A Role for the Distal Carboxyl Tails in Generating the Novel Pharmacology and G Protein Activation Profile of μ and δ Opioid Receptor Hetero-oligomers
Bibliographic record
Abstract
Opioid receptor pharmacology in vivo has predicted a greater number of receptor subtypes than explained by the profiles of the three cloned opioid receptors, and the functional dependence of the receptors on each other shown in gene-deleted animal models remains unexplained. One mechanism for such findings is the generation of novel signaling complexes by receptor hetero-oligomerization, which we previously showed results in significantly different pharmacology for mu and delta receptor hetero-oligomers compared with the individual receptors. In the present study, we show that deltorphin-II is a fully functional agonist of the mu-delta heteromer, which induced desensitization and inhibited adenylyl cyclase through a pertussis toxin-insensitive G protein. Activation of the mu-delta receptor heteromer resulted in preferential activation of Galpha(z), illustrated by incorporation of GTPgamma(35)S, whereas activation of the individually expressed mu and delta receptors preferentially activated Galpha(i). The unique pharmacology of the mu-delta heteromer was dependent on the reciprocal involvement of the distal carboxyl tails of both receptors, so that truncation of the distal mu receptor carboxyl tail modified the delta-selective ligand-binding pocket, and truncation of the delta receptor distal carboxyl tail modified the mu-selective binding pocket. The distal carboxyl tails of both receptors also had a significant role in receptor interaction, as evidenced by the reduced ability to co-immunoprecipitate when the carboxyl tails were truncated. The interaction between mu and delta receptors occurred constitutively when the receptors were co-expressed, but did not occur when receptor expression was temporally separated, indicating that the hetero-oligomers were generated by a co-translational mechanism.
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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) | 0.001 | 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".