Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance
| Autor: | Andrew G. McArthur, Sommer Chou, Finlay Maguire, Haley L. Zubyk, Gerard D. Wright, Arman Edalatmand, Kara K. Tsang, Robert G. Beiko |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
In silico
Computational biology Biology medicine.disease_cause Genome beta-Lactamases Genotype phenotype 03 medical and health sciences Antibiotic resistance genotype–phenotype Drug Resistance Bacterial medicine Escherichia coli Computer Simulation Genomic Methodologies: Genome-phenotype association antimicrobial resistance Gene Data Curation 030304 developmental biology 0303 health sciences Whole Genome Sequencing 030306 microbiology Pseudomonas aeruginosa Gene Expression Profiling Computational Biology High-Throughput Nucleotide Sequencing General Medicine bioinformatics prediction Gene Expression Regulation Bacterial Phenotype 3. Good health Anti-Bacterial Agents Logistic Models Algorithms Research Article |
| Zdroj: | Microbial Genomics |
| ISSN: | 2057-5858 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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