A Role for DNA Hypomethylation and Histone Acetylation in Maintaining Allele-Specific Expression of Mouse NKG2A in Developing and Mature NK Cells
Bibliographic record
Abstract
The repertoire of receptors that is expressed by NK cells is critical for their ability to kill virally infected or transformed cells. However, the molecular mechanisms that determine whether and when NK receptor genes are transcribed during hemopoiesis remain unclear. In this study, we show that hypomethylation of a CpG-rich region in the mouse NKG2A gene is associated with transcription of NKG2A in ex vivo NK cells and NK cell lines. This observation was extended to various developmental stages of NK cells sorted from bone marrow, in which we demonstrate that the CpGs are methylated in the NKG2A-negative stages (hemopoietic stem cells, NK progenitors, and NKG2A-negative NK cells), and hypomethylated specifically in the NKG2A-positive NK cells. Furthermore, we provide evidence that DNA methylation is important in maintaining the allele-specific expression of NKG2A. Finally, we show that acetylated histones are associated with the CpG-rich region in NKG2A positive, but not negative, cell lines, and that treatment with the histone deacetylase inhibitor trichostatin A alone is sufficient to induce NKG2A expression. Treatment with the methyltransferase inhibitor 5-azacytidine only is insufficient to induce transcription, but cotreatment with both drugs resulted in a significantly greater induction, suggesting a cooperative role for DNA methylation and histone acetylation status in regulating gene expression. These results enhance our understanding of the formation and maintenance of NK receptor repertoires in developing and mature NK cells.
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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 | 0.001 |
| 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".