Active β-catenin is regulated by the PTEN/PI3 kinase pathway: a role for protein phosphatase PP2A
Bibliographic record
Abstract
Dysregulation of Wnt/β-catenin signaling has been associated with the development and progression of many cancers. The stability and subcellular localization of β-catenin, a dual functional protein that plays a role in intracellular adhesion and in regulating gene expression, is tightly regulated. However, little is known about the transcriptionally active form of β-catenin, Active Beta Catenin (ABC), that is unphosphorylated at serine 37 (Ser37) and threonine 41 (Thr41). Elucidating the mechanism by which β-catenin is activated to generate ABC is vital to the development of therapeutic strategies to block β-catenin signaling for cancer treatment. Using melanoma, breast and prostate cancer cell lines, we show that while cellular β-catenin levels are regulated by the Wnt pathway, cellular ABC levels are mainly regulated by the PI3K pathway and are dependent on the phosphatase activity of the protein phosphatase PP2A. Furthermore, we demonstrate that although the PI3K/PTEN pathway does not regulate total β-catenin protein levels within the cell, it plays a role in regulating the subcellular localization of β-catenin. Our results support a novel functional interaction/cross-talk between the PTEN/PI3K and Wnt pathways in the regulation of the subcellular/nuclear levels of ABC, which is crucially important for the protein's activity as a transcription factor and its biological effects in health and disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".