- Open Access
Within-host and between-host evolutionary rates across the HIV-1 genome
© Alizon and Fraser; licensee BioMed Central Ltd. 2013
- Received: 12 October 2012
- Accepted: 3 April 2013
- Published: 2 May 2013
HIV evolves rapidly at the epidemiological level but also at the within-host level. The virus’ within-host evolutionary rates have been argued to be much higher than its between-host evolutionary rates. However, this conclusion relies on analyses of a short portion of the virus envelope gene. Here, we study in detail these evolutionary rates across the HIV genome.
We build phylogenies using a relaxed molecular clock assumption to estimate evolutionary rates in different regions of the HIV genome. We find that these rates vary strongly across the genome, with higher rates in the envelope gene (env). Within-host evolutionary rates are consistently higher than between-host rates throughout the HIV genome. This difference is significantly more pronounced in env. Finally, we find weak differences between overlapping and non-overlapping regions.
We provide a genome-wide overview of the differences in the HIV rates of molecular evolution at the within- and between-host levels. Contrary to hepatitis C virus, where differences are only located in the envelope gene, within-host evolutionary rates are higher than between-host evolutionary rates across the whole HIV genome. This supports the hypothesis that HIV strains that are less adapted to the host have an advantage during transmission. The most likely mechanism for this is storage and then preferential transmission of viruses in latent T-cells. These results shed a new light on the role of the transmission bottleneck in the evolutionary dynamics of HIV.
- Evolutionary Rate
- Molecular Clock
- Envelope Gene
- External Branch
- Strict Molecular Clock
HIV evolves rapidly over the course of an infection due to its short generation time and to the selective pressure exerted by the host’s immune response [1, 2]. The virus is therefore subject to multi-level selective pressures: at the within-host level, natural selection favours virus strains that grow rapidly inside the host and/or that escape the immune response, whereas at the between-host level it favours strains that spread rapidly in the host population. Within-host and between-host selective pressures can be conflicting as mutations that confer adaptation to exploit one host can impede the transmission rate to other hosts or can even be detrimental in another host . Understanding the interplay between these levels of selection is fundamental to developing epidemiological models for the spread of drug resistant and immune escape mutants [4, 5]. Here, we estimate HIV evolutionary rates at the within-host (WH) and between-host (BH) levels, and across the HIV-1 genome.
If all HIV strains inside an infected individual are equally likely to be transmitted to another host, evolutionary rates should have similar values at the WH and BH levels. On the contrary, current (but limited) evidence suggests that BH rates are lower than WH rates by an order of magnitude: the former tend to be close to 10-2 substitutions per site per year (subst ·site-1·year-1), whereas the latter are closer to 10-3 subst ·site-1·year-1[4, 6]. However, this conclusion is based on only a portion of the envelope (env) gene (using data from ) and evidence obtained on hepatitis C virus shows that different regions of the genome can evolve differently WH and BH .
We focus on the virus molecular rate of evolution, i.e. the number of mutations that are fixated per unit of time in the virus population. This substitution rate indicates the evolutionary potential of a population and is often referred to as the ‘evolutionary rate’ (ER). Importantly, the ER should not be confused with the mutation rate , which is the rate at which mutational errors occur during genome replication: the ER is a property of a viral population and is the result of evolutionary processes such as natural selection or drift, whereas the mutation rate is the result of the interaction between a virus and a host cell. The ER can be measured at the within-host level, by collecting longitudinal sequence data from the same infected host, but also at the between-host level by collecting sequence data from different hosts. Technically, we estimate the ER by assuming a relaxed molecular clock , when building the phylogeny using Bayesian inference methods . This allows us to alleviate the limiting assumption that evolutionary rates are constant among lineages of the phylogeny and through time, i.e. the strict molecular clock hypothesis . Note that we do test that the relaxed molecular clock assumption explains the sequence data better than the strict clock assumption (see the Methods).
Our assumption of a relaxed molecular clock allowed us to estimate ER on internal and on external branches of the virus phylogeny separately . At the WH level, we know that internal branches correspond to viruses that will have an offspring. For external branches however, this is not always the case. Therefore, we expect some of the viruses sampled to bear more deleterious mutations in their genome. In other words, at the WH level, we can expect the substitution rate on external branches to be higher and closer to the virus mutation rate. At the BH level, we do not expect much differences between ER on internal and external branches because selection has had the time to act.
Concerning HIV, it is known that BH substitution rates in the env gene are higher than that in the gag gene . We are not aware of studies that compare WH and BH evolutionary rates in different genomic regions. Here, we provide a genome-wide overview of molecular rates of evolution of HIV-1 both at the within- and at the between-host levels.
Presence of molecular signal
As stressed by several studies [8, 9], before analysing evolutionary rates, it is necessary to check that there is actually molecular clock signal in the data, i.e. that there is accumulation of sequence divergence through time and that this temporal signal is not too over-dispersed. This can be done in several ways, which are further described in the Methods.
The second method, which tests for the temporal signal (i.e. evidence for the accumulation of sequence divergence), consists in performing a regression between root-to-tip divergence and sampling date in a ‘classical’ phylogenetic tree (with a strict molecular clock assumption). The R2 of the regression indicates the amount of molecular clock signal and we refer to it as the ‘root-to-tip’ method. We found that the WH sequences seemed to exhibit more signal than the BH host sequences, especially in the env region (Additional file 1: Figure S1).
We also used a third method, which tests for the temporal signal by randomising tip dates. We only applied this method to the C2V5 region for computational reasons and detected significant molecular clock signal (Additional file 1: Figure S2). Finally, the comparisons we performed between the likelihood of the strict clock model and the relaxed molecular clock models offers another way to test for the adequacy of the model.
Some segments exhibited weak molecular clock signal using both the CoV and the root-to-tip methods and were removed from the statistical analyses. At the BH level, the three (out of 21) segments ignored were REV-ENV, VIF-VPR and VPR-TAT, which altogether represent 2.3% of the total sequence length considered (see Additional file 2: Table S2 for the complete list of segments). At the WH level, the segments ignored for PIC1392 were ENV2, GAG, GAG-POL, TAT-REV-ENV and POL-VIF (i.e. 15.4% of the total sequence length). In the other four WH datasets, we removed segments C2V5, ENV1–3, ENV2, POL, POL-VIF, REV-ENV, TAT, TAT-REV, TAT-REV-ENV, VIF-VPR, VPR and VPR-TAT in PIC38417 (i.e. 42.4% of the total sequence length), segments ENV1–1, ENV1–3, LTR3, GAG-POL, POL-VIF, TAT-REV, TAT-REV-ENV, VIF-VPR, VPR, VPR-TAT and VPU in PIC71101 (i.e. 21.4% of the total sequence length), segments GAG-POL, POL-VIF, TAT, TAT-REV, TAT-REV-ENV, VIF, VIF-VPR, VPR-TAT, VPR and VPU-ENV in PIC83747 (i.e. 11.2% of the total sequence length) and segments ENV1–4, GAG, GAG-POL, LTR3, POL-VIF, REV-ENV, TAT, TAT-REV-ENV, VIF-VPR and VPU-ENV in PIC90770 (i.e. 14.3% of the total sequence length). The fact that many of these segments belong to overlapping reading frames is discussed below. Overall, dataset PIC1362 was our WH dataset with the best coverage (see Additional file 2: Figure S1). In the following we use it as our reference dataset to stress some specific points.
Comparing rates on internal and external branches
In the other WH datasets, the values of these ratios were much more dispersed. This could be due to the fact that these data come from acute infections.
In the following, in order to compare substitution rates at the WH and at the BH level, we only use rates estimated from internal branches of the phylogeny (otherwise, the fact that the WH evolutionary rate is closer to a mutation rate would bias the analyses).
Effect of level of study (WH or BH) and of the presence or not in the env gene on evolutionary rates in PIC1362 and US-up4
Effect of level of study (WH or BH) and the overlap factors on evolutionary rates
In order to check for the robustness of our substitution model assumption, we also measured these evolutionary rates on phylogenies inferred using a GTR substitution model (instead of a HKY+ Γ model). As shown in Additional file 1 (Figure S5), the substitution model did not seem to affect the results qualitatively but the HKY model yielded slightly higher estimates for the ER, both at the WH and at the BH levels.
WH longitudinal data appropriate for these types of analyses are rare. We analysed several whole-genome longitudinal data (patient 9213 studied in  and four patients –PIC38417, PIC71101, PIC83747 and PIC90770– studied in ) but none of the data matched that of patient PIC1362. Overall, the molecular clock signal (estimated using the root-to-tip divergence method and the coefficient of variation method) was low in many of the segments (see above for the list of the segments removed). Furthermore, in patient 9213, almost none of the phylogenies converged in BEAST but in the few segments that did converge (e.g. VPR-TAT), results were consistent with that obtained in PIC1362 (results not shown).
As mentioned above, our results are consistent with earlier studies that have shown a significant difference in evolutionary rates in part of the env gene . To further investigate the robustness of our results, we estimated evolutionary rates in part of the pol genes for other WH and BH datasets.
These additional results show that estimating within-host evolutionary rates requires detailed datasets that span over several years, with several sequences per time step. Appropriate data that is publicly available is limited but it is likely that there exist private datasets from which further insight could be gained.
HIV evolves during the course of an infection and adapts to its host. However, this evolution is ‘short-sighted’ in that it is unlikely to favour genotypes that are efficient at transmitting to new hosts. The hypothesis that there is a conflict between selective pressures acting on HIV at the WH and BH level is not new . However, it has regained interest with more recent analyses of a portion of the HIV genome (located in the env gene of the virus), which found that substitution rates seem to be much higher at the WH level than at the BH level [4, 6].
Here, we show that differences between WH and BH substitution rates previously observed in env are actually present throughout the whole genome. More precisely, the substitution rates do vary across genomic regions (with higher rates in env) but a difference of approximately one order of magnitude is nevertheless observed between the WH and the BH rates. This pattern supports the hypothesis that some HIV variants are stored early in the infection in latent cells and preferentially transmitted when re-activated later on . Indeed, it is more parsimonious to assume that a virus is stored for several generations rather than assuming that there would be reverse mutations throughout the whole genome.
We found that the difference between WH and BH evolutionary rates was slightly (but significantly) more pronounced in the envelope gene (env). This suggests that another process could be at play in env, namely that some of the mutations acquired in this genomic region reverse rapidly in the early stage of an infection, which supports earlier results . It is noteworthy that while longitudinal analysis of early evolution in whole genomes supports some reversion to wild-type in env, it does not support sustained reversion throughout the genome .
We have not considered synonymous and non-synonymous mutations explicitly. The main reason for this is that since we are carrying a whole-genome analysis, we include many regions of the HIV genome that have overlapping reading frame (in which there are no synonymous substitutions). Furthermore, codon usage bias is high for HIV  and, because of secondary and tertiary RNA structure, many synonymous mutations may turn out to be non-synonymous. However, we can still draw some conclusions from our results because we partitioned the genome according to regions of overlap, i.e. regions that simultaneously code for multiple genes. As expected, we found significantly lower rates in overlapping regions at the WH level (and in some cases, no evidence of clock-like evolution at all). At the BH level, this difference seemed to go in the other direction, which could be explained by more time for negative selection to act on deleterious mutations at the BH level. This would be consistent with the absence of differences in BH rates between internal and external branches but these results would require more data (especially BH data) to be confirmed.
Results shown in earlier reviews (though never described in depth) [4, 6] have stimulated research on virus evolution at different levels. However, it is difficult to compare these results to ours because, due to lack of space, their authors did not describe the protocol they used. For instance, we do not know which substitution model they used or, more importantly, on which type of branches (all branches or internal branches only) they measured the evolutionary rates.
We are only aware of one other study that compared WH and BH evolutionary rates across a whole virus genome . This other study was conducted on hepatitis C virus (HCV). Since HCV has no overlapping reading frames, the authors could cut the virus genome into segments of similar size. The main difference between their study and ours is that the WH evolutionary rates were estimated by pooling data from 15 different individuals, who were all infected by the same source via blood transfusion. Our results corroborate these results on HCV in that evolutionary rates vary across the genome and that the difference between WH and BH evolutionary rates is more pronounced in the envelope region. However, contrary to HCV, there is a difference between WH and BH rates even outside env, which allows us to hypothesise that the nature of transmitted strains differ for these two viruses.
A notable limitation to the generality of our results is that we were only able to analyse full genome sequence data of few patients to estimate WH rates. In order to generalise these results, one should analyse more WH genomes (preferentially sampled from patients with infections progressing at different rates).
Overall, this illustrates that estimating within-host evolutionary rates requires extremely good quality datasets that have both a long longitudinal coverage and a deep sampling at each time point. This limitation is not technical and in fact it might be that such data already exists. However, it is not publicly available so far.
We show that evolutionary rates vary strongly across the HIV genome, with higher rates in the envelope gene (env). Furthermore, within-host evolutionary rates are consistently higher than between-host rate throughout the HIV genome. This difference is significantly more pronounced in env. While this result is based on the analysis of only one patient with a long time-series and four patients followed during acute infection and for a short period afterwards, it is an extension of a result that is already established from variation in env in several other patients. Finally, we find only weak differences between overlapping and non-overlapping regions. This study provides the first genome-wide overview of the differences in the HIV rates of molecular evolution at the within- and between-host levels. Contrary to hepatitis C virus, for which this difference is only located in the envelope gene, within-host evolutionary rates are higher than between-host evolutionary rates across the whole HIV genome. This supports the hypothesis that HIV strains that are less adapted to the host have an advantage during transmission. The most likely mechanism for this is storage and then preferential transmission of viruses in latent T-cells. These results shed a new light on the role of the transmission bottleneck in the evolutionary dynamics of HIV. Further studies involving more data (especially within-host data) are needed to determine how these results can be affected by host specificity.
Cut the genome sequences into segments
Remove recombining sequences
Check for the existence of molecular clock signal in the data
Balancing datasets (to maximise clock-likeness)
Select the most appropriate substitution model
Compare molecular clock models and coalescent models
Run the bayesian phylogeny inference package (BEAST)
Analyse substitution rates on internal and external branches
i) Cutting the genome into segments
Sequences were cut into HIV genes using the Gene Cutter algorithm (http://www.hiv.lanl.gov/content/sequence/GENE_CUTTER/cutter.html). These genes were checked using SeaView v.4.3.3  and cut according to overlapping regions using the ape package in R v.2.14.2 .
ii) Removing recombinant sequences
Each segment was analysed with 6 different methods to detect recombination using the RDP software . According to the designer of RDP, any sequence where at least one of the methods detects recombination can be considered as a recombinant. We applied this criterion here (with a p-value of 0.05).
We did not find any evidence for recombination in the WH dataset. In the BH dataset, some sequences were recombinant and were removed.
iii) Controlling for molecular clock signal
An important step before estimating evolutionary rates with a relaxed molecular clock is to check that there is actually molecular clock signal in the data. Indeed, software packages such as BEAST  will always provide the user with an estimate of substitution rate, even if there is no molecular clock signal in the data. The presence of such signal, i.e. the ‘clock-likeness’ of the data, can be checked in different ways. Here we present three of these.
First, we checked that the posterior distribution of the coefficient of variation statistics (CoV), i.e. the scaled variance in ER among lineages , does not impinge substantially on the boundary at zero, which is a way to test between relaxed and strict molecular clock models .
Second, we estimated the root-to-tip divergence . This provided us with an R-squared of the regression between root-to-tip divergence that indicates the amount of sequence divergence explained by the sampling date. To do so, we first generated phylogenies using a ML likelihood approach (using the software PhyML v.3.0 ). We then estimated the clock-like behaviour of the data by performing a regression between root-to-tip distance in the ML tree and the date of sampling of each sequence using the software Path-O-Gen v1.3 . Trees were rooted at the position that was likely to be the most compatible with the assumption of the molecular clock. This method estimated the amount of variation in genetic distances that can be explained by the sampling time.
Third, we built phylogenies using datasets with randomised sampling dates (in order to scramble the temporal structure) and then estimated the evolutionary rate (ER) on the C2V5 segment. If the difference between the substitution rate obtained on the real phylogeny and those obtained on the randomised phylogenies is significant, it supports the existence of a temporal structure .
iv) Balancing datasets
In order to maximise the ‘clock-likeness’ of the data, it helps to have a balanced dataset, i.e. a similar number of sequences from each time point and as many time points as possible . This was obtained by removing samples (randomly) from the most overrepresented time points. For the WH dataset, we kept up to 13 sequences for each time point and for the BH dataset up to 4 (these numbers were chosen to maximise the signal in the C2V5 segment).
v) Determining the substitution model
The substitution model was chosen using the software jModelTest v.0.1 . We selected the HKY+ Γ model, which had the advantage to often provide a good (if not the best) fit to the data without being too complicated (Additional file 2: Table S1). This model also has the advantage to allow for comparisons with other studies, such as .
Note that for the WH data, a GTR substitution model sometimes fitted the data better than an HKY model. However, as we show, our results were not influenced qualitatively by the substitution model.
vi) Determining molecular clock and coalescent models
The model with a relaxed log-normal molecular clock and a Bayesian skyline coalescent model  was selected using a Bayes Factors criterion [7, 34] in the C2V5 region. The Bayes Factor (BF) is based on the difference between the log marginal likelihoods of each model. The classical rule of thumb is that if the difference in Bayes Factors is greater than 3, this is positive evidence for a difference between the two models, and if it is greater than 10, this is strong evidence.
vii) Building the phylogenies
Phylogenies were inferred using BEAST v.1.6.2  with default parameters. Simulations were run until convergence (i.e. an effective sample size greater than 200 for all parameters) and the results were summarised using Tracer v.1.5.
Evolutionary rates were first estimated on each posterior tree distribution using Tracer. Approximately 10% of the output was used as a burn-in. We also estimated the coefficient of variation of the evolutionary rate for each region.
For each data set, there was one tree posterior distribution for each genome segment and each of these posterior distributions were inferred using two different substitution models (GTR and HKY+ Γ).
viii) Analysing substitution rates on internal and external branches
Using a relaxed molecular clock in BEAST allows us to estimate ER on different parts of the phylogeny. We obtained final estimates of ER on internal and external branches for 200 trees from the posterior distribution using the program RateAnalyzer.jar . Note that even though part of the trees of the posterior distribution (200) were used in RateAnalyzer.jar, the results obtained were consistent with that obtained with Tracer, which used the full posterior distributions.
We selected full genomes from untreated US patients infected by HIV-1 subtype B from the Los Alamos HIV database http://www.hiv.lanl.gov/. The GenBank accession numbers of all the sequences we used are provided in Additional file 3.
There was only one excellent quality longitudinal dataset that fitted our criteria (subject PIC1362, a homosexual caucasian male who refused treatment during the whole infection [11, 12]). The dataset consisted of 65 full genome sequences with sampling dates ranging from 1998 to 2002. Earlier studies show that, within a constraint of subsampling sequences that have been collected at a set of distinct sampling times, having an equal number per distinct time is best to maximise the molecular clock signal , this is why here we kept up to 13 sequences from each time point and ended up with a dataset of 65 sequences.
The Los Alamos HIV database did contain two other studies with longitudinal data of full genome sequences. With one of these datasets , the analyses were largely unsuccessful, probably because of a lack of molecular signal. More precisely, this dataset was based on 11 sequences sampled from a German patient (patient 9213) from 2004 to 2008 at 4 different time points. Four datasets from the study by Herbeck et al.  could be analysed. A common feature of these sequences is that they were all obtained during the acute phase of the infection (the longest longitudinal timespan was 11 months). This means that the unit for the estimation of the evolutionary rates was months instead of years. The patient codes were PIC38417, PIC71101, PIC83747 and PIC90770.
Finally, we measured evolutionary rates in the POL segment (a coverage of at least 900 nucleotides between positions 2300 and 4000 of the HIV genome). This looser selection criterion allowed us to include data from two other studies: patient WC3 from a study by Kemal et al.  and patient 1005 from a study by Kearney et al.  (other patients were analysed in this study but there was no molecular signal in their sequence data, data not shown).
For the BH dataset, we applied the same selection criteria (sequences had to be from HIV-1 subtype B, sampled in drug naive individuals from the US, with known sampling dates). Many sequences came from a study conducted in Boston  and additional sequences came from other studies [23–25]. As for the WH dataset, we homogeneised our sampling by keeping up to 4 sequences from each time point and ended up with a dataset of 30 sequences with sampling dates ranging from 1985 to 2007.
As for the WH level, we analysed other BH datasets for the POL segment. We thus obtained 35 sequences from France, 16 sequences from Switzerland and 106 sequences from the UK, all from the Los Alamos HIV database.
We thank I. Bravo, J. Herbeck, K. Lythgoe, Y. Michalakis, O. Pybus, G. Shirreff, T. Stadler for discussion. We are grateful to P. Lemey for sharing his code for the estimation of substitution rates on internal and external branches of the phylogeny. SA is funded by an ATIP-Avenir grant from CNRS and INSERM, by the CNRS and by the IRD. CF is funded by a Royal Society fellowship.
- Shankarappa R, Margolick JB, Gange SJ, Rodrigo AG, Upchurch D, Farzadegan H, Gupta P, Rinaldo CR, Learn GH, He X, Huang XL, Mullins JI: Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. J Virol. 1999, 73 (12): 10489-10502.PubMed CentralPubMedGoogle Scholar
- Rambaut A, Posada D, Crandall KA, Holmes EC: The causes and consequences of HIV evolution. Nat Rev Genet. 2004, 5: 52-61. 10.1038/nrg1246.View ArticlePubMedGoogle Scholar
- Levin BR, Bull JJ: Short-sighted evolution and the virulence of pathogenic microorganisms. Trends Microbiol. 1994, 2 (3): 76-81. 10.1016/0966-842X(94)90538-X.View ArticlePubMedGoogle Scholar
- Lemey P, Rambaut A, Pybus OG: HIV evolutionary dynamics within and among hosts. AIDS Rev. 2006, 8 (3): 125-140.PubMedGoogle Scholar
- Alizon S, Luciani F, Regoes RR: Epidemiological and clinical consequences of within-host evolution. Trends Microbiol. 2011, 19: 24-32. 10.1016/j.tim.2010.09.005.View ArticlePubMedGoogle Scholar
- Pybus OG, Rambaut A: Evolutionary analysis of the dynamics of viral infectious disease. Nat Rev Genet. 2009, 10 (8): 540-550. 10.1038/nrg2583.View ArticlePubMedGoogle Scholar
- Gray RR, Parker J, Lemey P, Salemi M, Katzourakis A, Pybus OG: The mode and tempo of hepatitis C virus evolution within and among hosts. BMC Evol Biol. 2011, 11: 131-10.1186/1471-2148-11-131.PubMed CentralView ArticlePubMedGoogle Scholar
- Drummond AJ, Pybus OG, Rambaut A, Forsberg R, Rodrigo AG: Measurably evolving populations. Trends Ecol Evol. 2003, 18 (9): 481-488. 10.1016/S0169-5347(03)00216-7.View ArticleGoogle Scholar
- Drummond AJ, Ho SYW, Phillips MJ, Rambaut A: Relaxed phylogenetics and dating with confidence. PLoS Biol. 2006, 4 (5): e88-10.1371/journal.pbio.0040088.PubMed CentralView ArticlePubMedGoogle Scholar
- Drummond AJ, Rambaut A: BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007, 7: 214-10.1186/1471-2148-7-214.PubMed CentralView ArticlePubMedGoogle Scholar
- Cao J, McNevin J, Malhotra U, McElrath MJ: Evolution of CD8+ T cell immunity and viral escape following acute HIV-1 infection. J Immunol. 2003, 171 (7): 3837-3846.View ArticlePubMedGoogle Scholar
- Liu Y, McNevin J, Cao J, Zhao H, Genowati I, Wong K, McLaughlin S, McSweyn MD, Diem K, Stevens CE, Maenza J, He H, Nickle DC, Shriner D, Holte SE, Collier AC, Corey L, McElrath MJ, Mullins JI: Selection on the human immunodeficiency virus type 1 proteome following primary infection. J Virol. 2006, 80 (19): 9519-9529. 10.1128/JVI.00575-06.PubMed CentralView ArticlePubMedGoogle Scholar
- Herbeck JT, Rolland M, Liu Y, McLaughlin S, McNevin J, Zhao H, Wong K, Stoddard JN, Raugi D, Sorensen S, Genowati I, Birditt B, McKay A, Diem K, Maust BS, Deng W, Collier AC, Stekler JD, McElrath MJ, Mullins JI: Demographic processes affect HIV-1 evolution in primary infection before the onset of selective processes. J Virol. 2011, 85 (15): 7523-7534. 10.1128/JVI.02697-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang YE, Li B, Carlson JM, Streeck H, Gladden AD, Goodman R, Schneidewind A, Power KA, Toth I, Frahm N, Alter G, Brander C, Carrington M, Walker BD, Altfeld M, Heckerman D, Allen TM: Protective HLA class I alleles that restrict acute-phase CD8+ T-cell responses are associated with viral escape mutations located in highly conserved regions of human immunodeficiency virus type 1. J Virol. 2009, 83 (4): 1845-1855. 10.1128/JVI.01061-08.PubMed CentralView ArticlePubMedGoogle Scholar
- Lemey P, Kosakovsky Pond SL, Drummond AJ, Pybus OG, Shapiro B, Barroso H, Taveira N, Rambaut A: Synonymous substitution rates predict HIV disease progression as a result of underlying replication dynamics. PLoS Comput Biol. 2007, 3 (2): e29-10.1371/journal.pcbi.0030029.PubMed CentralView ArticlePubMedGoogle Scholar
- Lemey P, Pybus OG, Rambaut A, Drummond AJ, Robertson DL, Roques P, Worobey M, Vandamme AM: The molecular population genetics of HIV-1 group O. Genetics. 2004, 167 (3): 1059-1068. 10.1534/genetics.104.026666.PubMed CentralView ArticlePubMedGoogle Scholar
- Henn MR, Boutwell CL, Charlebois P, Lennon NJ, Power KA, Macalalad AR, Berlin AM, Malboeuf CM, Ryan EM, Gnerre S, Zody MC, Erlich RL, Green LM, Berical A, Wang Y, Casali M, Streeck H, Bloom AK, Dudek T, Tully D, Newman R, Axten KL, Gladden AD, Battis L, Kemper M, Zeng Q, Shea TP, Gujja S, Zedlack C, Gasser O, Brander C, Hess C, Günthard HF, Brumme ZL, Brumme CJ, Bazner S, Rychert J, Tinsley JP, Mayer KH, Rosenberg E, Pereyra F, Levin JZ, Young SK, Jessen H, Altfeld M, Birren BW, Walker BD, Allen TM: Whole genome deep sequencing of HIV-1 reveals the impact of early minor variants upon immune recognition during acute infection. PLoS Pathog. 2012, 8 (3): e1002529-10.1371/journal.ppat.1002529.PubMed CentralView ArticlePubMedGoogle Scholar
- Lythgoe K, Fraser C: New insights into the evolutionary rate of HIV-1 at the within-host and epidemiological levels. Proc R Soc Lond B. 2012, 279 (1741): 3367-3375. 10.1098/rspb.2012.0595.View ArticleGoogle Scholar
- Sharp PM: What can AIDS virus codon usage tell us?. Nature. 1986, 324 (6093): 114-10.1038/324114a0.View ArticlePubMedGoogle Scholar
- Seo TK, Thorne JL, Hasegawa M, Kishino H: A viral sampling design for testing the molecular clock and for estimating evolutionary rates and divergence times. Bioinformatics. 2002, 18: 115-123. 10.1093/bioinformatics/18.1.115.View ArticlePubMedGoogle Scholar
- Kemal KS, Beattie T, Dong T, Weiser B, Kaul R, Kuiken C, Sutton J, Lang D, Yang H, Peng YC, Collman R, Philpott S, Rowland-Jones S, Burger H: Transition from long-term nonprogression to HIV-1 disease associated with escape from cellular immune control. J Acquir Immune Defic Syndr. 2008, 48 (2): 119-126. 10.1097/QAI.0b013e31816b6abd.View ArticlePubMedGoogle Scholar
- Kearney M, Maldarelli F, Shao W, Margolick JB, Daar ES, Mellors JW, Rao V, Coffin JM, Palmer S: Human immunodeficiency virus type 1 population genetics and adaptation in newly infected individuals. J Virol. 2009, 83 (6): 2715-2727. 10.1128/JVI.01960-08.PubMed CentralView ArticlePubMedGoogle Scholar
- Fang G, Burger H, Chappey C, Rowland-Jones S, Visosky A, Chen CH, Moran T, Townsend L, Murray M, Weiser B: Analysis of transition from long-term nonprogressive to progressive infection identifies sequences that may attenuate HIV type 1. AIDS Res Hum Retroviruses. 2001, 17 (15): 1395-1404. 10.1089/088922201753197060.View ArticlePubMedGoogle Scholar
- Wei X, Decker JM, Wang S, Hui H, Kappes JC, Wu X, Salazar-Gonzalez JF, Salazar MG, Kilby JM, Saag MS, Komarova NL, Nowak MA, Hahn BH, Kwong PD, Shaw GM: Antibody neutralization and escape by HIV-1. Nature. 2003, 422 (6929): 307-312. 10.1038/nature01470.View ArticlePubMedGoogle Scholar
- Blankson JN, Bailey JR, Thayil S, Yang HC, Lassen K, Lai J, Gandhi SK, Siliciano JD, Williams TM, Siliciano RF: Isolation and characterization of replication-competent human immunodeficiency virus type 1 from a subset of elite suppressors. J Virol. 2007, 81 (5): 2508-2518. 10.1128/JVI.02165-06.PubMed CentralView ArticlePubMedGoogle Scholar
- Gouy M, Guindon S, Gascuel O: SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol. 2010, 27 (2): 221-224. 10.1093/molbev/msp259.View ArticlePubMedGoogle Scholar
- Paradis E, Claude J, Strimmer K: APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004, 20 (2): 289-290. 10.1093/bioinformatics/btg412.View ArticlePubMedGoogle Scholar
- Martin DP, Lemey P, Lott M, Moulton V, Posada D, Lefeuvre P: RDP3: a flexible and fast computer program for analyzing recombination. Bioinformatics. 2010, 26 (19): 2462-2463. 10.1093/bioinformatics/btq467.PubMed CentralView ArticlePubMedGoogle Scholar
- Guindon S, Gascuel O: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003, 52 (5): 696-704. 10.1080/10635150390235520.View ArticlePubMedGoogle Scholar
- Drummond A, Pybus OG, Rambaut A: Inference of viral evolutionary rates from molecular sequences. Adv Parasitol. 2003, 54: 331-358.View ArticlePubMedGoogle Scholar
- Ramsden C, Holmes EC, Charleston MA: Hantavirus evolution in relation to its rodent and insectivore hosts: no evidence for codivergence. Mol Biol Evol. 2009, 26: 143-153.View ArticlePubMedGoogle Scholar
- Posada D: jModelTest: phylogenetic model averaging. Mol Biol Evol. 2008, 25 (7): 1253-1256. 10.1093/molbev/msn083.View ArticlePubMedGoogle Scholar
- Drummond AJ, Rambaut A, Shapiro B, Pybus OG: Bayesian coalescent inference of past population dynamics from molecular sequences. Mol Biol Evol. 2005, 22 (5): 1185-1192. 10.1093/molbev/msi103.View ArticlePubMedGoogle Scholar
- Suchard MA, Weiss RE, Sinsheimer JS: Bayesian selection of continuous-time Markov chain evolutionary models. Mol Biol Evol. 2001, 18 (6): 1001-1013. 10.1093/oxfordjournals.molbev.a003872.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.