Suppression of canonical TGF-β signaling enables GATA4 to interact with H3K27me3 demethylase JMJD3 to promote cardiomyogenesis
Bibliographic record
Abstract
Summary Direct reprogramming of fibroblasts into cardiomyocytes (CMs) represents a promising strategy to regenerate CMs lost after ischemic heart injury. Overexpression of G ATA4, H AND2, M EF2C, T BX5, miR-1, and miR-133 (GHMT2m) along with transforming growth factor beta (TGF-β) inhibition efficiently promotes reprogramming. However, the mechanisms by which TGF-β blockade promotes cardiac reprogramming remain unknown. Here, we identify interactions between the histone H3 lysine 27 trimethylation (H3K27me3) – demethylase JMJD3, the SWI/SNF remodeling complex subunit BRG1, and cardiac transcription factors. Furthermore, canonical TGF-β signaling regulates the interaction between GATA4 and JMJD3. TGF-β activation impairs the ability of GATA4 to bind target genes and prevents demethylation of H3K27 at cardiac gene promoters during cardiac reprogramming. Finally, a mutation in GATA4 (V267M) exhibits reduced binding to JMJD3 and impaired cardiomyogenesis. Thus, we have identified an epigenetic mechanism wherein canonical TGF-β pathway activation impairs cardiac gene programming by interfering with GATA4-JMJD3 interactions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".