BET bromodomain inhibition suppresses innate inflammatory and profibrotic transcriptional networks in heart failure
Bibliographic record
Abstract
Despite current standard of care, the average 5-year mortality after an initial diagnosis of heart failure (HF) is about 40%, reflecting an urgent need for new therapeutic approaches. Previous studies demonstrated that the epigenetic reader protein bromodomain-containing protein 4 (BRD4), an emerging therapeutic target in cancer, functions as a critical coactivator of pathologic gene transactivation during cardiomyocyte hypertrophy. However, the therapeutic relevance of these findings to human disease remained unknown. We demonstrate that treatment with the BET bromodomain inhibitor JQ1 has therapeutic effects during severe, preestablished HF from prolonged pressure overload, as well as after a massive anterior myocardial infarction in mice. Furthermore, JQ1 potently blocks agonist-induced hypertrophy in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). Integrated transcriptomic analyses across animal models and human iPSC-CMs reveal that BET inhibition preferentially blocks transactivation of a common pathologic gene regulatory program that is robustly enriched for NFκB and TGF-β signaling networks, typified by innate inflammatory and profibrotic myocardial genes. As predicted by these specific transcriptional mechanisms, we found that JQ1 does not suppress physiological cardiac hypertrophy in a mouse swimming model. These findings establish that pharmacologically targeting innate inflammatory and profibrotic myocardial signaling networks at the level of chromatin is effective in animal models and human cardiomyocytes, providing the critical rationale for further development of BET inhibitors and other epigenomic medicines for HF.
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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".