关键词:
痛风性关节炎
余甘子
网络药理学
摘要:
目的:本研究应用网络药理学探究余甘子治疗临床难治性疾病GA的主要活性成分和潜在机制,为云南特色药用植物余甘子用于GA的防治提供科学参考。方法:按照《网络药理学评选方法指南》采用网络数据库TCMSP和DisGeNET等获取余甘子的主要活性成分及其相关的蛋白质靶点和GA相关病理靶点。应用Venny2.1.0获取余甘子和GA靶点的集合,继而构建PPI网络,利用Cytoscape 3.9.1基于MCC算法得到核心靶点。进一步对靶点进行GO和KEGG富集分析。结果:筛选获得余甘子有效成分18个,余甘子和GA交集39个靶点,PPI网络中Degree值排序最重要的是TNF、VEGFA、MAPK14。GO分析表明余甘子治疗GA涉及小胶质细胞分化、凋亡细胞清除的正调节等生物过程。KEGG分析表明余甘子治疗GA的关键信号通路为Toll-like受体信号通路、NF-κB信号通路及IL-17信号通路。结论:通过本研究揭示了余甘子中槲皮素、木犀草素、山奈酚、鞣花酸等重要功效物质,通过调控关键炎症免疫信号通路及蛋白靶点,降低血液尿酸水平、减轻尿酸所致关节炎反应作为防治GA的潜在分子机制。Objective: This study used network pharmacology to explore the main active ingredients and potential mechanisms of Phyllanthus emblica in the treatment of clinical refractory disease GA and to provide a scientific reference for the use of Phyllanthus emblica, a Yunnan specialty medicinal plant, in the prevention and treatment of GA. Method: According to the “Guidelines for Network Pharmacology Selection”, the main active ingredients of Phyllanthus emblica and their related protein and GA-related pathological targets were obtained using network databases such as TCMSP and DisGeNET. Venny2.1.0 was used to obtain the set of Phyllanthus emblica and GA targets, and then a PPI network was constructed. The core targets were obtained using Cytoscape 3.9.1 based on the MCC algorithm. GO and KEGG enrichment analyses were performed on the targets further. Results: A total of 18 active ingredients of Phyllanthus emblica and 39 targets in the intersection of Phyllanthus emblica and GA were screened and obtained. The most important degree values in the PPI network were TNF, VEGFA, and MAPK14. GO analysis showed that Phyllanthus emblica in the treatment of GA involved biological processes such as positive regulation of microglia differentiation and apoptotic cell clearance. KEGG analysis showed that the key signaling pathways of Phyllanthus emblica in treating GA were the Toll-like receptor signaling pathway, NF-κB signaling pathway, and IL-17 signaling pathway. Conclusion: This study revealed that quer