Restriction of V3 region sequence divergence in the HIV-1 envelope gene during antiretroviral treatment in a cohort of recent seroconverters

Application of deep sequencing methods for inferring viral population diversity

The first deep sequencing method was announced in 2005. Due to an increasing number of sequencing data and a reduction in the costs of each sequencing dataset, this innovative technique was soon applied to genetic investigations of viral genome diversity in various viruses, particularly RNA viruses. These deep sequencing findings documented viral epidemiology and evolution and provided high-resolution data on the genetic changes in viral populations. Here, we review deep sequencing platforms that have been applied in viral quasispecies studies. Further, we discuss recent deep sequencing studies on viral inter- and intrahost evolution, drug resistance, and humoral immune selection, especially in emerging and re-emerging viruses. Deep sequencing methods are becoming the standard for providing comprehensive results of viral population diversity, and their applications are discussed.

Measurements of intrahost viral diversity require an unbiased diversity metric

Viruses exist within hosts at large population sizes and are subject to high rates of mutation. As such, viral populations exhibit considerable sequence diversity. A variety of summary statistics have been developed which describe, in a single number, the extent of diversity in a viral population; such measurements allow the diversities of different populations to be compared, and the effect of evolutionary forces on a population to be assessed. Here we highlight statistical artefacts underlying some common measures of sequence diversity, whereby variation in the depth of genome sequencing may substantially affect the extent of diversity measured in a viral population, making comparisons of population diversity invalid. Specifically, naive estimation of sequence entropy provides a systematically biased metric, a lower read depth being expected to produce a lower estimate of diversity. The number of polymorphic loci per kilobase of genome is more unpredictably affected by read depth, giving potentially flawed results at lower sequencing depths. We show that the nucleotide diversity statistic π provides an unbiased estimate of diversity in the sense that the expected value of the statistic is equal to the correct value of the property being measured. Our results are of importance for studies interpreting genome sequence data; we describe how diversity may be assessed in viral populations in a fair and unbiased manner.

Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach

Determining the factors modulating the genetic diversity of HIV-1 populations is essential to understand viral evolution. This study analyzes the relative importance of clinical factors in the intrahost HIV-1 subtype B (HIV-1B) evolution and in the fixation of drug resistance mutations (DRM) during longitudinal pediatric HIV-1 infection. We recovered 162 partial HIV-1B pol sequences (from 3 to 24 per patient) from 24 perinatally infected patients from the Madrid Cohort of HIV-1 infected children and adolescents in a time interval ranging from 2.2 to 20.3 years. We applied machine learning classification methods to analyze the relative importance of 28 clinical/epidemiological/virological factors in the HIV-1B evolution to predict HIV-1B genetic diversity (d), nonsynonymous and synonymous mutations (dN, dS) and DRM presence. Most of the 24 HIV-1B infected pediatric patients were Spanish (91.7%), diagnosed before 2000 (83.3%), and all were antiretroviral therapy experienced. They had from 0.3 to 18.8 years of HIV-1 exposure at sampling time. Most sequences presented DRM. The best-predictor variables for HIV-1B evolutionary parameters were the age of HIV-1 diagnosis for d, the age at first antiretroviral treatment for dN and the year of HIV-1 diagnosis for ds. The year of infection (birth year) and year of sampling seemed to be relevant for fixation of both DRM at large and, considering drug families, to protease inhibitors (PI). This study identifies, for the first time using machine learning, the factors affecting more HIV-1B pol evolution and those affecting DRM fixation in HIV-1B infected pediatric patients.

Effect of HIV/HCV Co-Infection on the Protease Evolution of HIV-1B: A Pilot Study in a Pediatric Population

This pilot study evaluates in pediatric patients the impact of HIV/HCV coinfection in the molecular evolution of the HIV-1 subtype B protease (HIV-1BPR). For this study, HIV-1B/HCV coinfected (15) and HIV-1B monoinfected (56) patients with available HIV-1B pol sequences were enrolled. Both groups of patients had comparable gender frequencies and average age, time of infection, antiretroviral treatment (ART) exposure and time under ART. Prevalence of drug resistance mutations (DRM), genetic diversity, number of synonymous (d_S) and non-synonymous (d_N) mutations per site and selection pressures (d_N - d_S) in the HIV-1BPR were estimated and compared between mono- and coinfected patients. Both HIV-1B populations presented similar genetic diversity (0.050 ± 0.02 vs. 0.045 ± 0.01) and d_S (0.074 ± 0.03 vs. 0.078 ± 0.04). In turn, in coinfected patients the HIV-1BPR had higher d_N (0.045 ± 0.01 vs. 0.024 ± 0.01) and d_N-d_S (-0.026 ± 0.02 vs. -0.048 ± 0.04) values, and less amino acid sites under purifying selection (4.2% vs. 42.1%) than in monoinfected patients. Accordingly, in co-infection with HCV, the HIV-1BPR sites 50, 53, 82, 84 and 88 - associated with resistance to PIs - were under neutral evolution, whereas these sites were under purifying selection in monoinfected patients. This pilot study suggests that HIV-1B may evolve differently in the presence than in the absence of HCV.

Ultrasensitive single-genome sequencing: accurate, targeted, next generation sequencing of HIV-1 RNA

BACKGROUND

Although next generation sequencing (NGS) offers the potential for studying virus populations in unprecedented depth, PCR error, amplification bias and recombination during library construction have limited its use to population sequencing and measurements of unlinked allele frequencies. Here we report a method, termed ultrasensitive Single-Genome Sequencing (uSGS), for NGS library construction and analysis that eliminates PCR errors and recombinants, and generates single-genome sequences of the same quality as the "gold-standard" of HIV-1 single-genome sequencing assay but with more than 100-fold greater depth.

RESULTS

Primer ID tagged cDNA was synthesized from mixtures of cloned BH10 wild-type and mutant HIV-1 transcripts containing ten drug resistance mutations. First, the resultant cDNA was divided and NGS libraries were generated in parallel using two methods: uSGS and a method applying long PCR primers to attach the NGS adaptors (LP-PCR-1). Second, cDNA was divided and NGS libraries were generated in parallel comparing 3 methods: uSGS and 2 methods adapted from more recent reports using variations of the long PCR primers to attach the adaptors (LP-PCR-2 and LP-PCR-3). Consistently, the uSGS method amplified a greater proportion of cDNAs, averaging 30% compared to 13% for LP-PCR-1, 21% for LP-PCR-2 and 14% for LP-PCR-3. Most importantly, when the uSGS sequences were binned according to their primer IDs, 94% of the bins did not contain PCR recombinant sequences versus only 55, 75 and 65% for LP-PCR-1, 2 and 3, respectively. Finally, when uSGS was applied to plasma samples from HIV-1 infected donors, both frequent and rare variants were detected in each sample and neighbor-joining trees revealed clusters of genomes driven by the linkage of these mutations, showing the lack of PCR recombinants in the datasets.

CONCLUSIONS

The uSGS assay can be used for accurate detection of rare variants and for identifying linkage of rare alleles associated with HIV-1 drug resistance. In addition, the method allows accurate in-depth analyses of the complex genetic relationships of viral populations in vivo.

Clinical Determinants of HIV-1B Between-Host Evolution and their Association with Drug Resistance in Pediatric Patients

Understanding the factors that modulate the evolution of virus populations is essential to design efficient control strategies. Mathematical models predict that factors affecting viral within-host evolution may also determine that at the between-host level. Although HIV-1 within-host evolution has been associated with clinical factors used to monitor AIDS progression, such as patient age, CD4 cells count, viral load, and antiretroviral experience, little is known about the role of these clinical factors in determining between-host HIV-1 evolution. Moreover, whether the relative importance of such factors in HIV-1 evolution vary in adult and children patients, in which the course of infection is different, has seldom been analysed. To address these questions, HIV-1 subtype B (HIV-1B) pol sequences of 163 infected children and 450 adults of Madrid, Spain, were used to estimate genetic diversity, rates of synonymous and non-synonymous mutations, selection pressures and frequency of drug-resistance mutations (DRMs). The role and relative importance of patient age, %CD4, CD4/mm3, viral load, and antiretroviral experience in HIV-1B evolution was analysed. In the pediatric HIV-1B population, three clinical factors were primary predictors of virus evolution: Higher HIV-1B genetic diversity was observed with increasing children age, decreasing CD4/mm3 and upon antiretroviral experience. This was mostly due to higher rates of non-synonymous mutations, which were associated with higher frequency of DRMs. Using this data, we have also constructed a simple multivariate model explaining between 55% and 66% of the variance in HIV-1B evolutionary parameters in pediatric populations. On the other hand, the analysed clinical factors had little effect in adult-infecting HIV-1B evolution. These findings highlight the different evolutionary dynamics of HIV-1B in children and adults, and contribute to understand the factors shaping HIV-1B evolution and the appearance of drug-resistance mutation in pediatric patients.

SAMFIRE: multi-locus variant calling for time-resolved sequence data

An increasingly common method for studying evolution is the collection of time-resolved short-read sequence data. Such datasets allow for the direct observation of rapid evolutionary processes, as might occur in natural microbial populations and in evolutionary experiments. In many circumstances, evolutionary pressure acting upon single variants can cause genomic changes at multiple nearby loci. SAMFIRE is an open-access software package for processing and analyzing sequence reads from time-resolved data, calling important single- and multi-locus variants over time, identifying alleles potentially affected by selection, calculating linkage disequilibrium statistics, performing haplotype reconstruction and exploiting time-resolved information to estimate the extent of uncertainty in reported genomic data.

AVAILABILITY AND IMPLEMENTATION

C ++ code may be found at https://github.com/cjri/samfire/

CONTACT

chris.illingworth@gen.cam.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dynamics of immunoglobulin sequence diversity in HIV-1 infected individuals

Advances in immunoglobulin (Ig) sequencing technology are leading to new perspectives on immune system dynamics. Much research in this nascent field has focused on resolving immune responses to viral infection. However, the dynamics of B-cell diversity in early HIV infection, and in response to anti-retroviral therapy, are still poorly understood. Here, we investigate these dynamics through bulk Ig sequencing of samples collected over 2 years from a group of eight HIV-1 infected patients, five of whom received anti-retroviral therapy during the first half of the study period. We applied previously published methods for visualizing and quantifying B-cell sequence diversity, including the Gini index, and compared their efficacy to alternative measures. While we found significantly greater clonal structure in HIV-infected patients versus healthy controls, within HIV patients, we observed no significant relationships between statistics of B-cell clonal expansion and clinical variables such as viral load and CD4⁺ count. Although there are many potential explanations for this, we suggest that important factors include poor sampling resolution and complex B-cell dynamics that are difficult to summarize using simple summary statistics. Importantly, we find a significant association between observed Gini indices and sequencing read depth, and we conclude that more robust analytical methods and a closer integration of experimental and theoretical work is needed to further our understanding of B-cell repertoire diversity during viral infection.

Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection

Human immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput "deep" sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different time points during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intrahost viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this article we develop a new method for inference using HIV deep sequencing data, using an approach based on importance sampling of ancestral recombination graphs under a multilocus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions, a quantity of key interest when approximating coalescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different time points and missing data without extra computational difficulty. We apply our method to a data set of HIV-1, in which several hundred sequences were obtained from an infected individual at seven time points over 2 years. We find mutation rate and effective population size estimates to be comparable to those produced by the software BEAST. Additionally, our method is able to produce local recombination rate estimates. The software underlying our method, Coalescenator, is freely available.

Envelope gene evolution and HIV-1 neuropathogenesis

In the era of combined antiretroviral therapy (cART), HIV-associated neurocognitive disorders (HAND) account for 40 to 56% of all HIV⁺ cases. During the acute stage of HIV-1 infection (<6 months), the virus invades and replicates within the central nervous system (CNS). Compared to peripheral tissues, the local CNS cell population expresses distinct levels of chemokine receptors, which levels exert selective pressure on the invading virus. HIV-1 envelope (env) sequences recovered from the brains and cerebrospinal fluid (CSF) of neurocognitively impaired HIV⁺ subjects often display higher nucleotide variability as compared to non-impaired HIV⁺ subjects. Specifically, env evolution provides HIV-1 with the strategies to evade host immune response, to reduce chemokine receptor dependence, to increase co-receptor binding efficiency, and to potentiate neurotoxicity. The evolution of env within the CNS leads to changes that may result in the emergence of novel isolates with neurotoxic and neurovirulent features. However, whether specific factors of HIV-1 evolution lead to the emergence of neurovirulent and neurotropic isolates remains ill-defined. HIV-1 env evolution is an ongoing phenomenon that occurs independently of neurological and neurocognitive disease severity; thus HIV env evolution may play a pivotal and reciprocal role in the etiology of HAND. Despite the use of cART, the reactivation of latent viral reservoirs represents a clinical challenge because of the replenishment of the viral pool that may subsequently lead to persistent infection. Therefore, gaining a more complete understanding of how HIV-1 env evolves over the course of the disease should be considered for the development of future therapies aimed at controlling CNS burden, diminishing persistent viremia, and eradicating viral reservoirs. Here we review the current literature on the role of HIV-1 env evolution in the setting of HAND disease progression and on the impact of cART on the dynamics of viral evolution.

Detection of HIV-1 matrix protein p17 quasispecies variants in plasma of chronic HIV-1-infected patients by ultra-deep pyrosequencing

BACKGROUND

The HIV-1 matrix protein p17 (p17MA) is a pleiotropic protein that plays a key role in the HIV-1 life cycle. It has been long believed to have a highly conserved primary amino acid sequence and a well-preserved structural integrity to avoid severe fitness consequences. However, recent data revealed that the carboxy (COOH)-terminus of p17MA possesses high levels of predicted intrinsic disorder, which would subtend to at least partially unfolded status of this region. This finding pointed to the need of investigating p17MA heterogeneity.

METHODS

The degree of intrapatient variations in the p17MA primary sequence was assessed on plasma viral RNA by using ultra-deep pyrosequencing.

RESULTS

Data obtained support a complex nature of p17MA quasispecies, with variants present at variable frequency virtually in all patients. Clusters of mutations were scattered along the entire sequence of the viral protein, but they were more frequently detected within the COOH-terminal region of p17MA. Moreover, deletions and insertions also occurred in a restricted area of the COOH-terminal region.

CONCLUSIONS

On the whole, our data show that the intrapatient level of sequence diversity in the p17MA is much higher than predicted before. Our results pave the way for further studies aimed at unraveling possible correlations between the presence of distinct p17MA variants and peculiar clinical evolutions of HIV-1 disease. The presence of p17MA quasispecies diversity may offer new tools to improve our understanding of the viral adaptation during the natural history of HIV-1 infection.

Antiretroviral therapy: when to start

In this article, the scientific evidence and professional guidelines regarding the timing of antiretroviral therapy initiation are reviewed, with discussion of the increasingly persuasive evidence in favor of starting treatment early in the course of human immunodeficiency virus disease.

Association between latent proviral characteristics and immune activation in antiretrovirus-treated human immunodeficiency virus type 1-infected adults

Generalized immune activation during HIV infection is associated with an increased risk of cardiovascular disease, neurocognitive disease, osteoporosis, metabolic disorders, and physical frailty. The mechanisms driving this immune activation are poorly understood, particularly for individuals effectively treated with antiretroviral medications. We hypothesized that viral characteristics such as sequence diversity may play a role in driving HIV-associated immune activation. We therefore sequenced proviral DNA isolated from peripheral blood mononuclear cells from HIV-infected individuals on fully suppressive antiretroviral therapy. We performed phylogenetic analyses, calculated viral diversity and divergence in the env and pol genes, and determined coreceptor tropism and the frequency of drug resistance mutations. Comprehensive immune profiling included quantification of immune cell subsets, plasma cytokine levels, and intracellular signaling responses in T cells, B cells, and monocytes. These antiretroviral therapy-treated HIV-infected individuals exhibited a wide range of diversity and divergence in both env and pol genes. However, proviral diversity and divergence in env and pol, coreceptor tropism, and the level of drug resistance did not significantly correlate with markers of immune activation. A clinical history of virologic failure was also not significantly associated with levels of immune activation, indicating that a history of virologic failure does not inexorably lead to increased immune activation as long as suppressive antiretroviral medications are provided. Overall, this study demonstrates that latent viral diversity is unlikely to be a major driver of persistent HIV-associated immune activation.

IMPORTANCE

Chronic immune activation, which is associated with cardiovascular disease, neurologic disease, and early aging, is likely to be a major driver of morbidity and mortality in HIV-infected individuals. Although treatment of HIV with antiretroviral medications decreases the level of immune activation, levels do not return to normal. The factors driving this persistent immune activation, particularly during effective treatment, are poorly understood. In this study, we investigated whether characteristics of the latent, integrated HIV provirus that persists during treatment are associated with immune activation. We found no relationship between latent viral characteristics and immune activation in treated individuals, indicating that qualities of the provirus are unlikely to be a major driver of persistent inflammation. We also found that individuals who had previously failed treatment but were currently effectively treated did not have significantly increased levels of immune activation, providing hope that past treatment failures do not have a lifelong "legacy" impact.

Universal amplification, next-generation sequencing, and assembly of HIV-1 genomes

Whole HIV-1 genome sequences are pivotal for large-scale studies of inter- and intrahost evolution, including the acquisition of drug resistance mutations. The ability to rapidly and cost-effectively generate large numbers of HIV-1 genome sequences from different populations and geographical locations and determine the effect of minority genetic variants is, however, a limiting factor. Next-generation sequencing promises to bridge this gap but is hindered by the lack of methods for the enrichment of virus genomes across the phylogenetic breadth of HIV-1 and methods for the robust assembly of the virus genomes from short-read data. Here we report a method for the amplification, next-generation sequencing, and unbiased de novo assembly of HIV-1 genomes of groups M, N, and O, as well as recombinants, that does not require prior knowledge of the sequence or subtype. A sensitivity of at least 3,000 copies/ml was determined by using plasma virus samples of known copy numbers. We applied our novel method to compare the genome diversities of HIV-1 groups, subtypes, and genes. The highest level of diversity was found in the env, nef, vpr, tat, and rev genes and parts of the gag gene. Furthermore, we used our method to investigate mutations associated with HIV-1 drug resistance in clinical samples at the level of the complete genome. Drug resistance mutations were detected as both major variant and minor species. In conclusion, we demonstrate the feasibility of our method for large-scale HIV-1 genome sequencing. This will enable the phylogenetic and phylodynamic resolution of the ongoing pandemic and efficient monitoring of complex HIV-1 drug resistance genotypes.