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Nrf2-mediated chromatin activation of hyperosmotic response genes attenuates heat-induced renal injury [RNA-seq]

GSE266023 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/07/29 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 8 conditions, mostly in duplicateGroups 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
Nrf2-mediated epigenetic priming of osmoprotective genes enhances cellular adaptation to hyperosmotic stressThe Journal of biological chemistry · 2026
Summary (written by the submitters)
The escalating frequency and duration of extreme weather events, driven by ongoing climate change, are heightening health hazards associated with heat stress. Heat stress can cause whole-body hyperosmolarity and increase risk of chronic kidney disease. Here, we confirmed Nrf2 as the core transcription factor for cellular resistance to hyperosmotic stress through drug screening.
Read the full summary ↓
Meanwhile, we constructed a mouse model of renal injury caused by high temperature dehydration, genetic deletion of Nrf2 further increased the renal injury and fibrosis. The natural compound SFN, found in cruciferous plants, can be used to activate Nrf2 and alleviate kidney damage caused by high-temperature dehydration. Mechanistically, we performed hyperosmotic stress in kidney collecting duct cells, and through ATAC-seq and ChIP-seq analysis of multiple histone modifications, found that most of the hyperosmotic response genes changed their chromatin state after pharmacological activation of Nrf2. The activation of the chromatin state in hyperosmotic response genes enhances the binding affinity of Nfat5 following exposure to hyperosmotic stress. Together, activation of Nrf2 maintains the active chromatin state of hyperosmotic response genes. Under hyperosmotic stress, the core transcription factor Nfat5 efficiently activates gene expression, thereby assisting cells in resisting hyperosmotic stress. Additionally, activation of Nrf2 by SFN may serve as a potential therapeutic approach for preventing renal injury caused by high-temperature dehydration.
Published in
Nrf2-mediated epigenetic priming of osmoprotective genes enhances cellular adaptation to hyperosmotic stress
Lu Y, Li N, Dou X et al. · The Journal of biological chemistry 2026 · PMID 42331111 · doi:10.1016/j.jbc.2026.113278
This dataset
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.

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 GSE266023_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1105357 and SRA study SRP504401. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in duplicate
mIMCD3 cells, DMSO_1h ×2 mIMCD3 cells, DMSO_6h ×2 mIMCD3 cells, DMSO_Ctrl ×2 mIMCD3 cells, Nrf2KO_SFN ×2 mIMCD3 cells, Nrf2KO_SFN_HS1h ×2 mIMCD3 cells, SFN_1h ×2 mIMCD3 cells, SFN_6h ×2 mIMCD3 cells, SFN_Ctrl ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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
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