The substitution rate of HIV-1 subtypes: a genomic approach

Abstract HIV-1M causes most infections in the AIDS pandemic. Its genetic diversity is defined by nine pure subtypes and more than sixty recombinant forms. We have performed a comparative analysis of the evolutionary rate of five pure subtypes (A1, B, C, D, and G) and two circulating recombinant forms (CRF01_AE and CRF02 AG) using data obtained from nearly complete genome coding sequences. Times to the most recent common ancestor (tMRCA) and substitution rates of these HIV genomes, and their genomic partitions, were estimated by Bayesian coalescent analyses. Genomic substitution rate estimates were compared between the HIV-1 datasets analyzed by means of randomization tests. Significant differences in the rate of evolution were found between subtypes, with subtypes C and A1 and CRF01_AE displaying the highest rates. On the other hand, CRF02_AG and subtype D were the slowest evolving types. Using a different molecular clock model for each genomic partition led to more precise tMRCA estimates than when linking the same clock along the HIV genome. Overall, the earliest tMRCA corresponded to subtype A1 (median = 1941, 95% HPD = 1943–55), whereas the most recent tMRCA corresponded to subtype G and CRF01_AE subset 3 (median = 1971, 95% HPD = 1967–75 and median = 1972, 95% HPD = 1970–75, respectively). These results suggest that both biological and epidemiological differences among HIV-1M subtypes are reflected in their evolutionary dynamics. The estimates obtained for tMRCAs and substitution rates provide information that can be used as prior distributions in future Bayesian coalescent analyses of specific HIV-1 subtypes/CRFs and genes.


Background
HIV is a retrovirus of the genus Lentivirus and is characterized by a very high genetic diversity. There exist two types of HIV, HIV-1 and HIV-2. The former causes the AIDS pandemics and comprises four phylogenetically distinct groups: M, N, O, and P. Groups N and O are found almost exclusively in West-Central Africa (Hahn et al. 2000). Only two strains from group P have been reported so far, both in Cameroon (Plantier et al. 2009;Vallari et al. 2011). HIV-1 group M is the main driver of the HIV pandemics. Within this group, there exist nine subtypes (denoted as A, B, C, D, F, G, H, J, and K) and at least sixty-one circulating recombinant forms (Kuiken et al. 2012).
High mutation and substitution rates favor the genetic diversity of HIV. These result from three main causes: (1) polymerization errors of the reverse transcriptase (Roberts et al. 1988), (2) genetic recombination that produces viral chimeras (Temin 1993), and (3) an explosive within-host proliferation and a large, and still growing, number of infected persons that lead to very large population sizes (Pennings et al. 2014). Although some of these factors may facilitate a fast pace of evolutionary change V C The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com (Moya et al., 2004), other factors act in the opposite direction. For instance, Simon-Loriere et al. (2013) showed that gene overlapping, which affects all genes in the HIV genome, is negatively correlated to the rate of evolution due to a reduction in the number of synonymous substitutions, although it would be less relevant in cases of terminal gene overlaps, which are the predominant type in HIV. Genetic bottlenecks during transmission also act slowing the pace of evolution in this virus, because many mutations accumulated within a host are lost after transmission. Because adaptive changes at the within-host level are lost or reverted after transmission, higher intra-host than interhost substitution rates of HIV-1 are regularly reported (Alizon & Fraser 2013;Duchê ne et al. 2014;Lin et al. 2015). The speed of spread of HIV in an epidemic also influences its substitution rate (Maljkovic Berry et al. 2007). Hence, differences in selective pressures, mutation rates, replication capacity, and/or epidemic dynamics may explain differences in substitution rates among HIV-1 subtypes.
There are important differences in the prevalence of the different subtypes around the world. Subtype C is the most prevalent group of HIV-1, occurring mainly in Africa (which presents the highest diversity of HIV-1) and Asia, accounting for almost 50 per cent of the infections. Subtype B is the main HIV-1 group in Western and Central Europe, the Americas, and Australia. It is also common in different countries of Southeast Asia, Northern Africa, the Middle East, and among South African and Russian men who have sex with men. Subtypes A, D, F, G, H, J, and K display their highest prevalence in Sub-Saharan Africa. It is important to mention the increasing prevalence of circulating recombinant forms, especially CRF01_AE and CRF02_AG, which cause most of the infections in South-East Asia and Western Africa, respectively (Buonaguro et al. 2007).
Differences among subtypes in the intensity of selection have also been reported (Choisy et al. 2004). Its importance on the differential pace at which HIV-1M subtypes evolve has been addressed by analyses of partial genes (Abecasis et al. 2009;Wertheim et al. 2012), thus ignoring the differences in mutation rates and/or selective constraints that are known to exist between genomic regions (Geller et al. 2015).
Here, we present a comparative analysis of the evolution of the main HIV-1 subtypes using Bayesian coalescent reconstructions. The primary goal of our study was to compare the substitution rates of HIV-1 subtypes from a genomic perspective, by using near-full viral coding sequences, which should be more informative for the inference of the substitution rates and diversification dates than the individual genomic regions used so far.

Datasets
Full coding-region sequences (CDS) were retrieved from the Los Alamos HIV Sequence Database, LANL (http://www.hiv.lanl.gov/), on October 2015. Independent datasets were obtained for subtypes A1, B, C, D and G, and the CRF01_AE and CRF02_AG circulating recombinant forms. Although subtype F1 was initially considered, it was excluded from the study due to the low number of sequences retrieved. The criteria for the selection of these sequences were (1) removal of problematic sequences (defined in LANL as sequences with a high proportion of non-ACTG characters or stop codons, presenting hypermutations, deletions or being either contaminants, synthetic constructs or reverse complements); (2) only one sequence per patient was used; and (3) sequences with large deletions or undetermined regions (>5% of the sequence length) were excluded. We also removed sequences without a known sampling date. In order to exclude recombinant or incorrectly subtyped sequences, the retained sequences were re-subtyped with the Comet HIV-1 (http://comet. retrovirology.lu) and REGAv3 HIV-1 subtyping tools (Pineda-Peña et al. 2013). All the sequences were also analyzed with five recombination detection methods implemented using the RDP4 software, RDP, Geneconv, Bootscan, Maxchi, and Chimaera (Smith 1992;Padidam et al. 1999;Martin & Rybicki 2000;Posada 2002;Martin et al. 2005Martin et al. , 2015. Sequences in which at least one method suggested recombination, with a P value <0.05, were considered for exclusion. In order to remove redundant sequences, alignments of the concatenated sequences were processed with CD-HIT (Huang et al. 2010) using a similarity threshold at 0.98. One sequence from each of the clusters found at this level was retained for further analysis.
Due to the high number of B, C, and CRF01_AE sequences that fulfilled the selection criteria and the computational limitations associated with the analysis of large genomic datasets, three different subsets (each with n ¼ 100 sequences) for each of these HIV-1 groups were generated by random sampling with replacement from the original data. These subsets also allowed to check the robustness of the estimates obtained for these subtypes. To reduce uncertainty in the estimates of evolutionary parameters (Wilkinson et al. 2015), eleven early subtype B sequences, corresponding to samples obtained between 1978 and 1983 (Worobey et al. 2016), were also included in all the subtype B subsets.

Molecular clock signal analysis
We checked the clock-likeness of each dataset by performing linear regression analyses between the parameters 'root-to-tip divergence' and 'sampling date' with TempEST (Rambaut et al. 2016). For each subtype and CRF, a tree reconstructed with Fasttree2.1 (Price et al. 2010) was used as input, and the root was chosen as the branch that maximized the coefficient of correlation (R), under the assumption of a strict molecular clock.

Evolutionary analyses
Times to the most recent common ancestor (tMRCA) and genomic substitution rate estimates of each HIV-1 subtype and CRF were obtained by independent Bayesian Markov Chain Monte Carlo (MCMC) coalescent analyses, as implemented in BEAST v1.8.1 (Drummond & Rambaut 2007). Initially, the same partition tree and clock models were applied to all gene regions. All the analyses were performed with the HKYþC (four categories) substitution model, combined with either an uncorrelated lognormal relaxed or the strict molecular clock model and three different demographic models (Bayesian Skyline Plot, and exponential or logistic demographic change). The best demographic model was chosen as that with the lowest Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC) (AICM) (Baele et al. 2012). We repeated the coalescent analyses using the GTRþC model, obtaining identical tMRCA and substitution rate results (data not shown).
For each viral group, we also estimated the tMRCA and substitution rate of each gene partition by repeating the BEAST analyses by assigning a different molecular clock model to each gene.
At least two independent runs of BEAST were performed for each alignment, with MCMC chain lengths ranging between 30 Â 10 6 and 20 Â 10 7 states. Convergence of the estimated parameters was confirmed with Tracer (http://tree.bio.ed.ac.uk/ software/tracer/) checking that effective sample sizes were larger than 200 for all the parameters.
Because substitutions in external branches may include recent, deleterious mutations leading to overestimates of the actual substitution rates, we compared the genomic substitution rates in internal and external branches for each subtype/CRF, using a Perl script (available upon request), to estimate the substitution rate (mean substitution rate) parameter independently for internal and external branches. As the estimates for internal and external branches were almost identical (data not shown), tMRCA and substitution rate estimates reported in this work correspond to values estimated from both external and internal branches.

Pairwise comparisons of genomic substitution rates
We tested whether the genomic substitution rate distributions estimated from BEAST were significantly different among subtypes/CRFs comparing pairwise posterior distributions by means of randomization tests, in which P-values were calculated by counting the number of times that one substitution rate was lower than the other, considering 5,000 tree states chosen randomly (with replacement) of each subtype/CRF. The obtained value (v) divided by 5,000 (number of comparisons) was considered as the probability that the compared values belong to different distributions (Abecasis et al. 2009). P-values were obtained as P ¼ 1 À v, and were adjusted with the false discovery rate method (Benjamini & Hochberg 1995). These comparisons were performed using an in-house R script available upon request (R Core Team 2014).

Datasets
We initially retrieved 2,399 HIV-1M sequences from LANL. After applying the filtering criteria detailed above, 862 different sequences were kept for further analysis: 96 were subtype A1, 248 subtype B, 234 subtype C, 45 subtype D, 32 subtype G, 177 CRF01_AE, and 30 CRF02_AG. As mentioned in the Materials and methods section, three different random subsets (each of n ¼ 100) were obtained for each of the subtypes B and C and CRF01_AE. Information on HIV-1 subtype/CRF, country of origin, sampling year, and accession number of the sequences used in each dataset is provided in Supplementary File S1.

Molecular clock signal analyses
The clock-like signal present in the analyzed datasets was evaluated by calculating the correlation coefficients (R) between the root-to-tip divergence and sampling date. R ranged between 0.50 (CRF02_AG) and 0.90 (subtype G) ( Table 1). The possible existence of over-dispersion of the HIV-1 molecular clock, which could be a major limitation for our comparisons, was rejected by ensuring that plots produced in the linear regression analyses of root-to-tip divergence versus sampling date for the concatenates did not present large dispersed clouds of points around the regression line. Residual mean squared values, which estimate the variance of the rates, were lower than 2 Â 10 À4 for all the subtypes (Table 1).

Substitution rate and tMRCA estimates and comparisons
Genomic substitution rate estimates of each HIV-1 subtype and CRF were obtained by Bayesian MCMC coalescent analyses, as implemented in BEAST. The best-fitting demographic and molecular clock models for each HIV-1 subtype/CRF are shown in Table 2, and dated phylogenetic trees obtained from the near full CDS of each dataset under the best-fitting demographic and clock model are shown in Supplementary File S1.
Bayesian coalescent analyses were also performed, unlinking the molecular clock models of the different genomic partitions. Median tMRCAs estimated from this approach were very similar to those obtained when the same clock model was used (largest difference ¼ 6 years, for CRF02_AG). However, 95% HPDs were more precise (narrower) than when applying the same molecular clock model along the whole CDS (Table 3). The 95% HPDs of tMRCAs estimated unlinking the molecular clock models were narrower than 15 years for all datasets, with the only exceptions of CRF02_AG (27 years). However, when the same clock model was applied to the whole CDS, 95% HPDs narrower than 15 years were obtained only for subtypes B (B-1 and B-2 datasets), C (all three datasets), G, and CRF1_AE (all three datasets). Regarding the tMRCA estimates obtained from the different subsets of subtypes B and C and CRF01_AE, the largest difference between medians of the same HIV-1 variant was found for the random subsets C-1 and C-3 (3 years). Overall, the earliest tMRCA corresponded to subtype A1 (median ¼ 1941, 95% HPD ¼ 194395% HPD ¼ -1955, whereas the most recent tMRCAs corresponded to subtype G and CRF01_AE subset 3 (median ¼ 1971, 95% HPD ¼ 1967-1975and median ¼ 1972, 95% HPD ¼ 1970-1975. In all cases, the substitution rates of the 5 0 half of HIV-1 genome (gag, pol, and vif) were lower than those of the 3 0 half (vprto-vpu, env, and nef). Specifically, pol presented the lowest substitution rate and env the highest in all HIV-1 subtypes/CRFs (Table 3).

Discussion
We have estimated and compared the genomic substitution rates of different HIV-1 subtypes (A1, B, C, D, and G) and CRFs (CRF01_AE and CRF02_AG). To obtain representative datasets of the publicly available genomes for each HIV-1 variant, we included sequences from the most complete geographical, temporal, and genetic range as possible and removed epidemiologically related variants, including those obtained from the same patient.
Our analyses were performed using tip-dates of heterochronous samples as the only calibration method. They have revealed differences in the substitution rates between the analyzed subtypes and CRFs. Subtypes C and A1 and CRF01_AE presented the fastest substitution rates among the studied HIV-1 datasets, with CRF02_AG and subtype D being the slowest evolving groups.
As expected, substitution rate estimates for the different subtypes and CRFs differed from previous analyses working only with partial pol and/or env regions (Abecasis et al. 2009;Wertheim et al. 2012  For subtypes B, C, and CRF01_AE, only the sub-dataset with highest molecular clock signal was subjected to model comparison. b AICM value obtained using the best-fitting demographic model. and env sequences of up to 799 and 931 nt, respectively, and found that for these two genes subtype G and CRF02_AG had the highest substitution rate and subtype D had the lowest rate. On the other hand, Wertheim et al. (2012) analyzed complete pol sequences and found subtype B to be evolving faster than subtypes D and C. These incongruences between different studies can be explained by the different genomic regions analyzed, focusing on the substitution rates of short genomic regions, but ignoring differences in selective constraints or mutation rates that exist along the whole HIV CDS (Geller et al. 2015). This could explain why the genomic substitution rate of the subsets from subtype B were very similar to that estimated recently from complete coding regions by Worobey et al. (2016). The discrepancies between works could also be explained by differences in the size of the analyzed datasets. Although larger datasets have been analyzed previously for CFR02_AG and subtype G (Abecasis et al. 2009), and for subtype D (Wertheim et al. 2012), we have analyzed larger datasets than previous works for the remaining HIV-1 groups. All the HIV-1 subtypes and CRFs analyzed displayed a similar pattern in the estimated substitution rates along their genomes: genes gag, pol, and vif presented lower rates than the vpr-to-vpu segment and env and nef genes, with pol and env presenting the slowest and fastest substitution rates, respectively. Li et al. (2015) found higher levels of amino acid diversity in the proteins encoded by tat, rev, vpu, env, and nef genes, and associated their higher levels of variability to different factors. Firstly, a higher variability might be associated with the presence of CD4 T-cell and antibody epitopes, which would favor diversifying selective pressures. Secondly, these proteins were found to present higher numbers of HIV-human associations, which may lead these proteins to present a higher structural flexibility.
Using nearly complete genome coding regions, the 95% HPD intervals obtained for the tMRCA of each subtype and gene were in most cases in agreement with previous estimates (Abecasis et al. 2009;Gray et al. 2009;Yebra et al. 2016). However, tMRCA estimates for subtypes B and C were discordant with respect to those obtained previously (Gilbert et al. 2007;Faria et al. 2014;Worobey et al. 2016). These works estimated the tMRCA of subtype B to have occurred in the 60 s. Faria et al. (2014) also estimated the tMRCA of subtype C to have occurred in the late 30 s. The most plausible reasons for such discrepancies is that the aforementioned studies analyzed older sequences, obtained from samples existing in the geographical locations from which these subtypes initially diversified, such as Kinshasa (Democratic Republic of the Congo) and the Caribbean. Indeed, the tMRCA of our subtype C datasets are more similar to that obtained by Wilkinson et al. (2015) for the origin of the southern African epidemic (median: year 1960), the geographic region from which most of our sequences were obtained. It is also noteworthy that, although the tMRCA that we estimated for subtype B differed from that reported by Worobey et al (2016), the genomic substitution rates (as well as the rates for gag, pol, and env) that they reported are very similar to our estimates. This highlights that, although the tMRCA that we obtained for subtype B may not represent the actual diversification date of this HIV-1 group, the substitution rate estimates that we have obtained are robust.
tMRCA estimated for CRF02_AG presented the broadest 95% HPD among all the HIV subtypes/CRFs analyzed in this work, probably because it was also the dataset with the lowest molecular clock signal. However, the median tMRCA obtained for this CRF was only 8 years older than that estimated by Yebra et al. (2016) in West Africa (median tMRCA between 1962 and 1963 as Table 3. Estimates (median and 95% HPD lower and upper limit) for the tMRCA, and for the substitution rates (Â 10e À3 ) of each genomic partition of the HIV-1 datasets analyzed, as obtained using the best-fitting demographic and molecular clock models.
Dataset ( estimated from PR and gp41, respectively). These tMRCA estimates obtained for CRF02_AG suggest an earlier origin than that of HIV-1 subtype G, which was supposed to be one of its parental subtypes. These estimates support previous results revealing that, indeed, CRF02_AG is the parent of subtype G, which is not an actual pure subtype although it remains classified as one (Abecasis et al. 2007).
Overall, analyzing nearly complete coding regions has produced more precise tMRCA and substitution rate estimates than previous analyses (Abecasis et al. 2009;Gray et al. 2009;Wertheim et al. 2012), especially when different molecular clock models were used for the different gene partitions comprising the CDS. However, it is noteworthy that for some HIV-1 groups (subtypes D and G and CRF02_AG) the number of available genomes was much lower than for the others. This work aimed at obtaining the most representative CDS datasets as possible, and the analyzed datasets represent the currently available genomes in public databases for each HIV-1 subtype/CRF. However, it is possible that the limited number of complete CDS sequences analyzed for subtypes D and G and CRF02_AG may have affected our estimates. Despite this potential caveat, estimating tMRCA and substitution rates from independent datasets for each genome partition would lack the statistical power that confers the information present in the different genome partitions in BEAST. Furthermore, it might introduce another bias, arising from sampling differences between genomic regions from the same HIV subtype/CRF.
Another bias in the estimates may result from the time dependence of the substitution rates. Rates can be very different when analyzing viral datasets from different timescales, with shorter timescales associated with higher rates (Duchê ne et al. 2014;Aiewsakun & Katzourakis 2015. Meyer et al. (2015) assessed the effect of time dependence of the substitution rate of influenza during the 2009 pandemic outbreak, and found that at least 9 months of temporal divergence were necessary to obtain precise estimates for long-term values. In our work, we have used datasets with similar sampling times, which ranged between 21 and 31 years. For this reason, such phenomenon should not introduce a bias in our comparisons.
In conclusion, we have estimated and compared the tMRCAs and genomic substitution rates of the main HIV-1M subtypes and CRFs from a genomic perspective, using the longest possible non-overlapping coding regions. The results obtained show that substitution rates differ significantly among HIV-1 subtypes and CRFs and that the accuracy of the estimated evolutionary parameters increases when independent molecular clock models are applied to each genomic partition. The results obtained provide information that can be used as prior distributions in future Bayesian coalescent analyses of specific HIV-1 subtypes/CRFs and genes, given that the substitution rates of HIV-1 vary among subtypes/CRFs and genomic regions.

Supplementary data
Supplementary data are available at Virus Evolution online.