通过网络药理学、机器学习、分子对接和实验验证探索参芪抗肾复康片治疗肾小球肾炎的潜在机制。
Exploring the Underlying Mechanism of Shenyankangfu Tablet in the Treatment of Glomerulonephritis Through Network Pharmacology, Machine Learning, Molecular Docking, and Experimental Validation.
机构信息
Department of Nephrology, Beijing-Chaoyang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
Department of Nephrology, Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Institute of Nephrology, State Key Laboratory of Kidney Diseases (2011DAV00088), National Clinical Research Center for Kidney Diseases, Beijing, 100853, People's Republic of China.
出版信息
Drug Des Devel Ther. 2021 Nov 9;15:4585-4601. doi: 10.2147/DDDT.S333209. eCollection 2021.
PURPOSE
This study aimed to explore the underlying mechanisms of Shenyankangfu tablet (SYKFT) in the treatment of glomerulonephritis (GN) based on network pharmacology, machine learning, molecular docking, and experimental validation.
METHODS
The active ingredients and potential targets of SYKFT were obtained through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, the targets of GN were obtained through GeneCards, etc. Perl and Cytoscape were used to construct an herb-active ingredient-target network. Then, the clusterProfiler package of R was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We also used the STRING platform and Cytoscape to construct a protein-protein interaction (PPI) network, as well as the SwissTargetPrediction server to predict the target protein of the core active ingredient based on machine-learning model. Molecular-docking analysis was further performed using AutoDock Vina and Pymol. Finally, we verified the effect of SYKFT on GN in vivo.
RESULTS
A total of 154 active ingredients and 255 targets in SYKFT were screened, and 135 targets were identified to be related to GN. GO enrichment analysis indicated that biological processes were primarily associated with oxidative stress and cell proliferation. KEGG pathway analysis showed that these targets were involved mostly in infection-related and GN-related pathways. PPI network analysis identified 13 core targets of SYKFT. Results of machine-learning model suggested that STAT3 and AKT1 may be the key target. Results of molecular docking suggested that the main active components of SYKFT can be combined with various target proteins. In vivo experiments confirmed that SYKFT may alleviate renal pathological injury by regulating core genes, thereby reducing urinary protein.
CONCLUSION
This study demonstrated for the first time the multicomponent, multitarget, and multipathway characteristics of SYKFT for GN treatment.
目的
本研究旨在基于网络药理学、机器学习、分子对接和实验验证，探讨参雁康复片（SYKFT）治疗肾小球肾炎（GN）的潜在机制。
方法
通过中药系统药理学数据库与分析平台（TCMSP）获取 SYKFT 的活性成分和潜在靶点，通过 GeneCards 等获取 GN 的靶点。利用 Perl 和 Cytoscape 构建草药-活性成分-靶点网络。然后，使用 R 中的 clusterProfiler 包进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路分析。我们还使用 STRING 平台和 Cytoscape 构建蛋白质-蛋白质相互作用（PPI）网络，并使用 SwissTargetPrediction 服务器基于机器学习模型预测核心活性成分的靶蛋白。进一步使用 AutoDock Vina 和 Pymol 进行分子对接分析。最后，我们在体内验证了 SYKFT 对 GN 的作用。
结果
筛选出 SYKFT 中的 154 种活性成分和 255 个靶点，鉴定出 135 个与 GN 相关的靶点。GO 富集分析表明，生物过程主要与氧化应激和细胞增殖有关。KEGG 通路分析表明，这些靶点主要参与感染相关和 GN 相关途径。PPI 网络分析确定了 SYKFT 的 13 个核心靶点。机器学习模型的结果表明，STAT3 和 AKT1 可能是关键靶标。分子对接的结果表明，SYKFT 的主要活性成分可以与各种靶蛋白结合。体内实验证实，SYKFT 可能通过调节核心基因来减轻肾脏病理损伤，从而减少尿蛋白。
结论
本研究首次证明了 SYKFT 治疗 GN 的多成分、多靶点和多途径特征。
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