Role of IL-17A, IL-17F, and the IL-17 Receptor in Regulating Growth-Related Oncogene-α and Granulocyte Colony-Stimulating Factor in Bronchial Epithelium: Implications for Airway Inflammation in Cystic Fibrosis
Bibliographic record
Abstract
IL-17R signaling is critical for pulmonary neutrophil recruitment and host defense against Gram-negative bacteria through the coordinated release of G-CSF and CXC chemokine elaboration. In this study, we show that IL-17R is localized to basal airway cells in human lung tissue, and functional IL-17R signaling occurs on the basolateral surface of human bronchial epithelial (HBE) cells. IL-17A and IL-17F were potent inducers of growth-related oncogene-alpha and G-CSF in HBE cells, and significant synergism was observed with TNF-alpha largely due to signaling via TNFRI. The activities of both IL-17A and IL-17F were blocked by a specific anti-IL-17R Ab, but only IL-17A was blocked with a soluble IL-17R, suggesting that cell membrane IL-17R is required for signaling by both IL-17A and IL-17F. Because IL-17A and IL-17F both regulate lung neutrophil recruitment, we measured these molecules as well as the proximal regulator IL-23p19 in the sputum of patients with cystic fibrosis (CF) undergoing pulmonary exacerbation. We found significantly elevated levels of these molecules in the sputum of patients with CF who were colonized with Pseudomonas aeruginosa at the time of pulmonary exacerbation, and the levels declined with therapy directed against P. aeruginosa. IL-23 and the downstream cytokines IL-17A and IL-17F are critical molecules for proinflammatory gene expression in HBE cells and are likely involved in the proinflammatory cytokine network involved with CF pathogenesis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".