Anti‐nociception mediated by a κ opioid receptor agonist is blocked by a δ receptor agonist
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The opioid receptor family comprises four structurally homologous but functionally distinct sub-groups, the μ (MOP), δ (DOP), κ (KOP) and nociceptin (NOP) receptors. As most opioid agonists are selective but not specific, a broad spectrum of behaviours due to activation of different opioid receptors is expected. In this study, we examine whether other opioid receptor systems influenced KOP-mediated antinociception. EXPERIMENTAL APPROACH: We used a tail withdrawal assay in C57Bl/6 mice to assay the antinociceptive effect of systemically administered opioid agonists with varying selectivity at KOP receptors. Pharmacological and genetic approaches were used to analyse the interactions of the other opioid receptors in modulating KOP-mediated antinociception. KEY RESULTS: Etorphine, a potent agonist at all four opioid receptors, was not anti-nociceptive in MOP knockout (KO) mice, although etorphine is an efficacious KOP receptor agonist and specific KOP receptor agonists remain analgesic in MOP KO mice. As KOP receptor agonists are aversive, we considered KOP-mediated antinociception might be a form of stress-induced analgesia that is blocked by the anxiolytic effects of DOP receptor agonists. In support of this hypothesis, pretreatment with the DOP antagonist, naltrindole (10 mg·kg(-1) ), unmasked etorphine (3 mg·kg(-1) ) antinociception in MOP KO mice. Further, in wild-type mice, KOP-mediated antinociception by systemic U50,488H (10 mg·kg(-1) ) was blocked by pretreatment with the DOP agonist SNC80 (5 mg·kg(-1) ) and diazepam (1 mg·kg(-1) ). CONCLUSIONS AND IMPLICATIONS: Systemic DOP receptor agonists blocked systemic KOP antinociception, and these results identify DOP receptor agonists as potential agents for reversing stress-driven addictive and depressive behaviours mediated through KOP receptor activation. LINKED ARTICLES: This article is part of a themed section on Opioids: New Pathways to Functional Selectivity. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-2.
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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) | 0.004 | 0.001 |
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".