Cystic Fibrosis Transmembrane Conductance Regulator Modulates Neurosecretory Function in Pulmonary Neuroendocrine Cell-Related Tumor Cell Line Models
Bibliographic record
Abstract
The pulmonary neuroendocrine cell (PNEC) system consists of solitary cells and distinctive cell clusters termed neuroepithelial bodies (NEB) localized in the airway epithelium. PNEC/NEB express a variety of bioactive substances, including amine (serotonin, 5HT) and neuropeptides. We have previously shown that NEB cells are O(2) sensors expressing nicotinamide adenine diphosphate oxidase complex and O(2) sensitive K(+) channel. Recently, we demonstrated expression of functional cystic fibrosis transmembrane conductance regulator (CFTR) and Cl(-) conductances in NEB cells of rabbit neonatal lung. Because PNEC/NEB are sparsely distributed and difficult to study in native lung, we investigated small-cell lung carcinoma (SCLC) and carcinoid tumor cell lines (tumor counterparts of normal PNEC/NEB) as models for PNEC/NEB. SCLC (H146, H345) and carcinoid (H727) cell lines express neuroendocrine cell markers, including chromogranin A, neural cell adhesion molecule (N-CAM), 5HT, and tryptophan hydroxylase. We report that H146, H345, and H727 express CFTR messenger RNA (reverse transcription polymerase chain reaction) and protein (immunoblotting) and possess functional CFTR Cl(-) conductance, demonstrated by an iodide efflux assay inhibitable by transfection with antisense CFTR. Using an immunoassay to quantitate 5HT secretion, we also show that downregulation of CFTR abolishes hypoxia-induced 5HT release, and reduces secretory response to high potassium. Our findings suggest that CFTR may modulate neurosecretory activity of PNEC/NEB possessing O(2) sensor function. We propose that these tumor cell lines may be useful models for investigating the role of CFTR in PNEC/NEB functions in health and disease.
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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 | 0.001 | 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".