Cofilin promotes actin turnover and flexibility to drive coordinated cell movements <i>in vivo</i>
Bibliographic record
Abstract
Abstract Embryos display a striking ability to repair wounds rapidly, with no inflammation or scarring. Embryonic wound healing is driven by the collective movement of the cells adjacent to the wound. The cells at the wound edge polarize actin and the molecular motor non-muscle myosin II, forming a supracellular cable around the wound that generates force and coordinates cell movements to close the lesion. Actin network contraction has been associated with the disassembly of the actin filaments that form the network. We found that the actin-severing protein Cofilin and its co-factor Aip1 accumulated at the edge of epidermal wounds in Drosophila embryos. Reducing Cofilin activity or levels slowed down wound closure, indicating that Cofilin is necessary for rapid wound healing. Using quantitative microscopy, we showed that Cofilin controls F-actin turnover at the wound edge, but not F-actin polarity or contractile force generation. Combining genetic and pharmacological manipulations, we found that F-actin turnover at the wound edge must be tightly regulated for wounds to close rapidly. Computational modelling suggested that Cofilin may contribute to rapid wound repair by maintaining a flexible actin network around the wound. Consistent with this model, fluorescence fluctuation analysis revealed that F-actin networks at the wound edge were significantly more rigid when we reduced Cofilin activity. Together, our results indicate that Cofilin promotes F-actin turnover at the wound margin to maintain a flexible actin network and facilitate rapid contraction and wound healing.
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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 | 0.001 |
| 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".