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Methionine-supplemented Longevity diet increases GLP-1 and FGF21 to reduce frailty and promote healthspan

GSE297351 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2026/06/23 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 10 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
10 samples: 5 × SD vs 5 × LDGroups 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)
Southern European countries have some of the highest life expectancies in the world, yet they display high frailty. Here, we examined different diets to identify compositions that promote both healthspan and strength. In genetically diverse mice, the western and ketogenic diets increased fat mass, insulin resistance, and cholesterol levels.
Read the full summary ↓
In contrast, a low-protein pescatarian Longevity Diet (LD), combining traditional Mediterranean and Okinawan features with moderate methionine supplementation (LDMM) or a fasting-mimicking diet (FMD), reduced fat mass and frailty while improving cardiometabolic markers. The normocaloric LDMM reduced IGF-1 while increasing growth hormone, GLP-1, and FGF21, which was crucial for diet-induced fat loss and improved insulin sensitivity. These findings indicate that high methionine intake may partially explain the major increase in overweight and diabetes in epidemiological studies of subjects who maintain a healthy lifestyle but consume a high animal protein diet, but not in those consuming a high plant protein diet.
This dataset
Open GSE297351 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 GSE297351_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 PRJNA1263616 and SRA study SRP585729. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 × SD vs 5 × LD

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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

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