Molecular Prediction of the O157:H-Negative Phenotype Prevalent in Australian Shiga Toxin-Producing Escherichia coli Cases Improves Concordance of In Silico Serotyping with Phenotypic Motility.

Shiga toxin-producing Escherichia coli (STEC) is a foodborne pathogen, and serotype O157:H7 is typically associated with severe disease. Australia is unique in its STEC epidemiology, as severe cases are typically associated with non-O157 serogroups, and locally acquired O157 isolates are H-negative/nonmotile. The H-negative phenotype and reduced severity of disease compared to that associated with H7/motile strains are distinct features of Australian O157 strains, but the molecular mechanism behind this phenotype has not been reported. Accurate characterization of the H-negative phenotype is important in epidemiological surveillance of STEC. Serotyping is moving away from phenotype-based methods, as next generation sequencing allows rapid extrapolation of serotype through in silico detection of the O-antigen processing genes, wzx, wzy, wzm, and wzt, and the H-antigen gene, fliC The detection and genotyping of fliC alone is unable to determine the motility of the strain. Typically, most Australian O157:H-negative strains carry an H7 genotype yet phenotypically are nonmotile; thus, many are mischaracterized as H7 strains by in silico serotyping tools. Comparative genomic analysis of flagellar genes between Australian and international isolates was performed and an insertion at nucleotide (nt) 125 in the flgF gene was identified in H-negative isolates. Chi-square results showed that this insertion was significantly associated with the H-negative phenotype (P < 0.0001). Phylogenetic analysis was also completed and showed that the Australian H-negative isolates with the insertion in flgF represent a clade within the O157 serogroup, distinct from O157:H7 serotypes. This study provides a genetic target for inferring the nonmotile phenotype of Australian O157 STEC, which increases the predictive value of in silico serotyping. © Crown copyright 2018.