Dominant‐Negative Effects of p53 R337 Variants in Li–Fraumeni Syndrome: Impact on Tetramer Formation and Transcriptional Activity
Bibliographic record
Abstract
Li-Fraumeni syndrome (LFS) is an inherited cancer predisposition disorder caused by heterozygous TP53 mutations. Among these, missense mutations at Arg337-such as R337C and R337H-are common in LFS patients. Although many studies have characterized individual p53 variants in LFS, the impact of tetramerization domain (TD) mutations on wild-type (WT) p53 function remains unclear. Herein, a novel FRET-based assay system that enables the simultaneous detection of heterotetramer formation and p53-dependent transcriptional activity in live cells is developed. These results show that the heteromultimerization of the R337C variant with WT p53 is only slightly reduced compared to WT homotetramers, yet its transcriptional activity is diminished by over 50%. In contrast, the R337H variant forms heterotetramers at near-normal levels but exhibits markedly compromised transcriptional activity. These findings reveal a previously unrecognized dominant-negative-like effect, suggesting reduced p53 function is due not only to decreased tetramer formation but also to diminished heterotetramer stability. Moreover, the LFS-associated p53TD variants show a greater loss of activity against the low-affinity, apoptosis-inducing bax response element than against the high-affinity, cell cycle arrest-related CDKN1A response element. Collectively, this study demonstrates that p53TD mutations can exert dominant-negative effects, advancing the understanding of p53 heteromultimer function in LFS pathogenesis. These mechanistic insights into p53 heterotetramer stability may not only inform genetic screening strategies for LFS but also support future therapeutic approaches aimed at restoring p53 function by stabilizing mutant tetramers.
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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 | 0.000 | 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 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".