Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance

Kara K. Tsang; Finlay Maguire; Haley L. Zubyk; Sommer Chou; Arman Edalatmand; Gerard D. Wright; Robert G. Beiko; Andrew G. McArthur

doi:10.1099/mgen.0.000500

Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance

Kara K. Tsang, Finlay Maguire, Haley L. Zubyk, Sommer Chou, Arman Edalatmand, Gerard D. Wright, Robert G. Beiko, Andrew G. McArthur

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

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 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.

Original language	English
Article number	000500
Pages (from-to)	1-13
Number of pages	13
Journal	Microbial genomics
Volume	7
Issue number	1
DOIs	https://doi.org/10.1099/mgen.0.000500
Publication status	Published - 2021

Bibliographical note

Funding Information:
This research was funded by the Canadian Institutes of Health Research (PJT-156214 to A. G. M., MT-14981 to G. D. W.), the Ontario Research Fund (to G. D. W.), Genome Canada (to R. G. B.), a Canada Research Chair to G. D. W. and a Cisco Research Chair in Bioinformatics to A. G. M., supported by Cisco Systems Canada, Inc. K. K. T. was supported by an Ontario Graduate Scholarship, McMaster University’s MacDATA Institute Graduate Fellowship and Michael G. DeGroote Institute for Infectious Disease Research Michael Kamin Hart Memorial Scholarship. F. M., was supported by a Donald Hill Family Fellowship in Computer Science. Computer resources were supplied by the McMaster Service Lab and Repository computing cluster, funded in part by grants to A. G. M. from the Canadian Foundation for Innovation (34531).

Publisher Copyright:
© 2021 The Authors.

ASJC Scopus Subject Areas

Epidemiology
Microbiology
Molecular Biology
Genetics

PubMed: MeSH publication types

Journal Article
Research Support, Non-U.S. Gov't

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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 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.",

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note = "Funding Information: This research was funded by the Canadian Institutes of Health Research (PJT-156214 to A. G. M., MT-14981 to G. D. W.), the Ontario Research Fund (to G. D. W.), Genome Canada (to R. G. B.), a Canada Research Chair to G. D. W. and a Cisco Research Chair in Bioinformatics to A. G. M., supported by Cisco Systems Canada, Inc. K. K. T. was supported by an Ontario Graduate Scholarship, McMaster University{\textquoteright}s MacDATA Institute Graduate Fellowship and Michael G. DeGroote Institute for Infectious Disease Research Michael Kamin Hart Memorial Scholarship. F. M., was supported by a Donald Hill Family Fellowship in Computer Science. Computer resources were supplied by the McMaster Service Lab and Repository computing cluster, funded in part by grants to A. G. M. from the Canadian Foundation for Innovation (34531). Publisher Copyright: {\textcopyright} 2021 The Authors.",

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AU - Wright, Gerard D.

AU - Beiko, Robert G.

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PY - 2021

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N2 - 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.

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Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance

Abstract

Bibliographical note

ASJC Scopus Subject Areas

PubMed: MeSH publication types

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