Differential Regulation of hERG Current and Expression by Activation of Protein Kinase C Using Phorbol Ester Treatment
Bibliographic record
Abstract
The human ether-à-go-go-related gene (hERG) encodes the pore-forming alpha subunit of the channel that conducts the rapidly activating delayed rectifier potassium current (IKr) in the heart. Reductions in IKr cause long QT syndrome (LQTS), which predisposes individuals to potentially fatal arrhythmias that can be triggered by stress. One potential link between stress and hERG function is protein kinase C (PKC) activation. However, PKC regulation of hERG is complex and seemingly conflicting results have been reported. In the present study, both acute and chronic effects of PKC activation were investigated using phorbol 12-myristate 13-acetate (PMA) on hERG channels expressed in human embryonic kidney (HEK) 293 cells. Western blot analyses demonstrate that chronic PKC activation increases expression of intracellular and membrane-bound hERG protein. However, the increased channel abundance is accompanied by a decrease in hERG current (IhERG) after chronic PMA treatment. Furthermore, patch clamp data reveal that acute PKC activation reduces IhERG, and this effect is dependent on the presence of the N-terminus of the channel. Upon truncation of the N-terminus of hERG, chronic activation of PKC increases both hERG protein expression and current. The increase in hERG protein is partially mediated by reduced degradation of mature hERG channels. This results from increased phosphorylation of neural precursor cell expressed developmentally down-regulated protein 4 subtype 2 (Nedd4-2), an E3 ubiquitin ligase that mediates hERG degradation. These findings demonstrate that PKC regulates hERG in a balanced manner, increasing expression while decreasing current.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".