The leading edge during dorsal closure as a model for epithelial plasticity: Pak is required for recruitment of the Scribble complex and septate junction formation
Bibliographic record
Abstract
Dorsal closure (DC) of the Drosophila embryo is a model for the study of wound healing and developmental epithelial fusions, and involves the sealing of a hole in the epidermis through the migration of the epidermal flanks over the tissue occupying the hole, the amnioserosa. During DC, the cells at the edge of the migrating epidermis extend Rac- and Cdc42-dependent actin-based lamellipodia and filopodia from their leading edge (LE), which exhibits a breakdown in apicobasal polarity as adhesions are severed with the neighbouring amnioserosa cells. Studies using mammalian cells have demonstrated that Scribble (Scrib), an important determinant of apicobasal polarity that functions in a protein complex, controls polarized cell migration through recruitment of Rac, Cdc42 and the serine/threonine kinase Pak, an effector for Rac and Cdc42, to the LE. We have used DC and the follicular epithelium to study the relationship between Pak and the Scrib complex at epithelial membranes undergoing changes in apicobasal polarity and adhesion during development. We propose that, during DC, the LE membrane undergoes an epithelial-to-mesenchymal-like transition to initiate epithelial sheet migration, followed by a mesenchymal-to-epithelial-like transition as the epithelial sheets meet up and restore cell-cell adhesion. This latter event requires integrin-localized Pak, which recruits the Scrib complex in septate junction formation. We conclude that there are bidirectional interactions between Pak and the Scrib complex modulating epithelial plasticity. Scrib can recruit Pak to the LE for polarized cell migration but, as migratory cells meet up, Pak can recruit the Scrib complex to restore apicobasal polarity and cell-cell adhesion.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".