TRPV1 promotes opioid analgesia during inflammation
Bibliographic record
Abstract
Pain and inflammation are inherently linked responses to injury, infection, or chronic diseases. Given that acute inflammation in humans or mice enhances the analgesic properties of opioids, there is much interest in determining the inflammatory transducers that prime opioid receptor signaling in primary afferent nociceptors. Here, we found that activation of the transient receptor potential vanilloid type 1 (TRPV1) channel stimulated a mitogen-activated protein kinase (MAPK) signaling pathway that was accompanied by the shuttling of the scaffold protein β-arrestin2 to the nucleus. The nuclear translocation of β-arrestin2 in turn prevented its recruitment to the μ-opioid receptor (MOR), the subsequent internalization of agonist-bound MOR, and the suppression of MOR activity that occurs upon receptor desensitization. Using the complete Freund's adjuvant (CFA) inflammatory pain model to examine the role of TRPV1 in regulating endogenous opioid analgesia in mice, we found that naloxone methiodide (Nal-M), a peripherally restricted, nonselective, and competitive opioid receptor antagonist, slowed the recovery from CFA-induced hypersensitivity in wild-type, but not TRPV1-deficient, mice. Furthermore, we showed that inflammation prolonged morphine-induced antinociception in a mouse model of opioid receptor desensitization, a process that depended on TRPV1. Together, our data reveal a TRPV1-mediated signaling pathway that serves as an endogenous pain-resolution mechanism by promoting the nuclear translocation of β-arrestin2 to minimize MOR desensitization. This previously uncharacterized mechanism may underlie the peripheral opioid control of inflammatory pain. Dysregulation of the TRPV1-β-arrestin2 axis may thus contribute to the transition from acute to chronic pain.
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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) | 0.002 | 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".