Protein Kinase C Mediates the Synergistic Interaction Between Agonists Acting at α<sub>2</sub>-Adrenergic and Delta-Opioid Receptors in Spinal Cord
Bibliographic record
Abstract
Coactivation of spinal alpha(2)-adrenergic receptors (ARs) and opioid receptors produces antinociceptive synergy. Antinociceptive synergy between intrathecally administered alpha(2)AR and opioid agonists is well documented, but the mechanism underlying this synergy remains unclear. The delta-opioid receptor (DOP) and the alpha(2A)ARs are coexpressed on the terminals of primary afferent fibers in the spinal cord where they may mediate this phenomenon. We evaluated the ability of the DOP-selective agonist deltorphin II (DELT), the alpha(2)AR agonist clonidine (CLON) or their combination to inhibit calcitonin gene-related peptide (CGRP) release from spinal cord slices. We then examined the possible underlying signaling mechanisms involved through coadministration of inhibitors of phospholipase C (PLC), protein kinase C (PKC) or protein kinase A (PKA). Potassium-evoked depolarization of spinal cord slices caused concentration-dependent release of CGRP. Coadministration of DELT and CLON inhibited the release of CGRP in a synergistic manner as confirmed statistically by isobolograpic analysis. Synergy was dependent on the activation of PLC and PKC, but not PKA, whereas the effect of agonist administration alone was only dependent on PLC. The importance of these findings was confirmed in vivo, using a thermal nociceptive test, demonstrating the PKC dependence of CLON-DELT antinociceptive synergy in mice. That inhibition of CGRP release by the combination was maintained in the presence of tetrodotoxin in spinal cord slices suggests that synergy does not rely on interneuronal signaling and may occur within single subcellular compartments. The present study reveals a novel signaling pathway underlying the synergistic analgesic interaction between DOP and alpha(2)AR agonists in the spinal cord.
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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) | 0.001 | 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".