The Human Ether-a-go-go-related Gene (hERG) Potassium Channel Represents an Unusual Target for Protease-mediated Damage
Bibliographic record
Abstract
The human ether-a-go-go-related gene (hERG) encodes the pore-forming subunit of the rapidly activating delayed rectifier potassium channel (IKr), which is important for cardiac repolarization. Dysfunction of hERG causes long QT syndrome and sudden death, which occur in patients with cardiac ischemia. Cardiac ischemia is also associated with activation, up-regulation, and secretion of various proteolytic enzymes. Here, using whole-cell patch clamp and Western blotting analysis, we demonstrate that the hERG/IKr channel was selectively cleaved by the serine protease, proteinase K (PK). Using molecular biology techniques including making a chimeric channel between protease-sensitive hERG and insensitive human ether-a-go-go (hEAG), as well as application of the scorpion toxin BeKm-1, we identified that the S5-pore linker of hERG is the target domain for proteinase K cleavage. To investigate the physiological relevance of the unique susceptibility of hERG to proteases, we show that cardiac ischemia in a rabbit model was associated with a reduction in mature ERG expression and an increase in the expression of several proteases, including calpain. Using cell biology approaches, we found that calpain-1 was actively released into the extracellular milieu and cleaved hERG at the S5-pore linker. Using protease cleavage-predicting software and site-directed mutagenesis, we identified that calpain-1 cleaves hERG at position Gly-603 in the S5-pore linker of hERG. Clarification of protease-mediated damage of hERG extends our understanding of hERG regulation. Damage of hERG mediated by proteases such as calpain may contribute to ischemia-associated QT prolongation and sudden cardiac death.
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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".