TM-10 * ALTERATIONS IN METABOLIC PATHWAYS IN GLIOBLASTOMA MULTIFORME CELLS IN RESPONSE TO HEXOKINASE-2 INHIBITION
Bibliographic record
Abstract
INTRODUCTION: GBMs are characterized by elevated glycolysis, however to date effective therapies targeting tumor glycolysis have not been identified. Also, the contribution of metabolic pathways other than glycolysis, such as glutamine and fatty acid oxidation, that are key in providing carbon to TCA cycle, have not been explored in GBMs. Aim: In this study we aim to decipher the adaptive mechanisms brought about by the inhibition of tumor glycolysis in GBMs. We have investigated the metabolic changes occurring in GBM cells after the knockdown of key glycolytic enzyme, hexokinase2 (HK2). METHODS AND RESULTS: We have generated doxycyclin-inducible HK2 knockdown GBM cells by transfecting shRNA in U87 and three Glioma Stem Cell lines with different baseline molecular characteristics We confirm specific knockdown of HK2, with no change in HK1 or HK3, using western blot. HK2 knockdown reduced cell number and proliferative capacity, in addition to decrease in lactate production and O2 consumption. HK2 loss significantly increased NADP:NADPH ratio. Metabolic profiling by LC/MS targeted metabolomics was conducted after exposing GBM cells to C13-labeled glucose media. LC/MS metabolomics demonstrate that decrease in HK2 significantly reduced the levels of metabolites belonging to glycolysis and Pentose Phosphate Pathway. These two pathways are known to be associated with NADPH and biomass production for rapidly proliferating tumor cells. In contrast, metabolites in TCA cycle increased in response to HK2 inhibition. CONCLUSION: Our data strongly suggests that GBM cells activate alternate metabolic pathways in response to inhibition of HK2 dependent glycolysis. These results have important clinical implications in designing combinatorial targeted therapy for blocking tumor metabolism in GBMs. Our ongoing work is focused on establishing the metabolites involved in glutamine catabolism and fatty acid oxidation, both in-vitro and in-vivo GBM models.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".