Stem cell-derived cardiomyocytes expressing a dominant negative pacemaker HCN4 channel do not reduce the risk of graft-related arrhythmias
Bibliographic record
Abstract
Background Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) show tremendous promise for cardiac regeneration following myocardial infarction (MI), but their transplantation gives rise to transient ventricular tachycardia (VT) in large-animal MI models, representing a major hurdle to translation. Our group previously reported that these arrhythmias arise from a focal mechanism whereby graft tissue functions as an ectopic pacemaker; therefore, we hypothesized that hPSC-CMs engineered with a dominant negative form of the pacemaker ion channel HCN4 (dnHCN4) would exhibit reduced automaticity and arrhythmogenic risk following transplantation. Methods We used CRISPR/Cas9-mediated gene-editing to create transgenic dnHCN4 hPSC-CMs, and their electrophysiological behavior was evaluated in vitro by patch-clamp recordings and optical mapping. Next, we transplanted WT and homozygous dnHCN4 hPSC-CMs in a pig MI model and compared post-transplantation outcomes including the incidence of spontaneous arrhythmias and graft structure by immunohistochemistry. Results In vitro dnHCN4 hPSC-CMs exhibited significantly reduced automaticity and pacemaker funny current (If) density relative to wildtype (WT) cardiomyocytes. Following transplantation with either dnHCN4 or WT hPSC-CMs, all recipient hearts showed transmural infarct scar that was partially remuscularized by scattered islands of human myocardium. However, in contrast to our hypothesis, both dnHCN4 and WT hPSC-CM recipients exhibited frequent episodes of ventricular tachycardia (VT). Conclusions While genetic silencing of the pacemaker ion channel HCN4 suppresses the automaticity of hPSC-CMs in vitro, this intervention is insufficient to reduce VT risk post-transplantation in the pig MI model, implying more complex mechanism(s) are operational in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".