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基于网络药理学和分子对接的桑叶(Morus alba Linne)治疗糖尿病作用机制的系统评价。

Systematic Evaluation of the Mechanisms of Mulberry Leaf (Morus alba Linne) Acting on Diabetes Based on Network Pharmacology and Molecular Docking.

机构信息

Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.

Department of Pharmacy, Anqing Medical College, Anqing 246052, China.

出版信息

Comb Chem High Throughput Screen. 2021;24(5):668-682. doi: 10.2174/1386207323666200914103719.

Abstract

BACKGROUND

Diabetes mellitus is one of the most common endocrine metabolic disorder- related diseases. The application of herbal medicine to control glucose levels and improve insulin action might be a useful approach in the treatment of diabetes. Mulberry leaves (ML) have been reported to exert important activities of anti-diabetic.

OBJECTIVE

In this work, we aimed to explore the multi-targets and multi-pathways regulatory molecular mechanism of Mulberry leaves (ML, Morus alba Linne) acting on diabetes.

METHODS

Identification of active compounds of Mulberry leaves using Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was carried out. Bioactive components were screened by FAF-Drugs4 website (Free ADME-Tox Filtering Tool). The targets of bioactive components were predicted from SwissTargetPrediction website, and the diabetes related targets were screened from GeneCards database. The common targets of ML and diabetes were used for Gene Ontology (GO) and pathway enrichment analysis. The visualization networks were constructed by Cytoscape 3.7.1 software. The biological networks were constructed to analyze the mechanisms as follows: (1) compound-target network; (2) common target-compound network; (3) common targets protein interaction network; (4) compound-diabetes protein-protein interactions (ppi) network; (5) target-pathway network; and (6) compound-target-pathway network. At last, the prediction results of network pharmacology were verified by molecular docking method.

RESULTS

17 active components were obtained by TCMSP database and FAF-Drugs4 website. 51 potential targets (11 common targets and 40 associated indirect targets) were obtained and used to build the PPI network by the String database. Furthermore, the potential targets were used for GO and pathway enrichment analysis. Eight key active compounds (quercetin, Iristectorigenin A, 4- Prenylresveratrol, Moracin H, Moracin C, Isoramanone, Moracin E and Moracin D) and 8 key targets (AKT1, IGF1R, EIF2AK3, PPARG, AGTR1, PPARA, PTPN1 and PIK3R1) were obtained to play major roles in Mulberry leaf acting on diabetes. And the signal pathways involved in the mechanisms mainly include AMPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, insulin signaling pathway and insulin resistance. The molecular docking results show that the 8 key active compounds have good affinity with the key target of AKT1, and the 5 key targets (IGF1R, EIF2AK3, PPARG, PPARA and PTPN1) have better affinity than AKT1 with the key compound of quercetin.

CONCLUSION

Based on network pharmacology and molecular docking, this study provided an important systematic and visualized basis for further understanding of the synergy mechanism of ML acting on diabetes.

摘要

背景

糖尿病是最常见的内分泌代谢紊乱相关疾病之一。应用草药控制血糖水平和改善胰岛素作用可能是治疗糖尿病的一种有效方法。桑叶(Mulberry leaves,ML)已被报道具有重要的抗糖尿病作用。

目的

本研究旨在探讨桑叶(Morus alba Linne)治疗糖尿病的多靶点、多途径调控分子机制。

方法

采用中药系统药理学(TCMSP)数据库对桑叶的活性化合物进行鉴定。通过 FAF-Drugs4 网站(Free ADME-Tox Filtering Tool)筛选生物活性成分。从 SwissTargetPrediction 网站预测生物活性成分的靶点,从 GeneCards 数据库筛选糖尿病相关靶点。将 ML 和糖尿病的共同靶点用于基因本体(GO)和通路富集分析。使用 Cytoscape 3.7.1 软件构建可视化网络。构建生物网络以分析以下机制:(1)化合物-靶点网络;(2)共同靶点-化合物网络;(3)共同靶点蛋白相互作用网络;(4)化合物-糖尿病蛋白-蛋白相互作用(ppi)网络;(5)靶点-通路网络;(6)化合物-靶点-通路网络。最后,通过分子对接方法验证网络药理学的预测结果。

结果

通过 TCMSP 数据库和 FAF-Drugs4 网站获得 17 种活性成分。通过 String 数据库获得 51 个潜在靶点(11 个共同靶点和 40 个间接相关靶点),并构建 PPI 网络。此外,对潜在靶点进行 GO 和通路富集分析。得到 8 种关键活性化合物(槲皮素、鸢尾苷 A、4-prenylresveratrol、桑辛素 H、桑辛素 C、异拉帕酮、桑辛素 E 和桑辛素 D)和 8 个关键靶点(AKT1、IGF1R、EIF2AK3、PPARG、AGTR1、PPARA、PTPN1 和 PIK3R1)在桑叶治疗糖尿病中起主要作用。涉及的信号通路主要包括 AMPK 信号通路、PI3K-Akt 信号通路、mTOR 信号通路、胰岛素信号通路和胰岛素抵抗。分子对接结果表明,8 种关键活性化合物与 AKT1 的关键靶点具有良好的亲和力,而 5 种关键靶点(IGF1R、EIF2AK3、PPARG、PPARA 和 PTPN1)与关键化合物槲皮素的亲和力优于 AKT1。

结论

基于网络药理学和分子对接,本研究为进一步了解 ML 治疗糖尿病的协同作用机制提供了重要的系统和可视化基础。

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