Biology subjects

Lythgoe, K.

Publications and source records attributed to Lythgoe, K..

10 recordsLinked to original sources

bioRxiv2026

Natural selection driven by escape from shifting antibody classes shapes SARS-CoV-2 evolution

The phenotypic fitness landscape defines the action of natural selection on pathogens, linking changes in their phenotypes to transmission and evolution. The rapidly changing nature of epidemic spread and antigenic landscapes pushes viruses to evolve on fitness seascapes. As a result, evolution of viruses such as SARS-CoV-2 proceeds in a neverending series of waves, driven by epistatic interactions and by the arms race between viral adaptation and human immunity. Phenotypic characterisation of these rapidly changing fitness seascapes is an open challenge. Using a Phenotypic Selection Inference framework that links phylogenetic estimates of mutation fitness effects with deep mutational scanning data, we traced how selective pressures on viral phenotypes have shifted throughout the COVID-19 pandemic. Natural selection has favoured enhanced ACE2 binding since the emergence of SARS-COV-2, with relatively constant selective pressure even for the most recent variants. The strength of selection for antibody escape was comparable to ACE2 binding during early evolution, but as population immunity rose, escape from class 3 and then class 2 antibodies became dominant. For variants circulating in 2024, natural selection shifted toward class 3 antibody escape, while those circulating in 2025 have experienced dynamic, rapidly changing pressures for escape from all antibody classes. These transitions reflect an ongoing arms race between viral adaptation and human immunity. Our findings reveal that SARS-CoV-2 antigenic evolution is governed by dynamic, class-specific immune pressures, and that selection for replication capacity has been continuously present during the pandemic, presumably to compensate for the effects of antigenic escape on viral replication. Our approach for the inference of phenotypic selection provides a framework to understand and anticipate the evolution of future variants.

evolutionary biology↗

bioRxiv2026

Identification and Masking of Artefactual and Misleading Within-Host Variants in Deep-Sequencing SARS-CoV-2 Data

Deep sequencing data are increasingly used to study within-host viral diversity and to inform evolutionary inference. For SARS-CoV-2, analyses based on intra-host single-nucleotide variants (iSNVs) have been widely applied to quantify within-host diversity and infer transmission dynamics. However, these applications critically depend on the reliable identification of low-frequency variants, which remain vulnerable to systematic and technical artefacts. In this study, we show that recurrent artefactual iSNVs are common in large-scale SARS-CoV-2 sequencing data and can persist even under conservative minor allele frequency (MAF) thresholds. Using data from the UKs Office for National Statistics COVID-19 Infection Survey, we demonstrate that such artefacts are predominantly sequencing centre-rather than protocol-specific. Each centre exhibits a modest, distinct set of recurrent artefactual variants showing little overlap with sites routinely masked at the consensus level. To address this, we developed a systematic, dataset-aware framework that uses recurrence within sequencing datasets to identify small, noise-adapted sets of artefactual iSNVs to mask. Applying this framework reduces spurious sharing of low-frequency variants between samples and qualitatively alters downstream inferences, including estimates of within-host diversity and transmission bottleneck sizes. Together, these findings highlight the importance of explicit, dataset-aware artefact control for robust inference from within-host variation, particularly as genomic studies increasingly seek to exploit sub-consensus diversity in rapidly evolving pathogens such as SARS-CoV-2.

genomics↗

bioRxiv2025

Attenuation of HIV severity by slightly deleterious mutations can explain the long-term trajectory of virulence evolution.

HIV-1 is a well-studied example of a pathogen that has evolved an intermediate level of virulence that maximises transmission. For a trait to evolve it must be heritable, and although viral load--a proxy for disease severity--has been shown to be a heritable trait, it is surprising that specific heritable viral factors remain mostly elusive. Rapid within-host evolution is also expected to diminish heritability. We hypothesised that rather than a small number of mutations of large effect determining viral load, the number of slightly deleterious mutations could be key. As a proof of principle, we explored how viral load is expected to evolve within and between hosts using a nested modelling approach that links within-host evolution with epidemiological outcomes. For mutations of sufficiently small effect, a mutation-selection balance is gradually reached during infection, resulting in slow changes in viral load despite rapid rates of genomic evolution. In simulated epidemics, we generated realistic population distributions of viral loads and estimates of heritability. The existence of many slightly deleterious mutations provides a mechanism that can help to explain why viral loads change slowly during infection, broad distributions of viral loads among individuals, and why searches for viral factors that determine viral load have had limited success.

evolutionary biology↗

bioRxiv2023

Directional predictions of HIV transmission with optimised genomics in cohorts of serodiscordant couples

Viral genetic information from people living with HIV can deepen our understanding of the infections epidemiology at many scales. To better understand the potentials and limits of tools that utilise such information, we show the performance of two representative tools (HIV-TRACE and phyloscanner) in describing HIV transmission dynamics, with different types of genetic data, and compare with previous findings. The samples were collected from three cohort studies in Sub-Saharan Africa and were deep sequenced to produce both short Illumina reads and long PacBio reads. By comparing phyloscanners performance with short and long reads, we show that long reads provide improved phylogenetic resolution for the classic transmission topology in joint within-host trees. Our pipeline accurately predicted the direction of transmission 88%-92% of the time. We also show that the timing of sample collection plays an important role in the reconstruction of directionality using deep sequencing data. Consensus sequences were also generated and used as HIV-TRACE input to show different patterns of clustering sensitivity and specificity for data from different genomic regions or the entire genome. Finally, we discuss adjusting expectations about sensitivity and specificity of different types of sequence data, considering rapid pathogen evolution, and highlight the potentials of high within-host phylogenetic resolution in HIV. In conclusion, viral genetic data collected and presented differently could greatly influence our ability to describe the underlying dynamics. Methods for source attribution analysis have reached levels of superior accuracy. However, residual uncertainty emphasizes sequence analysis alone cannot conclusively prove linkage at the individual level. ImportanceUnderstanding HIV transmission dynamics is key to designing effective HIV testing and prevention strategies. By using different sequencing techniques on well-characterised cohorts, we were able to evaluate the effect of genetic data resolution on the accuracy of identifying likely transmission pairs and the direction of transmission within pairs. We find that the longer reads generated by PacBio sequencing are more suitable for transmission analyses.

genomics↗

bioRxiv2022

Viral burdens are associated with age and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection related sampling bias.

In this study, we evaluated the impact of viral variant, in addition to other variables, on within-host viral burdens, by analysing cycle threshold (Ct) values derived from nose and throat swabs, collected as part of the UK COVID-19 Infection Survey. Because viral burden distributions determined from community survey data can be biased due to the impact of variant epidemiology on the time-since-infection of samples, we developed a method to explicitly adjust observed Ct value distributions to account for the expected bias. Analysing the adjusted Ct values using partial least squares regression, we found that among unvaccinated individuals with no known prior infection, the average Ct value was 0.94 lower among Alpha variant infections, compared those with the predecessor strain, B.1.177. However, among vaccinated individuals, it was 0.34 lower among Delta variant infections, compared to those with the Alpha variant. In addition, the average Ct value decreased by 0.20 for every 10 year age increment of the infected individual. In summary, within-host viral burdens are associated with age, in addition to the interplay of vaccination status and viral variant.

genetics↗

bioRxiv2022

Analysis and comprehensive lineage identification for SARS-CoV-2 genomes through scalable learning methods

Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and globally caused multiple waves of infection. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern (VoC). They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta, but not Omicron. Phylogenetic methods provide the gold standard for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges together with the increasing volumes of genomic data available provide a motivation to develop complementary methods that can incorporate all of the genetic data available, without down-sampling, to extract meaningful information rapidly and with minimal curation. Here, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies, and while not serving as a substitute for current phylogenetic analyses the proposed methods can be used as a complementary approach to identify and confirm new emerging variants.

bioinformatics↗

bioRxiv2020

Limited evolution despite years of measurable viremia in a cART-treated seronegative HIV-1 positive individual

Understanding the role that antibodies play in controlling HIV-1 infection and in the dynamics that underpin the formation of the HIV-1 reservoir are important steps towards combatting this global disease. To address these gaps, we performed whole-genome, deep sequence analysis of longitudinal plasma HIV-1 samples from an individual who failed to develop detectable anti-HIV-1 antibodies for 4 years post infection. These analyses reveal limited evolution despite months of measurable viremia during treatment with cART. We used a mathematical model to simultaneously analyse the viral and evolutionary dynamics of this unique individual. We propose a role for antibodies in reducing viral infectivity and demonstrate how our data are consistent with a theory of rapid activation of latently infected cells prior to effective viral suppression. Our study supports and elucidates a recent finding that although the latent reservoir persists for years once virus is effectively suppressed, prior to suppression, viral strains within the reservoir turn over rapidly. The implications for a cure are significant.

evolutionary biology↗

bioRxiv2019

A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection

In the absence of effective antiviral therapy, HIV-1 evolves in response to the within-host environment, of which the immune system is an important aspect. During the earliest stages of infection, this process of evolution is very rapid, driven by a small number of CTL escape mechanisms. As the infection progresses, immune escape variants evolve under reduced magnitudes of selection, while competition between an increasing number of polymorphic alleles (i.e., clonal interference) makes it difficult to quantify the magnitude of selection acting upon specific variant alleles. To tackle this complex problem, we developed a novel multi-locus inference method to evaluate the role of selection during the chronic stage of within-host infection. We applied this method to targeted sequence data from the p24 and gp41 regions of HIV-1 collected from 34 patients with long-term untreated HIV-1 infection. We identify a broad distribution of beneficial fitness effects during infection, with a small number of variants evolving under strong selection and very many variants evolving under weaker selection. The uniquely large number of infections analysed granted a previously unparalleled statistical power to identify loci at which selection could be inferred to act with statistical confidence. Our model makes no prior assumptions about the nature of alleles under selection, such that any synonymous or non-synonymous variant may be inferred to evolve under selection. However, the majority of variants inferred with confidence to be under selection were non-synonymous in nature, and in nearly all cases were associated with either CTL escape in p24 or neutralising antibody escape in gp41. Sites inferred to be under selection in multiple hosts have high within-host and between-host diversity albeit not all sites with high between-host diversity were inferred to be under selection at the within-host level. Our identification of selection at sites associated with resistance to broadly neutralising antibodies (bNAbs) highlights the need to fully understand the role of selection in untreated individuals when designing bNAb based therapies.\n\nAuthor SummaryDuring the within-host evolution of HIV-1, the diversity of the viral population increases, with many beneficial variants competing against each other. This competition, known as clonal interference, makes the identification of variants under positive selection a challenging task. We here apply a novel method for the inference of selection to targeted within-host sequence data describing changes in the p24 and gp41 genes during HIV-1 infection in 34 patients. Our method adopts a parsimonious approach, assigning selection to the smallest number of variants necessary to explain the evolution of the system. The large size of our dataset allows for the confident identification of variants under selection, alleles at certain loci being repeatedly inferred as under selection within multiple individuals. While early CTL escape mutations have been identified to evolve under strong positive selection, we identify a distribution of beneficial fitness effects in which a large number of mutations are under weak selection. Variants that were confidently identified under selection were primarily found to be associated with either CTL escape in p24 or neutralising antibody escape in gp41, including sites associated with escape from broadly neutralising antibodies. We find that the most frequently selected loci have high diversity both within-host and at the between-host level.

evolutionary biology↗

bioRxiv2019

CpG-creating Mutations are Costly in Many Human Viruses

Mutations can occur throughout the virus genome and may be beneficial or deleterious. We are interested in mutations that yield a C next to a G, producing CpG sites. CpG sites are rare in eukaryotic and viral genomes. For the eukaryotes, it is thought that CpG sites are rare because they are prone to mutation when methylated. In viruses, we know less about why CpG sites are rare. A previous study in HIV suggested that CpG-creating transition mutations are more costly that similar non-CpG-creating mutations. To determine if this is the case in other viruses, we analyzed the allele frequencies of CpG-creating and non-CpG-creating mutations across various strains, subtypes, and genes of viruses using existing data obtained from Genbank, HIV Databases, and Virus Pathogen Resource. Our results suggest that CpG sites are costly for most viruses. By understanding the cost of CpG sites, we can obtain further insights into the evolution and adaptation of viruses.

evolutionary biology↗

bioRxiv2018

Link between the numbers of particles and variants founding new HIV-1 infections depends on the timing of transmission

Understanding which HIV-1 variants are most likely to be transmitted is important for vaccine design and predicting virus evolution. Since most infections are founded by single variants, it has been suggested that selection at transmission has a key role in governing which variants are transmitted. We show that the composition of the viral population within the donor at the time of transmission is also important. To support this argument, we developed a probabilistic model describing HIV-1 transmission in an untreated population, and parameterised the model using both within-host next generation sequencing data and population-level epidemiological data on heterosexual transmission. The most basic HIV-1 transmission models cannot explain simultaneously the low probability of transmission and the non-negligible proportion of infections founded by multiple variants. In our model, transmission can only occur when environmental conditions are appropriate (e.g. abrasions are present in the genital tract of the potential recipient), allowing these observations to be reconciled. As well as reproducing features of transmission in real populations, our model demonstrates that, contrary to expectation, there is not a simple link between the number of viral variants and the number of viral particles founding each new infection. These quantities depend on the timing of transmission, and infections can be founded with small numbers of variants yet large numbers of particles. Including selection, or a bias towards early transmission (e.g. due to treatment) acts to enhance this conclusion. In addition, we find that infections initiated by multiple variants are most likely to have derived from donors with intermediate set-point viral loads, and not from individuals with high set-point viral loads as might be expected. We therefore emphasise the importance of considering viral diversity in donors, and the timings of transmissions, when trying to discern the complex factors governing single or multiple variant transmission.

epidemiology↗