H3K36 Methylation - a Guardian of Epigenome Integrity
Bibliographic record
Abstract
Abstract H3K36 methylation is emerging as a key epigenetic modification with strong implications in genetic disease and cancer. However, the mechanisms through which H3K36me impacts the epigenome and asserts its functional consequences are far from understood. Here, we use mouse mesenchymal stem cell lines with successive knockouts of the H3K36 methyltransferases: NSD1, NSD2, SETD2, NSD3, and ASH1L, which result in progressive depletion of H3K36me and its complete absence in quintuple knockout cells, to finely dissect the role of H3K36me2 in shaping the epigenome and transcriptome. We show that H3K36me2, which targets active enhancers, is important for maintaining enhancer activity, and its depletion results in downregulation of enhancer-dependent genes. We demonstrate the roles of H3K36me2/3 in preventing the invasion of gene bodies by the repressive H3K27me modifications. Finally, we observe a previously undescribed relationship between H3K36me and H3K9me3: Following the depletion of H3K36me2, H3K9me3 is redistributed away from large heterochromatic domains and towards euchromatin. This results in a drastic decompartmentalization of the genome, weakening the boundaries between active and inactive compartments, and a catastrophic loss of long-range inter-compartment interactions. By studying cells totally devoid of H3K36 methyltransferase activity, we uncover a broad range of crucial functions of H3K36me in maintaining epigenome integrity.
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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".