Spatial Distribution of Di- and Tri-methyl Lysine 36 of Histone H3 at Active Genes
Bibliographic record
Abstract
Methylation of lysine 4 of histone H3 (K4/H3) is linked to transcriptional activity, whereas methylation of K9/H3 is tightly associated with gene inactivity. These are well characterized sites of methylation within histones, but there are numerous other, less characterized, sites of modification. In Saccharomyces cerevisiae, methylation of K36/H3 has been linked to active genes, but little is known about this methylation in higher eukaryotes. Here we analyzed for the first time the levels and spatial distribution of di- and tri-methyl (di- and tri-Me) K36/H3 in metazoan genes. We analyzed chicken genes that are developmentally regulated, constitutively active, or inactive. We found that active genes contain high levels of these modifications compared with inactive genes. Furthermore, in actively transcribed regions the levels of di- and tri-Me K36/H3 peak toward the 3' end of the gene. This is in striking contrast to the distributions of di- and tri-Me K4/H3, which peak early in actively transcribed regions. Thus, di/tri-Me K4/H3 and di/tri-Me K36/H3 are both useful markers of active genes, but their genic distribution indicates differing roles. Our data suggest that the unique spatial distribution of di- and tri-Me K36/H3 plays a role in transcriptional termination and/or early RNA processing.
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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 | 0.001 | 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".