Genomic and machine learning approaches to predict antimicrobial resistance in Stenotrophomonas maltophilia

作者：Xin Liu, Shanshan Long, Fangyuan Chen, Chang Liu, Peng Han, Hua Yu, Xiaobo Huang, Chun Pan, Ruiming Yue, Wentao Feng, Guanhua Rao, Han Shen, Lingai Pan · 发表于：Microbiology Spectrum · 年份：2025 · DOI：10.1128/spectrum.02632-24 · 被引用次数：4 · 研究领域：Infections and bacterial resistance、Bacterial biofilms and quorum sensing

ABSTRACT Stenotrophomonas maltophilia is a multidrug-resistant pathogen, which poses a major challenge to clinical management due to its increasing resistance to common antibiotics, such as levofloxacin (LEV) and trimethoprim-sulfamethoxazole (SXT), and poor clinical response to treatment. There is an urgent need for rapid and reliable antimicrobial susceptibility testing (AST) methods to improve treatment outcomes. This study collected 441 S. maltophilia strains, performed whole-genome sequencing, and used machine learning to identify key resistance determinants for LEV and SXT, constructing predictive models for resistance phenotypes. The 441 S . maltophilia strains we collected show significant genomic diversity and representative lineage distribution. Machine learning identified key resistance markers for LEV and SXT, improving area under the curve values to 92.80% for LEV and 95.44% for SXT. Validation accuracies reached 94.87% for LEV and 96.27% for SXT. Mutations in parC, smeT, and gyrA were strongly associated with LEV resistance. The gene presence of sul1, sul2, and CEQ03_18740, as well as gene mutations in Gsh2, prmA, and gspD, were highly correlated with SXT resistance. These findings suggest that integrating genome-based markers can enhance the prediction of antimicrobial resistance, offering a robust method for clinical application. Genotypic AST can reliably predict resistance phenotypes, providing a promising alternative to traditional AST methods for S. maltop...