An intricate regulatory circuit between FLI1 and GATA1/GATA2/LDB1/ERG dictates erythroid vs. megakaryocytic differentiation
Bibliographic record
Abstract
during hematopoiesis, megakaryocytic erythroid progenitors (MePs) differentiate into megakaryocytic or erythroid lineages in response to specific transcriptional factors, yet the regulatory mechanism remains to be elucidated.using the MeP-like cell line Hel western blotting, RT-qPCR, lentivirus-mediated downregulation, flow cytometry as well as chromatin immunoprecipitation (chip) assay demonstrated that the e26 transformation-specific (eTS) transcription factor friend leukemia integration factor 1 (Fli-1) inhibits erythroid differentiation.The present study using these methods showed that while Fli1-mediated downregulation of GaTa binding protein 1 (GaTa1) suppresses erythropoiesis, its direct transcriptional induction of GaTa2 promotes megakaryocytic differentiation.GaTa1 is also involved in megakaryocytic differentiation through regulation of GaTa2.By contrast to Fli1, the eTS member erythroblast transformation-specific-related gene (ERG) negatively controls GaTa2 and its overexpression through exogenous transfection blocks megakaryocytic differentiation.in addition, Fli1 regulates expression of liM domain Binding 1 (ldB1) during erythroid and megakaryocytic commitment, whereas shrna-mediated depletion of ldB1 downregulates Fli1 and GaTa2 but increases GaTa1 expression. in agreement, ldB1 ablation using shrna lentivirus expression blocks megakaryocytic differentiation and modestly suppresses erythroid maturation.These results suggested that a certain threshold level of ldB1 expression enables Fli1 to block erythroid differentiation.overall, Fli1 controlled the commitment of MeP to either erythroid or megakaryocytic lineage through an intricate regulation of GaTa1/GaTa2, ldB1 and erG, exposing multiple targets for cell fate commitment and therapeutic intervention.
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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) | 0.001 | 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".