Genome-wide screening in human embryonic stem cells identifies genes and pathways involved in the p53 pathway
Bibliographic record
Abstract
BACKGROUND: The tumor suppressor protein, p53, which is mutated in half of human tumors, plays a critical role in cellular responses to DNA damage and maintenance of genome stability. Therefore, increasing our understanding of the p53 pathway is essential for improving cancer treatment and diagnosis. METHODS: This study, which aimed to identify genes and pathways that mediate resistance to p53 upregulation, used genome-wide CRISPR-Cas9 loss-of-function screening done with Nutlin-3a, which inhibits p53-MDM2 interaction, resulting in p53 accumulation and apoptotic cell death. We used bioinformatics analysis for the identification of genes and pathways that are involved in the p53 pathway and cell survival assays to validate specific genes. In addition, we used RNA-seq to identify differentially expressed p53 target genes in gene knockout (KO) cell lines. RESULTS: Our screen revealed three significantly enriched pathways: The heparan sulfate glycosaminoglycan biosynthesis, diphthamide biosynthesis and Hippo pathway. Notably, TRIP12 was significantly enriched in our screen. We found that TRIP12 is required for the p53-dependent transcription of several pro-apoptotic genes. CONCLUSION: Our study has identified two novel pathways that play a role in p53-mediated growth restriction. Moreover, we have highlighted the interaction between the Hippo and the p53 pathways. Interestingly, we have shown that TRIP12 plays an important function in the p53 pathway by selectively affecting its role as a transcription factor.
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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 | 0.001 | 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".