Differential Association of Receptor-Gβγ Complexes with β-Arrestin2 Determines Recycling Bias and Potential for Tolerance of Delta Opioid Receptor Agonists
Bibliographic record
Abstract
Opioid tendency to generate analgesic tolerance has been previously linked to biased internalization. Here, we assessed an alternative possibility; whether tolerance of delta opioid receptor agonists (DORs) could be related to agonist-specific recycling. A first series of experiments revealed that DOR internalization by DPDPE and SNC-80 was similar, but only DPDPE induced recycling. We then established that the non-recycling agonist SNC-80 generated acute analgesic tolerance that was absent in mice treated with DPDPE. Furthermore, both agonists stabilized different conformations, whose distinct interaction with Gβγ subunits led to different modalities of β-arrestin2 (βarr2) recruitment. In particular, bioluminescence resonance energy transfer (BRET) assays revealed that sustained activation by SNC-80 drew the receptor C terminus in close proximity of the N-terminal domain of Gγ2, causing βarr2 to interact with receptors and Gβγ subunits. DPDPE moved the receptor C-tail away from the Gβγ dimer, resulting in βarr2 recruitment to the receptor but not in the vicinity of Gγ2. These differences were associated with stable DOR-βarr2 association, poor recycling, and marked desensitization following exposure to SNC-80, while DPDPE promoted transient receptor interaction with βarr2 and effective recycling, which conferred protection from desensitization. Together, these data indicate that DORs may adopt ligand-specific conformations whose distinct recycling properties determine the extent of desensitization and are predictive of analgesic tolerance. Based on these findings, we propose that the development of functionally selective DOR ligands that favor recycling could constitute a valid strategy for the production of longer acting opioid analgesics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".