Establishing Genotypic Cutoff Values To Measure Antimicrobial Resistance in Salmonella

• Published in: Antimicrobial agents and chemotherapy Vol. 61; no. 3
• Main Authors: Tyson, Gregory H, Zhao, Shaohua, Li, Cong, Ayers, Sherry, Sabo, Jonathan L, Lam, Claudia, Miller, Ron A, McDermott, Patrick F
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 01.03.2017
• Subjects: Aminoglycosides - pharmacology; Anti-Bacterial Agents; Anti-Bacterial Agents - pharmacology; Cephalosporins - pharmacology; Drug Resistance, Multiple, Bacterial - genetics; Fluoroquinolones - pharmacology; Genome, Bacterial; Genotype; High-Throughput Nucleotide Sequencing; Mechanisms of Resistance; Microbial Sensitivity Tests; Penicillins - pharmacology; Pharmacogenetics; Salmonella; Salmonella - drug effects; Salmonella - genetics; Salmonella - growth & development; Salmonella; breakpoints; antibiotic resistance; whole-genome sequencing
• ISSN: 0066-4804; 1098-6596
• DOI: 10.1128/AAC.02140-16

Whole-genome sequencing (WGS) has transformed our understanding of antimicrobial resistance, helping us to better identify and track the genetic mechanisms underlying phenotypic resistance. Previous studies have demonstrated high correlations between phenotypic resistance and the presence of known resistance determinants. However, there has never been a large-scale assessment of how well resistance genotypes correspond to specific MICs. We performed antimicrobial susceptibility testing and WGS of 1,738 nontyphoidal strains to correlate over 20,000 MICs with resistance determinants. Using these data, we established what we term genotypic cutoff values (GCVs) for 13 antimicrobials against For the drugs we tested, we define a GCV as the highest MIC of isolates in a population devoid of known acquired resistance mechanisms. This definition of GCV is distinct from epidemiological cutoff values (ECVs or ECOFFs), which currently differentiate wild-type from non-wild-type strains based on MIC distributions alone without regard to genetic information. Due to the large number of isolates involved, we observed distinct MIC distributions for isolates with different resistance gene alleles, including for ciprofloxacin and tetracycline, suggesting the potential to predict MICs based on WGS data alone.