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BMAL1–HIF2A heterodimer modulates circadian variations of myocardial injury

GSE260643 Homo sapiens (human) RNA-seq (gene expression) 155 samples Submitted 2026/02/01 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 155 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
155 samples: 155 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
BMAL1-HIF2A heterodimer modulates circadian variations of myocardial injuryNature · 2025
Summary (written by the submitters)
Cardiac injury following myocardial infarction exhibits a circadian pattern, yet the underlying mechanism remains unclear. To elucidate genes governing circadian variation of myocardial injury, we conducted transcriptomic profiling of left-ventricular tissues from mice or humans experiencing myocardial injury at different daytimes. Through comprehensive analyses, including transgenic mouse models and functional studies, we identified BMAL1 as a pivotal transcription factor modulating diurnal variation of myocardial injury.
Read the full summary ↓
Remarkably, we discovered that BMAL1 regulates circadian-dependent cardiac injury by forming a transcriptionally active heterodimer with HIF2A. Substantiating this finding, we determined the cryo-EM structure of the BMAL1/HIF2F/DNA complex, revealing a previously unknown capacity for structural rearrangement within BMAL1. Furthermore, we confirmed amphiregulin (AREG) as a transcriptional target of the BMAL1/HIF2A heterodimer, critical for modulating circadian variation of myocardial injury. Finally, targeting the BMAL1/HIF2A-AREG pathway via timed AREG administration or enhancing circadian rhythm pharmacologically offered significant cardioprotection, implicating this pathway in treating ischemic heart disease.
Published in
BMAL1-HIF2A heterodimer modulates circadian variations of myocardial injury
Ruan W, Li T, Bang IH et al. · Nature 2025 · PMID 40269168 · doi:10.1038/s41586-025-08898-z
This dataset
Open GSE260643 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 GSE260643_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 PRJNA1082392 and SRA study SRP492707. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40269168) ↗
Study design
155 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 155 sample titles: 155 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

+ 115 more — browse all 155 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.