Mis-splicing Drives Loss of Function of p53 <sup>E224D</sup> Point Mutation
Bibliographic record
Abstract
Abstract Background Tp53 is the most commonly mutated gene in cancer. Canonical Tp53 DNA damage response pathways are well characterized and classically thought to underlie the tumor suppressive effect of Tp53. Challenging this dogma, mouse models have revealed that p53 driven apoptosis and cell cycle arrest are dispensable for tumor suppression. Here, we investigated the inverse context of a p53 mutation predicted to drive expression of canonical targets, but is detected in human cancer. Methods We established a novel mouse model with a single base pair mutation (GAG>GAC, p53 E221D ) in the DNA-Binding domain that has wild-type function in screening assays, but is paradoxically found in human cancer in Li-Fraumeni syndrome. Using mouse p53 E221D and the analogous human p53 E224D mutant, we evaluated expression, transcriptional activation, and tumor suppression in vitro and in vivo. Results Expression of human p53 E224D from cDNA translated to a fully functional p53 protein. However, p53 E221D/E221D RNA transcribed from the endogenous locus is mis-spliced resulting in nonsense mediated decay. Moreover, fibroblasts derived from p53 E221D/E221D mice do not express a detectable protein product. Mice homozygous for p53 E221D exhibited increased tumor penetrance and decreased life expectancy compared to p53 WT animals. Conclusions Mouse p53 E221D and human p53 E224D mutations lead to splice variation and a biologically relevant p53 loss of function in vitro and in vivo.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".