GEO series

DTMB mitigates diet-induced metabolic dysfunction-associated steatohepatitis by targeting inflammation and lipid metabolism

GSE315019 Mus musculus (mouse) RNA-seq (gene expression) 22 samples Submitted 2026/07/31 Platform GPL34328

At a glance

RNA-seq (gene expression) · mouse · 22 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: 22 samples: 6 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)

Metabolic dysfunction-associated steatohepatitis (MASH) is an advanced stage of metabolic dysfunction-associated steatotic liver disease (MASLD), and is characterized by steatosis, inflammation, and fibrosis. Without effective treatment, MASH can progress to cirrhosis and hepatocellular carcinoma. Despite recent therapeutic advances, there remains an unmet need for agents that address both chronic inflammation and metabolic dysfunction.

Read the full summary ↓

Here, we investigate the effects of DTMB, a peroxisome proliferator-activated receptor gamma (PPAR-γ)-modulating compound, in multiple diet-induced models of MASLD and MASH. DTMB treatment was associated with reduced hepatic inflammation, accompanied by suppression of NF-κB/NLRP3 inflammasome-related signaling and attenuation of hepatic steatosis, alongside increased adipose triglyceride lipase (ATGL) expression. Notably, unlike full PPAR-γ agonists, DTMB exerts these beneficial effects without causing weight gain. Collectively, our study highlights the promising protective effect of DTMB as a selective PPAR-γ modulator for treating metabolic dysfunction-associated liver disease.

This dataset

Open GSE315019 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 GSE315019_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 PRJNA1394873 and SRA study SRP658368. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly with about 4 replicates each

HFD cohort_HFD_Veh_biol ×4 HFD cohort_HFD_DTMB_biol ×4 MCD cohort_ND_Veh_biol ×4 MCD cohort_MCD_Veh_biol ×4 HFD cohort_ND_Veh_biol ×3 MCD cohort_MCD_DTMB_biol ×3

Supports a between-group comparison across 22 samples.

How we read this: 6 replicated groups read from 22 sample titles; they account for 22 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

GSM9420184 HFD cohort_ND_Veh_biol rep 1 HFD cohort_ND_Veh_biol
GSM9420185 HFD cohort_ND_Veh_biol rep 2 HFD cohort_ND_Veh_biol
GSM9420186 HFD cohort_ND_Veh_biol rep 3 HFD cohort_ND_Veh_biol
GSM9420187 HFD cohort_HFD_Veh_biol rep 1 HFD cohort_HFD_Veh_biol
GSM9420188 HFD cohort_HFD_Veh_biol rep 2 HFD cohort_HFD_Veh_biol
GSM9420189 HFD cohort_HFD_Veh_biol rep 3 HFD cohort_HFD_Veh_biol
GSM9420190 HFD cohort_HFD_Veh_biol rep 4 HFD cohort_HFD_Veh_biol
GSM9420191 HFD cohort_HFD_DTMB_biol rep 1 HFD cohort_HFD_DTMB_biol
GSM9420192 HFD cohort_HFD_DTMB_biol rep 2 HFD cohort_HFD_DTMB_biol
GSM9420193 HFD cohort_HFD_DTMB_biol rep 3 HFD cohort_HFD_DTMB_biol
GSM9420194 HFD cohort_HFD_DTMB_biol rep 4 HFD cohort_HFD_DTMB_biol
GSM9420195 MCD cohort_ND_Veh_biol rep 1 MCD cohort_ND_Veh_biol
GSM9420196 MCD cohort_ND_Veh_biol rep 2 MCD cohort_ND_Veh_biol
GSM9420197 MCD cohort_ND_Veh_biol rep 3 MCD cohort_ND_Veh_biol
GSM9420198 MCD cohort_ND_Veh_biol rep 4 MCD cohort_ND_Veh_biol
GSM9420199 MCD cohort_MCD_Veh_biol rep 1 MCD cohort_MCD_Veh_biol
GSM9420200 MCD cohort_MCD_Veh_biol rep 2 MCD cohort_MCD_Veh_biol
GSM9420201 MCD cohort_MCD_Veh_biol rep 3 MCD cohort_MCD_Veh_biol
GSM9420202 MCD cohort_MCD_Veh_biol rep 4 MCD cohort_MCD_Veh_biol
GSM9420203 MCD cohort_MCD_DTMB_biol rep 1 MCD cohort_MCD_DTMB_biol
GSM9420204 MCD cohort_MCD_DTMB_biol rep 2 MCD cohort_MCD_DTMB_biol
GSM9420205 MCD cohort_MCD_DTMB_biol rep 3 MCD cohort_MCD_DTMB_biol

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