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Volume 7, Issue 1

Identifying novel β-lactamase substrate activity through prediction of antimicrobial resistance Open Access

Abstract

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 and 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.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antimicrobial resistance , bioinformatics , genotype–phenotype and prediction
Funding
This study was supported by the:
  • Canadian Institutes of Health Research (Award PJT-156214)
    • Principle Award Recipient: AndrewG. McArthur
© 2021 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2021-01-08
2024-04-25
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Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance
M Gen 7, 000500 (2021); https://doi.org/10.1099/mgen.0.000500
/content/journal/mgen/10.1099/mgen.0.000500
/content/journal/mgen/10.1099/mgen.0.000500
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