Mis-splicing drives loss of function of p53E224D point mutation
Bibliographic record
Abstract
BACKGROUND: The tumor suppressor p53 (Trp53), also known as p53, is the most commonly mutated gene in cancer. Canonical p53 DNA damage response pathways are well characterized and classically thought to underlie the tumor suppressive effect of p53. 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 the expression of canonical targets but is detected in human cancer. METHODS: We established a novel mouse model with a single base pair mutation (GAG>GAT, p53E221D) 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 p53E221D and the analogous human p53E224D mutants, we evaluated expression, transcriptional activation, and tumor suppression in vitro and in vivo. RESULTS: Expression of human p53E224D from cDNA translated to a fully functional p53 protein. However, p53E221D/E221D RNA transcribed from the endogenous locus is mis-spliced resulting in nonsense-mediated decay. Moreover, fibroblasts derived from p53E221D/E221D mice do not express a detectable protein product. Mice homozygous for p53E221D exhibited increased tumor penetrance and decreased life expectancy compared to p53WT/WT animals. CONCLUSIONS: Mouse p53E221D and human p53E224D 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".