Multi-omic network analysis identifies dysregulated neurobiological pathways in opioid addiction

Abstract

Opioid addiction constitutes a public health crisis in the United States and opioids cause the most drug overdose deaths in Americans. Yet, opioid addiction treatments have limited efficacy. To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies (GWAS) of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex (dlPFC) in opioid overdose victims. We identified 211 highly interrelated genes identified by GWAS or dysregulation in the dlPFC of individuals with opioid overdose victims that implicated the Akt, BDNF, and ERK pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. This included drugs used to treat other substance use disorders and antidepressant drugs. Our synthesis of multi-omics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.