Distinct Mu, Delta, and Kappa Opioid Receptor Mechanisms Underlie Low Sociability and Depressive-Like Behaviors During Heroin Abstinence
Bibliographic record
Abstract
Addiction is a chronic disorder involving recurring intoxication, withdrawal, and craving episodes. Escaping this vicious cycle requires maintenance of abstinence for extended periods of time and is a true challenge for addicted individuals. The emergence of depressive symptoms, including social withdrawal, is considered a main cause for relapse, but underlying mechanisms are poorly understood. Here we establish a mouse model of protracted abstinence to heroin, a major abused opiate, where both emotional and working memory deficits unfold. We show that delta and kappa opioid receptor (DOR and KOR, respectively) knockout mice develop either stronger or reduced emotional disruption during heroin abstinence, establishing DOR and KOR activities as protective and vulnerability factors, respectively, that regulate the severity of abstinence. Further, we found that chronic treatment with the antidepressant drug fluoxetine prevents emergence of low sociability, with no impact on the working memory deficit, implicating serotonergic mechanisms predominantly in emotional aspects of abstinence symptoms. Finally, targeting the main serotonergic brain structure, we show that gene knockout of mu opioid receptors (MORs) in the dorsal raphe nucleus (DRN) before heroin exposure abolishes the development of social withdrawal. This is the first result demonstrating that intermittent chronic MOR activation at the level of DRN represents an essential mechanism contributing to low sociability during protracted heroin abstinence. Altogether, our findings reveal crucial and distinct roles for all three opioid receptors in the development of emotional alterations that follow a history of heroin exposure and open the way towards understanding opioid system-mediated serotonin homeostasis in heroin abuse.
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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".