Two Distinct Actin Networks Mediate Traction Oscillations to Confer Focal Adhesion Mechanosensing
Bibliographic record
Abstract
Focal adhesions (FAs) are integrin-based transmembrane assemblies that connect a cell to its extracellular matrix (ECM). They are mechanosensors through which cells exert actin cytoskeleton-mediated traction forces to sense the ECM stiffness. Interestingly, FAs themselves are dynamic structures that adapt their growth in response to mechanical force. It is unclear how the cell manages the plasticity of the FA structure and the associated traction force to accurately sense ECM stiffness. Strikingly, FA traction forces oscillate in time and space, and govern the cell mechanosensing of ECM stiffness. However, precisely how and why the FA traction oscillates is unknown. We developed a model of FA growth that integrates the contributions of the branched actin network and stress fibers (SFs). Using the model in combination with experimental tests, we show that the retrograde flux of the branched actin network promotes the proximal growth of the FA and contributes to a traction peak near the FA's distal tip. The resulting traction gradient within the growing FA favors SF formation near the FA's proximal end. The SF-mediated actomyosin contractility further stabilizes the FA and generates a second traction peak near the center of the FA. Formin-mediated SF elongation negatively feeds back with actomyosin contractility, resulting in central traction peak oscillation. This underpins the observed FA traction oscillation and, importantly, broadens the ECM stiffness range over which FAs can accurately adapt to traction force generation. Actin cytoskeleton-mediated FA growth and maturation thus culminate with FA traction oscillation to drive efficient FA mechanosensing.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".