Regulation of Human p53 Activity and Cell Localization by Alternative Splicing
Bibliographic record
Abstract
The development of cancer is a multistep process involving mutations in proto-oncogenes, tumor suppressor genes, and other genes which control cell proliferation, telomere stability, angiogenesis, and other complex traits. Despite this complexity, the cellular pathways controlled by the p53 tumor suppressor protein are compromised in most, if not all, cancers. In normal cells, p53 controls cell proliferation, senescence, and/or mediates apoptosis in response to stress, cell damage, or ectopic oncogene expression, properties which make p53 the prototype tumor suppressor gene. Defining the mechanisms of regulation of p53 activity in normal and tumor cells has therefore been a major priority in cell biology and cancer research. The present study reveals a novel and potent mechanism of p53 regulation originating through alternative splicing of the human p53 gene resulting in the expression of a novel p53 mRNA. This novel p53 mRNA encodes an N-terminally deleted isoform of p53 termed p47. As demonstrated within, p47 was able to effectively suppress p53-mediated transcriptional activity and impair p53-mediated growth suppression. It was possible to select for p53-null cells expressing p47 alone or coexpressing p53 in the presence of p47 but not cells expressing p53 alone. This showed that p47 itself does not suppress cell viability but could control p53-mediated growth suppression. Interestingly, p47 was monoubiquitinated in an Mdm2-independent manner, and this was associated with its export out of the nucleus. In the presence of p47, there was a reduction in Mdm2-mediated polyubiquitination and degradation of p53, and this was also associated with increased monoubiquitination and nuclear export of p53. The expression of p47 through alternative splicing of the p53 gene thus has a major influence over p53 activity at least in part through controlling p53 ubiquitination and cell localization.
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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) | 0.000 | 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 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".