Multi-pathway approach for the correction of CF
Bibliographic record
Abstract
Cystic fibrosis (CF) is an early onset disease characterized by a defect in the apical chloride channel, cystic fibrosis transmembrane conductance regulator (CFTR). The most common disease causing mutation is a 3 base pair deletion resulting in loss of Phe 508 (ΔF508), which leads to misfolding and efficient endoplasmic reticulum associated degradation (ERAD) of the protein, a hallmark of misfolding diseases. Many efforts have been centered on the idea of correcting ΔF508 by directly targeting the ΔF508 channel with small molecules (pharmacological chaperones). More recently, global alterations of the cellular proteostasis environment by small molecule approaches or biologics including siRNA to key components of protein folding environments have been successful in correcting many misfolding diseases, supporting a role for a multi-pathway approach in the correction of these pathologies. We now show that correction of ΔF508 stability and function can be achieved by alteration of the multiple homeostasis environments of the cell. Treatment of cells with select modifiers of homeostasis environments leads to an increase in the stability and function of ΔF508 in both lung cells expressing ΔF508 as a transgene as well as in human lung primary cells from a ΔF508 homozygous patient. The level of correction observed in the latter is thought to be corrective for CF. These data lead us to suggest that correction of ΔF508 will require modification of numerous CFTR intersecting pathways in order to regain stability and function in the native tissue environment through modulation of cellular homeostasis pathways. DMH is supported by a fellowship from the Canadian Cystic Fibrosis Foundation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.012 |
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".