AI Article Synopsis

  • Identifying drug targets for brain diseases is difficult, so researchers created a new computational method called CRAFT that integrates gene regulatory info with causal reasoning.
  • CRAFT analyzes gene expression data to predict important cell membrane receptors that influence gene profiles related to diseases.
  • The framework successfully identified Csf1R as a potential target for epilepsy treatment, and experiments showed that blocking this receptor reduced seizures, indicating CRAFT's potential for discovering targets in various diseases.

Article Abstract

The identification of drug targets is highly challenging, particularly for diseases of the brain. To address this problem, we developed and experimentally validated a general computational framework for drug target discovery that combines gene regulatory information with causal reasoning ("Causal Reasoning Analytical Framework for Target discovery"-CRAFT). Using a systems genetics approach and starting from gene expression data from the target tissue, CRAFT provides a predictive framework for identifying cell membrane receptors with a direction-specified influence over disease-related gene expression profiles. As proof of concept, we applied CRAFT to epilepsy and predicted the tyrosine kinase receptor Csf1R as a potential therapeutic target. The predicted effect of Csf1R blockade in attenuating epilepsy seizures was validated in three pre-clinical models of epilepsy. These results highlight CRAFT as a systems-level framework for target discovery and suggest Csf1R blockade as a novel therapeutic strategy in epilepsy. CRAFT is applicable to disease settings other than epilepsy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6120885PMC
http://dx.doi.org/10.1038/s41467-018-06008-4DOI Listing

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