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Professor Daniel J Wilson

Research Area: Genetics and Genomics
Scientific Themes: Genetics & Genomics and Immunology & Infectious Disease
Keywords: Evolution, Pathogens, Microbiology, Epidemiology and Statistical genetics
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My research interests centre on the application of tools for evolutionary analysis, in particular population genetics, to understanding human pathogens. I am primarily involved in the UK CRC Consortium Modernising Medical Microbiology, an ambitious project with the aim of tracing and tracking clinically important microorganisms in near to real-time using whole genome next generation sequencing. Through statistical analysis, we wish to elucidate the evolution and epidemiology of these pathogens.

Name Department Institution Country
Dr Rory Bowden Wellcome Trust Centre for Human Genetics University of Oxford United Kingdom
Professor Derrick Crook Experimental Medicine Division University of Oxford United Kingdom
Professor Peter Donnelly FRS Wellcome Trust Centre for Human Genetics University of Oxford United Kingdom
Professor Tim Peto Experimental Medicine Division University of Oxford United Kingdom
Dr Zamin Iqbal Wellcome Trust Centre for Human Genetics University of Oxford United Kingdom
Dr David Wyllie Jenner Institute University of Oxford United Kingdom
Dr Chris Spencer Wellcome Trust Centre for Human Genetics University of Oxford United Kingdom

Didelot X, Wilson DJ. 2015. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol, 11 (2), pp. e1004041. Read abstract | Read more

Recombination is an important evolutionary force in bacteria, but it remains challenging to reconstruct the imports that occurred in the ancestry of a genomic sample. Here we present ClonalFrameML, which uses maximum likelihood inference to simultaneously detect recombination in bacterial genomes and account for it in phylogenetic reconstruction. ClonalFrameML can analyse hundreds of genomes in a matter of hours, and we demonstrate its usefulness on simulated and real datasets. We find evidence for recombination hotspots associated with mobile elements in Clostridium difficile ST6 and a previously undescribed 310kb chromosomal replacement in Staphylococcus aureus ST582. ClonalFrameML is freely available at Hide abstract

Hedge J, Wilson DJ. 2014. Bacterial phylogenetic reconstruction from whole genomes is robust to recombination but demographic inference is not. MBio, 5 (6), pp. e02158. Read abstract | Read more

UNLABELLED: Phylogenetic inference in bacterial genomics is fundamental to understanding problems such as population history, antimicrobial resistance, and transmission dynamics. The field has been plagued by an apparent state of contradiction since the distorting effects of recombination on phylogeny were discovered more than a decade ago. Researchers persist with detailed phylogenetic analyses while simultaneously acknowledging that recombination seriously misleads inference of population dynamics and selection. Here we resolve this paradox by showing that phylogenetic tree topologies based on whole genomes robustly reconstruct the clonal frame topology but that branch lengths are badly skewed. Surprisingly, removing recombining sites can exacerbate branch length distortion caused by recombination. IMPORTANCE: Phylogenetic tree reconstruction is a popular approach for understanding the relatedness of bacteria in a population from differences in their genome sequences. However, bacteria frequently exchange regions of their genomes by a process called homologous recombination, which violates a fundamental assumption of phylogenetic methods. Since many researchers continue to use phylogenetics for recombining bacteria, it is important to understand how recombination affects the conclusions drawn from these analyses. We find that whole-genome sequences afford great accuracy in reconstructing evolutionary relationships despite concerns surrounding the presence of recombination, but the branch lengths of the phylogenetic tree are indeed badly distorted. Surprisingly, methods to reduce the impact of recombination on branch lengths can exacerbate the problem. Hide abstract

Everitt RG, Didelot X, Batty EM, Miller RR, Knox K, Young BC, Bowden R, Auton A et al. 2014. Mobile elements drive recombination hotspots in the core genome of Staphylococcus aureus. Nat Commun, 5 pp. 3956. Read abstract | Read more

Horizontal gene transfer is an important driver of bacterial evolution, but genetic exchange in the core genome of clonal species, including the major pathogen Staphylococcus aureus, is incompletely understood. Here we reveal widespread homologous recombination in S. aureus at the species level, in contrast to its near-complete absence between closely related strains. We discover a patchwork of hotspots and coldspots at fine scales falling against a backdrop of broad-scale trends in rate variation. Over megabases, homoplasy rates fluctuate 1.9-fold, peaking towards the origin-of-replication. Over kilobases, we find core recombination hotspots of up to 2.5-fold enrichment situated near fault lines in the genome associated with mobile elements. The strongest hotspots include regions flanking conjugative transposon ICE6013, the staphylococcal cassette chromosome (SCC) and genomic island νSaα. Mobile element-driven core genome transfer represents an opportunity for adaptation and challenges our understanding of the recombination landscape in predominantly clonal pathogens, with important implications for genotype-phenotype mapping. Hide abstract

Eyre DW, Cule ML, Wilson DJ, Griffiths D, Vaughan A, O'Connor L, Ip CL, Golubchik T et al. 2013. Diverse sources of C. difficile infection identified on whole-genome sequencing. N Engl J Med, 369 (13), pp. 1195-1205. Read abstract | Read more

BACKGROUND: It has been thought that Clostridium difficile infection is transmitted predominantly within health care settings. However, endemic spread has hampered identification of precise sources of infection and the assessment of the efficacy of interventions. METHODS: From September 2007 through March 2011, we performed whole-genome sequencing on isolates obtained from all symptomatic patients with C. difficile infection identified in health care settings or in the community in Oxfordshire, United Kingdom. We compared single-nucleotide variants (SNVs) between the isolates, using C. difficile evolution rates estimated on the basis of the first and last samples obtained from each of 145 patients, with 0 to 2 SNVs expected between transmitted isolates obtained less than 124 days apart, on the basis of a 95% prediction interval. We then identified plausible epidemiologic links among genetically related cases from data on hospital admissions and community location. RESULTS: Of 1250 C. difficile cases that were evaluated, 1223 (98%) were successfully sequenced. In a comparison of 957 samples obtained from April 2008 through March 2011 with those obtained from September 2007 onward, a total of 333 isolates (35%) had no more than 2 SNVs from at least 1 earlier case, and 428 isolates (45%) had more than 10 SNVs from all previous cases. Reductions in incidence over time were similar in the two groups, a finding that suggests an effect of interventions targeting the transition from exposure to disease. Of the 333 patients with no more than 2 SNVs (consistent with transmission), 126 patients (38%) had close hospital contact with another patient, and 120 patients (36%) had no hospital or community contact with another patient. Distinct subtypes of infection continued to be identified throughout the study, which suggests a considerable reservoir of C. difficile. CONCLUSIONS: Over a 3-year period, 45% of C. difficile cases in Oxfordshire were genetically distinct from all previous cases. Genetically diverse sources, in addition to symptomatic patients, play a major part in C. difficile transmission. (Funded by the U.K. Clinical Research Collaboration Translational Infection Research Initiative and others.). Hide abstract

Eyre DW, Cule ML, Griffiths D, Crook DW, Peto TE, Walker AS, Wilson DJ. 2013. Detection of mixed infection from bacterial whole genome sequence data allows assessment of its role in Clostridium difficile transmission. PLoS Comput Biol, 9 (5), pp. e1003059. Read abstract | Read more

Bacterial whole genome sequencing offers the prospect of rapid and high precision investigation of infectious disease outbreaks. Close genetic relationships between microorganisms isolated from different infected cases suggest transmission is a strong possibility, whereas transmission between cases with genetically distinct bacterial isolates can be excluded. However, undetected mixed infections-infection with ≥2 unrelated strains of the same species where only one is sequenced-potentially impairs exclusion of transmission with certainty, and may therefore limit the utility of this technique. We investigated the problem by developing a computationally efficient method for detecting mixed infection without the need for resource-intensive independent sequencing of multiple bacterial colonies. Given the relatively low density of single nucleotide polymorphisms within bacterial sequence data, direct reconstruction of mixed infection haplotypes from current short-read sequence data is not consistently possible. We therefore use a two-step maximum likelihood-based approach, assuming each sample contains up to two infecting strains. We jointly estimate the proportion of the infection arising from the dominant and minor strains, and the sequence divergence between these strains. In cases where mixed infection is confirmed, the dominant and minor haplotypes are then matched to a database of previously sequenced local isolates. We demonstrate the performance of our algorithm with in silico and in vitro mixed infection experiments, and apply it to transmission of an important healthcare-associated pathogen, Clostridium difficile. Using hospital ward movement data in a previously described stochastic transmission model, 15 pairs of cases enriched for likely transmission events associated with mixed infection were selected. Our method identified four previously undetected mixed infections, and a previously undetected transmission event, but no direct transmission between the pairs of cases under investigation. These results demonstrate that mixed infections can be detected without additional sequencing effort, and this will be important in assessing the extent of cryptic transmission in our hospitals. Hide abstract

Golubchik T, Batty EM, Miller RR, Farr H, Young BC, Larner-Svensson H, Fung R, Godwin H et al. 2013. Within-host evolution of Staphylococcus aureus during asymptomatic carriage. PLoS One, 8 (5), pp. e61319. Read abstract | Read more

BACKGROUND: Staphylococcus aureus is a major cause of healthcare associated mortality, but like many important bacterial pathogens, it is a common constituent of the normal human body flora. Around a third of healthy adults are carriers. Recent evidence suggests that evolution of S. aureus during nasal carriage may be associated with progression to invasive disease. However, a more detailed understanding of within-host evolution under natural conditions is required to appreciate the evolutionary and mechanistic reasons why commensal bacteria such as S. aureus cause disease. Therefore we examined in detail the evolutionary dynamics of normal, asymptomatic carriage. Sequencing a total of 131 genomes across 13 singly colonized hosts using the Illumina platform, we investigated diversity, selection, population dynamics and transmission during the short-term evolution of S. aureus. PRINCIPAL FINDINGS: We characterized the processes by which the raw material for evolution is generated: micro-mutation (point mutation and small insertions/deletions), macro-mutation (large insertions/deletions) and the loss or acquisition of mobile elements (plasmids and bacteriophages). Through an analysis of synonymous, non-synonymous and intergenic mutations we discovered a fitness landscape dominated by purifying selection, with rare examples of adaptive change in genes encoding surface-anchored proteins and an enterotoxin. We found evidence for dramatic, hundred-fold fluctuations in the size of the within-host population over time, which we related to the cycle of colonization and clearance. Using a newly-developed population genetics approach to detect recent transmission among hosts, we revealed evidence for recent transmission between some of our subjects, including a husband and wife both carrying populations of methicillin-resistant S. aureus (MRSA). SIGNIFICANCE: This investigation begins to paint a picture of the within-host evolution of an important bacterial pathogen during its prevailing natural state, asymptomatic carriage. These results also have wider significance as a benchmark for future systematic studies of evolution during invasive S. aureus disease. Hide abstract

Dearlove B, Wilson DJ. 2013. Coalescent inference for infectious disease: meta-analysis of hepatitis C. Philos Trans R Soc Lond B Biol Sci, 368 (1614), pp. 20120314. Read abstract | Read more

Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible-infectious (SI), susceptible-infectious-susceptible (SIS) and susceptible-infectious-recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number (R(0)) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference. Hide abstract

Wilson DJ. 2012. Insights from genomics into bacterial pathogen populations. PLoS Pathog, 8 (9), pp. e1002874. Read abstract | Read more

Bacterial pathogens impose a heavy burden of disease on human populations worldwide. The gravest threats are posed by highly virulent respiratory pathogens, enteric pathogens, and HIV-associated infections. Tuberculosis alone is responsible for the deaths of 1.5 million people annually. Treatment options for bacterial pathogens are being steadily eroded by the evolution and spread of drug resistance. However, population-level whole genome sequencing offers new hope in the fight against pathogenic bacteria. By providing insights into bacterial evolution and disease etiology, these approaches pave the way for novel interventions and therapeutic targets. Sequencing populations of bacteria across the whole genome provides unprecedented resolution to investigate (i) within-host evolution, (ii) transmission history, and (iii) population structure. Moreover, advances in rapid benchtop sequencing herald a new era of real-time genomics in which sequencing and analysis can be deployed within hours in response to rapidly changing public health emergencies. The purpose of this review is to highlight the transformative effect of population genomics on bacteriology, and to consider the prospects for answering abiding questions such as why bacteria cause disease. Hide abstract

Young BC, Golubchik T, Batty EM, Fung R, Larner-Svensson H, Votintseva AA, Miller RR, Godwin H et al. 2012. Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease. Proc Natl Acad Sci U S A, 109 (12), pp. 4550-4555. Read abstract | Read more

Whole-genome sequencing offers new insights into the evolution of bacterial pathogens and the etiology of bacterial disease. Staphylococcus aureus is a major cause of bacteria-associated mortality and invasive disease and is carried asymptomatically by 27% of adults. Eighty percent of bacteremias match the carried strain. However, the role of evolutionary change in the pathogen during the progression from carriage to disease is incompletely understood. Here we use high-throughput genome sequencing to discover the genetic changes that accompany the transition from nasal carriage to fatal bloodstream infection in an individual colonized with methicillin-sensitive S. aureus. We found a single, cohesive population exhibiting a repertoire of 30 single-nucleotide polymorphisms and four insertion/deletion variants. Mutations accumulated at a steady rate over a 13-mo period, except for a cluster of mutations preceding the transition to disease. Although bloodstream bacteria differed by just eight mutations from the original nasally carried bacteria, half of those mutations caused truncation of proteins, including a premature stop codon in an AraC-family transcriptional regulator that has been implicated in pathogenicity. Comparison with evolution in two asymptomatic carriers supported the conclusion that clusters of protein-truncating mutations are highly unusual. Our results demonstrate that bacterial diversity in vivo is limited but nonetheless detectable by whole-genome sequencing, enabling the study of evolutionary dynamics within the host. Regulatory or structural changes that occur during carriage may be functionally important for pathogenesis; therefore identifying those changes is a crucial step in understanding the biological causes of invasive bacterial disease. Hide abstract

Didelot X, Bowden R, Wilson DJ, Peto TEA, Crook DW. 2012. Transforming clinical microbiology with bacterial genome sequencing Nature Reviews Genetics, 13 (9), pp. 601-612. Read abstract | Read more

Whole-genome sequencing of bacteria has recently emerged as a cost-effective and convenient approach for addressing many microbiological questions. Here, we review the current status of clinical microbiology and how it has already begun to be transformed by using next-generation sequencing. We focus on three essential tasks: identifying the species of an isolate, testing its properties, such as resistance to antibiotics and virulence, and monitoring the emergence and spread of bacterial pathogens. We predict that the application of next-generation sequencing will soon be sufficiently fast, accurate and cheap to be used in routine clinical microbiology practice, where it could replace many complex current techniques with a single, more efficient workflow. © 2012 Macmillan Publishers Limited. All rights reserved. Hide abstract

Wilson DJ, Hernandez RD, Andolfatto P, Przeworski M. 2011. A population genetics-phylogenetics approach to inferring natural selection in coding sequences. PLoS Genet, 7 (12), pp. e1002395. Read abstract | Read more

Through an analysis of polymorphism within and divergence between species, we can hope to learn about the distribution of selective effects of mutations in the genome, changes in the fitness landscape that occur over time, and the location of sites involved in key adaptations that distinguish modern-day species. We introduce a novel method for the analysis of variation in selection pressures within and between species, spatially along the genome and temporally between lineages. We model codon evolution explicitly using a joint population genetics-phylogenetics approach that we developed for the construction of multiallelic models with mutation, selection, and drift. Our approach has the advantage of performing direct inference on coding sequences, inferring ancestral states probabilistically, utilizing allele frequency information, and generalizing to multiple species. We use a Bayesian sliding window model for intragenic variation in selection coefficients that efficiently combines information across sites and captures spatial clustering within the genome. To demonstrate the utility of the method, we infer selective pressures acting in Drosophila melanogaster and D. simulans from polymorphism and divergence data for 100 X-linked coding regions. Hide abstract

Genome Evolution in Bacterial Infectious Diseases

Why do bacteria cause disease? Are there genetic differences between bacteria that affect disease severity? Does natural selection act on bacteria within the body to promote disease or attenuate infection?We are using statistical genetics and evolutionary biology to understand how mutations in the genome and changes in gene expression affect virulence - the severity or frequency of infection.Bacterial diseases are leading causes of mortality worldwide, exerting a profound effect onglobal health, ...

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Developing a vaccine against Staphylococcus aureus

The high human and economic burden of S. aureus disease in man, as well as its impact in agriculture, continue to stimulate interest in an effective vaccine against Staphylococcus aureus.Successful selection of antigens for such a vaccine represents a significant challenge, due to variation in S. aureus behaviour between strains and between in vitro conditions, where it is relatively easily studied, and in vivo infection.  We have recently developed tools which identify both virulent and less vi ...

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