Regulation of RUNX1 dosage is crucial for efficient blood formation from hemogenic endothelium
Bibliographic record
Abstract
ABSTRACT During ontogeny, hematopoietic stem and progenitor cells arise from hemogenic endothelium through an endothelial-to-hematopoietic transition that is strictly dependent on the transcription factor RUNX1. Although it is well established that RUNX1 is essential for the onset of hematopoiesis, little is known about the role of RUNX1 dosage specifically in hemogenic endothelium and during the endothelial-to-hematopoietic transition. Here, we used the mouse embryonic stem cell differentiation system to determine if and how RUNX1 dosage affects hemogenic endothelium differentiation. The use of inducible Runx1 expression combined with alterations in the expression of the RUNX1 co-factor CBFβ allowed us to evaluate a wide range of RUNX1 levels. We demonstrate that low RUNX1 levels are sufficient and necessary to initiate an effective endothelial-to-hematopoietic transition. Subsequently, RUNX1 is also required to complete the endothelial-to-hematopoietic transition and to generate functional hematopoietic precursors. In contrast, elevated levels of RUNX1 are able to drive an accelerated endothelial-to-hematopoietic transition, but the resulting cells are unable to generate mature hematopoietic cells. Together, our results suggest that RUNX1 dosage plays a pivotal role in hemogenic endothelium maturation and the establishment of the hematopoietic system.
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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 | 0.001 |
| 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".