Interaction between the Cardiac Rapidly (IKr) and Slowly (IKs) Activating Delayed Rectifier Potassium Channels Revealed by Low K+-induced hERG Endocytic Degradation
Bibliographic record
Abstract
Cardiac repolarization is controlled by the rapidly (I(Kr)) and slowly (I(Ks)) activating delayed rectifier potassium channels. The human ether-a-go-go-related gene (hERG) encodes I(Kr), whereas KCNQ1 and KCNE1 together encode I(Ks). Decreases in I(Kr) or I(Ks) cause long QT syndrome (LQTS), a cardiac disorder with a high risk of sudden death. A reduction in extracellular K(+) concentration ([K(+)](o)) induces LQTS and selectively causes endocytic degradation of mature hERG channels from the plasma membrane. In the present study, we investigated whether I(Ks) compensates for the reduced I(Kr) under low K(+) conditions. Our data show that when hERG and KCNQ1 were expressed separately in human embryonic kidney (HEK) cells, exposure to 0 mM K(+) for 6 h completely eliminated the mature hERG channel expression but had no effect on KCNQ1. When hERG and KCNQ1 were co-expressed, KCNQ1 significantly delayed 0 mM K(+)-induced hERG reduction. Also, hERG degradation led to a significant reduction in KCNQ1 in 0 mM K(+) conditions. An interaction between hERG and KCNQ1 was identified in hERG+KCNQ1-expressing HEK cells. Furthermore, KCNQ1 preferentially co-immunoprecipitated with mature hERG channels that are localized in the plasma membrane. Biophysical and pharmacological analyses indicate that although hERG and KCNQ1 closely interact with each other, they form distinct hERG and KCNQ1 channels. These data extend our understanding of delayed rectifier potassium channel trafficking and regulation, as well as the pathology of 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".