Pulmonary Ionocytes Regulate Airway Surface Liquid pH in Primary Human Bronchial Epithelial Cells
Bibliographic record
Abstract
Abstract Rationale Pulmonary ionocytes are a newly discovered airway epithelial cell type proposed to be a major contributor to cystic fibrosis (CF) lung disease based on observations they express the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel at a higher level than any other cell type in the airway epithelia. Moreover, genetically manipulated experimental models that lack ionocytes develop NaCl transport abnormalities and airway surface liquid (ASL) dehydration consistent with CF. However, no direct evidence indicates ionocytes engage in NaCl transport or contribute to ASL formation, questioning the relevance of ionocytes to CF lung disease. Objectives To determine the ion transport properties of pulmonary ionocytes and club cells in genetically intact healthy and CF airway epithelia. Methods We measured ion transport at the single-cell level using a self-referencing ion-selective microelectrode technique in primary human bronchial epithelial cell culture. Measurements and Main Results cAMP-stimulated non-CF ionocytes do not secrete Na+ or Cl− into the ASL, but rather modulate its pH by secreting bicarbonate via CFTR-linked Cl−/bicarbonate exchange. Non-CF club cells secrete Na+ and Cl− to the lumen side after cAMP stimulation. CF ionocytes and club cells do not transport ions in response to cAMP stimulation, but incubation with CFTR modulators elexacaftor/tezacaftor/ivacaftor restores transport properties. Conclusions We conclude that ionocytes do not contribute to ASL formation but regulate ASL pH. Club cells secrete the bulk of airway fluid. In CF, abnormal ionocyte and club cell function results in acidic and dehydrated ASL, causing reduced antimicrobial properties and mucociliary clearance.
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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 | 0.000 |
| 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".