Synergistic action of actin binding proteins regulate actin network organization and cell shape
Bibliographic record
Abstract
Abstract Animal cell shape is largely determined by the organization of the actin cytoskeleton. Spread shapes result from a balance between protrusive actin networks and contractile stress fibers, while rounded shapes are supported by a contractile actomyosin cortex. The assembly and regulation of distinct types of actin networks have been extensively studied, yet, what determines which networks dominate in a given cell remains unclear. In this Brief Report, we explore the molecular regulation of overall actin organization and resulting cell shape. We use our recently published comparison of the F-actin interactome in spread interphase and rounded mitotic cells to establish a list of candidate regulators of actin networks in spread cells. Utilizing micropatterning and automated image analysis we quantitatively analyze how these candidates affect actin organization. Out of our initial 16 candidates, we identify subsets of proteins promoting stress fibers or regulating their arrangement. Interestingly, no single regulator depletion caused significant cell shape change. However, perturbing two hits simultaneously, supervillin and myosin II, led to stress fiber disassembly and cell rounding. Overall, our systematic investigation shows that actin networks are robust to perturbations, and identifies regulatory modules controlling overall actin organization and resulting cell shape.
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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".