Src Family Protein-tyrosine Kinases Alter the Function of PTEN to Regulate Phosphatidylinositol 3-Kinase/AKT Cascades
Bibliographic record
Abstract
Src family protein-tyrosine kinases, which play an important role in signal integration, have been implicated in tumorigenesis in multiple lineages, including breast cancer. We demonstrate, herein, that Src kinases regulate the phosphatidylinositol 3-kinase (PI3K) signaling cascade via altering the function of the PTEN tumor suppressor. Overexpression of activated Src protein-tyrosine kinases in PTEN-deficient breast cancer cells does not alter AKT phosphorylation, an indicator of signal transduction through the PI3K pathway. However, in the presence of functional PTEN, Src reverses the activity of PTEN, resulting in an increase in AKT phosphorylation. Activated Src reduces the ability of PTEN to dephosphorylate phosphatidylinositols in micelles and promotes AKT translocation to cellular plasma membranes but does not alter PTEN activity toward water-soluble phosphatidylinositols. Thus, Src may alter the capacity of the PTEN C2 domain to bind cellular membranes rather than directly interfering with PTEN enzymatic activity. Tyrosine phosphorylation of PTEN is increased in breast cancer cells treated with pervanadate, suggesting that PTEN contains sites for tyrosine phosphorylation. Src kinase inhibitors markedly decreased pervanadate-mediated tyrosine phosphorylation of PTEN. Further, expression of activated Src results in marked tyrosine phosphorylation of PTEN. SHP-1, a SH2 domain-containing protein-tyrosine phosphatase, selectively binds and dephosphorylates PTEN in Src transfected cells. Both Src inhibitors and SHP-1 overexpression reverse Src-induced loss of PTEN function. Coexpression of PTEN with activated Src reduces the stability of PTEN. Taken together, the data indicate that activated Src inhibits PTEN function leading to alterations in signaling through the PI3K/AKT pathway.
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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".