Membrane tension can enhance adaptation to maintain polarity of migrating cells
Bibliographic record
Abstract
Abstract Migratory cells are known to adapt to environments that contain wide-ranging levels of chemoattractant. While biochemical models of adaptation have been previously proposed, here we discuss a different mechanism based on mechanosensing, where the interaction between biochemical signaling and cell tension facilitates adaptation. We describe and analyze a model of mechanochemical-based adaptation coupling a mechanics-based physical model of cell tension coupled with the wave-pinning reaction-diffusion model for Rac activity. Mathematical analysis of this model, simulations of a simplified 1D cell geometry, and 2D finite element simulations of deforming cells reveal that as a cell protrudes under the influence of high stimulation levels, tension mediated inhibition of GTPase signaling causes the cell to polarize even when initially over-stimulated. Specifically, tension mediated inhibition of GTPase activation, which has been experimentally observed in recent years, facilitates this adaptation by countering the high levels of environmental stimulation. These results demonstrate how tension related mechanosensing may provide an alternative (and potentially complementary) mechanism for cell adaptation. Statement of Significance Migratory cells such as human neutrophils encounter environments that contain wide-ranging levels of chemoattractant. In order to move, these cells must maintain an organized front-rear signaling polarity despite this wide variation in environmental stimuli. Past research has demonstrated a number of biochemical based mechanisms by which cells adapt to variable signal levels. Here we demonstrate that the interplay between Rho GTPase signaling and tension mediated feedbacks may provide an alternative mechanochemical mechanism for adaptation to high levels of signaling.
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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".