Network-based drug repurposing for schizophrenia

• Published in: Neuropsychopharmacology (New York, N.Y.) Vol. 49; no. 6; pp. 983 - 992
• Main Authors: Truong, Trang T. T., Liu, Zoe S. J., Panizzutti, Bruna, Kim, Jee Hyun, Dean, Olivia M., Berk, Michael, Walder, Ken
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.05.2024; Springer International Publishing
• Subjects: Algorithms; Antipsychotic Agents - pharmacology; Antipsychotic Agents - therapeutic use; Cell adhesion; Computational neuroscience; Computer programs; Drug Repositioning - methods; Energy metabolism; Gene Regulatory Networks - drug effects; Genes; Humans; Immune response; Immunosuppressive agents; Kaempferol; Mental disorders; Protein interaction; Schizophrenia; Schizophrenia - drug therapy; Schizophrenia - genetics; Schizophrenia - metabolism; Signal transduction; Transcription Factors - genetics; Transcription Factors - metabolism
• DOI: 10.1038/s41386-024-01805-6
• ISSN: 0893-133X; 1740-634X

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.