Evidence map›Paper›PMID 39403933›Full record

ArticleThe Journal of clinical investigation2024

An emerging multi-omic understanding of the genetics of opioid addiction.

Eric O Johnson, Heidi S Fisher, Kyle A Sullivan, Olivia Corradin, Sandra Sanchez-Roige, Nathan C Gaddis, Yasmine N Sami, Alice Townsend, Erica Teixeira Prates, Mirko Pavicic and 7 more

Abstract read
In one paragraph

Article in The Journal of clinical investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Eric O JohnsonGenOmics and Translational Research Center and.
Heidi S FisherThe Jackson Laboratory, Bar Harbor, Maine, USA.
Kyle A SullivanBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Olivia CorradinWhitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Sandra Sanchez-RoigeDepartment of Psychiatry, UCSD, La Jolla, California, USA.
Nathan C GaddisGenOmics and Translational Research Center and.
Yasmine N SamiWhitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Alice TownsendBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Erica Teixeira PratesBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Mirko PavicicBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Peter KruseBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Elissa J CheslerThe Jackson Laboratory, Bar Harbor, Maine, USA.
Abraham A PalmerDepartment of Psychiatry, UCSD, La Jolla, California, USA.
Vanessa TroianiGeisinger College of Health Sciences, Scranton, Pennsylvania, USA.
Jason A BubierThe Jackson Laboratory, Bar Harbor, Maine, USA.
Daniel A JacobsonBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Brion S MaherDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Funding

Synergy Core (SynC)P50DA054071 · NIDA · RESEARCH TRIANGLE INSTITUTE · PI Vanessa Troiani · 2022 to 2026
$13.1M
Integrating Multiple Omics to Illuminate Gene Networks Underlying Cigarette Smoking and Opioids.R01DA051913 · NIDA · RESEARCH TRIANGLE INSTITUTE · PI HANCOCK, DANA B, JACOBSON, DANIEL A · 2020 to 2024
$2.9M
Genetic Variation of Ultra-Potent Synthetic Opioid Sensitivity in MiceR01DA059060 · NIDA · JACKSON LABORATORY · PI BUBIER, JASON A · 2023 to 2025
$1.3M
DOE DE-AC05-00OR22725NIDA NIH HHS P50 DA054071NIDA NIH HHS R01 DA051913NIDA NIH HHS R01 DA059060
6 · The paper itself

Abstract

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

Indexed as

Opioid-Related DisordersAnimalsBrainGenome-Wide Association StudyHumansMultiomicsReceptors, Opioid, muOPRM1 protein, humanReceptors, Opioid, mu

Identifiers

PMID39403933
PMCPMC11473141

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.