Biology subjects

du Plessis, L.

Publications and source records attributed to du Plessis, L..

14 recordsLinked to original sources

bioRxiv2025

TyCHE enables time-resolved lineage tracing of heterogeneously-evolving populations

Phylogenetic methods for cell lineage tracing have driven significant insights into organismal development, immune responses, and tumor evolution. While most methods estimate mutation trees, time-resolved lineage trees are more interpretable and could relate events like cellular migration and differentiation to perturbations like vaccines and drug treatments. However, somatic mutation rates vary dramatically by cell type, significantly biasing existing methods. We introduce TyCHE (Type-linked Clocks for Heterogeneous Evolution), a Bayesian phylogenetics package that infers time-resolved phylogenies of populations with distinct evolutionary rates. We demonstrate that TyCHE improves tree accuracy using a new simulation package SimBLE (Simulator of B cell Lineage Evolution). We use TyCHE to infer patterns of memory B cell differentiation during HIV infection, dynamics of recall germinal centers following influenza vaccination, evolution of a glioma tumor lineage, and progression of a bacterial lung infection. TyCHE and SimBLE are available as open-source software packages compatible with the BEAST2 and Immcantation ecosystems.

immunology↗

bioRxiv2025

Characterizing Co-Circulating Respiratory Virus Genomic Diversity in Switzerland with Hybrid-Capture Sequencing and Phylogenetic Reconstructions: Insights into the 2023/24 Season

Respiratory viruses circulate yearly with strain-specific patterns. Although SARS-CoV-2 and Influenza A/B genomic surveillance is well-developed, most respiratory viruses are unevenly monitored, lacking geographical diversity to capture wider population dynamics. Consequently, insights into respiratory virus evolution are limited. We investigated the genetic diversity of these viruses within one country. During the 2023/24 season, we conducted whole-genome sequencing of 1129 clinical samples using a hybrid-capture protocol. These samples were pre-tested by real-time PCR panels throughout Switzerland. Leveraging publicly-available full-length genomes, we constructed background datasets representative of geographical diversity and built phylogenies for all viruses with more than 40 new high-quality genomes from this study. We detected 981 viruses and recovered 461 high-quality genomes, including 18 co-infections, from 437 PCR-positive and 6 PCR-negative samples. All viruses detected by PCR were also detected by sequencing in 56% of samples. The four most prevalent viruses were Influenza A/H1N1, SARS-CoV-2, RSV-A, and HPIV-3, and their seasonal spread was consistent with wastewater monitoring and influenza-like illness reports. Swiss viral genomes were representative of the global genomic diversity, with evidence for multiple introductions into Switzerland, and we identified putative Swiss clusters. In this proof-of-concept study, we focus on 3 viruses (Influenza A/H1N1, RSV-A/B, and HPIV-3), and we demonstrate the streamlined implementation of a broad respiratory virus genomic surveillance workflow with an off-the-shelf protocol and publicly-available software. In addition, we highlight additional evolutionary insights that can only be derived from genomic surveillance. Going forward, this dataset will be a useful resource for future investigations into respiratory viral genomic diversity.

genetics↗

bioRxiv2025

Highly pathogenic avian influenza management in high-density poultry farming areas

The continuous spread of highly pathogenic avian influenza H5 viruses poses significant challenges, particularly in regions with high poultry farm densities where conventional control measures are less effective. Using phylogeographic and phylodynamic tools, we analysed virus spread in Southwestern France in 2020-21, a region with recurrent outbreaks. Following a single introduction, the virus spread regionally, mostly affecting duck farms, peaking in mid-December with a velocity of 27.8 km/week and an effective reproduction number between farms (Re) of 3.8, suggesting the virus can spread beyond current control radii. Transmission declined after late December following preventive culling. Farm infectiousness was estimated around 9 days. Duck farm density was the main driver of virus spread and we identified farm density and proximity thresholds required to maintain effective control (Re < 1). These findings offer actionable guidance to support regional biosecurity and to improve the robustness of the poultry sector to mitigate future outbreaks.

evolutionary biology↗

bioRxiv2023

EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering

Accurately estimating the effective reproduction number (Rt) of a circulating pathogen is a fundamental challenge in the study of infectious disease. The fields of epidemiology and pathogen phylodynamics both share this goal, but to date, methodologies and data employed by each remain largely distinct. Here we present EpiFusion: a joint approach that can be used to harness the complementary strengths of each field to improve estimation of outbreak dynamics for large and poorly sampled epidemics, such as arboviral or respiratory outbreaks, and validate it for retrospective analysis. We propose a model of Rt that estimates outbreak trajectories conditional upon both phylodynamic (time-scaled trees estimated from genetic sequences) and epidemiological (case incidence) data. We simulate stochastic outbreak trajectories that are weighted according to epidemiological and phylodynamic observation models and fit using particle Markov Chain Monte Carlo. To assess performance, we test EpiFusion on simulated outbreaks in which transmission and/or surveillance rapidly changes and find that using EpiFusion to combine epidemiological and phylodynamic data maintains accuracy and increases certainty in trajectory and Rt estimates, compared to when each data type is used alone. Finally, we benchmark EpiFusions performance against existing methods to estimate Rt and demonstrate advances in efficiency and accuracy. Importantly, our approach scales efficiently with dataset size, including the use of phylogenetic trees generated from large genomic datasets. EpiFusion is designed to accommodate future extensions that will improve its utility, such as introduction of population structure, accommodations for phylogenetic uncertainty, and the ability to weight the contributions of genomic or case incidence to the inference. Author SummaryUnderstanding infectious disease spread is fundamental to protecting public health, but can be challenging as disease spread is a phenomenon that cannot be directly observed. So, epidemiologists use data in conjunction with mathematical models to estimate disease dynamics. Often, combinations of different models and data can be used to answer the same questions - for example traditional epidemiology commonly uses case incidence data (the number of people who have tested positive for a disease at a certain time) whereas phylodynamic models use pathogen genomic sequence data and our knowledge of their evolution to model disease population dynamics. Each of these approaches have strengths and limitations, and data of each type can be sparse or biased, particularly in rapidly developing outbreaks or lower-middle income countries. An increasing number of approaches attempt to fix this problem by incorporating diverse concepts and data types together in their models. We aim to contribute to this movement by introducing EpiFusion, a modelling framework that makes improvements on efficiency and temporal resolution. EpiFusion uses particle filtering to simulate epidemic trajectories over time and weight their likelihood according to both case incidence data and a phylogenetic tree using separate observation models, resulting in the inference of trajectories in agreement with both sets of data. Improvements in our ability to accurately and confidently model pathogen spread help us to respond to infectious disease outbreaks and improve public health.

evolutionary biology↗

bioRxiv2023

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation

The large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, adaptation to higher sample coverage, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting two large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.

bioinformatics↗

bioRxiv2023

Inferring patterns of recombination and divergence with ancient and modern treponemal genomes

BackgroundTreponemal diseases pose significant global health risks, presenting severe challenges to public health due to their serious health impacts if left untreated. Despite numerous genomic studies on Treponema pallidum and the known possible biases introduced by the choice of the reference genome used for mapping, few investigations have addressed how these biases affect phylogenetic and evolutionary analysis of these bacteria. In this study, we assessed the impact of selecting an appropriate genomic reference on phylogenetic and evolutionary analyses of T. pallidum. ResultsWe designed a multiple-reference-based (MRB) mapping strategy using four different reference genomes and compared it to traditional single-reference mapping. To conduct this comparison, we created a genomic dataset comprising 77 modern and ancient genomes from the three subspecies of T. pallidum, including a newly sequenced 17th-century genome (35X coverage) of a syphilis-causing strain (designated as W86). Our findings show that recombination detection was consistent across different references, but the choice of reference significantly affected ancient genome reconstruction and phylogenetic inferences. The high-coverage W86 genome obtained here also provided a new calibration point for Bayesian molecular clock dating, improving the reconstruction of the evolutionary history of treponemal diseases. Additionally, we identified novel recombination events, positive selection targets, and refined dating estimates for key events in the species history. ConclusionsThis study highlights the importance of considering methodological implications and reference genome bias in High-Throughput Sequencing-based whole-genome analysis of T. pallidum, especially of ancient or low-coverage samples, contributing to a deeper understanding of this pathogen and its subspecies.

microbiology↗

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↗

bioRxiv2020

A computationally tractable birth-death model that combines phylogenetic and epidemiological data

Inferring the dynamics of pathogen transmission during an outbreak is an important problem in both infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each data type provides different, and potentially complementary, insight; recent studies have recognised that combining data sources can improve estimates of the transmission rate and number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the number of unreported infections. Using simulated data we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace. Author summaryMathematical epidemiologists typically studies time series of cases, ie the epidemic curve, to understand the spread of pathogens. Genetic epidemiologists study similar problems but do so using measurements of the genetic sequence of the pathogen which also contain information about the transmission process. There have been many attempts to unite these approaches so that both data sources can be utilised. However, striking a suitable balance between model flexibility and fidelity, in a way that is computationally tractable, has proven challenging; there are several competing methods but for large datasets they are intractable. As sequencing of pathogen genomes becomes more common, and an increasing amount of epidemiological data is collected, this situation will only be exacerbated. To bridge the gap between the time series and genomic methods we developed an approximation scheme, called TimTam, which can accurately and efficiently estimate key features of an epidemic such as the prevalence of the infection and the effective reproduction number, ie how many people are currently infected and the degree to which the infection is spreading.

genomics↗

bioRxiv2020

Ancient bacterial genomes reveal a formerly unknown diversity of Treponema pallidum strains in early modern Europe

Sexually transmitted (venereal) syphilis marked European history with a devastating epidemic at the end of the 15th century, and is currently re-emerging globally. Together with non-venereal treponemal diseases, like bejel and yaws, found in subtropical and tropical regions, it poses a prevailing health threat worldwide. The origins and spread of treponemal diseases remain unresolved, including syphilis potential introduction into Europe from the Americas. Here, we present the first genetic data from archaeological human remains reflecting a previously unknown diversity of Treponema pallidum in historical Europe. Our study demonstrates that a variety of strains related to both venereal syphilis and yaws were already present in Northern Europe in the early modern period. We also discovered a previously unknown T. pallidum lineage recovered as a sister group to yaws and bejel. These findings imply a more complex pattern of geographical prevalence and etiology of early treponemal epidemics than previously understood.

genetics↗

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↗

bioRxiv2019

Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has been responsible for substantial impacts on public, veterinary, and wildlife health. WNV isolates have been sampled at various times and locations across North America since its introduction to New York twenty years ago. We exploit this genetic data repository to demonstrate that factors hypothesised to affect viral dispersal and demography can be formally tested. Specifically, we detail and apply an analytical workflow consisting of state-of-the art methods that we further improve to test the impact of environmental factors on the dispersal locations, velocity, and frequency of viral lineages, as well as on the genetic diversity of the viral population through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. Using a simulation procedure, we find no evidence that viral lineages preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient. Finally, we also separately apply our testing approaches on the three WNV genotypes that circulated in North America in order to understand and compare their dispersal ability. Our study demonstrates that the development and application of statistical approaches, coupled with comprehensive pathogen genomic data, can address epidemiological questions that might otherwise be difficult or impractically expensive to answer.

evolutionary biology↗

bioRxiv2019

An integrated framework for the joint inference of demographic history and sampling intensity from genealogies or genetic sequences

Estimating past population dynamics from molecular sequences that have been sampled longitudinally through time is an important problem in infectious disease epidemiology, molecular ecology and macroevolution. Popular solutions, such as the skyline and skygrid methods, infer past effective population sizes from the coalescent event times of phylogenies reconstructed from sampled sequences, but assume that sequence sampling times are uninformative about population size changes. Recent work has started to question this assumption by exploring how sampling time information can aid coalescent inference. Here we develop, investigate, and implement a new skyline method, termed the epoch sampling skyline plot (ESP), to jointly estimate the dynamics of population size and sampling rate through time. The ESP is inspired by real-world data collection practices and comprises a flexible model in which the sequence sampling rate is proportional to the population size within an epoch but can change discontinuously between epochs. We show that the ESP is accurate under several realistic sampling protocols and we prove analytically that it can at least double the best precision achievable by standard approaches. We generalise the ESP to incorporate phylogenetic uncertainty in a new Bayesian package (BESP) in BEAST2. We re-examine two well-studied empirical datasets from virus epidemiology and molecular evolution and find that the BESP improves upon previous coalescent estimators and generates new, biologically-useful insights into the sampling protocols underpinning these datasets. Sequence sampling times provide a rich source of information for coalescent inference that will become increasingly important as sequence collection intensifies and becomes more formalised.

genetics↗

bioRxiv2018

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments.\n\nDeveloping such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.\n\nAuthor summaryBayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

evolutionary biology↗

bioRxiv2017

Epidemic establishment and cryptic transmission of Zika virus in Brazil and the Americas

Zika virus (ZIKV) transmission in the Americas was first confirmed in May 2015 in Northeast Brazil1. Brazil has the highest number of reported ZIKV cases worldwide (>200,000 by 24 Dec 20162) as well as the greatest number of cases associated with microcephaly and other birth defects (2,366 confirmed cases by 31 Dec 20162). Following the initial detection of ZIKV in Brazil, 47 countries and territories in the Americas have reported local ZIKV transmission, with 22 of these reporting ZIKV-associated severe disease3. Yet the origin and epidemic history of ZIKV in Brazil and the Americas remain poorly understood, despite the value of such information for interpreting past trends in reported microcephaly. To address this we generated 53 complete or partial ZIKV genomes, mostly from Brazil, including data generated by the ZiBRA project - a mobile genomics lab that travelled across Northeast Brazil in 2016. One sequence represents the earliest confirmed ZIKV infection in Brazil. Joint analyses of viral genomes with ecological and epidemiological data estimate that the ZIKV epidemic first became established in NE Brazil by March 2014 and likely disseminated from there, both nationally and internationally, before the first detection of ZIKV in the Americas. Estimated dates of the international spread of ZIKV from Brazil coincide with periods of high vector suitability in recipient regions and indicate the duration of pre-detection cryptic transmission in those regions. NE Brazils role in the establishment of ZIKV in the Americas is further supported by geographic analysis of ZIKV transmission potential and by estimates of the virus basic reproduction number.\n\nOne Sentence SummaryVirus genomes reveal the establishment of Zika virus in Northeast Brazil and the Americas, and provide an appropriate timeframe for baseline (pre-Zika) microcephaly in different regions.

genomics↗