Cooperative Phosphorylation of the Tumor Suppressor Phosphatase and Tensin Homologue (PTEN) by Casein Kinases and Glycogen Synthase Kinase 3β
Bibliographic record
Abstract
The phosphatase and tensin homologue (PTEN) tumor suppressor is a phosphatidylinositol D3-phosphatase that counteracts the effects of phosphatidylinositol 3-kinase and negatively regulates cell growth and survival. PTEN is itself regulated by phosphorylation on multiple serine and threonine residues in its C terminus. Previous work has implicated casein kinase 2 (CK2) as the kinase responsible for this phosphorylation. Here we showed that CK2 does not phosphorylate all sites in PTEN and that glycogen synthase kinase 3beta (GSK3beta) also participates in PTEN phosphorylation. Although CK2 mainly phosphorylated PTEN at Ser-370 and Ser-385, GSK3beta phosphorylated Ser-362 and Thr-366. More importantly, prior phosphorylation of PTEN at Ser-370 by CK2 strongly increased its phosphorylation at Thr-366 by GSK3beta, suggesting that the two may synergize. Using RNA interference, we showed that GSK3 phosphorylates PTEN in intact cells. Finally, PTEN phosphorylation was affected by insulin-like growth factor in intact cells. We concluded that multiple kinases, including CK2 and GSK3beta, participate in PTEN phosphorylation and that GSK3beta may provide feedback regulation of PTEN.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".