Receptor-Specific Regulation of Phosphatidylinositol 3′-Kinase Activation by the Protein Tyrosine Phosphatase Shp2
Bibliographic record
Abstract
Receptor tyrosine kinases (RTKs) play distinct roles in multiple biological systems. Many RTKs transmit similar signals, raising questions about how specificity is achieved. One potential mechanism for RTK specificity is control of the magnitude and kinetics of activation of downstream pathways. We have found that the protein tyrosine phosphatase Shp2 regulates the strength and duration of phosphatidylinositol 3'-kinase (PI3K) activation in the epidermal growth factor (EGF) receptor signaling pathway. Shp2 mutant fibroblasts exhibit increased association of the p85 subunit of PI3K with the scaffolding adapter Gab1 compared to that for wild-type (WT) fibroblasts or Shp2 mutant cells reconstituted with WT Shp2. Far-Western analysis suggests increased phosphorylation of p85 binding sites on Gab1. Gab1-associated PI3K activity is increased and PI3K-dependent downstream signals are enhanced in Shp2 mutant cells following EGF stimulation. Analogous results are obtained in fibroblasts inducibly expressing dominant-negative Shp2. Our results suggest that, in addition to its role as a positive component of the Ras-Erk pathway, Shp2 negatively regulates EGF-dependent PI3K activation by dephosphorylating Gab1 p85 binding sites, thereby terminating a previously proposed Gab1-PI3K positive feedback loop. Activation of PI3K-dependent pathways following stimulation by other growth factors is unaffected or decreased in Shp2 mutant cells. Thus, Shp2 regulates the kinetics and magnitude of RTK signaling in a receptor-specific manner.
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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".