Protease-activated receptor-2 activation enhances epithelial wound healing via epidermal growth factor receptor
Bibliographic record
Abstract
The intestinal barrier function relies on the presence of a single layer of epithelial cells. Barrier dysfunction is associated with the inflammatory bowel diseases (IBD). Understanding the mechanisms involved in intestinal wound healing in order to sustain the barrier function has a great therapeutic potential. Activation of protease-activated receptor-2 (PAR2) induces COX-2 expression in intestinal epithelial cells via EGFR transactivation. COX-2 is well known for its protective effects in the gastrointestinal tract. Therefore, we hypothesized that PAR-2 activation induces a wound healing response in intestinal epithelial cells through COX-2-derived lipid mediators and EGFR transactivation. Immunofluorescence and calcium assay were used to characterize CMT-93 mouse colonic epithelial cell line for PAR2 expression and its activity, respectively. Treatment with PAR2 activating peptide 2-furoyl-LIGRLO-NH2 (2fLI), but not by its inactive reverse-sequence peptide (2fO) enhanced wound closure in scratch wounded monolayers. The EGFR tyrosine kinase inhibitor (PD153035), broad-spectrum matrix metalloproteinase inhibitor (GM6001) and Src tyrosine kinase inhibitor (PP2) inhibited PAR2-induced wound healing. However, PAR2 activation did not induce COX-2 expression in CMT-93 cells and inhibition of COX-2 by COX-2 selective inhibitor (NS-398) did not alter PAR2-induced wound healing. In conclusion, PAR2 activation drives wound healing in CMT-93 cells via EGFR transactivation. Matrix metalloproteinases and Src tyrosine kinase activity may involve in EGFR transactivation and PAR2-induced wound healing is independent of COX-2 activity. These findings provide a mechanism whereby PAR2 can participate in the resolution of intestinal wounds in gastrointestinal inflammatory diseases.
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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) | 0.003 | 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".