Proteinase-activated receptor 2 is an anti-inflammatory signal for colonic lamina propria lymphocytes in a mouse model of colitis
Bibliographic record
Abstract
The proteinase-activated receptor 2 (PAR-2) is a member of a family of G protein-coupled receptors for proteases. Proteases cleave PARs within the extracellular N-terminal domains to expose tethered ligands that bind to and activate the cleaved receptors. PAR-2 is highly expressed in colon in epithelial and neuronal elements. In this study we show that PAR-2 activation prevents the development and induces healing of T helper cell type 1-mediated experimental colitis induced by intrarectal administration of 2,4,6-trinitrobenzene sulfonic acid (TNBS) in mice. A role for PAR-2 in the protection against colon inflammation was explored by the use of SLIGRL-NH(2), a synthetic peptide that corresponds to the mouse tethered ligand exposed after PAR-2 cleavage. TNBS-induced colitis was dose-dependently reduced by the administration of SLIGRL-NH(2), whereas the scramble control peptide, LSIGRL-NH(2), was uneffective. This beneficial effect was reflected by increased survival rates, improvement of macroscopic and histologic scores, decrease in mucosal content of T helper cell type 1 cytokines, protein, and mRNA, and a diminished myeloperoxidase activity. SLIGRL-NH(2), but not the scramble peptide, directly inhibited IFN-gamma secretion and CD44 expression on lamina propria T lymphocytes. Protection exerted by PAR-2 in TNBS-treated mice was reverted by injecting mice with a truncated form of calcitonin gene-related peptide and by sensory neurons ablation with the neurotoxin capsaicin. Collectively, these studies show that PAR-2 is an anti-inflammatory receptor in the colon and suggest that PAR-2 ligands might be effective in the treatment of inflammatory bowel 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".