Bystander activation across a TAD boundary supports a cohesin-dependent transcription cluster model for enhancer function
Bibliographic record
Abstract
Mammalian enhancers can regulate genes over large genomic distances, often skipping over other genes. Despite this, precise developmental regulation suggests that mechanisms exist to ensure enhancers only activate their correct targets. Sculpting of three-dimensional chromosome organization through cohesin-dependent loop extrusion is thought to be important for facilitating and constraining enhancer action. The boundaries of topologically associating domains (TADs) are thought to prevent enhancers acting on genes in adjacent TADs. However, there are examples where enhancers appear to act across TAD boundaries, but it has remained unclear whether a single enhancer can simultaneously activate genes in different TADs. Here we show that some Shh enhancers can activate transcription concurrently not only at Shh but also at Mnx1 located in an adjacent TAD. This occurs in the context of a chromatin conformation maintaining genes and enhancers in close proximity and is influenced by cohesin. To our knowledge, this is the first report of two endogenous mammalian genes transcribed concurrently under the control of the same enhancer and across a TAD boundary. These findings have implications for understanding the design rules of gene regulatory landscapes and are consistent with a transcription cluster model of enhancer–promoter communication.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".