Biology subjects

Robertson, D. L.

Publications and source records attributed to Robertson, D. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Flu Mutation Explorer: an Interactive Platform for Mapping Host Adaptation Mutations in Influenza A Viruses

A rapid expansion of influenza A virus (IAV) genome sequencing has transformed global surveillance but has also created major challenges for interpreting the biological significance of viral mutations, particularly amino acid replacements associated with host adaptation. Resources have been created to support mutation annotation and phylogenetic analysis, but there is a need for a tool that integrates experimentally derived phenotypic evidence with evolutionary context in a framework suitable for users without prior training in bioinformatics. Here, we present the Flu Mutation Explorer, an interactive web application that combines large-scale influenza phylogenies with a manually curated database of reported mammalian adaptation mutations, to enable the exploration and interpretation of IAV genetic variation. The underlying database comprises over 1.5 million publicly available IAV sequences and over 1000 mutations associated with mammalian adaptation. The Flu Mutation Explorer enables users to query protein sequences, visualise amino acid distributions across viral lineages, examine host-specific conservation patterns, and identify adaptation mutation with links to supporting literature. We include case studies which demonstrate the platforms use in assessing amino acid conservation at sites of interest and in rapidly identifying candidate mammalian adaptation mutations during the ongoing H5N1 panzootic. By integrating genomic, phylogenetic, and functional information into an intuitive interface, the Flu Mutation Explorer lowers the barriers to interpreting influenza sequences for specialists and non-specialists alike.

microbiology↗

bioRxiv2026

Protein language models learn underlying mutation biases alongside fitness landscapes

Protein language models (PLMs) score the effects of amino acid replacements as pseudo-probabilities, which are widely utilised to map protein fitness landscapes. However, because their training data relies on natural amino acid sequences, these models conflate protein structural constraints with nucleotide mutation biases and codon accessibility. Using the rapid emergence of the divergent influenza A H3N2 K lineage as a stress test, we investigate how base PLMs (ESM-2 and ESM-C) versus fine-tuned versions of these models capture mutational processes. We systematically implement a parameter sweep to explicitly couple (or decouple) empirical nucleotide mutational supply from PLM-assessed amino acid substitution pseudo-probabilities across evolutionary forecasting tasks. We find that base PLMs implicitly learn generic nucleotide-level mutational constraints, an effect strongly amplified by virus-specific fine-tuning. Incorporating explicit mutational accessibility significantly improves the binary prediction of observed amino acid changes. Conversely, when predicting the final circulating frequency of variants that have already emerged, adding mutational supply degrades performance, confirming that selection dominates post-emergence dynamics. Additionally, we perform amino-acid-level epistatic scanning to investigate protein structural constraints in the context of genetic background. This indicates the improbable antigenic substitution I160K is dependent on co-occurring S144N and N158D mutations in the H3N2 K lineage. Ultimately, current PLM pseudo-probabilities are a composite metric that conflates protein structural fitness with historical biases in mutational supply. Explicitly decoupling these independent evolutionary processes optimises predictive accuracy for real-world pathogen forecasting and isolates pure protein fitness for synthetic design pipelines.

evolutionary biology↗

bioRxiv2026

Understanding the emergence of the influenza A/H3N2 K subclade in its historical and evolutionary context

The emergence in 2025/26 of the influenza A/H3N2 K substrain (H3N2/K) was the cause of significant public health concern. This genetically divergent virus was assessed to have a strongly decreased reactivity to contemporary vaccine strains. Respectively prolonged and early influenza seasons in the Southern and Northern Hemispheres contributed to concerns about vaccine efficacy. Here we retrospectively assessed the genetic and antigenic properties of this virus, combining epidemiological surveillance data, computational antigenic analysis, and serological data using samples from a well-stratified UK cohort. In contrast to initial indications, we found that despite the genetic distinctiveness of H3N2/K the virus had undergone limited antigenic change, suggesting that its emergence was instead the result of selection for non-antigenic properties. We confirmed previous results showing that contemporary vaccines produced an enhanced neutralising response to H3N2/K but, in a stratified serological analysis, showed that responses to the J and K substrains were age-dependent, largely driven by patterns of vaccination. Our results have implications for antigenic surveillance and for public communication strategies in future influenza seasons.

evolutionary biology↗

bioRxiv2026

CD8scape: an accessible, command-line tool for predicting viral escape from the CD8+ T cell response

The CD8+ T cell response is a critical component of antiviral immunity, particularly in hosts who are immunocompromised or undergoing B cell-depleting therapy, such as rituximab. As viral evolution can lead to escape from CD8+ T cell recognition, tools that predict such escape are increasingly relevant. Here, we present CD8scape, an accessible command-line tool designed to predict viral escape from the CD8+ T cell response based on within-host sequence variation and HLA class I genotype. CD8scape is primarily a Julia wrapper for NetMHCpan v4.2, a neural network-based predictor trained on mass spectrometry-derived peptide presentation data. CD8scape integrates variant data and viral reading frames to identify all overlapping 8-11mer peptides at variant sites in both ancestral and derived states. These peptides are evaluated using NetMHCpan, which outputs eluted ligand (EL) scores as allele-specific percentile ranks to account for differences in MHC binding fastidiousness, and these are passed back to CD8scape itself. For each variant, the best-ranking peptide across all alleles is identified, and a harmonic mean is used to summarize presentation likelihood across the hosts HLA genotype. A fold-change between ancestral and derived harmonic means quantifies the likelihood of immune escape, with values >1 indicating reduced predicted presentation, and therefore a potential escape from the CD8+ T cell response. This is converted to a log2 value of this fold-change so that the metric is symmetric around 0, with positive values representing predicted escape. CD8scape can operate with known HLA genotypes or a representative HLA supertype panel for generalizable predictions. We demonstrate our method by application to within-host SARS-CoV-2 evolution in a rituximab-treated patient and discuss its implications for population-level CD8+ T cell escape.

bioinformatics↗

bioRxiv2025

COVID-19 vaccination induces cross-neutralisation of sarbecoviruses related to SARS-CoV-2

Close relatives of SARS-CoV-1 and SARS-CoV-2 continue to circulate in wildlife, posing an ongoing threat of zoonotic spillover. While vaccination played a key role in controlling the COVID-19 pandemic, variants of concern (VOC) with immune evasive substitutions emerged on multiple occasions, causing widespread breakthrough infections. The combined threats of zoonosis and newly emerging VOCs, coupled with the potential for recombination, underscore the need to assess the breadth of existing vaccine-mediated protection. Here, we investigate a cohort of older individuals (median age 68.5 years) for the potential of cross-neutralisation against Omicron lineage VOC and animal sarbecoviruses induced by four COVID-19 vaccine doses. Despite the recent use of a bivalent mRNA vaccine dose (encoding spike from Wu-1 and omicron), we observed that neutralisation of Omicron lineage VOCs such as BA.1 and BA.2 were reduced compared to SARS-CoV-2 Wu-1, suggesting an imprinted immune response from pre-Omicron lineage viruses. Similarly, both SARS-CoV-1 and a SARS-CoV-1-related bat CoV were neutralised less efficiently than SARS-CoV-2 Wu-1. Unexpectedly, however, we observed that two animal SARS-CoV-2-related viruses, BANAL-20-52 (from bats) and a pangolin-CoV, were more sensitive to serum neutralising antibodies than SARS-CoV-2 Wu-1 itself. These surprising findings suggest that vaccine-mediated adaptive immunity may provide efficient cross-protection against certain animal sarbecoviruses.

microbiology↗

bioRxiv2024

Viro3D: a comprehensive database of virus protein structure predictions

Viruses are intracellular parasites of organisms from all domains of life. They infect and cause disease in humans, animals and plants but also play crucial roles in the ecology of microbial communities. Tolerance to genetic change, high-mutation rates, adaptations to hosts and immune escape has driven high divergence of viral genes, hampering their functional annotation and phylogenetic inference. The protein structure is more conserved than sequence and can be used for searches of distant homologs and evolutionary analysis of divergent proteins. Structures of viral proteins are traditionally underrepresented in public databases, but recent advances in protein structure prediction allows us to address this issue. Combining two state-of-the-art approaches, AlphaFold2-ColabFold and ESMFold, we predicted models for 85,000 proteins from 4,400 human and animal viruses, expanding the structural coverage for viral proteins by 30 times compared to experimental structures. We also performed structural and network analyses of the models to demonstrate their utility for functional annotation and inference of distant phylogenetic relationships. Taking this approach, we examined the deep evolutionary history of viral class-I fusion glycoproteins, gaining insights on the origins of coronavirus spike protein. To enable further discoveries, we have created Viro3D (https://viro3d.cvr.gla.ac.uk/), a virus species-centred protein structure database. It allows users to search, browse and download protein models from a virus of interest and explore similar structures present in other virus species. This resource will facilitate fundamental molecular virology, investigation of virus evolution, and may enable structure-informed design of therapies and vaccines.

microbiology↗

bioRxiv2024

XRN1 supplies free nucleotides to feed alphavirus replication

Several RNA viruses induce widespread degradation of cellular mRNAs upon infection; however, the biological significance and mechanistic details of this phenomenon remain unknown. Here, we make use of a model alphavirus, Sindbis virus (SINV), to fill this knowledge gap. We found that SINV triggers cellular RNA decay through the exonuclease XRN1 and the 5-to-3 degradation machinery (5-3DM). These proteins accumulate at viral replication organelles (VROs) and interact with the non-structural protein 1 (nsP1), bringing mRNA degradation into proximity with vRNA synthesis. Our data suggest that monophosphate nucleotides released by cellular RNA decay are recycled through the salvage pathway to feed viral replications. Our work thus reveals a fundamental connection between cellular mRNA degradation and viral replication via nucleotides repurposing. Research highlightsO_LI5-3 RNA decay is essential for the replication of a wide range of viruses. C_LIO_LIXRN1 directly interacts with transcripts which are degraded during infection. C_LIO_LIRNA decay factors and salvage pathway members localise to viral factories. C_LIO_LISupplying nucleosides to several 5-3DM deficient cells facilitates SINV infection. C_LI

biochemistry↗

bioRxiv2024

PLM-interact: extending protein language models to predict protein-protein interactions

Computational prediction of protein structure from amino acid sequences alone has been achieved with unprecedented accuracy, yet the prediction of protein-protein interactions (PPIs) remains an outstanding challenge. Here we assess the ability of protein language models (PLMs), routinely applied to protein folding, to be retrained for PPI prediction. Existing PPI prediction models that exploit PLMs use a pre-trained PLM feature set, ignoring that the proteins are physically interacting. Our novel method, PLM-interact, goes beyond a single protein, jointly encoding protein pairs to learn their relationships, analogous to the next-sentence prediction task from natural language processing. This approach provides a significant improvement in performance: Trained on human-human PPIs, PLM-interact predicts mouse, fly, worm, E. coli and yeast PPIs, with 16-28% improvements in AUPR compared with state-of-the-art PPI models. Additionally, it can detect changes that disrupt or cause PPIs and be applied to virus-host PPI prediction. Our work demonstrates that large language models can be extended to learn the intricate relationships among biomolecules from their sequences alone.

bioinformatics↗

bioRxiv2024

Multi-omics analysis of virus-permissive versus hostile cellular states reveals protein networks controlling virus infection

The capacity of host cells to sustain or restrict virus infection is influenced by their proteome. Understanding the compendium of proteins defining cellular permissiveness is key to many questions in fundamental virology. Here, we apply a multiomic approach to determine the proteins that are associated with highly permissive, intermediate, and hostile cellular states. We observed two groups of differentially regulated genes: i) with robust changes in mRNA and protein levels, and ii) with protein/RNA discordances. Many of the latter are classified as interferon stimulated genes (ISGs) but have no reported antiviral activity. This suggests that IFN-dependent changes in mRNA levels do not imply antiviral function. Phosphoproteomics revealed an additional regulatory layer involving non-signalling proteins with altered phosphorylation. Indeed, we confirmed that several permissiveness-associated proteins with changes in abundance or phosphorylation regulate infection fitness. Altogether, our study provides a comprehensive and systematic map of the cellular alterations driving virus susceptibility.

pathology↗

bioRxiv2024

From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences

Protein language models (PLMs) capture features of protein three-dimensional structure from amino acid sequences alone, without requiring multiple sequence alignments (MSA). The concepts of grammar and semantics from natural language have the potential to capture functional properties of proteins. Here, we investigate how these representations enable assessment of variation due to mutation. Applied to SARS-CoV-2s spike protein using in silico deep mutational scanning (DMS) we demonstrate the PLM, ESM-2, has learned the sequence context within which variation occurs, capturing evolutionary constraint. This recapitulates what conventionally requires MSA data to predict. Unlike other state-of-the-art methods which require protein structures or multiple sequences for training, we show what can be accomplished using an unmodified pretrained PLM. We demonstrate that the grammaticality and semantic scores represent novel metrics. Applied to SARS-CoV-2 variants across the pandemic we show that ESM-2 representations encode the evolutionary history between variants, as well as the distinct nature of variants of concern upon their emergence, associated with shifts in receptor binding and antigenicity. PLM likelihoods can also identify epistatic interactions among sites in the protein. Our results here affirm that PLMs are broadly useful for variant-effect prediction, including unobserved changes, and can be applied to understand novel viral pathogens with the potential to be applied to any protein sequence, pathogen or otherwise.

bioinformatics↗

bioRxiv2023

HIHISIV: a database of gene expression in HIV and SIV host immune response

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV). Both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories. Database URLhttps://hihisiv.github.io

bioinformatics↗

bioRxiv2023

Lysogeny destabilizes computationally simulated microbiomes

BackgroundThe Anna Karenina Principle predicts that stability in host-associated microbiomes correlates with health in the host. Microbiomes are ecosystems, and classical ecological theory suggests that predators impact ecosystem stability. Phages can act as predators on bacterial species in microbiomes. However, our ability to extrapolate results from existing ecological theory to phages and microbiomes is limited because some phages can stage lysogenic infections, a process with no precise analog in classical ecology. In lysogenic infections, so-called "temperate" phages integrate into the cells of their hosts where they can remain dormant as prophages for many generations. Prophages can later be induced by environmental conditions, whereupon they lyse their host cells and phage progeny are released. It has been suggested that prophages can act as biological timebombs that destabilize microbial ecosystems, but formal theory to support this hypothesis is lacking. ResultsWe studied the effects of temperate and virulent phages on diversity and stability in computationally simulated microbiomes. The presence of either phage type in a microbiome increased bacterial diversity. Bacterial populations were more likely to fluctuate over time when there were more temperate phages in the system. When microbiomes were disturbed from their steady states, both phage types affected return times, but in different ways. Bacterial species returned to their pre-disturbance densities more slowly when there were more temperate phage species, but cycles engendered by disturbances dampened more slowly when there were more virulent phage species. ConclusionsPhages shape the diversity and stability of microbiomes, and temperate and virulent phages impact microbiomes in different ways. A clear understanding of the effects of phage life cycles on microbiome dynamics is needed to predict the role of microbiome composition in host health, and for applications including phage therapy and microbiome transplants. The results we present here provide a theoretical foundation for this body of work.

ecology↗

bioRxiv2023

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Wholesale Seafood Market, the site with the most reported wildlife vendors in the city of Wuhan, China. Here, we analyze publicly available qPCR and sequencing data from environmental samples collected in the Huanan market in early 2020. We demonstrate that the SARS-CoV-2 genetic diversity linked to this market is consistent with market emergence, and find increased SARS-CoV-2 positivity near and within a particular wildlife stall. We identify wildlife DNA in all SARS-CoV-2 positive samples from this stall. This includes species such as civets, bamboo rats, porcupines, hedgehogs, and one species, raccoon dogs, known to be capable of SARS-CoV-2 transmission. We also detect other animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them to those from other markets. This analysis provides the genetic basis for a short list of potential intermediate hosts of SARS-CoV-2 to prioritize for retrospective serological testing and viral sampling.

genomics↗

bioRxiv2023

The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 has led to increased sampling of related sarbecoviruses circulating primarily in horseshoe bats. These viruses undergo frequent recombination and exhibit spatial structuring across Asia. Employing recombination-aware phylogenetic inference on bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed just [~]1-3 years prior to their emergence in humans. Phylogeographic analyses examining the movement of related sarbecoviruses demonstrate that they traveled at similar rates to their horseshoe bat hosts and have been circulating for thousands of years in Asia. The closest-inferred bat virus ancestor of SARS-CoV likely circulated in western China, and that of SARS-CoV-2 likely circulated in a region comprising southwest China and northern Laos, both a substantial distance from where they emerged. This distance and recency indicate that the direct ancestors of SARS-CoV and SARS-CoV-2 could not have reached their respective sites of emergence via the bat reservoir alone. Our recombination-aware dating and phylogeographic analyses reveal a more accurate inference of evolutionary history than performing only whole-genome or single gene analyses. These results can guide future sampling efforts and demonstrate that viral genomic fragments extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.

evolutionary biology↗

bioRxiv2023

SARS-CoV-2's evolutionary capacity is mostly driven by host antiviral molecules

The COVID-19 pandemic has been characterised by sequential variant-specific waves shaped by viral, individual human and population factors. SARS-CoV-2 variants are defined by their unique combinations of mutations and there has been a clear adaptation to human infection since its emergence in 2019. Here we use machine learning models to identify shared signatures, i.e., common underlying mutational processes, and link these to the subset of mutations that define the variants of concern (VOCs). First, we examined the global SARS-CoV-2 genomes and associated metadata to determine how viral properties and public health measures have influenced the magnitude of waves, as measured by the number of infection cases, in different geographic locations using regression models. This analysis showed that, as expected, both public health measures and not virus properties alone are associated with the rise and fall of regional SARS-CoV-2 reported infection numbers. This impact varies geographically. We attribute this to intrinsic differences such as vaccine coverage, testing and sequencing capacity, and the effectiveness of government stringency. In terms of underlying evolutionary change, we used non-negative matrix factorisation to observe three distinct mutational signatures, unique in their substitution patterns and exposures from the SARS-CoV-2 genomes. Signatures 0, 1 and 3 were biased to C[->]T, T[->]C/A[->]G and G[->]T point mutations as would be expected of host antiviral molecules APOBEC, ADAR and ROS effects, respectively. We also observe a shift amidst the pandemic in relative mutational signature activity from predominantly APOBEC-like changes to an increasingly high proportion of changes consistent with ADAR editing. This could represent changes in how the virus and the host immune response interact, and indicates how SARS-CoV-2 may continue to accumulate mutations in the future. Linkage of the detected mutational signatures to the VOC defining amino acids substitutions indicates the majority of SARS-CoV-2s evolutionary capacity is likely to be associated with the action of host antiviral molecules rather than virus replication errors.

genomics↗

bioRxiv2023

Prediction of virus-host association using protein language models and multiple instance learning

Predicting virus-host associations is essential to determine the specific host species that viruses interact with, and discover if new viruses infect humans and animals. Currently, the host of the majority of viruses is unknown, particularly in microbiomes. To address this challenge, we introduce EvoMIL, a deep learning method that predicts the host species for viruses from viral sequences only. It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than sequence composition features, including amino acids, physiochemical properties, and DNA k-mers. In multi-host prediction tasks, EvoMIL achieves median F1 score improvements of 8.6%, 12.3%, and 4.1% in prokaryotic hosts, and 0.5%, 1.8% and 3% in eukaryotic hosts. EvoMIL binary classifiers achieve impressive AUC over 0.95 for all prokaryotic and range from roughly 0.8 to 0.9 for eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction task and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together, highlighting the ability of EvoMIL to capture key proteins in virus-host specificity. Author summaryBeing able to predict which viruses can infect which host species, and identifying the specific proteins that are involved in these interactions, are fundamental tasks in virology. Traditional methods for predicting these interactions rely on common manual features among proteins, overlooking the structure of the protein "language" encoded in individual proteins. We have developed a novel method that combines a protein language model and multiple instance learning to allow host prediction directly from protein sequences, without the need to extract manual features. This method significantly improved prediction accuracy and revealed key proteins involved in virus-host interactions.

bioinformatics↗

bioRxiv2022

The SARS-CoV-2 Spike Protein Mutation Explorer: Using an Interactive Application to Improve the Public Understanding of SARS-CoV-2 Variants of Concern

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic, which began in late 2019 and has resulted in millions of death globally. The need to understand the pandemic means that detailed descriptions of features of this virus are now of interest to non-expert audiences. In particular, there has been much public interest in the spike protein that protrudes from the surface of the SARS-CoV-2 virus particle. The spike is the major determinant of viral infectivity and the main target for protective immune responses, and included in vaccines, and so its properties influence the impact of the pandemic on peoples lives. This protein is rapidly evolving, with mutations that enhance transmissibility or weaken vaccine protection creating new variants of concern (VOCs) and associated sub-lineages. The spread of SARS-CoV-2 VOCs has been tracked by groups such as the COVID-19 Genomics UK consortium (COG-UK). Their online mutation explorer (COG-UK/ME), which analyses and shares SARS-CoV-2 sequence data, contains information about VOCs that is designed primarily for an expert audience but is potentially of general interest during a pandemic. We wished to make this detailed information about SARS-CoV-2 VOCs more widely accessible. Previously work has shown that visualisations and interactivity can facilitate active learning and boost engagement with molecular biology topics, while animations of these topics can boost understanding on protein structure, function, and dynamics. We therefore set out to develop an educational graphical resource, the SARS-CoV-2 Spike Protein Mutation Explorer (SSPME), which contains interactive 3D molecular models and animations explaining SARS-CoV-2 spike protein variants and VOCs. We performed user-testing of the original COG-UK/ME website and of the SSPME, using a within-groups design to measure knowledge acquisition and a between-groups design to contrast the effectiveness and usability. Statistical analysis demonstrated that, when compared to the COG-UK/ME, the SSPME had higher usability and significantly improved participant knowledge confidence and knowledge acquisition. The SSPME therefore provides an example of how 3D interactive visualisations can be used for effective science communication and education on complex biomedical topics, as well as being a resource to improve the public understanding of SARS-CoV-2 VOCs.

molecular biology↗

bioRxiv2022

The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK

The first SARS-CoV-2 variant of concern (VOC) to be designated was lineage B.1.1.7, later labelled by the World Health Organisation (WHO) as Alpha. Originating in early Autumn but discovered in December 2020, it spread rapidly and caused large waves of infections worldwide. The Alpha variant is notable for being defined by a long ancestral phylogenetic branch with an increased evolutionary rate, along which only two sequences have been sampled. Alpha genomes comprise a well-supported monophyletic clade within which the evolutionary rate is more typical of SARS-CoV-2. The Alpha epidemic continued to grow despite the continued restrictions on social mixing across the UK, and the imposition of new restrictions, in particular the English national lockdown in November 2020. While these interventions succeeded in reducing the absolute number of cases, the impact of these non-pharmaceutical interventions was predominantly to drive the decline of the SARS-CoV-2 lineages which preceded Alpha. We investigate the only two sampled sequences that fall on the branch ancestral to Alpha. We find that one is likely to be a true intermediate sequence, providing information about the order of mutational events that led to Alpha. We explore alternate hypotheses that can explain how Alpha acquired a large number of mutations yet remained largely unobserved in a region of high genomic surveillance: an under-sampled geographical location, a non-human animal population, or a chronically-infected individual. We conclude that the last hypothesis provides the best explanation of the observed behaviour and dynamics of the variant, although we find that the individual need not be immunocompromised, as persistently-infected immunocompetent hosts also display a higher within-host rate of evolution. Finally, we compare the ancestral branches and mutation profiles of other VOCs to each other, and identify that Delta appears to be an outlier both in terms of the genomic locations of its defining mutations, and its lack of rapid evolutionary rate on the ancestral branch. As new variants, such as Omicron, continue to evolve (potentially through similar mechanisms) it remains important to investigate the origins of other variants to identify ways to potentially disrupt their evolution and emergence.

evolutionary biology↗