Deciphering UBE4B phosphorylation dynamics: a key mechanism in p53 accumulation and cancer cell response to DNA damage
Bibliographic record
Abstract
The p53 tumor suppressor protein plays a crucial role in detecting and eliminating various oncogenic threats by promoting processes such as cell cycle arrest, DNA repair, senescence, and apoptosis. UBE4B is essential for negatively regulating p53 during normal conditions and following DNA damage. In previous studies, we demonstrated that UBE4B targets phosphorylated p53 for degradation in response to DNA damage. However, the regulation of UBE4B in relation to DNA damage in cancer is not well understood. In this study, we show that the UBE4B protein is regulated through a phosphorylation and dephosphorylation mechanism in response to DNA damage. Phosphorylation of UBE4B reduces its binding affinity to p53, leading to an accumulation of p53 in the cell. Wip1 plays a crucial role in the dephosphorylation of UBE4B, which stabilizes the activity of the UBE4B protein in response to DNA damage. UBE4B is primarily phosphorylated through ATR-mediated signaling, which reduces its binding affinity with p53, resulting in the accumulation and activation of p53. When Wip1 is inhibited, there is a significant increase in UBE4B phosphorylation, leading to more p53 accumulation and a reduction in cell growth. Therefore, understanding how UBE4B is regulated in cancer cells in response to DNA-damaging agents could help develop new therapeutic strategies to improve the prognosis for cancer patients.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".