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通过网络药理学揭示肠道微生物群衍生代谢产物对抑郁症的影响。

Revealing the impact of gut microbiota-derived metabolites on depression through network pharmacology.

作者信息

Su Si, A Tengarile, A Ruhan, Wu Riga, Wei Lisi, Ta La, Huo Wenfeng, Tong Lijun, Zhang Jing, Hu Rilebagen, Li Li

机构信息

Faculty of Mongolian Medicine, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.

Basic Medical Faculty, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.

出版信息

Artif Cells Nanomed Biotechnol. 2025 Dec;53(1):327-342. doi: 10.1080/21691401.2025.2531752. Epub 2025 Jul 24.

DOI:10.1080/21691401.2025.2531752
PMID:40708240
Abstract

A total of 208 metabolites and 223 targets were initially extracted from the gutMGene v1.0 database. In addition, 1,630 and 1,321 targets were identified using the Similarity Ensemble Approach and Swiss Target Prediction databases, respectively, resulting in 921 overlapping targets. By integrating data from gutMGenev1.0, 13 core targets were finally identified. A microbiota-metabolite-target-signal pathway-disease network was constructed using Cytoscape 3.9.1, revealing 15 metabolites associated with the IL-17, TLR, and PI3K-Akt signalling pathways. Among these, five metabolites exhibited favourable drug similarity and acceptable toxicological profiles. Molecular docking confirmed the stable binding of two key metabolites-succinate and phenylacetylglutamine-to their respective targets. The results showed that succinate and phenylacetylglutamine demonstrated strong binding affinities to IL-1β and GSK3B, both involved in the IL-17, TLR, and PI3K-Akt signalling pathways. IL-17 and TLR4, as important pro-inflammatory cytokines, are closely associated with the development of depression, while the PI3K/AKT signalling pathway plays a key role in its pathogenesis. The present study reveals the potential mechanisms by which gut microbiota influence MDD and provides a scientific basis and a comprehensive research framework for future investigations.

摘要

最初从gutMGene v1.0数据库中提取了总共208种代谢物和223个靶点。此外,分别使用相似性集成方法和瑞士靶点预测数据库鉴定出1630个和1321个靶点,从而得到921个重叠靶点。通过整合gutMGene v1.0的数据,最终确定了13个核心靶点。使用Cytoscape 3.9.1构建了微生物群-代谢物-靶点-信号通路-疾病网络,揭示了与IL-17、TLR和PI3K-Akt信号通路相关的15种代谢物。其中,5种代谢物表现出良好的药物相似性和可接受的毒理学特征。分子对接证实了两种关键代谢物——琥珀酸和苯乙酰谷氨酰胺——与其各自靶点的稳定结合。结果表明,琥珀酸和苯乙酰谷氨酰胺对IL-1β和GSK3B表现出强烈的结合亲和力,二者均参与IL-17、TLR和PI3K-Akt信号通路。IL-17和TLR4作为重要的促炎细胞因子,与抑郁症的发生密切相关,而PI3K/AKT信号通路在其发病机制中起关键作用。本研究揭示了肠道微生物群影响重度抑郁症的潜在机制,并为未来的研究提供了科学依据和全面的研究框架。

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