Activation of Spinal μ- and δ-Opioid Receptors Potently Inhibits Substance P Release Induced by Peripheral Noxious Stimuli
Bibliographic record
Abstract
Over the past few years, δ-opioid receptors (DOPRs) and μ-opioid receptors (MOPRs) have been shown to interact with each other. We have previously seen that expression of MOPR is essential for morphine and inflammation to potentiate the analgesic properties of selective DOPR agonists. In vivo, it is not clear whether MOPRs and DOPRs are expressed in the same neurons. Indeed, it was recently proposed that these receptors are segregated in different populations of nociceptors, with MOPRs and DOPRs expressed by peptidergic and nonpeptidergic fibers, respectively. In the present study, the role and the effects of DOPR- and MOPR-selective agonists in two different pain models were compared. Using preprotachykinin A knock-out mice, we first confirmed that substance P partly mediates intraplantar formalin- and capsaicin-induced pain behaviors. These mice had a significant reduction in pain behavior compared with wild-type mice. We then measured the effects of intrathecal deltorphin II (DOPR agonist) and DAMGO (MOPR agonist) on pain-like behavior, neuronal activation, and substance P release following formalin and capsaicin injection. We found that both agonists were able to decrease formalin- and capsaicin-induced pain, an effect that was correlated with a reduction in the number of c-fos-positive neurons in the superficial laminae of the lumbar spinal cord. Finally, visualization of NK(1) (neurokinin 1) receptor internalization revealed that DOPR and MOPR activation strongly reduced formalin- and capsaicin-induced substance P release via direct action on primary afferent fibers. Together, our results indicate that functional MOPRs and DOPRs are both expressed by peptidergic nociceptors.
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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".