Biology subjects

Pybus, O.

Publications and source records attributed to Pybus, O..

11 recordsLinked to original sources

bioRxiv2026

Dairy cattle movement networks govern the spread and persistence of H5N1 B3.13 in the United States

Avian influenza virus H5N1 is predominantly found in wild and domestic birds. H5N1 genotype B3.13 was first detected in United States dairy cattle in March 2024 and spread through much of the country during 2024 and 2025, resulting in the largest known H5N1 outbreak in domestic mammals. By analysing viral genomic data jointly with historical cattle movement patterns, we find that live cattle movement is the predominant contributor to H5N1 spatial spread, rather than wild or domestic birds. Using phylogeographic methods we estimate that >40% of interstate viral transmissions occurred before B3.13's first detection and >70% occurred before interstate dairy cattle movement restrictions were implemented in April 2024. After this implementation, interstate transmission slowed but within-state spread persisted, driven by local cattle network density and movement volume. Our epidemic simulations demonstrate that the connectivity of counties within and around the Texas Panhandle - where the earliest cases were documented - predisposed the region to become an outbreak epicentre. Counterfactual simulations indicate that interstate restrictions needed to have begun 2-3 months earlier in order to limit the epizootic substantively, given the extent of cryptic spread before detection. Network-informed interventions outperformed geographically-defined containment, but response speed (rather than intervention strength) was the limiting factor in our epidemic models, underscoring the need for surveillance systems capable of detecting pathogen emergence before widespread dissemination.

evolutionary biology↗

bioRxiv2025

Quantifying virus load and characterising virus diversity in wildlife samples with target enrichment sequencing

Metagenomics is a powerful tool for characterising viruses, with broad applications across diverse disciplines, from understanding the ecology and evolutionary history of viruses to identifying causative agents of emerging outbreaks with unknown aetiology. Additionally, metagenomic data contains valuable information about the amount of virus present within samples. However, we have yet to leverage metagenomics to assess viral load, which is a key epidemiological parameter. To effectively use sequencing outputs to inform transmission, we need to understand the relationship between read depth and viral load across a diverse set of viruses. Here, using target enrichment sequencing, we investigated the detection and recovery of virus genomes by spiking known concentrations of DNA and RNA viruses into wild rodent faecal samples. In total, 15 experimental replicates were sequenced with target enrichment sequencing and compared to shotgun sequencing of the same background samples. Target enriched sequencing recovered all spike-in viruses at every concentration (102, 103, and 105 {+/-} 1 log genome copies) and showed a log-linear relationship between spike-in concentration and mean read depth. Background viruses (including Kobuvirus and Cardiovirus) were recovered consistently across all biological and technical replicates, but genome coverage was variable between virus genera and likely reflected the composition of target enrichment probe panel. Overall, our study highlights the strengths and weaknesses of using commercially available panels to quantify and characterise wildlife viromes, and underscores the importance of probe panel design for accurately interpreting coverage and read depth. To advance the use of metagenomics for understanding virus transmission, further research will be needed to elucidate how sequencing strategy (e.g. library depth, pooling), virome composition, and probe design influence viral read counts and genome coverage.

genomics↗

bioRxiv2022

Comparing the evolutionary dynamics of predominant SARS-CoV-2 virus lineages co-circulating in Mexico

Over 200 different SARS-CoV-2 lineages have been observed in Mexico by November 2021. To investigate lineage replacement dynamics, we applied a phylodynamic approach and explored the evolutionary trajectories of five dominant lineages that circulated during the first year of local transmission. For most lineages, peaks in sampling frequencies coincided with different epidemiological waves of infection in Mexico. Lineages B.1.1.222 and B.1.1.519 exhibited similar dynamics, constituting clades that likely originated in Mexico and persisted for >12 months. Lineages B.1.1.7, P.1 and B.1.617.2 also displayed similar dynamics, characterized by multiple introduction events leading to a few successful extended local transmission chains that persisted for several months. For the largest B.1.617.2 clades, we further explored viral lineage movements across Mexico. Many clades were located within the south region of the country, suggesting that this area played a key role in the spread of SARS-CoV-2 in Mexico.

evolutionary 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↗

bioRxiv2021

Pango lineage designation and assignment using SARS-CoV-2 spike gene nucleotide sequences

More than 2 million SARS-CoV-2 genome sequences have been generated and shared since the start of the COVID-19 pandemic and constitute a vital information source that informs outbreak control, disease surveillance, and public health policy. The Pango dynamic nomenclature is a popular system for classifying and naming genetically-distinct lineages of SARS-CoV-2, including variants of concern, and is based on the analysis of complete or near-complete virus genomes. However, for several reasons, nucleotide sequences may be generated that cover only the spike gene of SARS-CoV-2. It is therefore important to understand how much information about Pango lineage status is contained in spike-only nucleotide sequences. Here we explore how Pango lineages might be reliably designated and assigned to spike-only nucleotide sequences. We survey the genetic diversity of such sequences, and investigate the information they contain about Pango lineage status. Although many lineages, including the main variants of concern, can be identified clearly using spike-only sequences, some spike-only sequences are shared among tens or hundreds of Pango lineages. To facilitate the classification of SARS-CoV-2 lineages using subgenomic sequences we introduce the notion of designating such sequences to a "lineage set", which represents the range of Pango lineages that are consistent with the observed mutations in a given spike sequence. These data provide a foundation for the development of software tools that can assign newly-generated spike nucleotide sequences to Pango lineage sets.

genomics↗

bioRxiv2021

Prisoner of War dynamics explains the time-dependent pattern of substitution rates in viruses

Molecular clock dating is widely used to estimate timescales of phylogenetic histories and to infer rates at which species evolve. One of the major challenges with inferring rates of molecular evolution is the observation of a strong correlation between estimated rates and the timeframe of their measurements. Recent empirical analysis of virus evolutionary rates suggest that a power-law rate decay best explains the time-dependent pattern of substitution rates and that the same pattern is observed regardless of virus type (e.g. groups I-VII in the Baltimore classification). However there exists no explanation for this trend based on molecular evolutionary mechanisms. We provide a simple predictive mechanistic model of the time-dependent rate phenomenon, incorporating saturation and host constraints on the evolution of some sites. Our model recapitulates the ubiquitous power-law rate decay with a slope of -0.65 (95% HPD: -0.72, -0.52) and can satisfactorily account for the variation in inferred molecular evolutionary rates over a wide range of timeframes. We show that once the saturation of sites starts - typically after hundreds of years in RNA viruses and thousands of years in DNA viruses - standard substitution models fail to correctly estimate divergence times among species, while our model successfully re-creates the observed pattern of rate decay. We apply our model to re-date the diversification of genotypes of hepatitis C virus (HCV) to 396,000 (95% HPD: 326,000 - 425,000) years before present, a time preceding the dispersal of modern humans out of Africa, and also showed that the most recent common ancestor of sarbecoviruses dates back to 23,500 (95% HPD: 21,100 - 25,300) years ago, nearly thirty times older than previous estimates. This not only creates a radical new perspective for our understanding the origins of HCV but also suggests a substantial revision of evolutionary timescales of other viruses can be similarly achieved.

evolutionary biology↗

bioRxiv2020

A dynamic nomenclature proposal for SARS-CoV-2 to assist genomic epidemiology

The ongoing pandemic spread of a novel human coronavirus, SARS-COV-2, associated with severe pneumonia disease (COVID-19), has resulted in the generation of thousands of virus genome sequences. The rate of genome generation is unprecedented, yet there is currently no coherent nor accepted scheme for naming the expanding phylogenetic diversity of SARS-CoV-2. We present a rational and dynamic virus nomenclature that uses a phylogenetic framework to identify those lineages that contribute most to active spread. Our system is made tractable by constraining the number and depth of hierarchical lineage labels and by flagging and declassifying virus lineages that become unobserved and hence are likely inactive. By focusing on active virus lineages and those spreading to new locations this nomenclature will assist in tracking and understanding the patterns and determinants of the global spread of SARS-CoV-2.

microbiology↗

bioRxiv2020

Are skyline plot-based demographic estimates overly dependent on smoothing prior assumptions?

In Bayesian phylogenetics, the coalescent process provides an informative framework for inferring changes in the effective size of a population from a phylogeny (or tree) of sequences sampled from that population. Popular coalescent inference approaches such as the Bayesian Skyline Plot, Skyride and Skygrid all model these population size changes with a discontinuous, piecewise-constant function but then apply a smoothing prior to ensure that their posterior population size estimates transition gradually with time. These prior distributions implicitly encode extra population size information that is not available from the observed coalescent data i.e., the tree. Here we present a novel statistic, {Omega}, to quantify and disaggregate the relative contributions of the coalescent data and prior assumptions to the resulting posterior estimate precision. Our statistic also measures the additional mutual information introduced by such priors. Using {Omega} we show that, because it is surprisingly easy to over-parametrise piecewise-constant population models, common smoothing priors can lead to overconfident and potentially misleading inference, even under robust experimental designs. We propose {Omega} as a useful tool for detecting when effective population size estimates are overly reliant on prior assumptions and for improving quantification of the uncertainty in those estimates.

genetics↗

bioRxiv2018

The multifurcating skyline plot

A variety of methods based on coalescent theory have been developed to infer demographic history from gene sequences sampled from natural populations. The \"skyline plot\" and related approaches are commonly employed as exible prior distributions for phylogenetic trees in the Bayesian analysis of pathogen gene sequences. In this work we extend the classic and generalised skyline plot methods to phylogenies that contain one or more multifurcations (i.e. hard polytomies). We use the theory of {Lambda}-coalescents (specifically, Beta(2,)-coalescents) to develop the \"multifurcating skyline plot\", which estimates a piecewise constant function of effective population size through time, conditional on a time-scaled multifurcating phylogeny. We implement a smoothing procedure and extend the method to serially-sampled (heterochronous) data, but we do not address here the problem of estimating trees with multifurcations from gene sequence alignments. We validate our estimator on simulated data using maximum likelihood and find that parameters of the Beta(2,)-coalescent process can be estimated accurately. Lastly we apply the multifurcating skyline plot to a molecular clock phylogeny of 1,610 Ebola virus sequences from the 2014-2016 West African outbreak. We artificially collapse short branches in this empirical phylogeny in order to mimic different levels of multifurcation and show that variance in the reproductive success of the pathogen through time can be estimated by combining the skyline plot with epidemiological case count data.

bioinformatics↗

bioRxiv2017

Zika virus outbreak in Cabo Verde Islands, West Africa: early epidemiological findings

IntroductionThe Zika virus (ZIKV) outbreak in the island nation of Cabo Verde was of unprecedented magnitude in Africa and the first to be associated with microcephaly in the continent.\n\nMethodsUsing a simple mathematical framework we present a first epidemiological assessment of attack and observation rates from 7,580 ZIKV notified cases and 18 microcephaly reports between July 2015 and May 2016.\n\nResultsIn line with observations from the Americas and elsewhere, the single-wave Cabo Verdean ZIKV epidemic was characterized by a basic reproductive number of 1.85 (95% CI, 1.5 -2.2), with overall the attack rate of 51.1% (range 42.1 - 61.1) and observation rate of 2.7% (range 2.29 - 3.33).\n\nConclusionCurrent herd-immunity may not be sufficient to prevent future small-to-medium epidemics in Cabo Verde. Together with a small observation rate, these results highlight the need for rapid and integrated epidemiological, molecular and genomic surveillance to tackle forthcoming outbreaks of ZIKV and other arboviruses.

epidemiology↗

bioRxiv2017

Optimal Point Process Filtering for Birth-Death Model Estimation

The discrete space, continuous time birth-death model is a key process for describing phylogenies in the absence of coalescent approximations. Extensively used in macroevolution for analysing diversification, and in epidemiology for estimating viral dynamics, the birth-death process (BDP) is an important null model for inferring the parameters of reconstructed phylogenies. In this paper we show how optimal, point process (Snyder) filtering techniques can be used for parametric inference on BDPs. Specifically, we introduce the Bayesian Snyder filter (SF) to estimate birth and death rate parameters, given a reconstructed phylogeny. Our estimation procedure makes use of the equivalent Markov birth process description for a reconstructed birth-death phylogeny (Nee et al, 1994). We first analyse the popular constant rate BDP and show that our method gives results consistent with previous work. Among these results is an analytic solution to the special case of the Yule-Furry model. We also find an equivalence between the SF Poisson likelihood and two standard conditioned birth-death model likelihoods. We then generalise our estimation problem to BDPs with time varying rates and numerically solve the SF for two illustrative cases. Our results compare well with a recent Markov chain Monte Carlo method by Hohna et al (2016) and we numericaly show that both methods are solving the same likelihood functions. Lastly we apply the SF to a model selection problem on empirical data. We use the Australian Agamid dataset and predict the same relative model fit as that of the original maximum likelihood technique developed and used by Rabosky (2006) for this dataset. While several capable parametric and non-parametric birth-death estimators already exist, ours is the first to take the Nee et al approach, and directly computes the posterior distribution of the parameters. The SF makes no approximations, beyond those required for parameter space discretisation and numerical integration, and is mean square error optimal. It is deterministic, easily implementable and flexible. We think SFs present a promising alternative parametric BDP inference engine.

ecology↗