Serum and glucocorticoid inducible kinase regulates hERG channels through inhibition of Nedd4–2 and activation of Rab11 recycling endosomes
Bibliographic record
Abstract
The human ether‐a‐go‐go‐related gene (hERG) encodes the rapidly activating delayed rectifier potassium channel (I Kr ). Loss of function of hERG channels due to mutations or drug‐induced blockage can cause long QT syndrome, leading to ventricular arrhythmias or sudden death. Physical or emotional stress is known to affect ion channel activity. Stress hormones such as cortisol regulate the expression of various genes including the serum‐ and glucocorticoid‐inducible kinase (SGK). Using patch clamp, Western blot, co‐immunoprecipitation, and immunocytochemistry methods, we demonstrate that SGK isoforms SGK1 and SGK3 increased hERG current ( I HERG ) and the expression level of mature hERG proteins. We recently showed that mature hERG channels are degraded by ubiquitin ligase Nedd4–2 via enhanced ubiquitination. We observed that SGK1 and SGK3 overexpression enhanced Nedd4–2 phosphorylation which is known to inhibit Nedd4–2 activity. However, removal of the Nedd4–2 effect on hERG channels by disrupting Nedd4–2 target motif in hERG reduced but did not eliminate the SGK‐induced increase in hERG expression. Additional disruption of Rab11 proteins led to a complete elimination of SGK‐mediated hERG increase. We conclude that SGK isoforms 1 and 3 enhance hERG stability by inhibiting Nedd4–2 ubiquitin ligase and activating Rab11 recycling endosomes. Supported by: Canadian Institutes of Health Research (CIHR)
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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".