Genomic and machine learning approaches to predict antimicrobial resistance in .

Journal: Microbiology spectrum
Published Date: Aug 5, 2025

Abstract

UNLABELLED: 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 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 . 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 infections.

Authors

  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Shanshan Long
    Department of Laboratory Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Fangyuan Chen
    Genskey Medical Technology Co., Ltd, Beijing, China.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Peng Han
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Hua Yu
    School of Computer Science and Technology, Tianjin University, Nankai District, Tianjin 300072, China. yuhua@tju.edu.cn.
  • Xiaobo Huang
    Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China.
  • Chun Pan
    Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Ruiming Yue
    Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Wentao Feng
    Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Guanhua Rao
    Genskey Medical Technology Co., Ltd., Beijing, China.
  • Han Shen
    School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, 51006, PR China. Electronic address: shenhanbc@163.com.
  • Lingai Pan
    Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

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