identified in this study and predicted their reactivity with sera from individuals infected with T. cruzi strains representative of each DTU (TcI to TcVI). To this end, we first subjected the peptide sequences to an AlaScan analysis [44] to identify the amino acid residues critical to antibody binding. We found that the pattern GXXXXMRQNE in the carboxy-terminal region of conserved peptide C6_30_cons is important for the interaction with the antibodies generated in infection caused by the three T. cruzi strains (Figures 3A, B, and C). As for the polymorphic epitopes, the patterns PPXDXSLXXP in peptide A6_30_col (Figure 3D), QPQPXPQXXXQP in B2_30_y (Figure 3E), and DEXXXXG in B9_30_cl (Figure 3F) are critical for binding with the antibodies generated by Colombiana, Y, and CL Brener infections, respectively.
We then sequenced the genomic DNA encoding these four epitopes in strains representative of the six T. cruzi DTUs to predict whether the peptides would be recognized in infections caused by different parasite DTUs. It is expected that a peptide would be recognized in infections caused by a specific strain if the amino acid residues critical for antibody recognition are encoded by its genome. Based on this criterion, we predicted that conserved peptide C6_30 would be able to identify infection caused by four of the six T. cruzi DTUs (Figure 4A), whereas peptides A6_30_col and B2_30_y are expected to identify infections caused only by TcI and TcVI (Figure 4B) and TcII and TcVI (Figure 4C), respectively. Interestingly, the A6_30_col and B2_30_y epitopes are identical to the Non-Esmo- and Esmo-like CL Brener haplotypes, respectively, reinforcing the hypothesis that the nature of the CL Brener hybrid may have contributions of both the TcI and TcII genomes. B9_30_cl is predicted to identify patients infected with TcIII or TcVI (Figure 4D).
Although all peptides are derived from the CL Brener genome, the sera from mice infected with this strain had lower antibody affinities for the A6_30_col and B2_30_y peptides than did the sera from mice infected with the Colombiana or Y strain (Figure 2). Because CL Brener is a hybrid strain [34], [42], [45], the polymorphic epitopes encoded by its pairs of alleles may have distinct expression levels that could explain the differences in their reactivity. To investigate this further, we designed allele-specific primers for the genes that encode the epitopes to evaluate their expression levels in the trypomastigote and amastigote forms, the parasite stages found in mammalian hosts (Figure 5). As expected based on the T. cruzi phylogeny [46], Y expressed only the Esmo-like variants, and Colombiana expressed only the Non-Esmo variants, except for the B9_30 transcript. CL Brener expressed both alleles of all genes, except for the B9_30 transcript. The conserved peptide was expressed by both the Esmo and Non-Esmo haplotypes of CL Brener (Figure 5A). The polymorphic Non-Esmo peptide A6_30_col was expressed by the Colombiana and CL Brener strains (Figure 5B), and CL Brener also expressed the Esmo-like allele for this peptide. The opposite profile was observed for the polymorphic Esmo B2_30_y peptide, whereby only the Y and CL Brener strains expressed the Esmo-like allele and CL Brener also expressed the Non-Esmo allele of this peptide (Figure 5C). CL Brener only expressed the Esmo-like variant of the B9_30_CL epitope, and its level of expression was approximately 5 times higher than in the Y strain (Figure 5D).
All previous results were based on a mouse model because the amount of the inoculum, infective strain, and time of infection can be adequately controlled. To test the potential application of these peptides for serodiagnosis and serotyping of human infection, we performed ELISA experiments with sera from chagasic patients with parasites genotyped as TcI or TcII, and healthy individuals. The conserved peptide C6_30_cons showed 95.8% sensitivity, 88.5% specificity, and 92.7% accuracy for the identification of chagasic patients, and no significant differences in the reactivity of sera from patients infected with TcI or TcII was observed (Figure 6). As expected, peptide A6_30_col showed much higher reactivity with the sera from patients infected with TcI (Figure 7A), with 100% sensitivity, 91.9% specificity, and 92.6% accuracy; peptide B2_30_y identified most of the individuals infected with TcII (Figure 7B), with 80% sensitivity, 94.8% specificity, and 92.6% accuracy. Additionally, none of the sera from patients infected with TcI recognized the B2_30_y peptide, and peptide B9_30_cl showed a low reactivity with both TcI and TcII (Figure 7C). All peptides were also T. cruzi specific because the majority of the sera from the patients infected with L. braziliensis were non-reactive (Supplementary Figure S4).
Despite efforts to identify new targets for the immunodiagnosis of Chagas disease, the impressive genetic variability of T. cruzi strains has imposed serious limitations on the development of high-sensitivity methods [47]–[50]. Additionally, serological cross-reactivity with Leishmania and T. rangeli infections [11], [12], [16] compromises the specificity of Chagas disease diagnosis. Therefore, the identification of new T. cruzi-specific antigens conserved among the parasite strains has been recognized as an important research area for Chagas disease diagnosis and control [47]. The polymorphic nature of T. cruzi isolates, on the other hand, opens new avenues for the development of serotyping methodologies to identify the parasite DTU causing infection based on a serological survey. For instance, this would allow large-scale epidemiological studies aimed at correlating the strain causing the infection with the clinical forms of Chagas disease, an open question that has been hampered by the limited number of samples that can be analyzed by the current genotyping methodologies [48]. To the best of our knowledge, only one study has identified a polymorphic epitope among the T. cruzi DTUs [49]. This marker is an immunodominant B-cell epitope of TSSA (trypomastigote small surface antigen), a representative of the TcMUC III gene family. The TSSA-I and TSSA-II isoforms serologically discriminate between animals infected with T. cruzi I from those infected with T. cruzi II, according to the previous DTU classification (TcII-VI in the current classification), respectively. In a serological survey of chagasic patients from Argentina, Brazil, and Chile, anti-TSSA antibodies recognized only the TSSA-II isoform, suggesting that the TcII-VI DTUs are the cause of Chagas disease in those regions. In a more recent study, however, this same research group analyzed the diversity of the TSSA gene in several representatives of each of the six T. cruzi DTUs and found a complex pattern of sequence polymorphism. Based on their analysis, the epitope considered to be specific for TcII-VI was shown to identify the TcII, V, and VI DTUs. In addition, the peptide previously described as TcI specific shares key features with TcIII and IV. Therefore, there is no T. cruzi DTU-specific serological marker identified thus far.
The goal of this work was to identify conserved and polymorphic linear B-cell epitopes of T. cruzi for Chagas disease serodiagnosis and serotyping using ELISA. This technique was selected because it is a quantitative assay and easily automated, thus allowing the analysis of a large number of samples. In recent years, synthetic peptides used as antigens have shown high sensitivity and specificity in diagnostic tests [50]. Peptides have several advantages over chemically purified or recombinant antigens because their production does not involve the manipulation of living organisms and can be obtained with a high level of purity [51]. Recently, the use of peptide arrays has allowed the immunoscreening of a large number of epitope candidates [39]. Thus, an approach based on a synthetic peptide array was chosen to screen of a large number of potential antigens by immunoblotting, followed by ELISA validation.
Initially, we screened the CL Brener genome to predict epitopes that are polymorphic and conserved between the Esmo and Non-Esmo haplotypes. The rationale underlying this strategy is that, because the CL Brener strain is a recent hybrid between the TcII and TcIII DTUs and there is evidence suggesting that the latter is an ancient hybrid between TcI and TcII [46], it is likely that the polymorphic epitopes between the CL Brener alleles would also be polymorphic among distinct T. cruzi strains. The Colombiana (TcI) and CL Brener (TcVI) clones and Y (TcII) strain were selected for this study to evaluate the degree of polymorphism of epitopes in TcII, a direct representative of one CL Brener parental DTU, and TcI, a more distant DTU of CL Brener, along with CL Brener.
The immunoscreening of 150 high-scoring peptides resulted in the identification of 36 novel epitopes, indicating that our computational approach for the prediction and prioritization of epitope candidates was successful. Our rate of success (24%) was slightly higher than previously described (19.5%) for T. cruzi using a similar validation approach [50]. We found that only 11% (4/36) of the reactive peptides are shared among the three parasite strains (Figure 1E), highlighting the problem with identifying high-sensitivity antigens for the serodiagnosis of Chagas disease due to the high degree of T. cruzi polymorphism. One of the conserved epitopes identified in this study, peptide C6_30_cons, has proven to be a new conserved T. cruzi antigen with a potential application in Chagas disease serodiagnosis (Figures 2A, 6A, S2, and S3).
Together, the three polymorphic epitopes were able to discriminate among infections caused by the three different T. cruzi strains included in this study and, thus, have the potential to be used for the serotyping of infections caused by this parasite. ELISA experiments using human sera confirmed the predictive reactivity of A6_30_col and B2_30_y (Figure 6). A6_30_col was able to identify 100% of the patients infected with TcI. As expected, the serum samples obtained from Brazilian patients known to be infected with TcII were reactive only with the C6_30 conserved and B2_30_y peptides. These results confirm the potential use of this peptide set for Chagas disease serotyping.
The peptide A6_30_col and B9_30_cl are derived from RNA binding proteins and RNA polymerase III, respectively (Supplementary Table S3). Both are predicted to have an intracellular localization. Indeed, humoral response against intracellular antigens is quite common in trypanosomatids as shown by the work described by da Rocha et al., 2002 [52] that performed immunoscreening of an amastigote cDNA library using sera from chagasic patients. About 70% of the amastigote antigens identified in this study is derived from intracellular parasite proteins. Similar to Leishmania infection, it is postulated that during T. cruzi infection a proportion of trypomastigotes/amastigotes cells are destroyed, thus releasing substantial amounts of multicomponent complexes containing intracellular antigens [53]. This reactivity could be the result of high abundance of these antigens as circulating complexes during the parasite infection due to high and constant expression of nuclear and house-keeping genes; higher stability due to formation of nucleoprotein particles more resistant to degradation; and their increased capacity to be processed by antigen-presenting cells because multicomponent particles are taken into the cell more efficiently than soluble antigens [54].
It is worth noting that the conserved and polymorphic epitopes identified in this study encompass repetitive regions. Interestingly, two of these peptides have proline-rich regions (Figure 4) that may be involved in protein-protein interactions in prokaryotes [55] and eukaryotes [56]. It has been demonstrated that the overall immunogenicity of proteins harboring tandem repeats is increased, as is the antigenicity of epitopes contained within repetitive units [52], [57]. Therefore, one expects that repeats receive a high B-cell epitope prediction score. Furthermore, the polymorphic epitopes containing repeats were top ranked for an additional reason: our polymorphic scale applied to the CL Brener pair of alleles attributes the highest score to a gap position in the alignment, a situation always present when the contraction or expansion of a repetitive region occurs in one sequence but not in another. This criterion was used because it is well known that repetitive sequences evolve faster than other regions of the genome [58], hence it is expect that they display a high level of polymorphism among distinct parasite strains. Additionally, because it is known that the number of repetitive antigenic motifs may affect antibody binding affinity [59], we hypothesized that polymorphic repetitive epitopes would be differentially recognized by the sera of animals and human infected with distinct parasite DTUs, an assumption that was reinforced by our results.
The cross-reactivity of the epitopes among the sera from mice infected with distinct parasite strains is in agreement with an origin hypothesis of the different T. cruzi DTUs. In two-way comparisons, the CL Brener and Y strains, the two more phylogenetically related strains, shared a higher number of epitopes (4) compared to Y and Colombiana (1), whereas CL Brener and Colombiana did not share any epitope (Figure 1E).
Interestingly, despite the fact that all of the peptides are derived from the CL Brener genome, a smaller number of epitopes were identified in this strain compared with Y and Colombiana (Figure 1E). We speculate that the co-expression of alleles that encode the polymorphic epitopes in CL Brener may affect the titer of the antibody and/or its affinity for the variant epitopes. For example, the pattern of expression and the reactivity of the polymorphic peptides A6_30_col and B2_30_y (Figures 4B, 4C, 5B, and 5C) suggest that the co-expression of polymorphic epitopes in CL Brener could induce low-affinity antibodies. A similar phenomenon has been described for the polymorphic T. cruzi trans-sialidase (TS) multigene family, whereby TS displays a network of B-cell cross-reactive and polymorphic epitopes that delays the generation of high-affinity neutralizing antibodies and hamper an effective elicitation of a humoral response against these proteins [60]. Whether this is a more general adaptive immune evasion strategy that affects antibody affinity maturation, particularly in the case of hybrid strains, remains to be investigated.
Altogether, the results demonstrated that peptide C6_30_cons is a new T. cruzi antigen conserved in the majority of DTUs of this parasite. Using this peptide, in association with other T. cruzi antigens, may improve the serodiagnosis of Chagas disease. The three polymorphic epitopes identified were able to discriminate among infections caused by the three different T. cruzi strains included in this study and, thus, have the potential to be used for the serotyping of infections caused by this parasite. This is the first study on the genomic scale to identify DTU-specific antigens. The genome sequencing of other T. cruzi strains will help identify new strain-specific and conserved epitopes and increase the number of antigen candidates for Chagas disease serodiagnosis and serotyping. The development of a robust panel of strain-specific epitopes may allow large-scale epidemiological studies aimed at correlating the infective strain with the variability in clinical outcomes observed in chagasic patients.
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10.1371/journal.pcbi.1004948 | Neuroprosthetic Decoder Training as Imitation Learning | Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user’s intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user’s intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.
| There are various existing methods for rapidly learning a decoder during closed-loop brain computer interface (BCI) tasks. While many of these methods work well in practice, there is no clear theoretical foundation for parameter learning. We offer a unification of closed-loop decoder learning setting as an imitation learning problem. This has two major consequences: first, our approach clarifies how to derive “intention-based” algorithms for any BCI setting, most notably more complex settings like control of an arm; and second, this framework allows us to provide theoretical results, building from an existing literature on the regret of related algorithms. After first demonstrating algorithmic performance in simulation on the well-studied setting of a user trying to reach targets by controlling a cursor on a screen, we then simulate a user controlling an arm with many degrees of freedom in order to grasp a wand. Finally, we describe how extensions in the online-imitation learning literature can improve BCI in additional settings.
| Brain-computer interfaces (BCI, or brain-machine interfaces) translate noisy neural activity into commands for controlling an effector via a decoding algorithm [1–4]. While there are various proposed and debated encoding mechanisms describing how motor-relevant variables actually relate to neural activity [5–9], in practice decoders are successful at leveraging the statistical relationship between the intended movements of the user and firing rates of recorded neural signals. Under the operational assumption that some key variables of interest (e.g. effector kinematics) are linearly encoded by neural activity, the Kalman filter (KF) is a reasonable decoding approach [10], and empirically it yields state-of-the-art decoding performance [11] (see [12] for review). Once a decoder family (e.g. KF) is specified, a core objective in decoder design is to obtain good performance by learning specific parameter values during a training phase. For a healthy user who is capable of making overt movements (as in a laboratory setup with non-human primates [1–3, 11]), it is possible to observe neural activity and overt movements simultaneously in order to directly learn the statistical mapping—implicitly, we assume the overt movements reflect intention, so this mapping provides a relationship between neural activity and intended movement.
However, in many cases of interest the user is not able to make overt movements, so intended movements must be inferred or otherwise determined. This insight that better decoder parameters can be learned by training against some form of assumed intention appears in [11], and extensions have been explored in [13, 14]. In these works, it is assumed that the user intends to move towards the current goal or target in a cursor task, resulting in parameter training algorithms that result in dramatically improved decoder performance on a cursor task.
Specifically, in the recalibrated feedback intention-trained Kalman filter formulation (ReFIT, [11]), the decoder is trained in two stages. First, the subject makes some number of reaches using its real arm. The hand kinematics and neural data are used to train a Kalman filter decoder. Next, the subject engages in the reach-task in an online setting using the fixed Kalman filter decoder. The decoder could be updated naively with the data from this second stage (gathered via closed loop control of the cursor). However, the key parameter-fitting insight of ReFIT is that a demonstrably better decoder is learned by first modifying this closed-loop data to reflect the assumption that the user intended at each timestep to move towards the target (rather than the movement that the decoder actually produced). Specifically, the modification is that the instantaneous velocity from the closed-loop cursor control is rotated to point towards the goal to create a goal-oriented dataset. The decoder is then trained on this modified dataset. ReFIT additionally proposes a modified decoding algorithm. However, we emphasize the distinction between the problem of learning parameters and selection of the decoding algorithm—this paper focuses on the problem of learning parameters (for discussion concerning decoding algorithm selection, see [12]).
Shortcomings of ReFIT include both a lack of understanding the conditions necessary for successful application of its parameter-fitting innovation, as well as the inability for the user to perform overt movements required for the initial data collection when the user is paralyzed (as would be the norm for clinical settings [4, 15, 16]). But even more critical an issue is that ReFIT is exclusively suited to the cursor setting by requiring the intuitively-defined, goal-rotated velocities. The closed-loop decoder adaptation (CLDA) framework has made steps towards generalizing the ReFIT parameter-fitting innovation [13]. The CLDA approach built on ReFIT, effectively proposing to update the decoder online as new data streamed in using an adaptive scheme [13, 14]. While these developments significantly improve the range of applicability, they still rely on rotated velocities and do not address the key issue of extending these insights to more complex tasks, such as control with a realistic multi-joint arm effector. In the present work, we provide a clear approach which generalizes this problem to arbitrary effectors and contextualizes the style of parameter fitting employed in both ReFIT and CLDA approaches as special cases of a more general online learning problem, called “imitation learning.”
In imitation learning (or “apprenticeship learning”), an agent must learn what action to take when in a particular situation (or state) via access to an expert or oracle which provides the agent with a good action at each timestep. The agent can thereby gradually learn a policy for determining which action to select in various settings. This setting is related to online learning [17], wherein an agent makes sequential actions and receives feedback from the environment regarding the quality of the action. We propose that, in the BCI setting, instead of a policy that asserts which action to take in a given state, we have a decoder that determines the effector update in response to the current kinematic state and neural activity. Formally, the decoder serves the role of the policy; the neural activity and the current kinematic pose of the effector comprise the state; and the incremental updates to the effector pose correspond to actions. We also formalize knowledge of the user’s instantaneous “true” intention as an intention-oracle. With this oracle, we can train the decoder in an online-imitation data collection process using update rules that follow from supervised learning.
Our work helps to resolve core issues in the application of intention-based parameter fitting methods. (1) By explicitly deriving intention-based parameter fitting from an imitation learning perspective, we can describe a family of algorithms, provide general guarantees for the closed-loop training process, and provide specific guarantees for standard choices of parameter update rules. (2) We generalize intention-based parameter fitting to more general effectors through the use of an optimal control solver to generate an intention-oracle. We provide a concrete approach to derive goal-directed intention signals for a model monkey arm in a reaching task. Simulations of the arm movement task demonstrate the feasibility of leveraging intention-based parameter fitting in higher dimensional tasks—something fundamentally ambiguous given existing work, because it was not possible to infer intention for high-dimensional tasks or arbitrary effector DOF representations.
In the next section, we formulate the learning problem. We then present a family of CLDA-like algorithms which encompasses existing approaches. By relating BCI learning algorithms to their general online learning counterparts in this way, we can leverage the results from the larger online learning literature. We theoretically characterize the algorithms in terms of bounds on “regret.” Regret is a measure of the performance of a learning algorithm relative to the performance if that algorithm were set to its optimal parameters. However, while bounds are highly informative about dominant terms, they are often ambiguous up to proportionality constants. Therefore, we employ simulations to give a concrete sense of how well these algorithms can perform and provide a demonstration that even learning to control a full arm is now feasible using this approach.
The problem that arises in BCI parameter fitting is to learn the parameters of the model in an online fashion. In an ideal world, this could be performed by supervised learning, where we observe both the neural activity and overt movements, which reflect user intention. In a closed-loop setting, we would then simply use supervised online learning methods. However, for supervised learning we need labelled movement data. Neither overt movements nor user intent are actually observable in a real-world prosthetic setting. Imitation learning, through the usage of an oracle or expert, helps us circumvent this issue. To begin, we describe the core components of BCI algorithms that follow the imitation learning paradigm—effector, task objective, oracle, decoding algorithm, and update rule (Fig 1).
The effector for a BCI is the part of the system that is controlled in order to interact with the environment (e.g. a cursor on a computer screen [11] or a robotic arm [15, 18, 19]). Minimally, the degrees of freedom (DOF) that are able to be controlled must be selected. For example, when controlling a robotic arm, it might be decided that the user only controls the hand position of the robotic arm (e.g. as if it were a cursor in 3D) and the updates to the arm joint angles are computed by the algorithm to accommodate that movement. A model of effector dynamics provides a probabilistic state transition model, which permits the use of filtering techniques as the decoding algorithm. The default assumption for dynamics is that the effector does not move discontinuously, which yields smoothed trajectories.
The task objective refers to the performance measure of the task. For example, in a cursor task, the objective could be for the cursor to be as close as possible to the goal as rapidly as possible, or it may be for the cursor to acquire as many targets as possible in some time interval. Other objectives related to holding the cursor at the target with a required amount of stability have also been proposed (e.g. “dial-in-time” as in [11]). The objective may include be additional components related to minimizing exertion (i.e. energy) or having smooth/naturalistic movements. Insofar as this task has been communicated to the user (verbally in the human case or via training in the case of non-human subjects), the user’s intention should be consistent with this objective, so it is appropriate to consider the task objective to correspond to the user’s intended objective.
Imitation learning requires an oracle or expert to provide the labelled data. When overt movements are available, we use overt movements as a proxy for the intended movements. Retrospectively, we re-interpret the parameter-fitting innovation of ReFIT in the imitation learning framework—specifically, the choice to train using goal-directed velocity vectors [11] was an implicit selection of intention-oracle (a model of the user’s intention). Indeed this is a reasonable choice of oracle as it is goal-directed, presumably reflects user intent, and provides a sensible heuristic for the magnitude of the instantaneous oracle velocities. More generally, the oracle should be selected to match the user’s intention as closely as possible (for example by compensating for sensory delays as in [20]). When the task objective is well-specified and there exists a dynamics model for the effector, routine optimal control theory can be used to produce the oracle (along the lines of [21]). That is, from the current position, the incremental update to the effector state in the direction of the task objective can be computed. For a cursor, a simple mean-squared error (MSE) objective will result in optimal velocities directed towards the goal/target, with extra assumptions governing the magnitudes of those velocities.
Different BCI algorithms also differ in their choice of decoder family and update rule. We can abstract these decoders as learned functions mapping neural activity and current effector state to kinematic updates (e.g. this is straightforward for the steady-state Kalman filter, see methods). The parameters of the model will be adapted by an update rule, which makes use of the observed pairs of data (i.e the intention-oracle and the neural activity). We note two complementary perspectives—we can use our data to directly update decoder parameters or alternatively we can update the encoding model parameters and compute the corresponding updated optimal decoder (i.e. using Bayes rule to combine the encoding model and the effector dynamics model to decode via Bayesian filtering). In principle, either of these approaches work, but in this work we will directly adapt decoder parameters because it is simpler and closer to the convention in online learning.
In very general decision process settings, a function mapping from states to actions is called a policy [22, 23]—in BCI settings, this is the decoder. The details of this mapping can be specified in a few essentially equivalent ways. Most consistent with the state-action mapping is for the policy to produce an action corresponding to an update to the state of the effector. If the effector state consists of positions, then these updates are velocities; but the effector state could also be instantaneous velocities, forces, or other variables, in which case the actions correspond to updates to these state variables and imply updates to the pose of the effector.
Relatively more familiar in BCI research is the use of a policy as decoder when reinforcement learning (RL) is being used (see [24–27], or even with error feedback derived from neural activity in other brain regions [28]). Reinforcement learning and imitation learning involve similar formalisms. However, the most suitable learning framework depends on the available information. Conventional RL only provides information when feedback is available (e.g. when the task is successful), whereas use of an oracle in imitation learning allows for training informed by every state. This will yield considerably more rapid learning than RL. There are various ways to learn a policy using frameworks between these extremes. In an actor-critic RL framework [29], the policy (a.k.a. actor) is trained from a learned value function (a.k.a. critic)—readers familiar with this framework may see this as a conceptual bridge between imitation learning and RL, where imitation learning uses oracle examples rather than a learned value function. It is also possible to learn an expert’s reward function from examples and directly train the policy [30]. Perhaps most usefully, a policy could also be learned from hybrid RL and imitation updates, and this would be well-advised if the oracle is noisy or of otherwise low quality (see Discussion).
We next present a BCI meta-algorithm which formalizes closed-loop data collection and online parameter updating as a variant of imitation learning. This perspective is consistent with the CLDA framework [13], but by formalizing the entire approach as a meta-algorithm, we gain additional theoretical leverage. BCI training as described in this meta-algorithm amounts to a non-standard imitation learning setting insofar as the oracle comes from a task-constrained model of user intention, and the decoder is a policy that is conditioned on noisy neural activity. The imitation learning formalization of this BCI learning procedure is consistent with the online-imitation learning framework and meta-algorithm dataset aggregation (DAgger) [17]. We will subsequently show the online-imitation learning framework encompasses a range of reasonable closed-loop BCI approaches.
We set up the process such that the data is split into reach trajectories k = 1, …, K that each contain a sequence of Tk < T discretized time points, and K is not necessarily known a priori. Each Tk corresponds to the time it takes for a single successful reach. The kth reach is successful when some task objective, such as the distance between the cursor position and a goal position gkt, is satisfied to within some ϵ (more generally, the goal gkt corresponds to any sort of target upon which the objective depends). At each time point within a reach, t, we assume that we have the current state of the effector xkt, as well as a vector nkt that corresponds to neural activity (e.g. spike counts). Bold lower-case letters (x, n, g, …) denote column vectors. The decoder will update the state of the effector based on the combined neural state and previous effector state, {nkt, xkt} (in a limiting case, the decoder may only rely on neural activity, but inclusion of previous effector states allows for smoothing of effector trajectories).
Formally, we want a decoder π ∈ Π (i.e., a policy π within the space of policies Π) that transforms the state information (x, n) into an action that matches the intention of the user. An imitation learning algorithm trains the policy to mimic as closely as possible the oracle policy π*, which gives the oracle actions okt = π*(xkt, nkt, gkt). Note that the oracle policy is not a member of Π (i.e. π* ∉ Π): this distinction is important as the learnable policies π ∈ Π do not have access to goal information. Because we have finite samples, we use an instantaneous loss ℓ(π(xkt, nkt), okt) (note this is a surrogate loss because it depends only on the available decoded and oracle variables, and not the unavailable “true” user intention). In the cursor control case, this loss could be the squared error between the oracle velocity and the decoder/policy velocity. We write L ( π , D ( 1 : k ) ) as shorthand for ∑ k = 1 K ∑ t = 1 T k ℓ ( π ( x k t , n k t ) , o k t ), where D ( k ) refers to the set of data { xkt,nkt,okt }t∈1…Tk from just the kth reach, and D ( 1 : k ) refers to the combined set of data { xk′t,nk′t,ok′t }k′∈ 1…k,t∈1…Tk′ from reaches up to k.
Algorithm 1: Imitation learning perspective of decoder training
Initialize dataset D ( 0 ) ← ∅
Initialize decoder π(0)
Input/select β1, …, βK
for k = 1 to K trajectories do
Initialize effector state, xk1 ← x0, (or continue from end of previous trajectory)
Randomly select goal state, gkt from set of valid goals
Initialize t ← 1
while distance(xkt,gkt) > ϵ and t < T do
Acquire neural data nkt
Query oracle update okt = π*(xkt,nkt,gkt)
Update state via assisted decoder:
xk, t+1 ← βk π*(xkt,nkt,gkt) + (1 − βk)π(k)(xkt,nkt)
t ← t + 1
end
Aggregate D ( 1 : k + 1 ) ← D ( 1 : k ) ∪ { ( x k t , n k t , o k t ) } t = 1 , … , T k
π ( k + 1 ) ← UPDATE ( π ( k ) , D ( 1 : k + 1 ) ) (See Alg. 2)
end
return best or last π
The core imitation learning meta-algorithm is presented in Alg 1. This meta-algorithm describes the general structure for different learning algorithms, and the update line is distinct for alternative learning methods (each update takes the current decoder and dataset and produces the new decoder). We emphasize that this meta-algorithm is specified only once the effector, task objective, oracle, decoding algorithm, and parameter update rule are determined. The DAgger process gradually aggregates a dataset D with pairs of state information and oracle actions at each time point. The dataset is used to train a stationary, deterministic decoder, which is defined as the deterministic optimal action (lowest average loss) based on the state information, which includes both the neural activity (n) and the effector state (x) in the BCI setting.
The meta-algorithm begins with an initial decoder (i.e. stable, albeit poorly performing) and uses this decoder, possibly blended with the oracle, to explore states. Specifically, the effective decoder is given by βi π* + (1 − βi)π(k), where π* is the oracle policy and π(k) is the current decoder. When this mixing is interpreted as a weighted linear sum, this approach is equivalent to assisted decoding in the BCI literature (as in [31] or [32]), where the effective decoder during training is a mixture of the oracle policy and the decoder driven by the neural activity —in [17], the policy blending is probabilistic (see S1 Text for detailed distinction). The assisted decoder may reduce user frustration from poor initial decoding, and helps provide more task-relevant sampling of states. As training proceeds, the effective decoder relies less on the oracle and is ultimately governed only by the decoder. For example, βi may be set to decrease according to a particular schedule with iterations, or as an abrupt example, β1 = 1 and βi > 1 = 0.
For each time point in each trajectory, the state information and oracle pair are incorporated into the stored dataset. The decoder is updated by a chosen rule at the end of each trajectory (or alternatively after each time step). We note that computational and memory requirements are less for updates that only require data from the most recent stage (D ( k + 1 )); however, using the whole dataset is more general, may improve performance, and can stabilize updates.
Imitation learning with an intention-oracle is a natural framework to reinterpret and understand the parameter fitting insights that were proposed in the ReFIT algorithm [11]. In the ReFIT work, the authors used modified velocity vectors in order to update parameters in a fashion which incorporated the user’s presumed goal-directed intention, and this approach was empirically justified. We can re-interpret the rotated vectors as an ad hoc oracle, with these vectors and the single batch re-update being specific choices, hand-tailored for the task.
The CLDA framework extracted the core parameter-fitting principle from ReFIT, allowing for the updates to occur multiple times and take different forms [13]. The simplest update consistent with this framework is gradient-based decoder adaptation. Under this scheme the decoder is repeatedly updated and the updates correspond to online gradient descent (OGD). This general class of BCI algorithms take observations in an online fashion, perform updates to the parameters using the gradient, and do not pass over the “old” data again. This update takes the form:
π ( k + 1 ) = π ( k ) - 1 η k ∇ π L ( π ( k ) , D ( k + 1 ) ) , (1)
which simply means that decoder parameters are updated by taking a step in the direction of the negative gradient of the loss with respect to those parameters. 1 η k corresponds to the learning rate.
A second option for parameter updating is to smoothly average previous parameter estimates with recent (temporally localized) estimates of those parameters computed from a mini-batch—that is, to perform a moving average (MA) over recent optimal parameters. This update takes the form:
π ( k + 1 ) = ( 1 - λ ) π ( k ) + λ arg min π L ( π , D ( k + 1 ) ) (2)
for λ ∈ [0, 1]. In practice, the second term here corresponds to maximum likelihood estimation of the parameters. An update of this sort is presented as part of the CLDA framework as smoothBatch [13].
A third parameter update option in the BCI setting is to peform a full re-estimation of the parameters given all of the observed data at every update stage. This can be interpreted as a follow-the-leader (FTL) update [33]. This update takes the form:
π ( k + 1 ) = arg min π L ( π , D ( 1 : k + 1 ) ) . (3)
Here all data pairs are used as part of the training of the next set of parameters. We will show in the next section that this update can provide especially good guarantees on performance. DAgger was originally presented using this FTL update, utilizing the aggregated dataset [17]. We note that this sort of batch maximum likelihood update is discussed as a CLDA option in [14], where a computationally simpler, exponentially weighted variant is explored, termed recursive maximum likelihood (RML). For BCI settings, data is costly relative to the memory requirements, so it makes sense to aggregate the whole dataset without discarding old samples. For all of these updates, especially early on, it can be useful to include regularization, and we also incorporate this into the definition of the loss. We summarize the parameter update procedures in Alg 2.
Algorithm 2: Selected direct decoder update options
Switch:
Case—Online gradient descent (OGD), Eq 1 :
π ( k + 1 ) = π ( k ) - 1 η k ∇ π L ( π ( k ) , D ( k + 1 ) )
Case—Moving average (MA), Eq 2 :
π ( k + 1 ) = ( 1 - λ ) π ( k ) + λ arg min π L ( π , D ( k + 1 ) )
Case—Follow the (regularized) leader (FTL), Eq 3 :
π ( k + 1 ) = arg min π L ( π , D ( 1 : k + 1 ) )
return π(k+1)
Adaptive filtering techniques in engineering are closely related to the online machine learning updates we consider in this work. OGD is a generic update rule. In the special case of linear models with a mean square error cost, the solution that has a long history in engineering is called the least mean square (LMS) algorithm [34]. Also, in the same setting, when FTL corresponds to a batch LS optimization, its solution could be computed exactly in an online fashion using recursive least squares (RLS) [35] (for more background on LMS or RLS see [36]) or by keeping a running total of sufficient statistics and recomputing the LS solution.
We will more concretely discuss the guarantees of these algorithms in the subsequent section. We remark that all of the algorithms described so far make use of our generalization of the key parameter-fitting innovation from ReFIT, but they differ in parameter update rule. Additionally, algorithms can differ in the selection of the decoding algorithm, effector, task objective, and oracle. For example, if some objective other than mean squared error (MSE) were prioritized (e.g. rapid cursor stopping) and it was believed that user intention should reflect this priority, then the task objective and oracle could be designed accordingly.
In this section we provide theoretical guarantees for the BCI learning algorithms introduced above. Our formalization of the BCI setting allows us to provide new theory for closed-loop BCI learning by combining core theory for DAgger [17] with adaptations of results from the online learning literature. We provide specific terms and rates for the representative choices of parameter update rules (discussed in previous sections, summarized in Table 1).
The standard way of assessing the quality of an online learning algorithm is through a regret bound [33], which calculates the excess loss after K trajectories relative to having used an optimal, static decoder from the set of possible decoders Π:
Regret K ( Π ) = max π ♭ ∈ Π ∑ k = 1 K ∑ t = 1 T k ℓ ( π ( k ) ( x k t , n k t ) , o k t ) - ℓ ( π ♭ ( x k t , n k t ) , o k t ) . (4)
A smaller regret bound or a regret bound that decays more quickly is indicative of an algorithm with better worst-case performance. Note that π♭ is the best realizable decoder (Π is the set of feasible decoders, which may have a specific parameterization and will not depend on the goal), so π♭ is not equivalent to the oracle. Since π♭ will need to make use of noisy neural activity, the term ℓ(π♭(xkt, nkt), okt) is not likely to be zero.
Because we have been able to formulate closed-loop BCI learning as imitation learning, we inherit a variant of the core theorem of [17] (see S1 Text for our restatement), which can be paraphrased as stating: Alg 1 will result in a policy (i.e. decoder) that has an expected total loss bounded by the sum of three terms: (1) a term corresponding to the loss if the best obtainable decoder had been used for the whole duration; (2) a term that compensates for the assisted training terms (βk ); (3) a term that corresponds to the regret of the online learning parameter update rule used.
We emphasize that the power of this theorem is that it allows analysis of imitation learning through regret bounds for well-established online optimization methods. Regret that accumulates sublinearly with respect to observations implies that the trial-averaged loss can be expected to converge. We usually want the regret accumulation to occur as slowly as possible. A goal of online learning is to provide no-regret algorithms, which refers to the property that limK → ∞ RegretK(Π)/K = 0.
In this work, we have introduced three update methods that serve as a representative survey of the simple, intuitive space of algorithms proposed for the BCI setting (see Table 1). We provide regret bounds for the imitation learning variants, here specifically assuming linear decoding and a quadratic loss (see S1 Text for full details). This analysis is based on the steady-state Kalman filter (SSKF) (see methods), but could be generalized to other settings.
OGD is a classical online optimization algorithm, and is well-studied both generally and in the linear regression case. The regret scales as O ( K ) [37] (recall k indexes the reach trajectory). We note that in order to saturate the performance of OGD, the learning rate must be selected carefully, and the optimal learning rate essentially requires knowledge of the scaling of the parameters. Each parameter may require a distinct learning rate for optimal performance [39]. OGD is most useful in an environment where data is cheap because the updates have very low computational overhead—this is relevant for many modern large-data problems. In BCI applications, data is costly due to practical limits on collecting data from a single subject, so a more computationally intensive update may be preferable if it outperforms OGD.
Under certain conditions, OGD can achieve a regret rate of O ( log K ) [38], which is an improved rate (and the same order as the more computationally-intensive FTL strategy we discuss below). This rate requires additional assumptions that are realistic only for certain practical settings. Asymptotically, any learning rate ηk that scales as O ( k ) will achieve this logarithmic rate, but choosing the wrong scale will dramatically negatively impact performance, especially during the crucial, initial learning period. For this reason, we may desire methods without step-size tuning.
We next provide guarantees available for the moving average update. This algorithm suffers from regret that is O(K), so it is not a no-regret algorithm (see analysis presented in [13] where there is an additional steady-state error). Conceptually this is because old data has decaying weight, so there is estimation error due to prioritization of a recent subset of the data. While this method has poor regret when analyzed for a static model (i.e. neural tuning is stable), it may be useful when some of the data is meaningless (i.e. a distracted user who is temporarily not paying attention), or when the parameters of the model may change over time. Also, in practice, if λ is large enough, the algorithm may be close “enough” to an optimal solution.
Motivated by findings from online learning, we also expect that Follow-the-leader (FTL) (or if regularization is used, Follow-the-regularized-leader (FTRL, a.k.a. FoReL)) may improve regret rates relative to OGD, generally at the expense of additional computational cost [33] (though without much computational burden if RLS can be used). We derive that under mild conditions that hold for the SSKF learned with mild regularization, FTL obtains a regret rate of O ( log K ) [38] (see S1 Text for details and discussion of constants). Thus, keeping in mind these bounds are worst-case, we expect that using FTL updates will provide improved performance relative to OGD or MA. We validate our theoretical results in simulations in the next section. We note that BCI datasets remain small enough that FTL updates for sets of reaches should be tractable, at least for initial decoder learning in closed-loop settings.
While the focus here is on static models, we note that there is additional literature concerning online optimization for dynamic models. Here dynamic refers to situations where the neural tuning drifts in a random fashion over time. Intuitively, something more like OGD is reasonable, and specific variants have been well characterized [40]. If the absolute total deviation of the time-varying parameters is constrained, these approaches can have regret of order O ( K ) [40]. A dynamic model may provide better fit and therefore provide lower MSE despite potential for additional regret.
The first set of simulations concerns decoding from a set of neurons that are responsive to intended movement velocity (see methods for full details). In these simulations, there is a cursor that the user intends to move towards a target, and we wish to learn the parameters of the decoder to enable this. The cursor task (leftside panel of Fig 2) is relatively simple, but the range of results we obtain for well-tuned algorithm variants is consistent with our theoretically-motivated expectations. Indeed, in the right panel of Fig 2, we see that the OGD algorithm, which takes only a single gradient step after each reach, performs less well than the FTL algorithm that performs batch-style learning using all data acquired to the current time. MA performs least well, though for large values of λ (i.e. .9 in this simulation), the performance can become reasonable.
We also note that updates may require regularization to be stable, so we provide all algorithms with equal magnitude ℓ2 regularization (the regularization coefficient per OGD update was equal to 1/K times the regularization coefficient of the other algorithms). After fewer than 10 reaches the OGD and FTL appear to plateau—this task is sufficiently simple that good performance is quickly obtained when SNR is adequate. We note that we have opted to show sum squared error (SSE) rather than MSE (in Fig 2 and elsewhere), because it reflects the aggregated single timestep error combined with differences in acquisition time—MSE normalizes for the different lengths of reach trajectories, thereby only providing a sense of single timestep error (compare to S1 Fig).
To get a sense of the magnitude of the performance improvements (i.e. the scale of the error in Fig 2), we can visualize poorly-performed reaches from early in training and compare these against well-performed reaches from a later decoder (Fig 3). While the early decoder performs essentially randomly, the learned decoder performs quite well, with trajectories that move rapidly towards the target location. See S1 Movie for an example movie of cursor movements during the learning process.
We emphasize that FTL essentially has no learning-related parameters (aside from the optional ℓ2-regularization coefficient). On the other hand, OGD and MA have additional learning parameters that must be set, which may require tuning in practical settings. The OGD experiments presented here are the result of having run the experiment for multiple learning rates and we reported only the results of a well-performing learning rate (since this requires tuning, it may be non-trivial to immediately achieve this rate of improvement in a practical setting where the learning rate is likely to be set more conservatively). Too large a learning rate leads to divergence during learning, and too small a learning rate leads to needlessly slow improvement.
In this section we introduce a new opportunity, moving beyond BCI settings where intention-based algorithmic capabilities have yet been explored. We validate the imitation learning framework through simulation results on a high dimensional task—BCI control of a simulated robotic/virtual-arm (Fig 4). Whereas existing algorithms cannot be generalized to more complicated tasks, our results allow for generalization to an arm effector. The simple ReFIT-style oracle of rotating instantaneous velocities towards the “goal” is ill-posed in general cases—the goal position could be non-unique and the different degrees of freedom (DOF) may interact nonlinearly in producing the end-effector position (both of these issues are present for an arm). Instead, we introduce an optimal control derived intention-oracle. As our proof of concept, we present a set of simulated demonstrations of reaches of an arm towards a target-wand. We envision this being incorporated into a BCI setting such as that described in [19], where a user controls a virtual arm in a virtual environment. Extension to a robotic arm is also conceptually straightforward, if a model of the robotic arm is available.
For these simulations, we implement the reach task using a model of a rhesus macaque arm in MuJoCo, a software that provides a physics engine and optimal control solver [41]. The monkey arm has 26 DOF, corresponding to all joint-angles at the shoulder, elbow, wrist, and fingers. The task objective we specified corresponds to the arm reaching towards a target “wand,” placed in a random location for each reach, and touching the wand with two fingers. Following from the task objective, at each timestep the optimal control solver receives the current position of the arm and the position of the goal (i.e. wand position), from which it computes incremental updates to the joint angles. These incremental updates to the joint angles correspond to oracle angular velocities and we wish to learn a decoder that can reproduce these updates via Alg 1. See methods for complete details of the simulations.
Given that this arm task is ostensibly more complicated than cursor control, it may be initially surprising that we see that task performance rapidly improves with a small number of reaches (Right panel Fig 4, and see S2 Movie for an example movie of arm reaches during the learning process). However, this relatively rapid improvement makes sense when we consider that the data is not collected independently, rather there is a closed-loop sequential process (see Alg 1). Consequently we expect that early improvement should occur by leveraging the most widely used DOF (i.e. shoulder, elbow, and to a lesser extent wrist). More gradually, the other degrees of freedom should improve (i.e. finger and less-relevant wrist DOF).
To empirically examine the rate at which we can learn about distinct DOF, we conduct an analysis to see how well we can characterize the mapping between intention (per DOF) and neural activity. At each stage of the learning process (k = 1…K), we use the aggregated dataset D ( 1 : k ) to estimate the encoding model by regression (see methods, Eq 5). The encoding model corresponds to the mapping from intention to neural activity and our ability to recover this (per DOF) reflects the amount of data we have about the various DOF. To quantify this, we compute correlation coeffcients (per DOF, across neurons) between the true encoding model parameters (known in simulation) and the encoding model parameters estimated from data aggregated up through a given reach. We expect this correlation to generally improve with increasing dataset size; however, regret bounds do not provide direct guarantees on this parameter convergence. The key empirical observation is that DOF more integral to task performance are learned rapidly, whereas certain finger DOF which are less critical are learned more gradually (Fig 5).
Similarly to the cursor tasks, we want to examine the magnitude of the performance improvements. For this case, it is difficult to statically visualize whole reaches. Instead, we look at an example shoulder DOF and depict the trajectory of that joint during a reach (Fig 6). Branching off of the actual trajectory, we show local, short-term oracle trajectories which depict the intended movement. Note that the oracle update takes into account other DOF and optimizes the end-effector cost, so it may change over time as other DOF evolve. We see that the early decoder does not yield trajectories consistent with the intention—the decoded pose does not move rapidly, nor does it always move in the direction indicated by the oracle. The late decoder is more responsive, moving more rapidly in a direction consistent with the the oracle. In the four examples using the late decoder, the arm successfully reaches the target, so the reach concludes before the maximum reach time.
An important potential class of model mismatch arises when there is a discrepancy between the “oracle” policy and the true intention of the user (in such cases the oracle is not a proper oracle and is better thought of as an attempt at approximating an oracle). We can consider this setting to suffer from “intention mismatch” (see [42] for a distinct, but related concept of discrepant “internal models”).
In our results thus far, we have assumed we have a true intention oracle. When such an oracle is available, we are in the ideal statistical setting, and our simulations provide a sense of quality of algorithmic variants in this setting. In order to characterize the robustness of this approach, we consider the realism of this assumption and the consequences when it is violated. This point concerning mismatch is not restricted to a specific oracle. Rather, it arises when comparing the degree of discrepancy between actual user intention and any particular oracle. There are a few classes of deviations we might expect between a true user’s intention and the intention oracle.
A simple class of intention mismatch corresponds to random noise applied to the user intention. This would be a simple model of single timebin variability arising from sensory feedback noise, inherent variability in biological control, or inconsistent task engagement. For such a case, we perform simulations identical to those performed previously, but we model the actual user intention (that drives the simulated neural activity) as a combination of a random intention and the oracle intention. The magnitude of the intention noise here corresponds to the magnitude of the random intention relative to the oracle (i.e. 100% noise indicates that actual user intention is a linear combination of the oracle intention and a randomly directed vector of equal magnitude norm). We emphasize that here the oracle is not correct and there is additional noise in the system that is from the random intention. We can verify empirically that performance decreases with noise level at a reasonable rate for this intention noise variant of model mismatch (see Fig 7). While naturally performance (i.e. loss between noise-free oracle and decoded intention) decreases when there is additional noise, we see gradual rather than catastrophic decline in performance.
Although intention noise mismatch is realistic under certain assumptions, we may have concerns regarding more systematically structured model mismatch. We next consider a class of intention mismatch where the user intention is consistently biased by a fixed linear operator with respect to the oracle (i.e. user intention arises from a gain and/or rotation applied to the oracle). If this linear mismatch is always present, then—crucially—the performance of the resulting decoder will be equivalent under our loss, which compares the decoder output against the oracle. This is because the algorithm would learn a decoder that undoes this consistent linear transform between the user intent and the oracle, resulting in good task performance. Note that after training, there would remain a persistent discrepancy between the decoder output and the actual user intention. Also note that changes in gain should only affect decoding performance if such changes modulated the SNR of the neural activity.
While linear intention mismatch does not affect the ability to imitate the oracle, it is not entirely realistic. For example, if the oracle and the user intention differ by a rotation that is consistent over time, either the oracle or the user intention would not efficiently complete the task (e.g. the intended cursor trajectory won’t be directed towards the target). Therefore, efficient completion of the task serves to constrain plausible intention trajectories. This motivates us to characterize a remaining class of nonlinear intention mismatch—wherein user intention and the oracle both solve the task but do so in ways that are discrepant. While there may be many satisfactory trajectories from the beginning of the task, as the effector nears goal acquisition, the discrepancy amongst efficient oracle solutions reduces. This means that the while the oracle is systematically and reliably wrong, the discrepancy differs in a way that depends upon the current pose and objective.
For the cursor task, we designed a conceptually illustrative second oracle that solves the task and is not simply a linear transform of the first oracle (i.e. not gain mismatch). We consider trajectories that arc towards the goal—this oracle can be generated by having a distance dependent linear transform, where a sigmoid function of distance determines whether the actual user intention is offset by zero up to some maximal ϕ from the standard straight-line oracle (see Fig 8). At far distances, this model of user intention and the straight-line oracle differ by a moderate rotation, and as the cursor nears the goal, the discrepancy decreases. It would be impossible for a simple decoder to compensate for this kind of mismatch because the decoder will not generally have access to distance between cursor and the goal. Instead, we expect the decoder will partly compensate for this arc-shape intention by learning to “undo” a rotation relative to the straight-line oracle. Since the correct rotation-compensation varies, the decoder will (at most distances) be incorrectly undercompensating or overcompensating (see Fig 8).
We show empirical performance curves for the cursor arc-trajectory user intention and see that for increasing levels of arc-angle, learned performance only gradually declines. Note that these simulations were for a 3D cursor, so rotation corresponds to a rotation in all 3 planes of the same magnitude. For minor discrepancy, the resulting performance is very robust. At the largest level (45° angle), performance is noticeably worse but still suffices to perform the task. The center panel of Fig 8 depicts many example single trial reach trajectories (projected into 2D and rotated to align with the cartoon in the middle panel).
While it is not feasible for us to test all forms of model mismatch here, the simulation framework we presented allows for empirical investigation of any specific class or mismatch details of interest that may arise. The representative classes of mismatch explored in this section illustrate the reasonable robustness of this framework.
In this work, we have unified closed-loop decoder training approaches by providing a meta-algorithm for BCI training, rooted in imitation learning. Specifically, we have focused on the parameter learning problem, complementing other research that focuses on the problem of selecting a good decoder family [12]. Our approach allows the parameter learning problem to be established on a firmer footing within online learning, for which theoretical guarantees can be made. This is crucial since ReFIT-based approaches are being translated to human clinical applications where performance is of paramount concern [16, 43]. Moreover, we have demonstrated that this approach now permits straightforward extension to higher dimensional settings, enabling rapid learning even in the higher dimensional case. In scaling existing algorithms to an arm-control task, we have provided generic approaches to solve two issues. First, imitation learning (using data aggregation) serves as the generic framework for updating parameters. Second, we have employed a generic, optimal control approach, which can be used to compute intention-oracle kinematics in a broad range of BCI settings.
For simulations in this work, we employ linear encoding of kinematic variables because, in addition to having a history in the BCI literature [10], this corresponds to an operationally useful encoding model employed in recent, well-performing applications in the closed-loop BCI [11, 16]. We do not intend to claim that simple, linear encoding models as assumed when employing Kalman filter decoders correspond to the reality of innate neural computation in motor cortex. Nonlinear filtering approaches that make more realistic assumptions about neural encoding have been explored offline [44–46]. However, it is not clear that offline results employing more realistic encoding models always translate performance gains to closed-loop settings [47]. Nevertheless, there have been successes using more complicated decoding algorithms in closed-loop experiments [48–50]. Following on recent scientific work that has sought to understand a role of intrinsic dynamics in motor cortices [9], dynamics-aware decoders are also being developed [51–53]. While many decoder forms may be considered, in line with the variety of theories about the motor cortex, the precise choice is orthogonal to the work here. Intention-based parameter fitting does not depend, in any general way, on the encoding model assumed by the decoding algorithm. Consequently, a key benefit of the theoretical statements we present are that the algorithm performance guarantees hold for general classes of decoders, and the meta-algorithm we describe is largely agnostic to the details of the encoding.
It is a key point that Alg 1 results in preferential acquisition of data that enables learning of the most task-relevant DOF. This follows from the fact that the sampling of states in closed-loop is non-uniform, since the current decoder induces the distribution of states visited during the next reach. Exploration is not explicitly optimized, but more time is spent in relevant sets of states as a consequence of preferential sampling of certain parts of what can be a high dimensional movement space. This clarifies the potential utility of assisted decoding, which may serve to facilitate initial data collection in positions in the movement space that are especially task-relevant. This non-uniform exploration of the movement space provides intuition for the generality of the theoretical guarantees for DAgger-like learning. The decoder used in this work is of a relatively simple form (steady-state velocity Kalman filter, described in methods), but the theoretical results hold for general stationary, deterministic decoders.
While we have focused on a simple, parametric decoder, the parameter learning approach presented in this paper extends to more complicated decoders. For example, we may wish to allow the neural activity to be decoded differently depending on the current state of the effector. In conventional imitation learning, policies are trained to yield sequences of actions (without user input), so this general problem is extremely state-dependent. By building into the decoder an expressive mapping that captures state-transition probabilities, we could design a policy-decoder hybrid to exploit regularities in the dynamics of intended movements and heavily regularize trajectories based on their plausibility. Additionally, we could augment the state with extra information (e.g. extra data from sensors on the physical effector could be added to the current kinematics and neural activity) such that decoding relies on autonomous graceful execution of trajectories in addition to neural activity (see [54]). Similarly, this framework accommodates decoders which operate in more abstract spaces (such as if the available neural activity sent action-intention commands rather than low-level velocity signals).
A particularly interesting opportunity that corresponds to an augmentation of follow-the-leader (FTL, Eq 3) would be to enrich the decoder family as the dataset grows. We can imagine a system with decoders of increasing complexity (more parameters or decreasing regularization) as the aggregated dataset of increasing size becomes available. While we focused on a simple decoder (i.e. the Kalman filter) which makes sense for small-to-moderate datasets, some work suggests that complicated decoders trained on huge datasets can perform well (e.g. using neural networks [50]). We anticipate that data aggregation would allow us to start with a simple decoder, and we could increase the expressive power of the decoder parameterization as more data streams in.
Our formalization of BCI learning most closely resembles the DAgger setting, but novel extensions to the BCI learning setting follow from related imitation learning formulations. Some particularly relevant opportunities are surveyed here. When starting from an initial condition of an unknown decoder-policy, it may be hard to directly train towards an optimal decoder-policy. Training incrementally towards the optimal policy via intermediate policies has been proposed [55]. Under such a strategy, a “coach” replaces the oracle, and the coach provides demonstration actions which are not much worse than the oracle but are easier to achieve. For example, in BCI, it may be hard to learn to control all DOF simultaneously, so a coach could provide intention-trajectories that use fewer DOF. It has also been observed that DAgger explores using partially optimized policies, and these might cause harm to the agent/system. Especially early in training, the policies may produce trajectories which take the agent through states which may be dangerous to the agent or the environment. An appropriate modification to solve this is to execute the oracle/expert action at timesteps when a second-system suspects there may be an issue carrying out the policy action, thereby promoting safer exploration [56].
As touched upon in the results, we also want to be aware of the performance impact of model mismatch and mitigate this problem. While we expect performance will erode with increasing intention mismatch, our results indicated robustness to small levels of mismatch (see Figs 7 & 8). In settings where, even after carefully designing the intention oracle there is persistent mismatch, a combined imitation learning and reinforcement learning approach may produce better results [57]. This amounts to a hybrid optimization that combines the error-ridden expert signals with RL signals obtained by successful goal acquisitions.
Finally, in this work we have assumed there is not gradual “drift” in the neural encoding model—it is probably a fair assumption that neural encoding drift is not a dominant issue during rapid training [58, 59]. We highlight a distinction between general closed-loop adaptation (where the decoder should adapt as fast as possible), versus settings designed for the user to productively learn, termed co-adaptive (for a review of co-adaptation, see [60]). We have focused on the setting with user learning in other work [61, 62], but we here focused on optimizing parameter learning under the assumption that the user’s neural tuning is fixed, allowing us to rigorously compare algorithms. In future work, it may prove fruitful to attempt to unify this analysis with co-adaptation. We also anticipate future developments that couple the sort formalization of decoder learning explored in this work with more expressive decoders. We are optimistic that progress in these directions will enable robust, high-dimensional brain-computer interface technology.
In this work we present two sets of simulations. The first set of simulations consist of simulated closed-loop experiments of 3D cursor control. In these simulations, the cursor serves as the effector, and this cursor is maneuverable in all three dimensions. Goals are placed at random locations and the task objective is to minimize the squared error loss between the cursor and the current goal. Goals are acquired when the cursor is moved to within a small radius of the target. The oracle for this task is determined from optimal control. When there is a quadratic penalty on instantaneous movement velocity, the optimal trajectory from the cursor towards the goal will be equal-length vectors directed towards the target. So at each timestep, we take the oracle to correspond to a goal-directed vector from the current cursor position.
The second set of simulations are similar, but involve controlling an arm to reach towards a “wand”. As the effector, we use an arm model with dimensions corresponding to those of a rhesus macaque monkey used for BCI research (from Pesaran Lab, Center for Neural Science, New York University, http://www.pesaranlab.org, as in [19]). For simplicity we treat each joint as a degree of freedom (DOF) yielding 26 joint angles and 26 corresponding angular velocities. We specify the task objective to be a spring-like penalty between the wrist position (3D spatial coordinates) and the wand position. Specifically, in addition to the 26 joint-angle DOF, there are also identifiers corresponding to the x-y-z coordinates of the wrist and select fingertips, as well as points on the wand. Objective functions in terms of the x-y-z coordinates of these markers can be specified, and the MuJoCo solver computes trajectories (in terms of the specified joint angles) in order to optimize the objective. We defined the initial objective in terms of the Euclidean distance between the wrist and the wand. Once the wrist is within a radius δ of the wand, a new sping-like penalty is placed on the distance between tip of the middle finger and a point on the wand and also between the tip of the thumb and a point on the wand—this causes the fingertips to touch two points of the wand (a simple “grasp”). This explicit task objective allows us to compute the oracle solution for the reach trajectory, and this oracle is computed via an iterative optimal control solver on all joint angles in the model. The model, simulation, and optimal control solver are implemented in an early release of the software simulation package MuJoCo [41]. At each timestep, given the wand position and current arm position, the optimal control solver produces an incremental update to all (26) of the joint angles of the arm, and this goal-directed angular velocity vector is taken as the oracle. As an alternative to explicitly posing the objective function and computing the oracle, one can imagine using increasingly naturalistic optimal-control-based oracles that use more elaborate motor models trained on real data [63].
We produce synthetic neural activity similarly in both sets of simulations. In the cursor task, we want the neurons to be tuned to intended cursor velocity. In the arm task, neurons should encode velocities of the joint angles. To produce simulated neural data that reflects the “user’s” intention, we have a convenient choice—the oracle itself. The simulation cycle entails: (1) computing the intention-oracle (given the current state, goal, and task objective), (2) simulating the linear-Gaussian neural activity from the intention-oracle (Eq 5), (3) using the current decoder to update the effector, and (4) updating the decoder between reaches. We note that the oracle is used twice, first to produce the neural activity and subsequently in the imitation learning decoder updates.
Specifically, we simulate neural activity via the neural encoding matrix A that maps intended velocity to neural activity:
n t = A o t + c t (with c t ∼ N ( 0 , C ) ) . (5)
where the noise covariance C was taken to be a scaled identity matrix, such that the signal-to-noise ratio (SNR) was ≈1 per neuron (i.e. noise magnitude set to be roughly equal in magnitude to signal magnitude per neuron, which we considered reasonable for single unit recordings). In real settings this neural activity might be driven by some intended movement x t * (where here the star denotes intention as in [12]).
These simulations assume the intention-oracle is “correct”. As such, a feature of all ReFIT-inspired algorithms is that there is model mismatch if the user is not engaged in the task or has a meaningfully different intention than these algorithms presume. This problem affects any algorithm that trains in closed-loop and makes assumptions about the user’s intention (see discussion for extensions to handle the case when the oracle is known to be imperfect). For the model mismatch section of the results, we perform simulations with “intention mismatch” by perturbing the oracle signal that drives the neural activity (i.e. by operating on ot before applying the neural encoding matrix A).
For the simulations, A was selected to consist of independently drawn random values. For both tasks, we randomly sampled a new matrix A for each repeat of the simulated learning process. For the cursor simulations, we simply sampled entries of A independently from a normal distribution. For the higher dimensional arm simulations, we wanted to have neurons which did not encode all DOFs, so for the results presented here we similarly sampled A from a normal distribution, but then set any negative entries of A to zero (results were essentially the same if negative entries were included).
Assisted decoding (see Alg 1) was not heavily used. To provide stable initialization, β0 was set to 1 (and noise was injected into the oracle for numerical stability), and all subsequent βk were set to 0. For the cursor simulations, we used 10 neurons and the maximum reach time T was set to 200 timesteps. For arm simulations, we used 75 neurons and the maximum reach time T was set to 150 timesteps. We consider simulated timesteps to correspond to real timesteps of order 10–50ms.
For both sets of simulated experiments the decoding algorithm was chosen to be the steady-state velocity Kalman Filter (SSVKF), which is a simple decoder and representative of decoders used in similar settings (i.e. it corresponds to a 2nd order physical system according to the interpretation in [12]). The SSVKF has a fixed parametrization as a decoder, but it also has a Bayesian interpretation. When the encoding model of the neural activity is linear-Gaussian with respect to intended velocity, the velocity Kalman filter is Bayes-optimal, and the steady state form is a close approximation for BCI applications.
The steady state Kalman Filter (SSKF) generally has the form:
x t + 1 = F n t + G x t (6)
Here G can be interpreted as a prior dynamics model and F can be interpreted as the function mediating the update to the state from the current neural data. In practice, a bias term can be included in the neural activity to compensate for non-zero offset in the neural signals. The generic SSKF equation can be expanded into a specific SSVKF equation, where the state consists of both position and velocity. At the same time we will constrain the position to be physically governed by the velocity, and we will only permit neural activity to relate to velocity.
p t + 1 v t + 1 = 0 0 F v b v n t 1 + I d t × I 0 G v p t v t (7)
It is straightforward to augment the decoder to include past lags of neural activity or state. A very straightforward training scheme that is apparent for this specific decoder is to simply perform regression to fit {Fv, bv, Gv}, from the function:
v t + 1 = F v n t + b v + G v v t + e t (8)
where et denotes an additive Gaussian noise term.
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10.1371/journal.pntd.0000707 | Decreased Prevalence of Lymphatic Filariasis among Diabetic Subjects Associated with a Diminished Pro-Inflammatory Cytokine Response (CURES 83) | Epidemiological studies have shown an inverse correlation between the incidence of lymphatic filariasis (LF) and the incidence of allergies and autoimmunity. However, the interrelationship between LF and type-2 diabetes is not known and hence, a cross sectional study to assess the baseline prevalence and the correlates of sero-positivity of LF among diabetic subjects was carried out (n = 1416) as part of the CURES study. There was a significant decrease in the prevalence of LF among diabetic subjects (both newly diagnosed [5.7%] and those under treatment [4.3%]) compared to pre-diabetic subjects [9.1%] (p = 0.0095) and non-diabetic subjects [10.4%] (p = 0.0463). A significant decrease in filarial antigen load (p = 0.04) was also seen among diabetic subjects. Serum cytokine levels of the pro-inflammatory cytokines—IL-6 and GM-CSF—were significantly lower in diabetic subjects who were LF positive, compared to those who were LF negative. There were, however, no significant differences in the levels of anti-inflammatory cytokines—IL-10, IL-13 and TGF-β—between the two groups. Although a direct causal link has yet to be shown, there appears to be a striking inverse relationship between the prevalence of LF and diabetes, which is reflected by a diminished pro-inflammatory cytokine response in Asian Indians with diabetes and concomitant LF.
| Childhood helminth infections can reduce the risk and severity of allergies and autoimmune diseases, by means of immunomodulation, and a decrease in helminth infections could potentially account for the increased prevalence of these diseases in the western world (hygiene hypothesis). We hypothesized that the same immunomodulatory effect can have an impact on metabolic diseases like obesity, diabetes, hypertension and atherosclerosis, wherein inflammation plays a crucial role (extended hygiene hypothesis). To test this hypothesis, we examined the prevalence of lymphatic filariasis (LF) among diabetic, pre-diabetic and non-diabetic subjects who were part of the CURES (Chennai Urban Rural Epidemiology Study) study. In accordance with our hypothesis, we found reduced prevalence of LF among diabetic subjects compared to non-diabetic and pre-diabetic subjects. This was associated with decreased filarial antigen load and anti-filarial antibody levels. The association remained significant even after adjusting for socioeconomic status, age and gender. Interestingly, within the diabetic subjects, those who were filarial positive had reduced levels of pro-inflammatory markers (TNF-α, IL-6 and GM-CSF) compared to those who were filarial negative. In light of these findings, the decreasing incidence of filarial infection due to mass drug administration could potentially have an unexpected adverse impact on the prevalence of diabetes in India.
| Global epidemiological studies have shown a marked increase in the incidence of diabetes worldwide. India leads the world in absolute numbers of diabetic subjects [1]. Type-2 diabetes mellitus constitutes about ∼90% of the entire diabetic population. The association between diabetes mellitus and increased susceptibility to infections is well known. Many diseases such as tuberculosis and candidiasis are more common in diabetic patients, while some such as invasive otitis externa and rhinocerebral mucomycosis occur almost exclusively in people with diabetes [2]. In addition, infections with group B streptococcus and Klebsiella spp. occur with increased severity in patients with diabetes and may be associated with an increased risk of complications [2].
Infection with systemic helminths, in addition to causing morbidity by themselves, may contribute to increased morbidity due to diabetes. But, there is very little data available on the prevalence of lymphatic filariasis (LF) among people with diabetes, although studies have examined the coexistence of LF with HIV [3], malaria [4] and tuberculosis [5]. Current estimates suggest that 129 million persons worldwide are infected with one of the three lymph-dwelling filariae (Wuchereria bancrofti, Brugia malayi or B. timori), the major causative agents of LF. The disease burden from LF is concentrated in tropical and sub-tropical countries (such as India) where the prevalence of type-2 diabetes is greatest [6]. This is particularly true in South India where the prevalence of LF caused by W. bancrofti is between 6–20% based on circulating filarial antigenemia [5]. Thus, in the present study, the influence of LF on diabetes was examined as part of an ongoing, prospective epidemiological study in Chennai, Southern India.
Institutional ethical committee approval from the Madras Diabetes Research Foundation Ethics Committee was obtained (Ref No-MDRF-EC/SOC/2009//05) and written informed consent was obtained from all the study subjects. Study subjects were recruited from the Chennai Urban Rural Epidemiology Study (CURES), an ongoing epidemiological study conducted on a representative population of Chennai (formerly Madras), the fourth largest city in India. The methodology of the study and the prevalence of diabetes in Chennai have been published elsewhere [7], [8]. Briefly, in Phase 1 of the urban component of CURES, 26,001 individuals were recruited based on a systematic sampling technique with random start. Fasting capillary blood glucose was determined using the OneTouch Basic glucometer (Lifescan, Johnson & Johnson, Milpitas, CA) in all subjects. Details of the sampling are described on our website (http://www.drmohansdiabetes.com/bio/WORLD/pages/pages/chennai.html). In Phase 2, detailed studies of diabetic complications, including nephropathy and retinopathy, were performed, and in Phase 3, every 10th individual in Phase 1 was invited to participate in more detailed studies. As part of the questionnaire, the socio-economic details of the study participants was collected and recorded. For the present study, the following groups were randomly selected from Phase 3 of CURES, Group 1- 943 normal glucose tolerance subjects (NGT); Group 2- 154 subjects with impaired glucose tolerance (IGT); Group 3- 158 newly diagnosed type-2 diabetes subjects (ND-DM) and Group 4- 161 known type-2 diabetes subjects under treatment (KDM). A larger NGT group was included based on sample size calculations and to obtain baseline values for the normal population. The filarial status of these individuals was not known at the time of recruitment into the study.
Anthropometric measurements, including height, weight, and waist circumference, were obtained using standardized techniques. The body mass index (BMI) was calculated as the weight in kilograms divided by the square of height in meters. Fasting plasma glucose (FPG) (glucose oxidase-peroxidase method), serum cholesterol (cholesterol oxidase-peroxidase- amidopyrine method), serum triglycerides (glycerol phosphate oxidase-peroxidase-amidopyrine method), high density lipoprotein cholesterol (HDL-C) (direct method-polyethylene glycol-pretreated enzymes), and creatinine (Jaffe's method) were measured using a Hitachi-912 Autoanalyser (Hitachi, Mannheim, Germany). The intra- and inter assay coefficient of variation for the biochemical assays ranged between 3.1% and 5.6%. Glycated hemoglobin (HbA1c) was estimated by high pressure liquid chromatography using a variant machine (Bio-Rad, Hercules, CA). The intra- and inter-assay coefficient of variation of HbA1c was less than 5%.
To quantify the filarial antigen levels and prevalence, sera were analyzed using the W. bancrofti Og4C3 antigen-capture enzyme-linked immunosorbent assay (Tropbio, James Cook University, Townsville, Queensland, Australia) according to the manufacturer's instructions.
The serum antibody (IgG and IgG4) titer against Brugia malayi antigen (BmA) was determined by ELISA as described previously [9].
The levels of cytokines (TNF-α, IL-6, IL-1β, GM-CSF, IFN-γ, IL-13 and IL-10) in the undiluted serum were measured using a Bioplex multiplex cytokine assay system (Biorad, Hercules, CA). The lowest detection limit for the various cytokines were IL-1β -2.7pg/ml, IL-2-1.16 pg/ml, IL-4-0.3 pg/ml, IL-5- 2.08 pg/ml, IL-6- 2.31 pg/ml, IL-10- 2.2 pg/ml, IL-12- 2.78 pg/ml, IL-13- 2.22 pg/ml, IL-17 2.57, GM-CSF- 0.67 pg/ml, IFN-γ- 2.14 pg/ml and TNF-a- 4.89 pg/ml. TGF-β was estimated by conventional ELISA following manufacturer's protocol (R&D, Minneapolis, MN). The lowest detection limit for TGF-β was 7.8 pg/ml.
All statistical analyses were performed using SPSS software (Version 15.0.0, Chicago).The prevalence of filarial infections among the different groups was analyzed by Pearson's Chi-Square test. The antigenic load and antibody titers were analyzed by Mann Whitney U test. The clinical and biochemical characteristics of the study subjects were compared using one-way ANOVA analysis. To account for multiple comparisons, the cytokine levels in controls (DM−LF−), DM+LF+ and DM+LF− groups were compared by multinomial logistic regression analysis. P values<than 0.05 were considered significant.
The baseline characteristics including demographics, clinical and biochemical features of the study population are shown in Table 1. As can be seen in the table, compared to subjects with normal glucose tolerance (NGT), those with glucose intolerance (i.e. IGT, ND-DM, KDM) had higher BMIs, systolic and diastolic blood pressure, serum cholesterol, LDL and triglycerides levels but lower HDL cholesterol levels. Since, socio-economic differences could be a confounding factor for the prevalence of both diabetes [10] and LF [11], the average monthly income of study subjects was recorded (Table 2). We also examined the occupation profile of the study subjects (Table S1). As can be seen, there were no significant differences in the socio-economic status between the various groups.
The prevalence of LF among the various groups was determined by quantitative TropBio ELISA (>128 U/ml) and differences in the prevalence of LF among the groups were observed. The prevalence of LF was found to be 10.4% in the NGT, 9.1% in IGT, 5.7% in ND-DM and 4.3% in KDM respectively. The differences in the prevalence rate between NGT and KDM (p = 0.0463) and NGT and ND-DM (p = 0.0095) were significant (Fig 1a).
To examine more quantitatively the association of type-2 diabetes and LF, we next quantified the serum circulating filarial antigen (CFA) levels among the filarial positive subjects. Not only was there a difference in prevalence rates between those with NGT and those with glucose intolerance but there was also a clear difference in the absolute levels of CFA in the LF-infected individuals, with CFA levels being lower among the diabetic groups compared to the NGT group (Fig 1b). The geometric mean (range) of CFA levels in the four groups were: NGT-1,594 (127–32,768), IGT-1,520 (209–16,345), ND-DM-929 (129–32,768) and KDM-351 (163–1,126) with the differences in the antigen levels between the KDM and the NGT (p = 0.04) and the KDM and the IGT (p = 0.04) being statistically significant.
We next quantified the serum anti-filarial antibody levels among those with LF in the four groups (Fig 2). Even though the mean IgG4 levels were not different among the four groups, the mean IgG levels were significantly lower in the KDM compared to NGT (p<0.0023),
We next quantified pro- and anti-inflammatory cytokine levels among control (DM−LF−), diabetic only (DM+LF−) or both LF and diabetic (DM+LF+) subjects (Fig 3). In comparison to controls, the diabetes only (DM+LF−) group had high levels of IL-6 and GM-CSF, which was significant (p<0.01) (Fig 3b and c). There was no significant difference in the levels of TNF- α (Fig 3a). In DM+LF+ subjects, there was a significant reduction in the levels of IL-6 and GM-CSF compared to the diabetic only group (DM+LF−) (geometric mean (GM) of 13.57 pg/ml versus 45.13 pg/ml for IL-6, p<0.05; and GM of 0.81 pg/ml versus 2.24 pg/ml for GM-CSF, p<0.05). TNF- α levels were not significantly different between the two groups. IL-1β levels were not statistically different among the three groups (data not shown). IFN-γ levels were significantly elevated in diabetic only group compared to controls but was unaltered by the LF status (Fig 3d).
When the levels of anti-inflammatory cytokines were measured, both the diabetic groups (DM−LF+ and DM+LF+) had comparable levels of IL-13 and TGF-β (Fig 3e and f), but TGF-β was significantly higher in both the diabetic groups compared to controls (p<0.01). No significant difference was seen in IL-10 levels among the three groups (Fig 3g).
It is well known that individuals with diabetes are at increased risk of susceptibility to several infectious diseases including tuberculosis [12], urinary tract infection [13] and mucormycosis [14]. It is generally assumed that diabetes increases the susceptibility to all infections [2]. But in this study, we clearly demonstrate that the prevalence of LF is lower in subjects with type-2 diabetes. A serial decline in the prevalence of LF was seen as individuals progressed from NGT to IGT to ND-DM to KDM with the significance being maintained even after adjusting for age and gender. The decrease in prevalence was associated with decreased antigen load and anti-filarial IgG antibody titer but the anti-filarial IgG4 titer was unaffected. The reduced IgG levels in diabetic subjects was expected since previous reports have shown reduced levels of IgG and increased levels of IgA among diabetic subjects [15], [16]. In terms of humoral responses, both subjects with active filarial infection and those who are exposed but resistant to infection mount vigorous antibody responses to parasite antigen, most specifically, IgG4 [9], [17]. Thus, BmA- specific IgG4 can be considered to be a good surrogate marker for exposure. The fact that IgG4 levels were not significantly different between the four groups, is further evidence to show that, differences in exposure to infection is less likely a reason for the differences in prevalence between the diabetic and non-diabetic groups. It is very unlikely that the decreased prevalence of LF among diabetic subjects was due to LF-mediated mortality, as LF is a chronic, non-lethal disease. Since, differences in socio-economic status could be a confounding factor; the average monthly income and occupation of the study subjects were analyzed. No significant difference was seen between the study groups, indicating that, the socio-economic status is unlikely to be a potential confounding factor for the differences seen in the prevalence of LF. Another confounding factor that could be of significance is the nutritional differences [18] that could arise due to calorie restriction among diabetic subjects. But, there was no difference with respect to average protein intake (approximately 12%) among different groups, again suggesting that nutritional differences or dietary intake are unlikely to be potential confounding variables in the study. More likely is the interplay between two chronic conditions in which the longstanding regulatory environment seen in LF may play a role in conferring resistance to type-2 diabetes. There are some reports that have documented a similar inverse relationship between diabetes and hepatitis C infection [19], but other studies failed to reproduce the association [20]. More studies are thus needed on the coincidence of diabetes and other infectious diseases to have a better understanding about the interplay of infection/inflammation and diabetes.
In mice, there is evidence to show that filarial infection can prevent type-1 diabetes (“hygiene hypothesis”) [21], but whether the same immunomodulatory effect can dampen inflammation and protect against type-2 diabetes is currently not known. To better understand the mechanism associated with the decreased prevalence of LF among diabetic subjects, we studied the serum cytokine levels. The main focus was to determine the effect of LF on diabetes in terms of the serum cytokine profile. Although filarial parasites elicit a broad spectrum of inflammatory and regulatory responses mediated by cytokines, whether this type of immunomodulation occurs in co-incident diabetes has not been well-studied. The serum cytokine profile of LF only patients has already been reported by Satapathy et al. [22] and us, previously [23]. Diabetes in conjunction with LF had decreased levels of TNF-α and IL-6 (cytokines that have already been associated with insulin resistance (IR) [24]), compared to those with DM alone. Diabetic subjects (without LF) had a typical pro-inflammatory phenotype with high levels of TNF-α, IL-6 and GM-CSF. IL-1β, however, was not elevated in these subjects, although it has been previously been shown to act synergistically with TNF-α and IL-6 in inducing IR [24]. The contribution of GM-CSF (seen to be elevated in diabetic subjects) in mediating IR is currently not known. Interestingly, in those with both diabetes and LF, the pro-inflammatory cytokines - TNF-α, IL-6 and GM-CSF were reduced, compared to those without LF, suggesting that LF-mediated reduction of pro-inflammatory cytokines negatively influences the development of IR.
Although pro-inflammatory cytokine levels are typically elevated in diabetic subjects, the data on the balance of effector T cell phenotypes in this condition has been confusing with some studies reporting Th1 polarization [25], others reporting Th2 skewing [26], and still others reporting a balanced response [27]. In the present study, diabetic subjects (who were LF negative) had very high levels of IFN-γ and IL-13 suggestive of a mixed (relatively non-polarized) phenotype. Whether this immune phenotype is the cause or effect of IR remains to be established.
Although the anti-inflammatory cytokines - IL-10 and TGF-β - have largely been associated with immune-regulation in LF (with IL-10 playing the major role), the down regulation of pro-inflammatory cytokines in type-2 diabetic subjects with LF seems to be due to TGF-β and not IL-10. However, as TGF-β is often elevated in diabetic subjects, other immunomodulatory molecules such as CTLA-4, PD-1, IDO might act in concert with TGF-β in the modulation of the immune responses in these groups [28].
Our study suggests that one mechanism by which LF can potentially protect against type-2 diabetes, is by modulating the pro-inflammatory environment seen in diabetes. Helminth infections, such as LF could modulate diabetes by inducing a chronic, non-specific, low-grade, immune regulation mediated by Th2/Tregs (modified Th2 response) which in turn can suppress the pro-inflammatory responses [29]. One can speculate that, childhood filarial infection reduces TNF-α, IL-6 and GM-CSF levels thereby conferring protection against type-2 diabetes. An alternate hypothesis could be that the inflammation associated with diabetes may also promote anti-filarial immunity by enhancing anti-filarial antibody responses that could mediate parasite clearance [30].
Although this study suffers from the limitation of being a cross-sectional study and therefore not providing a direct causative mechanism for the decreased prevalence of LF among diabetic subjects, the study highlights the importance of the need to understand the complex interactions between an infectious disease (LF) and a metabolic disorder (Type-2 diabetes). In addition, the immune as well as non-immune mechanisms by which the interplay between LF and DM occurs needs to be explored in detail. Finally, the decreasing incidence of LF (due to mass eradication programs) could have an impact on the incidence of type-2 diabetes in the future and obviously this is an important area for future study.
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10.1371/journal.ppat.1003984 | ChIP-Seq and RNA-Seq Reveal an AmrZ-Mediated Mechanism for Cyclic di-GMP Synthesis and Biofilm Development by Pseudomonas aeruginosa | The transcription factor AmrZ regulates genes important for P. aeruginosa virulence, including type IV pili, extracellular polysaccharides, and the flagellum; however, the global effect of AmrZ on gene expression remains unknown, and therefore, AmrZ may directly regulate many additional genes that are crucial for infection. Compared to the wild type strain, a ΔamrZ mutant exhibits a rugose colony phenotype, which is commonly observed in variants that accumulate the intracellular second messenger cyclic diguanylate (c-di-GMP). Cyclic di-GMP is produced by diguanylate cyclases (DGC) and degraded by phosphodiesterases (PDE). We hypothesized that AmrZ limits the intracellular accumulation of c-di-GMP through transcriptional repression of gene(s) encoding a DGC. In support of this, we observed elevated c-di-GMP in the ΔamrZ mutant compared to the wild type strain. Consistent with other strains that accumulate c-di-GMP, when grown as a biofilm, the ΔamrZ mutant formed larger microcolonies than the wild-type strain. This enhanced biofilm formation was abrogated by expression of a PDE. To identify potential target DGCs, a ChIP-Seq was performed and identified regions of the genome that are bound by AmrZ. RNA-Seq experiments revealed the entire AmrZ regulon, and characterized AmrZ as an activator or repressor at each binding site. We identified an AmrZ-repressed DGC-encoding gene (PA4843) from this cohort, which we named AmrZ dependent cyclase A (adcA). PAO1 overexpressing adcA accumulates 29-fold more c-di-GMP than the wild type strain, confirming the cyclase activity of AdcA. In biofilm reactors, a ΔamrZ ΔadcA double mutant formed smaller microcolonies than the single ΔamrZ mutant, indicating adcA is responsible for the hyper biofilm phenotype of the ΔamrZ mutant. This study combined the techniques of ChIP-Seq and RNA-Seq to define the comprehensive regulon of a bifunctional transcriptional regulator. Moreover, we identified a c-di-GMP mediated mechanism for AmrZ regulation of biofilm formation and chronicity.
| Pathogenic bacteria such as Pseudomonas aeruginosa utilize a wide variety of systems to sense and respond to the changing conditions during an infection. When a stress is sensed, signals are transmitted to impact expression of many genes that allow the bacterium to adapt to the changing conditions. AmrZ is a protein that regulates production of several virulence-associated gene products, though we predicted that its role in virulence was more expansive than previously described. Transcription factors such as AmrZ often affect the expression of a gene by binding and promoting or inhibiting expression of the target gene. Two global techniques were utilized to determine where AmrZ binds in the genome, and what effect AmrZ has once bound. This approach revealed that AmrZ represses the production of a signaling molecule called cyclic diguanylate, which is known to induce the formation of difficult to treat communities of bacteria called biofilms. This study also identified many novel targets of AmrZ to promote future studies of this regulator. Collectively, these data can be utilized to develop treatments to inhibit biofilm formation during devastating chronic infections.
| Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen that is a major burden on the health care industry. Up to 10% of all nosocomial infections are attributed to P. aeruginosa, with mortality rates approaching 40% in patients with bacteremia [1], [2]. This bacterium is often a causative agent of sepsis, as well as acute and chronic infections of the airway, burn wounds, skin, and medical devices such as catheters [1], [3].
Additionally, P. aeruginosa forms biofilms that contribute significantly to disease [4]. The formation of a biofilm by P. aeruginosa confers resistance to antibiotic treatment and immune cells [5]–[7]. The classical definition of a biofilm involves a community of bacteria adhered to a surface encased in a self-produced matrix [3], [8]–[11]. P. aeruginosa forms these biofilms in the environment, on implanted devices such as catheters, and in wound infections [12]. In addition, P. aeruginosa forms biofilms suspended in the dehydrated pulmonary mucus plugs of cystic fibrosis patients [13], [14]. Biofilms are often recalcitrant to antibiotics, have anti-phagocytic properties, and are difficult to treat, commonly accounting for the persistence of chronic infections [7], [15]–[17].
Our laboratory has identified the ribbon-helix-helix transcription factor AmrZ (alginate and motility regulator Z) as a modulator of P. aeruginosa biofilm development and virulence [18], [19]. Five AmrZ-regulated virulence factors have been identified through targeted molecular approaches; however, the global effect of AmrZ on expression of P. aeruginosa genes is unknown. AmrZ directly represses transcription of fleQ and thus motility [20], [21], and its own transcription in a feedback loop [22], [23]. Additionally, AmrZ inhibits production of the extracellular polysaccharide Psl by repressing transcription of the psl operon [19]. In contrast, AmrZ activates alginate production by binding the algD promoter [24], [25] and is essential for twitching motility and formation of a type IV pilus [26]. Each of these AmrZ-regulated genes have been linked to biofilms and P. aeruginosa pathogenicity. The major limitation of the previous approaches is that they are biased towards genes that produce an easily observed phenotype, potentially overlooking many AmrZ-regulated genes that are important in infection. Here, we present a systems-level analysis of the AmrZ regulon utilizing ChIP-Seq and RNA-Seq [27], [28]. By combining these two high-throughput techniques, the genome can be scanned for functional AmrZ binding sites. Additionally, these data allow classification of members of the AmrZ regulon into activated or repressed promoters, as well as direct vs. indirect regulation. Herein, we identified 398 regions of the genome bound by AmrZ (≥3-fold enrichment). The RNA-Seq identified 333 genes that were differentially expressed when comparing a ΔamrZ mutant to a complemented strain (≥2-fold difference). Comparison of AmrZ-bound and AmrZ-regulated genes identified 9 genes directly activated by AmrZ and 49 genes that were directly repressed. Many of these genes have been implicated in pathogenesis, highlighting the importance of AmrZ in P. aeruginosa virulence. Finally, these data allow comparisons of the sequence specificity of AmrZ bound promoters, further defining the consensus AmrZ binding site and lending insight into the mechanism of regulation by AmrZ.
One AmrZ-dependent pathway was investigated in detail since it provided important insights into earlier findings that ΔamrZ mutants form hyper biofilms compared with the parental strain, PAO1 [19]. The present study provides a molecular basis for this finding since we discovered that AmrZ directly represses a predicted diguanylate cyclase-encoding gene (PA4843), which we named adcA (AmrZ dependent cyclase A,). Repression of adcA led to reduced amounts of the second messenger c-di-GMP. This regulation event explains the hyper- aggregative and -biofilm phenotype of a ΔamrZ strain, as elevated c-di-GMP is often associated with the rugose small colony variant phenotype that shares these characteristics [29]–[31]. Recent reports indicate that reducing c-di-GMP in P. aeruginosa biofilm infections leads to biofilm dissolution [32], [33]. Regulation of c-di-GMP by AmrZ could lend insights into the establishment and persistence of chronic P. aeruginosa infections and open novel avenues of treatment.
Upon observation of overnight growth on VBMM, the wild-type strain PAO1 formed a smooth colony, while the ΔamrZ mutant formed an aggregated rugose small colony variant (RSCV) morphology. (Figure 1A). Prevention of rugosity was dependent on AmrZ binding DNA, as the DNA binding deficient R22A AmrZ mutant is also an RSCV (Figure 1A). Chromosomal complementation of the ΔamrZ mutant relieves the rugose phenotype and returns the colony morphology to that of the smooth parental strain (Figure 1A). We have included the well-defined RSCV ΔwspF for comparison [29], [34], [35]. The RSCV phenotype of the ΔwspF mutant has been attributed to the loss of repression of the diguanylate cyclase WspR, leading to elevated intracellular c-di-GMP [32], [36]. Cyclic di-GMP modulates the activity of the transcriptional regulator FleQ at the pel locus, switching FleQ from a repressor to an activator [37], [38]. Psl and Pel polysaccharide overproduction in these strains is responsible for the hyper aggregative phenotype and rugose colony morphology observed [35]. We therefore hypothesized that the ΔamrZ mutant displayed a RSCV phenotype due to elevated intracellular c-di-GMP. To test this, we purified nucleotide pools from plate-grown cells and measured the c-di-GMP via LC-MS/MS (Figure 1B) [39]. We observed that the ΔamrZ mutant accumulated nearly double the intracellular c-di-GMP compared to parental wild type PAO1 (p≤0.01). A two-fold change in c-di-GMP levels can have drastic effects on cell physiology and biofilm formation [39]–[42]. These data are consistent with our classification of ΔamrZ mutants as RSCV. Additionally, we observed that the DNA binding deficient R22A amrZ mutant had similar intracellular levels of c-di-GMP as the ΔamrZ strain (data not shown), indicating that the AmrZ contribution to low c-di-GMP is DNA binding dependent. This observation, in combination with elevated c-di-GMP in the amrZ mutants suggests that AmrZ-mediated modulation of c-di-GMP is either through transcriptional repression of a diguanylate cyclase or activation of a phosphodiesterase. Since AmrZ is a bifunctional transcriptional regulator [22], [24], [25], either of these mechanisms is possible. Therefore, to provide a comprehensive analysis of the AmrZ regulon and to define the mechanistic basis for c-di-GMP accumulation in the ΔamrZ mutant, RNA-Seq and ChIP-Seq strategies were undertaken.
Previous studies identified four AmrZ-bound promoters utilizing standard molecular methods such as DNA footprinting and Electrophoretic Mobility Shift Assays (EMSA) [18], [19], [22]–[26], [43]. Though these methods are recognized as the standard for DNA binding analysis, we wished to perform a genome-wide screen for AmrZ binding sites. Chromatin immunoprecipitation (ChIP) allows us to purify DNA bound AmrZ directly from cells [27], [28], [44], [45]. In this assay, chromatin bound AmrZ was cross-linked, the DNA sheared, nonspecific proteins and nucleic acids removed, and the DNA was purified and quantified using high-throughput parallel DNA sequencing. The resulting ChIP-Seq tags were analyzed using HOMER (Hypergeometric Optimization of Motif EnRichment) a suite of tools for ChIP-Seq analysis and motif discovery [46]. This generated a complete map of genomic areas to which AmrZ binds (Table S1). Conditions were optimized by using previously studied positive control DNA (algD, amrZ) and a negative control region (algB) [22], [24], [25]. Consistent with the literature, algD and amrZ promoters were significantly enriched over input DNA (6.68 and 4.80 fold, respectively), while the algB promoter demonstrated no significant enrichment compared to input DNA. The previously published AmrZ interaction at the fleQ and pslA promoters was also confirmed with this data set (Table 1), indicating the stringency of the analysis. The relatively low enrichment of these two previously described promoters by AmrZ provides a reference to which other interactions can be compared, suggesting that the interactions described here (≥3-fold enrichment) are biologically significant in the cell. In total, we identified 398 regions of the genome that were bound by AmrZ (≥3-fold enrichment over input) (Table S1).
Output for one significantly enriched region is included for reference (Figure 2A). In this example, AmrZ binds upstream of the PA4843 gene in the immunoprecipitated sample (green histogram), however, this enrichment is not present in the input sample (grey histogram). Other regions of the genome that were bound by AmrZ appeared similar. Specific AmrZ binding to the PA4843 promoter region was confirmed using an Electrophoretic Mobility Shift Assay (Figure 2B).
The ChIP-Seq analysis allows one to predict consensus-binding sites based on identification of common sequences within enriched DNA. Based on these analyses we defined a consensus AmrZ binding site (Figure 3). The 13 nt motif was present in 54.7% of all enriched DNA fragments, but in only 10.93% of background reads, producing a significant enrichment of this sequence (p = 1e–120). This motif resembles that reported elsewhere for AmrZ binding using DNA binding and mutagenesis studies [18], [23]–[26], [43]. This motif is also contained in the crystal structure of AmrZ bound to the amrZ1 binding site identified by Pryor et al. [23]. Putative AmrZ binding sites were assigned to a selection of the AmrZ-enriched DNA fragments based on these consensus sequences and analysis of ChIP-Seq reads.
Previous work demonstrated that AmrZ regulates genes in a variety of pathways, many of which are implicated in virulence [19], [21], [25], [26]. However, the extent of the AmrZ regulon is unknown. RNA-Seq allows comparison of sequences of the total mRNA from a ΔamrZ mutant to a complemented strain, elucidating the effect of AmrZ on all genes in the cell, both positive and negative. Total RNA was isolated from a mid-exponential culture (OD600 0.5±0.1) of a ΔamrZ mutant containing the empty pHERD20T vector and a complemented strain containing the arabinose inducible AmrZ expression vector pCJ3. These growth conditions were chosen to match those utilized in the ChIP-Seq experiment. cDNA was synthesized and the resulting product was tagged and quantified using high-throughput parallel DNA sequencing. mRNA expression levels and differential expression analysis was performed using the Bioconductor package DEseq [47]. Three hundred and thirty eight genes were significantly regulated at least 2-fold (Benjamini-Hochberg adjusted p value <0.05), with 89 genes activated- and 249 genes repressed- by AmrZ (Table S2). Several of the AmrZ-regulated genes described in the literature were identified in this analysis, including algD (activated by AmrZ 19.74 fold) and fleQ (repressed by AmrZ 8.05 fold).
The RNA-Seq data indicate that AmrZ strongly, though indirectly, represses many genes involved in iron acquisition, suggesting a novel mechanism for AmrZ mediated control of virulence (Table 1). AmrZ significantly repressed many genes in the pyochelin and pyoverdine synthesis operons, including ppyR. In addition, the Fe(III)-pyochelin receptor fptA and ferripyoverdine receptors fpvA and fpvB were all significantly repressed by AmrZ, (5.44, 2.01, and 1.45 fold, respectively), suggesting that reliance on the iron acquisition systems is reduced in strains where AmrZ is highly expressed, such as in mucoid isolates from the CF lung. Previous reports indicate that the iron concentration in the CF sputum and lung is elevated [48]-[50], supporting the hypothesis that there is sufficient iron in the CF lung for bacterial growth with reduced dependence on the high-affinity iron acquisition systems. Many virulence factors are iron-regulated, so the impact of AmrZ-mediated siderophore repression may contribute significantly to the establishment of chronic infections [51], [52]. There was no alteration of the transcription of the iron-dependent master regulator Fur in the ΔamrZ mutant, implying that AmrZ regulates these iron acquisition genes independent of Fur, perhaps through small RNAs or downstream members of the Fur regulon that have yet to be identified. Future studies will explore the relationship of AmrZ and iron acquisition during infection.
The results from the RNA-Seq and ChIP-Seq were further evaluated to determine genes potentially directly regulated by AmrZ. To accomplish this, the list of AmrZ-bound genomic regions (at least 3-fold enrichment) was filtered using the list of target genes regulated by AmrZ as determined by the RNA-Seq (at least 2-fold regulation). This approach allows classification of genes based on AmrZ binding status and AmrZ-mediated regulation. Interestingly, only 9 of the AmrZ-activated and 49 of the AmrZ-repressed genes were identified in the ChIP-Seq as also containing an AmrZ binding site within 500 base pairs of the start of the coding region of the gene (Table 2), suggesting that there are 80 activated and 200 repressed genes with promoters that were not directly bound by AmrZ, suggesting indirect regulation. One AmrZ directly activated gene is algD, a known AmrZ-dependent gene [24]. Other AmrZ-activated genes in Table 2A include a putative alginate lyase and members of the pel operon. These two genes, in combination with activation of the algD operon, suggest that when expressed, AmrZ affects the P. aeruginosa polysaccharide profile. Additionally, AmrZ directly activates the cyclic di-GMP response gene cdrA, which is correlated with polysaccharide overexpression [30]. Table 2B depicts genes directly repressed by AmrZ. In addition to the previously described fleQ, this list includes many genes that are known or predicted to be involved in virulence including: pyochelin synthesis (pchG), aggregation (siaA), flagellum synthesis (fleQ, flgG, flgE, fliF), alternative type IV pili production (flp), chemotaxis (PA2867, pctC, PA4844), multidrug transport (PA3401), and rhamnolipid production and quorum sensing (rhlR). Several of the directly AmrZ-repressed genes are predicted to be involved in Type VI secretion: PA1657, PA1664, PA1668. Type VI secretion is a recently-described system that is involved in P. aeruginosa pathogenesis and fratricide [53]–[56]. Specifically, the Type VI genes repressed by AmrZ belong to the HSI-II locus, which is involved in P. aeruginosa pathogenicity. HSI-II mutant strains exhibit a delay in mortality in both murine lung and burn wound infections [54]. This regulation may contribute to the role of AmrZ during infection.
Another group of AmrZ directly repressed genes are those predicted to be involved in cyclic diguanylate signaling. These include a predicted diguanylate cyclase (PA4843), a predicted phosphodiesterase (PA2567), and hypothetical proteins that are proposed c-di-GMP effector proteins containing PilZ domains (PA4324, PA3353). PA4324 does not appear to be part of an operon, while PA3353 is in the flgM operon and may have a function in flagella motility [57]. Dysregulation of c-di-GMP signaling could account for the hyper-aggregative phenotype of a ΔamrZ mutant. We explore this system further in this study.
Transcriptional start sites were obtained from RNA-Seq data by observing where the sequence reads begin upstream of a coding region [58]. By performing this analysis to a selection of directly AmrZ-regulated promoters, the proximity of the AmrZ binding site was observed relative to the transcription start site. Promoters with strong AmrZ binding (≥4-fold enrichment) and regulation (≥4-fold regulation) were chosen for an alignment of the AmrZ binding site to the start of transcription. The two strongly activated promoters did not suggest a common mechanism (Figure 4A). However, with the exception of PA3235, each of the directly AmrZ-repressed promoters observed contained an AmrZ binding site from −100 to +15 relative to the transcription start site (Figure 4B). This implies that during repression, AmrZ interferes with the binding of RNA polymerase to the promoter, a common mechanism of bacterial transcriptional repression.
Previous publications have identified two AmrZ binding sites in the amrZ promoter, amrZ1 and amrZ2 [22], [23]. The amrZ1 binding site was identified by the ChIP-Seq (Figure 4B, red binding site). The previously identified amrZ2-binding site was not specifically identified by ChIP-Seq, however, this is likely due to the reduced AmrZ affinity for the amrZ2 binding site [18], [22]. Analysis of the read alignment of the immunoprecipitated sample reveals a biphasic peak including both the amrZ1 and amrZ2 binding sites. One gene (PA3235) that was repressed by AmrZ lacked a binding site in the promoter. However, AmrZ did bind 70 bp downstream of the observed PA3235 start of transcription. This may indicate a second mechanism of AmrZ repression, where bound AmrZ interferes with the elongation of the transcript.
Analysis of the proximal promoter regions of AmrZ-regulated genes indicates that AmrZ may affect RNA polymerase assembly directed by several sigma factors. For example, the −10 and −35 boxes of siaA appear to indicate that this promoter is RpoD-dependent (−35TTGaCc/−10TAtAAT), while the promoter of PA4843 appears to match the consensus sequence for a σN dependent promoter (−24GG/−12GC) [59]. There was no discernable pattern in the relation of the AmrZ binding site to the start of transcription in the AmrZ-activated genes, indicating that there may be several mechanisms of AmrZ-mediated direct activation.
The gene most highly repressed by AmrZ was PA4843 (40-fold) (Table 2B). Predictions based on the structure and function of PleD from Caulobacter crescentus indicates that PA4843 contains two component receiver domains (Rec), an I-site, and a GGEEF cyclase domain (Figure S1). Previously, PA4843 was described as a putative diguanylate cyclase [60] since it contains a conserved cyclase domain; however, no reports demonstrate functional cyclase activity for the PA4843-encoding gene. Additionally, deletion of this gene in strain PA14 did not impact attachment or host cell cytotoxicity [60]. Because PA4843 was the most highly repressed AmrZ target gene and ΔamrZ mutants have an RSCV phenotype and elevated levels of c-di-GMP (Figure 1B), we hypothesized that PA4843 encoded a diguanylate cyclase that is de-repressed in ΔamrZ mutants. To address this, PA4843 was cloned into the arabinose inducible vector pHERD20T [61] and the plasmids transferred to wild type PAO1 or a strain lacking PA4843. c-di-GMP levels in both PAO1 or ΔPA4843 containing the induced vector control exhibited low levels of c-di-GMP (∼3 fmol/µg total protein) (Figure 5). However, expression of PA4843 in these strains generated nearly thirty fold more c-di-GMP (87 fmol/µg total protein for PAO1, and 92 fmol/µg total protein for ΔPA4843; Figure 5), supporting the hypothesis that PA4843 is a functional diguanylate cyclase. Based on these results and others below, we named PA4843 adcA, for AmrZ-dependent cyclase A. Additionally, a deletion of adcA in a ΔamrZ mutant returns the c-di-GMP to wild-type levels, (ΔamrZ mutant 7.33 fmol/µg total protein, ΔamrZ ΔadcA double mutant 2.33 fmol/µg total protein) indicating that the elevated c-di-GMP in a ΔamrZ mutant is dependent on AdcA.
As previously reported, a ΔamrZ mutant forms robust biofilms with more biomass and taller microcolonies than the parental strain, PAO1 [19]. This report demonstrated that direct repression of the psl operon by AmrZ could abrogate the hyper biofilm phenotype of the ΔamrZ mutant [19]. Here, we present data that AmrZ also regulates c-di-GMP concentrations in the cell, thus providing an additional level of control. We hypothesized that the ΔamrZ mutant hyper biofilm phenotype is due to adcA derepression and c-di-GMP accumulation in this strain. To test this hypothesis, we grew 24-hour flow cell biofilms in a PAO1 and ΔamrZ mutant background while modulating the amount of adcA expression in the cells (Figure 6A). We reasoned if the hyper biofilm phenotype is dependent on derepression of adcA and accumulation of c-di-GMP in the ΔamrZ mutant, biofilm cells formed by a ΔamrZ ΔadcA double mutant should have less intracellular c-di-GMP and biofilms with less biomass and microcolony height. Consistent with this hypothesis, the ΔamrZ ΔadcA double mutant produces biofilms with significantly less biomass than the ΔamrZ mutant (Figure 6A, Figure S3). Additionally, we observed that the ΔamrZ ΔadcA double mutant produced significantly lower c-di-GMP compared to the ΔamrZ mutant (2.33 vs 5.98 fmol/µg total protein, respectively) while the adcA overexpressing ΔamrZ mutant had significantly higher c-di-GMP (67.00 fmol/µg total protein). These data indicate that the hyper biofilm phenotype of the ΔamrZ mutant is due to loss of repression of adcA and elevated intracellular c-di-GMP. This mechanism, in addition to the previously reported direct repression of the psl-encoded biofilm polysaccharide [19], indicates that AmrZ-dependent regulation of the psl operon at multiple levels may amplify the effect on Psl production, with significant changes in the biofilm phenotype.
We also reasoned that if dysregulation of c-di-GMP production is responsible for the hyper biofilm phenotype of ΔamrZ mutants, then reducing intracellular c-di-GMP in these strains by overexpressing a phosphodiesterase (PDE) should ablate biofilm formation. For this, a plasmid encoding the arabinose inducible PDE PA2133 (pJN2133) or the empty vector pHERD20T was transformed into the ΔamrZ mutant and 16 hour flow cell biofilms were grown in the presence of inducer [32]. CLSM analysis demonstrates that PDE overexpression significantly reduces biofilm biomass and microcolony height in these biofilms (Figure. 6B). These data further support the hypothesis that the hyper biofilm phenotype of ΔamrZ mutants is dependent on elevated c-di-GMP.
Understanding how bacteria respond to varying conditions in the environment and during infection is clearly of importance. Here, we present a comprehensive analysis of a bacterial transcription factor regulon obtained by combining ChIP-Seq and RNA-Seq. The power of these techniques stems from the unbiased and genome-wide production of the entire regulon, but also the activity of the transcription factor at these binding sites. These techniques have been established in eukaryotes [62], [63], however they have recently been adapted as powerful tools to investigate the activity of bacterial transcription factors [27], [28], [58], [64]–[67]. We were able to identify 398 regions bound by AmrZ in the P. aeruginosa genome. Additionally, we developed a transcriptional profile of both the ΔamrZ mutant and its complemented strain. This allowed us to combine the results of ChIP-Seq and RNA-Seq and divide loci into several categories, either activated, repressed, or unaffected by AmrZ. Each of these groups were then further categorized into directly or indirectly regulated.
Our prior studies revealed that wild type bacteria have a competitive advantage over ΔamrZ mutant bacteria in a mixed acute pulmonary model of infection [18]. By combining ChIP-Seq and RNA-Seq analysis, we identified many genes that are AmrZ-regulated and may be important for colonization and disease progression. One of the directly AmrZ-repressed genes, a diguanylate cyclase we named adcA (PA4843), emerged as the most highly regulated AmrZ target. Deletion of adcA in a ΔamrZ mutant eliminated the accumulation of c-di-GMP and the hyper biofilm phenotype. The modulation of c-di-GMP by AmrZ is a novel observation and enhances the molecular explanation for the earlier studies regarding the role of AmrZ in biofilm phenotypes [19]. c-di-GMP has diverse functions in P. aeruginosa, regulating polysaccharide production, motility, virulence factor production, and biofilm formation [60], [68]. When competed against the wild type PAO1 in an acute pulmonary infection model, both a ΔadcA mutant and a ΔamrZ ΔadcA double mutant retained the virulence defect observed for the ΔamrZ mutant (Figure S2). We propose that AmrZ-dependent gene regulation is most important in the establishment of chronic infections, as in the cystic fibrosis lung. Therefore, lack of a phenotype in an acute model of infection does not negate a role for AmrZ in chronic infections and future studies are geared towards this line of investigation. It should be noted that suitable chronic lung infection models that faithfully reproduce CF pathology are limited, though there are several very promising developments in this area [69].
Regulation of the numerous DGC and PDE enzymes in P. aeruginosa presents a complex network of integrated stimuli sensation and physiological response. Work in other systems has demonstrated that c-di-GMP is freely diffusible in the cytoplasm and is detected by many sensors [31], [70], [71]. This work highlights the regulation of one DGC, however, deciphering the regulation of c-di-GMP production and cellular response to diverse signals is currently an area of great interest.
In addition to the DGC activity described here, AdcA contains a predicted N-terminal two-component receiver domain. This combination of receiver domain and DGC is also observed in the well-characterized PleD of C. crescentus [72], [73]. Previous studies have revealed PleC-dependent activation of the PleD receiver domain by phosphorylation, leading to dimerization and c-di-GMP production [72]. The end result of this signaling cascade is the loss of flagellum and development of the stalk leading to a sessile lifestyle. Another example of a hybrid response regulator/diguanylate cyclase with biofilm effects is WspR of P. aeruginosa [32]. Surface growth leads to phosphorylation of WspR, inducing clustering of the protein and activation of cyclase activity [74], [75]. This model of clustered cyclases suggests that such subcellular foci can lead to regional increases of c-di-GMP, which may be an explanation for why subtle changes in whole-cell c-di-GMP pools can have drastic and varied effects on biofilm and motility phenotypes [73]–[76]. Analysis of AdcA for conserved domains indicates that the aspartate at residue 300 is a probable phosphorylation site. Activation of AdcA in P. aeruginosa leads to a hyper biofilm phenotype, suggesting that AdcA, PleD, and WspR have similar cellular effects. Based on the homology between these proteins, future studies will identify the partner sensor kinase and evaluate the effects of AdcA phosphorylation.
AmrZ activates alginate transcription and twitching motility, but represses Psl, flagella, and c-di-GMP production (Figure 7). Each of these pathways have been implicated in biofilm formation and disease chronicity [77]–[85]. The complete analysis of the AmrZ regulon indicates that AmrZ may serve as a molecular switch that triggers biofilm maturation in P. aeruginosa. We have observed that nonmucoid, environmental strains produce a low amount of AmrZ, allowing for high production of the adherent and aggregative polysaccharide Psl [19]. Additionally, low AmrZ in these strains allows expression of fleQ and flagellum production, further enhancing the attachment phenotypes [21], [80]. We present here that low AmrZ also permits expression of the diguanylate cyclase adcA, producing elevated c-di-GMP in the cell. This signaling molecule can affect the production of all of the above pathways in addition to the direct regulation by AmrZ [29], [32], [37], [38]. Cumulatively, the result of de-repression of these genes results in a motile strain that is primed to colonize and form biofilms by expressing the adhesive polysaccharide Psl. We observe a hyper aggregative and hyper biofilm phenotype in the ΔamrZ mutant, supporting this hypothesis. A similar phenomenon is observed in a ΔretS mutant, where elevated c-di-GMP leads to hyper biofilm formation [40], [86]. The GacS/RetS sensor systems are involved in the transition from acute to chronic infections by regulating polysaccharide production, motility, and secretion systems [40], [86], [87]. These systems regulate virulence genes through RsmA, which has a vast regulon [41], [88]. Though AmrZ was not identified as regulating any of the members of the Gac/Rsm signaling cascade, the ultimate effects of the two pathways are strikingly similar. Further work will investigate how AmrZ is interacting or overlapping with these well-established regulators of acute to chronic transition. Identification of the signal activating AdcA will enhance the understanding of the interactions of these two functionally similar pathways.
Strains of P. aeruginosa that infect patients are Psl-producing, nonmucoid, and form biofilms more readily than mucoid strains [79], [89]. Once a cystic fibrosis patient is infected with a nonmucoid strain, there is an aggressive neutrophil influx into the lungs [90]. These neutrophils produce many antimicrobial products, including reactive oxygen species, antimicrobial peptides, and neutrophil nets [91], [92]. Additionally, CF patients with active infections are treated with high doses of antibiotics. These factors, coupled with the high salinity, low oxygen, and high viscosity of the mucus in the CF lung, provide an environment that is highly selective for bacterial variants able to persist [93]. One clear phenotype that emerges in this environment is the production of alginate (mucoidy), which provides resistance to phagocytosis and protection against antibiotics and reactive oxygen species [7], [16], [94]–[96]. Mucoid strains express AmrZ at levels much higher than those observed in nonmucoid counterparts [24], [25], [43]. We propose that AmrZ acts as a molecular switch that transitions P. aeruginosa from a motile, adherent, colonizing strain causing acute virulence and tissue damage to a nonmotile, mucoid, chronic strain that is more adept at persistence and immune evasion. We suggest that the enhanced virulence of the wild type is due to the expression of various virulence factors such as the type III secretion system regulator ExsC and iron sequestration proteins such PchC and FptA. AmrZ represses these genes (−3.4, −9.01, and −5.45-fold, respectively), though their promoters were not identified as bound by AmrZ in the ChIP-Seq analysis, suggesting that this repression is indirect. When AlgT/U is active, as in mucoid strains, the amount of AmrZ rises. This rise in AmrZ could reduce production of these proteins and limit the acute virulence of the strains, allowing for the establishment of a chronic infection. Additionally, we demonstrate that AmrZ activates expression of cdrA, encoding a biofilm matrix protein and the pel polysaccharide operon. Previous reports indicate that AmrZ can directly repress the psl operon, leading to multifactorial control of this polysaccharide [19]. Combined with the published knowledge of the effect of c-di-GMP on the psl operon through FleQ, these data further reinforce the potential for additive effects of AmrZ at multiple points of polysaccharide and matrix protein regulation. Cumulatively, these experiments suggest that the high production of AmrZ in mucoid strains during chronic infections could lead to a polysaccharide transition from expressing Psl to alginate and Pel. Additionally, CdrA has been reported to stabilize biofilm structure [30]. The overlap of these regulatory networks with the inclusion of c-di-GMP signaling could provide insight to the complexity of the contributions of polysaccharides to virulence during different stages of infection. Future work will delve into virulence contribution by the AmrZ-regulated genes to identify the molecular basis for the acute virulence defect in the ΔamrZ mutant.
All animals were maintained in the OSU College of Medicine IACUC-approved vivarium located in the Biomedical Research Tower. The University has many veterinarians and trained animal caretakers available for consultation on the studies. The protocol for these studies has been approved by the OSU IACUC committee (Protocol # 2009A0177). There is adequate space for the animals to be housed in the vivarium. Animals are monitored frequently during the infection. Animals that meet the criteria for removal from study will be euthanized via CO2 inhalation. Each room contains sentinel mice that are sacrificed at regular time points for examination for infectious agents by vivarium staff. During infection, mice were lightly sedated with isoflurane and inoculated intranasally with bacteria suspended in sterile PBS. Thirty µL of the PBS solution is pipetted onto the nares of the mouse as soon as the anesthetic administration is discontinued. The animal rapidly recovers under supervision from the researcher. The mice are not in discomfort or distress during this procedure. There are no restraining devices utilized during this study. Mice were sacrificed via CO2 inhalation. This method of euthanasia causes minimal discomfort to the animals. Cardiac puncture was used as a second method of euthanasia. These methods are consistent with the recommendations of the American Veterinary Medical Association Guidelines on Euthanasia.
The bacterial strains used along with genotypes are provided in Table S3. P. aeruginosa strains were inoculated in LBNS (10 g l−1 tryptone, 5 g l−1 yeast extract, pH 7.5) at 37°C for overnight cultures under shaking conditions unless otherwise noted. Strains were grown at 37°C on LANS (LBNS with 1.5% agar) or Pseudomonas Isolation Agar (Difco, Detroit, MI) agar plates. E. coli was routinely cultured at 37°C in lysogeny broth (LB, 10 g L−1 tryptone, 5 g L−1 yeast extract, 5 g L−1 NaCl). Semi-solid media was prepared by adding 1.5% Bacto agar to LB. Colony morphology was imaged on modified Vogel-Bonner minimal medium (VBMM) plates (0.2 g L−1 MgSO4 7H2O, 2.0 g L−1 citric acid, 3.5 g L−1 NaNH4HPO4 4H2O, and 10 g L−1 K2HPO4, 1 g L−1 casamino acids, and 5 mM CaCl2. Congo Red (40 µg/mL) and Brilliant Blue R (15 µg/mL) were added to VBMM to aid in visualization of morphology. Antibiotics were added to maintain or select for plasmids in P. aeruginosa as follows: gentamicin (Gm) at 100 µg/mL, Rifampicin (Rif) at 100 µg/mL and carbenicillin (Cb) at 300 µg/mL. Antibiotics were added to maintain or select for plasmids in E. coli as follows: gentamicin (Gm) at 10 µg/mL and spectinomycin (Sp) at 50 µg/mL.
Plasmids and primers used in genetic manipulations are listed in Tables S4 and S5, respectively.
Primers AmrZF2 and AmrZR2 amplified the 324 bp DNA sequence of amrZ. NEB Q5 High Fidelity DNA Polymerase was used in PCR following manufacturer's instructions. The PCR product of amrZ was inserted into pET29a (Novogen) through NdeI and NotI restriction sites. The 432 bp DNA sequence of the amrZ gene, ribosome binding site, and C-terminal 6x His tag were amplified from the resulting plasmid using primers AmrZF3 and AmrZR3. The PCR product was inserted into pHERD20T [61] through XbaI and HindIII restriction sites. The resulting construct (pCJ3) was verified by DNA sequencing.
A deletion allele for PA4843 was assembled by removing an in-frame, 1593 bp fragment of coding sequence from the PA4843 open reading frame (ORF), leaving a scar ORF encoding a 10-amino acid peptide. In a first step, two PCR products were amplified using primers that targeted the adjacent upstream and downstream regions of the chromosome flanking PA4843. Subsequently, these PCR products were joined by splicing by overlapping extension (SOE) PCR [97] to create the ΔPA4843 allele. The upstream forward and downstream reverse primers used to generate this deletion allele were tailed with attB1 or attB2 sequences as described in the Gateway Cloning Technology Manual (Invitrogen). Using Gateway technology, the ΔPA4843 allele was first recombined with pDONR223 using BP Clonase II (Invitrogen) to create pJJH125, which was sequenced using M13F and M13R primers. Finally, the ΔPA4843 allele from pJJH125 was recombined with pEX18GmGW using LR Clonase II (Invitrogen) to create the allelic exchange vector pJJH129.
The adcA overexpression plasmid pBX22 was constructed by inserting adcA coding sequence into the arabinose-inducible vector pHERD20T [61]. The 1659 bp DNA sequence of the adcA gene was amplified by primers PA4843_F and PA4843_R. NEB Q5 High Fidelity DNA Polymerase was used in PCR following manufacturer's instructions. The PCR product of adcA was inserted into pHERD20T through XbaI and HindIII restriction sites. The adcA coding sequence in pBX22 was verified by Sanger-based DNA sequencing.
c-di-GMP was extracted and quantified as described previously with minor modifications [39]. Cells were cultured overnight on LANS plates. An isolated colony was transferred to a fresh LANS plate and incubated at 37°C for 24 hrs before harvesting. Colonies were scraped from agar plates and resuspended in 990 µL of LC/MS grade water (Optima). 2-chloro-adenosine-5′-O-monophosphate (2Cl-AMP, 10 µL of 10 µM, Biolog), was added as an internal standard. Nucleotides were extracted from cells by the addition of 94 µl of 70% perchloric acid and incubated for 30 min on ice. Cell debris were removed by centrifugation and reserved for subsequent protein quantification. The supernatant containing c-di-GMP was neutralized by the addition of 219 µL of 2.5 M KHCO3. The resulting precipitate was removed by centrifugation. The supernatant was stored at -80°C until LC/MS analysis. Pure c-di-GMP standards (Biolog) were extracted in parallel and treated identically to samples.
Compounds were separated on an Acuity UPLC equipped with a C18 Guard Cartridge (Phenomenex) and Synergi 4 µ Hydro RP 80A column (50×2 mm, Phenomenex). The injection volume was 20–30 µL. A gradient was established starting with 98% aqueous (10 mM formic acid in water) and 2% organic (acetonitrile). The aqueous concentration was adjusted to 70% at 2 min, 20% at 2.5 min, 100% at 3 min, and finally held at 98% from 5–7.5 min. Compounds were detected using multiple reaction monitoring on a Premier XL triple-quadrupole electrospray mass spectrometer (Waters) in positive-ionization mode. The m/z 691>152 transition was used for the identification of c-di-GMP and 382>170 for 2Cl-AMP. The cone voltages and collision energies were 40 V/30 eV and 35 V/20 eV, respectively. The capillary voltage used was 3.5 kV. The desolvation temperature was 350°C and source temperature was 120°C. Nitrogen was used as a drying gas with a flow rate of 800 L/hr. The concentrations of c-di-GMP were calculated by comparison of the peak area ratio of c-di-GMP to 2Cl-AMP to a standard curve. Moles of c-di-GMP were normalized to total protein determined from Pierce protein assay. Data represent averages of three independent cultures.
For protein quantification, cell pellets were resuspended in 220 µL of 10 mM Tris-Cl buffer (pH 8.5). The remaining acid in the pellets was neutralized by the addition of 30 µl of 1 M NaOH. Cells were lysed by the addition of 250 µl of 2X concentrated Laemilli Buffer and boiled for 30-90 min at 100°C or until the pellet had dissolved. Protein concentration was determined using Pierce 660 nm Protein Reagent with Ionic Detergent Compatibility Reagent (IDCR) as recommended by the manufacturer.
Chromatin immunoprecipitation was modified from existing protocols [44], [45]. Cultures were induced with 0.5% arabinose at an OD600 of 0.1 and allowed to grow for two hours at 37°C in a roller. Protein-DNA complexes were cross-linked by addition of formaldehyde- to a final concentration of 1.0% and incubated at room temperature for ten minutes. Cross-linking was quenched by addition of glycine (final concentration 250 mM). The final OD600 was recorded and cells were collected from 1 OD600 of culture via centrifugation and washed once in LBNS. The supernatant was removed and pellets were stored for further processing at -80°C.
Cell pellets were resuspended in 1.0 mL of lysis buffer (20 mM HEPES, pH 7.9; 50 mM KCl; 0.5 mM DTT; 500 mM NaCl; 10 mM imidazole; 1% BSA; 1 µg/mL leupeptin/pepstatin; and 400 µM PMSF) per 1 OD600 of culture. Samples were sonicated on Covaris with the following conditions: Duty Cycle 20%, Intensity 8, Cycles per burst 200, with frequency sweeping 20 min total shearing time (60 sec cycles, 20 cycles). Lysate was cleared via centrifugation (20,000× g, 30 minutes, 4°C) and the supernatant was transferred to a fresh tube as the input sample. Magne-HIS beads (Promega V8560) were blocked at room temperature in lysis buffer for 30 minutes, and then 500 µl of the input sample was added to the beads. After 30 minutes of binding at room temperature with agitation, the supernatant was removed from the beads via magnetic separation. Beads were washed five times in wash buffer (100 mM HEPES, pH 7.5, 10 mM imidazole, 500 mM NaCl, and 1% BSA). Elution buffer (100 mM HEPES, pH 7.5; and 500 mM imidazole) was added to the beads and incubated at room temperature for 30 minutes. Supernatant was collected after magnetic separation and combined with SDS (1.25% final concentration), then heated to 70°C for 30 minutes to reverse cross-links. DNA was purified via phenol:chloroform extraction and ethanol precipitation [98].
The chip DNA was quantified with Qubit 2 flurometer (Life Technologies) using Qubit dsDNA BR Assay. 10 ng of DNA was used to construct each Chip sequencing library, following NEXTflex ChIP-Seq kit (Bioo Scientific) instruction. NEXTflex ChIP-Seq Barcodes (Bioo Scientific) were used to index the library. The final DNA libraries were validated with Agilent 2100 Bioanalyzer using Agilent High Sensitivity DNA Kit. And the library concentrations were determined by Q-PCR using KAPA SYBR Fast qPCR kit. The libraries were then run on Single End flowcell on HiSeq2000.
HiSeq2000 sequencing was performed, resulting in approximately 255 million total single-end 52 bp reads from the six control and eight treatment samples. Reads were aligned using bwa (0.5.10) to the Pseudomonas aeruginosa PAO1 reference genome [99]. Approximately 220 million reads aligned uniquely to the reference (86.3%). A TDF file was created for each sample for visualization in IGV, which was scaled to reads per 10 million data using bedtools (2.17.0) and igvtools (2.3.3). ChIP-Seq analysis was performed using HOMER (4.2). First, aligned data was transformed into a platform-independent data structure for further HOMER analyses using the makeTagDirectory function. Secondly, HOMER's findPeaks-style factor was utilized to identify peaks, or regions of the genome where more reads are present than random. Lastly, HOMER's findMotifsGenome.pl was used to analyze genomic positions for de novo enriched motif regions of length 50 or 200 and identified peaks were annotated with the motifs using the annotatePeaks.pl function.
Cultures were induced with 0.5% arabinose at an OD600 of 0.1 and allowed to grow for two hours at 37°C in a roller. The final OD600 was recorded and 0.1 OD600 was centrifuged at 10,000-x g for 3 minutes. The supernatant was removed and pellets were resuspended in 1 mL of TRIzol (Invitrogen). Following a 5-minute incubation at room temperature, 0.2 mL of chloroform was added and the samples were shaken for 15 minutes. Phases were separated by centrifugation (12,000× g, 5 minutes, 4°C) and the aqueous phase was combined with 0.6 mL of 70% ethanol and transferred to an RNeasy mini column (Qiagen). After centrifugation, 0.7 mL of buffer RW1 (Qiagen) was added to the column and centrifuged. Samples were washed twice with 0.5 mL of Buffer RPE (Qiagen) and eluted in 50 µL of water.
Following assessment of the quality of total RNA using Agilent 2100 bioanalyzer and RNA Nano Chip kit (Agilent Technologies, CA), rRNA was removed from 2.5 µg of RNA with Ribo-Zero rRNA removal kit for Gram-negative bacteria (Epicentre Biotechnologies, WI). To generate directional signal in RNA seq data, libraries were constructed from first strand cDNA using ScriptSeq v2 RNA-Seq library preparation kit (Epicentre Biotechnologies, WI). Briefly, 50 ng of rRNA-depleted RNA was fragmented and reverse transcribed using random primers containing a 5′ tagging sequence, followed by 3′end tagging with a terminal-tagging oligo to yield di-tagged, single-stranded cDNA. Following purification by a magnetic-bead based approach, the di-tagged cDNA was amplified by limit-cycle PCR using primer pairs that anneal to tagging sequences and add adaptor sequences required for sequencing cluster generation. Amplified RNA-seq libraries were purified using AMPure XP System (Beckman Coulter). Quality of libraries were determined via Agilent 2100 Bioanalyzer using DNA High Sensitivity Chip kit, and quantified using Kappa SYBRFast qPCR kit (KAPA Biosystems, Inc, MA). 50 bp sequence reads were generated using the Illumina HiSeq 2000 platform.
HiSeq 2000 sequencing was performed, resulting in approximately 165 million total single-end 52-bp reads from the six total control and treatment samples. Reads were aligned using bwa (0.5.10) to the P. aeruginosa PAO1 reference genome [99]. Approximately 143 million reads aligned uniquely to non-ribosomal regions of the reference (86.9%). A TDF file was created for each sample for visualization in IGV, which was scaled to reads per million data using bedtools (2.17.0) and igvtools (2.3.3). A coverage file, describing the coverage for each feature in the PAO1 genome, was created using bedtools. These coverage's were normalized and the means of the control and treatment groups were tested for significant differences using the binomial test in the R package DESeq (1.10.1), producing fold changes and adjusted p-values for each feature. Resulting p-values were adjusted for multiple testing with the Benjamin-Hochberg procedure, which controls false discovery rate (FDR).
6FAM labeled DNA used for EMSA was amplified using Quick-load Taq 2X Mastermix (New England Biolabs), FAM-labeled forward primer and non-labeled reverse primer, and PAO1 genomic DNA as the template. The EMSA procedure is similar to that previously reported [18]. Each EMSA reaction contains 4 mM Tris-HCl (pH8.0), 40 mM NaCl, 4 mM MgCl2, 4% glycerol, 150 ng/ul Poly d[(I-C)] (non-specific DNA control), 100 µg/mL BSA (non-specific protein control), 5 nM FAM labeled DNA, and a defined concentrations of AmrZ or AmrZR22A. Protein-DNA binding was equilibrated at room temperature (25°C) for 20 min after adding all reagents to each reaction. 10 µL of each reaction was loaded onto a 4% non-denaturing acrylamide gel. Electrophoresis was conducted for 22 min at 200 V in 0.5% TBE. 6FAM fluorescence was detected with a Typhoon scanner (GE Lifescience). A similar length DNA sequence within the algD coding sequence but lacking an AmrZ binding site was selected as the specificity control.
Protein sequence was submitted to the Phyre2 server for analysis of homology [100]. Predicted structure was imaged in Jmol (http://www.jmol.org).
Inoculation of flow cells was done by normalizing overnight cultures to an optical density of 0.5 and injecting into an Ibidi μ-Slide VI0.4 (Ibidi 80601). To seed the flow cell surface, the media flow was suspended and the bacteria allowed to adhere at room temperature for 3 hours. Flow of 5% v/v LBNS with 0.5% arabinose was initiated at a rate of 0.15 mL*min−1 and continued for 24 h. Following the biofilm growth period, the flow was terminated and the biofilms were fixed with 4% paraformaldehyde. Confocal images were taken at the Ohio State University Campus Microscopy and Imaging Facility on an Olympus Fluoview 1000 Laser Scanning Confocal microscope. Images were obtained with a 20X oil immersion objective. Images were processed using the Olympus FV1000 Viewer software. Quantitative analyses were performed using the COMSTAT software package [101] Total biomass was determined from Z-stack images using the BIOMASS command with the threshold set to 15. Three independent biofilms were imaged and analyzed. Statistical significance was determined using a Student's t-test.
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10.1371/journal.pcbi.1004542 | Bridging between NMA and Elastic Network Models: Preserving All-Atom Accuracy in Coarse-Grained Models | Dynamics can provide deep insights into the functional mechanisms of proteins and protein complexes. For large protein complexes such as GroEL/GroES with more than 8,000 residues, obtaining a fine-grained all-atom description of its normal mode motions can be computationally prohibitive and is often unnecessary. For this reason, coarse-grained models have been used successfully. However, most existing coarse-grained models use extremely simple potentials to represent the interactions within the coarse-grained structures and as a result, the dynamics obtained for the coarse-grained structures may not always be fully realistic. There is a gap between the quality of the dynamics of the coarse-grained structures given by all-atom models and that by coarse-grained models. In this work, we resolve an important question in protein dynamics computations—how can we efficiently construct coarse-grained models whose description of the dynamics of the coarse-grained structures remains as accurate as that given by all-atom models? Our method takes advantage of the sparseness of the Hessian matrix and achieves a high efficiency with a novel iterative matrix projection approach. The result is highly significant since it can provide descriptions of normal mode motions at an all-atom level of accuracy even for the largest biomolecular complexes. The application of our method to GroEL/GroES offers new insights into the mechanism of this biologically important chaperonin, such as that the conformational transitions of this protein complex in its functional cycle are even more strongly connected to the first few lowest frequency modes than with other coarse-grained models.
| Proteins and other biomolecules are not static but are constantly in motion. Moreover, they possess intrinsic collective motion patterns that are tightly linked to their functions. Thus, an accurate and detailed description of their motions can provide deep insights into their functional mechanisms. For large protein complexes with hundreds of thousands of atoms or more, an atomic level description of the motions can be computationally prohibitive, and so coarse-grained models with fewer structural details are often used instead. However, there can be a big gap between the quality of motions derived from atomic models and those from coarse-grained models. In this work, we solve an important problem in protein dynamics studies: how to preserve the atomic-level accuracy in describing molecular motions while using coarse-grained models? We accomplish this by developing a novel iterative matrix projection method that dramatically speeds up the computations. This method is significant since it promises accurate descriptions of protein motions approaching an all-atom level even for the largest biomolecular complexes. Results shown here for a large molecular chaperonin demonstrate how this can provide new insights into its functional process.
| Protein dynamics plays a key role in describing the function of most proteins and protein complexes. The importance of protein dynamics studies has been increasingly recognized alongside the importance of the structures themselves. Experimentally, protein dynamics can be studied using nuclear magnetic resonance (NMR) [1, 2], time-resolved crystallography [3], fluorescence resonance energy transfer (FRET) [4] and other single-molecule techniques [5], etc. Computationally, the study of protein dynamics most commonly relies upon molecular dynamics (MD) simulations [6–8]. Normal mode analysis (NMA) is another popular and powerful tool for studying protein dynamics and was first applied to proteins in the early 80’s [9–11]. The advantage of normal modes over MD is that they can most efficiently describe protein motions near the native state. To apply NMA, a structure is first energetically minimized. The minimized structure is then used to construct the Hessian matrix, from which normal modes can be obtained from its eigenvectors and eigen-frequencies. This method poses a huge demand on computational resources, especially memory, since some large supramolecules may have hundreds of thousands of atoms. The time spent on computing the eigenvalues/eigenvectors also is large, of the order of the cube of the number of atoms. Consequently, its applications are limited to smaller systems.
For this reason, many simplified models [12–33] have been developed for efficient normal mode computations. These models use simplified structural models or simplified force fields or commonly, both. One commonly applied type of coarse-grained model is the elastic network model [13, 16], which usually treats each residue as one node, and residue-residue interactions as Hookean springs. It has been demonstrated for a large number of cases that these extremely simplified models can still capture quite well the slow dynamics of a protein [12]. And because of their high level of simplicity, they have been successfully applied to study the normal mode motions of the largest structural complexes such as GroEL/GroES [18, 34–38], ribosome [22, 39–41], nuclear pore complex [29], etc.
However, along with the significant gains from this simplicity comes also some loss of accuracy, particularly in the accuracy of the normal modes [42, 43]. The validity of most simplified models was justified a posteriori, by comparing with experimental B-factors or sets of multiple experimental structures for example. How well they preserve the accuracy of the original NMA has rarely been assessed directly [33]. To overcome this problem of accuracy, we built a strong connection between NMA and elastic network models (ENMs) through a series of steps of simplification that began with NMA and ended with ENMs, and proposed a new way to derive accurate elastic network models in a top-down manner (by gradually simplifying NMA) [33]. Our derivation was based on the realization that the Hessian matrix of the original NMA can be written as a summation of two main terms, the spring-based terms and the force/torque-based terms, with the former contributing significantly more than the latter. By ignoring the latter term, we obtained at a new model, sbNMA (or spring-based NMA), that has high accuracy and closely resembles the original NMA and requires no energy minimization. sbNMA, like the original NMA, is force-field dependent and uses many parameters. By further simplifying it, we arrived at two force-field independent elastic network models, ssNMA (simplified spring-based NMA) and eANM (enhanced ANM), both of which use many fewer parameters and yet still preserve most of the accuracy of NMA [33]. For example, the mean square fluctuations predicted by ssNMA for a set of small to medium proteins have an average correlation of nearly 0.9 with those predicted with the original NMA [33]. It was shown [42] also that ssNMA modes are more accurate than those from other elastic network models. However, this bridging, as detailed in Ref. 33, connected NMA only with all-atom elastic network models but not with coarse-grained ones. Both ssNMA and eANM, though strongly resembling NMA, are by nature all-atom models and cannot be directly applied to coarse-grained structures.
There is little doubt that for very large biomolecular systems, coarse-grained structure representations are needed, since all-atom normal mode analyses for such systems are computationally often out of reach. Our aim in this work is to extend the idea of bridging between NMA and elastic network models to coarse-grained models while preserving sufficient accuracy to obtain accurate protein dynamics even for very large systems. Is it possible to efficiently construct coarse-grained models whose description of the dynamics of a coarse-grained structure remains as accurate as that given by all-atom models? Coarse-grained models, such as Cα-based models, obviously do not have all the structural details of all-atom models. But can they produce the dynamics of the Cα atoms as accurately as all-atom models? Is it possible to have both the simplicity of coarse-grained structures and the accuracy of all-atom interactions? These questions are the focus of this work. And we demonstrate affirmative answers to these questions by employing a novel iterative matrix projection technique.
While our earlier work [33] connects between NMA and all-atom elastic network models and represents a force-field simplification of NMA while maintaining most of its accuracy, the present work presents an additional structural simplification from all-atom elastic network models to coarse-grained elastic network models. Combined together, the two pieces of work provide a bridge between all-atom NMA and coarse-grained elastic network models and should reveal deep insights for how to develop coarse-grained elastic network models that preserve most of the accuracy of all-atom NMA.
A coarse-grained model has two key components: i) a coarse-grained structure representation, and ii) an interaction model for the coarse-grained structure. The challenge that one normally faces in developing coarse-grained models is that there is no prescription for how to represent the interactions among the coarse-grained structure precisely [44]. Most semi-empirical force field potentials are for atomic models. Highly simplified Hookean springs were commonly used to model residue-residue interactions. They provide only a very rough approximation to the atomic interactions. Other studies that link atomic and coarse-grained models apply force-matching [44] or require their frequency spectra to have similar distributions [45]. A statistical mechanical foundation was developed by the same research group [46] to show that many-body potentials of mean force that govern the motions of the coarse-grained sites could be generated. Regarding coarse-grained structure representation, Cα atoms are normally used to represent residues, although other coarse-grained representations also have been investigated [47].
In this work, to extend the accurate all-atom models to coarse-grained models without losing accuracy in the dynamics, we take two steps. First, we show that it is possible to define a precise interaction model for the coarse-grained structure so that its dynamics are the same as that of its all-atom counterpart. Second, we show that the construction of such a precise interaction model can be performed efficiently and straightforwardly.
It is useful first to perform an operation that separates out the atoms used for the coarse-graining from the remainder of the atoms. Mathematically, it is possible to define a precise interaction model (in the form of a Hessian matrix) for the coarse-grained structure by first rearranging the original Hessian matrix Hall into parts for the coarse-grained atoms and the remainder of the atoms in separate subspaces, as was done by Eom et. al. [48] and Zhou and Siegelbaum [49]:
H a l l = ( H c c H c r H c r ⊤ H r r ) , (1) H ˜ c c = H c c - H c r H r r - 1 H r c ⊤ , (2)
where c stands for the atoms used for the coarse-graining, r stands for the residual part of the structure, and ⊤ represents the matrix transpose. It can be shown mathematically [50, 51] that H ˜ c c maintains the same description of the mean-square fluctuations and cross-correlations of the coarse-grained structure as the original Hessian matrix. All elements in H ˜ c c - 1 are the same as their corresponding elements in H a l l - 1. A similar idea of using matrix projection to obtain the motions for subsystems was previously used also by Brooks and Zheng and their co-workers [52, 53] to develop their VSA (vibration subsystem analysis) model.
However, this mathematical rearrangement in Eq (2) requires the inversion of Hrr, which appears to be nearly as difficult as computing the inverse of the original all-atom Hessian matrix, assuming the number of atoms in the coarse-grained structure is much smaller than that of the original all-atom model. Therefore, unless H ˜ c c can be computed in an efficient way, the precise interaction model defined in Eq (2) would be computationally too expensive to apply for very large systems and thus of little practical utility.
In the next section, we present a novel way for computing H ˜ c c efficiently, without directly inverting Hall or Hrr. As a result, this permits an efficient construction of coarse-grained models that can represent the dynamics of the coarse-grained structure as accurately as all-atom models.
To efficiently obtain the Hessian matrix H ˜ c c from Eq (2) without having to directly invert Hrr, we take advantage of the fact that the Hessian matrix Hall, the second derivatives of the potential, can be highly sparse for some all-atom models. Hall is not so sparse for the conventional NMA, due to the persistence of electrostatic interactions to long distances. However, it is sparse for ssNMA, an accurate all-atom model that closely resembles NMA as mentioned above.
The potential for ssNMA includes most of the same interaction terms as for NMA, except for the electrostatic interactions [33]. As a simplified model of spring-based NMA (or sbNMA), ssNMA uses one single uniform spring constant for all bond stretching terms, one uniform spring constant for all the bond-bending terms, and one for the torsional terms. Its non-bonded van der Waals interactions are truncated near the equilibrium distance to avoid negative spring constants in the Hessian matrix [33]. A single set of van der Waals radii are used for all van der Waals interactions. All the equilibrium values such as bond lengths, bond angles, and torsional angles are taken from the reference structure. Consequently, most of the off-diagonal elements in the ssNMA Hessian matrix are zero.
In the following, we use ssNMA to construct the all-atom Hessian matrix Hall and show how a precise interaction model H ˜ c c can be efficiently constructed through an iterative matrix projection procedure. We call this model coarse-grained ssNMA, or CG-ssNMA. CG-ssNMA preserves the same accuracy as the all-atom ssNMA in its description of the dynamics of the coarse-grained structure.
The procedure, as detailed below, takes full advantage of the sparseness of the Hessian matrix. Given a protein that has n atoms, one can iteratively reduce its size (or coarse-grain it) by removing one atom, or a group of r atoms, at a time without losing accuracy in depicting the motions of the remaining atoms. This can be done by adding a correction term to the interactions among the remaining atoms. Define by H the Hessian matrix with n atoms as follows:
H = ( H k k H k r H k r ⊤ H r r ) , (3)
where Hkk is the block matrix of H for the kept n − r atoms, Hrr the block matrix for r atoms to be removed, and Hkr represents the interactions between the group of atoms to be removed and the remaining atoms. The effective Hessian matrix H ˜ k k of the kept atoms after taking into account the correction term can be written as [42, 48, 49]:
H ˜ k k = H k k - H k r H r r - 1 H k r ⊤ , (4)
with H k r H r r - 1 H k r ⊤ being the correction term.
It can be shown that the motions of the remaining atoms as described by H ˜ k k is the same as from the original Hessian matrix H. This numerical preservation is crucial when an all-atom Hessian matrix is gradually coarse-grained by repeatedly removing non-Cα atoms, since it guarantees that the quality of the description of the Cα atoms remains the same while the size of the Hessian matrix is being reduced.
Note that each atom interacts only with a few, say m on average, atoms due the sparseness of the Hessian matrix. As a result, Hkr has only a small number (rm) of non-zero elements, representing the interactions between the group of atoms to be removed and the kept atoms. Therefore, the term H k r H r r - 1 H k r ⊤ in Eq (4) can be computed in O(r3 + r2 m2) time. Coarse-graining the whole protein structure takes roughly n/r iterations and thus requires a total time of O((r2 + rm2)n), which is linear in the protein size n.
To further reduce the running time, matrix elements that are near zero (weak interactions) are set to zero if their absolute values are less than a predetermined threshold value ξ. A properly chosen ξ can further improve computation speed while preserving the accuracy, by effectively reducing the number of interactions, especially those between the atoms being removed and the remaining atoms. Different ξ values are tested, as detailed in the next section.
Fig 1 illustrates how the sparseness of the Hessian matrix is maintained throughout the iterative matrix projection procedure. At the initial step, atoms are shuffled so that Cα atoms are grouped together and placed on the left-most side of the Hessian matrix, as shown in Fig 1(A), where the grouped Cα and non-Cα atoms are represented by dark and light gray blocks, respectively. Blue dots represent the non-zero elements of the Hessian matrix. The non-Cα atoms can then be further rearranged, for example, using the Cuthill-McKee algorithm [54], so that the atoms that interact with one another are placed close together in the matrix. As a result, the non-zero elements are relocated near the diagonal of the matrix (see Fig 1(B)). In such a sparse matrix, Fig 1(C) shows the effect of applying one matrix projection using Eq (4), where the red dots represent the elements of the matrix whose values are modified. Note that the sparseness of the non-Cα region is mostly unaffected by the projection. The sparseness of the white region (interactions with Cα atoms) can be maintained by using an appropriate threshold value ξ mentioned earlier.
Algorithm 1 lists the steps that iteratively reduce the all-atom Hessian matrix to a coarse-grained one. The algorithm takes as input the all-atom Hessian matrix H, a set of Cα atom indices {k1, …, kn}, and a threshold value ξ. All matrix elements whose absolute values are less than ξ are set to 0. In practice, it turns out that lines 4–11 run more efficiently if each iteration of the coarse-graining process removes not a single atom but a group of atoms (Ri as in line 2). Removing a group of adjacent atoms reduces the average number of interactions (m in the above Big-O notation) with the remaining atoms. These groups of atoms are determined by spatially partitioning the whole structure (3-D) into cubic blocks (18~Å for each dimension). These blocks represent initial groups of atoms. The reason why atoms are partitioned in this way is to minimize the number of interactions among the different groups. Blocks are then sorted by their sizes (i.e., the number of atoms) in descending order. Next, starting with the smallest one, blocks on the “small” end (usually blocks on the outsides of a structure) are iteratively merged together with the next smallest block as long as the size of the merged group does not exceed the size limit (which is about 500 atoms per group, the number of atoms in a regular cubic block). The merging process stops when there are no small blocks left to be merged. In lines 7 and 9, sparse(A, b) returns a sparse matrix of A by setting to zero A’s elements that satisfy |Ai,j| < b, where |Ai,j| is the absolute value of Ai,j. Threshold ξ/m is used in line 9 since the addition (or subtraction) in line 10 is accumulated m times. Line 9 prevents very small values from being added to H in line 10 and then removed in line 7 at the next iteration.
Algorithm 1 CoarseGrain(H, {k1, …, kn}, ξ)
1: K ← {k1, …, kn}
2: R ← {R1, R2, …, Rm}
3: H ← Hessian matrix of H reshaped in the order of K, R1, R2, …, Rm
4: for i = m, m − 1, …, 1 do
5: k ← | K | + ∑ j = 1 i - 1 | R j |
6: r ← k + ∣Ri∣
7: B ← sparse(H1..k, k + 1..r, ξ)
8: D ← Hk + 1..r, k + 1..r
9: E ← sparse(BD−1 B⊤, ξ/m)
10: H1..k,1..k ← H1..k, 1..k − E
11: end for
12: H ← sparse(H1..|K|,1..|K|, ξ)
13: return H
In this section, we first verify computationally that the coarse-grained ssNMA model constructed according to the proposed procedure indeed not only preserves the accuracy of all-atom models in its description of the motions of the coarse-grained structure but also is computationally efficient. To this end, we first show, by applying it to a dataset of 177 small to medium proteins, that with a properly chosen threshold value ξ, the coarse-grained ssNMA preserves full accuracy. We then extend the same coarse-graining procedure, using the same ξ value, to construct coarse-grained ssNMA Hessian matrices for 80 large superamolecules of different sizes and show that the construction of these ssNMA Hessian matrices requires only a nearly linear time and can thus be carried out quickly, even for large systems.
To validate the accuracy of the method, Algorithm 1 is applied to 177 small-to-medium sized proteins whose sizes are greater or equal to 60 residues but less than 150. This is the same set of proteins that was used in our earlier work [33]. Only small to medium sized proteins are used at this stage due to the high computational costs of running all-atom models, which have also been computed here for comparison purposes.
Each protein structure is first energy minimized. From the all-atom ssNMA Hessian matrix, two coarse-grained Hessian matrices, H and H ^, are computed. H is computed by direct matrix projection (as in Eq (2)), which is an exact but very expensive computation, while H ^ is computed with the proposed iterative projections as in Algorithm 1. To show that H ^ preserves the same accuracy as H, we compute the correlations between mean square fluctuations (MSF) computed with H and those with H ^, and the eigenvalue-weighted overlaps between modes by H and those by H ^. The eigenvalue-weighted mode overlap is defined as:
∑ i = 7 3 n w i w | m i · m ^ i | , (5)
where n is the number of atoms, mi (and m ^ i) is the ith mode of H (and H ^), wi = 1/λi is the relative weight and is set to be the inverse of the ith eigenvalue of H, and w = ∑ i = 7 3 n w i is the normalization factor. The reason why we use the modes with the same indices (mi and m ^ i) instead of the best matching modes when computing the weighted-overlap is to measure also how well the order of the modes is preserved. Lower frequency modes are given higher weights in this weighted overlap measure. The intuition behind this weighted mode scheme is that it represents how similar the modes (including their orders) are between the two models.
Table 1 shows the levels of accuracy that can be achieved when different threshold values ξ are applied to ssNMA [33]. It is seen that ssNMA preserves the full accuracy (1.0 in correlations and overlaps) in mean square fluctuations and modes when a threshold value (ξ) as large as 0.01 is used. Similar results are also seen for the enhanced ANM model (eANM) [33], another all-atom model that closely resembles NMA. Using a large threshold value allows the sparseness of the Hessian matrix to be maintained during the iterative matrix projection process and consequently the construction of the coarse-grained ssNMA Hessian matrix to be carried out quickly.
For conventional NMA, however, the iterative coarse-Graining approach as described above does not work nearly as well (see Table 1). This is due to the slowly-decreasing, long-range electrostatic interactions.
Secondly, we look at the efficiency, i.e., how much time does this iterative coarse-graining procedure require? To this end, we apply the same iterative coarse-graining procedure to construct coarse-grained ssNMA Hessian matrices for a number of large proteins and protein complexes. The same threshold value, ξ = 0.01, is used, which has been shown in the previous section to preserve the full accuracy.
Fig 2 shows the efficiency (computational time) of the proposed method as a function of the system size. In the figure, each blue and red point represent respectively, for a protein of that size, the coarse-graining time, i.e., the time required to construct the coarse-grained ssNMA Hessian matrix (with ξ = 0.01), and the diagonalization time of that coarse-grained Hessian matrix. The dashed lines show the growth rates of the time cost as a function of the system size. The curves are obtained from the least squares fitting to a non-linear function f(x) = axb. As shown in the figure, the diagonalization time (red curve) grows approximately as the cube, while the coarse-graining time grows approximately linearly. Especially for large complexes, the time needed for coarse-graining the all-atom Hessian matrix using Algorithm 1 becomes increasingly smaller relative to the diagonalization time. As a result, the total time for computing the normal modes for such large protein complexes using the coarse-grained ssNMA Hessian matrices is about the same as for other coarse-grained elastic network models such as ANM.
In summary, the results in this section demonstrate that the proposed iterative coarse-graining procedure not only preserves the accuracy in depicting the motions of the coarse-grained structures but is also computationally highly efficient.
This result is significant since it means that we can construct coarse-grained models that preserve all-atom accuracy even for very large protein complexes, which was not previously possible. Next, as an application, we apply the proposed procedure to compute and analyze the dynamics of the GroEL/GroES complex.
The GroEL/GroES complex [55] is a molecular chaperone that assists the unfolding of partially folded or misfolded proteins, by providing them with the chance to refold. GroEL consists of cis and trans rings, each of which has 7 subunits. Each subunit is 547 residues. GroES also has 7 chains and each chain contains 97 residues. The GroEL cis-ring and GroES form a capped chamber that can hold proteins and facilitate protein unfolding partly through their intrinsic collective motions, such as compressing, stretching, twisting, shearing, and relaxing. Fig 3 shows the GroEL/GroES structure (pdbid: 1AON) in top and front views. In Fig 3(A), the three domains of the cis and trans rings are distinguished with different colors: equatorial (green), intermediate (yellow), and apical (blue) domains.
To understand its functional mechanisms, it is informative to obtain the intrinsic motions of this complex. However, for large protein complexes such as GroEL/GroES that has over 8,000 residues, standard all-atom NMA will take a prohibitively large memory and a long time to run. Consequently, past normal mode studies on this complex were limited to coarse-grained models [18, 36], or all-atom models of single subunits [34]. Though a more accurate description of its normal modes is highly desirable and may provide deeper insights into the functional mechanism of the complex, it was lacking due to computational constraints.
Here, we apply the proposed iterative procedure to obtaine a coarse-grained ssNMA Hessian matrix for the entire GroEL/GroES complex. This coarse-grained ssNMA (or CG-ssNMA) model preserves the same all-atom accuracy in its description of the motions of the coarse-grained structure as the original ssNMA.
First, we apply CG-ssNMA to compute mean-square fluctuations. To this end, we use the GroEL-GroES-(ADP)7 complex (pdbid: 1AON) [55] as the initial structure. This structure is composed of the co-chaperone GroES, the cis-ring whose subunits are bound with 7 ADPs, and the trans-ring (see Fig 3).
The motion correlation (or cooperativity) Ci,j between the i-th and j-th residues can be expressed as follows:
C i , j = ⟨ r i · r j ⟩ ( ⟨ r i · r i ⟩ ⟨ r j · r j ⟩ ) 1 / 2 , (6)
where ri and rj are the displacement vectors for the i-th and j-th residues in a given mode, respectively, a ⋅ b is the dot product of two vectors a and b, and ⟨a⟩ is the average value of a within the first k lowest frequency modes. Fig 5 shows the cooperativity of residue motions within each subunit and across the whole protein complex. The cooperativity plot is generated from the first 15 dominant (i.e., lowest frequency) modes given by the coarse-grained ssNMA.
Fig 5(A) shows the cooperativity among residue pairs within a single set of subunits: one subunit from the cis ring (chain A of 1AON), one from the trans ring (chain N), and one from GroES (chain O). The cooperativity of residue pairs is color coded: red for strong correlated motions (Ci,j = 1), cyan for uncorrelated (Ci,j = 0), and purple/blue for anti-correlated (Ci,j = −1). The most noticeable difference between the cis and trans rings is the involvement of the intermediate domain in the motions of the apical or equatorial domain. In the cis ring, the red regions indicate that the motions of intermediate domain (I1 and I2) are strongly correlated with those of the equatorial domain (E1 and E2), while the motions of the apical domain (A) are largely independent of them. In the trans ring, however, the motions of intermediate domains (I1’ and I2’) are more correlated with those of the apical domain (A’) than with the equatorial domain (E1’ and E2’). A similar cooperativity plot for the ANM model is given in Supporting information (S1 Fig). Overall, the two methods give similar correlation patterns. The main noticeable difference is that the relative motions between equatorial (E1’ and E2’) and apical (A’) domains of the trans-ring subunit are more clearly shown as anti-correlated (i.e., the region appears to be bluer) in Fig 5 (given by ssNMA) than with ANM shown in S1 Fig.
One general role of the intermediate domain is connecting the apical and equatorial domains and facilitating the communication between them. The results in Fig 5 imply that the dynamics or motion partner of the intermediate domain depends on the structural form of the GroEL ring: cis or trans. Considering the structure transitions of cis → trans and trans → cis that take place during the GroEL/GroES functional cycle, it is not surprising that the transition path in the former case may be different from a simple reverse of the latter. Additionally, Fig 5(A) shows that the motions of GroES and the apical domain (A) of the cis ring also are highly correlated.
The cooperativity of all the residues in the complex is presented in Fig 5(B). Along the off-diagonal there are four dark blue mesh bands, implying that the apical domains of the subunits that sit on opposite sides across the rings, such as chain C/D and chain A, are strongly anti-correlated. Another interesting observation is that the motions of GroES are strongly anti-correlated to the equatorial domain of the cis ring.
The ssNMA model presented in this work, though coarse-grained in structure, maintains an all-atom level accuracy in its description of the interactions and consequently an all-atom level accuracy in its description of the normal mode motions of the coarse-grained structure. Such an accurate description of the normal mode motions is highly desirable but has not been performed before for large protein complexes such as GroEL/GroES that has over 8,000 residues. In the following, we will examine closely the first few lowest frequency modes of ssNMA and characterize their motions. The quality of these modes is then assessed. A comparison with Cα-based ANM modes is made at the end.
Fig 6 characterizes the slow dynamics of GroEL/GroES in individual modes or pairs of modes. The first lowest frequency mode portrays a rotational motion around the cylindrical axis of the complex. This mode matches with the first mode of ANM nearly perfectly, with a high overlap of 0.97. The third mode is about opening the gate of the trans ring to receive substrates into its chamber, by moving its apical domains to conform its structure to resemble that of the cis ring. The second and fourth modes are mainly about a swing motion of the trans ring. This motion also helps to open the chamber gate of the trans ring. In ssNMA, this gate opening motion in the trans ring is clearly captured by these three distinct modes, especially the third mode, whose importance is manifested also in the conformation transitions during the GroEL/GroES functional cycle that will be described in the next section. In ANM, there is not a single mode that closely matches the third mode of ssNMA. The gating opening motion seems to spread into several modes in ANM and be mingled with other motions. The 5th–6th modes are shearing motions of the GroES cap and the apical domains of the cis ring. This motion causes them to shift significantly relative to the equatorial domains. This motion (in the 5th/6th modes) is similar, to some extent, to that in the second and third modes of ANM, which in turn have some resemblance also to the second/fourth modes of ssNMA. The 7th–10th modes display alternating motions of compression and extension of the whole complex. The 11th mode is mainly about stretching/compressing the chamber of the cis-ring. To some extent, this motion (of the 11th mode) changes the structure of the cis ring towards the shape of the trans ring. The 12th–13th modes are mainly about tilting the cis/trans rings and the GroES cap.
The animations of the top 13 dominant modes (lowest frequency) of ssNMA (and ANM) are made available at http://www.cs.iastate.edu/~gsong/CSB/coarse.
Next, we compare more quantitatively the modes of ssNMA and ANM.
In this section, we apply CG-ssNMA to interpret the conformation transitions in the functional cycle of GroEL/GroES. Our hypothesis is that the intrinsic normal mode motions of the complex should facilitate its conformation transitions. To measure how well the modes are related to the conformation transitions, we compute the overlaps between normal modes and a given transition. We then repeat the computations and analysis using ANM and compare the results with those from CG-ssNMA.
In total there are six conformation transitions among the five known conformation states of the complex (see Table 3) considered: T → R, T → R′′′, R ′ ′ → R flipped ′ ′, R nocap ′ ′ → R nocap,flipped ′ ′, R′′ → S, and S → R′′, where “nocap” stands for the absence of the GroES cap. Table 4 summarizes, for these transitions, the top 3 largest overlaps found using CG-ssNMA and ANM. The indices of the modes that give the largest overlaps also are given. The first two cases represent transitions from the apo form to ATP/GroES bound forms. The transitions R ′ ′ → R flipped ′ ′ and R nocap ′ ′ → R nocap,flipped ′ ′ were thought to take place during the functional cycle of GroEL/GroES [58], in which the two GroEL rings alternate as a functional chaperone. However, recent work [59] suggested that in vivo the GroEL/GroES complex assumes a football shape in the functional process and that both GroELs might work simultaneously as protein unfolding chaperones. For this reason, we consider also the functional transitions between states R′′ and S. Table 4 lists the results.
T → R and T → R * ′ ′: Transitions T → R and R * ′ ′ in Table 4 show that these are mostly achieved with a torsional motion along the vertical axis of the structure. Both the CG-ssNMA and ANM models capture this torsional motion, but their mode indices are different. It is the fourth mode in CG-ssNMA that gives the largest overlap while it is the first in ANM. The results clearly show that the motion to R (as induced by ATP binding) is along the path to R * ′ ′, as observed by Roseman et al. [60] from low resolution cryo-EM images.
R′′→Rflipped′′: Ranson et al. [58] suggested that the functional process of GroEL/GroES involves alternations to the two GroEL rings as functional units and the complex is bullet-shaped [55] in vivo.
Here we consider the transition from a bullet-shaped complex (R′′) to its flipped counterpart. In this transition, one of the GroEL rings goes from the trans form to the cis form, while the other ring changes from cis to trans. Results in Table 4 show that the coarse-grained ssNMA captures well the transition from trans to cis using its fourth mode, which has the second largest overlap, while the 17th mode has the best overlap and characterizes mostly the transition from cis to trans ring, as well as a partial transition from trans to cis. ANM, on the other hand, describes the transition of trans → cis and cis → trans using the 17th and 18th modes, each of which is a mixture of both cis-ring and trans-ring deformations.
It is thought that after the binding of the ATPs to the trans ring, the GroES cap is removed and the substrate protein is released. Then the two GroEL rings go through trans → cis and cis → trans transitions, respectively, and another GroES will bind the opposite ring, completing a cycle. The GroES cap stabilizes the cis ring in its conformation and prevents its transition to a trans conformation. However, after the ATP binding at the opposite ring, the GroES cap is removed, which makes the transition from a cis to a trans conformation easier. The larger overlap seen in this transition without the GroES cap (see Table 4) provides evidence that GroES is probably removed first before the cis ↔ trans conformation transitions take place rather than occurring simultaneously. This agrees with the idea that structures facilitate functional transitions.
R′′ → S (opening the trans ring gate): Recent work by Fei et al. [59] suggested that the GroEL/GroES complex in vivo should have a football shape. The formation of a football-shaped GroEL/GroES complex was thought to be promoted by substrate protein (SP), and that “SP shifts the equilibrium between the footballs and bullets in favor of the former, consequently making them the predominant species.” [59]
Here, we examine the transitions between a football-shaped complex and a bullet-shaped complex. Transition R′′ → S opens the gate of the trans ring to receive a substrate protein (unfolded or misfolded) in its chamber. This is accomplished by conforming the structure of its apical domain to that of a cis ring (see the third mode in Fig 6 and in S1 Video). S2 Fig highlights the conformation change that takes place within a trans-ring monomer in this transition. The overlaps between the transition and normal modes reveal a large contribution by the torsional rotation along the vertical axis (mode 1), as the trans ring of S is rotated about 8 degree counter-clockwise from that of R′′ [59]. Secondly, this transition is captured by the third ssNMA mode that mainly depicts a chamber-opening motion. In contrast, CA-ANM provides this transition mainly using its 20th mode, which is a mixture of the chamber opening motion and some other deformation of the cis ring and the GroES cap.
S → R′′ (closing the cis ring gate): Transition S → R′′ closes the gate of the cis ring to conform its structure to that of a trans ring. Similar to the transition R′′ → S, this transition requires torsional rotations and gate-closing motions. The coarse-grained ssNMA captures this transition using the second and third low frequency modes. CA-ANM captures the torsional rotation properly using the third mode, but has to rely on higher-frequency modes to capture the gate-closing transition (See Table 4, last column).
Normal mode analysis (NMA) is an indispensable tool for obtaining the patterns of intrinsic collective dynamics of biomolecular systems around their native states. Such dynamics studies and computations are important since dynamics is tightly linked to functional mechanisms and can reveal insights that studies based on static structures alone cannot provide. For very large complexes and eventually even a cell, all-atom descriptions of the dynamics of the system are neither feasible nor necessary. A coarse-grained structure representation is often sufficient. But what about the dynamics for a coarse-grained structure? Even though the structure representation is coarse-grained, we still would like to have an accurate description of its dynamics, ideally as close in accuracy to an all-atom model as possible.
It was by the use of coarse-grained models that past normal mode studies of very large biomolecular systems were carried out and remarkable insights were gained in these studies [18, 22, 29, 36, 39, 40]. There is little doubt that the levels of coarse-graining chosen for studying these large systems were appropriate. However, what was not previously assessed was the quality of the dynamics that was provided by those coarse-grained models. Since most coarse-grained models use extremely simple potentials to model the interactions within the coarse-grained structures, the dynamics they render are likely to have some deficiencies.
In this work, we have successfully bridged this gap and have presented a new method for constructing coarse-grained models that can preserve all-atom accuracy in dynamics. The method takes advantage of the sparseness of the Hessian matrix and iteratively reduces its size through projection until it is reduced to that of the desired coarse-grained structure. Since the projections maintain the accuracy of the interactions, the final Hessian matrix represents the precise interactions within the coarse-grained structure. Compared with the RTB (rotation-translation block) method [61] or BNM (block normal modes) [19], which assumes rigidity and ignores flexibility within each block, our method provides a more accurate description of the motions of the coarse-grained systems. Compared with the VSA model (vibration subsystem analysis) [52, 53], the advantage of our method is that it is computationally significantly more efficient.
Results presented in this work are highly significant since they promise to provide descriptions of normal mode motions at the all-atom level of accuracy even for the largest biomolecule complexes. While preserving all-atom accuracy through matrix projection is not new and has been done previously [49, 50, 52, 53], one of our key contributions here is developing a new algorithm that can carry out this matrix projection highly efficiently and therefore make it applicable to very large structure complexes, which has not been done previously. Such accurate descriptions of the intrinsic dynamics may help reveal new insights into the functional mechanisms of many biomolecular systems. It should be noted that because we are able to efficiently obtain a precise interaction model (the Hessian matrix) for the coarse-grained systems, we can solve it not only for the first few lowest frequency modes, but for all the modes. This is in line with the overarching aim of our work: to bridge between NMA and coarse-grained elastic network models while preserving the all-atom accuracy. If only the first few lowest frequency modes are needed, then there are some alternative methods that may be more efficient.
Our application of the method to GroEL/GroES reveals some new insights into the functional process of this biologically important chaperonin. For example, our results show that the conformational transitions of this protein complex in its functional cycle are even more closely linked to the low frequency modes than was previously observed using other coarse-grained models.
This work is a continuation of our previous work that aimed to bridge NMA with elastic network models [33]. While the previous work bridged between NMA and all-atom elastic network models, this work represents the second half of developing this bridge, namely between all-atom elastic network models and coarse-grained elastic network models. Combined together, the two pieces of work demonstrate how one can bridge between the conventional NMA that uses an all-atom model with a full force-field and coarse-grained elastic network models that are nowadays the preferred choice for normal mode computations due to their simplicity. This bridging reveals novel insights on how one may develop coarse-grained models that are not only simple to use, but also maintain most of the accuracy of the conventional NMA.
Although the proposed iterative coarse-graining procedure can be used to efficiently construct coarse-grained models whose description of the dynamics of the coarse-grained structures preserves all-atom accuracy, it is limited in that it can be applied only to some of the models, such as ssNMA, eANM, or sbNMA (see S1 Table). It cannot be applied to the conventional NMA. This is because the potential of NMA contains electrostatic interactions that decay rather slowly and consequently the NMA Hessian matrix is not sparse; however, there remain some uncertainties about how to best compute the electrostatics.
A possible partial solution is to add a switch function to the non-bonded interactions of NMA and make it decay to zero at some cutoff distance, as is commonly done in MD simulations. This will make the Hessian matrix much sparser and make it possible to apply the proposed iterative procedure to NMA. We have shown this to be the case (see results in S1 Table). However, this is only a partial solution since it recovers only the short range part of the electrostatics. The long range electrostatic interactions, which may have a pronounced contribution to long-range collective motions and cooperativity, are still missing. Additionally, the cumbersome energy minimization (which ssNMA does not require) becomes necessary, which can be a challenge when working with large biomolecular complexes.
One possible future work is to study the effects of electrostatic interactions on normal modes, specifically the extent of contributions by short-range or long-range electrostatic interactions. If the short-range component of the electrostatic interactions dominates the long range component in contributing to normal modes, then the aforementioned partial solution will provide an excellent approximation.
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10.1371/journal.pntd.0004801 | Impact of the Mycobaterium africanum West Africa 2 Lineage on TB Diagnostics in West Africa: Decreased Sensitivity of Rapid Identification Tests in The Gambia | MPT64 rapid speciation tests are increasingly being used in diagnosis of tuberculosis (TB). Mycobacterium africanum West Africa 2 (Maf 2) remains an important cause of TB in West Africa and causes one third of disease in The Gambia. Since the introduction of MPT64 antigen tests, a higher than expected rate of suspected non-tuberculous mycobacteria (NTM) was seen among AFB smear positive TB suspects, which led us to prospectively assess sensitivity of the MPT64 antigen test in our setting.
We compared the abundance of mRNA encoded by the mpt64 gene in sputa of patients with untreated pulmonary TB caused by Maf 2 and Mycobacterium tuberculosis (Mtb). Subsequently, prospectively collected sputum samples from presumptive TB patients were inoculated in the BACTEC MGIT 960 System. One hundred and seventy-three acid fast bacilli (AFB)-positive and blood agar negative MGIT cultures were included in the study. Cultures were tested on the day of MGIT positivity with the BD MGIT TBc Identification Test. A random set of positives and all negatives were additionally tested with the SD Bioline Ag MPT64 Rapid. MPT64 negative cultures were further incubated at 37°C and retested until positive. Bacteria were spoligotyped and assigned to different lineages. Maf 2 isolates were 2.52-fold less likely to produce a positive test result and sensitivity ranged from 78.4% to 84.3% at the beginning and end of the recommended 10 day testing window, respectively. There was no significant difference between the tests. We further showed that the decreased rapid test sensitivity was attributable to variations in mycobacterial growth behavior and the smear grades of the patient.
In areas where Maf 2 is endemic MPT64 tests should be cautiously used and MPT64 negative results confirmed by a second technique, such as nucleic acid amplification tests, to avoid their misclassification as NTMs.
| Diagnostics for rapid confirmation of positive liquid cultures presumptive of Mycobacterium tuberculosis bacteria, based on the detection of the MPT64 antigen, are being used in many TB diagnostic laboratories worldwide. Of note, diagnostic performance of these tests in West Africa, where TB is uniquely caused by the geographically restricted Mycobacterium africanum (Maf 1 and 2) and Mycobacterium tuberculosis lineages, has not been properly assessed. Although M. tuberculosis and M. africanum are genetically related, they differ in various aspects. Amongst several differences, Maf 2 grows significantly slower than Mtb bacteria. Because secretion of the MTP64 protein is dependent on the bacterial growth rate, we found that the MPT64 rapid test performance for detecting Maf 2 was lower in our setting in The Gambia. These findings might be relevant for other West African Maf 2 endemic countries where this rapid test is commonly used, as Maf 2 infected patients might have been missed in the past. Our finding emphasizes the need to thoroughly consider the presence of bacterial variants specific to certain regions during product development and implementation of novel diagnostic tests.
| Tuberculosis remains a significant public health problem in Africa. Key to interrupting transmission, which is an essential step to reduce the incidence of TB, is timely identification and treatment of diseased individuals. This objective has fueled research into new generations of diagnostics and drugs. MPT64 is a 24kD secreted protein that has been explored in diagnostics and vaccine design due to several properties: it has been associated with virulence, is highly immunogenic and is produced solely by members of the Mycobacterium tuberculosis complex [1,2,3]. MPT64 is the target of three widely used rapid speciation lateral flow assays for the identification of the MTBc in culture; BD MGIT TBc Identification Test (BD TBc ID) (Becton Dickinson Diagnostics, Becton, Dickinson and Company, Sparks, Maryland, USA), SD Bioline Ag MPT64 Rapid (SD Bioline) (Standard diagnostics, Inc., Yongin-si, Gyeonggi-do, Republic of Korea), and Capilia TB-Neo (TAUNS Laboratories, Inc., Numazu, Shizuoka, Japan). Despite the advantage of a lateral flow assay, there have been reports of the failure of MPT64 tests to detect MTBc isolates, resulting in erroneous reporting of Non-tuberculous mycobacteria (NTM) isolation [2,3,4,5]. Inability to identify the MTBc will lead to delays in initiating appropriate treatment with dire consequences for the patient and their communities given the continued risk of TB transmission.
The global phylogeographical distribution of the MTBc suggests that strain diversity is greatest in West Africa, with a representation of all major MTBc lineages [6]. Differences between these MTBc lineages might affect the performance of diagnostics and vaccines [7]. Thus, West Africa is ideal for providing a global snapshot of the performance of TB diagnostics and vaccines. Interestingly, few studies assessing commercially available MPT64 MTBc rapid speciation tests were undertaken within African populations. They were mostly done in Asia, America and Europe where strain diversity is significantly lower [8].
Lineage 6, Mycobacterium africanum (Maf) West African 2, is geographically restricted to West Africa, causing up to one third of clinically reported TB. We and others have previously described inherent genotypic and phenotypic differences between Mycobacterium tuberculosis sensu stricto (Mtb) and Maf 2 strains in vitro and within the host [9,10,11,12]. Since we previously observed differences in various virulence factors between Maf 2 and Mtb and MPT64 is a described virulence factor, we hypothesized that the sensitivity of MPT64 rapid tests for MTBc identification could be different for Maf 2 relative to Mtb strains.
In The Gambia, where Maf 2 is commonly isolated, we compared the abundance of mRNA encoded by the mpt64 gene in Maf 2 strains versus Mtb strains in the sputa of untreated TB patients. The mpt64 (Rv1980c) mRNA transcript was significantly less abundant in the sputa of TB patients infected with Maf 2 compared with Mtb. Therefore we concluded that Maf 2 might either produce the MPT64 protein at a slower rate and possibly below the limit of detection of the rapid tests. We confirmed this hypothesis and found a reduced Maf 2 sensitivity of rapid tests. Further, we compared the time to detection of the MPT64 antigen by the BD TBc ID and SD Bioline rapid tests between clinical isolates of Mtb and Maf 2. We report lineage dependent and time specific differences in conversion to MPT64 test positivity. Our findings have direct implications on the performance of these and other MPT64 based tools in West Africa.
This study was nested within an intervention trial of Enhanced Case Finding in The Gambia (Clinicaltrials.gov NCT01660646). The parent study, including bacterial sub-studies, received ethical approval from the Joint Gambia Government/MRC Ethics Committee and the Institute of Tropical Medicine (ITM), Antwerp Institutional Review Board. Written informed consent was obtained from all participants who were assigned unique identifiers for purposes of anonymity and confidentiality.
Sputum samples were prospectively collected from individuals with suspected TB between April and October 2014 and were all initially screened for the presence of AFB by Auramine microscopy. Fresh samples were decontaminated by the NALC-NaOH method as described previously [13]. The purity of all decontaminated samples was subsequently checked on blood agar for 48 hours at 37°C and screened for AFB by Ziehl-Neelsen [14] staining, during which time the decontaminated sputa were stored at -20°C, prior to inoculation.
Sputa from 11 adult patients with smear positive TB that had not started therapy were collected and stored in Guanidine Isothiocyanate. Later 5 Maf 2 and 6 Mtb sputum specimens were re-suspended in Trizol for total RNA isolation. Gene expression of mpt64 gene was performed using Multiplex qPCR with previously published TaqMan primer-probe sets as described previously [15,16]. Multiplex RT-PCR data were normalized and analysed according to a previously published method. For a detailed description please refer to Garcia et al. [17].
Confirmed blood agar negative and AFB positive samples were cultured within the BACTEC MGIT 960 System (MGIT 960; Becton Dickinson Microbiology Systems, Sparks, Maryland, USA) at 37°C according to the manufactures’ instructions. Instrument positive vials were removed from the machine and again subjected to purity check using blood agar and ZN microscopy to confirm the presence of AFB. Cultures with growth on blood agar and/or AFB negative were excluded from the study.
Blood agar negative and AFB positive MGIT cultures were tested on the day of MGIT positivity (day 0, T0) with the BD TBc ID rapid speciation lateral flow assay, following the manufacturers’ instructions. The BD TBc ID manual specifies that tests should be performed on AFB-positive MGIT tubes only if AFB-positive organisms predominate on a smear and cautions on the possibility of false results due to the presence of non-AFB organisms in cultures. In order to test whether the lower sensitivity of the MPT64 assay was specific to one manufacturer, we additionally tested all BD TBc ID negative samples at T0, including a random number of positive samples, with the SD Bioline kit at the same time points. Tests were always independently evaluated by at least two blinded readers; in case of discordance between the readers a third blinded reader was consulted. Faint bands were considered as positive results. Results were recorded after 15 minutes and negative cultures at T0 were retested at 3, 10, 15 and 90 days with both the BD TBc ID and SD Bioline rapid speciation kits.
Aliquots of the MGIT cultures were taken at T0 and heat killed. These lysates were genotyped by spoligotyping as previously described [18]. Patient isolates were assigned to specific TB lineages using the TB-lineage tool [19] within the TB-Insight public database.
We plotted the survival curves for Maf 2 and Mtb conversion to rapid test positivity at 1, 3, 10, 15 and 90 days and used the generalized Log-rank test for interval-censored failure time [20] to compare the two survivor functions. We estimated the effect of MTBc lineage (Maf 2 and Mtb) on the time-to-conversion to rapid test positivity using a Weibull regression model for interval-censored failure time [20] after controlling for testing time interval, age, gender, smear grade, duration spent in the MGIT, mycobacterial growth units, and whether patients had already initiated therapy. The BD TBc ID manual indicated that tests could be performed within 10 days after MGIT tube positivity, while SD Bioline did not specify any time interval for the performance of tests. Therefore, for comparative evaluation we stratified both analyses by a 0–10 days testing window and 10–90 days follow-up. A paired interval-censored analysis was used to compare BD TBc ID and SD Bioline performance. All analyses were performed using STATA 13.1 (Stata Corp., College Station, Texas).
On comparing the abundance of the mtp64 mRNA transcript in sputum samples from 6 Mtb and 5 Maf 2 infected patients, who had not received any TB treatment, the mpt64 transcript was significantly less abundant in the sputa of Maf 2-infected patients, compared to sputa of Mtb-infected patients (fold change = 2.52, p = 0.006). To validate our expression data, we measured the abundance of mRNA transcripts of the gene (Rv2355) responsible for the transposition of IS6110 elements in Maf 2 samples since Maf 2 is known to have lower copy numbers of IS6110 elements than Mtb [21]. Interestingly, we found a significantly reduced abundance of mRNA transcripts of this transposase in Maf 2 samples. In vitro under expression of the polyketide synthesis loci responsible for production of sulfolipids in Maf 2 has been reported [22], which were also significantly downregulated in Maf 2 in our ex vivo data. For a detailed overview of mRNA expression for each individual patient please see S3 Table.
Altogether, 193 sputum samples from 193 presumptive TB patients were cultured in the MGIT 960 System. For a summary of patients’ characteristics see Table 1. Of the 193 cultures, 20 (10.4%) were excluded from the study due to missing patient data (n = 4; (2.1%)), being AFB negative and contaminated with other microorganisms on blood agar (n = 4; (2.1%)), AFB negative without contamination (n = 9; (4.7%)), or AFB positive and contaminated (n = 3; (1.5%)) (see Fig 1).
A hundred and seventy-three positive cultures (89.6%), from 168 AFB smear positive and 5 AFB smear negative sputa, were blood agar negative, AFB positive by ZN staining and were tested with the BD TBc ID kit (Fig 1). One hundred and fifty samples tested positive with the BD TBc ID speciation kit on T0, with 23 testing negative at this time point, representing 13.2% of cultures tested.
All 173 samples were spoligotyped and assigned to a lineage within the MTBc. Samples genotyped belonged to lineages 1 (Indo-oceanic), 2 (East-Asian/Beijing), 4 (Euro-American) or 6 (Maf 2).
The conversion time of the BD TBc ID assay did not differ among the three Mtb lineages- East-Asian, Euro-American and Indo-Oceanic (East-Asian and Euro-American (p = 0.55), East-Asian and Indo-Oceanic (p = 0.89) and Euro-American and Indo-Oceanic (p = 0.63) (S1 Table). Therefore, we combined the three lineages into one group, Mtb.
We found an overall reduced rapid test sensitivity for Maf 2 when compared to Mtb (see Fig 2 and Table 2).
There was strong evidence of a difference in the time to detection between Maf 2 and Mtb (P = 0.001), with Mtb strains having a higher rate of conversion to BD TBc ID rapid test positivity than Maf 2 (Fig 2 and Table 2).
After controlling for age, gender, smear grade, duration spent in the MGIT automated culture system, mycobacterial growth units, and whether patients had already received some TB therapy, in a multivariable analysis, there was strong evidence of association between species and conversion to BD TBc ID positivity during the first 10 days (p<0.0001, Table 3). The rate of rapid test positivity in Mtb-infected patients was almost 4 times higher compared to Maf 2-infected patients within the first 10 days. From 10–90 days, there was no evidence of difference in the time to a positive test between Mtb and Maf 2 (p = 0.15, Table 3).
There was strong evidence of association between smear grade and conversion to positivity (p = 0.0001, Table 3) after controlling for lineage, testing time interval, gender, age, therapy, duration spent in the MGIT and mycobacterial growth units. The rate of conversion to positivity in patients having smear grade +++ was about 3 times higher than patients with a scanty smear. Generally, cultures that spent longer in the MGIT automated culture system before turning culture positive were more likely to have a BD TBc ID positive rapid test result at T0 (p<0.0001).
As shown in Table 3, there were no associations between conversion to BD TBc ID positivity and gender, age, therapy or mycobacterial growth units.
As only a subset of MGIT cultures were tested with both MPT64 assays, firstly, we assessed if there was any evidence of selection bias. Finding none (S2 Table), we compared their performance in a paired interval-censored analysis restricted to samples with results from both assays, correcting for the same explanatory variables as used in the BD TBc ID survival analysis.
Within each time interval and for each strain, there was no evidence of difference in time to rapid test positivity between BD TBc ID and SD Bioline (Table 4), suggesting that the decreased sensitivity may not be manufacturer specific yet intrinsic to the MPT64 target.
Our findings suggest 2.5-fold decreased expression of the mpt64 gene in Lineage 6 of M. africanum compared to M. tuberculosis and a significant decrease in sensitivity (78% on day T0) of the MPT64 based lateral flow assays for speciation of Lineage 6 cultures relative to M. tuberculosis, as members of the MTBc.
Among the smear microscopy positive TB suspects enrolled in the present study, all were ultimately confirmed as MTBc infected, although 22% of Maf 2 patients, and 10% of MTB patients, would have been misclassified as NTMs if the tests had not been repeated after T0, the day the MGIT culture turned positive. During the 10-day MGIT positive window recommended by the BD TBc ID manufacturer, only 84% of all Maf 2 were detected by a positive test versus 98% of Mtb strains. Given the relatively low cost, limited technical expertise and shorter turnaround time associated with using rapid speciation tests compared to alternative speciation methods, MPT64 rapid tests will likely remain one of the preferred options for timely diagnosis of suspected TB despite the possibility of false negative results. Therefore, a negative MPT64 result would require confirmation by an alternative method, such as molecular tests or culture on para-nitrobenzoic acid (PNB), depending on laboratory infrastructure and resources. As BD and SD Bioline MPT64 rapid tests have been on the market for over a decade now, several groups have now evaluated them. The inability of the MPT64 tests to detect strains that have lost the mpt64 gene or acquired mutations has been reported previously [4,23]. A recent meta-analysis reported a sensitivity ≥ 95% yet studies evaluating these tests were conducted using a very limited panel of MTBc lineages, the majority belonging to M. tuberculosis sensu stricto lineages.
Two studies including Maf were biased by the low number of isolates evaluated compared to Mtb strains tested [24,25]. In their analysis of the sensitivity of the BD TBc ID test using reference strains, Yu et al included one Maf strain among 24 NTM strains, 18 mixtures of M. tuberculosis and NTM strains, 2 M. bovis strains and 1 Nocardia spp. strain. In the same study, 171 clinical respiratory specimen were prospectively analyzed yet these were all M. tuberculosis isolates [25]. Gaillard and co-workers, in their assessment of both the SD Bioline and BD TBc ID tests, included 20 Maf among 318 MTBc consisting of 242 M. tuberculosis, 53 M. canettii, 2 M. bovis and 1 M. bovis BCG Pasteur [24]. Although all Maf strains were detected, neither of the studies specified whether Maf 1 or Maf 2 isolates were tested. Interestingly, an earlier report hinted on the possibility of MPT64 rapid test sensitivity being influenced by the amount of antigen secreted by the metabolically growing MTBc cells and emphasised the need to determine factors driving secretion [26]. Notably, Gaillard and co-workers reported the production of a faint band after the standard 15 min incubation period by 8 out of 20 Maf strains tested during their evaluation of both the SD Bioline and BD TBc ID tests. They suggested further incubation of cultures which produce faint bands to allow production of greater amounts of antigen by the MTBc strain that could be clearly detected as positive [24]. Interestingly, in this study, we also observed an association between the duration spent by cultures in the MGIT automated culture system and positive rapid test results. The longer cultures spent in the MGIT culture system, the more likely they were to produce detectable amounts of MPT64 and therefore a positive rapid test result. Furthermore, Gagneux and colleagues detected a non-synonymous SNP in the mpt64 gene of all isolates from Maf 1, which they hypothesized could affect the sensitivity of MPT64 tests in West Africa [7]. As no Maf 1 was identified in our study, the sensitivity of the MPT64 rapid tests to detect Maf 1 will need to be validated elsewhere. However, in a report from Nigeria, no failures of the MPT64 test were reported in detecting Maf 1, albeit not all positive cultures were accounted for [27].
One possible explanation for the evidence of association between high smear grade and rapid test positivity could be the presence of a greater number of actively dividing cells secreting the MPT64 antigen leading to an abundance of antigen in the growth medium. Interestingly, Mtb strains reportedly divide significantly faster than Maf 2 strains [9]. Since the secretion of MPT64 has previously been linked to active cell division [1] the differential growth behaviour could also support our finding of lower detection of MPT64 rapid tests for Maf 2. Additionally future studies should ascertain if the observed under expression of mpt64 in Maf 2 was due to mutations in the gene. Whole genome sequencing will also enable us to confirm if these Maf 2 strains belong to a unique clone that is currently spreading in the Gambia or whether it is a general trait of Maf 2.
Our findings indicate that MPT64 tests need to be cautiously used in settings where Maf 2 is common. Different modifications of workflow can be considered, such as repeating the MPT64 assay after 10 days on all T0 MPT64 negative cultures, and molecular confirmation as MTBc vs NTMs in AFB positive cultures that test MPT64 negative. A preferred approach will be to apply the Xpert MTB/RIF assay or Line-Probe-Assays (LPA) to all AFB positive, MPT64 negative cultures. Generally, our findings strongly emphasize the need to consider strain diversity during TB product development. Our study further demonstrates that a careful evaluation and validation of novel tests before implementation, especially in regions with geographically restricted MTBC lineages, such as M. africanum in West Africa, is imperative.
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10.1371/journal.ppat.1002512 | Mechanisms of Pathogenesis, Infective Dose and Virulence in Human Parasites | The number of pathogens that are required to infect a host, termed infective dose, varies dramatically across pathogen species. It has recently been predicted that infective dose will depend upon the mode of action of the molecules that pathogens use to facilitate their infection. Specifically, pathogens which use locally acting molecules will require a lower infective dose than pathogens that use distantly acting molecules. Furthermore, it has also been predicted that pathogens with distantly acting immune modulators may be more virulent because they have a large number of cells in the inoculums, which will cause more harm to host cells. We formally test these predictions for the first time using data on 43 different human pathogens from a range of taxonomic groups with diverse life-histories. We found that pathogens using local action do have lower infective doses, but are not less virulent than those using distant action. Instead, we found that virulence was negatively correlated with infective dose, and higher in pathogens infecting wounded skin, compared with those ingested or inhaled. More generally, our results show that broad-scale comparative analyses can explain variation in parasite traits such as infective dose and virulence, whilst highlighting the importance of mechanistic details.
| We found that mechanisms used by parasites to infect hosts are able to explain variation in two key pathogen traits: infective dose and virulence. In pathogens where the molecules secreted to facilitate infection acted locally, the number of cells required to start an infection (infective dose), was lower than in pathogens where the secreted molecules act more distantly. Parasite virulence showed no correlation with local versus distant action, but was negatively correlated with infective dose, and greater in species that infect via wounded skin. By showing how such parasite life history details matter, our results help explain why classical trade-off models have been relatively unsuccessful in explaining broad scale variation across parasite species.
| There is huge variation across pathogen species in the number of cells required to successfully infect a host. This number is known as the ‘infective dose’. At one end of the scale, species such as Shigella and Giardia lamblia require about 10 cells to start an infection. In contrast, species such as Vibrio cholera and Staphylococcus aureus require 103–108 cells in order for an infection to develop [1]–[3]. It is unclear why infective dose varies, with large differences occurring even between closely related pathogens [2], [3].
Schmid-Hempel and Frank [2] predicted that the variation in infective dose could be explained by the different biochemical mechanisms that pathogens use to infect hosts. Pathogens secrete a number of molecules which facilitate the suppression and/or evasion of host immune responses, and hence aid parasite growth. If these molecules act locally, in the vicinity of the pathogenic cell, then only small numbers of molecules may be required for successful growth and so infections can be established from small numbers of pathogenic cells. In contrast, if the pathogenic molecules diffuse and therefore act at a distance, then large numbers of molecules may be required for evading the host immune system. In these cases greater numbers of pathogenic cells could be needed to establish an infection. However, while this prediction is consistent with anecdotal data [2], [3], it has yet to be tested formally.
Here, we test Schmid-Hempel and Frank's [2] prediction that infective dose is determined by whether pathogenesis is locally or distantly acting. We use data from 43 species of human pathogens across a range of enteropathogenic bacteria, protozoa, fungi and viruses. A possible problem with comparative studies across species is that closely related species can share characters through common descent rather than independent evolution. Consequently, analysing species as independent data points can lead to misleading correlations [4]–[6]. For example, all viruses are locally acting, and so this could lead to patterns between viruses and bacteria, rather than local or distant action. We account for this potential problem of shared ancestry by using multivariate nested taxonomic models [7], [8].
We then extend this work in two ways. First, Schmid-Hempel and Frank [2], [3], [9] further predicted that pathogens with distantly acting immune modulators will be more virulent, possibly because they would have a large numbers of cells in the inoculums, and higher parasite density would overwhelm the host immune system causing more harm to hosts. We therefore test whether the virulence of pathogens with distantly acting immune modulators is greater than that of pathogens with locally acting molecules. Second, we test the influence of two other factors that could affect infective dose and virulence: mode of transmission (direct or indirect) and route of infection (ingestion, inhalation or wounded skin) [1], [10]–[12]. These factors could influence dose and virulence for a number of reasons, including their affect on: the extent to which virulence reduces pathogen transmission; the types of immune response they encounter; and the genetic diversity (or relatedness) of the pathogens either competing for or cooperating to exploit the host [2], [3], [9]–[28].
We found that pathogens with immune modulators that act distantly within the host have significantly higher infective doses than pathogens with locally acting molecules, (Figure 1 and Table S2 in Text S1: F1, 40 = 25.79, P<0.0001). This supports the prediction by Schmid-Hempel and Frank [2] that local pathogenic action requires only a small number of molecules, and thus relatively few cells are needed to start an infection, compared to distantly acting mechanisms where a large number of diffusible molecules need to accumulate in order to overwhelm the host's immune clearance.
Contrary to the hypothesis that pathogens with distantly acting immune modulators are more virulent, we found no significant relationship between case fatality rate or severity of infection and the mechanism of pathogenesis (Table S3 and Table S4 in Text S1: P>0.05). However, case fatality rate was significantly negatively related to infective dose of pathogens (Figure 2 and Table S3 in Text S1: F1, 38 = 3.94, P = 0.05). We suggest this correlation arises because for a given dose, pathogens that are locally acting and have lower infective doses are more likely to establish an infection. For this relationship to hold, we reasonably assume that the actual dose in natural infections is largely determined by factors such as mode of transmission, and so does not show a strong covariance with whether a parasite acts locally or globally within the host. We attempted to collect data on mean parasite dose in different transmission modes during natural infections so we could examine how this correlates with local/global within-host parasite action, but we were unable to obtain sufficient data.
There are at least two possible alternative explanations for the negative relationship between case fatality rate and infective dose of parasites, although we suggest these are less likely than the above explanation. First, recent theory suggests parasites might adapt to low infective doses by evolving a higher per-parasite growth rate, causing greater host exploitation and virulence [29]. However, the reduction in dose in this model results from increased host resistance, and there is no reason to assume that selection for host resistance consistently differs between global and local acting parasites.
Second, a low infective dose may reduce the incidence of multiple genotype pathogen infections since there are fewer parasites in the inoculum, which could favour higher levels of cooperation between parasites, and hence lead to greater growth and virulence [24], [25]. However, the extent to which this will be of general importance will be limited by the fact that different biological details can lead to different relationships between strain diversity and virulence. For example, when different parasite strains compete for host resources, higher strain diversity is expected to lead to greater virulence [10], [11], [22], [29]–[33]. Alternatively, antagonistic interaction between strains, such as chemical warfare, can lead to a predicted domed relationship between strain diversity and virulence [26]. Nonetheless, it is possible that all three explanations could play a role, with their importance varying across species.
We found that pathogens infecting hosts through wounded skin result in significantly higher case fatality rates than pathogens inhaled or ingested (Figure 3 and Table S3 in Text S1: F2, 26 = 5.30, P = 0.01). Given that infection via wounded skin includes transmission via bites of insect vectors and contaminated water, this result supports theory on virulence-transmission trade-offs which proposes that vectors and water systems circumvent the need for an ambulatory host to transmit pathogens, selecting for the evolution of higher virulence [12], [18], [21]. However, another potentially important factor is that the type of immune response that pathogens are confronted with will affect virulence. Pathogens that infect hosts through wounded skin evade mechanical immunity and directly enter the circulatory system. Hence, they may cause virulent systemic infections more readily than ingested or inhaled pathogens, which must overcome other anti-infection barriers such as stomach acid and mucus membranes before causing systemic infections.
More generally, our results emphasise the importance of life-history or mechanistic details for the evolution of parasite traits. Theoretical models for the evolution of parasite traits such as virulence have generally relied on simple trade-offs between virulence and transmission. These models have been able to explain variation in virulence both within species, and between closely related species with similar life histories [15], [20], [28], [33]–[37]. In contrast, this body of theory has been less successful at explaining broad scale variation across species [2], [9], [25], [27]. One possible explanation for this is that the predictions of virulence theory can depend upon the mechanisms that parasites use to infect and exploit hosts, which are not considered in the classical models; hence our expectations of data fitting the model may be too high. If the details of how parasites infect hosts really matter, this would limit the extent to which we would expect to find broad empirical patterns to match theory [25]. Our results show that transmission, dose and virulence can be influenced by mechanistic details such as distance at which molecules act and route of infection.
We obtained data on the number of pathogen cells required to start an infection (infective dose) by searching: (a) databases from the United States Food and Drug Administration [38], Health Canada [39], Medscape [40], the Centre for Disease Control and Prevention [41], the World Health Organisation [42]; (b) empirical studies found via keyword searches in the ISI Web of Knowledge database [43]. Where ranges or more than one estimate of infective dose were given, we calculated the median infective dose to use in our analyses.
We emphasise that uncertainties exist in infective dose measurements: often they were extrapolated from epidemiologic investigations, were obtained by human feeding studies on healthy, young adult volunteers, or are best or worst estimates based on a limited data from outbreaks. Where known, we give methods of estimation in Table S1 in Text S1.
We classified pathogens as having local or distant action according to the framework of Schmid-Hempel and Frank [2]. For local action, pathogens directly interact with host cells via surface-bound molecules or by injecting proteins into host cells by a type III or IV secretion systems. For example, Yersinia entricolata/tuberculosis injects Yop protein into target cells via a type III secretion system, leading to cytotoxicity [44], and Ebola virus binds to different cell surfaces and replicates leading to cell necrosis [45].
For distant action, pathogens indirectly interact with host cells by secreting proteins that diffuse into their surroundings and only exert pathogenic effects when they bind to host cells. This may arise, for example, through immune modulators delivered by the general secretary pathway, or the type I, II and V secretary systems. For example, the well known virulence factor lysteriolysin O of Listeria monocytogenes and exotoxins of Staphylococcus aureus, are secreted via the general secretory pathway [46]. We do not classify between interactions with host and immune cells specifically since we are concerned with how far the interaction occurs from the infecting parasite, not with what cell the interaction occurs with.
To capture both the short and long term consequences of pathogen infection on host health, we use case fatality rate and a ‘disease severity’ score to measure pathogen virulence. These are two of the three criteria used to estimate ‘burden of disease’ in a recent protocol for prioritising infectious disease in public health [47].
We rated each pathogen according to its severity, as described in Table 1. We gave a score of 0 to pathogens of average importance, or pathogens for which a lack of data precluded another score. Incidence data are the estimated mean number of new cases per year in the USA. Case fatality rates are estimates of fatality without treatment or co-morbidities and represent the number of cases of a disease ending in death compared to the number of cases of the disease. We obtained data from the before-mentioned databases, plus various reports in the literature (see Table S1 in Text S1).
We emphasise that while a “case” should represent an infected individual, in practice it may involve infection of some severity, -hospitalization even. Thus overall our definition of case fatality may overestimate virulence. For example, a benign parasite that infects many hosts asymptomatically, but cause severe disease in a small proportion of hosts, may be classified as virulent. By contrast, a virulent parasite that causes disease of equal severity in its hosts may be classed as less virulent. To correct for this potential bias, we assessed whether case fatality rate is linked to incidence rate, and examined the effects of the other variables after controlling for variation in incidence rate.
We obtained data on transmission mode and route of infection using the before-mentioned databases. We classified pathogens as either direct or indirectly transmitted: direct transmission requires physical contact between an infected and susceptible host, and indirect transmission requires an agent to transfer the pathogen from an infected to a susceptible host. We classified the routes of infection used by pathogens as entry through wounded skin, inhalation, or ingestion. For example, Bordatella pertussis is usually spread by infected people coughing or sneezing while in close contact with susceptible others who then inhale the pertussis bacteria [41] (i.e. direct transmission). Where pathogens can use more than one mechanism of transmission or infection, we used the mechanism stated in the infective dose data for our analyses.
We performed three analyses. First, we tested whether minimum infective dose (log transformed) was related to the mechanism of infection (2 level fixed factor: local or distant), infection route (3 level fixed factor: ingestion, inhalation, wounded skin) and the transmission mode of pathogens (2 level fixed factor: direct, indirect) using a linear mixed effects model (LMM) with restricted maximum likelihood estimation (REML). Second we tested if case fatality rate (% of cases resulting in death) was influenced by infective dose (covariate log transformed), incidence (covariate log transformed), mechanism of infection, infection route and transmission mode using a generalised linear mixed effects model (GLMM) with a binomial error distribution. Finally, we analysed the severity of infection (−1, 0, 1) in relation to the same explanatory variables as the second analysis using a GLMM with an ordered multinomial error distribution.
The data (Table S1 in Text S1) encompass a diverse range of pathogens. We obtained information on the taxonomic classification of pathogens from the National Center for Biotechnology Information (NCBI) [48]. We accounted for the non-independence of data arising from phylogenetic relationships between pathogens in all LMMs and GLMMs using nested taxonomic random effects structures whereby each taxonomic level (genus, order, class and kingdom) was nested within all higher taxonomic levels (see Tables S2–4 in Text S1 for details). We only entered genus, order, class and kingdom into models because of poor replication at other taxonomic levels. We examined the significance of fixed effects (factors and covariates) using Wald type adjusted F statistics and the effect with the highest P value was sequentially dropped until only significant terms (P<0.05) remained [49]. Prior to all analyses covariates were Z-transformed (mean = 0, standard deviation = 1). We used the Kenward and Roger (1997) method for estimating standard errors for parameter estimates and denominator degrees of freedom since it is specifically designed for models with multiple random effects and unbalanced data, increasing the accuracy of significance tests [50]–[52]. We assessed the significance of random effects using log-likelihood ratio tests (LRTs) [53]. All analyses were conducted in SAS version 9.2.
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10.1371/journal.pbio.2005756 | BRAF and AXL oncogenes drive RIPK3 expression loss in cancer | Necroptosis is a lytic programmed cell death mediated by the RIPK1-RIPK3-MLKL pathway. The loss of Receptor-interacting serine/threonine-protein kinase 3 (RIPK3) expression and necroptotic potential have been previously reported in several cancer cell lines; however, the extent of this loss across cancer types, as well as its mutational drivers, were unknown. Here, we show that RIPK3 expression loss occurs progressively during tumor growth both in patient tumor biopsies and tumor xenograft models. Using a cell-based necroptosis sensitivity screen of 941 cancer cell lines, we find that escape from necroptosis is prevalent across cancer types, with an incidence rate of 83%. Genome-wide bioinformatics analysis of this differential necroptosis sensitivity data in the context of differential gene expression and mutation data across the cell lines identified various factors that correlate with resistance to necroptosis and loss of RIPK3 expression, including oncogenes BRAF and AXL. Inhibition of these oncogenes can rescue the RIPK3 expression loss and regain of necroptosis sensitivity. This genome-wide analysis also identifies that the loss of RIPK3 expression is the primary factor correlating with escape from necroptosis. Thus, we conclude that necroptosis resistance of cancer cells is common and is oncogene driven, suggesting that escape from necroptosis could be a potential hallmark of cancer, similar to escape from apoptosis.
| Necroptosis is a regulated process that triggers cell death, resulting in necrosis and inflammation. Cancer cells have been shown to lose their ability to die via necroptosis, but the genetic factors that drive this resistance remain unknown. Here, we have analyzed 941 different cancer cell types and found that 83% of them are fully resistant to necroptosis. In order to identify the mechanisms underlying necroptosis resistance in these cells, we performed bioinformatics analyses to identify genes whose overexpression or mutation correlate with this effect. We show that two major genes, which are frequently deregulated in cancer (also known as oncogenes), are key drivers of the resistance to necroptosis, and that targeting these oncogenes with specific drugs reversed this resistance. We conclude that resistance to necroptosis is a common event in cancer that can be overcome by targeting the genes that drive this resistance, which subsequently allows stimulation of cancer cell death via necroptosis.
| Necroptosis is a necrotic programmed cell death pathway mediated by the RIPK1-RIPK3-MLKL signaling cascade [1–4]. Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) can be activated when cells are stimulated by Tumor necrosis factor alpha (TNFα), Fas, or TRAIL ligands as well as downstream of Toll-like receptors [5,6]. Cells can be sensitized to necroptosis by repressing function of the inhibitor of apoptosis proteins (IAPs: cIAP1, cIAP2, and XIAP) by Smac mimetics, such as SM-164, while caspase inhibition by a pan-caspase inhibitor such as zVAD.fmk also further sensitizes cells to necroptosis [5,7,8].
During necroptosis activation, RIPK1 interacts with Receptor-interacting serine/threonine-protein kinase 3 (RIPK3) to form the necrosome, which in turn phosphorylates pseudokinase Mixed lineage kinase domain-like protein (MLKL) to mediate necrotic cell death via plasma membrane rupture [9–17]. In addition to necroptosis [9,17–21], RIPK3 has been implicated in regulation of antitumor immunity [22], apoptosis [6,11,23–29], and cytokine production [30,31]. While RIPK3 expression has been shown to be lost in several cancer cell lines and cancer types [18,21,32–34], no systematic evidence for the extent of this loss across cancer types or the mechanisms driving this loss have been reported.
The Tyro3, Axl, Mer (TAM) receptor family of tyrosine kinases plays a role in regulating cell growth, survival, and proliferation [35,36]. TAM kinases are oncogenes, frequently amplified in a variety of cancers, in which their overexpression correlates with poor patient survival [36–40]. Importantly, while TAM kinases are anti-apoptotic and are established as important mediators of resolution of inflammation [41], their roles in the context of necroptosis have not been studied.
BRAF is a major regulator of protein synthesis, cell survival, growth, and proliferation [42]. Overactivation of BRAF is observed in a vast majority of cancers [42–45]. Importantly, while BRAF is an established anti-apoptotic kinase, its role in the regulation of necroptosis is unknown.
In this study, we performed a necroptosis sensitivity screen in 941 human cancer cell lines to identify the mutational drivers of the RIPK3 expression loss and the consequent escape from necroptosis. We identified the oncogenic kinases BRAF and AXL, which were validated as potential mediators of this process, because their inhibition can rescue the loss of RIPK3 expression and result in regain of sensitivity to necroptosis. Interestingly, our tumor xenograft studies, as well as transcriptomics analyses of published RNAseq/microarray datasets of patient tumor biopsy samples, show that RIPK3 expression is lost progressively during tumorigenesis. Our results reveal a potential role of BRAF and AXL oncogenes in driving the loss of RIPK3 expression and escape from necroptosis in various cancers.
In order to understand the relevance of necroptosis in tumor growth and the in vivo kinetics of the RIPK3 expression loss during tumorigenesis, we evaluated the changes in RIPK3 mRNA levels in published transcriptomics datasets. Six patient tumor biopsy studies [46–51] and one cancer cell line xenograft study [52] were analyzed. We found that RIPK3 mRNA levels were progressively lost during tumor growth in colorectal, gastric, and ovarian cancer patients (Fig 1A). Notably, the loss of RIPK3 expression also associated with the progression to metastasis in human prostate tumors, and higher-grade adrenocortical and breast tumors (Fig 1A). Moreover, RIPK3 expression was also progressively lost during in vivo passaging of tumor xenografts using 47 human cancer cell lines, in which the majority of the cell lines showed a strong loss of RIPK3 expression at passage 10, compared to passage 1, with some heterogeneity in the extent of the loss in a fraction of the cell lines (Fig 1B and 1C).
Because the most robust RIPK3 expression loss was observed in ovarian cancer biopsies (Fig 1A), we performed a newly derived patient-derived xenograft (PDX) study using primary cells obtained from high-grade serous ovarian cancer biopsies, in order to determine whether necroptosis is physiologically activated in tumors and whether RIPK3 protein levels indeed are lost during tumorigenesis progression. We found that the expression of RIPK3, but not that of RIPK1, was progressively reduced during xenograft tumor growth in four out of five PDX samples derived from high-grade serous ovarian cancer biopsies (Fig 1D and 1E, S1A Fig). In addition, we found that MLKL was phosphorylated at Ser358 in tumors at early in vivo xenograft passages (passage 0), revealing that the necroptosis pathway is endogenously activated in tumors. Consistent with the loss of RIPK3 expression, MLKL phospho-Ser358 levels decreased as a function of serial in vivo passage of the PDXs (Fig 1D and 1E). Importantly, while ex vivo–cultured tumor xenograft cells were sensitive to TNFα+SM-164+zVAD.fmk (TSZ)-induced necroptosis at passage zero, they were fully resistant after the third in vivo serial xenograft, and because of the resistance to cell death, this treatment of TSZ did not induce cell death, but rather induced cell growth resulting in an approximately 140% survival rate (Fig 1F and S1B Fig). The TSZ-induced necroptosis in these cells was potently blocked by 10 μM of the RIPK1 inhibitor Nec-1s and 10 μM of the RIPK3 inhibitor GSK’872, and was also blocked by 10 μM of the MLKL inhibitor necrosulfonamide (NSA) (S1B and S1C Fig).
These findings reveal that the loss of RIPK3 expression occurs progressively during tumorigenesis in vivo and that necroptosis is activated in tumors that express RIPK3.
In order to identify the mechanisms driving RIPK3 expression loss in cancer cells, we performed a necroptosis sensitivity screen using a panel of 941 human cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) collection, which represent various cancer types from 28 tissues [53,54]. A potent TNFα + SM-164 + zVAD.fmk (TSZ) treatment was used to stimulate necroptotic cell death under nine different SM-164 concentration conditions in the 4–1,024 nM range (Fig 2A). Remarkably, we found that 780 (83%) of these cell lines were fully resistant to necroptosis induced by TSZ even at the highest SM-164 concentration (Fig 2B and 2C, S1 and S2 Tables). These screen results were validated by testing 23 randomly selected cancer cell lines, which showed a complete resistance to TSZ- and TNFα+Cycloheximide+zVAD.fmk (TCZ)-induced necroptosis, lack of RIPK3 expression, and lack of MLKL Ser358 phosphorylation upon stimulation with TSZ treatment (Fig 2D and 2E and S2A Table). Out of 28 tissue types from which the cancer cell lines were derived, 8 tissue types were found to have no sensitive cell lines, and no tissue type was found to lack resistant lines (S2B and S2C Fig).
Together, these results suggest that the escape from necroptosis is found in most cancer cell lines, independent of tissue and cancer type.
Having established that RIPK3 expression loss is observed during tumorigenesis (Fig 1) and that this loss is prevalent across cancer types (Fig 2B), we next set out to identify drivers of this loss. We performed genome-wide Pearson correlation analysis using the mRNA expression datasets from both GDSC and Broad-Novartis Cancer Cell Line Encyclopedia [55] (CCLE) in order to identify genes whose elevated expression correlates with high TSZ-IC50 values (i.e., resistance to necroptosis). We used both databases because the GDSC and the CCLE database cell line collections overlap and the expression values obtained from two independent sources would increase the confidence in the obtained correlation results. Our correlation analyses revealed 634 genes whose expression positively correlated with the resistance to necroptosis (p < 0.01, Bonferroni correction). RIPK3 expression was the most negatively correlated with resistance to necroptosis (Pearson coefficient = −0.43, p = 4.11 × 10−24) and its low expression was significantly enriched in necroptosis-resistant (NR) cell lines, confirming the validity of the screen and the analysis strategy (Fig 2F and S3A Fig). Consistently with its key role in necroptosis, MLKL expression also negatively correlated with resistance to necroptosis (Pearson coefficient = −0.25, p = 8.45 × 10−7), while RIPK1 expression did not (Fig 2F). Importantly, 20 of these genes were known to be classified as oncogenes or genes that promote oncogenic transformation (see Materials and methods for the bioinformatics analysis description) (S3B Fig).
Out of the 20 oncogene-related genes, we focused our subsequent experiments on AXL, because (a) its family member TYRO3 was also among the 634 genes that positively correlate with resistance to necroptosis; (b) out of the two TAM kinase family members, AXL expression showed the strongest positive correlation with TSZ-IC50 (AXL: Pearson coefficient = 0.21, p = 2.91 × 10−5; TYRO3: Pearson coefficient = 0.10, p = 0.017); and (c) AXL is the predominant TAM kinase family member that is frequently overexpressed in cancer. Importantly, transcriptomics analysis of the screened 941 cancer cell lines revealed that high AXL and TYRO3 mRNA levels predict both resistance to necroptosis and low RIPK3 mRNA levels (Figs 2F and 3A–3D, S3 Table), but not those of RIPK1, MLKL, or any other pro-necroptotic genes (S4A Fig).
AXL expression levels also negatively correlated with RIPK3 expression in stomach adenocarcinoma tumors and acute myeloid leukemia (S4B Fig), based on the analysis of the Cancer Genome Atlas (TCGA) database [56] using cBio Cancer Genomics Portal [51] and according to both Pearson and Spearman correlation analyses.
A similar positive correlation between AXL expression and TSZ-IC50, as well as a negative correlation between AXL-RIPK3 expression levels, was observed when expression values from the CCLE database were used for the analysis (S5A and S5B Fig). Quartile analysis of the data also confirmed these Pearson correlation observations (S5C and S5D Fig).
Clustering analysis (Fig 3D) revealed that majority of the analyzed cancer cell lines that are resistant to necroptosis (high IC50, cluster 1, about 83%) are either AXLhigh (cluster 2, about 28%) or TYRO3high (cluster 3, about 14%), while the majority of those that are sensitive to necroptosis (low IC50) are RIPK3high and have low/medium AXL/TYRO3 levels (cluster 4, about 19%). While the majority of the cells in the cluster 4 were RIPK3high, a fraction were RIPK3low, suggesting that high RIPK3 mRNA levels are not a prerequisite to undergo necroptosis and that sufficient RIPK3 protein is expressed in these cells to undergo necroptotic cell death. However, RIPK3 expression levels were more heterogeneous than the TSZ-IC50 values, and about 18% of the cell lines were fully resistant to necroptosis despite the presence of RIPK3 expression (clusters 5 and 6), suggesting that the escape from necroptosis may not be only due to loss of RIPK3 expression. Moreover, not all cell lines with high AXL/TYRO3 levels had lost RIPK3 expression (cluster 5). Additionally, about 14% of the cell lines with low RIPK3 levels and resistance to necroptosis did not have high AXL/TYRO3 levels, suggesting that other RIPK3 loss-driving forces may exist (cluster 7). Overall, this analysis revealed a great degree of heterogeneity in AXL/TYRO3 and RIPK3 expression levels and resistance to necroptosis in the screened lines, as well as the presence of high AXL/TYRO3 and concomitant low RIPK3 expression levels in about 56% of the NR lines, suggesting that high expression levels of AXL/TYRO3 could be potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer.
A 4-day treatment of A375 and SkMel28 cancer cell lines, which have no initial RIPK3 expression (but also no genetic mutations of RIPK3), with low concentrations of AXL/TYRO3 inhibitor BMS-777607 resulted in a regain of RIPK3 expression at both mRNA and protein levels (Fig 3E). Importantly, this treatment also restored the sensitivity of these cells to TSZ-induced necroptosis (Fig 3F).
Overall, these findings suggest that AXL/TYRO3 overexpression, frequently seen in cancers, promotes the loss of RIPK3 expression and escape from necroptosis, which may be reversed upon inhibition of these kinases. Moreover, high AXL/TYRO3 levels are potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer.
Using the differential sensitivity to necroptosis data from the cell-based screen, we performed a second round of bioinformatics analysis with a focus on genome-wide mutational enrichment in NR (fully resistant to necroptosis even at 1 μM of SM-164) versus necroptosis-sensitive (NS) cell lines. Our analysis revealed that several oncogenic mutations, including those of BRAF, are strongly enriched in the NR cell lines, compared to the NS cell lines (Fig 4A and 4B). Interestingly, 75 of the NR cell lines were found to have high RIPK3 expression (S6A Fig). Mutational enrichment analysis of the NR-RIPK3high versus NR-RIPK3low populations revealed 73 interesting genes, mutations of which may lead to necroptosis resistance via alternative pathways, independent of the RIPK3 expression suppression (S6B Fig).
Due to the importance of BRAF overactivation in cancer, we next focused on this oncogene. Transcriptomics analysis of the screened cell lines showed that mutations that lead to overactivation of BRAF can predict the loss of RIPK3 expression levels in cancer, despite many of the BRAFWT cell lines displaying low RIPK3 expression, consistent with its heterogeneous nature (Fig 4C, S4 and S5 Tables), similar to that of high AXL expression levels.
These results raised the question of whether inhibition of BRAF, similar to that of AXL, could also result in reversal of the RIPK3 expression loss. Indeed, a transcriptomics study [57] analyzing melanoma patient tumor biopsies before and after treatments with BRAF inhibitors Dabrafenib and Vemurafenib revealed that RIPK3 expression was increased by at least 1.2-fold in 58.3% of the patients and decreased by least 1.2-fold in 25% of the patients, while no change was observed in 16.7% of the patients, consistent with the heterogeneous nature of RIPK3 expression loss (Fig 4D). Importantly, treatment of ES2 and SkMel28 cell lines, both of which carry an activating BRAF V600E mutation and have no initial RIPK3 expression, with low concentrations of BRAF inhibitor TAK-632 for 4 days resulted in an up-regulation of RIPK3 expression (Fig 4E). Importantly, this treatment also restored the sensitivity of these cells to TSZ-induced necroptosis (Fig 4F).
These findings suggest that oncogenic BRAF overactivation promotes the loss of RIPK3 expression and escape from necroptosis, which may be reversed upon inhibition of BRAF. Moreover, BRAF overactivating mutations are potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer.
Here, we establish that necroptosis resistance can be found in high percentages of cancer cell lines derived from cancers of different tissue and cell type origins. We discover BRAF and AXL as the first two oncogenes that can drive the loss of RIPK3 expression in cancer cells (Fig 5). BRAF gain-of-function mutations and AXL overexpression, which are both observed in various cancers at high frequencies, are important therapeutic targets for the treatment of cancers. Interestingly, we found that the expression of RIPK3 may be restored upon inhibition of BRAF and AXL.
The loss of RIPK3 is a heterogeneous event, and its extent differs across various cancer cases, as can be seen from the screen data (Fig 2 and Fig 3) and the xenograft data (Fig 1 and S1A Fig). However, the prevalent loss of RIPK3 expression (Fig 3D) and resistance to necroptosis may be an important factor to consider during design of anticancer therapies. Our results suggest that therapies targeting key oncogenes BRAF and AXL result in a regain of RIPK3 expression in cancers that have lost it. Therefore, combinations of the compounds targeting these oncogenes with strategies that aim to induce necroptosis in tumors might augment the therapeutic benefit, because the regain of RIPK3 expression induced by the BRAF or AXL inhibitors is expected to render the tumors necroptosis sensitive.
However, because we show tumors undergo necroptosis in vivo, this inflammatory mode of cell death could positively contribute to tumor growth. Therefore, one needs to consider the potentially negative consequences of reactivating necroptosis by inducing the lost RIPK3 expression, because increase in necroptosis and inflammation can fuel tumor growth. On the other hand, RIPK3 has been shown to be important for CD8+ T-cell cross-priming and antitumor immunity [22]; therefore, inducing RIPK3 expression in tumor cells could increase their clearance by CD8+ T cells. Thus, induction of RIPK3 expression in cancer could prove to be a double-edged sword. Furthermore, while RIPK3-induced cytokine production and necroptosis-induced inflammation (or necroinflammation) [58] can fuel the tumor cell growth, such RIPK3-dependent processes may also promote antitumor immunity and programmed cell death of the tumor cells. It is conceivable that uncoupling necroptotic cell death, the pro-growth inflammation it brings, and CD8+ T-cell cross-priming induction could bring forward the benefits of RIPK3 expression induction in cancer (i.e., antitumor immunity stimulation via cross-priming) and diminish its disadvantages (inflammation, increased tumor growth and necrosis).
The presence of MLKL phospho-Ser358 marker in the tumor xenografts (Fig 1D) may also indicate that other roles of MLKL unrelated to cell death are at play during tumorigenesis, because phosphorylation of MLKL at this residue has been shown to be not sufficient to commit to necrotic cell death, as demonstrated in a recent study that links the ESCRT-III complex downstream of MLKL [59]. For instance, during tumorigenesis, MLKL/ESCRT-III pathway could be promoting CD8+ T-cell cross-priming and enhancing antitumor immunity, as ESCRT-III was found to be involved in cross-priming by necroptotic cells [59].
Investigating the role of the RIPK3 expression regain in cancer resistance and tumor regrowth in patients following BRAF inhibitor therapies (e.g., melanoma) could be of importance to explain this clinically vital phenomenon. We found that 38 out of 39 melanoma cell lines that have an activating BRAF mutation are fully resistant to necroptosis and have lost RIPK3 expression (S5 Table). Thus, according to our findings, RIPK3 expression is expected to be induced in most anti-melanoma therapies that employ mutant BRAF-specific inhibitors. It would be important to investigate if the regain of RIPK3 expression plays a role in the success or failure of BRAF-targeting therapies, in order to enhance the success rate and overcome the failures.
We analyzed the mutational status of BRAF and AXL kinases in the cell lines used for the aforementioned xenograft transcriptomics study. Consistent with the notion that oncogenic BRAF and AXL kinases promote the loss of RIPK3 expression in cancer cells, 14 out of 20 cell lines that harbor mutations promoting BRAF activation or high levels of AXL (or TYRO3) experienced loss of RIPK3 expression during in vivo passaging, while 13 out of 16 cell lines that lack such mutations did not experience that effect (S6 Table). The latter set of cells provides a crucial negative control and further supports that BRAF and AXL overactivation in cancer may drive the loss of RIPK3 during tumor progression.
It is possible that the selective pressure to lose RIPK3 expression during tumorigenesis comes from the necessity to evade immunity. For example, loss of RIPK3 in tumors would result in decreased cross-priming [22] and increased escape from immunity, thus benefiting tumor survival and growth, but inadvertently it would also result in loss of necroptosis potential because of the essentiality of RIPK3 for necroptosis. Thus, the cost/benefit for a tumor to lose RIPK3 expression could be dependent on the extent of necessity for the tumor cells to evade the immunity of the patient. This could explain why some cell lines obtained from the patients still had not lost RIPK3 expression but lose it when xenografted into mice.
Our in vivo results and published tumor xenograft experiments using immunocompromised animals show that the adaptive immune response (e.g., T cells) is not necessary for the loss of RIPK3 expression in tumor cells. Hence, our findings suggest that RIPK3 loss may be dependent on a tumor cell–intrinsic mechanism in vivo or due to interactions with stromal or innate immune cells.
RAS isoforms, known to be upstream of BRAF, were found among 20 oncogenes identified to positively correlate with resistance to necroptosis, further suggesting the involvement of BRAF in escape from necroptosis (S3A Fig). Cancer cell lines with BRAF mutations did not show as high correlation between AXL overexpression and RIPK3 as those with wild-type BRAF, suggesting that oncogenic pressure from either BRAF or AXL is sufficient to promote RIPK3 expression loss, and escape from necroptosis in cancer (S7 Fig). Overall, these observations strongly suggest that pathways downstream of BRAF and AXL are responsible for RIPK3 expression suppression and escape from necroptosis in cancer.
RIPK3 expression has been previously shown to be controlled via transcriptional repression mechanisms that include promoter hypermethylation and regulation via transcription factor Sp1 [21,60]. BRAF and AXL pathways are known to regulate many transcription factors, including JUN, FOS, ETS, and MYC. It is possible that the pathways overactivated upon mutational overactivation of BRAF/AXL converge on a set of transcription factors that control RIPK3 expression during tumorigenesis. Interestingly, BRAF overactivating mutations have been previously linked to promoter hypermethylation of various genes [61–63]. The delineation of the exact mechanistic details downstream of BRAF/AXL and upstream of transcription factors that control RIPK3 transcription is likely to be of importance to our understanding of cancer escape from necroptosis and will be elucidated in future studies.
Notably, both ABIN-1 and OPTN expression levels were found to strongly correlate with AXL expression across the analyzed 1,000 cell lines (S4A Fig). It is noteworthy that both of these ubiquitin-binding proteins have recently been linked to the regulation of RIPK1 activation in necroptosis [64,65]. Whether AXL regulates apoptosis and necroptosis via controlling expression of these ubiquitin chain adapters will be elucidated in future studies.
It is interesting that both BRAF and RIPK3 are in the same kinome branch, namely, in the tyrosine-kinase like (TKL) family of kinases [66]. Notably, several BRAF inhibitors have been reported to inhibit RIPK3 kinase activity, highlighting this similarity in the kinase domain structure [67]. Such structural similarity suggests a potential convergence and importance of the TKL family in the regulation of processes involving RIPK3, including necroptosis, cytokine production, and immunity. In fact, many of the members of the TKL family include regulators of these processes, such as RIPK1, RIPK2, RIPK3, MLKL, and TAK1 as well as IRAK and LRRK kinases [66].
In conclusion, we provide the first systematic evidence that most human cancer cell lines escape from necroptosis, independent of their tissue of origin or cancer type, and identify the first two oncogenic alterations upstream of the RIPK3 expression suppression. We show that BRAF and AXL oncogene overactivation in cancers is likely to be among the driving forces for the loss of RIPK3 during tumorigenesis and the consequent escape from necroptosis, as well as other RIPK3-driven processes. Understanding the mechanism of escape from necroptosis in tumorigenesis is likely to pave the way for development of better anticancer therapies.
BMS-777607 and TAK-632 were purchased from SelleckChem (Houston, TX). Luminol (A8511), p-coumaric acid (C9008), Tween 20, and zVAD.fmk were from Sigma (St. Louis, MO). DMSO (sc-20258) was from Santa Cruz Biotechnology (Santa Cruz, CA). The following antibodies were used in this study: RIPK1 (Cell Signallng Technology [Danvers, MA], #3493); p-MLKL (S358) (Abcam (Cambridge, UK), ab187091); hMLKL (Abcam [Cambridge, UK], ab183770); and Actin (Santa Cruz Biotechnology (Santa Cruz, CA), sc-81178). Smac mimetic SM-164 was custom synthesized (SelleckChem [Houston, TX]) [7]. TNFα was from Cell Sciences (Newburyport, MA).
All cell lines were grown in RPMI or DMEM medium (Corning, with L-glutamine, with 4.5 g/L glucose, without pyruvate) supplemented with 10% FBS (Sigma), 1× penicillin/streptomycin (Life Technologies), 1 μg/mL amphotericin B (Santa Cruz Biotechnology, sc-202462A), 1× non-essential amino acids mix (NEAA MEM) (Gibco, Life Technologies) and 1 mM sodium pyruvate (Gibco, Life Technologies).
High-throughput drug screening and sensitivity modeling (curve fitting and IC50 estimation) was performed essentially as described previously [53]. Cells were cultured in RPMI or DMEM/F12 containing 5% FBS and penicillin/streptomycin. Cells were incubated at 37°C in a humidified atmosphere with 5% CO2. Cells were grown in RPMI or DMEM/F12 in order to minimize the potential effect of different cell culture media on the drug sensitivity during the screening. A panel of 92 SNPs was profiled for each cell line (Sequenom, San Diego, CA), in order to authenticate the cell lines and thus rule out cross-contamination. A pairwise comparison score was calculated for this purpose. Moreover, short tandem repeat (STR) analysis (AmpFlSTR Identifiler, Applied Biosystems, Carlsbad, CA) was done on the cell lines and the results were matched to existing STR signatures from the repository that provided the cell lines. Briefly, cells were seeded in 384-well plates at variable density to ensure optimal proliferation during the assay. Drugs were added to the cells the day after seeding for adherent cell lines and the day of seeding for suspension cell lines. For tumor subtypes containing both adherent and suspension cells, all lines were drugged the same day (small cell lung cancer cell lines, for example, were all drugged the day after seeding). A series of nine doses was used using a 2-fold dilution factor for a total concentration range of 256-fold. Maximum concentration was chosen for each drug based on prior knowledge of activity on target and in cells. Viability was determined using resazurin after 5 days of drug exposure. Cell lines were treated with TSZ: TNFα (fixed dose 20 ng/mL) + ZVAD (fixed dose 20 μM) + Variable dose of SM-164 (Max of 1.024 μM).
Total cell lysates (20–30 μg) were heated at 90° for 5 minutes in 1× SDS-PAGE sample buffer (2% SDS, 1% beta-mercaptoethanol, 0.01% bromophenol blue, 50% glycerol, 63 mM Tris-HCl, pH 6.8), subjected to 10% SDS-PAGE using Bio-Rad’s Mini-PROTEAN Electrophoresis System, and then electrotransferred onto 0.2-μm nitrocellulose membranes (buffer: 5.82 g/L Tris, 2.93 g/L glycine, 20% ethanol) for 2 hours at 0.4 A current, with the wet transfer tank submerged into an ice/water bath using Bio-Rad’s Trans-Blot cell. Membranes were blocked for 1 hour in TBST buffer containing 5% (w/v) nonfat milk and probed with the indicated antibodies in TBST containing 5% (w/v) BSA for 16 hours at 4°. Detection was performed using HRP-conjugated secondary antibodies and in-house-made chemiluminescence reagent (2.5 mM luminol, 0.4 mM p-coumaric acid, 100 mM Tris-HCl, pH 8.6, 0.018% H2O2).
Cells were seeded into 24-well plates in 1 mL of medium at 15%–20% confluence. Cells were treated 16–24 hours later with BMS-777607, TAK-632, or Vemurafenib (0.3–3 μM) for 96 hours. Cells were washed twice with 1 mL of medium (5-minute incubation at 37° for each wash) and pretreated with 0.5 μM SM-164 and 30 μM zVAD.fmk for 30 min with a subsequent treatment with 25 ng/mL hTNFα for 24 hours to induce necroptosis. Cell survival was determined using CellTiterGlo (Promega) kit according to manufacturer’s instructions. Equal volumes of the reagent were added to the culture medium and the 24-well plates were incubated at 25° for 10 minutes in the dark, with agitation. A total of 25 μL of the obtained lysates were transferred into opaque 384-well plates and luminescence was measured at 100 sensitivity setting with 0.2 seconds integration time, using BioTek Synergy 2 plate reader. For RIPK3 expression analysis, cells were lysed in RLT buffer of the RNeasy kit (Qiagen).
RNA was isolated using RNeasy kit (Qiagen) and cDNA synthesis was performed using RNA to cDNA EcoDry Premix (Double Primed) (Takara Bio). A total of 1 μg of RNA was used per premix tube. Quantitative real-time PCR (qRT-PCR) was done using SYBR Green Real-Time PCR Master Mix (Thermo Fisher Scientific), with QuantStudio 7 Flex Real-Time PCR System (Thermo Fisher Scientific). RIPK3 qRT primer sequences (hRIPK3_F, CAAGGAGGGACAGAAATGGA; hRIPK3_R, GCCTTCTTGCGAACCTACTG) were as described elsewhere [21].
Experiments were performed as previously described [68]. Tumor ascites from patients with advanced ovarian cancer (IRB approved protocols at Dana-Farber Cancer Institute) were implanted orthotopically (intraperitoneal injection) in NOD-SCID mice (8 weeks old, Jackson labs). Mice were followed weekly for abdominal distension and were humanely killed 3–8 months after injection of the original patient tumor ascites (passage 0) to harvest tumor ascites for serial passaging. Ascites harvested from the xenografts were processed for red blood cell lysis and serially passaged (up to 3 passages) in new NOD-SCID mice. Tumors were frozen in liquid nitrogen for storage. Tumors were lysed in NP-40 lysis buffer (25 mM HEPES [pH 7.5], 0.2% NP-40, 120 mM NaCl, 0.27 M sucrose, 5 mM EDTA, 5 mM EGTA, 50 mM NaF, 10 mM b-glycerophosphate, 5 mM sodium pyrophosphate, 1 mM Na3VO4 (fresh), 1 mM benzamidine [fresh], 0.1% BME [fresh], 1 mM PMSF [fresh], 2× Complete protease inhibitor cocktail [Roche]) using VWR 200 Homogenizer, on ice. Lysates were cleared by centrifugation at 16,000g, 15 minutes, 4°. Protein concentrations were determined using Bradford reagent (BioRad). Protein samples were mixed with 5× SDS-PAGE sample buffer and frozen at −80° for storage.
For all experiments, unless otherwise indicated, n was at least 3. Statistical analyses were performed using GraphPad Prism 7 or Microsoft Excel. Violin and bean plots were made using BoxPlotR (http://shiny.chemgrid.org/boxplotr/) [69]. Data were analyzed using one-way analysis of variance (ANOVA) test with Bonferroni posttest for non-paired datasets. Student t test was used for paired datasets. Data points are shown as means ± SEM. ClustVis was used for heatmap generation [70]. The heatmap in Fig 2D was generated as follows. The data IC50 values from the screen and gene expression values from GCSD database were analyzed by z-test and the heatmap was generated from these z-scores. ClustVis Data Pre-Processing settings were as follows: no row centering, unit variance scaling. Column settings were as follows: clustering distance—Manhattan; clustering method—single; tree ordering—original. Row settings were as follows: no clustering.
The following databases were used for bioinformatics analysis of published datasets: cBio Cancer Genomics Portal (http://www.cbioportal.org/) [51], Broad-Novartis Cancer Cell Line Encyclopedia [55] (http://www.broadinstitute.org/ccle/home, CCLE_Expression_Entrez_2012-10-18.res microarray dataset), Genomics of Drug Sensitivity in Cancer [54] (http://www.cancerrxgene.org) and Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/).
The following datasets were included in this manuscript: GDS5336 [46], GDS4367 [47], GDS3894 [48], GDS2546 [49], microarray datasets from Ma and colleagues [50], and GSE48433 [52] (see S7 Table).
The oncogene-related gene database was obtained by searching Uniprot database for key word “oncogene” (QUERY: keyword:oncogene AND organism:"Homo sapiens (Human) [9606]"). Intersections of gene lists were made with CrossCheck [71] and Venny (http://bioinfogp.cnb.csic.es/tools/venny/).
Mutational enrichment was done by dividing the number of mutations identified in NR cells by those identified in cells that were sensitive to the necroptosis treatment (NS) and then performing a z-test on this dataset. NR cells were defined as those that had no reduction in cell viability at 1 μM SM-164 concentration (the highest concentration used in the screen). The rest of the cell lines that exhibited reduction in cell viability were defined as NS.
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10.1371/journal.ppat.1007263 | Structure of the Cladosporium fulvum Avr4 effector in complex with (GlcNAc)6 reveals the ligand-binding mechanism and uncouples its intrinsic function from recognition by the Cf-4 resistance protein | Effectors are microbial-derived secreted proteins with an essential function in modulating host immunity during infections. CfAvr4, an effector protein from the tomato pathogen Cladosporium fulvum and the founding member of a fungal effector family, promotes parasitism through binding fungal chitin and protecting it from chitinases. Binding of Avr4 to chitin is mediated by a carbohydrate-binding module of family 14 (CBM14), an abundant CBM across all domains of life. To date, the structural basis of chitin-binding by Avr4 effector proteins and of recognition by the cognate Cf-4 plant immune receptor are still poorly understood. Using X-ray crystallography, we solved the crystal structure of CfAvr4 in complex with chitohexaose [(GlcNAc)6] at 1.95Å resolution. This is the first co-crystal structure of a CBM14 protein together with its ligand that further reveals the molecular mechanism of (GlcNAc)6 binding by Avr4 effector proteins and CBM14 family members in general. The structure showed that two molecules of CfAvr4 interact through the ligand and form a three-dimensional molecular sandwich that encapsulates two (GlcNAc)6 molecules within the dimeric assembly. Contrary to previous assumptions made with other CBM14 members, the chitohexaose-binding domain (ChBD) extends to the entire length of CfAvr4 with the reducing end of (GlcNAc)6 positioned near the N-terminus and the non-reducing end at the C-terminus. Site-directed mutagenesis of residues interacting with (GlcNAc)6 enabled the elucidation of the precise topography and amino acid composition of Avr4’s ChBD and further showed that these residues do not individually mediate the recognition of CfAvr4 by the Cf-4 immune receptor. Instead, the studies highlighted the dependency of Cf-4-mediated recognition on CfAvr4’s stability and resistance against proteolysis in the leaf apoplast, and provided the evidence for structurally separating intrinsic function from immune receptor recognition in this effector family.
| Microbes mobilize an array of secreted effectors to manipulate their hosts during infections, whereas in response, hosts utilize cognate immune receptors to perceive effectors and mount a defense. To date, the structural basis of effector function and recognition by immune receptors are still poorly understood. Here we present the crystal structure in complex with chitohexaose of CfAvr4, a CBM14 lectin and the founding member of a fungal effector family that binds and protects chitin in fungal cell-walls from chitinases. This is the first structure of a CBM14 protein to be co-crystalized with its ligand that further reveals how Avr4 effectors function. Specifically, by leveraging structural and functional data, we elucidate the molecular basis for ligand-binding by CfAvr4 and show that two effector molecules are brought together through the ligand to form a sandwich structure that laminates two chitohexaose molecules within the dimeric assembly. We further show that recognition of CfAvr4 by the cognate Cf-4 immune receptor is not mediated through residues directly interacting with chitohexaose, thereby structurally uncoupling the ligand-binding function of Avr4 from recognition by Cf-4 and challenging early postulations that the broad recognition of Avr4 effectors by Cf-4 stems from perceiving residues implicated in binding their ligand.
| Effectors are intriguing and enigmatic proteins deployed by microbes during host-pathogen interactions [1, 2]. Although inhibition of plant immunity during host infection is the main function of these proteins, the manner by which individual effectors perform this task is poorly understood [2]. One of the better understood effectors is CfAvr4, a 135-residue effector protein from the tomato pathogen Cladosporium fulvum, which utilizes a carbohydrate-binding module of family 14 (CBM14) to bind chitin present in fungal cell walls and protect it from hydrolysis by plant-derived chitinases during infection [3–5]. To date, functional orthologues of Avr4 have been identified in a number of fungal species within the Dothideomycete class of fungi and beyond, including the tomato pathogen Pseudocercospora fuligena [6], the banana pathogen Pseudocercospora fijiensis [4], and several others [7]. The majority of Avr4 homologs share a similar cysteine-spacing pattern and contain a distinctive CBM14 domain in their structure, indicating that members of the Avr4 effector family have a conserved role in binding and protecting chitin in fungal cell walls against chitinases [4, 6]. Moreover, biochemical analysis between CfAvr4 and its PfAvr4 homolog from P. fuligena has shown that the specificity of these proteins extends further into binding the same length chito-oligosaccharide, i.e. (GlcNAc)6, suggesting that they share a similar binding-site topography and mechanism of interacting with the ligand [6].
Although protection of chitin seems to be the predominant biological function of the Avr4 family members [4, 6], a molecular-level mechanistic understanding of how these effectors bind to their substrate is currently lacking. In this respect, the presence of a CBM14 module in the structure of Avr4 is likely key to its biological function and interaction with chito-oligomers. CBM14s are short modules of approximately 70 residues that bind explicitly to chitin, a long-chain polymer of β(1–4) linked N-acetylglucosamine (GlcNAc) [7]. Currently, only limited information exists as to how CBM14 family members bind to their ligand, but the inclusion of the CBM14 family within the Type C class of CBMs suggests that they lack the extended binding site found in CBM Types A and B [7, 8]. However, an experimental validation of this assumption is currently lacking.
Despite the lack of information regarding the structural basis for ligand-binding by CBM14 proteins, clues to how Avr4 could possibly interact with its ligand were recently provided by the elucidation of the crystal structure of PfAvr4 from P. fuligena [6]. Like CfAvr4, PfAvr4 utilizes a CBM14 domain to bind specifically to (GlcNAc)6 oligomers, while binding to higher molecular weight chitin may be facilitated through positive cooperative protein-protein interactions of PfAvr4 molecules [6, 9]. Although, a co-crystal of PfAvr4 with chito-oligomers was not attainable, the chitohexaose-binding domain (ChBD) of PfAvr4 was predicted to be located to the C-terminus of the protein [6]. The predicted location of the ChBD in PfAvr4 matches to that of CfAvr4, which used NMR titration experiments to identify residues that may interact with chito-oligomers but was unable to resolve its three-dimensional structure [9]. Next to PfAvr4, the only known structures of CBM14 members are those of tachycitin [10], a small antimicrobial protein from horseshow crab, Der p 23 [11], an allergen from the dust mite Dermatophagoides pteronyssinus, and the ChBD of the human chitotriosidase CHIT1 (ChBDCHIT1) [12, 13]. All four structures share a common core containing two β-sheets in a distorted β-sandwich arrangement, a seemingly common fold among CBMs [14].
Next to a conserved biological function, another unexpected commonality shared among several Avr4 family members is their ability to elicit a hypersensitive response (HR) in the presence of the cognate Cf-4 resistance protein from tomato [4, 6]. As most Avr4 orthologues share little sequence similarity, it was hypothesized that the indispensability of the CBM14 domain for the function of the protein would make it a prime target for recognition by Cf-4 [4]. However, structure-function analysis with PfAvr4 has shown that point mutations in residues within the predicted ChBD of the protein do not abolish recognition by Cf-4, suggesting that amino acids that directly interact with (GlcNAc)6 do not form part of CfAvr4’s epitope that is recognized by Cf-4 [6]. However, a shortcoming of the studies on PfAvr4 was that the analysis was based on a predictive model of Avr4’s ChBD instead of structural information regarding the actual carbohydrate-protein interaction.
Here, we report the crystal structure of CfAvr4 bound to (GlcNAc)6, thereby accurately now defining the architecture and amino acid composition of the ChBD and elucidating the underlying molecular mechanism employed by Avr4 family members to bind (GlcNAc)6 oligosaccharides. The CfAvr4-(GlcNAc)6 complex showed that, contrary to previous assessments, the ChBD of CfAvr4 extents nearly to the entire length of the protein, which in the presence of the ligand forms a dimeric assembly that laminates two (GlcNAc)6 oligosaccharides within its structure. Subsequent detailed functional profiling of residues involved in binding (GlcNAc)6 has further enabled us to quantify their individual contribution to binding affinity, thereby identifying the ones that are most critical to ligand-binding. Further on, by leveraging structural and functional data we reassessed whether the pleiotropic recognition of Avr4 effectors by Cf-4 is based on the perception of residues that directly interact with (GlcNAc)6 and established that such residues are not targets for recognition by Cf-4, thus provided now strong evidence for structurally separating the ligand-binding function in the Avr4 effector family from recognition by Cf-4.
Our previous crystal structure of PfAvr4 proved recalcitrant to structures complexed with chito-oligosaccharides [6]. Consequently, we undertook crystallization of CfAvr4, which has a 10-fold higher binding affinity for (GlcNAc)6 than PfAvr4, and were successful in obtaining crystals of CfAvr4 in complex with (GlcNAc)6. CfAvr4 bound in a 1:1 stoichiometric ratio with the (GlcNAc)6 ligand and the final structure of the CfAvr4-(GlcNAc)6 complex was solved at 1.95Å resolution, with R-factor and R-free values of 16.7% and 21.4%, respectively (S1 Table).
The X-ray crystal structure of the CfAvr4 monomer spans residues Gln35-Thr113 and is composed of an N-terminal α-helix (H1), a distorted β-sandwich fold formed by a central β-sheet (A) with three anti-parallel β-strands (A1, A2, and A3), a small β-sheet (B) of two anti-parallel β-strands (B4 and B5), and a short C-terminal α-helix (H2) (Fig 1A). Nearly 47% of the structure is organized into α/β secondary structure with the remaining 53% residing in highly ordered loops. The structure clearly shows four disulfide bonds that match the previously determined disulfide pairs of Cys40-Cys70, Cys50-Cys56, Cys64-Cys109, and Cys86-Cys101 [6, 15]. A comparison with the PfAvr4 structure shows that the two proteins share a similar fold, with the two structures aligning with a root-mean-squared deviation (RMSD) of 0.794 Å over 53 α-carbons (Fig 1B and S1A Fig). The only notable discrepancy resides in the proteins’ C-terminus, which in both is comprised of a β-sheet B and an α-helix H2, but CfAvr4 has a two-residue insertion extending the loop connecting the β-strands B1 and B2 (Fig 1C). A search using the DaliLite server [16] revealed that despite the lack of similarity at the amino acid level, CfAvr4 also shares significant structural homology to the CBM14 family members tachycitin (PDB Id: 1DQC), Der p 23 (PDB Id: 4ZCE), and ChBDCHIT1 (PDB Id: 5HBF), aligning to these proteins at an RMSD of 2.019 Å, 0.699 Å, and 0.686 Å, over 36, 16, and 38 α-carbons, respectively (Fig 1B and S1B–S1D Fig). However, all three CBM14 proteins lack the N-terminal helix and the large extended loop connecting β-strands A2 and A3 present in CfAvr4 and PfAvr4, plus Der p 23 and ChBDCHIT1 also lack the C-terminal helix. Moreover, three of the four disulfide bonds in CfAvr4 are conserved in tachycitin but only two appear in the Der p 23 and the ChBDCHIT1 structures (S1B–S1D Fig).
CfAvr4 was crystallized with (GlcNAc)6 as an asymmetric unit consisting of two CfAvr4 dimers, with each dimer respectively binding to two (GlcNAc)6 molecules (S2 Fig and S1 Movie). However, only ~610 Å2 of surface area is buried between the two dimers, suggesting that the tetrameric unit is likely crystallographically induced and that the biologically relevant assembly is a dimer, as previously proposed for PfAvr4 [6, 9]. Each dimer consists of two CfAvr4 monomers bound to two parallel (GlcNAc)6 molecules stacked on top of each other (Fig 2A, S3 Fig and S2 Movie). A single (GlcNAc)6 chain nearly extends along the entire length of the longitudinal axis of each CfAvr4, with the reducing end located near the N-terminus of the protein and the non-reducing end at the C-terminus. Within each dimer, the stacked (GlcNAc)6 molecules shift by translation of one sugar ring, with GlcNAc-1 (reducing end) of chain B stacking on top of GlcNAc-2 of chain A and so forth (Fig 2A and S4 Fig). The dimeric assembly creates a 2-fold screw-like rotation of each monomer, such that both sugar and protein are rotated 180° and translated by one sugar unit to create a molecular sandwich that almost entirely encapsulates the parallel-stacked (GlcNAc)6 molecules within its structure. Surprisingly, the CfAvr4 dimer interface is completely mediated by carbohydrate interactions and no intermolecular protein-protein interactions are observed across the dimer, suggesting that dimerization is a consequence of ligand binding. When considering individual CfAvr4 monomers in the crystallographic asymmetric unit, all four show nearly identical conformations and (GlcNAc)6 interactions (Fig 2B). Aligned to chain A, chains B, C, and D have an RMSD of 0.309 Å, 0.099 Å, and 0.265 Å, over 67, 73, and 68 α-carbons, respectively (Fig 2B). In chains B and D, however, the GlcNAc-6 ring bends toward the protein, deviating from the linearity that is seen in the other two chains.
Interestingly, PfAvr4 has been previously crystalized as a dimer as well [6] but a comparison of the CfAvr4-(GlcNAc)6 dimeric assembly to the ligand-free PfAvr4 dimer showed that two dimer arrangements vary substantially (S5 Fig). For instance, in PfAvr4, the dimeric contacts were mostly water-mediated and only three direct hydrogen-bond interactions between monomer chains were detected [6]. In contrast, CfAvr4 dimerization is mediated exclusively by (GlcNAc)6, as no direct protein-protein interactions are observed in the CfAvr4-(GlcNAc)6 dimeric assembly. Instead, several cross linkages are formed where each monomer of CfAvr4 interact with both (GlcNAc)6 molecules to stabilize the dimeric state of the complex. Further comparison of the dimeric assemblies reveals that upon superposition of the A chain monomers of PfAvr4 and CfAvr4, the B subunit of CfAvr4 is shifted out ~6.7Å and rotated ~54° relative to the B subunit of PfAvr4 (S5 Fig). This increased separation between the CfAvr4 monomers is caused by enclosing the ligands at the dimerization interface and creating a space large enough for accommodating two (GlcNAc)6 molecules between the monomers.
By modelling the PfAvr4 crystal structure on other chitin-binding proteins, we have previously predicted that PfAvr4’s ChBD resides in the C-terminal domain of the protein consisting of residues on β-strands B4 and B5 and their connecting β-hairpin loop. However, its exact topography and composition could not be precisely defined as even after repeated crystallization attempts a PfAvr4-(GlcNAc)6 co-crystal was never obtained [6].
Contrary to previous assessments [6, 10, 15], the chitin hexasaccharide is accommodated in a shallow trench across the longitudinal axis of CfAvr4 with the face of the pyranose rings binding to the protein by means of nonpolar and CH-π interactions, and the ring substituents pointing into the protein core and forming hydrogen bonds with both the main chain and side chains (Fig 3A and S4 Fig). The main facial interaction between CfAvr4 and (GlcNAc)6 is a CH-π bond between Trp100 and GlcNAc-5. Met51 and Pro53 are also in van der Waals bonding distances of GlcNAc-1 and GlcNAc-3, respectively and contribute to ligand binding. In addition, a number of amino acids in CfAvr4 are also shown to interact with individual sugar ring substituents of the (GlcNAc)6 substrate (Fig 3B, S4 Fig and S2 Table). For instance, starting from GlcNAc-1, the C6 hydroxyl of GlcNAc-1 forms a hydrogen bond with the main chain carbonyl oxygen of Lys49. The N-acetyl group nitrogen of GlcNAc-2 hydrogen bonds with the main chain carbonyl oxygen of Cys50, while its C3 hydroxyl group hydrogen bonds to the amide oxygen of Gln69. The N-acetyl group nitrogen of GlcNAc-4 forms a water-mediated hydrogen bond interaction with the main chain carbonyl oxygens of both Pro53 and Lys99, an association that is observed in all 4 monomers. The C6 hydroxyl of GlcNAc-5 forms a hydrogen bond with the main chain carbonyl of Cys101, whereas the hydroxyl of Tyr103 is also within hydrogen bonding distance to both the GlcNAc-6 N-acetyl carbonyl and the C3 hydroxyl. In chain B, GlcNAc-6, which does not stack with a carbohydrate subunit from chain A, bends towards the protein so that its N-acetyl group comes within hydrogen bonding distance to the side chain of Asp102 (Fig 3B, S4 Fig and S2 Table). Taken together, the interactions of a CfAvr4 monomer with the (GlcNAc)6 molecule is mediated by residues Lys49, Cys50, Met51, Pro53, Lys99, Trp100, Cys101, Asp102, and Tyr103, which collectively form at least two water-mediated and nine direct hydrogen bonds with (GlcNAc)6. Of these, only Trp100 (Trp94 in PfAvr4), Asp102 (Asp96 in PfAvr4), and Trp103 (Tyr97 in PfAvr4) have been previously identified as ChBD residues in PfAvr4 [6].
Interestingly, while no direct protein-protein interactions are observed in the CfAvr4-(GlcNAc)6 dimeric assembly, there are plenty of sugar-facilitated cross-linkages between the two protein chains (Fig 3B, S4 Fig and S2 Table). For instance, the hydroxyl of Tyr67/A hydrogen bonds to the C6 hydroxyl of the GlcNAc-2/B, whereas the same interaction occurs between Tyr67/B and the GlcNAc-4/A. In a similar way, the amine of Lys84/A hydrogen bonds to the N-acetyl carbonyl of GlcNAc-1/B, whereas Lys84/B interacts with the N-acetyl carbonyl of GlcNAc-3/A. The amide nitrogen of Gln69/A hydrogen bonds with the C3 hydroxyl of GlcNAc-1/B, while Gln69/B hydrogen bonds to C3 hydroxyl of GlcNAc-2/A. Finally, the hydroxyl of Tyr103/A hydrogen bonds to the N-acetyl group of GlcNAc-5/B, but this interaction is not seen between Tyr103/B and the A sugar, as it would have to occur with a GlcNAc-7 unit on the chain A.
To assess the functional role that each residue in CfAvr4 involved in binding (GlcNAc)6 has, we mutated these residues individually and investigated the binding properties via Isothermal Titration Calorimetry (ITC). Specifically, we made the alanine-substitution mutations M51A, P53A, K84A, P87A, W100A, and D102A, and the more conservative mutations Q69N, Y67F and Y103F, to minimize potential protein instability effects. We excluded Lys49 from the mutational analysis since only the main chain of this residue interacts with (GlcNAc)6, as well as Cys50 and Cys101 because it has been previously shown that mutating these residues compromises the stability of the protein [9, 15].
ITC experiments determined that WT-CfAvr4 bound to (GlcNAc)6 with a dissociation constant (Kd) of 6.73 ± 1.49 μM, a binding enthalpy (ΔH) of -38.37 ± 2.96 kJ/mol, and a stoichiometric ratio n of 1.09 ± 0.12 (S2 Table and S6 Fig). The parameters remained similar irrespectively of whether or not the purification tag was removed from the protein or whether a reverse titration was run, in which concentrated CfAvr4 was titrated into a dilute solution of (GlcNAc)6, with no evidence of allosteric or dimer dissociation (S2 Table and S6 Fig). The values are also in good agreement with previously reported data except for the stoichiometric ratio [9]. Specifically, our ITC experiment resulted in a stoichiometric ratio of 1:1, which agrees with the structure, but contrasts the 2:1 protein:(GlcNAc)6 stoichiometry proposed previously [9]. Due to this discrepancy, we took great care to verify the concentration of CfAvr4 using a Bradford assay, and quantitated the carbohydrate concentration [17, 18]. When assaying the ChBD mutants, mutations M51A, P53A, Y67F, P87A, and Y103F did not affect the Kd substantially and had a small decrease in the ΔH magnitude (S2 Table and S6 Fig). However, mutations W100A and D102A abolished all detectable binding to (GlcNAc)6, in agreement with the previously reported analogous mutations made in PfAvr4 [6]. Mutations Q69N and K84A both displayed a ~20-fold increase in the Kd raising it to 130.95 ± 31.47 μM and 120.67 ± 18.77 μM, respectively (S2 Table and S6 Fig).
To determine if the reduced affinity for (GlcNAc)6 of the W100A, D102A, Q69N and K84A mutants was biologically meaningful, we evaluated whether they were able to protect germlings of Trichoderma viride against hydrolysis from chitinases and compared their protective potency to that of the WT-CfAvr4 (S7 Fig). Similar in vitro protection assays were used before to demonstrate and compare the protective properties of CfAvr4, PfAvr4 and other Avr4 family members or mutants thereof [4–6]. As expected, combined addition to pre-germinated germlings of T. viride of BSA (negative control) with chitinases supplemented with basic β-1,3-glucanases inhibited fungal growth, whereas combined application of the enzyme mixture with the WT-CfAvr4 (positive control) enabled fungal growth and survival (S7 Fig). When examining the ChBD mutants, mutants W100A and D102A that do not exhibit any detectable binding to (GlcNAc)6 (S2 Table and S6 Fig), essentially failed to protect the fungal hyphae against chitinases, thus resulting in poor fungal growth that was comparable to that of the BSA control. Mutants Q69N and K84A that exhibit reduced affinity for (GlcNAc)6 (S2 Table and S6 Fig), enabled fungal growth to levels comparable to those of the WT-CfAvr4, although at closer inspection of the hyphae many were now seen to bore signs of osmotic injuries such as swollen segments and coagulated cytoplasm (S7 Fig). This indicates that the chitinase treatment had a stronger effect on hyphae treated with the Q69N and K84A mutants as compared to hyphae treated with the WT-CfAvr4 that remained morphologically intact. Collectively, results from the protection assays show that mutations in the ChBD of CfAvr4 that decrease or abolish its affinity for (GlcNAc)6 also reduce the protein’s ability to protect fungal germlings against chitinases and thus perform its biological function.
A previous NMR study of CfAvr4, while unable to determine the structure, showed amide (1HN) backbone chemical shifts upon addition of (GlcNAc)3 that were assigned to Asn93, Asp94, Asn95, (NDN motif), Asp102, and Tyr103 [9]. The structure of CfAvr4 in complex with (GlcNAc)6 confirms that Asp102 and Tyr103 directly interact with the ligand. In contrast, the NDN motif is located on the B4-B5 β-hairpin loop and is pointing away from the binding site, where it is at a distance of more than 10Å from the hexasaccharide (S8 Fig). To address this discrepancy between the NMR and crystallographic data, we made mutations N93A, D94A and N95A, and characterized their affinity for (GlcNAc)6 using ITC (S2 Table). Surprisingly, all three mutations affected the thermodynamic parameters that were indicative of lower affinity for the hexasaccharide, as they increased the Kd by a factor of six (N93A), five (D94A) and three (N95A) (S2 Table). One of the characteristics of CH-π interactions, such as observed between Trp100 and GlcNAc-5, is that they are very sensitive to the electronic structure of the aromatic residues involved in binding [19]. It is thus likely that the nearby NDN motif helps to create a proper electronic environment to facilitate binding, while also providing structural integrity to the C-terminal domain containing Trp100 (see below). The amide side chain of Asn93 is 4.0 Å from the indole ring nitrogen of Trp100, and further hydrogen bonds to both side chain and main chain of Asn95, thus stabilizing the NDN loop. Therefore, the binding of the hexasaccharide to Trp100 could shift this loop, resulting in the observed backbone NMR chemical shifts [9].
We have previously determined that residues in PfAvr4 that are critical to binding (GlcNAc)6 do not individually have an effect on PfAvr4’s interaction with Cf-4, as alanine substitution of these residues yields avirulent forms of the protein that elicit a Cf-4-mediated HR. Instead, a strong correlation between receptor activation and Avr4 stability was observed, as ChBD mutants that escape detection by Cf-4 are unstable proteins that are susceptible to proteolytic cleavage in the protease-rich environment of the leaf apoplast [6]. These observations prompt us to suggest that the ligand-binding function of Avr4 is structurally distinct or does not fully overlap with the property of recognition by Cf-4. This is important because it alluded that the molecular basis for the pleiotropic recognition of core effector proteins by single immune receptors is not based on the perception of individual amino acids that define the effectors intrinsic function, as previously hypothesized [4].
The elucidation of the precise structure and amino acid composition of CfAvr4’s ChBD enabled us to address this postulation with higher accuracy and reexamine whether recognition of Avr4 by Cf-4 is mediated through residues directly interacting with (GlcNAc)6. Therefore, we assessed the ability of Cf-4 to mount an HR upon perception of the WT-CfAvr4 and the ChBD mutants (S2 Table). We also assessed mutants N93A, D94A, N95A, as the NDN motif indirectly affects the protein’s affinity for (GlcNAc)6. The HR-inducing properties of the effector variants was initially examined by infiltrations of the purified proteins into tomato leaves of cv Purdue 135 (+ Cf-4) and cv Moneymaker (–Cf-4) at concentrations of 5 μg/ml and 10 μg/ml (Fig 4A, S9A Fig and S2 Table). When infiltrated into the leaves of cv Purdue, the WT-CfAvr4 and mutants M51A, P53A, Y67F, K84A, P87A, N95A, W100A and Y103F, all elicited a strong and equal in intensity HR at 5 days post-infiltration (dpi). In contrast, mutants Q69N and D102A elicited an HR at infiltrations with 10 μg/ml but only a weak response at 5 μg/ml, whereas mutants N93A and D94A failed to elicit an HR at both concentrations tested (Fig 4A, S9A Fig and S2 Table). As expected, none of the mutants or the WT-CfAvr4 induced an HR when infiltrated into leaves of cv Moneymaker (S9A Fig). Collectively, these results suggest that residues Gln69, Asn93, Asp94 and Asp102 could be direct targets of recognition by Cf-4 or, alternatively, that they are critical to protein stability and resistance of the protein against proteolytic degradation in the leaf apoplast.
To discriminate between these two possibilities, we next transiently co-expressed Cf-4 with WT-CfAvr4 or its individual mutant alleles into leaves of Nicotiana benthamiana, using an Agrobacterium tumefaciens-mediated transformation assay (ATTA) [20]. Cf-4:effector co-infiltrations at cell densities of A6000.5:A6001.0 (0.5:1 ratio), A6000.5:A6000.5 (1:1 ratio), and A6000.5:A6000.25 (2:1 ratio) all induced an HR in the infiltrated leaf sectors thus resolving that none of our mutants can effectively escape recognition by Cf-4 when present in sufficient amounts in the leaf apoplast (Fig 4B, S9B Fig and S2 Table). We next compared the resilience of the WT-CfAvr4 and of the Q69N, N93A, D94A and D102A mutants against proteolytic degradation by subtilisin. We additionally included in these assays mutants K84A and W100A as, although they trigger a full and equal in intensity HR as the WT-CfAvr4 (S9 Fig), they nonetheless exhibit less affinity for (GlcNAc)6 (S6 Fig and S2 Table). In all cases, treatment with subtilisin resulted in rapid cleavage reducing the full-length protein to the true mature form of CfAvr4 [3, 6] (Fig 4C and S10 Fig). The protease assay showed that with the exception of WT-CfAvr4 and the K84A and W100A mutants (S10 Fig and S2 Table), all other mutants are susceptible to further proteolytic degradation, with mutants N93A and D94A being more so than mutants Q69N and D102A, evidenced by the almost complete disappearance of the band corresponding to the mature CfAvr4 for the N93A and D94A mutants and the decreased intensity of this band for the Q69N and D102A mutants, as compared to the WT-CfAvr4 (Fig 4C). The vulnerability of the mutants to proteolytic degradation is inversely correlated with their ability to elicit a Cf-4 mediated HR (Fig 4B and S2 Table), thus indicating that residues Gln69, Asn93, Asp94 and Asp102 make important contributions to protein stability but they likely do not mediate a direct interaction with Cf-4. Conversely the W100A and K84A mutants, which also show reduced affinity for (GlcNAc)6 (S6 Fig and S2 Table) but elicited a full HR in tomato and N. benthamiana (S9 Fig), are as resilient to proteolysis as the WT-CfAvr4, evidenced by the equal in intensity band corresponding to the mature CfAvr4 obtained in the subtilisin assay for the three proteins (S10 Fig and S2 Table). Taken together, these results indicate that mutations in residues within the ChBD that decrease or abolish the protein’s affinity for (GlcNAc)6 do not individually affect recognition by Cf-4 if they do not perturb the stability of the protein.
During the past two decades, a great deal of effort has been placed towards deciphering the molecular determinants that define the structural basis of protein–carbohydrate interactions. Such interactions are ubiquitous in nature and at the heart of diverse biological processes of profound importance to human health, plant growth, and microbial disease [8]. Although CBMs may interact with their oligosaccharide in various ways, generally they do not undergo conformational changes when binding to their ligands. Instead the tertiary structure provides a platform for substrate-binding. Binding-site topography is thus key to their binding mode and can be very diverse, ranging from planar surfaces with aromatic residues that stack against the pyranose rings of polysaccharides (Type A CBMs), to grooves or clefts that contain both aromatic and hydrogen-bonding interactions that accommodate long polysaccharide chains (Type B CBMs), and small pockets that bind short oligosaccharide ligands (Type C CBMs) [8]. A recent refinement of these classes further proposed that Type B CBMs bind glycan chains internally (endo-type), whereas Type C modules interact with short mono-, di-, tri-saccharides or the termini of glycans (exo-type) [21].
To date, only limited information exists on how CBM14 family members bind chito-oligomers, although it is assumed that they would exhibit the structural and functional characteristics of Type C lectins [7, 8]. The assumption mostly stems from indirect evidence and observations that representative members of this family interact with small oligosaccharide ligands, yet the structural basis of this interaction was so far unknown. The structural characterization of CfAvr4 and of its mode of interaction with (GlcNAc)6 now provides experimental support that Avr4 may be unique in the CBM14 family in that it has an extended ChBD and can be classified rather as a Type B CBM instead of Type C. This is supported by the fact that Avr4 binds longer polysaccharide chains, whereas its mode of interaction with the substrate is mediated through aromatic residues (i.e. Trp100) and numerous hydrogen bonds with both side chains and main chains. Furthermore, unlike the other structurally-characterized CBM14 family members, CfAvr4 has an additional N-terminal α-helix and an extended loop connecting the α-helix to the first β-strand. The main chain of this loop hydrogen-bonds to (GlcNAc)6 (Cys50 to GlcNAc-2) and contains the Cys50-Cys56 disulfide bond along with residues Met51 and Pro53 that stack against the pyranose rings of GlcNAc-1 and GlcNAc-3, respectively. Notably, although mutating each side chain individually only slightly reduces the affinity for (GlcNAc)6, the overall additive effect would be increased. These interactions, along with residues Gln69 and Lys84 that greatly affect binding, are not conserved in tachycitin, Der p 23, and ChBDCHIT1, which are thus likely to have a smaller ChBD that is more similar to the hevein fold and to bind shorter oligosaccharides in par with other Type C CBMs.
Next to Avr4, the only information available on the ligand-binding mechanism of a CBM14 family member with a known structure is the ligand-free structure of ChBD of the human chitotriosidase CHIT1 (ChBDCHIT1) [12, 13]. CfAvr4 and ChBDCHIT1 share a similar overall fold but the residues that dictate the binding affinity of the two proteins for their ligand are different. For instance, the residues in CfAvr4 essential for binding (GlcNAc)6, Trp100 and Asp102, are replaced by cysteine (Cys462) and threonine (Thr464) in the ChBDCHIT1 structure (S1D Fig). In ChBDCHIT1, residues Pro451, Leu454, and Trp465 that were identified as crucial for chitin binding [12] are conserved in CfAvr4, aligning to Pro87, Leu90, and Tyr103, respectively. However, although Pro87 and Leu90 sit on the loop connecting the two β-sheets and point toward the ligand binding site suggesting that they will have a role in binding (GlcNAc)6, the P87A mutation does not have an effect on the binding thermodynamics of CfAvr4, whereas Leu90 is not within binding distance to (GlcNAc)6, as the closest contact is 3.3Å between the terminal methyl of the sidechain of chain A and the C6 hydroxyl of GlcNAc-4 of chain B. Interestingly, ChBDCHIT1 binding assays showed that there was no thermodynamic effect on binding when increasing the degree of polymerization beyond the disaccharide of GlcNAc, whereas, Avr4 showed a greatly decreased Kd and more negative ΔH upon an increase to the degree of polymerization of the oligosaccharide. This may indicate a very different binding mechanism between the two ChBDs of the same CBM14 family, which have a conserved fold, or the difference may highlight the vastly different nature of the proteins. CHIT1 is a chitinase with distinct catalytic and ChBD domains and has been implicated in the immune response in humans, whereas Avr4 is a fungal lectin protecting the fungus from plant chitinases. The Avr4 protein most likely needs to bind its ligand with a higher affinity than CHIT1.
The structure of the CfAvr4-(GlcNAc)6 complex further showed that two molecules of CfAvr4 can be joined through the ligand to form a sandwich structure that laminates two (GlcNAc)6 molecules within the dimeric assembly. This mechanism of ligand-mediated dimerization, to some extent, appears to be unique among carbohydrate-binding proteins, as in most cases dimeric binding involves two molecules of a CBM interacting with the same molecule of the ligand [22–25]. Although it is conceivable that such a dimeric assembly of Avr4 could create a sheltered environment for (GlcNAc)6, what cannot be determined is whether it is biologically relevant in terms of how the protein interacts with the network of chitin microfibrils present in the fungal cell wall that more accurately represents the biological substrate of Avr4 under in vivo conditions. The cell wall of fungi consists mainly of β-1,3- and β-1,6 glucans cross-linked to randomly oriented microfibrils of chitin, mannans, and glycoproteins that collectively form a three-dimensional layered structure in which glucans and chitin are most frequently positioned closer to the plasma membrane and are overlaid by mannans and glycoproteins [26, 27]. Chitin microfibrils in the fungal cell wall are mainly found in the form of randomly oriented short microcrystalline rodlets and to a lesser extent as a network of longer interlaced microfibrils [28, 29]. Such an arrangement of a tightly knitted network of chitin would seem to preclude the dimer formation that is seen in the crystal structure. Since, Avr4 binds (GlcNAc)6 in a 1:1 stoichiometric ratio, it is plausible that the protein rests as a monomer on the solvent-exposed surface of these microfibrils, thus creating a protective layer against endochitinases. If the case, it suggests that Avr4 may interact differently with free in solution chito-oligosaccharides as compared with chitin fixed in microfibrils. Conditional dimer formation has also been observed in the wheat germ agglutinin (WGA), a chitin binding-lectin with a hevein-like fold which, depending on the solution conditions, forms weak non-obligate and transient homodimers. Specifically, it is shown that although dimerization enables WGA to maximize its ligand binding affinity, the monomers exhibit significant binding affinity as well, thus making the formation of the dimers less mandatory for the function of the protein [30]. Another possibility is that binding of the Avr4 monomer to the chitin aggregate may lift a chitin chain from a microfibril, thus loosening the structure and enabling another monomer to grab hold of the opposite strand, thereby capping the ends of it that would otherwise be accessible to exochitinases. This mode of interaction is somewhat analogous to the model proposed for the enzymatic decrystallization of cellulose by celluloses, in which case the CBM appended to the catalytic domain functions as a wedge that lifts cellulose chains from the cellulose network [31, 32]. It should be noted that, depending on the orientation and hydrogen-bonding pattern of its GlcNAc chains, crystalline chitin assembles in nature into mainly three allomorphic forms known as α-, β- or γ-chitin. α-chitin forms antiparallel chains of GlcNAc and is most commonly found in crustaceans, insects, and the cell walls of fungi [33, 34], whereas β-chitin forms parallel chains of GlcNAc and is found mainly in diatoms and cephalopods [35–37]. Lastly, γ-chitin forms a mixture of parallel and antiparallel chains of GlcNAc and is found mainly in cocoon fibers of the Ptinus beetle and the stomach of the Loligo squid [38, 39]. Despite the differential orientation of the GlcNAc chains in the three crystalline structures of chitin, they all share a similar stacking pattern of these chains, which is similar to the oligosaccharide stacking observed in the CfAvr4-(GlcNAc)6 complex (S11 Fig). This suggests that Avr4 is likely able to bind all three forms of crystalline chitin, which might explain the broad distribution of CBM14 proteins across nearly all domains of life and their involvement in various biological processes [7].
Our previous work on PfAvr4 led us to hypothesize that the ligand-binding function of Avr4 is structurally distinct or does not fully overlap with the property of recognition by Cf-4 [6]. Instead, a strong correlation between receptor activation and Avr4 stability was observed, as ChBD mutants that evade recognition by Cf-4 embody unstable proteins that are susceptible to proteolytic cleavage in the protease-rich environment of the leaf apoplast [6]. The structural determination of the CfAvr4-(GlcNAc)6 complex and the elucidation of the precise topography and amino acid composition of CfAvr4’s ChBD enabled us to readdress this postulation now with accuracy and examine whether individual residues that directly interact with (GlcNAc)6 are targets for recognition by Cf-4. Our results corroborated the previous findings with PfAvr4, as site-directed mutagenesis of residues in CfAvr4’s ChBD yielded effector mutants that are able to trigger a Cf-4 mediated HR when present at sufficient amounts in the leaf apoplast. The studies further highlighted the dependency of Cf-4-mediated HR on CfAvr4’s stability and resistance against proteolysis in the leaf apoplast, as an inverse correlation exists between the intensity of the HR induced by the mutants when infiltrated into tomato leaves of cv. Purdue 135 (+Cf-4) and their susceptibility to subtilisin. These results are also in agreement with early studies showing that race 4 field isolates of C. fulvum produce unstable and protease sensitive isoforms of CfAvr4 as a means of evading recognition by Cf-4 [3, 15]. Collectively, these studies emphasize the importance for recognition by Cf-4 of a stable tertiary structure of Avr4 and challenge early postulations that the broad recognition of Avr4 effectors by Cf-4 stems from perceiving residues implicated in binding (GlcNAc)6. Instead, we hypothesize that immune receptors like Cf-4 could have exploited, during evolution, the need of apoplastic effectors to adopt a well-ordered stable structure in order to withstand proteolytic attack during infections in leaf apoplast, thus perceiving globular fold properties of Avr4. However, it was previously shown that Pro87, a conserved residue among Avr4 effectors, is essential for the Cf-4-mediated HR, as mutating this amino acid to an arginine resulted in a loss of recognition [40]. Our studies show that Pro87 is in close proximity to the ligand and mutating it to alanine results in minimal effect in ligand binding and no effect on Cf-4 recognition, thus corroborating previous finding with PfAvr4 [6]. This suggests that it is likely not the proline that is essential but a small aliphatic residue that is required in this position in order to ensure the structural integrity of the protein.
Chitin hexasaccharide was purchased from Megazyme, Inc (Dublin, Ireland). Concentration of WT Avr4 was determined using the theoretical ϵ280(Avr4_WT) = 17460 M-1 cm-1, which was similar to the calculated ϵ280 determined from a Bradford Assay. Mutants of Avr4 that included aromatic residues had extinction coefficients different from the WT protein and those values, and that of the WT protein, were determined using the ExPASy server [18].
A stock solution of the monomer of N-acetylglucosamine was prepared by dissolving the dry powder in a buffer containing 10 mM Bis-Tris, pH 6.5, 100 mM NaCl to a concentration of 0.100 g/L using analytic techniques. Standard solution of 0, 0.010, 0.025, 0.050, 0.075 g/L were prepared by serial dilutions. Using glass test tubes and pipets, 100 μL of each standard was mixed with 300μL of concentrated sulfuric acid and vortexed for 10 sec to mix. Solutions were incubated at RT for 10 min and then placed on ice to stop the reaction. Solutions had a maximum absorbance at 322 nm and the concentration curve was determined using the absorbance of each standard at this wavelength. Analytically prepared solution of the di- and tri-saccharide scaled linearly to the calibration curve. To determine the concentration of the chitin hexasaccharide solutions used in ITC, the solutions were diluted into the dynamic range of the calibration curve (S12 Fig) and subjected to the above procedure.
The part of the CfAvr4 gene that encodes for the true mature form of the protein (i.e. Lys30-Gln115) [3] was cloned into a modified pCDG-duet-1 vector containing two 6x His tags and a rhinovirus 3C protease cleavage site immediately N-terminal of the MCS1 using the SalI and NotI restriction sites. The vector was transformed into Escherichia coli Rosetta-gami B cells (Novagen) to facilitate formation of disulfide bonds. For expression, 1L cultures were grown at 37°C until OD600 = 0.5–0.7 and then cooled to 15°C. Cultures were induced with IPTG at a concentration of 1mM and allowed to express for 18–24 hours.
For purification, cells were resuspended in buffer containing 50mM Tris:HCl, pH 8.0, 300mM NaCl, and 5mM imidazole. Cells were lysed using a microfluidizer and the lysate was cleared by centrifugation (39,000xg for 45 min). Cleared lysate was loaded on a 1mL HiFliQ-NiNTA (Anatrace). The column was washed with 10 column volumes (CV) of lysis buffer containing 30mM imidazole followed by a gradient wash, which increased the imidazole concentration to 60mM over 20CV, then washed with an additional 10CV of buffer containing 60mM imidazole. Protein was eluted from the column using lysis buffer containing 300mM imidazole. Collected protein fractions were concentrated to 1/5th original volume, then diluted back to original volume using lysis buffer to reduce imidazole concentration. Protein concentration was checked using A280 and a theoretical ϵ280 = 17460M-1cm-1 The purification tag was removed by adding His-tagged Rhinovirus 3C protease in a 50:1 Avr4-to-protease ratio and 10μM of BME. The cleavage reaction was conducted at 4°C with stirring for 15 hours. The cleaved tag and protease were removed by flowing cleavage product back over the Ni-NTA column. The collected flow-through was checked for purity on SDS gel. CfAvr4 proved to be unamenable to dialysis. Buffer exchange was conducted via Amicon Ultra-15 Centrifugal Filters (Millipore, 3000 NMWL). The recovered protein was concentrated down to <3mL and diluted to 14mL using the crystallization buffer, 10mM Bis-Tris, pH 6.5, 100mM NaCl. This concentration and dilution was repeated twice more before the final concentration step for full buffer exchange.
E. coli-produced CfAvr4 was concentrated to 100mg/mL and mixed with chitin hexasaccharide to reach a final concentration of 80mg/mL and 8mM carbohydrate, a 1:1 stoichiometric ratio. The protein and ligand were co-crystallized by sitting-drop vapor diffusion in 0.1M Tris:HCl, pH 8.5, 30% (w/v) PEG-4000, 0.8M LiCl at 4°C. Crystals were harvested and briefly soaked in reservoir buffer supplemented with 30% ethylene glycol prior to flash-cooling in liquid nitrogen. X-ray diffraction data were collected at ALS beamline 8.3.1. Crystals belong to space group P21 with unit cell parameters of: a = 39.83 Å, b = 41.08 Å, c = 121.30 Å, β = 97.871°. A Matthews coefficient [41] was calculated to be 2.14 Å3/Da (solvent content = 42.5%) assuming four monomers per asymmetric unit. Phases were determined by molecular replacement using PfAvr4 (PDB: 4Z4A) as a search model. The resulting structure was refined to a resolution of 1.95Å in the space group P21. The structure was refined to a Rfactor = 16.70% and an Rfree = 21.45% using Phenix Refine [42]. Data collection and refinement statistics are shown in S1 Table. The refinement restraints for the hexasaccharide of GlcNAc were generated using Phenix eLBOW [43]. Atomic coordinates along with structure factors have been deposited in the Protein Data Bank, PDB ID: 6BN0.
Primers for the site-directed mutagenesis were designed using the Agilent QuikChange Primer Design tool. Primers were purchased from Integrated DNA Technologies (IDT) and mutagenesis reactions were performed using Accuzyme PCR mix. Reaction products were purified and transformed into BL21 (DE3) cells. Colonies were selected and a Miniprep was performed to amplify the DNA. Collected DNA was sequenced to confirm proper, in-frame mutation (QuintaraBio). Some reactions repeatedly resulted in primer duplications and further experiments were performed using the half-reaction procedure to prevent the duplications. Sequence-confirmed mutations were transformed into the Rosetta-gami B cells for expression.
All ITC experiments were performed on a TA Instruments low volume NanoITC with a 186uL reaction cell and a reference cell filled with degassed Milli-Q grade water. Reactions were run in a buffer containing 10mM Bis-Tris, pH 6.5, 100 mM NaCl. The reservoir solution was filled with protein solution and continuously stirred while Chitin hexasaccharide solutions titrated in 2.5μL aliquots for 19 injections, with an initial 1μL injection, for a total injection volume of 48.5μL. The carbohydrate solutions were made by dissolving the dry powder in buffer to a concentration of 20mM (confirmed using methods described above) and then diluted to the working concentration using the final buffer exchange flow through to match the buffers as closely as possible. The integrated heats, after correction for the heat of dilution were analyzed using the NanoAnalyze software from ITC Technologies. An individual binding site model was used to fit the binding isotherm. All experiments were replicated a minimum of three times.
The ability of the WT-CfAvr4 and of ChBD mutants to protect against chitinases was evaluated in in vitro protection assays conducted as described before [6]. Briefly, spores of T. viride (3∙103) were pre-germinated overnight at room temperature in 100 μl of half-strength PDB medium in 96 well plates. The following day, 5 μM of WT-CfAvr4 or ChBD mutants were mixed with 7.5 units of Zymolyase (Zymo Research cat. no. E1004) and 0.2 units of bacterial chitinases (Sigma-Aldrich cat. no. C6137) and the mixture was added to the microtitre plate wells containing the pre-germinated spores in a final volume of 150 μl. Plates were incubated at 25°C for 6–8 h before evaluating under the microscope fungal growth and the ability of the proteins to provide protection against chitinases. Zymolase has β-1,3 glucanase and β-1,3-glucan laminaripentaohydrolase activity and is added to the mixture in order to remove the surface glucans from the fungal cell wall and thus expose the underlying chitin layer.
Tomato plants of cv Moneymaker (MM), which does not carry Cf-4 or any other functional Cf resistance genes against C. fulvum, and of cv Purdue 135, which expresses a functional Cf-4 resistance gene, were grown in a growth chamber with 16 h of artificial light and 70% humidity at 27°C for 6 weeks. Tomato seeds were obtained from the Tomato Genetic Resource Center (TGRC) at UC Davis. WT-CfAvr4 and mutants thereof with single point mutations at selected amino acids were produced and purified from E. coli Rosetta-gami B cells as description above. Proteins were infiltrated at a concentration of 5 μg/mL and 10 μg/mL into the back side of MM or Purdue 135 tomato leaves using a one mL syringe and the HR response was recorded at 6 days post-infiltrations.
The Agrobacterium tumefaciens transient transformation assay (ATTA) was used for the transient co-expression into leaves of Nicotiana benthamiana of Cf-4 with the WT-CfAvr4 and the mutants thereof tested in this study [6, 44]. Briefly, N. benthamiana plants were grown in a growth chamber with 16 h of artificial light and 70% humidity, at 25°C for 4 weeks. The true mature form of the WT-CfAvr4 (i.e. between Lys30-Gln115) [3] and of the mutants thereof were singly cloned into the binary expression vector pICH47742 downstream of the PR1A signal sequence of Nicotiana tabacum for targeted secretion into the apoplast, and under the control of CaMV 35S promoter and NOS terminator. The tomato Cf-4 was cloned into pMOG800 as descripted before [20]. Binary vector plasmids were transformed into A. tumefaciens strain GV3101 by electroporation. For the ATTA assay, agrobacteria transformed with pMOG800:Cf-4 was grown in 10mL LB-mannitol medium (LB medium with 10 g/L mannitol) amended with 50 μg/mL kanamycin and 25 μg/mL rifampicin, whereas agrobacteria transformed with pICH47742:WT-CfAvr4 or pICH4774:CfAvr4-mutants were grown in LB-mannitol amended with 100 μg/mL carbenicillin and 25 μg/mL rifampicin. All cultures were incubated at 28°C for 2 days, after which period they were pelleted at 2800g for 15 min, re-suspended in MMAi medium (5 g/L Murashige and Skoog basal salts, 20 g/L sucrose, 10 mM MES, and 200 mM acetosyringone) at an optical cell density (A600) of 2.0, and further incubated for 2–4 h at room temperature. Agroinfiltrations were performed by mixing agrobacteria containing pMOG800:Cf-4 with agrobacteria containing pICH47742_WT-CfAvr4 or pICH47742:CfAvr4-mutants at three different ratios of optical cell densities, i.e. A6000.5:A6001.0, A6000.5:A6000.5, and A6000.5:A6000.25 of. The mixture of agrobacteria was infiltrated into the leaves of N. benthamiana plants using a 1 mL syringe and the induction of an HR in the infiltrated leaf sectors was evaluated at 5 days post-infiltrations. Control infiltrations were made using only the pMOG800:Cf-4 transformed agrobacteria and the agrobacteria containing pICH47742:CfAvr4 or the mutants thereof without mixing the two cultures, at cell densities of A6000.25, A6000.5, and A6001.0.
The WT-CfAvr4 and selected mutants thereof were exposed to the non-specific protease subtilisin in order to determine their resistance against proteolytic degradation. Treatments with subtilisin were made as previously described with minor modifications [6]. Briefly, 40 μM of the E. coli-produced CfAvr4 and the selected CfAvr4 mutants were mixed with 10 mM of calcium chloride and 500 ng/μL of subtilisin (Sigma-Aldrich) in a total volume of 15 μL. Samples were incubated for 30 min at room temperature, after which period they were denatured by mixing with 10 mM PMSF and SDS-PAGE loading buffer, and heating at 98°C for 10 min. Digestion products were visualized on an SDS-PAGE after staining with Coomassie blue.
The atomic coordinates and structure factors have been deposited in the Protein Data Bank, www.wwpdb.org (PDB ID code 6BN0).
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10.1371/journal.pmed.1002912 | Simplified clinical algorithm for identifying patients eligible for same-day HIV treatment initiation (SLATE): Results from an individually randomized trial in South Africa and Kenya | The World Health Organization recommends "same-day" initiation of antiretroviral therapy (ART) for HIV patients who are eligible and ready. Identifying efficient, safe, and feasible procedures for determining same-day eligibility and readiness is now a priority. The Simplified Algorithm for Treatment Eligibility (SLATE) study evaluated a clinical algorithm that allows healthcare workers to determine eligibility for same-day treatment and to initiate ART at the patient’s first clinic visit.
SLATE was an individually randomized trial at three outpatient clinics in urban settlements in Johannesburg, South Africa and three hospital clinics in western Kenya. Adult, nonpregnant, HIV-positive, ambulatory patients presenting for any HIV care, including HIV testing, but not yet on ART were enrolled and randomized to the SLATE algorithm arm or standard care. The SLATE algorithm used four screening tools—a symptom self-report, medical history questionnaire, physical examination, and readiness assessment—to ascertain eligibility for same-day initiation or refer for further care. Follow-up was by record review, and analysis was conducted by country. We report primary outcomes of 1) ART initiation ≤28 days and 2) initiation ≤28 days and retention in care ≤8 months of enrollment. From March 7, 2017 to April 17, 2018, we enrolled 600 patients (median [IQR] age 34 [29–40] and CD4 count 286 [128–490]; 63% female) in South Africa and 477 patients in Kenya (median [IQR] age 35 [29–43] and CD4 count 283 [117–541]; 58% female). In the intervention arm, 78% of patients initiated ≤28 days in South Africa, compared to 68% in the standard arm (risk difference [RD] [95% confidence interval (CI)] 10% [3%–17%]); in Kenya, 94% of intervention-arm patients initiated ≤28 days compared to 89% in the standard arm (6% [0.5%–11%]). By 8 months in South Africa, 161/298 (54%) intervention-arm patients had initiated and were retained, compared to 146/302 (48%) in the standard arm (6% [(2% to 14%]). By 8 months in Kenya, the corresponding retention outcomes were identical in both arms (137/240 [57%] of intervention-arm patients and 136/237 [57%] of standard-arm patients). Limitations of the trial included limited geographic representativeness, exclusion of patients too ill to participate, missing viral load data, greater study fidelity to the algorithm than might be achieved in standard care, and secular changes in standard care over the course of the study.
In South Africa, the SLATE algorithm increased uptake of ART within 28 days by 10% and showed a numerical increase (6%) in retention at 8 months. In Kenya, the algorithm increased uptake of ART within 28 days by 6% but found no difference in retention at 8 months. Eight-month retention was poor in both arms and both countries. These results suggest that a simple structured algorithm for same-day treatment initiation procedures is feasible and can increase and accelerate ART uptake but that early retention on treatment remains problematic.
Clinicaltrials.gov NCT02891135, registered September 1, 2016. First participant enrolled March 6, 2017 in South Africa and July 13, 2017 in Kenya.
| Both the World Health Organization and many national governments in sub-Saharan Africa now recommend that patients diagnosed with HIV start antiretroviral treatment (ART) as quickly as they can and, if possible, on the same day as their HIV diagnosis, known as “same-day initiation.”
Despite the recommendations, initiation still usually requires 2–4 clinic visits before medications are dispensed, and there is little guidance on how to implement same-day initiation, in particular on exactly how to determine if a patient is eligible and ready to start treatment and how to provide the specific services required for ART initiation in a single clinic visit.
In the SLATE study, we evaluated a simple clinical algorithm to guide nurses and other clinical staff on how to offer same-day initiation to most patients while still providing all the care that HIV patients need.
We individually randomized patients coming to three public-sector clinics in South Africa and three in Kenya for an HIV test or pretreatment care to be offered either same-day ART initiation under the SLATE algorithm or regular (standard-of-care) procedures for ART initiation.
The intervention allowed half of the patients in South Africa and 70% of them in Kenya to initiate ART on the same day (i.e., in a single visit); most who could not had symptoms of tuberculosis (TB) and required a TB test before starting ART.
The proportions starting treatment within 7 and 28 days of study enrollment increased by 27% and 10% in South Africa and by 13% and 6% in Kenya.
There was little or no difference in retention in care or viral suppression rates 8 months after study enrollment, with very poor retention observed in both countries.
Nearly every patient in the study (98%) said that they would like to start treatment on the same day if they could.
The SLATE study demonstrates that at least half of all HIV-positive patients who come to clinics and are not yet on HIV treatment are eligible and ready for same-day initiation; initiation can safely be done without waiting for laboratory test results, and the vast majority of patients would like this option.
Common reasons for delaying ART initiation, such as the presence of TB symptoms or providers’ concerns about treatment adherence, should be investigated further because the benefits of offering medications on the day of diagnosis may outweigh some of these risks.
Loss of patients from care after starting ART remains a major challenge regardless of the manner or speed of initiation.
| In July 2017, the World Health Organization (WHO) revised its guidelines for antiretroviral therapy (ART) for HIV to recommend “same-day” treatment initiation (on day of diagnosis) whenever possible and “rapid” initiation (within 7 days of diagnosis) for all HIV-positive patients [1]. The guidelines cited evidence from clinical trials suggesting that offering treatment to patients at their first clinical encounter has the potential to increase relative uptake of ART within 90 days by 30% and from observational studies that showed an overall relative increase of 53%. These studies varied widely from one another in the clinical approaches used, intervention design (same-day initiation or rapid initiation), and the populations studied [1].
Both WHO’s new guidelines [1] and national guidelines in South Africa [2] and Kenya [3] recommend same-day initiation if the patient is “ready” (WHO), “clinically ready and willing to commit” (South Africa), or “as soon as patient is ready” (Kenya). Little guidance is provided, however, on exactly how to determine if a patient is ready to start treatment or how to provide the specific services required for ART initiation in a single clinic visit.
All of the trials cited by WHO relied on point-of-care (POC) instruments, which are not feasible in most routine care settings. A cost-effectiveness analysis of one of them, the RapIT trial in South Africa [4], found that the POC instruments used increased the cost per patient initiated substantially [5], and requirements for power, internet access, maintenance, and quality assurance make scale-up of POC instruments infeasible in most low-resource settings. In late 2015, a technical consultation to develop a post-RapIT research agenda on how to accelerate ART initiation proposed a clinical algorithm intended to allow nurses and other clinicians to determine eligibility for same-day initiation and start ART without relying on POC tests or waiting for laboratory results, using a comprehensive, standardized algorithm [6]. The Simplified Algorithm for Treatment Eligibility (SLATE) trial evaluated a refined version of that algorithm, powered separately in South Africa and Kenya. The study’s goal was to determine whether an algorithm for same-day ART initiation that can be implemented in routine care settings without reliance on laboratory results can safely and effectively increase and accelerate uptake of ART in the general adult population. We report primary and secondary outcomes, including ART initiation within 7 and 28 days and retention in care at 8 months after study enrollment.
SLATE was an unblinded, individually randomized trial of an intervention that allows clinicians to determine eligibility for same-day initiation and dispense antiretroviral medications (ARVs) at any clinic visit, including the first visit for an HIV test, using a clinical algorithm that does not require laboratory test results prior to initiation and can be implemented by typical clinic staff. It received ethics approval from the institutional review boards of Boston University (BUMC H-35634), the University of the Witwatersrand (HREC 160910), and the Kenya Medical Research Institute (SERU 3408) and is registered with ClinicalTrials.gov, number NCT02891135. Study procedures have been described in detail previously [7] and are illustrated in Fig 1. The research protocol is included as S1 Text and the CONSORT checklist as S1 Checklist.
During the period of study enrollment (March, 2017–April, 2018), all HIV-positive individuals were eligible for ART under South Africa’s and Kenya’s universal treatment guidelines, regardless of CD4 count. Under standard care, guidelines called for a preinitiation CD4 count, creatinine clearance test, and hemoglobin in both countries and alanine aminotransferase in South Africa. Guidelines also recommended that patients with symptoms of tuberculosis (TB; any cough, fever, night sweats, or weight loss) be asked for a sputum sample, to be tested at a centralized (South Africa) or on-site (Kenya) laboratory using Xpert MTB/RIF. Blood samples from patients with CD4 counts ≤100 cells/mm3 were reflexively tested for cryptococcal antigen (CrAg). Treatment-naïve patients were initiated on the standard first-line ARV regimen of tenofovir, emtricitabine (South Africa)/lamivudine (Kenya), and efavirenz, dispensed in a combined once-daily tablet. After initiation, patients in South Africa were asked to return to the clinic for monitoring at 1, 2, 3, 6, and 12 months, and 6-monthly thereafter [8]; patients in Kenya were asked to return at 2 weeks and 4 weeks and then monthly until virally suppressed, with visits every 3 months thereafter. Medication refill visit schedules depended on inventories available and provider judgment, with a 1–3 months’ supply typically dispensed at each visit.
SLATE was conducted at a convenience sample of three public-sector primary care clinics serving densely settled, urban formal and informal populations around Johannesburg, South Africa and three public-sector HIV outpatient clinics located within county hospitals in western Kenya. All study sites received some level of support from nongovernmental partners of the U.S. President’s Emergency Plan For AIDS Relief (PEPFAR), as was typical of most large facilities in both countries. Details of infrastructure and staffing varied by country. All study staff completed ethics and study-specific training.
In South Africa, an interview room and an examination room located either in the clinic building or in a mobile trailer on the clinic grounds were designated for study procedures and storage of study equipment and supplies. Clinical procedures and administration of the algorithm among those randomized to the intervention arm were performed by study nurses with the same clinical qualifications as existing primary healthcare nurses responsible for ART initiation. Nonclinical procedures (recruitment, consent, questionnaire, patient flow management, data capturing onto mobile tablets) were implemented by study assistants, some of whose qualifications were comparable to those of experienced lay counselors at the sites.
In Kenya, an interview room and an examination room were located in clinic buildings at all three sites. Clinical procedures and administration of the algorithm were performed by the same clinical officers who were already responsible for ART initiation, with one per site employed part-time by the study. Nonclinical procedures (recruitment, consent, questionnaire, patient flow management, data capturing onto mobile tablets) were implemented by study assistants who were trained clinic nurses, again working on a part-time basis for the study.
The study enrolled adult (≥18 years old), nonpregnant, HIV-positive patients not yet on ART who presented at one of the study clinics to have an HIV test, enroll in care or prepare to start or restart ART if already diagnosed, or receive other unrelated medical care that led to referral for an HIV test. Pregnant women were excluded because standard-of-care prevention of mother-to-child transmission (PMTCT) is typically provided by the antenatal clinic rather than the general ART clinic. During screening, patients who were unwilling to hear about the study, were currently on ART or had been dispensed ARVs in the preceding 90 days, indicated that they planned to seek HIV care during the next 12 months at a different clinic, or were judged by clinic or study staff to be physically or emotionally unable to provide consent or participate in all study procedures were excluded. Female patients found postenrollment to be pregnant were withdrawn prior to randomization and referred for antenatal ART initiation.
After obtaining written informed consent, each study participant was assigned a unique study ID that was electronically scanned and used to link all electronic forms captured on mobile tablets in REDCap Mobile [9]. The study assistant then administered a questionnaire to all study participants asking questions about the patients’ demographic characteristics, HIV history and treatment preferences, employment and primary activities, and visit costs.
Each participant was offered compensation for participation equivalent in value to US$5–$15, in the form of a shopping voucher that could be used at nearby grocery/general goods stores in South Africa and cash in Kenya, as illustrated in Fig 1.
Participants were individually randomized 1:1 to the intervention arm or to standard of care using block randomization in blocks of six. Allocations were generated by MM using a computerized random-number generator under oversight of the principal investigator and numbered sequentially. They were then placed in opaque envelopes and sealed by ATB. The envelopes were kept in sequential, numbered order at the study sites, with an equal number distributed to each site to balance enrollment by site. On completion of the questionnaire, the study assistant opened the next sequentially numbered randomization envelope to reveal the patient’s allocation. No blinding of patients or providers was possible because each arm entailed different procedures and staff.
Study staff interaction with participants in the standard arm was limited to screening for study eligibility, obtaining written informed consent, administering a questionnaire, and referral back to standard care. Standard-arm patients were accompanied to the appropriate location in the clinic (e.g., counselor station, registration desk, or TB room) to continue with a standard care visit. After referral, patients in the standard arm were followed passively through medical record review and had no further interaction with study staff.
Standard-of-care procedures for ART initiation generally followed national guidelines in each country as outlined below, though exact procedures varied somewhat by site. During the period of study enrollment in South Africa (2017), guidelines recommended ART initiation “as soon as the patient is ready and within two weeks of CD4 count being done” for most patients and within 1 week for those presenting very ill [8,10]. A national evaluation conducted in South Africa in 2017 reported that an average of three visits to a clinic were still required before ARVs were dispensed: one visit for an HIV test, TB symptom screen, and sputum sample if symptomatic, and an initial adherence session; a second visit for remaining adherence sessions and return of laboratory results; and a third visit for a clinical examination and dispensing of ARVs. These visits were typically completed over a 2–4 week period [11,12]. Patients with positive TB test results or other conditions entailing additional care required more visits over a longer period of time. All laboratory tests were processed at centralized public-sector laboratories, with results available at the patient’s next visit. Patients who had already completed some preinitiation steps, such as an HIV test and blood draw for a CD4 count, at the time of study enrollment required few or no additional visits under standard care, such that some patients enrolled in the study could be dispensed ARVs by clinic staff on the day of study enrollment because they had already completed all preinitiation steps.
In Kenya, standard-of-care guidelines at the time of SLATE I enrollment recommended that all patients initiate within 2 weeks of HIV care enrollment but allowed same-day initiation for those thought to have “strong motivation” [13]. An informal prestudy review of clinic records suggested that the use of same-day initiation varied by site but was quite common overall. During study enrollment, the standard-of-care ART initiation process required an HIV test (if not already done) and confirmatory HIV test, a complete medical and psychosocial history, a thorough physical exam, HIV-specific and nonspecific laboratory investigations (but not as a prerequisite to ART initiation), screening for TB, and a variety of other assessments and counseling activities addressing reproductive health, noncommunicable diseases, mental health, nutrition, alcohol and substance abuse, and education on HIV and its treatment.
For patients randomized to the intervention arm, the SLATE algorithm (Fig 2 and S1 Table) was administered by a study nurse in South Africa and a clinical officer in Kenya. The algorithm comprised four “screens”: symptom report, medical history, physical examination, and readiness assessment. Each screen was designed to identify clinical, historical, or personal reasons for which a patient should be referred for additional care, investigations, or services needed before ART could be initiated without compromising patient welfare.
For patients who “screened in” under the algorithm—i.e., did not report or demonstrate any reason to delay ART initiation—28 days/14 days of medication was dispensed by the study clinician in South Africa/Kenya, and the patient was accompanied to the clinic booking office to schedule their next clinic appointment. For those who “screened out” on any one of the four SLATE algorithm screens, indicating at least one condition or concern that suggested additional services were needed, the study clinician referred the patient back to the site for further clinical investigation or care following the site’s routine procedures as warranted. All patients who screened out of same-day initiation in the intervention arm were offered a referral letter to give to the site clinic detailing the condition or concern reported by the study nurse. After the study enrollment visit, all patients received follow-up care from clinic staff under routine procedures. The previously published protocol for the study provides further details about procedures [7].
Data were collected from four sources. First, at the study enrollment visit, a case report form (CRF) was completed, with eligibility and questionnaire data for all participants and SLATE algorithm data for intervention-arm participants. CRF data were entered by study staff onto tablet computers programmed for data collection using REDCap Mobile [9]. Second, baseline blood tests (e.g., CD4 counts) and TB test results were extracted directly from laboratory electronic records or paper-based registers kept at each site. Third, follow-up data for the period from enrollment to study endpoints were collected from routinely generated clinical record data from patient records in electronic and paper format. And fourth, viral load test results were obtained from national laboratory databases maintained by the National Health Laboratory Service in South Africa and the National AIDS & STI Control Programme (NASCOP) in Kenya. Viral load outcomes were thus not limited to tests originating at the study sites, though all other data were. Further details about data sources and quality are provided in S2 Text.
Patients had no personal interaction with the study team after the enrollment visit because all follow-up was based on record review only. Study patients received no support for retention or adherence from the study. Clinics varied in their efforts to trace patients lost to follow-up, but we observed little active tracing during the study period.
The primary outcomes of the study were 1) ART initiation ≤28 days of study enrollment and 2) ART initiation ≤28 days and retention in care 8 months after study enrollment (both conditions had to be met to achieve this outcome). Previous studies have found that 28 days is a sufficient time interval for a majority of patients found eligible for ART to complete the steps required to start treatment under routine care [10,13]. We note that 28 days was a relatively generous interval to allow for achievement of this outcome since standard care guidelines at the time of study enrollment called for initiation within 14 days [14,15]. In light of the recent WHO recommendation for “rapid” initiation of all patients, defined as initiation within 7 days, we also report this additional outcome alongside the original primary outcomes.
The 8-month interval allowed patients up to 1 month (28 days) for ART initiation, 6 months to reach the routine 6-month clinic visit called for by guidelines, and 1 additional month for the 6-month visit to be completed. We defined a patient as “retained” if the patient initiated within 28 days of enrollment and a clinic visit was made or a viral load test observed between 5 and 8 months after enrollment, allowing a broad window for irregular visit schedules. We reviewed patients’ records for up to 3 months after the 8-month outcome window to ensure that we captured information generated within 8 months but only recorded in the EMR or paper files up to 3 months later. Patients who were not retained were reported as known to have died, known to have transferred care to another facility, or, most often, lost to follow-up from the study site (i.e., did not have visit or test 5–8 months after enrollment).
Secondary outcomes reported here include time to initiation in days, proportion of patients initiating within 7 and 14 days, proportion of patients who screened in and out of the SLATE algorithm, reasons for screening out, and self-reported patient preferences on the timing of ART initiation, using baseline questionnaire data. We also report the secondary outcome of viral suppression (<400 copies/mL) between months 5 and 8 after study enrollment, conditional on achieving the 8-month primary outcome, to capture the routine 6-month viral load test called for in national guidelines. We found, however, that many patients who achieved the study’s 8-month outcome had not had a viral load test by 8 months. Viral load results were thus missing for a large number of patients.
The study was designed to detect an absolute increase of 15% in patients achieving our second primary outcome from an estimated baseline of 65%, as observed in the RapIT trial [4], to an intervention outcome of 80%. With an α of 0.05, power of 90%, 1:1 randomization, and an uncorrected Fisher’s exact test, we estimated that we would need to enroll at least 197 patients per arm, which we increased to a maximum of 240 per arm to ensure sufficient power after accounting for anticipated postconsent withdrawal and ineligibility. In South Africa, the sample size was further increased to a maximum of 330 per arm (660 in total). We suspected fairly early in the study that results would vary quite a lot by site, particularly in South Africa. Increasing the sample size facilitated analysis of effect modification by site.
Characteristics at study enrollment of all randomized participants were summarized using simple proportions and medians with interquartile ranges (IQRs), stratified by treatment arm. We compared the proportions of patients achieving each dichotomized study outcome and present crude risk differences (RDs) and crude relative risks (RRs) with 95% confidence intervals (CIs) stratified by group. RDs were estimated using a linear probability model with robust standard errors. RRs were estimated using a log-linear generalized linear model, also with robust standard errors. All analyses were by modified intention to treat: patients randomized to the intervention arm who screened out of immediate ART initiation under the SLATE algorithm remained in the intervention arm for data analysis, with the exception of participants who were excluded because there was not enough time for them to complete study procedures by the end of the day. We looked for absolute effect modification by important predictors of each outcome: age, sex, site, CD4 count, and reason for clinic visit. We used a simple stratification of the primary analysis by the potential modifier and report crude RDs and risk ratios and their corresponding 95% CIs.
During the South Africa study enrollment period from March 6, 2017 to July 28, 2017, 760 patients were screened for study eligibility (Fig 3A). Of these, 609 were eligible and provided written informed consent. Of the 151 screened who did not meet study eligibility criteria, 54 intended to seek further care elsewhere, and 40 refused participation. Other reasons for ineligibility are shown in Fig 3A. Six female patients had positive pregnancy tests after consent and were excluded, and one patient withdrew after consenting, making the total randomized 602. After randomization, two patients were unable to complete study procedures on the day of enrollment because of lack of time and were removed from further analyses, leaving 600 patients in the final South Africa analytic cohort. Of these, 302 were randomized to the standard arm and 298 to the intervention arm. Follow-up continued through June 30, 2018, when all participants had reached the 8-month follow-up interval at which the primary outcome was assessed.
During the Kenya study enrollment period from July 13, 2017 to April 17, 2018, 507 patients were screened for study eligibility (Fig 3B). Of these, 480 were eligible and provided written informed consent. Of the 27 screened who did not meet study eligibility criteria, nine were pregnant, and eight intended to seek further care elsewhere. Other reasons for ineligibility are shown in Fig 3B. Three female patients had positive pregnancy tests after consent and were excluded, making the total randomized and remaining in the analytic cohort 477. Of these, 237 were randomized to the standard arm and 240 to the intervention arm. Follow-up continued through December 23, 2018, when all participants had reached the 8-month interval for the primary outcome.
Baseline demographic, clinical, and economic characteristics of participants stratified by study arm at time of enrollment are reported in Table 1. A majority of participants (63% in South Africa and 58% in Kenya) were female; the median ages were 34 and 36 years, respectively, and median baseline CD4 counts were 277 cells/mm3 (IQR 141–484) and 283 cells/mm3 (IQR 117–541). As Table 1 indicates, there were small differences in baseline CD4 count strata between arms in both countries, but medians and IQRs were similar. In South Africa, more intervention-arm patients than standard-arm patients presented with CD4 counts ≤100 (20% versus 15%), but the proportion with CD4 counts <200 was similar between the arms (36% versus 35%). In Kenya, 39% of standard-arm patients were missing CD4 count results, mainly because of the site laboratories’ equipment failures or lack of reagents. There were small differences between the study arms in each country for some other variables, but none that appear meaningful for interpretation of trial results.
In South Africa, 69% of the study sample said that they had never been to that clinic for any reason before the day of study enrollment, and 52% reported that one of the reasons for coming to the clinic on the day of study enrollment was to test for HIV. In Kenya, 81% of subjects said that they had never been to that clinic for any reason before the day of study enrollment, and 47% reported that one of the reasons for coming to the clinic on the day of study enrollment was to test for HIV. In both countries, well over 90% of patients indicated that they would want to start ART on the same day if they could, regardless of the reason for their clinic visit.
As reported in Table 2, initiation of ART within 0, 7, 14, and 28 days of study enrollment was higher in the intervention arm in both countries. In South Africa, 78% (232/298) of intervention-arm patients were documented to have initiated ART within 28 days, compared to 68% (204/302) of patients in the standard arm, for an absolute RD of 10% (3%–17%). In Kenya, 94% (226/240) of intervention-arm patients were documented to have initiated ART within 28 days, compared to 89% (210/237) of patients in the standard arm, for an RD of 6% (1%–11%).
Within 7 days of study enrollment—the WHO definition of “rapid” initiation—65% (193/298) of intervention-arm patients and 38% (114/302) of standard-arm patients in South Africa had started treatment, for an RD of 27% (19%–35%). An additional 24 (8%) patients in the intervention arm and 34 (11%) patients in the standard arm initiated between 28 and 90 days after enrollment. By 3 months after study enrollment, there was still no record of ART initiation for 14% of intervention-arm patients and 21% of standard-arm patients (–7% [–13% to 1%]).
In Kenya, 86% (207/240) of intervention-arm patients and 73% (173/237) of standard-arm patients had started treatment by 7 days, for an RD of 13% (6 to 20%). An additional 5 (2%) patients in the intervention arm and 12 (5%) patients in the standard arm initiated between 28 and 90 days after enrollment. By 3 months after study enrollment, there was still no record of ART initiation for 4% of intervention-arm patients and 6% of standard-arm patients (–3% [–7% to 1%]).
Finally, just over half of intervention-arm patients (161/298) in South Africa initiated on the same day as study enrollment, as did 11% of standard-arm patients (33/302). Of the 149 intervention-arm patients screened out of same-day initiation, cumulative numbers initiating within 0, 7, 14, and 28 days were 12 (8%), 44 (30%), 58 (39%), and 83 (56%), respectively; 52 patients screened out of same-day initiation did not initiate within 28 days, and no records were found for the remaining 14 patients.
In Kenya, 70% (167/240) of intervention-arm patients initiated on the same day as study enrollment, as did 54% of standard-arm patients (127/237). A total of 109 patients randomized to the intervention were deemed ineligible to received same-day treatment through the SLATE I study; of these patients, cumulative numbers initiating within 0, 7, 14, and 28 days were 41 (38%), 78 (72%), 86 (79%), and 95 (87%), respectively; 6 patients screened out of same-day initiation did not initiate within 28 days, and no records were found for the remaining 8 patients.
The second protocol-defined primary outcome was initiation by 28 days and retained in care 8 months after study enrollment, as indicated by a clinic visit or observed laboratory test between 5 and 8 months after enrollment. In the intervention arm in South Africa, 161 of 298 (54%) patients achieved the second primary outcome, compared to 146/302 (48%) in the standard arm, showing a numerical 6% [–2% to 14%] increase in absolute risk. Results were nearly identical between arms in Kenya: 57% of patients in each study arm achieved the second primary outcome.
As defined by the study, viral suppression as an outcome pertains only to the subset of patients who achieved the second primary outcome (initiated within 28 days and retained at 8 months’ postenrollment). In South Africa and Kenya, 60% and 67% of these patients had viral load test results reported in their records by 8 months after enrollment and were virally suppressed, respectively (Table 2). We found no difference in known viral suppression by 8 months among these patients.
In South Africa, among the 298 patients in the intervention arm, exactly half (n = 149) were eligible for same-day initiation according to the SLATE algorithm. Among the remaining 149 patients who screened out of same-day initiation—many for multiple reasons—109 (73%) had one or more symptoms of TB, 17 (11%) reported persistent headache, 14 (9%) had previously defaulted ART, 6 (4%) said they were not ready, 6 (4%) reported substance abuse issues, and 6 (4%) presented with a concerning clinical condition unrelated to TB or other serious opportunistic infection. Among the 52 intervention-arm patients who screened out of same-day initiation and did not initiate within 28 days, 33 (63%) had TB symptoms, 3 (6%) had persistent headache, 7 (13%) were previous defaulters, 2 (4%) reported substance abuse, and 7 (13%) were not ready to start. A further 12 patients (8%) who screened out of same-day ART in the intervention arm were initiated on the day of enrollment by clinic staff following study referral, resulting in a total of 161 intervention-arm patients who initiated ART on the day of study enrollment. The 12 patients initiated by the clinic had screened out of the SLATE algorithm because of TB symptoms (n = 9), headache (n = 2), or substance abuse (n = 1).
In Kenya, among the 240 patients in the intervention arm, 131 (55%) were eligible for same-day initiation according to the SLATE algorithm. Among the remaining 109 patients who screened out of same-day initiation—many for multiple reasons—93 (85%) had one or more symptoms of TB, 31 (28%) reported persistent headache, 18 (17%) had previously defaulted ART, 3 (3%) said they were not ready, 12 (11%) reported substance abuse issues, and 7 (6%) presented with a concerning clinical condition unrelated to TB or another serious opportunistic infection. Among the 14 intervention-arm patients who screened out of same-day initiation and did not initiate within 28 days, 14 (100%) had TB symptoms, 4 (29%) had persistent headache, 1 (7%) was a previous defaulter, 2 (14%) reported substance abuse, and 1 (7%) was not ready to start. Forty-one of 109 (38%) patients who screened out of same-day ART in the intervention arm were initiated on the day of study enrollment by clinic staff following study referral. The 41 patients initiated by the clinic had screened out of the SLATE algorithm because of TB symptoms (n = 30), headache (n = 8), and/or substance abuse (n = 3).
Secondary outcomes included an analysis of absolute effect modification by selected variables (S2 Table). We note that the study was not powered to identify effect modifications, and results of this analysis should be interpreted as hypothesis-generating. The most important modifier in both countries was site. In South Africa, for both primary outcomes, most of the difference seen was due to Site 3. For initiation by 28 days, the absolute RD at Site 3 was 22% (6%–37%). At the other two sites, initiation ≤28 days showed a modest increase of 4%–6%, though with wide CIs. For retention at 8 months, the RD for Site 3 was 15% (1%–29%), while differences at the other sites were small. In Kenya, for initiation ≤28 days, most of the difference seen was due to Sites 1 (11% [2%–21%]) and 3 (7% [0%–15%]), not Site 2, where the difference was small. For our second primary outcome, all three sites differed: Site 1 showed equally poor retention in both study arms; at Site 2, the RD was negative with a wide CI (–13% [–29% to 2%]), while for Site 3, the RD was positive (15% [–2% to 31%]). Other modifiers of effect included sex, age, and reason for clinic visit in South Africa and sex in Kenya, with greater initiation ≤28 days in general for men in both countries. No other variables, including baseline CD4 count, showed an effect modification.
In this randomized evaluation, we found that a simple algorithm for initiating ART in a single visit, without awaiting laboratory tests or additional services, enabled exactly half of HIV-positive adults presenting at primary care clinics in Johannesburg and 70% of this population in Kenya to initiate treatment on the same day. In both countries, roughly half of those had been diagnosed that day. The proportion initiating within 7 days increased by 27% and within 28 days by 10% in South Africa and by 13% within 7 days and 6% within 28 days in Kenya. After 8 months’ follow-up, there was a numerical 6% increase between the arms in retention in care in South Africa and no difference in retention in care in Kenya or known viral suppression in either country.
In much of the world, uptake of ART among those already diagnosed remains far below the global target of 90% [14]. In Gauteng Province, where Johannesburg is located, only an estimated 55% of known HIV-positive persons were on treatment in 2016 [15]. As a result, 42% of AIDS-related deaths nationally were among people diagnosed but not yet on ART that year [16]. In western Kenya, roughly 20% of AIDS-related deaths were estimated to be among those diagnosed but not on ART between 2010 and 2015 [17]. While there are barriers to starting treatment at a number of levels [18], making procedures for ART initiation more efficient—with efficiency encompassing clinical effectiveness, patient behavior, and resource utilization by both providers and patients—is important if high-prevalence countries like South Africa and Kenya are to achieve the 90–90–90 targets for HIV treatment.
Other trials of same-day or accelerated ART initiation have generally reported larger increases in ART initiation, compared to standard care, but similar outcomes after starting ART [4,19–21]. Further details can be found in Ford and colleagues’ 2017 recent review of these studies [19], which informed WHO’s guideline revision in favor of rapid or same-day initiation.
To our knowledge, SLATE is the first algorithm evaluated that does not require technology or infrastructure typically not available in public-sector clinics and is, we believe, simpler to perform than other approaches. For nonpregnant patients, all randomized studies we are aware of to date have relied on POC testing instruments for CD4 staging, TB diagnosis, and/or creatinine clearance, which we have come to believe are not feasible or affordable to place in typical primary health clinics in low- and middle-income countries outside study or demonstration settings. These include, e.g., the RapIT trial, which used POC CD4 counts, TB tests, and creatinine tests [4]; the START trial in Uganda, which relied on POC CD4 counts [20]; and the CASCADE trial in Lesotho, which also utilized an array of POC tests [22]. Unlike the START trial, SLATE attempted no changes to clinic management; unlike CASCADE, it took place entirely in existing facilities. Our hope is that SLATE will provide an alternative that can more readily be implemented in routine care settings, particularly those that currently have the longest delays under standard of care.
In both of the SLATE study countries, most of the benefits of the intervention accrued at a subset of the three study sites. In South Africa, the large improvement in ART initiation at Site 3 was not a surprise to our study team, as Site 3 appeared to be the least efficient of the three sites, with frequent staff turnover and absences, poor procedures for filing records and tracing patients, and long queues and waiting times. Similarly, in Kenya, the intervention did little to improve outcomes at Site 2, which was the best organized of our Kenya sites and initiated 94% of standard-arm patients within 28 days, but it increased ART uptake ≤28 days by 11% at Site 1. It is reasonable to speculate that an intervention like SLATE, which is intended to improve the efficiency of clinic procedures, is most effective at facilities that are least efficient to start with and thus have more room for improvement. If the SLATE intervention were to be rolled out in the study countries, targeting facilities with the worst indicators for placing new patients on treatment, rather than all facilities at once, would thus make sense.
TB symptoms were by far the most common reason for screening out of the SLATE algorithm, though relatively few patients were in fact diagnosed with TB. Persistent headache did not identify any CrAg-positive patients among those with CD4 counts ≤100 who were reflex-tested. Most other reasons for screening out were behavioral rather than clinical, such as prior default from ART or current substance abuse; whether these should trigger referral for additional services before ART initiation is debatable. For many if not most of these patients, the benefits of same-day ART initiation may well outweigh the costs, even for previous defaulters and others who may face adherence challenges.
Although the study was powered to detect an absolute increase of 15% of patients achieving our second primary outcome, from 65% to 80%, the observed increase was a modest 6%, from 48% to 54%, in South Africa, and no improvement was seen in Kenya. Standard care achieved faster ART initiation than expected in both countries. A recent observational study in South Africa estimated that the median interval between diagnosis and initiation fell from 27 to 6 days during this period [23].
The poor postinitiation retention rates in both countries and study arms, even for patients who initiated within 28 days (48% in the standard arm and 54% in the intervention arm for primary outcome 2 in South Africa and 43% in both arms for Kenya) suggest that retaining patients on ART in their first half year of treatment remains a major challenge. Facility support for adherence to and retention in ART varied by site and country and probably also by month. We do not have complete information on what types of postinitiation adherence/retention support were provided nor whether study patients participated in available services because routine data systems did not record uptake of such things as adherence clubs and tracing.
We speculate that for some minority of patients, the offer of same-day initiation simply shifts the point of attrition from before to after starting ART [24]. These patients simply do not wish to be treated, at least at the time of the offer; they may return to care later (and likely sicker) or not at all. The lack of a difference in 8-month retention between the study arms in both countries suggests that the manner of initiation is not in itself the driver of loss to follow-up after initiation. Same-day initiation prompts those who do make it to the clinic at least once to give ART a try, rather than being sent away empty-handed; new interventions will be needed for the critical postinitiation period.
As previously anticipated [7], SLATE had several limitations. First, while the study sites were all typical primary healthcare clinics in South Africa and typical hospital-based HIV clinics in Kenya, they were geographically clustered in each country, making generalizability to the rest of the country uncertain. Second, by necessity, we excluded prior to randomization patients who were not physically or emotionally able to participate, leaving us with a potentially healthier sample than the overall population. Third, because we relied on routine data collection systems for outcomes and follow-up, we had a modest amount of missing data. This was mainly problematic in comparing viral load suppression rates: a majority of patients did not have a viral load test recorded by the 8-month study endpoint because of poor record keeping, nonoperational equipment, or patient or provider decision not to do the routine 6-month test. For the same reason—reliance on routinely collected data for follow-up—we cannot determine what proportion of patients who appear to be lost to follow-up at the 8-month endpoint were in fact undocumented transfers to other healthcare facilities. Fourth, the intervention arm of the study was implemented by trained study staff who achieved near-perfect fidelity to intervention procedures; we might not expect such consistent implementation in routine care settings, and the effect reported may thus not reflect what would be seen in practice. Fifth, participation payments to intervention-arm patients were made after all other study procedures were completed, potentially incentivizing these patients to remain for the full set of procedures. Finally, because this was a pragmatic trial that made no effort to “control” the standard arm, services provided to the standard-of-care comparison arm fluctuated over the enrollment period and by study site. When enrollment into SLATE started, same-day initiation was regarded as a bold and perhaps risky proposition, not addressed in prevailing guidelines; by the time the study ended, roughly a year and a half later, it was a widely accepted practice. As a result, the SLATE algorithm as implemented in this study may be relatively conservative compared to the current (but not former) standard of care.
In conclusion, SLATE demonstrated that South African public-sector, primary healthcare clinics and Kenyan public-sector, hospital-based HIV clinics can feasibly and safely initiate 50%–70% of all new HIV patients onto ART during the patients’ first clinic visit, without the use of expensive POC assays, laboratory results, or additional adherence education or other services for patients. While practice has to some extent caught up with the study—initiation on the same day as diagnosis is now a commonly accepted practice in Kenya [3], South Africa [2], and many other countries—there remains little research on how it should be implemented in a way that maximizes patient benefits. SLATE offers a way to standardize procedures and minimize the burden on eligible patients while still assuring appropriate care for those who need it. Same-day initiation under the SLATE algorithm achieved better uptake of ART in both countries and modest improvement in retention in care at 8 months in South Africa. For both study arms in both countries, though, the proportion of patients achieving the 8-month retention outcome was abysmal. Early retention after initiation, regardless of the speed or manner of initiation, will continue to require additional intervention. For accelerating ART initiation, the next step in is to look more carefully at the large proportion of patients who screened out of the SLATE algorithm to see whether some of those patients too, could be started on ART the same day.
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10.1371/journal.ppat.1004598 | In Vivo Approaches Reveal a Key Role for DCs in CD4+ T Cell Activation and Parasite Clearance during the Acute Phase of Experimental Blood-Stage Malaria | Dendritic cells (DCs) are phagocytes that are highly specialized for antigen presentation. Heterogeneous populations of macrophages and DCs form a phagocyte network inside the red pulp (RP) of the spleen, which is a major site for the control of blood-borne infections such as malaria. However, the dynamics of splenic DCs during Plasmodium infections are poorly understood, limiting our knowledge regarding their protective role in malaria. Here, we used in vivo experimental approaches that enabled us to deplete or visualize DCs in order to clarify these issues. To elucidate the roles of DCs and marginal zone macrophages in the protection against blood-stage malaria, we infected DTx (diphtheria toxin)-treated C57BL/6.CD11c-DTR mice, as well as C57BL/6 mice treated with low doses of clodronate liposomes (ClLip), with Plasmodium chabaudi AS (Pc) parasites. The first evidence suggesting that DCs could contribute directly to parasite clearance was an early effect of the DTx treatment, but not of the ClLip treatment, in parasitemia control. DCs were also required for CD4+ T cell responses during infection. The phagocytosis of infected red blood cells (iRBCs) by splenic DCs was analyzed by confocal intravital microscopy, as well as by flow cytometry and immunofluorescence, at three distinct phases of Pc malaria: at the first encounter, at pre-crisis concomitant with parasitemia growth and at crisis when the parasitemia decline coincides with spleen closure. In vivo and ex vivo imaging of the spleen revealed that DCs actively phagocytize iRBCs and interact with CD4+ T cells both in T cell-rich areas and in the RP. Subcapsular RP DCs were highly efficient in the recognition and capture of iRBCs during pre-crisis, while complete DC maturation was only achieved during crisis. These findings indicate that, beyond their classical role in antigen presentation, DCs also contribute to the direct elimination of iRBCs during acute Plasmodium infection.
| Malaria is a significant health issue, particularly in the tropical and subtropical regions of the world. The red pulp (RP) of the spleen is a major site for the control of blood-borne infections such as malaria. Macrophages and dendritic cells (DCs) form a complex phagocyte network inside the splenic RP. DCs are usually thought of as highly efficient antigen-presenting cells that play an essential role in the activation of adaptive immunity. However, the direct role of DCs in the clearance of pathogens is still unclear. To clarify these issues, we took advantage of in vivo experimental approaches that enabled us to deplete or visualize DCs. The depletion of phagocytes demonstrated that DCs are key participants in the protection against blood stages of experimental malaria. Using confocal intravital microscopy, we observed that splenic RP DCs efficiently recognized and phagocytized infected erythrocytes during acute infection. We also showed that splenic DCs were crucial for the CD4+ T cell response to infection, but full DC maturation was achieved only after the peak of parasitemia. This study help to elucidate the protective mechanisms against Plasmodium parasites, and it shows that in vivo imaging is a reliable tool to evaluate iRBC phagocytosis during experimental malaria.
| The spleen is a primary site for the control of blood-borne infectious diseases in humans and rodents [1], [2]. Although splenic phagocytic activity has been well documented in vitro[3]-[5] and ex vivo [3], [6]-[8], few studies have reported on the in vivo three-dimensional (3D) interactions between splenic phagocytes and pathogens [9]. Addressing this issue is particularly important in the case of malaria, a disease characterized by splenic involvement that is critical for controlling blood-stage Plasmodium parasites [10]. In recent years, confocal intravital microscopy (CIVM) [11] has been used to study host-pathogen interactions during infectious diseases caused by viruses [12], [13], bacteria [14] and protozoan parasites [15]. For example, CIVM revealed important aspects of the Plasmodium life cycle [16], [17]. Other works described Plasmodium-induced immune responses inside the placenta [18] and the dermis using fluorescent stereomicroscopy [19]. A single publication reported the movements of Plasmodium-infected red blood cells (iRBCs) inside the spleen [20]. However, no in vivo study has addressed the interactions between blood-stage Plasmodium parasites and the splenic immune system.
Splenectomized patients with acute Plasmodium falciparum infections have an impaired ability to remove parasites from circulation [21], similar to splenectomized mice infected with the blood-stages of Plasmodium chabaudi (Pc) [22]. In humans and mice, the phagocytosis of iRBCs or free merozoites by splenic phagocytes begins soon after infection and helps to control the parasitemia and induce the lymphocyte response [23], [24]. This occurs primarily inside the red pulp (RP) and the marginal zone (MZ) of the spleen [23], [24], where a complex phagocyte network is formed by heterogeneous populations of macrophages and dendritic cells (DCs) [25], [26]. In an effort to characterize the role of splenic phagocytes in Pc malaria, a recent study identified migrating monocytes as major participants in the clearance of iRBCs [8]. However, previous studies that quantified the ex vivo phagocytosis of iRBCs by flow cytometry reported low percentages of splenic phagocytes containing Pc remnants [8], [27]. This observation is not fully compatible with the notion that the role of the spleen is of the utmost importance in parasite control.
DCs are phagocytes that are highly specialized in presenting antigens to T cells [28]. Splenic DCs are efficient antigen presenting cells (APCs) during the massive T and B cell responses to acute Pc malaria [29]-[31]. Within the first week of Pc infection, splenic DCs up-regulate the expression of major histocompatibility complex (MHC) and costimulatory molecules, secrete pro-inflammatory cytokines, and stimulate T cell proliferation and IFN-γ production [32]-[34]. Nevertheless, it is still unclear whether DCs are unique in their ability to initiate CD4+ T cell responses to Pc blood-stages in the spleen, as observed in Plasmodium berghei(Pb) malaria [35]. Moreover, many details concerning the dynamics of splenic DCs in malaria remain unknown, limiting our understanding of the involvement of these cells in the protective immune response. After taking on antigens, immature DCs lose the ability to phagocytize and migrate towards T cell-rich areas to initiate the adaptive immune response [28]. Thus, it would be expected that DCs leave the RP soon after phagocytizing iRBCs or free merozoites and no longer contribute to parasite clearance, although this is as yet only a supposition.
In this study, we took advantage of experimental approaches that enabled us to deplete or visualize splenic DCs in vivo to clarify these issues. The in vivo depletion of phagocytes clearly demonstrated that DCs are key participants in the early control of the blood stage of infection with Pc and Plasmodium yoelii (Py) iRBCs, as well as the blood stage of infection with Pb sporozoites. The phagocytosis of Pc iRBCs by splenic DCs was analyzed by CIVM, as well as by flow cytometry and immunofluorescence, in three distinct situations: at the first encounter, at a pre-crisis phase concomitant with parasitemia growth and at a crisis phase, when parasitemia has dramatically dropped and changes in the splenic architecture have culminated in spleen closure [36]. CIVM allowed us to visualize the phagocytosis of Pc iRBCs by the RP DC network, the movement dynamics and morphological changes of DCs and the interaction between DCs and CD4+ T cells at the different phases of acute Pc malaria. To our knowledge, this is the first description of the in vivo interaction between Plasmodium iRBCs and the splenic immune system.
To evaluate whether DCs are important for the early control of blood-stage Pc malaria, C57BL/6.CD11c-DTR (B6.CD11c-DTR) mice were treated with diphtheria toxin (DTx). The great majority of splenic CD11c+I-A+ cells were eliminated in DTx-treated B6.CD11c-DTR mice (Fig. 1A). No effect was observed on F4/80+ RP macrophages, but the already small population of MARCO/MOMA-1+ MZ macrophages was depleted (S1 Fig.). Starting in the earliest days of infection, DTx-treated B6.CD11c-DTR mice had higher parasitemia (Fig. 1B) and weight loss (Fig. 1C) in comparison to their PBS-treated counterparts, leading to an accumulated mortality of 75% of mice on day 15 p.i. (Fig. 1D). On day 4 p.i., DTx-treated B6.CD11c-DTR mice had reduced numbers of CD4+ T cells per spleen (Fig. 1E). DTx treatment also completely abrogated the CD4+ T cell proliferation and IFN-γ production in vitro in response to iRBCs (Fig. 1F). None of these effects were observed in DTx-treated C57BL/6 (B6) mice (Figs. 1 and S1). Furthermore, the selective elimination of MZ macrophages by treating B6 mice with a low dose of clodronate liposomes (ClLip) did not affect the course of parasitemia, IFN-γ production by splenic CD4+ T cells or mouse survival (S2 Fig.). Similarly to what was observed for the Pc parasite, DTx treatment in B6.CD11c-DTR mice exacerbated Py malaria from the beginning of infection (S3A–S3C Fig.). The role of DCs in the early control of parasitemia was also evaluated in B6 and B6.CD11c-DTR mice that were treated with DTx on day 2 p.i. with Pb sporozoites. DTx-treated B6.CD11c-DTR mice presented with higher parasitemias (S3D–S3E Fig.). In this case, however, DTx treatment prolonged the survival of infected B6.CD11c-DTR mice by protecting them from cerebral malaria (S3F Fig.).
To investigate whether splenic DCs phagocytize iRBCs in recently infected mice, we analyzed the interaction between YFP+ cells and mCherry-Pc iRBCs in the subcapsular RP of C57BL/6.CD11c-YFP (B6.CD11c-YFP) mice using CIVM [26]. Mice were infected by i.v. administration of mature iRBCs (>95% late trophozoites/schizonts), as these cells are known to be recognized and phagocytized by DCs [37]. In naïve mice, YFP+ cells were non-motile and actively extended protrusions and dendrites (S1 Video). At 15 min p.i., mCherry-Pc iRBCs were present in the subcapsular RP (Fig. 2A, S2 Video). CIVM 3D animations showed mCherry-Pc iRBC remnants inside YFP+ cells (yellow spots of merged mCherry/YFP-3D signal; Fig. 2B, S3 Video). At this time, 16% of YFP+ cells contained mCherry-Pc fragments (Fig. 2C). We also observed several mCherry-Pc iRBCs trapped by YFP+ cells without visible signs of internalization (Fig. 2A, S4 Video). Thus, a substantial proportion of subcapsular RP YFP+ cells trapped or internalized iRBCs soon after Pc infection. These cells were not activated, as indicated by small YFP+ cell volume and sphericity (Fig. 2D).
The phagocytic activity of splenic DCs from recently infected B6 mice was also analyzed ex vivo by immunofluorescence and flow cytometry. Immunofluorescence revealed approximately 5% CD11c pixels that were colocalized with GFP pixels in those spleens (Fig. 3A and 3B). The majority of GFP-Pc iRBCs were trapped inside the RP and MZ (Fig. 3B). Nearly 2% of CD11c+ cells internalized Cell Tracer Violet (CTV)-Pc parasites (4 × 104 CTV+CD11c+ cells/spleen), as revealed by flow cytometry (Fig. 3C). Comparable data were obtained with Green Fluorescent Protein (GFP)-Pc iRBCs (S1 Table). This phagocytic activity was not restricted to a DC subtype, as subsets of CD11c+ cells co-expressing CD11b, CD8, B220 or CD4 were CTV+ (S4A Fig.). Considering the numbers of cells per spleen, CD11b+CD11c+ cells were responsible for most of the parasite clearance carried out by CD11c+ cells in recently infected mice (S4B Fig.).
Although 61% of YFP+ cells in recently infected B6.CD11c-YFP mice had a DC phenotype, expressing CD11c and MHC class II (I-A) but not F4/80, 20% displayed the phenotype of F4/80+ RP macrophages (S5 Fig.). Therefore, we also analyzed the phagocytic activity of the YFP+ cell subsets by CIVM and flow cytometry. With injection of a fluorescent anti-F4/80 mAb into mice, CIVM revealed that 17% of cells in the subcapsular RP YFP+ cell population were F4/80+ soon after infection (Fig. 2E and 2F, S5 Video). Approximately 15% of F4/80+YFP+ and F4/80-YFP+ cells internalized Cell Tracker Red CMTPX (CMTPX)-Pc parasites (Fig. 2G), but only 20% of the CMTPX+YFP+ cells were F4/80+ (Fig. 2H). Flow cytometry analysis of the YFP+ cell subsets showed that a proportion of CD11c+ and F4/80+ cells was CTV+ in B6.CD11c-YFP mice that were recently infected with CTV-Pc iRBCs (Fig. 3D). The CD11c+ cells made up 63% of the CTV+YFP+ cell population (4.5 × 104 CTV+CD11c+YFP+ cells/spleen), while 37% of CTV+YFP+ cells expressed F4/80 (2.5 × 104 CTV+F4/80+YFP+ cells/spleen) (Fig. 3E and 3F).
Next, we evaluated the dynamics of splenic DCs during early Pc malaria. At 12 h p.i., the subcapsular RP YFP+ cells from B6.CD11c-YFP mice displayed higher speed and displacement (Fig. 4A). This enhanced motility of YFP+ cells correlated with their migration towards CD4+ T cell-rich areas. This was evident in immunofluorescences, at 2 h and 24 h p.i., by the presence of yellow areas of merged FITC/PE signal (Fig. 4B) and higher percentages of CD11c-CD4 pixel colocalization (Fig. 4C). We also adoptively transferred CD4+ T cells expressing Cyan Fluorescent Protein (CFP) into B6.CD11c-YFP mice to evaluate the interaction of subcapsular RP DCs with CD4+ T cells during early Pc malaria. In naïve mice, most CFP+CD4+ cells made transient contacts with YFP+ cells (Fig. 4D, S6 Video), and CFP+CD4+ cells were actively moving inside spleen (Fig. 4E). At 24 h p.i., CFP+CD4+ cells contacted YFP+ cells more stably (Fig. 4D, S7 Video), as indicated by a decrease in CFP+CD4+ cell speed and an increase in arrest coefficient (Fig. 4E).
To investigate whether splenic DCs have a direct role in parasite clearance during pre-crisis, we analyzed the interactions between splenic DCs and iRBCs after five days of infection in vivo and ex vivo. This possibility was suggested by our data showing that, on day 5 p.i., splenic DCs had an enhanced expression of the phagocytic receptor FcγRI (S6A–S6B Fig.). Notably, we visualized many mCherry-Pc iRBCs inside the subcapsular RP, and YFP+ cells displayed intense phagocytic activity (Fig. 5A, S8 Video). The presence of intense vacuolization in these DCs was also clear, and we observed some YFP+ cells (containing iRBC remnants from previous internalization events) phagocytizing mCherry-Pc iRBCs (Fig. 5A, S9 Video). CIVM 3D animations confirmed the internalization of mCherry-Pc parasites by YFP+ cells (Fig. 5B, S10 Video). This phenomenon was observed in 45% of the YFP+ cells (Fig. 5C). At five days p.i., YFP+ cells were activated and displayed higher cell volume and lower cell sphericity than those from recently infected mice (Fig. 5D; S1 Table). On day 5 p.i., the CD11c+ cells also expressed higher levels of MHC class II, CD80 and CD86 compared to those from naïve mice (S6C–S6D Fig.).
Immunofluorescence corroborated the significant role of splenic DCs in the widespread iRBC phagocytosis observed during pre-crisis. The percentages of CD11c pixels that colocalized with GFP pixels reached up to 40% in spleens from B6 mice on day 5 p.i. (Fig. 6A and 6B). Flow cytometry confirmed that splenic DCs were able to phagocytize iRBCs during pre-crisis. When mature CTV-Pc iRBCs were i.v. injected into B6 mice on day 5 p.i., approximately 4% of splenic DCs were CTV+ (1.4 × 105 CTV+CD11c+ cells/spleen) (Fig. 6C and 6D). Phagocytic activity was not restricted to a particular DC subtype, as a proportion of all subsets studied internalized iRBCs during pre-crisis (S4A Fig.). However, CTV+ CD11b+CD11c+ and CD8+CD11c+ cell numbers were significantly higher per spleen than those of other DC subsets (S4B Fig.). In addition, on day 5 p.i., 10% of CD11c+ cells from mice infected with GFP-Pc iRBCs were GFP+ (4 × 105 CTV+CD11c+ cells/spleen) (Fig. 6E and 6F). Comparatively, we observed substantially higher activation and phagocytic activity both in vivo and ex vivo in the splenic DCs during pre-crisis (S1 Table). Furthermore, a significantly higher frequency of iRBC uptake was detected using CIVM in comparison with flow cytometry.
Notably, flow cytometry analysis of splenic YFP+ cells from B6.CD11c-YFP mice during pre-crisis showed a sharp reduction in the percentages of F4/80+ cells so that the great majority of the YFP+ cell population presented with a classical DC phenotype (S5 Fig.). Moreover, a large fraction of CD11c+YFP+ cells in these mice expressed higher levels of MHC class II molecules in comparison to those in recently infected B6.CD11c-YFP mice. This observation was confirmed by CIVM, which revealed a reduction of F4/80+YFP+ cells in the subcapsular RP of B6.CD11c-YFP mice on day 5 p.i. (Fig. 5E and 5F, S11 Video). Due to the incremental number of CD11c+ cells in the YFP+ cell population, almost all of the phagocytic activity of YFP+ cells was imputed to DCs during pre-crisis, as observed by CIVM (Fig. 5G and 5H) and by flow cytometry (Fig. 6G, 6H and 6I).
During the crisis phase of acute Pc malaria, profound modifications in the splenic architecture occur, resulting in RP closure [36]. Therefore, we extended our study into this phase of the disease. CIVM revealed only occasional mCherry-Pc iRBCs trapped by subcapsular RP YFP+ cells in B6.CD11c-YFP mice on day 8 p.i. (Fig. 7A and 7B, S12 Video), and yellow spots of merged mCherry/YFP-3D signal were infrequent (Fig. 7C). At that same time point, YFP+ cell volumes were smaller than during pre-crisis (Fig. 7D, S1 Table). YFP+ cell sphericity was reduced in mice on days 5 and 8 p.i. compared with naïve mice (Fig. 7D, S1 Table). Flow cytometry also revealed poor phagocytosis by splenic DCs, a process that was investigated both when mice were re-infected i.v. with mature CTV-Pc iRBCs and when mice were i.p. infected with GFP-Pc iRBCs (Fig. 7E, 7F, 7G and 7H). These data indicate that splenic DCs could be primarily involved in antigen presentation rather than in phagocytosis during crisis, as CD11c+ cells expressed high levels of MHC class II and CD80 on day 8 p.i. (S6C–S6D Fig.).
The depletion of phagocytes in vivo allowed us to clearly demonstrate the key role of DCs in the protection against experimental blood-stage malaria. Abundant CD11c expression is a well-known marker for DCs, which are primary targets of DTx treatment in B6.CD11c-DTR mice [38]. Nevertheless, MZ macrophages are also depleted in DTx-treated B6.CD11c-DTR mice due to ectopic expression of the DTx receptor transgene [39]. The role of DCs was established in our study by comparing the disease progression in DTx-treated B6.CD11c-DTR mice and in B6 mice treated with a low dose of ClLip, which selectively depletes MZ macrophages within splenic phagocytes [39], [40]. The significant contribution of DCs in the control of Pc malaria was suggested by data showing the worsening of the disease in DTx-treated B6.CD11c-DTR mice, while the elimination of MZ macrophages by the ClLip treatment did not alter the course of infection in B6 mice. Our data also showed that splenic DCs are required for CD4+ T cell proliferation and IFN-γ production during Pc infection. The complete abrogation of these responses in DTx-treated B6.CD11c-DTR mice, but not in ClLip-treated B6 mice, demonstrated that other splenic phagocytes such as MZ and RP macrophages did not replace DCs in the initiation of CD4+ T cell responses to Pc infection.
Our first evidence suggesting that DCs could directly contribute to parasite clearance was the effect of DC depletion on the increase of parasitemia and the reduction of body weight during the first days of blood-stage Pc and Py malaria. DCs were also required to control the early parasitemia following infection with Pb sporozoites. The early protective role of DCs could not be completely attributed to the need for these cells to activate T cells, which take longer to produce IFN-γ and induce antibody secretion during experimental malaria. The splenocytes obtained four and five days after Pc infection still require further stimulation with iRBCs in vitro to differentiate into effector cells [41], [42], while the ex vivo production of IFN-γ and antibodies coincides with the drop of parasitemia a week after infection [42], [43]. Using in vivo and ex vivo approaches, we unequivocally demonstrated here that the subcapsular RP DCs recognize and phagocytize mature iRBCs during the first encounter and pre-crisis, while spleen closure coincides with limited Pc phagocytosis by DCs during crisis. Although the splenic DCs are thought to be a major DC population in intimate contact with the bloodstream, these cells may act together with other DCs outside the spleen to clear Plasmodium parasites. This idea is supported by studies in splenectomized mice showing that other reticuloendothelial organs, such as the liver, effectively substitute for the phagocytic functions of the spleen in protecting against Pc malaria [22], [44]. In fact, hepatic CD11c+ DCs are also capable of internalizing iRBCs in the liver sinusoids during acute Pc infection [45].
CIVM allowed us to visualize the interaction between subcapsular RP DCs and iRBCs in great detail. In naïve mice, these cells actively extended protrusions and dendrites, as previously shown [26]. Soon after infection, we observed iRBCs being trapped by DCs that had a non-activated phenotype. The majority of these cells showed a classical DC phenotype, but a proportion of them exhibited strong labeling for F4/80, a marker of RP macrophages that is also expressed by a subset of DCs in the skin [46]. Another study reporting a similar observation concluded that, based on their dendritic morphology, subcapsular RP F4/80+YFP+ cells represent a subset of peripheral tissue DCs [26]. Although we did not visualize phagocytosis of iRBCs in recently infected mice, the detection of Pc remnants inside subcapsular RP DCs suggests that iRBC uptake had occurred. In fact, parasite antigen presentation is likely to occur soon after Pc infection, similar to the process observed during L. monocytogenes infection [47]. During the first day p.i., subcapsular RP DCs displayed high motility and made stable contacts with CD4+ T cells. DCs also migrated rapidly to T cell-rich areas following Pc infection, a process that might involve chemokine signaling as suggested by studies in CCR7-knockout mice [48].
Here, for the first time, we observed the phagocytosis of iRBCs during pre-crisis in vivo. This occurred in a large number of subcapsular RP DCs, such that up to half of this population presented with Pc remnants. The great majority of these cells had a classical DC phenotype, which was characterized by negative staining for F4/80 and high expression of both MHC class II and costimulatory molecules. It is notable that these cells displayed an activated phenotype. Even if most subcapsular RP DCs during pre-crisis are immature cells that recently migrated to the spleen [49], it is expected that DC activation leads to their maturation and consequent blockade of phagocytic activity, allowing the cellular machinery to be restructured for antigen presentation [28]. In agreement with our data, a previous report determined that the peak of in vitro iRBC uptake by splenic DCs occurred at five days p.i., in parallel with the increase in the expression of MHC class II and costimulatory molecules [34]. In both studies, the phagocytic activity was not restricted to a particular DC subset. Our ex vivo data implicate CD11b+ and CD8+ DCs in most of the parasite clearance imputed to splenic DCs in mice both soon after infection and at the pre-crisis phase. Consistent with the immune response to acute Pc malaria, the CD11b+ and CD8+ DC subsets are known to be specifically involved in antigen presentation to CD4+ T cells and IL-12 production, respectively [50], [51]. Furthermore, both subsets of DCs are able to induce IFN-γ production by parasite-specific T cells during Pc infection [29]. Another important observation during pre-crisis was a sharp decline in the population of F4/80+YFP+ cells, a phenomenon that also occurred to splenic F4/80+ macrophages after the parasitemia peak (unpublished data). Because DCs have a higher turnover than F4/80+ macrophages [47], a possible explanation for our results is that a proportion of these phagocytes died after ingesting Pc parasites and only DCs were rapidly replaced. This process would substitute F4/80+ macrophages, a resident RP population that is primarily required to maintain tissue homeostasis [52], to inflammatory phagocytes. An alternative explanation is the down-regulation of the F4/80 molecule due to macrophage activation as reported during mycobacterial infection [53]. The F4/80+YFP+ cells could also have migrated to other locations such as the splenic T cell-rich areas.
During crisis, the down-regulation of the phagocytic function of splenic DCs coincided with the period of spleen closure. This was demonstrated here by in vivo images showing a few iRBCs in the subcapsular RP at eight days p.i., when parasitemias were even higher than at five days p.i.. The decline in iRBC uptake was also associated with the maximum expression of MHC class II and CD80 molecules by splenic DCs, which indicates that complete DC maturation was only achieved during crisis. This idea is corroborated by a previous study that reported a decrease to baseline levels of the in vitro uptake of the iRBCs by splenic DCs at day 8 p.i. [34]. Thus, in addition to spleen closure and the subsequent blockade of iRBC entry inside the RP, splenic DCs seem to lose the ability to phagocytize parasites, while concomitantly increasing their ability to present cognate antigens. This is an interesting observation because, during crisis, most of the lymphocytes that are activated during early Pc infection undergo apoptosis [54], [55]. Thus, it is possible that mature DCs are required to expand and differentiate the few remaining T cells, giving rise to the memory response to malaria [56], [57].
The quantification of iRBC phagocytosis ex vivo by flow cytometry yielded substantially lower percentages of Pc+ DCs compared with in vivo data obtained by CIVM. This discrepancy may result from differences in the fluorescence detection thresholds of CIVM and flow cytometry, the DC subpopulations examined by these techniques (subcapsular RP DCs or total splenic DCs, respectively) or the fluorochrome labeling of the iRBCs (mCherry, GFP, CTV or CMTPX). Another possible explanation for the low detection of iRBC uptake by flow cytometry is the rapid iRBC degradation or fluorochrome quenching [8], such that Pc remnants were only identified inside DCs shortly after phagocytosis. Previously, low frequencies of iRBC uptake were also detected by flow cytometry in migrating monocytes [8], [27]. Immunofluorescence confirmed that splenic DCs, particularly those localized inside the RP and MZ, play a major role in the clearance of iRBCs during acute Pc infection. Although this technique did not efficiently discriminate single cells, the percentages of CD11c-GFP pixel co-localization were comparable to those of Pc+ DCs obtained by CIVM.
The in vivo approaches used in this study indicate that, beyond the classical role of DCs in antigen presentation, these cells also contribute to the direct elimination of iRBCs during acute Plasmodium infection. For several days after Pc infection, subcapsular RP DCs were highly efficient in the recognition and capture of iRBCs. Complete DC maturation appeared to be achieved only during crisis when restructuring of the spleen might facilitate the development of the acquired immunity. Taking into account the specifics of different parasite-host interactions, we speculate whether our findings in mouse models could be applied to human malaria. The adhesion of P. falciparum iRBCs to human monocyte-derived DCs through the scavenger receptor CD36 has been shown to inhibit DC maturation and subsequently reduce their capacity to activate T cells [58]. This observation was interpreted as the impairment of the DC function during P. falciparum infection. However, our data showing the induction of FcγRI in splenic DCs during pre-crisis open the possibility that recognition of opsonized iRBCs through this receptor can overcome the down-regulatory activity of CD36 signaling. Thus, the opposite effects of malaria on DC function could be related to the different activation profiles of DCs, which are greatly influenced by the surrounding tissue microenvironment, rather than other factors previously discussed such as different species and strains of hosts and parasites [59]. Together, our data add novel information to this area of immunology and demonstrate that in vivo imaging may help to unravel the mechanisms underlying protective immunity against malaria.
Six- to eight-week-old B6, B6.CD11c-DTR [28], B6.CFP [60] and B6.CD11c-YFP mice [61] were bred under specific pathogen-free conditions at the Animal Facilities of Instituto Gulbenkian de Ciência (IGC), Instituto de Ciências Biomédicas at the Universidade de São Paulo (ICB-USP) or Institut de Transgénose Orléans-Villejuif. Pc (AS strain), Py (XL strain) and mCherry-Pc were maintained previously as described [62], [63]. GFP-Pc parasites were selected by treatment with pyrimethamine (Sigma-Aldrich, USA) [64]. The Instituto de Medicina Molecular at the Universidade de Lisboa provided Anopheles stephensi mosquitoes infected with Pb (ANKA strain). Mice were infected intraperitoneally (i.p.) with 1 × 106 iRBCs (blood from infected mice), and intravenously (i.v.) with 1 × 108 iRBCs or 1 × 103 sporozoites. Purified iRBCs were used where specified. The iRBCs were obtained during a period of the circadian cycle in which mature stages predominated (>95% late trophozoites/schizonts).
All procedures were in accordance with the national regulations of Conselho Nacional de Saúde and Colégio Brasileiro em Experimentação Animal (COBEA) and Federation of European Laboratory Animal Science Associations (FELASA). The protocols were approved by the Comissão de Ética no Uso de Animais (CEUA) of ICB-USP, São Paulo, Brazil under permit numbers 0036/2007 and 0174/2011, and by FELASA under permit number AO10/2010.
To deplete CD11c+ cells, B6.CD11c-DTR mice were injected i.p. with a single dose of 2 ng/g body weight of DTx (Sigma-Aldrich) 24 h before iRBC infection or 48 h after sporozoite infection. This dose is half of the one previously established to deplete CD11c+ cells [65] and it was used to reduce drug toxicity. To deplete MARCO+/MOMA1+ cells, B6 mice were injected i.v. with 8.5 µg/g body weight of ClLip 24 h before infection [40]. Phosphate buffered saline (PBS) or PBS liposomes (PBSLip) were injected as controls. The procedures to obtain ClLip and PBSLip were described elsewhere [66].
Blood from infected B6 mice was resuspended in 1 ml PBS, pipetted over 5 ml of 74% Percoll (GE Healthcare, USA) and centrifuged (2500 x g, acceleration/break 5/0) for 30 min at room temperature (RT). The top cell layers were collected and washed with complete RPMI 1640 medium (supplemented with 10% heat-inactivated fetal calf serum, 100 U/ml penicillin, 100 µg/ml streptomycin, 50 µM 2-mercaptoethanol, 2 mM L-glutamine and 1 mM sodium pyruvate; Life Technologies, USA). Purified iRBCs (>95% purity) were stained with CTV or CMTPX, following the manufacturer’s instructions (Life Technologies).
B6.CD11c-YFP mice infected with mCherry-Pc iRBCs were deeply anesthetized i.p. with 55 ng/g body weight of ketamine (Imalgene 1000, Merial, USA) and 0.85 ng/g body weight of xylazine (Rompun 2%, Bayer, Germany). Spleens were externalized by a 1 cm incision just below the ribcage. Mice were placed above a metal plate with a coverslip and immobilized without disrupting the vasculature or splenic connective tissue. Live imaging was carried out with an Eclipse Ti microscope (Nikon Instruments Inc., Japan) equipped with an Andor Revolution XD system (Andor Technology, UK), a Yokogawa CSU-X1 spinning disk unit (Andor Technology), a 20x PLAN APO VC objective (Nikon Instruments Inc.) and a 1.5x auxiliary magnification system (Nikon Instruments Inc.). Data were processed with MicroManager 1.2 (General Public License, NIH, USA). For each movie, 28 µm Z-sections with 4 µm Z-steps were acquired for 30 min. Imaris X64 7.0.0. (Andor Technology) was used to edit images and to determine the percentage of mCherry+YFP+ cells, as well as the CD11c+ cell volume and sphericity. In other cases, B6.CD11c-YFP mice were adoptively transferred with 5 × 106 splenic CD4+ T cells from B6.CFP mice (purified by FACS sorting using a FACSAria device; BD Biosciences). These mice were infected as described above and processed 24 h later. Imaris was used to edit images and to determine CD11c+ cell speed and displacement, as well as the coefficients of CFP+CD4+ T cell speed and arrest.
B6.CD11c-YFP mice infected with CMTPX-Pc iRBCs were injected i.v. with PE-conjugated anti-F4/80 mAbs (200 ng/g body weight) and deeply anesthetized to externalize the spleen as described above. Live imaging was carried out with a Zeiss LSM 780-NLO confocal microscope (Zeiss, Germany). Data were processed with Zen 2012 software (Zeiss, Germany). In each movie, 28 µm Z-sections with 2 µm Z-steps were acquired for 30 min. Imaris was used to edit images and to determine the percentages of CMTPX+ cells.
Mice were sacrificed and PBS-perfused to remove circulating iRBCs. Spleens were harvested, and the remaining RBCs were lysed with ACK lysis buffer. Splenocytes (1 × 106) were stained with fluorescent monoclonal antibodies (mAbs) against CD3, CD4, CD11c, CD69, CD11b, CD80, CD86, I-Ab, B220, CD36, CD64 (FcγRI), DX5 and Ter119 (BD Biosciences, USA), F4/80 (eBiosciences, USA), and MOMA-1 and MARCO (Abcam, UK). Cells were analyzed by flow cytometry (FACSCanto; BD Biosciences) with FlowJo 9.5.3. (Tree Star Inc., USA).
Splenocytes (3 × 107) were resuspended in 1 ml PBS with 0.1% BSA (bovine serum albumin; Sigma-Aldrich) and stained with CFSE (carboxyfluorescein succinimidyl ester; Life Technologies) at a final concentration of 5 μM for 20 min at 37°C. Cells (1 × 106) were cultured in complete RPMI 1640 medium for 72 h at 37°C with 5% CO2 in the presence of iRBCs (3 × 106). Cells were then stained with fluorescent mAbs against CD3 and CD4, and proliferation was assessed by flow cytometry. IFN-γ was quantified in the supernatants using the OptEIA IFN-γ kit (BD Biosciences).
GFP-Pc iRBC-infected B6 mice were sacrificed and PBS-perfused. Spleens were removed and frozen in Tissue-Tek OCT (Sakura Fineteck, Japan). Sections 8 µm thick were cut with a CM3050S Cryostat (Leica, USA) and fixed with 1% paraformaldehyde (Alfa Aesar, USA) for 30 min at RT. Sections were incubated with anti-CD16/CD32 mAb (Fc block; BD Biosciences) for 30 min followed by incubation in a humidified dark chamber with fluorescent mAbs against CD11c, CD19, CD3, CD4 (BD Biosciences) and MOMA-1 (Abcam) for 2 h at RT. Sections were then stained for 5 min with 0.5 μg/ml DAPI (4',6-diamidino-2-phenylindole; Sigma-Aldrich), washed with PBS and mounted with Fluoromount-G (Southern Biotechnologies, USA). Images were acquired with a DMRA2 fluorescence microscope (Leica) and MetaMorph software (Molecular Devices Inc., USA). Image analysis was performed with Photoshop CS4 (Adobe Inc., USA). Percentages of CD11c-GFP/CD11c-CD4 pixel colocalization and of GFP pixel distribution in the spleen were calculated using FIJI for Windows 64-bit (Colocalization threshold and Mixture Modeling Thresholding plugins, respectively; General Public License, NIH, USA).
Results were analyzed with Prism 5 software (Graph Pad) using ANOVA or Student’s t-tests. The existence of a normal distribution was confirmed using the Kolmogorov-Smirnov test. Differences were considered statistically significant at p < 0.05.
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10.1371/journal.pntd.0000414 | Marriage, Sex, and Hydrocele: An Ethnographic Study on the Effect of Filarial Hydrocele on Conjugal Life and Marriageability from Orissa, India | Lymphatic filariasis (LF), a leading cause of permanent and long-term disability, affects 120 million people globally. Hydrocele, one of the chronic manifestations of LF among 27 million people worldwide, causes economic and psychological burdens on patients and their families. The present study explores and describes the impact of hydrocele on sexual and marital life as well as on marriageability of hydrocele patients from rural areas of Orissa, an eastern state of India.
This paper is based on ethnographic data collected through focus group discussions and in-depth interviews with hydrocele patients, wives of hydrocele patients, and other participants from the community. The most worrisome effect of hydrocele for patients and their wives was the inability to have a satisfactory sexual life. The majority of patients (94%) expressed their incapacity during sexual intercourse, and some (87%) reported pain in the scrotum during intercourse. A majority of hydrocele patients' wives (94%) reported dissatisfaction in their sexual life. As a result of sexual dissatisfaction and physical/economic burden, communication has deteriorated between the couples and they are not living happily. This study also highlights the impact on marriageability. The wives of hydrocele patients said that a hydrocele patient is the “last choice” and that girls show reluctance to marry hydrocele patients. In some cases, the patients were persuaded by their wives to remove hydrocele by surgery (hydrocelectomy).
The objective of the morbidity management arm of the Global Programme to Eliminate LF should be to increase access to hydrocelectomy, as hydrocelectomy is the recommended intervention. Though the study area is covered by the programme, like in other endemic areas, hydrocelectomy has not been emphasised by the national LF elimination programme. The policy makers and programme managers should be sensitised by utilising this type of research finding.
| Lymphatic filariasis, the second leading cause of permanent and long-term disability, affects 120 million people globally. Hydrocele, an accumulation of fluid in the scrotum that causes it to swell, is one of the chronic manifestations of LF among men and there are about 27 million men with hydrocele worldwide. We conducted ethnographic interviews and discussions with patients, women whose husbands have hydrocele, and the general public in a rural community of eastern India. The study describes how hydrocele impacts patients' sexual and marital life. It reveals the most worrisome effect of hydrocele for patients and their wives due to the inability to have a satisfactory sexual life. Patients expressed their incapacity during sexual intercourse. A majority of hydrocele patients' wives reported that their married life became burdened and couples were not living happily. This study also highlights the impact on marriageability, and some women expressed that a hydrocele patient is the “last choice”. In some cases, the patients were persuaded by their wives to remove hydrocele by surgery (hydrocelectomy). Hence, access to hydrocelectomy has to be strengthened under the Global Programme to Eliminate Lymphatic Filariasis, which is operational in several endemic areas in the world. Also, this activity may be integrated with primary healthcare services and interventions of other neglected tropical diseases.
| Lymphatic filariasis (LF), the second leading cause of permanent and long-term disability [1], affects 120 million people globally [2]. It is a mosquito-borne parasitic disease caused by Wuchereria bancrofti, which accounts for approximately 90% of all LF cases, followed by Brugia malayi and Brugia timoti. India contributes about 40% of the total global burden of LF. In India, a total of 554 million people are at risk of infection, and there are approximately 21 million people with symptomatic LF and 27 million asymptomatic microfilaria carriers [3]. The manifestations of disease are mostly irreversible and a cause of socioeconomic and psychological problems for patients and often their families [4]–[7]. Hydrocele, an accumulation of fluid in the tunica vaginalis in the scrotum that causes it to swell, is one of the chronic manifestations of LF among men (Figure S1 and Figure S2). There are 26.79 million cases of hydrocele worldwide and 48% of these cases are in India [2]. However, little information is available from India, specifically on disability due to hydrocele, except for a few studies on productivity [6]–[9]. The authors undertook one-year round case-control studies to investigate the economic burden, in terms of treatment costs and loss of work, on people affected with chronic and acute forms of LF in rural communities of Orissa [7],[10]. As part of these studies, epidemiological investigations were also carried out in these communities to identify the cases [11],[12]. The authors' interaction with people during these studies revealed several problems related to marriage and sex due to hydrocele. In addition, it is a neglected area in disease burden research [13]. Thus, it is hypothesised that hydrocele has an impact on marital life and marriageability of affected individuals. The authors have a strong rapport with the communities in this study, given that these were sites for the socioeconomic studies mentioned above. The present study was undertaken using ethnographic methods to explore and describe the impact of hydrocele on marital and sexual life, and on marriageability.
The present study was conducted in the Khurda district of Orissa, an eastern state of India. This district is known for endemicity of LF caused by W. bancrofti, transmitted by Culex quinquefasciatus [11]. The study area is rural in nature and its inhabitants are mostly small farmers and daily wage labourers. The economic opportunities for daily wage labourers are greater in the monsoon period and sparse during the rest of the year.
This paper is based on ethnographic data, for which the protocols were developed during the LF socioeconomic studies mentioned above [7],[10]. The data for this paper were collected during 2003. Prior to this, the project protocol was approved by the Scientific Advisory Committee (SAC) of the Regional Medical Research Centre, Bhubaneswar. The SAC reviews and approves the research projects for their scientific and ethical merits. A summary of the methodology is presented in Table 1.
Eight focus group discussions were conducted among hydrocele patients and community members separately. Focus groups are widely used to examine people's experience of diseases [14],[15]. They are useful for studying dominant cultural values, such as narratives about sexuality [16],[17], and actively facilitate the discussion of taboo topics. Participants can also provide mutual support in expressing feelings that are common to their group but which they consider to deviate from mainstream culture [16]. Standard guidelines were followed for conducting focus group discussions [16],[18],[19]. Due to the sensitivity of the topic, focus group discussions with community members were conducted separately among men and women. The focus group discussions were conducted in Oriya, the language of the state of Orissa. Question guides were used to ensure that the moderator addressed all the issues to be discussed by the group. Separate guides were prepared for patient groups and the general community. Initially, these guides were prepared in English and translated into Oriya, and all translated guides were reviewed for linguistic reliability and correctness. Later, these guides were piloted with groups similar to the participants' groups, but from villages that were not included in the study, to check appropriateness, clarity, and flow of questions. The duration of focus group discussions varied between 60 and 90 minutes.
To elicit the views and experiences on the impact of hydrocele, in-depth interviews were conducted with hydrocele patients, wives of hydrocele patients, and key-informants in the community. In-depth interviewing offers the respondents the opportunity to express their own ideas and address themes that the researchers may not have anticipated [20]. Key-informants are individuals in the community who possess special knowledge and who are willing to share their knowledge with the researchers. They have access to the culture under study in a way that the researcher does not [18]. The procedures for selecting key-informants and conducting in-depth interviews were based on standard guidelines [18],[21]. All of the interviews were held in Oriya and the duration of these interviews ranged from 30 to 75 minutes. Separate interview guides were made for the three categories of participants. These guides were prepared and finalised in the same manner as the focus group guides.
Participants for focus group discussions and one-to-one in-depth interviews were selected by purposive sampling from 12 villages of Khurda district, Orissa. In purposive sampling, researchers choose study sites or informants to represent the range of variation on those characteristics that seem to be meaningful for the topic under study. In this situation, a small number of specially chosen informants can yield valid and generalisable information [18]. Participants were selected from all of the villages to the extent possible. During epidemiological investigations [11], a door-to-door survey was conducted and all individuals in these villages were physically examined for different signs and symptoms of LF. A cohort of 115 patients exclusively with hydrocele was available. These patients were identified for in-depth interviews and focus group discussions, and only those who were married and below 50 years of age were selected. Hydrocele patients who possessed other LF symptoms like lymphoedema were not included. To conduct a focus group discussion with hydrocele patients, six to eight patients from two to three villages were consulted and requested to arrive to a particular place at a particular time. These villages are closely situated within a distance of 2–3 kilometres. Further, they share several common places like agricultural fields, markets, a health centre, etc. Hence, people of these villages were known to each other. Two focus groups consisted of eight patients each and a third group consisted of six patients. The hydrocele patients who were chosen for in-depth interviews were not included in focus groups. The selection of women whose husbands suffer from hydrocele was also made by identifying hydrocele patients from the cohort. These patients were not covered for in-depth interviews and focus group discussions.
Key-informants were also selected from all of the villages, based on the guidelines mentioned above. The key-informants were village/community heads, members of local administrative bodies, and teachers residing in these villages. All of these informants were educated, having at least a school education, and were up to the age of 70 years. For focus groups with community members, participants were selected from within a village. Two groups of women (consisting of six and eight members) and three groups of men (consisting of seven, eight, and eight members) in the age range of 20 to 50 years were selected. Women participants were housewives and the men were farmers and agricultural labourers. Some of the participants did not have any formal education, while others had received formal educations of up to 5 years. During the recruitment of focus group participants, it was ensured that all participants knew each other, and care was taken to maintain homogeneity within each group. Participants having similar socioeconomic characteristics (like education, income, occupation, and caste affiliation) were grouped together.
The purpose of the study was explained and consent to participate in the study was obtained orally from all of the participants, as suggested by the SAC. Oral consent was obtained instead of written consent, as the majority of the study participants were illiterate. The consent of the participants was recorded along with the recording of the interview/discussion. In addition to obtaining consent from each participant, the consent of the community leaders and village heads was obtained to conduct the surveys in their villages and communities. None of the participants declined to particpate in the study.
The researchers and staff who assisted during the conducting of focus groups (SM and ANN, and those listed in Acknowledgments) are aware of local language and culture. The discussion/interview guides were prepared initially in English to ensure that all the issues and objectives of the study were covered comprehensively. The focus group moderators were trained medical anthropologists who were experienced with conducting focus group discussions on LF-related issues. The moderator conducted the focus group discussions, while observers watched and took notes on the discussion, as well as on participants' actions, gestures, and emotions that could not be captured on audiotape. The observers transcribed the focus group discussions. The in-depth interviews were conducted by the same researchers who acted as moderators of the focus group discussions. Focus groups as well as interviews with female and male participants were conducted by female and male staff, respectively. After each discussion/interview, the team along with the primary researcher discussed what had transpired and reviewed the observers' field notes on impressions and observations.
The entire discussion/interview was recorded on audiocassettes. Later, the audiocassettes were played back and transcribed into Oriya with the help of field notes. The Oriya scripts were translated to English and were entered into a personal computer in MS Word as text files [22]. The analysis was done by using ATLAS.ti for Windows 4.1 (Scientific Software Development, Berlin). This computer-based analysis facilitated selecting relevant quotations from the text, coding, annotating, and comparing the quotations. The coding plan was developed based on the issues related to the effect of hydrocele on conjugal life and marriageability. The quotations of each code were retrieved for each category of participants by using a text-based selection option. Thus, by summarising the quotations of each code by category, a matrix was developed to examine the qualitative data.
Data revealed that hydrocele is always a burden to the patient and to his family. In addition, the most worrisome effect of hydrocele for patients and their spouses was the inability to have a satisfactory sexual life. Of the 32 patients interviewed, 30 patients (93.7%) expressed their frustration due to their incapacity during sexual intercourse. All respondents interviewed except four (87.5%) reported that they get severe pain during intercourse and hence they avoid sex. They also perceived the dissatisfaction of their spouse due to their incapability during sexual intercourse. A young hydrocele patient during an interview said, “The hydrocele has affected my sexual life. I could not do much … . My wife is now not interested to have sex with me. Seems that she hates me… and advises me to go for hydrocele surgery.” Another patient narrated, “My spouse has no complaint, rather she is very cooperative in this matter. However, my sexual desire has declined drastically over the years. I develop an acute pain in the scrotum during and immediately after the intercourse.” However, in focus group discussions with hydrocele patients, a few participants argued that it has no gross impact on sexual life, though they do experience some pain. However, the groups reached consensus that their condition has influenced their ability to have sexual intercourse.
Of the 35 women whose husbands are hydrocele patients, 33 women (94%) reported dissatisfaction with their sexual life. Some women (25.7%) complained that a failure of erection and penetration occurred during intercourse due to the larger size of the hydrocele scrotum. About half of them said that hydrocele patients have lesser sexual potency than normal men and they thought that hydrocele is responsible for male impotency. Many of these women (68.6%) also reported that their husbands became hesitant to have sex and subsequently lost interest in it. In the voice of the wife of a hydrocele patient, “When the disease becomes prolonged, the front portion of male genital becomes very small and the scrotum gets unusually large. Eventually that leads to male impotency.” However, about 6% of these women reported that they did not perceive any problem. This reporting might be due to the following: (i) some men are in an early stage of hydrocele and might not be affected much, and/or (ii) these women may not choose to reveal this private information. A few women could not speak openly, as the issue is sensitive and related to their family. A 28-year-old woman, whose husband is a hydrocele patient, said, “Yes, there is problem. How there shall be no problem? There is a major problem at the time of intercourse. But I do not like to reveal it to everybody. For instance, when we are served food by our guests, we normally accept it irrespective of whether or not we like that food. For certain things, I cannot tell to my husband, he may feel bad about me. Though we are not happy on bed, I manage it without showing sense of dissatisfaction.” This study found that the wives of many hydrocele patients persuaded their husbands to get a hydrocelectomy. In one case, it was found that the wife of a hydrocele patient was no longer interested in sleeping with her husband. In another case, as narrated by a woman respondent, a woman had left her husband and gone back to her parents' house out of frustration. During one interview, a woman reported, “A newly married girl of our village recently left her husband and went back to her parents' house due to this problem. We stay with our husbands because we are old and have children. The younger generation girls are not that much adjustable.”
The general community also reported similar views about the effect of hydrocele on the sexual life of patients. Of the nine key-informants in the community, eight informants (89%) revealed that the hydrocele patients feel pain during sexual intercourse. They also said that, as the penis of hydrocele patients get shorter, they fail to satisfy their wives during sexual intercourse. These respondents became aware of these issues through casual discussions and gossiping with the affected people and other community members. Hence, these respondents discussed these issues in a generalised manner without referring to a particular person.
Concerning marital life, the hydrocele patients revealed several issues. During in-depth interviews, half of the 32 patients agreed that they and their wives are not as happy as they were before the development of hydrocele. When they were asked whether or not both of them live just like others, about 40% of patients said that they are not like others, as their condition hampered their economic situation, because of loss of work capacity, and led to dissatisfaction in their sexual life. About one-third of patients said that their spouses were hesitant and did not like to go out with them. Approximately one-fifth of patients revealed that their wives expressed dislike towards them. The focus groups of patients also revealed that communication had deteriorated between the couples and the quality of the marriage had been affected.
About half of the women (17/35) whose husbands have hydrocele told the interviewers that their husbands' activities were affected adversely due to hydrocele. These respondents reported that patients get severe pain in their genitals (scrotum) and feel too weak to perform hard work. Approximately 60% of women felt sorry for the condition of their husbands. About one-third of the respondents reported that the disease has affected the economic condition of their family. Some of these respondents (11.4%) reported that their husbands drink alcohol to get rid of the pain and other problems associated with hydrocele. A few women said that they frequently quarrel because of the disease and their relations are deteriorating. The following statement of a 40-year-old woman reveals the agony of such women: “My husband has a big hydrocele. He is not able to move and work freely. He feels ashamed to make his appearance in public places. He is now not even fit for conjugal life.”
The community members also perceived the problem of hydrocele and revealed that the disease is affecting people physically and psychologically. They mentioned several instances in which the deterioration of relations between wife and husband and disturbances in conjugal life had occurred.
The hydrocele patients also revealed the impact of hydrocele on marriageability. They said that one should remove it by surgery before going for marriage proposals. All patients agreed that it is difficult to get a bride for a hydrocele patient. Some patients in focus groups said that some men remained unmarried due to their condition, but this was denied by a few patients. However, all focus groups of patients agreed that it is problem for young men to get married, as the patient cannot work or even walk properly. In the present study, the hydrocele patients anticipated problems in getting their children married due to their disease. This disease is considered hereditary and people think that the diseases get transmitted to the next generation. When their sons suffer from hydrocele, it then becomes increasingly difficult for the parents to obtain spouses for their sons.
The women whose husbands have hydrocele said that the hydrocele patient is the “last choice” and that some girls are reluctant to marry hydrocele patients. A woman who is the wife of a hydrocele patient reported, “Had I know about the hydrocele of my husband, I could have refused to marry him.” About half of the women whose husbands were suffering from hydrocele said that their husbands had this disease before marriage. Approximately two-thirds of them alleged that the husbands' families had not disclosed their husbands' disease prior to the marriage, and these women expressed the feeling of having been deceived. Some women also expressed their inability to choose a bridegroom due to their economic situation and family customs. The women were asked a hypothetical question querying whether they think getting a spouse for a hydrocele patient is difficult. More than half of these participants said that it is difficult to get spouse for a hydrocele patient. Specifically, if a girl knows beforehand about the disease of the bridegroom, she may not accept that proposal. However, some women respondents (34%) opined that it is not a problem, as hydrocele can be removed by surgery.
The community members also felt similarly and said that people do not prefer to give their daughters to a hydrocele patient. Therefore, the hydrocele patients marry with little or no dowry under a compulsive situation. Often they marry girls from lower socioeconomic strata. One person explained, “It is difficult for a filariasis patient to get a girl by choice for marriage. Normally he gets married to a girl of lower economic status. … Parents offer their daughter to such a patient only under compulsive situations.”
In LF-endemic areas, hydrocele develops from asymptomatic infection through acute clinical manifestations. There are about 73 million people with LF infection worldwide and men in this group are at the risk of developing hydrocele, in addition to 27 million existing hydrocele patients [2]. The peak incidence of noticeable hydrocele seems to occur in early adulthood, between the age of 19–34 years [2] (Figure S1). It is demonstrated through several studies that hydrocele has an immense impact on economic activities and productivity [6],[7],[9],[23],[24] and quality of life [25],[26]. Addiss and Brady [27] reported that hydrocele patients reported both “enacted stigma” and “felt stigma”, based on studies in some endemic areas [28],[29]. Some patients often described themselves as frustrated, losing hope, and even suicidal [5],[30],[31].
The present study highlights the impact of hydrocele on conjugal life and relations between hydrocele patients and their wives. In addition, the paper describes how hydrocele impacts the marriageability of patients and their sons. Hydrocele affects men during the prime age when they pursue social and family goals. Women married to hydrocele patients were “silent sufferers” of their husbands' disease. The dissatisfaction of the patient and his wife leads to the deterioration of the marital relationship. However, in a society like rural India, the institution of marriage is strong and women usually do not separate for reasons like sexual dissatisfaction. The family and society do not appreciate and support such women, in addition to the existence of a strong community sanction against divorce and even temporary separation. However, an incidence of temporary separation due to this problem was reported by a woman participant of this study.
Similar findings were reported from other endemic areas. Dreyer and her colleagues found problems among clinic-based patients from Brazil such as marriages devoid of physical and sexual intimacy, a “conspiracy of silence” that includes both the patient and his wife, and profound shame and suicidal thoughts among men with hydrocele [5]. In Ghana, unmarried men found it difficult to find a spouse of their choice, and various degrees of sexual dysfunction were reported amongst married men [30]. This study associated sexual dysfunction with the size of the hydrocele. However, in the present study, no such attempt to find an association with the size of hydrocele was made due to lack of sufficient data. A similar study from another endemic area of Ghana reported the inability of hydrocele patients to have satisfactory sexual intercourse [28]. In addition, hydrocele prevented patients from getting a marriage partner, and there were a few cases of divorce due to hydrocele [28]. Another study, based on the extended Euro quality of life scale among South Indian hydrocele patients, reported that hydrocele adversely affected the patients' sexual functioning and caused moderate problems with anxiety/depression [32]. Though these studies reported sexual dysfunction and its effect on married life, the strength of the present study is that it could capture the feelings of the wives of hydrocele patients. Many wives persuaded their husbands to remove hydrocele by surgery. Women in endemic areas may have an important role in advocating for better access to hydrocelectomy, at least on a household level. However, it is felt that these issues, specifically the feelings of the women, should be understood with gender perspectives.
There are methodological limitations in this study, as is usual with this type of research design and methods. The topics of discussion and interviews are sensitive, and participants may not express their views openly, as they think that their responses may damage their reputation or their family. Sometimes, in this type of research, participants may also report the behaviour that is believed to be consistent with their culture, rather than the actual behaviour [33]. In focus groups, some participants were inactive and did not reveal much in the discussion, while some others were active. Though that is a limitation, it has been managed by the trained moderators.
It is clear that men with hydrocele need psychological and social support, as opined by Dreyer and her colleagues [5]. As there is a strong feeling of shame and embarrassment among hydrocele patients, the problem of sexual disability is usually not acknowledged unless the patients are specifically probed by health care staff. The desire of some of the hydrocele patients as well as their wives was to have surgery to remove the hydrocele (hydrocelectomy). In addition, a majority of these people were aware of the remedy of hydrocele through the surgery [34]. However, most hydrocele patients have not had a hydrocelectomy due to the costs involved, loss of working days/wages during hospitalisation and recuperation after surgery, and lack of a surgical facility in rural public health institutions. The surgical facilities for hydrocelectomy are available in private hospitals in urban areas, and these hospitals charge more than US$100 for surgery and a bed. In addition, the patient has to bear the other expenditures like medicines, food, travel, etc. This expenditure, along with the loss of work and wage of the patient as well as the escort, prevents patients from accessing surgery for the cure of hydrocele.
This study area is covered by the Global Programme to Eliminate LF. The alleviation of disability and control of morbidity among LF patients is the second arm of the programme [35]. This arm has not received much attention and therefore lags behind the first arm of the programme, i.e., interruption of LF infection through mass drug administration (MDA). It is evident by the fact that 48 of the 83 endemic countries had implemented MDA by the end of 2007, whereas only 27 of the 48 countries that implemented MDA have initiated morbidity management activities [36]. Even in those areas where morbidity management activities are conducted, more emphasis is given to the management of lymphedema, rather than to the repair of hydrocele. This could be due to several reasons, including lack of resources at health institutions of an implementation level and lack of adequate information on the burden and impact of hydrocele. The policy makers and programme managers should be sensitised by using research findings on the burden and impact of hydrocele. The objective of any LF morbidity management programme should be to increase access to hydrocelectomy. One of the initial activities of the programme should be to detect hydrocele cases using existing community-based surveys, such as enumeration during MDA. In addition, a house-to-house morbidity census may be conducted to acquire a better understanding of hydrocele burden. Individuals with hydrocele should be referred to a facility for surgery, if necessary. Mass hydrocelectomy camps may be feasible initially to reduce the burden of hydrocele in high LF-endemic areas. In a study from Ghana, patients reported that within 3 to 6 months of the post-surgery period, they had experienced a significant improvement in self-esteem, sexual function, and work capacity, and they participated more in community activities [28].
Because MDA may be used as a primary prevention measure for disabilities caused by LF, opportunities for synergy between MDA and disability management and prevention activities need to be explored [37]. Endemic countries have become convinced of the benefits of the programme and real progress in arresting transmission has been reported from countries that commenced MDA early [38]. Also, an unpredicted outcome of MDA, i.e., reduction of incidence of hydrocele following MDA, has been reported from Papua New Guinea [39] and India [40]. However, a common finding was that even after an aggressive control programme to arrest the transmission of infection, chronic manifestations such as hydrocele have persisted for decades [41]. Hence, the programme's two arms should go hand in hand. There is a need to incorporate LF disability management and prevention into primary health care services, which are well established in many areas. Also, these activities may be integrated with health interventions of other neglected tropical diseases. The integration among these programmes helps improve both efficiency and effectiveness [42]. Recently, there has been significant discussion on potential challenges, opportunities, and estimated potential benefits including cost savings [42]. The momentum of the Global Programme must be sustained to remove the impediments that prevent hydrocele patients from leading a decent life and to stop generation of new hydrocele patients.
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10.1371/journal.pgen.1007936 | Spatial soft sweeps: Patterns of adaptation in populations with long-range dispersal | Adaptation in extended populations often occurs through multiple independent mutations responding in parallel to a common selection pressure. As the mutations spread concurrently through the population, they leave behind characteristic patterns of polymorphism near selected loci—so-called soft sweeps—which remain visible after adaptation is complete. These patterns are well-understood in two limits of the spreading dynamics of beneficial mutations: the panmictic case with complete absence of spatial structure, and spreading via short-ranged or diffusive dispersal events, which tessellates space into distinct compact regions each descended from a unique mutation. However, spreading behaviour in most natural populations is not exclusively panmictic or diffusive, but incorporates both short-range and long-range dispersal events. Here, we characterize the spatial patterns of soft sweeps driven by dispersal events whose jump distances are broadly distributed, using lattice-based simulations and scaling arguments. We find that mutant clones adopt a distinctive structure consisting of compact cores surrounded by fragmented “haloes” which mingle with haloes from other clones. As long-range dispersal becomes more prominent, the progression from diffusive to panmictic behaviour is marked by two transitions separating regimes with differing relative sizes of halo to core. We analyze the implications of the core-halo structure for the statistics of soft sweep detection in small genomic samples from the population, and find opposing effects of long-range dispersal on the expected diversity in global samples compared to local samples from geographic subregions of the range. We also discuss consequences of the standing genetic variation induced by the soft sweep on future adaptation and mixing.
| When a species is spread out over a large geographic range, different regions may adapt to the same selection pressure by acquiring distinct beneficial mutations. The resulting pattern of genetic variation in the population is called a soft sweep. Dispersal strongly influences soft sweep patterns, as it determines how a mutation that arose in one region might spread to others. Although most plant and animal populations experience some amount of dispersal over very long distances, the impact of such long-range dispersal events on soft sweep patterns remains poorly understood. We use computer simulations and mathematical analysis to study patterns of genetic variation in a model of soft sweeps including long-range dispersal. We show that long-range dispersal leaves distinct signatures in the genetic makeup of the population, which can be detected in genetic samples from individuals across the range. Our results are important for correctly interpreting patterns of genetic diversity in populations that have undergone recent adaptation.
| Rare beneficial alleles can rapidly increase their frequency in a population in response to a new selective pressure. When adaptation is limited by the availability of mutations, a single beneficial mutation may sweep through the entire population in the classical scenario of a “hard sweep”. However, populations may exploit a high availability of beneficial mutations due to standing variation, recurrent new mutation, or recurrent migration [1–5] to respond quickly to new selection pressures. As a result, multiple adaptive alleles may sweep through the population concurrently, leaving genealogical signatures that distinguish them from hard sweeps. Such events are termed soft sweeps. Soft sweeps are now known to be frequent and perhaps dominant in many species [6, 7]. Well-studied examples in humans include multiple origins for the sickle cell trait which confers resistance to malaria [8], and of lactose tolerance within and among geographically separated human populations [9, 10].
Soft sweeps rely on a supply of beneficial mutations on distinct genetic backgrounds, which has two main origins. One is when selection acts on an allele which has multiple copies in the population due to standing genetic variation—a likely source of soft sweeps when the potentially beneficial alleles were neutral or only mildly deleterious before the appearance of the selective pressure [3]. In this work, we focus on the other important scenario of soft sweeps due to recurrent new mutations which arise after the onset of the selection pressure. Soft sweeps become likely when the time taken for an established mutation to fix in the entire population is long compared to the expected time for additional new mutations to arise and establish. In a panmictic population, the relative rate of the two processes is set primarily by the rate at which new mutations enter the population as a whole [5].
Most examples of soft sweeps in nature, however, show patterns consistent with arising in a geographically structured rather than a panmictic population [7]. Spatial structure promotes soft sweeps [11]: when lineages spread diffusively (i.e. when offspring travel a restricted distance between local fixation events), a beneficial mutation advances as a constant-speed wave expanding outward from the point of origin, much slower than the logistic growth expected in a well-mixed population. Therefore, fixation is slowed down by the time taken for genetic information to spread through the range, making multi-origin sweeps more likely. However, the detection of such a spatial soft sweep crucially depends on the sampling strategy: the wavelike advance of distinct alleles divides up the range into regions within which a single allele is predominant. If genetic samples are only taken from a small region within the species’ range, the sweep may appear hard in the local sample even if it was soft in the global range.
Between the two limits of wavelike spreading and panmictic adaptation lies a broad range of spreading behaviour driven by dispersal events that are neither local nor global. Many organisms spread through long-range jumps drawn from a probability distribution of dispersal distances (dispersal kernel) that does not have a hard cutoff in distance but instead allows large, albeit rare, dispersal events that may span a significant fraction of the population range [12, 13]. A recent compilation of plant dispersal studies showed that such so-called “fat-tailed” kernels provided a good statistical description for a majority of data sets surveyed [14]. Fat-tailed dispersal kernels accelerate the growth of mutant clones, whose sizes grow faster-than-linearly with time and ultimately overtake growth driven by a constant-speed wave [12, 15]. Besides changing the rate at which beneficial alleles take over the population, long-range dispersal also breaks up the wave of advance [16]: the original clone produces geographically separated satellites which strongly influence the spatial structure of regions taken over by distinct alleles.
Despite its prominence in empirically measured dispersal behaviour and its strong effects on mutant clone structure and dynamics, the impact of long-range dispersal on soft sweeps is poorly understood. Past work incorporating fat-tailed dispersal kernels in spatial soft sweeps [11] relied on deterministic approximations of the jump-driven spreading behaviour of a single beneficial allele [12]. However, recent analysis has shown that deterministic approaches are accurate only in the two extreme limits of local (i.e. wavelike) and global (i.e. panmictic) spreading, and break down over the entire regime of intermediate long-range dispersal [17]. Away from the limiting cases, the correct long-time spreading dynamics is obtained only by explicitly including rare stochastic events which drive the population growth. Deterministic approaches also do not account for the disconnected satellite structure, which has consequences for soft sweep detection in local samples.
Here, we study soft sweeps driven by the stochastic spreading of alleles via long-range dispersal. We perform simulations of spatial soft sweeps in which beneficial alleles spread via fat-tailed dispersal kernels which fall off as a power law with distance, focusing on the regime in which multiple alleles arise concurrently. We find that long-range dispersal gives rise to distinctive spatial patterns in the distribution of mutant clones. In particular, when dispersal is sufficiently long-ranged, mutant clones are discontiguous in space, in contrast to the compact clones expected from wavelike spreading models. We identify qualitatively different regimes for spatial soft sweep patterns depending on the tail of the jump distribution. We show that analytical results for the stochastic jump-driven growth of a solitary allele [17], combined with a mutation-expansion balance relevant for spatial soft sweeps [11], allow us to predict the range sizes beyond which soft sweeps become likely. We also analyze how stochastic aspects of growth of independent alleles, particularly the establishment of satellites disconnected from the initial expanding clone, influence the statistics of observing soft sweeps in a small sample from the large population. We find that long-range dispersal has contrasting effects on the likelihood of soft sweep detection, depending on whether the population is sampled locally or globally.
We consider a haploid population that lives in a d-dimensional habitat consisting of demes that are arranged on an integer lattice (e.g. square lattice in d = 2). Local resource limitation constrains the deme population to a fixed size n ^, assumed to be the same for all demes. Denoting the linear dimension of the lattice as L, the total population size is N = L d n ^. The population is panmictic within each deme. With a rate m per generation, individuals migrate from one deme to another. For each dispersal event, the distance r to the target deme is chosen from a probability distribution with weight J(r), appropriately discretized, with the normalization ∫ 1 ∞ J ( r ) d r = 1. The function J(r) is called the jump kernel. The dispersal direction is chosen uniformly at random from the unit sphere in d dimensions. New mutations arise in all demes at a constant rate u per individual per generation. Each new mutation is distinguishable from previous mutations (e.g. due to different genomic backgrounds), but all mutations confer the same selective advantage s. Back mutations are ignored. To minimize the effect of the specific boundary geometry, periodic boundary conditions are assumed.
To focus on the effects of long-range dispersal over local dynamics, we now impose a set of bounds on the individual-based parameters following [11]. In particular, we consider only situations where s n ^ ≫ 1; u n ^ ⪡ 1; m n ^ ⪡ 1 (strong selection, and low mutation and migration rates at the deme level). Mutations are also assumed to be fully redundant, i.e. a second mutation confers no additional advantage. The strong selection condition implies that genetic drift within a deme is irrelevant relative to selection: a new mutation, upon surviving stochastic drift and fixing within a deme (which happens with probability 2s) cannot be subsequently lost due to genetic drift. The bounds on mutation and migration rates meanwhile imply that the fixation dynamics of a beneficial mutation within a deme is fast compared to the dynamics of mutation within a deme or of migration among demes. The time to fixation of a beneficial allele from a single mutant individual in the deme, log ( n ^ s ) / s, is a few times 1/s. When u n ^ ⪡ 1 and m n ^ ⪡ 1, the fixation time scale is much shorter than the establishment time scales of new alleles arising due to mutation or migration, which are ( 2 s m n ^ ) - 1 and ( 2 s u n ^ ) - 1 respectively. Therefore, the first beneficial allele that establishes in a deme, whether through mutation or migration, fixes in that deme without interference from other alleles. Furthermore, the assumption of mutual redundancy means that subsequent mutations that arrive after the first fixation event also have no effect. As a result, the first beneficial allele that establishes in a deme excludes any subsequent ones—a situation termed allelic exclusion [11].
Taken together, these assumptions lead to a simplified model that ignores the microscopic dynamics of mutations within demes. For each deme, we keep track of a single quantity: the allelic identity (whether wildtype or one of the unique mutants that has arisen) that has fixed in the deme. At the deme level, new mutations fix within wildtype demes at the rate 2 s n ^ u, and each mutated deme sends out migrants at rate 2 s n ^ m with the target deme selected according to the dispersal kernel J(r) (the rates explicitly include the fixation probability 2s of a single mutant in a wildtype deme). The first successful mutant to arrive at a wildtype deme, whether through mutation or migration, immediately fixes within that deme. The state of the deme thereafter is left unchanged by mutation or migration events, because of allelic excusion.
When time is measured in units of the expected interval ( 2 s n ^ m ) - 1 between successive dispersal events per deme, the reduced model is characterized by just three quantities: L; J(r); and the per-deme rate of mutations per dispersal attempt u ˜ ≡ 2 s n ^ u / ( 2 s n ^ m ) = u / m, which we call the rescaled mutation rate of our model. Simulations are begun with a lattice of demes of size Ld all occupied by the wildtype. Each discrete simulation step is either a mutation or an attempted migration event, with the relative rates determined by u ˜ and the fraction of wildtype sites at that step. Mutation events flip a randomly-selected wildtype deme into a new allelic identity. Migration events first pick a mutated origin and then pick a target deme according to the jump kernel. If the target site is wildtype, it acquires the allelic identity of the origin; otherwise the migration is unsuccessful. Simulations are run until all demes have been taken over by mutants.
The fat-tailed jump kernels we use are of the form J(r) = μr−(1+μ), with μ > 0 to ensure that the kernel is normalizable. The exponent μ characterizes the “heaviness” of the tail of the distribution. We have chosen power-law kernels because they span a dramatic range of outcomes that connect the limiting cases of well-mixed and wavelike growth upon varying a single parameter. The growth dynamics of more general fat-tailed kernels in the stochastic regime of interest (i.e. driven by rare long jumps) are largely determined by the power-law falloff of the tail, and details of the dispersal kernel at shorter length scales are less consequential. Therefore, our qualitative results should extend to kernels sharing the same power law behaviour of the tail, provided the typical clones are large enough so that rare jumps picked from the tail of the distribution become relevant. The underlying analysis leading to the results is even more general, and can be applied to any jump kernel that leads to faster-than-linear growth in the extent of an individual clone with time.
The output of a simulation at a given set of L, μ and u ˜ values is the final configuration of mutants, which can be grouped into distinct clones of the same allelic identity. Note that we have ignored the post-sweep mixing of alleles which are now relatively neutral to each other due to migration; this is justified by the separation of time scales between fast fixation and slow neutral migration [11]. In addition, although we restrict ourselves to weak mutation and migration at the deme level, the population-level mutation and migration rates Nu, Nm are typically large which allows for soft sweeps with strong migration effects.
While our theoretical results are valid for all dimensions, computational limitations prevented us from running extensive simulations in dimensions higher than one. Therefore, we primarily report simulations of linear habitats (d = 1) in the main text. Preliminary results from planar simulations (d = 2) are reported in S1 Appendix, Section B and are consistent with our theoretical arguments, although quantitative comparisons are limited by finite-size effects.
Some typical outcomes of the simulation model are shown in Fig 1 for both two-dimensional (2D) and one-dimensional (1D) ranges. To emphasize variations in the spatial patterns for the same average clone size, simulations were chosen in which the final state has exactly ten unique alleles; this required varying the rescaled mutation rate as μ was increased. This feature, which is tied to the slower growth of individual clones apparent in the space-time plots of Fig 1(b), is explored in depth in Section Characteristic scales via mutation-expansion balance.
In both 2D and 1D, the spatial soft sweep patterns of Fig 1 display systematic differences as the kernel exponent is varied. Clones are increasingly fragmented as the kernel exponent is reduced; i.e. as long-range dispersal becomes more prominent. At the highest value of μ in each dimension, the range is divided into compact, essentially contiguous domains each of which shares a unique mutational origin. As the kernel exponent μ is reduced, the contiguous structure of clones is lost as they break up into disconnected clusters of demes. For most clones, however, a compact region can still be identified in the range which is dominated by that clone (i.e. the particular allele reaches a high occupancy that is roughly uniform within the region but begins to fall with distance outside it) and in turn contains a significant fraction of the clone. We call this region the core of the clone. The remainder of the clone is distributed among many satellite clusters which produce local regions of high occupancy for a particular clone. The satellites become increasingly sparse and smaller in size as we move away from the core. For the broadest kernels (μ = 0.5 in 2D and μ = 0.7 in 1D), most clones also include isolated demes which do not form a cluster but are embedded within cores and satellite clusters of a different allele. We term the collection of satellites and isolated demes the halo region surrounding the core of the clone. The circles in the second panel of Fig 1(a) illustrate the extent of core and halo, quantified via distance measures which we introduce later on for a particular clone (the fifth clone entering the population, colored light green). The spatial extent of the clone including the halo can be many times the extent of the core alone, and increases relative to the core extent as μ is reduced. (We will use “extent” to refer to linear dimensions, and “mass” or “size” to refer to the number of demes).
The space-time evolution displayed in Fig 1(b) for linear simulations reveals the role of jump-driven growth in producing the observed spatial structures. At μ = 2.5, the growth of clones appears nearly deterministic, with fronts separating mutant from wildtype advancing outwards from the originating mutations at near-constant velocity. These fronts are arrested when they encounter advancing fronts of other clones, leaving behind a tessellation of the range into contiguous clones. By contrast, at the lower values μ = 1.3 and 0.7, the stochastic nature of jump-driven growth becomes apparent. Clones advance through long-distance dispersal events, which seed satellite clusters that may merge with each other before the sweep is complete. For all except the smallest clones, the originating mutation is surrounded by a region which is dominated by that particular allele—these form the core regions defined above. Satellites are seeded by stochastic jumps that extend over regions which either were occupied by a different allele already, or get filled in by a different allele before the satellite has a chance to merge with the core. For μ = 1.3, haloes extend only a short distance out from the core, whereas at μ = 0.7 the haloes often extend over a distance many times the core extent.
The increased fragmentation of clones with broader dispersal kernels has a marked impact on local diversity in sub-regions of the range. Haloes belonging to different alleles overlap to produce regions of high diversity, as exemplified by the dashed box in Fig 1(a) for μ = 1.5, which contains demes belonging to six of the 10 unique alleles despite being a small fraction of the total range area. By contrast, the same region contains only one allele at μ = 3.5 for which clones form contiguous domains. Other effects of broadening the dispersal kernel are also visible in Fig 1: the spread in clone sizes becomes larger, and individual clones take many more generations to attain a given size.
To build a quantitative understanding of these variations, we begin by noting that at early times in Fig 1(b), each clone grow largely unencumbered by other clones. We can therefore gain insight from existing results on the jump-driven growth of a solitary advantageous clone expanding into a wildtype background [17]. The key features are summarized here and illustrated for the blue clone in Fig 2. Consider a clone that grows from a mutation that originated at time t = 0 at the origin. At times longer than a short transient, the clone fills most sites out to some distance from the origin. In line with the terminology established above, we call this region of high occupancy the core of the growing clone. Its typical extent over time (i.e. the average radius of a core that has grown for time t) is quantified by a function ℓ(t) which itself depends on the dispersal kernel (a precise definition is given at the end of this section). As sites in the core get filled, they send out offspring through long-range dispersal events drawn from the specified kernel, which then grow into independent satellite clusters. As a result, at any time t there are also demes outside the core which are occupied by the mutant. However, the occupancy of sites outside the core decays as r−(d+μ) with distance r from the originating mutation [17], fast enough that the total mass of the clone at time t is proportional to ℓd(t).
As sketched in Fig 2, the core grows through mergers of satellite clusters that grew out of rare but consequential “key jumps” out of the core at earlier times (solid arrows in Fig 2). [17] identified qualitative differences in the behaviour of key jumps and the resulting functional forms of ℓ(t) as the kernel exponent is varied. When μ > d + 1, the extent of typical key jumps remains constant over time, which implies that they must originate and land within a fixed distance from the boundary of the high-occupancy region at all times. As a result, clones advance via a constant-speed front similar to the case of wavelike growth; i.e. ℓ(t) ∝ t. Furthermore, the separation between the core and satellites is insignificant at long times, giving rise to essentially contiguous clones. By contrast, for μ < d + 1, growth is increasingly driven by jumps that originated in the interior of the core at earlier times, and key jumps become longer with time. The resulting growth of ℓ(t) is faster-than-linear with time. The value μ = d is an important marginal case which separates two distinct types of long-time asymptotic behaviour for ℓ(t): power-law growth for d < μ < d + 1 and stretched-exponential growth for 0 < μ < d (see the second column of Table 1 for the asymptotic growth forms in all regimes). As μ → 0, spatial structure becomes increasingly irrelevant and the growth dynamics approaches the exponential growth of a well-mixed population.
These features of solitary-clone growth can be directly connected to the spatial patterns in Fig 1 when recurrent mutations are allowed. The tessellation of the range into contiguous domains for the highest values of μ is exactly as expected from the wavelike growth situation when μ > d + 1. When μ < d + 1, by contrast, each clone consists of a growing core and well-separated satellite clusters at any time. Unlike the solitary-mutant case, satellites belonging to a particular clone are no longer guaranteed to merge with the core or with each other at later times: due to allelic exclusion, mergers are obstructed by cores and satellites with a different allelic identity, as shown schematically in Fig 2. The final pattern of frozen-in satellite clusters comprises the previously identified halo structure around each core when μ < d + 1.
Notation and definitions: Before we proceed, we summarize the various quantities in our analysis, and the conventions used in representing them. (A complete list of variables and definitions is provided in Table 2). One set of physical quantities, represented as Latin symbols without a time argument, measures properties of individual clones after the soft sweep has been completed; i.e. quantities measured from the final simulation outputs such as those displayed in Fig 1. (These quantities could also, in principle, be measurable from a real spatial population that has recently experienced a sweep). Of these, quantities that have dimensions of length are the mass-equivalent clone radius req and the clone extent rmax (defined in Table 2). The solid and dotted circles in Fig 1(a) illustrate these quantities for a specimen clone. The final clone mass is designated by the symbol X. Ensemble averages of these quantities for a given set of model parameter values, obtained by averaging first over all clones within a single simulation and then across many independent simulations, are denoted by 〈…〉.
Our analysis connects these properties of the final, static soft sweep pattern to the dynamic growth behaviour of a solitary clone under the same dispersal kernel, in the absence of interference from other clones. For a given dispersal kernel, the typical growth behaviour is captured by the core growth function ℓ(t) which we introduced previously. A precise definition of ℓ(t) requires making a choice about how to identify the core region. In contrast to the case of wavelike growth, there is no sharp advancing front which separates the high-occupancy region of a growing clone from its surroundings; the average radial occupancy profile at time t (defined as the probability that a deme at distance r from its point of origin is occupied by the clone) is close to one out to some distance from the origin, beyond which it crosses over to a profile that decays as a power law with increasing distance. One possibility, proposed in [11], is to define ℓ(t) as the distance at which the average occupancy profile falls below some low threshold probability ε. Here, we make a different choice motivated by the property, proved in [17], that the total mass of the clone (which we call M(t)) is proportional to ℓd(t). We define ℓ(t) as the expected mass-equivalent radius of the clone at time t: ℓ(t) ≡ E[(M(t)/ωd)1/d], where ωd is the volume of the d-sphere of radius 1 (ω1 = 2, ω2 = π). For a particular solitary-clone growth simulation, M(t) is straightforward to measure since the clone mass is readily accessible. For a particular value of μ, ℓ(t) is then estimated using an ensemble average over many independent solitary-clone simulations (see S1 Appendix, Section A for details). Our choice of ℓ(t) is proportional to ℓ(t) defined using an occupancy threshold, provided ε is small enough. We expect that using other definitions of ℓ(t) which scale proportionately with the core region will not significantly change our results, at most shifting the magnitude of reported quantities by constant factors of order unity as long as we are sufficiently far from the well-mixed limit μ → 0.
Finally, the interplay between the expansion of individual clones and the introduction of new mutations is used to derive various time-independent characteristic lengths, which are represented as Greek symbols. These length scales depend on the dispersal kernel via the functional form of ℓ(t), and the rescaled mutation rate u ˜. Precise definitions of the characteristic length scales are provided in Table 2 and in the forthcoming sections.
So far, we have focused on the spatial structure of individual clones within a soft sweep, and have shown that many aspects of this structure can be understood from the theory of growth of a solitary clone under the same dispersal kernel. To address questions of global and local allelic diversity, however, we need to explicitly consider the concurrent growth of multiple clones. We now show how the balance between jump-driven growth and the dynamics of introduction of new mutations sets the typical size and spatial extent of clones.
Unlike our simulations, studies of real populations do not have access to complete allelic information over the entire range. Instead, the allelic identity of a small number of individuals is sampled from the population. The likelihood of detecting a soft sweep in such a random sample is determined not only by the total number of distinct clones in the range, but also by their size distribution: if the range contains many clones, but all but one are at extremely small frequency (defined as the fraction of demes in the range that belong to that clone), the sweep is likely to appear “hard” in a small random sample which would with high probability contain only the majority allele. Long-range dispersal can therefore influence soft sweep detection not only by setting the average clone size, but also by modifying the distribution of clone sizes around the average. Having already established that the dispersal kernel has a significant effect on the average clone size (Fig 4), we now analyze its effects on the clone size distribution and the consequences for soft sweep detection.
Clone size distributions were quantified by computing the allele frequency spectrum f(x), defined such that f(x)δx is the expected number of alleles which have attained frequencies between x and x + δx in the population [18]. The allele frequency spectrum is related to the average probability distribution of clone sizes, but has a different normalization ∫ 1 / N 1 x f ( x ) d x = 1 which allows sampling statistics to be expressed as integrals involving f(x) (we will exploit this fact in Section Global sampling statistics below). Analytical results for f(x) can be derived for the deterministic wavelike growth limit μ ≫ d + 1 in 1D by mapping the spatial soft sweep on to a grain growth model [19], and for the panmictic limit μ → 0 in any dimension via a different mapping to an urn model [20]. The resulting functions, termed fw and f∞ for the two limits respectively, provide bounds on the expected frequency spectra at intermediate μ. Details of the mappings and complete forms for the functions fw and f∞ are provided in S1 Appendix, Section D.
Fig 6(a) shows allele frequency spectra computed from the outcomes of 1D soft sweep simulations for system size L = 107 and mutation rate u ˜ = 10 - 4. We find that the frequency spectra vary strongly with the dispersal kernel, and approach the exact forms f∞ and fw for small and large μ respectively. Generically, spectra become broader as the kernel exponent is reduced: as μ → 0, more high-frequency clones are observed. Although this broadening is partly explained by the increase in the average clone size due to accelerated expansion, which would lead to more high-frequency alleles, there are also systematic changes in the overall shapes of the distribution as the dispersal kernel is varied. Upon reducing the rescaled mutation rate to u ˜ = 10 - 6 [Fig 6(b)], all frequency spectra broaden due to the increase in the average clone size, but the variations in shapes of the f(x) curves with μ remain consistent across the two mutation rates. These observations suggest that spatial soft sweep patterns with similar numbers of distinct alleles in a range might nevertheless have vastly different clone size distributions due to different dispersal kernels, with implications for sampling statistics.
To uncover variations due to long-range dispersal beyond changes in the average clone size, we rescaled the frequency spectra by the expected dependence on Xave, which we have already established as being set by the mutation-expansion balance via the characteristic size χ. To establish the form of this rescaling, we assume that for a given dispersal kernel, soft sweep patterns at different mutation rates are self-similar when distances are rescaled by the characteristic length χ. Under this assumption, the probability distribution of clone sizes in an infinitely large range is a function only of the rescaled clone mass s ≡ X/Xave; i.e. the probability of finding a clone between s and s + δs is Pμ(s)δs, where the density function Pμ depends only on the dispersal kernel and not on the rescaled mutation rate.
For finite ranges of extent L much larger than χ, we can now express the average allele frequency spectrum in terms of Pμ. The expected number of unique alleles in the range is Ld/Xave. Within these alleles, the probability of finding an allele in the frequency range (x, x + δx) is Pμ(Ldx/Xave) × Ldδx/Xave. Therefore, the expected number of alleles with frequencies between x and x + δx is
( L d X ave ) 2 P μ ( L d x X ave ) δ x .
Upon comparing this expression the definition of the allele frequency spectrum for the finite range, we arrive at
f ( x ) = ( L d X ave ) 2 P μ ( L d x X ave ) , (4)
Eq 4 implies that for a given dispersal kernel, the dependence of the allele frequency spectrum on mutation rate and range size is completely captured by the ratio Ld/Xave. In particular, when f(x) is multiplied by (Xave/Ld)2 and the frequency by Ld/Xave, frequency spectra for different values of u ˜ ought to collapse onto a single curve for each μ. Fig 6(c) shows that upon such a rescaling (with 〈X〉 used as a simulation-derived estimate of Xave), curves for the same value of μ from panels (a) and (b) largely coincide, confirming that most of the dependence of the frequency spectrum on mutation rate is captured by the variation of the single length scale χ and, through it, the expected clone mass Xave. Note that we can use the fact that Xave ∝ χd with a kernel-independent prefactor to rewrite Eq 4 as f(x) = (L/χ)2dGμ(Ldx/χd), where Gμ is independent of u ˜, which explicitly shows the role of χ in scaling the allele frequency spectrum.
The arguments leading to Eq 4 relied on the assumption that only one characteristic scale exists for the soft sweep patterns. For our class of kernels, this assumption is only exact in the regime of power-law growth, for which the halo extent scale ψ is proportional to χ. In the stretched-exponential and marginal growth cases, by contrast, ψ acts as an independent length scale from χ with its own mutation-rate dependence. In S1 Appendix, Section E, we show that the consequent corrections to Eq 4 are weak (logarithmic in mutation rate and system size) and are strongest when μ approaches 0, validating the effectiveness of the proposed rescaling over all regimes away from the well-mixed limit.
The scaled frequency spectra show that broader dispersal kernels favour broader allele frequency spectra even after accounting for changes in the average clone size. At μ = 4, the steep decline in the frequency spectrum occurs near the frequency expected of an average clone, x ≈ 〈X〉/L. As μ is reduced, the falloff occurs at higher frequencies; at μ = 0.4, for instance, clones with frequencies an order of magnitude higher than the average clone are still likely. Qualitatively, this trend is a result of the increased nonlinearity of the growth functions ℓ(t) for broader dispersal. If we assume no interference among distinct clones until the time t*, the size of an allele which arrives at time ti is proportional to ℓd(t* − ti). For a given spread of arrival times of mutations, the spread of final clone sizes is significantly enhanced by nonlinearity in ℓ(t). Therefore, the increased departure from linear growth in ℓ(t) as μ → 0 gives rise to broader clone size distributions. Deterministic approximations to the clone size distributions expected for the asymptotic ℓ(t) forms in 1D, described in S1 Appendix, Section E, support this heuristic picture.
Although we do not have analytical expressions for the frequency spectra at intermediate μ, the measured curves and deterministic calculations suggest a simple approximate form for the allele frequency spectra: extend the power-law behaviour observed at intermediate frequencies [straight parts of the curves in Fig 6(a)–6(b)] from x = 0 up to a cutoff frequency corresponding to the location of the sharp dropoff in f(x). Quantitatively, we consider an ansatz for the frequency spectra with two parameters:
f ( x ) = { p + 2 x c p + 2 x p, x < x c 0 , x > x c , (5)
i.e. a power-law behaviour characterized by exponent p, up to some maximal frequency xc, with the constant of proportionality determined by the normalization. The values p and xc are determined from the numerical data, but are also consistent with theoretical arguments (S1 Appendix, Section E). The small-x behaviour of the two limiting spectra, f∞(x) ∼ x−1 and fw(x) ∼ x as x → 0, imply that p is restricted to vary from −1 to 1 as μ increases from zero. Despite its simplicity, this approximation can be used to quantify the relationships among various features of the clone size distributions as we show in S1 Appendix, Section F. For instance, the power-law ansatz predicts a relation between the average clone size and the cutoff frequency, Lxc/Xave = (p + 1)/(p + 2), which matches the trends observed in the rescaled frequency spectra, see inset to Fig 6(c).
Population genomic studies are often limited not only in the number of independent samples available, but in their geographic distribution as well. Samples tend to be clustered in regions chosen for a variety of reasons such as anthropological or ecological significance, or practical limitations. The analysis of the last section would apply to comparing samples across different regions, provided that these are relatively well spread out in the range. Here we focus on the variation within local samples from a subrange of the entire population. As illustrated by the wide variation in local diversity within the highlighted subranges (dashed boxes) in Fig 1(a), inferences based on local sampling can be significantly different from inferences based on global information, and may be very sensitive to modes of long-range dispersal.
Long-range dispersal enhances local diversity. When clones extend over a much wider spatial range than required by their mass (Fig 5), local subranges contain alleles whose origins lie far away from the subrange, and are consequently more diverse than expected from the diversity of the range as a whole. To quantitatively illustrate this effect, we compute sampling statistics for different dispersal kernels and subrange sizes from 1D simulations with a global range size much larger than the characteristic length scale χ (Fig 8). (Subrange size, denoted by Ls, and extent are equivalent in our 1D simulations). We observe that the smaller clones expected at higher values of μ favour the detection of soft sweeps globally (Fig 8a), but the diversity is less detectable in samples from subranges that are smaller than the characteristic size shared by the compact domains at μ = 4. By contrast, samples from smaller subranges continue to show signatures of soft sweeps for broader dispersal kernels (Fig 8b and 8c).
To compare the sensitivity of soft sweep detection to subrange size across different dispersal kernels and mutation rates, we focus on the probability of detecting the same allele in a pair of individuals randomly sampled from a subrange, Phard,s(2) (also called the species homoallelicity of the subrange). This probability is high only when the subrange is mostly occupied by the core of a single clone; it is low if the subrange contains cores belonging to different clones, or a combination of cores and haloes. Therefore, we expect χ (or equivalently the average mass-equivalent radius 〈req〉, which we may use as a simulation-derived estimate for χ in 1D) to also be the relevant scale to compare Ls values across different situations. Fig 9(a) shows the dependence of Phard,s(2) on Ls/〈req〉 for different dispersal kernels and mutation rates in the χ ≪ L limit. As with the global sampling probabilities reported in Fig 7(b), we find that the rescaling of subrange size with 〈req〉 captures much of the variation among different mutation rates (symbols) for a given dispersal kernel. In contrast with the global sampling statistics, however, hard sweep detection probabilities are suppressed (or equivalently, soft sweeps are easier to detect for the same rescaled subrange size) as the jump kernel is broadened. At high values of μ in the wavelike expansion limit, the shape of the curves is well-approximated by the null expectation for an idealized clone size distribution where all clones are perfectly contiguous segments of equal size Xave. As μ falls below d + 1, the prevalence of overlapping haloes increases local diversity at the scale of satellite clusters, much smaller than the typical clone size would dictate. The effect is especially strong in the marginal and stretched-exponential growth regimes (μ ≤ d), which was associated with the halo dominating over the core (Figs 3 and 5).
A different measure of subrange diversity is the total number of distinct alleles present in a subrange on average, which we call nc,s. Unlike the subrange homoallelicity, which was dominated by the most prevalent clone in the subrange, this measure gives equal weight to all clones, and is sensitive to haloes that overlap with the subrange. The expected number of distinct cores in the subrange is Ls/(2〈req〉); in the absence of haloes, we would expect nc,s to be equal to this value. However, haloes of clones whose cores are outside the subrange would cause nc,s to exceed the number of cores in the subrange. This enhancement in diversity due to encroaching haloes would be expected to occur only when the subrange is smaller than the average clone extent including the halo, i.e., when Ls < 2〈rmax〉. When the subrange is larger than the typical halo extent, the cores of clones whose haloes contribute to nc,s are also expected to lie within the subrange, and are accounted for in Ls/(2〈req〉). This expectation is confirmed in Fig 9(b). When the subrange size is rescaled by the extent of the clone including the halo, the average number of distinct alleles in the subrange follows nc,s = Ls/(2〈req〉) (solid line) in all cases, provided Ls/〈rmax〉 > 2. For smaller subrange sizes, nc,s lies above this estimate, reflecting the enhancement of local diversity due to encroaching haloes.
Adaptation in a spatially extended population often uses different alleles in different geographic regions, even if the selection pressure is homogeneous across the entire range. The probability of such convergent adaptation [21] and the patterns of spatial soft sweeps that result depend on two factors: the potential for the population to recruit adaptive variants from either new mutations or from the standing genetic variation, and the mode of dispersal. Previous work has focused on the two extremes of dispersal phenomena: panmictic populations without spatial structure [3–5] or wavelike spreading due to local diffusion of organisms [11, 21]. However, gene flow in many natural populations does not conform strictly to either limit. Many species experience some long-distance dispersal either through active transport or through passive hitchhiking on wind, water, or migrating animals including humans [12–14]. The dynamics of adaptation of populations with a large range can be strongly influenced by long-distance dispersal even when dispersal events are rare [22].
We have described spatial patterns of convergent adaptation for a general dispersal model, with jump rates taken from a kernel that falls off as a power-law with distance. Although the underlying analysis is applicable to more general dispersal kernels, our specific choice of kernel allows us to span a wide range of outcomes using a single parameter. We have shown that long-range dispersal tends to break up mutant clones into a core region dominated by the clone, surrounded by a disconnected halo of satellite clusters and isolated demes which mingle with other alleles. A key result of our analysis is that although the total mass of a clone is well-captured by the extent of the core region, the sparse halo can extend out to distances that are significantly larger than the core, sometimes by orders of magnitude. Therefore, understanding clone masses alone provides incomplete information about spatial soft sweep patterns, and can vastly underestimate the true extent of mutant clones.
By analyzing the balance between the jump-driven expansion of solitary clones and the introduction of new mutations, we have identified three characteristic length scales that quantify the spatial relationships between core and halo: the characteristic core extent χ, which sets the average clone mass; the radial extent ψ within which well-developed satellite clusters are expected; and the outer limit ζ within which both satellite clusters and isolated demes are typically found. As the kernel exponent μ is varied, these length scales demarcate three regimes with qualitatively different core-halo relationships: compact cores with insignificant haloes, similar to the case of wavelike growth, for μ > d + 1; a dominant high-occupancy core surrounded by a halo of well-developed satellite clusters which extend to a size-independent multiple of the core radius (ζ ∼ ψ ∝ χ) when d < μ < d + 1; and a halo including a significant number of isolated demes in addition to satellite clusters, which may extend over a region orders of magnitude larger than the core (ζ ≫ ψ ≫ χ) when μ < d.
We have also studied the signatures left behind by these patterns on population samples that are taken either from a local region, or globally from the entire range. Under which conditions, and for which types of samples, can we expect to observe a soft sweep? We have found that when ranges with similar overall diversity (as judged by the number of distinct clones in the entire range) are compared, broadening the dispersal kernel has opposing effects on soft sweep detection at global and local scales: soft sweeps become harder to detect in a global random sample, but easier to detect in samples from smaller subranges.
Besides having consequences for detecting and interpreting evidence for spatial soft sweeps, the breakup of mutant clones by long-range dispersal also impacts future evolution after the soft sweep has completed. Our analysis describes the spatial patterns arising in the regime of strong selection, where the large advantage of beneficial mutants over the wildtype dominates the evolutionary dynamics. Once the entire population has adapted to the driving selection pressure, smaller fitness differences among the distinct alleles will become significant, and modify the spatial patterns on longer time scales. Selection is most sensitive to these fitness differences at the boundaries separating demes belonging to different clones. For the same global diversity, the total length of these boundaries is strongly influenced by the connectivity of clones, and grows significantly as the kernel exponent is reduced, thereby modifying the post-sweep evolution of the population. The post-sweep evolution could also favour well-developed satellite clusters over isolated demes of one allele within a region dominated by another: isolated demes are likely to be taken over by their surrounding allele through local diffusion of individuals. Therefore, the characteristic length ψ may prove to be a relevant spatial scale for the post-sweep evolution, even in the regime μ < d where ζ sets the extent of the halo in the sweep patterns.
Although a quantitative evaluation of our model using real-world genomic data is beyond the scope of this work, some qualitative features of long-range dispersal can be identified in previous studies of spatial soft sweeps. The evolution of resistance to widely-adopted drugs in the malarial parasite Plasmodium falciparium is a well-studied example of a soft sweep arising in response to a broadly applied selective pressure. While multiple mutant haplotypes conferring resistance to pyrimethamine-based drugs have been observed across Africa and South-east Asia, the number of distinct haplotypes is smaller than would have been expected if resistance-granting mutations were confined to their area of origin [23]; this feature has been linked to long-distance migration of parasites through their human hosts, which allowed individual haplotypes to quickly spread across disconnected parts of the globe [24]. Within the same soft sweep, high levels of spatial mixing of distinct resistant lineages was also observed in some sub-regions [25]. These observations are consistent with the contrasting effects of long-range dispersal we have quantified in our model: at a given rescaled mutation rate, dispersal reduces diversity globally, but increases the mixing of alleles locally. Advances in sequencing technology have driven rapid improvements in the spatiotemporal resolution of drug-resistance evolution studies [26], making them a promising candidate for quantitative analysis of the spatial soft sweep patterns we have described.
Many interesting questions remain to be explored. Our simulation studies in d = 2 could be significantly expanded. We have also focused on the limit in which the average clone size is many times smaller than the entire range. It would also be interesting to study the statistics of soft sweeps when the extent of the range is comparable to the characteristic length scale χ, making a soft sweep an event of low but significant probability which may vary significantly with the dispersal kernel.
The applicability of our results to continuous populations without an imposed deme structure is an open problem. In our model, the deme structure is used to impose a local population density and allows us to separate the local dynamics of fixation from the large-scale behaviour driven by rare but consequential jumps. However, the theoretical picture of growth via the merger of satellite outbreaks with an expanding core does not rely on the deme structure. Therefore, we expect aspects of our results to also hold in continuous populations under certain parameter regimes. However, explicitly translating the parameters and defining the correct continuum limit of deme-based models is known to be challenging [27], and presents an interesting avenue for future work. Our simulations could also be modified to exploit advances in computational modeling of continuum populations [28].
The model can also be extended to include additional mechanisms involved in parallel adaptation. Besides recurring mutations, standing genetic variation (SGV) in the population is a important source of diversity for soft sweeps [3]. Long-range dispersal could impact both the spatial distribution of SGV before selection begins to act, and the spreading of alleles from distinct variational origins during the sweep [21]; both situations can be explored through extensions of our model. In the latter case, we expect the distinct regimes of core-halo patterns for different jump kernels to persist, but with the characteristic core size set by the initial distribution of variational origins rather than mutation-expansion balance.
The necessity of including heterogeneity motivates a natural set of extensions of the model. When soft sweeps arise due to mutations at different loci producing similar phenotypic effects, some variation in fitness among the distinct variants is inevitable. In panmictic models, fitness variations do not significantly affect the probability of observing a soft sweep, provided that the variations are small relative to the absolute fitness advantage of mutants over the wildtype [5]. Since spatial structure restricts competition to the geographic neighbourhood of a clone, we expect the effect of fitness variation to be even weaker than for panmictic populations, and our results should be robust to a small amount of variation in fitness effects. However, when fitness variations among mutations are large enough to be significant, the impact of the variations could depend on the dispersal kernel, and show qualitatively different behaviours in the distinct regimes of power-law and stretched-exponential growth. Similarly, spatial heterogeneities in the selection pressures could lead to so-called “patchy” landscapes which lead to certain mutations being highly beneficial in some patches but neutral or even deleterious in others [29]. Convergent adaptation on patchy landscapes is likely to be significantly impacted by long-range dispersal which would allow mutations to spread efficiently to geographically separated patches.
Finally, the assumptions of strong selection and weak mutation/migration allowed us to ignore the dynamics of introduction of beneficial mutations within a deme. Relaxing these assumptions would lead us to a more general model with an additional time scale characterizing the local well-mixed dynamics at the deme level. The interplay between this time scale and the time scales governing the large-scale dynamics driven by long-range dispersal could lead to new patterns of genetic variation during convergent adaptation.
Simulations were written in the C++ programming language, and utilized the standard Mersenne Twister engine to generate pseudorandom numbers. A simulation of linear size L in d dimensions is begun by initializing an array of integers of size Ld. Each array position corresponds to a single deme, and the associated integer value stores the allelic type. The array is initialized with all demes bearing the value 0 signifying the wildtype (WT).
As described in the text, the simulations only need to incorporate the two types of events which could potentially change the identity of a deme: a mutation of a WT deme, or an attempted migration from a mutant deme. To accomplish this, each deme is assigned a weight of u ˜ if WT, and 1 if a mutant deme. At each discrete simulation step, a deme is picked at random with probability proportional to its weight. If the deme chosen is WT, it is assigned a unique integer that was not previously present in the array. If the deme chosen contains a mutant allele, a jump is attempted. The jump distance r is obtained by drawing a random number X evenly distributed between 0 and 1, and computing the variable r = X−1/μ; this produces a variable with normalized probability density function P(r) = μr−(1+μ) for kernel exponent μ. The distance is then multiplied with a random d-dimensional unit vector (simply ±1 in d = 1, and evenly distributed on the unit circle in d = 2). Each vector component is rounded to the nearest integer to obtain a jump vector on the lattice. The target position for the migration attempt is obtained by adding this jump vector to the source position, and wrapping the result into the range of size Ld assuming periodic boundary conditions.
If the target deme is WT, its value is updated with the allelic identity of the source; otherwise the migration attempt is unsuccessful. If the simulation step ends in a mutation or a successful migration, the probability weights associated with the demes are updated and the next step is executed. The simulation continues until all Ld array positions contain nonzero integers signifying the completion of the sweep. The final array of Ld integers constitutes the simulation output.
A single simulation took between a few minutes and 24h of CPU time depending on the parameter values. Simulation results were processed using scripts written in the Python programming language. All reported results were obtained by averaging over 20-100 independent simulations for each set of parameters, depending on system size.
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10.1371/journal.pntd.0000722 | The Global Burden of Alveolar Echinococcosis | Human alveolar echinococcosis (AE) is known to be common in certain rural communities in China whilst it is generally rare and sporadic elsewhere. The objective of this study was to provide a first estimate of the global incidence of this disease by country. The second objective was to estimate the global disease burden using age and gender stratified incidences and estimated life expectancy with the disease from previous results of survival analysis. Disability weights were suggested from previous burden studies on echinococcosis.
We undertook a detailed review of published literature and data from other sources. We were unable to make a standardised systematic review as the quality of the data was highly variable from different countries and hence if we had used uniform inclusion criteria many endemic areas lacking data would not have been included. Therefore we used evidence based stochastic techniques to model uncertainty and other modelling and estimating techniques, particularly in regions where data quality was poor. We were able to make an estimate of the annual global incidence of disease and annual disease burden using standard techniques for calculation of DALYs. Our studies suggest that there are approximately 18,235 (CIs 11,900–28,200) new cases of AE per annum globally with 16,629 (91%) occurring in China and 1,606 outside China. Most of these cases are in regions where there is little treatment available and therefore will be fatal cases. Based on using disability weights for hepatic carcinoma and estimated age and gender specific incidence we were able to calculate that AE results in a median of 666,434 DALYs per annum (CIs 331,000-1.3 million).
The global burden of AE is comparable to several diseases in the neglected tropical disease cluster and is likely to be one of the most important diseases in certain communities in rural China on the Tibetan plateau.
| Human alveolar echinococcosis (AE), caused by the larval stage of the fox tapeworm Echinococcus multilocularis, is amongst the world's most dangerous zoonoses. Transmission to humans is by consumption of parasite eggs which are excreted in the faeces of the definitive hosts: foxes and, increasingly, dogs. Transmission can be through contact with the definitive host or indirectly through contamination of food or possibly water with parasite eggs. We made an intensive search of English, Russian, Chinese and other language databases. We targeted data which could give country specific incidence or prevalence of disease and searched for data from every country we believed to be endemic for AE. We also used data from other sources (often unpublished). From this information we were able to make an estimate of the annual global incidence of disease and disease burden using standard techniques for calculation of DALYs. Our studies suggest that AE results in a median of 18,235 cases globally with a burden of 666,433 DALYs per annum. This is the first estimate of the global burden of AE both in terms of global incidence and DALYs and demonstrates the burden of AE is comparable to several diseases in the neglected tropical disease cluster.
| Human alveolar echinococcosis (AE) is caused by the larval stage of the fox tapeworm Echinococcus multilocularis. It is amongst the world's most dangerous zoonoses. Transmission of AE to humans is by consumption of parasite eggs which are excreted in the faeces of the definitive hosts: foxes and, increasingly, dogs. Naturally the parasite transmits between foxes or dogs and small mammals whilst humans are aberrant intermediate hosts (Figure 1). Human infection can be through direct contact with the definitive host or indirectly through contamination of food or possibly water with parasite eggs. Geographically E. multilocularis is confined to the northern hemisphere, but within that range has a wide distribution (Figure 2) [1]. In humans, infection results in a metacestode in the liver. This is a slowly growing infiltrative space occupying lesion. If untreated, this lesion will result in clinical signs such as abdominal mass and/or pain, jaundice, and ultimately liver failure [2]. In the late stages of the disease, the parasitic lesion can metastasize resulting in a variety of symptoms. Treatment options include liver resection to remove the parasite mass and chemotherapy using benzimadazoles is now being increasingly used. Survival analysis indicates that with judicious surgical and chemotherapeutic treatment the prognosis is relatively good [3]. However, chemotherapy is required continuously for many years, sometimes for the remainder of the patient's life to achieve success. In the absence of this expensive treatment the disease normally has a fatal course.
Human AE is an emerging disease in Europe. Studies in wildlife are detecting the parasite in new areas [4]. It is not yet clear if the parasite range is spreading or increasing surveillance has lead to greater detection rates. What is certain is that increasing fox populations in Europe are correlated with the greater numbers of cases of AE reported in Switzerland [5]. In Asia a major endemic focus was detected in China [6] with large numbers of human cases – in some communities 5% or more of the population is infected [7]. In such areas there is, not only high infection rates in humans, but a high prevalence of infection with the adult parasite in the dog population [8]. In central Asia there are also reports of a spill over of E. multilocularis into the dog population [9] and this may indicate an increasing threat of transmission to humans. Elsewhere there are increasing reports of AE being detected by surgeons in countries such as Russia and Turkey.
Control of the parasite is possible, for example, through the judicious use of praziquantel baits distributed to foxes [10]. Some studies have indicated that risk factors such as contamination of food or water are a likely conduit of human infection [11], [12] and could suggest alternative strategies to prevent human infection. However, whatever the intervention strategy, the economic efficiency of control will depend upon the societal burden of disease. The purpose of this study was to estimate the annual global burden of AE.
Initially all countries, endemic for E. multilocularis, were identified. These were countries that were known to have autochthonous human cases of AE and/or E. multilocularis identified in animal populations. In addition, neighbouring countries where there were no known reports were also identified as likely endemic areas. The list of countries believed to be endemic for E. multilocularis is given in Table 1.
Literature searches were undertaken in any relevant databases that could be accessed. These included all the following scientific databases: Pubmed, Medline, Science Citation Index, Scopus, East View (Chinese and Russian databases), Russian Scientific electronic library, and Google Scholar. This was supplemented by direct contacts with known persons working in the field in various countries. In addition, further information was solicited by directly contacting individuals who had authored manuscripts by the email address in the correspondence section. Key words used were Echinococcus multilocularis and alveolar echinococcosis for initial screening. Where appropriate the search term was also translated into the language of the relevant database. For each country a systematic search was undertaken to locate data from that country eg Echinococcus multilocularis AND France. All literature was initially screened. Most literature was not useful for calculating incidence rates (for example individual surgical case reports); although in a number of cases such individual reports confirmed that the disease was endemic in the country or region of interest. Inclusion criteria depended on the amount of available information from that country. Because of wide variability in the quality of the data from different countries it was not possible to use a standard procedure across all endemic countries. When there were extensive prevalence and/or incidence reports, particularly indicating whole country incidences, these were used as the primary data sources. However, for many if not most countries, such data was not available. In these cases the reports of individual cases or case studies were utilized to, at the very least, prove the presence of the disease (and in a few cases there were only reports from animal infections). The body of literature on experimental research (eg experimental infections of definitive or intermediate hosts) was, in the whole, of little relevance to this study.
For a number of countries such as Switzerland [5] or Germany [13], accurate figures for the annual numbers of cases were easily identified. This is because they had up to date national databases and/or accurate methodology for capturing the estimated numbers of cases each year. In other countries, such as Kyrgyzstan, accurate reporting figures were available based on histological confirmed cases presented for treatment from hospitals (unpublished). However, it is believed that, in low-income countries such as Kyrgyzstan, the reported cases are likely to substantially underestimate the total numbers of cases as a substantive number of cases are likely to remain undiagnosed because of the relative expense of seeking medical treatment.
China is believed to account for the majority of global AE cases. Estimates were based on mass screenings by ultrasound giving a prevalence estimate. Many such reports also indicated groups (such as Tibetan pastoralists) who were at particular risk of infection. In this region there were a number of large prevalence studies and epidemiological studies based on ultrasound confirmation of diagnosis. These often consisted of several thousand individuals and hence gave samples of populations at risk. In addition particular groups at risk such as Tibetan pastoralists were identified. The studies used are given in Table 2. In total in studies spanning the first decade of the 21st century over 36,000 individuals have been screen by ultrasound over large areas of Ningxia autonomous region, Sichuan, Gansu, and Qinghai provinces. The total number of individuals currently affected with AE was estimated from these prevalence studies and the total populations at risk. This data was then converted into an annual incidence of AE for these populations. Survival analysis of a series of cases from Switzerland suggested that the 50% survival rate of approximately 8 years if treatment is not available for individuals with a mean age of presentation in their early 50s [3]. This can rise to approximately 11 years for younger subjects. If endemic stability is assumed then there will be approximately 6.1% of infected young adults dying from the disease rising to approximately 8.2% of infected adults who are aged in their early 50s. Hence the annual incidence is approximate 6.1%–8.2% of the detected ultrasound prevalence in the population of these respected age groups.
China has large populations in rural areas that are potentially exposed to this parasite. An estimate was performed of the population at risk by examining population data county by county from Chinese Census data. The estimates of the mean prevalence in the population at risk were estimated from relative risk of various ethnic communities from population studies and the proportions these communities make up in the general population. There are also reports of AE in inner Mongolia and Xinjiang but these tend to have considerably few cases then the main endmic area of the Tibetan plateau.This prevalence data was extrapolated to the estimated population at risk and then converted to incidence based on the results of survival analysis [3].
A flow chart illustrating the methodology used in the study is given in figure 3.
In Turkey and Russia total numbers of cases for echinococcosis are recorded. In Russia in 2002 there were 3,274 cases of cystic echinococcosis (CE) notified [14]. In Turkey 14,789 cases of CE were notified in the 5 years 2001–2005 [15]. Separate information for AE was not available. This may be because it has only recently been made notifiable such as in Turkey [16] or because CE and AE cases are not distinguished in official figures. Despite this, in Turkey there are some nationwide figures which give a minimum estimate. However, there are a number of case series of echinococcosis published by surgical units which differentiate between CE and AE. The relative proportion of AE to CE cases in such surveys can be used to estimate the likely number of AE cases for the whole country. These case series (Tables 3 and 4) are a means to calculate the total incidence of AE from the relative incidence and total country incidence of CE. In addition, in Turkey there was one detailed case series of echinococcosis with CNS involvement that identified 16 cases of AE with cerebral involvement over a 5 year period from neurosurgical units in Turkey [17]. A large study in China suggested that 4% of AE cases had neurological involvement [18]. Likewise, a large European study found 17 of 559 (3%) cases of AE had brain involvement [19]. The likely number of AE cases can then be estimated by assuming that the proportion of cerebral AE cases in Turkey was similar to these reported case series.
To calculate DALYs standard techniques were used [20]. The years of life lost (YLLs) were calculated on the assumption that the disease is fatal within an average of 8 years of diagnosis if untreated. If treatment is available then the prognosis was assumed to be reasonable with just 2–3 YLLs. These assumptions are based on previously published survival analysis [3]. In order to calculate the years lived with disability (YLDs) is was necessary to assign a disability weight. As no accepted disability weight has yet been assigned to alveolar echinococcosis the disability weight for carcinoma of the liver was used as previously [7]. The years lived with the disability again depends on where the cases are presented and available treatment options. Where there is no treatment, death can be assumed within a mean of 8 years for patients in their 50s, but this increases to 11 years for someone presenting in their 20s [3]. In low income countries a disability weight for pre terminal liver cancer (0.200) was assigned for 6–9 years with a disability weight for 2 years living at the disability weight for metastatic and terminal stages (0.75–0.81) [21] .The number of years at these weights depended on the age specific incidence (see below). In those countries where advanced medical treatments lead to a successful outcome, a disability weight of 0.200 for mild disease for the average length of treatment (7 years) [3]. This was based on the fact that liver cancer has similar symptomatology to AE [7].
This data are important in estimating the burden of disease. For Europe a large data set of 559 [19] was used as the basis for the age and gender distribution of cases and hence the age weighting and YLLs for the estimated DALYs. For China prevalence data only was available, but much was age and gender stratified and was used to calculate the age and gender specific incidence after adjusting for bias by comparing the sampled population with the age and gender profile of the general population using census data [7]. Other counties including Turkey had case series reports which indicated age and gender of cases, although in some instances only the mean age was given. Unpublished data for case series from Kyrgyzstan were used and assumed to be representative of similar ex-Soviet states where data was not available.
Based on the quality of the data we were able to assume some countries (eg Switzerland) had accurate estimates of incidence, whilst others it was much more uncertain, particularly when estimates had to be created from modelling or extrapolation from neighbouring regions. A Monte-Carlo routine was written to resample estimates of incidence from each country based on the likely probability distribution of the total case incidence. This was similar to methods described previously [7], [22]. Distributions were based on a number of factors from the available data including possibilities of missing data.
For China in total we estimated there are 230,000 individuals presently suffering from AE and a total population at risk of some 22.6 million in 7 provinces (Table 5). Assuming most of these go untreated and hence have a fatal outcome, this was used to estimate the incidence from age stratified life expectancy following diagnosis. This gives an annual incidence of approximately 16,629 new cases per annum.
Russia is a huge endemic area stretching from Eastern Europe to Siberia. We estimated that there are approximately 1,180 cases per year in this country. AE is found throughout the northern parts of Asia with important foci in central Asia and Turkey. The estimates for the annual numbers of cases in Asia excluding Russia are given in Table 6. The estimated numbers of cases from Europe from countries that are endemic for AE are given in the Tables 7 and 8, with the references supporting the estimate. Although North America is endemic for E. multilocularis in animal hosts there is very little evidence for transmission to humans presently.
We estimate that the median estimate of the total numbers of AE cases in the world is 18,235cases per year with 95% CIs of 11,932–28,156. Of these 91% of cases are believed to be in China with just 1,606 occurring outside China.
Globally YLLs due to AE was estimated at 616,897 (CIs 296,485 – 1.2 million). Again China had most of YLLs and as a proportion was even higher than the incidence as it was assumed that the majority of cases in China did not receive treatment. The age of onset was also younger then compared to Europe. Thus, whilst China had 91% of the global number of cases it is believed that it has 95% of the YLLs due to alveolar echinococcosis. The total number of DALYs per annum for the world is estimated at a median of 666,433 (CIs 331,539 – 1.3 million).
This report represents a first attempt to estimate the global burden of AE although the global geographical distribution of E. multilocularis has been reviewed previously (e.g. [2]). Throughout much of its geographical range AE is sporadic in humans. In high income countries such as Germany and Switzerland the numbers of cases were the actual number reported (Switzerland) [5]. Alternatively in Germanyreported figures were based on a reported capture recapture technique which modelled underreporting [13]. These are believed to be accurate reports of the numbers of cases. Reviews of published data also gave estimates for a number of other upper income countries.
For some lower income studies there was limited official data (unpublished) reporting the total number of cases presenting for treatment. However in such countries a major underestimation of the numbers of cases is possible as only relatively wealthy individuals can pay for medical care and the majority of cases may not present for treatment and hence go undiagnosed. A criticism of our approach is that we did not use consistent inclusion criteria for data in different countries. What we did use was the best available data and balanced this inconsistency by using a stochastic approach to model uncertainty. In the countries where we believed the data was accurate a very narrow probability distribution was chosen for the Monte-Carlo routine. In contrast where there was poor data, a very wide distribution was used to model this uncertainty. Hence, the median incidence and estimated DALYs together with the 95% confidence limits give a good estimate of the burden of AE.
In certain districts of China several studies have indicated a very high prevalence of AE through mass screening studies. Surveys have consistently shown a high prevalence of AE using ultrasound studies across Sichuan, Gansu, Qinghai, and Ninxia (Table 2). This confirms that there are large numbers of AE cases in China and these represent at least 91% of the global incidence. In some communities the prevalence of AE is similar to that of tuberculosis [23]. Because of the large population at risk and the consistent finding of high prevalences we believe the estimate of the number of cases in China is representative. However, the prevalence is not uniform with variations within these districts of between <1% to 12%. The calculations have tried to accommodate these variations in arriving at an overall incidence figure. In the Tibet Autonomous Region (TAR), the incidence could be much higher than the figures suggested. Tibetan communities in neighbouring districts of Sichuan for example have very high prevalences. The parasite is known to be endemic in TAR, but there is no published human surveillance data. We were only able to assume that prevalences in the eastern most part of TAR were similar to prevalences in neighbouring counties of Sichuan or Qinghai. It is possible, therefore, that the true incidence could be thousands rather than the hundreds that are suggested. There is a single case report of cerebral AE in a Tibetan monk who originated from Lhasa which is quite some distance west of the known highly endemic areas of Gansu, Qinghai, and Sichaun [24] which is evidence that the parasite has a greater range than our conservative assumption. In Xingjiang the parasite is endemic, but human studies have mainly uncovered cases along the northwest of the province and hence the population at risk and actual case numbers are calculated accordingly.
Russia is a large endemic area for alveolar echinococcosis. In some districts, particularly in Siberia there are reports of a number of human cases whilst elsewhere the disease is sporadic. Detailed data is somewhat lacking despite intensive search of English and Russian language databases. Most data is reported in terms of hospital reports and is therefore estimated as an annual incidence. There are no mass ultrasound surveillance studies as in China although there are a few mass serological studies which tend to confirm the potential for large numbers of cases, especially in Siberia. It is clear, however, that the disease occurs sporadically across almost the entire country. Even in districts where there are no reported human cases there are reports of the parasite in animal hosts so transmission to humans is likely. The estimates for the whole of Russia initially relied on extrapolating data from districts where there were known reports or unpublished data. Bassonov wrote an extensive monograph [25] detailing the epidemiology of echinococcosis throughout countries of the former Soviet Union including summaries of otherwise difficult to obtain material. This was also used as a basis for estimating the numbers of cases in Russia. In addition, we were able to access some local Russian language reports including articles in local newspapers and these largely confirmed our assumptions [26]. Estimates based on the samples from case series (essentially a type of capture recapture technique) described in the text tended to confirm these estimates. We assumed a maximum ratio of 1∶2 for AE∶CE. However, one study of surgical cases from Omsk in western Siberia described 84 cases of AE and 44 cases of CE [27]. As most of these cases were from Siberian districts such as Kemerova, Altai, Yakutia and Tomsk which could indicate higher numbers of AE than the ratios we used in our calculations.
The mountainous regions of Kazakhstan, Kyrgystan, Uzbekistan, and Tadjikistan are all endemic for E. multilocularis [28]. Here the cycle of infection has been well described in terms of the animal hosts. The most accurate figures available are from Kyrgyzstan where approximately 35 cases per years are now being reported in the hospitals (unpublished figures from the Government Epidemiological Surveillance Unit, Bishkek). This figure is likely to be accurate in terms of numbers being treated. However, it may underestimate the actual numbers of cases occurring as the country is poor and expensive medical treatment is not available to a large part of the population.
Although ethnically distinct, in terms of geography and economy, Tadjikistan is very similar to Kyrgyzstan. Therefore, as it is also endemic for E. multilocularis, similar numbers of cases per year could be expected as there is likely a similar population size at risk. Kazakhstan and Uzebkistan are much larger countries in area and population but as much of their territory is in low or non-endemic areas the proportion of population at risk is smaller, but absolute numbers are similar. Turkmenistan is likely to have few cases of AE, as much of the territory is arid desert which is inimical to transmission. Mongolia has a number of reported cases. From unpublished data there were 9 cases of AE in both 2006 and 2007.
India, Nepal, Bhutan and Pakistan border on endemic zones and may have a few cases. The disease has been reported in India (Kashmir) [29], [30]. For Afghanistan, data is not available. However there is a case report of a patient originating from Afghanistan who was treated in the UK (which is non endemic) [31]. Therefore, further cases are likely, especially in the north of the country. Iran is endemic for AE but there is little data. Between 1948 and 1993, 37 cases of AE were reported [32] or less than 1 case per year. In view of the fact that neighbouring Turkey is known to be highly endemic this is likely underreported. There is a single case report of AE from northern Iraq [33].
Turkey is highly endemic for echinococcosis. In total, approximately 3000 cases are recorded annually. The number of cases of AE is uncertain. The proportion of cases of echinococcosis that are AE have been reported in a number of case series (Table 3). Assuming a similar ratio of AE to CE nationwide as found in the case report series this would suggest as many as 500 cases of AE per year. However, such selected case series may overestimate the true incidence of AE and these data contrast somewhat with the report of 206 cases of AE in Turkey for the period 1980–2000 or just 10 cases per year [34]. AE has only recently been made reportable in Turkey [16] and so more accurate figures are unavailable. Also of interest is a case finding study which specifically searched through the records of 47 neurosurgical units for cases of cerebral echinococcosis. This study found a total of 219 cases of intracranial echinococcosis in the five year period 1994–1999 [17]. Of these 16 were AE and 2 were AE with no extra CNS involvement. Another report found 4 CNS cases of AE in just 27 months at one centre [35]. Intracranial AE is thought to be rare with CNS involvement usually a manifestation of metastases from a primary lesion. A large study in China suggested that 4% of AE cases had neurological involvement [18]. Likewise, a large European study found 17 of 559 (3%) cases of AE had brain involvement [19]. The incidence of diagnosed intracranial cases of AE therefore gives strong evidence there must be at least 100 cases of AE per year in Turkey. This type of approach may, nevertheless, substantially underestimate AE cases in resource poor endemic regions, as a smaller proportion of AE cases my receive hospital treatment then are actually present in the community. For example, in south Ningxi in China, CE represented 96% of hospital treated cases of echinococcosis, but ultrasound studies in the local community suggest that 56% of cases of echinococcosis are AE [36].
The USA and Canada are endemic for E. multilocularis. However there is very little transmission to man. There have been reports from Native American communities of high incidence rates in Alaska [37] but only locally and these have been eliminated by appropriate intervention programs. Otherwise, there have only been single cases reports in Minnesota [38] and Manitoba [39]. Thus there is very little evidence for autochthonous human cases presently in North America despite the active transmission in animal hosts.
Eastern Europe has highly variable data. The parasite is endemic in most of the former Soviet States. Lithuania has the best data and reports incidence rates [40] and it is likely that the other Baltic States have similar incidences due to similarities in culture, geography, and population. From central and western Europe there is usually high quality data giving details of the numbers of cases (Table 7). The core endemic area is centred on Switzerland, southern Germany, and eastern France.
In North Africa, there have been two reports of autochthonous AE from Tunisia and a further case from Morocco diagnosed by histological examination of the lesions [41], [42]. However the parasite has never been recorded in animals from Africa and in the absence of molecular confirmation there is insufficient evidence to confirm any part of North Africa is presently endemic for E. multilocularis.
AE is a serious disease and, although the prognosis is reasonably good when treatment is available [3], the prognosis is equally bleak in the absence of treatment. The overwhelming number of cases comes from an area of rural China which forms part of the Tibetan plateau. This population is remote and with few financial resources with an estimated annual income per head of less than US$500 [43]. Therefore it is reasonable to assume that most of these cases will be fatal and hence, the annual mortality due to AE is similar to the incidence. This is also likely to be true of most other cases outside of Europe. An annual mortality due to AE of approximately 18,000 is greater than one tenth of the total mortality of 177,000 as a result of the 10 diseases of the neglected tropical disease cluster (trypanosomiasis, Chagas disease, schistosomiasis, leishmaniasis, lymphatic filariasis, onchocerciasis, intestinal nematode infections, Japanese encephalitis, dengue, and leprosy ) [44]. The disease burden can also be compared to that of rabies. Annual AE mortality is approximately one third of that due to rabies which has been estimated at approximately 55,000 [45], [46]. Nearly all is from Africa and Asia. Unlike rabies, there is also no vaccine for canid echinococcosis and control requires repeated treatment of foxes or dogs with praziquantel. Thus, although AE is rare on a global scale it has a high burden in some highly endemic communities in China where it is likely to be one of the leading causes of death.
Likewise the global burden of disease, in terms of DALYs, is high. This is again due to the very high fatality rate resulting in a large number of YLLs but additionally due to the expected high disability weight that individuals have during the course of the disease. Diseases with similar magnitudes of DALYs include neglected tropical diseases such as onchocerciasis, and Chagas disease [47]. An initial estimate of the global burden of cystic echinococcosis was approximately 1 million DALYs [22]. However, this is likely to be an underestimate [48] and a re-evaluation of the global burden of CE is ongoing.
Control of this disease depends on the risk factors for transmission. The wild life cycle can be disturbed through the treatment of foxes with praziquantel impregnated baits [10]. However, the feasibility of applying such control measures over large parts of the Tibetan plateau, where the main burden of AE is found, would be questionable. Dog contact is a known risk factor for transmission to man [49] and dogs are highly susceptible to infection with this parasite [50]. However, it is not known if dogs participate in the cycle or are aberrant definitive hosts. If they are aberrant hosts, periodic treatment of dogs will not disturb the transmission cycle and will have much less effect on long term transmission rates to humans [43]. Other means of reducing the disease burden would be through better control of food or water supplies which may be contaminated with parasite eggs. Such control would be dependent on the attributable fraction of disease burden due to these transmission pathways and the cost effectiveness of such intervention strategies.
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10.1371/journal.pcbi.1003925 | A Comparative Study and a Phylogenetic Exploration of the Compositional Architectures of Mammalian Nuclear Genomes | For the past four decades the compositional organization of the mammalian genome posed a formidable challenge to molecular evolutionists attempting to explain it from an evolutionary perspective. Unfortunately, most of the explanations adhered to the “isochore theory,” which has long been rebutted. Recently, an alternative compositional domain model was proposed depicting the human and cow genomes as composed mostly of short compositionally homogeneous and nonhomogeneous domains and a few long ones. We test the validity of this model through a rigorous sequence-based analysis of eleven completely sequenced mammalian and avian genomes. Seven attributes of compositional domains are used in the analyses: (1) the number of compositional domains, (2) compositional domain-length distribution, (3) density of compositional domains, (4) genome coverage by the different domain types, (5) degree of fit to a power-law distribution, (6) compositional domain GC content, and (7) the joint distribution of GC content and length of the different domain types. We discuss the evolution of these attributes in light of two competing phylogenetic hypotheses that differ from each other in the validity of clade Euarchontoglires. If valid, the murid genome compositional organization would be a derived state and exhibit a high similarity to that of other mammals. If invalid, the murid genome compositional organization would be closer to an ancestral state. We demonstrate that the compositional organization of the murid genome differs from those of primates and laurasiatherians, a phenomenon previously termed the “murid shift,” and in many ways resembles the genome of opossum. We find no support to the “isochore theory.” Instead, our findings depict the mammalian genome as a tapestry of mostly short homogeneous and nonhomogeneous domains and few long ones thus providing strong evidence in favor of the compositional domain model and seem to invalidate clade Euarchontoglires.
| The non-uniformity of DNA composition in mammalian genomes has been known for over four decades. Many attempts have been made to provide a concise description of this heterogeneity and to identify the evolutionary driving forces behind this compositional phenomenology. The first concise description of the genome suggested an isochoric structure according to which the mammalian genome consists of a mosaic of long, compositionally homogenous DNA sequences. With the advent of genome sequencing, this description was found to be inappropriate. We have recently proposed an alternative “compositional domains” model that depicts the human and cow genomes as composed of mixture of compositionally homogeneous and nonhomogeneous domains. Most of these domains are very short. Since its proposal, this model has been validated in plethora of invertebrate genomes. Here, we test the validity of this model on eleven mammalian and avian genomes using seven attributes of compositional domains and discuss their evolution. We also use these attributes to decide between two competing phylogenetic hypotheses. Our findings provide strong supporting evidence for the “compositional domains” model and indicate that rodents are not as close to primates as envisioned by the Euarchontoglires hypothesis.
| Human and cow genomes have been shown to possess a complex architecture, in which compositionally homogeneous and nonhomogeneous domains of varying lengths and nucleotide composition are interspersed with one another [1], [2]. These empirically derived compositional architectures are mostly incompatible with the “isochore theory” [3]–[6], according to which the genomes of warm-blooded vertebrates are depicted as mosaics of fairly long isochores —typically 300 kb or more—each possessing a characteristic GC content that differs significantly from that of its neighbors, and each classifiable by GC content into six or less isochore families [7]–[14].
Numerous methods for segmenting DNA sequences into contiguous compositionally-coherent domains have been proposed in the literature. These methods differ from one another in the number and types of parameters used in the segmentation process, as well as in the levels of user intervention. Unfortunately, even methods that limit user input to a few parameters yield incongruent results with one another [15], whereas methods that rely on subjective user intervention [e.g., 16] preclude independent replication of the results and are, thus, unscientific. Through comparison of performances against benchmark simulations, Elhaik, Graur, and Josić [2] identified a segmentation method, DJS [17], that outperformed all others. However, DJS failed to partition sequences with low compositional dispersion and had difficulties in identifying short homogeneous domains. To rectify these inadequacies, Elhaik et al. [15] devised IsoPlotter—a recursive segmentation algorithm that employs a dynamic threshold, which takes into account the composition and length of each segment. Most importantly, IsoPlotter is an unsupervised algorithm, i.e., it requires no subjective user intervention, and through benchmark validation, it was shown to yield unbiased results [15].
The compositional domains identified by IsoPlotter are contiguous genomic segments, each with a characteristic GC content that differs significantly from the GC contents of its adjacent upstream and downstream compositional domains. By comparing the GC content variance of compositional domains with that of the chromosomes on which they reside, compositional domains can be further classified into two types: “compositionally homogeneous domains,” or simply “homogeneous domains,” and “compositionally nonhomogeneous domains” or “nonhomogeneous domains.” A subset of long homogeneous domains, where “long” is arbitrarily defined as ≥300 kb, are termed “isochoric” domains (sensu [12]). By segmenting the human genome with IsoPlotter, we found that one-third of the genome is composed of compositionally nonhomogeneous domains and the remaining is a mixture of many short compositionally homogeneous domains and relatively few long ones [15]. “Isochoric” domains cover less than a third of the human genome. Similar results were obtained for the cow genome [1].
Here, we characterize the compositional architecture of ten completely sequenced mammalian genomes and an avian outgroup, and attempt to identify quantitative trends in the evolution of homogeneous and nonhomogeneous domains. Seven attributes of compositional domains are used, many of which were previously used to characterize compositional architectures [1], [18]–[28]. Each genome is defined by: (1) the number of compositional domains, (2) compositional domain-length distribution, (3) density of compositional domains, (4) genome coverage by the different domain types, (5) degree of fit to a power-law distribution, (6) compositional domain GC content, and (7) the joint distribution of GC content and length of the different domain types. Our results are interpreted in light of two currently competing phylogenetic hypotheses depicting the evolution of eutherian mammals for which traditional phylogenetic tools provided ambiguous answers [e.g.], [ 29, 30] (Figure 1). Further, our results support the so-called “murid shift” hypothesis, and suggest that homogeneous and nonhomogeneous domains are biologically different.
This evolutionary study represents a dramatic departure from earlier studies that either extrapolated from a few genes to the entire genome [e.g.], [ 10], [ 31, 32], used unreliable proxies to infer the composition of domains [e.g.], [ 31, 33], or used irreproducible methodologies [e.g.], [ 16, 34]. Our results will be compared with claims made by proponents of the “isochore theory.” Sadly, we are forced yet again to confront the “isochore theory,” because despite its being refuted numerous times [e.g.], [ 18], [ 35], [ 36, 37], proponents of the theory and those invested in it continue to pursue the notion of isochores aggressively, relentlessly, and vociferously [e.g.], [ 31], [ 38], [ 39]–[46]. It seems that T. H. Huxley's dictum on “the great tragedy of science” being “the slaying of a beautiful theory by an ugly fact” does not easily apply to the concept of “isochores.”
All mammalian genomes in our study are similar in size, ranging from 2 Gb in horse to 3.4 Gb in opossum. At 1 Gb, the size of the chicken genome is considerably smaller than the average mammalian genome. The genomic characteristics of the compositional domains for the 11 species under study are listed in Table 1.
Genome statistics for compositional, homogeneous, nonhomogeneous, and “isochoric” domains are shown in Table 1. In Table S1 we present the same data partitioned by individual chromosomes. The mean number of compositional domains in a mammalian genome in our sample is approximately 96,000, with opossum having the largest number of domains (107,000), and rat having the smallest (∼63,000).
On average, over two thirds of all mammalian domains are homogeneous, but this proportion varies with taxon (Table 1). Opossum has the smallest fraction of homogeneous domains (59%) followed by murids (62%). By contrast, pig (71%) and horse (74%) genomes are the most enriched for homogeneous domains. Isochoric domains constitute only a tiny fraction of the compositional domains, from 0.7% in horse and dog to 2.1% in rat.
The mean compositional-domain length varies from ∼25,700 bp in primates to ∼38,500 bp in murids (Table 1). The median length is much smaller in all taxa, indicating an extreme skewed distribution towards very short domains. For example, half of the compositional domains in rat are shorter than 9,216 bp. The mean and median lengths of homogeneous and nonhomogeneous domains within a taxon are practically indistinguishable. The largest homogeneous domain among all species is one 10.5-megabase (Mb) long (GC content of 36%) found in the opossum genome. In the human genome, the largest homogenous domain is about half that length (5.2 Mb).
Almost all the distributions of homogeneous domain lengths in all studied species (Figure 2) are significantly different from each other (Kolmogorov-Smirnov goodness-of-fit test, p<0.01), however, this is due to the large sample sizes. The magnitude of the differences between homogeneous and nonhomogeneous domain lengths is very small in all species (area overlap>98%, Cohen's d<0.05) with the chicken genome exhibiting borderline similarity (area overlap 97%, Cohen's d<0.05).
A comparison of the cumulative distributions of domain lengths indicates that the top percentile in murids consists of domains larger than 511 kb, whereas the top percentile in the laurasiatherian genomes consists of domains larger than 281 kb (Figure 3). In mammalian genomes, the proportion of long homogeneous domains (≥300 kb), i.e., “isochoric” domains, out of all domains is 1% and twice that in murids (2.02%). Similar cumulative distributions were observed for compositional and nonhomogeneous domains (Figure S1).
Domain density measures the average number of compositional domains per Mb. When divided into GC-poor and GC-rich compositional domains it ranges from 0 to 90 domains/Mb for GC-poor domains and up to 121 domains/Mb for GC-rich domains (Figure 4). Homogeneous domains are more dense for both GC-poor (0–57 domains/Mb) and GC-rich (0–98 domains/Mb) domains compared to nonhomogeneous GC-poor (0–43 domains/Mb) and GC-rich (0–64 domains/Mb) domains, respectively. In regions of low domain densities, the density of GC-rich domains is higher than the density of GC-poor domains. That is, genomic regions with fewer domains are more likely to be GC-rich, whereas denser genomic regions are more likely to harbor GC-poor domains (Figure S2a).
On average, murid chromosomes are the least dense (26 domains/Mb), whereas the horse genome is the most dense (49 domains/Mb). The chromosomal domain densities of opossum are as low as murids for homogeneous domains (21 and 16 domains/Mb, respectively) and as high as primates for nonhomogeneous domains (13 domains/Mb). The overall chromosomal domain densities position opossum (34 domains/Mb) between murids (26 domains/Mb) and other mammals (43 domains/Mb).
Similar patterns were observed when comparing the compositional domain densities of GC- rich and GC-poor domains (Figure S3); the opossum and primate genomes have the highest density for GC-rich domains (21 and 18 domains/Mb, respectively). By contrast, the opossum's genome low density for GC-poor domains (10 domains/Mb) is lower even than that of murids (16 domains/Mb). The overall domain density in opossum (31 domains/Mb) is between that of murids (25.5 domains/Mb) and primates (∼38.5 domains/Mb).
Domain density largely varies among chromosomes and chromosome types. Density differences between chromosomes can reach 100% (Figures 4, S2) with sex chromosomes having a lower density than the average autosome (Table S1). These results indicate that the processes that shaped the inter-chromosomal domain organization acted non-uniformly on all chromosomes and their effect on domain lengths was highly variable in different lineages implying the existence of a compositional constraint on chromosomal heterogeneity.
In Figure 5, we show the relative genomic coverage of compositional domains as a function of domain homogeneity and length. The genomic coverage by homogeneous domains ranges from ∼79% in primates and murids to ∼85% in horse. By defining “isochoric” domains as compositionally homogeneous domains longer than 300 kb, we find that the genomic coverage by “isochores” in mammals is a trifling 27%, compared to 16% in the chicken. Murids and opossum have the largest genomic coverage by “isochoric” domains (34% and 37%, respectively). Relaxing the “isochore” definition to include homogeneous domains larger than 100 kb, as proposed by Nekrutenko and Li [47], slightly increases the “isochoric” portion of the genome to 38%. These results, in themselves, are sufficient to invalidate the “isochore theory” or at least diminish its applicability.
The distribution of domain lengths in the human genome is commonly depicted as a power-law distribution over a large range of length scales [e.g.], [ 18], [ 48, 49]. A distribution is said to follow a power-law if its histogram is a straight line when plotted on a log-log scale [50], [51]. To gauge the power-law model, we used two approaches: first, we compared the cumulative distributions of homogeneous domain lengths to the maximum likelihood power-law fits. In all cases, the complementary cumulative distribution function P(x) and their maximum likelihood power-law fits deviate from a straight line, and the p-value is sufficiently small (Kolmogorov-Smirnov, p<0.01) that the power-law model can be ruled out (Figure 6). In other words, there is a very small probability that the data can be modeled by a power-law. An even weaker fit was obtained using compositional domains and nonhomogeneous domains (Figure S4). Next, we tested the power-law behavior of domain lengths using the random group formation model. We found that the same deviations from a power-law-like behavior were also predicted by the random group formation model [52] (Figure S5).
The deviations of the data from power-law behavior are caused by the excess of short domains and low frequency of long domains. These findings are at odds with earlier contentions that the mammalian genome is a mosaic of long homogeneous domains with very few short domains [e.g.], [ 12], [ 49, 53]. However, we note that earlier results are not based on the length distribution of actual domains as some authors chose to avoid the excess of short domains – that cause the deviation from power-law – by concatenating them to form artificially long domains [e.g.], [ 54, 55]. We believe that the decision as to whether or not neighboring domains should be concatenated should rely solely on their homogeneity rather than on attempts to make the data fit a preconceived model.
Moreover, if domain lengths are truly drawn from a power-law distribution, the power-law model should fit the data over more than three orders of magnitude [50]. In reality, the power-law fit is quite poor and should thus be rejected (Figures 6, S4, S5). Our findings are in agreement with previous studies that rejected the power-law behavior of compositional domains, although they relied on a small dataset and incomplete genomic sequences [56]–[61]. We reported similar findings in three ant genomes [19]–[21].
The GC contents of the homogeneous and nonhomogeneous domains in eutherians exhibit a non-normal distribution (Lilliefors goodness-of-fit test, p<0.05) with a mean of 42–44% and a standard deviation of 5.7–8.5%. The GC distributions of compositional domains of the same type are significantly different from one another, particularly between related taxa (Kolmogorov-Smirnov goodness-of-fit test, p<0.01); however, this is due to the large sample sizes. Similar to the patterns observed in compositional domain lengths, the small differences in the GC contents of homogeneous and nonhomogeneous domains allow grouping the species into five taxonomic groups: Primates, Laurasiatheria, Muridae, opossum, and chicken (Figure 7). Of these groups, only the Primate and Laurasiatheria exhibit a high degree of similarity in compositional domain length distribution. Murids and opossum have the most variable GC distribution (38% area nonoverlap) (Figure 7).
With the exception of the murid genomes (γ≈0.29), the low frequency of short GC-poor domains and the abundance of medium GC-rich domains causes mammalian GC distributions to be highly right-skewed (0.56<γ<0.77) (Figure 7, Table S2). Opossum (γ≈1.12) and chicken (γ≈0.86) are the most right-skewed of all species, due to the high abundance of short GC-rich and medium-short GC-rich domains, respectively.
To further study the GC content fluctuations within compositional domains, we looked at their compositional variability. Compositional variability is measured from the standard deviation (GCσ) of the GC content of each domain calculated over short nonoverlapping windows within the domain (see Materials and Methods). Figure 8 presents two-dimensional joint distribution of homogeneous domain GC content and GCσ. Interestingly, the GCσ values of most mammalian domains are narrowly distributed around 11% GCσ and, with the exception of opossum that exhibits a smaller variation. In other words, GC-rich domains are more erratic in their composition (high GCσ) than GC-poor domains (low GCσ). The high compositional variability of horse and dog is also reflected in the wide range of GCσ values compared with those of the Cetartiodactyla species.
The opossum is exceptional in exhibiting a GCσ gradient toward smaller GCσ. The opossum compositional makeup characterized by its low GC content and narrow GCσ distribution appears to be an intermediate between mammals and murids. The narrow GCσ distribution in the murid genomes is also confounding. The murid joint distributions are largely symmetric about the x-axis (Figure 8), suggesting that the evolutionary processes that shaped the compositional organization of the genome were symmetrical. Similar trends were obtained for the nonhomogeneous domains (Figure S6).
The two-dimensional joint distributions of homogeneous domain GC content and length are shown in Figure 9. These measures are not correlated (r = ∼0). As shown before, the majority of domains in all genomes are short (6–8 kb), and their GC content distributes close to the mammalian genome mean GC content. With the exception of murids, homogeneous domains are significantly more AT-rich than nonhomogeneous domains (Kolmogorov-Smirnov goodness-of-fit test, p<0.01). The genomic landscape topologies of primates, laurasiatherian, and murids are remarkably similar with short (103–104 bp) GC-rich domains 1.3–1.7 times more frequent than GC-poor domains and medium-large (105–106 bp) GC-rich domains 1–2 times more frequent than GC-poor domains (Table S2). This ratio is opposite for both domain size groups (0.7 and 0.32, respectively) in opossum, which implies a major domain fusion process that affected the tetrapod genome.
Domains in the murid genome have a distinct length distribution compared to other mammals. The murid genome has an abundance of over 2,500 medium-long (105–106 bp) and long (>106 bp) GC-rich domains compared to all other genomes (∼500–1,591) (Table S2). By comparison, in the AT-rich opossum genome, GC-poor domains are twice more frequent than GC-rich domains. The opossum genome is particularly enriched in over 3,500 medium-long and long GC-poor domains compared with only 486 GC-rich domains. Similar results were observed for nonhomogeneous domains (Figure S7).
Table 2 summarizes the supporting evidence for the two phylogenetic hypotheses contrasting the validity of Euarchontoglires clade based on the defined genetic attributes. Although the attributes are not independent, qualitatively they provide a strong support for the second hypothesis that places Primates with Laurasiatheria to the exclusion of Muridae, thereby invalidating clade Euarchontoglires (Figure 1).
One of the most fascinating features of mammalian genomes is the uniformity of GC content over hundreds and hundreds of thousands base-pairs termed short- and long-range correlations, respectively. Although these structures have been known for over three decades [3], only few explanations were proposed in an evolutionary framework. Most of the explanations for the long-range correlations were related to the “isochore theory.” The “isochore theory” posits the mammalian genome is composed of a mosaic of isochores, long homogeneous domains (typically ≥300 kb) that cover the majority of the genome of “warm-blooded” vertebrates; whereas only a small portion of the genome consists of non-“isochoric” regions. The “cold-blooded” vertebrate genome was described as less compositionally heterogeneous and devoid of GC-rich isochores [5], [12]. Although the theory failed to explain the compositional patterns later found in fish and reptiles [e.g.], [ 43, 62], its importance has been in stimulating follow-up studies that attempted to correlate various biological phenomena with compositional and organizational features. Eventually, following conflicting findings [e.g.], [ 15], [ 36], [ 37], [ 62, 63], ambiguity as to the interpretation of the theory predictions [18] and contradictory revisions of the theory's main principles [e.g., 55](Table S3), the original theory was de facto abandoned by most scientists (with the exception of its proponents), leaving open the basic questions: how, when, and why in the course of evolution, did mammalian genomes acquire their current composition and organization?
The most effective approach to understanding the compositional organization of human and mammalian genomes is by comparative analysis – preferably a large-scale one. In a previous analysis of the human genome, Elhaik et al. [15] proposed a compositional domain model to explain its genomic architecture. The compositional domain model portrays the human genome as a mixture of mostly short and very few long homogeneous and nonhomogeneous domains in a ratio of 2∶1. Under this model, “isochoric” domains consist of only a small fraction of all compositional domains [15]. Here, we extended the analysis to ten mammalian genomes and tested whether the outcomes fit within the isochoric or the compositional-domain models using seven genomic attributes. Our findings are discussed under two different phylogenetic hypotheses, for which traditional phylogenetic analyses provided ambiguous answers (Figure 1). Table 2 summarizes the evidence in support of either hypothesis.
The mammalian genome is covered by a complex medley of nonhomogeneous domains of various lengths (32%), short (103–104 bp) homogeneous domains (36%), medium-short (104–105 bp) homogeneous domains (26%), medium-long (105–106 bp) homogeneous domains (4%), and only a miniscule fraction of 0.16% long (106–107 bp) homogeneous domains (Table S2). On average, homogeneous domains longer than 300 kb, i.e., isochores, constitute less than 2% of all domains and cover less than 28% of the mammalian genome (Table 1). Short homogeneous domains have wide GC content distributions and the GC content of long homogeneous domains is distributed slightly below the mammalian genome mean GC content (Figure 9)m whereas the GC content of long nonhomogeneous domains is distributed slightly above it.
Under the “isochore model” where the vast portion of the genome was considered to be composed of long homogeneous domains, their length distribution was thought to display a power-law distribution [18], [49], [53], [64]. We demonstrated that the power-law model is inconsistent with the data due to the high abundance of short domains and the scarcity of long domains (Figures 6, S4, S5). Short domains are major components of the mammalian genome and cannot be dismissed as “false positives“ [55]. Overall, our results support the compositional domain model and limit the applicability of the isochore model to less than 30% of the average mammalian genome.
Homogeneous or “relatively homogeneous” [9] domains were speculated to be biologically different from nonhomogeneous domains [7], [18], [55], yet we found only minor differences between and within chromosomes, most of which stemmed from the differences in the proportions of the two domain types (Tables 1, S2). Interestingly, with the exception of murid genomes, we found that homogeneous domains are significantly more AT-rich than nonhomogeneous domains (Figures 9, S7), which may suggest biological importance. To support such hypothesis, additional biological properties should be used to test whether or not this distinction is biologically meaningful.
Most genome characteristics within higher taxa follow phylogenetic relatedness. For example, the genomes of the three primates are very similar to each other, as are the genomes of the two murids. The genome characteristics of the Pegasoferae (horse and dog) differ slightly from those of cetartiodactyls (cow and pig), possibly adding support for the validity of clade Pegasoferae (Figure 1). However, the possibility that the similarity between horse and dog is due to the poor quality of their genomic sequences cannot be excluded. We have evidence obtained by comparing a draft of the cow genome (build 3.1) with the finished version (build 4.0) [1] that draft genomes contain an abundance (∼90%) of short compositional domains (<10 kb), thus rendering drafts genomes artificially similar to one another.
Overall, the laurasiatherian genomes are more similar to the primate genomes than the murid genomes, which, in turn, are more similar to the opossum genome than to any other genome (Table 2). The murid genome is distinguished from the primate and laurasiatherian genomes mainly by its narrow GC content distribution (Figure 7), larger GC-rich domains (Figures 2, 3), smaller GC content standard deviation for both GC-poor and -rich domains (Figure 8), and the unique shape of its joint distribution of compositional domain GC content and length (Figure 9). Differences in the compositional patterns between murids and other mammals were previously termed the “murid pattern” [65] or “murid shift” [66]. The “shift” was attributed to a smaller variation in the composition of isochoric domains compared to other mammals [66]; however, we found that the differences between the murid lineage to other mammals are found in the entire murid genomes and are not unique to “isochoric” domains. A possible explanation to the “shift” may be in the different evolutionary origin of murids (Figure 1b). Moreover, the similarity between the murid and opossum genomes suggests the effect was not unique to murids and may have originated in the eutherian ancestor.
The two phylogenetic hypotheses tested here differ in the validity of clade Euarchontoglires. According to the first hypothesis (Figure 1a), murids arose relatively late in mammalian evolution and are grouped with Primates under Euarchontoglires. Considering the relatively fast mutation rate of the murids [67], the most parsimonious explanation would be that their genomic organization is a derived state, possibly as a result of a “shift” or a genomic transition that affected the entire linage. Under this hypothesis, the genomic transition resulted in the fusion of nearly half of the short domains of extreme GC content together with other domains. Elongated domains were created due to the decrease in GC content variability and the fusion of neighboring domains. Subsequently, domain density was reduced and the compositional fluctuations were “flattened” resulting in higher homogeneity between domains. The process dramatically decreased the proportion of short domains (52%) that are highly frequent in other mammalian genomes (60%). Conversely, these fusions increased the proportion of longer domains (medium-short = 40%, medium-long = 7.5%, long = 0.28%) compared to all other mammalian domains (medium-short = 36%, medium-long = 4%, long = 0.15%). The proportion of long GC-poor domains increased as well but in smaller proportion than GC-rich domains. Further evidence for this transition can be found in the frequency distribution of GC content standard deviation that is relatively devoid of heterogeneous domains compared to other mammalian genomes (Figure 8). Moreover, Muridae have genomes that are markedly homogeneous in both poor- and GC-rich domains, as opposed to mammalians genomes that are highly heterogeneous in their GC-rich domains and homogeneous in their GC-poor domains (Table S2). We note that genome elongation could also result from segmental duplication; however, we do not know of a segmental duplication that acts selectively on segments with certain composition.
According to the second hypothesis (Figure 1b), murids arose early in the mammalian evolution and their genomic architecture reflects an ancestral state. The “typical” mammalian genome thus evolved from this ancestral pattern leading to a wider compositional distribution and shorter domains. This view is supported by the similar genomic structure (Tables 1, S2) and genome homogeneity shared between the murid and opossum genomes. A similar hypothesis was tested by Mouchiroud, Gautier, and Bernardi [68]; however, because they assumed the existence of isochores that cover the mammalian genome, their conclusions are limited to few “isochoric” domains.
Unfortunately, the representation of marsupial mammal as outgroup yielded more questions than answers as opossum reflected either unique genomic characteristics or oscillated between murid and non-murid characteristics (Tables 1–2). Thus, although the results showed a high resemblance between murids and opossum in support of the second hypothesis (Table 2), additional evidence would be necessary before ruling out the first hypothesis (Figure 1). It is possible that with the accumulation of additional genomic sequences of intermediate species this question would be answered. In light of these findings, it will be intriguing to identify which evolutionary mechanisms shaped the transitions that affected the murid and opossum genomes. Understanding these biological mechanisms and their evolutionary implications is a key factor in reconstructing the evolutionary history of mammalian genome evolution.
Nine eutherian genomes that are either fully sequenced or have reliable genomic drafts were included in this study: human (Homo sapiens build 37.1), chimpanzee (Pan troglodytes build 2.1), orangutan (Pongo abelii build 1.2), mouse (Mus musculus build 37.1), rat (Rattus norvegicus build 4.1), horse (Equus caballus build 2.1), dog (Canis familiaris build 2.1), pig (Sus scrofa build 2.1), and cow (Bos taurus build 4.1). The gray short-tailed opossum (Monodelphis domestica build 2.1) was used as an outgroup to the eutherians, and chicken (Gallus gallus build 2.1) was used as an outgroup to the mammals. Genomes were downloaded from http://www.ncbi.nlm.nih.gov/Genomes/. Nulls, i.e., unknown, undetermined, or ambiguous characters in the genomic sequences, were discarded.
There are two phylogenetic hypotheses in the literature for the taxa under study (Figure 1). The two hypotheses are supported by molecular data though differ in their outcome. The difference between the two phylogenetic trees concerns the relative kinship of murids (mouse and rat) and laurasiatherians (horse, dog, cow, and pig) to primates (human, chimpanzee, and orangutan). In the first scheme [e.g.], [ 29], [ 69], [ 70]–[72], primates cluster with the murids within clade Euarchontoglires (Figure 1a). In the second scheme [e.g.], [ 30, 73], primates cluster with the laurasiatherians to the exclusion of murids (Figure 1b). The clustering of Perissodactyla (horse) and Carnivora (dog) into Pegasoferae to the exclusion of Cetartiodactyla (cow and pig) is accepted by both alternative phylogenies [69].
Version 2 of IsoPlotter [15] of the IsoPlotter+ pipeline [28] was obtained from https://github.com/sean-dougherty/isoplotter/and used to partition each of the genomes into compositionally distinct domains. IsoPlotter recursively maximizes the difference in GC content between adjacent segments, as measured by the Jensen-Shannon divergence statistic [17]. The halting criterion was obtained via a dynamic threshold calculated in real-time according to the length of each segment and the standard deviation of its GC content. The compositional domains inferred by the segmentation procedure were classified into homogeneous and nonhomogeneous as in Elhaik et al. [15]. For convenience, domains are sometimes divided by order of magnitude of their length into: short (103–104 bp), medium-short (104–105 bp), medium-long (105–106 bp), and long (106–107 bp) domains.
The mean GC content of all mammalian genomes in this study (40.9%) was used as a critical value. A compositional domain was defined as GC-rich or GC-poor if its GC content was higher or lower, respectively, than the critical value.
For each species and for each domain category, log domain-lengths were sorted and smoothed. Smoothing was carried by dividing the log domain-lengths into 1,000 groups of equal size and then using the mean domain length of each group to calculate a histogram with 38 bins ranging from 8 to 16. To test whether or not two distributions are different, we used the Kolmogorov-Smirnov goodness-of-fit test and the False Discovery Rate (FDR) correction for multiple tests [74]. Because the differences between the distributions were highly significant due to the huge sample sizes, we also calculated the effect size, first by using the nonoverlapping percentage of the two distributions, and then by using Hedges' g estimator of Cohen's d [75]. If the area overlap was larger than 98% and Cohen's d was smaller than 0.05, we considered the magnitude of the difference between the two distributions to be too small to be biologically meaningful.
The distributions of domain GC contents were calculated in a similar manner. To smooth the GC content distributions, domain GC contents were divided into 1,000 groups of equal size, and the mean domain GC content of each group was used to calculate a histogram with 38 bins ranging from 0 to 1. The remaining calculations were carried as described above.
To test whether the GC-content distributions of homogeneous and nonhomogeneous domains fit a normal distribution, we used the Lilliefors (1967) test. This test is a two-sided goodness-of-fit test suitable when a fully-specified null distribution is unknown and its parameters must be estimated. It tests the null hypothesis that domain GC contents come from a distribution in the normal family, against the alternative that they do not come from a normal distribution.
We also estimated the standardized skewness (γ) of the GC content distributions using the “skewness” function in Matlab, which first centralizes the distribution by subtracting it from its mean, calculates its third (k3) and second (k2) moments, and then computes the skewness, so that: GC0 = GC – μ(GC), k3 = μ(GC03), k2 = μ(GC02), and γ = k3/k21.5.
We used two approaches to test the fit of the domain-length distributions to power-laws. First, the minimum domain length and the power-law exponent were estimated for the domain lengths of each genome according to the goodness-of-fit based method described in Clauset, Shalizi, and Newman [51]. The observed domain lengths were then compared to the domain lengths generated from the parameters previously estimated, and the similarity between the two distributions was calculated using the Kolmogorov-Smirnov statistic [76]. Based on the observed goodness-of-fit, we calculated a p-value that quantifies the probability that the data were drawn from the hypothesized distribution. We used the Matlab scripts plfit.m (version 1.0.5), plpva.m (version 1.0.6), and plplot.m (version 1.0) in www.santafe.edu/~aaronc/powerlaws/(Clauset, Shalizi, and Newman [51]. Second, Baek and et al. [52] showed that the random group formation (RGF) model is a form of general distribution, free from system-specific assumptions, of which pure power-laws are a special case. We used this model to test the data fitting into the power-law model using the online application http://www.tp.umu.se/~garuda/Comp.html.
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10.1371/journal.pgen.1004002 | Phosphate Flow between Hybrid Histidine Kinases CheA3 and CheS3 Controls Rhodospirillum centenum Cyst Formation | Genomic and genetic analyses have demonstrated that many species contain multiple chemotaxis-like signal transduction cascades that likely control processes other than chemotaxis. The Che3 signal transduction cascade from Rhodospirillum centenum is one such example that regulates development of dormant cysts. This Che-like cascade contains two hybrid response regulator-histidine kinases, CheA3 and CheS3, and a single-domain response regulator CheY3. We demonstrate that cheS3 is epistatic to cheA3 and that only CheS3∼P can phosphorylate CheY3. We further show that CheA3 derepresses cyst formation by phosphorylating a CheS3 receiver domain. These results demonstrate that the flow of phosphate as defined by the paradigm E. coli chemotaxis cascade does not necessarily hold true for non-chemotactic Che-like signal transduction cascades.
| Bacteria use chemotaxis and chemotaxis-like signal transduction pathways to quickly sense and adapt to a constantly changing environment. The purple photosynthetic bacterium Rhodospirillum centenum is able to withstand long periods of desiccation by forming metabolically dormant cyst cells, the development of which is regulated by the Che3 chemotaxis-like pathway. Using a combination of genetic and biochemical approaches, we demonstrate that hybrid histidine kinase (HHK) CheA3 encoded in the che3 gene cluster is essential for cyst formation while another HHK CheS3 inhibits cyst formation. We further show that the appended receiver domains of these kinases regulate their respective histidine kinase domains and are critical in controlling the timing of cyst formation. Finally, we demonstrate that CheA3 functions upstream of CheS3 and promotes cyst formation by phosphorylating CheS3.
| Rhodospirillum centenum is a photosynthetic member of the Azospirillum clade, members of which associate with root rhizospheres in a broad range of plants. These aerobic nitrogen fixating organisms are capable of promoting plant growth by the donation of both fixed nitrogen and plant hormones [1]. Inoculating fields and/or seeds with Azospirillum sp. have significantly enhanced crop yields of a wide diversity of cultivars including corn and wheat [2], [3]. An additional feature of this group is the capability of forming metabolically dormant cysts that promotes survival during droughts [4]. Encystment involves several morphological transitions during which cells round up and form a thick outer exopolysaccharide coat termed the exine layer [5]. The formation of cysts also correlates with the appearance of intracellular poly-β-hydroxybutyrate (PHB) granules that are presumably used as energy reserves [6]. Once water and nutrients are available, cysts germinate by reforming vegetative cells that emerge from the exine coat [5].
Azospirillum species are morphologically similar to myxospores synthesized by Myxobacteria. Both groups are soil-dwelling, Gram-negative proteobacteria that form highly desiccation resistant resting cells. In Myxococcus xanthus a two-component system (TCS) comprised of a membrane bound histidine kinase (HK) CrdS, which phosphorylates a DNA binding response regulator (RR) CrdA to control myxospore development. The Che-like Che3 signaling cascade negatively regulates CrdA by functioning as a phosphatase [7]. As is the case with Myxobacteria, cyst formation in R. centenum also utilizes a novel chemotaxis-like signal transduction cascade (Che3) to control the timing of development [8]. The R. centenum che3 gene cluster (Figure 1) is comprised of eight genes coding for homologs of CheA (CheA3), CheW (CheW3a and CheW3b), CheB (CheB3), CheR (CheR3), a methyl-accepting chemorecepter (MCP3) and CheY (CheY3). CheA3 is a CheA-CheY hybrid (Figure 1) belonging to Class II HKs, which include homologs of the E. coli CheA with a conserved histidine residue located in a histidine phosphotransfer (Hpt) domain rather than a dimerization and hisitidine phosphotransfer (DHp) domain found in Class I HKs. In addition to CheA3, the che3 cluster also codes for a second HK (CheS3). CheS3 has two REC domains followed by a PAS (Per, Arnt, Sim) domain and a HWE Class I HK domain (Figure 1); however, only one of the CheS3 REC domains contains a predicted phosphorylatable aspartate (D54 in REC1, Figure 1) with the comparable position in the second REC being substituted by an alanine (A191 in REC2, Figure 1).
Clearly the presence of a second HK and two additional phosphorylatable REC domains in the R. centenum Che3 cascade indicates that the flow of phosphate is more complex in this signaling pathway than for the E. coli Che signaling cascade. In the classic Escherichia coli chemotaxis model, CheA is tethered to the MCP-CheW complex and its autophosphorylation at a conserved His in the Hpt domain is enhanced upon repellents binding to MCP and inhibited upon binding of attractants. CheA phosphorylates a conserved Asp in CheY; phosphorylated CheY in turn binds to the flagellum's rotor causing reversal of flagellar rotation. Similar to the smooth-swimming and tumbling phenotypes exhibited in E. coli chemotaxis mutants, in-frame deletions of individual che3 genes produce distinctly opposing phenotypes [8]. Deletions of cheS3, cheY3, or cheB3 lead to a hyper-cyst phenotype characterized by premature formation of cysts, whereas null mutants of mcp3, cheW3a, cheW3b, cheR3, or cheA3 produce hypo-cyst strains that are defective for cyst development [8]. These genetic studies indicate that CheS3 and CheY3 may constitute cognate partners in a TCS that suppresses encystment, and that CheA3 either inhibits phosphorylation of the CheS3-CheY3 TCS or is part of a separate pathway. Here we report that CheY3 indeed accepts phosphates from CheS3 and not CheA3, and that CheA3 derepresses cyst formation by phosphorylating the REC1 domain of CheS3.
We previously reported that deletions of hybrid histidine kinase (HHK) genes cheA3 and cheS3 lead to opposing defects in the timing of cyst formation [8]. Specifically, a deletion of cheA3 resulted in severely defective encystment, while a deletion of cheS3 resulted in enhanced encystment. We also observed that a cheY3 null mutation is indistinguishable from the hypercyst phenotype exhibited by a null mutation of cheS3. In order to further probe the importance of the linked CheA3 and CheS3 REC domains we introduced alanine substitutions at the predicted Asp sites of phosphorylation and recombined these mutations into the native R. centenum chromosomal loci (Figure 1). Mutated strains were subsequently assayed for cyst development by growth on either nutrient-rich CENS medium that promotes vegetative growth or on cyst-inducing CENBA medium. Phase contrast microscopy was then used to visually assess cyst production coupled with flow cytometry quantitation of vegetative/cyst cell populations (Figure 2).
As observed in previous studies, growth of wild type cells in CENS medium visibly leads to >99% vegetative cells (Figure 2A), whereas growth in CENBA medium produces large cyst clusters (Figure 2B). Separation of individual vegetative cells from cyst clusters using flow cytometry indicates that the large population of vegetative cells present in CENS medium form a tight pattern near the origin of a side scatter (SSC) versus forward scatter (FSC) flow cytometry plot (Figure S1). In contrast, wild type cells grown in CENBA medium, which microscopically have a large number of cysts clusters, shows a distinct “comet tail” comprised of larger cyst cells that separate from the tight clustering of smaller single vegetative cells during flow cytometry (Figure S1). The tight clustering of vegetative cells is indicative of a high degree of uniformity of cell size (∼1 µm) [9] and internal complexity whereas the “comet tail” distribution of the cyst cell population shows that there is a wider distribution of sizes (2–8 µm) [10] present with varying internal complexity due in part to varying numbers and sizes of large PHB storage granules inside cysts [10], [11]. Because each cyst cluster typically contains 2 to 6 cells, the number of cyst cells is significantly higher (estimated to be ∼4-fold higher) than what is measured by flow cytometry quantitation of cyst clusters.
Flow cytometry analysis of wild type cells grown on cyst inducing CENBA medium show that ∼10% of the cell culture can be separated from the vegetative cell population as larger cyst clusters (Figure 2B). In contrast, growth of the ΔcheA3 mutant in cyst inducing CENBA shows a two-log reduction in cyst formation (Figure 2B) to a level that is comparable with that of wild type cells growth in vegetative CENS medium (Figure 2A). Not surprisingly, the cheA3:H49A HK mutant resembles a ΔcheA3 mutant, as this strain also contains a large predominance of vegetative cells irrespective of growth on nutrient-rich CENS or cyst-inducing CENBA medium. Interestingly, the cheA3:D663A REC mutant exhibits an opposing phenotype in that it forms large numbers of cysts in both CENS and CENBA growth media (Figure 2). Indeed the level of cyst production by the cheA3:D663A REC mutant exceeds that of wild type cells grown in CENBA.
The cyst deficient phenotypes exhibited by the ΔcheA3 and cheA3:H49A mutants are markedly contrasted by the ΔcheS3 and cheS3:H453A HK mutant strains that produce cysts in both CENS and CENBA medium. Interestingly, similar to what was observed in the CheA3 HK and REC mutant strains, the cheS3:D54A REC mutant exhibits a cyst defective phenotype that is opposite of the hypercyst phenotype exhibited by the ΔcheS3 and cheS3:H453A HK mutant strains (Figure 2). The opposing encystment phenotypes produced by the cheA3 and cheS3 HK and REC domain mutations indicates that the REC domains have regulatory control over the linked HK domains in both kinases. Similar to the ΔcheS3 and cheS3:H453A mutants, both the ΔcheY3, and cheY3:D64A mutants produced cyst cells when grown in both vegetative CENS and cyst inducing CENBA growth media (Figure 2).
Finally, to determine the hierarchy of CheA3 and CheS3 within the Che3 signaling cascade, we constructed ΔcheA3ΔcheS3 and ΔcheA3ΔcheY3 double mutants and assayed for encystment. These double mutations resulted in hyper-cyst strains that resemble the ΔcheS3 and ΔcheY3 phenotypes (Figure 2), suggesting that CheA3 functions upstream of CheS3 and CheY3 in this developmental signaling pathway.
HHKs are generally able to undergo four reactions in the presence of ATP and divalent metal cations: (1) autophosphorylation, where the conserved His residue within the HK domain is phosphorylated by the adjacent catalytic and ATP-binding domain (CA) using ATP as a substrate; (2) autodephosphorylation of the phospho-His residue within the HK domain; (3) phosphotransfer, where the REC domain dephosphorylates phospho-His and transfers the phosphate to its conserved Asp; and (4) autodephosphorylation of the phospho-Asp residue within the REC domain to yield inorganic phosphates (Pi) (Figure S2). In addition, phosphoryl group transfer from a response regulator back to its cognate HK is also possible. This reverse reaction has been observed in the EnvZ-OmpR TCS [12] as well as in phosphorelay systems involving a Hpt domain where the forward phosphorylation reaction (His1→Asp1→His2→Asp2) is partially reversible (Asp2→His2→Asp1→Pi) [13]. In the presence of ATP, HHKs may therefore exist as a mixture of four different phosphorylation states as illustrated in Figure 3A: unphosphorylated, His-phosphorylated (His∼P), Asp-phosphorylated (Asp∼P), and His-and-Asp-phosphorylated (His∼P/Asp∼P).
In order to characterize potential phosphorylation states of wild type CheA3 and CheS3, we isolated CheA3 and CheS3 with hexahistidine tags at their N-termini and performed in vitro phosphorylation assays. In early experiments we observed little radioactive labeling on CheA3 with [γ-33P] ATP in buffers containing Na+ and Mg2+, which made it difficult to biochemically characterize CheA3. Earlier studies showed that potassium but not sodium stimulates autophosphorylation of E. coli CheA [14]. Additionally, the Salmonella typhimurium CheY∼P autodephosphorylates at a high rate in the presence of Mg2+ leading to a low amount of 32P protein labeling, whereas in the presence of Ca2+ autodephosphorylation is impeded leading to a high level of 32P labeling [15]. To test whether different metal ions affected HHK phosphorylation, we performed kinase assays on wild type CheA3, CheS3 and on CheA3, CheS3 REC domain mutants in 14 buffers containing 25 mM Tris pH 7.5 and varying in 100 mM monovalent and 6 mM total divalent salt compositions (Table S1, Buffers 1–14). As shown in Figure 3B, CheA3 exhibited nearly undetectable labeling in Buffers 1 and 3–7, all of which contain NaCl as a monovalent salt. CheA3 labeled considerably better in all K+-containing buffers with maximum labeling observed in Buffer 9 containing Ca2+ as the sole divalent ion. When D663 was replaced with an alanine, 33P labeling was greatly improved in nearly all buffer conditions (Figure 3B). The enhanced labeling of CheA3:D663A compared with wild type CheA3 suggests that the N-terminal HK domain transfers the phosphate to D663 in the REC domain, which subsequently undergoes rapid autodephosphorylation. The D663A REC domain mutation would thus effectively trap the phosphate at H49, thereby allowing increased accumulation of phosphates. Regarding the enhanced phosphorylation of wild type CheA3 observed in Buffer 9, we propose that phosphate is captured at both H49 and D663 residues due to Ca2+ mediated inhibition of receiver domain autodephosphorylation. This conclusion is further supported by acid-base stability assays described below. Unlike CheA3, CheS3 shows no particular metal ion preference (Figure 3B). CheS3:D54A exhibits much lower 33P incorporation in Buffers 2 and 9, which contain Ca2+ as the only divalent metal ion.
HHKs are found in most bacterial genomes [16] with the role of the linked REC domain not well established in most cases. However, in several studies it has been shown that the HK domain favors intramolecular phosphotransfer to the linked REC domain [17]–[19]. We tested whether intramolecular phosphotransfer occurs in CheA3 and CheS3 by determining the phosphorylation states of the HK and REC domains. To capture the His∼P, Asp∼P, and His∼P/Asp∼P forms of these phospho-kinases, we used an acid-base stability assay based on differential pH sensitivity of His and Asp phosphorylated residues. Specifically, His∼P (Figure 3A-2) bonds are labile in acidic conditions but stable in basic conditions [20] while acylphosphates like Asp∼P (Figure 3A-3) are both acid- and base-labile [21].
In this experiment, we phosphorylated CheA3 and CheS3 in Buffer 9 (containing Ca2+ as the only divalent cation) for 30 min, denatured the phospho-proteins with SDS and treated samples with Tris buffer, HCl, or NaOH. Samples were then assayed for 33P-labeling by SDS-PAGE, with the assumption being that phosphorylation is preserved in a buffered solution with a physiological pH (Tris pH 7.5) and thus would represent 100% phosphorylation of the kinases before acid or base treatment.
In Figure 4A we show that ∼50% of wild type CheA3∼P was hydrolyzed by exposure to 0.1 M HCl and that it increased to ∼90% hydrolysis by exposure to 1 M HCl. This is contrasted by >90% hydrolysis of phosphate observed with the mutant (CheA3:D663A∼P) in both low and high HCl concentrations. The different stability profiles of the wild type CheA3 and D663A mutant suggest that Asp∼P likely exists in the wild type CheA3∼P. This is confirmed by treatment with NaOH, which dephosphorylates only Asp∼P. In this case nearly 100% CheA3:D663A∼P withstood high pH while the phosphate on wild type CheA3 is extremely labile (Figure 4A). This demonstrates that CheA3:D663A∼P is indeed only phosphorylated on a His residue and that wild type CheA has the majority (>90%) of its phosphate located at D663. Collectively these data suggest that the phosphate group flows from the HK domain to the REC domain within wild type CheA3. This conclusion is also confirmed by observing direct transfer of phosphate from CheA3:D663A∼P to a truncated version of CheA3 comprised of only the C-terminal receiver domain (CheA3-REC) (Figure 4C). Intermolecular phosphoryl transfer to CheA3-REC was also detected using the wild type CheA3∼P as the donor (Figure S3A) that has a linked REC domain competing with intermolecular phosphoryl transfer to the truncated REC domain.
Tethered receiver domains in HHKs can either function as an intermediate within a multicomponent phosphorylation cascade, or as a phosphate sink, removing phosphate from the HK domain to impede it from phosphorylating an untethered cognate REC domain. We believe the latter is the case with CheA3 as the half-life of the phosphate on the CheA3:D663A mutant is nearly 3-fold higher (80 min, Table 1) than is observed with wild type CheA3 (31 min, Table 1). Taken together, it appears that the REC domain in CheA3 functions to modulate the phosphorylation state of the HK domain by accepting a phosphate that is then rapidly lost by hydrolysis.
In contrast to the acid and base stability of CheA3, CheS3∼P is only acid-labile (Figure 4B). Furthermore, substitution of the predicted D54 phosphorylation site to an alanine in the first REC domain does not alter pH sensitivity. These results indicate that His∼P (Figure 3A-2) is the primary autophosphorylation form of CheS3∼P. Because CheS3:D54A showed reduced 33P incorporation in Buffer 9 (Figure 3B), we repeated this assay with CheS3∼P and CheS3:D54A∼P prepared in Buffer 5 (containing both Ca2+ and Mg2+) in order to rule out any ion effects imparted upon the phosphorylation equilibriums discussed above (Figure S2). We observed the same results of high HCl sensitivity and NaOH resistance regardless of the buffer conditions (Figure S4). In agreement with this conclusion, no phosphoryl transfer was detected from CheS3∼P to a truncated version of CheS3 comprised of only the N-terminal CheS3-REC1 domain (Figure 4D). Interestingly, despite evidence against CheS3 intramolecular phosphoryl transfer, the >4 hour stability of CheS3:D54A∼P is substantially greater than the 55 min stability observed with CheS3∼P (Table 1) suggesting that D54 may play a role in promoting autodephosphorylation of the HK domain.
Based on the CheA-CheY paradigm from E. coli, we tested the ability of CheA3 to phosphorylate CheY3. In our assays CheA3∼P and the more stable CheA3:D663A∼P mutant did not exhibit any detectable ability to transfer a phosphate to CheY3 (Figure 5A, 5B) in Buffer 9. Since the E. coli CheY and other response regulators exhibit a wide range of binding affinities to divalent metals (Kd of 0.4–47 mM under pH 6.0–10.0 have been reported [15], [22]–[24]), we also assayed CheA3 phosphorylation of CheY3 in Buffers 15–21 with higher (18 mM) total divalent metal concentrations (Table S1). This assay condition also failed to obtain phosphoryl transfer from CheA3∼P or CheA3:D663A∼P to CheY3 (Figure S5).
In contrast to the hypo-cyst phenotype exhibited by null mutation of cheA3, null mutations in cheS3 and cheY3 both exhibit indistinguishable hyper-cyst phenotypes (Figure 2) indicating that CheS3 might be the cognate kinase of CheY3. To test whether CheS3 can phosphorylate CheY3 we phosphorylated CheS3 for 30 min and then added CheY3. Upon addition of CheY3, rapid phosphoryl transfer from CheS3∼P to CheY3 was observed within 30 sec (Figure 5C). We also observed that CheS3:D54A is capable of phosphorylating CheY3 (Figure 5D) and that the H453A point mutation renders CheS3 unable to autophosphorylate (Figure S6). Thus, the phosphoryl group appears to transfer directly from H453 from CheS3 to CheY3. We also note that CheY3 appears to have a fast autodephosphorylation rate similar to chemotaxis CheYs [25]–[27].
Since the REC1 domain of CheS3 is not phosphorylated by the tethered HK domain, we questioned whether CheA3 participates in the CheS3 pathway by phosphorylating the REC1 domain of CheS3. We initially performed a phosphotransfer assay using CheA3∼P as the phospho-donor and did not observe CheS3-REC1 phosphorylation in Buffer 9 (Figure S3B) or Buffer 15 (Figure S3C). We reasoned that it may be difficult to observe an in vitro intermolecular transfer of phosphate from CheA3 to CheS3 as the intramolecular transfer from the HK domain of CheA3 to the tethered REC domain of CheA3 may outcompete this reaction. We therefore repeated the assay using the CheA3:D663A mutant as the tethered mutated REC domain would not compete with this intermolecular transfer. As shown in Figure 5E, CheA3:D663A does indeed transfer a phosphate to the CheS3-REC1 domain. This transfer from CheA3 to CheS3-REC1 also demonstrates a level of specificity typically exhibited between cognate HK-RR partners, as phosphate does not flow from CheS3 to CheA3-REC (Figure 5F, Figure S3D).
As shown in Figure 2, a D54A mutation in the CheS3 REC1 domain that would be unable to accept a phosphate in the REC domain exhibits a cyst deficient hypo-cyst phenotype. This is opposite of the hyper-cyst phenotype exhibited by a H453A mutation (Figure 2) that would disrupt CheS3 kinase activity. These opposing phenotypes suggest that phosphorylation of the CheS3 REC1 domain by the HK domain from CheA3 would have an inhibitory effect on autophosphorylation of CheS3 or a stimulating effect on autodephosphorylation of CheS3. This conclusion is also supported by genetic and epistasis studies which indicates that cheS3 null mutants are hyper-cyst and also epistatic to the hypo-cyst phenotype exhibited by cheA3 null mutants (Figure 2).
Chemotaxis and chemotaxis-like signaling pathways represent some of the more complex multicomponent signal transduction systems present in prokaryotes. A recent bioinformatic analysis of 450 non-redundant prokaryotic genomes found that 245 contained at least one chemotaxis-like protein [28]. In these 245 genomes there are a total of 416 chemotaxis-like systems that contain at least an MCP, CheA, and CheW homologs, which together are considered a minimum chemotaxis core [28]. Together, Che-like signal transduction cascades are known to control three classes of function: flagellar motility, type IV pili-based motility (TFP), and alternative cellular functions (ACF) [28]. The ACF class comprises approximately 6% of all the identified chemotaxis systems, regulating cellular processes such as cell development [29], [30], biofilm formation [31], exopolysaccharide production [32], cell-cell interactions [33], [34], and flagellum biosynthesis [35]. In fact, most identifiable Che-like signal transduction cascades are yet to be genetically disrupted so the function of many of these pathways remains to be elucidated.
Chemotaxis systems either exhibit typical chemotaxis architecture as found in E. coli, or have evolved to include additional auxiliary proteins and/or multi-domain hybrid components. Only a few of the more complex Che-like systems containing auxiliary proteins have been biochemically and genetically assayed for the flow of phosphate among protein components. Consequently, it remains unclear whether the CheA-CheY paradigm from E. coli will hold true for the many other, and often more complex, Che-like cascades from other species. Clearly the results of this study indicate that the Che3 cascade from R. centenum differs from this paradigm in that CheA3 functions to regulate the CheS3-CheY3 TCS. In some respects this is similar to the Che3 cascade from M. xanthus where a CheA homolog controls developmental program by acting as a phosphatase to the DNA binding RR CrdA [7].
HHKs with appended REC domains are often present in organisms that adopt complex life styles such as M. xanthus [36]–[39] and R. centenum [29], [40], allowing for added layers of regulation within signaling systems. In some cases, intramolecular phosphoryl transfer occurs within HHKs. For example, RodK from M. xanthus has three REC domains that are all essential for fruiting body formation but the HK domain selectively transfers a phosphate to its third REC domain [36]. In E. coli, the HK and REC domains of RcsC are involved in a HK→REC→Hpt→REC phosphorelay, which regulates capsular synthesis and swarming [41]. In other cases, the receiver domain can either prevent the HK from autophosphorylating, presumably by an occluding mechanism [42], or enhance gene expression by interacting with the cognate response regulator of the HHK [43].
Cysts are a dormant, non-growing state needed for survival in poor growth conditions, so the decision to form or impede this developmental pathway must involve multiple inputs and checkpoints. In the R. centenum Che3 cascade, there are three receivers that are capable of accepting a phosphate from two HHKs (CheB3 is not discussed here since CheB homologs are typically involved in MCP modification and not downstream signaling). CheA3 and CheS3 are HHKs containing respective C-terminal and N-terminal REC domains whereas CheY3 is a stand-alone receiver without an identifiable output domain. The presence of three REC domains and two HK domains encoded in this gene cluster potentially makes the Che3 signaling cascade quite complex with the possibility of multiple inputs and check points, which are presumably necessary to control the decision to induce cyst formation.
We showed that CheA3∼P is acid- and base-labile, indicating that an intramolecular phosphoryl transfer occurs between the tethered HK and REC domains. This transfer is inhibited when D663 is substituted with an Ala, giving rise to a His-phosphorylated CheA3:D663A∼P that is stable at high pH. The phosphate on CheA3:D663A is much more stable than observed with wild type CheA3, indicating that the tethered REC domain likely functions as a phosphate sink, attenuating phosphorylation of its own HK domain. Fused REC domains serving as phosphate sinks are not unprecedented. CheAY2, a CheA-CheY hybrid in Helicobacter pylori has also been shown to use its REC domain as a phosphate sink by rapidly dephosphorylating the linked kinase domain [27].
Unlike CheA3, the REC1 domain of CheS3 appears to serve a different function. CheS3∼P is acid-labile and base-resistant and also does not phosphorylate its receiver truncation (CheS3-REC1) in vitro. This indicates that the CheS3 HK domain does not phosphorylate its own REC1 domain. While it is unclear whether the CheS3 REC1 domain directly interacts with the HK domain, it is evident that the REC1 domain greatly affects the phosphorylation state of H453. This is evidenced by the half-life of CheS3:D54A∼P that is prolonged by many hours relative to wild type CheS3∼P (Table 1). Furthermore, the CheS3:D54A mutant has an opposing in vivo phenotype from a CheS3:H453A mutant thereby indicating that the CheS3 REC1 domain has regulatory control over phosphorylation of the CheS3 HK domain. Based on these results, we propose that D54 stimulates autodephosphorylation of the C-terminal HK domain by a mechanism other than transferring and accepting phosphates from the CheS3 HK domain. Although we do not yet have molecular details on how Asp-phosphorylated CheS3 inhibits the HK domain of CheS3, genetic and biochemical results clearly suggest that CheA3 promotes cyst formation by phosphorylating the REC1 domain in CheS3. It is likely that Asp-phosphorylated REC1 domain causes a conformational adoption that either inhibits CheS3 autophosphorylation or accelerates autodephosphorylation of the tethered HK domain.
The results of this study allow us to establish a working model for the Che3 signal transduction cascade in R. centenum (Figure 6). Under cyst non-inducing conditions, CheA3 has low basal level of kinase activity that directs intramolecular phosphate flow in the direction of His→Asp→Pi. The REC1 domain of CheS3 remains unphosphorylated so the HK domain of CheS3 operates at a high level of activity that effectively transfers phosphoryl groups to CheY3. CheY3∼P subsequently activates downstream components that repress cyst formation (Figure 6A). Upon starvation or desiccation (cyst inducing conditions), a signal is sensed by MCP3, which fully activates the kinase activity of CheA3 (Figure 6B). Activated CheA3 is now able to phosphorylate the REC1 domain of CheS3 thereby turning off the HK domain of CheS3 leading to unphosphorylated CheY3 that induces cyst formation (Figure 6B).
This model also readily explains the opposing phenotypes of the cheA3:D663A and cheS3:D54A REC mutant strains (Figure S7). In the cheA3:D663A REC mutant, intramolecular phosphoryl transfer, which acts as a CheA3 phosphate sink, would be blocked. The resulting elevated phosphate concentration at the CheA3 HK domain would subsequently lead to elevated phosphoryl transfer from CheA3 to the REC1 domain of CheS3 under both vegetative and cyst inducing growth conditions. Constitutive phosphorylation of the REC1 domain of CheS3 by CheA3:D663A would lead to a reduction in the HK activity of CheS3 and subsequent reduction in phosphorylation of CheY3. Thus, the cheA3:D663A strain should have a cyst defective phenotype under all growth conditions, which is what is observed (Figure S7A and B).
For the cheS3 REC1 mutant, CheA3 is no longer capable of phosphorylating the CheS3 REC1 domain due to the D54A substitution (Figure S7C). Therefore the CheS3 HK domain is able to autophosphorylate and phosphorylate CheY3 under all growth conditions. This would result in constitutive repression of cyst formation, which is also observed (Figure S7C and D).
Even though details of the Che3 phosphorylation cascade have been revealed, several features of this pathway still require clarification. First, based on the E. coli chemotaxis model, CheA3 should be activated by an extracellular signal received by MCP3, the nature of which is currently unknown. Second, it is unclear whether CheS3 is regulated only from phosphorylation by CheA3 or if it also directly senses changes in metabolism during encystment via a PAS domain. Third, the outputs and the downstream components of the Che3 signal transduction cascade remain elusive. One possibility is that CheY3 passes its phosphate onto unidentified downstream components.
Also not yet reconciled is how the Che3 pathway is integrated with the cGMP signaling in R. centenum. This signaling nucleotide is synthesized as cells transition from vegetative growth into the cyst developmental phase [44]. While this is a newly identified signaling pathway, a cGMP responsive CRP-like transcription factor has been identified and is required to induce cyst development [44]. How these two seemingly independent pathways together control the induction and timing of cyst formation constitutes a significant challenge in our understanding of this Gram-negative developmental pathway.
cheS3 was PCR amplified with 500 bp of flanking DNA as two fragments using wild type cells as template for colony PCR with primer pairs listed in Table S2. PCR amplified fragments were separately cloned and sequenced in pTOPO. Using a Quikchange (Stratagene) point mutagenesis kit, the D54A mutation was made within the 5′ cheS3 fragment harboring plasmid, whereas a H453A mutation was made in the plasmid harboring the 3′ cheS3 fragment using primers described in Table S2. Suicide vector constructs for cheS3 containing D54A or H453A mutations were then constructed by ligating the appropriate 5′ and 3′ cheS3 fragments directly into pZJD29a using external BamHI and XbaI sites and were internally joined by a BbsI site common to both fragments. After sequence confirmation, plasmids were mated from E. coli S17-1 (λpir) into an R. centenum ΔcheS3 strain [8]. Initial recombinants were selected for on CENSGm and second recombinants with chromosomal cheS3 point mutants were identified by phenotypic (GmS/SucR) and colony PCR analyses. Suicide vector constructs for cheA3:H49A, cheA3:D663A, and cheY3:D64A were similarly constructed using point mutagenesis primers detailed in Table S2, with cheA3 internally ligated using a ClaI site and cheY3 cloned as one fragment. See Table S3 for a complete list of R. centenum strains used in this study.
Two types of media were used to assay for encystment: CENS was used for vegetative growth [45], and CENBA for inducing cyst formation [46]. Encystment uninduced cells were prepared by overnight growth in CENS at 37°C. Encystment induced cells were prepared by washing overnight CENS cultures twice in CENBA, subculturing 1∶40 into CENBA and then incubating at 37°C for 3 days.
For microscopic observations, phase-contrast microscopy was performed on a Nikon E800 light microscope equipped with a 100× Plan Apo oil objective. For flow cytometry, CENS and CENBA cultures were diluted in 40 mM phosphate buffer and sonicated briefly (∼1 sec) at lower power to disaggregate cyst cells. All samples were stained in 2 µM Syto-9 (Life Technologies/Molecular Probes, Grand Island, NY) for 1.5 hours. Syto-9 is a permeant DNA stain that was shown microscopically to penetrate both vegetative and cyst cells similarly (data not shown). Initially fluorescent calibration beads of 880 nanometers and 10 microns were used to set the limits for background. After staining, cells were diluted ∼1∶10–1∶20 in 40 mM phosphate buffer just prior to running to achieve ∼1000 events per second on a Becton Dickenson FACS Calibur flow cytometer running CellQuest Pro data collection software using an argon laser (488 nm). 100,000 events were collected per sample with two biological replicates analyzed for each bacterial strain grown in each media. Forward and side scatter (SSC vs FSC) were plotted in logarithmic scales. Hypo-cyst ΔcheA3 and hyper-cyst ΔcheS3 strains were used to determine the appropriate gating to use for vegetative cells versus cyst cells. FlowJo version 10 (Tree Star, Inc.) was used to analyze the data and plot the data for publication. Statistical analysis was performed using Prism version 5.0 (GraphPad Software, Inc.).
Coding regions of CheS3, CheA3, CheY3, and the receiver domains of CheA3 and CheS3 (CheA3-REC and CheS3-REC1) were PCR amplified from R. centenum genomic DNA with primers listed in Table S1. Gel-purified PCR products were cloned into pBluescript SK+ or pGEM-T, sequenced, then subcloned into the NdeI and XhoI sites in vector pET28a. pET28a plasmids for overexpression of CheS3 and CheA3 point mutants were generated using the appropriate pZJD29a vector as template for PCR using primers detailed in Table S1. All pET28a constructs were transformed into E. coli BL21 Rosetta 2 (DE3) cells (Novagen). See Table S3 for a complete list of E. coli strains used in this study. For overexpression, overnight cultures of E. coli Rosetta 2 (DE3) cells were subcultured 1∶100 into 1 L LB medium and shaken at 37°C to an OD600 of 0.5. Protein overexpression was induced at an isopropyl β-D-1-thiogalactopyranoside concentration of 0.4 mM and cultures were incubated overnight at 16°C with gentle agitation. Cells were pelleted by centrifugation and stored at −80°C until further use. For purification of all proteins, cell pellets were resuspended and lysed by ultrasonication in lysis buffer (20 mM Tris-HCl pH 7.5, 500 mM NaCl, 25 mM imidazole and 10% glycerol). Purification was performed on 1 mL HisTrap HP (GE Healthcare) columns using an FPLC system. His-tagged proteins were eluted in 20 mM Tris-HCl (pH 7.5) buffers with a gradient of 25–500 mM imidazole. Fractions containing purified proteins were dialyzed into a storage buffer (25 mM Tris-HCl pH 7.5, 100 mM NaCl in 30% glycerol) and stored at −20°C until further use.
Twenty-one Tris buffers containing common mono- and divalent metal ions were used in this study for HHK phosphorylation and phosphotransfer assays (see the full list of buffer compositions in Table S1). All kinase reactions and phospho-transfers were performed in 0.2 mM final ATP concentration except for the half-life determination experiments. All reactions were stopped by addition of 6× SDS-PAGE sample loading buffer. All phospho-proteins were separated by SDS-PAGE and gels were examined by autoradiography on a Typhoon 9100 scanner (GE Healthcare) located in the Indiana University Physical Biochemistry Instrumentation Facility.
In the metal ion dependency assays (shown in Figure 3B and Figure S3), isolated kinases were diluted in the indicated buffers to 2–5 µM. Kinase reactions were initiated by adding 1/20 volume of ATP/[γ-33P] ATP mix in 25 mM Tris pH 7.5 and allowed to proceed for 30 min at room temperature. For phosphoryl transfer to CheY3 shown in Figure S3, 1/10 volume of 65 mM CheY3 was also added to each reaction mixture at the end of 30 min autophosphorylation for another 30 min incubation at room temperature.
In assays assessing intermolecular phosphoryl transfer shown in Figure 4C, Figure 5, and Figure 6, ATP mixes and protein dilutions were made in same buffer as indicated in the text. 2–5 µM kinases were first phosphorylated in 0.2 mM ATP for 30 min followed by addition of 1/10 volume of 65 µM CheY3 or receiver domain truncations (CheS3-REC1 or CheA3-REC). The time of receiver addition was set to time 0. Phosphoryl transfer was then assessed at various time intervals.
To determine the half-lives of phosphorylated kinases, 2–5 µM CheA3 and CheA3:D663A were pre-autophosphorylated in the presence of 10 µM ATP mix in Buffer 9 for 50 min before passing through Bio-Rad Micro Spin 6 chromatography columns to remove excess ATP. Dephosphorylation was monitored at room temperature by removing 10 µL of the filtrates at various time intervals. Phosphorylation of the kinases was quantified using ImageJ software by integrating the grayscale density of the radioactive bands. % Kinase phosphorylation was plotted over 300 min and data points were fitted to one phase exponential decay using Prism. Half-lives of CheS3 and CheS3:D54A were measured with the same protocol with the exception that Buffer 5 was used in place of Buffer 9.
The phosphorylation state of all CheS3 and CheA3 variants was determined by assaying phosphoprotein stability under acidic or basic conditions using a non-filter based assay [47]. Kinases were allowed to autophosphorylate at room temperature for 30 min, after which phosphoproteins were denatured by adding 0.1 volume of 20% SDS. Aliquots were withdrawn and mixed with equal volumes of 0.1 or 1.0 M Tris pH 7.5, HCl or NaOH and incubated for 30 min at 37°C before being neutralized with 1.0 M Tris-HCl pH 7.5. Samples were then mixed with 6× loading dye and resolved by SDS-PAGE and assayed for phosphorylation by autoradiography. Phosphorylation of the kinases was quantified using ImageJ software by integrating the grayscale density of the radioactive bands.
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10.1371/journal.pntd.0002856 | Hemoglobin Uptake by Paracoccidioides spp. Is Receptor-Mediated | Iron is essential for the proliferation of fungal pathogens during infection. The availability of iron is limited due to its association with host proteins. Fungal pathogens have evolved different mechanisms to acquire iron from host; however, little is known regarding how Paracoccidioides species incorporate and metabolize this ion. In this work, host iron sources that are used by Paracoccidioides spp. were investigated. Robust fungal growth in the presence of the iron-containing molecules hemin and hemoglobin was observed. Paracoccidioides spp. present hemolytic activity and have the ability to internalize a protoporphyrin ring. Using real-time PCR and nanoUPLC-MSE proteomic approaches, fungal growth in the presence of hemoglobin was shown to result in the positive regulation of transcripts that encode putative hemoglobin receptors, in addition to the induction of proteins that are required for amino acid metabolism and vacuolar protein degradation. In fact, one hemoglobin receptor ortholog, Rbt5, was identified as a surface GPI-anchored protein that recognized hemin, protoporphyrin and hemoglobin in vitro. Antisense RNA technology and Agrobacterium tumefaciens-mediated transformation were used to generate mitotically stable Pbrbt5 mutants. The knockdown strain had a lower survival inside macrophages and in mouse spleen when compared with the parental strain, which suggested that Rbt5 could act as a virulence factor. In summary, our data indicate that Paracoccidioides spp. can use hemoglobin as an iron source most likely through receptor-mediated pathways that might be relevant for pathogenic mechanisms.
| Fungal infections contribute substantially to human morbidity and mortality. During infectious processes, fungi have evolved mechanisms to obtain iron from high-affinity iron-binding proteins. In the current study, we demonstrated that hemoglobin is the preferential host iron source for the thermodimorphic fungus Paracoccidioides spp. To acquire hemoglobin, the fungus presents hemolytic activity and the ability to internalize protoporphyrin rings. A putative hemoglobin receptor, Rbt5, was demonstrated to be GPI-anchored at the yeast cell surface. Rbt5 was able to bind to hemin, protoporphyrin and hemoglobin in vitro. When rbt5 expression was inhibited, the survival of Paracoccidioides sp. inside macrophages and the fungal burden in mouse spleen diminished, which indicated that Rbt5 could participate in the establishment of the fungus inside the host. Drugs or vaccines could be developed against Paracoccidioides spp. Rbt5 to disturb iron uptake of this micronutrient and, thus, the proliferation of the fungus. Moreover, this protein could be used in routes to introduce antifungal agents into fungal cells.
| Iron is an essential micronutrient for almost all organisms, including fungi. Because iron is a transition element, iron can participate as a cofactor in a series of biological processes, such as respiration and amino acid metabolism, as well as DNA and sterol biosynthesis [1]. However, at high levels, iron can be toxic, generating reactive oxygen species (ROS). The regulation of iron acquisition in fungi is one of the most critical steps in maintaining iron homeostasis because these micro-organisms have not been described as possessing a regulated mechanism of iron egress [2].
The mammal host actively regulates intracellular and systemic iron levels as a mechanism to contain microbial infection and persistence. Because of this, microbial iron acquisition is an important virulence attribute. One strategy to protect the body against iron-dependent ROS cascades and to keep iron away from microorganisms is to tightly bind the metal to many proteins, including hemoglobin, ferritin, transferrin and lactoferrin [3]. In human blood, 66% of the total circulating body iron is bound to hemoglobin. Each hemoglobin molecule possesses four heme groups, and each heme group contains one ferrous ion (Fe2+) [4]. Iron that is bound to the glycoprotein transferrin, which presents two ferric ion (Fe3+) high affinity binding sites, circulates in mammalian plasma [5]. Lactoferrin is present in body fluids, such as serum, milk, saliva and tears [6]. Additionally, similar to transferrin, lactoferrin possesses two Fe3+ binding sites [7]. Lactoferrin functions as a defense molecule due to its ability to sequester iron [8]. Although these proteins are important in sequestering extracellular iron, ferritin is primarily an intracellular iron storage protein [9] and is composed of 24 subunits that are composed of approximately 4500 Fe3+ ions [10].
Most microorganisms can acquire iron from the host by utilizing high-affinity iron-binding proteins. Preferences for specific host iron sources and strategies to gain iron that is linked to host proteins are under study. It has been revealed, for example, that Staphylococcus aureus preferentially uses iron from heme rather than from transferrin during early infection [11]. However, thus far, there is a scarcity of data from pathogenic fungi. It has been suggested that Cryptococcus neoformans preferentially uses transferrin as the host iron source through a reductive iron uptake system because Cft1 (Cryptococcus Fe Transporter) is required for transferrin utilization and is essential for full virulence [12]. Histoplasma capsulatum seems to preferentially use transferrin as the host iron source but also uses hemin and ferritin [13], [14]. Candida albicans can also mediate iron acquisition from transferrin [15]. Moreover, the Als3 (Agglutinin-like sequence) protein functions as a receptor at the surface of C. albicans hyphae, which could support iron acquisition from ferritin [16].
The strategy for iron acquisition from hemoglobin by C. albicans is the best characterized. C. albicans presents hemolytic activity and utilizes hemin and hemoglobin as iron sources [17]–[20]. For erythrocyte lyses, C. albicans most likely possesses a hemolytic factor that is attached to the fungal cell surface [21]. After hemoglobin release, surface receptors, e.g., Rbt5 (Repressed by Tup1), Rbt51, Wap1/Csa1 (Candida Surface Antigen), Csa2 and Pga7 (Predicted GPI-Anchored), could function in the uptake of hemoglobin [19]. Those receptors possess a CFEM domain, which is characterized by a sequence of eight spaced cysteine residues [22], that might bind heme through the iron atom [23]. It has been demonstrated that rbt5 and wap1 are transcriptionally activated during low iron conditions (10 µM) in comparison with high iron conditions (100 µM), which indicates that these encoding proteins are important in high-affinity iron uptake pathways [24]. Rbt5, which is a glycosylphosphatidylinositol (GPI)-anchored protein, appears to have a central role in hemin/hemoglobin uptake because the rbt5 deletion impaired C. albicans growth in the presence of hemin and hemoglobin as iron sources [19]. However, rbt5 deletion did not affect C. albicans virulence in a mouse model of systemic infection or during rabbit corneal infection [25], which indicates that other compensatory mechanisms could act in the absence of Rbt5 [19]. It is suggested that after hemoglobin binds to Rbt5, the host iron source is internalized by endocytosis into vacuoles [20].
It has been proposed that the C. neoformans mannoprotein cytokine-inducing glycoprotein (Cig1) acts as a hemophore at the cell surface, which sequesters heme for internalization via a receptor that has not yet been described [26]. After heme binding, the molecule is most likely internalized via endocytosis with the participation of the ESCRT pathway [27], as described for C. albicans [20]. In C. neoformans, the deletion of vps23, which is an ESCRT-I component, resulted in a growth defect on heme and reduced susceptibility to non-iron metalloporphyrins, which have heme-uptake dependent toxicity, indicating that the endocytosis pathway is important for hemoglobin utilization by this fungus [27].
In the host, macrophages play an important role in maintaining adequate levels of plasma iron. Those cells phagocyte aged or damaged erythrocytes and internally recycle iron from senescent erythrocytes [5]. Macrophages are the first host defense cells that interact with Paracoccidioides spp. [28], which is a complex of two suggested species (P. brasiliensis and P. lutzii) of thermodimorphic fungi [29]. Here, this complex is designated as Paracoccidioides. All strains of Paracoccidioides that have been described thus far are causative agents of paracoccidioidomycosis (PCM) [29], which is a systemic mycosis [30]. Non-activated macrophages are permissive to intracellular Paracoccidioides multiplication, functioning as a protected environment against complement systems, antibodies and innate immune components and thus leading to fungal dissemination from the lungs to other tissues [31], [32]. Possible strategies that are used by Paracoccidioides to survive inside macrophages include (i) the downregulation of macrophage genes that are involved in the inflammatory response and in the activation against pathogens [33], [34], (ii) the inhibition of phagosome-endosome fusion [35] and (iii) the detoxification of ROS that are produced by the phagocyte NADPH oxidase system [36]. Moreover, iron availability inside monocytes is required for Paracoccidioides survival because the effect of chloroquine on fungal survival is reversed by FeNTA, which is an iron compound that is soluble in the neutral to alkaline pH range [37].
The host iron sources that are used by Paracoccidioides have not been established to date. In this work, we demonstrate that Paracoccidioides can use hemoglobin as an iron source through a receptor-mediated pathway during infection. This observation unravels new mechanisms by which Paracoccidioides species might interfere with the physiology of host tissues.
All animals were treated in accordance with the guidelines provided by the Ethics Committee on Animal Use from Universidade Federal de Goiás based on the International Guiding Principles for Biomedical Research Involving Animals (http://www.cioms.ch/images/stories/CIOMS/guidelines/1985_texts_of_guidelines.htm) and their use was approved by this committee (131/2008).
Paracoccidioides strains Pb01 (ATCC MYA-826; Paracoccidioides lutzii) [29], Pb18 (ATCC 32069; Paracoccidioides brasiliensis, phylogenetic species S1) and Pb339 (ATCC 200273; Paracoccidioides brasiliensis, phylogenetic species S1) [38] were used in this work. The fungus was maintained in brain heart infusion (BHI) medium, which was supplemented with 4% (w/v) glucose at 36°C to cultivate the yeast form. For growth assays, Paracoccidioides yeast cells were incubated in chemically defined MMcM medium [39] with no iron addition for 36 h at 36°C under rotation to deplete intracellular iron storage. Cells were collected and washed twice with phosphate buffered saline solution 1X (1X PBS; 1.4 mM KH2PO4, 8 mM Na2HPO4, 140 mM NaCl, 2.7 mM KCl; pH 7.3). Cell suspensions were serially diluted and spotted on plates with MMcM medium, which contained 50 µM of bathophenanthroline disulfonic acid (BPS) that was supplemented or not (no iron condition) with different iron sources: 30 µM inorganic iron [Fe(NH4)2(SO4)2], 30 µM hemoglobin, 120 µM hemin, 30 µg/ml ferritin, 30 µM transferrin or 3 µM lactoferrin. All host iron sources were purchased from Sigma-Aldrich, St. Louis, MO, USA.
Paracoccidioides yeast cells were maintained in MMcM medium for 36 h. Those cells were pre-incubated or not with hemoglobin for 1 h at room temperature. After this time, the cells were incubated on MMcM medium, which was supplemented or not with different concentrations of zinc protoporphyrin IX (zinc-PPIX) (Sigma-Aldrich, St. Louis, MO, USA) for different times at 36°C under rotation. Cells were collected, washed twice with 1X PBS and observed by live fluorescence microscopy using an Axio Scope A1 microscope with a 40x objective and the software AxioVision (Carl Zeiss AG, Germany). The Zeiss filter set 15 was used to detect intrinsic zinc-PPIX fluorescence. The camera exposition time was fixed in 710 ms for all pictures. The fluorescence background was determined in the absence of zinc-PPIX in the MMcM medium.
The hemolytic activity of Paracoccidioides was evaluated as described previously [17], with modifications. Briefly, the fungus was cultivated in MMcM medium with no iron addition for 36 h at 36°C, under rotation. After this period, the yeast cells were harvested and washed twice with 1X PBS. Then, 107 cells were incubated with 108 sheep erythrocytes (Newprov Ltda, Pinhais, Paraná, Brazil) for 2 h, at 36°C in 5% CO2. As negative or positive controls, respectively, erythrocytes were incubated with 1X PBS or water. After incubation, the cells were resuspended by gentle pipetting, and then pelleted by brief centrifugation. The optical densities of the supernatants were determined using an ELISA plate reader at 405 nm. The experiment was performed in triplicate, and the average of the optical density was obtained for each condition. The average optical density of each condition was used to calculate the relative hemolysis of the experimental conditions or the negative control against the positive control. The relative hemolysis data were plotted in a bar graph. Student's t-test was applied to compare the experimental values to the negative control values.
The amino acid sequences of putative members of the Paracoccidioides hemoglobin receptor family were obtained from the Dimorphic Fungal Database of the Broad Institute site at (http://www.broadinstitute.org/annotation/genome/paracoccidioides_brasiliensis/MultiHome.html) based on a homology search. The sequences for Pb01 Rbt5, Wap1 and Csa2 have been submitted to GenBank with the following respective accession numbers: XP_002793022, XP_002795519 and XP_002797192. For Pb18 Wap1, the accession number is EEH49284. And for Pb03 Rbt51 and Csa2, the accession numbers are, respectively, EEH22388 and EEH19315. SMART (http://smart.embl-heidelberg.de/), SignalP 4.1 Server (http://www.cbs.dtu.dk/services/SignalP/) and big-PI Fungal Predictor (http://mendel.imp.ac.at/gpi/fungi_server.html) protein analysis tools were used to search for conserved domains, signal peptides and GPI modification sites, respectively, in Paracoccidioides and C. albicans sequences. The amino acid sequences of Paracoccidioides and C. albicans orthologs were aligned using the CLUSTALX2 program [40].
Pb01 yeast cells were incubated in MMcM medium without iron or in MMcM medium supplemented with different iron sources: 10 or 100 µM inorganic iron or 10 µM hemoglobin. Cells were harvested after 30, 60 or 120 min of incubation, and total RNA was extracted using TRIzol (TRI Reagent, Sigma-Aldrich, St. Louis, MO, USA) and mechanical cell rupture (Mini-Beadbeater - Biospec Products Inc., Bartlesville, OK). After in vitro reverse transcription (SuperScript III First-Strand Synthesis SuperMix; Invitrogen, Life Technologies), the cDNAs were submitted to a qRT-PCR reaction, which was performed using SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) in a StepOnePlus Real-Time PCR System (Applied Biosystems Inc.). The expression values were calculated using the transcript that encoded alpha tubulin (XM_002796593) as the endogenous control as previously reported [41]. The primer pairs for qRT-PCR were designed such that one primer in each pair spanned an intron, which prevented genomic DNA amplification. The sequences of the oligonucleotide primers that were used were as follows: rbt5-S, 5′- ATATCCCACCTTGCGCTTTGA -3′; rbt5-AS, 5′- GGGCAGCAACGTCGCAAGA -3′; wap1-S, 5′- AAGTCTGTGATAGTGCTGGAG - 3′; wap1-AS, 5′- AGGGGGTTCAGGGAGAGGA -3′; csa2-S, 5′- GCAAAATTAAAGAATCTCTCACG -3′; csa2-AS, 5′- ATGAAACGGCAAATCCCACCA-3′; alpha-tubulin-S, 5′- ACAGTGCTTGGGAACTATACC -3′; alpha-tubulin-AS, 5′- GGGACATATTTGCCACTGCC -3′. The annealing temperature for all primers was 62°C. The qRT-PCR reaction was performed in triplicate for each cDNA sample, and a melting curve analysis was performed to confirm single PCR products. The relative standard curve was generated using a pool of cDNAs from all the conditions that were used, which was serially diluted 1∶5 to 1∶625. Relative expression levels of transcripts of interest were calculated using the standard curve method for relative quantification [42]. Student's t-test was applied in the statistical analyses.
Pb01 yeast cells were cultivated in MMcM medium with 10 µM inorganic iron [Fe(NH4)2(SO4)2] or with 10 µM bovine hemoglobin (H2500-Sigma-Aldrich, St. Louis, MO, USA) at 36°C under constant agitation. After 48 h, the cells were harvested, and the cell rupture was performed as described above, in the presence of Tris-Ca buffer (Tris-HCl 20 mM, pH 8.8; CaCl2 2 mM) with 1% proteases inhibitor (Protease Inhibitor mix 100x, Amersham). The mixtures were centrifuged at 12,000 g at 4°C for 10 min. The supernatant was collected and centrifuged again, at the same conditions for 20 min. Then, the protein extracts were washed twice with 50 mM NH4HCO3 buffer and concentrated using a 10 kDa molecular weight cut-off in an Ultracel regenerated membrane (Amicon Ultra centrifugal filter, Millipore, Bedford, MA, USA). The proteins extracts concentration were determined using the Bradford assay [43]. These extracts were prepared as previously described [44] for analyses using nano-scale ultra-performance liquid chromatography combined with mass spectrometry with data-independent acquisitions (nanoUPLC-MSE). In this way, the trypsin-digested peptides were separated using a nanoACQUITY UPLC System (Waters Corporation, Manchester, UK). The MS data that were obtained via nanoUPLC-MSE were processed and examined using the ProteinLynx Global Server (PLGS) version 2.5 (Waters Corporation, Manchester, UK). Protein identification and quantification level analyses were performed as described previously [45]. The observed intensity measurements were normalized with the identified peptides of the digested internal standard rabbit phosphorylase. For protein identification, the Paracoccidioides genome database was used. Protein tables that were generated by PLGS were merged using the FBAT software [46], and the dynamic range of the experiment was calculated using the MassPivot software (kindly provided by Dr. Andre M. Murad) by setting the minimum repeat rate for each protein in all replicates to 2 as described previously [45]. Proteins were considered regulated when p<0.05 (determined by PLGS) and when the fold change between protein quantification in the presence of hemoglobin x presence of inorganic iron was ±0.2. Proteins were classified according to MIPS functional categorization (http://mips.helmholtz-muenchen.de/proj/funcatDB/) with the help of the online tools UniProt (http://www.uniprot.org/), PEDANT (http://pedant.helmholtz-muenchen.de/pedant3htmlview/pedant3view?Method=analysis&Db=p3_r48325_Par_brasi_Pb01) and KEGG (http://www.genome.jp/kegg/). Graphics that indicated the quality of the proteomic data were generated using the Spotfire software (http://spotfire.tibco.com/).
Oligonucleotide primers were designed to amplify the 585 bp complete coding region of Rbt5: rbt5-S, 5′- GGTGTCGACCAGCTCCCTAATATCCCAC -3′; rbt5-AS, 5′- GGTGCGGCCGCGACATAATTTACAGGTAAGC -3′ (underlined regions correspond to NotI and SalI restriction sites, respectively). The PCR product was subcloned into the NotI/SalI sites of pGEX-4T-3 (GE Healthcare). The DNA was sequenced on both strands and was used to transform the E. coli C41 (DE3). The transformed cells were grown at 37°C, and protein expression was induced by the addition of 1 mM isopropyl β-D- thiogalactopyranoside (IPTG) for 5 h. The bacterial extract was centrifuged at 2,700 g and was resuspended in 1X PBS. The fusion protein Rbt5 was expressed in the soluble form in the heterologous system and was purified by affinity chromatography under non-denaturing conditions using glutathionesepharose 4B resin (GE Healthcare). Subsequently, the fusion protein was cleaved by the addition of thrombin protease (50 U/ml). The purity and size of the recombinant protein were evaluated by resuspending the protein in SDS-loading buffer [50 mM Tris-HCl, pH 6.8; 100 mM dithiothreitol, 2% (w/v) SDS; 0.1% (w/v) bromophenol blue; 10% (v/v) glycerol]. Subsequently the sample was boiled for 5 min, followed by running the purified molecule on a 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and finally, staining with Coomassie blue.
The purified Rbt5 was used to generate a specific rabbit polyclonal serum. Rabbit preimmune serum was obtained and stored at −20°C. The purified recombinant protein (300 µg) was injected into rabbit with Freund's adjuvant three times at 10-day intervals. The obtained serum was sampled and stored at −20°C.
Yeast cells were frozen in liquid nitrogen and disrupted by using a mortar and pestle. This procedure was performed until the cells completely ruptured, which was verified by optical microscopic analysis. The ground material was lyophilized, weighed, and resuspended in 25 µl Tris buffer (50 mM Tris-HCl, pH 7.8) for each milligram of dry weight as described previously [47]. The supernatant was separated from the cell wall fraction by centrifugation at 10,000 g for 10 min at 4°C. To remove proteins that were not covalently linked and intracellular contaminants, the isolated cell wall fraction was washed extensively with 1 M NaCl, was boiled three times in SDS-extraction buffer (50 mM Tris-HCl, pH 7.8, 2% [w/v] SDS, 100 mM Na-EDTA, 40 mM β-mercaptoethanol) and pelleted by centrifugation at 10,000 g for 10 min [48]. The washed pellet containing the cell wall enriched fraction was washed six times with water, lyophilized, and weighed. The cell wall fraction, which was prepared as described above, was treated with hydrofluoric acid-pyridine (HF-pyridine) (10 µl for each milligram of dry weight of cell walls) for 4 h at 0°C [49], [50]. After centrifugation, the supernatant that contained the HF-pyridine extracted proteins was collected, and HF-pyridine was removed by precipitating the supernatant in 9 volumes of methanol buffer (50% v/v methanol, 50 mM Tris-HCl, pH 7.8) at 0°C for 2 h. The pellet was washed three times in methanol buffer and resuspended in approximately 10 times the pellet volume in SDS-loading buffer, as described previously [50].
Twenty micrograms of protein samples were loaded onto a 12% SDS-PAGE gel and were separated by electrophoresis. Proteins were transferred from gels to nitrocellulose membrane at 20 V for 16 h in buffer that contained 25 mM Tris-HCl pH 8.8, 190 mM glycine and 20% (v/v) methanol. Membranes were stained with Ponceau red to confirm complete protein transfer. Next, each membrane was submerged in blocking buffer [1X PBS, 5% (w/v) non-fat dried milk, 0.1% (v/v) Tween-20] for 2 h. Membranes were washed with wash buffer [1X PBS, 0.1% (v/v) Tween-20] and incubated with primary antibody, which was used at a 1/3,000 (v/v) ratio of antibody to buffer, for 1 h at room temperature. This step was followed by three 15 min washes with wash buffer. Membranes were incubated with the conjugated secondary antibody [anti-rabbit immunoglobulin G coupled to alkaline phosphatase (Sigma-Aldrich, St. Louis, MO, USA)] in a 1/5,000 (v/v) ratio, for 1 h at room temperature, and developed with 5-bromo-4-chloro-3-indolylphosphate–nitroblue tetrazolium (BCIP-NBT). Reactions were also performed with sera from patients with PCM, sera from control individuals (all diluted 1∶100) and with 1 µg of purified recombinant Rbt5. After incubation with peroxidase conjugate anti-human IgG (diluted 1∶1000), the reaction was developed with hydrogen peroxide and diaminobenzidine (Sigma-Aldrich, St. Louis, MO, USA) as the chromogenic reagent.
For the ultrastructural and immunocytochemistry studies, the protocols that were previously described by Lima and colleagues [51] were employed. Transmission electron microscopy was performed using thin sections from Pb01 yeast that were fixed in 2% (v/v) glutaraldehyde, 2% (w/v) paraformaldehyde and 3% (w/v) sucrose in 0.1 M sodium cacodylate buffer pH 7.2. The samples were post-fixed in a solution that contained 1% (w/v) osmium tetroxide, 0.8% (w/v) potassium ferricyanide and 5 mM CaCl2 in sodium cacodylate buffer, pH 7.2. The material was embedded in Spurr resin (Electron Microscopy Sciences, Washington, PA). Ultrathin sections were stained with 3% (w/v) uranyl acetate and lead citrate. For immunolabeling, the cells were fixed in a mixture that contained 4% (w/v) paraformaldehyde, 0.5% (v/v) glutaraldehyde and 0.2% (w/v) picric acid in 0.1 M sodium cacodylate buffer at pH 7.2 for 24 h at 4°C. Free aldehyde groups were quenched with 50 mM ammonium chloride for 1 h. Block staining was performed in a solution containing 2% (w/v) uranyl acetate in 15% (v/v) acetone. After dehydration, samples were embedded in LR Gold resin (Electron Microscopy Sciences, Washington, PA.). For ultrastructural immunocytochemistry studies, the ultrathin sections were incubated for 1 h with the polyclonal antibody raised against the recombinant Pb01 Rbt5, which was diluted 1∶100, and for 1 h at room temperature with the labeled secondary antibody anti-rabbit IgG Au-conjugated (10 nm average particle size; 1∶20 dilution; Electron Microscopy Sciences, Washington, PA). The nickel grids were stained as described above and observed using a Jeol 1011 transmission electron microscope (Jeol, Tokyo, Japan). Controls were incubated with a rabbit preimmune serum, which was diluted 1∶100, followed by incubation with the labeled secondary antibody.
The recombinant proteins Rbt5 was pre-incubated with hemoglobin or with 1X PBS, as a control, for 1 h at room temperature. After this time, the protein was incubated with a hemin-agarose resin (Sigma-Aldrich, St. Louis, MO, USA) for 1 h at 4°C. The recombinant protein, Enolase, previously obtained in our laboratory [52], was independently incubated with the hemin-agarose resin to function as a specificity control. After the batch binding strategy, the resin was washed three times with cold 1X PBS, resuspended in SDS-loading buffer and boiled for 5 min to elute proteins that were bound to the resin. The samples were submitted to SDS-PAGE, and the proteins were transferred to nitrocellulose membranes as cited above. For Western blot analyses, the primary antibodies anti-Rbt5 and anti-Enolase were used at 1/3,000 and 1/40,000 (v/v) ratios, respectively, and developed with BCIP-NBT, as cited above.
Rbt5 binding affinities for hemoglobin and protoporphyrin were evaluated by flow cytometry. Yeast cells of Paracoccidioides [strains Pb01, Pb339 (PbWt), Pbrbt5-aRNA and PbWt+EV] were cultivated as described above, washed with 1X PBS and blocked for 1 h at room temperature in 1X PBS, which was supplemented with 1% bovine serum albumin (PBS-BSA). Fungal cells were then separated in two groups: the first group was initially treated with 20 µM protoporphyrin or 10 µM hemoglobin and further incubated with the anti-Rbt5 antibodies and an Alexa Fluor 488-labeled anti-rabbit IgG (10 µg/ml). The second group was sequentially incubated with primary and secondary antibodies as described above. The cells were then treated with 20 µM protoporphyrin or with 10 µM hemoglobin. All incubations were performed for 30 min at 37°C, followed by washing with 1X PBS. Control cells were not exposed to hemoglobin or to protoporphyrin. Fluorescence levels of yeast cells were analyzed using a FACSCalibur (BD Biosciences) flow cytometer, and the data were processed using the FACS Express software.
The antisense-RNA (aRNA) strategy was used as described previously [53], [54]. Briefly, DNA from wild-type Pb339 (PbWt) exponentially growing yeast cells was obtained after cell rupture as described above. Platinum Taq DNA Polymerase High Fidelity (Invitrogen, USA) and the oligonucleotides asrbt5-S, 5′ - CCGCTCGAGCGGTCTCGGAAACGACGGGTGC - 3′ and asrbt5-AS, 5′ - GGCGCGCCCGCAAGATTTCTCAACGCAAG - 3′ were employed to amplify aRNA from PbWt rbt5 DNA. Plasmid construction for aRNA gene repression and A. tumefaciens-mediated transformation (ATMT) of PbWt was performed as previously described [55], [56]. The amplified rbt5-aRNA fragments were inserted into the pCR35 plasmid, which was flanked by the calcium-binding protein promoter region (P-cbp-1) from H. capsulatum and by the cat-B termination region (T-cat-B) of Aspergillus fumigatus [57]. The pUR5750 plasmid was used as a parental binary vector to harbor the aRNA cassette within the transfer DNA (T-DNA). The constructed binary vectors were introduced into A. tumefaciens LBA1100 ultracompetent cells by electroporation [58] and were isolated by kanamycin selection (100 mg/ml). The A. tumefaciens cells that were positive for pUR5750 transformation were used to perform the ATMT of Paracoccidioides yeast cells. The hygromycin (Hyg)-resistance gene, hph, from E. coli was used as a selection mark and was flanked by the glyceraldehyde-3-phosphate dehydrogenase promoter region (P-gapdh) and the trpC termination region (T-trpC) from Aspergillus nidulans. The selection of transformants (Pbrbt5-aRNA) was performed in BHI solid media with Hyg B (75 µg/ml Hyg) during 15 days of incubation at 36°C. Randomly selected Hyg resistant transformants were tested for mitotic stability by subculturing the fungus three times in Hyg 75 µg/ml and three more times in Hyg 150 µg/ml. Paracoccidioides yeast cells were also transformed with the empty parental vector pUR5750 (PbWt+EV) as a control during the assays that were performed in this study. The investigation of rbt5 gene expression was performed by qRT-PCR after consecutive subculturing.
Macrophages from the cell line J774 A.1 (BCRJ Cell Bank, Rio de Janeiro, accession number 0121), which were maintained in RPMI medium (RPMI 1640, Vitrocell, Brazil) that was supplemented with 10% (v/v) fetal bovine serum (FBS) at 37°C in 5% CO2, were used in this assay. In total, 1×106 macrophages were seeded into each well of a 24-well tissue culture plate, and 100 U/ml of murine IFN-γ (PeproTech, Rocky Hill, New Jersey, USA) was added for 24 h at 37°C in 5% CO2 for macrophage activation as described previously [59]. Prior to co-cultivation, Paracoccidioides yeast cells (PbWt, Pbrbt5-aRNA and PbWt+EV) were cultivated in BHI liquid medium for 72 h at 36°C. For infection, 2.5×106 Paracoccidioides yeast cells for each strain were added to the macrophages independently. The cells were co-cultivated for 24 h at 37°C in 5% CO2 to allow fungal internalization. Infected macrophages were first washed three times with 1X PBS, and then macrophages were lysed with distilled water. Dilutions of the lysates were plated in BHI medium, which was supplemented with 5% (v/v) fetal bovine serum (FBS), at 36°C. Colony forming units (CFUs) were counted after growth for 10 days. CFUs were expressed as the mean value ± the standard error of the mean (SEM) from triplicates, and statistical analyses were performed using Student's t-test.
For the mouse infection experiment, PbWt, Pbrbt5-aRNA and PbWt+EV were cultivated for 48 h in BHI medium, which was supplemented with 4% glucose. Thirty-day-old male BALB/c mice (n = 4) were inoculated intraperitoneally with 107 yeast cells of each strain independently, as previously described [60]. After 2 weeks of infection, mouse spleens were removed and were homogenized using a grinder in 3 mL of sterile 0.9% (w/v) NaCl. In total, 50 µl of the homogenized sample was plated on BHI agar, which was supplemented with 4% (v/v) fetal calf serum and 4% (w/v) glucose. The plates were prepared in triplicates for each organ of each animal and were incubated at 36°C. After 15 days, the CFUs for each organ that was infected with each strain were determined by counting, and a mean for each condition was obtained. The data were expressed as the mean value ± the SEM from quadruplicates, and statistical analyses were performed using Student's t-test.
The Paracoccidioides strains Pb01 and Pb18 were grown in the absence of iron (by adding 50 µM BPS, an iron chelator), or in the presence of different iron sources, after 36 h of iron scarcity to deplete intracellular iron storage (Figure 1). The host iron sources that were tested in this work included hemoglobin, ferritin, transferrin and lactoferrin. An inorganic iron source, ferrous ammonium sulfate, was also used. In all conditions, 50 µM BPS was added to verify that the chelator itself does not interfere with Paracoccidioides growth. Although some subtle differences were observed in the growth profiles, Pb01 and Pb18 were able to grow efficiently in the presence of different host iron sources, primarily hemoglobin and ferritin for both strains, and transferrin primarily for the Pb01 strain. In iron-depleted medium, Paracoccidioides grew poorly. Notably, both Pb01 and Pb18 presented a robust growth in the presence of hemoglobin or hemin as sole iron sources (Figure 1), which suggested that the increased growth in the presence of hemoglobin was not only due to the amino acid portion but also due to the heme group. These results indicate that hemoglobin could represent an important iron source for Paracoccidioides in the host environment.
The robust growth in the presence of hemoglobin and hemin led us to investigate the ability of Paracoccidioides to internalize protoporphyrin rings. For this assay, the fungus was incubated in the presence or absence of different concentrations of zinc-protoporphyrin IX (Zn-PPIX). The protoporphyrin ring is intrinsically fluorescent, but iron is an efficient quencher of this fluorescence. Consequently, the heme group is not fluorescent, but Zn-PPIX is [61]. Both Pb01 and Pb18 presented the ability to internalize the protoporphyrin ring because the fluorescence was observed only in fungi that were cultivated with Zn-PPIX (Figure 2). The cellular uptake of the compound was concentration- and time- dependent. As the Zn-PPIX concentration increased, the uptake increased in both strains (Figure 2). Similarly, increasing the incubation time also enhanced the uptake of Zn-PPIX by both strains (Figure S1). To test if another protoporphyrin ring-containing molecule could compete with Zn-PPIX for internalization, a pre-incubation with hemoglobin was performed before incubating the fungus in presence of Zn-PPIX. It was observed that the pre-incubation with hemoglobin, inhibited the Zn-PPIX uptake (Figure 3), suggesting that both compounds occupy the same sites for cell internalization. These observations suggest that, to acquire iron from heme, Paracoccidioides may internalize the entire molecule to release the iron intracellularly, instead of promoting the iron extraction outside before taking this ion up into cells.
To use hemoglobin as an effective iron source, microorganisms need to lyse host erythrocytes to expose the intracellular hemoglobin. The hemolytic ability of Paracoccidioides was assessed by incubating the fungus for 2 hours, after iron starvation, with sheep erythrocytes. Both Pb01 and Pb18 demonstrated the ability to lyse erythrocytes compared with phosphate buffered saline solution (PBS), which was used as a negative control (Figure 4). Sterile water was used as a positive control. Additionally, when Paracoccidioides was cultivated in iron presence, the fungus still presented ability to promote erythrocytes lysis (data not shown). These results suggest that Paracoccidioides produces a hemolytic factor that can be secreted or that is associated with the fungus surface.
In silico searches in the Paracoccidioides genome database (http://www.broadinstitute.org/annotation/genome/paracoccidioides_brasiliensis/MultiHome.html) were performed to verify whether the Pb01 and Pb18 genomes contain genes that encode hemoglobin receptors that are orthologous to genes in the C. albicans hemoglobin receptor gene family [19]. The Pb01 genome presents three putative hemoglobin receptors that are orthologous to C. albicans Rbt5, Wap1/Csa1 and Csa2, respectively. However, Pb18 presents only one ortholog to C. albicans Wap1/Csa1. Additionally, Pb03, which is the other Paracoccidioides strain that has its genome published, presents two orthologs, one to C. albicans Rbt51 and the other one to C. albicans Csa2, as suggested by the in silico analysis (Table S1). All four putative identified proteins are predicted to have a CFEM domain, which presents eight spaced cysteine residues [22]. These in silico analyses suggest that Paracoccidioides could uptake hemoglobin through a receptor-mediated process.
To analyze the expression of Pb01 genes that encode putative hemoglobin receptors, real-time qRT-PCRs were performed with the transcripts that encode the Pb01 proteins Rbt5, Wap1/Csa1 and Csa2 in different iron supplementation conditions. As depicted in Figure 5, the expression of Pb01 putative hemoglobin receptors were regulated at the transcriptional level. One can observe that rbt5, wap1/csa1 and csa2 transcripts were, in general, up-regulated in iron depletion in comparison to the other conditions tested in this work, at 30 min time point. The expression of Pb01 rbt5, for example, increased 20 times in iron-depleted condition in comparison to the presence of 100 µM inorganic iron during 30 min of incubation (data not shown). The exception is wap1/csa1 in presence of hemoglobin, suggesting that Pb01 Wap1/Csa1 might be involved in earlier stages of hemoglobin acquisition. Pb01 rbt5 was strongly activated after 120 min in the presence of 10 µM inorganic iron or 10 µM hemoglobin, in comparison with the no iron addition condition. This result suggests that Pb01 Rbt5 may participate in an iron acquisition pathway that is involved in hemoglobin iron uptake. The Pb01 csa2 transcript presented the same profile, which suggests that Pb01 Csa2 may also work in hemoglobin binding/uptake.
Since Paracoccidioides appears to use hemoglobin as an iron source, a nanoUPLC-MSE-based proteomics approach was employed to identify Pb01 proteins that were induced or repressed in the presence of 10 µM hemoglobin as the iron source, compared with 10 µM ferrous ammonium sulfate, which was used as an inorganic iron source. In total, 282 proteins were positively or negatively regulated. In this group, 159 proteins were induced (Table S2) and 123 proteins were repressed (Table S3) in the presence of hemoglobin, compared with proteins that were produced in the presence of inorganic iron. The false positive rates of protein identification in the presence of hemoglobin and in the presence of inorganic iron were 0.58% and 0.30%, respectively. In total, 75.42% and 76.87% of the peptides that were obtained in the presence of hemoglobin and in the presence of inorganic iron, respectively, were identified in a 5 ppm error range (Figure S2). The resulting peptide data that were generated by the PLGS process are shown in Figure S3. Some selected proteins that were induced or repressed in the presence of hemoglobin, are depicted in Tables 1 and 2, respectively. Many proteins that were detected are involved in amino acid, nitrogen and sulfur metabolism (Tables 1 and 2). Proteins that are involved in alanine, lysine, tryptophan or aspartate and glutamate metabolism, as well as those proteins that are involved in arginine, cysteine, histidine, serine or threonine biosynthesis, were upregulated in the presence of hemoglobin (Table 1). In contrast, proteins that are involved in asparagine or phenylalanine degradation were down regulated in the presence of hemoglobin (Table 2). This result suggests that the fungus could use hemoglobin not only as iron source, as demonstrated by the induction of proteins that are involved with the iron-sulfur cluster assembly, such as cysteine desulfurase (Table 1), but also as nitrogen and sulfur sources because many proteins that are involved in amino acid metabolism were upregulated in the presence of hemoglobin. This observation reinforces the notion that Paracoccidioides internalizes the entire hemoglobin molecule instead of promoting the iron release extracellularly. This internalization could occur by endocytosis because proteins that are involved with lysosomal and vacuolar protein degradation, including carboxypeptidase Y and a vacuolar protease A orthologs, were upregulated (Table 1). Among the induced proteins, it is important to highlight the Pb01 Csa2 detection only in presence of hemoglobin (Table 1), which corroborates the hypothesis that hemoglobin uptake by Paracoccidioides is receptor-mediated. Among the repressed proteins, those proteins that are involved with porphyrin biosynthesis, including uroporphyrinogen decarboxylase and a glutamate-1-semialdehyde 2,1-aminomutase orthologs, were detected only in the presence of inorganic iron (Table 2), which reinforces the hypothesis that hemoglobin is efficiently used by the fungus.
We continued our studies with the Pb01 ortholog of Rbt5, the best-characterized hemoglobin receptor in C. albicans [20]. As described above, Pb01 rbt5 was the transcript that was most efficiently regulated in the presence of hemoglobin. To investigate this result further, a recombinant GST-tagged Pb01 Rbt5 protein, which presents 42.5 kDa, was produced in Escherichia coli and purified using the GST tag. After purification, the GST tag was removed after thrombin digestion, and the resultant protein presented a molecular mass of 22 kDa (Figure S4A). Polyclonal antibodies were raised against the recombinant protein in rabbit. To verify the reactivity of the obtained antibody against the recombinant protein, Western blots were performed (Figures S4B and S4C). Only a 22 kDa immunoreactive species was obtained in the Western blot analysis after GST tag cleavage (Figure S4B, lane 3). No cross-reactivity was observed with pre-immune sera (Figure S4C).
In silico analysis identified a predicted signal peptide and a putative GPI anchor in the Pb01 Rbt5 ortholog, which was similar to C. albicans Rbt5 (Figure S5), suggesting that this protein could localize at the Paracoccidioides cell surface. In this way, the GPI-anchored proteins of the Pb01 cell wall were extracted using HF-pyridine. A Western blot assay was performed using anti-Pb01 Rbt5 polyclonal antibodies against the GPI-anchored protein extract, and a single immunoreactive 60 kDa species was identified in this fraction (Figure 6A, lane 2). The mass shift from 22 kDa to 60 kDa suggests post-translational modifications (PTMs) of the native protein, which is in agreement with the occurrence of glycosylation [62].
To confirm the cell wall localization, an immunocytochemical analysis of Pb01 yeast cells using anti-Pb01 Rbt5 polyclonal antibodies was prepared for analysis using transmission electron microscopy (Figure 6B, panels 2 and 3). Rbt5 was abundantly detected on the Pb01 yeast cell wall. Some gold particles were observed in the cytoplasm, which is consistent with intracellular synthesis for further surface export. The control sample was free of label when incubated with the rabbit preimmune serum (Figure 6B, panel 1).
The fact that Pb01 Rbt5 is homologous to C. albicans Rbt5 (Figure S5) suggests that Pb01 Rbt5 may participate in hemoglobin uptake in Paracoccidioides. In this way, the protein's ability to interact with the heme group was investigated. Affinity assays were performed using the recombinant Pb01 Rbt5 and a hemin-agarose resin (Figure 7A). A specific ability to interact with hemin was demonstrated to Pb01 Rbt5, since enolase, which is also present at the Pb01 yeast surface [52], did not present ability to bind to the hemin resin (data not shown). Moreover, when pre-incubating the recombinant Rbt5 with hemoglobin, the Rbt5 was not able anymore to interact with the hemin resin, suggesting that hemoglobin compete with hemin for the Rbt5 binding sites (Figure 7A).
To confirm the ability of Rbt5 to recognize heme-containing molecules, Pb01 yeast cells were submitted to binding assays for further flow cytometry analyses. Background fluorescence levels were determined using yeast cells alone (Figure 7B, black lines). Positive controls were composed of systems where Pb01 cells were incubated with polyclonal antibodies raised against PbRbt5, followed by incubation with a fluorescent secondary antibody (Figure 7B, red lines). For the determination of binding activities, Pb01 yeast cells were incubated with protoporphyrin or hemoglobin before or after exposure to the anti-PbRbt5 antibodies, followed by incubation with the secondary antibody (Figure 7B, green and blue lines, respectively). When yeast cells were incubated with protoporphyrin or hemoglobin before exposure to primary and secondary antibodies, fluorescence intensities were at background levels, suggesting that the heme-containing molecules blocked surface sites that are also recognized by the anti-PbRbt5 antibodies (Figure 7B). When the cells were exposed to protoporphyrin or to hemoglobin after incubation with the antibodies, the fluorescence levels were similar to those levels that were obtained in systems where incubation with the heme-containing proteins was omitted. These results suggest a high-affinity binding between Rbt5 and heme-containing molecules, which corroborates the hypothesis that Rbt5 could act as a hemoglobin receptor at the fungus cell surface.
To verify whether Rbt5 deficiency could influence the ability of the fungus to acquire heme groups or to survive inside the host, an antisense-RNA (aRNA) strategy was applied (Figure 8A). For this analysis, Pb339 was used, since the Agrobacterium tumefaciens-mediated transformation (ATMT) of this strain has been standardized [63]. The knockdown strategy was demonstrated to be efficient because the quantification of rbt5 transcripts in two isolates of knockdown strain (Pbrbt5-aRNA 1 and Pbrbt5-aRNA 2) was 60% lower than in the wild type strain (PbWt) (Figure 8B). The strain that was transformed with the empty vector (PbWt+EV) showed a similar level of rbt5 transcripts compared with PbWt (Figure 8B). Because of its higher stability, the Pbrbt5-aRNA 1 isolate was selected for the next experiments. The flow cytometry results with PbWt and PbWt+EV strains (Figure 8C) were similar to those results that are described in Figure 7B. In contrast, fluorescence intensities were all at background levels when the Pbrbt5-aRNA strain was assessed. These results indicate that the gene silencing was efficient also at protein level.
Despite the efficiency of the knockdown strategy, the Pbrbt5-aRNA strain demonstrated an identical ability to grow in the presence of hemoglobin as the iron source, compared to the other strains (Figure S6), which suggests that either a low amount of Rbt5 at the cell surface is sufficient to allow hemoglobin acquisition or that the other putative hemoglobin receptors could compensate for the Rbt5 deficiency. The identical growth ability of all three strains was also observed in media without iron and with ferrous ammonium sulfate as an inorganic iron source (Figure S6). However, the incubation in presence of Zn-PPIX demonstrated a decreased fluorescence of the Pbrbt5-aRNA strain in comparison to the other control strains (Figure S7), corroborating the hypothesis that Paracoccidioides Rbt5 could function as a hemoglobin receptor at the cell surface.
To test the ability of Paracoccidioides mutant strains to survive inside the host, two strategies were employed. First, Pbrbt5-aRNA and PbWt+EV were co-cultivated with macrophages. PbWt was used as a control. After 24 h, macrophages were first washed with PBS to remove the weakly bounded yeast cells and then were lysed with distilled water. Lysates were plated on BHI solid medium to recover the internalized fungi. After 10 days, the colony forming units (CFUs) were counted, and the Pbrbt5-aRNA presented approximately 98% reduction in the number of CFUs in comparison with PbWt and PbWt+EV (Figure 9A). The second strategy included a murine model of infection. Mice were inoculated intraperitoneally with PbWt, PbWt+EV and Pbrbt5-aRNA, independently. After 2 weeks of infection, the mice were sacrificed, and the spleens were removed. The organs were macerated, and the homogenized sample was plated on BHI agar for CFU determination. The number of CFUs after the infection with the Pbrbt5-aRNA strain was approximately 6 times lower than the CFUs that were observed after the infection with PbWt or with PbWt+EV (Figure 9B). These results indicate that the rbt5 knockdown could reduce the virulence of the fungi and/or increase the stimulation of the host defense cells to kill the fungus.
To verify whether PbRbt5 had antigenic properties, sera of five PCM patients were used in immunoblot assays against the recombinant protein. All sera presented strong reactivity against the recombinant Pb01 Rbt5 that was immobilized in the nitrocellulose membrane (Figure 9C, lanes 1–5). No cross-reactivity was observed with control sera of patients who were not diagnosed with PCM (Figure 9C, lanes 6–10). This result suggests that Pb01 Rbt5 is an antigenic protein that is produced by Paracoccidioides during human infection.
Because pathogenic fungi face iron deprivation in the host, these microorganisms have evolved different mechanisms to acquire iron from the host's iron-binding proteins [64]. C. albicans, for example, can use transferrin, ferritin and hemoglobin as host iron sources [15], [16], [19], [20]. It has been demonstrated in Paracoccidioides that genes that are involved in iron acquisition are not upregulated during the incubation of the fungus with human blood, which suggests that this condition is not iron-limiting for this fungus [60]. This observation, coupled with the Paracoccidioides preference for heme iron in culture, suggests heme iron scavenging during infection.
In this study, we observed that Paracoccidioides presented the ability to internalize a zinc-bound protoporphyrin ring in a dose- and time- dependent pattern. It seems that hemoglobin and Zn-PPIX occupy the same receptor sites, since hemoglobin blocked Zn-PPIX internalization. Moreover, the fungus could promote erythrocyte lysis. A hemolysin-like protein (XP_002797334) has been evidenced in a mycelium to yeast transition cDNA library [65], which indicates that Paracoccidioides could access the intracellular heme in the host by producing a hemolytic factor that can be secreted or associated with the fungus surface. The ability to internalize the zinc-bound protoporphyrin ring has been demonstrated for C. albicans, but not for C. glabrata [61]. The absence of protoporphyrin internalization by C. glabrata is most likely because heme receptors are not present in this fungus, as suggested by the fact that genes that encode these receptors have not been identified in the C. glabrata genome [61]. In contrast, a hemoglobin-receptor gene family that is composed of the genes rbt5, rbt51, wap1/csa1, csa2 and pga7 has been identified in C. albicans [19]. To access the heme group inside the erythrocytes, C. albicans also produces a hemolytic factor that is able to promote the lysis of erythrocytes [17].
By performing an in silico analysis, iron-related genes were identified in the Paracoccidioides genome, which were composed of rbt5, wap1/csa1 and csa2, that were orthologous to C. albicans genes that encode hemoglobin receptors [19], providing further evidence that Paracoccidioides has the ability to use hemoglobin in its regular metabolic pathways. In C. albicans, the transcripts rbt5 and wap1 are activated during low iron condition compared with high iron abundance conditions [24], which corroborate the hypothesis that these transcripts are involved in an iron acquisition mechanism, more specifically, in hemoglobin uptake. Similar results were obtained with Paracoccidioides. The rbt5, wap1/csa1 and csa2 transcripts were also induced in the fungus Pb01. Moreover, most of these transcripts were induced in a low-inorganic iron condition or in the presence of hemoglobin compared with iron depletion, after 30 minutes of incubation. These results suggest that the proteins that are encoded by the analyzed transcripts could be involved in hemoglobin utilization in Pb01.
The proteomic analysis of the Pb01 strain demonstrated that Csa2 was detected only in the presence of hemoglobin, which suggests that its uptake by Paracoccidioides is receptor-mediated, as described for C. albicans [19] and C. neoformans [26]. Among the three Pb01 hemoglobin-receptor orthologs, Csa2 is the only one that was not predicted to have a GPI-anchor (Table S1). Because no specific protocol to purify GPI-surface proteins has been used for proteomic analyses, no additional hemoglobin-binding proteins than Csa2 were identified in this proteome. The Pb01 proteome in the presence of hemoglobin demonstrated that proteins that are involved in amino acid, nitrogen and sulfur metabolism and in iron-sulfur cluster assembly were induced in comparison with the fungus that was cultivated in presence of inorganic iron. Moreover, proteins that are involved in porphyrin biosynthesis were detected only when the fungus was cultivated in the presence of inorganic iron. These results suggest that the fungus could use hemoglobin as an efficient source of nitrogen, sulfur, iron and porphyrin, internalizing the entire hemoglobin molecule. This internalization hypothesis is corroborated by the fact that proteins that are involved with lysosomal and vacuolar protein degradation were also induced in the presence of hemoglobin. Similar mechanisms have been suggested for C. albicans and C. neoformans. In C. albicans, hemoglobin is taken up by endocytosis after Rbt5/51 binding [20]. In C. neoformans, Cig1, a recently described extracellular mannoprotein that functions as a receptor or hemophore at the cell surface [26], potentially helps the fungus to take up heme before iron release, perhaps by endocytosis [27].
Pb01 rbt5 presented a high level of transcriptional regulation in the presence of hemoglobin, as observed in this work. In this way, Pb01 Rbt5 was investigated and demonstrated characteristics that were similar to C. albicans Rbt5, such as the presence of a CFEM domain [9] and a GPI anchor [66]. Pb01 Rbt5 was identified in the cell wall extract, which was enriched with GPI proteins, obtained as previously described [49], and was visualized at the Pb01 yeast surface. These results indicate that Pb01 Rbt5 is anchored at the fungal surface through a GPI anchor. To function as a hemoglobin receptor, a protein must be able to bind heme. It has been suggested that the CFEM domain is able to bind to ferrous and ferric iron, including the iron atom present in the center of the heme group [23], suggesting that Pb01 Rbt5 potentially binds to heme group. Pb01 Rbt5 heme group-binding ability and the competition between hemoglobin and hemin for the same Pb01 Rbt5 binding sites were demonstrated using batch ligand affinity chromatography, with a hemin-resin and the Pb01 Rbt5 recombinant protein. Moreover, flow cytometry assays using the whole Pb01 yeast cells and the anti-Rbt5 antibodies, which were raised against the Pb01 Rbt5 recombinant protein, in the presence of protoporphyrin and hemoglobin also demonstrated the Pb01 Rbt5 affinity for these two heme-containing molecules. In C. neoformans, the Cig1 heme-binding ability was detected using spectrophotometric titration and isothermal titration calorimetry assays with recombinant Cig1-GST protein purified from E. coli [26]. These results demonstrated that Pb01 Rbt5 is able to bind hemin, protoporphyrin and hemoglobin, which corroborate the hypothesis that Pb01 Rbt5 could function as a heme group receptor, which could help in the acquisition of iron from host sources.
Functional genomic studies in Paracoccidioides are recent because little is known regarding the fungi's life cycle. For instance, mechanisms of homologous recombination or haploid segregation in Paracoccidioides cells remain obscure. This paucity of data compromises the development of efficient classical genetic techniques [56]. Thus, to modulate the expression of target genes in Paracoccidioides, antisense RNA (aRNA) technology is applicable by A. tumefaciens-mediated transformation (ATMT) [53], [56], [63]. In this work, Paracoccidioides rbt5 knockdown strains were generated using the same methodologies. Reductions in gene and protein expression in Pbrbt5-aRNA strains were demonstrated by qRT-PCR and flow cytometry assays, respectively, in comparison with the control strains (wild type and empty vector transformed strains). It was also observed a reduced ability of the knock down strain to uptake heme groups, as demonstrated by the decreased Zn-PPIX internalization. Despite the knockdown of PbRbt5, no growth difference was observed in the presence of inorganic iron or hemoglobin sources. In contrast, in C. albicans, a Δrbt5 mutant strain presented reduced growth in the presence of hemin and hemoglobin as iron sources [19]. These results suggest that other hemoglobin receptors could function at the Paracoccidioides surface; this possibility is the focus of future studies in our laboratory.
Paracoccidioides is a thermodimorphic fungus that can infect the host by airborne propagules. After the mycelium-yeast transition in the host lungs, the fungus can disseminate to different organs and tissues through the hematogenous or lymphatic pathways [67], [68]. In the host tissues, including the lungs, the fungus can be internalized by macrophages [31], [35]. One of the functions of the macrophages is to recycle senescent red cell iron, primarily in the spleen. Hemoglobin-derived heme is catabolized and the heme iron is released by a hemoxygenase inside macrophages [5]. In this way, Paracoccidioides has at least two different opportunities to be exposed to the heme group: during (i) fungal dissemination by the hematogenous route or (ii) macrophage infection. Because it has been suggested that monocyte intracellular iron availability is required for Paracoccidioides survival [37], the ability of the rbt5 knockdown strain to survive inside macrophages was investigated. The rbt5 knockdown strain presented decreased survival inside macrophages in comparison with control strains, which indicates that Rbt5 could be a virulence factor and/or could affect macrophage stimulation to kill the internalized yeast cells. In addition, the fungal burden in mouse spleen that was infected with the rbt5 knockdown strain was lower than the fungal burden of the mice that were infected with the control strains, indicating that Rbt5 could be important for infection establishment and/or maintenance by Paracoccidioides. The differences observed between the in vitro and in vivo conditions may be due to host defense against Paracoccidioides in animals and macrophage. In contrast, the rbt5 deletion did not affect C. albicans virulence in animal models of infection [25], which indicates that other compensatory mechanisms could act in the absence of Rbt5 in this fungus [19]. The ability of Rbt5 to function as an antigen in Paracoccidioides was demonstrated by Pb01 Rbt5 recombinant protein recognition using sera of five PCM patients in immunoblot assays. Similar results were obtained for C. albicans because Rbt5 and Csa1 were found among 33 antigens that were recognized by sera from convalescent candidemia patients [69]. These results reinforce that Rbt5 could act in the host-pathogen interface.
Fungal surface proteins that are involved in iron uptake might be attractive targets for vaccines or drugs that block microbial proliferation. Moreover, these proteins could be considered as routes to introduce antifungal agents into fungal cells [70]. In that way, iron acquisition mechanisms could be important targets to prevent or treat fungal diseases. This study constitutes evidence that Paracoccidioides could acquire heme groups through a receptor-mediated mechanism. In that way, Rbt5 may be a good target for developing vaccines, for blocking Paracoccidioides proliferation inside phagocytes, or for using a Trojan horse strategy for introducing antifungal agents into yeast cells.
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10.1371/journal.pntd.0002965 | Wolbachia Enhances West Nile Virus (WNV) Infection in the Mosquito Culex tarsalis | Novel strategies are required to control mosquitoes and the pathogens they transmit. One attractive approach involves maternally inherited endosymbiotic Wolbachia bacteria. After artificial infection with Wolbachia, many mosquitoes become refractory to infection and transmission of diverse pathogens. We evaluated the effects of Wolbachia (wAlbB strain) on infection, dissemination and transmission of West Nile virus (WNV) in the naturally uninfected mosquito Culex tarsalis, which is an important WNV vector in North America. After inoculation into adult female mosquitoes, Wolbachia reached high titers and disseminated widely to numerous tissues including the head, thoracic flight muscles, fat body and ovarian follicles. Contrary to other systems, Wolbachia did not inhibit WNV in this mosquito. Rather, WNV infection rate was significantly higher in Wolbachia-infected mosquitoes compared to controls. Quantitative PCR of selected innate immune genes indicated that REL1 (the activator of the antiviral Toll immune pathway) was down regulated in Wolbachia-infected relative to control mosquitoes. This is the first observation of Wolbachia-induced enhancement of a human pathogen in mosquitoes, suggesting that caution should be applied before releasing Wolbachia-infected insects as part of a vector-borne disease control program.
| Current methods to control mosquitoes and the pathogens they transmit are ineffective, partly due to insecticide and drug resistance. One novel control method involves exploiting naturally occurring Wolbachia bacteria in insects. Wolbachia are bacterial symbionts that are attractive candidates for mosquito-borne disease control due to their ability to inhibit pathogens infecting humans. Additionally, Wolbachia affects insect reproduction to facilitate its own transmission to offspring, which has been exploited to establish the bacterium in naturally uninfected field populations. Most Wolbachia pathogen control research has focused on Aedes and Anopheles mosquitoes, but Culex mosquitoes also transmit pathogens that affect human health. We evaluated impacts of Wolbachia infection on West Nile virus (WNV) in the naturally uninfected mosquito Culex tarsalis. Wolbachia was able to efficiently establish infection in Cx. tarsalis but contrary to other studies, Wolbachia enhanced rather than inhibited WNV infection. Enhancement occurred in conjunction with suppression of mosquito anti-viral immune gene expression. This study indicates that Wolbachia control strategies to disrupt WNV via pathogen interference may not be feasible in Cx. tarsalis, and that caution should be used when releasing Wolbachia infected mosquitoes to control human vector-borne diseases.
| Efforts to control vector-borne pathogens have been hindered by evolution of insecticide resistance and failing drug therapies. Evidence suggests bed nets and indoor residual spraying with insecticides are losing efficacy in developing countries [1], [2]. To improve the sustainability and efficacy of control efforts, alternative vector control strategies are being considered, including methods that suppress the pathogen instead of the vector [3], [4]. Wolbachia are a genus of maternally-inherited bacterial endosymbionts that naturally occur in numerous arthropod taxa [5]. Wolbachia can inhibit viruses and parasites in fruit flies and mosquitoes [6]–[11] and influence reproduction of their host to facilitate spread through populations [12]. Mosquito-borne disease management programs that use Wolbachia are currently under investigation [13]. In field trials in Australia, Wolbachia reached fixation in naturally uninfected populations of Aedes aegypti [11] and the DENV blocking phenotype has been maintained [14], but the impacts of Wolbachia on reducing the incidence of disease are yet to be investigated.
Pathogen interference conferred by Wolbachia depends on various factors, including Wolbachia strain, pathogen type, infection type (natural versus artificial) and host and is not a guarantee [7], [15], [16]. For example, Wolbachia increases Plasmodium berghei, P. yoelii and P. gallinaceum oocyst loads in Anopheles gambiae, An. stephensi, and Aedes fluviatilis, respectively [17]–[19], and P. relictum sporozoite prevalence in Culex pipiens [20]. These Wolbachia-mediated pathogen enhancement studies suggest that careful examination of Wolbachia is required, since the bacterium influences insect-pathogen interactions in ways that may negatively impact pathogen control efforts.
Few studies have investigated the effect of Wolbachia on pathogen transmission by Culex mosquitoes, despite the fact they transmit viruses impacting human health [9], [21], [22]. Culex tarsalis is a mosquito species associated with agriculture and urban areas in the western United States [23] and is highly competent for West Nile virus (WNV), St. Louis encephalitis virus (SLEV) and western equine encephalitis virus (WEEV) [24]–[26]. Cx. tarsalis are naturally uninfected with Wolbachia [27]. We established Wolbachia infections in this mosquito by intrathoracic injection of purified symbionts into adult females, characterized the extent of the infection by fluorescence in situ hybridization and quantitative PCR, and assessed the ability for Wolbachia to block WNV infection, dissemination and transmission at multiple time points. We found that, in contrast to other systems, Wolbachia infection enhanced WNV infection rates 7 days post-blood feeding. This is the first observation of Wolbachia-induced enhancement of a human pathogen in mosquitoes and suggests that caution should be applied before using Wolbachia as part of a vector-borne disease control program.
Mosquitoes were maintained on commercially available bovine blood using a membrane feeder. WNV infection experiments were performed under biosafety-level 3 (BSL3) and arthropod-containment level 3 (ACL3) conditions.
The Cx. tarsalis YOLO strain was used for experiments. The colony was originally established from Yolo County, CA in 2009. Mosquitoes were reared and maintained at 27°C±1°C, 16∶8 hour light∶dark diurnal cycle at approximately 45% relative humidity in 30×30×30 cm cages. The wAlbB Wolbachia strain was purified from An. gambiae Sua5B cells according to published protocols [28]. Viability and density of the bacteria was assessed using the Live/Dead BacLight Kit (Invitrogen) and a hemocytometer. The experiment was replicated twice; wAlbB concentrations were: replicate one, 5.3×109 bacteria/mL; replicate two, 1.3×1011 bacteria/mL. Two- to four-day-old adult female Cx. tarsalis were anesthetized with CO2 and intrathoracically (IT) injected with approximately 0.1 uL of either wAlbB or Schneider's insect media (Sigma Aldrich) as a control. Mosquitoes were provided with 10% sucrose ad libitum and maintained at 27°C in a growth chamber. WNV strain WN02-1956 (GenBank: AY590222) was originally isolated in African green monkey kidney (Vero) cells from an infected American crow in New York in 2003 [29] and amplified in Aedes albopictus cells (C6/36) to a final titer of 5.0×109 PFU/ml. WNV was added to 5 mL defibrinated bovine blood (Hema-Resource & Supply, Aurora, OR) with 2.5% sucrose solution. Replicate titers were: replicate one, 8.0×107 PFU/mL; replicate two, 3.0×107 PFU/mL. Seven days post Wolbachia injection mosquitoes were fed a WNV infectious blood meal via Hemotek membrane feeding system (Discovery Workshops, Accrington, UK) for approximately one hour. Partially- or non-blood fed females were excluded from the analysis.
To characterize Wolbachia infections in Cx. tarsalis tissues, we performed fluorescence in situ hybridization (FISH) on mosquitoes at 12 dpi according to published protocols [10] with slight modifications. Briefly, mosquitoes were fixed in acetone, embedded in paraffin wax and sectioned with a microtome. Slides were dewaxed with three successive xylene washes for 5 minutes, followed by two 5-minute washes with 100% ethanol and one wash in 95% ethanol before treatment with alcoholic hydrogen peroxide (6% H2O2 in 80% ethanol) for 3 days to minimize autofluorescence. Sectioned tissues were hybridized overnight in 1 ml of hybridization buffer (50% formamide, 5× SSC, 200 g/liter dextran sulfate, 250 mg/liter poly(A), 250 mg/liter salmon sperm DNA, 250 mg/liter tRNA, 0.1 M dithiothreitol [DTT], 0.5× Denhardt's solution) with Wolbachia specific probes W1 and W2 labeled with a 5-prime rhodamine fluorophore [30]. After hybridization, tissues were successively washed three times in 1× SSC, 10 mM DTT and three times in 0.5× SSC, 10 mM DTT. Slides were mounted with SlowFade Gold antifade reagent (Invitrogen) and counterstained with DAPI (Roche). Images were captured with a LSM 510 META confocal microscope (Zeiss) and epifluorescent BX40 microscope (Olympus). Images were processed using LSM image browsers (Zeiss) and Photoshop 7.0 (Adobe) software. No-probe, competition probe and RNAse treatment controls were conducted (Figure S1).
Virus infection and transmission assays were performed as described at 7 and 14 days post blood feeding [31]–[33]. Female mosquitoes were anesthetized with triethylamine (Sigma, St. Louis, MO), legs from each mosquito were removed and placed separately in 1 mL mosquito diluent (MD: 20% heat-inactivated fetal bovine serum [FBS] in Dulbecco's phosphate-buffered saline, 50 ug/mL penicillin/streptomycin, 50 ug/mL gentamicin and 2.5 ug/mL fungizone). The proboscis of each mosquito was positioned in a tapered capillary tube containing 10 uL of a 1∶1 solution of 50% sucrose and FBS to induce salivation. After 30 minutes, the contents were expelled into 0.3 mL MD and bodies were placed individually into 1 mL MD. Mosquito body, legs and salivary secretion samples were stored at −70°C until tested for WNV presence and Wolbachia titers. Mosquito bodies and legs were homogenized for 30 seconds utilizing Qiagen Tissue Lyser at 24 cycles/second, followed by clarification via centrifugation for one minute. Mosquito samples were tested for WNV infectious particles by plaque assay on Vero cells [34]. Infection was defined as the proportion of mosquitoes with WNV positive bodies. Dissemination and transmission were defined as the proportion of infected mosquitoes with WNV positive legs and salivary secretions, respectively. Proportions were compared using Fisher's exact test. The experiment was replicated twice.
To evaluate Wolbachia density in individual mosquitoes from vector competence experiments, DNA was extracted using DNeasy Blood and Tissue kits (Qiagen) and used as template for qPCR on a Rotor Gene Q (Qiagen) with the SYBR green PCR kit (Qiagen). Wolbachia DNA was amplified with primers Alb-GF and Alb-GR [35] and was normalized to the Cx. tarsalis actin gene [36] (Table 1). Wolbachia to host genome ratios were calculated using Qgene [37]. PCRs were performed in duplicate. Comparisons of Wolbachia titers between treatments were analyzed using Mann-Whitney U test.
To explore Wolbachia effects on mosquito immune gene expression, one- to four- day old adult female Cx. tarsalis were anesthetized with CO2 and injected as described above with Wolbachia (wAlbB) or Schneider's insect media as control. Mosquitoes were provided with 10% sucrose ad libitum and maintained at 27°C in a growth chamber. At 7 dpi, mosquitoes were blood fed on bovine blood via glass membrane feeder. At 2 dpf, five mosquitoes per treatment were harvested and RNA extracted using RNeasy mini kits (Qiagen). Extracted RNA was DNase treated (Ambion #AM1906) and converted to cDNA using Superscript III with random hexamers (Invitrogen #18080-51) according to the manufacturers' protocols. qPCRs were performed using the Rotor Gene Q (Qiagen) and SYBR Green qPCR kit (Qiagen) according to the manufacturer's protocol. Five target immune genes in the Toll and IMD innate immune pathways (REL1, REL2, cactus, defensin and diptericin) were selected, primers designed based on homologous genes in the Anopheles gambiae, Aedes aegypti and Culex pipiens genomes and normalized to host actin (Table 1). Gene expression was analyzed by calculating ratios of target to host gene and tested for significance using Mann-Whitney U test. All qPCRs were technically replicated twice.
Using fluorescence in situ hybridization, we observed that wAlbB establishes an infection in both somatic and germline tissue in Cx. tarsalis 12 days post injection. Wolbachia disseminated to various tissues including the head, proboscis, thoracic flight muscles, fat body and ovarian follicles (Figure 1). Cx. tarsalis appeared heavily infected, suggesting that adult microinjection is an effective method to experimentally infect this mosquito species.
We evaluated the vector competence of Wolbachia-infected and uninfected Cx. tarsalis for WNV in mosquito bodies, legs and salivary secretions to determine infection, dissemination and transmission rates, respectively. Replicate results were similar, and results from pooled replicates or analysis of individual replicates were identical, so the pooled analysis is presented for clarity; results from individual replicates are available as Table S1. wAlbB-infected Cx. tarsalis displayed significantly higher WNV infection rates 7 days post-feeding (dpf) (P = 0.04). A similar but non-significant trend was observed 14 dpf (Figure 2). If mosquitoes were infected, virus dissemination and transmission rates did not differ statistically (Table S1).
To determine if there was a Wolbachia density effect on WNV phenotype, qPCR was used to compare Wolbachia titers in mosquitoes either positive or negative WNV. Wolbachia titers in WNV-infected versus uninfected Cx. tarsalis did not differ statistically; similarly, no significant titer differences were found in individuals that disseminated versus non-disseminated or transmitted vs. non-transmitted (Figure 3).
To elucidate the mechanism behind Wolbachia mediated WNV infection enhancement in Cx. tarsalis, we evaluated mosquito immune gene expression in response to Wolbachia using qPCR. Unlike other systems [38]–[40], Wolbachia did not induce a significant immune response in Cx. tarsalis females compared to the control. In contrast, REL1 (the NF kappa B activator of the antiviral Toll pathway) was significantly reduced in Wolbachia-infected mosquitoes compared to controls (one-tailed P = 0.008) (Figure 4).
It should be noted that these experiments were performed with mosquitoes transiently infected in the somatic tissues with Wolbachia, rather than a stable maternally inherited infection. It remains to be seen whether a stable wAlbB infection will enhance WNV in a similar way. Wolbachia density in mosquito somatic tissues (as opposed to germline) was found to explain differences in virus infection in Aedes mosquitoes [41]. Thus, it seems likely that if stable infection in Cx. tarsalis has a similar somatic tissue distribution to a transient infection it may induce a similar virus enhancement phenotype. However, this must be tested empirically. It is also unknown whether virus enhancement is limited to WNV or occurs more broadly. Finally, we tested a single Wolbachia strain, and it is unknown whether virus enhancement is specific to wAlbB or occurs with diverse Wolbachia strains.
Previous studies have shown that pathogen suppression by Wolbachia has the potential to be a novel method for controlling vector-borne diseases [4], [42]–[44]. Not all mosquito species are naturally infected with Wolbachia, but non-infected species may support infection once introduced and these novel infections often effectively inhibit various pathogens [5], [45]. Our experiments indicate that following adult microinjection, Wolbachia is capable of establishing both somatic and germline infection in Cx. tarsalis but does not inhibit WNV infection, dissemination or transmission. In contrast with other studies showing pathogen inhibition by Wolbachia, our data suggest that Wolbachia may in fact increase WNV infection rates in Cx. tarsalis, particularly at early time points. Increased early infection has the potential to shorten the extrinsic incubation period of the pathogen, which can dramatically increase the reproductive rate of the virus [19]. It has become increasingly clear that Wolbachia does not always suppress pathogens in insects [46]. For example, the cereal crop pest Spodoptera exempta is more susceptible to nucleopolydrovirus mortality in the presence of Wolbachia [47]. In the mosquitoes An. gambiae An. stephensi, Ae. fluviatilis and Cx. pipiens, Wolbachia enhances Plasmodium berghei, P. yoelii, P. gallinaceum and P. relictum, respectively [17]–[20]. Enhancement may be dependent on the host-Wolbachia strain-pathogen system of interest, as Wolbachia strains that block one pathogen yet enhance another have been documented [9], [17]. Wolbachia-mediated pathogen enhancement may be a common yet often ignored phenomenon, which merits attention when designing Wolbachia-based strategies for disease control [46].
Intracellular infection with bacteria may alter the cellular environment in multiple ways, including bacterial manipulation to avoid host immune defenses [48]. Though the exact Wolbachia-mediated inhibition mechanism is unknown, studies have suggested that Wolbachia indirectly modulates mosquito immunity [40], [49]. Wolbachia can activate the Toll pathway, stimulating a cascade of events that have been correlated with inhibition of dengue and Plasmodium in mosquitoes [39], [50], [51]. In contrast, in Cx. tarsalis, wAlbB infection significantly downregulated REL1 (the activator of the Toll pathway), suggesting that in this system Wolbachia may down regulate antiviral Toll-based immunity leading to increased virus infection. However, while statistically significant, this decrease in REL1 expression was modest, and further study is required to determine the precise mechanism of Wolbachia-based WNV enhancement in this system.
To our knowledge this is first study showing Wolbachia can potentially enhance a vector-borne pathogen that causes human disease. Our results, combined with other Wolbachia enhancement studies [17]–[20], [46]–[47], suggest that field deployment of Wolbachia-infected mosquitoes should proceed with caution. Wolbachia effects on all potential pathogens in the study area should be determined. Additionally, several studies have shown that Wolbachia is capable of horizontal transfer to other insect species which could have unforeseen effects on non-target insects [52]–[54]. A lack of understanding of Wolbachia-pathogen-mosquito interactions could impact efficacy of disease control programs. Cx. tarsalis is a competent vector for many human pathogens, and further studies that assess alternative Wolbachia strains and viruses in Cx. tarsalis may elucidate the importance of host background on pathogen interference phenotypes in this medically important mosquito species.
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10.1371/journal.pntd.0000702 | The Sudden Dominance of blaCTX–M Harbouring Plasmids in Shigella spp. Circulating in Southern Vietnam | Plasmid mediated antimicrobial resistance in the Enterobacteriaceae is a global problem. The rise of CTX-M class extended spectrum beta lactamases (ESBLs) has been well documented in industrialized countries. Vietnam is representative of a typical transitional middle income country where the spectrum of infectious diseases combined with the spread of drug resistance is shifting and bringing new healthcare challenges.
We collected hospital admission data from the pediatric population attending the hospital for tropical diseases in Ho Chi Minh City with Shigella infections. Organisms were cultured from all enrolled patients and subjected to antimicrobial susceptibility testing. Those that were ESBL positive were subjected to further investigation. These investigations included PCR amplification for common ESBL genes, plasmid investigation, conjugation, microarray hybridization and DNA sequencing of a blaCTX–M encoding plasmid.
We show that two different blaCTX-M genes are circulating in this bacterial population in this location. Sequence of one of the ESBL plasmids shows that rather than the gene being integrated into a preexisting MDR plasmid, the blaCTX-M gene is located on relatively simple conjugative plasmid. The sequenced plasmid (pEG356) carried the blaCTX-M-24 gene on an ISEcp1 element and demonstrated considerable sequence homology with other IncFI plasmids.
The rapid dissemination, spread of antimicrobial resistance and changing population of Shigella spp. concurrent with economic growth are pertinent to many other countries undergoing similar development. Third generation cephalosporins are commonly used empiric antibiotics in Ho Chi Minh City. We recommend that these agents should not be considered for therapy of dysentery in this setting.
| Shigellosis is a disease caused by bacteria belonging to Shigella spp. and is a leading cause of bacterial gastrointestinal infections in infants in unindustrialized countries. The Shigellae are dynamic and capable of rapid change when placed under selective pressure in a human population. Extended spectrum beta lactamases (ESBLs) are enzymes capable of degrading cephalosporins (a group of antimicrobial agents) and the genes that encode them are common in pathogenic E. coli and other related organisms in industrialized countries. In southern Vietnam, we have isolated multiple cephalosporin-resistant Shigella that express ESBLs. Furthermore, over two years these strains have replaced strains isolated from patients with shigellosis that cannot express ESBLs. Our work describes the genes responsible for this characteristic and we investigate one of the elements carrying one of these genes. These finding have implications for treatment of shigellosis and support the growing necessity for vaccine development. Our findings also may be pertinent for other countries undergoing a similar economic transition to Vietnam's and the corresponding effect on bacterial populations.
| Enterobacteriaceae that have the capability to express CTX-M (so named because of their hydrolytic activity against cefotaxime) family extended spectrum beta lactamases (ESBLs) have emerged as a major health threat worldwide [1], [2]. Most of the research in this area is conducted in industrialized countries, where organisms, such as Escherichia coli and Klebsiella spp., mostly from urinary tract infections are the commonest source [3], [4], [5]. Relatively little is known about the distribution of such genes in organisms found developing or countries undergoing an economic transition, where the circulating pathogens may differ.
Enterobacteriaceae capable of producing ESBLs have been described previously in South East Asia [6], [7]. Ho Chi Minh City in southern Vietnam is typical of many cities where patterns of infectious diseases are changing due to rapid economic growth, better access to health care and improving infrastructure. We recently showed that 42% of healthy people carried ESBL producing bacteria as part of their regular intestinal flora [8]. This previous work suggested that commensal organisms play a role in the dissemination and maintenance of such antimicrobial resistance genes in the population. Furthermore, the uncontrolled use of antimicrobials in the human population and in livestock rearing may lead to further problems with drug resistance and even more limited therapeutic options.
Shigellosis is a gastrointestinal infection caused by members by Shigella spp. Due to the faecal oral route of transmission of the Shigellae, children less than five years old and living in developing countries have the highest incidence [9], [10]. In our hospital in Ho Chi Minh City, shigellosis is the leading cause of paediatric diarrhoeal admission with bacterial aetiology. The infection is typically self limiting, although antimicrobial treatment is necessary for the young and those that are severely ill as it ensures fewer complications and curtails the duration of the disease [11].
Fluoroquinolones are the drugs of choice to treat Shigella infections in both adults and children [12]. However, as with many other members of the Enterobacteriaceae, mutations in the genes encoding the target proteins for fluoroquinolones are common in Shigella [13], [14]. Our recent findings show that patients with shigellosis are staying in hospital for longer periods compared with 5 and 10 years ago and the disease severity has concurrently increased [15]. Interestingly, at the same time there has been a significant species shift from S. flexneri to S. sonnei isolated from patients [15]. Patients here are treated with fluoroquinolones, however, those patients that do not respond to the standard therapy are treated with third generation cephalosporins (mainly ceftriaxone). The intravenous third generation cephalosporins are amongst the most commonly used antimicrobials in hospitals in Ho Chi Minh City and the oral second and third generation cephalosporins are also widely available in the community.
Antimicrobial resistance in the Shigellae is common; these organisms are closely related to E. coli and are readily transformed by exogenous DNA [16], [17], [18]. The distribution of antimicrobial resistance is, however, often different depending on the species. A multi-centre study across Asia demonstrated that S. flexneri were more likely to be resistant to ampicillin, whilst S. sonnei were more likely to be resistant to co-trimoxazole [19]. Resistance patterns and species dominance are variable depending on the specific location [20], [21], [22].
We have previously reported the rapid emergence of third generation cephalosporin resistant Shigella in Vietnam, where we noted the routine isolation of a number of ESBL producing microorganisms [15]. Here, we present data suggesting that ESBL negative organisms have been replaced with ESBL positive organisms.
This study was conducted according to the principles expressed in the Declaration of Helsinki. This study was approved by the scientific and ethical committee of the HTD and Oxford tropical research ethics committee (OXTREC) number 010-06 (2006). All parents of the subject children were required to provide written informed consent for the collection of samples and subsequent analysis.
The work was conducted on the paediatric gastrointestinal infections ward at the hospital for tropical diseases (HTD) in Ho Chi Minh City in Vietnam. The HTD is a 500 bed tertiary referral hospital treating patients from the surrounding provinces and from the districts within Ho Chi Minh City. All patients from which Shigella spp. were isolated were enrolled into a randomized controlled trial comparing treatment with ciprofloxacin and gatifloxicin as described previously [15] (trial number ISRCTN55945881). Briefly, all children (aged 0–14 years) with dysentery (defined as passing bloody diarrhoea or mucoid stools with additional abdominal pain or tenesmus) whose parent or guardian gave fully informed written consent were eligible for admission to the study. The primary outcome of the trial was treatment failure, defined as the patient not clearing symptoms after five days of antimicrobial treatment.
Stool samples were collected from patients and cultured directly on the day of sampling. Samples were cultured overnight in selenite F broth (Oxoid, Basingstoke, UK) and plated onto MacConkey and XLD agar (Oxoid) at 37°C. Colonies suggestive of Shigella were sub-cultured on to nutrient agar and were identified using a ‘short set’ of sugar fermentation reactions (Kliger iron agar, urea agar, citrate agar, SIM motility-indole media (Oxoid, United Kingdom)). Serologic identification was performed by slide agglutination with polyvalent somatic (O) antigen grouping sera, followed by testing with available monovalent antisera for specific serotype identification as per the manufacturer's recommendations (Denka Seiken, Japan).
Antimicrobial susceptibility testing of all Shigella isolates against ampicillin (AMP), chloramphenicol (CHL), trimethoprim – sulfamethoxazole (SXT), tetracycline (TET), nalidixic acid (NAL), ofloxacin (OFX;) and ceftriaxone (CRO) was performed by disk diffusion (Oxoid, United Kingdom). The minimum inhibitory concentrations (MICs) were additionally calculated for all isolates by E-test, according to manufacturer's recommendations (AB Biodisk, Sweden).
Those strains that were identified as resistant to ceftriaxone using the disk diffusion susceptibility test were further subjected to the combination disc method to confirm ESBL production [23], [24]. The combination disc method utilizes discs containing only cefotaxime (CTX) (30 µg) and ceftazidime (CAZ) (30 µg) and both antimicrobials combined with clavulanic acid (CLA) (10µg). ESBL producing strains were identified as those with a greater than 5 mm increase in zone with the single antimicrobial compared to the combined antimicrobial, i.e. demonstrating ESBL inhibition [25]. All antimicrobial testing was performed on Mueller-Hinton agar, data was interpreted according to the Clinical and Laboratory Standards Institute guidelines [26].
Genomic DNA was isolated from strains that were subjected to PCR and DNA microarray hybridisation from 1 ml of a 5 ml overnight bacterial culture using the wizard genomic DNA extraction kit (Promega, USA), as per the manufacturer's recommendations.
For characterization of gene content of isolated Shigella strains, genomic DNA was hybridized to an active surveillance of pathogens (ASP) oligonucleotide microarray [27], [28]. The ASP array contains over 6,000 gene markers, including species signature genes, virulence genes and antimicrobial resistance genes from over a hundred bacterial species. Thus the ASP array provides data for assessing horizontally transferred genes, such data is helpful for diagnosis and for guiding antimicrobial therapy.
The ASP array used in this study was version 6.2 and was designed and constructed as described previously [28]. Test samples were labelled and hybridised as described previously [29]. Briefly, 5 µg genomic DNA was labelled with Cy5 and hybridised with a formamide based hybridisation buffer solution in a final volume of 48 µl at 50°C for 16–20 hours. The ASP arrays were washed as described previously but with the initial wash at 50°C [29]. The ASP arrays were scanned using a 418 microarray Scanner (Affymetrix, USA) and intensity fluorescence data acquired using ImaGene 7.5 (BioDiscovery, USA). Data was analysed as described previously by Stabler et al. [28]. Briefly, a reporter was considered positive if the background corrected mean reporter signal from duplicate spots was both greater than one standard deviation of reporter signal (reporter variation) and the mean reporter signal was greater than the whole background corrected microarray mean plus one standard deviation, as shown for S. sonnei EG1007 in Dataset S1 in supporting information. The raw microarray data for all isolates is presented in Dataset S2 in supporting information.
Plasmid DNA was isolated from ESBL positive and ESBL negative Shigella isolates using a modified version of the methodology previously described by Kado and Liu [30]. The resulting plasmid DNA was separated by electrophoresis in 0.7% agarose gels made with 1× E buffer. Gels were run at 90 V for 3 h, stained with ethidium bromide and photographed. For DNA sequencing plasmid DNA containing an ESBL gene was extracted from an E. coli transconjugant using a NucleoBond® Xtra Midi kit as per the manufacturers recommendations (Clontech, USA)
Genomic DNA was subjected to PCR amplification targeting known classes of bla genes using, initially, primers that would recognise sequences encoding SHV, (F; 5′ TCTCCCTGTTAGCCACCCTG, R; 5′; CCACTGCAGCAGCTGC) TEM (F; 5′ TGCGGTATTATCCCGTGTTG, R; 5′ TCGTCGTTTGGTATGGCTTC) and CTX-M (F; 5′ CGATGTGCAGTACCAGTAA, R; 5′ TTAGTGACCAGAATCAGCGG) class ESBLs [31], [32]. Further characterisation of the various sub-group of blaCTX ESBL genes was performed using primers, CTX-M-1; (F 5′ ATGGTTAAAAAATCACTGCG, R 5′ TTACAAACCGTCGGTGAC), CTX-M-2; (F 5′ TGGAAGCCCTGGAGAAAAGT and R 5′ CTTATCGCTCTCGCTCTGT) and CTX-M-9; (F 5′ATGGTGACAAAGAGAGTGCAAC, R 5′ TTACAGCCCTTCGGCGATG) using previously outlined PCR amplification conditions [31], [32].
To identify an association with CTX-M genes and the adjacent ISEcp1 transposase, all ESBL positive strains were subjected to PCR with primers forward primers Tnp24F 5′ CACTCGTCTGCGCATAAAGCGG, Tnp15F 5′ CCGCCGTTTGCGCATA CAGCGG (for blaCTX-M-24 and blaCTX-M-15 respectively) and reverse primer TnpR 5′ AGATATGTAATCATGAAGTTGTCGG. The Tnp24F and Tnp15F were located within the blaCTX-M-24 and blaCTX-M-15 genes respectively and TnpR was located within the ISEcp1 transposase gene. The bla-transposase PCR was performed under the following conditions; 95°C for 1 minute, 30 cycles of 95°C for 30 seconds, 56°C for 30 seconds, 72°C for 1 minute 30 seconds and 72°C for 2 minutes. All PCRs were performed using Taq DNA polymerase and appropriate recommended concentrations of reagents (Bioline, UK).
Positive PCR amplicons were cloned into cloning vector pCR 2.1 (Invitrogen, USA) and sequencing reactions were carried out as recommended by the manufacturer using big dye terminators in forward and reverse orientation on an ABI 3700 sequencing machine (ABI, USA). All sequencing reactions were performed twice to ensure correct sequencing and sequences were verified, aligned and manipulated using Bioedit software (http://www.mbio.ncsu.edu/BioEdit/bioedit.html). All ESBL gene sequences were compared to other ESBL sequences by BLASTn at NCBI. The DNA sequence of various classes of blaCTX were downloaded and aligned with the produced sequences.
Bacterial conjugation experiments were performed by combining equal volumes (3 ml) of overnight Luria-Bertani cultures of donor and recipient strains. The donor strains were Shigella clinical isolates carrying blaCTX genes and the recipient was E. coli J53 (sodium azide resistant). Bacteria were conjugated for 12 hours at 37°C and transconjugants were selected on Luria-Bertani media containing sodium azide (100 µg/ml) and ceftriaxone (6 µg/ml). Potential transconjugants were verified by serotyping and plasmid extraction.
Plasmid pEG356 was selected for DNA sequencing and annotation as previously described [33]. The DNA sequence was annotated to identify coding sequences and repeat sequences in Artemis. To identify plasmids with similar sequences, pEG356 was compared by BLASTn at NCBI. pAPEC-01-ColBM (Ac. DQ381420) [34] was downloaded and aligned with pEG356 and viewed in Artemis Comparison Tool (ACT) [35]. Schematic drawing of the sequence of pEG356 was constructed using DNAplotter [36]. Artemis, ACT and DNAplotter are freely available at (http://www.sanger.ac.uk/Software). The full sequence and annotation of pEG356 was submitted to EMBL with the accession number FN594520.
During a 24 month period between April 2007 and March 2009 we isolated 94 Shigella strains from the stools of children admitted with dysentery. Of these 94 strains, 24 were S. flexneri and 70 were S. sonnei, confirming the species substitution previously noted from isolates in this region [15]. The general antibiotic sensitivity patterns in these strains were variable, although resistance to trimethoprim – sulfamethoxazole, tetracycline and latterly nalidixic acid were ubiquitous and there was an overall propensity of sensitivity towards older generation antimicrobials such as chloramphenicol (Table 1). A reversion of sensitivity to older therapies highlights how antimicrobial resistance genes can be maintained (or otherwise) by selective antimicrobial pressure in the population.
The first isolation of a ceftriaxone resistant organism during the transitional period occurred in May 2007 and similar strains were isolated in low numbers for the following months (Figure 1). The numbers of Shigellae isolated that were resistant to ceftriaxone fluctuated over the following 18 months. However, there was increase in the proportion of resistant to sensitive isolates 19% to 41% (5 to 11) between the periods from April 2007–September 2007 and April 2008–September 2008, respectively. This trend peaked in March 2009, with six out of seven Shigella strains isolated resistant to ceftriaxone (MIC>256). The overall rate of resistance to ceftriaxone between September 2008 and March 2009 was 75%.
We initially cultured a ceftriaxone resistant S. sonnei strain in 2001 (DE 0611) (Table 1), however, this strain was a single, isolated organism and a secondary ceftriaxone resistant Shigella was not isolated again until 2007. Between 2007 and 2009, 35 (34%) Shigella isolates cultured were resistant to ceftriaxone (Table 1). Of these strains, 33 were S. sonnei and the other two isolates were S. flexneri. In total, we isolated 36 ceftriaxone resistant organisms between 2001 and 2009.
The mechanism of ceftriaxone resistance was examined by the double disc inhibition method to identify ESBL producing organisms. All the S. sonnei and one S. flexneri strain (35 from 36 ceftriaxone resistant Shigella) produced the characteristic ESBL pattern on investigation, whereas the hydrolysing activity of the other S. flexneri organism was not inhibited by clavulanic acid [23], [24] (Table 1).
The median age of patients harbouring third generation cephalosporin resistant Shigellae was 32 months (range; 8 to 120 months), the median age of shigellosis patients during the same period was 30 months [15]. Owing to the rapid increase in the rate isolation of such organisms we hypothesised that an individual dominant strain had began circulating in one area of Ho Chi Minh City. However, residence data procured on the time of admission showed that such strains were circulating over a wide area of the city and not purely limited to an isolated outbreak (Table 1). 12 patients were resident in surrounding provinces, some 150 km from the hospital.
In conjunction with ceftriaxone, all strains were examined for resistance to an additional five antimicrobials by disc diffusion and MIC (Table 1). As predicted, all strains demonstrated co-resistance to ampicillin. Thirty five of the 36 strains (97%) were resistant to trimethoprim – sulfamethoxazole and tetracycline, whilst 33/36 were resistant to nalidixic acid. Only three isolates; DE0611, EG0419 and EG0471 were co-resistant to chloramphenicol, of which two, EG0419 and EG0471 (6%), were resistant to five of the six antimicrobials tested (Table 1).
The most common mechanism of dissemination of ESBL genes in the Enterobacteriaceae is plasmid mediated transfer. Our previous studies have suggested that Vietnam (and other parts of South East Asia) may be hotspot for the origin and further transmission of antimicrobial resistant organisms [8], [13], [37], [38]. Enterobacteriaceae which carry MDR plasmids are common in Vietnam and the isolation of MDR Shigella strains has been repeatedly reported [19], [20], [39].
We hypothesised that the ESBL phenotype was related to the insertion of a transposon carried on an MDR plasmid that had permeated into and was circulating within the Shigella population. To investigate the genetic nature of the ESBL positive isolates compared to the ESBL negative isolates we hybridised genomic DNA to an active surveillance of pathogens (ASP) DNA microarray. In total, 15 isolates (seven ESBL positive and eight ESBL negative) were compared. The ASP array is designed to monitor gene flux, genetic content and the nature of horizontally transferred DNA in a bacterial population. The resulting hybridisation is shown in Figure 2. Concurrently, plasmid DNA was isolated and compared from the same bacterial isolates to assess plasmid content.
Figure 2 is a heatmap representation of the 142 ASP microarray reporters which demonstrated positive hybridisation to DNA in two or more of the S. sonnei samples and the 11 reporters representing the S. sonnei Ss046 plasmid pSS_046. The overall hybridisation data and the names and predicted functions of the genes are presented in Dataset S2 (supporting information).
The pattern of relative hybridisation across all strains was remarkably homogenous, with only 30% (42/142+11 pSS_046) of the total proportion of the positive coding sequences demonstrating variable hybridisation patterns. The coding sequences demonstrating common hybridisation patterns across all 15 strains included a number of signature E. coli, Shigella spp. regions and sequences corresponding to virulence and antimicrobial resistance (Figure 2 and Supporting information Datasets S1 and S2).
The common antimicrobial resistance genes identified between isolates included genes conferring resistance to streptomycin, macrolides, tetracycline, beta lactams and also some unspecific antimicrobial resistance efflux genes. The homogenous nature of hybridisation suggests that variation between isolates is limited and dependent on plasmid content. All the ESBL producing strains demonstrated significant hybridisation to sequences corresponding to bla genes, highlighted in Figure 2, DNA from the ESBL negative strains failed to hybridise to these targets.
Plasmid visualisation of plasmid DNA by agarose gel electrophoresis with all hybridised strains revealed that in contrast to the ESBL negative isolates, all the ESBL producing isolates had a large plasmid, we roughly estimated to be greater than 63 Kbp in size (according to the marker plasmid). Despite the ESBL negative isolates lacking a large plasmid; these strains demonstrated similar resistance profiles, with the obvious exception of ceftriaxone (data not shown). These data suggested that the ESBL genes may be located on simple (none MDR) extrachromosomal elements. This hypothesis was supported by evidence of in vivo horizontal plasmid transfer; two strains cultured two days apart from the same patient were identical in serotype, plasmid content and MIC resistance profile, with the exception of the secondary strain carrying a large plasmid and displaying resistance to ceftriaxone (data not shown). Furthermore, sequencing of a conjugative, ESBL encoding plasmid confirmed our suggestion of a simple extrachromosomal element.
PCR was performed to detect the blaTEM, blaSHV and blaCTX-M genes. Further PCR amplifications were performed on DNA from all strains that produced amplicons with the blaCTX-M primers. Primers that were specific for the three major CTX-M clusters, blaCTX-M-9, blaCTX-M-1 and blaCTX-M-2 were selected [40]. Three strains (DE0611, EG0187 and EG0356) produced amplicons with the blaCTX-M-9 primers and the remaining 32 isolates produced amplicons with the blaCTX-M-1 primers (Table 2). All 35 PCR amplicon were sequenced.
Sequence analysis of the PCR amplicons demonstrated that there were two differing blaCTX-M genes present in the Shigella population, these were, blaCTX-M-24 (n = 3, 8%) and blaCTX-M-15 (n = 32, 92%) (Table 2). Both genes (blaCTX-M-24 and blaCTX-M-15) share 74% DNA homology with each other; blaCTX-M-15 and blaCTX-M-24 differ by 12 and 6 nucleotides from the precursor genes within their respective parent groups, (blaCTX-M-1 and blaCTX-M-9).
Plasmid sizing, by visualisation of the previous agarose gel electrophoresis demonstrated that the estimated plasmid size corresponded with either the blaCTX-M gene (Table 2); blaCTX-M-15 was consistently located on a plasmid larger than that associated with blaCTX-M-24. These observations were confirmed by Southern blotting hybridisation of plasmid DNA extractions (data not shown). The differing plasmid sizes and ESBL genes correlated precisely with two distinct zone clearance areas when strains were susceptibility tested with ceftazidime. The strains expressing CTX-M-24 demonstrated less activity against ceftazidime when compared to CTX-M-15 (median zone size, CTX-M-24; 28mm, CTX-M-15; 20mm) (Table 2).
All blaCTX-M harbouring plasmids with the exception of the plasmid in strain EG1020 were transmissible with high conjugation frequencies, ranging from 4.84×102 to 4.88×106 (median 1.55×102) per donor cell (Table 2). The mobilisation of one of these blaCTX harbouring plasmids was further demonstrated by conjugative transfer of the plasmid originally from S. sonnei EG356 from an E.coli transconjugant back into a fully susceptible, naive S. sonnei strain at a similarly high frequency.
The ESBL encoding gene blaCTX-M-24 appears to be generally restricted to Enterobacteriaceae in Asia [41], [42], with only sporadic reports of this gene in other locations [43]. Therefore, we selected the plasmid from isolate EG0356, carrying a blaCTX-M-24, as it is applicable to this location, for further characterisation by DNA sequencing.
Plasmid pEG356 was found to be a circular replicon consisting of 70,275 nucleotides, similar in size to another blaCTX-M-24 encoding plasmid from Asia; pKP96. pKP96 was isolated from a Klebsiella pneumoniae strain from China in 2002, yet demonstrates limited DNA homology to pEG356, with exception to the ESBL encoding region [44]. pEG356 was comparatively GC neutral (52.26%) and belonged to incompatibility group FI (on the basis of the DNA sequence homology to the replication region) (Figure 3). pEG356 was predicted to contain 104 coding sequences, of which 14 were considered to be pseudogenes on the basis of apparent premature stop codons, frameshifts or missing start codons. The density of coding sequencing approached 95% and contained four main structural features, a replication region, the ESBL gene encoding region with predicted homology to an ISEcp1 element, an iron ABC transport system and a DNA transfer region (labelled red, pink, dark blue and light blue, respectively in Figure 3).
pEG356 encoded the complete tra gene-set encoding a conjugative pilus with high sequence similarity to the transfer region from the F plasmid sequence from E. coli K12 [45] (Ac. AP001918). This is consistent with the in vitro data demonstrating that this plasmid is transmissible into an E. coli recipient. The IncFI replication region was highly similar to other IncF plasmids, including the recently described CTX-M-15 encoding plasmid pEK499 (Ac. EU935739) isolated from an E. coli O25:H4-ST131 epidemic strain circulating in the United Kingdom [46]. Additionally, pEG356 shared another 30 Kbp (position 15,152 to 44,255 in pEG356) of high sequence similarity with pEK499 [46]. This region contains multiple common hypothetical plasmid genes of unknown function, genes involved in conjugative transfer (traM to traC), plasmid partitioning and a predicted single stranded DNA binding protein (ssb). Unlike pEK499 the mok and hok post segregational killing genes are missing from within the plasmid maintenance region [46]. With respect to pEK499 and other ESBL carrying plasmids, pEG356 does not carry multiple antimicrobial resistance genes, transposons, insertion sequences or any additional virulence associated genes [44], [46], [47](Chen et al. 2007; Shen et al. 2008; Woodford et al. 2009)(Chen et al. 2007; Shen et al. 2008; Woodford et al. 2009).
In overall structure, but not size, pEG356 shared the most DNA sequence similarity with the ColBM plasmid pAPEC-O1 (Ac. DQ381420), isolated from an avian pathogenic E. coli strain [34] (Figure 4). pEG356 shared around 80% of the gene content with pAPEC-O1, including the conjugation (tra), replication (rep) and a putative ATP iron transport system (iro). The iro region consisted of four coding sequences, which include, a putative permease, an iron binding protein and an export associated protein.
The blaCTX-M-24 was located on an ISEcp1 like element. The overall sequence of the ISEcp1 variant on pEG356 is 4,725 bp and 3,000 bp shares 99% DNA homology with an ESBL gene encoding element from an E. coli strain that was isolated in China; pOZ174 (AF252622) [48]. The blaCTX-M-24 carrying region is also highly similar (99% DNA homology) to the equivalent region in the previously described plasmid, pKP96, including the IS903D downstream of the blaCTX-M-24 gene (Figure 4) [44].The ISEcp1 element contains two pairs of inverted repeat (Figure 4): the larger inverted repeat (31 bp) flanks the complete element, inclusive of six coding sequences. The 3′end of the ISEcp1 element contained a ISEcp1 transposase and a small hypothetical coding sequence of unknown function which is spanned by two IS1380 elements. The blaCTX-M-24 isadjacent to two pseudogenes, which were understood to have encoded a conserved hypothetical transposon protein and a maltose-inducible porin precursor, it is not clear what significance, if any, these genes are to the overall functionality of the element or the plasmid.
All ESBL producing Shigella were subjected to PCR to demonstrate if all bla genes were associated with the ISEcp1 transposase. The location of the PCR primers Tnp24F and TnpR are highlighted in Figure 4 and were designed to produce an amplicon if the bla gene and the adjacent ISEcp1 transposase were in the same location and orientation in strains with a blaCTX-M-24. A secondary forward primer was designed in equivalent location for those strains with a blaCTX-M-15 (Tnp15F). Therefore, if blaCTX-M-24 or the blaCTX-M-15 was consistently adjacent to the ISEcp1 transposase it would produce an amplicon of 414 bp in all strains. All ESBL positive strains (CTX-M-15 and CTX-M-24) generated a PCR amplicon of the predicted size (Table 2). Sequencing of all PCR products demonstrated that all the blaCTX-M-15 and the blaCTX-M-24 gene were associated with an ISEcp1 transposase, The DNA sequence from all PCR products was identical from within the transposase gene up to and including the IS1380.
Members of the Enterobacteriaceae that carry CTX-M family ESBLs have been isolated from many parts of the world since the mid 1990s [40]. CTX-M genes have been previously identified from pathogenic Enterobacteriaceae circulating in South East Asia; such as Vietnam, Thailand, Cambodia and Singapore [6], [7], [49], [50]. Additionally, our work has shown that ESBLs are commonly found in organisms which constitute the “normal” gastrointestinal flora in the general population living in Ho Chi Minh City [8]. Such data predicts that intestinal flora may be a considerable reservoir of ESBL encoding genes and the genetic elements they circulate on, permitting potential transmission to their pathogenic counterparts.
CTX-M genes in the Shigellae have been previously reported in Argentina, (CTX-M-2) [51], Korea (CTX-M-14) [52] and from a traveler returning from India (CTX-M-15) [53]. More recently, Nagano et al. described a novel CTX-M-64 hybrid from a shigellosis patient infected with S. sonnei after returning to Japan from China [54]. The S. sonnei strains isolated here in Ho Chi Minh City harbored the blaCTX-M-15 and blaCTX-M-24 genes. Current data suggests that blaCTX-M-24 is found mainly in Asia [41], [42], yet may have been transferred to other locations [43]. MDR CTX-M-15 producing E. coli is emerging worldwide as an important pathogen causing hospital-acquired infections [2]. The potential impact of MDR Shigella combined with CTX-M-15/24 carrying plasmids is substantial, with implications for local treatment policy and the transportation of such plasmids into other countries as has been implicated in Canada [43], [55].
The structure of pEG356 as a vector for transferring blaCTX-M-24 implies that such plasmids may be common. The streamlined nature of pEG356, remarkably high conjugation frequency may ensure onward circulation of the genetic cargo as it becomes stable in the bacterial population. The simplistic nature of pEG356, with a lack of additional resistance genes suggests that this is a contemporary element, with the blaCTX-M-24 a recent acquisition. The blaCTX-M-24 gene has been located on a relatively uncomplicated plasmid in Asia, however, pKP96 only demonstrates limited homology to pEG356 [44].
All ESBL gene were located adjacent to a ISEcp1 transposase (as identified by PCR). We are currently unable to substantiate if it is the ISEcp1-like element, the plasmids or the circulation of bacterial clone is responsible for the increasing rate of isolation. However, the geographical spread of these strains suggests that they are widely disseminated throughout southern Vietnam. S. sonnei is a monophyletic bacterial pathogen, and owing to the lack of sensitivity of existing sequence based methods such as multi locus sequence typing [56], we are currently unable to confirm clonality satisfactorily (data not shown). Further epidemiological investigation of CTX-M containing strains combined with a more sensitive sequenced based methodology, such as is used for Salmonella Typhi is required [57]. We are currently assessing the genetic nature of the strain and the plasmids carrying the ESBL genes.
Our findings show a transfer from 0% to 75% ceftriaxone resistance in S. sonnei over just two years in the key age group (1 to 3 years) for this disease. By sampling across the Ho Chi Minh City area, covering approximately 150 sq kilometres of Vietnam and a population of approximately 15 million people we have shown that the genetic explanation for this resistance pattern is the dissemination two distinct ESBL genes, of which one is dominant. These are the leading source of ESBLs in clinical Shigella cases and their rapid spread suggests that these organisms are under strong selection pressure. The use of third generation cephalosporins, such as oral cefpodoxime and cefixime in the community is common in Vietnam, and places the even the short term usage of ceftriaxone and other broad-spectrum cephalosporins in jeopardy.
Shigella spp. are capable of carrying multiple plasmids with an array of phenotypes including virulence and antimicrobial resistance [16], [18]. The presence of Shigella in the gastrointestinal tract of humans is an ideal environment to acquire horizontally transferred genetic material. Small highly transmissible plasmids that impinge on the fitness of the host may be rapidly disseminated under appropriate conditions.
Vietnam is a country that in many respects is representative of many parts of the world. The Vietnamese economy is developing rapidly and the country is undergoing transition with an increasing population, urbanisation and shifting patterns of infectious diseases. In the past decade there has been a transition in species from S. flexneri to S. sonnei in the Southern provinces of Vietnam. With this shift has come the emergence of ESBL S. sonnei. These findings from the Vietnamese population should perhaps serve as a warning for other countries encountering the same economic transition. The progressive evolution of pan-resistant Shigella makes vaccine development an increasingly important objective.
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10.1371/journal.ppat.1002280 | Evolutionarily Divergent, Unstable Filamentous Actin Is Essential for Gliding Motility in Apicomplexan Parasites | Apicomplexan parasites rely on a novel form of actin-based motility called gliding, which depends on parasite actin polymerization, to migrate through their hosts and invade cells. However, parasite actins are divergent both in sequence and function and only form short, unstable filaments in contrast to the stability of conventional actin filaments. The molecular basis for parasite actin filament instability and its relationship to gliding motility remain unresolved. We demonstrate that recombinant Toxoplasma (TgACTI) and Plasmodium (PfACTI and PfACTII) actins polymerized into very short filaments in vitro but were induced to form long, stable filaments by addition of equimolar levels of phalloidin. Parasite actins contain a conserved phalloidin-binding site as determined by molecular modeling and computational docking, yet vary in several residues that are predicted to impact filament stability. In particular, two residues were identified that form intermolecular contacts between different protomers in conventional actin filaments and these residues showed non-conservative differences in apicomplexan parasites. Substitution of divergent residues found in TgACTI with those from mammalian actin resulted in formation of longer, more stable filaments in vitro. Expression of these stabilized actins in T. gondii increased sensitivity to the actin-stabilizing compound jasplakinolide and disrupted normal gliding motility in the absence of treatment. These results identify the molecular basis for short, dynamic filaments in apicomplexan parasites and demonstrate that inherent instability of parasite actin filaments is a critical adaptation for gliding motility.
| Cellular movement is key to life and in the case of intracellular parasites, provides a vital mechanism to gain access to the protected niche they require. The parasite Toxoplasma gondii is a model for a group of parasites called apicomplexans, which move by an actin-dependent process referred to as gliding motility. This form of motility is distinct from that used by ciliated or flagellated cells, and from the crawling behavior of amoeba and many mammalian cells. We demonstrate that the normally highly conserved protein actin is divergent in these parasites and that it displays unusual kinetic properties that result in formation of short unstable filaments, in contrast to the highly stable nature of mammalian actin. Our findings reveal that the short dynamic nature of parasite actins is due to a small number of amino acid differences that affect stability of the filament. Moreover, these properties are essential to normal parasite motility since reversion of these residues to match those seen in mammalian cells was detrimental to gliding movement. The dependence of parasites on rapid turnover of highly unstable actins renders them extremely sensitive to toxins that stabilize actin filaments, thus providing a potential target for development of specific intervention.
| Actin is an essential protein that is highly conserved in sequence and function in eukaryotic cells. Despite this conservation, parasites within the phylum Apicomplexa encode divergent actins that remain largely in an unpolymerized state in vivo and only form short, unstable filaments in vitro, in contrast to conventional actins from yeast to mammals. Apicomplexan parasites are obligate intracellular protozoan pathogens of animals including humans. Two notable members of this phylum are Toxoplasma gondii [1], an opportunistic pathogen, and Plasmodium falciparum, the most severe cause of malaria [2], a devastating global disease. Apicomplexan parasites move by a unique form of gliding motility that is actin-dependent [3]. Initial studies demonstrated that host cell invasion by T. gondii [4] is blocked by cytochalasin D, and it was later shown using a combination of genetic mutants in the host vs. parasite that the primary target of these treatments was parasite actin filaments, which are essential for motility and cell invasion [5]. Gliding motility is considered to be a conserved feature of the phylum [6] and has been described in T. gondii tachyzoites [7], Plasmodium spp. sporozoites [8], Cryptosporidium spp. sporozoites [9], Eimeria sporozoites [10] and the more distantly related gregarines [11]. Gliding motility powers migration through tissues, traversal of biological barriers, and invasion into and egress from host cells [12].
T. gondii contains a single actin gene, TgACTI, which shows 83% amino acid identity with mammalian muscle actin [13]. P. falciparum contains two actin genes, PfACTI, that is closely related to TgACTI, sharing 93% identity at the protein level, and PfACTII, which is more divergent and has only 79% similarity to PfACTI [14]. Transcriptional analysis demonstrates that PfACTI is expressed throughout the parasite life cycle while PfACTII is most highly expressed in gametocytes [15]. Parasite actins have been shown to exist mostly in an unpolymerized state, as defined by sedimentation at 100,000g and an absence of staining in fixed cells with fluorescently labeled phalloidin [16], [17]. In contrast, the majority of actin in mammalian, yeast, and amoeba cells is found in long filamentous networks, or bundled fibers, which are readily stained with phalloidin and sedimented by centrifugation at 100,000g [18]. Although very uncommon in apicomplexans, actin filaments have been visualized by freeze fracture electron microscopy beneath the parasite membrane in gliding T. gondii tachyzoites [17]. PfACTI from P. falciparum has been shown to form short filaments in vitro [19], and similar short filaments of ∼100 nm in length were detected in lysates from asexually propagating stages (i.e. merozoites) following sedimentation at 500,000g [16]. Apicomplexans are also unusual in having a streamlined set of actin binding proteins consisting of actin depolymerizing factor, cyclase associated protein, profilin, and capping protein [20], [21], while they lack Arp2/3 [22] and many other regulatory proteins found in more complex systems.
Actin dynamics are controlled in part by an inherent ability of actin monomers to polymerize head-to-tail into parallel helical strands that form filaments [18]. Polymerization is dependent on Mg2+, salt (i.e. KCl), and ATP-actin and is thermodynamically favored above the so-called critical concentration (Cc) [18]. Above the Cc, filaments are typically highly stable, although gradual hydrolysis of ATP and release of Pi increases disassembly and susceptibility to severing [18]. TgACTI also requires high salt and Mg2+ for polymerization, and somewhat surprisingly it initiates polymerization more readily than conventional actins, and yet it only forms short transient filaments in vivo [23]. Consistent with this finding, in vitro polymerization of TgACTI results in formation of short, irregular filaments that rapidly disassemble in the absence of stabilizing compounds such as phalloidin [23]. TgACTI fails to copolymerize with mammalian actin [23]; however, copolymerization of PfACTI with rabbit muscle actin reveals differences in monomer stacking and a larger helical pitch in parasite actin [24]. Consistent with this, previous modeling studies have suggested that instability of parasite actin filaments might arise from structural changes [23], although this hypothesis has not been directly tested.
Highly motile cells often exhibit rapid actin turnover [18], suggesting that the unusual dynamics of apicomplexan actins may be important in gliding motility. Indirect evidence that actin turnover is important in T. gondii comes from treatment with agents that stabilize actin filaments, such as the heterocyclic compound jasplakinolide (JAS), which is produced by marine sponges and acts to stabilize actin filaments [25]. JAS treatment disrupts motility and cell invasion in T. gondii [17], [26], as well as invasion of merozoites [27], motility of ookinetes [28], and endocytic trafficking in trophozoites [29] of Plasmodium. Collectively, previous studies indicate that apicomplexan actins spontaneously polymerize into short filaments that are intrinsically unstable; however, the molecular basis and functional significance of these unusual properties are largely unknown.
The present study was undertaken to address two questions: 1) what intrinsic properties govern actin filament instability in apicomplexans?, and 2) are the unusual dynamic properties of filamentous actin important for efficient motility in apicomplexans? Here, we demonstrate that two divergent residues partially explain the inherent instability of parasite actin filaments and reveal that this feature is important for efficient gliding motility in T. gondii.
Despite overall conservation in sequence, apicomplexan actins are functionally divergent from actins in yeast, animals, and plants; likely due to molecular differences in parasite actins that affect function (see supplemental Figure S1). To visualize these shared differences, homology models were created to compare parasite actins: TgACTI and PfACTI are highly similar (Figure 1A, yellow spheres highlight differences) while PfACTII is more divergent (Figure 1B). We expressed recombinant actins from T. gondii (TgACTI), P. falciparum (PfACTI, PfACTII), and yeast (ScACT) using baculovirus and purified these proteins to study their properties in vitro, as described previously [23] (Figure 1C). Recombinant actins contained an N-terminal His6 tag that was used for purification; previous studies have shown that the presence of this tag does not alter polymerization [23]. The kinetics of actin polymerization was examined by light scattering following addition of filamentation (F) buffer. TgACTI only polymerized to a very limited extent (Figure 1D, red), while both PfACTI and PfACTII showed modest levels of polymerization (Figure 1D, blue and green, respectively). In contrast, polymerization of ScACT (Figure 1D, orange) was much more efficient, indicating that the inefficiency of parasite actin polymerization was not a consequence of expression in baculovirus, or the N-terminal tag shared by all of the proteins.
To test the ability of parasite actins to polymerize under stabilizing conditions, purified actins were incubated with different amounts of phalloidin during polymerization in F buffer. Consistent with previous reports [26], [30], filaments were not detected for TgACTI in the presence of low levels of fluorescently labeled phalloidin (i.e. 0.13 µM) that was added to visualize filamentous actin (Figure 2A). Short, punctate filaments were observed when a slightly higher level of labeled phalloidin (i.e. 0.33 µM) was added to TgACTI (Figure 2A,B). In contrast, long clusters of filaments were observed when TgACTI was allowed to polymerize in the presence of equimolar levels of unlabeled phalloidin combined with lower levels of labeled phalloidin for visualization (i.e. 0.33 µM) (Figure 2A,B). A similar dose-response to increasing phalloidin was seen for PfACTI and PfACTII, although these actins also occasionally formed small clusters of short filaments even in low levels of labeled phalloidin (i.e. 0.13 µM) (although rare, a representative example is shown in Figure 2A,B). Both PfACTI and PfACTII formed more abundant clusters of short filaments in slightly higher levels of labeled phalloidin (i.e. 0.33 µM) and these were further stabilized by equimolar unlabeled phalloidin (Figure 2A,B). As expected, ScACT formed long, stable filaments regardless of the phalloidin concentration (Figure 2A). Interestingly, the filaments formed by ScACT and PfACTII in the presence of high levels of phalloidin (equimolar) were often curved, while those of TgACTI and PfACTI where extremely straight (Figure 2). Measurement of the sizes of individual filaments formed by these different actins in response to phalloidin confirmed the general patterns seen by microscopy. Both PfACTI and PfACTII formed significantly longer filamentous structures than TgACTI in the presence of low levels of phalloidin used to visualize filaments (i.e. 0.13 or 0.33 µM), and all three actins showed a shift to longer filaments with with equimolar phalloidin treatment (Figure 2B).
Filaments formed by parasite actins were examined by negative staining and electron microscopy to reveal ultrastructural details. Similar to the fluorescent phalloidin assays, EM visualization of abundant parasite actin filaments required incubation in F buffer containing equimolar phalloidin (Figure 3). In the absence of added phalloidin, the parasite actins were observed to form irregular globular aggregates. Although we did not detect structures by EM that were similar to the small clusters seen by fluorescence staining of PfACTI and PfACTII in low levels of phalloidin (Figure 2A), this may reflect the low frequency of these forms or a requirement for low levels of phalloidin to stabilize them. Enlarged images of the phalloidin stabilized filaments formed by the three parasite actins revealed a spiral pattern of the actin helix and striations along the filament, which are typical characteristics of conventional actin filaments, as observed in ScACT filaments formed under all polymerizing conditions (Figure 3). Both the parasite actins and yeast actin showed prominent filament bundles, which are also seen in the fluorescence images mentioned above. Collectively, these studies verify that the instability of parasite actin filaments generated from recombinant tagged actins is intrinsic and that polymerization is rescued by high concentrations of phalloidin.
To investigate the molecular basis of phalloidin binding, we used structures from our molecular dynamics (MD) simulation of the muscle actin filament and performed molecular docking studies with phalloidin (Figures 4A, 4B). Our predicted phalloidin binding site is similar to that reported previously [31], but also provides more precise information on specific binding contacts that stem from the following improvements: 1) unconstrained docking analyses were based on a new higher resolution actin filament model [32]; 2) flexible protein conformations were included by choosing multiple snapshots from MD simulations and multiple binding sites were included within each snapshot; 3) induced fit was accommodated by simulated annealing. Together these analyses precisely mapped the phalloidin binding site in mammalian actin to the loop formed by residues 196–200 in the lower actin monomer, the 72–74 loop of the middle monomer, and the 285–290 loop of the upper monomer (Figure 4A). These three regions closely coincide with those identified in previous experimental studies as important for phalloidin interactions [31], [33], [34]. Importantly, residues including D179, Y198, S199, K284, I287 and R290, which were previously observed to be close to the phalloidin binding site [31], were also within 4 Å of phalloidin in our model. Moreover, our more precise placement of phalloidin predicts maximum interaction between the Cys3-Pro (OH)4-Ala5-Trp6 ring in phalloidin and actin residues, while Leu(OH)7 in phalloidin faces out of the binding pocket and is accessible to solvent. This orientation corresponds well with experimental studies [31], [33], [34] showing that derivatives of phalloidin with a fluorophore linked to Leu(OH)7, bind actin filaments in the same conformation.
Homology models for TgACTI and PfACTII were built using the muscle actin filament obtained by simulated annealing. Docking studies were repeated using TgACTI and PfACTII homology models and they yielded very similar conformations although the specific amino acid contacts lying within 4 Å varied slightly between proteins. Residues previously shown by mutational analysis to mediate phalloidin binding in yeast [35] (i.e. R177, D179), were conserved in all three models (Figure 4C). Residues R177 and D179 in mammalian actin, corresponding to R178 and D180 in parasite TgACTI, both lie within 4 Å of phalloidin (Figure 4C). Six specific differences in the residues contacting phalloidin in mammalian muscle actin vs. TgACTI and PfACTII were noted (Figure 4C, see supplemental Figure S1). Together, these differences may mediate the less efficient binding to phalloidin observed for parasite actin filaments.
Molecular modeling was also used to identify residues that differ between human muscle actin and TgACTI in regions that are predicted to be critical for stabilizing the actin filament. Divergent residues at positions G200 and K270 in T. gondii were identified as candidates that likely affect monomer-monomer interactions across the filament. However, the previously identified difference R277 in TgACTI, corresponding to glutamate in muscle [23], no longer made close contact in the new filament model, and consistent with this, no change in polymerization of substituted TgACTI-R277E was observed (data not shown). Instead, our refined model now points to S199 in human muscle actin as forming an important hydrogen bond with D179 of a monomer across the filament (Figure 5A). This hydrogen bonding was observed in a majority of inter-monomer contacts predicted in the MD simulations of the filament. However, at this position TgACTI contains a glycine that would eliminate the hydrogen bond and potentially adversely impact filament stability (Figure 5B). The second residue of interest identified was M269 in muscle actin (Figure 5A) that corresponds to K270 in TgACTI (Figure 5B). Mutational studies in yeast have previously demonstrated that loss of hydrophobicity in this loop leads to destabilization of the actin filament [36], and it has previously been suggested that this natural difference may affect parasite actin stability [23].
All three of the parasite actins studied here contain the alteration K/R270, whereas G200 is found in both TgACTI and PfACTI, while PfACTII has a threonine at this position (see supplemental Figure S1). In comparing other actins to those studied here, the alteration in G200 seen in T. gondii is conserved only in ACTI homologues found in a subset of apicomplexans that rely on gliding motility (Figure 6). In contrast, the substitution of K/R in the hydrophobic plug at residue 269/270 is seen in a wider variety of protozoa including dinoflagelates, ciliates, and apicomplexans (Figure 6). The distribution of these residues among taxa on the phylogenetic trees suggests a very different ancestry for these two alterations. The presence of a positive charged residue at 269/270 is polyphyletic, being found in a wide diversity of taxa, although not in higher plants or animals. In contrast, the G200, which was found in combination with K270, is confined to a subset of apicomplexans, which rely on gliding motility for cell invasion (Figure 6). These same patterns were confirmed by an independent phylogeny based on maximum likelihood (see supplemental Figure S2), indicating they are robust to different phylogenetic methods of analysis.
We chose T. gondii to test the importance of these two altered residues, since it is more amenable to genetic analyses. TgACTI residues were substituted with the corresponding amino acids from human muscle actin. The substituted proteins TgACTI-G200S (hydrogen bond substitution), TgACTI-K270M (hydrophobic loop substitution) and TgACTI-G200S/K270M (double substitution) were expressed using baculovirus and purified with Ni-affinity chromatography (Figure 5C). To determine if the substituted TgACTI alleles were more stable, purified proteins were incubated in F buffer and light scattering was used to examine polymerization. Wild type TgACTI underwent only limited polymerization while the TgACTI-K270M substituted protein showed a modest enhancement (Figure 5D). However, TgACTI-G200S and TgACTI-G200S/K270M showed increased polymerization, with both the rate (slope of the initial phase) and maximum extent being greater than wild type protein (Figure 5D).
The results of the light scattering assays were confirmed using fluorescent phalloidin staining and visualization via fluorescence microscopy. In all cases, filaments were visualized in the presence of low levels of labeled phalloidin (0.33 µM) combined with different amounts of unlabeled phalloidin. Wild type TgACTI (WT) required addition of 0.25 µM of unlabeled phalloidin to form small filaments and long filaments only formed upon addition of 5 µM (a 1∶1 ratio with actin) (Figure 7A, top row). The TgACTI-K270M protein showed a slight enhancement in polymerization with small filaments appearing in the presence of low levels of phalloidin and reaching longer lengths with addition of 1 µM molar ratio of unlabeled phalloidin (Figure 7A, second row). Interestingly, the TgACTI-G200S substitution showed much more robust polymerization with short filaments being detected even in the presence of low levels of labeled phalloidin (0.33 µM) and longer filaments appearing with addition of 0.25 µM of unlabeled phalloidin (Figure 7A, third row). TgACTI-G200S/K270M also formed longer filaments than seen with wild type TgACTI, similar to the TgACTI-G200S single mutant (Figure 7A, bottom row). Quantitation of the filament lengths formed by different TgACTI alleles confirmed that the features seen in microscopy were consistent across different replicate samples. All three of the mutant actins formed significantly longer filaments in low levels of phalloidin needed for visualization (i.e. 0.33 µM) compared to wild type TgACTI (Figure 7B). At higher levels of phalloidin, all of the actins formed long filaments of approximately equal length (Figure 7B). Taken together, these results show that TgACTI filaments may be inherently unstable due to the presence of only a few differences from conventional actins and that mutations designed to mimic mammalian actin in TgACTI result in formation of more stable actin filaments in vitro. We also investigated the effects of altering yeast actin to mimic residues found in TgACTI (i.e. converting S199 to G and L269 to K): neither mutation alone, nor the combination, showed dramatic change in filament assembly or length (data not shown), indicating there also must be other structural and kinetic differences that explain the extremely stable nature of actin filaments in yeast and likely other conventional actins. However, these data are not inconsistent with the gain of function results seen in mutant forms of TgACTI, where the magnitude of polymerization is still relatively modest when compared to yeast.
Despite the relatively small changes in actin stability observed in TgACTI mutants in vitro, we reasoned that such changes might still adversely affect actin-based processes in the parasite, such as gliding motility, which is exquisitely sensitive to actin stabilizing drugs like JAS [17]. To examine the effect of expressing stabilized mutants of TgACTI in T. gondii, we generated transgenic parasites expressing a second copy of TgACTI fused to an N-terminal degradation domain (DD), which allows regulated expression in the presence of Shield-1 [37]. This approach was chosen over allelic replacement, since we reasoned that the mutant alleles might be detrimental, hence compromising attempts to evaluate their functions. The regulated nature of the DD-stabilized proteins also allows the timing of expression to be controlled, thus minimizing the chance for pleomorphic downstream effects or compensatory changes that can occur using conventional dominant negative strategies. Transgenic lines expressing the DD-fusion proteins were tested for regulated expression by Western blot using an antibody against TgACTI (Figure 8A) and by immunofluorescence detection of the c-myc tag also present at the N-terminus (Figure 8B). The level of DD-tagged actins was approximately 50% of wild type actin and the patterns of staining was diffuse in the cytosol, similar to the pattern for endogenous actin described previously [13]. The expression of DD-tagged actins was similar at 6, 12, 24, and 40 hr (the point of natural egress) (see supplemental Figure S3). This relatively rapid induction with continued expression maintained over time allowed us to test different biological phenotypes at different time points. Initially, the impact of expressing DD-TgACTI fusions on the life cycle of the parasite was tested under continuous treatment with Shield-1 using a plaque assay, which monitors the normal intracellular growth cycle (Figure 8C). Although parasites expressing DD-G200S or DD-G200S/K270M formed plaques comparable to the controls in the absence of Shield-1, plaque formation was almost non-existent when parasites were treated with Shield-1 (Figure 8C), demonstrating that expression of stabilized TgACTI disrupts the parasite life cycle. In contrast, expression of the wild type DD-TgACTI (DD-wild type) had no effect on plaque formation either in the absence or presence of Shield-1 (Figure 8C). Additionally, growth in Shield-1 did not significantly alter cell division during the first 36 hr (see supplementary Figure S3), indicating that the expression of wild type or mutant actins does not affect endodyogeny, although we cannot rule out the possible effects on growth from expression of these actins over longer time frames.
Based on the observation that culturing parasites in Shield-1 for an entire lytic cycle does not affect replication, we treated cells for 40 hr and harvested parasites to examine the distribution and polymerization state of actin. Parasites expressing DD-TgACTI fusion proteins revealed a diffuse pattern of actin staining with some discrete puncta (Figure 9A; data not shown). The absence of detectable long filaments in cells expressing stabilized actins suggest that they behave somewhat differently in vivo than in vitro, perhaps as a result of other proteins that regulate actin turnover. Actin dynamics are highly sensitive to actin-stabilizing compounds like JAS, which permeates cells and stabilizes actin filaments [38]. Hence, we examined the distribution of actins in parasites expressing DD-TgACTI alleles following treatment with low levels of JAS (i.e. 0.25 µM). Filamentous actin structures were revealed emanating from both the apical and posterior poles in parasites expressing the stabilized TgACTI mutants grown in the presence of Shield-1, whereas staining of wild type DD-TgACTI relocalized to the poles without forming visible filaments (Figure 9A). The actin filaments seen in parasites expressing stabilized mutants of TgACTI formed spiral patterns beneath the surface of the parasite, as visualized in sequential slices of a z-series (Figure 9B). Measurement of the size of actin structures in parasites revealed that larger puncta were found in parasites expressing DD-G200S and DD-G200S/K270M vs. DD-wild type TgACTI in the absence of JAS, and that these mutant actins formed considerably longer filaments in the presence of low levels of JAS (Figure 9C). Co-staining of parasites expressing DD-tagged actins and treated with higher level of JAS (i.e. 1 µM), revealed that the tagged alleles co-localized in actin-filament rich apical projections, which are induced by JAS (see supplemental Figure S4).
To determine if the stabilized DD-TgACTI alleles polymerized more readily in vivo, we examined the proportion of globular and filamentous actin based on sedimentation at 350,000g, conditions previously found to be necessary to pellet short filaments that form in parasites [16]. Although no change in sedimented actin was detected in control lysates, treatment with low levels of JAS (i.e. 0.25 µM) induced much greater polymerization of the DD-G200S and DD-G200S/K270M mutants compared to DD-wild type (Figure 9D). Collectively, these studies demonstrate that stabilized forms of TgACTI were more sensitive to JAS-induced polymerization in vivo.
To examine the impact of stabilized TgACTI alleles on parasite motility, we employed video microscopy to analyze the typical circular and helical motions that are characteristic of gliding, as described previously [7]. In contrast to DD-wild type TgACTI expressing parasites that underwent normal circular gliding (Figure 10A, see supplemental Video S1), a large percentage of the circular movements in parasites expressing DD-G200S and DD-G200S/K270M actins were aberrant (Figures 10B, 10C). For example, DD-G200S and DD-G200S/K270M expressing parasites often stalled, were unable to complete circles, or went off-track during gliding (Figures 10B, 10C see supplemental Videos S3, S4, S5). Quantification of these results indicated that expression of the DD-wild type allele resulted in a higher frequency of circular gliding than helical, relative to untransfected parasites, however these movements were largely normal (Figure 10D). Although mutants expressing DD-G200S and DD-G200S/K270M underwent wild type motility in the absence of Shield-1, significantly more cells exhibited aberrant forms of gliding motility in the presence of Shield-1 (Figure 10D). Comparison of the radii of tracks made by DD-wild type and DD-G200S and DD-G200S/K270M expressing parasites undergoing circular gliding motility revealed that the mutants traced out partial arcs that were significantly larger than circular tracks formed by wild type parasites (Figure 10E). The larger arc traced by parasites expressing stabilized actins was in part due to a modest change in the shape of the cell, as shown by measuring the curvature of the parasite body during gliding, although this difference was not significant (Figure 10F). Collectively, these results indicate that expression of the mutant DD-G200S and DD-G200S/K270M forms of TgACTI disrupts normal circular gliding motility.
Consistent with previous descriptions of normal helical motility [7], DD-wild type expressing parasites underwent helical gliding at a relatively fast rate and moved through numerous corkscrew motions, noted in the example shown (Figure 10A see supplemental Video S2). In contrast, DD-G200S and DD-G200S/K270M expressing parasites were delayed in their movements and went through fewer flips and turns (Figures 10B, 10C, see supplemental Video S6). Parasites expressing the stabilized TgACTI alleles were significantly slower in both helical and circular gliding compared to the untransfected or DD-wild type parasites (Table 1). Taken together, these findings reveal that expression of stabilized mutants of TgACTI significantly disrupts gliding motility in T. gondii.
Since earlier data had shown that DD-tagged alleles are induced to similar levels after 6 hr of treatment, we wanted to test the effects of shorter treatments on gliding motility, in order to rule out possible indirect effects of long-term treatment. Hence, Shield-1 was added to infected monolayers during only the last 6 hr of development, and parasites were harvested following natural egress. As described above, defects in gliding motility were seen in T. gondii parasites expressing either the DD-G200S and DDG200S/K270M mutant actins (Figure 10G). Following Shield-1 treatment, parasites expressing mutant, but not wild type actin, showed increased frequency of aberrant circular trails including stalled, incomplete and off-track patterns, as well as aberrant helical patterns (Figure 10G). The similar phenotypes observed at 6 hr and 40 hr of Shield-1 treatment for the mutant actins, and absence of defects in parasites expressing wild type tagged actin, indicates that the aberrant gliding phenotypes are due to expression of stabilized actin alleles, rather than non-specific effects.
Our studies suggest that the instability of parasite actin filaments is an inherent property that results in part from differences in monomer-monomer interactions that normally stabilize the filament. Although parasite actins only form clusters of small filaments when visualized with low levels of phalloidin, equimolar levels of phalloidin rescued parasite actin filament instability in vitro, resulting in long stable filaments that resembled conventional actin. Reversion of two key residues in T. gondii actin to match those predicted to stabilize mammalian muscle actin, also partially restored filament stability in vitro. Furthermore, in vivo expression of these stabilized actins led to disruption of gliding in T. gondii. These findings provide insight into the molecular basis of parasite actin filament dynamics and reveal formation of short, highly dynamic actin filaments is an important adaptation for parasite motility.
Our studies are in agreement with previous work on the polymerization properties of parasite actins and extend these findings by examining the molecular basis for instability of actin filaments. We have previously reported that TgACTI undergoes polymerization in vitro as determined by tryptophan quenching and sedimentation, although the extent of this process was not compared to conventional actins [23]. In the present report, we examined actin polymerization by staining with fluorescently labeled phalloidin, sedimentation, electron microscopy, and light scattering, which provides a convenient method to study dynamics. Our findings indicate that while TgACTI undergoes polymerization, it has a very limited capacity to do so in comparison to yeast actin. There have been previous studies on the polymerization differences between muscle actin and actins from either budding [39] or fission yeast [40], however the differences observed in these cases are relatively minor compared to what we observe here between parasite and yeast actins. Rather than subtle shifts in the polymerization kinetics or critical concentration, the extent of polymerization with these parasite actins is fundamentally different from yeast or mammalian actins.
The inefficient polymerization of TgACTI was rescued by conditions that stabilize the filament including treatment with phalloidin in vitro. Other studies have previously examined PfACTI produced in yeast and concluded that it also polymerizes poorly in vitro [19]. In this prior study, stabilization with phalloidin (1∶4 molar ratio) was used to achieve modest levels of polymerization of PfACTI purified from yeast and copolymerized with bovine ß-actin [19]. In a separate study, the ability of PfACTI purified from merozoites to be stabilized by phalloidin showed a pH dependence, with greater polymerization detected at pH 6.0 than 8.0 [24], although the basis of this response is unknown. Our findings with PfACTI and PfACTII demonstrate that these actins, while modestly better at polymerization than TgACTI, also fail to polymerize robustly on their own. The reasons for this apparent instability have not been definitively resolved but could result from a lower capacity for elongation, more rapid disassembly, or a lower capacity to anneal, as described previously [41]. In contrast to yeast and vertebrate actins, our studies show that apicomplexan actins are highly dependent on addition of high levels (i.e. equimolar ratios) of phalloidin to form long stable filaments. Because the phalloidin binding site sits at the interface between protomers within the filament, it may overcome inherent instability caused by changes that affect monomer-monomer contacts within parasite actin filaments.
Several new F-actin models have been produced in the past few years [32], [42] and these models have given us new insight into the structural details of protomer interactions within the filament. However, the difficulties with interpreting a single, uniform F-actin structure have also been highlighted [43], and this is precisely why we make use of molecular modeling studies in our work here. Based on our dynamics simulations, we see that actin filaments are stabilized by interactions across the width (inter-strand) of the filament through two key regions including the “hydrophobic plug” encompassing residues 265–270 and a helix from residues 191–199 [32], [42]. Our studies further suggest that relatively few changes in these critical regions account for the instability of parasite actin filaments. Among these alterations, a change in the hydrophobic plug (i.e. K270 in T. gondii) plays a modest role while an alteration in the helix (i.e. G200) has a larger affect on filament stability. The substitution of K270M in TgACTI resulted in filaments that were detected by fluorescent staining at low concentrations of phalloidin, although this change had less effect on actin polymerization as monitored by light scattering assays in the absence of phalloidin. As this residue lies within the phalloidin pocket, it suggests that hydrophobic residues here result in enhanced phalloidin binding. Mutations designed to reduce hydrophobicity in the corresponding residue in yeast actin (i.e. L269) have no affect on polymerization, while those at the other end of the hydrophobic plug are much more severe [44]. Hence, these results indicate that K270 contributes to normally low phalloidin binding of parasite actins, while it likely plays a lesser role in intrinsic filament instability. Modeling predictions also indicate that S199 in muscle actin plays a role in filament stabilization via a hydrogen bond network with R177 and D179. Consistent with this, mutation of G200S had a larger impact on the in vitro polymerization of TgACTI as shown by increased light scattering, even in the absence of phalloidin. Collectively, the absence of these two stabilizing interactions in TgACTI partially explains the inherent instability of parasite actin filaments. Intriguingly, both PfACTI and PfACTII polymerized slightly better than TgACTI in the absence of stabilizing agents, and the introduction of two alterations in TgACTI (G200S and K270M) resulted in polymerization to levels that approximated with wild type levels of the Plasmodium actins (compare Figures 1,2 to 5,7). Together, these findings indicate that other sequence and structural differences between these actins must contribute to their inherent differences in polymerization kinetics, which is not surprising in light of findings that even conventional actins such as yeast and muscle differ significantly [39], [40].
The intrinsic properties of actins may be highly significant in controlling dynamics in apicomplexan parasites since they contain only a streamlined set of actin-binding proteins [20], [21]. PfACTI contains similar substitutions to those described for TgACTI above, while PfACTII contains a K at 270 and T at 200 instead of S199 in muscle. PfACTI is expressed throughout the Plasmodium life cycle including merozoites, while PfACTII is expressed primarily in sexual stages ([14], [15] and EupathDB.org). Sporozoites and ookinetes undergo actin-dependent gliding motility on substrates and cells, while merozoites do not show substrate-dependent gliding but rely on a similar actin-dependent process for invading red blood cells [45]. In comparing the two different actin isoforms in Plasmodium, PfACTII was slightly more stable than PfACTI as shown by fluorescent phalloidin staining of filaments, raising the possibility that T200 is capable of partial hydrogen bonding, analogous to the interaction of S199 in muscle actin. Increased actin filament stability may be important in non-motile forms such as gametocytes where PfACTII is highly expressed [15]. It is also possible that the natural variation in actins found in parasite actins, and the specific changes in TgACTI mutants studied here, are influenced by interactions with actin binding proteins.
Actin filament instability is evidently an important adaptation since expression of stabilized TgACTI within the parasite had a detrimental effect on gliding motility, while only modestly affecting cell division over the first 24–36 hr. Although we have not directly measured the effects on invasion or egress, these processes also depend on gliding motility and therefore are likely affected by expression of the stabilized mutants of TgACTI. Collectively, these phenotypes likely have an additive effect in the plaque assay, which captures successive rounds of invasion, replication, egress, and motility, thus leading to a more dramatic phenotype. The effects of expressing mutant actins in T. gondii partially mimic the effects of treatment with JAS, supporting the conclusion that they arise by stabilizing actin filaments. Previous studies using actin stabilizing agents such as JAS have revealed that increased polymerization of TgACTI filaments adversely effects motility and host cell invasion [17], [26], [30]. In the present study, stabilized mutants of TgACTI were more sensitive than wild type parasites to JAS, as shown by formation of spiral actin filaments and increased sedimentation. The spiral patterns seen here are similar to those reported previously from wild type T. gondii treated with high levels of JAS [17]; however, notably here they occur with low levels of JAS and are only seen in mutants expressing stabilized TgACTI forms. Stabilized DD-TgACTI mutants also had a profound effect on disrupting normal motility in the absence of treatment, revealing that this phenotype is not simply due to enhanced binding to JAS or phalloidin. Intriguingly, parasites expressing stabilized actins formed circles with larger radii, moved more slowly, and stalled in the process of gliding. These larger arcs were in part due to a more relaxed curvature of parasites expressing mutant actins, although this difference was much less pronounced than that seen in the trails. Hence, the increased trail radii likely results from the parasite slipping off its track as it migrates around the circle. Previous studies have also shown that the degree of actin polymerization can influence adhesive strength and hence the gliding behavior of Plasmodium sporozoites [46]. Collectively these data suggest that short, highly dynamic actin filaments are required for parasites to complete the tight arcs and corkscrew turns that are characteristic for circular and helical gliding [47].
The current model for gliding motility predicts that short, highly dynamic actin filaments attached to transmembrane adhesive proteins are translocated along the surface of the parasite by a small myosin [48]. The myosin motor, which is anchored in the inner membrane complex [49], is also highly nonprocessive [50], meaning it does not stay attached to a single filament for long periods. Instead, this model predicts that short actin filaments, tethered to transmembrane adhesins, are passed sequentially between motor complexes that operate independently. Consistent with this, where actin filaments have been seen in parasites, they are quite short (i.e. 50–100 nm) [16], [23]. Actin in apicomplexans may be adapted for rapid turnover of short filaments, since long filaments would increase the likelihood of multiple motors being engaged simultaneously, potentially leading to conflicting forces on the same filament. Although we were not able to discern distinct filaments in parasites expressing DD-TgACTI proteins, the observed punctate staining pattern may reflect clusters of short filaments that are below the resolving power of the light microscope (in theory ∼200 nm, but in practice likely ∼400 nm). Nonetheless, we would predict based on their in vitro properties that the G200S and G200S/K270M mutants would form more stable filaments, which could inhibit motility by reducing free monomers needed for new filament assembly, or by physically disrupting productive motor-actin filament complexes. Alternatively, stabilized DD-TgACTI mutants could affect interactions with actin-binding proteins in vivo, including those involved in polymerization or depolymerization. Although apicomplexans lack an Arp2/3 complex [22], they express several formins that act to increase actin polymerization [51], [52] and actin depolymerization factor, which acts primarily to sequester monomers and prevent polymerization [53]. Regardless of the exact mechanism, our results indicate that even subtle changes in actin filament stability significantly affect function, underscoring the importance of rapid actin dynamics in apicomplexans.
In comparing apicomplexans to other organisms, the G200S mutation is found in a subset of apicomplexans including Toxoplasma, Neospora, Eimeria, and Plasmodium spp. but excluding Theileria, Babesia, Cryptosporidium, and gregarines. Hence it is uncertain if conversion to G200 arose in the common ancestor of coccidians (monoxenous and tissue cyst forming) and hematozoa and was subsequently lost by some members, or if arose independently in Plasmodium and the coccidian. Gliding motility has not been described in Thieleria, which enters lymphocytes by a very different process than other apicomplexans [54]. However, Babesia enters red cells by a process very analogous to that seen in Plasmodium [55], and so likely has a conserved mechanism for actin-based motility. Cryptosporidium and gregarines also move by gliding motility [6], although the polymerization properties of actins from these organisms have not been examined. Hence it is unclear whether these other apicomplexans rely on more stable actins, or if other divergent residues impart similar properties to those observed in Toxoplasma and Plasmodium.
The substitution of K/R in the hydrophobic plug at residue 269/270 is seen in a wider variety of protozoa including dinoflagelates, ciliates, and apicomplexans. Consistent with this, diverse actins from protozoans Leishmania [56], Giardia [57], and Tetrahymena [58] have also been reported not to bind well to phalloidin and to display unusual polymerization kinetics or novel actin structures. This pattern further suggests that stable actin filaments are a more recent evolutionary development, found in amoeba, yeast, plants and animals, but not shared by many protozoans. There are some exception to this pattern, such as Giardia, which expresses a very divergent actin that nonetheless forms stable filamentous structures [57]. Although no kinetic measurements have been reported for Giardia actin as of yet, when available they will provide extremely useful comparisons to other systems. Overall these differences in actin filament stability likely reflect adaptations for stable vs. dynamic actin cytoskeletons that are designed for very different life strategies. The importance of dynamic actin turnover in apicomplexans is shown by introduction of stabilizing residues in TgACTI, changes that were sufficient to dramatically slow the speed of gliding and result in aberrant forms of motility. Collectively, these findings demonstrate that actin filament instability and rapid turnover are important adaptations for productive gliding in apicomplexans, and suggest that small molecules designed to selectively stabilize parasite actins may have potential for preventing infection.
Recombinant Toxoplasma, Plasmodium and yeast actins were expressed in baculovirus, as previously described [23]. Recombinant viruses were created by amplification from 3D7 strain of Plasmodium falciparum cDNA or Saccharomyces cerevisiae cDNA using gene-specific primers (Table S1) and the resulting products were cloned into the viral transfer vector pAcHLT-C (BD Biosciences Pharmingen). Recombinant viruses were obtained by cotransfection with linearized baculogold genomic DNA into Sf9 insect cells (BD Biosciences Pharmingen), according to manufacturer's instructions. Recombinant viruses for mutant TgACTI alleles and were created via site-directed mutagenesis using wild type TgACTI as a template and allele-specific primers (Table S1).
Hi5 insect cells were maintained as suspension cultures in Express-Five SFM media (Invitrogen). Hi5 cells were harvested at 2.5 days postinfection with recombinant virus and lysed in BD BaculoGold Insect Cell Lysis Buffer (BD Biosciences Pharmingen) supplemented with 0.2 mM CaCl2, 0.2 mM ATP, 0.2 mM NaN3, and protease inhibitor cocktail (E64, 1 µg ml−1 AEBSB, 10 µg ml−1; TLCK, 10 µg ml−1; leupeptin, 1 µg ml−1). His-tagged actins were purified using Ni-NTA agarose (Invitrogen). After binding for 2 hr, the column was washed sequentially with G actin buffer without DTT (G-DTT buffer) (5 mM Tris-Cl, pH 8.0, 0.2 mM CaCl2, 0.2 mM ATP), then G-DTT buffer with 10 mM imidazole, G-DTT buffer with 0.5 M NaCl and 10 mM imidazole, G-DTT buffer with 0.5 M KCl and 10 mM imidazole, and finally G-DTT buffer with 25 mM imidazole. Proteins were eluted with serial washes of G-DTT buffer containing 50 mM, 100 mM, and 200 mM imidazole, pooled together and dialyzed overnight in G-actin buffer containing 0.5 mM DTT with 100 µM sucrose. Purified recombinant actins were clarified by centrifugation at 100,000g, 4°C, for 30 min using a TL100 rotor and a Beckman Optima TL ultracentrifuge (Becton Coulter) to remove aggregates. Purified proteins were resolved on 12% SDS-PAGE gels followed by SYPRO Ruby (Molecular Probes) staining, visualized using a FLA-5000 phosphorimager (Fuji Film Medical Systems), and quantified using Image Gauge v4.23. Purified actins were stored at 4°C and used within 2–3 days.
Purified recombinant actins were clarified as described above and incubated (5 µM) in F buffer (50 mM KCl, 2 mM MgCl2, 1 mM ATP), and treated with different molar ratios of unlabeled phalloidin to actin from 0∶1 to 1∶1 (Molecular Probes). In addition, final concentrations of 0.13 µM or 0.33 µM Alexa-488 phalloidin (Molecular Probes) were added to each sample to visualize filaments. Following polymerization for 1 hr, samples were placed on a slide and viewed with a Zeiss Axioskop (Carl Zeiss) microscope using 63× Plan-NeoFluar oil immersion lens (1.30 NA). Images were collected using a Zeiss Axiocam with Axiovision v3.1 and processed using linear adjustments in Adobe Photoshop v8.0. Filament lengths were determined using the measurement feature of Axiovision software (Zeiss). For each actin sample, filaments were measured from 8–10 fields (63×) within three biological replicates.
Purified recombinant actins were clarified as described above and incubated (5 µM) in G buffer containing 1 mM EGTA and 50 µM MgCl2 for 10 min (to replace bound Ca2+ with Mg2+). Samples were placed in a submicrocuvette (Starna Cells) and following addition of 1/10th volume of 10× F buffer, light scattering was monitored with the PTI Quantmaster spectrofluorometer (Photon Technology International) with excitation 310 nm (1 nm bandpass) and emission 310 nm (1 nm bandpass). Curves were processed by second order smoothing with 15–30 neighbors using Prism (Graph Pad).
Homology models for TgACTI, PfACTI, and PfACTII sequences were built on the ADP-actin crystal structure (1J6Z) [59] using Modeller [60]. Homology models were aligned and visualized using VMD [61]. Protein sequences for actins from Homo sapiens (muscle α-actin), gi: 6049633; Saccharomyces cerevisiae, gi: 38372623; Toxoplasma gondii, gi: 606857; Plasmodium falciparum ACTI, gi: 160053; and Plasmodium falciparum ACT2, gi: 160057; were aligned using DNASTAR Lasergene MegAlign v7 and modified using Adobe Illustrator v10.
An atomic model of phalloidin was derived from the solid state structure of a synthetic derivative [62], modified to contain dihydroxy-Leu7 using Maestro (Schrödinger LLC,) and energy minimized using MacroModel (Schrödinger LLC,) with a MMFF94s forcefield. The model was further optimized in continuum solvent using Jaguar (Schrödinger LLC), with DFT level of theory using a hybrid B3LYP functional and 6-31G** basis set. The actin filament model based on X-ray fiber diffraction data [32] was used to create an 8-monomer filament of muscle F-actin. A 50 ns molecular dynamics (MD) simulation in explicit water was carried out using NAMD [63] in an NpT ensemble with a pressure of 1 atm and a temperature of 300 K with explicit TIP3P water. CHARMM27 forcefield was used with a 10 Å cut off for van der Waals with a 8.5 Å switching distance, and Particle Mesh Ewald for long-range electrostatics. Bonded hydrogens were kept rigid to allow 2 fs time steps. A simulated annealed structure of muscle filament model with phalloidin in the binding site was used as the template for building parasitic actin filament homology models using Modeller [60].
Docking of phalloidin to different sites along the filament was captured using multiple snapshots taken at intervals of 200 ps from the 50 ns simulation. AutoDock [64] was used to perform large scale docking runs with a coarse grid that covered the six binding sites on the filament. To determine the correct orientation of phalloidin in the binding site, higher resolution docking studies were performed on each binding site using both AutoDock and Glide (Schrödinger LLC) in independent trials and clustered to derive the most probable docking orientation. For AutoDock, flexible ligand docking was performed using Lamarckian genetic algorithm with a population size of 200, 10 million energy evaluations, and a local search probability frequency at 0.2. Grid spacings of 0.325 Å and 0.25 Å were used for coarse and high resolution docking, respectively, and the results were clustered at RMSD of 3.0 Å from the lowest docked energy conformer. Gasteiger-Marsili charges were assigned to the ligand using Sybyl (Tripos Inc.,). Default parameters were used for Glide; ligand charges were derived from the quantum optimization calculation and protein charges were derived from the OPLS2001 forcefield.
TgACTI alleles were amplified by PCR and inserted into a modified vector pTUB-DD-myc-YFP-CAT-Pst1 [37] at unique Pst1-AvrII sites to generate DD-TgACTI fusions. The resulting plasmids were transfected into tachyzoites of the RH strain of Toxoplasma and parasites were single celled cloned on monolayers of HFF cells and propagated as previously described [65].
For intracellular staining, parasites were allowed to invade HFF monolayers on glass coverslips for 24 hr in the presence or absence of 4 µM Shield-1. The coverslips were then fixed with 4% formaldehyde and stained with mouse anti-c-myc (Zymed) to detect the DD-fusion proteins followed by goat anti-mouse IgG conjugated to AlexaFluor 488 (Molecular Probes) and mAb DG52 (anti-TgSAG1) directly conjugated to AlexaFluor 594 to detect the parasite. To examine the pattern of actin following expression of DD-tagged actins, parasites were cultured in Shield-1 for one lytic cycle (i.e. 40 hr) and then harvested following natural egress. Freshly harvested parasites were treated ±0.25 µM JAS (Invitrogen) for 15 min and allowed to glide for 15 min on glass coverslips coated with 50 µg ml−1 BSA. Coverslips were fixed and stained with mouse anti-cmyc (Zymed) followed by goat anti-mouse IgG conjugated to AlexaFluor 488 and mAb DG52 labeled with AlexaFluor 594. Coverslips were mounted in Pro-Long Gold anti-fade reagent (Invitrogen) and viewed with a Zeiss Axioskop (Carl Zeiss) microscope using 63× Plan-NeoFluar oil immersion lens (1.30 NA). Images were collected using a Zeiss Axiocam and deconvolved using a nearest neighbor algorithm in Axiovision v3.1. Images were processed using linear adjustments in Adobe Photoshop v8.0. To determine the length of actin filament structures, the longest continuously staining patterns (i.e. puncta, filaments, or spirals) were determined using the measurement feature of Axiovision software (Zeiss). Measurements were made from 6–8 separate parasites from each of the groups (i.e. DD-wild type, DD-G200S, and DD-G200S/K270M) ± JAS treatment.
Plaque assays were conducted by adding 300 purified parasites to HFF monolayers in 6-well dishes containing medium+DMSO or medium +3 µM Shield-1 in DMSO and incubated at 37°C with 5% CO2 for 7 days. Plates were then fixed with 70% ethanol and stained with 0.01% crystal violet to visualize plaques.
Freshly lysed parasites were used to infect HFF monolayers with the addition of 4 µM Shield-1 for 6, 12, 24 or 40 hr prior to egress. Following natural egress, parasites were filtered, spun at 400g for 10 min and resuspended in Laemmli sample buffer. Parasite lysates from each time point were resolved on 12% SDS-PAGE gels, Western blotted with anti-TgACTI antibody, visualized using a FLA-5000 phosphorimager (Fuji Film Medical Systems) and quantified using Image Gauge v4.23.
Analysis of replication of parasites expressing DD-TgACTI alleles were conducted by adding freshly egressed parasites to HFF monolayers on coverslips in 24 well plates containing medium +DMSO or medium+4 µM Shield-1 in DMSO and incubated at 37°C with 5% CO2 for 24 or 36 hr at which time the coverslips were fixed and stained with mAb DG52 labeled with AlexaFluor 488. Coverslips were mounted in Pro-Long Gold anti-fade reagent (Invitrogen) and viewed with a Zeiss Axioskop (Carl Zeiss) microscope using 63× Plan-NeoFluar oil immersion lens (1.30 NA). The numbers of parasites per vacuole were counted in triplicate from at least 50 vacuoles per coverslip from three replicate experiments.
Parasite strains expressing DD-tagged actins were treated ±0.5 µM JAS for 30 min, lysed with Triton-X-100 for 1 hr, centrifuged at 1,000g, 4°C for 2 min and supernatants centrifuged at 350,000g, 4°C for 1 hr using a TL100 rotor and a Beckman Optima TL ultracentrifuge (Becton Coulter). Supernatant proteins were acetone precipitated and washed with 70% ethanol. All pellets were resuspended in 1× sample buffer, resolved on 12% SDS-PAGE gels, Western blotted with anti-TgACTI antibody, visualized using a FLA-5000 phosphorimager (Fuji Film Medical Systems), and quantified using Image Gauge v4.23.
Parasite gliding was monitored by video microscopy as previously described [7]. Parasites were treated with DMSO or 4 µM Shield-1 for 6 or 40 hr, resuspended in Ringer's solution and allowed to glide on uncoated glass coverslips. Images were captured with 50–100 ms exposure times at 1 sec intervals, combined into composites with Openlab v4.1 (Improvision), analyzed using ImageJ and saved as QuickTime videos. Cell motility was tracked using the ParticleTracker plug-in to evaluate average speeds from a 3–15 tracks. The percentage of parasites undergoing different forms of motility was quantified from 4 or more separate videos, 60 sec in length and containing 10–40 motile parasites each, using Cell Counter, as described [66]. Radii of circular trail patterns and of the curvature of gliding parasites were determined using the measurement feature of Axiovision software (Zeiss). Measurements of the radii of trails were made from tracks of individual parasites from 4 separate videos containing 10–40 motile parasites each. The curvatures of individual parasites were determined from parasites undergoing circular (DD-wild type) vs. off-track and stalled (DD-G200S, G200S/K270M) patterns of motility. The curvature of individual parasites was measured independently from 3–5 separate frames from a single motility track taken from representative time-lapse recordings.
Sequences of actins for 83 organisms including a variety of protists, plants, fungi and animals, were obtained from GenBank and aligned using Clustal [67] with a gap opening penalty of 30 and extension penalty of 0.75. The alignment (see supplementary Figure 5) was imported as a NEXUS file in the PAUP* [68] and used to generate tress by Neighbor-Joining distance using BioNJ and 1000 bootstrap replicates. Only branches of >50% were retained in building the consensus tree. Unrooted trees were drawn in TreeView [69]. Separately, the alignment file was imported as a nexus file into HyPhy [70] and used to generate a maximum likelihood tree under the HKY85 model with 100 bootstrap replicates.
Statistics were calculated in Excel or Prism (Graph Pad) using unpaired, two-tailed Student's t-tests for normally distributed data with equal variances, and two-tailed Mann-Whitney analysis for analysis of samples with small samples sizes of unknown distribution. Significant differences were defined as P≤0.05.
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10.1371/journal.pgen.1000909 | Whole-Genome SNP Association in the Horse: Identification of a Deletion in Myosin Va Responsible for Lavender Foal Syndrome | Lavender Foal Syndrome (LFS) is a lethal inherited disease of horses with a suspected autosomal recessive mode of inheritance. LFS has been primarily diagnosed in a subgroup of the Arabian breed, the Egyptian Arabian horse. The condition is characterized by multiple neurological abnormalities and a dilute coat color. Candidate genes based on comparative phenotypes in mice and humans include the ras-associated protein RAB27a (RAB27A) and myosin Va (MYO5A). Here we report mapping of the locus responsible for LFS using a small set of 36 horses segregating for LFS. These horses were genotyped using a newly available single nucleotide polymorphism (SNP) chip containing 56,402 discriminatory elements. The whole genome scan identified an associated region containing these two functional candidate genes. Exon sequencing of the MYO5A gene from an affected foal revealed a single base deletion in exon 30 that changes the reading frame and introduces a premature stop codon. A PCR–based Restriction Fragment Length Polymorphism (PCR–RFLP) assay was designed and used to investigate the frequency of the mutant gene. All affected horses tested were homozygous for this mutation. Heterozygous carriers were detected in high frequency in families segregating for this trait, and the frequency of carriers in unrelated Egyptian Arabians was 10.3%. The mapping and discovery of the LFS mutation represents the first successful use of whole-genome SNP scanning in the horse for any trait. The RFLP assay can be used to assist breeders in avoiding carrier-to-carrier matings and thus in preventing the birth of affected foals.
| Genetic disorders affect many domesticated species, including the horse. In this study we have focused on Lavender Foal Syndrome, a seizure disorder that leads to suffering and death in foals soon after birth. A recessively inherited disorder, its occurrence is often unpredictable and difficult for horse breeders to avoid without a diagnostic test for carrier status. The recent completion of the horse genome sequence has provided new tools for mapping traits with unprecedented resolution and power. We have applied one such tool, the Equine SNP50 genotyping chip, to a small sample set from horses affected with Lavender Foal Syndrome. A single genetic location associated with the disorder was rapidly identified using this approach. Subsequent sequencing of functional candidate genes in this location revealed a single base deletion that likely causes Lavender Foal Syndrome. From a practical standpoint, this discovery and the development of a diagnostic test for the LFS allele provides a valuable new tool for breeders seeking to avoid the disease in their foal crop. However, this work also illustrates the utility of whole-genome association studies in the horse.
| Heritable disorders affect many domestic species, including the horse. In the Arabian breed of horse a neurological disorder has been reported that is lethal soon after birth [1]. Affected foals can display an array of neurological signs including tetanic-like seizures, opisthotonus, stiff or paddling leg movements and nystagmus (Figure 1) [2]. Mild leucopenia is sometimes observed [2], [3]. These neurologic impairments prevent the foal from standing and nursing normally and, if not lethal on their own, are often cause for euthanasia. In addition to these abnormalities, affected foals possess a characteristic diluted “lavender” coat color. This resulting coat color, variously described as pale gray, pewter, and light chestnut, as well as lavender, has coined the name “Lavender Foal Syndrome” (LFS) [2]. Also called “Coat Color Dilution Lethal” [2], there is currently no treatment for LFS available. Additionally, initial diagnosis can be difficult as the clinical signs of LFS can easily be confused with a number of neonatal conditions including neonatal maladjustment syndrome and encephalitis [2]. The inheritance of Lavender Foal Syndrome is suspected to be recessive, although extensive pedigree analysis has not, to date, been published. Outwardly healthy horses can sire lethally affected foals; therefore a recessive mode of inheritance for LFS is most likely.
Historically developed by the Bedouin tribesman on the Arabian Peninsula, the Arabian horse is one of the oldest recognized breeds of horse. Valued for its beauty and athleticism, the Arabian has contributed to the development of many light horse breeds, most notably the Thoroughbred, a breed used extensively in horse racing across the world [4]. The majority of documented cases of Lavender Foal Syndrome have been reported in the Egyptian Arabian, a sub-group of the Arabian breed found originally in Egypt but extensively exported and popular in the United States. Egyptian Arabians have their own registry, although they are also part of the main Arabian studbook. It is estimated that there are 49,000 living registered Egyptian Arabians worldwide (personal communication, Beth Minnich, Pyramid Society). Identifying the genetic basis of this condition and developing a diagnostic test for the LFS allele will enable breeders to make more informed selection of mating pairs, thus avoiding the production of affected foals and potentially lowering the frequency of this allele in the population, without wholesale culling of valuable stock.
Over the past 15 years the Horse Genome Project has produced several generations of analytical and diagnostic resources (genetic tools) that permit interrogation of polymorphisms across the entire equine genome [5], [6]. Previous mapping efforts using ∼300 microsatellite markers yielded results for several heritable diseases (for examples see [7], [8]). However, this small number of markers limited genetic studies in the horse to simple traits in closely related families with fairly large numbers of samples. The recently completed 6.8x whole genome sequence of the horse and the associated identification of approximately 1.5 million Single Nucleotide Polymorphisms (SNPs) located throughout the horse genomic sequence [9] has enabled the construction of a 56,402 element SNP chip for rapid whole genome scanning (Equine SNP50, Illumina, San Diego, CA). SNP-based whole genome association studies have proven exceptionally successful when studying simple mendelian traits in domesticated species. Two notable examples can be found in studies of coat traits in the dog [10] and recessive diseases of cattle [11].
Previously described mutations in mice and humans provide several comparative phenotypes similar to Lavender Foal Syndrome. Two genes in particular, Ras-associated protein RAB27a (RAB27A) and myosin Va (MYO5A), yield phenotypes with striking parallels to LFS. These two proteins, along with melanophilin (MLPH) are part of a transportation complex responsible for the trafficking of melanosomes to the periphery of the cell where they are transferred to the keratinocyte (reviewed in [12]). The myosin Va transport complex is also utilized in the dendrite of the neuron where it has been shown to move various cargo, including mRNAs, glutamate receptors, and secretory granules [13], [14]. Disruption of these diverse functions could explain the constellation of defects observed in RAB27A and MYO5A mutants. In mice, 71 mutations in MYO5A and 106 in RAB27A have been recorded in the MGD database [15]. In humans, several unique recessive mutations in these two genes have been shown to cause similar disorders. The severity of the phenotype, known as Griscelli syndrome, varies with the gene and location of the mutation [16]. Griscelli syndromes have been divided in to three categories based on the gene responsible; MYO5A in type 1, RAB27A in type 2, and MLPH in type 3 [17]. There are subtle differences in the phenotype of each of these subtypes. For example, RAB27A mutations in both human and mouse disrupt granule exocytosis in T lymphocytes. This leads to immunodeficiency and leukocyte infiltration in to vital organs, including the brain. Thus, although neurological defects are often present in RAB27A mutants they are usually secondary to this infiltration [17]. In contrast, MYO5A mutants exhibit a primary neurologic dysfunction and have normal immune function. Based on this distinction MYO5A was chosen as the primary candidate gene for Lavender Foal Syndrome.
Pedigree data from the six affected foals available at the time of genotyping supported a recessive mode of inheritance. A single common ancestor was identified six to eight generations from these six affected foals (Figure S1). This common ancestor is present on both sides of the pedigree in each foal. This stallion may represent a founder among this group and this convergence in the pedigree supports identity by descent for the LFS mutation. Average inbreeding (Fi) was 0.0861 for affected foals, versus 0.0394 for parents of foals. The extended pedigree also allowed for the calculation of the coancestry coefficient between each living relative and the nearest affected foal in the pedigree. Based on this calculation we predicted that the frequency of the LFS allele would be 0.42 among the 30 relatives used for genotyping.
Genotypic association tests using the six affected foals and their 30 healthy relatives revealed a single region on chromosome 1 (ECA1) with statistical significance above that of the rest of the genome (Figure 2). These 14 highly significant SNPs encompassed a region spanning 10.5 Mb (ECA1:129228091 to 139718117). Although extensive inbreeding and relatedness between affected individuals produced a high number of coincidentally significant (p<0.05) SNPs across the genome, the high peak significance of SNPs in the candidate region (p = 4.62e-6) was convincing evidence for association. In total there were 14 SNPs at this locus that were more significantly associated with the LFS trait than any other region in the genome. The twelve LFS bearing chromosomes from the six affected horses represented only four unique haplotypes for this 10 Mb candidate region. These four haplotypes possessed one large block of 27 SNPs in common. This 1.6 Mb region was homozygous in all six affected horses and heterozygous in obligate carriers as well as many of the living relatives, as was predicted by the coancestry in the pedigree. The linkage disequilibrium (LD) structure and p-values in this likely location for a recessive mutation are plotted in Figure 3. Only 10 Ensembl Gene Predictions fell within this region, including MYO5A, but not RAB27A (UCSC Genome Browser [9]).
Genome-wide observed homozygosity from the genotypes obtained using the EquineSNP50 chip was on average 65.14%. This was much higher than expected considering the homozygosity of the inbred mare chosen for whole genome sequencing was estimated at only 46% [9]. The ten founder Egyptian Arabian individuals from this study, as well as an additional 10 unrelated individuals from the Thoroughbred, Arabian (non-Egyptian) and Saddlebred breeds were used to calculate average genome-wide LD (Figure S2). This calculation revealed that the length of LD in the Egyptian was similar to that of the Thoroughbred, a breed with a long history of a closed studbook and relatively small foundation population. LD in the Egyptian was also much longer than that of the Arabian population as a whole, which was most similar to the Saddlebred. The Saddlebred breed registry was closed in 1917 and derived from fairly diverse types of horse suitable for use as transportation under saddle and in harness.
Individual PCR amplification and sequencing of the 39 exons of MYO5A from a LFS affected foal revealed three SNPs and one polymorphic microsatellite in intronic sequence, as well as a single base deletion in exon 30 of MYO5A (Table 1). This deletion was further confirmed by sequencing in a second foal and its heterozygous parents (Figure 4). The deletion is termed ECA1 g.138235715del per Human Genome Variation Society (http://www.hgvs.org/mutnomen/) nomenclature. This deletion changes the reading frame, creating a premature stop codon in the translation of exon 30, 12 amino acids following the mutation. A multiple alignment of the predicted LFS exon 30 amino acid sequence, as well as the wild type sequence from eight species, shows that this region of the myosin Va protein is highly conserved (Figure S3). The four intronic polymorphisms were not predicted to change the function of myosin Va and were therefore not investigated further.
We designed a PCR-RFLP assay using the Fau I restriction enzyme to detect this deletion (Figure S4). Digestion of the PCR product produces a positive control fragment of 289 bp in all genotypes. Presence of the deletion abolishes a Fau I site, changing the normal pattern of a 386 bp and a 90 bp fragment in to a single 476 bp product. All seven affected foals (the six originally submitted for mapping plus one additional obtained after mapping was completed) were homozygous for the deletion (Table 2). Eight out of the 14 parents of these affected foals were available for sampling and all carried the deletion. Among 23 relatives of affected foals 16 were identified as carriers of the deletion. A sample group of 114 Arabian horses was tested to provide a rough estimate of the frequency of the MYO5A exon 30 deletion, and therefore Lavender Foal Syndrome, in the breed as a whole (Table 3). 10.3% of Egyptian Arabians (six out of 58 horses) and 1.8% of non-Egyptian Arabians (one out of 56 horses) were identified as carriers.
Here we describe the first successful use of the EquineSNP50 genotyping platform in identification of the mutation responsible for a genetic disorder in the horse. We have described a frameshift mutation in the MYO5A gene that leads to Lavender Foal Syndrome in the Egyptian Arabian breed of horse. This task was made more challenging by the small number (six) of DNA samples from available affected foals. We improved our chances of success by using pedigree data to select control samples from the extended family and by utilizing a genotype association rather than allelic association statistic in combination with identification of regions of homozygosity.
The extreme predicted impact on function resulting from the single base deletion in MYO5A exon 30 makes it a very logical cause of LFS. Indeed, an alignment of MYO5A exon 30 amino acid sequences from 8 diverse species shows that the exon is completely conserved in horses, humans, mice, dogs and cattle and contains only a few changes in the possum, chicken, and zebrafish (Figure S3). As LFS affected foals do not have an immunodeficiency consistent with RAB27A mutations, and the genomic region containing this gene was not inherited as predicted by our recessive model, it is doubtful that this gene plays a role in Lavender Foal Syndrome.
The newly discovered deletion in exon 30 of MYO5A leads to a frame shift and premature termination of transcription. Loss of the 379 amino acids at the C-terminus of the protein, which encode a portion of the secretory vesicle-specific binding domains of the globular tail, would likely impair binding of myosin Va to those cargo organelles bearing the appropriate receptors [18]. Although this truncation leaves intact the melanocyte specific alternative exon, exon F, it has been previously shown that binding function is nonetheless destroyed without the cooperative action of downstream motifs [19]. Additionally, the quantity of MYO5A protein may be significantly reduced, as is often observed in experimentally truncated constructs of this gene [19]. The resulting loss of vesicle traffic could easily interfere with the normal function of melanocytes and neurons. The neurologic deficits exhibited by LFS affected foals are relatively more severe than the symptoms reported in human cases of Griscelli Syndrome, which are most often due to changes in a single amino acid rather than loss of a significant portion of the transcript [16]. However, in the mouse a broad spectrum of phenotypes are observed, owing to the variety of causative mutations available for study.
There is some speculation that a mild, survivable epileptic condition of young foals may represent a non-lethal phenotype of LFS carriers [2] as the two conditions are often seen in the same pedigrees. However, this association has not been scientifically validated and samples from horses diagnosed with this condition were not available for study at this time. Based on comparative phenotypes in the mouse this is a plausible scenario. Several MYO5A alleles in the mouse, most notably mutations of the globular tail region like d-n and d-n2J, exhibit neurological and behavioral defects in juvenile homozygotes [20]. These deficits improve with age and are often survivable, as has been described in the rumored condition of the horse. Discovery of the mutation responsible for LFS will enable future studies to evaluate association of this allele with juvenile neurological dysfunction.
Our results suggest the population frequency of carriers of this deletion is 10.3% in the Egyptian Arabian. It is possible that this may be an over estimation of carriers, as owners who suspect they have LFS carrying horses may have been more motivated to participate in the study. However it is equally as likely that this figure is an underestimation as there is social stigma associated with producing LFS foals, thus motivating breeders to hide the carrier status of their breeding stock. Despite strict policies regarding the confidential nature of identifying information in research projects, this still influences some breeders to avoid association with Lavender Foal Syndrome research out of fear of being rumored to own carrier horses. Notably, three of the six carriers identified were reported to be breeding stallions. Data from the Egyptian Arabian horse registry indicates that approximately 850 young horses are registered each year (personal communication, Beth Minnich, Pyramid Society). Given our estimate of the number of carriers in the population we expect that around nine LFS foals would be born in the US each year. This is a small number; however rumors of carrier status can very quickly negatively impact the breeding career of high-priced stallions and lead to large economic losses. This estimate also assumes mating at random. In the case of the Egyptian Arabian horse this is not a realistic assumption given the commonplace use of inbreeding and line-breeding in the industry. The allele frequency for LFS of 5.2% is not unlike the frequencies of other heritable diseases in various breeds of horse [21], [22].
We identified a conserved block of 1.6 Mb in common in the four LFS bearing haplotypes. This is somewhat smaller than would be expected considering the average rate of decay of LD across just the six to eight generations that separate these four haplotypes. Indeed, upon further research of the pedigrees from carriers identified during screening for the LFS allele in our sample of 107 Arabian horses, we identified carriers who did not possess this candidate founder in their pedigree. Therefore it is likely the true founder of this mutation occurred far earlier. The appearance of a more recent common ancestor is not surprising given the prolific use of popular sires and the prevalence of inbreeding in this population.
Prevention of the economic and emotional losses associated with lethal conditions in foals, included those affected with LFS is a high priority among Arabian breeders. The market for Egyptian Arabian horses particularly values certain popular bloodlines. This leads to close breeding as owners seek to increase the percentage of this ancestry in their foal crop. This breeding strategy thus increases the need for vigilant prevention of recessive genetic disorders. The test developed here will be a pivotal tool for breeders seeking to breed within lines segregating for LFS, yet minimize or eliminate the production of affected foals.
Widespread application of the EquineSNP50 chip in genetic research is just beginning. In the case of Lavender Foal Syndrome, the limited availability of samples had impeded the progress of research using existing mapping tools for many years. Although whole genome association using large numbers of SNP markers is heralded as a tool for complex, polygenic traits, here we have shown that it can be very successfully applied to a simple trait in a small number of individuals. This work is the first use of the EquineSNP50 genotyping chip to successfully identify a causative mutation. While whole genome association is often the tool of choice for mapping complex traits and QTLs, we have demonstrated that it can also be a much anticipated solution for simple traits that face additional challenges in phenotyping and/or sample number. Testing for the LFS allele will be a valuable aid to breeders seeking to avoid losing foals while still using many of the popular lines that may carry Lavender Foal Syndrome. As the Arabian horse was used to develop many of the modern light horse breeds it is possible that the LFS allele is present in these breeds as well. In future work we will test additional sub-types of Arabian, as well as a variety of light horse breeds to better assess the population frequency in these groups. It is possible that LFS segregates in these groups at a low frequency without detection, as it is easy to confuse with other neonatal disorders of the foal.
Procedures in living animals were limited to the collection of blood by jugular venipuncture or hairs pulled from the mane or tail. Both procedures were conducted according to standard veterinary protocol and inflict minimal, if any pain. All samples were voluntarily submitted by horse owners and/or attending veterinarians to the Antczak or Brooks laboratories according to protocols approved by the Cornell Institutional Animal Care and Use Committee protocol #1986-0216.
Six initial samples from affected foals plus one foal obtained mid-way through the study, their 31 relatives, as well as 114 individual horses from the general Arabian horse population were available for study. The diagnosis of Lavender Foal Syndrome was made by the attending veterinarian and was consistent with the previously published case reports [2], [3]. Population samples were voluntarily submitted by horse owners from across the US. As multiple samples received from a single owner often included closely related individuals, these horses were selected so as no horse included in the study was related to any other within a single generation. Although the identity and pedigree of study horses were made available, those data are not provided here to protect confidentiality. Each of the six affected horses had unique parents, although they were often related farther back in the pedigree (Figure S1). Samples were coded numerically during use to protect the anonymity of participating farms and owners.
Although Lavender Foal Syndrome is widely-known among breeders of Arabian horses, the number of documented cases available for study and genetic analysis is very low. The six affected foals and their 30 relatives used for SNP genotyping in this study were collected over a 9 year period from Arabian breeders in various locations in the US. The average SNP density in the horse has been estimated at 1 per 2,000 bp [9]. It has been proposed that 100,000 SNPs should be sufficient for genome wide association mapping in the horse, given the moderate level of linkage disequilibrium both within and across breeds [9]. Due to the small number of available affected horses and the smaller than optimal horse SNP chip (only 56K SNPs), we decided to employ a modified family study using all of the available affected foals and their closest relatives, plus extensive pedigrees information available from the Arabian Horse DataSource (Arabian Horse Association, Aurora CO).
Genomic DNA was isolated from fresh or frozen tissue or peripheral blood lymphocytes using the DNeasy Blood and Tissue kit (Qiagen Inc., Valencia, CA) following the manufacturer's protocol. DNA was eluted, as well as diluted in, MilliQ (Millipore Corp., Billerica, MA) water before use in downstream applications. Hair lysates were prepared for PCR from hair bulbs as previously published [23].
The Lineage v.1.06 pedigree analysis program (Personal Communication, John Pollack, Animal Breeding Group Cornell University) was used to construct a pedigree and calculate Fi statistics as well as the coancestry coefficient for the 36 horses submitted for genotyping (Figure S1). For Figure S1 the “prune” option was used to hide individuals with fewer than two offspring as well as those with no known ancestors in order to simplify the pedigree for easier viewing.
We selected six affected foals, seven of their parents (all those from which samples were available) and 23 close relatives from amongst banked samples held at the Antczak laboratory. Genotyping on the EquineSNP50 chip was performed by the Genotyping Shared Resource at the Mayo Clinic, (Rochester, MN) using 75 µL of approximately 75 ng/µL genomic DNA. Across the 36 samples the genotyping call rate averaged 98% with a minor allele frequency of 0.47, on average. Genotypes were filtered to remove SNPs with a MAF <0.05 and missingness >0.5 using the Plink Whole Genome Analysis Toolset [24]. A Fisher's exact 3×2 test for a significant genotypic association between each SNP and the affected status was performed using the R statistical package v2.8.1 [25]. Statistical results were visualized and LD plots generated using Haploview [26] or the JMP v7.0 software package (SAS Institute Inc., Cary, NC). 281 SNPs from the significantly associated region were phased in to haplotypes using the Phase v2.1.1 [27]. Genome wide LD was estimated using the r2 statistic in the Plink Whole Genome Analysis Toolset under the following filters: minor allele frequency <0.05 and deviation from Hardy Weinburg Equilibrium p<0.0001. Ten individuals previously genotyped on the EquineSNP50 chip were chosen from the Arabian, Thoroughbred and Saddlebred breeds and compared to ten unrelated founder Egyptian Arabians typed for this study. Values were binned in groups of 5000 and average r2 and inter SNP distance graphed using Excel 2007 (Microsoft Corp., New York, NY).
As no full length mRNA sequence is currently available for MYO5A in the horse, exons were identified based on homology to the human mRNA sequence (NM_000259) aligned in the UCSC Genome Browser [28]. This human transcript, comprising 12,238 nt of mRNA (spanning 114 kb of genomic sequence) encoding 1855 amino acids, is 97.9% identical to the homologous equine sequence. Primers spanning these 39 exons were designed based on the EquCab2.0 genomic sequence from the UCSC Genome Browser using the Primer3 software [29] and purchased from Integrated DNA Technologies (Coralville, IA). These primers and their optimal annealing conditions are listed in Table S1. PCR products were submitted to the Cornell Core Life Sciences Laboratories Center for sequencing using standard ABI chemistry on a 3730 DNA Analyzer (Applied Biosystems Inc., Foster City, CA). All sequences were submitted to Genbank under the following accession numbers: GU183550 and GU183551. Sequences were aligned and screened for mutations using the Contig Express program in the Vector NTI Advance v10 suite (Invitrogen Corp., Carlsbad CA) or the CodonCode Aligner (CodonCode Corp., Dedham, MA)(Figure S3). The exon 30 sequence from an LFS horse was translated using Vector NTI Advance v10 and a multiple alignment constructed in Clustal X v.2 [30] using the following amino acid sequences from Genbank: horse XP_001918220.1, human EAW77447.1, mouse CAX15575.1, dog XP_535487.2, cow XP_615219.4, possum XP_001380677.1, chicken CAA77782.1, zebrafish AAI63575.1.
25 ng of genomic DNA or 2 µL of hair lysate were amplified by PCR using the following primers: Myo5a.Exon30.RFLP.F 5′-CAG GGC CTT TGA GAA CTT TG-3′ and Myo5a.Exon30.R 5′-CAG CCA TGA AAG ATG GGT TT-3′. Reactions were assembled in a 10 µL total volume using FastStart Taq DNA Polymerase and included all reagents per the manufacturers recommended conditions (Roche Diagnostics, Indiananpolis, IN). Thermocycling on an Eppendorf Mastercycler Ep Gradient (Eppendorf Corp., Westbury, NY) was also according to the manufacturer's recommendations with an annealing temperature of 60°C and a total of 40 cycles for this primer pair. The restriction digest used 10 µL of PCR product, 1.5 units Fau I (New England Biolabs Inc. (NEB), Ipswitch, MA), 1x NEB Buffer 4 and enough MilliQ water to bring the reaction volume to 20 uL. Digests were incubated at 55°C for 1 hour. The resulting products were combined with loading buffer (Gel Loading Dye (6X), NEB) and separated alongside a size standard (100 bp DNA Ladder, NEB) by electrophoresis following standard conditions on a 3% agarose gel (Omnipur Agarose, EMD Chemicals Inc, Gibbstown, NJ). Agarose gels were stained (SYBRsafe DNA gel stain (10,000X) concentrate, Invitrogen Molecular Probes, Eugene, OR) and visualized under UV illumination (FluroChem HD2, Alpha Innotec Corp., San Leandro CA).
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