Post-transcriptional regulation in hematopoiesis: RNA binding proteins take control
Bibliographic record
Abstract
Normal hematopoiesis is sustained through a carefully orchestrated balance between hematopoietic stem cell (HSC) self-renewal and differentiation. The functional importance of this axis is underscored by the severity of disease phenotypes initiated by abnormal HSC function, including myelodysplastic syndromes and hematopoietic malignancies. Major advances in the understanding of transcriptional regulation of primitive hematopoietic cells have been achieved; however, the post-transcriptional regulatory layer that may impinge on their behavior remains underexplored by comparison. Key players at this level include RNA-binding proteins (RBPs), which execute precise and highly coordinated control of gene expression through modulation of RNA properties that include its splicing, polyadenylation, localization, degradation, or translation. With the recent identification of RBPs having essential roles in regulating proliferation and cell fate decisions in other systems, there has been an increasing appreciation of the importance of post-transcriptional control at the stem cell level. Here we discuss our current understanding of RBP-driven post-transcriptional regulation in HSCs, its implications for normal, perturbed, and malignant hematopoiesis, and the most recent technological innovations aimed at RBP-RNA network characterization at the systems level. Emerging evidence highlights RBP-driven control as an underappreciated feature of primitive hematopoiesis, the greater understanding of which has important clinical implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".