RNA-dependent dynamic histone acetylation regulates MCL1 alternative splicing
Bibliographic record
Abstract
Histone deacetylases (HDACs) and lysine acetyltransferases (KATs) catalyze dynamic histone acetylation at regulatory and coding regions of transcribed genes. Highly phosphorylated HDAC2 is recruited within corepressor complexes to regulatory regions, while the nonphosphorylated form is associated with the gene body. In this study, we characterized the nonphosphorylated HDAC2 complexes recruited to the transcribed gene body and explored the function of HDAC-complex-mediated dynamic histone acetylation. HDAC1 and 2 were coimmunoprecipitated with several splicing factors, including serine/arginine-rich splicing factor 1 (SRSF1) which has roles in alternative splicing. The co-chromatin immunoprecipitation of HDAC1/2 and SRSF1 to the gene body was RNA-dependent. Inhibition of HDAC activity and knockdown of HDAC1, HDAC2 or SRSF1 showed that these proteins were involved in alternative splicing of MCL1. HDAC1/2 and KAT2B were associated with nascent pre-mRNA in general and with MCL1 pre-mRNA specifically. Inhibition of HDAC activity increased the occupancy of KAT2B and acetylation of H3 and H4 of the H3K4 methylated alternative MCL1 exon 2 nucleosome. Thus, nonphosphorylated HDAC1/2 is recruited to pre-mRNA by splicing factors to act at the RNA level with KAT2B and other KATs to catalyze dynamic histone acetylation of the MCL1 alternative exon and alter the splicing of MCL1 pre-mRNA.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".