目的探讨轻盈祛浊汤治疗糖尿病肾病(DN)的作用机制。方法利用网络药理学预测轻盈祛浊汤治疗DN的作用机制,并通过分子对接预测活性成分的结合部位。结合网络药理学及分子对接结果,构建DN大鼠模型,2023年1—6月,将60只大鼠分为正常组、模型组、马来酸依那普利组、轻盈祛浊汤组,每组15只。治疗8周后比较四组大鼠24 h尿微量白蛋白(urinary microalbuminexcretion rate,UAER)、尿素氮、血肌酐水平和钠/葡萄糖协同转运蛋白1(recombinant sodium/glucose cotransporter 1,SGLT1),A1腺苷受体(A1 adenosine receptor,A1AR)蛋白表达水平。结果网络药理学共筛选出轻盈祛浊汤有效成分1114种,作用靶点269个,DN相关靶点2020个,其交集靶点174个。基因本体富集(GO)和基因组的京都百科全书(KEGG)分析得出主要涉及信号转导、炎症反应、细胞凋亡等一系列的生物学反应过程,主要参与丝裂原活化蛋白激酶/核因子κB(mitogen-activated protein kinase/nuclear factor kappa-B,pMAPK/NF-κB)、NOD样受体家族蛋白3/白细胞介素-1β(NOD-like receptor protein 3/interleukin-1β,NLRP3/IL-1β)、白细胞介素6/信号传导和转录激活因子3(interleukin-6/signal transducer and activator of transcription 3,IL-6/STAT3)、肿瘤坏死因子(tumor necrosis factor,TNF)、肿瘤蛋白p53(tumor protein 53,P53)和前列腺素内过氧化物合酶(prostaglandin-endoperoxide synthase 2,PTGS2)等信号通路的调控。分子对接表明,轻盈祛浊汤主要成分与DN靶点的结合活性较强。模型组大鼠24 h UAER[(4539.71±516.03)μg/24 h比(226.59±72.71)μg/24 h]、血肌酐[(85.63±12.96)mL·kg^(-1)·min^(-1)比(0.48±0.12)mL·kg^(-1)·min^(-1)]、SGLT1(1.17±0.07比0.82±0.06)高于正常组,而模型组大鼠尿素氮、A1AR低于正常组(P<0.05)。马来酸依那普利片组大鼠24 h UAER、血肌酐、SGLT1低于模型组,马来酸依那普利片组大鼠尿素氮、A1AR高于模型组(P<0.05)。轻盈祛浊汤组大鼠24 h UAER�
目的:通过网络药理学与孟德尔随机化的结合探讨姜黄素治疗2型糖尿病的药理学机制。方法:姜黄素的药物靶点通过pharmapper数据库、Swiss Target Prediction数据库获得,2型糖尿病的疾病靶点通过GeneCards数据库获得,将药物靶点与疾病靶点进行合并以获得潜在的治疗靶点,蛋白质之间相互作用(PPI)数据从String数据库中获得,通过R语言对治疗靶点进行GO与KEGG分析,通过IEU OPEN GWAS Project数据库获得暴露数据与结局数据的相关信息,并通过语言进行孟德尔随机化分析。结果:姜黄素的药物靶点预测到了111个,2型糖尿病的疾病靶点有921个,治疗靶点有27个,对这些治疗靶点KEGG分析发现主要通路为凋亡信号通路、TNF信号通路、IL-17信号通路等,核心治疗靶点为CASP3、MMP2、SIRT1等。结论:姜黄素通过多靶点、多通路、多生物学过程对2型糖尿病起到治疗作用,这与中医学核心观念相一致。Objective: To explore the pharmacological mechanism of curcumin in the treatment of type 2 diabetes through the combination of network pharmacology and Mendel randomization. Methods: The drug targets of curcumin were obtained from pharmapper database and Swiss Target Prediction database, the disease targets of type 2 diabetes were obtained from GeneCards database, the drug targets and disease targets were combined to obtain potential therapeutic targets, the protein-protein interaction (PPI) data was obtained from String database, the treatment targets were analyzed by GO and KEGG through R language, the relevant information of exposure data and outcome data was obtained through IEU OPEN GWAS Project database, and Mendelian randomization analysis was conducted through language. Results: 111 drug targets were predicted for curcumin, 921 disease targets for type 2 diabetes, and 27 therapeutic targets. KEGG analysis of these therapeutic targets found that the main pathways were apoptosis signaling pathway, TNF signaling pathway, IL-17 si
