Resveratrol: Multi-Targets Mechanism on Neurodegenerative Diseases Based on Network Pharmacology.

Abstract

Resveratrol is a natural polyphenol in lots of foods and traditional Chinese medicines, which has shown promising treatment for neurodegenerative diseases (NDs). However, the molecular mechanisms of its action have not been systematically studied yet. In order to elucidate the network pharmacological prospective effects of resveratrol on NDs, we assessed of pharmacokinetics (PK) properties of resveratrol, studied target prediction and network analysis, and discussed interacting pathways using a network pharmacology method. Main PK properties of resveratrol were acquired. A total of 13,612 genes related to NDs, and 138 overlapping genes were determined through matching the 175 potential targets of resveratrol with disease-associated genes. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed to obtain more in-depth understanding of resveratrol on NDs. Accordingly, nodes with high degrees were obtained according using a PPI network, and AKT1, TP53, IL6, CASP3, VEGFA, TNF, MYC, MAPK3, MAPK8, and ALB were identified as hub target genes, which showed better affinity with resveratrol in silico studies. In addition, our experimental results demonstrated that resveratrol markedly enhanced the decreased levels of Bcl-2 and significantly reduced the increased expression of Bax and Caspase-3 in hippocampal neurons induced by glutamate exposure. Western blot results confirmed that resveratrol inhibited glutamate-induced apoptosis of hippocampal neurons partly by regulating the PI3K/AKT/mTOR pathway. In conclusion, we found that resveratrol could target multiple pathways forming a systematic network with pharmacological effects.

• Authors: Wenjun Wang, Shengzheng Wang, Tianlong Liu, Yang Ma, Shaojie Huang, Lu Lei, Aidong Wen, Yi Ding
• Journal: Frontiers in pharmacology (Front Pharmacol) Vol. 11 Pg. 694 ( 2020) ISSN: 1663-9812 [Print] Switzerland
• PMID: 32477148 (Publication Type: Journal Article)
• Copyright: Copyright © 2020 Wang, Wang, Liu, Ma, Huang, Lei, Wen and Ding.