A <i>Sox2</i> Enhancer Cluster Regulates Region-Specific Neural Fates from Mouse Embryonic Stem Cells
Bibliographic record
Abstract
Abstract Embryonic development depends on spatially and temporally orchestrated gene regulatory networks. Expressed in neural stem and progenitor cells (NSPCs), the transcription factor sex-determining region Y box 2 (Sox2) is critical for embryogenesis and stem cell maintenance in neural development. Whereas Sox2 is regulated by a distal cluster of enhancers in embryonic stem cells (ESCs), enhancers closer to the gene have been implicated in Sox2 transcriptional regulation in the neural lineage. Using functional genomics data, and deletion analysis we show that a downstream enhancer cluster regulates Sox2 transcription in NSPCs derived from mouse ESCs. By generating allelic mutants using CRISPR-Cas9 mediated deletions, we show that this proximal enhancer cluster, termed Sox2 regulatory regions 2-18 (SRR2-18), is a cis regulator of Sox2 transcription during neural differentiation. Transcriptome analyses demonstrate that loss of even one copy of SRR2-18 disrupts the region-specific identity of NSPCs. Biallelic deletion of this Sox2 neural enhancer cluster causes reduced SOX2 protein, less frequent interaction with transcriptional machinery, and leads to perturbed chromatin accessibility genome-wide further affecting the expression of neurodevelopmental and anterior-posterior regionalization genes. Furthermore, homozygous NSPC deletants exhibit self-renewal defects and impaired differentiation into cell types found in the brain. Altogether, our data define a cis -regulatory enhancer cluster controlling Sox2 transcription in NSPCs and highlight the sensitivity of neural differentiation processes to decreased Sox2 transcription, which influences their differentiation into posterior neural fates, specifically the caudal neural tube.
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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) | 0.001 | 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".