Phosphoproteomic Analysis of Dopamine D2 Receptor Signaling Reveals Interplay of G Protein- and β-Arrestin-Mediated Effects
Bibliographic record
Abstract
Leveraging biased signaling of G protein-coupled receptors has been proposed as a promising strategy for the development of drugs with higher specificity. However, the consequences of selectively targeting G protein- or β-arrestin-mediated signaling on cellular functions are not comprehensively understood. In this study, we utilized phosphoproteomics to gain a systematic overview of signaling induced by the four biased and balanced dopamine D2 receptor (D 2 R) ligands MS308, BM138, quinpirole, and sulpiride in an in vitro D 2 R transfection model. Quantification of 14,160 phosphosites revealed a low impact of the partial G protein agonist MS308 on cellular protein phosphorylation, as well as surprising similarities between the balanced agonist quinpirole and the inverse agonist sulpiride. Analysis of the temporal profiles of ligand-induced phosphorylation events showed a transient impact of the G protein-selective agonist MS308, whereas the β-arrestin-preferring agonist BM138 elicited a delayed, but more pronounced response. Functional enrichment analysis of ligand-impacted phosphoproteins and treatment-linked kinases confirmed multiple known functions of D 2 R signaling while also revealing novel effects, for example of MS308 on sterol regulatory element-binding protein-related gene expression. All raw data were deposited in MassIVE (MSV000089457).
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".