Network-based drug repurposing for schizophrenia.

Area of Science:

• Computational neuroscience
• Genomics
• Pharmacology

Background:

• Schizophrenia drug discovery faces persistent challenges.
• Computational drug repurposing offers a promising avenue by utilizing extensive biomedical databases.
• Gene regulatory networks (GRNs) are crucial for understanding transcription factor (TF) regulatory effects.

Purpose of the Study:

• To identify topological differences in TF-gene regulatory networks between schizophrenia patients and healthy controls.
• To leverage these differences for computational drug repurposing to find novel schizophrenia treatments.

Main Methods:

• Utilized the PANDA algorithm to construct and analyze TF-gene regulatory networks from RNA-seq data (CommonMind project, 532 samples).
• Integrated binding motifs, protein interactions, and gene co-expression data.
• Employed differential network analysis (CLUEreg tool) to identify drug candidates targeting disease-associated gene signatures.

Main Results:

• Constructed GRNs for 15,831 genes and 413 TFs in schizophrenia cases and controls.
• Identified 18 promising drug repurposing candidates for schizophrenia.
• Schizophrenia-associated TFs predominantly regulate pathways in energy metabolism, immune response, cell adhesion, and thyroid hormone signaling.

Conclusions:

• Drug repurposing candidates likely target identified TF-regulated pathways.
• Rimonabant and kaempferol are highlighted as candidates warranting further investigation.
• This network-based approach provides a robust strategy for identifying novel therapeutic targets for schizophrenia.