Frequent interferon regulatory factor 1 (IRF1) binding at remote elements without histone modification
Bibliographic record
Abstract
Transcriptional activators bind DNA and recruit cofactors to modify chromatin. The extent to which these two events are separable is unclear. Here, using a custom ChIP tiling array to map chromatin modifications, we show that interferon-γ-induced DNA binding of signal transducer and activator of transcription 1 (STAT1), typically associated with the transcription factor interferon regulatory factor 1 (IRF1), causes histone acetylation (H3ac, H4ac). In contrast, among IRF1 sites lacking concomitant STAT1 recruitment, only 25% underwent inducible histone acetylation, 31% exhibited constitutive histone acetylation, and 44% had no histone acetylation. These latter "orphan sites" also lacked other activating modifications ( e.g. H3K4me1, H3K4me2) and were typically remote from transcription start sites. In these cases the closest gene was typically an IFNγ-inducible locus that did not respond to IFNγ in this setting. Orphan sites were detected in different cell types, suggesting broad relevance. Despite an atypical downstream response ( i.e. no histone modifications), IRF1 binding depended on SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4 (SMARCA4 or BRG1), as is typical of active IRF1 enhancers. Although SMARCA4 permitted IRF1 access to the orphan sites, there was no corecruitment of the histone acetyltransferases CREB-binding protein (CBP) and p300. Orphan sites were constitutively unacetylated, and several were marked with repressive chromatin modifications ( e.g. H3K27me3). In conclusion, although IRF1 can trigger enhanceosome formation independently of STAT1, its ability to do so depends on local chromatin cues.
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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) | 0.003 | 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".