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Viral integration promotes SV40T-induced immortalization by disturbing the expression of DNA/chromosome- and ECM-associated functional genes

GSE247743 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/03/10 Platform GPL21273
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 6 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.
Paper
Viral integration promotes SV40T-induced immortalization by disturbing the expression of DNA/chromosome- and ECM-associated functional genesGene · 2024
Summary (written by the submitters)
Lentivirus containing simian virus 40 large T antigen (SV40T) is routinely used to induce cell immortalization. However, the roles of viral integration itself in this progress is still controversial. Here, we transformed primary mouse embryonic fibroblasts (MEFs) with SV40T lentivirus and studied the roles of viral integration in the immortalization using RNA-seq and whole genome sequencing (WGS).
Read the full summary ↓
During the immortalization, differentially expressed genes (DGEs) are enriched in viral infection and several diverse activities. However, DEGs between immortalized and aging cells are significantly enriched in DNA/chromosome- and extracellular matrix (ECM)-associated activities. Gene regulatory network (GRN) analysis shows that although p53 is a key regulatory factor, many other transcription factors also play critical roles in the process, like STAT1. Of these DEGs, 32 genes have viral integration in their coding and/or regulatory regions. Our findings suggest that viral integration may promote SV40T-mediated immortalization by disturbing the expression of DNA/chromosome- and ECM-associated genes.
Published in
Viral integration promotes SV40T-induced immortalization by disturbing the expression of DNA/chromosome- and ECM-associated functional genes
Li Z, Xu T, Li X et al. · Gene 2024 · PMID 38048968 · doi:10.1016/j.gene.2023.148060
This dataset
Open GSE247743 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 GSE247743_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 PRJNA1040240 and SRA study SRP471830. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38048968) ↗
Study design
6 conditions, mostly in triplicate
1_SV40T ×3 1_Control ×3 1_Vehicle ×3 2_SV40T ×3 2_Control ×3 2_Vehicle ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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