REGγ modulates p53 activity by regulating its cellular localization
Bibliographic record
Abstract
The proteasome activator REGγ mediates a shortcut for the destruction of intact mammalian proteins. The biological roles of REGγ and the underlying mechanisms are not fully understood. Here we provide evidence that REGγ regulates cellular distribution of p53 by facilitating its multiple monoubiquitylation and subsequent nuclear export and degradation. We also show that inhibition of p53 tetramerization by REGγ might further enhance cytoplasmic relocation of p53 and reduce active p53 in the nucleus. Furthermore, multiple monoubiquitylation of p53 enhances its physical interaction with HDM2 and probably facilitates subsequent polyubiquitylation of p53, suggesting that monoubiquitylation can act as a signal for p53 degradation. Depletion of REGγ sensitizes cells to stress-induced apoptosis, validating its crucial role in the control of apoptosis, probably through regulation of p53 function. Using a mouse xenograft model, we show that REGγ knockdown results in a significant reduction of tumor growth, suggesting an important role for REGγ in tumor development. Our study therefore demonstrates that REGγ-mediated inactivation of p53 is one of the mechanisms involved in cancer progression.
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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 | 0.000 |
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".