Madeleine Cule

Diverse sources of C. difficile infection identified on whole-genome sequencing.

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.).

Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease

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.

Microevolutionary analysis of Clostridium difficile genomes to investigate transmission

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.

Within-Host Evolution of Staphylococcus aureus during Asymptomatic Carriage

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.

Detection of mixed infection from bacterial whole genome sequence data allows assessment of its role in Clostridium difficile transmission.

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.

A modified RNA-Seq approach for whole genome sequencing of RNA viruses from faecal and blood samples

To date, very large scale sequencing of many clinically important RNA viruses has been complicated by their high population molecular variation, which creates challenges for polymerase chain reaction and sequencing primer design. Many RNA viruses are also difficult or currently not possible to culture, severely limiting the amount and purity of available starting material. Here, we describe a simple, novel, high-throughput approach to Norovirus and Hepatitis C virus whole genome sequence determination based on RNA shotgun sequencing (also known as RNA-Seq). We demonstrate the effectiveness of this method by sequencing three Norovirus samples from faeces and two Hepatitis C virus samples from blood, on an Illumina MiSeq benchtop sequencer. More than 97% of reference genomes were recovered. Compared with Sanger sequencing, our method had no nucleotide differences in 14,019 nucleotides (nt) for Noroviruses (from a total of 2 Norovirus genomes obtained with Sanger sequencing), and 8 variants in 9,542 nt for Hepatitis C virus (1 variant per 1,193 nt). The three Norovirus samples had 2, 3, and 2 distinct positions called as heterozygous, while the two Hepatitis C virus samples had 117 and 131 positions called as heterozygous. To confirm that our sample and library preparation could be scaled to true high-throughput, we prepared and sequenced an additional 77 Norovirus samples in a single batch on an Illumina HiSeq 2000 sequencer, recovering >90% of the reference genome in all but one sample. No discrepancies were observed across 118,757 nt compared between Sanger and our custom RNA-Seq method in 16 samples. By generating viral genomic sequences that are not biased by primer-specific amplification or enrichment, this method offers the prospect of large-scale, affordable studies of RNA viruses which could be adapted to routine diagnostic laboratory workflows in the near future, with the potential to directly characterize within-host viral diversity.

Healthcare-associated outbreak of meticillin-resistant Staphylococcus aureus bacteraemia: role of a cryptic variant of an epidemic clone.

Summary Background New strains of meticillin-resistant Staphylococcus aureus (MRSA) may be associated with changes in rates of disease or clinical presentation. Conventional typing techniques may not detect new clonal variants that underlie changes in epidemiology or clinical phenotype. Aim To investigate the role of clonal variants of MRSA in an outbreak of MRSA bacteraemia at a hospital in England. Methods Bacteraemia isolates of the major UK lineages (EMRSA-15 and -16) from before and after the outbreak were analysed by whole-genome sequencing in the context of epidemiological and clinical data. For comparison, EMRSA-15 and -16 isolates from another hospital in England were sequenced. A clonal variant of EMRSA-16 was identified at the outbreak hospital and a molecular signature test designed to distinguish variant isolates among further EMRSA-16 strains. Findings By whole-genome sequencing, EMRSA-16 isolates during the outbreak showed strikingly low genetic diversity (P < 1 × 10−6, Monte Carlo test), compared with EMRSA-15 and EMRSA-16 isolates from before the outbreak or the comparator hospital, demonstrating the emergence of a clonal variant. The variant was indistinguishable from the ancestral strain by conventional typing. This clonal variant accounted for 64/72 (89%) of EMRSA-16 bacteraemia isolates at the outbreak hospital from 2006. Conclusions Evolutionary changes in epidemic MRSA strains not detected by conventional typing may be associated with changes in disease epidemiology. Rapid and affordable technologies for whole-genome sequencing are becoming available with the potential to identify and track the emergence of variants of highly clonal organisms.

Stochastic modelling and inference in electronic hospital databases for the spread of infections: Clostridium difficile transmission in Oxfordshire hospitals 2007–2010

The combination of genetic information with electronic patient records promises to provide a powerful new resource for understanding human disease and its treatment. Here we develop and apply a novel stochastic compartmental model to a large dataset on Clostridium difficile infection (CDI) in three Oxfordshire hospitals over a 2.5 year period which combines genetic information on 858 confirmed cases of CDI with a database of 750,000 patient records. C. difficile is a major cause of healthcare-associated diarrhoea and is responsible for substantial mortality and morbidity, with relatively little known about its biology or its transmission epidemiology. Bayesian analysis of our model, via Markov chain Monte Carlo, provides new information about the biology of CDI, including genetic heterogeneity in infectiousness across different sequence types, and evidence for ward contamination as a significant mode of transmission, and allows inferences about the contribution of particular individuals, wards, or hospitals to transmission of the bacterium, and assessment of changes in these over time following changes in hospital practice. Our work demonstrates the value of using statistical modelling and computational inference on large-scale hospital patient databases and genetic data.

Towards eliminating bias in cluster analysis of TB genotyped data

The relative contributions of transmission and reactivation of latent infection to TB cases observed clinically has been reported in many situations, but always with some uncertainty. Genotyped data from TB organisms obtained from patients have been used as the basis for heuristic distinctions between circulating (clustered strains) and reactivated infections (unclustered strains). Naïve methods previously applied to the analysis of such data are known to provide biased estimates of the proportion of unclustered cases. The hypergeometric distribution, which generates probabilities of observing clusters of a given size as realized clusters of all possible sizes, is analyzed in this paper to yield a formal estimator for genotype cluster sizes. Subtle aspects of numerical stability, bias, and variance are explored. This formal estimator is seen to be stable with respect to the epidemiologically interesting properties of the cluster size distribution (the number of clusters and the number of singletons) though it does not yield satisfactory estimates of the number of clusters of larger sizes. The problem that even complete coverage of genotyping, in a practical sampling frame, will only provide a partial view of the actual transmission network remains to be explored.