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Gestational Low-Protein Diet Impairs Mitochondrial Function and Skeletal Muscle Development by Inducing Immune Responses in Male Offspring

GSE307422 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/10/14 Platform GPL34328
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 6 × NN vs 6 × LNGroups 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
Gestational low-protein diet impairs mitochondrial function and skeletal muscle development by inducing immune responses in male offspringRedox biology · 2025
Summary (written by the submitters)
To elucidate the potential underlying mechanisms of protein restriction during gestation, we compared the transcriptomic profiles of GAS muscles from 21-day-old offspring of mothers on a low-protein diet during gestation with those of a control group using RNA-seq analysis.
Published in
Gestational low-protein diet impairs mitochondrial function and skeletal muscle development by inducing immune responses in male offspring
Altinpinar AE, Alameddine M, Ersoy U et al. · Redox biology 2025 · PMID 41101209 · doi:10.1016/j.redox.2025.103890
This dataset
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.

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 GSE307422_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1321603 and SRA study SRP618253. Searching any of these in the dataset finder brings you back here.

Study design
6 × NN vs 6 × LN

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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
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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.