Zhao Jie, Mo Chao, Shi Wei, Meng LiFeng, Ai Jun
Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China.
Department of Nephrology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi 530023, China.
Evid Based Complement Alternat Med. 2021 Jul 24;2021:9980981. doi: 10.1155/2021/9980981. eCollection 2021.
(AR)- (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of - treating DN based on network pharmacology combined with bioinformatics.
The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of -. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of - putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.
Twenty-two bioactive ingredients and 189 putative targets of were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of against DN were identified. GO and KEGG analyses revealed that candidate targets of against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.
against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.
黄芪-茯苓这一经典药对在治疗糖尿病肾病（DN）方面已显示出显著效果。然而，其治疗DN的内在机制仍不清楚。本研究旨在基于网络药理学结合生物信息学阐明其治疗DN的机制及分子靶点。
利用中药系统药理学数据库筛选黄芪-茯苓的生物活性成分。随后，利用DrugBank数据库预测生物活性成分的潜在靶点，并在UniProtKB数据库中转化为基因。通过分析基因表达综合数据库中已发表的微阵列数据（GSE30528）检索DN相关靶点。使用Cytoscape 3.8.0建立黄芪-茯苓潜在靶点与DN相关靶点的蛋白质-蛋白质相互作用网络以鉴定候选靶点。使用Cytoscape的插件ClueGO对候选靶点进行GO和KEGG富集分析。使用AutoDock Vina软件进行分子对接，并通过Pymol软件可视化结果。通过受试者工作特征（ROC）曲线验证枢纽基因的诊断能力。
获得了22种生物活性成分和189个黄芪-茯苓的潜在靶点。筛选出850个与DN相关的差异表达基因。蛋白质-蛋白质相互作用网络显示，鉴定出115个黄芪-茯苓抗DN的候选靶点。GO和KEGG分析表明，黄芪-茯苓抗DN的候选靶点主要参与细胞凋亡、氧化应激、细胞周期和炎症反应，并调节PI3K-Akt信号通路、细胞周期和MAPK信号通路。此外，MAPK1、AKT1、GSK3B、CDKN1A、TP53、RELA、MYC、GRB2、JUN和EGFR被认为是核心潜在治疗靶点。分子对接表明，这些核心靶点与槲皮素、山柰酚、异鼠李素和芒柄花素成分具有很强的结合亲和力。ROC曲线分析表明，AKT1、TP53、RELA、JUN、CDKN1A和EGFR在区分DN与对照方面有效。
黄芪-茯苓抗DN可能通过多种生物活性化学成分及相关药理途径发挥肾脏保护作用，涉及多个分子靶点，这可能是一种有前途的治疗DN的药对。然而，这些结果仍需实验证据进一步验证。
