Functionally selective activation of the dopamine receptor D2 is mirrored by the protein expression profiles
Bibliographic record
Abstract
Abstract The development of functionally selective or biased ligands is a promising approach towards drugs with less side effects. Biased ligands for G protein-coupled receptors can selectively induce G protein activation or β-arrestin recruitment. The consequences of this selective action on cellular functions, however, are not fully understood. Here, we investigated the impact of five biased and balanced dopamine D2 receptor agonists and antagonists on the global protein expression in HEK293T cells by untargeted nanoscale liquid chromatography–tandem mass spectrometry. The proteome analysis detected 5290 protein groups. Hierarchical clustering and principal component analysis based on the expression levels of 1462 differential proteins led to a separation of antagonists and balanced agonist from the control treatment, while the biased ligands demonstrated larger similarities to the control. Functional analysis of affected proteins revealed that the antagonists haloperidol and sulpiride regulated exocytosis and peroxisome function. The balanced agonist quinpirole, but not the functionally selective agonists induced a downregulation of proteins involved in synaptic signaling. The β-arrestin-preferring agonist BM138, however, regulated several proteins related to neuron function and the dopamine receptor-mediated signaling pathway itself. The G protein-selective partial agonist MS308 influenced rather broad functional terms such as DNA processing and mitochondrial translation.
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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".