Cluster-specific gene markers enhance Shigella and enteroinvasive Escherichia coli in silico serotyping

Author:

Zhang Xiaomei1,Payne Michael1ORCID,Nguyen Thanh1ORCID,Kaur Sandeep1,Lan Ruiting1ORCID

Affiliation:

1. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia

Abstract

Shigella and enteroinvasive Escherichia coli (EIEC) cause human bacillary dysentery with similar invasion mechanisms and share similar physiological, biochemical and genetic characteristics. Differentiation of Shigella from EIEC is important for clinical diagnostic and epidemiological investigations. However, phylogenetically, Shigella and EIEC strains are composed of multiple clusters and are different forms of E. coli , making it difficult to find genetic markers to discriminate between Shigella and EIEC. In this study, we identified 10 Shigella clusters, seven EIEC clusters and 53 sporadic types of EIEC by examining over 17000 publicly available Shigella and EIEC genomes. We compared Shigella and EIEC accessory genomes to identify cluster-specific gene markers for the 17 clusters and 53 sporadic types. The cluster-specific gene markers showed 99.64% accuracy and more than 97.02% specificity. In addition, we developed a freely available in silico serotyping pipeline named Shigella EIEC Cluster Enhanced Serotype Finder (ShigEiFinder) by incorporating the cluster-specific gene markers and established Shigella and EIEC serotype-specific O antigen genes and modification genes into typing. ShigEiFinder can process either paired-end Illumina sequencing reads or assembled genomes and almost perfectly differentiated Shigella from EIEC with 99.70 and 99.74% cluster assignment accuracy for the assembled genomes and read mapping respectively. ShigEiFinder was able to serotype over 59 Shigella serotypes and 22 EIEC serotypes and provided a high specificity of 99.40% for assembled genomes and 99.38% for read mapping for serotyping. The cluster-specific gene markers and our new serotyping tool, ShigEiFinder (installable package: https://github.com/LanLab/ShigEiFinder, online tool: https://mgtdb.unsw.edu.au/ShigEiFinder/), will be useful for epidemiological and diagnostic investigations.

Publisher

Microbiology Society

Subject

General Medicine

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