The role of hospital transmission in Clostridium difficile infection

This week the Modernising Medical Microbiology consortium at Oxford published the findings of a six-year study into the transmission of the hospital "superbug" Clostridium difficile. The research, which appears in the New England Journal of Medicine, shows that the majority of new cases cannot be traced to other infections in hospital, and indicates instead that there must be a large, as yet unidentified, reservoir of C. difficile infectious to humans. This finding is important because it suggests that there is a limit to which more and more intense hospital cleaning - important though it has been - can continue to have in reducing C. difficile infection.

The research, which is the result of a tireless effort by a large number of my colleagues - notably David Eyre, Tim Peto and Sarah Walker - used bacterial whole genome sequencing to detect within-hospital transmission by searching for extremely closely related bacterial strains among more than 1200 cases of C. difficile infection that occurred in Oxfordshire between September 2007 and March 2011. The consortium is currently developing the approach for routine microbiology diagnostics and infection control, with a view to eventual roll-out across the NHS.

Postdoctoral Position in Statistical Genomics

The position of Postdoctoral Scientist is available in my group to lead research on the Wellcome Trust and Royal Society funded project Statistical Methods for Whole Genome Phenotype Mapping in Bacterial Populations.

Bacteria cause disease throughout the world. Different strains vary in disease severity, but the genetic variants responsible remain largely undiscovered. Recent breakthroughs in whole genome sequencing provide new opportunities for discovery, but the lack of statistical analysis tools tailored to the special structure of bacterial populations presents a roadblock. The goal of the project is to develop an analysis framework for mapping genes underlying naturally variable traits in bacterial populations. Focusing on the hospital-associated pathogens Staphylococcus aureus and Clostridium difficile, we will investigate the role of bacterial variants on disease severity.

The role of the Postdoctoral Scientist is to develop novel statistical methods for analysing genotype-phenotype associations in bacteria at the whole genome level. The successful candidate will write software implementing the statistical methods and apply them to design and carry out investigations into the genetic basis of virulence in natural populations of bacterial pathogens. The ideal candidate would be a recently graduating PhD student with experience of statistical genetics and computer programming, with evidence of publicly released software. Experience of population genetics or microbiology would be advantageous but is not essential.

The post is available immediately, and is available for up to 3 years in the first instance. For more details on this position, including salary, job description, selection criteria and how to apply, please see the University of Oxford recruitment page.

Applications for this vacancy are to be made online. The closing date is 12.00 noon on Monday 4 November 2013. Applicants will be asked to upload a CV and a supporting statement as part of the online application. For informal enquiries, please email me. More information about the group's research is available here.

Sir Henry Dale Fellowship

I am pleased to report that I have been awarded a Wellcome Trust and Royal Society funded Sir Henry Dale Fellowship. The subject of the fellowship, to be held in the Nuffield Department of Medicine at the University of Oxford, is Statistical Methods for Whole Genome Phenotype Mapping in Bacterial Populations.

The project addresses the question of how to detect genes or mutations in bacteria responsible for variability in important traits such as the tendency to cause human disease. Focusing on the hospital-associated pathogens Staphylococcus aureus and Clostridium difficile, the project has the potential to help identify genetic variants that explain why some bacteria cause more severe infections, knowledge that could help develop new drugs and tests that improve patient treatment.

The fellowship runs for five years, and includes support for a postdoctoral research assistant and laboratory costs. I will be advertising a position shortly. If you are interested, please get in touch.

I want to thank the funders and reviewers for supporting this project, and my colleagues who helped me write and re-write the research proposal.

Detecting mixed strain infections with whole genome sequencing

Whole genome sequencing in near-to-real time is set to become a routine tool for outbreak detection by hospital and public health microbiology labs, following successful pilot studies in the UK last year. Typically, the bacteria are cultured from a clinical sample, and a single colony is picked for sequencing. Since a bacterial colony grows from a single cell, this procedure ensures that all the cells picked for sequencing are genetically identical, and this in turn helps piece the genome back together again following sequencing.

But it exposes the system to a flaw. What would happen if a patient sick with two strains transmitted one, but not the other to a second patient? Characterizing the genome of just one of the strains in the first patient risks missing the transmission event entirely, because the "wrong" strain might have been sequenced.

One safeguard would be to sequence multiple bacterial colonies per sample, three for example. But this would increase the cost of routine surveillance three-fold.

In a new paper published this month in PLoS Computational Biology, with David Eyre, Madeleine Cule, Sarah Walker and others, we have investigated an alternative solution, where by a large number of colonies gets sequenced all together. The cost is the same as that of sequencing a single colony. But the downstream bioinformatics analysis is complicated considerably by the presence of multiple strains. To cope with this, we developed a new computational method that reconstructs the identities of the multiple strains, using a panel of reference genomes to help where possible.

By applying the approach to 26 clinical samples of Clostridium difficile hospital infections with known epidemiological relationships, we detected four mixed strain infections, one of which revealed a previously undetected transmission event within the hospital. For full details, read the open access paper.

Within-host evolution of Staphylococcus aureus during asymptomatic carriage

Given its notoriety as one of the world's major causes of infection-related deaths, it may come as a surprise that one in three healthy adults carry the human pathogen Staphylococcus aureus in their noses without adverse effects. Indeed, most people carry the bacteria at some point in their lives. So carriage must be seen as the normal state of affairs in the human-S. aureus interaction, and by understanding this state better we can improve our understanding of why, in some people, the bacteria go on to cause life-threatening invasive disease.

This month sees publication of an investigation by my colleagues and me into the evolution of S. aureus during this normal healthy carriage state. The carriers in our study harboured populations of the bacteria that were very closely related but typically not identical, implying that the bacteria had evolved within the human body. The nose appears to be a microcosm of evolution for S. aureus, showing all the different types of genetic variation known at the species level within the noses of these individual carriers. For the most part, within-host evolution of the bacteria was very conservative, but certain proteins expressed on the surface of the bacteria and toxins secreted by the bacteria showed evidence of involvement in a host-pathogen arms race.

The paper, whose lead authors include Tanya Golubchik, Liz Batty, Derrick Crook and Rory Bowden, has received coverage on the EveryONE blog and F1000. I liked Gerald Pier's conclusion, made on the post-publication peer review website: "Given that about 30% of the world's seven billion-plus humans, and an unknown number of animals, are chronically colonized with S. aureus, the tremendous opportunity provided to this organism for generating genetic variation to counteract human efforts to prevent S. aureus infections may be one of the most formidable barriers to overcome in order to develop vaccines and highly effective interventions to lessen the impact of this organism on human and animal health."

Coalescent inference for infectious disease

Today my student Bethany Dearlove has her first paper published, called Coalescent inference for infectious disease: meta-analysis of hepatitis C. In this paper, published in Philosophical Transactions of the Royal Society B, we have developed coalescent-based population genetics methods for popular, deterministic, epidemiological models known as SI (susceptible-infectious), SIS (susceptible-infectious-susceptible) and SIR (susceptible-infectious-recovered). By implementing these methods in BEAST, we were able to re-analyse previously published hepatitis C virus datasets and directly estimate epidemiological parameters. Our results show that, in the absence of co-infection, the widely-used exponential growth and logistic growth models of changing population size correspond directly to SI and SIS dynamics. We were also able to examine the limitations to genetic approaches to reconstructing epidemiological dynamics.

This paper appears as part of an issue on Next-generation molecular and evolutionary epidemiology of infectious disease, which accompanies a Royal Society discussion meeting organized by Oli Pybus, Christophe Fraser and Andrew Rambaut. The Royal Society has made audio recordings of the talks at this meeting, and the accompanying satellite meeting, available online, including my talk on Bethany's paper.