p53 CRISPR Deletion Affects DNA Structure and Nuclear Architecture
Bibliographic record
Abstract
The TP53 gene is a key tumor suppressor. Although the tumor suppressor p53 was one of the first to be characterized as a transcription factor, with its main function potentiated by its interaction with DNA, there are still many unresolved questions about its mechanism of action. Here, we demonstrate a novel role for p53 in the maintenance of nuclear architecture of cells. Using three-dimensional (3D) imaging and spectral karyotyping, as well as super resolution microscopy of DNA structure, we observe significant differences in 3D telomere signatures, DNA structure and DNA-poor spaces as well gains or losses of chromosomes, between normal and tumor cells with CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-deleted or wild-type TP53. Additionally, treatment with Nutlin-3 results in differences in nuclear architecture of telomeres in wild-type but not in p53 knockout MCF-7 (Michigan Cancer Foundation-7) cells. Nutlin-3 binds to the p53-binding pocket of mouse double minute 2 (MDM2) and blocks the p53-MDM2 interaction. Moreover, we demonstrate that another p53 stabilizing small molecule, RITA (reactivation of p53 and induction of tumor cell apoptosis), also induces changes in 3D DNA structure, apparently in a p53 independent manner. These results implicate p53 activity in regulating nuclear organization and, additionally, highlight the divergent effects of the p53 targeting compounds Nutlin-3 and RITA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".