Regulated Expression of the Human CFTR Gene in Epithelial Cells
Bibliographic record
Abstract
We developed an epithelium-specific, inducible cystic fibrosis transmembrane conductance regulator (CFTR) expression system. In this system we used a human cytokeratin 18 expression cassette to drive epithelium-specific expression of the reverse tetracycline transactivator (rtTA), which turns on CFTR expression from a Tet-inducible promoter in the presence of doxycycline. CFTR expression was monitored by reverse-transcription polymerase chain reaction, immunostaining, and Western blotting. We confirmed that protein expression was dose-dependent in double stable transfected cell lines, with no detectable protein in the absence of doxycycline. However, low levels of CFTR mRNA could be detected in the uninduced state. When clones capable of inducing high levels of CFTR expression were analyzed, we observed a decrease in cell proliferation, consistent with reports in other cell lines (NIH3T3 and BTS). We generated transgenic mice expressing rtTA from the K18 expression cassette and demonstrated that the system retained its tissue specificity for lacZ reporter expression in vivo. When mice were induced with doxycycline, high levels of expression were found in the trachea, upper bronchi, and submucosal glands. Therefore, this inducible system can improve our understanding of the role of CFTR in the lung and should help in the design of safe and effective CF therapies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".