Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

Trang T T Truong; Zoe S J Liu; Bruna Panizzutti; Olivia M Dean; Michael Berk; Jee Hyun Kim; Ken Walder

doi:10.1016/j.jad.2024.01.034

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

J Affect Disord. 2024 Apr 1:350:230-239. doi: 10.1016/j.jad.2024.01.034. Epub 2024 Jan 6.

Authors

Trang T T Truong¹, Zoe S J Liu¹, Bruna Panizzutti¹, Olivia M Dean², Michael Berk³, Jee Hyun Kim², Ken Walder⁴

Affiliations

¹ Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia.
² Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia.
³ Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, The Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, University of Melbourne, Parkville 3010, Australia.
⁴ Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia. Electronic address: ken.walder@deakin.edu.au.

PMID: 38190860
DOI: 10.1016/j.jad.2024.01.034

Abstract

Background: Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies.

Methods: This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database.

Results: Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments.

Limitations: Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes.

Conclusions: Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Keywords: Bipolar disorder; Drug discovery; Mental disorders; Network analysis; Neuroscience; Psychiatry.

MeSH terms

Bipolar Disorder* / drug therapy
Bipolar Disorder* / genetics
Bipolar Disorder* / metabolism
Brain / metabolism
Gene Expression Profiling
Gene Expression Regulation
Gene Regulatory Networks
Humans