Mast Cell Tryptase Controls Paracellular Permeability of the Intestine
Bibliographic record
Abstract
Tight junctions between intestinal epithelial cells prevent ingress of luminal macromolecules and bacteria and protect against inflammation and infection. During stress and inflammation, mast cells mediate increased mucosal permeability by unknown mechanisms. We hypothesized that mast cell tryptase cleaves protease-activated receptor 2 (PAR2) on colonocytes to increase paracellular permeability. Colonocytes expressed PAR2 mRNA and responded to PAR2 agonists with increased [Ca2+]i. Supernatant from degranulated mast cells increased [Ca2+]i in colonocytes, which was prevented by a tryptase inhibitor, and desensitized responses to PAR2 agonist, suggesting PAR2 cleavage. When applied to the basolateral surface of colonocytes, PAR2 agonists and mast cell supernatant decreased transepithelial resistance, increased transepithelial flux of macromolecules, and induced redistribution of tight junction ZO-1 and occludin and perijunctional F-actin. When mast cells were co-cultured with colonocytes, mast cell degranulation increased paracellular permeability of colonocytes. This was prevented by a tryptase inhibitor. We determined the role of ERK1/2 and of beta-arrestins, which recruit ERK1/2 to PAR2 in endosomes and retain ERK1/2 in the cytosol, on PAR2-mediated alterations in permeability. An ERK1/2 inhibitor abolished the effects of PAR2 agonist on permeability and redistribution of F-actin. Down-regulation of beta-arrestins with small interfering RNA inhibited PAR2-induced activation of ERK1/2 and suppressed PAR2-induced changes in permeability. Thus, mast cells signal to colonocytes in a paracrine manner by release of tryptase and activation of PAR2. PAR2 couples to beta-arrestin-dependent activation of ERK1/2, which regulates reorganization of perijunctional F-actin to increase epithelial permeability. These mechanisms may explain the increased epithelial permeability of the intestine during stress and inflammation.
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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 | 0.000 |
| 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".