Biology subjects

Xavier Didelot

Publications and source records attributed to Xavier Didelot.

7 recordsLinked to original sources

bioRxiv2016

New method to reconstruct phylogenetic and transmission trees with sequence data from infectious disease outbreaks

Whole-genome sequencing (WGS) of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and WGS data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but applications are tailored to specific datasets with matching model assumptions and code, or otherwise make simplifying assumptions that break up the dependency between the four processes. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with WGS data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.\n\nAuthor SummaryIt is becoming easier and cheaper to obtain whole genome sequences of pathogen samples during outbreaks of infectious diseases. If all hosts during an outbreak are sampled, and these samples are sequenced, the small differences between the sequences (single nucleotide polymorphisms, SNPs) give information on the transmission tree, i.e. who infected whom, and when. However, correctly inferring this tree is not straightforward, because SNPs arise from unobserved processes including infection events, as well as pathogen growth and mutation within the hosts. Several methods have been developed in recent years, but none so generic and easily accessible that it can easily be applied to new settings and datasets. We have developed a new model and method to infer transmission trees without putting prior limiting constraints on the order of unobserved events. The method is easily accessible in an R package implementation. We show that the method performs well on new and previously published simulated data. We illustrate applicability to a wide range of infectious diseases and settings by analysing five published datasets on densely sampled infectious disease outbreaks, confirming or improving the original results.

Epidemiology↗

bioRxiv2016

Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks

Genomic data is increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom - a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by colouring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch colouring approach can incorporate a variable number of unique colours to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented our technique in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

Epidemiology↗

bioRxiv2016

Bayesian Inference of the Evolution of a Phenotype Distribution on a Phylogenetic Tree

The distribution of a phenotype on a phylogenetic tree is often a quantity of interest. Many phenotypes have imperfect heritability, so that a measurement of the phenotype for an individual can be thought of as a single realisation from the phenotype distribution of that individual. If all individuals in a phylogeny had the same phenotype distribution, measured phenotypes would be randomly distributed on the tree leaves. This is however often not the case, implying that the phenotype distribution evolves over time. Here we propose a new model based on this principle of evolving phenotype distribution on the branches of a phylogeny, which is different from ancestral state reconstruction where the phenotype itself is assumed to evolve. We develop an efficient Bayesian inference method to estimate the parameters of our model and to test the evidence for changes in the phenotype distribution. We use multiple simulated datasets to show that our algorithm has good sensitivity and specificity properties. Since our method identifies branches on the tree on which the phenotype distribution has changed, it is able to break down a tree into components for which this distribution is unique and constant. We present two applications of our method, one investigating the association between HIV genetic variation and human leukocyte antigen, and the other studying host range distribution in a lineage of Salmonella enterica, and we discuss many other potential applications. All the methods described in this paper are implemented in a software package called TreeBreaker which is freely available for download at https://github.com/ansariazim/TreeBreaker

Evolutionary Biology↗

bioRxiv2016

Declaring a tuberculosis outbreak over with genomic epidemiology

We report an updated method for inferring the time at which an infectious disease was transmitted between persons from a time-labelled pathogen genome phylogeny. We applied the method to 48 Mycobacterium tuberculosis genomes as part of a real-time public health outbreak investigation, demonstrating that although active tuberculosis (TB) cases were diagnosed through 2013, no transmission events took place beyond mid-2012. Subsequent cases were the result of progression from latent TB infection to active disease and not recent transmission. This evolutionary genomic approach was used to declare the outbreak over in January 2015.

Genomics↗

bioRxiv2015

Evolutionary history of the global emergence of the Escherichia coli epidemic clone ST131

BackgroundEscherichia coli sequence type 131 (ST131) has emerged globally as the most predominant lineage within this clinically important species, and its association with fluoroquinolone and extended-spectrum cephalosporin resistance impacts significantly on treatment. The evolutionary histories of this lineage, and of important antimicrobial resistance elements within it, remain unclearly defined.\n\nResultsThis study of the largest worldwide collection (n = 215) of sequenced ST131 E. coli isolates to date demonstrates that clonal expansion of two previously recognized antimicrobial-resistant clades, C1/H30R and C2/H30Rx, started around 25 years ago, consistent with the widespread introduction of fluoroquinolones and extended-spectrum cephalosporins in clinical medicine. These two clades appear to have emerged in the United States, with the expansion of the C2/H30Rx clade driven by the acquisition of a blaCTX-M-15-containing IncFII-like plasmid that has subsequently undergone extensive rearrangement. Several other evolutionary processes influencing the trajectory of this drug-resistant lineage are described, including sporadic acquisitions of CTX-M resistance plasmids, and chromosomal integration of blaCTX-M within sub-clusters followed by vertical evolution. These processes are also occurring for another family of CTX-M gene variants more recently observed amongst ST131, the blaCTX-M-14/14-like group.\n\nConclusionsThe complexity of the evolutionary history of ST131 has important implications for antimicrobial resistance surveillance, epidemiological analysis, and control of emerging clinical lineages of E. coli. These data also highlight the global imperative to reduce specific antibiotic selection pressures, and demonstrate the important and varied roles played by plasmids and other mobile genetic elements in the perpetuation of antimicrobial resistance within lineages.

Genomics↗

bioRxiv2015

Worldwide population structure, long term demography, and local adaptation of Helicobacter pylori

Helicobacter pylori is an important human pathogen associated with serious gastric diseases. Owing to its medical importance and close relationship with its human host, understanding genomic patterns of global and local adaptation in H. pylori may be of particular significance for both clinical and evolutionary studies. Here we present the first such whole-genome analysis of 60 globally distributed strains, from which we inferred worldwide population structure and demographic history and shed light on interesting global and local events of positive selection, with particular emphasis on the evolution of San-associated lineages. Our results indicate a more ancient origin for the association of humans and H. pylori than previously thought. We identify several important perspectives for future clinical research on candidate selected regions that include both previously characterized genes (e.g. transcription elongation factor NusA and tumor Necrosis Factor Alpha-Inducing Protein Tip) and hitherto unknown functional genes.

Evolutionary Biology↗

bioRxiv2013

Bayesian inference of infectious disease transmission from whole genome sequence data

Genomics is increasingly being used to investigate disease outbreaks, but an important question remains unanswered - how well do genomic data capture known transmission events, particularly for pathogens with long carriage periods or large within-host population sizes? Here we present a novel Bayesian approach to reconstruct densely-sampled outbreaks from genomic data whilst considering within-host diversity. We infer a time-labelled phylogeny using BEAST, then infer a transmission network via a Monte-Carlo Markov Chain. We find that under a realistic model of within-host evolution, reconstructions of simulated outbreaks contain substantial uncertainty even when genomic data reflect a high substitution rate. Reconstruction of a real-world tuberculosis outbreak displayed similar uncertainty, although the correct source case and several clusters of epidemiologically linked cases were identified. We conclude that genomics cannot wholly replace traditional epidemiology, but that Bayesian reconstructions derived from sequence data may form a useful starting point for a genomic epidemiology investigation.

Genomics↗