Exploring the Underlying Mechanism of Shenyankangfu Tablet in the Treatment of Glomerulonephritis Through Network Pharmacology, Machine Learning, Molecular Docking, and Experimental Validation.
Exploring the Underlying Mechanism of Shenyankangfu Tablet in the Treatment of Glomerulonephritis Through Network Pharmacology, Machine Learning, Molecular Docking, and Experimental Validation.
复制标题
通过网络药理学、机器学习、分子对接和实验验证探索肾炎康夫片治疗肾小球肾炎的潜在机制。
DOI：
10.2147/dddt.s333209
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Li D
中科院分区：
文献类型：
--
作者：
Jin M;Ren W;Zhang W;Liu L;Yin Z;Li D
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，探讨肾炎康夫片（SYKFT）治疗肾小球肾炎（GN）的潜在机制。 SYKFT的活性成分和潜在靶标通过中药系统药理学数据库和分析平台获得，GN的靶标通过GeneCards等获得。使用Perl和Cytoscape构建草药-活性成分-靶标网络。然后，使用R的clusterProfiler包进行基因本体论（GO）和京都基因和基因组百科全书（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，以及SwissTargetPrediction服务器基于机器学习模型预测核心活性成分的目标蛋白质。使用 AutoDock Vina 和 Pymol 进一步进行分子对接分析。最后，我们在体内验证了SYKFT对GN的作用。共筛选了SYKFT中的154个活性成分和255个靶点，并鉴定出135个靶点与GN相关。 GO富集分析表明生物过程主要与氧化应激和细胞增殖相关。 KEGG通路分析显示这些靶点主要涉及感染相关和GN相关通路。 PPI网络分析确定了SYKFT的13个核心目标。机器学习模型的结果表明STAT3和AKT1可能是关键目标。分子对接结果表明SYKFT的主要活性成分可以与多种靶蛋白结合。体内实验证实SYKFT可能通过调节核心基因减轻肾脏病理损伤，从而降低尿蛋白。该研究首次证明了SYKFT治疗肾小球肾炎的多成分、多靶点和多途径特征。
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. 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. 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. This study demonstrated for the first time the multicomponent, multitarget, and multipathway characteristics of SYKFT for GN treatment.