IL‐22 increases CFTR expression and host defence capacity of airway epithelial cells
Bibliographic record
Abstract
Interleukin (IL)‐22 is a pleiotropic cytokine which predominately targets epithelial cells. IL‐22 has been implicated in increasing epithelial host defence via a number of different mechanisms and we hypothesized its IL‐22 application would stimulate several protective mechanisms in airway epithelial cells. Using the model human airway bronchial epithelial cell line 16HBEo− we investigated the ability of IL‐22 to increase CFTR expression, and determined that treatment for 24 hours increased CFTR gene and protein expression, resulting in increased CFTR activity measured via iodide efflux assays. Furthermore, IL‐22 increased mRNA expression for the anti‐microbial peptides human beta‐defensin‐2 (hDB‐2) and neutrophil gelatinase‐associated lipocalin (NGAL), but had no effect on expression of the cathelicidin LL‐37. Additionally, we investigated the ability of IL‐22 to enhance epithelial repair using a mechanical wounding model, and determined that its presence significantly enhanced would healing in this model. IL‐22 application was associated with increased STAT‐3 phosphorylation. This is the first report of IL‐22 acting to increase CFTR expression and functionality, which together with its ability to increase anti‐microbial peptide expression and would healing ability, confirm its role as a potentially important factor in the airway epithelial host defence and repair. CFCanada and NSERC.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".