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基于代谢通量分布和转录组表达的新型药物研发的药物聚类策略

Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development.

作者信息

Ruan Yao, Chen Xiao-Hui, Jiang Feng, Liu Yan-Guang, Liang Xiao-Long, Lv Bo-Min, Zhang Hong-Yu, Zhang Qing-Ye

机构信息

Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

出版信息

Biomedicines. 2021 Nov 8;9(11):1640. doi: 10.3390/biomedicines9111640.

Abstract

The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs.

摘要

基于网络模块的方法已被用于药物重新定位。传统的药物重新定位方法仅使用药物的基因特征,却忽略了药物引发的代谢变化。代谢网络系统地描述了基因、蛋白质和代谢反应之间的联系。作为药物代谢特征的差异代谢通量分布,被用于对具有相似作用机制(MoA)的药物进行聚类。在本研究中,具有相同药理学特性的药物被聚类为一组,基于差异代谢通量分布,来自CMap数据库的总共1309种药物被聚类为98组。转录因子(TF)富集分析表明,同一组中的药物(如第7组和第26组)被证实具有相似的作用机制。通过这种药物聚类策略,确定了能够抑制日本脑炎病毒(JEV)感染的候选药物。本研究为药物重新定位及其作用机制提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4280/8615746/17b87f1e9b19/biomedicines-09-01640-g001.jpg

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