Dynamic Histone Acetylation of H3K4me3 Nucleosome Regulates <i>MCL1</i> Pre‐mRNA Splicing
Bibliographic record
Abstract
Pre-mRNA splicing is a cotranscriptional process affected by the chromatin architecture along the body of coding genes. Recruited to the pre-mRNA by splicing factors, histone deacetylases (HDACs) and K-acetyltransferases (KATs) catalyze dynamic histone acetylation along the gene. In colon carcinoma HCT 116 cells, HDAC inhibition specifically increased KAT2B occupancy as well as H3 and H4 acetylation of the H3K4 trimethylated (H3K4me3) nucleosome positioned over alternative exon 2 of the MCL1 gene, an event paralleled with the exclusion of exon 2. These results were reproduced in MDA-MB-231, but not in MCF7 breast adenocarcinoma cells. These later cells have much higher levels of demethylase KDM5B than either HCT 116 or MDA-MB-231 cells. We show that H3K4me3 steady-state levels and H3K4me3 occupancy at the end of exon 1 and over exon 2 of the MCL1 gene were lower in MCF7 than in MDA-MB-231 cells. Furthermore, in MCF7 cells, there was minimal effect of HDAC inhibition on H3/H4 acetylation and H3K4me3 levels along the MCL1 gene and no change in pre-mRNA splicing choice. These results show that, upon HDAC inhibition, the H3K4me3 mark plays a critical role in the exclusion of exon 2 from the MCL1 pre-mRNA. J. Cell. Physiol. 231: 2196-2204, 2016. © 2016 Wiley Periodicals, Inc.
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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) | 0.001 | 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".