Regulation of δ-Opioid Receptor Trafficking via μ-Opioid Receptor Stimulation: Evidence from μ-Opioid Receptor Knock-Out Mice
Bibliographic record
Abstract
We recently demonstrated that prolonged treatment with morphine increases the antinociceptive potency of the delta-opioid receptor (deltaOR) agonist deltorphin and promotes cell surface targeting of deltaORs in neurons of the dorsal horn of the rat spinal cord (Cahill et al., 2001b). In the present study we examined whether these effects were mediated selectively via muOR. Using the same intermittent treatment regimen as for morphine, we found that methadone and etorphine, but not fentanyl, enhanced [D-Ala2]-deltorphin-mediated antinociception. However, continuous delivery of fentanyl for 48 hr resulted in augmented deltaOR-mediated antinociception when compared with saline-infused animals. Time course studies confirmed that a 48 hr treatment with morphine was necessary for the establishment of enhanced deltaOR-mediated antinociception. The observed increases in deltaOR agonist potency and deltaOR plasma membrane density were reversed fully 48 hr after discontinuation of morphine injections. Wild-type C57BL/6 mice pretreated with morphine for 48 hr similarly displayed enhanced deltaOR-mediated antinociception in a tonic pain paradigm. Accordingly, the percentage of plasma membrane-associated deltaOR in the dorsal horn of the spinal cord, as assessed by immunogold electron microscopy, increased from 6.6% in naive to 12.4% in morphine-treated mice. In contrast, morphine treatment of muOR gene knock-out (KO) mice did not produce any change in deltaOR plasma membrane density. These results demonstrate that selective activation of muOR is critical for morphine-induced targeting of deltaOR to neuronal membranes, but not for basal targeting of this receptor to the cell surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".