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Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen, Shigella sonnei
Shigella sonnei is the most common agent of shigellosis in high-income countries, and causes a significant disease burden in low- and middle-income countries. Antimicrobial resistance is increasingly common in all settings. Whole genome sequencing (WGS) is increasingly utilised for S. sonnei outbrea...
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Published in: | Nature communications 2021-05, Vol.12 (1), p.2684-2684, Article 2684 |
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Main Authors: | , , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Shigella sonnei
is the most common agent of shigellosis in high-income countries, and causes a significant disease burden in low- and middle-income countries. Antimicrobial resistance is increasingly common in all settings. Whole genome sequencing (WGS) is increasingly utilised for
S. sonnei
outbreak investigation and surveillance, but comparison of data between studies and labs is challenging. Here, we present a genomic framework and genotyping scheme for
S. sonnei
to efficiently identify genotype and resistance determinants from WGS data. The scheme is implemented in the software package Mykrobe and tested on thousands of genomes. Applying this approach to analyse >4,000
S. sonnei
isolates sequenced in public health labs in three countries identified several common genotypes associated with increased rates of ciprofloxacin resistance and azithromycin resistance, confirming intercontinental spread of highly-resistant
S. sonnei
clones and demonstrating the genomic framework can facilitate monitoring the spread of resistant clones, including those that have recently emerged, at local and global scales.
Whole genome sequencing is increasingly being adopted for Shigella sonnei outbreak investigation and surveillance, but there is no global classification standard. Here, the authors develop and validate a genomic framework implemented using open-source software, and demonstrate its application using surveillance data. |
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ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-021-22700-4 |