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Effects of different diets on autophagy control and unfolded protein response in the aging heart

GSE299132 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2026/01/28 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 5 × normal protein (NP) animal vs 5 × high-fat diet (HF) animal vs 5 × high-fat diet + low protein (HF+LP) animal vs 4 × low protein (LP) animalGroups 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
Dietary Protein Restriction Ameliorates Cardiac Inflammaging via AMPK-ULK1-Mediated Mitochondrial Quality ControlAging cell · 2026
Summary (written by the submitters)
We reported a decrease in mitochondrial function in the hearts of aging mice undergone a low protein diet, but an increased lifespan. For that, we hypothesized that the increased autophagy and unfolded protein response plays an important role in this phenomenon. For that, we want to identify changes in the heart transcriptome profiling (RNA-seq) that could explain this lifespan increased in aging mice.
Published in
Dietary Protein Restriction Ameliorates Cardiac Inflammaging via AMPK-ULK1-Mediated Mitochondrial Quality Control
Dantas WS, Zunica ERM, Heintz EC et al. · Aging cell 2026 · PMID 41549510 · doi:10.1111/acel.70386
This dataset
Open GSE299132 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 GSE299132_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 PRJNA1272631 and SRA study SRP590229. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41549510) ↗
Study design
5 × normal protein (NP) animal vs 5 × high-fat diet (HF) animal vs 5 × high-fat diet + low protein (HF+LP) animal vs 4 × low protein (LP) animal

Supports a between-group comparison across 19 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 19 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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.