Airway surface hyperviscosity and defective mucociliary transport by IL-17/TNF-α are corrected by β-adrenergic stimulus
Bibliographic record
Abstract
The fluid covering the surface of airway epithelia represents a first barrier against pathogens. The chemical and physical properties of the airway surface fluid are controlled by the activity of ion channels and transporters. In cystic fibrosis (CF), loss of CFTR chloride channel function causes airway surface dehydration, bacterial infection, and inflammation. We investigated the effects of IL-17A plus TNF-α, 2 cytokines with relevant roles in CF and other chronic lung diseases. Transcriptome analysis revealed a profound change with upregulation of several genes involved in ion transport, antibacterial defense, and neutrophil recruitment. At the functional level, bronchial epithelia treated in vitro with the cytokine combination showed upregulation of ENaC channel, ATP12A proton pump, ADRB2 β-adrenergic receptor, and SLC26A4 anion exchanger. The overall result of IL-17A/TNF-α treatment was hyperviscosity of the airway surface, as demonstrated by fluorescence recovery after photobleaching (FRAP) experiments. Importantly, stimulation with a β-adrenergic agonist switched airway surface to a low-viscosity state in non-CF but not in CF epithelia. Our study suggests that CF lung disease is sustained by a vicious cycle in which epithelia cannot exit from the hyperviscous state, thus perpetuating the proinflammatory airway surface condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".