Tension modulation of actomyosin ring assembly and <scp>RhoGTPases</scp> activity: Perspectives from the <i>Xenopus</i> oocyte wound healing model
Bibliographic record
Abstract
Abstract Cells are remarkably resilient structures; they are able to recover from injuries to their plasma membrane (PM) and cytoskeleton that would normally constitute existential threats. This capacity is exemplified by Xenopus laevis oocytes which can recover from very large PM defects through exocytotic and endocytic events and can repair damaged cortical cytoskeleton structures through the formation of a contractile actomyosin ring (AMR). Formation of the AMR involves the localized Ca 2+ ‐dependent activation of RhoA and Cdc42, and the pre‐patterning of guanine nucleotide exchange factors (GEFs) and GTPase‐activating proteins (GAPs). However, this model fails to account for observations that suggest a link between cytoskeletal dynamics, intracellular tension, and AMR formation. It also does not explain why the formation of an AMR is not involved in the cytoskeletal repair program of adherent cells. We show here evidence for the support of tension as an essential regulatory signal for the formation of AMR. Indeed, oocytes in which global tension has been experimentally reduced were unable to form a functional AMR following injury, showing severely diminished RhoA activity at the wound site. These new insights place the cytoskeleton at the center of events involving changes in cell shape such as cytokinesis which also involves the formation and closure of an AMR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".