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Transcriptomic alterations associated with the progression of metabolic dysfunction-associated steatotic liver disease in CC042 Collaborative Cross mice.

GSE275176 Mus musculus (mouse) RNA-seq (gene expression) 60 samples Submitted 2025/07/09 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 60 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
60 samples: 60 samples, each with a distinct title — no replicated groupsGroups 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
Signature gene expression model for quantitative evaluation of MASH-like liver injury in miceToxicology and applied pharmacology · 2025
Summary (written by the submitters)
Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as nonalcoholic fatty liver disease, has emerged as the most common chronic liver disease globally, affecting more than one-third of the world’s population. Its rate has increased by 13%, rising from 25% to 38% over the past three decades.
Read the full summary ↓
MASLD is characterized by excessive fat accumulation in hepatocytes and, if left untreated, progresses to its more severe form, metabolic dysfunction-associated steatohepatitis (MASH), which poses a significant global health concern as one of the leading causes of cirrhosis, cirrhotic complications, and hepatocellular carcinoma. The molecular determinants of MASLD stratification are not clearly defined and require additional investigation. In this study, we used male and female CC042 mice, a Collaborative Cross mouse strain susceptible to the development of MASLD, to analyze the global transcriptomic profile in liver and to clarify the molecular mechanisms involved in the progression of the disease. Male and female CC042 mice were maintained on a high fat/high sucrose (HF/HS) diet for 20, 40, or 60 weeks, and hepatic gene expression profiles were determined by next-generation RNA sequencing. Chronic feeding of the HF/HS diet induced profound alterations in gene expression at all time points. The number of significantly differentially expressed genes (DEGs) in the livers of male mice was greater than female mice at 20 and 40 weeks and reached a maximum of 2764 genes at 40 weeks. In contrast, the number of DEGs in the livers of female mice steadily increased with the time on diet reaching a maximum of 2974 DEGs at 60 weeks after HF/HS diet initiation. The number of upregulated DEGs was higher than downregulated DEGs in both male and female mice at all time points. Functional analysis of DEGs showed that in both sexes, the most dysregulated liver toxicity-related pathways were associated with liver fibrosis, liver necrosis/cell death, inflammation, steatosis, liver damage, and liver proliferation/hyperplasia. In male mice, pathways associated with fibrosis, necrosis/cell death, inflammation, and steatosis remained constant from 20 to 40 weeks, and activation of liver hyperplasia pathway was constant from 40 and 60 weeks. In contrast, in female mice, dysregulation of genes associated with these pathways gradually increased from 20 to 60 weeks after HF/HS diet initiation. Importantly, we found that the expression of 701 and 871 genes significantly correlated with total NASH pathology scores (Pearson r > |0.8|) in the livers of male and female mice, respectively. These findings provide comprehensive insights into the dynamics of time- and sex-specific transcriptomic changes during NAFLD progression. This knowledge can be helpful for identifying promising targets for prevention and treatment, as well as for determining key potential biomarkers of NAFLD to NASH progression.
Published in
Signature gene expression model for quantitative evaluation of MASH-like liver injury in mice
Tryndyak VP, Willett RA, Song Z et al. · Toxicology and applied pharmacology 2025 · PMID 40527436 · doi:10.1016/j.taap.2025.117442
This dataset
Open GSE275176 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 GSE275176_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 PRJNA1149728 and SRA study SRP527332. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40527436) ↗
Study design
60 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 60 sample titles: 60 distinct titles with little repetition. 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.