Protein tyrosine phosphatase-α complexes with the IGF-I receptor and undergoes IGF-I-stimulated tyrosine phosphorylation that mediates cell migration
Bibliographic record
Abstract
Protein tyrosine phosphatase-alpha (PTPalpha) is a widely expressed receptor-type phosphatase that functions in multiple signaling systems. The actions of PTPalpha can be regulated by its phosphorylation on serine and tyrosine residues, although little is known about the conditions that promote PTPalpha phosphorylation. In this study, we tested the ability of several extracellular factors to stimulate PTPalpha tyrosine phosphorylation. The growth factors IGF-I and acidic FGF induced the highest increase in PTPalpha phosphorylation at tyrosine 789, followed by PMA and lysophosphatidic acid, while EGF had little effect. Further investigation of IGF-I-induced PTPalpha tyrosine phosphorylation demonstrated that this occurs through a novel Src family kinase-independent mechanism that does not require focal adhesion kinase, phosphatidylinositol 3-kinase, or MEK. We also show that PTPalpha physically interacts with the IGF-I receptor. In contrast to IGF-I-induced PTPalpha phosphorylation, this association does not require IGF-I. The interaction of PTPalpha and the IGF-I receptor is independent of PTPalpha catalytic activity, and expression of exogenous PTPalpha does not promote IGF-I receptor tyrosine dephosphorylation, indicating that PTPalpha does not act as an IGF-I receptor phosphatase. However, PTPalpha mediates IGF-I signaling, because IGF-I-stimulated fibroblast migration was reduced by approximately 50% in cells lacking PTPalpha or in cells with mutant PTPalpha lacking the tyrosine 789 phosphorylation site. Our results suggest that PTPalpha tyrosine phosphorylation can occur in response to diverse stimuli and can be mediated by various tyrosine kinases. In the case of IGF-I, we propose that IGF-I-induced tyrosine 789 phosphorylation of PTPalpha, possibly catalyzed by the PTPalpha-associated IGF-I receptor tyrosine kinase, is required for efficient cell migration in response to this growth factor.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".