Mu opioid receptors in GABAergic neurons of the forebrain promote alcohol reward and drinking
Bibliographic record
Abstract
Mu opioid receptors (MORs) are widely distributed throughout brain reward circuits and their role in drug and social reward is well established. Substantial evidence has implicated MOR and the endogenous opioid system in alcohol reward, but circuit mechanisms of MOR-mediated alcohol reward and intake behavior remain elusive, and have not been investigated by genetic approaches. We recently created conditional knockout (KO) mice targeting the Oprm1 gene in GABAergic forebrain neurons. These mice (Dlx-MOR KO) show a major MOR deletion in the striatum, whereas receptors in midbrain (including the Ventral Tegmental Area or VTA) and hindbrain are intact. Here, we compared alcohol-drinking behavior and rewarding effects in total (MOR KO) and conditional KO mice. Concordant with our previous work, MOR KO mice drank less alcohol in continuous and intermittent two-bottle choice protocols. Remarkably, Dlx-MOR KO mice showed reduced drinking similar to MOR KO mice, demonstrating that MOR in the forebrain is responsible for the observed phenotype. Further, alcohol-induced conditioned place preference was detected in control but not MOR KO mice, indicating that MOR is essential for alcohol reward and again, Dlx-MOR KO recapitulated the MOR KO phenotype. Taste preference and blood alcohol levels were otherwise unchanged in mutant lines. Together, our data demonstrate that MOR expressed in forebrain GABAergic neurons is essential for alcohol reward-driven behaviors, including drinking and place conditioning. Challenging the prevailing VTA-centric hypothesis, this study reveals another mechanism of MOR-mediated alcohol reward and consumption, which does not necessarily require local VTA MORs but rather engages striatal MOR-dependent mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".