WGS accurately predicts antimicrobial resistance in Escherichia coli

Gregory H Tyson, Patrick F McDermott, Cong Li, Yuansha Chen, Daniel A Tadesse, Sampa Mukherjee, Sonya Bodeis-Jones, Claudine Kabera, Stuart A Gaines, Guy H Loneragan, Tom S Edrington, Mary Torrence, Dayna M Harhay, Shaohua Zhao
Journal of Antimicrobial Chemotherapy 2015, 70 (10): 2763-9

OBJECTIVES: The objective of this study was to determine the effectiveness of WGS in identifying resistance genotypes of MDR Escherichia coli and whether these correlate with observed phenotypes.

METHODS: Seventy-six E. coli strains were isolated from farm cattle and measured for phenotypic resistance to 15 antimicrobials with the Sensititre(®) system. Isolates with resistance to at least four antimicrobials in three classes were selected for WGS using an Illumina MiSeq. Genotypic analysis was conducted with in-house Perl scripts using BLAST analysis to identify known genes and mutations associated with clinical resistance.

RESULTS: Over 30 resistance genes and a number of resistance mutations were identified among the E. coli isolates. Resistance genotypes correlated with 97.8% specificity and 99.6% sensitivity to the identified phenotypes. The majority of discordant results were attributable to the aminoglycoside streptomycin, whereas there was a perfect genotype-phenotype correlation for most antibiotic classes such as tetracyclines, quinolones and phenicols. WGS also revealed information about rare resistance mechanisms, such as structural mutations in chromosomal copies of ampC conferring third-generation cephalosporin resistance.

CONCLUSIONS: WGS can provide comprehensive resistance genotypes and is capable of accurately predicting resistance phenotypes, making it a valuable tool for surveillance. Moreover, the data presented here showing the ability to accurately predict resistance suggest that WGS may be used as a screening tool in selecting anti-infective therapy, especially as costs drop and methods improve.

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