Regulation of normal and cystic fibrosis airway epithelial repair processes by TNF-α after injury
Bibliographic record
Abstract
Chronic infection and inflammation have been associated with progressive airway epithelial damage in patients with cystic fibrosis (CF). However, the effect of inflammatory products on the repair capacity of respiratory epithelia is unclear. Our objective was to study the regulation of repair mechanisms by tumor necrosis factor-α (TNF-α), a major component of inflammation in CF, in a model of mechanical wounding, in two bronchial cell lines, non-CF NuLi and CF CuFi. We observed that TNF-α enhanced the NuLi and CuFi repair rates. Chronic exposure (24-48 h) to TNF-α augmented this stimulation as well as the migration rate during repair. The cellular mechanisms involved in this stimulation were then evaluated. First, we discerned that TNF-α induced metalloproteinase-9 release, epidermal growth factor (EGF) shedding, and subsequent EGF receptor transactivation. Second, TNF-α-induced stimulation of the NuLi and CuFi wound-closure rates was prevented by GM6001 (metalloproteinase inhibitor), EGF antibody (to titrate secreted EGF), and EGF receptor tyrosine kinase inhibitors. Furthermore, we recently reported a relationship between the EGF response and K(+) channel function, both controlling bronchial repair. We now show that TNF-α enhances KvLQT1 and K(ATP) currents, while their inhibition abolishes TNF-α-induced repair stimulation. These results indicate that the effect of TNF-α is mediated, at least in part, through EGF receptor transactivation and K(+) channel stimulation. In contrast, cell proliferation during repair was slowed by TNF-α, suggesting that TNF-α could exert contrasting actions on repair mechanisms of CF airway epithelia. Finally, the stimulatory effect of TNF-α on airway wound repair was confirmed on primary airway epithelial cells, from non-CF and CF patients.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".