Utility of Zebrafish Models of Acquired and Inherited Long QT Syndrome
Bibliographic record
Abstract
Long-QT Syndrome (LQTS) is a cardiac electrical disorder, distinguished by irregular heart rates and sudden death. Accounting for ∼40% of cases, LQTS Type 2 (LQTS2), is caused by defects in the Kv11.1 (hERG) potassium channel that is critical for cardiac repolarization. Drug block of hERG channels or dysfunctional channel variants can result in acquired or inherited LQTS2, respectively, which are typified by delayed repolarization and predisposition to lethal arrhythmia. As such, there is significant interest in clear identification of drugs and channel variants that produce clinically meaningful perturbation of hERG channel function. While toxicological screening of hERG channels, and phenotypic assessment of inherited channel variants in heterologous systems is now commonplace, affordable, efficient, and insightful whole organ models for acquired and inherited LQTS2 are lacking. Recent work has shown that zebrafish provide a viable in vivo or whole organ model of cardiac electrophysiology. Characterization of cardiac ion currents and toxicological screening work in intact embryos, as well as adult whole hearts, has demonstrated the utility of the zebrafish model to contribute to the development of therapeutics that lack hERG-blocking off-target effects. Moreover, forward and reverse genetic approaches show zebrafish as a tractable model in which LQTS2 can be studied. With the development of new tools and technologies, zebrafish lines carrying precise channel variants associated with LQTS2 have recently begun to be generated and explored. In this review, we discuss the present knowledge and questions raised related to the use of zebrafish as models of acquired and inherited LQTS2. We focus discussion, in particular, on developments in precise gene-editing approaches in zebrafish to create whole heart inherited LQTS2 models and evidence that zebrafish hearts can be used to study arrhythmogenicity and to identify potential anti-arrhythmic compounds.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".