Whole-Genome Analysis of Escherichia coli from One Health Sources: Evaluating Genetic Relatedness and Antimicrobial Resistance Carriage

<b>Background/Objectives</b>: Due to the numerical dominance of environmental and commensal strains, understanding antimicrobial resistance (AMR) transmission in <i>Escherichia coli</i> requires consideration of non-clinical as well as pathogenic isolates. In this cross-sectional study, associations between the genetic context of non-clinical <i>E. coli</i> and AMR carriage are examined in isolates sampled from different niches within a One Health continuum. <b>Methods</b>: Two hundred eighty-eight <i>E. coli</i> isolates collected in Alberta, Canada (2018-2019) from wastewater, well water, feces of broiler chickens and feedlot cattle, and retail beef and chicken meat were selected from existing surveillance collections using a stratified random sampling structure. Using short-read whole genome assemblies, phylogenetic relationships were inferred from pan-genome multiple sequence alignments. Principal coordinate analysis and permutational analysis of variance (PERMANOVA) of a Jaccard dissimilarity matrix derived from gene presence/absence data were used to investigate contributions of source and AMR strata to observe genetic dissimilarity. Population clustering and gene under- or over-representation by source and cluster were also explored. <b>Results</b>: Minimal phylogenetic segregation of isolates was noted based on source or AMR strata, and both contributed significant but small proportions of observed genetic dissimilarity, with the largest proportion attributed to phylogroup. There was notable diversity of <i>E. coli</i> within and between sources; however, in some larger clusters, differential gene presence/absence was potentially linked to ecological niche rather than source of isolation. <b>Conclusions</b>: This study highlights the ecological complexity of AMR in <i>E. coli</i> in non-clinical contexts, offering a novel lens on how niche-specific factors can influence population structure and AMR carriage. It also provides insight into apparent discrepancies in the literature regarding clustering of <i>E. coli</i> by source. These findings support a more integrative One Health approach to AMR surveillance, emphasizing the need to account for microbial diversity and niche-specific adaptation across interconnected systems.