Down-regulation of miR-1/miR-133 Contributes to Re-expression of Pacemaker Channel Genes HCN2 and HCN4 in Hypertrophic Heart*
Post-publication record
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Bibliographic record
Abstract
Cardiac hypertrophy is characterized by electrical remolding with increased risk of arrhythmogenesis. Enhanced abnormal automaticity of ventricular cells contributes critically to hypertrophic arrhythmias. The pacemaker current I(f), carried by the hyperpolarization-activated channels encoded mainly by the HCN2 and HCN4 genes in the heart, plays an important role in determining cardiac automaticity. Their expressions reportedly increase in hypertrophic and failing hearts, contributing to arrhythmogenesis under these conditions. We performed a study on post-transcriptional regulation of expression of HCN2 and HCN4 genes by microRNAs. We experimentally established HCN2 as a target for repression by the muscle-specific microRNAs miR-1 and miR-133 and established HCN4 as a target for miR-1 only. We unraveled robust increases in HCN2 and HCN4 protein levels in a rat model of left ventricular hypertrophy and in angiotensin II-induced neonatal ventricular hypertrophy. The up-regulation of HCN2/HCN4 was accompanied by pronounced reduction of miR-1/miR-133 levels. Forced expression of miR-1/miR-133 by transfection prevented overexpression of HCN2/HCN4 in hypertrophic cardiomyocytes. The serum-responsive factor protein level was found significantly decreased in hypertrophic hearts, and silencing of this protein by RNA interference resulted in increased levels of miR-1/miR-133 and concomitant increases in HCN2 and HCN4 protein levels. We conclude that down-regulation of miR-1 and miR-133 expression contributes to re-expression of HCN2/HCN4 and thereby the electrical remodeling process in hypertrophic hearts. Our study also sheds new light on the cellular function and pathological role of miR-1/miR-133 in the heart.
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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) | 0.001 | 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".