Abstract 19038: Epigenetic Regulation of Canonical TNFa Pathway by HDAC1 Determines Survival of Cardiac Myocytes
Bibliographic record
Abstract
Gene transcription is regulated by post-translation modifications. Histone deacetylases (HDACs) remove acetyl-groups from histone and non-histone factors inhibiting transcription. Pro-inflammatory cytokines such as TNFα activate the canonical Nuclear Factor -κB (NF-κB) pathway. Earlier we established a cytoprotective role for NF-κB in the heart. Though a causal relationship for HDAC1 and NF-κB has been established, the impact of HDAC1 on TNFα signaling is unknown. Herein, we demonstrate HDAC1 provides a molecular switch for determining cell survival in the TNFα pathway. In contrast to vehicle treated control cells, TNFα treated cells displayed a marked increase in NF-κB gene transcription. Notably, cells treated with TNFα were indistinguishable from vehicle controls cells with respect to viability. Interestingly, HDAC activity was reduced in cells treated with TNFα. Conversely, in the presence of HDAC1, NF-κB gene transcription by TNFα was repressed, resulting in mitochondrial perturbations and widespread cell death. Heterologous fusion proteins comprised of yeast Gal4 DNA binding domain fused in frame to the NF-κB p65 transactivation domain were preferentially repressed by HDAC1. Moreover, transcription mediated by Gal4VP16 protein from herpes virus was unaffected by HDAC1 in cardiac myocytes. Mutations that abrogate known catalytic activities of HDAC1, small interference RNA or pharmacological inhibition of HDAC1, restored NF-κB signaling and suppressed cell death induced by TNFα. These data provide the first evidence for an obligate link between HDAC1 and canonical TNFα pathway for cell survival of cardiac myocytes.
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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) | 0.011 | 0.003 |
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".