The cellular abundance of the essential transcription termination factor TTF-I regulates ribosome biogenesis and is determined by MDM2 ubiquitinylation
Bibliographic record
Abstract
The ARF tumour suppressor stabilizes p53 by negatively regulating the E3 ubiquitin ligase MDM2 to promote cell cycle arrest and cell death. However, ARF is also able to arrest cell proliferation by inhibiting ribosome biogenesis. In greater part this is achieved by targeting the transcription termination factor I (TTF-I) for nucleolar export, leading to an inhibition of both ribosomal RNA synthesis and processing. We now show that in the absence of ARF, TTF-I is ubiquitinylated by MDM2. MDM2 interacts directly with TTF-I and regulates its cellular abundance by targeting it for degradation by the proteasome. Enhanced TTF-I levels inhibit ribosome biogenesis by suppressing ribosomal RNA synthesis and processing, strongly suggesting that exact TTF-I levels are critical for efficient ribosome biogenesis. We further show that concomitant with its ability to displace TTF-I from the nucleolus, ARF inhibits MDM2 ubiquitinylation of TTF-I by competitively binding to a site overlapping the MDM2 interaction site. Thus, both the sub-nuclear localization and the abundance of TTF-I are key regulators of ribosome biogenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".