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Peroxisome Proliferator-activated Receptor Agonist IVA337 Alleviates Inflammation

GSE292999 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2025/06/18 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 14 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
Homo sapiens (human)
Samples
14 samples: 7 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
Peroxisome Proliferator-Activated Receptor Agonist IVA337 Alleviates Inflammation and Fibrosis in MASH by Restoring Lipid HomeostasisThe American journal of pathology · 2025
Summary (written by the submitters)
Metabolic dysfunction-associated steatohepatitis (MASH), an advanced stage of metabolic dysfunction-associated steatotic liver disease (MASLD), is characterized by significant hepatic fibrosis and inflammation. The pan-peroxisome proliferator-activated receptor (pan-PPAR) agonist IVA337 (lanifibranor) has demonstrated potential as an anti-MASH therapeutic, although its mechanisms of action remain incompletely understood.
Read the full summary ↓
This study explores the effects and mechanisms of IVA337 using two distinct MASH models: 2D primary human hepatic stellate cells (HSCs) stimulated with TGFß1, and 3D liver spheroids comprising primary hepatocytes, HSCs, and nonparenchymal cells. In TGFß1-stimulated HSCs, IVA337 effectively suppressed the expression of fibrosis-related genes, including PAI1, COL1A1, and SMA, as well as the inflammatory gene IL-6. We successfully established 3D mouse and human liver spheroid models of MASH, characterized by reduced lipid content and elevated fibrotic gene expression. IVA337 treatment not only attenuated fibrotic gene expression but also restored lipid content in the MASH spheroids, as evidenced by BODIPY staining. Immunostaining further confirmed a reduction in SMA and collagen levels following IVA337 treatment. Bulk RNA sequencing and Gene Ontology (GO) analysis revealed lipid metabolism-related genes as key effectors downstream of IVA337. Additionally, IVA337 modulated multiple signaling pathways, including IL-17, TNF, NF-kappa B, PI3K-AKT, and MAPK. Collectively, these findings demonstrate that IVA337 effectively mitigates fibrosis development in both 2D and 3D MASH models by restoring lipid homeostasis and regulating crucial fibrotic and inflammatory pathways.
Published in
Peroxisome Proliferator-Activated Receptor Agonist IVA337 Alleviates Inflammation and Fibrosis in MASH by Restoring Lipid Homeostasis
Li N, Wu S, Li X et al. · The American journal of pathology 2025 · PMID 40712710 · doi:10.1016/j.ajpath.2025.06.012
This dataset
Open GSE292999 in finder → Find more human 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 GSE292999_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 PRJNA1242327 and SRA study SRP573360. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40712710) ↗
Study design
7 conditions, mostly in duplicate
00SC-CTRL ×2 00SC-IVA ×2 55SC-CTRL ×2 55SC-IVA ×2 Spheroid-CTRL ×2 Spheroid-NASH ×2 Spheroid-IVA ×2

Supports a between-group comparison across 14 samples.

How we read this: 7 replicated groups read from 14 sample titles; they account for 14 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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