The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.     

Soft sweeps: molecular population genetics of adaptation from standing genetic variation

A population can adapt to a rapid environmental change or habitat expansion in two ways. It may adapt either through new beneficial mutations that subsequently sweep through the population or by using alleles from the standing genetic variation. We use diffusion theory to calculate the probabilities for selective adaptations and find a large increase in the fixation probability for weak substitutions, if alleles originate from the standing genetic variation. We then determine the parameter regions where each scenario-standing variation vs. new mutations-is more likely. Adaptations from the standing genetic variation are favored if either the selective advantage is weak or the selection coefficient and the mutation rate are both high. Finally, we analyze the probability of "soft sweeps," where multiple copies of the selected allele contribute to a substitution, and discuss the consequences for the footprint of selection on linked neutral variation. We find that soft sweeps with weaker selective footprints are likely under both scenarios if the mutation rate and/or the selection coefficient is high.     

Loss and Recovery of Genetic Diversity in Adapting Populations of HIV

The evolution of drug resistance in HIV occurs by the fixation of specific, well-known, drug-resistance mutations, but the underlying population genetic processes are not well understood. By analyzing within-patient longitudinal sequence data, we make four observations that shed a light on the underlying processes and allow us to infer the short-term effective population size of the viral population in a patient. Our first observation is that the evolution of drug resistance usually occurs by the fixation of one drug-resistance mutation at a time, as opposed to several changes simultaneously. Second, we find that these fixation events are accompanied by a reduction in genetic diversity in the region surrounding the fixed drug-resistance mutation, due to the hitchhiking effect. Third, we observe that the fixation of drug-resistance mutations involves both hard and soft selective sweeps. In a hard sweep, a resistance mutation arises in a single viral particle and drives all linked mutations with it when it spreads in the viral population, which dramatically reduces genetic diversity. On the other hand, in a soft sweep, a resistance mutation occurs multiple times on different genetic backgrounds, and the reduction of diversity is weak. Using the frequency of occurrence of hard and soft sweeps we estimate the effective population size of HIV to be ( confidence interval ). This number is much lower than the actual number of infected cells, but much larger than previous population size estimates based on synonymous diversity. We propose several explanations for the observed discrepancies. Finally, our fourth observation is that genetic diversity at non-synonymous sites recovers to its pre-fixation value within 18 months, whereas diversity at synonymous sites remains depressed after this time period. These results improve our understanding of HIV evolution and have potential implications for treatment strategies.     

Accessory Mutations Maintain Stability in Drug-Resistant HIV-1 Protease

The underlying mechanisms driving the evolution of drug resistance in human immunodeficiency virus (HIV) are only partially understood. We investigated the evolutionary cost of the major resistance mutations in HIV-1 protease in terms of protein stability. The accumulation of resistance mutations destabilizes the protease, limiting further adaptation. From an analysis of clinical isolates, we identified specific accessory mutations that were able to restore the stability of the protease or even increase it beyond the wild-type baseline. Resistance mutations were also found to decrease the activity of HIV protease near neutral pH values, while incorporating stabilizing mutations improved the enzyme's pH tolerance. These findings help us to explain the prevalence of mutations located far from the active site and underscore the importance of protein stability during the evolution of drug resistance in HIV.     

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.     

Recombination in HIV and the evolution of drug resistance: for better or for worse?

The rapid evolution of drug resistance remains a major obstacle for HIV therapy. The capacity of the virus for recombination is widely believed to facilitate the evolution of drug resistance. Here, we challenge this intuitive view. We develop a population genetic model of HIV replication that incorporates the processes of mutation, cellular superinfection, and recombination. We show that cellular superinfection increases the abundance of low fitness viruses at the expense of the fittest strains due to the mixing of viral proteins during virion assembly. Moreover, we argue that whether recombination facilitates the evolution of drug resistance depends critically on how resistance mutations interact to determine viral fitness. Contrary to the commonly held belief, we find that, under the most plausible biological assumptions, recombination is expected to slow down the rate of evolution of multi-drug-resistant virus during therapy.     

DNA Analysis of Herbarium Specimens of the Grass Weed Alopecurus myosuroides Reveals Herbicide Resistance Pre-Dated Herbicides

Acetyl-CoA carboxylase (ACCase) alleles carrying one point mutation that confers resistance to herbicides have been identified in arable grass weed populations where resistance has evolved under the selective pressure of herbicides. In an effort to determine whether herbicide resistance evolves from newly arisen mutations or from standing genetic variation in weed populations, we used herbarium specimens of the grass weed Alopecurus myosuroides to seek mutant ACCase alleles carrying an isoleucine-to-leucine substitution at codon 1781 that endows herbicide resistance. These specimens had been collected between 1788 and 1975, i.e., prior to the commercial release of herbicides inhibiting ACCase. Among the 734 specimens investigated, 685 yielded DNA suitable for PCR. Genotyping the ACCase locus using the derived Cleaved Amplified Polymorphic Sequence (dCAPS) technique identified one heterozygous mutant specimen that had been collected in 1888. Occurrence of a mutant codon encoding a leucine residue at codon 1781 at the heterozygous state was confirmed in this specimen by sequencing, clearly demonstrating that resistance to herbicides can pre-date herbicides in weeds. We conclude that point mutations endowing resistance to herbicides without having associated deleterious pleiotropic effects can be present in weed populations as part of their standing genetic variation, in frequencies higher than the mutation frequency, thereby facilitating their subsequent selection by herbicide applications.     

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.     

Persistence of transmitted drug resistance among subjects with primary human immunodeficiency virus infection

Following interruption of antiretroviral therapy among individuals with acquired drug resistance, preexisting drug-sensitive virus emerges relatively rapidly. In contrast, wild-type virus is not archived in individuals infected with drug-resistant human immunodeficiency virus (HIV) and thus cannot emerge rapidly in the absence of selective drug pressure. Fourteen recently HIV-infected patients with transmitted drug-resistant virus were followed for a median of 2.1 years after the estimated date of infection (EDI) without receiving antiretroviral therapy. HIV drug resistance and pol replication capacity (RC) in longitudinal plasma samples were assayed. Resistance mutations were characterized as pure populations or mixtures. The mean time to first detection of a mixture of wild-type and drug-resistant viruses was 96 weeks (1.8 years) (95% confidence interval, 48 to 192 weeks) after the EDI. The median time to loss of detectable drug resistance using population-based assays ranged from 4.1 years (conservative estimate) to longer than the lifetime of the individual (less conservative estimate). The transmission of drug-resistant virus was not associated with virus with reduced RC. Sexual transmission of HIV selects for highly fit drug-resistant variants that persist for years. The prolonged persistence of transmitted drug resistance strongly supports the routine use of HIV resistance genotyping for all newly diagnosed individuals.     

The evolutionary origins of pesticide resistance

Durable crop protection is an essential component of current and future food security. However, the effectiveness of pesticides is threatened by the evolution of resistant pathogens, weeds and insect pests. Pesticides are mostly novel synthetic compounds, and yet target species are often able to evolve resistance soon after a new compound is introduced. Therefore, pesticide resistance provides an interesting case of rapid evolution under strong selective pressures, which can be used to address fundamental questions concerning the evolutionary origins of adaptations to novel conditions. We ask: (i) whether this adaptive potential originates mainly from de novo mutations or from standing variation; (ii) which pre‐existing traits could form the basis of resistance adaptations; and (iii) whether recurrence of resistance mechanisms among species results from interbreeding and horizontal gene transfer or from independent parallel evolution. We compare and contrast the three major pesticide groups: insecticides, herbicides and fungicides. Whilst resistance to these three agrochemical classes is to some extent united by the common evolutionary forces at play, there are also important differences. Fungicide resistance appears to evolve, in most cases, by de novo point mutations in the target‐site encoding genes; herbicide resistance often evolves through selection of polygenic metabolic resistance from standing variation; and insecticide resistance evolves through a combination of standing variation and de novo mutations in the target site or major metabolic resistance genes. This has practical implications for resistance risk assessment and management, and lessons learnt from pesticide resistance should be applied in the deployment of novel, non‐chemical pest‐control methods.     

The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus'' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.     

Recombination Rate and Selection Strength in HIV Intra-patient Evolution

The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Yet, little is known about the distribution of selection differentials between individual viruses and the impact of single polymorphisms on viral fitness. Here, we estimate the rate of recombination and the distribution of selection coefficients from time series sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be ρ = 1.4±0.6×10⁻⁵ recombinations per site and generation. Furthermore, we provide evidence that the selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.     

HIV-1 Transmission during Early Infection in Men Who Have Sex with Men: A Phylodynamic Analysis

Background

Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection.

Methods and Findings

We analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%–46.4%) of transmissions occur during the first year of infection.

Conclusions

In this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals. Please see later in the article for the Editors'' Summary     

Drug-Sparing Regimens for HIV Combination Therapy: Benefits Predicted for “Drug Coasting”

Structured Treatment Interruptions (STI) during HIV drug therapy were thought to potentially reduce side effects and toxicity, boost immune involvement, and possibly lower the viral set-point. Clinical trials of STI regimens, however, have had mixed results. We use an established mathematical model of HAART to estimate possible therapeutic outcomes for STI and for other, similar patterns in HIV combination therapy. We perform an exhaustive search of patterns of up to 60 days, for triple-drug combinations involving accurate pharmacokinetics for 12 specific antiviral drugs. The results of this analysis are consistent with recent clinical trials which have demonstrated that STI-type patterns, involving therapy interruption of weeks or months, are rarely optimal. Our analysis predicts, however, that the benefit of treatment can often be improved by including very short drug-free periods, during which the patient effectively “coasts” for a day or two on adequate drug concentrations due to the long half-life of some pharmaceuticals. Our analysis predicts many cases in which this may be achieved without increasing the risk of drug-resistance. This suggests that “drug coasting” patterns, significantly shorter than STI patterns, may merit further clinical investigation in efforts to find drug-sparing regimens for HIV. Electronic Supplementary Material The online version of this article () contains supplementary material, which is available to authorized users.     

The temporal order of genetic and pathway alterations in tumorigenesis

Cancer evolves through the accumulation of mutations, but the order in which mutations occur is poorly understood. Inference of a temporal ordering on the level of genes is challenging because clinically and histologically identical tumors often have few mutated genes in common. This heterogeneity may at least in part be due to mutations in different genes having similar phenotypic effects by acting in the same functional pathway. We estimate the constraints on the order in which alterations accumulate during cancer progression from cross-sectional mutation data using a probabilistic graphical model termed Hidden Conjunctive Bayesian Network (H-CBN). The possible orders are analyzed on the level of genes and, after mapping genes to functional pathways, also on the pathway level. We find stronger evidence for pathway order constraints than for gene order constraints, indicating that temporal ordering results from selective pressure acting at the pathway level. The accumulation of changes in core pathways differs among cancer types, yet a common feature is that progression appears to begin with mutations in genes that regulate apoptosis pathways and to conclude with mutations in genes involved in invasion pathways. H-CBN models provide a quantitative and intuitive model of tumorigenesis showing that the genetic events can be linked to the phenotypic progression on the level of pathways.     

Estimating Trends in the Proportion of Transmitted and Acquired HIV Drug Resistance in a Long Term Observational Cohort in Germany

Objective

We assessed trends in the proportion of transmitted (TDR) and acquired (ADR) HIV drug resistance and associated mutations between 2001 and 2011 in the German ClinSurv-HIV Drug Resistance Study.

Method

The German ClinSurv-HIV Drug Resistance Study is a subset of the German ClinSurv-HIV Cohort. For the ClinSurv-HIV Drug Resistance Study all available sequences isolated from patients in five study centres of the long term observational ClinSurv-HIV Cohort were included. TDR was estimated using the first viral sequence of antiretroviral treatment (ART) naïve patients. One HIV sequence/patient/year of ART experienced patients was considered to estimate the proportion of ADR. Trends in the proportion of HIV drug resistance were calculated by logistic regression.

Results

9,528 patients were included into the analysis. HIV-sequences of antiretroviral naïve and treatment experienced patients were available from 34% (3,267/9,528) of patients. The proportion of TDR over time was stable at 10.4% (95% CI 9.1–11.8; p _{for trend} = 0.6; 2001–2011). The proportion of ADR among all treated patients was 16%, whereas it was high among those with available HIV genotypic resistance test (64%; 1,310/2,049 sequences; 95% CI 62–66) but declined significantly over time (OR 0.8; 95% CI 0.77–0.83; p _{for trend}<0.001; 2001–2011). Viral load monitoring subsequent to resistance testing was performed in the majority of treated patients (96%) and most of them (67%) were treated successfully.

Conclusions

The proportion of TDR was stable in this study population. ADR declined significantly over time. This decline might have been influenced by broader resistance testing, resistance test guided therapy and the availability of more therapeutic options and not by a decline in the proportion of TDR within the study population.