Biology subjects

Neher, R. A.

Publications and source records attributed to Neher, R. A..

At least 19 recordsLinked to original sources

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

Epistasis and the changing fitness landscapes of SARS-CoV-2

Since its emergence in late 2019, millions of SARS-CoV-2 genomes have been generated as part of global efforts to monitor the evolution and spread of the virus. This unprecedented volume of data provides a unique opportunity to study viral evolution at unparalleled resolution. In particular, individual genomic sites can be observed to have mutated independently thousands of times. These mutation counts have been used to estimate site-specific mutation rates and fitness effects for most mutations across the viral genome. Here, we use these data to investigate how the landscape of mutational fitness costs has changed over the course of the pandemic. SARS-CoV-2 evolution over the past six years has been characterized by the emergence of distinct variants separated by long branches corresponding to evolutionary saltations involving up to 50 mutations. We compare inferred fitness landscapes of the Spike protein across these variants and find that shifts in the estimated effects of non-synonymous mutations are linked to genetic differences between them. Sites with altered fitness costs are enriched near positions where the genetic backgrounds differ. To explain the observed changes, we introduce a model with pairwise epistatic interactions between mutations and residues that differ between variants. This model is able to explain about half of the variance in the shifts of fitness effects and suggests that each mismatch between variants substantially alters mutation effects at typically 1 to 3 additional positions.

evolutionary biology↗

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↗

bioRxiv2025

Synteny and linkage decay in bacteriophage pangenomes

Bacteriophages are rich in genetic diversity, due to frequent horizontal transfer and recombination. This makes traditional microbial phylogenetic analyses, often based on the assumption of vertical inheritance, not suitable for interpreting this diversity. Here, inspired by recent work on bacterial pangenomes, we investigate the evolution of a collection of 3425 actinobacteriophage genomes. We find that synteny is strongly conserved: core genes have a well-defined order, and most accessory genes are localized in a few locations along the core genome backbone. Within the core genome alignment, linkage disequilibrium decays rapidly with distance in some groups, while phylogenetic structure in other groups causes long-range linkage. Our quantitative characterizations extend across many groups of phages and indicate widespread homologous recombination restricted by strong gene order conservation.

genomics↗

bioRxiv2025

Evolutionary origin and functional diversification of plant GBF1-type ARF guanine-nucleotide exchange factors

Large ARF GTPase-activating guanine-nucleotide exchange factors (ARF-GEFs) are essential regulators of membrane trafficking across the eukaryotes. Although conserved, the GBF1-type ARF-GEFs underwent plant-specific evolution. Of the 3 paralogs in Arabidopsis thaliana, AtGNOM-LIKE1 (AtGNL1) performs the GBF1 task of retrograde Golgi-ER traffic whereas AtGNOM mediates polar recycling from endosomes to the basal plasma membrane. AtGNL2 is specific to pollen and is functionally equivalent to AtGNOM. To clarify their evolutionary origin and functional diversification in the plant lineage, we established a phylogenetic tree and ortho-synteny groups (OSGs), which enabled identification of AtGNOM and AtGNL1 orthologs. In addition, somatic paralogs from several species were functionally analysed in transgenic Arabidopsis plants and tested for GNOM-specific DCB-DCB domain interaction in yeast two-hybrid assays. Our results support the following scenario. The ancient plant-specific GBF1-type ARF-GEF was functionally equivalent to AtGNOM and mediated both polar recycling and secretory trafficking. GNL2 only arose in the early flowering plants and then evolved independently. Genome duplication in the basal eudicots - but neither in monocots nor in magnoliids - gave rise to the AtGNL1 ortholog, distinguished by its unique OSG. The AtGNL1 ortholog lost the ability of DCB-DCB interaction and was free to evolve independently, eventually acquiring AtGNL1 function in the rosid clade of core eudicots. In eudicot evolution, the AtGNOM ortholog underwent repeated transposition to different OSGs whereas the AtGNL1 ortholog was recurrently lost but eventually positively selected. This evolutionary process uncoupled polar recycling required for gravitropic response or lateral root initiation from secretory trafficking required for growth.

plant biology↗

bioRxiv2025

Automated Bacteriophage Evolution Studies with the Aionostat

Bacteriophages, the viruses that infect bacteria, are the most abundant and diverse biological entities on our planet. They play a critical role in shaping ecosystems and are increasingly recognized for their potential in treating bacterial infections. Yet, our comprehension of their biology and evolutionary dynamics is limited, largely because research has concentrated on a select few well-characterized phages or relies on broad metagenomic studies with limited follow-up analysis of individual phages. This knowledge gap hinders our capacity to exploit their therapeutic and ecological possibilities - and while some studies have attempted to bridge it, such efforts typically require a lot of manual labor, highlighting the need for high-throughput, reproducible methods for in-depth study of phage evolution. To address this gap, we introduce the Aionostat, a novel automated continuous culture device designed to facilitate bacteriophage directed evolution experiments at scale. The Aionostats potential is showcased through two example experiments. In the first, phages from the BASEL collection rapidly adapted to a challenging E. coli strain, acquiring mutations and deletions that improved their infectivity. In the second experiment, we evolved a mixture of these phages on the same E.coli strain, leading to the emergence of recombinant phages with increased fitness. By automating these experiments, the Aionostat enables faster, more reproducible studies of phage evolution that would be impractical to perform by hand, thereby opening new avenues for investigating viral dynamics, engineering phage therapies, and studying evolutionary principles in broader biological contexts.

evolutionary biology↗

bioRxiv2025

The mutation rate of SARS-CoV-2 is highly variable between sites and is influenced by sequence context, genomic region, and RNA structure

RNA viruses like SARS-CoV-2 have a high mutation rate, which contributes to their rapid evolution. The rate of mutations depends on the mutation type (e.g., A[->]C, A[->]G, etc.) and can vary between sites in the viral genome. Understanding this variation can shed light on the mutational processes at play, and is crucial for quantitative modeling of viral evolution. Using the millions of available SARS-CoV-2 full-genome sequences, we estimate rates of synonymous mutations for all 12 possible nucleotide mutation types and examine how much these rates vary between sites. We find a surprisingly high level of variability and several striking patterns: the rates of four mutation types suddenly increase at one of two gene boundaries; the rates of most mutation types strongly depend on a sites local sequence context, with up to 56-fold differences between contexts; consistent with a previous study, the rates of some mutation types are lower at sites engaged in RNA secondary structure. A simple log-linear model of these features explains [~]15-60% of the fold-variation of mutation rates between sites, depending on mutation type; more complex models only modestly improve predictive power out of sample. We estimate the fitness effect of each mutation based on the number of times it actually occurs versus the number of times it is expected to occur based on the model. We identify several small regions of the genome where synonymous or noncoding mutations occur much less often than expected, indicative of strong purifying selection on the RNA sequence that is independent of protein sequence. Overall, this work expands our basic understanding of SARS-CoV-2s evolution by characterizing the viruss mutation process at the level of individual sites and uncovering several striking mutational patterns that arise from unknown mechanisms.

bioinformatics↗

bioRxiv2024

Evolutionary dynamics of genome structure and content among closely related bacteria

Bacterial genomes primarily diversify via gain, loss, and rearrangement of genetic material in their flexible accessory genome. Yet the dynamics of accessory genome evolution are very poorly understood, in contrast to the core genome where diversification is readily described by mutations and homologous recombination. Here, we tackle this problem for the case of very closely related genomes. We comprehensively describe genome evolution within n=222 genomes of E. coli ST131, which likely shared a common ancestor around one hundred years ago. After removing putative recombinant diversity, the total length of the phylogeny is 6000 core genome mutations. Within this diversity, we find 22 modifications to core genome synteny and estimate around 2000 structural changes within the accessory genome, i.e. one structural change for every 3 core genome mutations. 63% of loci with structural diversity could be resolved into individual gain and loss events with ten-fold more gains than losses, demonstrating a dominance of gains due to insertion sequences and prophage integration. Our results suggest the majority of synteny changes and insertions in bacterial genomes are likely deleterious and only persist for a short time before being removed by purifying selection.

evolutionary biology↗

bioRxiv2024

Lost in the woods: shifting habitats can lead phylogeography astray

Continuous phylogeographic inference is a popular method to estimate parameters of the dispersal process and to reconstruct the spatial location of ancestors of extant populations from samples viral genome sequences. However, these models typically ignore that replication and population growth are tightly coupled to spatial location: populations expand in areas with abundant susceptible hosts and contract in regions with limited resources. Here, I first investigate the sampling consistency of popular summary statistics of dispersal and show that estimators of "lineage velocities" are ill-defined. I then use simulations to investigate how local density regulation or shifting habitats perturb phylogeographic inference in continuous space and show that these can result in biased and overconfident estimates of ancestral locations and dispersal parameters. These, sometimes dramatic, distortions depend in complicated ways on the past dynamics of habitats and underlying population dynamics and dispersal processes. Consequently, the validity of continuous phylogeographic inferences is hard to assess and confidence can be much lower than suggested by the inferred posterior distributions, in particular when involving poorly sampled locations or extrapolations far into the past.

evolutionary biology↗

bioRxiv2023

Visualizing and quantifying structural diversity around mobile AMR genes

Understanding the evolution of mobile genes is important for understanding the spread of antimicrobial resistance (AMR). Many clinically important AMR genes have been mobilized by mobile genetic elements (MGEs) on the kilobase scale, such as integrons and transposons, which can integrate into both chromosomes and plasmids and lead to rapid spread of the gene through bacterial populations. Looking at the flanking regions of these mobile genes in diverse genomes can highlight common structures and reveal patterns of MGE spread. However, historically this has been a largely descriptive process, relying on gene annotation and expert knowledge. Here we describe a general method to visualize and quantify the structural diversity around genes using pangraph to find blocks of homologous sequence. We apply this method to a set of twelve clinically important beta-lactamase genes and provide interactive visualizations of their flanking regions at https://liampshaw.github.io/flanking-regions. We show that nucleotide-level variation in the mobile gene itself generally correlates with increased structural diversity in its flanking regions, demonstrating a relationship between rates of mutational evolution and rates of structural evolution, and find a bias for greater structural diversity upstream. Our framework is a starting point to investigate general rules that apply to the horizontal spread of new genes through bacterial populations. Impact statementUnderstanding the evolution and spread of mobile resistance genes is challenging because of the high variability in their genomic contexts. Here we outline a fast computational approach that identifies stretches of homologous sequence in the flanking regions of a gene, simultaneously producing interactive visualizations of these regions and quantifying the diversity within them. As an example, we apply the method to twelve clinically important beta-lactamase genes. We find that structural diversity around the resistance gene is correlated with mutations within it and that there is greater structural diversity upstream of many genes. There may be other general patterns about the evolution of mobile resistance genes that can be recovered with this kind of analysis.

bioinformatics↗

bioRxiv2023

Fitness effects of mutations to SARS-CoV-2 proteins

Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino-acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.

evolutionary biology↗

bioRxiv2022

Evolution of the SARS-CoV-2 mutational spectrum

SARS-CoV-2 evolves rapidly in part because of its high mutation rate. Here we examine whether this mutational process itself has changed during viral evolution. To do this, we quantify the relative rates of different types of single nucleotide mutations at four-fold degenerate sites in the viral genome across millions of human SARS-CoV-2 sequences. We find clear shifts in the relative rates of several types of mutations during SARS-CoV-2 evolution. The most striking trend is a roughly two-fold decrease in the relative rate of G[->]T mutations in Omicron versus early clades, as was recently noted by Ruis et al (2022). There is also a decrease in the relative rate of C[->]T mutations in Delta, and other subtle changes in the mutation spectrum along the phylogeny. We speculate that these changes in the mutation spectrum could arise from viral mutations that affect genome replication, packaging, and antagonization of host innate-immune factors--although environmental factors could also play a role. Interestingly, the mutation spectrum of Omicron is more similar than that of earlier SARS-CoV-2 clades to the spectrum that shaped the long-term evolution of sarbecoviruses. Overall, our work shows that the mutation process is itself a dynamic variable during SARS-CoV-2 evolution, and suggests that human SARS-CoV-2 may be trending towards a mutation spectrum more similar to that of other animal sarbecoviruses.

evolutionary biology↗

bioRxiv2022

A pseudovirus system enables deep mutational scanning of the full SARS-CoV-2 spike

A major challenge in understanding SARS-CoV-2 evolution is interpreting the antigenic and functional effects of emerging mutations in the viral spike protein. Here we describe a new deep mutational scanning platform based on non-replicative pseudotyped lentiviruses that directly quantifies how large numbers of spike mutations impact antibody neutralization and pseudovirus infection. We demonstrate this new platform by making libraries of the Omicron BA.1 and Delta spikes. These libraries each contain ~7000 distinct amino-acid mutations in the context of up to ~135,000 unique mutation combinations. We use these libraries to map escape mutations from neutralizing antibodies targeting the receptor binding domain, N-terminal domain, and S2 subunit of spike. Overall, this work establishes a high-throughput and safe approach to measure how ~105 combinations of mutations affect antibody neutralization and spike-mediated infection. Notably, the platform described here can be extended to the entry proteins of many other viruses.

microbiology↗

bioRxiv2022

Contributions of adaptation and purifying selection to SARS-CoV-2 evolution

Continued evolution and adaptation of SARS-CoV-2 has lead to more transmissible and immune-evasive variants with profound impact on the course of the pandemic. Here I analyze the evolution of the virus over 2.5 years since its emergence and estimate rates of evolution for synonymous and non-synonymous changes separately for evolution within clades - well defined mono-phyletic groups with gradual evolution - and for the pandemic overall. The rate of synonymous mutations is found to be around 6 changes per year. Synonymous rates within variants vary little from variant to variant and are compatible with the overall rate of 7 changes per year (or 7.5 x 10-4 per year and codon). In contrast, the rate at which variants accumulate amino acid changes (non-synonymous mutation) was initially around 12-16 changes per year, but in 2021 and 2022 dropped to 6-9 changes per year. The overall rate of non-synonymous evolution, that is across variants, is estimated to be about 26 amino acid changes per year (or 2.7 x 10-3 per year and codon). This strong acceleration of the overall rate compared to within clade evolution indicates that the evolutionary process that gave rise to the different variants is qualitatively different from that in typical transmission chains and likely dominated by adaptive evolution. I further quantify the spectrum of mutations and purifying selection in different SARS-CoV-2 proteins and show that the massive global sampling of SARS-CoV-2 is sufficient to estimate site specific fitness costs across the entire genome. Many accessory proteins evolve under limited evolutionary constraint with little short term purifying selection. About half of the mutations in other proteins are strongly deleterious.

evolutionary biology↗

bioRxiv2020

Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, its local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. We find trends in transmission dynamics correlated positively with trends in temperature, but not relative humidity nor school holidays. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network, while school children likely drove the spread within the remaining transmission network. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities. Author summaryAs shown with the current SARS-CoV-2 pandemic, respiratory diseases can quickly spread around the globe. While it can be hugely important to understand how diseases spread around the globe, local spread is most often the main driver of novel infections of respiratory diseases such as SARS-CoV-2 or influenza. We here use genetic sequence data alongside patient information to better understand what the drives the local spread of influenza by looking at the 2016/2017 influenza season in Basel, Switzerland as an example. The genetic sequence data allows us to reconstruct the how the transmission dynamics changed over the course of the season, which we correlate to changes, but not humidity or school holidays. Additionally, the genetic sequence data allows us to see how individual cases are connected. Using patient information, such as age and household status our analyses suggest that the elderly mainly transmit within their own transmission network. Additionally, they suggest that school aged children, but not pre-school aged children are important drivers of the local spread of influenza.

evolutionary biology↗

bioRxiv2020

Evolution, geographic spreading, and demographic distribution of Enterovirus D68

BackgroundWorldwide outbreaks of enterovirus D68 (EV-D68) in 2014 and 2016 have caused serious respiratory and neurological disease. MethodsWe collected samples from several European countries during the 2018 out-break and determined 53 near full-length genome ( whole genome) sequences. These sequences were combined with 718 whole genome and 1,987 VP1-gene publicly available sequences. FindingsIn 2018, circulating strains clustered into multiple subgroups in the B3 and A2 subclades, with different phylogenetic origins. Clusters in subclade B3 emerged from strains circulating primarily in the US and Europe in 2016, though some had deeper roots linking to Asian strains, while clusters in A2 traced back to strains detected in East Asia in 2015-2016. In 2018, all sequences from the USA formed a distinct subgroup, containing only three non-US samples. Alongside the varied origins of seasonal strains, we found that diversification of these variants begins up to 18 months prior to the first diagnostic detection during a EV-D68 season. EV-D68 displays strong signs of continuous antigenic evolution and all 2018 A2 strains had novel patterns in the putative neutralizing epitopes in the BC- and DE-loops. The pattern in the BC-loop of the USA B3 subgroup had not been detected on that continent before. Patients with EV-D68 in subclade A2 were significantly older than patients with a B3 subclade virus. In contrast to other subclades, the age distribution of A2 is distinctly bimodal and was found primarily among children and in the elderly. InterpretationWe hypothesize that EV-D68s rapid evolution of surface proteins, extensive diversity, and high rate of geographic mixing could be explained by substantial reinfection of adults. FundingUniversity of Basel and Swedish Foundation for Research and Development in Medical Microbiology

microbiology↗

bioRxiv2019

Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season

The 2017-2018 North American influenza season caused more hospitalizations and deaths than any year since the 2009 H1N1 pandemic. The majority of recorded influenza infections were caused by A(H3N2) viruses, with most of the viruss North American diversity falling into the A2 clade. Within A2, we observe a subclade which we call A2/re that rose to comprise almost 70% of A(H3N2) viruses circulating in North America by early 2018. Unlike most fast-growing clades, however, A2/re contains no amino acid substitutions in the hemagglutinin (HA) segment. Moreover, HI assays did not suggest substantial antigenic differences between A2/re viruses and viruses sampled during the 2016-2017 season. Rather, we observe that the A2/re clade was the result of a reassortment event that occurred in late 2016 or early 2017 and involved the combination of the HA and PB1 segments of an A2 virus with neuraminidase (NA) and other segments a virus from the clade A1b. The success of this clade shows the need for antigenic analysis that targets NA in addition to HA. Our results illustrate the potential for non-HA drivers of viral success and necessitate the need for more thorough tracking of full viral genomes to better understand the dynamics of influenza epidemics.

genomics↗

bioRxiv2018

Efficient estimation of evolutionary rates by covariance aware regression

Shared ancestry among individuals results in correlated traits and these dependencies need to be accounted for in probabilistic inference. In strictly asexual populations, the covariances have a particularly simple block-like structure imposed by the phylogenetic tree. Ho and Ane showed how this block-like structure can be exploited to efficiently invert covariance matrices and fit linear models on trees in linear time. In this short note, I use these methods to estimate evolutionary rates and to find the root of the tree that optimizes the time-divergence relationship. The algorithm is implemented in TreeTime and can be used to estimate evolutionary rates and their confidence intervals with computational cost scaling linearly in the number of tips.

evolutionary biology↗