基于多组学整合与计算机模拟的中药治疗脓毒症心肌病分子机制研究

Study on Molecular Mechanisms of Traditional Chinese Medicine in Treating Septic Cardiomyopathy Through Multi-Omics Integration and Computer Simulation

摘要: 目的：综合运用多组学整合与计算机模拟技术解析中药治疗脓毒症心肌病(SICM)的分子作用机制。方法：通过系统检索中国知网、万方及维普数据库，收集1910~2026年1月间相关文献，采用频次统计与关联规则分析筛选核心药对，结合TCMSP数据库和SwissTargetPrediction平台预测活性成分及靶点。整合GEO数据库SICM基因表达谱数据集，并筛选差异表达基因(DEGs)，通过加权基因共表达网络分析(WGCNA)构建疾病相关模块，确定关键靶标。基于Cytoscape构建药物–成分–靶点网络，利用STRING数据库进行蛋白质互作分析，GO/KEGG分析识别关键通路，基于分子对接与分子动力学模拟验证核心互作。结果：共纳入33首中药复方，筛选出高频药物：附子、丹参、黄芪、甘草、当归、人参。其中，当归–黄芪、附子–人参为关键配伍药对，筛选出药对的52个活性成分及1262个潜在作用靶点。基因表达分析得到1164个DEGs，WGCNA表明，1495个枢纽基因与SICM呈显著相关。网络拓扑分析和GO分析表明，核心靶标主要参与炎症反应、氧化应激及JAK-STAT信号通路等生物过程。KEGG分析显示，AGE-RAGE信号通路为关键调控途径，涉及IL6、STAT3等8个核心靶点。分子对接验证活性成分与核心蛋白具有稳定结合活性，其中STAT3-人参皂苷rh2复合物结合能最优，分子动力学模拟验证其结合构象稳定。结论：当归–黄芪与附子–人参药对通过多成分、多靶点、多通路协同调控SICM病理进程，其核心机制与STAT3介导的AGE-RAGE信号网络调控密切相关。该研究为阐释中药治疗SICM的生物学机制提供了理论与数据支撑。

Abstract: Objective: To comprehensively utilize multi-omics integration and computer simulation techniques to elucidate the molecular mechanism of traditional Chinese medicine (TCM) in treating sepsis-induced cardiomyopathy (SICM). Methods: Relevant literature published between 1910 and January 2026 was systematically retrieved from CNKI, Wanfang, and VIP databases. Frequency statistics and association rule analysis were used to screen core drug pairs. The TCMSP database and SwissTargetPrediction platform were employed to predict active ingredients and targets. The GEO database was integrated to obtain SICM gene expression profile datasets, and differentially expressed genes (DEGs) were screened. Disease-related modules were constructed through weighted gene co-expression network analysis (WGCNA) to identify key targets. A drug-ingredient-target network was constructed based on Cytoscape, and protein-protein interaction analysis was conducted using the STRING database. GO/KEGG analysis was used to identify key pathways, and molecular docking and molecular dynamics simulations were employed to verify core interactions. Results: A total of 33 TCM compounds were included, and high-frequency drugs were screened: aconite, salvia miltiorrhiza, astragalus, licorice, angelica, and ginseng. Among them, angelica-astragalus and aconite-ginseng were identified as key compound pairs, and 52 active ingredients and 1262 potential targets of the pairs were screened. Gene expression analysis yielded 1164 DEGs, and WGCNA indicated that 1495 hub genes were significantly correlated with SICM. Network topology analysis and GO analysis revealed that the core targets were mainly involved in biological processes such as inflammation, oxidative stress, and the JAK-STAT signaling pathway. KEGG analysis showed that the AGE-RAGE signaling pathway was a key regulatory pathway, involving 8 core targets such as IL6 and STAT3. Molecular docking verified that the active ingredients had stable binding activity with core proteins, with the STAT3-ginsenoside rh2 complex exhibiting the best binding energy. Molecular dynamics simulations confirmed the stability of its binding conformation. Conclusion: The angelica-astragalus and aconite-ginseng pairs synergistically regulate the pathological process of SICM through multi-component, multi-target, and multi-pathway mechanisms, and their core mechanism is closely related to STAT3-mediated AGE-RAGE signaling network regulation. This study provides theoretical and data support for elucidating the biological mechanism of TCM in treating SICM.