Enhancers mediate euchromatin hopping at chromatin contact points
Bibliographic record
Abstract
Summary Enhancer-mediated gene activation involves the recruitment of chromatin modifiers and RNA polymerase to target promoters, but it is unknown if enhancers influence chromatin beyond their target genes. Euchromatin and heterochromatin associated histone modifications separate the genome into opposing nuclear compartments. Whereas heterochromatin marks are known to spread from one modified nucleosome to another, no such ability has been ascribed to euchromatin. Using mono-allelic enhancer deletions, native ChIP-seq, and an engineered interaction between an enhancer and transcriptionally inert DNA, we show that enhancers mediate the acquisition of euchromatin features at distal regions through chromatin looping. We term this phenomenon euchromatin hopping and found it occurring on average ∼270kb bidirectionally from enhancers, redefining our understanding of enhancer-mediated chromatin architecture with implications on enhancer identification using chromatin features. Graphical Abstract Euchromatin hopping model Figure showing the proposed euchromatin hopping model. TFs recognize and bind to their binding sites in an active enhancer region. Upon activation enhancers recruit coactivators and RNAPII forming a condensate that supports gene activation. After an abundance of transcriptional machinery and coactivators are recruited, adjacent TF bound sites acquire euchromatin features through physical proximity to the active compartment, we call these regions “bystanders”. Upon enhancer deletion, condensate formation is lost and active euchromatin marks are not acquired at the gene promoter or other enhancer chromatin contacts. TFs are displayed in yellow, coactivators in green, RNAPII in pink, and histone modifications in red.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".