Recombination rate and selection strength in HIV intra-patient evolution

Tradeoffs between proliferation and transmission in virus evolution- insights from evolutionary and functional analyses of SARS-CoV-2

To be successful, a virus must maintain high between-host transmissibility while also effectively adapting within hosts. The impact of these potentially conflicting demands on viral genetic diversity and adaptation remains largely unexplored. These modes of adaptation can induce uncorrelated selection, bring mutations that enhance certain fitness aspects at the expense of others to high freqency, and contribute to the maintenance of genetic variation. The vast wealth of SARS-CoV-2 genetic data gathered from within and across hosts offers an unparalleled opportunity to test the above hypothesis. By analyzing a large set of SARS-CoV-2 sequences (~ 2 million) collected from early 2020 to mid-2021, we found that high frequency mutations within hosts are sometimes detrimental during between-host transmission. This highlights potential inverse selection pressures within- versus between-hosts. We also identified a group of nonsynonymous changes likely maintained by pleiotropy, as their frequencies are significantly higher than neutral expectation, yet they have never experienced clonal expansion. Analyzing one such mutation, spike M1237I, reveals that spike I1237 boosts viral assembly but reduces in vitro transmission, highlighting its pleiotropic effect. Though they make up about 2% of total changes, these types of variants represent 37% of SARS-CoV-2 genetic diversity. These mutations are notably prevalent in the Omicron variant from late 2021, hinting that pleiotropy may promote positive epistasis and new successful variants. Estimates of viral population dynamics, such as population sizes and transmission bottlenecks, assume neutrality of within-host variation. Our demonstration that these changes may affect fitness calls into question the robustness of these estimates.

A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses

Natural selection often acts on multiple traits simultaneously. For example, the virus HIV-1 faces pressure to evade host immunity while also preserving replicative fitness. While past work has studied selection during HIV-1 evolution, as in other examples where selection acts on multiple traits, it is challenging to quantitatively separate different contributions to fitness. This task is made more difficult because a single mutation can affect both immune escape and replication. Here, we develop an evolutionary model that disentangles the effects of escaping CD8+ T cell-mediated immunity, which we model as a binary trait, from other contributions to fitness. After validation in simulations, we applied this model to study within-host HIV-1 evolution in a clinical dataset. We observed strong selection for immune escape, sometimes greatly exceeding past estimates, especially early in infection. Conservative estimates suggest that roughly half of HIV-1 fitness gains during the first months to years of infection can be attributed to T cell escape. Our approach is not limited to HIV-1 or viruses and could be adapted to study the evolution of quantitative traits in other contexts.

Evolutionary Mechanisms of the Emergence of the Variants of Concern of SARS-CoV-2

The evolutionary origin of the variants of concern (VOCs) of SARS-CoV-2, characterized by a large number of new substitutions and strong changes in virulence and transmission rate, is intensely debated. The leading explanation in the literature is a chronic infection in immunocompromised individuals, where the virus evolves before returning into the main population. The present article reviews less-investigated hypotheses of VOC emergence with transmission between acutely infected hosts, with a focus on the mathematical models of stochastic evolution that have proved to be useful for other viruses, such as HIV and influenza virus. The central message is that understanding the acting factors of VOC evolution requires the framework of stochastic multi-locus evolution models, and that alternative hypotheses can be effectively verified by fitting results of computer simulation to empirical data.

Comparative Evaluation of Open-Source Bioinformatics Pipelines for Full-Length Viral Genome Assembly

The increasingly widespread application of next-generation sequencing (NGS) in clinical diagnostics and epidemiological research has generated a demand for robust, fast, automated, and user-friendly bioinformatics workflows. To guide the choice of tools for the assembly of full-length viral genomes from NGS datasets, we assessed the performance and applicability of four open-source bioinformatics pipelines (shiver-for which we created a user-friendly Dockerized version, referred to as dshiver; SmaltAlign; viral-ngs; and V-pipe) using both simulated and real-world HIV-1 paired-end short-read datasets and default settings. All four pipelines produced consensus genome assemblies with high quality metrics (genome fraction recovery, mismatch and indel rates, variant calling F1 scores) when the reference sequence used for assembly had high similarity to the analyzed sample. The shiver and SmaltAlign pipelines (but not viral-ngs and V-Pipe) also showed robust performance with more divergent samples (non-matching subtypes). With empirical datasets, SmaltAlign and viral-ngs exhibited an order of magnitude shorter runtime compared to V-Pipe and shiver. In terms of applicability, V-Pipe provides the broadest functionalities, SmaltAlign and dshiver combine user-friendliness with robustness, while the use of viral-ngs requires less computational resources compared to other pipelines. In conclusion, if a closely matched reference sequence is available, all pipelines can reliably reconstruct viral consensus genomes; therefore, differences in user-friendliness and runtime may guide the choice of the pipeline in a particular setting. If a matched reference sequence cannot be selected, we recommend shiver or SmaltAlign for robust performance. The new Dockerized version of shiver offers ease of use in addition to the accuracy and robustness of the original pipeline.

Linkage equilibrium between rare mutations

Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here, we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination and discuss their implications for the rates of horizontal gene transfer in bacteria.

Efficient epistasis inference via higher-order covariance matrix factorization

Epistasis can profoundly influence evolutionary dynamics. Temporal genetic data, consisting of sequences sampled repeatedly from a population over time, provides a unique resource to understand how epistasis shapes evolution. However, detecting epistatic interactions from sequence data is technically challenging. Existing methods for identifying epistasis are computationally demanding, limiting their applicability to real-world data. Here, we present a novel computational method for inferring epistasis that significantly reduces computational costs without sacrificing accuracy. We validated our approach in simulations and applied it to study HIV-1 evolution over multiple years in a data set of 16 individuals. There we observed a strong excess of negative epistatic interactions between beneficial mutations, especially mutations involved in immune escape. Our method is general and could be used to characterize epistasis in other large data sets.

A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses

Natural selection often acts on multiple traits simultaneously. For example, the virus HIV-1 faces pressure to evade host immunity while also preserving replicative fitness. While past work has studied selection during HIV-1 evolution, as in other examples where selection acts on multiple traits, it is challenging to quantitatively separate different contributions to fitness. This task is made more difficult because a single mutation can affect both immune escape and replication. Here, we develop an evolutionary model that disentangles the effects of escaping CD8+T cell-mediated immunity, which we model as a binary trait, from other contributions to fitness. After validation in simulations, we applied this model to study within-host HIV-1 evolution in a clinical data set. We observed strong selection for immune escape, sometimes greatly exceeding past estimates, especially early in infection. Conservative estimates suggest that roughly half of HIV-1 fitness gains during the first months to years of infection can be attributed to T cell escape. Our approach is not limited to HIV-1 or viruses, and could be adapted to study the evolution of quantitative traits in other contexts.

Lack of Resistance Mutations to the Novel HIV-1 Capsid Inhibitor Lenacapavir Among People Living with HIV in Guangdong, China

Background

The capsid inhibitor (CAI) lenacapavir (LEN) was approved for use in 2022, yet there are few reports about its drug resistance mutations (DRMs) and sensitivity.

Purpose

To delineate the prevalence of CAI DRMs and drug susceptibility among HIV-1 infected individuals living in Guangdong, China.

Patients and Methods

A total of 1035 individuals with HIV-1 infection, including 660 highly Active Anti-Retroviral Therapy (HAART) naive individuals and 375 hAART experienced individuals whose protease (PR)/ reverse transcriptase (RT) fragments were amplified successfully during drug resistance surveillance between October 2021 and December 2023, were randomly included in this study. The entire HIV-1 gag gene was amplified from plasma in LEN-naive individuals with or without antiretroviral therapy. The epidemiological and demographic information of the enrolled individuals were collected. The Stanford HIV Drug Resistance Database HIVdb program for Capsid was used to interpret the CAI DRMs and the LEN susceptibility.

Results

Among 1035 samples, 805 gag sequences were amplified, sequenced and assembled successfully from 518 hAART drugs naive individuals and 287 hAART drugs experienced individuals. Among them, 0.50% (4/805) carried at least one CAI DRM, of which 0.19% (1/518) from HAART naive individuals and 1.05% (3/287) from HAART experienced individuals. Among the individuals with CAI DRMs, two patients carried CAI major mutations (Q67H) conferring intermediate resistance to LEN and two patients carried CAI accessory mutation (T107A) conferring low level resistance to LEN.

Conclusion

Extremely low prevalence of CAI DRMs was detected among people living with HIV (PLWH) in Guangdong, China. Our observations indicate that LEN application may be promising when used in clinical practice in China. Before the administration of LEN, there is no need to consider detecting CAI mutations in PLWH through DRM examination for the time being.

Incorporating Intracellular Processes in Virus Dynamics Models

In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus-host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.

Elevated HIV Viral Load is Associated with Higher Recombination Rate In Vivo

HIV's exceptionally high recombination rate drives its intrahost diversification, enabling immune escape and multidrug resistance within people living with HIV. While we know that HIV's recombination rate varies by genomic position, we have little understanding of how recombination varies throughout infection or between individuals as a function of the rate of cellular coinfection. We hypothesize that denser intrahost populations may have higher rates of coinfection and therefore recombination. To test this hypothesis, we develop a new approach (recombination analysis via time series linkage decay or RATS-LD) to quantify recombination using autocorrelation of linkage between mutations across time points. We validate RATS-LD on simulated data under short read sequencing conditions and then apply it to longitudinal, high-throughput intrahost viral sequencing data, stratifying populations by viral load (a proxy for density). Among sampled viral populations with the lowest viral loads (<26,800 copies/mL), we estimate a recombination rate of 1.5×10-5 events/bp/generation (95% CI: 7×10-6 to 2.9×10-5), similar to existing estimates. However, among samples with the highest viral loads (>82,000 copies/mL), our median estimate is approximately 6 times higher. In addition to co-varying across individuals, we also find that recombination rate and viral load are associated within single individuals across different time points. Our findings suggest that rather than acting as a constant, uniform force, recombination can vary dynamically and drastically across intrahost viral populations and within them over time. More broadly, we hypothesize that this phenomenon may affect other facultatively asexual populations where spatial co-localization varies.

Characterization of HIV-1 CRF02_AG/A3/G unique recombinant forms identified among children in Larkana, Pakistan

Co-circulation of different human immunodeficiency virus type 1 HIV-1 subtypes among infected populations can lead to the generation of new recombinants. In Pakistan, subtype A1 and CRF02_AG are the dominant strains circulating among key populations. The high prevalence of new HIV infections among the key populations highlights the possibility of recombination between the dominant strains, which can lead to the generation of new recombinants. Here, we identified a recombinant cluster composed of CRF02_AG, sub-subtype A3, and subtype G among HIV-infected children in Larkana. For the study, 10 retrospectively collected samples, with recombination signals in the pol gene, were used to perform a near full-length genome NFLG sequencing. Of the 10 samples, NFLG was successfully sequenced from seven samples. Phylogenetic analysis of the seven NFLGs showed that all recombinants formed a distinct monophyletic cluster and were distinct from known HIV-1 circulating recombinant forms CRFs. Recombination analyses showed that all seven NFLGs shared a similar recombinant structure consisting of CRF02_AG, sub-subtype A3, and subtype G, with a sub-subtype A3 fragment inserted into pol and vif regions spanning from (HXB2: 4218-5518), and a subtype G fragment inserted into vpu, rev, tat and env regions spanning from (HXB2: 5957-8250) of the CRF02_AG backbone. The identification of unique recombinant forms may indicate the presence and transmission of several co-circulating lineages in Larkana, giving rise to newer CRFs. This study also highlights the importance of continuous molecular surveillance to fully understand HIV-1 genetic diversity in Pakistan, particularly in Larkana, which is the epicenter of HIV outbreaks.

Elevated HIV viral load is associated with higher recombination rate in vivo

HIV's exceptionally high recombination rate drives its intra-host diversification, enabling immune escape and multi-drug resistance within people living with HIV. While we know that HIV's recombination rate varies by genomic position, we have little understanding of how recombination varies throughout infection or between individuals as a function of the rate of cellular coinfection. We hypothesize that denser intra-host populations may have higher rates of coinfection and therefore recombination. To test this hypothesis, we develop a new approach (Recombination Analysis via Time Series Linkage Decay, or RATS-LD) to quantify recombination using autocorrelation of linkage between mutations across time points. We validate RATS-LD on simulated data under short read sequencing conditions and then apply it to longitudinal, high-throughput intra-host viral sequencing data, stratifying populations by viral load (a proxy for density). Among sampled viral populations with the lowest viral loads (< 26,800 copies/mL), we estimate a recombination rate of 1.5 × 10-5 events/bp/generation (95% CI: 7 × 10-6 - 2.9 × 10-5), similar to existing estimates. However, among samples with the highest viral loads (> 82,000 copies/mL), our median estimate is approximately 6 times higher. In addition to co-varying across individuals, we also find that recombination rate and viral load are associated within single individuals across different time points. Our findings suggest that rather than acting as a constant, uniform force, recombination can vary dynamically and drastically across intra-host viral populations and within them over time. More broadly, we hypothesize that this phenomenon may affect other facultatively asexual populations where spatial co-localization varies.

Long-range linkage effects in adapting sexual populations

In sexual populations, closely-situated genes have linked evolutionary fates, while genes spaced far in genome are commonly thought to evolve independently due to recombination. In the case where evolution depends essentially on supply of new mutations, this assumption has been confirmed by mathematical modeling. Here I examine it in the case of pre-existing genetic variation, where mutation is not important. A haploid population with [Formula: see text] genomes, [Formula: see text] loci, a fixed selection coefficient, and a small initial frequency of beneficial alleles [Formula: see text] is simulated by a Monte-Carlo algorithm. When the number of loci, L, is larger than a critical value of [Formula: see text] simulation demonstrates a host of linkage effects that decrease neither with the distance between loci nor the number of recombination crossovers. Due to clonal interference, the beneficial alleles become extinct at a fraction of loci [Formula: see text]. Due to a genetic background effect, the substitution rate varies broadly between loci, with the fastest value exceeding the one-locus limit by the factor of [Formula: see text] Thus, the far-situated parts of a long genome in a sexual population do not evolve as independent blocks. A potential link between these findings and the emergence of new Variants of Concern of SARS-CoV-2 is discussed.

Recombination smooths the time signal disrupted by latency in within-host HIV phylogenies

Within-host Human immunodeficiency virus (HIV) evolution involves several features that may disrupt standard phylogenetic reconstruction. One important feature is reactivation of latently integrated provirus, which has the potential to disrupt the temporal signal, leading to variation in the branch lengths and apparent evolutionary rates in a tree. Yet, real within-host HIV phylogenies tend to show clear, ladder-like trees structured by the time of sampling. Another important feature is recombination, which violates the fundamental assumption that evolutionary history can be represented by a single bifurcating tree. Thus, recombination complicates the within-host HIV dynamic by mixing genomes and creating evolutionary loop structures that cannot be represented in a bifurcating tree. In this paper, we develop a coalescent-based simulator of within-host HIV evolution that includes latency, recombination, and effective population size dynamics that allows us to study the relationship between the true, complex genealogy of within-host HIV evolution, encoded as an ancestral recombination graph (ARG), and the observed phylogenetic tree. To compare our ARG results to the familiar phylogeny format, we calculate the expected bifurcating tree after decomposing the ARG into all unique site trees, their combined distance matrix, and the overall corresponding bifurcating tree. While latency and recombination separately disrupt the phylogenetic signal, remarkably, we find that recombination recovers the temporal signal of within-host HIV evolution caused by latency by mixing fragments of old, latent genomes into the contemporary population. In effect, recombination averages over extant heterogeneity, whether it stems from mixed time signals or population bottlenecks. Furthermore, we establish that the signals of latency and recombination can be observed in phylogenetic trees despite being an incorrect representation of the true evolutionary history. Using an approximate Bayesian computation method, we develop a set of statistical probes to tune our simulation model to nine longitudinally sampled within-host HIV phylogenies. Because ARGs are exceedingly difficult to infer from real HIV data, our simulation system allows investigating effects of latency, recombination, and population size bottlenecks by matching decomposed ARGs to real data as observed in standard phylogenies.

Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1

Infusion of broadly neutralizing antibodies (bNAbs) has shown promise as an alternative to anti-retroviral therapy against HIV. A key challenge is to suppress viral escape, which is more effectively achieved with a combination of bNAbs. Here, we propose a computational approach to predict the efficacy of a bNAb therapy based on the population genetics of HIV escape, which we parametrize using high-throughput HIV sequence data from bNAb-naive patients. By quantifying the mutational target size and the fitness cost of HIV-1 escape from bNAbs, we predict the distribution of rebound times in three clinical trials. We show that a cocktail of three bNAbs is necessary to effectively suppress viral escape, and predict the optimal composition of such bNAb cocktail. Our results offer a rational therapy design for HIV, and show how genetic data can be used to predict treatment outcomes and design new approaches to pathogenic control.

HIV-1 Evolutionary Dynamics under Nonsuppressive Antiretroviral Therapy

Prolonged virologic failure on 2nd-line protease inhibitor (PI)-based antiretroviral therapy (ART) without emergence of major protease mutations is well recognized and provides an opportunity to study within-host evolution in long-term viremic individuals. Using next-generation sequencing and in silico haplotype reconstruction, we analyzed whole-genome sequences from longitudinal plasma samples of eight chronically infected HIV-1-positive individuals failing 2nd-line regimens from the French National Agency for AIDS and Viral Hepatitis Research (ANRS) 12249 Treatment as Prevention (TasP) trial. On nonsuppressive ART, there were large fluctuations in synonymous and nonsynonymous variant frequencies despite stable viremia. Reconstructed haplotypes provided evidence for selective sweeps during periods of partial adherence, and viral haplotype competition, during periods of low drug exposure. Drug resistance mutations in reverse transcriptase (RT) were used as markers of viral haplotypes in the reservoir, and their distribution over time indicated recombination. We independently observed linkage disequilibrium decay, indicative of recombination. These data highlight dramatic changes in virus population structure that occur during stable viremia under nonsuppressive ART. IMPORTANCE HIV-1 infections are most commonly initiated with a single founder virus and are characterized by extensive inter- and intraparticipant genetic diversity. However, existing literature on HIV-1 intrahost population dynamics is largely limited to untreated infections, predominantly in subtype B-infected individuals. The manuscript characterizes viral population dynamics in long-term viremic treatment-experienced individuals, which has not been previously characterized. These data are particularly relevant for understanding HIV dynamics but can also be applied to other RNA viruses. With this unique data set we propose that the virus is highly unstable, and we have found compelling evidence of HIV-1 within-host viral diversification, recombination, and haplotype competition during nonsuppressive ART.

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

Recombination is a major mechanism through which HIV type 1 (HIV-1) maintains genetic diversity and interferes with viral eradication efforts. There is growing evidence demonstrating a recombinant origin of primate lentiviruses including HIV-1 group M (HIV-1/M). Inferring the extent of recombination across the entire HIV-1/M genome is of great importance as it provides deeper insights into the origin, dynamics, and evolution of the global pandemic. Here we propose an alternative method that can reconstruct the extent of genome-wide recombination in HIV-1, uncover reticulate patterns, and serve as a framework for HIV-1 classification. Our method provides an alternative approach for understanding the roles of virus recombination in the early evolutionary history of zoonosis for other emerging viruses.

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

Evolution during primary HIV infection does not require adaptive immune selection

Modern HIV research depends crucially on both viral sequencing and population measurements. To directly link mechanistic biological processes and evolutionary dynamics during HIV infection, we developed multiple within-host phylodynamic models of HIV primary infection for comparative validation against viral load and evolutionary dynamics data. The optimal model of primary infection required no positive selection, suggesting that the host adaptive immune system reduces viral load but surprisingly does not drive observed viral evolution. Rather, the fitness (infectivity) of mutant variants is drawn from an exponential distribution in which most variants are slightly less infectious than their parents (nearly neutral evolution). This distribution was not largely different from either in vivo fitness distributions recorded beyond primary infection or in vitro distributions that are observed without adaptive immunity, suggesting the intrinsic viral fitness distribution may drive evolution. Simulated phylogenetic trees also agree with independent data and illuminate how phylogenetic inference must consider viral and immune-cell population dynamics to gain accurate mechanistic insights.

Fitness Estimation for Viral Variants in the Context of Cellular Coinfection

Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host’s infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally sampled H5N1 sequence data from ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics.

MPL resolves genetic linkage in fitness inference from complex evolutionary histories

Genetic linkage causes the fate of new mutations in a population to be contingent on the genetic background on which they appear. This makes it challenging to identify how individual mutations affect fitness. To overcome this challenge, we developed marginal path likelihood (MPL), a method to infer selection from evolutionary histories that resolves genetic linkage. Validation on real and simulated data sets shows that MPL is fast and accurate, outperforming existing inference approaches. We found that resolving linkage is crucial for accurately quantifying selection in complex evolving populations, which we demonstrate through a quantitative analysis of intrahost HIV-1 evolution using multiple patient data sets. Linkage effects generated by variants that sweep rapidly through the population are particularly strong, extending far across the genome. Taken together, our results argue for the importance of resolving linkage in studies of natural selection.

Evolutionary Genetics of Mycobacterium tuberculosis and HIV-1: "The Tortoise and the Hare"

The already enormous burden caused by Mycobacterium tuberculosis and Human Immunodeficiency Virus type 1 (HIV-1) alone is aggravated by co-infection. Despite obvious differences in the rate of evolution comparing these two human pathogens, genetic diversity plays an important role in the success of both. The extreme evolutionary dynamics of HIV-1 is in the basis of a robust capacity to evade immune responses, to generate drug-resistance and to diversify the population-level reservoir of M group viral subtypes. Compared to HIV-1 and other retroviruses, M. tuberculosis generates minute levels of genetic diversity within the host. However, emerging whole-genome sequencing data show that the M. tuberculosis complex contains at least nine human-adapted phylogenetic lineages. This level of genetic diversity results in differences in M. tuberculosis interactions with the host immune system, virulence and drug resistance propensity. In co-infected individuals, HIV-1 and M. tuberculosis are likely to co-colonize host cells. However, the evolutionary impact of the interaction between the host, the slowly evolving M. tuberculosis bacteria and the HIV-1 viral "mutant cloud" is poorly understood. These evolutionary dynamics, at the cellular niche of monocytes/macrophages, are also discussed and proposed as a relevant future research topic in the context of single-cell sequencing.

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.

Participation of Multifunctional RNA in Replication, Recombination and Regulation of Endogenous Plant Pararetroviruses (EPRVs)

Pararetroviruses, taxon Caulimoviridae, are typical of retroelements with reverse transcriptase and share a common origin with retroviruses and LTR retrotransposons, presumably dating back 1.6 billion years and illustrating the transition from an RNA to a DNA world. After transcription of the viral genome in the host nucleus, viral DNA synthesis occurs in the cytoplasm on the generated terminally redundant RNA including inter- and intra-molecule recombination steps rather than relying on nuclear DNA replication. RNA recombination events between an ancestral genomic retroelement with exogenous RNA viruses were seminal in pararetrovirus evolution resulting in horizontal transmission and episomal replication. Instead of active integration, pararetroviruses use the host DNA repair machinery to prevail in genomes of angiosperms, gymnosperms and ferns. Pararetrovirus integration - leading to Endogenous ParaRetroViruses, EPRVs - by illegitimate recombination can happen if their sequences instead of homologous host genomic sequences on the sister chromatid (during mitosis) or homologous chromosome (during meiosis) are used as template. Multiple layers of RNA interference exist regulating episomal and chromosomal forms of the pararetrovirus. Pararetroviruses have evolved suppressors against this plant defense in the arms race during co-evolution which can result in deregulation of plant genes. Small RNAs serve as signaling molecules for Transcriptional and Post-Transcriptional Gene Silencing (TGS, PTGS) pathways. Different populations of small RNAs comprising 21-24 nt and 18-30 nt in length have been reported for Citrus, Fritillaria, Musa, Petunia, Solanum and Beta. Recombination and RNA interference are driving forces for evolution and regulation of EPRVs.

Rapid screening and detection of inter-type viral recombinants using Phylo-K-Mers

Motivation

Novel recombinant viruses may have important medical and evolutionary significance, as they sometimes display new traits not present in the parental strains. This is particularly concerning when the new viruses combine fragments coming from phylogenetically distinct viral types. Here, we consider the task of screening large collections of sequences for such novel recombinants. A number of methods already exist for this task. However, these methods rely on complex models and heavy computations that are not always practical for a quick scan of a large number of sequences.

Results

We have developed SHERPAS, a new program to detect novel recombinants and provide a first estimate of their parental composition. Our approach is based on the precomputation of a large database of ‘phylogenetically-informed k-mers’, an idea recently introduced in the context of phylogenetic placement in metagenomics. Our experiments show that SHERPAS is hundreds to thousands of times faster than existing software, and enables the analysis of thousands of whole genomes, or long-sequencing reads, within minutes or seconds, and with limited loss of accuracy.

Availability and implementation

The source code is freely available for download at https://github.com/phylo42/sherpas.

Supplementary information

Supplementary data are available at Bioinformatics online.

Absence of Integrase Inhibitor-Associated Resistance Among Antiretroviral Therapy-Naïve HIV-1-Infected Adults in Guangdong Province, China, in 2018

Background

Antiretroviral therapy (ART) containing an integrase strand transfer inhibitor (INSTI) plus two nucleoside reverse-transcriptase inhibitors has been recommended as a first-line regimen for ART-naïve HIV-1-infected patients in the latest Chinese Guidelines for Diagnosis and Treatment of HIV/AIDS.

Objective

To determine the prevalence of INSTI-related mutations among ART-naïve HIV-1-infected adults in Guangdong, China, in 2018.

Methods

The entire integrase gene was amplified from blood plasma. Demographic and epidemiological information was collected. INSTI mutations and antiretroviral susceptibility were interpreted using the Stanford University HIV Drug Resistance Database HIVdb program.

Results

Of 927 samples, 827 integrase sequences were successfully obtained. Among them, no major resistance mutations to INSTIs were identified, and four accessory mutations, including T97A (0.12%, 1/827), A128T (0.24%, 2/827), E157Q (0.85%, 7/827), and G163R (0.24%, 2/827), were found in twelve individuals. Two patient samples contained the G163R mutation conferring low-level resistance to elvitegravir and raltegravir.

Conclusion

The overall prevalence of INSTI mutations remains low. Drug resistance mutation testing for the detection of INSTI drug resistance mutations in HIV treatment-naïve patients should be considered due to the circulation of polymorphisms contributing to INSTI resistance and the expected increasing use of this class of drugs.

An Evolutionary Model of Progression to AIDS

The time to the onset of AIDS symptoms in an HIV infected individual is known to correlate inversely with viremia and the level of immune activation. The correlation exists against the background of strong individual fluctuations demonstrating the existence of hidden variables depending on patient and virus parameters. At the moment, prognosis of the time to AIDS based on patient parameters is not possible. In addition, it is of paramount importance to understand the reason of progression to AIDS in untreated patients to be able to learn to control it by means other than anti-retroviral therapy. Here we develop a mechanistic mathematical model to predict the speed of progression to AIDS in individual untreated patients and patients treated with suboptimal therapy, based on a single-time measurement of several virological and immunological parameters. We show that the gradual increase in virus fitness during a chronic infection causes slow gradual depletion of CD4 T cells. Using the existing evolution models of HIV, we obtain general expressions predicting the time to the onset of AIDS symptoms in terms of the patient parameters, for low-viremia and high-viremia patients separately. We show that the evolution model of AIDS fits the existing data on virus-time correlations better than the alternative model of the deregulation of homeostatic response.

Evaluation of haplotype callers for next-generation sequencing of viruses

Currently, the standard practice for assembling next-generation sequencing (NGS) reads of viral genomes is to summarize thousands of individual short reads into a single consensus sequence, thus confounding useful intra-host diversity information for molecular phylodynamic inference. It is hypothesized that a few viral strains may dominate the intra-host genetic diversity with a variety of lower frequency strains comprising the rest of the population. Several software tools currently exist to convert NGS sequence variants into haplotypes. Previous benchmarks of viral haplotype reconstruction programs used simulation scenarios that are useful from a mathematical perspective but do not reflect viral evolution and epidemiology. Here, we tested twelve NGS haplotype reconstruction methods using viral populations simulated under realistic evolutionary dynamics. We simulated coalescent-based populations that spanned known levels of viral genetic diversity, including mutation rates, sample size and effective population size, to test the limits of the haplotype reconstruction methods and to ensure coverage of predicted intra-host viral diversity levels (especially HIV-1). All twelve investigated haplotype callers showed variable performance and produced drastically different results that were mainly driven by differences in mutation rate and, to a lesser extent, in effective population size. Most methods were able to accurately reconstruct haplotypes when genetic diversity was low. However, under higher levels of diversity (e.g., those seen intra-host HIV-1 infections), haplotype reconstruction quality was highly variable and, on average, poor. All haplotype reconstruction tools, except QuasiRecomb and ShoRAH, greatly underestimated intra-host diversity and the true number of haplotypes. PredictHaplo outperformed, in regard to highest precision, recall, and lowest UniFrac distance values, the other haplotype reconstruction tools followed by CliqueSNV, which, given more computational time, may have outperformed PredictHaplo. Here, we present an extensive comparison of available viral haplotype reconstruction tools and provide insights for future improvements in haplotype reconstruction tools using both short-read and long-read technologies.

HIV RNA Rebound in Seminal Plasma after Antiretroviral Treatment Interruption

If strategies currently in development succeed in eradicating HIV reservoirs in peripheral blood and lymphoid tissues, residual sources of virus may remain in anatomic compartments. Paired blood and semen samples were collected from 12 individuals enrolled in a randomized, double-blind, placebo-controlled therapeutic vaccine clinical trial in people with HIV (PWH) who began antiretroviral therapy (ART) during acute or early infection (ClinicalTrials registration no. NCT01859325). After the week 56 visit (postintervention), all participants interrupted ART. At the first available time points after viral rebound, we sequenced HIV-1 env (C2-V3), gag (p24), and pol (reverse transcriptase) regions amplified from cell-free HIV RNA in blood and seminal plasma using the MiSeq Illumina platform. Comprehensive sequence and phylogenetic analyses were performed to evaluate viral population structure, compartmentalization, and viral diversity in blood and seminal plasma. Compared to that in blood, HIV RNA rebound in semen occurred significantly later (median of 66 versus 42 days post-ART interruption, P < 0.01) and reached lower levels (median 164 versus 16,090 copies/ml, P < 0.01). Three of five participants with available sequencing data presented compartmentalized viral rebound between blood and semen in one HIV coding region. Despite early ART initiation, HIV RNA molecular diversity was higher in semen than in blood in all three coding regions for most participants. Higher HIV RNA molecular diversity in the genital tract (compared to that in blood plasma) and evidence of compartmentalization illustrate the distinct evolutionary dynamics between these two compartments after ART interruption. Future research should evaluate whether the genital compartment might contribute to viral rebound in some PWH interrupting ART.IMPORTANCE To cure HIV, we likely need to target the reservoirs in all anatomic compartments. Here, we used sophisticated statistical and phylogenetic methods to analyze blood and semen samples collected from 12 persons with HIV who began antiretroviral therapy (ART) during very early HIV infection and who interrupted their ART as part of a clinical trial. First, we found that HIV RNA rebound in semen occurred significantly later and reached lower levels than in blood. Second, we found that the virus in semen was genetically different in some participants compared to that in blood. Finally, we found increased HIV RNA molecular diversity in semen compared to that in blood in almost all study participants. These data suggest that the HIV RNA populations emerging from the genital compartment after ART interruption might not be the same as those emerging from blood plasma. Future research should evaluate whether the genital compartment might contribute to viral rebound in some people with HIV (PWH) interrupting ART.

A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection

In the absence of effective antiviral therapy, HIV-1 evolves in response to the within-host environment, of which the immune system is an important aspect. During the earliest stages of infection, this process of evolution is very rapid, driven by a small number of CTL escape mutations. As the infection progresses, immune escape variants evolve under reduced magnitudes of selection, while competition between an increasing number of polymorphic alleles (i.e., clonal interference) makes it difficult to quantify the magnitude of selection acting upon specific variant alleles. To tackle this complex problem, we developed a novel multi-locus inference method to evaluate the role of selection during the chronic stage of within-host infection. We applied this method to targeted sequence data from the p24 and gp41 regions of HIV-1 collected from 34 patients with long-term untreated HIV-1 infection. We identify a broad distribution of beneficial fitness effects during infection, with a small number of variants evolving under strong selection and very many variants evolving under weaker selection. The uniquely large number of infections analysed granted a previously unparalleled statistical power to identify loci at which selection could be inferred to act with statistical confidence. Our model makes no prior assumptions about the nature of alleles under selection, such that any synonymous or non-synonymous variant may be inferred to evolve under selection. However, the majority of variants inferred with confidence to be under selection were non-synonymous in nature, and in most cases were have previously been associated with either CTL escape in p24 or neutralising antibody escape in gp41. We also identified a putative new CTL escape site (residue 286 in gag), and a region of gp41 (including residues 644, 648, 655 in env) likely to be associated with immune escape. Sites inferred to be under selection in multiple hosts have high within-host and between-host diversity although not all sites with high between-host diversity were inferred to be under selection at the within-host level. Our identification of selection at sites associated with resistance to broadly neutralising antibodies (bNAbs) highlights the need to fully understand the role of selection in untreated individuals when designing bNAb based therapies.

The Genetic Diversity of HIV-1 Quasispecies Within Primary Infected Individuals

HIV has remarkable genetic diversity among populations. The diversity has critical impacts on transmission, immune escape, pathogenesis, and clinical management. HIV-1 diversity originates from frequent mutation and recombination during reverse transcription. This work focuses on the quasispecies genetic dynamics within individuals with primary infections. Eleven men who have sex with men from the Beijing PRIMO Clinical Cohort were identified as primary infection and had three or four series of their anticoagulant blood samples collected. Viral RNA was extracted and amplified using single-genome amplification. Products of the gp120 gene that met single-genome amplification requirements were sequenced. Subtype assortment of all collected sequences was performed using both the jumping profile hidden Markov model (jpHMM) and REGA. Quasispecies diversity at each time was estimated using Mega 6. Intrapatient recombination was analyzed using RDP4. According to the Fiebig classification system, YA-81 belongs to stage III and YA-113 belongs to stage IV. The other samples are all associated with the infection stage of V/VI. YA113 had a dual infection with subtype B and a new unique recombinant form involving CRF01_AE and C. The other eight were infected with CRF01_AE, one was infected with B/C recombinant, and the last one with B. Of the 10 single infections, 8 were caused by 1 founder virus. They all displayed a sharp increase of quasispecies diversity during the sampling times. Two were caused by at least two founder viruses. The diversity of these strains starts at a significantly high level and is followed by a relatively steady trend. Critically, the separate subtypes YA113-B and YA113-CRF01_AE/C both showed a similar trend to those infected by a single founder virus. Recombination analysis revealed that 5 of 11 cases underwent detectable intrapatient recombination. These findings indicate that tracing the dynamics of HIV-1 quasispecies during early infection may be relevant and valuable for understanding pathways of viral diversification and immune escape.

High-Resolution Evolutionary Analysis of Within-Host Hepatitis C Virus Infection

BACKGROUND

Despite recent breakthroughs in treatment of hepatitis C virus (HCV) infection, we have limited understanding of how virus diversity generated within individuals impacts the evolution and spread of HCV variants at the population scale. Addressing this gap is important for identifying the main sources of disease transmission and evaluating the risk of drug-resistance mutations emerging and disseminating in a population.

METHODS

We have undertaken a high-resolution analysis of HCV within-host evolution from 4 individuals coinfected with human immunodeficiency virus 1 (HIV-1). We used long-read, deep-sequenced data of full-length HCV envelope glycoprotein, longitudinally sampled from acute to chronic HCV infection to investigate the underlying viral population and evolutionary dynamics.

RESULTS

We found statistical support for population structure maintaining the within-host HCV genetic diversity in 3 out of 4 individuals. We also report the first population genetic estimate of the within-host recombination rate for HCV (0.28 × 10-7 recombination/site/year), which is considerably lower than that estimated for HIV-1 and the overall nucleotide substitution rate estimated during HCV infection.

CONCLUSIONS

Our findings indicate that population structure and strong genetic linkage shapes within-host HCV evolutionary dynamics. These results will guide the future investigation of potential HCV drug resistance adaptation during infection, and at the population scale.

Pervasive within-host recombination and epistasis as major determinants of the molecular evolution of the foot-and-mouth disease virus capsid

Although recombination is known to occur in foot-and-mouth disease virus (FMDV), it is considered only a minor determinant of virus sequence diversity. Analysis at phylogenetic scales shows inter-serotypic recombination events are rare, whereby recombination occurs almost exclusively in non-structural proteins. In this study we have estimated recombination rates within a natural host in an experimental setting. African buffaloes were inoculated with a SAT-1 FMDV strain containing two major viral sub-populations differing in their capsid sequence. This population structure enabled the detection of extensive within-host recombination in the genomic region coding for structural proteins and allowed recombination rates between the two sub-populations to be estimated. Quite surprisingly, the effective recombination rate in VP1 during the acute infection phase turns out to be about 0.1 per base per year, i.e. comparable to the mutation/substitution rate. Using a high-resolution map of effective within-host recombination in the capsid-coding region, we identified a linkage disequilibrium pattern in VP1 that is consistent with a mosaic structure with two main genetic blocks. Positive epistatic interactions between co-evolved variants appear to be present both within and between blocks. These interactions are due to intra-host selection both at the RNA and protein level. Overall our findings show that during FMDV co-infections by closely related strains, capsid-coding genes recombine within the host at a much higher rate than expected, despite the presence of strong constraints dictated by the capsid structure. Although these intra-host results are not immediately translatable to a phylogenetic setting, recombination and epistasis must play a major and so far underappreciated role in the molecular evolution of the virus at all scales.

There are 7 serotypes of Foot-and-Mouth Disease Virus and multiple strains of each serotype. The emergence of new strains can result in widespread outbreaks of disease and requires new vaccines to be developed. The major mechanisms driving variation are thought to be substitutions in the viral genome. Recombination in the capsid-coding region of the virus genome has been described at phylogenetic scales but not thought to play a major role in generating variants. In the current experiment, a co-infection of African buffaloes with closely related sub-populations of viruses allowed us to detect recombination events. For structural protein-coding sequences, the genetic composition of the population is driven by extensive within-host recombination. During the acute infection phase the intra-host recombination rates of 0.1 per base per year are comparable to the typical mutation rates of the virus. The recombination map reveals two strongly linked regions within the VP1 protein-coding sequence. Epistatic interactions between co-evolved mutations in VP1 are caused by intra-host selection at the RNA and protein level and are present both within and between the two regions. Our findings in this experimental setting support a major role for recombination and epistasis in the intra-host evolution of FMDV.

Quasispecies dynamics in disease prevention and control

Medical interventions to prevent and treat viral disease constitute evolutionary forces that may modify the genetic composition of viral populations that replicate in an infected host and influence the genomic composition of those viruses that are transmitted and progress at the epidemiological level. Given the adaptive potential of viruses in general and the RNA viruses in particular, the selection of viral mutants that display some degree of resistance to inhibitors or vaccines is a tangible challenge. Mutant selection may jeopardize control of the viral disease. Strategies intended to minimize vaccination and treatment failures are proposed and justified based on fundamental features of viral dynamics explained in the preceding chapters. The recommended use of complex, multiepitopic vaccines, and combination therapies as early as possible after initiation of infection falls under the general concept that complexity cannot be combated with simplicity. It also follows that sociopolitical action to interrupt virus replication and spread as soon as possible is as important as scientifically sound treatment designs to control viral disease on a global scale.

Molecular basis of genetic variation of viruses

Genetic variation is a necessity of all biological systems. Viruses use all known mechanisms of variation; mutation, several forms of recombination, and segment reassortment in the case of viruses with a segmented genome. These processes are intimately connected with the replicative machineries of viruses, as well as with fundamental physical-chemical properties of nucleotides when acting as template or substrate residues. Recombination has been viewed as a means to rescue viable genomes from unfit parents or to produce large modifications for the exploration of phenotypic novelty. All types of genetic variation can act conjointly as blind processes to provide the raw materials for adaptation to the changing environments in which viruses must replicate. A distinction is made between mechanistically unavoidable and evolutionarily relevant mutation and recombination.

Natural selection favoring more transmissible HIV detected in United States molecular transmission network

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters.

Epistasis detectably alters correlations between genomic sites in a narrow parameter window

Different genomic sites evolve inter-dependently due to the combined action of epistasis, defined as a non-multiplicative contribution of alleles at different loci to genome fitness, and the physical linkage of different loci in genome. Both epistasis and linkage, partially compensated by recombination, cause correlations between allele frequencies at the loci (linkage disequilibrium, LD). The interaction and competition between epistasis and linkage are not fully understood, nor is their relative sensitivity to recombination. Modeling an adapting population in the presence of random mutation, natural selection, pairwise epistasis, and random genetic drift, we compare the contributions of epistasis and linkage. For this end, we use a panel of haplotype-based measures of LD and their various combinations calculated for epistatic and non-epistatic pairs separately. We compute the optimal percentages of detected and false positive pairs in a one-time sample of a population of moderate size. We demonstrate that true interacting pairs can be told apart in a sufficiently short genome within a narrow window of time and parameters. Outside of this parameter region, unless the population is extremely large, shared ancestry of individual sequences generates pervasive stochastic LD for non-interacting pairs masking true epistatic associations. In the presence of sufficiently strong recombination, linkage effects decrease faster than those of epistasis, and the detection of epistasis improves. We demonstrate that the epistasis component of locus association can be isolated, at a single time point, by averaging haplotype frequencies over multiple independent populations. These results demonstrate the existence of fundamental restrictions on the protocols for detecting true interactions in DNA sequence sets.

The expected time to cross extended fitness plateaus

For a population to acquire a complex adaptation requiring multiple individually neutral mutations, it must cross a plateau in the fitness landscape. We consider plateaus involving three mutations, and show that large populations can cross them rapidly via lineages that acquire multiple mutations while remaining at low frequency, much faster than the ∝μ³ rate for simultaneous triple mutations. Plateau-crossing is fastest for very large populations. At intermediate population sizes, recombination can greatly accelerate adaptation by combining independent mutant lineages to form triple-mutants. For more frequent recombination, such that the population is kept near linkage equilibrium, we extend our analysis to find simple expressions for the expected time to cross plateaus of arbitrary width.

The Puzzle of HIV Neutral and Selective Evolution

HIV is one of the fastest evolving organisms known. It evolves about 1 million times faster than its host, humans. Because HIV establishes chronic infections, with continuous evolution, its divergence within a single infected human surpasses the divergence of the entire humanoid history. Yet, it is still the same virus, infecting the same cell types and using the same replication machinery year after year. Hence, one would think that most mutations that HIV accumulates are neutral. But the picture is more complicated than that. HIV evolution is also a clear example of strong positive selection, that is, mutants have a survival advantage. How do these facts come together?

DCA for genome-wide epistasis analysis: the statistical genetics perspective

Direct coupling analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the quasi-linkage equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of clonal competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3000 genomes of the human pathogen Streptococcus pneumoniae.

Evidence for a recombinant origin of HIV-1 Group M from genomic variation

Reconstructing the early dynamics of the HIV-1 pandemic can provide crucial insights into the socioeconomic drivers of emerging infectious diseases in human populations, including the roles of urbanization and transportation networks. Current evidence indicates that the global pandemic comprising almost entirely of HIV-1/M originated around the 1920s in central Africa. However, these estimates are based on molecular clock estimates that are assumed to apply uniformly across the virus genome. There is growing evidence that recombination has played a significant role in the early history of the HIV-1 pandemic, such that different regions of the HIV-1 genome have different evolutionary histories. In this study, we have conducted a dated-tip analysis of all near full-length HIV-1/M genome sequences that were published in the GenBank database. We used a sliding window approach similar to the 'bootscanning' method for detecting breakpoints in inter-subtype recombinant sequences. We found evidence of substantial variation in estimated root dates among windows, with an estimated mean time to the most recent common ancestor of 1922. Estimates were significantly autocorrelated, which was more consistent with an early recombination event than with stochastic error variation in phylogenetic reconstruction and dating analyses. A piecewise regression analysis supported the existence of at least one recombination breakpoint in the HIV-1/M genome with interval-specific means around 1929 and 1913, respectively. This analysis demonstrates that a sliding window approach can accommodate early recombination events outside the established nomenclature of HIV-1/M subtypes, although it is difficult to incorporate the earliest available samples due to their limited genome coverage.

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

Background

Our understanding of HIV-1 and antiretroviral treatment (ART) is strongly biased towards subtype B, the predominant subtype in North America and western Europe. Efforts to characterize the response to first-line treatments in other HIV-1 subtypes have been hindered by the availability of large study cohorts in resource-limited settings. To maximize our statistical power, we combined HIV-1 sequence and clinical data from every available study population associated with the Joint Clinical Research Centre (JCRC) in Uganda. These records were combined with contemporaneous ART-naive records from Uganda in the Stanford HIVdb database.

Methods

Treatment failures were defined by the presence of HIV genotype records with sample collection dates after the ART start dates in the JCRC database. Drug resistances were predicted by the Stanford HIVdb algorithm, and HIV subtype classification and recombination detection was performed with SCUEAL. We used Bayesian network analysis to evaluate associations between drug exposures and subtypes, and binomial regression for associations with recombination.

Results

This is the largest database of first-line treatment failures (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=1724$$\end{document}n=1724) in Uganda to date, with a predicted statistical power of 80% to detect subtype associations at an odds ratio of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 1.2$$\end{document}≥1.2. In the subset where drug regimen data were available, we observed that use of 3TC was associated with a higher rate of first line treatment failure, whereas regimens containing AZT and TDF were associated with reduced rates of failure. In the complete database, we found limited evidence of associations between HIV-1 subtypes and treatment failure, with the exception of a significantly lower frequency of failures among A/D recombinants that comprised about 7% of the population. First-line treatment failure was significantly associated with reduced numbers of recombination breakpoints across subtypes.

Conclusions

Expanding access to first-line ART should confer the anticipated public health benefits in Uganda, despite known differences in the pathogenesis of HIV-1 subtypes. Furthermore, the impact of ART may actually be enhanced by frequent inter-subtype recombination in this region.

Electronic supplementary material

The online version of this article (10.1186/s12981-019-0218-2) contains supplementary material, which is available to authorized users.

On the unfounded enthusiasm for soft selective sweeps II: Examining recent evidence from humans, flies, and viruses

Since the initial description of the genomic patterns expected under models of positive selection acting on standing genetic variation and on multiple beneficial mutations—so-called soft selective sweeps—researchers have sought to identify these patterns in natural population data. Indeed, over the past two years, large-scale data analyses have argued that soft sweeps are pervasive across organisms of very different effective population size and mutation rate—humans, Drosophila, and HIV. Yet, others have evaluated the relevance of these models to natural populations, as well as the identifiability of the models relative to other known population-level processes, arguing that soft sweeps are likely to be rare. Here, we look to reconcile these opposing results by carefully evaluating three recent studies and their underlying methodologies. Using population genetic theory, as well as extensive simulation, we find that all three examples are prone to extremely high false-positive rates, incorrectly identifying soft sweeps under both hard sweep and neutral models. Furthermore, we demonstrate that well-fit demographic histories combined with rare hard sweeps serve as the more parsimonious explanation. These findings represent a necessary response to the growing tendency of invoking parameter-heavy, assumption-laden models of pervasive positive selection, and neglecting best practices regarding the construction of proper demographic null models.

A long-standing debate in evolutionary biology revolves around the role of selective vs. stochastic processes in driving molecular evolution and shaping genetic variation. With the advent of genomics, genome-wide polymorphism data have been utilized to characterize these processes, with a major interest in describing the fraction of genomic variation shaped by positive selection. These genomic scans were initially focused around a hard sweep model, in which selection acts upon rare, newly arising beneficial mutations. Recent years have seen the description of sweeps occurring from both standing and rapidly recurring beneficial mutations, collectively known as soft sweeps. However, common to both hard and soft sweeps is the difficulty in distinguishing these effects from neutral demographic patterns, and disentangling these processes has remained an important field of study within population genetics. Despite this, there is a recent and troubling tendency to neglect these demographic considerations, and to naively fit sweep models to genomic data. Recent realizations of such efforts have resulted in the claim that soft sweeps play a dominant role in shaping genomic variation and in driving adaptation across diverse branches of the tree of life. Here, we reanalyze these findings and demonstrate that a more careful consideration of neutral processes results in highly differing conclusions.

Evolutionary Virology at 40

RNA viruses are diverse, abundant, and rapidly evolving. Genetic data have been generated from virus populations since the late 1970s and used to understand their evolution, emergence, and spread, culminating in the generation and analysis of many thousands of viral genome sequences. Despite this wealth of data, evolutionary genetics has played a surprisingly small role in our understanding of virus evolution. Instead, studies of RNA virus evolution have been dominated by two very different perspectives, the experimental and the comparative, that have largely been conducted independently and sometimes antagonistically. Here, we review the insights that these two approaches have provided over the last 40 years. We show that experimental approaches using in vitro and in vivo laboratory models are largely focused on short-term intrahost evolutionary mechanisms, and may not always be relevant to natural systems. In contrast, the comparative approach relies on the phylogenetic analysis of natural virus populations, usually considering data collected over multiple cycles of virus-host transmission, but is divorced from the causative evolutionary processes. To truly understand RNA virus evolution it is necessary to meld experimental and comparative approaches within a single evolutionary genetic framework, and to link viral evolution at the intrahost scale with that which occurs over both epidemiological and geological timescales. We suggest that the impetus for this new synthesis may come from methodological advances in next-generation sequencing and metagenomics.

QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data

Motivation

Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use.

Results

The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN.

Contact

pskums@gsu.edu or alexz@cs.gsu.edu or rahul@sfsu.edu or yek0@cdc.gov.

Supplementary information

Supplementary data are available at Bioinformatics online.

Relationship between latent and rebound viruses in a clinical trial of anti-HIV-1 antibody 3BNC117

A clinical trial was performed to evaluate 3BNC117, a potent anti-HIV-1 antibody, in infected individuals during suppressive antiretroviral therapy and subsequent analytical treatment interruption (ATI). The circulating reservoir was evaluated by quantitative and qualitative viral outgrowth assay (Q2VOA) at entry and after 6 mo. There were no significant quantitative changes in the size of the reservoir before ATI, and the composition of circulating reservoir clones varied in a manner that did not correlate with 3BNC117 sensitivity. 3BNC117 binding site amino acid variants found in rebound viruses preexisted in the latent reservoir. However, only 3 of 217 rebound viruses were identical to 868 latent viruses isolated by Q2VOA and near full-length sequencing. Instead, 63% of the rebound viruses appeared to be recombinants, even in individuals with 3BNC117-resistant reservoir viruses. In conclusion, viruses emerging during ATI in individuals treated with 3BNC117 are not the dominant species found in the circulating latent reservoir, but frequently appear to represent recombinants of latent viruses.

Within-patient mutation frequencies reveal fitness costs of CpG dinucleotides and drastic amino acid changes in HIV

HIV has a high mutation rate, which contributes to its ability to evolve quickly. However, we know little about the fitness costs of individual HIV mutations in vivo, their distribution and the different factors shaping the viral fitness landscape. We calculated the mean frequency of transition mutations at 870 sites of the pol gene in 160 patients, allowing us to determine the cost of these mutations. As expected, we found high costs for non-synonymous and nonsense mutations as compared to synonymous mutations. In addition, we found that non-synonymous mutations that lead to drastic amino acid changes are twice as costly as those that do not and mutations that create new CpG dinucleotides are also twice as costly as those that do not. We also found that G→A and C→T mutations are more costly than A→G mutations. We anticipate that our new in vivo frequency-based approach will provide insights into the fitness landscape and evolvability of not only HIV, but a variety of microbes.

Tracking HIV-1 recombination to resolve its contribution to HIV-1 evolution in natural infection

Recombination in HIV-1 is well documented, but its importance in the low-diversity setting of within-host diversification is less understood. Here we develop a novel computational tool (RAPR (Recombination Analysis PRogram)) to enable a detailed view of in vivo viral recombination during early infection, and we apply it to near-full-length HIV-1 genome sequences from longitudinal samples. Recombinant genomes rapidly replace transmitted/founder (T/F) lineages, with a median half-time of 27 days, increasing the genetic complexity of the viral population. We identify recombination hot and cold spots that differ from those observed in inter-subtype recombinants. Furthermore, RAPR analysis of longitudinal samples from an individual with well-characterized neutralizing antibody responses shows that recombination helps carry forward resistance-conferring mutations in the diversifying quasispecies. These findings provide insight into molecular mechanisms by which viral recombination contributes to HIV-1 persistence and immunopathogenesis and have implications for studies of HIV transmission and evolution in vivo.

On the effective depth of viral sequence data

Genome sequence data are of great value in describing evolutionary processes in viral populations. However, in such studies, the extent to which data accurately describes the viral population is a matter of importance. Multiple factors may influence the accuracy of a dataset, including the quantity and nature of the sample collected, and the subsequent steps in viral processing. To investigate this phenomenon, we sequenced replica datasets spanning a range of viruses, and in which the point at which samples were split was different in each case, from a dataset in which independent samples were collected from a single patient to another in which all processing steps up to sequencing were applied to a single sample before splitting the sample and sequencing each replicate. We conclude that neither a high read depth nor a high template number in a sample guarantee the precision of a dataset. Measures of consistency calculated from within a single biological sample may also be insufficient; distortion of the composition of a population by the experimental procedure or genuine within-host diversity between samples may each affect the results. Where it is possible, data from replicate samples should be collected to validate the consistency of short-read sequence data.

An in vitro Model to Mimic Selection of Replication-Competent HIV-1 Intersubtype Recombination in Dual or Superinfected Patients

The low frequency of HIV-1 recombinants within entire viral populations in both individual patients and culture-based infection models impedes investigation of the underlying factors contributing to either the occurrence of recombinants or the survival of recombinants once they are formed. So far, most of the related studies have no consideration of recombinants' functionality. Here, we established a functional recombinant production (FRP) system to produce pure and functional HIV-1 intersubtype Env recombinants and utilized 454 pyrosequencing to investigate the distribution of over 4000 functional and non-functional recombination breakpoints from either the FRP system or dual infection cultures. The results revealed that most of the breakpoints converged in gp41 (62%) and C1 (25.3%) domains of gp120, which has strong correlation with the similarity between the two recombining sequences. Yet, the breakpoints also appeared in C2 (5.2%) and C5 (4.6%) domains not correlated with the recombining sequence similarity. Interestingly, none of the intersubtype gp120 recombinants recombined between C1 and gp41 regions either from the FRP system or from the dual infection culture, and very few from the HIV epidemic were functional. The present study suggests that the selection of functional Env recombinants is one of the reasons for the predominance of C1 and gp41 Env recombinants in the HIV epidemic, and it provides an in vitro model to mimic the selection of replication-competent HIV-1 intersubtype recombination in dual or superinfected patients.