Bioinformatic Analyses of Broad H3K79me2 Domains in Different Leukemia Cell Line Data Sets
Bibliographic record
Abstract
A subset of expressed genes is associated with a broad H3K4me3 (histone H3 trimethylated at lysine 4) domain that extends throughout the gene body. Genes marked in this way in normal cells are involved in cell-identity and tumor-suppressor activities, whereas in cancer cells, genes driving the cancer phenotype (oncogenes) have this feature. Other histone modifications associated with expressed genes that display a broad domain have been less studied. Here, we identified genes with the broadest H3K79me2 (histone H3 dimethylated at lysine 79) domain in human leukemic cell lines representing different forms of leukemia. Taking a bioinformatic approach, we provide evidence that genes with the broadest H3K79me2 domain have known roles in leukemia (e.g., JMJD1C). In the mixed-lineage leukemia cell line MOLM-13, the HOXA9 gene is in a 100 kb broad H3K79me2 domain with other HOXA protein-coding and oncogenic long non-coding RNA genes. The genes in this domain contribute to leukemia. This broad H3K79me2 domain has an unstable chromatin structure, as was evident by enhanced chromatin accessibility throughout. Together, we provide evidence that identification of genes with the broadest H3K79me2 domain will aid in generating a panel of genes in the diagnosis and therapeutic treatment of leukemia in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".