Restoration of CFTR function in patients with cystic fibrosis carrying the F508del-CFTR mutation
Bibliographic record
Abstract
Restoration of BECN1/Beclin 1-dependent autophagy and depletion of SQSTM1/p62 by genetic manipulation or autophagy-stimulatory proteostasis regulators, such as cystamine, have positive effects on mouse models of human cystic fibrosis (CF). These measures rescue the functional expression of the most frequent pathogenic CFTR mutant, F508del, at the respiratory epithelial surface and reduce lung inflammation in Cftr(F508del) homozygous mice. Cysteamine, the reduced form of cystamine, is an FDA-approved drug. Here, we report that oral treatment with cysteamine greatly reduces the mortality rate and improves the phenotype of newborn mice bearing the F508del-CFTR mutation. Cysteamine was also able to increase the plasma membrane expression of the F508del-CFTR protein in nasal epithelial cells from F508del homozygous CF patients, and these effects persisted for 24 h after cysteamine withdrawal. Importantly, this cysteamine effect after washout was further sustained by the sequential administration of epigallocatechin gallate (EGCG), a green tea flavonoid, both in vivo, in mice, and in vitro, in primary epithelial cells from CF patients. In a pilot clinical trial involving 10 F508del-CFTR homozygous CF patients, the combination of cysteamine and EGCG restored BECN1, reduced SQSTM1 levels and improved CFTR function from nasal epithelial cells in vivo, correlating with a decrease of chloride concentrations in sweat, as well as with a reduction of the abundance of TNF/TNF-alpha (tumor necrosis factor) and CXCL8 (chemokine [C-X-C motif] ligand 8) transcripts in nasal brushing and TNF and CXCL8 protein levels in the sputum. Altogether, these results suggest that optimal schedules of cysteamine plus EGCG might be used for the treatment of CF caused by the F508del-CFTR mutation.
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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) | 0.001 | 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 | 0.001 | 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".