SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.

Journal: International journal of molecular sciences
PMID:

Abstract

Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential therapeutic target for DCI through integrated machine learning and molecular docking analyses. Transcriptomic data from cortical neuronal samples of T2DM patients were analysed using support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression, revealing SERPINA3 as a significantly upregulated gene in DCI. Experimental validation via Western blot confirmed elevated SERPINA3 protein levels in DCI patient plasma. Molecular docking demonstrated the stable binding of sulfonylurea hypoglycaemic agents, such as gliclazide and glimepiride, to SERPINA3, with binding energies of -6.8 and -6.6 kcal/mol, respectively. These findings suggest that SERPINA3 plays a pivotal role in DCI pathogenesis and that sulfonylurea drugs may exert neuroprotective effects through SERPINA3-mediated pathways. This study provides novel insights into the molecular mechanisms of DCI and highlights the potential of SERPINA3-targeted therapies for early intervention and treatment. Further research is warranted to validate these findings in larger cohorts and explore their clinical applicability.

Authors

  • Yu An
    School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology Wuhan 430070 PR China.
  • Zhaoming Cao
    School of Nursing, Peking University, Beijing 100191, China.
  • Yage Du
    School of Nursing, Peking University, Beijing 100191, China.
  • Guangyi Xu
    School of Nursing, Peking University, Beijing 100191, China.
  • Jingya Wang
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China. Electronic address: jingya.wang@std.uestc.edu.cn.
  • Yinchao Ma
    NHC Key Laboratory of Medical Immunology, School of Basic Medical Sciences, Peking University, Beijing 100191, China.
  • Ziyuan Wang
    Doheny Image Analysis Laboratory, Doheny Eye Institute, Los Angeles, CA, USA.
  • Jie Zheng
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
  • Yanhui Lu
    School of Nursing, Peking University, Beijing 100191, China.