High-content Functional Screen to Identify Proteins that Correct F508del-CFTR Function
Bibliographic record
Abstract
Cystic Fibrosis is caused by mutations in CFTR, with a deletion of a phenylalanine at position 508 (F508del-CFTR) representing the most common mutation. The F508del-CFTR protein exhibits a trafficking defect and is retained in the endoplasmic reticulum. Here we describe the development of a high-content screen based on a functional assay to identify proteins that correct the F508del-CFTR defect. Using a HEK293 MSR GripTite cell line that stably expresses F508del-CFTR, we individually co-expressed approximately 450 unique proteins fused to the Cl(-)-sensitive YFP(H148Q/I152L) mutant. We then tested correction of F508del-CFTR function by the CI(-)/l(-) exchange method following stimulation with forskolin/IBMX/genistein, using quantitative recordings in multiple individual cells with a high-content (high-throughput) Cellomics KSR imaging system. Using this approach, we identified several known and novel proteins that corrected F508del-CFTR function, including STAT1, Endothelin 1, HspA4, SAPK substrate protein 1, AP2M1, LGALS3/galectin-3, Trk-fused gene, Caveolin 2, PAP/REG3alpha, and others. The ability of these correctors to rescue F508del-CFTR trafficking was then validated by demonstrating their enhancement of maturation (appearance of band C) and by cell surface expression of F508del-CFTR bearing HA tag at the ectodomain using confocal microscopy and flow cytometry. These data demonstrate the utility of high-content analyses for identifying proteins that correct mutant CFTR and discover new proteins that stimulate this correction. This assay can also be utilized for RNAi screens to identify inhibitory proteins that block correction of F508del-CFTR, small molecule, and peptide screens.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".