Abstract：
BACKGROUND: Diabetic cardiomyopathy (DCM) is a common diabetes complication with limited medications. Gegen Qinlian decoction (GQD) has been used in the treatment of diabetes and its related complications in China for several decades.
OBJECTIVE: In this study, network pharmacology was employed to predict the active ingredients, key targets, and pathways involved in the treatment of DCM by GQD and to validate it by animal experiments.
METHODS: The active ingredients of GQD were retrieved from TCMSP and published literature. DCM-related gene targets were searched in Drugbank, Genecards, Disgenet, and OMIM disease databases. Protein-protein interaction networks were constructed using the STRING database and Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed using the Metascape platform. Moreover, a diabetic mouse model was established to evaluate the therapeutic effects of GQD by measuring serum biochemical markers and inflammation levels. Finally, the expression of predicted key target genes was determined using real-time quantitative PCR.
RESULTS: A total of 129 active ingredients were screened from GQD. Moreover, 146 intersecting genes related to DCM were obtained, with key targets, including AKT1, TNF, IL6, and VEGFA. Lipid and atherosclerosis, AGE-RAGE, PI3K-AKT, and MAPK pathways were identified. Blood glucose control, decreased inflammatory factors, and serum CK-MB levels were restored after GQD intervention, and the same occurred with the expressions of PPAR-γ, AKT1, APOB, and GSK3B genes.
CONCLUSIONS: Quercetin, kaempferol, wogonin, 7-methoxy-2-methyl isoflavone, and formononetin may exert major therapeutic effects by regulating key factors, such as AKT1, APOE, and GSK3B, in the inflammatory reaction, glycolipid oxidation, and glycogen synthesis related signaling pathways.
OBJECTIVE: In this study, network pharmacology was employed to predict the active ingredients, key targets, and pathways involved in the treatment of DCM by GQD and to validate it by animal experiments.
METHODS: The active ingredients of GQD were retrieved from TCMSP and published literature. DCM-related gene targets were searched in Drugbank, Genecards, Disgenet, and OMIM disease databases. Protein-protein interaction networks were constructed using the STRING database and Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed using the Metascape platform. Moreover, a diabetic mouse model was established to evaluate the therapeutic effects of GQD by measuring serum biochemical markers and inflammation levels. Finally, the expression of predicted key target genes was determined using real-time quantitative PCR.
RESULTS: A total of 129 active ingredients were screened from GQD. Moreover, 146 intersecting genes related to DCM were obtained, with key targets, including AKT1, TNF, IL6, and VEGFA. Lipid and atherosclerosis, AGE-RAGE, PI3K-AKT, and MAPK pathways were identified. Blood glucose control, decreased inflammatory factors, and serum CK-MB levels were restored after GQD intervention, and the same occurred with the expressions of PPAR-γ, AKT1, APOB, and GSK3B genes.
CONCLUSIONS: Quercetin, kaempferol, wogonin, 7-methoxy-2-methyl isoflavone, and formononetin may exert major therapeutic effects by regulating key factors, such as AKT1, APOE, and GSK3B, in the inflammatory reaction, glycolipid oxidation, and glycogen synthesis related signaling pathways.
摘要：
背景：糖尿病心肌病（DCM）是一种常见的糖尿病并发症，药物治疗有限。葛根芩连汤在我国应用于糖尿病及其相关并发症的治疗已有几十年的历史。
目的：在本研究中，网络药理学被用来预测活性成分，关键目标,以及GQD治疗DCM的途径，并通过动物实验进行验证。
方法：从TCMSP和已发表的文献中检索GQD的活性成分。在Drugbank中搜索DCM相关基因靶标，基因卡,Disgenet,和OMIM疾病数据库。使用STRING数据库和Cytoscape构建蛋白质-蛋白质相互作用网络。使用Metascape平台进行GO分析和KEGG途径富集分析。此外，建立糖尿病小鼠模型，通过测定血清生化指标和炎症水平来评价GQD的治疗效果。最后,使用实时定量PCR确定预测的关键靶基因的表达。
结果:从GQD中筛选出总共129种活性成分。此外，获得了146个与DCM相关的交叉基因，有了关键目标，包括AKT1，TNF，IL6和VEGFA。脂质和动脉粥样硬化，年龄-愤怒,PI3K-AKT,并鉴定了MAPK途径。血糖控制,降低炎症因子，GQD干预后血清CK-MB水平恢复,PPAR-γ的表达也是如此，AKT1、APOB、和GSK3B基因。
结论：槲皮素，山奈酚,Wogonin,7-甲氧基-2-甲基异黄酮，Formonoetin可能通过调节关键因素发挥主要治疗作用，如AKT1，APOE，GSK3B,在炎症反应中，糖脂氧化，与糖原合成相关的信号通路。
目的：在本研究中，网络药理学被用来预测活性成分，关键目标,以及GQD治疗DCM的途径，并通过动物实验进行验证。
方法：从TCMSP和已发表的文献中检索GQD的活性成分。在Drugbank中搜索DCM相关基因靶标，基因卡,Disgenet,和OMIM疾病数据库。使用STRING数据库和Cytoscape构建蛋白质-蛋白质相互作用网络。使用Metascape平台进行GO分析和KEGG途径富集分析。此外，建立糖尿病小鼠模型，通过测定血清生化指标和炎症水平来评价GQD的治疗效果。最后,使用实时定量PCR确定预测的关键靶基因的表达。
结果:从GQD中筛选出总共129种活性成分。此外，获得了146个与DCM相关的交叉基因，有了关键目标，包括AKT1，TNF，IL6和VEGFA。脂质和动脉粥样硬化，年龄-愤怒,PI3K-AKT,并鉴定了MAPK途径。血糖控制,降低炎症因子，GQD干预后血清CK-MB水平恢复,PPAR-γ的表达也是如此，AKT1、APOB、和GSK3B基因。
结论：槲皮素，山奈酚,Wogonin,7-甲氧基-2-甲基异黄酮，Formonoetin可能通过调节关键因素发挥主要治疗作用，如AKT1，APOE，GSK3B,在炎症反应中，糖脂氧化，与糖原合成相关的信号通路。
