A tale of two distinct actin networks underlies the entire life cycle of focal adhesion
Bibliographic record
Abstract
Abstract Cell assembles focal adhesion (FA) to transmit the stress fiber (SF)-based actomyosin contraction onto the extracellular matrix (ECM) for mesenchymal migration, essential for many physiological processes ( e.g. , development and wound healing). To transmit force efficiently, both FA and SF contractility are built as “clutches” and in positive feedback with each other; conversely, the SF-engaging FA imposes a strong cell-ECM anchorage and must be disassembled timely to facilitate the cell migration. How the cell balances the two opposing roles of FA in cell migration is an open question. Particularly, it is not well-understood how a cell builds the FA de novo to clutch with SF and disassemble the clutches when needed in a coherent manner. Combining theory and experiments, we show that the entire life cycle of FA is seamlessly orchestrated by the FA-localized spatiotemporal coordination between retrograde actin flow and SF, without destroying the FA constituent molecules. Retrograde actin flow drives the centripetal growth of nascent FA, paving the way for SF engagement. The SF further stabilizes the growing FA into maturation via the positive feedback that clutches the contractility with the FA. Finally, the retrograde actin flow increase, in coupling to the local cell edge retraction, tugs the mature FA in the proximal direction that relaxes the associated SF contractility, turns off the clutching, and triggers the FA disassembly. Our finding sheds light on the organizational principles that cell streamlines the mechanochemical interplay between FA, actin cytoskeleton, and cell edge dynamics for efficient cell 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".