Different SUMO Paralogs Determine the Fate of WT and Mutant CFTRs: Biogenesis vs. Degradation
Bibliographic record
Abstract
ABSTRACT A pathway for CFTR degradation is initiated by Hsp27 which cooperates with Ubc9 and binds to the common F508del mutant to modify it with SUMO-2/3. These SUMO paralogs form poly-chains, which are recognized by the ubiquitin ligase, RNF4, for proteosomal degradation. Here, protein array analysis identified the SUMO E3, PIAS4, which increased WT and F508del CFTR biogenesis in CFBE airway cells. PIAS4 increased immature CFTR three-fold and doubled expression of mature CFTR, detected by biochemical and functional assays. In cycloheximide chase assays, PIAS4 slowed immature F508del degradation 3-fold and stabilized mature WT CFTR at the PM. PIAS4 knockdown reduced WT and F508del CFTR expression by 40-50%, suggesting a physiological role in CFTR biogenesis. PIAS4 modified F508del CFTR with SUMO-1 in vivo and reduced its conjugation to SUMO-2/3. These SUMO paralog specific effects of PIAS4 were reproduced in vitro using purified F508del NBD1 and SUMOylation reaction components. PIAS4 reduced endogenous ubiquitin conjugation to F508del CFTR by ~50%, and blocked the impact of RNF4 on mutant CFTR disposal. These findings indicate that different SUMO paralogs determine the fates of WT and mutant CFTRs, and they suggest that a paralog switch during biogenesis can direct these proteins to different outcomes: biogenesis vs. degradation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".