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Muscle transcriptome at rest and during recovery from running in elite male ultra-endurance athletes acclimated to a high-carbohydrate versus low-carbohydrate diet

GSE273041 Homo sapiens (human) RNA-seq (gene expression) 60 samples Submitted 2025/03/19 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 60 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
60 samples
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
Muscle transcriptome profiles in elite male ultra-endurance athletes acclimated to a high-carbohydrate versus low-carbohydrate dietScientific reports · 2025
Summary (written by the submitters)
Low-carbohydrate diets enhance lipid metabolism and decrease reliance on glucose oxidation in athletes, but the associated gene expression patterns remain unclear. To provide mechanistic insight, we investigated the skeletal muscle transcriptome in elite ultra-endurance athletes habitually consuming a high-carbohydrate (HC, n=10, 33±6y, VO2max=63.4±6.2 mL O2•kg-1•min-1) or low-carbohydrate (LC, n=10, 34±7y, VO2max=64.7±3.7 mL O2•kg-1•min-1) diet.
Read the full summary ↓
Skeletal muscle gene expression was measured at baseline (BL), immediately-post (H0), and 2h (H2) after 3h submaximal treadmill running. Exercise induced a coordinated but divergent expression pattern. LC had higher expression of genes associated with lipid metabolism, particularly at BL. At H2, gene expression patterns were associated with differential pathway activity, including inflammation/immunity, suggesting a diet-specific influence on early muscle recovery. These results indicate that a habitual ketogenic diet leads to differences in resting and exercise-induced skeletal muscle gene expression patterns, underlying our previous findings of differential fuel utilization during exercise in elite male ultra-endurance athletes.
Published in
Muscle transcriptome profiles in elite male ultra-endurance athletes acclimated to a high-carbohydrate versus low-carbohydrate diet
Saenz C, Lavin KM, Lee EC et al. · Scientific reports 2025 · PMID 40069235 · doi:10.1038/s41598-025-88963-9
This dataset
Open GSE273041 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 GSE273041_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 PRJNA1139832 and SRA study SRP521999. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40069235) ↗
Study design
A cohort of 60 individual samples

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

How we read this: Sample titles run CC39020001 … CC39034060 and carry no group information.

Samples in this study

+ 20 more — browse all 60 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.