Inhibition of Wnt/β‐catenin signaling upregulates Na<sub>v</sub>1.5 channels in Brugada syndrome <scp>iPSC</scp>‐derived cardiomyocytes
Bibliographic record
Abstract
Abstract The voltage‐gated Nav1.5 channels mediate the fast Na+ current (INa) in cardiomyocytes initiating action potentials and cardiac contraction. Downregulation of INa, as occurs in Brugada syndrome (BrS), causes ventricular arrhythmias. The present study investigated whether the Wnt/β‐catenin signaling regulates Nav1.5 in human‐induced pluripotent stem cell‐derived cardiomyocytes (iPSC‐CMs). In healthy male and female iPSC‐CMs, activation of Wnt/β‐catenin signaling by CHIR‐99021 reduced (p < 0.01) both Nav1.5 protein and SCN5A mRNA. In iPSC‐CMs from a BrS patient, both Nav1.5 protein and peak INa were reduced compared to those in healthy iPSC‐CMs. Treatment of BrS iPSC‐CMs with Wnt‐C59, a small‐molecule Wnt inhibitor, led to a 2.1‐fold increase in Nav1.5 protein (p = 0.0005) but surprisingly did not affect SCN5A mRNA (p = 0.146). Similarly, inhibition of Wnt signaling using shRNA‐mediated β‐catenin knockdown in BrS iPSC‐CMs led to a 4.0‐fold increase in Nav1.5, which was associated with a 4.9‐fold increase in peak INa but only a 2.1‐fold increase in SCN5A mRNA. The upregulation of Nav1.5 by β‐catenin knockdown was verified in iPSC‐CMs from a second BrS patient. This study demonstrated that Wnt/β‐catenin signaling inhibits Nav1.5 expression in both male and female human iPSC‐CMs, and inhibition of Wnt/β‐catenin signaling upregulates Nav1.5 in BrS iPSC‐CMs through both transcriptional and posttranscriptional mechanisms.
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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 | 0.001 |
| 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".