RNA Interference Screen to Identify Kinases That Suppress Rescue of ΔF508-CFTR*
Bibliographic record
Abstract
Cystic Fibrosis (CF) is an autosomal recessive disorder caused by mutations in the gene encoding the Cystic fibrosis transmembrane conductance regulator (CFTR). ΔF508-CFTR, the most common disease-causing CF mutant, exhibits folding and trafficking defects and is retained in the endoplasmic reticulum, where it is targeted for proteasomal degradation. To identify signaling pathways involved in ΔF508-CFTR rescue, we screened a library of endoribonuclease-prepared short interfering RNAs (esiRNAs) that target ∼750 different kinases and associated signaling proteins. We identified 20 novel suppressors of ΔF508-CFTR maturation, including the FGFR1. These were subsequently validated by measuring channel activity by the YFP halide-sensitive assay following shRNA-mediated knockdown, immunoblotting for the mature (band C) ΔF508-CFTR and measuring the amount of surface ΔF508-CFTR by ELISA. The role of FGFR signaling on ΔF508-CFTR trafficking was further elucidated by knocking down FGFRs and their downstream signaling proteins: Erk1/2, Akt, PLCγ-1, and FRS2. Interestingly, inhibition of FGFR1 with SU5402 administered to intestinal organoids (mini-guts) generated from the ileum of ΔF508-CFTR homozygous mice resulted in a robust ΔF508-CFTR rescue. Moreover, combination of SU5402 and VX-809 treatments in cells led to an additive enhancement of ΔF508-CFTR rescue, suggesting these compounds operate by different mechanisms. Chaperone array analysis on human bronchial epithelial cells harvested from ΔF508/ΔF508-CFTR transplant patients treated with SU5402 identified altered expression of several chaperones, an effect validated by their overexpression or knockdown experiments. We propose that FGFR signaling regulates specific chaperones that control ΔF508-CFTR maturation, and suggest that FGFRs may serve as important targets for therapeutic intervention for the treatment of CF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".