p53 oligomerization status modulates cell fate decisions between growth, arrest and apoptosis
Bibliographic record
Abstract
Mutations in the oligomerization domain of p53 are genetically linked to cancer susceptibility in Li-Fraumeni Syndrome. These mutations typically alter the oligomeric state of p53 and impair its transcriptional activity. Activation of p53 through tetramerization is required for its tumor suppressive function by inducing transcriptional programs that lead to cell fate decisions such as cell cycle arrest or apoptosis. How p53 chooses between these cell fate outcomes remains unclear. Here, we use 5 oligomeric variants of p53, including 2 novel p53 constructs, that yield either monomeric, dimeric or tetrameric forms of p53 and demonstrate that they induce distinct cellular activities and gene expression profiles that lead to different cell fate outcomes. We report that dimeric p53 variants are cytostatic and can arrest cell growth, but lack the ability to trigger apoptosis in p53-null cells. In contrast, p53 tetramers induce rapid apoptosis and cell growth arrest, while a monomeric variant is functionally inactive, supporting cell growth. In particular, the expression of pro-arrest CDKN1A and pro-apoptotic P53AIP1 genes are important cell fate determinants that are differentially regulated by the oligomeric state of p53. This study suggests that the most abundant oligomeric species of p53 present in resting cells, namely p53 dimers, neither promote cell growth or cell death and that shifting the oligomeric state equilibrium of p53 in cells toward monomers or tetramers is a key parameter in p53-based cell fate decisions.
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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".