Interleukin-17A modulates human airway epithelial responses to human rhinovirus infection
Bibliographic record
Abstract
Human rhinovirus (HRV) infections are associated with exacerbations of asthma and chronic obstructive pulmonary disease that are characterized by a selective neutrophil infiltration. IL-17A, a cytokine derived primarily from activated T cells, has been linked to neutrophilic inflammation of the airways. We hypothesized that IL-17A alters the response of HRV-infected epithelial cells to modulate airway inflammatory cell populations. IL-17A synergistically enhanced HRV-16-induced epithelial production of the neutrophil chemoattractant, IL-8, as well as human beta-defensin-2 (HBD-2), a chemoattractant for immature dendritic cells and memory T cells, but suppressed viral production of the eosinophil chemoattractant, RANTES. These effects were not due to alterations of viral uptake or replication by IL-17A. The synergy between HRV-16 and IL-17A for IL-8 protein production was both dose- and time-dependent. IL-8 induction by IL-17A or HRV-16, alone and in combination, was reduced by inhibitors of the p38 and p44/42 MAPK pathways. By contrast, induction of HBD-2 depended on the activation of the p38 and JNK pathways. The ability of IL-17A to synergistically enhance HRV-induced IL-8 is mediated posttranscriptionally, since IL-8 promoter activation by the combination of the two stimuli was merely additive, whereas the combination of IL-17A and HRV-16 led to stabilization of IL-8 mRNA. Similarly, stimulation of HBD-2 promoter constructs by the combination of IL-17A and HRV-16 was no more than the sum of the individual responses. Further studies are needed to examine HBD-2 mRNA stability. Taken together, these data represent the first demonstration that IL-17A can modify epithelial responses to HRV in a manner that would be expected to favor the recruitment of neutrophils, immature dendritic cells, and memory T cells to the airways.
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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) | 0.001 | 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".