Interaction of the mu-opioid receptor with GPR177 (Wntless) inhibits Wnt secretion: potential implications for opioid dependence
Bibliographic record
Abstract
BACKGROUND: Opioid agonist drugs produce analgesia. However, long-term exposure to opioid agonists may lead to opioid dependence. The analgesic and addictive properties of opioid agonist drugs are mediated primarily via the mu-opioid receptor (MOR). Opioid agonists appear to alter neuronal morphology in key brain regions implicated in the development of opioid dependence. However, the precise role of the MOR in the development of these neuronal alterations remains elusive. We hypothesize that identifying and characterizing novel MOR interacting proteins (MORIPs) may help to elucidate the underlying mechanisms involved in the development of opioid dependence. RESULTS: GPR177, the mammalian ortholog of Drosophila Wntless/Evi/Sprinter, was identified as a MORIP in a modified split ubiquitin yeast two-hybrid screen. GPR177 is an evolutionarily conserved protein that plays a critical role in mediating Wnt protein secretion from Wnt producing cells. The MOR/GPR177 interaction was validated in pulldown, coimmunoprecipitation, and colocalization studies using mammalian tissue culture cells. The interaction was also observed in rodent brain, where MOR and GPR177 were coexpressed in close spatial proximity within striatal neurons. At the cellular level, morphine treatment caused a shift in the distribution of GPR177 from cytosol to the cell surface, leading to enhanced MOR/GPR177 complex formation at the cell periphery and the inhibition of Wnt protein secretion. CONCLUSIONS: It is known that chronic morphine treatment decreases dendritic arborization and hippocampal neurogenesis, and Wnt proteins are essential for these processes. We therefore propose that the morphine-mediated MOR/GPR177 interaction may result in decreased Wnt secretion in the CNS, resulting in atrophy of dendritic arbors and decreased neurogenesis. Our results demonstrate a previously unrecognized role for GPR177 in regulating cellular response to opioid drugs.
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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".