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A polyphenol-rich extract recovered from an industrial byproduct regulates the intestinal microbiota and restricts enteric parasite infection

GSE328345 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2026/07/01 Platform GPL34328
At a glance

RNA-seq (gene expression) · mouse · 36 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
36 samples: 9 × Control Diet vs 9 × Red Clover Diet vs 9 × Control Diet + H polygyrus vs 9 × Red Clover Diet + H polygyrusGroups 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)
Dietary polyphenols have shown potential in limiting inflammation during noncommunicable diseases. However, interaction of polyphenols with the immune system and gut microbiome during enteric infections are not well defined. Moreover, efficient methods to recover high-value polyphenols from industrial processes are limited. Here, we describe the recovery of a polyphenol-rich extract from a byproduct of protein-producing biorefineries.
Read the full summary ↓
The extract was modified to allow incorporation into a semi-synthetic mouse diet. During infection with the intestinal nematode Heligmosomoides polygyrus, mice fed the polyphenol-enriched diet had significantly lower parasite burdens and enhanced mast cell–related gene expression in the jejunum, potentially accelerating worm expulsion. Effects on the gut microbiome were dependent on infection status. In uninfected animals, the extract significantly reshaped the gut microbiome by expanding a broad range of short-chain fatty acid (SCFA)-producing bacteria. However, during H. polygyrus infection, its modulatory effect on the microbiome was markedly diminished concomitant with an extensive increase in the abundance of the Lactobacillaceae and Enterobacteriaceae families in infected mice. However, the extract reduced the relative abundance of Clostridiaceae during infection, suggesting a role in restoring gut homeostasis by counteracting parasite-induced expansion of potentially pathogenic bacteria. Collectively, these findings demonstrate that byproducts from the biorefining industry can be repurposed as functional food components with the potential to limit enteric infection and inflammation.
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 GSE328345_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 PRJNA1454808 and SRA study SRP692686. Searching any of these in the dataset finder brings you back here.

Study design
9 × Control Diet vs 9 × Red Clover Diet vs 9 × Control Diet + H polygyrus vs 9 × Red Clover Diet + H polygyrus

Supports a between-group comparison across 36 samples.

How we read this: 4 replicated groups read from 36 sample titles; they account for 36 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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