David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.
M.G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.
Microb Genom. 2021 Jan;7(1). doi: 10.1099/mgen.0.000500.
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.
临床诊断抗菌药物耐药性（AMR）基于经验证据和当前的金标准实验室表型方法。基因型方法具有更快、更便宜的潜在优势，并且在机制解析方面优于表型方法。我们生成并应用基于规则和逻辑回归模型，从 和 种多药耐药临床分离株基因组中预测 AMR 表型。通过检查和评估这些模型，我们发现了以前未知的β-内酰胺酶底物活性。总共使用靶向基因表达研究对 22 种未知的β-内酰胺酶底物活性进行了实验验证。我们的结果表明，生成和分析预测模型有助于指导研究人员了解耐药机制，并提高 AMR 基因的注释和表型预测，同时表明我们不能仅依赖于已有的知识来预测耐药表型。
