Activation and targeting of mitogen-activated protein kinases by G-protein-coupled receptors
Bibliographic record
Abstract
Over the past decade, it has become apparent that many G-protein-coupled receptors (GPCRs) generate signals that control cellular differentiation and growth, including stimulation of Ras family GTPases and activation of mitogen-activated protein (MAP) kinase pathways. The mechanisms that GPCRs use to control the activity of MAP kinases vary between receptor and cell type but fall broadly into one of three categories: signals initiated by classical G protein effectors, e.g., protein kinase (PK)A and PKC, signals initiated by cross-talk between GPCRs and classical receptor tyrosine kinases, e.g., "transactivation" of epidermal growth factor (EGF) receptors, and signals initiated by direct interaction between beta-arrestins and components of the MAP kinase cascade, e.g., beta-arrestin "scaffolds". While each of these pathways results in increased cellular MAP kinase activity, emerging data suggest that they are not functionally redundant. MAP kinase activation occurring via PKC-dependent pathways and EGF receptor transactivation leads to nuclear translocation of the kinase and stimulates cell proliferation, while MAP kinase activation via beta-arrestin scaffolds primarily increases cytosolic kinase activity. By controlling the spatial and temporal distribution of MAP kinase activity within the cell, the consequences of GPCR-stimulated MAP kinase activation may be determined by the mechanism by which they are activated.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".