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Delayed molecular aging, preservation of energy metabolism and enhanced exercise response in exercise-trained human muscle

GSE330697 Homo sapiens (human) RNA-seq (gene expression) 93 samples Submitted 2026/06/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 93 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
Homo sapiens (human)
Samples
93 samples: 93 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)
Exercise is fundamental to healthy aging, yet how it mitigates age-related molecular changes and how fitness level shapes exercise responses remain unclear. To address this, we performed transcriptomics, lipidomics, and metabolomics on skeletal muscle of young and older adults with differing physical function, both before and after an acute bout of sub-maximal exercise.
Read the full summary ↓
At baseline, older adults exhibited reduced expression of genes associated with cellular respiration and energy metabolism compared to young adults with comparable activity levels. Here we show that in trained older adults, 50% of these age-related differences were absent, resulting in profiles resembling those of young adults. While all participants displayed transcriptional immune and stress responses upon acute exercise, the magnitude of these responses in older adults correlated positively with their physical fitness. Integrated multi-omic analyses further revealed links between mitochondrial respiration, lipid metabolism, stress responses and NAD⁺ biology. These findings demonstrate that sustained physical training transforms age-related molecular profiles and provides a molecular atlas for studying fitness-dependent aging mechanisms.
This dataset
Open GSE330697 in finder → Find more human 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 GSE330697_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 PRJNA1465274. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
93 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 93 sample titles: 93 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

+ 53 more — browse all 93 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.