μ-Opioid Receptors on Distinct Neuronal Populations Mediate Different Aspects of Opioid Reward-Related Behaviors
Bibliographic record
Abstract
μ-Opioid receptors (MORs) are densely expressed in different brain regions known to mediate reward. One such region is the striatum where MORs are densely expressed, yet the role of these MOR populations in modulating reward is relatively unknown. We have begun to address this question by using a series of genetically engineered mice based on the Cre recombinase/loxP system to selectively delete MORs from specific neurons enriched in the striatum: dopamine 1 (D1) receptors, D2 receptors, adenosine 2a (A2a) receptors, and choline acetyltransferase (ChAT). We first determined the effects of each deletion on opioid-induced locomotion, a striatal and dopamine-dependent behavior. We show that MOR deletion from D1 neurons reduced opioid (morphine and oxycodone)-induced hyperlocomotion, whereas deleting MORs from A2a neurons resulted in enhanced opioid-induced locomotion, and deleting MORs from D2 or ChAT neurons had no effect. We also present the effect of each deletion on opioid intravenous self-administration. We first assessed the acquisition of this behavior using remifentanil as the reinforcing opioid and found no effect of genotype. Mice were then transitioned to oxycodone as the reinforcer and maintained here for 9 d. Again, no genotype effect was found. However, when mice underwent 3 d of extinction training, during which the drug was not delivered, but all cues remained as during the maintenance phase, drug-seeking behavior was enhanced when MORs were deleted from A2a or ChAT neurons. These findings show that these selective MOR populations play specific roles in reward-associated behaviors.
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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".