目的 运用网络药理学探究黄芩治疗慢性鼻窦炎的潜在作用机制.方法 利用中药系统药理学数据库与分析平台(traditional Chinese medicine systematic pharmacology database and analysis platform,TCMSP)、中医药整合药理学研究平台(integrative pharmacology-based research platform of traditional Chinese medicine,TCMIP)和中药分子机制生物信息学分析工具(bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine,BATMAN-TCM)筛选出黄芩的活性成分;利用Swiss Target Prediction数据库收集黄芩活性成分的靶点;利用GeneCards、在线人类孟德尔遗传(online Mendelian inheritance in man,OMIM)、gharmGKB和DrugBank数据库筛选"慢性鼻窦炎"相关靶点;通过STRING(search tool for the retrieval of interaction gene/proteins)数据库构建活性成分靶点与疾病靶点之间的蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络;利用Cytoscape3.10.0 软件筛选核心靶点;进行京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路和基因本体(Gene Ontology,GO)富集分析以探索黄芩治疗慢性鼻窦炎的相关生物学过程及信号通路,利用Autodock tools 1.5.7 将前4 位关键靶点和前4 个活性成分进行分子对接.结果 共收集到35 个黄芩活性成分,1 743 个慢性鼻窦炎相关的疾病靶点,这些活性成分与慢性鼻窦炎有121 个交叉靶点,通过PPI网络分析,获得28 个核心靶点,4 个关键靶点,GO功能富集表明黄芩治疗慢性鼻窦炎主要涉及脂多糖、蛋白酪氨酸激酶活性、膜筏、膜微区和膜区域等生物学过程,KEGG通路富集提示主要与磷脂酰肌醇 3激酶/蛋白激酶 B(phosphoinositide 3-kinase-serine/threonine-protein kinase,PI3K-Akt)信号通路、高级糖基化终末产物-受体(advanced glycation end product-receptor of advanced glycation endproducts,AGE-RAGE)信号通路、Ras(reliability,availability and serviceability signaling pathway)信号通路、趋化因子信号通路等密切相关,分子对接结果显示表小檗碱与磷脂酰肌醇 4,5-二磷酸3-激酶催化亚基 δ(phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit δ,PIK3CD)对接良好.结论 本研究结果表明黄芩可能可通过多种活性成分、多个靶点及多条信号通路发挥其对慢性鼻窦炎治疗的作用.
The mechanism of Scutellaria baicalensis in treatment of chronic rhinosinusitis based on network pharmacology
Objective To explore the potential mechanisms of Scutellaria baicalensis in the treatment of chronic rhinosinusitis based on network pharmacology approach.Methods The active ingredients of Scutellaria baicalensis were screened on the traditional Chinese medicine systematic pharmacology database and analysis platform(TCMSP),traditional Chinese medicine integrated pharmacology research platform(TCMIP),and bioinformatics analysis tool for molecular mechanisms of traditional Chinese medicines(BATMAN-TCM).The targets of the active ingredients of Scutellaria baicalensis were collected by using the Swiss Target Prediction database;The GeneCards,online Mendelian inheritance in man(OMIM),gharmGKB and DrugBank databases were used to screen the relevant targets of chronic rhinosinusitis.The protein-protein interaction(PPI)networks between active ingredient targets and disease targets were constructed via the search tool for the retrieval of interaction gene/proteins(STRING)database.The Cytoscape 3.10.0 software was used to screen the core targets;Kyoto encyclopedia of genes and genomes(KEGG)pathway and Gene Ontology(GO)enrichment analyses were performed to explore the biological processes and signaling pathways related to the treatment of chronic rhinosinusitis with Scutellaria baicalensis.Autodock tools 1.5.7 was used to molecularly dock the top 4 key targets and the top 4 active ingredients.Results A total of 35 Scutellaria baicalensis active ingredients and 1 743 disease targets related to chronic rhinosinusitis were collected,including 121 cross-targets.A total of 28 core targets and 4 key targets were obtained by PPI network analysis.GO functional enrichment indicated that the treatment of chronic rhinosinusitis by Scutellaria baicalensis mainly involves biological processes such as lipopolysaccharide,protein tyrosine kinase activity,membrane rafts,membrane microregions and membrane regions.KEGG pathway enrichment suggests that it is mainly closely related to phosphoinositide 3-kinase-Serine/Threonine-protein kinase(PI3K-Akt)signaling pathway,advanced glycation end product-receptor of advanced glycation endproducts(AGE-RAGE)signaling pathway,reliability,availability and serviceability signaling pathway(Ras)signaling pathway,chemokine signaling pathway and so on.Epiberberine docked well with phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit δ(PIK3CD).Conclusion This study demonstrated that Scutellaria baicalensis can treat chronic rhinosinusitis through multiple active ingredients,multiple targets,and multiple signaling pathways.
NETL
NSTL
万方数据
维普
