Alpha-1 Antitrypsin Mitigates the Inhibition of Airway Epithelial Cell Repair by Neutrophil Elastase
Bibliographic record
Abstract
Neutrophil elastase (NE) activity is associated with many destructive lung diseases and is a predictor for structural lung damage in early cystic fibrosis (CF), which suggests normal maintenance of airway epithelium is prevented by uninhibited NE. However, limited data exist on how the NE activity in airways of very young children with CF affects function of the epithelia. The aim of this study was to determine if NE activity could inhibit epithelial homeostasis and repair and whether any functional effect was reversible by antiprotease alpha-1 antitrypsin (α1AT) treatment. Viability, inflammation, apoptosis, and proliferation were assessed in healthy non-CF and CF pediatric primary airway epithelial cells (pAECnon-CF and pAECCF, respectively) during exposure to physiologically relevant NE. The effect of NE activity on pAECCF wound repair was also assessed. We report that viability after 48 hours was significantly decreased by 100 nM NE in pAECnon-CF and pAECCF owing to rapid cellular detachment that was accompanied by inflammatory cytokine release. Furthermore, both phenotypes initiated an apoptotic response to 100 nM NE, whereas ≥ 50 nM NE activity significantly inhibited the proliferative capacity of cultures. Similar concentrations of NE also significantly inhibited wound repair of pAECCF, but this effect was reversed by the addition of α1AT. Collectively, our results demonstrate free NE activity is deleterious for epithelial homeostasis and support the hypothesis that proteases in the airway contribute directly to CF structural lung disease. Our results also highlight the need to investigate antiprotease therapies in early CF disease in more detail.
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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".