An extended genotyping framework for Salmonella enterica serovar Typhi, the cause of human typhoid

Vanessa K. Wong (), Stephen Baker, Thomas R. Connor, Derek Pickard, Andrew J. Page, Jayshree Dave, Niamh Murphy, Richard Holliman, Armine Sefton, Michael Millar, Zoe A. Dyson, Gordon Dougan and Kathryn E. Holt
Additional contact information
Vanessa K. Wong: The Wellcome Trust Sanger Institute
Stephen Baker: The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme
Thomas R. Connor: Cardiff University School of Biosciences, Cardiff University
Derek Pickard: The Wellcome Trust Sanger Institute
Andrew J. Page: The Wellcome Trust Sanger Institute
Jayshree Dave: Public Health Laboratory London, Public Health England
Niamh Murphy: Public Health Laboratory London, Public Health England
Richard Holliman: Public Health Laboratory London, Public Health England
Armine Sefton: Barts Health NHS Trust
Michael Millar: Barts Health NHS Trust
Zoe A. Dyson: Centre for Systems Genomics, University of Melbourne
Gordon Dougan: The Wellcome Trust Sanger Institute
Kathryn E. Holt: Centre for Systems Genomics, University of Melbourne

Nature Communications, 2016, vol. 7, issue 1, 1-11

Abstract: Abstract The population of Salmonella enterica serovar Typhi (S. Typhi), the causative agent of typhoid fever, exhibits limited DNA sequence variation, which complicates efforts to rationally discriminate individual isolates. Here we utilize data from whole-genome sequences (WGS) of nearly 2,000 isolates sourced from over 60 countries to generate a robust genotyping scheme that is phylogenetically informative and compatible with a range of assays. These data show that, with the exception of the rapidly disseminating H58 subclade (now designated genotype 4.3.1), the global S. Typhi population is highly structured and includes dozens of subclades that display geographical restriction. The genotyping approach presented here can be used to interrogate local S. Typhi populations and help identify recent introductions of S. Typhi into new or previously endemic locations, providing information on their likely geographical source. This approach can be used to classify clinical isolates and provides a universal framework for further experimental investigations.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/ncomms12827 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12827

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms12827

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().