利用生物信息学和小鼠抑郁模型鉴定及验证抗抑郁小分子

Identification and Validation of Antidepressant Small Molecules Using Bioinformatics and Mouse Depression Models.

作者信息

Qiao Yajun, Zhang Xingfang, Chen Hanxi, Liang Xinxin, Guo Juan, Wang Qiannan, Ding Yi, Wei Lixin, Bi Hongtao, Gao Tingting

机构信息

School of Psychology, Chengdu Medical College, Chengdu, Sichuan, People's Republic of China.

CAS Key Laboratory of Tibetan Medicine Research, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, People's Republic of China.

出版信息

Drug Des Devel Ther. 2025 Aug 20;19:7161-7183. doi: 10.2147/DDDT.S537918. eCollection 2025.

DOI:10.2147/DDDT.S537918

PMID:40859969

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12375355/

Abstract

BACKGROUND

Depression, a prevalent psychiatric disorder with limited effective treatments, can be addressed by repurposing existing small molecules via bioinformatics as a promising approach, though previous studies using tools like CMAP and GEO have successfully identified candidate drugs for neuropsychiatric disorders, few have combined in silico predictions with in vivo validation for it.

OBJECTIVE

The aim of this study was to employ bioinformatics and in vivo experimental validation to mine potential antidepressant small molecule compounds.

METHODS

This study utilized data from the GEO database, employing bioinformatics analysis methods to analyze the dataset. The CMAP platform was used to deeply explore potential antidepressant small-molecule compounds. In vivo experiments validated the antidepressant effects of the small-molecule compounds on a chronic restraint stress mouse model.

RESULTS

This study identified 311 differentially expressed genes (DEGs) from GSE182193-associated with the PI3K-Akt, MAPK, and neurotrophic factor signaling pathways, with key genes identified via Weighted Gene Co-expression Network Analysis (WGCNA) and immune correlation analysis-and screened 5 candidate compounds via CMAP, among which pyrimethamine, pifithrin-mu, and mibefradil significantly improved depressive behaviors in chronic restraint stress (CRS) model mice by regulating key protein expression in the PI3K-Akt and neurotrophic factor signaling pathways, as shown by a 33.44%-60.32% increase in movement distance in the open field test (P < 0.01 to P < 0.001) and a 20.25%-30.19% decrease in immobility time in the forced swim test (P < 0.01 to P < 0.001).

CONCLUSION

This study shows that pyrimethamine, pifithrin-mu, and mibefradil can regulate key proteins in the PI3K-Akt and neurotrophic factor pathways, improving depressive behaviors in mice and indicating their potential in alleviating depression; additionally, bioinformatics-driven repurposing of existing drugs for antidepressant discovery is more efficient than de novo development, and this study provides an exploratory demonstration of this.

摘要

背景

抑郁症是一种常见的精神疾病，有效治疗方法有限，通过生物信息学重新利用现有小分子药物是一种有前景的方法。尽管之前使用CMAP和GEO等工具的研究已成功识别出神经精神疾病的候选药物，但很少有研究将计算机模拟预测与体内验证相结合。

目的

本研究旨在利用生物信息学和体内实验验证来挖掘潜在的抗抑郁小分子化合物。

方法

本研究利用GEO数据库的数据，采用生物信息学分析方法对数据集进行分析。使用CMAP平台深入探索潜在的抗抑郁小分子化合物。体内实验在慢性束缚应激小鼠模型上验证了小分子化合物的抗抑郁作用。

结果

本研究从GSE182193中鉴定出311个差异表达基因（DEGs），这些基因与PI3K-Akt、MAPK和神经营养因子信号通路相关，通过加权基因共表达网络分析（WGCNA）和免疫相关性分析确定了关键基因，并通过CMAP筛选出5种候选化合物。其中，乙胺嘧啶、pifithrin-mu和米贝拉地尔通过调节PI3K-Akt和神经营养因子信号通路中的关键蛋白表达，显著改善了慢性束缚应激（CRS）模型小鼠的抑郁行为，旷场试验中运动距离增加了33.44%-60.32%（P<0.01至P<0.001），强迫游泳试验中不动时间减少了20.25%-30.19%（P<0.01至P<0.001）。