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 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

katholt/sonneityping: v20210201

A Python script to parse Mykrobe predict results for Shigella sonnei. Mykrobe v0.9.0+ can identify input genomes as S. sonnei, assign those identified as S. sonnei to hierarchical genotypes based on detection of single nucleotide variants (SNVs; defined in the file alleles.txt), and report known mutations in the quinolone-resistance determining region (QRDR) of genes gyrA (S83L, D87G, D87Y) and parC (S80I)

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 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