Revisiting the recombinant history of HIV-1 group M with dynamic network community detection.

The prevailing abundance of full-length HIV type 1 (HIV-1) genome sequences provides an opportunity to revisit the standard model of HIV-1 group M (HIV-1/M) diversity that clusters genomes into largely nonrecombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for simian immunodeficiency virus (SIV) and other HIV-1 groups. Here we develop an unsupervised nonparametric clustering approach, which does not rely on predefined nonrecombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (dynamic stochastic block model [DSBM]) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial generalized linear model (GLM), P<8×10−8), compared to other reference-free recombination detection programs (genetic algorithm for recombination detection [GARD], recombination detection program 4 [RDP4], and RDP5). When this method was applied to a representative sample of n = 525 actual HIV-1 genomes, we determined k = 29 as the optimal number of DSBM clusters and used change-point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and undocumented recombination hotspots in the HIV-1 genome and evidence of intersubtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative framework for HIV-1 classification.

Impact of variable sampling on estimates of HIV-1 reservoir formation dates.

Timing of HIV-1 reservoir formation is important for informing HIV cure efforts. It is unclear how much of the variability seen in dating reservoir formation is due to sampling and gene-specific differences. We used a Bayesian extension of root to tip regression (bayroot) to re-estimate formation date distributions in participants from Swedish and South African cohorts, and assessed the impact of variable timing, frequency, and depth of sampling on these estimates. Significant shifts in formation date distributions were only observed with use of faster-evolving genes, while timing, frequency, and depth of sampling had minor or no significant effect on estimates.

Phylogenetic Reconstruction and Functional Characterization of the Ancestral Nef Protein of Primate Lentiviruses.

Nef is an accessory protein unique to the primate HIV-1, HIV-2, and SIV lentiviruses. During infection, Nef functions by interacting with multiple host proteins within infected cells to evade the immune response and enhance virion infectivity. Notably, Nef can counter immune regulators such as CD4 and MHC-I, as well as the SERINC5 restriction factor in infected cells. In this study, we generated a posterior sample of time-scaled phylogenies relating SIV and HIV Nef sequences, followed by reconstruction of ancestral sequences at the root and internal nodes of the sampled trees up to the HIV-1 Group M ancestor. Upon expression of the ancestral primate lentivirus Nef protein within CD4+ HeLa cells, flow cytometry analysis revealed that the primate lentivirus Nef ancestor robustly downregulated cell-surface SERINC5, yet only partially downregulated CD4 from the cell surface. Further analysis revealed that the Nef-mediated CD4 downregulation ability evolved gradually, while Nef-mediated SERINC5 downregulation was recovered abruptly in the HIV-1/M ancestor. Overall, this study provides a framework to reconstruct ancestral viral proteins and enable the functional characterization of these proteins to delineate how functions could have changed throughout evolutionary history.

Using networks to analyze and visualize the distribution of overlapping genes in virus genomes.

Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may increase the information content of compact genomes or influence the creation of new genes. Here we report a global comparative study of overlapping open reading frames (OvRFs) of 12,609 virus reference genomes in the NCBI database. We retrieved metadata associated with all annotated open reading frames (ORFs) in each genome record to calculate the number, length, and frameshift of OvRFs. Our results show that while the number of OvRFs increases with genome length, they tend to be shorter in longer genomes. The majority of overlaps involve +2 frameshifts, predominantly found in dsDNA viruses. Antisense overlaps in which one of the ORFs was encoded in the same frame on the opposite strand (-0) tend to be longer. Next, we develop a new graph-based representation of the distribution of overlaps among the ORFs of genomes in a given virus family. In the absence of an unambiguous partition of ORFs by homology at this taxonomic level, we used an alignment-free k-mer based approach to cluster protein coding sequences by similarity. We connect these clusters with two types of directed edges to indicate (1) that constituent ORFs are adjacent in one or more genomes, and (2) that these ORFs overlap. These adjacency graphs not only provide a natural visualization scheme, but also a novel statistical framework for analyzing the effects of gene- and genome-level attributes on the frequencies of overlaps.

Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.

HyPhy 2.5-A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies.

HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.

Genetic Diversity, Compartmentalization, and Age of HIV Proviruses Persisting in CD4+ T Cell Subsets during Long-Term Combination Antiretroviral Therapy.

The HIV reservoir, which comprises diverse proviruses integrated into the genomes of infected, primarily CD4+ T cells, is the main barrier to developing an effective HIV cure. Our understanding of the genetics and dynamics of proviruses persisting within distinct CD4+ T cell subsets, however, remains incomplete. Using single-genome amplification, we characterized subgenomic proviral sequences (nef region) from naive, central memory, transitional memory, and effector memory CD4+ T cells from five HIV-infected individuals on long-term combination antiretroviral therapy (cART) and compared these to HIV RNA sequences isolated longitudinally from archived plasma collected prior to cART initiation, yielding HIV data sets spanning a median of 19.5 years (range, 10 to 20 years) per participant. We inferred a distribution of within-host phylogenies for each participant, from which we characterized proviral ages, phylogenetic diversity, and genetic compartmentalization between CD4+ T cell subsets. While three of five participants exhibited some degree of proviral compartmentalization between CD4+ T cell subsets, combined analyses revealed no evidence that any particular CD4+ T cell subset harbored the longest persisting, most genetically diverse, and/or most genetically distinctive HIV reservoir. In one participant, diverse proviruses archived within naive T cells were significantly younger than those in memory subsets, while for three other participants we observed no significant differences in proviral ages between subsets. In one participant, "old" proviruses were recovered from all subsets, and included one sequence, estimated to be 21.5 years old, that dominated (>93%) their effector memory subset. HIV eradication strategies will need to overcome within- and between-host genetic complexity of proviral landscapes, possibly via personalized approaches.IMPORTANCE The main barrier to HIV cure is the ability of a genetically diverse pool of proviruses, integrated into the genomes of infected CD4+ T cells, to persist despite long-term suppressive combination antiretroviral therapy (cART). CD4+ T cells, however, constitute a heterogeneous population due to their maturation across a developmental continuum, and the genetic "landscapes" of latent proviruses archived within them remains incompletely understood. We applied phylogenetic techniques, largely novel to HIV persistence research, to reconstruct within-host HIV evolutionary history and characterize proviral diversity in CD4+ T cell subsets in five individuals on long-term cART. Participants varied widely in terms of proviral burden, genetic diversity, and age distribution between CD4+ T cell subsets, revealing that proviral landscapes can differ between individuals and between infected cell types within an individual. Our findings expose each within-host latent reservoir as unique in its genetic complexity and support personalized strategies for HIV eradication.

Phylogenetic approach to recover integration dates of latent HIV sequences within-host.

Given that HIV evolution and latent reservoir establishment occur continually within-host, and that latently infected cells can persist long-term, the HIV reservoir should comprise a genetically heterogeneous archive recapitulating within-host HIV evolution. However, this has yet to be conclusively demonstrated, in part due to the challenges of reconstructing within-host reservoir establishment dynamics over long timescales. We developed a phylogenetic framework to reconstruct the integration dates of individual latent HIV lineages. The framework first involves inference and rooting of a maximum-likelihood phylogeny relating plasma HIV RNA sequences serially sampled before the initiation of suppressive antiretroviral therapy, along with putative latent sequences sampled thereafter. A linear model relating root-to-tip distances of plasma HIV RNA sequences to their sampling dates is used to convert root-to-tip distances of putative latent lineages to their establishment (integration) dates. Reconstruction of the ages of putative latent sequences sampled from chronically HIV-infected individuals up to 10 y following initiation of suppressive therapy revealed a genetically heterogeneous reservoir that recapitulated HIV's within-host evolutionary history. Reservoir sequences were interspersed throughout multiple within-host lineages, with the oldest dating to >20 y before sampling; historic genetic bottleneck events were also recorded therein. Notably, plasma HIV RNA sequences isolated from a viremia blip in an individual receiving otherwise suppressive therapy were highly genetically diverse and spanned a 20-y age range, suggestive of spontaneous in vivo HIV reactivation from a large latently infected cell pool. Our framework for reservoir dating provides a potentially powerful addition to the HIV persistence research toolkit.

Accumulation of integrase strand transfer inhibitor resistance mutations confers high-level resistance to dolutegravir in non-B subtype HIV-1 strains from patients failing raltegravir in Uganda.

BACKGROUND: Increasing first-line treatment failures in low- and middle-income countries (LMICs) have led to increased use of integrase strand transfer inhibitors (INSTIs) such as dolutegravir. However, HIV-1 susceptibility to INSTIs in LMICs, especially with previous raltegravir exposure, is poorly understood due to infrequent reporting of INSTI failures and testing for INSTI drug resistance mutations (DRMs). METHODS: A total of 51 non-subtype B HIV-1 infected patients failing third-line (raltegravir-based) therapy in Uganda were initially selected for the study. DRMs were detected using Sanger and deep sequencing. HIV integrase genes of 13 patients were cloned and replication capacities (RCs) and phenotypic susceptibilities to dolutegravir, raltegravir and elvitegravir were determined with TZM-bl cells. Spearman's correlation coefficient was used to determine cross-resistance between INSTIs. RESULTS: INSTI DRMs were detected in 47% of patients. HIV integrase-recombinant virus carrying one primary INSTI DRM (N155H or Y143R/S) was susceptible to dolutegravir but highly resistant to raltegravir and elvitegravir (>50-fold change). Two patients, one with E138A/G140A/Q148R/G163R and one with E138K/G140A/S147G/Q148K, displayed the highest reported resistance to raltegravir, elvitegravir and even dolutegravir. The former multi-DRM virus had WT RC whereas the latter had lower RCs than WT. CONCLUSIONS: In HIV-1 subtype A- and D-infected patients failing raltegravir and harbouring INSTI DRMs, there is high-level resistance to elvitegravir and raltegravir. More routine monitoring of INSTI treatment may be advised in LMICs, considering that multiple INSTI DRMs may have accumulated during prolonged exposure to raltegravir during virological failure, leading to high-level INSTI resistance, including dolutegravir resistance.

HIV Diversity and Genetic Compartmentalization in Blood and Testes during Suppressive Antiretroviral Therapy.

HIV's ability to persist during suppressive antiretroviral therapy is the main barrier to cure. Immune-privileged tissues, such as the testes, may constitute distinctive sites of HIV persistence, but this has been challenging to study in humans. We analyzed the proviral burden and genetics in the blood and testes of 10 individuals on suppressive therapy who underwent elective gender-affirming surgery. HIV DNA levels in matched blood and testes were quantified by quantitative PCR, and subgenomic proviral sequences (nef region) were characterized from single templates. HIV diversity, compartmentalization, and immune escape burden were assessed using genetic and phylogenetic approaches. Diverse proviruses were recovered from the blood (396 sequences; 354 nef-intact sequences) and testes (326 sequences; 309 nef-intact sequences) of all participants. Notably, the frequency of identical HIV sequences varied markedly between and within individuals. Nevertheless, proviral loads, within-host unique HIV sequence diversity, and the immune escape burden correlated positively between blood and testes. When all intact nef sequences were evaluated, 60% of participants exhibited significant blood-testis genetic compartmentalization, but none did so when the evaluation was restricted to unique sequences per site, suggesting that compartmentalization, when present, is attributable to the clonal expansion of HIV-infected cells. Our observations confirm the testes as a site of HIV persistence and suggest that individuals with larger and more diverse blood reservoirs will have larger and more diverse testis reservoirs. Furthermore, while the testis microenvironment may not be sufficiently unique to facilitate the seeding of unique viral populations therein, differential clonal expansion dynamics may be at play, which may complicate HIV eradication.IMPORTANCE Two key questions in HIV reservoir biology are whether immune-privileged tissues, such as the testes, harbor distinctive proviral populations during suppressive therapy and, if so, by what mechanism. While our results indicated that blood-testis HIV genetic compartmentalization was reasonably common (60%), it was always attributable to differential frequencies of identical HIV sequences between sites. No blood-tissue data set retained evidence of compartmentalization when only unique HIV sequences per site were considered; moreover, HIV immune escape mutation burdens were highly concordant between sites. We conclude that the principal mechanism by which blood and testis reservoirs differ is not via seeding of divergent HIV sequences therein but, rather, via differential clonal expansion of latently infected cells. Thus, while viral diversity and escape-related barriers to HIV eradication are of a broadly similar magnitude across the blood and testes, clonal expansion represents a challenge. The results support individualized analysis of within-host reservoir diversity to inform curative approaches.

First-line HIV treatment failures in non-B subtypes and recombinants: a cross-sectional analysis of multiple populations in Uganda.

BACKGROUND: Our understanding of HIV-1 and antiretroviral treatment (ART) is strongly biased towards subtype B, the predominant subtype in North America and western Europe. Efforts to characterize the response to first-line treatments in other HIV-1 subtypes have been hindered by the availability of large study cohorts in resource-limited settings. To maximize our statistical power, we combined HIV-1 sequence and clinical data from every available study population associated with the Joint Clinical Research Centre (JCRC) in Uganda. These records were combined with contemporaneous ART-naive records from Uganda in the Stanford HIVdb database. METHODS: Treatment failures were defined by the presence of HIV genotype records with sample collection dates after the ART start dates in the JCRC database. Drug resistances were predicted by the Stanford HIVdb algorithm, and HIV subtype classification and recombination detection was performed with SCUEAL. We used Bayesian network analysis to evaluate associations between drug exposures and subtypes, and binomial regression for associations with recombination. RESULTS: This is the largest database of first-line treatment failures ([Formula: see text]) in Uganda to date, with a predicted statistical power of 80% to detect subtype associations at an odds ratio of [Formula: see text]. In the subset where drug regimen data were available, we observed that use of 3TC was associated with a higher rate of first line treatment failure, whereas regimens containing AZT and TDF were associated with reduced rates of failure. In the complete database, we found limited evidence of associations between HIV-1 subtypes and treatment failure, with the exception of a significantly lower frequency of failures among A/D recombinants that comprised about 7% of the population. First-line treatment failure was significantly associated with reduced numbers of recombination breakpoints across subtypes. CONCLUSIONS: Expanding access to first-line ART should confer the anticipated public health benefits in Uganda, despite known differences in the pathogenesis of HIV-1 subtypes. Furthermore, the impact of ART may actually be enhanced by frequent inter-subtype recombination in this region.

Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison.

Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods' development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.

Quantitation of the latent HIV-1 reservoir from the sequence diversity in viral outgrowth assays.

BACKGROUND: The ability of HIV-1 to integrate into the genomes of quiescent host immune cells, establishing a long-lived latent viral reservoir (LVR), is the primary obstacle to curing these infections. Quantitative viral outgrowth assays (QVOAs) are the gold standard for estimating the size of the replication-competent HIV-1 LVR, measured by the number of infectious units per million (IUPM) cells. QVOAs are time-consuming because they rely on culturing replicate wells to amplify the production of virus antigen or nucleic acid to reproducibly detectable levels. Sequence analysis can reduce the required number of culture wells because the virus genetic diversity within the LVR provides an internal replication and dilution series. Here we develop a Bayesian method to jointly estimate the IUPM and variant frequencies (a measure of clonality) from the sequence diversity of QVOAs. RESULTS: Using simulation experiments, we find our Bayesian approach confers significantly greater accuracy over current methods to estimate the IUPM, particularly for reduced numbers of QVOA replicates and/or increasing actual IUPM. Furthermore, we determine that the improvement in accuracy is greater with increasing genetic diversity in the sample population. We contrast results of these different methods applied to new HIV-1 sequence data derived from QVOAs from two individuals with suppressed viral loads from the Rakai Health Sciences Program in Uganda. CONCLUSIONS: Utilizing sequence variation has the additional benefit of providing information on the contribution of clonality of the LVR, where high clonality (the predominance of a single genetic variant) suggests a role for cell division in the long-term persistence of the reservoir. In addition, our Bayesian approach can be adapted to other limiting dilution assays where positive outcomes can be partitioned by their genetic heterogeneity, such as immune cell populations and other viruses.

node.dating: dating ancestors in phylogenetic trees in R.

Summary: Phylogenetic trees encode the evolutionary distances between species or populations. With sufficient information, these evolutionary distances can be rescaled over time to provide estimates of the dates of the most recent ancestors of the species. Here we present the R program node.dating, divergence-time analysis software, which uses a maximum-likelihood method to estimate the dates of the internal nodes of a phylogenetic tree. Availability and Implementation: node.dating is available as a part of the R v3.30 package ape v4.0 (cran.r-project.org). node.dating is also available in the GitHub repository: https://github.com/brj1/node.dating , along with supplementary software and tests. Contact: brj1@sfu.ca. Supplementary information: Supplementary data are available at Bioinformatics online.

Genetic Cluster Analysis for HIV Prevention.

PURPOSE OF REVIEW: This review summarizes the use of genetic similarity clusters to understand HIV transmission and inform prevention efforts. RECENT FINDINGS: Recent emphases include the development of real-time cluster identification in order to interrupt transmission chains, the use of clusters to estimate rates of transmission along the HIV care cascade, and the extension of cluster analyses to understand transmission in the generalized epidemics of sub-Saharan Africa. Importantly, this recent empirical work has been accompanied by theoretical work that elucidates the processes that underlie HIV genetic similarity clusters; multiple studies suggest that clusters are not necessarily enriched with individuals with high transmission rates, but rather can reflect variation in sampling times within a population, with individuals sampled early in infection more likely to cluster. Analyses of genetic similarity clusters have great promise to inform HIV epidemiology and prevention. Future emphases should include the collection of additional sequence data from underrepresented populations, such as those in sub-Saharan Africa, and further development and evaluation of clustering methods.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

Genotypic susceptibility score (GSS) and CD4+ T cell recovery in HIV-1 patients with suppressed viral load.

OBJECTIVES: HIV drug resistance, measured by the genotypic susceptibility score (GSS), has a deleterious effect on the virological outcome of HIV-1-infected patients. However, it is not known if GSS retains any predictive value for CD4 recovery in patients with suppressed viral load. METHODS: Four hundred and six patients on virological failure (>500 copies/mL) with GSS : <6 months prior to switch therapy who achieved undetectable plasma viral load (<50 copies/mL) within 1 year, remained undetectable >1 year on an unchanged regimen and had CD4 data available during entire follow-up were included. Adjusted and unadjusted analyses of all characteristics at switch related to CD4 recovery were made for three time frames: (i) 'switch-suppression'; (ii) 'suppression-1 year'; and (iii) 'switch-1 year'. RESULTS: Higher GSS was associated with a greater CD4 recovery between 'switch' and '1 year' in the unadjusted analysis (P = 0.010); however, the effect of GSS was no longer statistically significant after adjusting for pre-switch clinical (CD4 count and plasma viral load) and demographic variables. Furthermore, only a lower pre-switch CD4 count was associated with increased CD4 recovery in the 'suppression-1 year' period in both unadjusted and adjusted models. The main CD4 recovery occurred in 'switch-suppression' and the variables associated, both unadjusted and adjusted, were CD4 and plasma viral load at switch, maintaining a trend for GSS (P = 0.06). CONCLUSIONS: In individuals who re-suppressed HIV viraemia after switching therapy, regimens having a higher GSS were associated with improved CD4 recovery only during the period from switch to virological suppression, but, once viral load is re-suppressed, the GSS of the new regimen has no further effect on subsequent CD4 recovery.

Population-Level Immune-Mediated Adaptation in HIV-1 Polymerase during the North American Epidemic.

UNLABELLED: Human leukocyte antigen (HLA) class I-associated polymorphisms in HIV-1 that persist upon transmission to HLA-mismatched hosts may spread in the population as the epidemic progresses. Transmission of HIV-1 sequences containing such adaptations may undermine cellular immune responses to the incoming virus in future hosts. Building upon previous work, we investigated the extent of HLA-associated polymorphism accumulation in HIV-1 polymerase (Pol) through comparative analysis of linked HIV-1/HLA class I genotypes sampled during historic (1979 to 1989; n = 338) and modern (2001 to 2011; n = 278) eras from across North America (Vancouver, BC, Canada; Boston, MA; New York, NY; and San Francisco, CA). Phylogenies inferred from historic and modern HIV-1 Pol sequences were star-like in shape, with an inferred most recent common ancestor (epidemic founder virus) sequence nearly identical to the modern North American subtype B consensus sequence. Nevertheless, modern HIV-1 Pol sequences exhibited roughly 2-fold-higher patristic (tip-to-tip) genetic distances than historic sequences, with HLA pressures likely driving ongoing diversification. Moreover, the frequencies of published HLA-associated polymorphisms in individuals lacking the selecting HLA class I allele was on average ∼2.5-fold higher in the modern than in the historic era, supporting their spread in circulation, though some remained stable in frequency during this time. Notably, polymorphisms restricted by protective HLA alleles appear to be spreading to a greater relative extent than others, though these increases are generally of modest absolute magnitude. However, despite evidence of polymorphism spread, North American hosts generally remain at relatively low risk of acquiring an HIV-1 polymerase sequence substantially preadapted to their HLA profiles, even in the present era. IMPORTANCE: HLA class I-restricted cytotoxic T-lymphocyte (CTL) escape mutations in HIV-1 that persist upon transmission may accumulate in circulation over time, potentially undermining host antiviral immunity to the transmitted viral strain. We studied >600 experimentally collected HIV-1 polymerase sequences linked to host HLA information dating back to 1979, along with phylogenetically reconstructed HIV-1 sequences dating back to the virus' introduction into North America. Overall, our results support the gradual spread of many-though not all-HIV-1 polymerase immune escape mutations in circulation over time. This is consistent with recent observations from other global regions, though the extent of polymorphism accumulation in North America appears to be lower than in populations with high seroprevalence, older epidemics, and/or limited HLA diversity. Importantly, the risk of acquiring an HIV-1 polymerase sequence at transmission that is substantially preadapted to one's HLA profile remains relatively low in North America, even in the present era.

Genotypic and functional impact of HIV-1 adaptation to its host population during the North American epidemic.

HLA-restricted immune escape mutations that persist following HIV transmission could gradually spread through the viral population, thereby compromising host antiviral immunity as the epidemic progresses. To assess the extent and phenotypic impact of this phenomenon in an immunogenetically diverse population, we genotypically and functionally compared linked HLA and HIV (Gag/Nef) sequences from 358 historic (1979-1989) and 382 modern (2000-2011) specimens from four key cities in the North American epidemic (New York, Boston, San Francisco, Vancouver). Inferred HIV phylogenies were star-like, with approximately two-fold greater mean pairwise distances in modern versus historic sequences. The reconstructed epidemic ancestral (founder) HIV sequence was essentially identical to the North American subtype B consensus. Consistent with gradual diversification of a "consensus-like" founder virus, the median "background" frequencies of individual HLA-associated polymorphisms in HIV (in individuals lacking the restricting HLA[s]) were ∼ 2-fold higher in modern versus historic HIV sequences, though these remained notably low overall (e.g. in Gag, medians were 3.7% in the 2000s versus 2.0% in the 1980s). HIV polymorphisms exhibiting the greatest relative spread were those restricted by protective HLAs. Despite these increases, when HIV sequences were analyzed as a whole, their total average burden of polymorphisms that were "pre-adapted" to the average host HLA profile was only ∼ 2% greater in modern versus historic eras. Furthermore, HLA-associated polymorphisms identified in historic HIV sequences were consistent with those detectable today, with none identified that could explain the few HIV codons where the inferred epidemic ancestor differed from the modern consensus. Results are therefore consistent with slow HIV adaptation to HLA, but at a rate unlikely to yield imminent negative implications for cellular immunity, at least in North America. Intriguingly, temporal changes in protein activity of patient-derived Nef (though not Gag) sequences were observed, suggesting functional implications of population-level HIV evolution on certain viral proteins.