Fibroblast Growth Factor-2-induced Signaling through Lipid Raft-associated Fibroblast Growth Factor Receptor Substrate 2 (FRS2)
Bibliographic record
Abstract
The plasma membrane is not homogeneous but contains specific subcompartments characterized by their unique lipid and protein composition. Based on their enrichment in various signaling molecules, these membrane microdomains are recognized to be sites of localized signal transduction for a number of extracellular stimuli. We have previously shown that fibroblast growth factor-2 (FGF2) induced a specific signaling response within a lipid raft membrane microdomain in human neuroblastoma cells characterized by the tyrosine phosphorylation of a p80 phosphoprotein. Herein, we show that this protein is the signaling adaptor FRS2 and that it is localized exclusively to lipid rafts in vitro and in vivo. We have examined how the tyrosine phosphorylation and serine-threonine phosphorylation of FRS2 within lipid rafts affect the response of cells to FGF2 signaling. Our data suggest that activation of protein kinase C, Src family kinases, and MEK1/2 are involved in regulating serine-threonine phosphorylation of FRS2, which can indirectly affect FRS2 phosphotyrosine levels. We also show that Grb2 is recruited to lipid rafts during signaling events and that activation of MEK1/2 by different mechanisms within lipid rafts may lead to different cellular responses. This work suggests that compartmentalized signaling within lipid rafts may provide a level of specificity for growth factor signaling.
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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) | 0.000 | 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".