The MEF2A transcription factor interactome in cardiomyocytes
Bibliographic record
Abstract
Transcriptional regulators encoded by the Myocyte Enhancer Factor 2 (MEF2) gene family play a fundamental role in cardiac development, homeostasis and pathology. Previous studies indicate that MEF2A protein-protein interactions serve as a network hub in several cardiomyocyte cellular processes. Based on the idea that interactions with regulatory protein partners underly the diverse roles of MEF2A in cardiomyocyte gene expression, we undertook a systematic unbiased screen of the MEF2A protein interactome in primary cardiomyocytes using an affinity purification-based quantitative mass spectrometry approach. Bioinformatic processing of the MEF2A interactome revealed protein networks involved in the regulation of programmed cell death, inflammatory responses, actin dynamics and stress signaling in primary cardiomyocytes. Further biochemical and functional confirmation of specific protein-protein interactions documented a dynamic interaction between MEF2A and STAT3 proteins. Integration of transcriptome level data from MEF2A and STAT3-depleted cardiomyocytes reveals that the balance between MEF2A and STAT3 activity exerts a level of executive control over the inflammatory response and cardiomyocyte cell survival and experimentally ameliorates Phenylephrine induced cardiomyocyte hypertrophy. Lastly, we identified several MEF2A/STAT3 co-regulated genes, including the MMP9 gene. Herein, we document the cardiomyocyte MEF2A interactome, which furthers our understanding of protein networks involved in the hierarchical control of normal and pathophysiological cardiomyocyte gene expression in the mammalian heart.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".