← BioTransfer GEO Dataset Finder
GEO series

Pharmacological inhibition of the epigenetic enzyme EZH2 attenuates cardiac dysfunction and mitigates lipid metabolism perturbations in a murine model of myocardial infarction

GSE304090 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2026/01/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 15 samples, each with a distinct title — no replicated groupsGroups 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)
Rational: EZH2, an epigenetic enzyme, acts as a transcriptional repressor for genes dysregulated after myocardial infarction (MI). Objective: This study aims to assess the benefits of GSK-343 (EZH2 inhibitor) on cardiac function and lipid metabolism after MI. Methods: Mice were treated daily with vehicle or GSK-343 for 7 days after MI and we evaluated cardiac function (echocardiography), gene expression profiling (RNA-seq) and circulating lipidome (untargeted lipidomics).
Read the full summary ↓
Results: The 28% MI decreased ejection fraction (EF) was improved by 20% with GSK-343. RNA-seq analysis revealed wide changes in MI normalized with GSK-343: 16 mitochondrial DNA-encoded genes, 57 genes related to mitochondrial function and 15 to lipid metabolism among the 561 down-regulated genes (FC 0.5 and 2; p=10-4). In plasma, while MI enhanced 18 triglycerides (1.24-2.43-fold; p<0.05) and decreased 32 choline glycerophospholipids (PC; 0.55-0.81-fold, p<0.05), GSK-343 normalized these perturbations. In MI, some of these lipids positively correlated with EF (e.g. PC40:6; R=0.84, p=0.004) or with cardiac hypertrophy (e.g. DG18:1_18:2; R=0.81, p=0.007) and GSK-343 abolished most of these correlations. Conclusion: Our study suggests that inhibiting EZH2 is of therapeutic interest to normalize lipid metabolism perturbations and improve cardiac function after MI.
This dataset
Open GSE304090 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 GSE304090_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 PRJNA1298897 and SRA study SRP604636. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
15 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 15 sample titles: 15 distinct titles with little repetition. 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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.