Viral Inhibition of IL-1- and Neutrophil Elastase-Induced Inflammatory Responses in Bronchial Epithelial Cells
Bibliographic record
Abstract
Previously, we elucidated the intracellular mechanisms by which neutrophil elastase (NE) up-regulates inflammatory gene expression in bronchial epithelial cells. In this study, we examine the effects of both IL-1 and NE on inflammatory gene expression in 16HBE14o- bronchial epithelial cells and investigate approaches to abrogate these inflammatory responses. IL-1 induced IL-8 protein production in time- and dose-dependent fashions, an important observation given that IL-8 is a potent neutrophil chemoattractant and a key inflammatory mediator. IL-1 and NE were shown to activate the p38 MAPK pathway in 16HBE14o- cells. Western blot analysis demonstrated IL-1R-associated kinase 1 (IRAK-1) degradation in response to stimulation with both IL-1 and NE. In addition, the expression of dominant negative IRAK-1 (IRAK-1delta), IRAK-2delta, or IRAK-4delta inhibited IL-1- and NE-induced NF-kappaB-linked reporter gene expression. Dominant negative versions of the intracellular adaptor proteins MyD88 (MyD88delta) and MyD88 adaptor-like (Mal P/H) abrogated NE-induced NF-kappaB reporter gene expression. In contrast, only MyD88delta was found to inhibit IL-1-induced NF-kappaB reporter activity. We also investigated the vaccinia virus proteins, A46R and A52R, which have been shown to antagonize IL-1 signaling. Transfection with A46R or A52R cDNA inhibited IL-1- and NE-induced NF-kappaB and IL-8R gene expression and IL-8 protein production in primary and transformed bronchial epithelial cells. Furthermore, cytokine array studies demonstrated that IL-1 and NE can up-regulate the expression of IL-6, oncostatin M, epithelial cell-derived neutrophil activating peptide-78, growth-related oncogene family members, vascular endothelial growth factor, and GM-CSF, with induction of these proteins inhibited by the viral proteins. These findings identify vaccinia virus proteins as possible therapeutic agents for the manifestations of several inflammatory lung diseases.
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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) | 0.001 | 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".