Abstract
Background: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed. Methods: To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals. Results: We identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) 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 or depression. Conclusions: Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.
| Original language | English |
|---|---|
| Journal | Biological Psychiatry |
| DOIs | |
| State | Accepted/In press - 2025 |
Funding
This work was supported by resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy (under Contract No. DE-AC05-00OR22725). This work was funded by the National Institutes of Health (Grant Nos. DA051908 [to EOJ and DAJ], DA051913 [to DBH and DAJ], DA046345 [to HRK], and DA054071 [to NCG, OC, BSM, SS-R, AAP, VT, EJC, EOJ, and DAJ]), Department of Veterans Affairs (Grant No. I01 BX004820 [to ACJ and HRK]), and an award from the Veterans Integrated Service Network Mental Illness Research, Education and Clinical Center (to HRK). This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by Grant No. I01 BX004820. This publication does not represent the views of the Department of Veteran Affairs or the U.S. government. DBH, EOJ, and DAJ were responsible for conceptualization. KAS was responsible for methodology. KAS, DK, ML, MC, and JIM were responsible for software. KAS was responsible for formal analysis. EOJ was responsible for investigation. KAS and EOJ were responsible for writing the original draft of the article. KAS, MRG, AT, BCQ, CW, NCG, RM, OC, BSM, PCS, SS-R, AAP, VT, EJC, RLK, HRK, ACJ, KX, BEA, DBH, EOJ, and DAJ were responsible for reviewing and editing the article. DBH, EOJ, and DAJ were responsible for supervision. HRK, ACJ, DBH, EOJ, and DAJ were responsible for funding acquisition. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes, and the publisher, by accepting the article for publication, acknowledges the U.S. government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the Department of Energy Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). No primary data were generated for the current study. All primary data from DNA methylation, GWAS summary statistics, H3K27ac ChIP-seq, LC/MS proteomics, and RNA-seq are from previously published manuscripts. GWAS summary statistics from the Million Veteran Program (8) are available on the National Institutes of Health database of Genotypes and Phenotypes (dbGaP) under accession phs001672. GWAS summary statistics from Deak et al. (7) are publicly available at https://medicine.yale.edu/lab/gelernter/stats/. GWAS summary statistics from Gaddis et al. (9) are available under dbGaP under accession phs000454.v1.p1. We used the top 10,000 SNPs from Sanchez-Roige et al. (6) GWAS summary statistics, which are publicly available at https://pmc.ncbi.nlm.nih.gov/articles/instance/8760042/bin/41380_2021_1335_MOESM2_ESM.xlsx. Previously published data from H3K27ac ChIP-seq are available under dbGaP accession No. phs002724.v1.p1. Previously published DNA methylation data are available at GEO accession No. GSE164822. Previously published LC/MS proteomics data are available at the ProteomeXchange PRIDE repository under PXD025269. Previously published RNA-seq datasets are available under dbGaP accession phs002724.v1.p1, GEO accession Nos. GSE221515 and GSE174409, and SRA accession No. SUB9455518. See the Supplement for personnel in the VA Million Veteran Program. All activities were approved by the Oak Ridge National Laboratory Institutional Review Board. The demographics of subjects from whom GWAS summary statistics were derived along with descriptions of institutional review boards to approve these studies have been characterized in previous publications, and all subjects provided informed consent. All postmortem brain tissue samples are exempt from human subject research. The GRIN software and multiplex network that was used is publicly available at http://github.com/sullivanka/GRIN. Publicly available R packages (ggplot2, tidyverse) were used for data analysis and visualization using R version 4.1.3, and ChIP-seq peaks were assigned using ChIPseeker (version 1.30.3). Additional code used to generate results is available upon request. A previous version of this manuscript was posted as a preprint on medRxiv: https://www.medrxiv.org/content/10.1101/2024.01.04.24300831v1. HRK is a member of advisory boards for Altimmune, Clearmind Medicine, Dicerna Pharmaceuticals, Enthion Pharmaceuticals, and Sophrosyne Pharmaceuticals; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies from Alkermes for an investigator-initiated study; a member of the American Society of Clinical Psychopharmacology's Alcohol Clinical Trials Initiative, which was supported in the past 3 years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi, and Otsuka; and is named as an inventor on PCT patent application #15/878,640 entitled: \u201CGenotype-guided dosing of opioid agonists,\u201D filed January 24, 2018. All other authors report no biomedical financial interests or potential conflicts of interest. A previous version of this manuscript was posted as a preprint on medRxiv. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work was funded by NIH grants DA051908 (EOJ, DAJ), DA051913 (DBH, DAJ), DA046345 (HRK), and DA054071 (NCG, OC, BSM, SS, AP, VT, EC, EOJ, DAJ), VA grant I01 BX004820 (ACJ, HRK), and the Veterans Integrated Service Network Mental Illness Research, Education and Clinical Center (HRK). This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award # I01 BX004820. This publication does not represent the views of the Department of Veteran Affairs or the United States Government.
Keywords
- Addiction
- Bioinformatics
- Multiomic
- Networks
- Opioids
- Systems biology