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Abstract Non-O157 STEC are increasingly linked to foodborne infections, yet little is known about the diversity and molecular epidemiology across locations. Herein, we used whole genome sequencing to examine genetic variation in 894 isolates collected from Michigan patients between 2001 and 2018. In all, 67 serotypes representing 69 multilocus sequence types were identified. Serotype diversity increased from an average of four (2001–2006) to 17 (2008–2018) serotypes per year. The top six serogroups reported nationally caused > 60% of infections in 16 of the 18 years; serogroups O111 and O45 were associated with hospitalization as were age ≥ 65 years, diarrhea with blood and female sex. Phylogenetic analyses of seven multilocus sequence typing (MLST) loci identified three clades as well as evidence of parallel evolution and recombination. Most (95.5%) isolates belonged to one clade, which could be further differentiated into seven subclades comprising isolates with varying virulence gene profiles and serotypes. No association was observed between specific clades and the epidemiological data, suggesting that serogroup- and serotype-specific associations are more important predictors of disease outcomes than lineages defined by MLST. Molecular epidemiological studies of non-O157 STEC are important to enhance understanding of circulating strain distributions and traits, genetic variation, and factors that may impact disease risk and severity.

Shiga toxin-producing Escherichia coli (STEC) causes high frequencies of foodborne infections worldwide and has been linked to numerous outbreaks each year. Pulsed-field gel electrophoresis (PFGE) has been the gold standard for surveillance until the recent transition to whole-genome sequencing (WGS). To further understand the genetic diversity and relatedness of outbreak isolates, a retrospective analysis of 510 clinical STEC isolates was conducted. Among the 34 STEC serogroups represented, most (59.6%) belonged to the predominant six non-O157 serogroups. Core genome single nucleotide polymorphism (SNP) analysis differentiated clusters of isolates with similar PFGE patterns and multilocus sequence types (STs). One serogroup O26 outbreak strain and another non-typeable (NT) strain, for instance, were identical by PFGE and clustered together by MLST; however, both were distantly related in the SNP analysis. In contrast, six outbreak-associated serogroup O5 strains clustered with five ST-175 serogroup O5 isolates, which were not part of the same outbreak as determined by PFGE. The use of high-quality SNP analyses enhanced the discrimination of these O5 outbreak strains into a single cluster. In all, this study demonstrates how public health laboratories can more rapidly use WGS and phylogenetics to identify related strains during outbreak investigations while simultaneously uncovering important genetic attributes that can inform treatment practices.