Extracellular K+ concentration controls cell surface density of IKr in rabbit hearts and of the HERG channel in human cell lines
Bibliographic record
Abstract
Although the modulation of ion channel gating by hormones and drugs has been extensively studied, much less is known about how cell surface ion channel expression levels are regulated. Here, we demonstrate that the cell surface density of both the heterologously expressed K+ channel encoded by the human ether-a-go-go-related gene (HERG) and its native counterpart, the rapidly activating delayed rectifier K+ channel (IKr), in rabbit hearts in vivo is precisely controlled by extracellular K+ concentration ([K+]o) within a physiologically relevant range. Reduction of [K+]o led to accelerated internalization and degradation of HERG channels within hours. Confocal analysis revealed colocalization between HERG and ubiquitin during the process of HERG internalization, and overexpression of ubiquitin facilitated HERG degradation under low [K+]o. The HERG channels colocalized with a marker of multivesicular bodies during internalization, and the internalized HERG channels were targeted to lysosomes. Our results provide the first evidence to our knowledge that the cell surface density of a voltage-gated K+ channel, HERG, is regulated by a biological factor, extracellular K+. Because hypokalemia is known to exacerbate long QT syndrome (LQTS) and Torsades de pointes tachyarrhythmias, our findings provide a potential mechanistic link between hypokalemia and LQTS.
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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".