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Whole-genome sequencing (WGS) has changed our understanding of bacterial pathogens, aiding outbreak investigations and advancing our knowledge of their genetic features. However, there has been limited use of genomics to understand antimicrobial resistance of veterinary pathogens, which would help identify emerging resistance mechanisms and track their spread. The objectives of this study were to evaluate the correlation between resistance genotypes and phenotypes for Staphylococcus pseudintermedius, a major pathogen of companion animals, by comparing broth microdilution antimicrobial susceptibility testing and WGS. From 2017-2019, we conducted antimicrobial susceptibility testing and WGS on S. pseudintermedius isolates collected from dogs in the United States as a part of the Veterinary Laboratory Investigation and Response Network (Vet-LIRN) antimicrobial resistance monitoring program. Across thirteen antimicrobials in nine classes, resistance genotypes correlated with clinical resistance phenotypes 98.4 % of the time among a collection of 592 isolates. Our findings represent isolates from diverse lineages based on phylogenetic analyses, and these strong correlations are comparable to those from studies of several human pathogens such as Staphylococcus aureus and Salmonella enterica. We uncovered some important findings, including that 32.3 % of isolates had the mecA gene, which correlated with oxacillin resistance 97.0 % of the time. We also identified a novel rpoB mutation likely encoding rifampin resistance. These results show the value in using WGS to assess antimicrobial resistance in veterinary pathogens and to reveal putative new mechanisms of resistance., (Published by Elsevier B.V.)

Background: Antimicrobial resistance (AMR) of bacterial pathogens is an emerging public health threat. This threat extends to pets as it also compromises our ability to treat their infections. Surveillance programs in the United States have traditionally focused on collecting data from food animals, foods, and people. The Veterinary Laboratory Investigation and Response Network (Vet-LIRN), a national network of 45 veterinary diagnostic laboratories, tested the antimicrobial susceptibility of clinically relevant bacterial isolates from animals, with companion animal species represented for the first time in a monitoring program. During 2017, we systematically collected and tested 1968 isolates. To identify genetic determinants associated with AMR and the potential genetic relatedness of animal and human strains, whole genome sequencing (WGS) was performed on 192 isolates: 69 Salmonella enterica (all animal sources), 63 Escherichia coli (dogs), and 60 Staphylococcus pseudintermedius (dogs)., Results: We found that most Salmonella isolates (46/69, 67%) had no known resistance genes. Several isolates from both food and companion animals, however, showed genetic relatedness to isolates from humans. For pathogenic E. coli, no resistance genes were identified in 60% (38/63) of the isolates. Diverse resistance patterns were observed, and one of the isolates had predicted resistance to fluoroquinolones and cephalosporins, important antibiotics in human and veterinary medicine. For S. pseudintermedius, we observed a bimodal distribution of resistance genes, with some isolates having a diverse array of resistance mechanisms, including the mecA gene (19/60, 32%)., Conclusion: The findings from this study highlight the critical importance of veterinary diagnostic laboratory data as part of any national antimicrobial resistance surveillance program. The finding of some highly resistant bacteria from companion animals, and the observation of isolates related to those isolated from humans demonstrates the public health significance of incorporating companion animal data into surveillance systems. Vet-LIRN will continue to build the infrastructure to collect the data necessary to perform surveillance of resistant bacteria as part of fulfilling its mission to advance human and animal health. A One Health approach to AMR surveillance programs is crucial and must include data from humans, animals, and environmental sources to be effective.