In vivo mapping of a GPCR interactome using knockin mice
Bibliographic record
Abstract
With over 30% of current medications targeting this family of proteins, G-protein-coupled receptors (GPCRs) remain invaluable therapeutic targets. However, due to their unique physicochemical properties, their low abundance, and the lack of highly specific antibodies, GPCRs are still challenging to study in vivo. To overcome these limitations, we combined here transgenic mouse models and proteomic analyses in order to resolve the interactome of the δ-opioid receptor (DOPr) in its native in vivo environment. Given its analgesic properties and milder undesired effects than most clinically prescribed opioids, DOPr is a promising alternative therapeutic target for chronic pain management. However, the molecular and cellular mechanisms regulating its signaling and trafficking remain poorly characterized. We thus performed liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses on brain homogenates of our newly generated knockin mouse expressing a FLAG-tagged version of DOPr and revealed several endogenous DOPr interactors involved in protein folding, trafficking, and signal transduction. The interactions with a few identified partners such as VPS41, ARF6, Rabaptin-5, and Rab10 were validated. We report an approach to characterize in vivo interacting proteins of GPCRs, the largest family of membrane receptors with crucial implications in virtually all physiological systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".