Compensatory cross-talk between autophagy and glycolysis regulates senescence and stemness in heterogeneous glioblastoma tumor subpopulations
Bibliographic record
Abstract
Abstract Despite tremendous research efforts, successful targeting of aberrant tumor metabolism in clinical practice has remained elusive. Tumor heterogeneity and plasticity may play a role in the clinical failure of metabolism-targeting interventions for treating cancer patients. Moreover, compensatory growth-related processes and adaptive responses exhibited by heterogeneous tumor subpopulations to metabolic inhibitors are poorly understood. Here, by using clinically-relevant patient-derived glioblastoma (GBM) cell models, we explore the cross-talk between glycolysis, autophagy, and senescence in maintaining tumor stemness. We found that stem cell-like GBM tumor subpopulations possessed higher basal levels of glycolytic activity and increased expression of several glycolysis-related enzymes including, GLUT1/SLC2A1, PFKP, ALDOA, GAPDH, ENO1, PKM2, and LDH, compared to their non-stem-like counterparts. Importantly, bioinformatics analysis also revealed that the mRNA expression of glycolytic enzymes positively correlates with stemness markers ( CD133/PROM1 and SOX2 ) in patient GBM tumors. While treatment with glycolysis inhibitors induced senescence in stem cell-like GBM tumor subpopulations, as evidenced by increased β-galactosidase staining and upregulation of the cell cycle regulators p21 Waf1/Cip1 / CDKN1A and p16 INK4A / CDKN2A , these cells maintained their aggressive stemness features and failed to undergo apoptotic cell death. Using various techniques including autophagy flux and EGFP-MAP1LC3B + puncta formation analysis, we determined that inhibition of glycolysis led to the induction of autophagy in stem cell-like GBM tumor subpopulations, but not in their non-stem-like counterparts. Similarly, blocking autophagy in stem cell-like GBM tumor subpopulations induced senescence-associated growth arrest without hampering stemness capacity or inducing apoptosis while reciprocally upregulating glycolytic activity. Combinatorial treatment of stem cell-like GBM tumor subpopulations with autophagy and glycolysis inhibitors blocked the induction of senescence while drastically impairing their stemness capacity which drove cells towards apoptotic cell death. These findings identify a novel and complex compensatory interplay between glycolysis, autophagy, and senescence that helps maintain stemness in heterogeneous GBM tumor subpopulations and provides a survival advantage during metabolic stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".