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Combinatorial Chemistry & High Throughput Screening

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ISSN (Online): 1875-5402

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Research Article

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

Author(s): Qiguo Wu* and Yeqing Hu

Volume 24, Issue 5, 2021

Published on: 14 September, 2020

Page: [668 - 682] Pages: 15

DOI: 10.2174/1386207323666200914103719

Price: $65

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.

Keywords: Mulberry leaf, diabetes, network pharmacology, active compounds, target, molecular docking.

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