G Protein: β-Arrestin Bias Confers Differential Regulation of Gα <sub>q</sub> Signaling by GPR17 Antagonists
Bibliographic record
Abstract
Therapies enhancing remyelination offer the exciting prospect of disease-modifying treatments across a number of poorly treated neurological disorders. The class A orphan GPCR, GPR17, is one of the most studied receptors in remyelination; and antagonists of GPR17 have attracted significant attention as potential pro-myelinating medicines. Despite this, the signaling pathways linking GPR17 to remyelination and the molecular mechanisms of action of GPR17 antagonists are not well-defined. In the present study, we characterized GPR17 signaling and inhibition by three chemically distinct GPR17 antagonists: pranlukast, HAMI-3379, and a patent literature antagonist, RWT9996. In HEK293 cells recombinantly expressing GPR17- and BRET-based biosensors, pranlukast preferentially inhibited G protein activation over β-arrestin-2 recruitment, whereas HAMI3379 and RWT9996 equally inhibited all signal transduction tested. Follow-up studies using pharmacological inhibitors and GPR17 antagonists in Oli-neu cells, an immortalized mouse oligodendrocyte precursor cell (OPC) line with endogenous GPR17 expression, corroborated the G protein and β-arrestin coupling profile observed in recombinant cells. Specifically, the generated bias profile suggests that β-arrestin potentiates Gα q signaling from GPR17, conferring differential regulation of Gα q signaling by biased GPR17 antagonists. These findings highlight an unappreciated potential for biased signaling in the pharmacology of GPR17 ligands. We anticipate that these insights will help to inform the translation of GPR17-targeted therapies and improve our understanding of GPR17-mediated signaling pathways in governing myelination.
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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".