ZIC2 and ZIC3 promote SWI/SNF recruitment to safeguard progression towards human primed pluripotency
Bibliographic record
Abstract
The primed epiblast acts as a transitional stage between the relatively homogeneous naïve epiblast and the gastrulating embryo. Its formation entails coordinated changes in regulatory circuits driven by transcription factors and epigenetic modifications. Using a multi-omic approach in human embryonic stem cell models across the spectrum of peri-implantation development, we demonstrate that the transcription factors ZIC2 and ZIC3 have overlapping but essential roles in opening primed-specific enhancers. Together, they are essential to facilitate progression to and maintain primed pluripotency. ZIC2/3 accomplish this by recruiting SWI/SNF to chromatin and loss of ZIC2/3 or degradation of SWI/SNF both prevent enhancer activation. Loss of ZIC2/3 also results in transcriptome changes consistent with perturbed Polycomb activity and a shift towards the expression of genes linked to differentiation towards the mesendoderm. Additionally, we find an intriguing dependency on the transcriptional machinery for sustained recruitment of ZIC2/3 over a subset of primed-hESC specific enhancers. Taken together, ZIC2 and ZIC3 regulate highly dynamic lineage-specific enhancers and collectively act as key regulators of human primed pluripotency.
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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".