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

Abstract

BackgroundOpioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. Yet, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed. MethodsTo help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies (GWAS) of opioid use disorder (OUD) and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex (dlPFC) of opioid overdose victims and controls. ResultsWe identified 211 highly interrelated genes identified by GWAS or dysregulation in the dlPFC of opioid overdose victims that implicated the Akt, BDNF, and ERK pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. Some of the identified drugs are approved to treat other substance use disorders (SUDs) or depression. ConclusionsOur 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.