TM-05 * PTEN INDUCED KINASE1 (PINK1) NEGATIVELY REGULATES AEROBIC GLYCOLYSIS IN GLIOBLASTOMA
Bibliographic record
Abstract
Aggressive cancer cells are characterized by high rates glycolysis and lactate production, a metabolic reprogramming event known as the Warburg effect. This ultimately provides tumor cells including GBM the most malignant and common primary brain tumor with intermediate metabolites for anabolic processes, cell proliferation and invasion. However, these biological processes generate oxidative stress that must be balanced through detoxification of reactive oxygen species (ROS). Using an unbiased retroviral loss of function screen in pre-disposed but non-transformed astrocytes, we demonstrate that PTEN Induced Kinase 1 (PINK1), a mitochondrial kinase is a crucial regulator of the Warburg effect. Mechanistically, loss of PINK1 mediates metabolic reprogramming in normal human astrocytes through ROS dependent hypoxia-inducible factor-1α (HIF1α) stabilization, a transcription factor that controls expression of several aerobic glycolysis genes. Overexpression of PINK1 in GBM cells suppresses ROS, HIF1a and the Warburg effect in vitro and in vivo. Surprisingly, loss of PINK1 in GBM cells that retain PINK1 expression increases oxidative stress and reduces cell viability suggesting ROS balance and maintenance is critical in tumor cells and can be therapeutically exploited. PINK1 loss was observed in GBM and correlated with poor patient survival. Collectively, we demonstrate that PINK1 is a negative regulator of the Warburg effect.
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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 | 0.000 | 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".