Electrophysiological abnormalities associated with a <i>CACNA1D</i> variant are rescued by AAV6-Cav1.3-C-terminus gene therapy in patient-iPSC-CMs
Bibliographic record
Abstract
Abstract Inherited arrhythmia syndromes are caused by genetic variants that alter cardiac ion channel function. We investigated a complex presentation in a pediatric patient with ventricular tachycardia and conduction abnormalities, harboring a de novo CACNA1D (c.3786G>T) variant, and two inherited variants, the SCN5A (c.2618C>G), and a DSP desmosome (c.1582C>G). The CACNA1D variant, which encodes Cav1.3 L-type calcium channel is the focus of this study, because the C-terminus fragment of Cav1.3 has recently been identified as a transcription auto-enhancer of its own gene and able to prevent arrhythmic events in a mouse model of ischemic heart failure. Leveraging this intrinsic property, we hypothesized that the Cav1.3-C-terminus could reverse the arrhythmic events associated with the CACNA1D variant. Patch-clamp and optical mapping experiments demonstrated a loss of Cav1.3 function, characterized by reduced L-type calcium current densities, and decrease of conduction velocity, leading to inducible re-entrant arrhythmias in human induced pluripotent stem cell-cardiomyocytes (hiPSC-CMs). RNA sequencing confirmed this loss-of-function via the downregulation CACNA1D gene expression. Interestingly, Cav1.3-C-terminus treatment of hiPSC-CMs successfully normalized Cav1.3 gene expression, restored calcium currents, and conduction velocity, and prevented the susceptibility to arrhythmias. These findings highlight the electrophysiological consequences resulting from a de novo Cav1.3 variant and demonstrate an important transcriptional role of Cav1.3-C-terminus as a transcriptional regulator and as a promising therapeutic tool to restore normal electrical properties in patients with calcium channels loss of function.
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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 | 0.001 |
| 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 | 0.001 | 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".