Evidence map›Paper›PMID 39725299›Full record

ArticleBiological psychiatry2025

Machine Learning Analysis of the Orbitofrontal Cortex Transcriptome of Human Opioid Users Identifies Shisa7 as a Translational Target Relevant for Heroin Seeking Leveraging a Male Rat Model.

Randall J Ellis, Jacqueline-Marie N Ferland, Tanni Rahman, Joseph L Landry, James E Callens, Gaurav Pandey, TuKiet Lam, Jean Kanyo, Angus C Nairn, Stella Dracheva and 1 more

Abstract read
In one paragraph

Article in Biological psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Randall J EllisDepartment of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Addiction Institute of Mount Sinai, New York, New York.
Jacqueline-Marie N FerlandDepartment of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Addiction Institute of Mount Sinai, New York, New York.
Tanni RahmanDepartment of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Joseph L LandryDepartment of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Addiction Institute of Mount Sinai, New York, New York.
James E CallensDepartment of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Gaurav PandeyDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York.
TuKiet LamKeck Mass Spectrometry and Proteomics Resource, WM Keck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, New Haven, Connecticut; Department of Biophysics and Biochemistry, Yale University, New Haven, Connecticut.
Jean KanyoKeck Mass Spectrometry and Proteomics Resource, WM Keck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, New Haven, Connecticut.
Angus C NairnDepartment of Psychiatry, Yale School of Medicine, New Haven, Connecticut.
Stella DrachevaDepartment of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Research and Development, James J. Peters VA Medical Center, Bronx, New York.
Yasmin L HurdDepartment of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Addiction Institute of Mount Sinai, New York, New York. Electronic address: yasmin.hurd@mssm.edu.

Funding

Yale/NIDA Neuroproteomics Research CenterP30DA018343 · NIDA · YALE UNIVERSITY · PI ANGUS C. NAIRN, Kenneth Robert WILLIAMS · 2004 to 2026
$37.1M
Transcription Factors in Stimulant and Opioid ActionP01DA047233 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Anne Schaefer · 2019 to 2026
$16.5M
Molecular Neurobiology of Human Opioid Use DisorderR01DA051191 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI HURD, YASMIN L. · 2021 to 2025
$2.9M
Opioid effects on cognition and addiction: Molecular underpinningsF31DA051183 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ELLIS, RANDALL JORDAN · 2021 to 2022
$78k
BLRD VA I01 BX006642BLRD VA IK6 BX006524NIDA NIH HHS F31 DA051183NIDA NIH HHS P01 DA047233NIDA NIH HHS P30 DA018343NIDA NIH HHS R01 DA051191
6 · The paper itself

Abstract

backgroundIdentifying neurobiological targets predictive of the molecular neuropathophysiological signature of human opioid use disorder (OUD) could expedite new treatments. OUD is characterized by dysregulated cognition and goal-directed behavior mediated by the orbitofrontal cortex (OFC), and next-generation sequencing could provide insights regarding novel targets.

methodsHere, we used machine learning to evaluate human postmortem OFC RNA sequencing datasets from heroin users and control participants to identify transcripts that were predictive of heroin use. To determine a causal link to OUD-related behaviors, we examined the effects of overexpressing the top target gene in a translational rat model of heroin seeking and behavioral updating. Additionally, we determined the effects of overexpression on the rat OFC transcriptome compared with that of human heroin users. Co-immunoprecipitation/mass spectrometry (co-IP/MS) from the rat OFC elucidated the protein complex of the novel target.

resultsOur machine learning approach identified SHISA7 as predictive of human heroin users. Shisa7 is understudied but appears to be an auxiliary protein of GABA

conclusionsOur findings suggest that OFC Shisa7 is a critical driver of neurobehavioral pathology related to drug-seeking behavior and behavioral updating, thus identifying a potential therapeutic target for OUD.

Indexed as

Drug-Seeking BehaviorHeroin DependenceMachine LearningPrefrontal CortexTranscriptomeAdultAnimalsDisease Models, AnimalHeroinHumansMaleRatsRats, Sprague-DawleyHeroinAddictionCognitionGenomicsMachine learningOpioid use disorderTranslation

Identifiers

PMID39725299
PMCPMC12490282

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Registered trials

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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.