Differential effect of plakoglobin in restoring the tumor suppressor activities of p53-R273H vs. p53-R175H mutants
Bibliographic record
Abstract
The six most common missense mutations in the DNA binding domain of p53 are known as "hot spots" and include two of the most frequently occurring p53 mutations (p53-R175H and p53-R273H). p53 stability and function are regulated by various post-translational modifications such as phosphorylation, acetylation, sumoylation, methylation, and interactions with other proteins including plakoglobin. Previously, using various carcinoma cell lines we showed that plakoglobin interacted with wild-type and several endogenous p53 mutants (e.g., R280K, R273H, S241F, S215R, R175H) and restored their tumor suppressor activities in vitro. Since mutant p53 function is both mutant-specific and cell context-dependent, we sought herein, to determine if plakoglobin tumor suppressive effects on exogenously expressed p53-R273H and p53-R175H mutants are similarly maintained under the same genetic background using the p53-null and plakoglobin-deficient H1299 cell line. Functional assays were performed to assess colony formation, migration, and invasion while immunoblotting and qPCR were used to examine the subcellular distribution and expression of specific proteins and genes that are typically regulated by or regulate p53 function and are altered in mutant p53-expressing cell lines and tumors. We show that though, plakoglobin interacted with both p53-R273H and p53-R175H mutants, it had a differential effect on the transcription and subcellular distribution of their gene targets and their overall oncogenic properties in vitro. Notably, we found that plakoglobin's tumor suppressive effects were significantly stronger in p53-R175H expressing cells compared to p53-R273H cells. Together, our results indicate that exploring plakoglobin interactions with p53-R175H may be useful for the development of cancer therapeutics focused on the restoration of p53 function.
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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) | 0.001 | 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".