Population genomics of intrapatient HIV-1 evolution

Superinfection with intact HIV-1 results in conditional replication of defective proviruses and nonsuppressible viremia in people living with HIV-1

During replication of some RNA viruses, defective particles can spontaneously arise and interfere with wild-type (WT) virus replication. Recently, engineered versions of these defective interfering particles (DIPs) have been proposed as an HIV-1 therapeutic. However, DIPs have yet to be reported in people with HIV-1 (PWH). Here, we find DIPs in PWH who have a rare, polyclonal form of non-suppressible viremia (NSV). While antiretroviral therapy (ART) rapidly reduces viremia to undetectable levels, some individuals experience sustained viremia due to virus production from cell clones harboring intact or defective proviruses. We characterized the source of NSV in two PWH who never reached undetectable viral load despite ART adherence. Remarkably, in each participant, we found a diverse set of defective viral genomes all sharing the same fatal deletions. We found that this paradoxical accumulation of mutations by viruses with fatal defects was driven by superinfection with intact viruses, resulting in mobilization of defective genomes and accumulation of additional mutations during untreated infection. We show that these defective proviruses interfere with WT virus replication, conditionally replicate, and, in one case, have an R 0 > 1, enabling in vivo spread. Despite this, clinical outcomes show no evidence of a beneficial effect of these DIPs.

Phylodynamics for Human Immunodeficiency Virus Prevention: A Miami-Dade County Case Study

BACKGROUND

To date, human immunodeficiency virus (HIV) molecular epidemiology has been primarily used to identify clusters of related infections (cluster detection and response) and then address interventions to these clusters. Community groups have raised concern regarding cluster detection and response related to privacy and ethical concerns. Here we demonstrate how an alternative approach to HIV molecular epidemiology can provide public health benefit.

METHODS

A limited data set for Miami-Dade County provided by the Florida Department of Health was curated and annotated by neighborhood health district (NBHD) and genetic linkage (using a genetic distance threshold of ≤0.5%) and phylodynamic analyses were performed. Phylodynamic analyses were used to infer viral transmissions into Miami-Dade County and between NBHDs within the county.

RESULTS

A total of 7274 HIV sequences from unique persons collected between 1 January 2015 and 31 December 2021 were analyzed, including 50% of the 7894 new diagnoses during this period. The proportion of sequences in local clusters increased over time. Higher ratios of local introductions, compared to viral egress (ie, source of local clusters in other NBHDs) were observed in 3 NBHDs in North Miami (range, 1.9-2.5), suggesting earlier diagnosis, but high numbers of susceptible persons not receiving preexposure prophylaxis. South Dade/Homestead had a low ratio (0.3) of local introductions compared with egress, suggesting later diagnosis and less durable suppression.

CONCLUSIONS

Phylodynamic and genetic linkage analyses can highlight populations and geographic regions that might benefit more from particular types of HIV prevention interventions. These findings will need to be explored by evaluating the impact of scaling up interventions informed by these analyses.

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.

Parallel HIV-1 fitness landscapes shape viral dynamics in humans and macaques that develop broadly neutralizing antibodies

Human immunodeficiency virus (HIV)-1 exhibits remarkable genetic diversity. For this reason, an effective HIV-1 vaccine must elicit antibodies that can neutralize many variants of the virus. While broadly neutralizing antibodies (bnAbs) have been isolated from HIV-1 infected individuals, a general understanding of the virus-antibody coevolutionary processes that lead to their development remains incomplete. We performed a quantitative study of HIV-1 evolution in two individuals who developed bnAbs. We observed strong selection early in infection for mutations affecting HIV-1 envelope glycosylation and escape from autologous strain-specific antibodies, followed by weaker selection for bnAb resistance later in infection. To confirm our findings, we analyzed data from rhesus macaques infected with viruses derived from the same two individuals. We inferred remarkably similar fitness effects of HIV-1 mutations in humans and macaques. Moreover, we observed a striking pattern of rapid HIV-1 evolution, consistent in both humans and macaques, that precedes the development of bnAbs. Our work highlights strong parallels between infection in rhesus macaques and humans, and it reveals a quantitative evolutionary signature of bnAb development.

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.

Evolution of evolvability in rapidly adapting populations

Mutations can alter the short-term fitness of an organism, as well as the rates and benefits of future mutations. While numerous examples of these evolvability modifiers have been observed in rapidly adapting microbial populations, existing theory struggles to predict when they will be favoured by natural selection. Here we develop a mathematical framework for predicting the fates of genetic variants that modify the rates and benefits of future mutations in linked genomic regions. We derive analytical expressions showing how the fixation probabilities of these variants depend on the size of the population and the diversity of competing mutations. We find that competition between linked mutations can dramatically enhance selection for modifiers that increase the benefits of future mutations, even when they impose a strong direct cost on fitness. However, we also find that modest direct benefits can be sufficient to drive evolutionary dead ends to fixation. Our results suggest that subtle differences in evolvability could play an important role in shaping the long-term success of genetic variants in rapidly evolving microbial populations.

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.

Chronic infections can generate SARS-CoV-2-like bursts of viral evolution without epistasis

Multiple SARS-CoV-2 variants have arisen during the first years of the pandemic, often bearing many new mutations. Several explanations have been offered for the surprisingly sudden emergence of multiple mutations that enhance viral fitness, including cryptic transmission, spillover from animal reservoirs, epistasis between mutations, and chronic infections. Here, we simulated pathogen evolution combining within-host replication and between-host transmission. We found that, under certain conditions, chronic infections can lead to SARS-CoV-2-like bursts of mutations even without epistasis. Chronic infections can also increase the global evolutionary rate of a pathogen even in the absence of clear mutational bursts. Overall, our study supports chronic infections as a plausible origin for highly mutated SARS-CoV-2 variants. More generally, we also describe how chronic infections can influence pathogen evolution under different scenarios.

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.

Comparison of short-read and long-read next-generation sequencing technologies for determining HIV-1 drug resistance

Accurate HIV-1 genome sequencing is necessary to identify drug resistance mutations (DRMs) in people with HIV-1 (PWH). Next-generation-sequencing (NGS) allows the detection of minor variants and is now available in many laboratories. Our study aimed to compare two NGS approaches, a "short read" sequencing protocol using DeepChek® Whole Genome HIV-1 Assay on Illumina, and a "long read" sequencing protocol of HIV-1 pol and env single-molecule real-time sequencing (SMRT) on Pacific Biosciences (PacBio). We analyzed 16 plasma samples and 13 cellular samples from PWH. HIV-1 whole genome was amplified into five amplicons using DeepChek® Whole Genome HIV-1 Assay and sequenced on an iSeq. 100. In parallel, HIV-1 pol and env genes were separately amplified and sequenced using PacBio SMRT system with the circular consensus sequencing mode on a Sequel IIe. Concordance rates for determining DRMs with both approaches varied depending on the HIV-1 region, with higher concordance in the integrase region compared to the reverse transcriptase and protease regions. DeepChek® Whole Genome HIV-1 Assay exhibited better sensitivity in HIV-1 RNA sequencing of plasmas with lower viral loads. In cell HIV-1 DNA sequencing, the DeepChek® Whole Genome HIV-1 Assay performed better in pol and env sequencing but detected more APOBEC-induced DRMs, which can represent defective proviruses. Our findings indicate that both DeepChek® Whole Genome HIV-1 Assay and PacBio SMRT sequencing exhibit good performance for subtype determination, detection, and quantification of DRMs of the HIV-1 genome. However, some discrepancies were found in cellular samples, highlighting the challenges of interpreting HIV-1 DNA DRMs.

Reconciling founder variant multiplicity of HIV-1 infection with the rate of CD4+ decline

HIV-1 transmission precipitates a stringent genetic bottleneck, with 75% of new infections initiated by a single genetic variant. Where multiple variants initiate infection, recipient set point viral load (SpVL) and the rate of CD4+ T cell decline may be elevated, but these findings remain inconsistent. Here, we summarised the evidence for this phenomenon, then tested whether previous studies possessed sufficient statistical power to reliably identify a true effect of multiple variant infection leading to higher SpVL. Next, we combined models of HIV-1 transmission, heritability and disease progression to understand whether available data suggest a faster CD4+ T cell decline would be expected to associated with multiple variant infection, without an explicit dependency between the two. First, we found that most studies had insufficient power to identify a true significant difference, prompting an explanation for previous inconsistencies. Next, our model framework revealed we would not expect to observe a positive association between multiple variant infections and faster CD4+ T cell decline, in the absence of an explicit dependency. Consequently, while empirical evidence may be consistent with a positive association between multiple variant infection and faster CD4+ T cell decline, further investigation is required to establish a causal basis.

Estimating Haplotype Structure and Frequencies: A Bayesian Approach to Unknown Design in Pooled Genomic Data

The estimation of haplotype structure and frequencies provides crucial information about the composition of genomes. Techniques, such as single-individual haplotyping, aim to reconstruct individual haplotypes from diploid genome sequencing data. However, our focus is distinct. We address the challenge of reconstructing haplotype structure and frequencies from pooled sequencing samples where multiple individuals are sequenced simultaneously. A frequentist method to address this issue has recently been proposed. In contrast to this and other methods that compute point estimates, our proposed Bayesian hierarchical model delivers a posterior that permits us to also quantify uncertainty. Since matching permutations in both haplotype structure and corresponding frequency matrix lead to the same reconstruction of their product, we introduce an order-preserving shrinkage prior that ensures identifiability with respect to permutations. For inference, we introduce a blocked Gibbs sampler that enforces the required constraints. In a simulation study, we assessed the performance of our method. Furthermore, by using our approach on two distinct sets of real data, we demonstrate that our Bayesian approach can reconstruct the dominant haplotypes in a challenging, high-dimensional set-up.

Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2

Background

Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission.

Methods

We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics.

Results

We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein.

Conclusions

This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.

Entourage: all-in-one sequence analysis software for genome assembly, virus detection, virus discovery, and intrasample variation profiling

BACKGROUND

Pan-virus detection, and virome investigation in general, can be challenging, mainly due to the lack of universally conserved genetic elements in viruses. Metagenomic next-generation sequencing can offer a promising solution to this problem by providing an unbiased overview of the microbial community, enabling detection of any viruses without prior target selection. However, a major challenge in utilising metagenomic next-generation sequencing for virome investigation is that data analysis can be highly complex, involving numerous data processing steps.

RESULTS

Here, we present Entourage to address this challenge. Entourage enables short-read sequence assembly, viral sequence search with or without reference virus targets using contig-based approaches, and intrasample sequence variation quantification. Several workflows are implemented in Entourage to facilitate end-to-end virus sequence detection analysis through a single command line, from read cleaning, sequence assembly, to virus sequence searching. The results generated are comprehensive, allowing for thorough quality control, reliability assessment, and interpretation. We illustrate Entourage's utility as a streamlined workflow for virus detection by employing it to comprehensively search for target virus sequences and beyond in raw sequence read data generated from HeLa cell culture samples spiked with viruses. Furthermore, we showcase its flexibility and performance on a real-world dataset by analysing a preassembled Tara Oceans dataset. Overall, our results show that Entourage performs well even with low virus sequencing depth in single digits, and it can be used to discover novel viruses effectively. Additionally, by using sequence data generated from a patient with chronic SARS-CoV-2 infection, we demonstrate Entourage's capability to quantify virus intrasample genetic variations, and generate publication-quality figures illustrating the results.

CONCLUSIONS

Entourage is an all-in-one, versatile, and streamlined bioinformatics software for virome investigation, developed with a focus on ease of use. Entourage is available at https://codeberg.org/CENMIG/Entourage under the MIT license.

SNP-slice resolves mixed infections: simultaneously unveiling strain haplotypes and linking them to hosts

MOTIVATION

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information sometimes have to discard mixed infection samples as many downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A scalable tool to learn and resolve the SNP-haplotypes from polygenomic data is an urgent need in molecular epidemiology.

RESULTS

We develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP-haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP-haplotypes and individual heterozygosities accurately without reference panels and outperforms the state-of-the-art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for using our method on empirical datasets.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNP-Slice algorithm, as well as scripts to analyze SNP-Slice outputs, are available at https://github.com/nianqiaoju/snp-slice.

Changing selection on amino acid substitutions in Gag protein between major HIV-1 subtypes

Amino acid preferences at a protein site depend on the role of this site in protein function and structure as well as on external constraints. All these factors can change in the course of evolution, making amino acid propensities of a site time-dependent. When viral subtypes divergently evolve in different host subpopulations, such changes may depend on genetic, medical, and sociocultural differences between these subpopulations. Here, using our previously developed phylogenetic approach, we describe sixty-nine amino acid sites of the Gag protein of human immunodeficiency virus type 1 (HIV-1) where amino acids have different impact on viral fitness in six major subtypes of the type M. These changes in preferences trigger adaptive evolution; indeed, 32 (46 per cent) of these sites experienced strong positive selection at least in one of the subtypes. At some of the sites, changes in amino acid preferences may be associated with differences in immune escape between subtypes. The prevalence of an amino acid in a protein site within a subtype is only a poor predictor for whether this amino acid is preferred in this subtype according to the phylogenetic analysis. Therefore, attempts to identify the factors of viral evolution from comparative genomics data should integrate across multiple sources of information.

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.

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.

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.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Using viral sequence diversity to estimate time of HIV infection in infants

Age at HIV acquisition may influence viral pathogenesis in infants, and yet infection timing (i.e. date of infection) is not always known. Adult studies have estimated infection timing using rates of HIV RNA diversification, however, it is unknown whether adult-trained models can provide accurate predictions when used for infants due to possible differences in viral dynamics. While rates of viral diversification have been well defined for adults, there are limited data characterizing these dynamics for infants. Here, we performed Illumina sequencing of gag and pol using longitudinal plasma samples from 22 Kenyan infants with well-characterized infection timing. We used these data to characterize viral diversity changes over time by designing an infant-trained Bayesian hierarchical regression model that predicts time since infection using viral diversity. We show that diversity accumulates with time for most infants (median rate within pol = 0.00079 diversity/month), and diversity accumulates much faster than in adults (compare previously-reported adult rate within pol = 0.00024 diversity/month [1]). We find that the infant rate of viral diversification varies by individual, gene region, and relative timing of infection, but not by set-point viral load or rate of CD4+ T cell decline. We compare the predictive performance of this infant-trained Bayesian hierarchical regression model with simple linear regression models trained using the same infant data, as well as existing adult-trained models [1]. Using an independent dataset from an additional 15 infants with frequent HIV testing to define infection timing, we demonstrate that infant-trained models more accurately estimate time since infection than existing adult-trained models. This work will be useful for timing HIV acquisition for infants with unknown infection timing and for refining our understanding of how viral diversity accumulates in infants, both of which may have broad implications for the future development of infant-specific therapeutic and preventive interventions.

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.

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.

Going beyond consensus genome sequences: An innovative SNP-based methodology reconstructs different Ugandan cassava brown streak virus haplotypes at a nationwide scale in Rwanda

Cassava Brown Streak Disease (CBSD), which is caused by cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV), represents one of the most devastating threats to cassava production in Africa, including in Rwanda where a dramatic epidemic in 2014 dropped cassava yield from 3.3 million to 900,000 tonnes (1). Studying viral genetic diversity at the genome level is essential in disease management, as it can provide valuable information on the origin and dynamics of epidemic events. To fill the current lack of genome-based diversity studies of UCBSV, we performed a nationwide survey of cassava ipomovirus genomic sequences in Rwanda by high-throughput sequencing (HTS) of pools of plants sampled from 130 cassava fields in thirteen cassava-producing districts, spanning seven agro-ecological zones with contrasting climatic conditions and different cassava cultivars. HTS allowed the assembly of a nearly complete consensus genome of UCBSV in twelve districts. The phylogenetic analysis revealed high homology between UCBSV genome sequences, with a maximum of 0.8 per cent divergence between genomes at the nucleotide level. An in-depth investigation based on Single Nucleotide Polymorphisms (SNPs) was conducted to explore the genome diversity beyond the consensus sequences. First, to ensure the validity of the result, a panel of SNPs was confirmed by independent reverse transcription polymerase chain reaction (RT-PCR) and Sanger sequencing. Furthermore, the combination of fixation index (FST) calculation and Principal Component Analysis (PCA) based on SNP patterns identified three different UCBSV haplotypes geographically clustered. The haplotype 2 (H2) was restricted to the central regions, where the NAROCAS 1 cultivar is predominantly farmed. RT-PCR and Sanger sequencing of individual NAROCAS1 plants confirmed their association with H2. Haplotype 1 was widely spread, with a 100 per cent occurrence in the Eastern region, while Haplotype 3 was only found in the Western region. These haplotypes' associations with specific cultivars or regions would need further confirmation. Our results prove that a much more complex picture of genetic diversity can be deciphered beyond the consensus sequences, with practical implications on virus epidemiology, evolution, and disease management. Our methodology proposes a high-resolution analysis of genome diversity beyond the consensus between and within samples. It can be used at various scales, from individual plants to pooled samples of virus-infected plants. Our findings also showed how subtle genetic differences could be informative on the potential impact of agricultural practices, as the presence and frequency of a virus haplotype could be correlated with the dissemination and adoption of improved cultivars.

SNP-Slice Resolves Mixed Infections: Simultaneously Unveiling Strain Haplotypes and Linking Them to Hosts

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information have to discard mixed infection samples, because existing downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A reliable tool to learn and resolve the SNP haplotypes from polygenomic data is an urgent need in molecular epidemiology. In this work, we develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP haplotypes and individual heterozygosities accurately without reference panels and outperforms the state of art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for the practical use of the method.

Adaptation of a transmitted/founder simian-human immunodeficiency virus for enhanced replication in rhesus macaques

Transmitted/founder (TF) simian-human immunodeficiency viruses (SHIVs) express HIV-1 envelopes modified at position 375 to efficiently infect rhesus macaques while preserving authentic HIV-1 Env biology. SHIV.C.CH505 is an extensively characterized virus encoding the TF HIV-1 Env CH505 mutated at position 375 shown to recapitulate key features of HIV-1 immunobiology, including CCR5-tropism, a tier 2 neutralization profile, reproducible early viral kinetics, and authentic immune responses. SHIV.C.CH505 is used frequently in nonhuman primate studies of HIV, but viral loads after months of infection are variable and typically lower than those in people living with HIV. We hypothesized that additional mutations besides Δ375 might further enhance virus fitness without compromising essential components of CH505 Env biology. From sequence analysis of SHIV.C.CH505-infected macaques across multiple experiments, we identified a signature of envelope mutations associated with higher viremia. We then used short-term in vivo mutational selection and competition to identify a minimally adapted SHIV.C.CH505 with just five amino acid changes that substantially improve virus replication fitness in macaques. Next, we validated the performance of the adapted SHIV in vitro and in vivo and identified the mechanistic contributions of selected mutations. In vitro, the adapted SHIV shows improved virus entry, enhanced replication on primary rhesus cells, and preserved neutralization profiles. In vivo, the minimally adapted virus rapidly outcompetes the parental SHIV with an estimated growth advantage of 0.14 days-1 and persists through suppressive antiretroviral therapy to rebound at treatment interruption. Here, we report the successful generation of a well-characterized, minimally adapted virus, termed SHIV.C.CH505.v2, with enhanced replication fitness and preserved native Env properties that can serve as a new reagent for NHP studies of HIV-1 transmission, pathogenesis, and cure.

A classification algorithm based on dynamic ensemble selection to predict mutational patterns of the envelope protein in HIV-infected patients

BACKGROUND

Therapeutics against the envelope (Env) proteins of human immunodeficiency virus type 1 (HIV-1) effectively reduce viral loads in patients. However, due to mutations, new therapy-resistant Env variants frequently emerge. The sites of mutations on Env that appear in each patient are considered random and unpredictable. Here we developed an algorithm to estimate for each patient the mutational state of each position based on the mutational state of adjacent positions on the three-dimensional structure of the protein.

METHODS

We developed a dynamic ensemble selection algorithm designated k-best classifiers. It identifies the best classifiers within the neighborhood of a new observation and applies them to predict the variability state of each observation. To evaluate the algorithm, we applied amino acid sequences of Envs from 300 HIV-1-infected individuals (at least six sequences per patient). For each patient, amino acid variability values at all Env positions were mapped onto the three-dimensional structure of the protein. Then, the variability state of each position was estimated by the variability at adjacent positions of the protein.

RESULTS

The proposed algorithm showed higher performance than the base learner and a panel of classification algorithms. The mutational state of positions in the high-mannose patch and CD4-binding site of Env, which are targeted by multiple therapeutics, was predicted well. Importantly, the algorithm outperformed other classification techniques for predicting the variability state at multi-position footprints of therapeutics on Env.

CONCLUSIONS

The proposed algorithm applies a dynamic classifier-scoring approach that increases its performance relative to other classification methods. Better understanding of the spatiotemporal patterns of variability across Env may lead to new treatment strategies that are tailored to the unique mutational patterns of each patient. More generally, we propose the algorithm as a new high-performance dynamic ensemble selection technique.

Inspecting the interaction between human immunodeficiency virus and the immune system through genetic turnover

Chronic infections of the human immunodeficiency virus (HIV) create a very complex coevolutionary process, where the virus tries to escape the continuously adapting host immune system. Quantitative details of this process are largely unknown and could help in disease treatment and vaccine development. Here we study a longitudinal dataset of ten HIV-infected people, where both the B-cell receptors and the virus are deeply sequenced. We focus on simple measures of turnover, which quantify how much the composition of the viral strains and the immune repertoire change between time points. At the single-patient level, the viral-host turnover rates do not show any statistically significant correlation, however, they correlate if one increases the amount of statistics by aggregating the information across patients. We identify an anti-correlation: large changes in the viral pool composition come with small changes in the B-cell receptor repertoire. This result seems to contradict the naïve expectation that when the virus mutates quickly, the immune repertoire needs to change to keep up. However, a simple model of antagonistically evolving populations can explain this signal. If it is sampled at intervals comparable with the sweep time, one population has had time to sweep while the second cannot start a counter-sweep, leading to the observed anti-correlation. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

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.

Estimating linkage disequilibrium and selection from allele frequency trajectories

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.

Bézier interpolation improves the inference of dynamical models from data

Many dynamical systems, from quantum many-body systems to evolving populations to financial markets, are described by stochastic processes. Parameters characterizing such processes can often be inferred using information integrated over stochastic paths. However, estimating time-integrated quantities from real data with limited time resolution is challenging. Here, we propose a framework for accurately estimating time-integrated quantities using Bézier interpolation. We applied our approach to two dynamical inference problems: Determining fitness parameters for evolving populations and inferring forces driving Ornstein-Uhlenbeck processes. We found that Bézier interpolation reduces the estimation bias for both dynamical inference problems. This improvement was especially noticeable for data sets with limited time resolution. Our method could be broadly applied to improve accuracy for other dynamical inference problems using finitely sampled data.

Inferring the multiplicity of founder variants initiating HIV-1 infection: a systematic review and individual patient data meta-analysis

BACKGROUND

HIV-1 infections initiated by multiple founder variants are characterised by a higher viral load and a worse clinical prognosis than those initiated with single founder variants, yet little is known about the routes of exposure through which transmission of multiple founder variants is most probable. Here we used individual patient data to calculate the probability of multiple founders stratified by route of HIV exposure and study methodology.

METHODS

We conducted a systematic review and meta-analysis of studies that estimated founder variant multiplicity in HIV-1 infection, searching MEDLINE, Embase, and Global Health databases for papers published between Jan 1, 1990, and Sept 14, 2020. Eligible studies must have reported original estimates of founder variant multiplicity in people with acute or early HIV-1 infections, have clearly detailed the methods used, and reported the route of exposure. Studies were excluded if they reported data concerning people living with HIV-1 who had known or suspected superinfection, who were documented as having received pre-exposure prophylaxis, or if the transmitting partner was known to be receiving antiretroviral treatment. Individual patient data were collated from all studies, with authors contacted if these data were not publicly available. We applied logistic meta-regression to these data to estimate the probability that an HIV infection is initiated by multiple founder variants. We calculated a pooled estimate using a random effects model, subsequently stratifying this estimate across exposure routes in a univariable analysis. We then extended our model to adjust for different study methods in a multivariable analysis, recalculating estimates across the exposure routes. This study is registered with PROSPERO, CRD42020202672.

FINDINGS

We included 70 publications in our analysis, comprising 1657 individual patients. Our pooled estimate of the probability that an infection is initiated by multiple founder variants was 0·25 (95% CI 0·21-0·29), with moderate heterogeneity (Q=132·3, p<0·0001, I²=64·2%). Our multivariable analysis uncovered differences in the probability of multiple variant infection by exposure route. Relative to a baseline of male-to-female transmission, the predicted probability for female-to-male multiple variant transmission was significantly lower at 0·13 (95% CI 0·08-0·20), and the probabilities were significantly higher for transmissions in people who inject drugs (0·37 [0·24-0·53]) and men who have sex with men (0·30 [0·33-0·40]). There was no significant difference in the probability of multiple variant transmission between male-to-female transmission (0·21 [0·14-0·31]), post-partum transmission (0·18 [0·03-0·57]), pre-partum transmission (0·17 [0·08-0·33]), and intra-partum transmission (0·27 [0·14-0·45]).

INTERPRETATION

We identified that transmissions in people who inject drugs and men who have sex with men are significantly more likely to result in an infection initiated by multiple founder variants, and female-to-male infections are significantly less probable. Quantifying how the routes of HIV infection affect the transmission of multiple variants allows us to better understand how the evolution and epidemiology of HIV-1 determine clinical outcomes.

FUNDING

Medical Research Council Precision Medicine Doctoral Training Programme and a European Research Council Starting Grant.

Community Insights in Phylogenetic HIV Research: The CIPHR Project Protocol

Inferring HIV transmission networks from HIV sequences is gaining popularity in the field of HIV molecular epidemiology. However, HIV sequences are often analyzed at distance from those affected by HIV epidemics, namely without the involvement of communities most affected by HIV. These remote analyses often mean that knowledge is generated in absence of lived experiences and socio-economic realities that could inform the ethical application of network-derived information in 'real world' programmes. Procedures to engage communities are noticeably absent from the HIV molecular epidemiology literature. Here we present our team's protocol for engaging community activists living in Nairobi, Kenya in a knowledge exchange process - The CIPHR Project (Community Insights in Phylogenetic HIV Research). Drawing upon a community-based participatory approach, our team will (1) explore the possibilities and limitations of HIV molecular epidemiology for key population programmes, (2) pilot a community-based HIV molecular study, and (3) co-develop policy guidelines on conducting ethically safe HIV molecular epidemiology. Critical dialogue with activist communities will offer insight into the potential uses and abuses of using such information to sharpen HIV prevention programmes. The outcome of this process holds importance to the development of policy frameworks that will guide the next generation of the global response.

Reversions to consensus are positively selected in HIV-1 and bias substitution rate estimates

Human immunodeficiency virus 1 (HIV-1) is a rapidly evolving virus able to evade host immunity through rapid adaptation during chronic infection. The HIV-1 group M has diversified since its zoonosis into several subtypes at a rate of the order of 10-3 changes per site per year. This rate varies between different parts of the genome, and its inference is sensitive to the timescale and diversity spanned by the sequence data used. Higher rates are estimated on short timescales and particularly for within-host evolution, while rate estimates spanning decades or the entire HIV-1 pandemic tend to be lower. The underlying causes of this difference are not well understood. We investigate here the role of rapid reversions toward a preferred evolutionary sequence state on multiple timescales. We show that within-host reversion mutations are under positive selection and contribute substantially to sequence turnover, especially at conserved sites. We then use the rates of reversions and non-reversions estimated from longitudinal within-host data to parameterize a phylogenetic sequence evolution model. Sequence simulation of this model on HIV-1 phylogenies reproduces diversity and apparent evolutionary rates of HIV-1 in gag and pol, suggesting that a tendency to rapidly revert to a consensus-like state can explain much of the time dependence of evolutionary rate estimates in HIV-1.

Inferring Epistasis from Genetic Time-series Data

Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.

Discordance between HIV-1 Population in Plasma at Rebound after Structured Treatment Interruption and Archived Provirus Population in Peripheral Blood Mononuclear Cells

Antiretroviral therapy (ART) can sustain the suppression of plasma viremia to below detection levels. Infected individuals undergoing a treatment interruption exhibit rapid viral rebound in plasma viremia which is fueled by cellular reservoirs such as CD4+ T cells, myeloid cells, and potentially uncharacterized cellular sources. Interrogating the populations of viruses found during analytical treatment interruption (ATI) can give insights into the biologically competent reservoirs that persist under effective ART as well as the nature of the cellular reservoirs that enable viral persistence under ART. We interrogated plasma viremia from four rare cases of individuals undergoing sequential ATIs. We performed next-generation sequencing (NGS) on cell-associated viral DNA and cell-free virus to understand the interrelationship between sequential ATIs as well as the relationship between viral genomes in circulating peripheral blood mononuclear cells (PBMCs) and RNA from rebound plasma. We observed population differences between viral populations recrudescing at sequential ATIs as well as divergence between viral sequences in plasma and those in PBMCs. This indicated that viruses in PBMCs were not a major source of post-ATI viremia and highlights the role of anatomic reservoirs in post-ATI viremia and viral persistence. IMPORTANCE Even with effective ART, HIV-1 persists at undetectable levels and rebounds in individuals who stop treatment. Cellular and anatomical reservoirs ignite viral rebound upon treatment interruption, remaining one of the key obstacles for HIV-1 cure. To further examine HIV-1 persistence, a better understanding of the distinct populations that fuel viral rebound is necessary to identify and target reservoirs and the eradication of HIV-1. This study investigates the populations of viruses found from proviral genomes from PBMCs and plasma at rebound from a unique cohort of individuals who underwent multiple rounds of treatment interruption. Using NGS, we characterized the subtypes of viral sequences and found divergence in viral populations between plasma and PBMCs at each rebound, suggesting that distinct viral populations appear at each treatment interruption.

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.

Genotypic and Phenotypic Diversity of the Replication-Competent HIV Reservoir in Treated Patients

In HIV infection, viral rebound after treatment discontinuation is considered to originate predominantly from viral genomes integrated in resting CD4⁺ T lymphocytes. Replication-competent proviral genomes represent a minority of the total HIV DNA. While the quantification of the HIV reservoir has been extensively studied, the diversity of genomes that compose the reservoir was less explored. Here, we measured the genotypic and phenotypic diversity in eight patients with different treatment histories. Between 4 and 14 (mean, 8) individual viral isolates per patient were obtained using a virus outgrowth assay, and their near-full-length genomes were sequenced. The mean pairwise distance (MPD) observed in different patients correlated with the time before undetectable viremia was achieved (r = 0.864, P = 0.0194), suggesting that the complexity of the replication-competent reservoir mirrors that present at treatment initiation. No correlation was instead observed between MPD and the duration of successful treatment (mean, 8 years; range, 2 to 21 years). For 5 of the 8 patients, genotypically identical viral isolates were observed in independent wells, suggesting clonal expansion of infected cells. Identical viruses represented between 25 and 60% of the isolates (mean, 48%). The proportion of identical viral isolates correlated with the duration of treatment (r = 0.822, P = 0.0190), suggesting progressive clonal expansion of infected cells during ART. A broader range of infectivity was also observed among isolates from patients with delayed viremia control (r = 0.79, P = 0.025). This work unveiled differences in the genotypic and phenotypic features of the replication-competent reservoir from treated patients and suggests that delaying treatment results in increased diversity of the reservoir.

IMPORTANCE In HIV-infected and effectively treated individuals, integrated proviral genomes may persist for decades. The vast majority of the genomes, however, are defective, and only the replication-competent fraction represents a threat of viral reemergence. The quantification of the reservoir has been thoroughly explored, while the diversity of the genomes has been insufficiently studied. Its characterization, however, is relevant for the design of strategies aiming the reduction of the reservoir. Here, we explored the replication-competent near-full-length HIV genomes of eight patients who experienced differences in the delay before viremia control and in treatment duration. We found that delayed effective treatment was associated with increased genetic diversity of the reservoir. The duration of treatment did not impact the diversity but was associated with higher frequency of clonally expanded sequences. Thus, early treatment initiation has the double advantage of reducing both the size and the diversity of the reservoir.

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.

Low Pathogenicity H7N3 Avian Influenza Viruses Have Higher Within-Host Genetic Diversity Than a Closely Related High Pathogenicity H7N3 Virus in Infected Turkeys and Chickens

Within-host viral diversity offers a view into the early stages of viral evolution occurring after a virus infects a host. In recent years, advances in deep sequencing have allowed for routine identification of low-frequency variants, which are important sources of viral genetic diversity and can potentially emerge as a major virus population under certain conditions. We examined within-host viral diversity in turkeys and chickens experimentally infected with closely related H7N3 avian influenza viruses (AIVs), specifically one high pathogenicity AIV (HPAIV) and two low pathogenicity AIV (LPAIVs) with different neuraminidase protein stalk lengths. Consistent with the high mutation rates of AIVs, an abundance of intra-host single nucleotide variants (iSNVs) at low frequencies of 2–10% was observed in all samples collected. Furthermore, a small number of common iSNVs were observed between turkeys and chickens, and between directly inoculated and contact-exposed birds. Notably, the LPAIVs have significantly higher iSNV diversities and frequencies of nonsynonymous changes than the HPAIV in both turkeys and chickens. These findings highlight the dynamics of AIV populations within hosts and the potential impact of genetic changes, including mutations in the hemagglutinin gene that confers the high pathogenicity pathotype, on AIV virus populations and evolution.

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.

Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time

Mutation is the driving force of species evolution, which may change the genetic information of organisms and obtain selective competitive advantages to adapt to environmental changes. It may change the structure or function of translated proteins, and cause abnormal cell operation, a variety of diseases and even cancer. Therefore, it is particularly important to identify gene regions with high mutations. Mutations will cause changes in nucleotide distribution, which can be characterized by natural vectors globally. Based on natural vectors, we propose a mathematical formula for measuring the difference in nucleotide distribution over time to investigate the mutations of human immunodeficiency virus. The studied dataset is from public databases and includes gene sequences from twenty HIV-infected patients. The results show that the mutation rate of the nine major genes or gene segment regions in the genome exhibits discrepancy during the infected period, and the Env gene has the fastest mutation rate. We deduce that the peak of virus mutation has a close temporal relationship with viral divergence and diversity. The mutation study of HIV is of great significance to clinical diagnosis and drug design.

Characterizing the Relationship Between Neutralization Sensitivity and env Gene Diversity During ART Suppression

Although antiretroviral therapy (ART) successfully suppresses HIV-1 replication, ART-treated individuals must maintain therapy to avoid rebound from an integrated viral reservoir. Strategies to limit or clear this reservoir are urgently needed. Individuals infected for longer periods prior to ART appear to harbor more genetically diverse virus, but the roles of duration of infection and viral diversity in the humoral immune response remain to be studied. We aim to clarify a role, if any, for autologous and heterologous antibodies in multi-pronged approaches to clearing infection. To that end, we have characterized the breadths and potencies of antibody responses in individuals with varying durations of infection and HIV-1 envelope (env) gene diversity as well as the sensitivity of their inducible virus reservoir to broadly neutralizing antibodies (bNAbs). Plasma was collected from 8 well-characterized HIV-1+ males on ART with varied durations of active infection. HIV envs from reservoir-derived outgrowth viruses were amplified and single genome sequenced in order to measure genetic diversity in each participant. IgG from plasma was analyzed for binding titers against gp41 and gp120 proteins, and for neutralizing titers against a global HIV-1 reference panel as well as autologous outgrowth viruses. The sensitivity to bNAbs of these same autologous viruses was measured. Overall, we observed that greater env diversity was associated with higher neutralizing titers against the global panel and also increased resistance to certain bNAbs. Despite the presence of robust anti-HIV-1 antibody titers, we did not observe potent neutralization against autologous viruses. In fact, 3 of 8 participants harbored viruses that were completely resistant to the highest tested concentration of autologous IgG. That this lack of neutralization was observed regardless of ART duration or viral diversity suggests that the inducible reservoir harbors 'escaped' viruses (that co-evolved with autologous antibody responses), rather than proviruses archived from earlier in infection. Finally, we observed that viruses resistant to autologous neutralization remained sensitive to bNAbs, especially CD4bs and MPER bNAbs. Overall, our data suggest that the inducible reservoir is relatively resistant to autologous antibodies and that individuals with limited virus variation in the env gene, such as those who start ART early in infection, are more likely to be sensitive to bNAb treatment.

Markov Chain-Based Stochastic Modelling of HIV-1 Life Cycle in a CD4 T Cell

Replication of Human Immunodeficiency Virus type 1 (HIV) in infected CD4+ T cells represents a key driver of HIV infection. The HIV life cycle is characterised by the heterogeneity of infected cells with respect to multiplicity of infection and the variability in viral progeny. This heterogeneity can result from the phenotypic diversity of infected cells as well as from random effects and fluctuations in the kinetics of biochemical reactions underlying the virus replication cycle. To quantify the contribution of stochastic effects to the variability of HIV life cycle kinetics, we propose a high-resolution mathematical model formulated as a Markov chain jump process. The model is applied to generate the statistical characteristics of the (i) cell infection multiplicity, (ii) cooperative nature of viral replication, and (iii) variability in virus secretion by phenotypically identical cells. We show that the infection with a fixed number of viruses per CD4+ T cell leads to some heterogeneity of infected cells with respect to the number of integrated proviral genomes. The bottleneck factors in the virus production are identified, including the Gag-Pol proteins. Sensitivity analysis enables ranking of the model parameters with respect to the strength of their impact on the size of viral progeny. The first three globally influential parameters are the transport of genomic mRNA to membrane, the tolerance of transcription activation to Tat-mediated regulation, and the degradation of free and mature virions. These can be considered as potential therapeutical targets.

Coevolutionary transitions emerging from flexible molecular recognition and eco-evolutionary feedback

Highly mutable viruses evolve to evade host immunity that exerts selective pressure and adapts to viral dynamics. Here, we provide a framework for identifying key determinants of the mode and fate of viral-immune coevolution by linking molecular recognition and eco-evolutionary dynamics. We find that conservation level and initial diversity of antigen jointly determine the timing and efficacy of narrow and broad antibody responses, which in turn control the transition between viral persistence, clearance, and rebound. In particular, clearance of structurally complex antigens relies on antibody evolution in a larger antigenic space than where selection directly acts; viral rebound manifests binding-mediated feedback between ecology and rapid evolution. Finally, immune compartmentalization can slow viral escape but also delay clearance. This work suggests that flexible molecular binding allows a plastic phenotype that exploits potentiating neutral variations outside direct contact, opening new and shorter paths toward highly adaptable states.

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A scale-crossing framework identifies key determinants of viral-immune coevolution

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Fast specific response influences slow broad response by shaping antigen dynamics

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Antibody footprint shift enables breadth acquisition and viral clearance

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Model explains divergent kinetics and outcomes of HCV infection in humans

Precision Tracing of Household Dengue Spread Using Inter- and Intra-Host Viral Variation Data, Kamphaeng Phet, Thailand

Dengue control approaches are best informed by granular spatial epidemiology of these viruses, yet reconstruction of inter- and intra-household transmissions is limited when analyzing case count, serologic, or genomic consensus sequence data. To determine viral spread on a finer spatial scale, we extended phylogenomic discrete trait analyses to reconstructions of house-to-house transmissions within a prospective cluster study in Kamphaeng Phet, Thailand. For additional resolution and transmission confirmation, we mapped dengue intra-host single nucleotide variants on the taxa of these time-scaled phylogenies. This approach confirmed 19 household transmissions and revealed that dengue disperses an average of 70 m per day between households in these communities. We describe an evolutionary biology framework for the resolution of dengue transmissions that cannot be differentiated based on epidemiologic and consensus genome data alone. This framework can be used as a public health tool to inform control approaches and enable precise tracing of dengue transmissions.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Genome Sequencing from Post-Mortem Formalin-Fixed, Paraffin-Embedded Lung Tissues

Implementation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing in the daily practice of pathology laboratories requires procedure adaptation to formalin-fixed, paraffin-embedded (FFPE) samples. So far, one study reported the feasibility of SARS-CoV-2 genome sequencing on FFPE tissues with only one contributory case of two. This study optimized SARS-CoV-2 genome sequencing using the Ion AmpliSeq SARS-CoV-2 Panel on 22 FFPE lung tissues from 16 deceased coronavirus disease 2019 (COVID-19) patients. SARS-CoV-2 was detected in all FFPE blocks using a real-time RT-qPCR targeting the E gene with crossing point (Cp) values ranging from 16.02 to 34.16. Sequencing was considered as contributory (i.e. with a uniformity >55%) for 17 FFPE blocks. Adapting the number of target amplification PCR cycles according to the RT-qPCR Cp values allowed optimization of the sequencing quality for the contributory blocks (i.e. 20 PCR cycles for blocks with a Cp value <28 and 25 PCR cycles for blocks with a Cp value between 28 and 30). Most blocks with a Cp value >30 were non-contributory. Comparison of matched frozen and FFPE tissues revealed discordance for only three FFPE blocks, all with a Cp value >28. Variant identification and clade classification was possible for 13 patients. This study validates SARS-CoV-2 genome sequencing on FFPE blocks and opens the possibility to explore correlation between virus genotype and histopathologic lesions.

Multiple haplotype reconstruction from allele frequency data

Because haplotype information is of widespread interest in biomedical applications, effort has been put into their reconstruction. Here, we propose an efficient method, called haploSep, that is able to accurately infer major haplotypes and their frequencies just from multiple samples of allele frequency data. Even the accuracy of experimentally obtained allele frequencies can be improved by re-estimating them from our reconstructed haplotypes. From a methodological point of view, we model our problem as a multivariate regression problem where both the design matrix and the coefficient matrix are unknown. Compared to other methods, haploSep is very fast, with linear computational complexity in the haplotype length. We illustrate our method on simulated and real data focusing on experimental evolution and microbial data.

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.