Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning

作者：Shanshan Li, Jun Wu, Nan Ma, Wenjia Liu, Mengjie Shao, Nanjiao Ying, Lei Zhu · 发表于：Journal of Medical Microbiology · 年份：2023 · DOI：10.1099/jmm.0.001657 · 被引用次数：7 · 研究领域：Antibiotic Resistance in Bacteria、Genomics and Phylogenetic Studies、Bacterial Identification and Susceptibility Testing

Introduction. The resistance rate of Klebsiella pneumoniae ( K. pneumoniae ) to imipenem is increasing year by year, and the imipenem resistance mechanism of K. pneumoniae is complex. Therefore, it is urgent to develop new strategies to explore the resistance mechanism of imipenem for its effective and accurate use in clinical practice. Hypothesis/Gap sStatement. Machine learning could identify resistance features and biological process that influence microbial resistance from whole-genome sequencing (WGS) data. Aims. This work aimed to predict imipenem resistance genetic features in K. pneumoniae from whole-genome k -mer features, and analyse their function for understanding its resistance mechanism. Methods. This study analysed WGS data of K. pneumoniae combined with resistance phenotype for imipenem, and established K. pneumoniae to imipenem genotype-phenotype model to predict resistance features using chi-squared test and random forest. An external clinical dataset was used to verify prediction power of resistance features. The potential genes were identified through alignment the resistance features with the K. pneumoniae reference genome using blast n, the functions of potential genes were further analysed to explore its resistance-related signalling pathways with GO and KEGG analysis, the resistance sequence patterns were screened using streme software. Finally, the resistance features were combined and modelled through four machine-learning algorithms (logistic regres...