Structural and functional interactions between six-transmembrane μ-opioid receptors and β2-adrenoreceptors modulate opioid signaling
Bibliographic record
Abstract
The primary molecular target for clinically used opioids is the μ-opioid receptor (MOR). Besides the major seven-transmembrane (7TM) receptors, the MOR gene codes for alternatively spliced six-transmembrane (6TM) isoforms, the biological and clinical significance of which remains unclear. Here, we show that the otherwise exclusively intracellular localized 6TM-MOR translocates to the plasma membrane upon coexpression with β2-adrenergic receptors (β2-ARs) through an interaction with the fifth and sixth helices of β2-AR. Coexpression of the two receptors in BE(2)-C neuroblastoma cells potentiates calcium responses to a 6TM-MOR ligand, and this calcium response is completely blocked by a selective β2-antagonist in BE(2)-C cells, and in trigeminal and dorsal root ganglia. Co-administration of 6TM-MOR and β2-AR ligands leads to substantial analgesic synergy and completely reverses opioid-induced hyperalgesia in rodent behavioral models. Together, our results provide evidence that the heterodimerization of 6TM-MOR with β2-AR underlies a molecular mechanism for 6TM cellular signaling, presenting a unique functional responses to opioids. This signaling pathway may contribute to the hyperalgesic effects of opioids that can be efficiently blocked by β2-AR antagonists, providing a new avenue for opioid therapy.
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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 | 0.001 | 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".