Impact of CCR5delta32 host genetic background and disease progression on HIV-1 intrahost evolutionary processes: efficient hypothesis testing through hierarchical phylogenetic models

The interplay between C-C chemokine receptor type 5 (CCR5) host genetic background, disease progression, and intrahost HIV-1 evolutionary dynamics remains unclear because differences in viral evolution between hosts limit the ability to draw conclusions across hosts stratified into clinically relevant populations. Similar inference problems are proliferating across many measurably evolving pathogens for which intrahost sequence samples are readily available. To this end, we propose novel hierarchical phylogenetic models (HPMs) that incorporate fixed effects to test for differences in dynamics across host populations in a formal statistical framework employing stochastic search variable selection and model averaging. To clarify the role of CCR5 host genetic background and disease progression on viral evolutionary patterns, we obtain gp120 envelope sequences from clonal HIV-1 variants isolated at multiple time points in the course of infection from populations of HIV-1-infected individuals who only harbored CCR5-using HIV-1 variants at all time points. Presence or absence of a CCR5 wt/Δ32 genotype and progressive or long-term nonprogressive course of infection stratify the clinical populations in a two-way design. As compared with the standard approach of analyzing sequences from each patient independently, the HPM provides more efficient estimation of evolutionary parameters such as nucleotide substitution rates and d(N)/d(S) rate ratios, as shown by significant shrinkage of the estimator variance. The fixed effects also correct for nonindependence of data between populations and results in even further shrinkage of individual patient estimates. Model selection suggests an association between nucleotide substitution rate and disease progression, but a role for CCR5 genotype remains elusive. Given the absence of clear d(N)/d(S) differences between patient groups, delayed onset of AIDS symptoms appears to be solely associated with lower viral replication rates rather than with differences in selection on amino acid fixation.     

Intercompartmental recombination of HIV-1 contributes to env intrahost diversity and modulates viral tropism and sensitivity to entry inhibitors

HIV-1 circulates within an infected host as a genetically heterogeneous viral population. Viral intrahost diversity is shaped by substitutional evolution and recombination. Although many studies have speculated that recombination could have a significant impact on viral phenotype, this has never been definitively demonstrated. We report here phylogenetic and subsequent phenotypic analyses of envelope genes obtained from HIV-1 populations present in different anatomical compartments. Assessment of env compartmentalization from immunologically discrete tissues was assessed utilizing a single genome amplification approach, minimizing in vitro-generated artifacts. Genetic compartmentalization of variants was frequently observed. In addition, multiple incidences of intercompartment recombination, presumably facilitated by low-level migration of virus or infected cells between different anatomic sites and coinfection of susceptible cells by genetically divergent strains, were identified. These analyses demonstrate that intercompartment recombination is a fundamental evolutionary mechanism that helps to shape HIV-1 env intrahost diversity in natural infection. Analysis of the phenotypic consequences of these recombination events showed that genetic compartmentalization often correlates with phenotypic compartmentalization and that intercompartment recombination results in phenotype modulation. This represents definitive proof that recombination can generate novel combinations of phenotypic traits which differ subtly from those of parental strains, an important phenomenon that may have an impact on antiviral therapy and contribute to HIV-1 persistence in vivo.     

Dynamic Correlation between Intrahost HIV-1 Quasispecies Evolution and Disease Progression

Quantifying the dynamics of intrahost HIV-1 sequence evolution is one means of uncovering information about the interaction between HIV-1 and the host immune system. In the chronic phase of infection, common dynamics of sequence divergence and diversity have been reported. We developed an HIV-1 sequence evolution model that simulated the effects of mutation and fitness of sequence variants. The amount of evolution was described by the distance from the founder strain, and fitness was described by the number of offspring a parent sequence produces. Analysis of the model suggested that the previously observed saturation of divergence and decrease of diversity in later stages of infection can be explained by a decrease in the proportion of offspring that are mutants as the distance from the founder strain increases rather than due to an increase of viral fitness. The prediction of the model was examined by performing phylogenetic analysis to estimate the change in the rate of evolution during infection. In agreement with our modeling, in 13 out of 15 patients (followed for 3–12 years) we found that the rate of intrahost HIV-1 evolution was not constant but rather slowed down at a rate correlated with the rate of CD4+ T-cell decline. The correlation between the dynamics of the evolutionary rate and the rate of CD4+ T-cell decline, coupled with our HIV-1 sequence evolution model, explains previously conflicting observations of the relationships between the rate of HIV-1 quasispecies evolution and disease progression.     

Evolution of the human immunodeficiency virus envelope gene is dominated by purifying selection

The evolution of the human immunodeficiency virus (HIV-1) during chronic infection involves the rapid, continuous turnover of genetic diversity. However, the role of natural selection, relative to random genetic drift, in governing this process is unclear. We tested a stochastic model of genetic drift using partial envelope sequences sampled longitudinally in 28 infected children. In each case the Bayesian posterior (empirical) distribution of coalescent genealogies was estimated using Markov chain Monte Carlo methods. Posterior predictive simulation was then used to generate a null distribution of genealogies assuming neutrality, with the null and empirical distributions compared using four genealogy-based summary statistics sensitive to nonneutral evolution. Because both null and empirical distributions were generated within a coalescent framework, we were able to explicitly account for the confounding influence of demography. From the distribution of corrected P-values across patients, we conclude that empirical genealogies are more asymmetric than expected if evolution is driven by mutation and genetic drift only, with an excess of low-frequency polymorphisms in the population. This indicates that although drift may still play an important role, natural selection has a strong influence on the evolution of HIV-1 envelope. A negative relationship between effective population size and substitution rate indicates that as the efficacy of selection increases, a smaller proportion of mutations approach fixation in the population. This suggests the presence of deleterious mutations. We therefore conclude that intrahost HIV-1 evolution in envelope is dominated by purifying selection against low-frequency deleterious mutations that do not reach fixation.     

More than meets the eye: detecting cryptic microgeographic population structure in a parasite with a complex life cycle

Nonrandom recruitment of parasites among hosts can lead to genetic differentiation among hosts and mating dynamics that promote inbreeding. It has been hypothesized that strictly aquatic parasites with intermediate hosts will behave as panmictic populations among hosts because ample opportunity exists for random mixing of unrelated individuals during transmission to the definitive host. A previous allozyme study on the marine trematode Lecithochirium fusiforme did not support this hypothesis; in that, there was genetic differentiation among, and significant heterozygote deficiencies within, definitive hosts. We revisit this system and use microsatellites to obtain multilocus genotypes. Our goal was to determine whether cryptic subgroups and/or the presence of clones could account for the apparent deviation from 'panmixia'. We find strong evidence for cryptic subdivision (three genetic clusters) that causes the Wahlund effect and differentiation among definitive hosts. After accounting for these cryptic groups, we see panmictic genetic structure among definitive hosts that is consistent with the 'high mixing in aquatic habitats' hypothesis. We see evidence for cotransmission of clones in all three clusters, but this level of clonal structure did not have a major impact in causing deviations from Hardy-Weinberg equilibrium, and only affected genetic differentiation among hosts in one cluster. A cursory examination of the data may have led to incorrect conclusions about nonrandom transmission. However, it is obvious in this system that there is more than meets the eye in relation to the actual make-up of parasite populations. In general, the methods we employ will be useful for elucidating hidden patterns in other organisms where cryptic structure may be common (e.g. those with limited morphology or complex life histories).     

Genome-wide patterns of intrahuman dengue virus diversity reveal associations with viral phylogenetic clade and interhost diversity

Analogous to observations in RNA viruses such as human immunodeficiency virus, genetic variation associated with intrahost dengue virus (DENV) populations has been postulated to influence viral fitness and disease pathogenesis. Previous attempts to investigate intrahost genetic variation in DENV characterized only a few viral genes or a limited number of full-length genomes. We developed a whole-genome amplification approach coupled with deep sequencing to capture intrahost diversity across the entire coding region of DENV-2. Using this approach, we sequenced DENV-2 genomes from the serum of 22 Nicaraguan individuals with secondary DENV infection and captured ～75% of the DENV genome in each sample (range, 40 to 98%). We identified and quantified variants using a highly sensitive and specific method and determined that the extent of diversity was considerably lower than previous estimates. Significant differences in intrahost diversity were detected between genes and also between antigenically distinct domains of the Envelope gene. Interestingly, a strong association was discerned between the extent of intrahost diversity in a few genes and viral clade identity. Additionally, the abundance of viral variants within a host, as well as the impact of viral mutations on amino acid encoding and predicted protein function, determined whether intrahost variants were observed at the interhost level in circulating Nicaraguan DENV-2 populations, strongly suggestive of purifying selection across transmission events. Our data illustrate the value of high-coverage genome-wide analysis of intrahost diversity for high-resolution mapping of the relationship between intrahost diversity and clinical, epidemiological, and virological parameters of viral infection.     

Faster Adaptation in Smaller Populations: Counterintuitive Evolution of HIV during Childhood Infection

Analysis of HIV-1 gene sequences sampled longitudinally from infected individuals can reveal the evolutionary dynamics that underlie associations between disease outcome and viral genetic diversity and divergence. Here we extend a statistical framework to estimate rates of viral molecular adaptation by considering sampling error when computing nucleotide site-frequencies. This is particularly beneficial when analyzing viral sequences from within-host viral infections if the number of sequences per time point is limited. To demonstrate the utility of this approach, we apply our method to a cohort of 24 patients infected with HIV-1 at birth. Our approach finds that viral adaptation arising from recurrent positive natural selection is associated with the rate of HIV-1 disease progression, in contrast to previous analyses of these data that found no significant association. Most surprisingly, we discover a strong negative correlation between viral population size and the rate of viral adaptation, the opposite of that predicted by standard molecular evolutionary theory. We argue that this observation is most likely due to the existence of a confounding third variable, namely variation in selective pressure among hosts. A conceptual non-linear model of virus adaptation that incorporates the two opposing effects of host immunity on the virus population can explain this counterintuitive result.     

Stochastic interplay between mutation and recombination during the acquisition of drug resistance mutations in human immunodeficiency virus type 1

The emergence of drug resistance mutations in human immunodeficiency virus (HIV) has been a major setback in the treatment of infected patients. Besides the high mutation rate, recombination has been conjectured to have an important impact on the emergence of drug resistance. Population genetic theory suggests that in populations limited in size recombination may facilitate the acquisition of beneficial mutations. The viral population in an infected patient may indeed represent such a population limited in size, since current estimates of the effective population size range from 500 to 10(5). To address the effects of limited population size, we therefore expand a previously described deterministic population genetic model of HIV replication by incorporating the stochastic processes that occur in finite populations of infected cells. Using parameter estimates from the literature, we simulate the evolution of drug-resistant viral strains. The simulations show that recombination has only a minor effect on the rate of acquisition of drug resistance mutations in populations with effective population sizes as small as 1,000, since in these populations, viral strains typically fix beneficial mutations sequentially. However, for intermediate effective population sizes (10(4) to 10(5)), recombination can accelerate the evolution of drug resistance by up to 25%. Furthermore, a reduction in population size caused by drug therapy can be overcome by a higher viral mutation rate, leading to a faster evolution of drug resistance.     

Analytical framework for identifying and differentiating recent hitchhiking and severe bottleneck effects from multi-locus DNA sequence data

Hitchhiking and severe bottleneck effects have impact on the dynamics of genetic diversity of a population by inducing homogenization at a single locus and at the genome-wide scale, respectively. As a result, identification and differentiation of the signatures of such events from DNA sequence data at a single locus is challenging. This paper develops an analytical framework for identifying and differentiating recent homogenization events at multiple neutral loci in low recombination regions. The dynamics of genetic diversity at a locus after a recent homogenization event is modeled according to the infinite-sites mutation model and the Wright-Fisher model of reproduction with constant population size. In this setting, I derive analytical expressions for the distribution, mean, and variance of the number of polymorphic sites in a random sample of DNA sequences from a locus affected by a recent homogenization event. Based on this framework, three likelihood-ratio based tests are presented for identifying and differentiating recent homogenization events at multiple loci. Lastly, I apply the framework to two data sets. First, I consider human DNA sequences from four non-coding loci on different chromosomes for inferring evolutionary history of modern human populations. The results suggest, in particular, that recent homogenization events at the loci are identifiable when the effective human population size is 50,000 or greater in contrast to 10,000, and the estimates of the recent homogenization events are agree with the "Out of Africa" hypothesis. Second, I use HIV DNA sequences from HIV-1-infected patients to infer the times of HIV seroconversions. The estimates are contrasted with other estimates derived as the mid-time point between the last HIV-negative and first HIV-positive screening tests. The results show that significant discrepancies can exist between the estimates.     

Intra-Host Diversity and Emergence of Unique GBV-C Viral Lineages in HIV Infected Subjects in Central China

GB virus C (GBV-C), which is highly prevalent among HIV/AIDS, seemed to slow the HIV disease progression. The HIV/GBV-C co-infected individuals may represent an interesting model for the investigation of the role played by HIV infection and/or the immune system in driving the evolution of the GBV-C viral populations. The present study investigated the prevalence and population dynamics of GB virus C in HIV infected individuals representing 13 geographic regions of Hubei Province of China. Approximately 37% of HIV-1 infected individuals were infected with GBV-C and genotype 3 is appeared to be predominant. Utilizing the 196 complete E2 nucleotide sequence data from 10 HIV/GBV-C infected individuals and employing coalescence based phylogenetic approaches; the present study has investigated the intra-host dynamics of GBV-C. The results revealed patient-specific unique GBV-C viral lineages and each viral lineage showed the evidence of rapid population expansion in respective HIV-1 infected patients, thus suggesting HIV-1 was unlikely to have been inhibiting effect on the GBV-C viral replication. GBV-C in all patients has experienced intense purifying selection, suggesting the GBV-C viral invasion and subsequent expansion within the HIV-1 infected hosts without any modification of the functional epitopes at their membrane protein. The finding of within host GBV-C recombinant sequences indicated recombination was one of the significant forces in the evolution and divergence of GBV-C.     

Viral biogeography revealed by signatures in Sulfolobus islandicus genomes

Viruses are a driving force of microbial evolution. Despite their importance, the evolutionary dynamics that shape diversity in viral populations are not well understood. One of the primary factors that define viral population structure is coevolution with microbial hosts. Experimental models predict that the trajectory of coevolution will be determined by the relative migration rates of viruses and their hosts; however, there are no natural microbial systems in which both have been examined. The biogeographic distribution of viruses that infect Sulfolobus islandicus is investigated using genome comparisons among four newly identified, integrated, Sulfolobus spindle-shaped viruses and previously sequenced viral strains. Core gene sequences show a biogeographic distribution where viral genomes are specifically associated with each local population. In addition, signatures of host–virus interactions recorded in the sequence-specific CRISPR (clustered regularly interspaced short palindromic repeats) system show that hosts have interacted with viral communities that are more closely related to local viral strains than to foreign ones. Together, both proviral and CRISPR sequences show a clear biogeographic structure for Sulfolobus viral populations. Our findings demonstrate that virus–microbe coevolution must be examined in a spatially explicit framework. The combination of host and virus biogeography suggests a model for viral diversification driven by host immunity and local adaptation.     

Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified viral load as a critical factor in variant identification. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology.     

The Price equation framework to study disease within-host evolution

The evolution of infectious diseases is known to affect epidemiological dynamics, but, for some viruses and bacteria, this evolution also takes place inside a host during the course of an infection. I develop an original approach to study intrahost evolutionary dynamics of quantitative disease traits. This approach can be expressed mathematically using the ‘Price equation’ framework recently developed in evolutionary epidemiology. This framework combines population genetics and within-host population dynamics models to identify trade-offs that affect disease intrahost evolution and to predict short-term evolutionary dynamics of life-history traits. I show that this can be applied to study the evolution of viruses competing for host cells or to study the coevolution between parasites and the immune system of the host. This framework can also easily incorporate experimental data. Studying intrahost evolutionary dynamics provides insight at the within-host level, because it allows us to better understand the course of chronic infections, and at the epidemiological level, because it helps to study multi-scale evolutionary processes. This framework can be used to address important biological issues, from immune escape to disease evolutionary response to treatments.     

A dimensionless number for understanding the evolutionary dynamics of antigenically variable RNA viruses

Antigenically variable RNA viruses are significant contributors to the burden of infectious disease worldwide. One reason for their ubiquity is their ability to escape herd immunity through rapid antigenic evolution and thereby to reinfect previously infected hosts. However, the ways in which these viruses evolve antigenically are highly diverse. Some have only limited diversity in the long-run, with every emergence of a new antigenic variant coupled with a replacement of the older variant. Other viruses rapidly accumulate antigenic diversity over time. Others still exhibit dynamics that can be considered evolutionary intermediates between these two extremes. Here, we present a theoretical framework that aims to understand these differences in evolutionary patterns by considering a virus's epidemiological dynamics in a given host population. Our framework, based on a dimensionless number, probabilistically anticipates patterns of viral antigenic diversification and thereby quantifies a virus's evolutionary potential. It is therefore similar in spirit to the basic reproduction number, the well-known dimensionless number which quantifies a pathogen's reproductive potential. We further outline how our theoretical framework can be applied to empirical viral systems, using influenza A/H3N2 as a case study. We end with predictions of our framework and work that remains to be done to further integrate viral evolutionary dynamics with disease ecology.     

Demographic processes affect HIV-1 evolution in primary infection before the onset of selective processes

HIV-1 transmission and viral evolution in the first year of infection were studied in 11 individuals representing four transmitter-recipient pairs and three independent seroconverters. Nine of these individuals were enrolled during acute infection; all were men who have sex with men (MSM) infected with HIV-1 subtype B. A total of 475 nearly full-length HIV-1 genome sequences were generated, representing on average 10 genomes per specimen at 2 to 12 visits over the first year of infection. Single founding variants with nearly homogeneous viral populations were detected in eight of the nine individuals who were enrolled during acute HIV-1 infection. Restriction to a single founder variant was not due to a lack of diversity in the transmitter as homogeneous populations were found in recipients from transmitters with chronic infection. Mutational patterns indicative of rapid viral population growth dominated during the first 5 weeks of infection and included a slight contraction of viral genetic diversity over the first 20 to 40 days. Subsequently, selection dominated, most markedly in env and nef. Mutants were detected in the first week and became consensus as early as day 21 after the onset of symptoms of primary HIV infection. We found multiple indications of cytotoxic T lymphocyte (CTL) escape mutations while reversions appeared limited. Putative escape mutations were often rapidly replaced with mutually exclusive mutations nearby, indicating the existence of a maturational escape process, possibly in adaptation to viral fitness constraints or to immune responses against new variants. We showed that establishment of HIV-1 infection is likely due to a biological mechanism that restricts transmission rather than to early adaptive evolution during acute infection. Furthermore, the diversity of HIV strains coupled with complex and individual-specific patterns of CTL escape did not reveal shared sequence characteristics of acute infection that could be harnessed for vaccine design.     

Stochastic or deterministic: what is the effective population size of HIV-1?

Various studies have attempted to estimate the effective population size of HIV-1 to determine the strength of stochastic effects in within-host evolution. The largely discrepant estimates, the complexity of the concept of the effective population size and the resulting uncertainty about the underlying assumptions make the interpretation of these estimates difficult. Here, we explain the concept and critically assess the current estimates. We discuss the biologically relevant factors that affect the estimate and use of the effective population size. We argue that these factors lead to an underestimation of the effective population size and, thus, to an overestimation of the strength of stochastic effects in HIV-1 evolution.     

High-resolution analysis of intrahost genetic diversity in dengue virus serotype 1 infection identifies mixed infections

Little is known about the rate at which genetic variation is generated within intrahost populations of dengue virus (DENV) and what implications this diversity has for dengue pathogenesis, disease severity, and host immunity. Previous studies of intrahost DENV variation have used a low frequency of sampling and/or experimental methods that do not fully account for errors generated through amplification and sequencing of viral RNAs. We investigated the extent and pattern of genetic diversity in sequence data in domain III (DIII) of the envelope (E) gene in serial plasma samples (n = 49) taken from 17 patients infected with DENV type 1 (DENV-1), totaling some 8,458 clones. Statistically rigorous approaches were employed to account for artifactual variants resulting from amplification and sequencing, which we suggest have played a major role in previous studies of intrahost genetic variation. Accordingly, nucleotide sequence diversities of viral populations were very low, with conservative estimates of the average levels of genetic diversity ranging from 0 to 0.0013. Despite such sequence conservation, we observed clear evidence for mixed infection, with the presence of multiple phylogenetically distinct lineages present within the same host, while the presence of stop codon mutations in some samples suggests the action of complementation. In contrast to some previous studies we observed no relationship between the extent and pattern of DENV-1 genetic diversity and disease severity, immune status, or level of viremia.     

Combination of Immune and Viral Factors Distinguishes Low-Risk versus High-Risk HIV-1 Disease Progression in HLA-B*5701 Subjects

HLA-B*5701 is the host factor most strongly associated with slow HIV-1 disease progression, although rates can vary within this group. Underlying mechanisms are not fully understood but likely involve both immunological and virological dynamics. The present study investigated HIV-1 in vivo evolution and epitope-specific CD8(+) T cell responses in six HLA-B*5701 patients who had not received antiretroviral treatment, monitored from early infection for up to 7 years. The subjects were classified as high-risk progressors (HRPs) or low-risk progressors (LRPs) based on baseline CD4(+) T cell counts. Dynamics of HIV-1 Gag p24 evolution and multifunctional CD8(+) T cell responses were evaluated by high-resolution phylogenetic analysis and polychromatic flow cytometry, respectively. In all subjects, substitutions occurred more frequently in flanking regions than in HLA-B*5701-restricted epitopes. In LRPs, p24 sequence diversity was significantly lower; sequences exhibited a higher degree of homoplasy and more constrained mutational patterns than HRPs. The HIV-1 intrahost evolutionary rate was also lower in LRPs and followed a strict molecular clock, suggesting neutral genetic drift rather than positive selection. Additionally, polyfunctional CD8(+) T cell responses, particularly to TW10 and QW9 epitopes, were more robust in LRPs, who also showed significantly higher interleukin-2 (IL-2) production in early infection. Overall, the findings indicate that HLA-B*5701 patients with higher CD4 counts at baseline have a lower risk of HIV-1 disease progression because of the interplay between specific HLA-linked immune responses and the rate and mode of viral evolution. The study highlights the power of a multidisciplinary approach, integrating high-resolution evolutionary and immunological data, to understand mechanisms underlying HIV-1 pathogenesis.     

Estimation of effective population size of HIV-1 within a host: a pseudomaximum-likelihood approach

Using pseudomaximum-likelihood approaches to phylogenetic inference and coalescent theory, we develop a computationally tractable method of estimating effective population size from serially sampled viral data. We show that the variance of the maximum-likelihood estimator of effective population size depends on the serial sampling design only because internal node times on a coalescent genealogy can be better estimated with some designs than with others. Given the internal node times and the number of sequences sampled, the variance of the maximum-likelihood estimator is independent of the serial sampling design. We then estimate the effective size of the HIV-1 population within nine hosts. If we assume that the mutation rate is 2.5 x 10(-5) substitutions/generation and is the same in all patients, estimated generation lengths vary from 0.73 to 2.43 days/generation and the mean (1.47) is similar to the generation lengths estimated by other researchers. If we assume that generation length is 1.47 days and is the same in all patients, mutation rate estimates vary from 1.52 x 10(-5) to 5.02 x 10(-5). Our results indicate that effective viral population size and evolutionary rate per year are negatively correlated among HIV-1 patients.     

Evolution of intrahost HIV-1 genetic diversity during chronic infection

HIV-1 is one of the fastest evolving entities known. Given that census population sizes of HIV-1 within individuals are much greater than the inverse mutation rate, every possible single point mutation in the viral genome occurs each generation. This enormous capability to generate genetic variation allows for escape from immune surveillance and antiviral therapy. However, compared to this potential, populations of HIV-1 within individuals exhibit little genetic variation. This discrepancy between the known mutation rate of HIV-1 and the average level of genetic variation in the env gene observed in vivo is reflected in comparisons of the actual numbers of productively infected cells, estimated as 10(7), and the effective population size, estimated as 10(3). Using approximate Bayesian computation, we evaluated several hypotheses based on a variety of selective and demographic processes to explain the low effective population size of HIV-1. Of the models we examined, the metapopulation model, in which HIV-1 evolves within an individual as a large collection of small subpopulations subject to frequent migration, extinction, and recolonization, was most consistent with the observed levels of genetic variation and the average frequencies of those variants. The metapopulation model links previous studies of viral dynamics and population genetics.