Decreasing Poly(ADP-Ribose) Polymerase Activity Restores ΔF508 CFTR Trafficking
Bibliographic record
Abstract
Most cystic fibrosis is caused by mutations in CFTR that prevent its trafficking from the ER to the plasma membrane and is associated with exaggerated inflammation, altered metabolism, and diminished responses to oxidative stress. PARP-1 is activated by oxidative stress and causes energy depletion and cell dysfunction. Inhibition of this enzyme protects against excessive inflammation and recent studies have also implicated it in intracellular protein trafficking. We hypothesized that PARP-1 activity is altered in CF and affects trafficking and function of the most common CF mutant ΔF508 CFTR. Indeed, PARP-1 activity was 2.9-fold higher in CF (ΔF508/ΔF508) human bronchial epithelial primary cells than in non-CF cells, and similar results were obtained by comparing CF vs. non-CF bronchial epithelial cell lines (2.5-fold higher in CFBE41o(-) vs. 16HBE14o(-), P < 0.002). A PARP-1 inhibitor (ABT-888, Veliparib) partially restored CFTR channel activity in CFBE41o(-) cells overexpressing ΔF508 CFTR. Similarly, reducing PARP-1 activity by 85% in ileum from transgenic CF mice (Cftr(tm1)Eur) partially rescued ΔF508 CFTR activity to 7% of wild type mouse levels, and similar correction (7.8%) was observed in vivo by measuring salivary secretion. Inhibiting PARP-1 with ABT-888 or siRNA partially restored ΔF508 CFTR trafficking in cell lines, and most ΔF508 CFTR was complex glycosylated when heterologously expressed in PARP-1(-/-) mouse embryonic fibroblasts. Finally, levels of the mature glycoform of CFTR were reduced by peroxynitrite, a strong activator of PARP-1. These results demonstrate that PARP-1 activity is increased in CF, and identify a novel pathway that could be targeted by proteostatic correctors of CFTR trafficking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".