Biology subjects

Bedford, T.

Publications and source records attributed to Bedford, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Fitness flux in SARS-CoV-2 and influenza H3N2

The tempo of viral adaptation is usually read indirectly from the composition of mutations, through measures such as dN/dS. Here we measure it directly from the dynamics of variant frequencies, where we use multinomial logistic regression to estimate a fitness for each co-circulating variant. We aggregate these estimates to derive the rate of change of mean population fitness, referred to as fitness flux. Tracing SARS-CoV-2 from its emergence, we find that it initially adapted rapidly, doubling in fitness every 6 months from Jan 2021 to Jun 2022, but slowing to every 2.4 years from Jul 2022 to Dec 2025. Seasonal influenza H3N2 sustained a slower, steadier pace doubling in fitness every 10.0 years. In both, the rate of fitness gain closely tracks the variance in fitness, matching the 1:1 expectation of Fishers fundamental theorem. Phylogenetic contrasts between parent and child lineages localize most fitness gain to spike, and within spike to the receptor-binding domain, where a simple count of spike S1 substitutions predicts lineage fitness about as well as deep-learning escape and protein-language-model scores. Measuring fitness directly thus offers a transparent, frequency-based alternative to mutational proxies for tracking and anticipating viral adaptation. The website blab.github.io/fitness-flux/ is the intended reading experience of this paper, providing responsive layout and interactive figures.

evolutionary biology↗

bioRxiv2026

SARS-CoV-2 saltational events are recurrent and trace to persistent human infections

SARS-CoV-2 evolution is characterized by gradual mutation accumulation but has been punctuated by rare yet impactful highly mutated variants. Whether such saltational jumps are a broad feature of SARS-CoV-2 evolution or rare anomalies remains unclear. We systematically investigate SARS-CoV-2 saltational evolution by developing a scalable framework to detect saltational events from 4.4 million high-quality viral genomes. Saltational events occurred at low but detectable rates during the pandemic and post-pandemic periods and across geographies. Their mutational signature closely matches that seen in persistent human infections but is inconsistent with the signatures of mink or deer infections. This points to persistent infection, rather than reverse zoonosis, as their primary source. While most saltational events lack evidence of onward transmission, those that do tend to carry mutations found in successful clades. Our work demonstrates that the emergence of highly mutated SARS-CoV-2 variants reflects a recurrent evolutionary process, with implications for preparedness.

evolutionary biology↗

bioRxiv2026

Nextstrain automates real-time phylodynamic analysis of open data for endemic and emerging pathogens

MotivationGenome sequencing provides an exceptional window into the evolutionary and epidemiological dynamics of endemic and emerging pathogens, and thus allows for better, more targeted, public health interventions. Online genomic surveillance platforms can provide near real-time insight into these dynamics. ResultsNextstrain provides continually updated real-time genomic surveillance for 21 viruses and the bacterial pathogen Mycobacterium tuberculosis, with most analyses relying solely on open sequence data. Each pathogen includes steps to fetch and curate open data, classify sequences using established nomenclature systems, perform phylogenetic analyses, and share the results publicly. These analyses are automated, with most running daily to provide continually updated snapshots of pathogen evolution. Availability and ImplementationAll source code is available at https://github.com/nextstrain. Phylogenetic results can be visualized and downloaded at https://nextstrain.org/pathogens, and open sequence data and curated metadata are available at https://nextstrain.org/pathogens/files.

bioinformatics↗

bioRxiv2026

Strain-specific differences in the response to egg-derived versus recombinant protein influenza vaccines

The 2023/2024 influenza vaccine included an updated H1N1 component designed to better match a new clade of H1N1 that had multiple mutations in antigenic epitopes of hemagglutinin. Despite this update, the vaccine trended towards being less effective against the vaccine-matched H1N1 clade than the parental H1N1 clade lacking the new antigenic mutations. Here we measure neutralization titers of serum antibodies from individuals who had received either a recombinant protein or an egg-derived vaccine against a set of viruses with hemagglutinins from 58 H1N1 strains representative of the diversity during the 2023/2024 season. We find that egg-derived vaccine recipients, but not recombinant protein vaccine recipients, had a relatively lower boost in neutralizing titers to the new clade that the updated vaccine was designed to target. We suggest that the difference in the extent that the egg-derived versus recombinant protein vaccines boosted neutralizing titers to the new H1N1 clade is because the seed strain for the egg-derived vaccine strain had acquired a reversion of a key antigenic mutation (K142R) present in that clade. Our results show how egg-derived versus recombinant protein vaccines can elicit different relative titer boosts against different subsets of viral strains, a phenomenon that could impact vaccine effectiveness. ImportanceInfluenza vaccines can be produced from virus grown in eggs, or grown in cells or made with recombinant protein. Egg-derived influenza vaccines often contain egg-adaptive mutations in the viral antigen hemagglutinin (HA) which can impact the antigenicity or immunogenicity of the HA. In this study, we compare neutralization titers from egg-derived and recombinant protein vaccine recipients against recently circulating influenza A(H1N1) strains. We find that the egg-derived vaccine induces less of a boost in titers than the recombinant protein vaccine to the new clade of viral strains that the vaccine was designed to target.

immunology↗

bioRxiv2026

Near real-time data on the human neutralizing antibody landscape to influenza virus as of early 2026 to inform vaccine-strain selection

Twice each year, a decision is made on whether to update the strains included in the seasonal influenza vaccine to better match the most recent circulating viral strains. To characterize the antigenic properties of current seasonal influenza A strains to inform the upcoming decision about which strains to include in the 2026-2027 Northern Hemisphere vaccine, here we perform high-throughput sequencing-based neutralization assays using a library of 57 H3N2 and 34 H1N1 influenza hemagglutinins reflecting the circulating diversity of strains in late 2025 to early 2026. We assay this library against 302 human sera collected in late 2025. The resulting data set encompasses 27,409 titers, and provides a near real-time portrait of the human neutralizing antibody landscape against influenza virus. We find that many human sera have lower titers against the K subclade of H3N2 and the D.3.1.1 subclade of H1N1; these subclades have recently become dominant among their respective subtypes. Our measurements also reveal variability in titers to different subvariants within the K subclade of H3N2, with titers especially low to subclade K strains with additional mutations in antigenic regions D and E. We make all our data and accompanying visualizations publicly available to enable their use in vaccine-strain selection and analyses of influenza evolution and immunity.

microbiology↗

bioRxiv2026

antigen-prime: Simulating coupled genetic and antigenic evolution of influenza virus

Seasonal influenza virus undergoes rapid antigenic drift to escape population immunity. Computational methods can be used to organize viral genetic diversity into antigenically similar variants and estimate variantspecific growth rates. However, benchmarking these methods is challenging because it can be difficult to accurately quantify antigenicity and growth rates in nature. Simulating viral evolution using defined selective pressures can provide ground-truth data for benchmarking. But, existing simulators do not link genetic sequences to antigenic phenotypes under selection from host populations. Here, we present a forward-time epidemic simulator called antigen-prime that links these factors. We use it to simulate viral evolution over 30 years and validate the simulation recapitulates genetic and antigenic patterns observed in natural influenza evolution. We then use the simulated data to benchmark methods for assigning variants and estimating their growth rates. We evaluated a sequencebased and a phylogenetics-based method for variant assignment, finding the former was slightly more effective at separating viruses into antigenically distinct groups. We also evaluated methods for estimating variant growth rates in one-year sliding windows. Estimates were accurate in most windows, but highly inaccurate in several others. Examining higherror windows revealed several examples of a previously unreported failure mode. In all, antigen-prime provides a simulation framework to benchmark models of influenza evolution, and could be used to help guide future development of these models. The source code is openly available at https://github.com/matsengrp/antigen-prime.

bioinformatics↗

bioRxiv2025

Hidden Markov Models Detect Recombination and Ancestry of SARS-CoV-2

When individuals are co-infected with distinct SARS-CoV-2 lineages, homologous recombination can generate mosaic genomes carrying mutations from both parental lineages. A variety of methods exist to detect recombinant sequences and their parental lineages in surveillance-scale datasets comprised of millions of SARS-CoV-2 genomes. However, these methods often rely on user-specified parameters, such as the probability a recombination breakpoint occurs between adjacent positions on the query sequence. In this study, we devise a hidden Markov model that detects recombinant SARS-CoV-2 sequences and identifies their parental lineages within a test set of sequences. Our method does not depend on user-specified parameters and can accommodate de novo mutations on the query sequence that are not present in the predicted parental lineages. To achieve this, we use maximum likelihood to estimate parameters that characterize the transition and emission probabilities in our hidden Markov model. Applying our method to 440,307 SARS-CoV-2 sequences sampled in England between September 2020 and March 2024, we detect 7,619 recombinant sequences corresponding to 1.73% (95% CI: [1.69%, 1.77%]) of all sampled sequences. We observe a positive association between the proportion of query sequences detected as recombinant in each week and community SARS-CoV-2 prevalence. This is consistent with higher prevalence increasing the risk of co-infection by distinct lineages and promoting the emergence of recombinant sequences. Finally, we observe localized clusters of recombination breakpoints within spike and in intergenic regions.

genomics↗

bioRxiv2025

Near real-time data on the human neutralizing antibody landscape to influenza virus to inform vaccine-strain selection in September 2025

The hemagglutinin of human influenza virus evolves rapidly to erode neutralizing antibody immunity. Twice per year, new vaccine strains are selected with the goal of providing maximum protection against the viruses that will be circulating when the vaccine is administered [~]8-12 months in the future. To help inform this selection, here we quantify how the antibodies in recently collected human sera neutralize viruses with hemagglutinins from contemporary influenza strains. Specifically, we use a high-throughput sequencing-based neutralization assay to measure how 188 human sera collected from Oct 2024 to April 2025 neutralize 140 viruses representative of the H3N2 and H1N1 strains circulating in humans as of the summer of 2025. This data set, which encompasses 26,148 neutralization titer measurements, provides a detailed portrait of the current human neutralizing antibody landscape to influenza A virus. The full data set and accompanying visualizations are available for use in vaccine development and viral forecasting.

microbiology↗

bioRxiv2025

Seasonal influenza viruses show distinct adaptive dynamics during growth in chicken eggs

Human influenza viruses are grown in chicken eggs for vaccine production. Sequences of these egg-passaged viruses give us the opportunity to examine the evolution that occurs when these human viruses are subjected to the selective pressure of growing in chicken eggs, which (among other things) express different sialic acid receptors. The repetition of this evolutionary experiment in hundreds of strains over the past several decades allows us to identify mutations that adapt the virus to eggs and epistatic constraints that influence them. We analyze influenza A/H3N2, A/H1N1pdm, B/Vic and B/Yam sequences that were passaged in eggs and find that almost all of the adaptive mutations are located around the receptor-binding pocket of hemagglutinin (HA). We observe epistatic interactions both between adaptive mutations and between these mutations and the continually-evolving human influenza HA background sequence. Our results show that this background dependence is greatest for influenza A/H3N2, then A/H1N1pdm, with B/Vic and B/Yam showing little-to-no background dependence. We find that the total number of adaptive mutations and the length of adaptive walk also follow the same pattern between the influenza subtypes, suggesting that background dependence, number of adaptive mutations, and extent of additive versus epistatic interactions may all be related features of the fitness landscape.

evolutionary biology↗

bioRxiv2025

High-throughput neutralization measurements correlate strongly with evolutionary success of human influenza strains

Human influenza viruses rapidly acquire mutations in their hemagglutinin (HA) protein that erode neutralization by antibodies from prior exposures. Here, we use a sequencing-based assay to measure neutralization titers for 78 recent H3N2 HA strains against a large set of children and adult sera, measuring [~]10,000 total titers. There is substantial person-to-person heterogeneity in the titers against different viral strains, both within and across age cohorts. The growth rates of H3N2 strains in the human population in 2023 are highly correlated with the fraction of sera with low titers against each strain. Notably, strain growth rates are less correlated with neutralization titers against pools of human sera, demonstrating the importance of population heterogeneity in shaping viral evolution. Overall, these results suggest that high-throughput neutralization measurements of human sera against many different viral strains can help explain the evolution of human influenza.

evolutionary biology↗

bioRxiv2024

High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains

The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we have developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes via a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately one month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals, and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year--although the titers six months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. This study demonstrates the utility of high-throughput sequencing-based neutralization assays that enable titers to be simultaneously measured against many different viral strains. We provide a detailed experimental protocol (DOI: https://dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and a computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.

microbiology↗

bioRxiv2024

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

bioinformatics↗

bioRxiv2023

Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin

ABSTRACTHuman influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population, and how this heterogeneity affects virus evolution. Here we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of the A/Hong Kong/45/2019 (H3N2) and A/Perth/16/2009 (H3N2) strains affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that fixed in influenza variants after 2020 cause the greatest escape from sera from younger individuals. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups, and suggest approaches to understand how this heterogeneous selection shapes viral evolution.

microbiology↗

bioRxiv2023

An Atlas of Adaptive Evolution in Endemic Human Viruses

Through antigenic evolution, viruses like seasonal influenza evade recognition by neutralizing antibodies elicited by previous infection or vaccination. This means that a person with antibodies well-tuned to an initial infection will not be protected against the same virus years later and that vaccine-mediated protection will decay. It is not fully understood which of the many endemic human viruses evolve in this fashion. To expand that knowledge, we assess adaptive evolution across the viral genome in 28 endemic viruses, spanning a wide range of viral families and transmission modes. We find that surface proteins consistently show the highest rates of adaptation, and estimate that ten viruses in this panel undergo antigenic evolution to selectively fix mutations that enable the virus to escape recognition by prior immunity. We compare overall rates of amino acid substitution between these antigenically-evolving viruses and SARS-CoV-2, showing that SARS-CoV-2 viruses are accumulating protein-coding changes at substantially faster rates than these endemic viruses.

evolutionary biology↗

bioRxiv2022

State-dependent evolutionary models reveal modes of solid tumor growth

Spatial properties of tumor growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumor cell division remains difficult to evaluate in clinical tumors. Here, we demonstrate that elevated cellular growth rates on the tumor periphery leave characteristic patterns in the genomes of cells sampled from different parts of a tumor, which become evident when they are used to construct a tumor phylogenetic tree. Namely, rapidly-dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing lineages in the tumor center. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential cell division rates between peripheral and central cells jointly from the branching and mutational patterns of single-time point, multi-region sequencing data. We validate this approach on simulated tumors by demonstrating its ability to accurately infer spatially-varying birth rates under a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods which ignore differential mutational acquisition. Finally, we apply SDevo to multi-region sequencing data from clinical hepatocellular carcinomas and find evidence that cells on the tumor edge divide 3-6x faster than those in the center. As multi-region and single-cell sequencing increase in resolution and availability, we anticipate that SDevo will be useful in interrogating spatial restrictions on tumor growth and could be extended to model non-spatial factors that influence tumor progression, including hypoxia and immune infiltration.

cancer biology↗

bioRxiv2021

Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2

Given the importance of variant SARS-CoV-2 viruses with altered receptor-binding or antigenic phenotypes, we sought to quantify the degree to which adaptive evolution is driving accumulation of mutations in the SARS-CoV-2 genome. Here we assessed adaptive evolution across genes in the SARS-CoV-2 genome by correlating clade growth with mutation accumulation as well as by comparing rates of nonsynonymous to synonymous divergence, clustering of mutations across the SARS-CoV-2 phylogeny and degree of convergent evolution of individual mutations. We find that spike S1 is the focus of adaptive evolution, but also identify positively-selected mutations in other genes that are sculpting the evolutionary trajectory of SARS-CoV-2. Adaptive changes in S1 accumulated rapidly, resulting in a remarkably high ratio of nonsynonymous to synonymous divergence that is 2.5X greater than that observed in HA1 at the beginning of the 2009 H1N1 pandemic.

evolutionary biology↗

bioRxiv2021

Limited within-host diversity and tight transmission bottlenecks limit SARS-CoV-2 evolution in acutely infected individuals

The recent emergence of divergent SARS-CoV-2 lineages has raised concerns about the role of selection within individual hosts in propagating novel variants. Of particular concern are variants associated with immune escape and/or enhanced transmissibility. Though growing evidence suggests that novel variants can arise during prolonged infections, most infections are acute. Understanding the extent to which variants emerge and transmit among acutely infected hosts is therefore critical for predicting the pace at which variants resistant to vaccines or conferring increased transmissibility might emerge in the majority of SARS-CoV-2 infections. To characterize how within-host diversity is generated and propagated, we combine extensive laboratory and bioinformatic controls with metrics of within- and between-host diversity to 133 SARS-CoV-2 genomes from acutely infected individuals. We find that within-host diversity during acute infection is low and transmission bottlenecks are narrow, with very few viruses founding most infections. Within-host variants are rarely transmitted, even among individuals within the same household. Accordingly, we also find that within-host variants are rarely detected along phylogenetically linked infections in the broader community. Together, these findings suggest that efficient selection and transmission of novel SARS-CoV-2 variants is unlikely during typical, acute infection. One Sentence SummaryPatterns of SARS-CoV-2 within hosts suggest efficient selection and transmission of novel variants is unlikely during typical, acute infection.

evolutionary biology↗

bioRxiv2021

Recombination patterns in coronaviruses

As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.

evolutionary biology↗