mTOR-Dependent Regulation of Ribosomal Gene Transcription Requires S6K1 and Is Mediated by Phosphorylation of the Carboxy-Terminal Activation Domain of the Nucleolar Transcription Factor UBF†
Bibliographic record
Abstract
Mammalian target of rapamycin (mTOR) is a key regulator of cell growth acting via two independent targets, ribosomal protein S6 kinase 1 (S6K1) and 4EBP1. While each is known to regulate translational efficiency, the mechanism by which they control cell growth remains unclear. In addition to increased initiation of translation, the accelerated synthesis and accumulation of ribosomes are fundamental for efficient cell growth and proliferation. Using the mTOR inhibitor rapamycin, we show that mTOR is required for the rapid and sustained serum-induced activation of 45S ribosomal gene transcription (rDNA transcription), a major rate-limiting step in ribosome biogenesis and cellular growth. Expression of a constitutively active, rapamycin-insensitive mutant of S6K1 stimulated rDNA transcription in the absence of serum and rescued rapamycin repression of rDNA transcription. Moreover, overexpression of a dominant-negative S6K1 mutant repressed transcription in exponentially growing NIH 3T3 cells. Rapamycin treatment led to a rapid dephosphorylation of the carboxy-terminal activation domain of the rDNA transcription factor, UBF, which significantly reduced its ability to associate with the basal rDNA transcription factor SL-1. Rapamycin-mediated repression of rDNA transcription was rescued by purified recombinant phosphorylated UBF and endogenous UBF from exponentially growing NIH 3T3 cells but not by hypophosphorylated UBF from cells treated with rapamycin or dephosphorylated recombinant UBF. Thus, mTOR plays a critical role in the regulation of ribosome biogenesis via a mechanism that requires S6K1 activation and phosphorylation of UBF.
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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 | 0.000 | 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".