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Impact of Intestinal Propionate Metabolism on Systemic Metabolic Homeostasis

GSE329264 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2026/05/02 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 5 conditions, mostly with about 4 replicates eachGroups 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)
Propionate is an abundant short-chain fatty acid in mammals, largely derived from gut microbiota production. Due to its abundance, propionate metabolism is essential to maintain systemic homeostasis. Inborn errors of essential metabolic enzymes in this pathway causes severe illness, such as the development of Propionic Acidemia in cases of Propionyl-CoA Carboxylase (PCC) deficiency.
Read the full summary ↓
We and others have previously developed mouse models deficient in Pcca, encoding the alpha subunit of PCC, to study the systemic and liver-specific mechanisms of propionic acidemia. However, the role of PCC in the intestine, where propionate is primarily absorbed, is understudied. To investigate the gut-specific role of PCC, we developed a mouse knockout of Pcca using Villin-Cre (Pcca Vill) on a floxed Pcca (Pcca ff) background. Here, we demonstrate the phenotypes present in this model provided both a standard (chow) and high fat (HFD) diets.
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 GSE329264_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 PRJNA1458231 and SRA study SRP695378. Searching any of these in the dataset finder brings you back here.

Study design
5 conditions, mostly with about 4 replicates each
Chow, PCCA ff, ×4 Chow, PCCA Vill, ×4 Chow, PCCA Alb, ×4 HFD, PCCA ff, ×4 HFD, PCCA Vill, ×4

Supports a between-group comparison across 20 samples.

How we read this: 5 replicated groups read from 20 sample titles; they account for 20 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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