A Mechanism of Cohesin-Dependent Loop Extrusion Organizes Zygotic Genome Architecture
Bibliographic record
Abstract
SUMMARY Fertilization triggers assembly of higher-order chromatin structure from a naïve genome to generate a totipotent embryo. Chromatin loops and domains are detected in mouse zygotes by single-nucleus Hi-C (snHi-C) but not bulk Hi-C. We resolve this discrepancy by investigating whether a mechanism of cohesin-dependent loop extrusion generates zygotic chromatin conformations. Using snHi-C of mouse knockout embryos, we demonstrate that the zygotic genome folds into loops and domains that depend on Scc1-cohesin and are regulated in size by Wapl. Remarkably, we discovered distinct effects on maternal and paternal chromatin loop sizes, likely reflecting loop extrusion dynamics and epigenetic reprogramming. Polymer simulations based on snHi-C are consistent with a model where cohesin locally compacts chromatin and thus restricts inter-chromosomal interactions by active loop extrusion, whose processivity is controlled by Wapl. Our simulations and experimental data provide evidence that cohesin-dependent loop extrusion organizes mammalian genomes over multiple scales from the one-cell embryo onwards. Highlights Zygotic genomes are organized into cohesin-dependent chromatin loops and TADs Loop extrusion leads to different loop strengths in maternal and paternal genomes Cohesin restricts inter-chromosomal interactions by altering chromosome surface area Loop extrusion organizes chromatin at multiple genomic scales
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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) | 0.002 | 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".