ZEB2 and MEIS1 independently contribute to hematopoiesis via early hematopoietic enhancer activation
Bibliographic record
Abstract
Cell differentiation is achieved by acquiring a cell type-specific transcriptional program and epigenetic landscape. While the cell type-specific patterning of enhancers has been shown to precede cell fate decisions, it remains unclear how regulators of these enhancers are induced to initiate cell specification and how they appropriately restrict cells that differentiate. Here, using embryonic stem cell-derived hematopoietic cell differentiation cultures, we show the activation of some hematopoietic enhancers during arterialization of hemogenic endothelium, a prerequisite for hematopoiesis. We further reveal that ZEB2, a factor involved in the transcriptional regulation of arterial endothelial cells, and a hematopoietic regulator MEIS1 are independently required for activating these enhancers. Concomitantly, ZEB2 or MEIS1 deficiency impaired hematopoietic cell development. These results suggest that multiple regulators expressed from an earlier developmental stage non-redundantly contribute to the establishment of hematopoietic enhancer landscape, thereby restricting cell differentiation despite the unrestricted expression of these regulators to hematopoietic cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".