Interplay between integrins and PI4P5K Sktl is crucial for cell polarization and reepithelialisation during Drosophila wound healing
Bibliographic record
Abstract
Phosphatidylinositol(4,5)-bisphosphate [PI(4,5)P2] regulates cell adhesion and actin dynamics during cell migration. PI(4,5)P2 binds various components of the cell adhesion machinery, but how these processes affect migration of the epithelial cell sheet is not well understood. Here, we report that PI(4,5)P2 and Sktl, the kinase that converts PI4P to PI(4,5)P2, are both localized to the rear side of cells during wound healing of the Drosophila larval epidermis. The Sktl localization requires JNK pathway activation and integrins, but not PVR. The sktl knockdown epidermis displays strong defects in would closure, reminiscent of the JNK-depleted epidermis, and shows severe disruption of cell polarity, as determined by myosin II localization. Sktl and βPS integrin colocalize at the rear side of cells forming the trailing edge during wound healing and the two are inter-dependent in that the absence of one severely disrupts the rear localization of the other. These results strongly suggest that the JNK pathway regulates the rear localization of Sktl and integrins and the interplay between Sktl and integrins sets up cell polarity, which is crucial for reepithelialisation during 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 | 0.000 |
| 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".