Control of Ribosomal RNA Synthesis by Hematopoietic Transcription Factors
Bibliographic record
Abstract
SUMMARY Ribosomal RNAs (rRNAs) are the most abundant cellular RNAs, and their synthesis from rDNA repeats by RNA Polymerase I accounts for the bulk of all transcription. Despite substantial variation in rRNA transcription rates across cell types, little is known about cell-type-specific factors that bind rDNA and regulate rRNA transcription to meet tissue-specific needs. Using hematopoiesis as a model system, we mapped about 2200 ChIP-Seq datasets for 250 transcription factors (TFs) and chromatin proteins to human and mouse rDNA, and identified robust binding of multiple TF families to canonical TF motifs on rDNA. Using a 47S-FISH-Flow assay developed for nascent rRNA quantification, we demonstrated that targeted degradation of CEBPA (C/EBP alpha), a critical hematopoietic TF with conserved rDNA binding, caused rapid reduction in rRNA transcription due to reduced Pol I occupancy. Our work identifies numerous potential rRNA regulators, and provides a template for dissection of TF roles in rRNA transcription. HIGHLIGHTS Multiple cell-type-specific transcription factors (TFs) bind canonical motifs on rDNA. The hematopoietic TF CEBPA binds to active rDNA alleles at a conserved site. CEBPA promotes Polymerase I occupancy and rRNA transcription in myeloid progenitors. We present ‘47S-FISH-Flow,’ a sensitive assay to quantify nascent rRNA.
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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".