Pro-survival function of MEF2 in cardiomyocytes is enhanced by β-blockers
Bibliographic record
Abstract
β1-Adrenergic receptor (β1-AR) stimulation increases apoptosis in cardiomyocytes through activation of cAMP/protein kinase A (PKA) signaling. The myocyte enhancer factor 2 (MEF2) proteins function as important regulators of myocardial gene expression. Previously, we reported that PKA signaling directly represses MEF2 activity. We determined whether (a) MEF2 has a pro-survival function in cardiomyocytes, and (b) whether β-adrenergic/PKA signaling modulates MEF2 function in cardiomyocytes. Initially, we observed that siRNA-mediated gene silencing of MEF2 induces cardiomyocyte apoptosis as indicated by flow cytometry. β1-AR activation by isoproterenol represses MEF2 activity and promotes apoptosis in cultured neonatal cardiomyocytes. Importantly, β1-AR mediated apoptosis was abrogated in cardiomyocytes expressing a PKA-resistant form of MEF2D (S121/190A). We also observed that a β1-blocker, Atenolol, antagonizes isoproterenol-induced apoptosis while concomitantly enhancing MEF2 transcriptional activity. β-AR stimulation modulated MEF2 cellular localization in cardiomyocytes and this effect was reversed by β-blocker treatment. Furthermore, Kruppel-like factor 6, a MEF2 target gene in the heart, functions as a downstream pro-survival factor in cardiomyocytes. Collectively, these data indicate that (a) MEF2 has an important pro-survival role in cardiomyocytes, and (b) β-adrenergic signaling antagonizes the pro-survival function of MEF2 in cardiomyocytes and β-blockers promote it. These observations have important clinical implications that may contribute to novel strategies for preventing cardiomyocyte apoptosis associated with heart pathology.
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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) | 0.001 | 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".