Discovery of the neuroprotective effects of alvespimycin by computational prioritization of potential anti-Parkinson agents.

Based on public gene expression data, we propose a computational approach to optimize gene expression signatures for the use with Connectivity Map (CMap) to reposition drugs or discover lead compounds for Parkinson's disease. This approach integrates genetic information from the Gene Expression Omnibus (GEO) database, the Parkinson's disease gene expression database (ParkDB), the Online Mendelian Inheritance in Man (OMIM) database and the Comparative Toxicogenomics Database (CTD), with the aim of identifying a set of interesting genes for use in computational drug screening via CMap. The results showed that CMap, using the top 20 differentially expressed genes identified by our approach as a gene expression signature, outperformed the same method using all differentially expressed genes (n = 535) as a signature. Utilizing this approach, the candidate compound alvespimycin (17-DMAG) was selected for experimental evaluation in a model of rotenone-induced toxicity in human SH-SY5Y neuroblastoma cells and isolated rat brain mitochondria. The results showed that 17-DMAG significantly attenuated rotenone-induced toxicity, as reflected by the increase of cell viability, the reduction of intracellular reactive oxygen species generation and a reduction in mitochondrial respiratory dysfunction. In conclusion, this computational method provides an effective systematic approach for drug repositioning or lead compound discovery for Parkinson's disease, and the discovery of the neuroprotective effects of 17-DMAG supports the practicability of this method.