Novel Role of the Serine Protease Inhibitor Elafin in Gluten-Related Disorders
Bibliographic record
Abstract
OBJECTIVES: Elafin, an endogenous serine protease inhibitor, modulates colonic inflammation. We investigated the role of elafin in celiac disease (CD) using human small intestinal tissues and in vitro assays of gliadin deamidation. We also investigated the potential beneficial effects of elafin in a mouse model of gluten sensitivity. METHODS: Epithelial elafin expression in the small intestine of patients with active CD, treated CD, and controls without CD was determined by immunofluorescence. Interaction of elafin with human tissue transglutaminase-2 (TG-2) was investigated in vitro. The 33-mer peptide, a highly immunogenic gliadin peptide, was incubated with TG-2 and elafin at different concentrations. The degree of deamidation of the 33-mer peptide was analyzed by liquid chromatography-mass spectrometry. Elafin was delivered to the intestine of gluten-sensitive mice using a recombinant Lactococcus lactis vector. Small intestinal barrier function, inflammation, proteolytic activity, and zonula occludens-1 (ZO-1) expression were assessed. RESULTS: Elafin expression in the small intestinal epithelium was lower in patients with active CD compared with control patients. In vitro, elafin significantly slowed the kinetics of the deamidation of the 33-mer peptide to its more immunogenic form. Treatment of gluten-sensitive mice with elafin delivered by the L. lactis vector normalized inflammation, improved permeability, and maintained ZO-1 expression. CONCLUSIONS: The decreased elafin expression in the small intestine of patients with active CD, the reduction of 33-mer peptide deamidation by elafin, coupled to the barrier enhancing and anti-inflammatory effects observed in gluten-sensitive mice, suggest that this molecule may have pathophysiological and therapeutic importance in gluten-related disorders.
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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".