Endocannabinoids enhance hKV7.1/KCNE1 channel function and shorten the cardiac action potential and QT interval
Bibliographic record
Abstract
Background Genotype-positive patients who suffer from the cardiac channelopathy Long QT Syndrome (LQTS) may display a spectrum of clinical phenotypes, with often unknown causes. Therefore, there is a need to identify factors influencing disease severity to move towards an individualized clinical management of LQTS. One possible factor influencing the disease phenotype is the endocannabinoid system, which has emerged as a modulator of cardiovascular function. In this study, we aim to elucidate whether endocannabinoids target the cardiac voltage-gated potassium channel K V 7.1/KCNE1, which is the most frequently mutated ion channel in LQTS. Methods We used two-electrode voltage clamp, molecular dynamics simulations and the E4031 drug-induced LQT2 model of ex-vivo guinea pig hearts. Findings We found a set of endocannabinoids that facilitate channel activation, seen as a shifted voltage-dependence of channel opening and increased overall current amplitude and conductance. We propose that negatively charged endocannabinoids interact with known lipid binding sites at positively charged amino acids on the channel, providing structural insights into why only specific endocannabinoids modulate K V 7.1/KCNE1. Using the endocannabinoid ARA-S as a prototype, we show that the effect is not dependent on the KCNE1 subunit or the phosphorylation state of the channel. In guinea pig hearts, ARA-S was found to reverse the E4031-prolonged action potential duration and QT interval. Interpretation We consider the endocannabinoids as an interesting class of hK V 7.1/KCNE1 channel modulators with putative protective effects in LQTS contexts. Funding ERC (No. 850622), Canadian Institutes of Health Research, Canada Research Chairs and Compute Canada, Swedish National Infrastructure for Computing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".