Negative Regulation of p53 Functions by Daxx and the Involvement of MDM2
Bibliographic record
Abstract
In normal cells p53 activity is tightly controlled and MDM2 is a known negative regulator. Here we show that via its acidic domain, Daxx binds to the COOH-terminal domain of p53, whose positive charges are critical for this interaction, as Lys to Arg mutations preserved, but Lys to Ala or Ser to Glu mutations abolished Daxx-p53 interaction. These results thus implicate acetylation and phosphorylation of p53 in regulating its binding to Daxx. Interestingly, whereas Daxx did not bind to p53 in cells as assessed by immunoprecipitation, MDM2 expression restored p53-Daxx interaction, and this correlated with deacetylation of p53. In p53/MDM2-null mouse embryonic fibroblasts (DKO MEF), Daxx repressed p53 target promoters whose p53-binding elements were required for the repression. Coexpression of Daxx and MDM2 led to further repression. p53 expression in DKO MEF induced apoptosis and Daxx expression relieved this effect. Similarly, in HCT116 cells, Daxx conferred striking resistance to 5-fluorouracil-induced apoptosis. As p53 is required for 5-fluorouracil-induced cell death, our data show that Daxx can suppress cell death induced by p53 overexpression and p53-dependent stress response. Collectively, our data reveal Daxx as a novel negative regulator of p53. Importantly, posttranslational modifications of p53 inhibit Daxx-p53 interaction, thereby relieving negative regulation of p53 by Daxx.
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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".