SNP-IT tool for identifying subspecies and associated lineages of Mycobacterium tuberculosis complex

Samuel Lipworth*, Rana Jajou, Albert De Neeling, Phelim Bradley, Wim Van Der Hoek, Gugu Maphalala, Maryline Bonnet, Elizabeth Sanchez-Padilla, Roland Diel, Stefan Niemann, Zamin Iqbal, Grace Smith, Tim Peto, Derrick Crook, Timothy Walker, Dick Van Soolingen

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    45 Citations (Scopus)

    Abstract

    The clinical phenotype of zoonotic tuberculosis and its contribution to the global burden of disease are poorly understood and probably underestimated. This shortcoming is partly because of the inability of currently available laboratory and in silico tools to accurately identify all subspecies of the Mycobacterium tuberculosis complex (MTBC). We present SNPs to Identify TB (SNP-IT), a single-nucleotide polymorphism-based tool to identify all members of MTBC, including animal clades. By applying SNP-IT to a collection of clinical genomes from a UK reference laboratory, we detected an unexpectedly high number of M. orygis isolates. M. orygis is seen at a similar rate to M. bovis, yet M. orygis cases have not been previously described in the United Kingdom. From an international perspective, it is possible that M. orygis is an underestimated zoonosis. Accurate identification will enable study of the clinical phenotype, host range, and transmission mechanisms of all subspecies of MTBCin greater detail.

    Original languageEnglish
    Pages (from-to)482-488
    Number of pages7
    JournalEmerging Infectious Diseases
    Volume25
    Issue number3
    DOIs
    Publication statusPublished - Mar 2019

    Bibliographical note

    Publisher Copyright:
    © 2019, Centers for Disease Control and Prevention (CDC). All rights reserved.

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