Nuclear exosome targeting complexes modulate cohesin binding and enhancer-promoter interactions in 3D
Bibliographic record
Abstract
Abstract Three-dimensional long-range contacts between enhancers and promoters are thought to be largely determined by loop extrusion driven by the cohesin complex and insulator factors. However, recent evidence also suggests a role for noncoding RNAs (ncRNAs), such as enhancer-associated RNAs (eRNAs) and promoter upstream transcripts (PROMPTs), in shaping enhancer-promoter connectivity. While nuclear RNA exosome, together with targeting complexes, PAXT and NEXT, control the decay of ncRNAs, it has not yet been determined whether these complexes regulate 3D contacts. Chromatin recruitment maps of ZCCHC8 (NEXT), ZFC3H1 (PAXT) and MTR4 helicase revealed that these factors that associate with sites of enhancer-promoter interactions. Depletion of NEXT, PAXT or MTR4 induced the accumulation of ncRNAs, notably enhancer-associated RNAs (eRNAs) and promoter upstream transcripts (PROMPTs). Strikingly, this further increased cohesin levels at sites accumulating ncRNAs. Chromatin conformation capture analysis revealed that MTR4 modulates the 3D long-range contacts between enhancers with their distant TSS targets. Upon loss of MTR4, contacts at anchor points increase while intraloop contacts decrease, suggesting that MTR4 facilitates loop extrusion. These data highlight a key interplay between cohesin-mediated enhancer-promoter interactions and the regulation of ncRNAs by nuclear RNA exosome that is consistent with a role for RNA in genome folding.
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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 | 0.000 |
| 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".