PAR<sub>2</sub> activation alters colonic paracellular permeability in mice via IFN‐γ‐dependent and ‐independent pathways
Bibliographic record
Abstract
Activation of colonic proteinase-activated receptor-2 (PAR(2)) caused inflammation and increased mucosal permeability in mouse colon. The present study was aimed at characterizing the possible links between these two phenomena. We evaluated the effects of intracolonic infusion of PAR(2)-activating peptide, SLIGRL, on colonic paracellular permeability and inflammation at two different doses, 5 and 100 microg per mouse, in an attempt to discriminate between both PAR(2)-mediated effects. We further investigated the possible involvement of interferon gamma (IFN-gamma) and calmodulin-dependent activation of myosin light chain kinase (MLCK), and alterations of zonula occludens-1 (ZO-1) localization in PAR(2)-induced responses. Thus, at the lower dose, SLIGRL increased colonic permeability without causing inflammation. Western blotting showed phosphorylation of mucosal myosin light chain (MLC) expression after both doses of SLIGRL. Moreover, while the MLCK inhibitor, ML-7, abolished the permeability effects of the low dose of SLIGRL, it only partially inhibited that of the high dose. In IFN-gamma-deficient mice (B6 ifng(-/-)), the increases in permeability were similar for both doses of SLIGRL and prevented by ML-7. In addition, MLCK immunoprecipitation revealed an increase of calmodulin binding to MLCK in the mucosa of mice treated with either dose of SLIGRL. Finally, we have shown that direct activation of PAR(2) on enterocytes is responsible for increased permeability and ZO-1 disruption. Moreover, SLIGRL at a dose that does not produce inflammation increases permeability via calmodulin activation, which binds and activates MLCK. The resulting tight junction opening does not depend upon IFN-gamma secretion, while the increased permeability in response to the high dose of PAR(2) agonist involves IFN-gamma secretion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".