Biology subjects

Barton, J. P.

Publications and source records attributed to Barton, J. P..

17 recordsLinked to original sources

bioRxiv2026

Sequence-structured fitness landscapes predict influenza evolution

Seasonal influenza viruses continually evolve to evade host immunity. Vaccines must be regularly updated to protect against new circulating strains, motivating efforts to forecast viral evolution. Recent experimental and computational methods have made progress on this problem, but learning the fitness landscape driving viral evolution remains challenging because genomic surveillance samples only a small fraction of the possible sequence space. Here, we developed a generative framework that shares information across genetically related variants, inferring effective mutation-level contributions to viral fitness from temporal sequence data. Applied to hemagglutinin and neuraminidase from A/H3N2 and A/H1N1 viruses, the inferred fitness landscapes recovers known determinants of antigenicity and reveals convergent spatial organization across subtypes consistent with common antibody-mediated selection. Forward simulations from these landscapes predicted both the fixation or loss of circulating mutations and the future composition of viral populations, outperforming models based on neutral evolution or multinomial-logistic growth. Collectively, our results show that interpretable representations of pathogen fitness landscapes can reveal biologically important features and improve prospective evolutionary forecasting.

evolutionary biology↗

bioRxiv2026

Evolutionary regimes determine the accuracy of epistasis inference from temporal genetic data

Epistasis, the non-additive effects of mutations, shapes fitness landscapes and evolutionary trajectories. Temporal genetic data reveal evolutionary dynamics and could be used to infer epistatic interactions, especially through linkage disequilibrium (LD) between interacting mutations. However, other evolutionary forces can also generate LD, challenging inference. Here, we systematically evaluated the accuracy of a variety of epistasis inference approaches across a range of selective pressures, recombination rates, and population sizes. In general, we found that inference accuracy depends on the evolutionary regime: methods based on marginal path likelihood (MPL) performed best under strong selection and low recombination, whereas quasi-linkage equilibrium (QLE) approaches were more accurate when recombination is frequent. We further showed that the strength of genetic drift can influence inference accuracy for approaches that learn from changes in allele frequencies over time. Collectively, our results show that the detectability of epistasis from temporal genetic data depends on the interplay between selection, recombination, and genetic drift, providing guidance for method selection across evolutionary contexts.

evolutionary biology↗

bioRxiv2026

Linkage-aware inference of fitness from short-read time-series genomic data

Inferring the fitness effect of mutations is a basic problem in understanding the evolution of populations over time. When multiple mutations are present in a population simultaneously, genetic linkage comes into play, and the fate of an individual mutation depends on both its fitness as well as the background on which it occurs. Accurate inference of fitness effects for evolutionary systems with multiple competing mutations is therefore contingent on resolving the confounding effects of genetic linkage, captured by the covariance between allele-pairs. Increasingly, evolutionary studies are using short-read sequencing technologies to produce detailed snapshots of evolving populations. This presents a problem as the frequencies of allele-pairs are not known beyond the read-length, hampering any attempt to resolve the effects of genetic linkage between pairs of loci residing on different reads. Here we present a computationally efficient pipeline for inferring selection from short-read time-series data with partial allele-pair frequency information, while accounting for linkage. Simulation results show that the method has good performance and is scalable to systems with several thousand variants. Additionally, we demonstrate the pipelines utility on real datasets of within-host HIV and SARS-CoV-2 evolution, showcasing its applicability in resolving linkage effects from complex evolutionary histories.

bioinformatics↗

bioRxiv2025

Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities

For genetically variable pathogens such as human immunodeficiency virus (HIV)-1, individual viral isolates can differ dramatically in their sensitivity to antibodies. The ability to predict which viruses will be sensitive and which will be resistant to a specific antibody could aid in the design of antibody therapies and help illuminate resistance evolution. Due to the enormous number of possible combinations, it is not possible to experimentally measure neutralization values for all pairs of viruses and antibodies. Here, we developed a simple and interpretable method called grouped neutralization learning (GNL) to predict neutralization values by leveraging viral genetic sequences and similarities in neutralization profiles between antibodies. Our method compares favorably to state-of-the-art approaches and is robust to model parameter assumptions. GNL can predict neutralization values for viruses with no observed data, an essential capability for evaluating novel viral strains. We also demonstrate that GNL can successfully transfer knowledge between independent data sets, allowing rapid estimates of viral sensitivity based on prior knowledge.

immunology↗

bioRxiv2025

Back-projection improves inference from sparsely sampled genomic surveillance data

Highly transmissible SARS-CoV-2 variants have emerged throughout the COVID-19 pandemic, driving new waves of infections. Genomic surveillance data can provide insights into the viruss evolution and biology. However, delayed and limited regional data can introduce biases in epidemiological models, potentially obscuring transmission patterns. To address this issue, we used a novel, variant-specific back-projection model to estimate a distribution of likely infection times from sample collection times. We combined this approach with epidemiological modeling to estimate selection for increased transmission in a way that accounts for the uncertainty in infection times. Tests in simulations demonstrated that our method can make the inference of selection more reliable. We also applied our approach to SARS-CoV-2 data, where it excelled in smoothing and extending data from geographic regions or times with poor sampling. Overall, our method can aid in the reliable identification of mutations and variants with higher transmission rates.

evolutionary biology↗

bioRxiv2025

Inferring fitness seascapes from evolutionary histories

Evolutionary dynamics are often conceptualized as walks on an adaptive landscape, with populations climbing toward optimal fitness peaks. However, environmental changes can transform static landscapes into dynamic "fitness seascapes" where natural selection fluctuates in time. Here, we used a path integral approach derived from statistical physics to reveal time-varying selection pressures from genetic sequence data. We found that constraints on the fitness seascape are determined by the screened Poisson equation, which also describes the screening of electric fields in media with mobile charge carriers. In our model, changes in mutation frequencies act as "charges" that reveal the underlying fitness seascape, which is analogous to an electrostatic potential. After validating our method in simulations, we applied it to study how HIV-1 evolves to escape immune control by T cells within individual hosts. Our analysis showed that the fitness benefit of immune escape declines as T cell responses approach their peak intensity, suggesting that functional exhaustion may impair the effectiveness of the immune response against HIV-1. Overall, our approach provides a general framework for capturing the complex dynamics of natural selection in rapidly evolving populations.

evolutionary biology↗

bioRxiv2024

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.

evolutionary biology↗

bioRxiv2024

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.

evolutionary biology↗

bioRxiv2024

Clonal heterogeneity and antigenic stimulation shape persistence of the latent reservoir of HIV

Drug treatment can control HIV-1 replication, but it cannot cure infection. This is because of a long-lived population of quiescent infected cells, known as the latent reservoir (LR), that can restart active replication even after decades of successful drug treatment. Many cells in the LR belong to highly expanded clones, but the processes underlying the clonal structure of the LR are unclear. Understanding the dynamics of the LR and the keys to its persistence is critical for developing an HIV-1 cure. Here we develop a quantitative model of LR dynamics that fits available patient data over time scales spanning from days to decades. We show that the interplay between antigenic stimulation and clonal heterogeneity shapes the dynamics of the LR. In particular, we find that large clones play a central role in long-term persistence, even though they rarely reactivate. Our results could inform the development of HIV-1 cure strategies.

immunology↗

bioRxiv2024

Parallel HIV-1 evolutionary dynamics in humans and rhesus macaques who develop broadly neutralizing antibodies

Human immunodeficiency virus (HIV)-1 evolves within individual hosts to escape adaptive immune responses while maintaining its capacity for replication. Coevolution between HIV-1 and the immune system generates extraordinary viral genetic diversity. In some individuals, this process also results in the development of broadly neutralizing antibodies (bnAbs) that can neutralize many viral variants, a key focus of HIV-1 vaccine design. However, a general understanding of the forces that shape virus-immune coevolution within and across hosts remains incomplete. Here we performed a quantitative study of HIV-1 evolution in humans and rhesus macaques, including 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. The inferred fitness effects of HIV-1 mutations in humans and macaques were remarkably similar. Moreover, we observed a striking pattern of rapid HIV-1 fitness gains 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.

immunology↗

bioRxiv2024

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.

evolutionary biology↗

bioRxiv2024

popDMS infers mutation effects from deep mutational scanning data

Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.

bioinformatics↗

bioRxiv2020

A quantitative model for the regulation of innate immune activation

The immune system employs a wide variety of strategies to protect the body from infection. Cells such as natural killer (NK) cells and macrophages can recognize and eliminate targets with aberrant surface ligand expression in a manner that is not antigen-specific. This innate mechanism of activation must be tightly regulated to prevent autoimmunity. Here we introduce a quantitative model of the regulation of nonspecific activation inspired by Bayesian inference. Our model captures known behaviors of innate immune cells, including adaptation to changing environments and the development of hyposensitivity after prolonged exposure to activating signals. Our analysis also reveals a tradeoff between precision and adaptation. Maintaining the ability to adapt to different environments leads to heterogeneous responses, even for hypothetical populations of immune cells and targets that have identical surface receptor and ligand expression. Collectively, our results describe an adaptive algorithm for self/nonself discrimination that functions even in the absence of antigen restriction. The same model could also apply more broadly to the adaptive regulation of activation for other immune cell types.

systems biology↗

bioRxiv2019

Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

evolutionary biology↗

bioRxiv2019

Resolving genetic linkage reveals patterns of selection in HIV-1 evolution

Identifying the genetic drivers of adaptation is a necessary step in understanding the dynamics of rapidly evolving pathogens and cancer. However, signals of selection are obscured by the complex, stochastic nature of evolution. Pervasive effects of genetic linkage, including genetic hitchhiking and clonal interference between beneficial mutants, challenge our ability to distinguish the selective effect of individual mutations. Here we describe a method to infer selection from genetic time series data that systematically resolves the confounding effects of genetic linkage. We applied our method to investigate patterns of selection in intrahost human immunodeficiency virus (HIV)-1 evolution, including a case in an individual who develops broadly neutralizing antibodies (bnAbs). Most variants that arise are observed to have negligible effects on inferred selection at other sites, but a small minority of highly influential variants have strong and far-reaching effects. In particular, we found that accounting for linkage is crucial for estimating selection due to clonal interference between escape mutants and other variants that sweep rapidly through the population. We observed only modest selection for antibody escape, in contrast with strong selection for escape from CD8+ T cell responses. Weak selection for escape from antibody responses may facilitate bnAb development by diversifying the viral population. Our results provide a quantitative description of the evolution of HIV-1 in response to host immunity, including selection on the viral population that accompanies bnAb development. More broadly, our analysis argues for the importance of resolving linkage effects in studies of natural selection.

evolutionary biology↗

bioRxiv2018

Characterization of intact proviruses in blood and lymph node from HIV-infected individuals undergoing analytical treatment interruption

The role of lymphoid tissue as a potential source of HIV-1 rebound following interruption of antiretroviral therapy is uncertain. To address this issue, we compared the latent viruses obtained from CD4+ T cells in peripheral blood and lymph nodes to viruses emerging during treatment interruption. Latent viruses were characterized by sequencing near full-length (NFL) proviral DNA, and env from viral outgrowth cultures (VOAs). 5 HIV-1 infected individuals on antiretroviral therapy (ART) were studied, 4 of whom participated in a clinical trial that included an analytical treatment interruption. Intact or replication competent clonal sequences from blood and lymph node overlapped. In contrast, there was no overlap between 205 latent reservoir and 125 rebound sequences in the 4 individuals who underwent treatment interruption. However, rebound viruses could be accounted for by recombination. The data suggests that CD4+ T cells carrying latent viruses circulate between blood and lymphoid tissues in individuals on ART and support the idea that recombination may play a role in the emergence of rebound viremia.

clinical trials↗

bioRxiv2018

Analysis of HIV-1 latent reservoir 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 (ART) and subsequent analytical treatment interruption (ATI). The circulating reservoir was evaluated by quantitative and qualitative outgrowth assay (Q2VOA) at entry and after 6 months, prior to ATI. Although there were no significant quantitative changes in the size of the reservoir, the composition of circulating reservoir clones varied over the 6_month period before treatment interruption in a manner that did not correlate with antibody sensitivity. The neutralization profile obtained from the reservoir by Q2VOA was predictive of time to rebound after ATI, and thus of antibody efficacy. Although 3BNC117 binding site amino acid variants found in rebound viruses pre_existed in the latent reservoir, only 3 of 217 rebound viruses were identical to 868 latent viruses. Instead many of the rebound viruses appeared to be recombinants, even in individuals with resistant reservoir viruses. By incorporating the possibility of recombination, 63% of the rebound viruses could have derived from the observed latent reservoir. In conclusion, viruses emerging during ATI in individuals treated with 3BNC117 are not the dominant species found in the circulating reservoir, but instead appear to represent recombinants.\n\nSummaryIn the setting of a clinical trial evaluating the anti_HIV_1 antibody 3BNC117, Cohen et al. demonstrate that rebound viruses that emerge following interruption of antiretroviral therapy are distinct from circulating latent viruses. However, rebound viruses often appear to be recombinants between isolated latent viruses.

clinical trials↗