Real-Time Genomic Epidemiological Evaluation of Human Campylobacter Isolates by Use of Whole-Genome Multilocus Sequence Typing
Open Access
- 1 August 2013
- journal article
- research article
- Published by American Society for Microbiology in Journal of Clinical Microbiology
- Vol. 51 (8), 2526-2534
- https://doi.org/10.1128/jcm.00066-13
Abstract
Sequence-based typing is essential for understanding the epidemiology of Campylobacter infections, a major worldwide cause of bacterial gastroenteritis. We demonstrate the practical and rapid exploitation of whole-genome sequencing to provide routine definitive characterization of Campylobacter jejuni and Campylobacter coli for clinical and public health purposes. Short-read data from 384 Campylobacter clinical isolates collected over 4 months in Oxford, United Kingdom, were assembled de novo . Contigs were deposited at the pubMLST.org /campylobacter website and automatically annotated for 1,667 loci. Typing and phylogenetic information was extracted and comparative analyses were performed for various subsets of loci, up to the level of the whole genome, using the Genome Comparator and Neighbor-net algorithms. The assembled sequences (for 379 isolates) were diverse and resembled collections from previous studies of human campylobacteriosis. Small subsets of very closely related isolates originated mainly from repeated sampling from the same patients and, in one case, likely laboratory contamination. Much of the within-patient variation occurred in phase-variable genes. Clinically and epidemiologically informative data can be extracted from whole-genome sequence data in real time with straightforward, publicly available tools. These analyses are highly scalable, are transparent, do not require closely related genome reference sequences, and provide improved resolution (i) among Campylobacter clonal complexes and (ii) between very closely related isolates. Additionally, these analyses rapidly differentiated unrelated isolates, allowing the detection of single-strain clusters. The approach is widely applicable to analyses of human bacterial pathogens in real time in clinical laboratories, with little specialist training required.Keywords
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