New paper: Severe infections emerge from commensal bacteria by adaptive evolution

Published this month in eLife, our new paper on the evolution and adaptation of Staphylococcus aureus during infection.

This study shows that the emergence of life-threatening infections of the major pathogen Staphylococcus aureus from bacteria colonizing the nose is associated with repeatable adaptive evolution inside the human body.

First author Bernadette Young has summarized the paper's findings on the Modernising Medical Microbiology blog.

SCOTTI wins PLoS Computational Biology Research Prize

Work from our group has been recognised in the PLoS Computational Biology 2017 Research Prizes. SCOTTI, which infers transmission routes from genetic and epidemiological information, won the Breakthrough in Advance/Innovation category. The citation reads

Our Breakthrough Advance/Innovation winning article presents a new computational tool, called SCOTTI (Structured COalescent Transmission Tree Inference), developed by Nicola De Maio of the University of Oxford (UK), and colleagues. De Maio says, “SCOTTI represents a convenient tool to reconstruct who-infected-whom within outbreaks… [and] has been used in particular for the study of bacterial hospital outbreaks”. It combines epidemiological information about patient exposure with genetic information about the infectious agent itself.

Work is nominated and selected as described in the announcement:

The journal invited the community to nominate their favorite 2016 published Research Articles. From these nominations the PLOS Computational Biology Research Prize Committee, made up of Editorial Board members Dina Schneidman, Nicola Segata, Maricel Kann, Isidore Rigoutsos, Avner Schlessinger, Lilia Iakoucheva, Ilya Ioshikhes, Shi-Jie Chen, and Becca Asquith, selected the winners. To help support future work, the authors of each winning paper will receive award certificates and a $2,000 (USD) prize.

You can read more about SCOTTI and the accompanying paper, written by Nicola De Maio, Jessie Wu and me, here.

Promiscuous bacteria have staying power

An insight article with Ruth Massey on John Lees' and Stephen Bentley's new paper was published in eLife on Friday:

Streptococcus pneumoniae is a notorious bacterial pathogen hiding in plain sight. A common resident of the nose and throat, between 68% and 84% of young infants will carry this species at any given time (Turner et al., 2012). In most cases it causes no harm, yet the presence of pneumococci – as the bacteria are known – can predispose a person to life-threatening infections like pneumonia or meningitis. Indeed, pneumococci are responsible for around 10% of all deaths in young children around the world (O'Brien et al., 2009), with the vast majority of cases being in developing countries.
Research into S. pneumoniae is complicated because the species is a patchwork of distinctive strains and some of these strains remain in the nose and throat for longer than others. Now, in eLife, John Lees and Stephen Bentley – both at the Wellcome Trust Sanger Institute – and colleagues report that strains rendered impotent by a virus do not linger for as long as other strains (Lees et al., 2017).

Click here to read the full piece.

New draft paper on combining p-values through the harmonic mean

In a preprint released today on Biorxiv I report a new method for improving the sensitivity to detect statistical signals by averaging over multiple alternative hypotheses using the harmonic mean p-value. The draft paper looks at example problems in genome-wide association studies (GWAS) in which signals of association may be apparent, but perhaps not sufficiently strong to meet the stringent threshold required to control for the millions of tests performed. Combining weak signals in arbitrary ways - for example across consecutive variants - can reveal signals sufficiently strong to meet the statistical significance threshold. This could be especially useful when looking for interactions, for example between host and pathogen genetics in their effect on infection, because it may be possible to conclude that a particular variant on the host side is involved, even if there is uncertainty over the specific pathogen variant it interacts with. Often such uncertainty arises because of the sheer number of possibilities. Similar ideas are beginning to gain traction in GWAS, and the ability to easily average over hypotheses is one of the strengths of Bayesian statistics. This new paper shows that the benefits of model averaging can be achieved easily in non-Bayesian statistics by taking the harmonic mean p-value from a range of tests. The test is very general and robust to a range of complexities including non-independence between the p-values.