PAR2 Activation Protects Against Colitis but Inhibits Epithelial Wound Healing
Bibliographic record
Abstract
Aims Protease‐activated receptor (PAR)2 activation increases the expression of cyclooxygenase (COX)‐2 in intestinal epithelial cells. We tested the hypothesis that PAR2‐induced COX‐2 regulates intestinal inflammation and modulates epithelial wound healing. Methods PAR2 was activated using the selective activating peptide 2f‐LIGRLO. Circular wounds in Caco2 cell monolayers were monitored with live‐cell imaging. In vivo, epithelial damage was induced by giving WT and PAR2 KO (C57Bl/6) mice 2.5% DSS for 5‐7 days. Results Activation of PAR2 in Caco2 cells significantly increased COX‐2 protein (4.2‐fold) and PGE 2 metabolites (9.6‐fold). In vivo , preliminary results showed less COX‐2 protein in PAR2 KO mice given DSS compared to WT mice. The PAR2 KO mice also lost significantly more weight and had higher histological damage scores compared to WT mice. We next determined the effect of PAR2‐induced COX‐2 on epithelial wound healing in vitro . Surprisingly, PAR2 activation significantly inhibited the rate of wound closure over 48 hr (79.3±2.5% wound closure) compared to control (94.3±0.5%), independently of COX‐2 activity. PAR2 activation had no effect on proliferation, but significantly inhibited cell migration. Conclusions PAR2 activation was shown to be protective in vivo , which correlated with the expression of COX‐2. We also uncovered a novel COX‐2‐independent effect of PAR2, reducing the rate of wound healing by inhibiting cell migration. Our results show that PAR2 plays differential roles in regulating epithelial response to injury and inflammation. Support: Crohn's and Colitis Canada, Alberta IBD Consortium, Alberta Cancer Foundation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".