Regulation of Epigenetic Modifiers, Including KDM6B, by Interferon-γ and Interleukin-4 in Human Macrophages
Bibliographic record
Abstract
Background. Interferon-γ (IFN-γ) or interleukin-4 (IL-4) drive widely different transcriptional programmes in macrophages. However, how IFN-γ and IL-4 alter expression of histone modifying enzymes involved in epigenetic regulation and how this affects the resulting phenotypic polarization is incompletely understood. Methods and Results. We investigated steady state messenger RNA levels of 84 histone modifying enzymes and related regulators in colony stimulating factor-1 differentiated primary human macrophages using quantitative polymerase chain reaction. IFN-γ or IL-4 treatment for 6 to 48 hours changed 11 mRNAs significantly. IFN-γ increased CIITA, KDM6B and NCOA1 and IL-4 also increased KDM6B by 6 hours. However, either cytokine decreased AURKB, ESCO2, SETD6, SUV39H1 and WHSC1, whereas IFN-γ alone decreased KAT2A, PRMT7 and SMYD3 mRNAs only after 18 hours, which coincided with decreased cell proliferation. Rendering macrophages quiescent by growth factor starvation or adenovirus-mediated over-expression of p27kip1 inhibited expression of AURKB, ESCO2, SUV39H1 and WHSC1 and mRNA levels were restored by overexpressing the S-phase transcription factor E2F1, implying their expression, at least partly, depended on proliferation. However, CIITA, KDM6B, NCOA1, KAT2A, PRMT7, SETD6 and SMYD3 were regulated independently of effects on proliferation. Silencing KDM6B, the only transcriptional activator upregulated by both IFN-γ and IL-4, pharmacologically or with short hairpin RNA, blunted a subset of responses to each cytokine. Conclusions. These findings demonstrate that IFN-γ or IL-4 can regulate the expression of histone acetyl transferases and histone methyl transferases independently of effects on proliferation and that up-regulation of the histone demethylase, KDM6B, assists phenotypic polarization by both cytokines.
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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".