Protein Phosphatase 2A Negatively Regulates Eukaryotic Initiation Factor 4E Phosphorylation and eIF4F Assembly through Direct Dephosphorylation of Mnk and eIF4E
Bibliographic record
Abstract
The eukaryotic translation initiation factor 4E (eIF4E) is frequently overexpressed in human cancers and is associated with cellular transformation, tumorigenesis, and metastatic progression. It is known that Mnks can phosphorylate eIF4E. Protein phosphatase 2A (PP2A) functions as a tumor suppressor, and it was previously suggested to regulate eIF4E phosphorylation. However, how PP2A regulates eIF4E phosphorylation has not been fully addressed. In this study, we have not only validated the role of PP2A in regulation of eIF4E phosphorylation but also demonstrated the mechanism underlying this process. Inhibition of PP2A using either okadaic acid or PP2A small interfering RNA (siRNA) increased eIF4E phosphorylation, which could be abolished by the presence of the Mnk inhibitor CGP57380 or deficiency of Mnk genes. Thus, Mnks are involved in PP2A-mediated regulation of eIF4E phosphorylation. Moreover, a dephosphorylation assay revealed that PP2A could directly dephosphorylate Mnk1 and eIF4E. m(7)GTP pull-down assay detected more eIF4G and phospho-eIF4E and less 4EBP-1 in PP2A siRNA-transfected cells than in control siRNA-transfected cells, indicating an increased cap binding of eIF4F complex. Accordingly, okadaic acid treatment or PP2A knockdown increased the levels of c-Myc and Mcl-1, which are proteins known to be regulated by a cap-dependent translation mechanism. Taken together, we conclude that PP2A negatively regulates eIF4E phosphorylation and eIF4F complex assembly through dephosphorylation of Mnk and eIF4E, thus suggesting a novel mechanism by which PP2A exerts its tumor-suppressive function.
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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 | 0.000 |
| 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".