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.
Highlights
The identification of drug targets is highly challenging, for diseases of the brain
As well as manifesting spontaneous epileptic seizures, these mice reflect several of the behavioral and cognitive disturbances associated with temporal lobe epilepsy (TLE) in humans, and their response to antiepileptic drugs (AEDs) therapy has been shown to be predictive of drug efficacy in human epilepsy[23]
In support of the validity of our causal reasoning results, we found that membrane receptors related to interleukin-1 type 1 receptor and Toll-like receptor 4 had a predicted direction of effect on epilepsy via a module enriched for relevant functional processes that was in agreement with the previously reported experimental evidence for that receptor[32]
Summary
The identification of drug targets is highly challenging, for diseases of the brain. Network-based drug discovery aims to harness this knowledge to identify drugs capable of restoring the expression of disease modules toward health[8,9] At this systems level, therapeutic compounds are judged not by their binding affinity to a particular protein, but by their ability to induce a transcriptional response (i.e., a gene expression profile) that is anti-correlated to the coordinated transcriptional program underpinning the disease state. Expression quantitative trait loci mapping of networks may identify only large genomic regions in which several candidate genes could be implicated whilst regulome approaches are not currently formulated to identify regulators that have tractability as drug targets Given these constraints we aimed to develop a new framework for drug target discovery based on identifying the regulators of disease-associated gene co-expression modules. In this study we applied CRAFT to epilepsy, CRAFT is applicable to any disease for which an underlying disease expression signature can be identified
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