Increased Concentrations of Fructose 2,6-Bisphosphate Contribute to the Warburg Effect in Phosphatase and Tensin Homolog (PTEN)-deficient Cells
Bibliographic record
Abstract
Unlike normal differentiated cells, tumor cells metabolize glucose via glycolysis under aerobic conditions, a hallmark of cancer known as the Warburg effect. Cells lacking the commonly mutated tumor suppressor PTEN exhibit a glycolytic phenotype reminiscent of the Warburg effect. This has been traditionally attributed to the hyperactivation of PI3K/Akt signaling that results from PTEN loss. Here, we propose a novel mechanism whereby the loss of PTEN negatively affects the activity of the E3 ligase APC/C-Cdh1, resulting in the stabilization of the enzyme PFKFB3 and increased synthesis of its product fructose 2,6-bisphosphate (F2,6P2). We discovered that when compared with wild-type cells, PTEN knock-out mouse embryonic fibroblasts (PTEN KO MEF) have 2-3-fold higher concentrations of F2,6P2, the most potent allosteric activator of the glycolytic enzyme phosphofructokinase-1 (PFK-1). Reintroduction of either wild-type or phosphatase mutant PTEN in the PTEN KO cells effectively lowers F2,6P2 to the wild-type levels and reduces their lactate production. PTEN KO cells were found to have high protein levels of PFKFB3, which directly contribute to the increased concentrations of F2,6P2. PTEN enhances interaction between PFKFB3 and Cdh1, and overexpression of Cdh1 down-regulates the PFKFB3 protein level in wild-type, but not in PTEN-deficient cells. Importantly, we found that the degradation of endogenous PFKFB3 in PTEN KO cells occurs at a slower rate than in wild-type cells. Our results suggest an important role for F2,6P2 in the metabolic reprogramming of PTEN-deficient cells that has important consequences for cell proliferation.
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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 | 0.001 |
| 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".