The Short N-Terminal Repeats of Transcription Termination Factor 1 Contain Semi-Redundant Nucleolar Localization Signals and P19-ARF Tumor Suppressor Binding Sites.
Bibliographic record
Abstract
(ARF) tumor suppressor provides an important link in the activation of p53 (TP53) by inhibiting its targeted degradation via the E3 ligases MDM2/HDM2. However, ARF also limits tumor growth by directly inhibiting ribosomal RNA synthesis and processing. Initial studies of the ARF tumor suppressor were compounded by overlap between the INK4A and ARF genes encoded by the CDKN2A locus, but mouse models of pure ARF-loss and its inactivation in human cancers identified it as a distinct tumor suppressor even in the absence of p53. We previously demonstrated that both human and mouse ARF interact with Transcription Termination Factor 1 (TTF1, TTF-I), an essential factor implicated in transcription termination and silencing of the ribosomal RNA genes. Accumulation of ARF upon oncogenic stress was shown to inhibit ribosomal RNA synthesis by depleting nucleolar TTF1. Here we have mapped the functional nucleolar localization sequences (NoLS) of mouse TTF1 and the sequences responsible for interaction with ARF. We find that both sequences lie within the 25 amino acid N-terminal repeats of TTF1. Nucleolar localization depends on semi-redundant lysine-arginine motifs in each repeat and to a minor extent on binding to target DNA sequences by the Myb homology domain of TTF1. While nucleolar localization of TTF1 predominantly correlates with its interaction with ARF, NoLS activity and ARF binding are mediated by distinct sequences within each N-terminal repeat. The data suggest that the N-terminal repeats of mouse TTF1, and by analogy those of human TTF1, cooperate to mediate both nucleolar localization and ARF binding.
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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".