GEO series

Aromatic Microbial Metabolite Hippuric Acid Potentiates Pro-Inflammatory Responses in Macrophages through TLR-MyD88 Signaling and Lipid Remodeling

GSE307919 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/12/23 Platform GPL19057

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: 3 × untreated, vs 3 × HA treated, vs 3 × LPS+IFNg treated, vs 3 × HA and LPS+IFNg treated,Groups 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: Aromatic microbial metabolite hippuric acid enhances inflammatory responses in macrophages via TLR-MyD88 signaling and lipid remodelingCell reports · 2026

Summary (written by the submitters)

The gut microbiome generates a diverse array of metabolites that actively shape host immunity, yet the pro-inflammatory potential of microbial metabolites remains incompletely understood. Using a non-targeted, liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics, we identified hippuric acid, an aromatic gut microbe-derived metabolite, as a potent enhancer of pro-inflammatory responses in Escherichia coli infection model.

Read the full summary ↓

Intraperitoneal administration of hippuric acid significantly heightened pro-inflammatory responses, promoted innate immune cell activation, and reduced survival in infected mice. Similar pro-inflammatory effects were observed in an LPS-induced inflammation model. In vitro, hippuric acid selectively potentiated M1-like macrophage polarization (LPS + IFNγ) but had no effect on M2-like polarization (IL-4). Hippuric acid further augmented responses to multiple myeloid differentiation primary response 88 (MyD88)-dependent toll-like receptor (TLR) ligands, but not to TRIF-dependent TLR3, or to cytosolic innate immune stimuli such as STING and NOD2 agonists, implicating TLR-MyD88 signaling as a likely mechanism of action. Genetic deletion of MyD88 abrogated the pro-inflammatory effects of hippuric acid both in vitro and in vivo, confirming its dependence on the MyD88 pathway. Transcriptomic and lipidomic analyses revealed that hippuric acid upregulated cholesterol biosynthesis and induced lipid accumulation. Pharmacological reduction of cellular cholesterol using fluvastatin or 25-hydroxycholesterol attenuated its pro-inflammatory effects. Notably, hippuric acid also enhanced pro-inflammatory responses in human macrophages, and its elevated levels correlated with increased sepsis mortality, underscoring its clinical relevance. Together, these findings identify hippuric acid as a previously unrecognized microbial-derived pro-inflammatory modulator that links gut microbial metabolism, lipid remodeling, and innate immune signaling, and offer new insights into its role in infection and inflammation.

Published in

Aromatic microbial metabolite hippuric acid enhances inflammatory responses in macrophages via TLR-MyD88 signaling and lipid remodeling

Mirji G, Bhat SA, El Sayed M et al. · Cell reports 2026 · PMID 41477766 · doi:10.1016/j.celrep.2025.116749

This dataset

Open GSE307919 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 GSE307919_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 PRJNA1327810 and SRA study SRP619649. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41477766) ↗

Study design

3 × untreated, vs 3 × HA treated, vs 3 × LPS+IFNg treated, vs 3 × HA and LPS+IFNg treated,

Supports a between-group comparison across 12 samples.

How we read this: 4 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

GSM9233978 untreated, replicate 1 untreated,
GSM9233979 untreated, replicate 2 untreated,
GSM9233980 untreated, replicate 3 untreated,
GSM9233981 HA treated, replicate 1 HA treated,
GSM9233982 HA treated, replicate 2 HA treated,
GSM9233983 HA treated, replicate 3 HA treated,
GSM9233984 LPS+IFNg treated, replicate 1 LPS+IFNg treated,
GSM9233985 LPS+IFNg treated, replicate 2 LPS+IFNg treated,
GSM9233986 LPS+IFNg treated, replicate 3 LPS+IFNg treated,
GSM9233987 HA and LPS+IFNg treated, replicate 1 HA and LPS+IFNg treated,
GSM9233988 HA and LPS+IFNg treated, replicate 2 HA and LPS+IFNg treated,
GSM9233989 HA and LPS+IFNg treated, replicate 3 HA and LPS+IFNg treated,

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