Spatiotemporal coupling of caveolae mechanosensing and RhoA-GEFs regulates cell polarity and directional migration
Bibliographic record
Abstract
Migrating cells dynamically adapt their morphogenetic programs in response to microenvironmental changes, requiring coordinated spatiotemporal integration of mechanical and biochemical signals. The plasma membrane, through membrane tension and actin dynamics modulation, is essential for cell motility. Caveolae, small plasma membrane invaginations, act as mechanosensors to buffer tension changes under mechanical stress. Recent evidence suggests a role for caveolae in cell migration. Here, we demonstrate that breast cancer cells exhibit a front-rear asymmetry in caveolae and caveolin-1 scaffolds, which is regulated by membrane tension and is crucial for persistent migration and cell directionality. RhoA-driven cell contraction relies on the spatiotemporally coordinated assembly of caveolae and recruitment of RhoA-GEFs at the cell rear. These results are supported by a physical model establishing a feedback loop between local membrane tension and contractility, through caveolae formation and disruption. Our findings underscore the importance of caveolae mechanosensing in regulating RhoA activation and guiding cell migration. This study reveals that caveolae and caveolin-1 form a front-rear asymmetry in migrating cells, coupling membrane tension to RhoA-GEF recruitment and activation. This mechanosensing feedback regulates cell polarity and promotes persistent migration.
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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) | 0.000 | 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 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".