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The SGLT2 Inhibitor Empagliflozin Enhances Metabolic Efficiency and Ameliorates Left Ventricular Hypertrophy in Myosin R403Q Mutant Hypertrophic Cardiomyopathy Mice

GSE270093 Mus musculus (mouse) RNA-seq (gene expression) 21 samples Submitted 2025/06/09 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 21 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
21 samples: 21 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.
Paper
Empagliflozin enhances metabolic efficiency and improves left ventricular hypertrophy in a hypertrophic cardiomyopathy mouse modelEuropean heart journal · 2025
Summary (written by the submitters)
Background: Hypertrophic cardiomyopathy (HCM) is a genetic cardiac disorder characterized by left ventricular hypertrophy (LVH), diastolic dysfunction, and impaired metabolic efficiency. This study investigates the therapeutic potential of the sodium-glucose cotransporter 2 inhibitor empagliflozin (EMPA) in ameliorating these pathological features in a mouse model carrying the myosin R403Q mutation.
Read the full summary ↓
Methods: Male mice harboring the R403Q mutation were treated with EMPA for 16 weeks. We utilized multinuclear magnetic resonance spectroscopy (31P, 13C, 23Na MRS), alongside echocardiography, transcriptomic, proteomic, and phosphoproteomic profiling to assess metabolic, structural, and functional changes. Results: EMPA facilitated the coupling of glycolysis with glucose oxidation and normalized elevated intracellular sodium levels. Treatment resulted in a significant reduction in LVH and myocardial fibrosis as evidenced by echocardiography and histopathology. These structural improvements correlated with enhancements in mitochondrial ATP synthesis, fatty acid oxidation (FAO), and branched-chain amino acid (BCAA) catabolism. Furthermore, empagliflozin improved left ventricular diastolic function and contractile reserve, underscored by improved ATP production and reduced energy cost of contraction. Notably, these benefits were linked to downregulation of the mTOR signaling pathway and normalization of myocardial substrate metabolic fluxes. Conclusion: EMPA significantly mitigates structural and metabolic dysfunctions in a mouse model of HCM, underscoring its potential as a therapeutic agent for managing this condition. These findings suggest broader applicability of SGLT2 inhibitors in cardiovascular diseases, including those due to myocardial-specific mutations, warranting further clinical investigation.
Published in
Empagliflozin enhances metabolic efficiency and improves left ventricular hypertrophy in a hypertrophic cardiomyopathy mouse model
Baka T, Moore J, Qin F et al. · European heart journal 2025 · PMID 40396194 · doi:10.1093/eurheartj/ehaf324
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 GSE270093_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 PRJNA1124979 and SRA study SRP514449. Searching any of these in the dataset finder brings you back here.

Study design
21 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 21 sample titles: 21 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
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