Genome-wide analysis of the p53 gene regulatory network in the developing mouse kidney
Bibliographic record
Abstract
Despite mounting evidence that p53 senses and responds to physiological cues in vivo, existing knowledge regarding p53 function and target genes is largely derived from studies in cancer or stressed cells. Herein we utilize p53 transcriptome and ChIP-Seq (chromatin immunoprecipitation-high throughput sequencing) analyses to identify p53 regulated pathways in the embryonic kidney, an organ that develops via mesenchymal-epithelial interactions. This integrated approach allowed identification of novel genes that are possible direct p53 targets during kidney development. We find the p53-regulated transcriptome in the embryonic kidney is largely composed of genes regulating developmental, morphogenesis, and metabolic pathways. Surprisingly, genes in cell cycle and apoptosis pathways account for <5% of differentially expressed transcripts. Of 7,893 p53-occupied genomic regions (peaks), the vast majority contain consensus p53 binding sites. Interestingly, 78% of p53 peaks in the developing kidney lie within proximal promoters of annotated genes compared with 7% in a representative cancer cell line; 25% of the differentially expressed p53-bound genes are present in nephron progenitors and nascent nephrons, including key transcriptional regulators, components of Fgf, Wnt, Bmp, and Notch pathways, and ciliogenesis genes. The results indicate widespread p53 binding to the genome in vivo and context-dependent differences in the p53 regulon between cancer, stress, and development. To our knowledge, this is the first comprehensive analysis of the p53 transcriptome and cistrome in a developing mammalian organ, substantiating the role of p53 as a bona fide developmental regulator. We conclude p53 targets transcriptional networks regulating nephrogenesis and cellular metabolism during kidney development.
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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".