Machine Learning and DIA Proteomics Reveal New Insights into Carbapenem Resistance Mechanisms in .

Journal: Journal of proteome research
Published Date: Jul 7, 2025

Abstract

The emergence of Carbapenem-resistant (CRKP) represents a major public health concern, primarily driven by its ability to evade a wide range of antibiotics. Despite extensive genomic studies, proteomic insights into antibiotic resistance mechanisms remain scarce. Here, we employed a Data-Independent Acquisition (DIA)-based quantitative proteomics approach to investigate proteomic differences between 78 CRKP and 18 Carbapenem-sensitive (CSKP) clinical isolates. A total of 3380 proteins were identified, with 946 showing significant differential expression. CRKP isolates exhibited increased expression of efflux pumps, beta-lactamases, and transcriptional regulators, while proteins associated with transport were enriched in CSKP isolates. To validate our findings, a quantitative proteomics analysis in an independent cohort of 10 CRKP and 11 CSKP isolates was performed. The key biomarkers identified via machine learning in the discovery cohort, including aldehyde dehydrogenase (KPN_03361), acyltransferase (KPN_02072), uncharacterized protein (YjeJ), plasmid partition protein B (ParaB), HTH-type transcriptional activator (RhaR), and beta-lactamase (Bla), were evaluated. They collectively achieved AUC > 0.7 in the validation cohort, confirming their discriminatory capacity as diagnostic markers. These findings provide novel insights into the molecular mechanisms of antibiotic resistance and identify promising biomarkers for diagnosing carbapenem-resistant , offering potential avenues for therapeutic intervention.

Authors

  • Guibin Wang
    State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
  • Ling Cao
    Key Laboratory of Marine, Center for Molecular Diagnosis and Precision Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, 1519 Dongyue Dadao, Nanchang 330209, China.
  • Lingli Lian
    Fujian Provincial Key Laboratory of Agroecological Processing and Safety Monitoring (School of Life Sciences, Fujian Agriculture and Forestry University), Fuzhou 350002, China.
  • Yuqian Wang
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.
  • Juanqi Lian
    National Engineering Research Center of JUNCAO Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
  • Ziqiu Liu
    Fujian Provincial Key Laboratory of Agroecological Processing and Safety Monitoring (School of Life Sciences, Fujian Agriculture and Forestry University), Fuzhou 350002, China.
  • Wenzhe Chen
    State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
  • Meichao Ji
    State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
  • Lanqing Gong
    Fujian Provincial Key Laboratory of Agroecological Processing and Safety Monitoring (School of Life Sciences, Fujian Agriculture and Forestry University), Fuzhou 350002, China.
  • Lishan Zhang
    Fujian Provincial Key Laboratory of Agroecological Processing and Safety Monitoring (School of Life Sciences, Fujian Agriculture and Forestry University), Fuzhou 350002, China.
  • Liping Li
    School of Public Health, Key Laboratory of Environment and Human Health of Hebei Medical University Shijiazhuang 050017 China xuxd@hebmu.edu.cn.
  • Xiangmin Lin
    Fujian Provincial Key Laboratory of Agroecological Processing and Safety Monitoring (School of Life Sciences, Fujian Agriculture and Forestry University), Fuzhou 350002, China.

Keywords

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