Tumor Necrosis Factor-α Triggers Mucus Production in Airway Epithelium through an IκB Kinase β-dependent Mechanism
Bibliographic record
Abstract
Excessive mucus production by airway epithelium is a major characteristic of a number of respiratory diseases, including asthma, chronic bronchitis, and cystic fibrosis. However, the signal transduction pathways leading to mucus production are poorly understood. Here we examined the potential role of IkappaB kinase beta (IKKbeta) in mucus synthesis in vitro and in vivo. Tumor necrosis factor-alpha (TNF-alpha) or transforming growth factor-alpha stimulation of human epithelial cells resulted in mucus secretion as measured by MUC5AC mRNA and protein. TNF-alpha stimulation induced IKKbeta-dependent p65 nuclear translocation, mucus synthesis, and production of cytokines from epithelial cells. TNF-alpha, but not transforming growth factor-alpha, induced mucus production dependent on IKKbeta-mediated NF-kappaB activation. In vivo, TNF-alpha induced NF-kappaB as determined by whole mouse body bioluminescence. This activation was localized to the epithelium as revealed by LacZ staining in NF-kappaB-LacZ transgenic mice. TNF-alpha-induced mucus production in vivo could also be inhibited by administration into the epithelium of an IKKbeta dominant negative adenovirus. Taken together, our results demonstrated the important role of IKKbeta in TNF-alpha-mediated mucus production in airway epithelium in vitro and in vivo.
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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 | 0.001 |
| 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".