Faculty Opinions recommendation of PTEN suppresses glycolysis by dephosphorylating and inhibiting autophosphorylated PGK1.
Bibliographic record
Abstract
The PTEN tumor suppressor is frequently mutated or deleted in cancer and regulates glucose metabolism through the PI3K-AKT pathway. However, whether PTEN directly regulates glycolysis in tumor cells is unclear. We demonstrate here that PTEN directly interacts with phosphoglycerate kinase 1 (PGK1). PGK1 functions not only as a glycolytic enzyme but also as a protein kinase intermolecularly autophosphorylating itself at Y324 for activation. The protein phosphatase activity of PTEN dephosphorylates and inhibits autophosphorylated PGK1, thereby inhibiting glycolysis, ATP production, and brain tumor cell proliferation. In addition, knockin expression of a PGK1 Y324F mutant inhibits brain tumor formation. Analyses of human glioblastoma specimens reveals that PGK1 Y324 phosphorylation levels inversely correlate with PTEN expression status and are positively associated with poor prognosis in glioblastoma patients. This work highlights the instrumental role of PGK1 autophosphorylation in its activation and PTEN protein phosphatase activity in governing glycolysis and tumorigenesis.Copyright © 2019 Elsevier Inc. All rights reserved. PMID: 31492635 Funding information This work was supported by: NCI NIH HHS, United States Grant ID: P30 CA086862
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.033 |
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".