* Rega Institute for Medical Research, Katholieke Universiteit Leuven, Belgium
Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles
Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology, Gent University, Belgium
Correspondence: E-mail: annemie.vandamme{at}uz.kuleuven.ac.be.
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Abstract |
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Key Words: HIV SIVcpz evolution Bayesian inference
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Introduction |
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Phylogenetic analysis of all the different HIV-1 groups indicated that they are genetically more closely related to the SIVcpz sequences isolated from the chimpanzee Pan troglodytes troglodytes than to any other simian viruses (Gao et al. 1999; Hahn et al. 2000; Sharp et al. 2000; 2001). There are four different subspecies of chimpanzees residing in the geographic area of Africa: P. t. verus and P. t. vellerosus in western Africa, P. t. troglodytes and P. t. schweinfurthii in western Africa and Eastern-Equatorial Africa, respectively (Gao et al. 1999; Corbet et al. 2000; Hahn et al. 2000). Until now, there is no evidence for SIV infection of P. t. verus (Prince et al. 2002), whereas the only infected chimpanzee identified among P. t. vellerosus was a single animal (Cam4) kept together with a P. t. troglodytes infected with SIVcpz (Cam3) (Corbet et al. 2000). The SIVcpzCam3 chimpanzee was recovered from Cameroon (Corbet et al. 2000). Until now, five additional SIVcpz strains from P. t. troglodytes have been characterized from chimpanzees: one isolated from Cameroon (Cam5), one from an animal kept in the United States (US) (Gao, et al. 1999; Corbet et al. 2000), two from Gabon (Gab1, Gab2) (Peeters et al. 1989), and two more genetically divergent SIV strains (ANT, TAN1) isolated from the subspecies P.t. schweinfurthii in the DRC (Congo) and Tanzania, respectively (Peeters et al. 1992; Santiago et al. 2003). The prevalence of SIVcpz antibodies in wild-caught animals, as indicated recently by analysis of 58 chimpanzees from Côte d'Ivoire, Uganda, and Tanzania (Santiago et al. 2002), was found to be as low as 2%, which is far less than in other primate species (Jin et al. 1994; Bibollet-Ruche et al. 1996; Beer et al. 2000; Hahn et al. 2000; Peeters et al. 2002). The extremely low seroprevalence of SIVcpz in the chimps living in the wild could possibly be explained by the limited number of the animals sampled up until now, or by a dramatic loss of the animals in their natural habitat in recent decades (Weiss and Wrangham 1999).
On the one hand, groups M and N of HIV-1 are genetically more closely related to SIVcpz sequences from P. t. troglodytes, and it has been suggested that they represent two different cross-species transmissions from this chimpanzee subspecies (Gao et al. 1999; Hahn et al. 2000; Sharp et al. 2001). Moreover, group N consists of genomic regions with discordant phylogenetic relationships with respect to group M and SIVcpz, which was highly suggestive of recombination (Simon et al. 1998; Gao et al 1999). On the other hand, the origin of group O, as well as the evolutionary relationships between the different SIVcpz and HIV-1 lineages throughout their complete genome, remains unclear. More specifically, based on phylogenetic analysis of full-length or partial genomic regions, group O was found to be an outgroup to HIV-1 and SIVcpz sequences from P. t. troglodytes (Gao et al. 1999; Corbet et al. 2000; Kuiken et al. 2001) and SIVcpzGab1 was found to cluster with the group of the SIVcpz sequences isolated from Cameroon (Corbet et al. 2000).
In the present study, we performed an in-depth analysis of the evolutionary relationships between HIV-1 groups M, O, and SIVcpz sequences isolated from P. t. troglodytes and P. t. schweinfurthii. Putative recombination between any of the HIV-1 and SIVcpz sequences was tested using bootscanning and Bayesian scanning plots, as well as a new method using a Bayesian multiple change-point (BMCP) model to infer parental sequences and crossing-over points.
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Materials and Methods |
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Investigation for Discordant Phylogenetic Relationships by Bootscanning Analysis
An exploratory analysis for the presence of any recombination events between all the different HIV-1/SIVcpz sequences was performed by bootscanning plots, using a sliding window of 500 nt, moving in steps of 50 nt and maximum likelihood estimated distances (F84 model with transitions/transversions = 2, default value) (Felsenstein 1984) as implemented in Simplot (3.2 beta version). The F84 model was used because it is the most complex model available in the current version of Simplot. The default value for ti/tv (transitions/transversion rate ratio) provided a good approximation of the value estimated by maximum likelihood (ti/tv = 2.14) using PAUP*4.0b10 (Swofford 1998). Bootscanning plots were then created for each monophyletic cluster separately, against all other HIV-1/SIVcpz sequences.
Bayesian Scanning Analysis
Bayesian scanning was performed including eight representative sequences within the five HIV-1/SIVcpz lineages: (1) SIVcpzGab1, (2) SIVcpzCam3/Cam5, (3) SIVcpzANT/TAN1, (4) ANT70 (HIV-1 group O), and (5) U455/WEAU160 (HIV-1 group M), using the GTR + model and run with a sliding window of 500 nt, moving in steps of 50 nt. The program used for the Bayesian scanning was MrBayes (version 3.0) (Huelsenbeck et al. 2001). For every single window, four Markov chains were run for 105 generations with a burn-in of 6 x 103 generations. The Markov chain Monte Carlo (MCMC) chain length for the Bayesian scanning was set to 105 generations; we found no differences between MCMC pre-run estimates in several separate regions using 105 and 106 generations, suggesting that 105 steps provided a "realistic" length for a full-length genomic scan using Bayesian inference. Moreover, Bayesian scanning was used as an initial tool to investigate the existence of any topological incongruence between HIV-1/SIVcpz lineages. The fixed number for burn-in was based on an inspection of the number of generations required to reach stationarity in all posterior output files. Burn-in was fixed to 2x the number of generations (3,000th) required to reach equilibrium in all regions of the alignment. The approximate posterior probabilities of the partitions: (1) SIVcpzANT/TAN-HIV-1 group O, (2) SIVcpzANT/TAN-SIVcpzGab1, (3) HIV-1 groupO-SIVcpzCamUS, (4) HIV-1 groupM-SIVcpzCamUS, and (5) SIVcpzGab1-SIVcpzCamUS, for which there was evidence of discordances by the bootscanning analysis, were plotted throughout the complete genome.
Bayesian Multiple Change-Point Model Analysis
The Bayesian multiple change-point (BMCP) model was initially developed to infer spatial phylogenetic variation (SPV) in DNA sequences from organisms that may have undergone recombination (Suchard et al. 2002, 2003). The strength of the BMCP approach is that it allows evolutionary selective pressures, rates, and reconstructed topologies to all vary independently, ensuring that selective pressure and rate changes do not lead to false inference regarding topological changes. The BMCP model employed in this study is a modification of Suchard et al. (2002) that allows for sampling of all 15 possible topologies relating the five distinct HIV-1/SIVcpz lineages. In this way, we did not need to assume a fixed reference tree for a subset of the lineages and test for recombination producing the remaining one; rather, we could test for discordance among all five simultaneously. For BMCP model analysis, we performed several runs using different sequences from SIVcpzCamUS (SIVcpzCam3, SIVcpzUS), group M (U455, WEAU160), and group O (ANT70, MVP5180) lineages together with SIVcpzGab1, and either SIVcpzANT or SIVcpzTAN1. We ran the model for 106 generations, assuming a value of 10, as was previously described (Suchard et al. 2003). To assess convergence, we ran five independent BMCP chains on a single data set including either SIVcpzANT or SIVcpzTAN1 (10 runs), and then two BMCP chains for each of the data sets including distinct sequences from each HIV-1/SIVcpz lineage (12 runs); thus there were 22 runs in total. Posterior probabilities of the five partitions listed above, and the marginal posterior estimates of the evolutionary parameters were plotted throughout the complete genome. All BMCP analyses were run using the same alignment as was used for the bootscanning and Bayesian scanning, as well for the conditionally independent phylogenetic analyses to be described.
Conditionally Independent Phylogenetic Analysis
In genomic regions with high evidence of discordant evolutionary relationships between the HIV-1/SIVcpz sequences based on the Bayesian approaches, separate phylogenetic analyses were done for the putative nonrecombinant fragments. The borders of these fragments were based on the previous Bayesian analyses. The best-fitting nucleotide substitution model was chosen according to the likelihood ratio test (Felsenstein 1981, Goldman 1993) among 64 different models using Modeltest (Posada and Crandall 1998) and PAUP*4.0b10 (Swofford 1998). Conditionally independent phylogenetic analysis was accomplished using a maximum likelihood (ML) approach with the best-fitting evolutionary model as implemented in PAUP* (Table 1). More specifically, all model parameters were initially estimated on a Neighbor-Joining tree, and tree topologies were subsequently evaluated using a heuristic search approach that implemented both tree bisection-reconnection and nearest-neighbor interchange perturbations. Bootstrapping was performed on the Neighbor-Joining trees (1,000 replicates) to assess the reliability of the obtained topologies. Phylogenetic trees were also obtained using Bayesian inference under the general time reversible (GTR) model including a distributed rates heterogeneity among sites as implemented in MrBayes (version 3.0) (Huelsenbeck et al. 2001). For Bayesian inference, four Markov chains run for 106 generations with a burn-in of 2 x 104, and four chains run for 105 generations with a burn-in of 2 x 103 for the nucleotide and the amino acid based sequences, respectively, were used for the reconstruction of the consensus tree. All MCMC analyses were repeated in five separate runs (using three heating chains and one cold chain) for both nucleotide and amino acid alignments. To assess for any significant differences between candidate topologies in different pieces of the alignment, we used the Approximately Unbiased (AU)-test (Shimodaira 2000) as implemented in CONSEL (Shimodaira and Hasegawa 2001). For each region, site-wise log-likelihoods were estimated for the candidate trees using PAUP* with the best-fitted evolutionary model.
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Results |
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Bootscanning plots of each of these four monophyletic groups and of the SIVcpzGab1 sequence, thus five lineages in total, were performed against the other four lineages. HIV-1 group N was not included, because this group is already known to be recombinant consisting of HIV-1 group M and SIVcpzUS-like regions in the 5' and 3' half of its genome, respectively (Gao et al. 1999; Hahn et al. 2000). Such analysis revealed discordant phylogenetic clustering of almost all lineages when comparing different genomic regions (fig. 1AE).
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Bayesian Scanning and Bayesian Multiple Change-Point (BMCP) Model Analysis
To explore in detail the evolutionary relationships and the boundaries of the genomic regions with discordant phylogenetic relationships between HIV-1/SIVcpz lineages, we performed two different kinds of analyses: (1) a genomic scan of the HIV-1/SIVcpz phylogenetic relationships by Bayesian inference with the general time reversible (GTR) model including a -distribution of rates among sites, and (2) a Bayesian analysis using a multiple change-point model (BMCP) as described recently (Suchard et al. 2002) and modified for a data set of five taxa.
The first approach resembles the bootscanning analysis; Bayesian inference analysis is performed in a sliding window of a specified length, and then the support of every single clade (approximate posterior probabilities instead of the bootstrap values) is plotted throughout the alignment. The Bayesian multiple change-model was developed to infer spatial phylogenetic variation (SPV) in DNA sequences from organisms that have undergone recombination. The BMCP model to infer recombination breakpoints extends the approaches of Husmeier, Wright and colleagues (McGuire et al. 2000; Husmeier and Wright 2001, 2002; Husmeier and McGuire 2002). Husmeier, Wright, and colleagues detect recombination by modeling multiple site evolution with a hidden Markov model (HMM), where the hidden states at each site along the alignment are the various possible topologies. The BMCP model not only allows for varying evolutionary rates, selective pressures, and topologies along the sequences, it also provides simultaneous estimates of uncertainty of all quantities including the number and locations of the inferred crossover points (Suchard et al. 2002, 2003). The model assumes that the sites along the data separate into an unknown number of contiguous segments, each with possibly different evolutionary relationships between organisms, evolutionary rates, and transition-transversion ratios.
The results of the Bayesian scanning are shown in two plots (fig, 2A, B) where the posterior probabilities of the five different partitions, (1) SIVcpzANT/TAN-group O, (2) SIVcpzANT/TAN-SIVcpzGab1, (3) groupO-SIVcpzCamUS, (4) groupM-SIVcpzCamUS, and (5) SIVcpzGab1-SIVcpzCamUS as indicated by the exploratory analysis, were plotted throughout the alignment. According to figure 2A, B, there was strong evidence for discordant phylogenetic variation clustering: (1) SIVcpzANT/TAN with group O (regions IV and VIIIb at positions 26003300 and 54005937, respectively) or SIVcpzGab1 (regions II, V, and VII at positions 8001500, 33003850, and 43004850, respectively), (2) group O with SIVcpzCamUS (region Ia at positions 1500), and (3) SIVcpzCamUS with group M (region II and between 29003400) or SIVcpzGab1 (region VIIIb) (8001500 and 54005937, respectively). At first glance, the results of the Bayesian approaches provided a clearer picture about the boundaries and the levels of confidence of the different partitions than the bootscanning analysis, even in regions where the bootscanning plots were unclear (e.g., position of the breakpoints between regions V, VI and VII, fig. 2A, B).
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To test for substitution saturation, we plotted the transition to transversion ratios for all the sites or for only the 1st plus 2nd codon positions versus the ML estimated evolutionary distances corrected for multiple substitutions for three fragments of the alignment belonging to partial gag, pol, and env regions, respectively (data not shown). According to the saturation plots, there was no evidence for substantial substitution saturation, especially after the elimination of the 3rd codon position, and thus phylogenetic inference that corresponds mainly to substitutions in the 1st and 2nd codon (analysis of amino acid changes) was found not to be substantially affected by substitution saturation bias.
Multiple Change-Point (BMCP) Model Analysis
To further confirm the findings about the spatial phylogenetic variation, we performed a BMCP model analysis using representative sequences for the five distinct lineages. Because the number of strains that can currently be analyzed with BMCP is restricted, we ran these analyses including only one sequence from each lineage at a time. We performed multiple analysis using different sequences from SIVcpzCamUS, group M and group O lineages, together with SIVcpzGab1 and either SIVcpzANT or SIVcpzTAN1, because there is considerable variability between these last two sequences. The results of all independent runs were almost identical within data sets including SIVcpzANT or within data sets including SIVcpzTAN1 sequences, thus prompting us to average the posterior probabilities for the SIVcpzANT- or SIVcpzTAN1- subset of sequences (fig. 4A). According to the BMCP analysis results, there was evidence of spatial phylogenetic variation throughout the HIV-1/SIVcpz genome (fig. 4A). Comparing the results of the BMCP analysis and the Bayesian scanning, there were some differences in the posterior probabilities of the different regions (fig. 2A, 4A). More specifically, the differences were more pronounced in region II (8001500), whereas there were some minor differences, also in region III (15002600) (fig. 2A, 4A). Interestingly, in region II none of the SIVcpzGab1 or group O sequences clustered significantly with SIVcpzANT/TAN (fig. 3II). We should note here that BMCP and Bayesian scanning were run with five and eight sequences, respectively, and thus the analyzed partitions were not identical. Bayesian scanning based on the exact data set used in BMCP analysis revealed almost identical results with the BMCP analysis (data not shown), thus suggesting that observed differences were due to the distinct datasets used in the analyses.
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Evolutionary Relationships as Deduced from These Analyses
Thus, phylogenetic analysis using ML with the best-fitting evolutionary model and Bayesian inference in both nucleotide and amino acid sequences revealed discordant evolutionary relationships between the HIV-1/SIVcpz sequences throughout the genome (shown schematically in figure 5). Although unrooted, the trees are drawn with SIVcpzANT/TAN as most external branch, because in most regions the root is stably put on that branch. As depicted in figure 5, our results indicate that, HIV-1 group M clusters significantly with the two SIV lineages from P. t. troglodytes (SIVcpsCamUS and SIVcpzGab1) and not with group O in regions IV and VI, and significantly only with SIVcpzCamUS in region II. In regions V and VII, HIV-1 group M clusters significantly with group O and SIVcpzCamUS, and not with SIVcpzGab1. In region I, group O clustered significantly with SIVczGab1 and SIVcpzCamUS and not with group M, whereas in regions III and VIII no significant clustering between the HIV-1/SIVcpz lineages could be found (fig. 5). Interestingly, HIV-1 group M and group O significantly clustered as a separate group in region VII.
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Discussion |
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To investigate the complex relationships between the HIV-1 and SIVcpz sequences, we performed an in-depth analysis of the five distinct HIV-1/SIVcpz lineages, using bootscanning, a sliding window Bayesian approach, ML and BMCP analysis. We found evidence that in case of highly divergent sequences, such as within HIV-1/SIVcpz, bootscanning analysis based on ML estimated distances (F84) provided an inadequate method for testing for discordant clustering; in several cases it did not provide clear results about the boundaries of the putative recombinant fragments. The inadequacy of the bootscanning method in deep phylogenies is not surprising because of the implementation of an oversimplified and fixed evolutionary model (F84) and a distance based tree-searching algorithm (NJ). In contrast, the Bayesian scanning method provided a clearer picture, not only about the boundaries of the regions with discordant evolutionary relationships but also about the levels of support of the phylogenetic clusters under study in different regions of the alignment.
Conditionally independent phylogenetic analyses, using different approaches, confirmed further the results obtained by the Bayesian scanning method about the discordant clustering within the HIV-1/SIVcpz lineage. In particular, the BMCP analysis provided additional evidence for spatial phylogenetic variation throughout the HIV-1/SIVcpz genome in accordance with the conditionally independent analyses. The BMCP analysis, which allows for sampling of all 15 possible topologies relating the five distinct HIV-1/SIVcpz lineages, is a new method, used complementarily to Bayesian scanning, to infer the spatial phylogenetic variation of the HIV-1/SIVcpz sequences. Because it is not currently computationally feasible to analyze more than five sequences using a BMCP approach, we chose one representative sequence from each lineage, HIV-1 group M, HIV-1 group O, SIVcpzCamUS, SIVcpzGab1, or SIVcpzANT/TAN. This analysis revealed some incongruence between the data sets when using either SIVcpzANT or SIVcpzTAN1, confirming the high divergence between the latter two sequences.
According to our findings, HIV-1 group M clusters significantly with two SIV lineages from P. t. troglodytes (SIVcpsCamUS and SIVcpzGab1) and not with group O in middle pol, and in partial vif/env, and significantly only with SIVcpzCamUS in 3'gag/pol. We should note here that in middle pol, the ML topology did not differ significantly from the phylogenetic tree inferred in neighboring regions, probably because of the high levels of similarity between HIV-1/SIVcpz lineages (middle of pol) compared to the other parts of the alignment. In pol/vif and in middle env, including V3, HIV-1 group M clusters significantly with group O and SIVcpzCamUS, and not with SIVcpzGab1, whereas in partial env the group M also clustered with group O. The gp120and especially the V3 regioncontain the binding site for the CD4 receptor; thus clustering of groups M and O in this region may be caused by convergent evolution to achieve virus entry into human cells and not to ancient recombination events. Furthermore, in p17/p24, group O clustered significantly with SIVczGab1 and SIVcpzCamUS and not with group M. Taken as a whole, these analyses are more supportive of SIVcpz from P. t.troglodytes as the origin of HIV-1 for both HIV-1 group M and group O, than SIVcpz from P. t. schweinfurthii.
The discordant phylogeny throughout the HIV-1/SIVcpz sequences suggests that either ancient recombination events between ancestral HIV-1/SIVcpz sequences altered rates of evolution, which in some regions may have been driven by convergent evolution, or a combination of these events has probably shaped the genomic makeup of the different HIV-1/SIVcpz lineages. The hypothesis of altered rates of evolution between the HIV-1/SIVcpz sequences was further supported by the BMCP analysis, where we found differences in the BMCP profiles when comparing analyses with either SIVcpzANT or SIVcpzTAN1, thus suggesting that the inferred evolutionary relationships between the HIV-1/SIVcpz sequences are not stable.
According to the inferred phylogenies, evolutionary distances between the HIV-1 and SIVcpz sequences isolated from P. t. troglodytes vary greatly with the genomic region, which is also supportive for a great impact of altered rates of evolution. Such a finding is not surprising because the SIVcpz viruses belonging to SIVcpzCam (Cam3, Cam5) and SIVcpzGab1 lineages were isolated from diverse geographic areas, Cameroon and Gabon, respectively, which means that a number of parameters (selective pressure, number of infected animals, etc.) could have shaped the evolution of the SIVcpz virus infecting chimpanzees, which remained separated in their natural habitat in a different way.
If recombination contributed greatly to the observed phylogenetic discordance, then it should have occurred early in the evolution process of SIVcpz and most probably before the interspecies transmission to human. Such events are not easily identifiable after a prolonged period of sequence divergence of the lineages after their separation.
Interestingly, it has recently been suggested that SIVcpz in chimpanzees originated from a recombination event between ancestral SIVs infecting red-capped mangabeys (SIVrcm) and greater spot-nosed monkeys (SIVgsn) (Courgnaud et al. 2002; Bailes et al. 2003); our findings indicate that additional recombination events or other factors continued to shape the evolutionary history of primate lentiviruses.
In conclusion, we find evidence for discordant phylogenetic relationships between HIV-1 and SIVcpz sequences throughout the genome, as shown schematically in figure 5. This discordance in topologies might have been caused by ancient recombination events, altered rates of evolution, concerted evolution, or a combination of these factors. Our analysis also suggests that SIV from P. t. troglodytes is the most probable origin for both HIV-1 group M and group O.
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Acknowledgements |
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Footnotes |
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Edward Holmes, Associate Editor
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