The effect of glucose restriction on cancer cell contractility: A threshold response in U-87 glioma
Bibliographic record
Abstract
Cells rely on contractility to proliferate, and cancerous ones exhibit an increased glucose dependence. It is therefore hypothesized that glucose restriction can mitigate cancer cell proliferation by ’stunting’ their contractility. However, glucose-restriction studies have mostly been based on experiments that have yielded conflicting results; some cells become less contractile under glucose-restriction, intuitively, while, others become surprisingly more contractile. Active mechanistic modeling may prove fruitful in resolving these conflicts. In this study, we develop a model for glucose-mediated cell contractility to capture the mechanical implications of glucose restriction. The model is calibrated on cell contraction data taken from 2D-cultured glioma cells, laying on a collagen substrate. The model predicts the existence of a critical level of glucose restriction that must be exceeded for contractility to be affected, and this is validated by our experiments. Our model provides an initial step toward a fundamental understanding of the metabolic implications of cell contractility, particularly in the context of glucose restriction: an essential step in cancer studies. significance This study advances our understanding of how glucose restriction affects cancer cell contractility, an essential factor in cell proliferation. Our findings reveal that cells require severe glucose deprivation before exhibiting reduced contractility, highlighting a threshold response. This indicates that the cytoskeleton, a key structural component, remains active until a significant reduction in energy supply forces the cell into a lower energy state. These insights provide critical knowledge about the metabolic hierarchy within cells, contributing to the broader study of cancer metabolism and potential therapeutic strategies aimed at disrupting cellular energy pathways.
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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".