Statistical omics data integration identifies potential therapeutic targets for synucleinopathies

Abstract

AbstractSynucleinopathies, such as Parkinson’s disease and multiple system atrophy, are characterized byα-synuclein aggregation in the brain. There is no disease-modifying therapy against synucleinopathies yet. To understand the neurobiological events underlyingα-synuclein aggregation, LUHMES cell lines have been utilized to measure transcriptomics and proteomics data and screen 1600 FDA-approved drugs for their toxicity-reducing ability. However, rather than an integrative approach, the statistical analyses were performed per dataset.We propose a novel statistical data integration method, POPLS-DA, to jointly model the transcriptomics and proteomics data and discriminate disease cells from controls. We also performed a dose-response test to validate screened FDA-approved drugs. We integrated the validated drugs with the selected genes/proteins using integrative bioinformatic analyses.We identified clusters of synaptic and lysosome-related genes targeted by the validated drugs. Furthermore,HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes. HSPA5 has been previously linked toα-synuclein pathology, showing the relevance of our findings.Our integrative approach results provide new directions for therapeutic targets for synucleinopathies.