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

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    57
    Author
    Wong, VK; Baker, S; Connor, TR; Pickard, D; Page, AJ; Dave, J; Murphy, N; Holliman, R; Sefton, A; Millar, M; ...
    Date
    2016-10-05
    Source Title
    Nature Communications
    Publisher
    NATURE PUBLISHING GROUP
    University of Melbourne Author/s
    Dyson, Zoe; Holt, Kathryn; Dougan, Gordon; Mulholland, Edward
    Affiliation
    Paediatrics (RCH)
    Biochemistry and Molecular Biology
    Microbiology and Immunology
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Wong, V. K., Baker, S., Connor, T. R., Pickard, D., Page, A. J., Dave, J., Murphy, N., Holliman, R., Sefton, A., Millar, M., Dyson, Z. A., Dougan, G. & Holt, K. E. (2016). An extended genotyping framework for Salmonella enterica serovar Typhi, the cause of human typhoid. NATURE COMMUNICATIONS, 7 (1), https://doi.org/10.1038/ncomms12827.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/260422
    DOI
    10.1038/ncomms12827
    NHMRC Grant code
    NHMRC/1061409
    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.

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