反向GSEA-通过整合的网络和转录组分析筛查阿尔兹海默症新型候选药物

Screening novel drug candidates for Alzheimer's disease by an integrated network and transcriptome analysis5.61Bioinformatics . 2020 Jun 9;btaa563. doi: 10.1093/bioinformatics/btaa563. Online ahead of print.

Abstract

Motivation: Alzheimer's disease (AD) is a serious degenerative brain disease and the most common cause of dementia. The current available drugs for AD provide symptomatic benefit, but there is no effective drug to cure the disease. The emergence of large-scale genomic, pharmacological data provides new opportunities for drug discovery and drug repositioning as a promising strategy in searching novel drug for AD. Results: In this study, we took advantage of our increasing understanding based on systems biology approaches on the pathway and network levels and perturbation data sets from the Library of Integrated Network-Based Cellular Signatures (LINCS) to introduce a systematic computational process to discover new drugs implicated in AD. Firstly, we collected 561 genes that have reported to be risk genes of AD, and applied functional enrichment analysis on these genes. Then, by quantifying proximity between 5595 molecule drugs and AD based on human interactome, we filtered out 1092 drugs that were proximal to the disease. We further performed an Inverted Gene Set Enrichment analysis on these drug candidates, which allowed us to estimate effect of perturbations on gene expression and identify 24 potential drug candidates for AD treatment. Results from this study also provided insights for understanding the molecular mechanisms underlying AD. As a useful systematic method, our approach can also be used to identify efficacious therapies for other complex diseases.

阿尔兹海默症（AD）作为一种严重的变性脑疾病，是痴呆症的最常见原因。但是目前尚无有效药物对症治疗。本文基于网络药理学，PPI网络，反向GSEA确定了24种潜在候选AD药物。