<i>CFTR</i> Gene Regulation in Human Pancreatic Duct, Bile Duct and Sweat Gland Epithelial Cells
Bibliographic record
Abstract
Epithelial cells at many sites in the body are affected by the inherited disorder cystic fibrosis (CF). The lung was the major focus of research until recently, when effective therapeutics became available for most people with CF. There is now renewed interest in CF aetiology in other locations in the body, where the regulatory mechanisms for the CF transmembrane conductance regulator (CFTR) gene are less well-characterised. The definition of the genomic elements controlling CFTR expression and their associated transcription factors is important for the design of gene-based therapies. Here we identify the cis-regulatory elements (CREs) associated with the CFTR locus by open chromatin mapping in pancreatic adenocarcinoma cell lines, primary human pancreatic and bile duct (cholangiocyte) organoids and single cells from tissues, as well as sweat gland coil and duct epithelial cells. We show that broadly these cell types use a combination of CREs that were characterised previously either in airway or intestinal epithelial cells, though not occurring together in these two cell lineages. Moreover, the chromatin structure of the CFTR locus in pancreatic cell lines is consistent with earlier models. We also use bioinformatic tools to predict the transcription factor network in these rare cell lineages from open chromatin peaks genome-wide.
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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".