Dataset Information

Clostridium difficile: Investigating Transmission Patterns Between Infected and Colonized Patients Using Whole Genome Sequencing.

ABSTRACT: Background:Whole genome sequencing (WGS) studies can enhance our understanding of the role of patients with asymptomatic Clostridium difficile colonization in transmission. Methods:Isolates obtained from patients with Clostridium difficile infection (CDI) and colonization identified in a study conducted during 2006-2007 at 6 Canadian hospitals underwent typing by pulsed-field gel electrophoresis, multilocus sequence typing, and WGS. Isolates from incident CDI cases not in the initial study were also sequenced where possible. Ward movement and typing data were combined to identify plausible donors for each CDI case, as defined by shared time and space within predefined limits. Proportions of plausible donors for CDI cases that were colonized, infected, or both were examined. Results:Five hundred fifty-four isolates were sequenced successfully, 353 from colonized patients and 201 from CDI cases. The NAP1/027/ST1 strain was the most common strain, found in 124 (62%) of infected and 92 (26%) of colonized patients. A donor with a plausible ward link was found for 81 CDI cases (40%) using WGS with a threshold of ?2 single nucleotide polymorphisms to determine relatedness. Sixty-five (32%) CDI cases could be linked to both infected and colonized donors. Exclusive linkages to infected and colonized donors were found for 28 (14%) and 12 (6%) CDI cases, respectively. Conclusions:Colonized patients contribute to transmission, but CDI cases are more likely linked to other infected patients than colonized patients in this cohort with high rates of the NAP1/027/ST1 strain, highlighting the importance of local prevalence of virulent strains in determining transmission dynamics.

SUBMITTER: Kong LY

PROVIDER: S-EPMC6321846 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clostridium difficile: Investigating Transmission Patterns Between Infected and Colonized Patients Using Whole Genome Sequencing.

Kong Ling Yuan LY Eyre David W DW Corbeil Jacques J Raymond Frederic F Walker A Sarah AS Wilcox Mark H MH Crook Derrick W DW Michaud Sophie S Toye Baldwin B Frost Eric E Dendukuri Nandini N Schiller Ian I Bourgault Anne-Marie AM Dascal Andrew A Oughton Matthew M Longtin Yves Y Poirier Louise L Brassard Paul P Turgeon Nathalie N Gilca Rodica R Loo Vivian G VG

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20190101 2

<h4>Background</h4>Whole genome sequencing (WGS) studies can enhance our understanding of the role of patients with asymptomatic Clostridium difficile colonization in transmission.<h4>Methods</h4>Isolates obtained from patients with Clostridium difficile infection (CDI) and colonization identified in a study conducted during 2006-2007 at 6 Canadian hospitals underwent typing by pulsed-field gel electrophoresis, multilocus sequence typing, and WGS. Isolates from incident CDI cases not in the init ...[more]

PMID: 29846557

Similar Datasets

Project description:No study to date has compared multilocus variable-number tandem-repeat analysis (MLVA) and whole-genome sequencing (WGS) in an investigation of the transmission of Clostridium difficile infection. Isolates from 61 adults with ongoing and/or recurrent C. difficile infections and 17 asymptomatic carriage episodes in children (201 samples), as well as from 61 suspected outbreaks affecting 2 to 41 patients in 31 hospitals in the United Kingdom (300 samples), underwent 7-locus MLVA and WGS in parallel. When the first and last samples from the same individual taken for a median (interquartile range [IQR]) of 63 days (43 to 105 days) apart were compared, the estimated rates of the evolution of single nucleotide variants (SNVs), summed tandem-repeat differences (STRDs), and locus variants (LVs) were 0.79 (95% confidence interval [CI], 0.00 to 1.75), 1.63 (95% CI, 0.00 to 3.59), and 1.21 (95% CI, 0.00 to 2.67)/called genome/year, respectively. Differences of >2 SNVs and >10 STRDs have been used to exclude direct case-to-case transmission. With the first serial sample per individual being used to assess discriminatory power, across all pairs of samples sharing a PCR ribotype, 192/283 (68%) differed by >10 STRDs and 217/283 (77%) by >2 SNVs. Among all pairs of cases from the same suspected outbreak, 1,190/1,488 (80%) pairs had concordant results using >2 SNVs and >10 STRDs to exclude transmission. For the discordant pairs, 229 (15%) had ?2 SNVs but ?10 STRDs, and 69 (5%) had ?2 SNVs but ?10 STRDs. Discordant pairs had higher numbers of LVs than concordant pairs, supporting the more diverse measure in each type of discordant pair. Conclusions on whether the potential outbreaks were confirmed were concordant in 58/61 (95%) investigations. Overall findings using MLVA and WGS were very similar despite the fact that they analyzed different parts of the bacterial genome. With improvements in WGS technology, it is likely that MLVA locus data will be available from WGS in the near future.

Project description:BackgroundWhole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing approaches. Many public health and clinical teams will increasingly use WGS to investigate clusters of potential pathogen transmission, making it crucial to understand the benefits and assumptions of the analytical methods for investigating the data. We aimed to understand how different approaches affect inferences of transmission dynamics and outline limitations of the methods.MethodsWe comprehensively searched electronic databases for studies that presented methods used to interpret WGS data for investigating tuberculosis (TB) transmission. Two authors independently selected studies for inclusion and extracted data. Due to considerable methodological heterogeneity between studies, we present summary data with accompanying narrative synthesis rather than pooled analyses.ResultsTwenty-five studies met our inclusion criteria. Despite the range of interpretation tools, the usefulness of WGS data in understanding TB transmission often depends on the amount of genetic diversity in the setting. Where diversity is small, distinguishing re-infections from relapses may be impossible; interpretation may be aided by the use of epidemiological data, examining minor variants and deep sequencing. Conversely, when within-host diversity is large, due to genetic hitchhiking or co-infection of two dissimilar strains, it is critical to understand how it arose. Greater understanding of microevolution and mixed infection will enhance interpretation of WGS data.ConclusionsAs sequencing studies have sampled more intensely and integrated multiple sources of information, the understanding of TB transmission and diversity has grown, but there is still much to be learnt about the origins of diversity that will affect inferences from these data. Public health teams and researchers should combine epidemiological, clinical and WGS data to strengthen investigations of transmission.

Project description: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.

Dataset Information

Clostridium difficile: Investigating Transmission Patterns Between Infected and Colonized Patients Using Whole Genome Sequencing.

Publications

Clostridium difficile: Investigating Transmission Patterns Between Infected and Colonized Patients Using Whole Genome Sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets