Differential Regulation of 6- and 7-Transmembrane Helix Variants of μ-Opioid Receptor in Response to Morphine Stimulation
Bibliographic record
Abstract
The pharmacological effect of opioids originates, at the cellular level, by their interaction with the μ-opioid receptor (mOR) resulting in the regulation of voltage-gated Ca2+ channels and inwardly rectifying K+ channels that ultimately modulate the synaptic transmission. Recently, an alternative six trans-membrane helix isoform of mOR, (6TM-mOR) has been identified, but its function and signaling are still largely unknown. Here, we present the structural and functional mechanisms of 6TM-mOR signaling activity upon binding to morphine. Our data suggest that despite the similarity of binding modes of the alternative 6TM-mOR and the dominant seven trans-membrane helix variant (7TM-mOR), the interaction with morphine generates different dynamic responses in the two receptors, thus, promoting the activation of different mOR-specific signaling pathways. We characterize a series of 6TM-mOR-specific cellular responses, and observed that they are significantly different from those for 7TM-mOR. Morphine stimulation of 6TM-mOR does not promote a cellular cAMP response, while it increases the intracellular Ca2+ concentration and reduces the cellular K+ conductance. Our findings indicate that 6TM-mOR has a unique contribution to the cellular opioid responses. Therefore, it should be considered as a relevant target for the development of novel pharmacological tools and medical protocols involving the use of opioids.
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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 | 0.000 |
| 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".