Enterohemorrhagic Escherichia coli O157 (O157) strains can be subdivided into clades based on their single-nucleotide polymorphisms, but such analysis using conventional methods requires intense effort by laboratories. Although multi-locus variable-number tandem repeat analysis (MLVA), which can be performed with low laboratory burden, has been used as a molecular epidemiological tool, it has not been evaluated whether MLVA can be used the clade subdivision of O157 strains like it can for that of other pathogenic bacteria. This study aimed to establish a method for subdividing O157 strains into clades using MLVA data. The standardized index of association, ISA, for O157 strains isolated in Chiba prefecture, Japan (Chiba isolates) revealed the presence of unique tandem repeat patterns in each major clade (clades 2, 3, 7, 8, and 12). A likelihood database of tandem repeats for these clades was then constructed using the Chiba isolates, and a formula for maximum a posteriori (MAP) estimation was constructed. The ratio of the number of O157 strains putatively subdivided into a clade by MAP estimation from MLVA data relative to the number of O157 strains subdivided using single-nucleotide polymorphism analysis (designated as the concordance ratio [CR]) was calculated using the Chiba isolates and O157 strains isolated in Yamagata prefecture (Yamagata isolates). The CRs for the major Chiba and Yamagata isolate clades, other than clade 2, were 89%-100%. Although the CR for clade 2 Chiba isolates was >95%, that of the Yamagata isolates was only 78.9%. However, these clade 2 CRs were not significantly different from one another, indicating that clade 2 strains can be subdivided correctly by MAP estimation. In conclusion, this study expands the utility of MLVA, previously applied predominantly for molecular epidemiological analysis, into a low-laboratory-burden tool for subdividing O157 strains into phylogenetic groups.
Copyright: © 2023 Hirai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.