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.).

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.

Summary. Let X 1 , ..., X n be independent and identically distributed random vectors with a (Lebesgue) density f. We first prove that, with probability 1, there is a unique log-concave maximum likelihood estimatorf n of f. The use of this estimator is attractive because, unlike kernel density estimation, the method is fully automatic, with no smoothing parameters to choose. Although the existence proof is non-constructive, we can reformulate the issue of computingf n in terms of a non-differentiable convex optimization problem, and thus combine techniques of computational geometry with Shor's r -algorithm to produce a sequence that converges tof n . An R version of the algorithm is available in the package LogConcDEAD-log-concave density estimation in arbitrary dimensions. We demonstrate that the estimator has attractive theoretical properties both when the true density is log-concave and when this model is misspecified. For the moderate or large sample sizes in our simulations,f n is shown to have smaller mean integrated squared error compared with kernel-based methods, even when we allow the use of a theoretical, optimal fixed bandwidth for the kernel estimator that would not be available in practice. We also present a real data clustering example, which shows that our methodology can be used in conjunction with the expectation-maximization algorithm to fit finite mixtures of log-concave densities.

BackgroundThe control of Clostridium difficile infection is a major international healthcare priority, hindered by a limited understanding of transmission epidemiology for these bacteria. However, transmission studies of bacterial pathogens are rapidly being transformed by the advent of next generation sequencing.ResultsHere we sequence whole C. difficile genomes from 486 cases arising over four years in Oxfordshire. We show that we can estimate the times back to common ancestors of bacterial lineages with sufficient resolution to distinguish whether direct transmission is plausible or not. Time depths were inferred using a within-host evolutionary rate that we estimated at 1.4 mutations per genome per year based on serially isolated genomes. The subset of plausible transmissions was found to be highly associated with pairs of patients sharing time and space in hospital. Conversely, the large majority of pairs of genomes matched by conventional typing and isolated from patients within a month of each other were too distantly related to be direct transmissions.ConclusionsOur results confirm that nosocomial transmission between symptomatic C. difficile cases contributes far less to current rates of infection than has been widely assumed, which clarifies the importance of future research into other transmission routes, such as from asymptomatic carriers. With the costs of DNA sequencing rapidly falling and its use becoming more and more widespread, genomics will revolutionize our understanding of the transmission of bacterial pathogens.