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Wild type and tumor-derived mutant p53 share a non-canonical mode of gene occupancy [RNA-seq]

GSE281318 Mus musculus (mouse) RNA-seq (gene expression) 35 samples Submitted 2025/01/04 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 35 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
35 samples: 11 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
To directly examine the interplay between mutant p53 or Mdm2 and wild type p53 in gene occupancy and expression, an integrated RNA-seq and ChIP-seq analysis was performed in vivo using isogenically matched mouse strains. Response to radiation was used as an endpoint to place findings in a biologically relevant context. Unexpectedly, mutant p53 and Mdm2 only inhibit a subset of wild type p53 gene expression.
Read the full summary ↓
In contrast to a dominant-negative or inhibitory role, the presence of either mutant p53 or Mdm2 actually enhances the occupancy of wild type p53 on many canonical targets. The C-terminal 19 amino acids of wild type p53 suppresses the p53 response allowing for survival at sublethal doses of radiation. Further, the mutant 172H p53 is shown to occupy genes and regulate their expression via a non-canonical means that is shared with wild type p53. This results in the heterozygous 172H/+ genotype having an expanded transcriptome compared to wild type p53+/+.
This dataset
Open GSE281318 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE281318_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1183367 and SRA study SRP543878. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
11 conditions, mostly in triplicate
WT_9_5_Gy ×3 SJ_9_5_Gy ×3 Null_9_5_Gy ×3 M_plus_9_5_Gy ×3 M_9_5_Gy ×3 BM_WT_p53.8w ×3 BM_Mdm2_Tg.8w ×3 BM_R172H.plus.8w ×3 +6 more

Supports a between-group comparison across 32 samples.

How we read this: 11 replicated groups read from 35 sample titles; they account for 32 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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