Open AccessIdentifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance
Author(s) -
Kara K. Tsang,
Finlay Maguire,
Haley L. Zubyk,
Sommer Chou,
Arman Edalatmand,
Gerard D. Wright,
Robert G. Beiko,
Andrew G. McArthur
Publication year - 2021
Publication title -
microbial genomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.476
H-Index - 28
ISSN - 2057-5858
DOI - 10.1099/mgen.0.000500
Subject(s) - phenotype , in silico , computational biology , pseudomonas aeruginosa , antibiotic resistance , biology , gene , antimicrobial , genome , multiple drug resistance , substrate (aquarium) , genetics , drug resistance , microbiology and biotechnology , bacteria , ecology
Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By inspecting and evaluating these models, we identified previously unknown β-lactamase substrate activities. In total, 22 unknown β-lactamase substrate activities were experimentally validated using targeted gene expression studies. Our results demonstrate that generating and analysing predictive models can help guide researchers to the mechanisms driving resistance and improve annotation of AMR genes and phenotypic prediction, and suggest that we cannot solely rely on curated knowledge to predict resistance phenotypes.