Compounds that correct F508del-CFTR trafficking can also correct other protein trafficking diseases: an in vitro study using cell lines
Bibliographic record
Abstract
BACKGROUND: Many genetic diseases are due to defects in protein trafficking where the mutant protein is recognized by the quality control systems, retained in the endoplasmic reticulum (ER), and degraded by the proteasome. In many cases, the mutant protein retains function if it can be trafficked to its proper cellular location. We have identified structurally diverse correctors that restore the trafficking and function of the most common mutation causing cystic fibrosis, F508del-CFTR. Most of these correctors do not act directly as ligands of CFTR, but indirectly on other pathways to promote folding and correction. We hypothesize that these proteostasis regulators may also correct other protein trafficking diseases. METHODS: To test our hypothesis, we used stable cell lines or transient transfection to express 2 well-studied trafficking disease mutations in each of 3 different proteins: the arginine-vasopressin receptor 2 (AVPR2, also known as V2R), the human ether-a-go-go-related gene (KCNH2, also known as hERG), and finally the sulfonylurea receptor 1 (ABCC8, also known as SUR1). We treated cells expressing these mutant proteins with 9 structurally diverse F508del-CFTR correctors that function through different cellular mechanisms and assessed whether correction occurred via immunoblotting and functional assays. Results were deemed significantly different from controls by a one-way ANOVA (p < 0.05). RESULTS: Here we show that F508del-CFTR correctors RDR1, KM60 and KM57 also correct some mutant alleles of other protein trafficking diseases. We also show that one corrector, the cardiac glycoside ouabain, was found to alter the glycosylation of all mutant alleles tested. CONCLUSIONS: Correctors of F508del-CFTR trafficking might have broader applications to other protein trafficking diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".