Contribution of the WNK1 kinase to corneal wound healing using the tissue‐engineered human cornea as an in vitro model
Bibliographic record
Abstract
Damage to the corneal epithelium triggers important changes in the extracellular matrix (ECM) to which basal human corneal epithelial cells (hCECs) attach. These changes are perceived by integrin receptors that activate different intracellular signalling pathways, ultimately leading to re-epithelialization of the injured epithelium. In this study, we investigated the impact of pharmacological inhibition of specific signal transduction mediators on corneal wound healing using both monolayers of hCECs and the human tissue-engineered cornea (hTEC) as an in vitro 3D model. RNA and proteins were isolated from the wounded and unwounded hTECs to conduct gene profiling analyses and protein kinase arrays. The impact of WNK1 inhibition was evaluated on the wounded hTECs as well as on hCECs monolayers using a scratch wound assay. Gene profiling and protein kinase arrays revealed that expression and activity of several mediators from the integrin-dependent signaling pathways were altered in response to the ECM changes occurring during corneal wound healing. Phosphorylation of the WNK1 kinase turned out to be the most striking activation event going on during this process. The inhibition of WNK1 by WNK463 reduced the rate of corneal wound closure in both the hTEC and hCECs grown in monolayer compared with their respective negative controls. WNK463 also reduced phosphorylation of the WNK1 downstream targets SPAK/OSR1 in wounded hTECs. These in vitro results allowed for a better understanding of the cellular and molecular mechanisms involved in corneal wound healing and identified WNK1 as a kinase important to ensure proper wound healing of the cornea.
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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".