Campylobacter jejuni disrupts polarized TLR9 signaling in colonic epithelial cells
Bibliographic record
Abstract
Campylobacter jejuni infection is associated with an increased risk of developing inflammatory bowel disease (IBD) via mechanisms that remain obscure. TLR9 signaling in response to bacterial DNA is essential to intestinal homeostasis, possibly by regulating the T helper (Th) 17‐cell line. IL‐25 is a known inhibitor of the proinflammatory Th17 pathway. Aim To assess whether C. jejuni contributes to the development of IBD by disrupting TLR9 signaling using a human colonic (T84) cell line and a DSS model of colitis. Results Apical application of a TLR9 agonist (ISS‐ODN, 5μg/mL) elicited a significant increase in transepithelial resistance across control T84 monolayers, indicating a tightening of the epithelial barrier. This response was lost in C. jejuni‐infected monolayers. Infected cells secreted significantly more IL‐8 in response to a basolaterally applied TLR9 agonist when compared to control cells. Real‐time PCR revealed a greater than 2 fold increase in TLR9 expression in human colonic biopsies exposed to C.jejuni. C57Bl/6 mice inoculated with 1 × 10 8 C.jejuni demonstrated an increase in occult blood score and a significant reduction in colonic IL‐25 levels following treatment with DSS compared to mice treated with C. jejuni or DSS alone. Conclusions C. jejuni may increase the risk of developing IBD by disrupting epithelial TLR9 signaling and reducing IL‐25 levels. Funded by CAG‐CIHR‐CCFC.
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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 | 0.001 |
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".