Recombination Pattern Reanalysis of Some HIV-1 Circulating Recombination Forms Suggest the Necessity and Difficulty of Revision

PLOS ONE, Sep 2014

Background Recombination is one of the major mechanisms underlying the generation of HIV-1 variability. Currently 61 circulating recombinant forms of HIV-1 have been identified. With the development of recombination detection techniques and accumulation of HIV-1 reference stains, more accurate mosaic structures of circulating recombinant forms (CRFs), like CRF04 and CRF06, have undergone repeated analysis and upgrades. Such revisions may also be necessary for other CRFs. Unlike previous studies, whose results are based primarily on a single recombination detection program, the current study was based on multiple recombination analysis, which may have produced more impartial results. Methods Representative references of 3 categories of intersubtype recombinants were selected, including BC recombinants (CRF07 and CRF08), BG recombinants (CRF23 and CRF24), and BF recombinants (CRF38 and CRF44). They were reanalyzed in detail using both the jumping profile hidden Markov model and RDP3. Results The results indicate that revisions and upgrades are very necessary and the entire re-analysis suggested 2 types of revision: (i) length of inserted fragments; and (ii) number of inserted fragments. The reanalysis also indicated that determination of small regions of about 200 bases or fewer should be performed with more caution. Conclusion Results indicated that the involvement of multiple recombination detection programs is very necessary. Additionally, results suggested two major challenges, one involving the difficulty of accurately determining the locations of breakpoints and the second involving identification of small regions of about 200 bases or fewer with greater caution. Both indicate the complexity of HIV-1 recombination. The resolution would depend critically on development of a recombination analysis algorithm, accumulation of HIV-1 stains, and a higher sequencing quality. With the changes in recombination pattern, phylogenetic relationships of some CRFs may also change. All these results may be critical to understand the role of recombination in a complex and dynamic HIV evolution.

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Recombination Pattern Reanalysis of Some HIV-1 Circulating Recombination Forms Suggest the Necessity and Difficulty of Revision

et al. (2014) Recombination Pattern Reanalysis of Some HIV-1 Circulating Recombination Forms Suggest the Necessity and Difficulty of Revision. PLoS ONE 9(9): e107349. doi:10.1371/journal.pone.0107349 Recombination Pattern Reanalysis of Some HIV-1 Circulating Recombination Forms Suggest the Necessity and Difficulty of Revision Jingyun Li 0 Lei Jia 0 Lin Li 0 Hanping Li 0 Siyang Liu 0 Xiaolin Wang 0 Zuoyi Bao 0 Tianyi Li 0 Daomin Zhuang 0 Yongjian Liu 0 Darren P. Martin, Institute of Infectious Disease and Molecular Medicine, South Africa 0 Department of AIDS Research, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology , Fengtai District, Beijing , China Background: Recombination is one of the major mechanisms underlying the generation of HIV-1 variability. Currently 61 circulating recombinant forms of HIV-1 have been identified. With the development of recombination detection techniques and accumulation of HIV-1 reference stains, more accurate mosaic structures of circulating recombinant forms (CRFs), like CRF04 and CRF06, have undergone repeated analysis and upgrades. Such revisions may also be necessary for other CRFs. Unlike previous studies, whose results are based primarily on a single recombination detection program, the current study was based on multiple recombination analysis, which may have produced more impartial results. Methods: Representative references of 3 categories of intersubtype recombinants were selected, including BC recombinants (CRF07 and CRF08), BG recombinants (CRF23 and CRF24), and BF recombinants (CRF38 and CRF44). They were reanalyzed in detail using both the jumping profile hidden Markov model and RDP3. Results: The results indicate that revisions and upgrades are very necessary and the entire re-analysis suggested 2 types of revision: (i) length of inserted fragments; and (ii) number of inserted fragments. The reanalysis also indicated that determination of small regions of about 200 bases or fewer should be performed with more caution. Conclusion: Results indicated that the involvement of multiple recombination detection programs is very necessary. Additionally, results suggested two major challenges, one involving the difficulty of accurately determining the locations of breakpoints and the second involving identification of small regions of about 200 bases or fewer with greater caution. Both indicate the complexity of HIV-1 recombination. The resolution would depend critically on development of a recombination analysis algorithm, accumulation of HIV-1 stains, and a higher sequencing quality. With the changes in recombination pattern, phylogenetic relationships of some CRFs may also change. All these results may be critical to understand the role of recombination in a complex and dynamic HIV evolution. - Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All 6 reanalyzed sequences are available in the Los Alamos HIV database (http://www.hiv.lanl.gov/content/index). Accession numbers are: AF286226, AY008715, AY900571, AY900574, FJ213783, FJ358521. Funding: This study is supported by the National Science and Technology Special Projects on Major Infectious Diseases (Grant No. 2012ZX10001-002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. One of the remarkable characteristics of HIV-1 is the high prevalence of variation. This has produced group M, group N, group O, and the newly described group P [1,2,3]. HIV-1 group M is predominant in HIV-1 infections worldwide and can be further divided into nine subtypes (AD, FH, J, and K) and five sub-subtypes (A1A3, F1, and F2). Intersubtype recombination is one major mechanisms contributing to HIV-1 variability, allowing the rapid generation of viral variants with high replicative capacity, drug resistance, and modified expression of antigenic epitopes (summarized in [4,5]). Analyses of recombination patterns can reveal as much about evolution as analyses of nucleotide substitution patterns do. Extensive experiments have been performed on HIV-1 and other retroviruses. Results have suggested that HIV-1 genetic recombination is caused by a copy choice mechanismthe alternating use of two templates during the synthesis of a single viral DNA molecule [6]. The recombination of HIV-1 occurs frequently, and can produce many recombinant, currently including 61 HIV-1 circulating recombinant forms (CRFs) and numerous unique recombinant forms (URFs). These CRFs have caused global epidemics. For example, CRF02_AG is common in West Central Africa, CRF07_BC and CRF08_BC are predominant in China [7,8]. BF is mainly found in South America [9,10]. A designation of CRF requires three representative strains to be identified in at least three HIV-infected persons without direct Methods determining original mosaic structure Recombinant structures were determined by Simplot. The programs Genconv, MaxChi, and GARD were used to locate more precisely the positions of breakpoints. (jpHMM) (CRF52, CRF57, and CRF61), and Simplot was preferred by the majority. Previous results regarding recombination were based primarily on single programs. A more impartial result may be obtained by using more forms of recombination analysis. In this study, representative references of 3 categories of intersubtype recombinants were selected (a total of 6CRFs), including BC recombinants (CRF07 and CRF08), BG recombinants (CRF23 and CRF24), and BF recombinants (CRF38 and CRF44). Both jpHMM and RDP3 were used to perform a reanalysis of the selected reference strains available from the Los Alamos HIV database [20,21,22]. Materials and Methods Sequences All the reference strains of 6 selected HIV-1 CRFs were extracted from the Los Alamos HIV database. The information of these CRFs and methods of determining original mosaic structure are shown in Table 1. Subtype reference alignments from the Los Alamos HIV database were used to make alignments with the selected strains. Sequences were aligned using muscle implemented in Mega 5 and minor manual adjustments were performed [23]. Recombination detection jpHMM was first used to perform recombination analysis. This tool is very intelligent and can produce a genome map based Table 1. Information of 6 reanalyzed CRFs. epidemiological linkage, and three near full-length genomic (NFLG) sequences are preferred [11]. Almost all published chimeric structures of CRFs have been summarized in the Los Alamos HIV database (http://www.hiv.lanl.gov/content/index). A more accurate determination of mosaic structure usually requires repeated analysis and upgrades. For example, the strain now designated CRF04_cpx was first identified as the prototype of subtype I based on gp120 sequences [12]. Full genome sequencing revealed this virus to be a complex mosaic with multiple breakpoints between regions of several distinct subtypes, including A, G, and I [13]. Subsequent analysis with previously unavailable complete genome sequences has revealed that the virus is in fact mosaic with regions associated with subtypes A, G, H, and K and unclassified regions [14]. The CRF06 recombinant was previously named CRF06_AGJ, but the subsequent identification of subtype K suggested that some regions of CRF06 are subtype K, so the subtype is now designated as CRF06_cpx and it includes subtypes A, G, J, and K [15]. The recombinant structure of the reference sequence was subsequently analyzed by Montavon et al. [16]. Similar progress has been made with other CRFs, like CRF13, CRF16, and CRF21 [17,18,19]. Considering the development of recombination detection technique and accumulation of appropriate reference sequences, such revisions may also be necessary to make further determinations of recombination patterns for other CRFs. Some of the previously identified CRF mosaic structures are derived completely from Recombination identification program (RIP) (CRF07 and CRF17), some are derived completely from jumping profile hidden Markov model Pairwise sequence comparisons Phylogenetic methods, Patterns of sites, Pairwise sequence comparisons Similarity/distance plot, Bootscanning Sawyers statistical test for gene conversion, Maximum chi-squared, Similarity/distance plot, Bootscanning, Difference in Sums of Squares method, Graphical recombination detection using Phylogenetic Profiles, Likelihood Analysis of Recombination in DNA, Sister scanning method, RDP method, Automated bootscanning (Recscan), Recombination detection using multiple approaches, Recombination detection using hyper-geometric random walks. directly on HXB2 numbering. This prediction method is based on a precalculated multiple alignment of the major HIV-1 subtypes including CRF01_AE references, and it is more accurate than the competing methods used for phylogenetic breakpoint detection [21]. In jpHMM, each HIV-1 subtype is represented by a profile hidden Markov model. All profile models are connected by empirical probabilities, allowing the detection of possible recombinants and related breakpoints by jumping from one profile to another. jpHMM performs best in predicting recombinants that involve subtypes that have had adequate sampling to build wellinformed profiles. It is less effective in cases related to subtypes H, J, and K because so few full-length genome sequences are available (N = 4, 3, and 2, respectively). In the present study, jpHMM was used to detect the recombination patterns in recombinants composed exclusively of subtypes B, C, F, and G. Each of these subtypes has enough data to form a good model of sequence variation. To confirm the data obtained by jpHMM analysis, another recombination analysis tool, RDP3, a software package for statistical identification and characterization of recombination events in DNA sequences, was used to perform further analysis [22]. RDP3 is also very intelligent and simultaneously utilizes a range of non-parametric recombination detection methods: RDP, GENECONV [24] BOOTSCAN [25,26], MAXCHI [27,28], CHIMAERA [27], SISCAN [29], and 3SEQ [30]. RDP3 treats every sequence within the analyzed alignment as a potential recombinant and systematically screens sequence triplets or quartets to identify viruses that contain a recombinant and two sequences that could serve as parents while performing a statistical evaluation of recombination signals [22]. Such an approach eliminates the need for reference sequences, which makes analysis of viral quasispecies from epidemiologically unlinked patients more practical [31]. The main strength of RDP3 is that it simultaneously uses a range of different recombination detection methods to both detect and characterize the recombination events that are evident within a sequence alignment without any prior user indication of a non-recombinant set of reference sequences. The sequences are set to linear. The highest acceptable P-value is set to 0.05. The other parameters are default RDP3 settings. The HIV-1 sequence would be considered to be recombinant when the recombination signal was supported by at least 3 methods with Pvalues of #0.05 after Bonferroni correction for multiple comparisons implemented in RDP3 [22,32]. The breakpoint position inferred were manually checked using recombination signal analysis implemented in RDP3. Recombinant breakpoint locations were designated relative to HXB2 (Genbank accession no. K03455). In the current study, when there is a conflict, recombination events detected by at least 2 of the 3 programs (jpHMM, RDP3, and the one determining the original mosaic structure) are considered the actual events. Methods and algorithms of the newly used recombination analysis tools and the original tools are listed in Table 2. Phylogenetic analysis Based on the newly inferred breakpoint locations, fragments with significant conflict were phylogenetically analyzed individually. The phylogenetic tree was constructed using the maximum likelihood method implemented in Mega 5 or using PhyML implemented in RDP3 [23,33]. The reliability of tree topologies was assessed by bootstrapping using 500 replications. Bootstrap support values of $70% were considered significant. Reanalysis of CRF07_BC indicated that there is no subtype B segment insertion in the middle of the gag region Both jpHMM and RDP3 revealed the very similar proposal parents and breakpoint locations of CRF07. An apparent revision is that the originally identified fragment of subtype B in gag gene (HXB2 nt 1270-1410) of CRF07 was, however, not detected (Figure 1A, Table 3). The reanalysis was expanded to other complete sequences of CRF07 in the Los Alamos HIV database and the same results were obtained (data not shown). In order to further confirm the results of the new recombination, this newly inferred recombinant region spanning HXB2 nt 790 to 2053 was used to construct a phylogenetic tree. The tree clearly showed the clustering of the fragment with the C references (Figure 1B) and thus supports the conclusion. RDP3 plus original reference sequences Segment assignment and breakpoint positions of CRF07_BC -indicates that the segment was not detected using this method. doi:10.1371/journal.pone.0107349.t003 Reanalysis of CRF08_BC indicated that it has a recombination pattern similar to that of CRF07_BC in the nef region Unlike CRF07, the first apparent revision found in CRF08 lies in that B segment originally spanning HXB2 nt 87979417 in the nef gene was reduced to HXB2 nt 88649026 by jpHMM and 88499002 by RDP3 (Figure 2A, Table 4), thus exhibiting a very similar recombination pattern to that of CRF07_BC. The remaining region of nef was reclassified as a C subtype. This newly inferred result was confirmed by Maximum Likelihood trees constructed using Mega 5. The results clearly indicate the clustering of the conflicted region together with the C reference strains (Figure 2B). The redetermination of recombination patterns is critical to understanding the phylogenetic relationship between different recombinants, because the interpretation of phylogenetic relationships depends critically on a more accurate genome map of CRFs. For example, the results of reanalysis of CRF07 and CRF08 in the nef region indicated a much closer phylogenetic relationship between these CRFs than had previously been believed (Figures 1A and 2A). With respect to the previously characterized B segment spanning HXB2 nt 12341680 in gag, RDP3 detected a little shorter B insertion spanning HXB2 nt 13251649, but it was reidentified as an uncertainty region (HXB2 nt 14141674) in jpHMM-derived results. The phylogenetic tree of the region spanning HXB2 nt 13251649 indicates that there indeed is a B segment (data not shown). Unlike the alignment in the RDP3 and in original Simplot, in jpHMM, each HIV-1 subtype is represented by a profile hidden Markov model. This difference may be a reason for the conflicted results by jpHMM. To further address the presence of new different recombination forms, the reference sequence of subtype B in original literatures of CRF07 and CRF08, RL42 from Yunnan, China (GenBank accession number U71182), was added to the subtype reference alignments to re-perform RDP analysis (The reference sequences of subtype C used in the bootscanning analysis of CRF07 and CRF08 in original literatures is eth2220 and 95IN21068, respectively [34,35]. Both strains have been included in the subtype reference alignments.). The results are shown in Table 3 (CRF07) and Table 4 (CRF08). Both show very similar results to that of the first round of analysis. For example, there is no subtype B insertion in middle region of gag of CRF07 and the original larger B fragment in nef region of CRF08 is modified to a pattern very similar to that of CRF07. Reanalysis of CRF24_BG and CRF44_BF indicate that revisions are necessary The results of reanalysis of CRF24 and CRF44 are summarized in Figure 3. The contents of both CRFs found are consistent with original ones. According to new schemes, the first apparent difference of CRF24 lies in region spanning 25524148. The original structure indicates that it is an interval of 3 B segments and 2 G segments. Both jpHMM and RDP3 revealed that it is a complete pure region of subtype B (details see Figure 3A and Table 5). Phylogenetic analysis further confirmed this (Figure 3A). The original very small B segment spanning 86978750 in env of 54 bases was not detected by either of these two programs. With short genes of less than 200 bases, a reliable tree cannot be produced so no phylogenetic tree was provided for this region. Similarly, a complete larger B segment was found in pol according to new schemes of CRF44 rather than the original interval of 2 B segments and 1 F1 segment (Figure 3B and Table 6). The subsequent phylogenetic analysis of HXB2 nt 24703705 supported these results closely. In addition, the originally characterized small insertion of subtype B segment spanning 63426446 (105 bases) in env was not found by either jpHMM or RDP3. In this way, both CRF24 and CRF44 present a clearer pattern of mosaicism than had previously been believed. In conclusion, reanalysis results of CRF07, CRF08, CRF24, and CRF44 suggest that the revisions mainly include 2 categories: (i) length of inserted fragments; and (ii) number of inserted fragments. Reanalysis of CRF23_BG and CRF38_BF indicate that recombination analysis is difficult Reanalysis of CRF23_BG and CRF38_BF produced impenetrable results and suggests the difficulty of identifying recombination patterns. As shown in Figure 4A and Table S1, all 3 recombination programs indicate similar endpoints but different starting points of the B segment in pol of CRF23. The jpHMMderived result indicates that the starting point was 2567. The RDP3-derived result indicates that the starting point is 2966. The starting point in original data set was 2552 with an additional G segment spanning 27952974 of 180 bases. As shown in Figure 4B and Table S2, all 3 recombination programs indicate similar starting point but different end point of the B segment in pol of CRF38. The endpoint in the original data set was 3712. The jpHMM-derived result indicated that the endpoint was 3832, 120 bases longer than original result. The RDP3-derived result Method of recombination analysis Segment assignment and breakpoint positions of CRF08_BC RDP3 plus original reference sequences -indicates that the segment was not detected using this method. doi:10.1371/journal.pone.0107349.t004 indicated that the endpoint was 3586, 126 bases shorter than original result. These results raised the first challenge that is the difficulty of locating the breakpoint accurately during recombination pattern analysis. The challenge also held true for the other 4 CRFs. Different programs always produced different breakpoint locations, even when the patterns were very similar. In addition, Method of recombination analysis Segment assignment and breakpoint positions of CRF24_BG RDP3 plus original reference sequences 2591 -indicates that the segment was not detected using this method. doi:10.1371/journal.pone.0107349.t005 Segment assignment and breakpoint positions of CRF44_BF -indicates that the segment was not detected using this method. doi:10.1371/journal.pone.0107349.t006 there was also a second challenge. The reanalysis indicated that determination of small regions of about 200 bases or fewer must be performed with caution. For example, previous results of subtype B insertion in gag region of CRF07 (141 bases), subtype B insertion in env regions of CRF23 and CRF24 (54 bases), subtype B insertion in env region of CRF44 (105 bases) were not all indicated by both jpHMM and RDP3. It is well known that phylogenetic tree analysis is a very general method used to confirm the recombination mosaic structures. However, this confirmation depends critically on the mosaic structure derived from the results of recombination tools such as Simplot, jpHMM, RIP, and RDP. In other words, no segment can be further confirmed using phylogenetic tree analysis unless it is first indicated to be imbedded in wild virus sequence by a recombination tool,. As an example of CRF07, in the current of re-analysis, neither of newly used methods based on different background alignments found any fragments of subtype B in the middle region of gag of CRF07. Thus no subtype B fragment can be further confirmed using phylogenetic analysis. Additionally, as has been discussed by Leitner et al., such small regions contain too little sequence information to produce reliable phylogenetic trees (http://www. hiv.lanl.gov/content/sequence/HIV/REVIEWS/RefSeqs2005/ RefSeqs05.html). Therefore, direct subtype re-assignment of the originally identified small B segment in CRF07 based on construction of a phylogenetic tree is unreliable. To better describe the issue, a maximum likelihood-based tree was constructed for the subtype B insertion in gag region of CRF07 spanning HXB2 nt 1270-1410 (141 bases). The unreliable tree Figure 4. Reanalysis of CRF23_BG and CRF38_BF reveal the challenges of recombination analysis. (A) Three recombination programs indicate similar endpoints but significantly different starting points of the B segment in pol region of CRF23. (B) These three recombination programs indicate similar starting points but different endpoints of the B segment in pol region of CRF38. doi:10.1371/journal.pone.0107349.g004 involved both incorrect clades for the subtype references and very low bootstrap values (data not shown). Both challenges described above indicated the complexity of HIV-1 recombination. It is here suggested that the involvement of multiple recombination detection programs may facilitate the production of impartial results. In original literatures, both phylogenetic and bootscanning analysis of CRF38 and CRF44 were also based on the subtype reference alignments. While bootscanning analysis of CRF23 and CRF24 were based on locally circulating strains, subtype B sequence of Cu19 (Accession number AY586542) and subtype G sequences of Cu74 (Accession number AY586547) [36]. Therefore, additional recombination analysis were performed using the subtype reference alignments plus these two strains for CRF23 and CRF24. The results showed that proposal parents of both CRFs were more closely related to this two locally circulating stains. Meanwhile, the results clearly indicated a very similar presence of new different recombination forms as the first round of analysis did, compared to the originally named CFRs in Los Alamos HIV1 sequence database (Table 5 and Table S1). With the development of a variety of tools for the detection of recombinant genomes and accumulation of HIV-1 reference stains, more accurate mosaic structures of CRFs, like CRF04 and CRF06, have undergone repeated analyses and upgrades. The present work included a large-scale reanalysis of 6 CRFs. Both the recombination analysis and subsequent phylogenetic analysis indicated that such revisions and upgrades are very necessary. In total, 2 types of revisions were made. It will become increasingly valuable to know, in full phylogeny detail, the circulating recombinant form of HIV-1, because the interpretation of phylogenetic relationships of different recombinants (including confirmation of mosaic structure by sub-region trees) depends critically on this information. The present study is the first to use RDP3 to perform recombination patterns analysis of HIV-1 CRFs. The main strength of RDP3 is that it simultaneously uses a range of different recombination detection methods to both detect and characterize the recombination events. It is more likely than other methods to produce impartial results. In addition to the algorithm, background alignments are also very important to recombination analysis. The jpHMM method used in this study is based on a precalculated multiple alignment of the major HIV-1 subtypes, including CRF01_AE references. Well-informed profiles are built using adequate sampling of each subtype. Then each HIV-1 subtype is represented by a profile hidden Markov model. In this way, at least from these two points, the newly derived results can reveal recombination patterns better than other methods can. These results illustrate two major challenges. One involves the difficulty of determining the location of the breakpoint accurately. The second is that determination of small regions about 200 bases or fewer should be performed with caution. During the current work, unreliable trees were found and contained both incorrect clades of subtype references and very low bootstrap values (data not shown). Both challenges indicated the complexity of HIV-1 recombination. The resolution was found to depend critically on development of recombination analysis algorithm, accumulation of HIV-1 stains (which can lead to more representative background alignments), and a higher quality of sequencing. Supporting Information Table S1 Comparison of newly identified segment assignment and breakpoint positions of CRF23_BG with original data. (DOCX) Table S2 Comparison of newly identified segment assignment and breakpoint positions of CRF38_BF with original data. (DOCX) Conceived and designed the experiments: LJ YJL JYL. Analyzed the data: LJ YJL LL HPL XLW. Contributed reagents/materials/analysis tools: SYL ZYB TYL. 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Lei Jia, Lin Li, Hanping Li, Siyang Liu, Xiaolin Wang, Zuoyi Bao, Tianyi Li, Daomin Zhuang, Yongjian Liu, Jingyun Li. Recombination Pattern Reanalysis of Some HIV-1 Circulating Recombination Forms Suggest the Necessity and Difficulty of Revision, PLOS ONE, 2014, DOI: 10.1371/journal.pone.0107349