基于逆向网络药理学及分子对接探讨新型冠状病毒感染后肺纤维化发病机制及中药组方预测

Exploring Pathogenesis of Pulmonary Fibrosis after COVID-19 and Predicting Traditional Chinese Medicine Prescription Based on Reverse Network Pharmacology and Molecular Docking

目的探讨新型冠状病毒感染(新冠感染,COVID-19)后肺纤维化的发病机制,并基于逆向网络药理学思维进行中药组方预测。方法于Genecards数据库获取COVID-19和PF的靶点取交集得出二者共有靶点后,通过String平台进行蛋白互作网络分析,运用Cytoscape软件计算Degree值以确定关键靶点,而后进行GO和KEGG富集分析以明确发病机制与通路。关键靶点通过Uniprot数据库转换后于TCMSP数据库逆向收集中药成分,选用度值较高的靶点与有效成分进行分子对接验证,再由有效成分收集对应中药,构建关键靶点-有效成分-中药网络,确定度值较高中药并整理其性、味、归经。结果共获取142个COVID-19与PF的共有靶点,保留度值较高的50个关键靶点,其中有32个关键靶点能于TCMSP数据库中匹配到中药成分化合物信息,筛选后有18个关键靶点匹配到31种有意义的中药成分,以度值最高的4个靶点蛋白与度值≥3的7种中药成分进行28次分子对接验证,结果稳定且良好。GO富集分析主要得出炎症反应、血管系统发育的调节等生物过程,KEGG富集分析显示冠状病毒病-新冠肺炎、Th17细胞分化、HIF-1信号通路、IL17信号通路等信号通路。逆向收集到287种中药,其中度值≥3的73种中药以寒性药、苦味药居多,其次为温性药、辛味药,并且肝、肺经居多,度值≥6的中药分别为苦参、连翘、木蝴蝶、山豆根。结论运用逆向网络药理学思维及分子对接技术对新冠感染后肺纤维化进行靶点、通路、成分和中药预测,为今后临床辨证论治中药组方拓宽思路。

Objective To explore the pathogenesis of pulmonary fibrosis after corona virus disease 2019(COVID-19)and predict the prescriptions of traditional Chinese medicine based on reverse network pharmacology.Methods The targets of COVID-19 and pulmonary fibrosis were obtained from the Genecards database and were intersected to obtain common targets.The protein-protein interaction network was conducted through String platform,the degree value was calculated by Cytoscape software to determine the key targets,and then Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were conducted to clarify the pathogenesis and pathway.Traditional Chinese medicine ingredients in Traditional Chinese Medicines Systems Pharmacology Platform(TCMSP)database were collected after the conversion of the key targets in Uniprot database.The targets with high degree value were selected for molecular docking with active ingredients.Then,traditional Chinese medicine corresponding to the active ingredients were collected to build the key target-active ingredient-traditional Chinese medicine network.Traditional Chinese medicine with high degree value was determined to sort its four properties,five tastes and channel tropism.Results A total of 142 common targets of COVID-19 and pulmonary fibrosis,50 key targets with high degree value,among which 32 key targets could match the information of traditional Chinese medicine ingredients in the TCMSP database.After screening,18 key targets matched to 31 meaningful traditional Chinese medicine ingredients.The molecular docking verification of 28 times was performed between the 4 target proteins with the highest degree value and the 7 traditional Chinese medicine ingredients with the degree value greater than or equal to 3,and the results were stable and good.GO enrichment analysis mainly concluded biological processes such as inflammatory response and regulation of vasculature development,while KEGG enrichment analysis showed coronavirus disease-COVID-19,Th17 cell differentiation,HIF-1 signaling pathway,and IL-17 signaling pathway.A total of 287 kinds of traditional Chinese medicines were collected in reverse,among which 73 kinds of traditional Chinese medicines with degree value greater than or equal to 3 were mostly cold and bitter medicines,followed by warm and spicy medicines,and liver and lung meridians were in the majority.The traditional Chinese medicines with degree value greater than or equal to 6 were Kushen(Sophorae Flavescentis Radix),Lianqiao(Forsythiae Fructus),Muhudie(Oroxyli Semen)and Shandougen(Sophorae Tonkinensis Radix Et Rhizoma).Conclusion In this study,reverse network pharmacological thinking and molecular docking were used to predict the targets,pathways,active ingredient and traditional Chinese medicine of pulmonary fibrosis after COVID-19,which broadened our thinking on the formulation of traditional Chinese medicine for clinical syndrome differentiation and treatment in the future.