Gene network inference and master regulator analysis identifies the estrogen-related receptor gamma (ERRγ) as a...

Differential gene expression is often inadequate to predict the activity of transcription factors and their contribution to the phenotypes associated with specific gene expression states. Here we used a systems biology approach based on gene network inference and master regulator analysis (MRA) to identify candidate drivers of the gene network dysregulations in the prefrontal cortex (PFC) of human subjects with a history of alcohol dependence. The estrogen-related receptor gamma (ERRγ) gene ESRRG, an orphan nuclear receptor protein that acts as a transcription activator, emerged as a high-ranking Master Regulator (MR) based on the expression of its targets and was selected for functional validation due to its translational and druggability potential. The ERRγ agonist, GSK4716, reduced alcohol drinking in the mouse binge drinking paradigm of drinking in the dark (DID) and in both non-dependent mice as well as in mice made dependent by chronic intermittent vapor exposure (CIE). GSK4716 also prevented alcohol-conditioned place preference without affecting saccharin intake or mouse locomotion. Similarly, in rats, GSK4716 reduced operant oral alcohol self-administration in non-dependent and dependent (by CIE) rats under fixed and progressive ratio schedules of reinforcement. Overall, these results support the efficacy of transcriptome-wide gene regulatory network approaches for the identification of key druggable regulators of long-term transcriptional adaptations that sustain the molecular and behavioral pathology of alcohol dependence and identify ERRγ as a regulator of excessive alcohol drinking and seeking, and a therapeutic target for AUD.