Li-Fraumeni Syndrome-Associated p53 Variants Disrupt Kidney and Urinary Tract Development
Bibliographic record
Abstract
Abstract Li-Fraumeni Syndrome (LFS) is a rare autosomal dominant disorder that increases the risk of various types of cancer. It is primarily caused by inherited mutations in the TP53 gene. While the tumor suppressor function of p53 is well established, its role in embryonic development, particularly in the formation of the kidney and urinary tract, remains poorly understood. Moreover, its contribution to human congenital anomalies has not been clearly defined. Here, we report that pathogenic TP53 variants can lead to congenital anomalies of the kidney and urinary tract (CAKUT), as well as genital defects (GD), in individuals with LFS. Among 28 unrelated TP53 mutation carriers, 28% (8/28) exhibited CAKUT and/or GD, with a higher frequency observed in individuals carrying structurally disruptive or dominant-negative mutations. We focused on two clinically observed variants: R242W, which destabilizes protein structure, and R282W, a dominant-negative hotspot mutation. AlphaFold modeling showed that both variants cluster within the DNA-binding domain and are predicted to disrupt tetramer formation. In Xenopus laevis, tp53 is expressed in developing nephric structures, consistent with findings from mouse models of nephrogenesis. Expression of either mutant TP53 mRNA in Xenopus embryos disrupted kidney morphogenesis in vivo , supporting a developmental loss-of-function effect. These findings indicate that pathogenic TP53 variants contribute to renal and urogenital defects in LFS. They reveal a previously unrecognized developmental role for p53 and expand the phenotypic spectrum associated with this cancer predisposition syndrome.
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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 | 0.001 | 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".