Ionocyte-Specific Regulation of Cystic Fibrosis Transmembrane Conductance Regulator
Bibliographic record
Abstract
Abstract CFTR (cystic fibrosis transmembrane conductance regulator) is a tightly regulated anion channel that mediates chloride and bicarbonate conductance in many epithelia and in other tissues, but whether its regulation varies depending on the cell type has not been investigated. Epithelial CFTR expression is highest in rare cells called ionocytes. We studied CFTR regulation in control and ionocyte-enriched cultures by transducing bronchial basal cells with adenoviruses that encode only eGFP or FOXI1 (forkhead box I1) + eGFP as separate polypeptides. FOXI1 dramatically increased the number of transcripts for ionocyte markers ASCL3 (Achaete-Scute Family BHLH Transcription Factor 3), BSND, ATP6V1G3, ATP6V0D2, KCNMA1, and CFTR without altering those for secretory (SCGB1A1), basal (KRT5, KRT6, TP63), goblet (MUC5AC), or ciliated (FOXJ1) cells. The number of cells displaying strong FOXI1 expression was increased 7-fold, and there was no evidence for a broad increase in background immunofluorescence. Total CFTR mRNA and protein levels increased 10-fold and 2.5-fold, respectively. Ionocyte-enriched cultures displayed elevated basal current, increased adenylyl cyclase 5 expression, and tonic suppression of CFTR activity by the phosphodiesterase PDE1C, which has not been shown previously to regulate CFTR activity. The results indicate that CFTR regulation depends on cell type and identifies PDE1C as a potential target for therapeutics that aim to increase CFTR function specifically in ionocytes.
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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 | 0.001 |
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".