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Sex- and age-associated factors drive the pathophysiology of MASLD

GSE274914 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2024/09/03 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 32 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
32 samples: 14 conditions, mostly in duplicateGroups 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
Sex- and age-associated factors drive the pathophysiology of MASLDHepatology communications · 2024
Summary (written by the submitters)
Metabolic dysfunction-associated steatotic liver disease (MASLD) is strongly associated with obesity. The use of animal models fed Western-style diets is vital for investigating the molecular mechanisms contributing to metabolic dysregulation and for facilitating novel drug target identification. However, the sex- and age-associated mechanisms underlying the pathophysiology remain poorly understood.
Read the full summary ↓
Here we show distinct responses to a HFD between males and females. Our study underscores the need for utilizing both sexes in drug target identification studies, and characterizing the molecular mechanisms contributing to the MASLD pathophysiology in aging animals. Hepatology Communications 2024
Published in
Sex- and age-associated factors drive the pathophysiology of MASLD
Yadav AK, MacNeill JJ, Krylov A et al. · Hepatology communications 2024 · PMID 39185904 · doi:10.1097/HC9.0000000000000523
This dataset
Open GSE274914 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 GSE274914_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 PRJNA1148454 and SRA study SRP526616. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39185904) ↗
Study design
14 conditions, mostly in duplicate
M2 ×2 M5 ×2 M3 ×2 M4 ×2 M7 ×2 M9 ×2 F2 ×2 F4 ×2 +10 more

Supports a between-group comparison across 28 samples.

How we read this: 14 replicated groups read from 32 sample titles; they account for 28 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

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.