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BMAL1 and YAP cooperate to hijack enhancers and promote inflammation in the aged epidermis [RNA-seq Col14]

GSE294339 Mus musculus (mouse) RNA-seq (gene expression) 48 samples Submitted 2026/06/25 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 48 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
48 samples: 12 conditions, mostly with about 4 replicates eachGroups 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)
Ageing is characterised by persistent low-grade inflammation that is linked to impaired tissue homeostasis and functionality. However, the molecular mechanisms driving age-associated inflammation remain poorly understood. The mammalian skin is a clinically relevant target of age-driven inflammation associated with compromised barrier function, inefficient wound healing, elevated oxidative stress, and DNA damage accumulation.
Read the full summary ↓
Here, we show that upon ageing a previously uncharacterised BMAL1–YAP transcriptional complex is hijacked from chromatin regions associated with homeostatic genes in adult epidermis and redirected to inflammation-related enhancers, amplifying the transcription of their target genes. Independently of its known role as a core circadian clock component, BMAL1 partners with the mechanosensitive transcriptional cofactor YAP at enhancer regions to regulate epidermal identity genes. In contrast, in aged skin, BMAL1–YAP complexes bind to enhancers of inflammation-related genes, co-regulated by NF-κB. Interestingly, aged pro-inflammatory signals from the IL-17 pathway activate YAP in a Hippo-independent manner. These findings unveil a transcriptional mechanism underlying epidermal ageing, linking chromatin dynamics to inflammatory transcriptional programs through BMAL1–YAP-bound enhancer rewiring. By elucidating how ageing reprograms transcriptional networks, our work highlights potential strategies to counteract chronic inflammation and restore tissue homeostasis across age-related loss of functionality.
This dataset
Open GSE294339 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 GSE294339_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 PRJNA1249051 and SRA study SRP578070. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly with about 4 replicates each
COLXIV_KO_ZT_12 biol ×4 COLXIV_KO_ZT_4 biol ×4 COLXIV_KO_ZT_8 biol ×4 COLXIV_KO_ZT_20 biol ×4 COLXIV_KO_ZT_0 biol ×4 COLXIV_WT_ZT_12 biol ×4 COLXIV_WT_ZT_16 biol ×4 COLXIV_WT_ZT_4 biol ×4 +5 more

Supports a between-group comparison across 47 samples.

How we read this: 12 replicated groups read from 48 sample titles; they account for 47 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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