Dates of HIV infection can be estimated for seroprevalent patients by coalescent analysis of serial next-generation sequencing data

Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates

In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since the divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as a signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the proposed approach to estimate age-specific sources of HIV infection in Amsterdam tranamission networks among men who have sex with men between 2010 and 2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models.

Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda

HIV incidence in eastern and southern Africa has historically been concentrated among girls and women aged 15-24 years. As new cases decline with HIV interventions, population-level infection dynamics may shift by age and gender. Here, we integrated population-based surveillance of 38,749 participants in the Rakai Community Cohort Study and longitudinal deep-sequence viral phylogenetics to assess how HIV incidence and population groups driving transmission have changed from 2003 to 2018 in Uganda. We observed 1,117 individuals in the incidence cohort and 1,978 individuals in the transmission cohort. HIV viral suppression increased more rapidly in women than men, however incidence declined more slowly in women than men. We found that age-specific transmission flows shifted: whereas HIV transmission to girls and women (aged 15-24 years) from older men declined by about one-third, transmission to women (aged 25-34 years) from men that were 0-6 years older increased by half in 2003 to 2018. Based on changes in transmission flows, we estimated that closing the gender gap in viral suppression could have reduced HIV incidence in women by half in 2018. This study suggests that HIV programmes to increase HIV suppression in men are critical to reduce incidence in women, close gender gaps in infection burden and improve men's health in Africa.

Inferring viral transmission time from phylogenies for known transmission pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously-described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time-calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda

HIV incidence in eastern and southern Africa has historically been concentrated among girls and women aged 15-24 years. As new cases decline with HIV interventions, population-level infection dynamics may shift by age and gender. Here, we integrated population-based surveillance of 38,749 participants in the Rakai Community Cohort Study and longitudinal deep sequence viral phylogenetics to assess how HIV incidence and population groups driving transmission have changed from 2003 to 2018 in Uganda. We observed 1,117 individuals in the incidence cohort and 1,978 individuals in the transmission cohort. HIV viral suppression increased more rapidly in women than men, however incidence declined more slowly in women than men. We found that age-specific transmission flows shifted, while HIV transmission to girls and women (aged 15-24 years) from older men declined by about one third, transmission to women (aged 25-34 years) from men that were 0-6 years older increased by half in 2003 to 2018. Based on changes in transmission flows, we estimated that closing the gender gap in viral suppression could have reduced HIV incidence in women by half in 2018. This study suggests that HIV programs to increase HIV suppression in men are critical to reduce incidence in women, close gender gaps in infection burden and improve men's health in Africa.

PIQMEE: Bayesian Phylodynamic Method for Analysis of Large Data Sets with Duplicate Sequences

Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient.

Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST

Many HIV prevention strategies are currently under consideration where it is highly informative to know the study participants' times of infection. These can be estimated using viral sequence data sampled early in infection. However, there are several scenarios that, if not addressed, can skew timing estimates. These include multiple transmitted/founder (TF) viruses, APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like)-mediated mutational enrichment, and recombination. Here, we suggest a pipeline to identify these problems and resolve the biases that they introduce. We then compare two modeling strategies to obtain timing estimates from sequence data. The first, Poisson Fitter (PF), is based on a Poisson model of random accumulation of mutations relative to the TF virus (or viruses) that established the infection. The second uses a coalescence-based phylogenetic strategy as implemented in BEAST. The comparison is based on timing predictions using plasma viral RNA (cDNA) sequence data from 28 simian-human immunodeficiency virus (SHIV)-infected animals for which the exact day of infection is known. In this particular setting, based on nucleotide sequences from samples obtained in early infection, the Poisson method yielded more accurate, more precise, and unbiased estimates for the time of infection than did the explored implementations of BEAST.IMPORTANCE The inference of the time of infection is a critical parameter in testing the efficacy of clinical interventions in protecting against HIV-1 infection. For example, in clinical trials evaluating the efficacy of passively delivered antibodies (Abs) for preventing infections, accurate time of infection data are essential for discerning levels of the Abs required to confer protection, given the natural Ab decay rate in the human body. In such trials, genetic sequences from early in the infection are regularly sampled from study participants, generally prior to immune selection, when the viral population is still expanding and genetic diversity is low. In this particular setting of early viral growth, the Poisson method is superior to the alternative approach based on coalescent methods. This approach can also be applied in human vaccine trials, where accurate estimates of infection times help ascertain if vaccine-elicited immune protection wanes over time.

Molecular dating and viral load growth rates suggested that the eclipse phase lasted about a week in HIV-1 infected adults in East Africa and Thailand

Most HIV-1 infected individuals do not know their infection dates. Precise infection timing is crucial information for studies that document transmission networks or drug levels at infection. To improve infection timing, we used the prospective RV217 cohort where the window when plasma viremia becomes detectable is narrow: the last negative visit occurred a median of four days before the first detectable HIV-1 viremia with an RNA test, referred below as diagnosis. We sequenced 1,280 HIV-1 genomes from 39 participants at a median of 4, 32 and 170 days post-diagnosis. HIV-1 infections were dated by using sequence-based methods and a viral load regression method. Bayesian coalescent and viral load regression estimated that infections occurred a median of 6 days prior to diagnosis (IQR: 9–3 and 11–4 days prior, respectively). Poisson-Fitter, which analyzes the distribution of hamming distances among sequences, estimated a median of 7 days prior to diagnosis (IQR: 15–4 days) based on sequences sampled 4 days post-diagnosis, but it did not yield plausible results using sequences sampled at 32 days. Fourteen participants reported a high-risk exposure event at a median of 8 days prior to diagnosis (IQR: 12 to 6 days prior). These different methods concurred that HIV-1 infection occurred about a week before detectable viremia, corresponding to 20 days (IQR: 34–15 days) before peak viral load. Together, our methods comparison helps define a framework for future dating studies in early HIV-1 infection.

HIV-1 infected individuals rarely know when they became infected but knowing when an infection occurred provides critical information regarding HIV-1 pathogenesis and epidemiology. Using a unique cohort in which infection was known to have occurred in a narrow interval, we investigated methods to estimate the timing of infections. Several methods suggested that HIV-1 infection typically occurs a median of one week before the infection can be detected by HIV-1 RNA testing. Going forward, we provide a strategy that can be used to elucidate the origin of an acute/early infection.

Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-infection Estimation towards Enhanced Vaccine Efficacy Assessment

Knowledge of the time of HIV-1 infection and the multiplicity of viruses that establish HIV-1 infection is crucial for the in-depth analysis of clinical prevention efficacy trial outcomes. Better estimation methods would improve the ability to characterize immunological and genetic sequence correlates of efficacy within preventive efficacy trials of HIV-1 vaccines and monoclonal antibodies. We developed new methods for infection timing and multiplicity estimation using maximum likelihood estimators that shift and scale (calibrate) estimates by fitting true infection times and founder virus multiplicities to a linear regression model with independent variables defined by data on HIV-1 sequences, viral load, diagnostics, and sequence alignment statistics. Using Poisson models of measured mutation counts and phylogenetic trees, we analyzed longitudinal HIV-1 sequence data together with diagnostic and viral load data from the RV217 and CAPRISA 002 acute HIV-1 infection cohort studies. We used leave-one-out cross validation to evaluate the prediction error of these calibrated estimators versus that of existing estimators and found that both infection time and founder multiplicity can be estimated with improved accuracy and precision by calibration. Calibration considerably improved all estimators of time since HIV-1 infection, in terms of reducing bias to near zero and reducing root mean squared error (RMSE) to 5-10 days for sequences collected 1-2 months after infection. The calibration of multiplicity assessments yielded strong improvements with accurate predictions (ROC-AUC above 0.85) in all cases. These results have not yet been validated on external data, and the best-fitting models are likely to be less robust than simpler models to variation in sequencing conditions. For all evaluated models, these results demonstrate the value of calibration for improved estimation of founder multiplicity and of time since HIV-1 infection.

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.

Ultradeep sequencing of B and non-B HIV-1 subtypes: Viral diversity and drug resistance mutations before and after one month of antiretroviral therapy in naive patients

BACKGROUND

Ultradeep pyrosequencing technologies permit an assessment of the genetic diversity and the presence and frequency of minority variants in a viral population. The effect of these parameters on the outcome of highly active antiretroviral therapy (HAART) in HIV-infected patients is poorly understood.

OBJECTIVES

The present study used the pyrosequencing Roche 454 prototype assay to determine whether antiretroviral efficacy is correlated with viral diversity and minority drug resistance mutations in HIV-infected treatment-naive patients and to compare assay performance in B and non-B subtypes.

STUDY DESIGN

The study included 30 HIV-1 infected naive patients (20 with subtype non-B and 10 with subtype B). Ultradeep pyrosequencing of protease and reverse transcriptase genes was performed at baseline and 1 month after HAART initiation. Plasma HIV VL was measured at 0 and after 1, 3, and 6 months of HAART.

RESULTS

Pre-HAART minority drug resistance mutations were observed to NRTI in 4 patients, to NNRTI in 6 patients, and to PI in 1 patient; there was no difference in HAART-induced VL decay between patients. Pre-HAART diversity was significantly correlated with the time elapsed since HIV-1 infection diagnosis, but not with the subtype, VL, or CD4 count. Patients with an undetectable VL after 3 months of HAART had a higher pre-HAART diversity. Pre- and post-HAART diversities were not statistically different. There was no difference in assay performance between subtype B and non-B.

CONCLUSIONS

A high pre-HAART viral diversity might have a positive effect on the outcome of HAART. Pre-therapeutic minority drug resistance mutations are uncommon in naive patients.

Pairwise diversity and tMRCA as potential markers for HIV infection recency

Intrahost human immunodeficiency virus (HIV)-1 diversity increases linearly over time. We assessed the extent to which mean pairwise distances and the time to the most recent common ancestor (tMRCA) inferred from intrahost HIV-1C env sequences were associated with the estimated time of HIV infection. Data from a primary HIV-1C infection study in Botswana were used for this analysis (N = 42). A total of 2540 HIV-1C env gp120 variable loop region 1 to conserved region 5 (V1C5) of the HIV-1 envelope gp120 viral sequences were generated by single genome amplification and sequencing, with an average of 61 viral sequences per participant and 11 sequences per time point per participant. Raw pairwise distances were calculated for each time point and participant using the ape package in R software. The tMRCA was estimated using phylogenetic inference implemented in Bayesian Evolutionary Analysis by Sampling Trees v1.8.2. Pairwise distances and tMRCA were significantly associated with the estimated time since HIV infection (both P < 0.001). Taking into account multiplicity of HIV infection strengthened these associations. HIV-1C env-based pairwise distances and tMRCA can be used as potential markers for HIV recency. However, the tMRCA estimates demonstrated no advantage over the pairwise distances estimates.

Promises and pitfalls of Illumina sequencing for HIV resistance genotyping

Genetic sequencing ("genotyping") plays a critical role in the modern clinical management of HIV infection. This virus evolves rapidly within patients because of its error-prone reverse transcriptase and short generation time. Consequently, HIV variants with mutations that confer resistance to one or more antiretroviral drugs can emerge during sub-optimal treatment. There are now multiple HIV drug resistance interpretation algorithms that take the region of the HIV genome encoding the major drug targets as inputs; expert use of these algorithms can significantly improve to clinical outcomes in HIV treatment. Next-generation sequencing has the potential to revolutionize HIV resistance genotyping by lowering the threshold that rare but clinically significant HIV variants can be detected reproducibly, and by conferring improved cost-effectiveness in high-throughput scenarios. In this review, we discuss the relative merits and challenges of deploying the Illumina MiSeq instrument for clinical HIV genotyping.

Overcoming the challenge of diagnosis of early HIV infection: a stepping stone to optimal patient management

Prompt identification of individuals during the highly infectious acute or early stage of HIV infection has implications for both patient management and public health interventions. The studies on natural history of HIV infection over the last three decades have uncovered several clinical features and virological markers to diagnose early infection. However, the brevity of the acute symptomatic phase combined with the difficulty in identifying non-specific signs and symptoms poses diagnosis of early HIV infection as a remaining challenge. Furthermore, underestimation of risky behavior in the absence of detailed patient history and possible concurrent sexually transmitted infections render the diagnosis of recent infection difficult. Herein, we focus on the multifaceted clinical manifestations and the best usage of technological advancements to detect early HIV infection. Early diagnosis of HIV infection contributes to further improving patient outcomes and preventing transmission.

A Generalized Entropy Measure of Within-Host Viral Diversity for Identifying Recent HIV-1 Infections

There is a need for incidence assays that accurately estimate HIV incidence based on cross-sectional specimens. Viral diversity-based assays have shown promises but are not particularly accurate. We hypothesize that certain viral genetic regions are more predictive of recent infection than others and aim to improve assay accuracy by using classification algorithms that focus on highly informative regions (HIRs).We analyzed HIV gag sequences from a cohort in Botswana. Forty-two subjects newly infected by HIV-1 Subtype C were followed through 500 days post-seroconversion. Using sliding window analysis, we screened for genetic regions within gag that best differentiate recent versus chronic infections. We used both nonparametric and parametric approaches to evaluate the discriminatory abilities of sequence regions. Segmented Shannon Entropy measures of HIRs were aggregated to develop generalized entropy measures to improve prediction of recency. Using logistic regression as the basis for our classification algorithm, we evaluated the predictive power of these novel biomarkers and compared them with recently reported viral diversity measures using area under the curve (AUC) analysis.Change of diversity over time varied across different sequence regions within gag. We identified the top 50% of the most informative regions by both nonparametric and parametric approaches. In both cases, HIRs were in more variable regions of gag and less likely in the p24 coding region. Entropy measures based on HIRs outperformed previously reported viral-diversity-based biomarkers. These methods are better suited for population-level estimation of HIV recency.The patterns of diversification of certain regions within the gag gene are more predictive of recency of infection than others. We expect this result to apply in other HIV genetic regions as well. Focusing on these informative regions, our generalized entropy measure of viral diversity demonstrates the potential for improving accuracy when identifying recent HIV-1 infections.

Combining the Estimated Date of HIV Infection with a Phylogenetic Cluster Study to Better Understand HIV Spread: Application in a Paris Neighbourhood

Objectives

To relate socio-demographic and virological information to phylogenetic clustering in HIV infected patients in a limited geographical area and to evaluate the role of recently infected individuals in the spread of HIV.

Methods

HIV-1 pol sequences from newly diagnosed and treatment-naive patients receiving follow-up between 2008 and 2011 by physicians belonging to a health network in Paris were used to build a phylogenetic tree using neighbour-joining analysis. Time since infection was estimated by immunoassay to define recently infected patients (very early infected presenters, VEP). Data on socio-demographic, clinical and biological features in clustered and non-clustered patients were compared. Chains of infection structure was also analysed.

Results

547 patients were included, 49 chains of infection containing 108 (20%) patients were identified by phylogenetic analysis. analysis. Eighty individuals formed pairs and 28 individuals were belonging to larger clusters. The median time between two successive HIV diagnoses in the same chain of infection was 248 days [CI = 176–320]. 34.7% of individuals were considered as VEP, and 27% of them were included in chains of infection. Multivariable analysis showed that belonging to a cluster was more frequent in VEP and those under 30 years old (OR: 3.65, 95 CI 1.49–8.95, p = 0.005 and OR: 2.42, 95% CI 1.05–5.85, p = 0.04 respectively). The prevalence of drug resistance was not associated with belonging to a pair or a cluster. Within chains, VEP were not grouped together more than chance predicted (p = 0.97).

Conclusions

Most newly diagnosed patients did not belong to a chain of infection, confirming the importance of undiagnosed or untreated HIV infected individuals in transmission. Furthermore, clusters involving both recently infected individuals and longstanding infected individuals support a substantial role in transmission of the latter before diagnosis.

Early immune adaptation in HIV-1 revealed by population-level approaches

BACKGROUND

The reproducible nature of HIV-1 escape from HLA-restricted CD8+ T-cell responses allows the identification of HLA-associated viral polymorphisms "at the population level" - that is, via analysis of cross-sectional, linked HLA/HIV-1 genotypes by statistical association. However, elucidating their timing of selection traditionally requires detailed longitudinal studies, which are challenging to undertake on a large scale. We investigate whether the extent and relative timecourse of immune-driven HIV adaptation can be inferred via comparative cross-sectional analysis of independent early and chronic infection cohorts.

RESULTS

Similarly-powered datasets of linked HLA/HIV-1 genotypes from individuals with early (median < 3 months) and chronic untreated HIV-1 subtype B infection, matched for size (N > 200/dataset), HLA class I and HIV-1 Gag/Pol/Nef diversity, were established. These datasets were first used to define a list of 162 known HLA-associated polymorphisms detectable at the population level in cohorts of the present size and host/viral genetic composition. Of these 162 known HLA-associated polymorphisms, 15% (occurring at 14 Gag, Pol and Nef codons) were already detectable via statistical association in the early infection dataset at p ≤ 0.01 (q < 0.2) - identifying them as the most consistently rapidly escaping sites in HIV-1. Among these were known rapidly-escaping sites (e.g. B*57-Gag-T242N) and others not previously appreciated to be reproducibly rapidly selected (e.g. A*31:01-associated adaptations at Gag codons 397, 401 and 403). Escape prevalence in early infection correlated strongly with first-year escape rates (Pearson's R = 0.68, p = 0.0001), supporting cross-sectional parameters as reliable indicators of longitudinally-derived measures. Comparative analysis of early and chronic datasets revealed that, on average, the prevalence of HLA-associated polymorphisms more than doubles between these two infection stages in persons harboring the relevant HLA (p < 0.0001, consistent with frequent and reproducible escape), but remains relatively stable in persons lacking the HLA (p = 0.15, consistent with slow reversion). Published HLA-specific Hazard Ratios for progression to AIDS correlated positively with average escape prevalence in early infection (Pearson's R = 0.53, p = 0.028), consistent with high early within-host HIV-1 adaptation (via rapid escape and/or frequent polymorphism transmission) as a correlate of progression.

CONCLUSION

Cross-sectional host/viral genotype datasets represent an underutilized resource to identify reproducible early pathways of HIV-1 adaptation and identify correlates of protective immunity.

Deep sequencing: becoming a critical tool in clinical virology

Population (Sanger) sequencing has been the standard method in basic and clinical DNA sequencing for almost 40 years; however, next-generation (deep) sequencing methodologies are now revolutionizing the field of genomics, and clinical virology is no exception. Deep sequencing is highly efficient, producing an enormous amount of information at low cost in a relatively short period of time. High-throughput sequencing techniques have enabled significant contributions to multiples areas in virology, including virus discovery and metagenomics (viromes), molecular epidemiology, pathogenesis, and studies of how viruses to escape the host immune system and antiviral pressures. In addition, new and more affordable deep sequencing-based assays are now being implemented in clinical laboratories. Here, we review the use of the current deep sequencing platforms in virology, focusing on three of the most studied viruses: human immunodeficiency virus (HIV), hepatitis C virus (HCV), and influenza virus.

An evaluation of phylogenetic methods for reconstructing transmitted HIV variants using longitudinal clonal HIV sequence data

A population of human immunodeficiency virus (HIV) within a host often descends from a single transmitted/founder virus. The high mutation rate of HIV, coupled with long delays between infection and diagnosis, make isolating and characterizing this strain a challenge. In theory, ancestral reconstruction could be used to recover this strain from sequences sampled in chronic infection; however, the accuracy of phylogenetic techniques in this context is unknown. To evaluate the accuracy of these methods, we applied ancestral reconstruction to a large panel of published longitudinal clonal and/or single-genome-amplification HIV sequence data sets with at least one intrapatient sequence set sampled within 6 months of infection or seroconversion (n = 19,486 sequences, median [interquartile range] = 49 [20 to 86] sequences/set). The consensus of the earliest sequences was used as the best possible estimate of the transmitted/founder. These sequences were compared to ancestral reconstructions from sequences sampled at later time points using both phylogenetic and phylogeny-naive methods. Overall, phylogenetic methods conferred a 16% improvement in reproducing the consensus of early sequences, compared to phylogeny-naive methods. This relative advantage increased with intrapatient sequence diversity (P < 10(-5)) and the time elapsed between the earliest and subsequent samples (P < 10(-5)). However, neither approach performed well for reconstructing ancestral indel variation, especially within indel-rich regions of the HIV genome. Although further improvements are needed, our results indicate that phylogenetic methods for ancestral reconstruction significantly outperform phylogeny-naive alternatives, and we identify experimental conditions and study designs that can enhance accuracy of transmitted/founder virus reconstruction.

IMPORTANCE

When HIV is transmitted into a new host, most of the viruses fail to infect host cells. Consequently, an HIV infection tends to be descended from a single "founder" virus. A priority target for the vaccine research, these transmitted/founder viruses are difficult to isolate since newly infected individuals are often unaware of their status for months or years, by which time the virus population has evolved substantially. Here, we report on the potential use of evolutionary methods to reconstruct the genetic sequence of the transmitted/founder virus from its descendants at later stages of an infection. These methods can recover this ancestral sequence with an overall error rate of about 2.3%-about 15% more information than if we had ignored the evolutionary relationships among viruses. Although there is no substitute for sampling infections at earlier points in time, these methods can provide useful information about the genetic makeup of transmitted/founder HIV.

Estimating the timing of mother-to-child transmission of the human immunodeficiency virus type 1 using a viral molecular evolution model

Background

Mother-to-child transmission (MTCT) is responsible for most pediatric HIV-1 infections worldwide. It can occur during pregnancy, labor, or breastfeeding. Numerous studies have used coalescent and molecular clock methods to understand the epidemic history of HIV-1, but the timing of vertical transmission has not been studied using these methods. Taking advantage of the constant accumulation of HIV genetic variation over time and using longitudinally sampled viral sequences, we used a coalescent approach to investigate the timing of MTCT.

Materials and Methods

Six-hundred and twenty-two clonal env sequences from the RNA and DNA viral population were longitudinally sampled from nine HIV-1 infected mother-and-child pairs [range: 277–1034 days]. For each transmission pair, timing of MTCT was determined using a coalescent-based model within a Bayesian statistical framework. Results were compared with available estimates of MTCT timing obtained with the classic biomedical approach based on serial HIV DNA detection by PCR assays.

Results

Four children were infected during pregnancy, whereas the remaining five children were infected at time of delivery. For eight out of nine pairs, results were consistent with the transmission periods assessed by standard PCR-based assay. The discordance in the remaining case was likely confused by co-infection, with simultaneous introduction of multiple maternal viral variants at the time of delivery.

Conclusions

The study provided the opportunity to validate the Bayesian coalescent approach that determines the timing of MTCT of HIV-1. It illustrates the power of population genetics approaches to reliably estimate the timing of transmission events and deepens our knowledge about the dynamics of viral evolution in HIV-infected children, accounting for the complexity of multiple transmission events.

The genealogical population dynamics of HIV-1 in a large transmission chain: bridging within and among host evolutionary rates

Transmission lies at the interface of human immunodeficiency virus type 1 (HIV-1) evolution within and among hosts and separates distinct selective pressures that impose differences in both the mode of diversification and the tempo of evolution. In the absence of comprehensive direct comparative analyses of the evolutionary processes at different biological scales, our understanding of how fast within-host HIV-1 evolutionary rates translate to lower rates at the between host level remains incomplete. Here, we address this by analyzing pol and env data from a large HIV-1 subtype C transmission chain for which both the timing and the direction is known for most transmission events. To this purpose, we develop a new transmission model in a Bayesian genealogical inference framework and demonstrate how to constrain the viral evolutionary history to be compatible with the transmission history while simultaneously inferring the within-host evolutionary and population dynamics. We show that accommodating a transmission bottleneck affords the best fit our data, but the sparse within-host HIV-1 sampling prevents accurate quantification of the concomitant loss in genetic diversity. We draw inference under the transmission model to estimate HIV-1 evolutionary rates among epidemiologically-related patients and demonstrate that they lie in between fast intra-host rates and lower rates among epidemiologically unrelated individuals infected with HIV subtype C. Using a new molecular clock approach, we quantify and find support for a lower evolutionary rate along branches that accommodate a transmission event or branches that represent the entire backbone of transmitted lineages in our transmission history. Finally, we recover the rate differences at the different biological scales for both synonymous and non-synonymous substitution rates, which is only compatible with the ‘store and retrieve’ hypothesis positing that viruses stored early in latently infected cells preferentially transmit or establish new infections upon reactivation.

Since its discovery three decades ago, the HIV epidemic has unfolded into one of the most devastating pandemics in human history. When HIV replication cannot be completely inhibited, the fast-evolving retrovirus continuously evades intra-host immune and drug selective pressure, but diversifies according to more neutral epidemiological dynamics at the interhost level. Limited evidence suggests that the virus may evolve faster in a single host than in a population of hosts, and various hypotheses have been put forward to explain this phenomenon. Here, we develop a new computational approach aimed at integrating host transmission information with pathogen genealogical reconstructions. We apply this approach to comprehensive sequence data sets sampled from a large HIV-1 subtype C transmission chain, and in addition to providing several insights into the reconstruction of HIV-1 transmissions histories and its associated population dynamics, we find that transmission decreases the HIV-1 evolutionary rate. The fact that we also identify this decline for substitutions that do not alter amino acid substitutions provides evidence against hypotheses that invoke selection forces. Instead, our findings support earlier reports that new infections start preferentially with less evolved variants, which may be stored in latently infected cells, and this may vary among different HIV-1 subtypes.

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.

Dynamics of viral evolution and neutralizing antibody response after HIV-1 superinfection

Investigating the incidence and prevalence of HIV-1 superinfection is challenging due to the complex dynamics of two infecting strains. The superinfecting strain can replace the initial strain, be transiently expressed, or persist along with the initial strain in distinct or in recombined forms. Various selective pressures influence these alternative scenarios in different HIV-1 coding regions. We hypothesized that the potency of the neutralizing antibody (NAb) response to autologous viruses would modulate viral dynamics in env following superinfection in a limited set of superinfection cases. HIV-1 env pyrosequencing data were generated from blood plasma collected from 7 individuals with evidence of superinfection. Viral variants within each patient were screened for recombination, and viral dynamics were evaluated using nucleotide diversity. NAb responses to autologous viruses were evaluated before and after superinfection. In 4 individuals, the superinfecting strain replaced the original strain. In 2 individuals, both initial and superinfecting strains continued to cocirculate. In the final individual, the surviving lineage was the product of interstrain recombination. NAb responses to autologous viruses that were detected within the first 2 years of HIV-1 infection were weak or absent for 6 of the 7 recently infected individuals at the time of and shortly following superinfection. These 6 individuals had detectable on-going viral replication of distinct superinfecting virus in the env coding region. In the remaining case, there was an early and strong autologous NAb response, which was associated with extensive recombination in env between initial and superinfecting strains. This extensive recombination made superinfection more difficult to identify and may explain why the detection of superinfection has typically been associated with low autologous NAb titers.

The power and pitfalls of HIV phylogenetics in public health

Phylogenetics is the application of comparative studies of genetic sequences in order to infer evolutionary relationships among organisms. This tool can be used as a form of molecular epidemiology to enhance traditional population-level communicable disease surveillance. Phylogenetic study has resulted in new paradigms being created in the field of communicable diseases and this commentary aims to provide the reader with an explanation of how phylogenetics can be used in tracking infectious diseases. Special emphasis will be placed upon the application of phylogenetics as a tool to help elucidate HIV transmission patterns and the limitations to these methods when applied to forensic analysis. Understanding infectious disease epidemiology in order to prevent new transmissions is the sine qua non of public health. However, with increasing epidemiological resolution, there may be an associated potential loss of privacy to the individual. It is within this context that we aim to promote the discussion on how to use phylogenetics to achieve important public health goals, while at the same time protecting the rights of the individual.

Future of phylogeny in HIV prevention

The success of the HIV Prevention Trials Network 052 trial has led to revisions in HIV-1 treatment guidelines. Antiretroviral therapy may reduce the risk of HIV-1 transmissions at the population level. The design of successful treatment as prevention interventions will be predicated on a comprehensive understanding of the spatial, temporal, and biological dynamics of heterosexual men who have sex with men and intravenous drug user epidemics. Viral phylogenetics can capture the underlying structure of transmission networks based on the genetic interrelatedness of viral sequences and cluster networks that could not be otherwise identified. This article describes the phylogenetic expansion of the Montreal men who have sex with men epidemic over the last decade. High rates of coclustering of primary infections are associated with 1 infection leading to 13 onward transmissions. Phylogeny substantiates the role of primary and recent stage infection in transmission dynamics, underlying the importance of timely diagnosis and immediate antiretroviral therapy initiation to avert transmission cascades.

Reconstructing the dynamics of HIV evolution within hosts from serial deep sequence data

At the early stage of infection, human immunodeficiency virus (HIV)-1 predominantly uses the CCR5 coreceptor for host cell entry. The subsequent emergence of HIV variants that use the CXCR4 coreceptor in roughly half of all infections is associated with an accelerated decline of CD4+ T-cells and rate of progression to AIDS. The presence of a ‘fitness valley’ separating CCR5- and CXCR4-using genotypes is postulated to be a biological determinant of whether the HIV coreceptor switch occurs. Using phylogenetic methods to reconstruct the evolutionary dynamics of HIV within hosts enables us to discriminate between competing models of this process. We have developed a phylogenetic pipeline for the molecular clock analysis, ancestral reconstruction, and visualization of deep sequence data. These data were generated by next-generation sequencing of HIV RNA extracted from longitudinal serum samples (median 7 time points) from 8 untreated subjects with chronic HIV infections (Amsterdam Cohort Studies on HIV-1 infection and AIDS). We used the known dates of sampling to directly estimate rates of evolution and to map ancestral mutations to a reconstructed timeline in units of days. HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm. We determined that the first mutations contributing to CXCR4 use emerged about 16 (per subject range 4 to 30) months before the earliest predicted CXCR4-using ancestor, which preceded the first positive cell-based assay of CXCR4 usage by 10 (range 5 to 25) months. CXCR4 usage arose in multiple lineages within 5 of 8 subjects, and ancestral lineages following alternate mutational pathways before going extinct were common. We observed highly patient-specific distributions and time-scales of mutation accumulation, implying that the role of a fitness valley is contingent on the genotype of the transmitted variant.

At the start of infection, human immunodeficiency virus (HIV) generally requires a specific protein receptor (CCR5) on the cell surface to bind and enter the cell. In roughly half of all HIV infections, the virus population eventually switches to using a different receptor (CXCR4). This ‘HIV coreceptor switch’ is associated with an accelerated rate of progression to AIDS. Although it is not known why this switch occurs in some infections and not others, it is thought to be shaped by constraints on how HIV can evolve from one mode to another. In this study, we test this hypothesis by reconstructing the evolutionary histories of HIV within 8 patients known to have undergone an HIV coreceptor switch. Each history is recreated from samples of HIV genetic sequences that were derived from repeated blood samples by next-generation sequencing, an emerging technology that is rapidly becoming an essential tool in the study of rapidly-evolving populations such as viruses or cancerous cells. Because we have samples from different points in time, we can use models of evolution to extrapolate back in time to the ancestors of each infection. Our analysis reveals patient-specific dynamics in HIV evolution that sheds new light on the determinants of the coreceptor switch.

Read length versus depth of coverage for viral quasispecies reconstruction

Recent advancements of sequencing technology have opened up unprecedented opportunities in many application areas. Virus samples can now be sequenced efficiently with very deep coverage to infer the genetic diversity of the underlying virus populations. Several sequencing platforms with different underlying technologies and performance characteristics are available for viral diversity studies. Here, we investigate how the differences between two common platforms provided by 454/Roche and Illumina affect viral diversity estimation and the reconstruction of viral haplotypes. Using a mixture of ten HIV clones sequenced with both platforms and additional simulation experiments, we assessed the trade-off between sequencing coverage, read length, and error rate. For fixed costs, short Illumina reads can be generated at higher coverage and allow for detecting variants at lower frequencies. They can also be sufficient to assess the diversity of the sample if sequences are dissimilar enough, but, in general, assembly of full-length haplotypes is feasible only with the longer 454/Roche reads. The quantitative comparison highlights the advantages and disadvantages of both platforms and provides guidance for the design of viral diversity studies.

Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data

Many viruses, including the clinically relevant RNA viruses HIV (human immunodeficiency virus) and HCV (hepatitis C virus), exist in large populations and display high genetic heterogeneity within and between infected hosts. Assessing intra-patient viral genetic diversity is essential for understanding the evolutionary dynamics of viruses, for designing effective vaccines, and for the success of antiviral therapy. Next-generation sequencing (NGS) technologies allow the rapid and cost-effective acquisition of thousands to millions of short DNA sequences from a single sample. However, this approach entails several challenges in experimental design and computational data analysis. Here, we review the entire process of inferring viral diversity from sample collection to computing measures of genetic diversity. We discuss sample preparation, including reverse transcription and amplification, and the effect of experimental conditions on diversity estimates due to in vitro base substitutions, insertions, deletions, and recombination. The use of different NGS platforms and their sequencing error profiles are compared in the context of various applications of diversity estimation, ranging from the detection of single nucleotide variants (SNVs) to the reconstruction of whole-genome haplotypes. We describe the statistical and computational challenges arising from these technical artifacts, and we review existing approaches, including available software, for their solution. Finally, we discuss open problems, and highlight successful biomedical applications and potential future clinical use of NGS to estimate viral diversity.