Scientific ReportsNeurosciencePeer-reviewed journal article
Potentially repurposable drugs for schizophrenia identified from its interactome
Kalyani Bindu KarunakaraniD, Srilakshmi ChaparalaiD, Madhavi K. GanapathirajuiD
Abstract
From the schizophrenia drug-target interactome,1 we studied the drugs that targeted multiple proteins in the interactome, or those that target proteins with many targets, or those that target novel (computationally predicted) interactors of schizophrenia associated proteins. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the selected drugs using the NextBio software suite, and shortlisted those that had a negative correlation with gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes which were differentially expressed on drug treatment, and had an anti-correlation with gene expression in schizophrenia, were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had pathophysiological overlap with schizophrenia. Drugs that are structurally similar to the shortlisted drugs, or targeted the same genes as these drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis may help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development, facing a high failure rate.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 31
- FWCI
- 2.98
- Field percentile
- top 9%
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