Network pharmacology analysis combined with experimental validation to explore the therapeutic mechanism of Schisandra Chinensis Mixture on diabetic nephropathy

系统药理学 计算生物学 五味子 药理学 微阵列分析技术 生物 分子药理学 机制(生物学) 药品 生物信息学 医学 中医药 基因 受体 遗传学 基因表达 认识论 哲学 病理 替代医学
作者
Yu Ma,Yuanyuan Deng,Na Li,Ao Dong,Hongdian Li,Shu Chen,Sai Zhang,Mianzhi Zhang
出处
期刊:Journal of Ethnopharmacology [Elsevier BV]
卷期号:302 (Pt A): 115768-115768 被引量:24
标识
DOI:10.1016/j.jep.2022.115768
摘要

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic nephropathy (DN) is one of the most common and serious microvascular complications of Diabetes mellitus (DM). The inflammatory response plays a critical role in DN. Schisandra Chinensis Mixture (SM) has shown promising clinical efficacy in the treatment of DN while the pharmacological mechanisms are still unclear. AIM OF THE STUDY: In this study, a network pharmacology approach and bioinformatic analysis were adopted to predict the pharmacological mechanisms of SM in DN therapy. Based on the predicted results, molecular docking and in vivo experiments were used for verification. MATERIALS AND METHODS: In this study, the candidate bioactive ingredients of SM were obtained via Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and supplementing according to the literature. SM putative targets and the verified targets were acquired from TCMSP and SiwssTartgetPrediction Database. DN-related target genes were collected from GeneCards, OMIM, DisGeNET databases, and microarray data analysis. Biological function and pathway analysis were performed to further explore the pharmacological mechanisms of SM in DN therapy. The protein-protein interaction (PPI) network was established to screen the hub gene. The Receiver Operating Characteristic (ROC) analysis and the molecular docking simulations were performed to validate the potential target-drug interactions. The fingerprint spectrum of multi-components of the SM was characterized by UPLC-MS/MS. The signaling pathways associated with inflammation and hub genes were partially validated in SD rats. RESULTS: A total of 36 bioactive ingredients were contained, and 666 component-related targets were screened from SM, of which 50 intersected with DN targets and were considered potential therapeutic targets. GO analyses revealed that the 50 intersection targets were mainly enriched in the inflammatory response, positive regulation of angiogenesis, and positive regulation of phosphatidylinositol 3-kinase(PI3K) signaling. KEGG analyses indicated that the PI3K-Akt signaling pathway was considered as the most important pathway for SM antagonism to the occurrence and development of DN, with the highest target count enrichment. PPI network results showed that the top 15 protein targets in degree value, VEGFA, JAK2, CSF1R, NOS3, CCR2, CCR5, TLR7, FYN, BTK, LCK, PLAT, NOS2, TEK, MMP1 and MCL1, were identified as hub genes. The results of ROC analysis showed that VEGFA and NOS3 were valuable in the diagnosis of DN. The molecular docking confirmed that the core bioactive ingredients had well-binding affinity for VEGFA and NOS3. The in vivo experiments confirmed that SM significantly inhibited the over-release of inflammatory cytokines such as interleukin (IL)-6 and tumor necrosis factor receptor (TNF)-α in DN rats, while regulating the PI3K-AKT and VEGFA-NOS3 signaling pathways. CONCLUSION: This study revealed the multi-component, multi-target and multi-pathway characteristics of SM therapeutic DN. SM inhibited the inflammatory response and improved renal pathological damage in DN rats, which was related to the regulation of the PI3K-Akt and VEGFA-NOS3 signaling pathways.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
四喜丸子完成签到,获得积分10
刚刚
xliang应助王老师采纳,获得10
1秒前
1秒前
KobeLaoda发布了新的文献求助30
1秒前
sunnyfriend完成签到,获得积分10
1秒前
2秒前
2秒前
Miracle发布了新的文献求助10
3秒前
yumu发布了新的文献求助10
4秒前
华仔应助xiaozhao采纳,获得10
4秒前
孙文远发布了新的文献求助10
5秒前
Tushar发布了新的文献求助10
5秒前
wangle_17应助火猫三张大王采纳,获得20
7秒前
CodeCraft应助火猫三张大王采纳,获得10
7秒前
zychaos发布了新的文献求助10
7秒前
Moonpie举报江睿曦求助涉嫌违规
7秒前
7秒前
lez完成签到,获得积分10
7秒前
7秒前
janeZ完成签到,获得积分10
7秒前
bkagyin应助Miracle采纳,获得10
8秒前
由秋尽发布了新的文献求助10
9秒前
waytohill发布了新的文献求助10
10秒前
内啡呔完成签到,获得积分10
10秒前
10秒前
打打应助海绵宝宝采纳,获得10
11秒前
11秒前
充电宝应助程璟曦采纳,获得10
12秒前
sagitar应助小样QQ星采纳,获得20
12秒前
12秒前
彤彤发布了新的文献求助10
12秒前
13秒前
13秒前
13秒前
zychaos完成签到,获得积分10
13秒前
15秒前
17秒前
呼伦贝尔大草原完成签到,获得积分10
17秒前
科研通AI6.4应助爱笑涔雨采纳,获得10
18秒前
皮凡发布了新的文献求助10
18秒前
高分求助中
Principles of Economics, 11th Edition 10000
Prescott's Microbiology: 2026 Release ISE 10000
University Physics with Modern Physics, 16th edition 10000
Cronologia da história de Macau 5000
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Interactions of Vowel Quality and Prosody in East Slavic 1000
Matrix Methods in Data Mining and Pattern Recognition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7155665
求助须知:如何正确求助?哪些是违规求助? 8800392
关于积分的说明 18598397
捐赠科研通 6756226
什么是DOI,文献DOI怎么找? 3161279
关于科研通互助平台的介绍 2295671
邀请新用户注册赠送积分活动 2135999