HLA-B-associated transcript 3 (Bat3)/Scythe is essential for p300-mediated acetylation of p53
Bibliographic record
Abstract
In response to DNA damage, p53 undergoes post-translational modifications (including acetylation) that are critical for its transcriptional activity. However, the mechanism by which p53 acetylation is regulated is still unclear. Here, we describe an essential role for HLA-B-associated transcript 3 (Bat3)/Scythe in controlling the acetylation of p53 required for DNA damage responses. Depletion of Bat3 from human and mouse cells markedly impairs p53-mediated transactivation of its target genes Puma and p21. Although DNA damage-induced phosphorylation, stabilization, and nuclear accumulation of p53 are not significantly affected by Bat3 depletion, p53 acetylation is almost completely abolished. Bat3 forms a complex with p300, and an increased amount of Bat3 enhances the recruitment of p53 to p300 and facilitates subsequent p53 acetylation. In contrast, Bat3-depleted cells show reduced p53-p300 complex formation and decreased p53 acetylation. Furthermore, consistent with our in vitro findings, thymocytes from Bat3-deficient mice exhibit reduced induction of puma and p21, and are resistant to DNA damage-induced apoptosis in vivo. Our data indicate that Bat3 is a novel and essential regulator of p53-mediated responses to genotoxic stress, and that Bat3 controls DNA damage-induced acetylation of p53.
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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".