Drug repurposing and personalized treatment strategies for bipolar disorder using transcriptomics: an exploratory study.

Abstract

The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify new bioactive compounds or Food and Drug Administration-approved drugs for the treatment of bipolar disorder (BD). Five transcriptomic datasets containing 165 blood samples from individuals with BD were selected from the Gene Expression Omnibus (GEO). The number of participants varied from six to 60, with a mean age between 35 and 48 years and a gender difference between them. Most of these patients were receiving pharmacological treatment. Master regulator analysis (MRA) and gene set enrichment analysis (GSEA) were performed to identify genes that were significantly different between patients with BD and healthy controls and their associations with mood states in patients with BD. In addition, molecules that could reverse the transcriptomic profiles of BD-altered regulons were identified from the Library of Network-Based Cellular Signatures Consortium (LINCS) and the Broad Institute Connectivity Map Drug Repurposing Database (cMap) databases. MRA identified 59 candidate master regulators (MRs) that modulate regulatory units enriched with BD-altered genes. In contrast, GSEA identified 134 enriched genes and 982 regulons whose activation state was determined. Both analyses revealed genes exclusively associated with mania, depression, or euthymia, and some genes were shared among these three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastic agents, as promising candidates for the treatment of BD. However, experimental validation is essential to confirm these findings in further studies. Although our data are still preliminary, they provide some insights into the biological patterns of different mood states in patients with BD and their potential therapeutic targets. The strategy of transcriptomics plus bioinformatics offers a way to advance drug discovery and personalized medicine by using gene expression information.