Transcription factor UBF depletion in mouse cells results in downregulation of both downstream and upstream elements of the rRNA transcription network
Bibliographic record
Abstract
Transcription/processing of the ribosomal RNA (rRNA) precursor, as part of ribosome biosynthesis, is intensively studied and characterized in eukaryotic cells. Here, we constructed shRNA-based mouse cell lines partially silenced for the Upstream Binding Factor UBF, the master regulator of rRNA transcription and organizer of open rDNA chromatin. Full Ubf silencing in vivo is not viable, and these new tools allow further characterization of rRNA transcription and its coordination with cellular signaling. shUBF cells display cell cycle G1 delay and reduced 47S rRNA precursor and 28S rRNA at baseline and serum-challenged conditions. Growth-related mTOR signaling is downregulated with the fractions of active phospho-S6 Kinase and pEIF4E translation initiation factor reduced, similar to phosphorylated cell cycle regulator retinoblastoma, pRB, positive regulator of UBF availability/rRNA transcription. Additionally, we find transcription-competent pUBF (Ser484) severely restricted and its interacting initiation factor RRN3 reduced and responsive to extracellular cues. Furthermore, fractional UBF occupancy on the rDNA unit is decreased in shUBF, and expression of major factors involved in different aspects of rRNA transcription is severely downregulated by UBF depletion. Finally, we observe reduced RNA Pol1 occupancy over rDNA promoter sequences and identified unexpected regulation of RNA Pol1 expression, relative to serum availability and under UBF silencing, suggesting that regulation of rRNA transcription may not be restricted to modulation of Pol1 promoter binding/elongation rate. Overall, this work reveals that UBF depletion has a critical downstream and upstream impact on the whole network orchestrating rRNA transcription in mammalian cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".