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SIRT6 Overexpression Counteracts Chromatin Aging [ChIP-Seq]

GSE294085 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 74 samples Submitted 2026/04/12 Platform GPL34290
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 74 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
74 samples: 20 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
SIRT6 overexpression counteracts chromatin aging in the male murine liverNature communications · 2026
Summary (written by the submitters)
Aging is associated with detrimental changes in chromatin structure and gene expression, contributing to inflammation, metabolic decline and tissue dysfunction. SIRT6, a histone deacetylase, plays a key role in maintaining chromatin integrity and promoting longevity. Here, we show that aging leads to increased chromatin accessibility in the murine liver, accompanied by upregulation of inflammation and downregulation of metabolic pathways.
Read the full summary ↓
Remarkably, SIRT6 overexpression reversed these changes, reducing inflammation and enhancing metabolic function. Notably, ETS family members were enriched in regions with increased accessibility during aging, while liver-enriched transcription factors (LETFs) were enriched in regions with reduced accessibility. ChIP-seq analyses of H3K9ac and H3K56ac binding showed that H3K9ac, but not H3K56ac, is associated with increased accessibility during aging, and that SIRT6 can reverse this effect. Furthermore, AAV-mediated SIRT6 overexpression in aged mice demonstrated that SIRT6 not only slows age-related chromatin changes but can also reverse them, rejuvenating chromatin accessibility to a youthful state.
Published in
SIRT6 overexpression counteracts chromatin aging in the male murine liver
Nagar R, Schwartz Z, Katz A et al. · Nature communications 2026 · PMID 42135312 · doi:10.1038/s41467-026-73115-y
This dataset
Open GSE294085 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE294085_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1248043 and SRA study SRP577283. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42135312) ↗
Study design
20 conditions, mostly in duplicate
H3K9ac_H3K56ac_Input_old_wt ×6 H3K9ac_H3K56ac_Input_old_sirt6_tg ×6 H3K9ac_old_wt ×6 H3K9ac_old_sirt6_tg ×6 H3K56ac_old_sirt6_tg ×6 H3K9ac_H3K56ac_Input_young_wt ×4 H3K9ac_H3K56ac_Input_young_sirt6_tg ×4 H3K9ac_young_wt ×4 +12 more

Supports a between-group comparison across 74 samples.

How we read this: 20 replicated groups read from 74 sample titles; they account for 74 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

+ 34 more — browse all 74 samples with per-sample file links →

Datasets on a similar topic

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