Rhinovirus and Bacteria Synergistically Induce IL-17C Release from Human Airway Epithelial Cells To Promote Neutrophil Recruitment
Bibliographic record
Abstract
Abstract Virus–bacteria coinfections are associated with more severe exacerbations and increased risk of hospital readmission in patients with chronic obstructive pulmonary disease (COPD). The airway epithelium responds to such infections by releasing proinflammatory and antimicrobial cytokines, including IL-17C. However, the regulation and role of IL-17C is not well understood. In this study, we examine the mechanisms regulating IL-17C production and its potential role in COPD exacerbations. Human bronchial epithelial cells (HBE) obtained from normal, nontransplanted lungs or from brushings of nonsmokers, healthy smokers, or COPD patients were exposed to bacteria and/or human rhinovirus (HRV). RNA and protein were collected for analysis, and signaling pathways were assessed with pharmacological agonists, inhibitors, or small interfering RNAs. HBE were also stimulated with IL-17C to assess function. HRV–bacterial coinfections synergistically induced IL-17C expression. This induction was dependent on HRV replication and required NF-κB–mediated signaling. Synergy was lost in the presence of an inhibitor of the p38 MAP kinase pathway. HBE exposed to IL-17C show increased gene expression of CXCL1, CXCL2, NFKBIZ, and TFRC, and release CXCL1 protein, a neutrophil chemoattractant. Knockdown of IL-17C significantly reduced induction of CXCL1 in response to HRV–bacterial coinfection as well as neutrophil chemotaxis. HBE from healthy smokers release less IL-17C than cells from nonsmokers, but cells from COPD patients release significantly more IL-17C compared with either nonsmokers or healthy smokers. These data suggest that IL-17C may contribute to microbial-induced COPD exacerbations by promoting neutrophil recruitment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".