Human «ether-a-go-go» related gene trafficking is dependent on a cytosolic chaperone network
Bibliographic record
Abstract
Long QT Syndrome is a cardiac disorder associated with ventricular arrhythmias that can lead to syncope and sudden death. Loss of function mutations in the human ether-a-go-go related gene (hERG/KCNH2) potassium channel underlie type 2 long-QT syndrome (LQT2). Most often this loss of function is a consequence of defective trafficking of hERG mutants to the cell surface, with channel retention and degradation at the endoplasmic reticulum. This study began with a proteomics screen conducted to search for hERG-interacting proteins that were important for its folding, trafficking, and degradation. The resulting list of putative interactors was huge and included several known components of the cytosolic chaperone network which became the focus of this work. Among them were Hsc70 (70 kDa heat shock cognate protein), Hsp90 (90 kDa heat shock protein), DJA1, Hop (Hsp-organizing protein) and Bag-2 (BCL-associated athanogene 2) as well as the membrane-bound FKBP38 (38 kDa FK506-binding protein; FKBP8). We first demonstrate that FKBP38 immunoprecipitates and co-localizes with hERG in our cellular system and that siRNA knockdown of FKBP38 causes a reduction in hERG trafficking. Next, we identify DJA1 (DNAJA1) and DJA2 (DNAJA2) as key modulators of hERG degradation. Overexpression of the DJAs reduces hERG trafficking efficiency; an effect eliminated by the proteasomal inhibitor lactacystin or with DJA mutants lacking their J domains essential for Hsc70/Hsp70 activation. Both DJA1 and DJA2 cause a decrease in the amount of hERG complexed with Hsc70, indicating a preferential degradation of the complex. Similar effects were observed with the E3 ubiquitin ligase CHIP (C terminus of Hsc70 interacting protein). Finally, we investigate the specific role that these chaperones play in hERG folding. Development of a limited proteolysis assay is used to determine the folding state of hERG as well as its chaperone-dependence. Our results suggest that only subtle differences
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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".