Lack of Vitamin D Receptor Leads to Hyperfunction of Claudin-2 in Intestinal Inflammatory Responses
Bibliographic record
Abstract
Background: Vitamin D3 and vitamin D receptor (VDR) are involved in the pathogenesis of inflammatory bowel disease (IBD) and bacterial infection. Claudin-2 is a junction protein that mediates paracellular water transport in epithelia. Elevation of Claudin-2 is associated with active IBD. However, VDR involved in epithelial junctions under inflammation and infection remains largely unknown. We investigated the mechanisms on how VDR and Claudin-2 are related in inflamed states. Methods: Using cultured VDR-/- enteroids, human intestinal epithelial cells, VDR-/- mice with Salmonella- or DSS-colitis, and human IBD samples, we investigated the mechanisms how VDR and Claudin-2 are related in inflamed states. Results: After Salmonella infection had taken place, we observed significantly enhanced Claudin-2 and an increased bacterial invasion and translocation. A lack of VDR regulation led to a robust increase of Claudin-2 at the mRNA and protein levels post-infection. In DSS-treated VDR-/- mice, Claudin-2 was significantly increased. Location and quantification of Claudin-2 protein in the mouse colons treated with DSS also confirmed these results. Inflammatory cytokines were significantly higher in the serum and mRNA levels in intestine, which are known to increase Claudin-2. Furthermore, in inflamed intestine of ulcerative colitis patients, VDR expression was low and Claudin-2 was enhanced. Mechanistically, we identified the enhanced Claudin-2 promoter activity through the binding sites of NF-κB and STAT in inflamed VDR-/- cells. Conclusions: Our studies have identified a new role for intestinal epithelial VDR in regulating barrier functions in the context of infection 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".