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ERα governs a specific gene expression program and metabolic function in white adipose tissue.

GSE121691 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 21 samples Submitted 2024/12/31 Platform GPL13112
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 21 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
21 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.
Summary (written by the submitters)
The White Adipose Tissue (WAT) has a major influence on energy metabolic functions and expresses the estrogen receptor ERα. Ovariectomized mice or mice carrying an ERα knock-out gene (ERKO), have a higher risk of becoming obese and developing metabolic disorders such as diabetes or dyslipidemia. To identify the direct target genes of ERα in visceral WAT, we treated ovariectomized mice with estradiol (E2) for 2 hours and proceeded to a genome-wide analysis of ERα binding sites to systemically identify them.
Read the full summary ↓
We demonstrate that the E2/ERα pathway increases the expression of genes involved in the balance between lipids and glucose catabolism, the energy expenditure through the TCA cycle and the respiratory membrane chain. ERα binding sites and gene expression analysis in visceral and subcutaneous WAT, brown adipose tissue and liver show a specific program for each tissue. Our results are consistent with E2/ERα signaling protecting against metabolic diseases such as obesity by regulating an important gene regulatory network that helps maintaining normal metabolic functions in WAT.
This dataset
Open GSE121691 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 GSE121691_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 PRJNA498107 and SRA study SRP166557. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in duplicate
vWATpE2_ERa_ChIPseq ×4 scWATpE2_ERa_ChIPseq ×3 vWATpE2_RNAseq ×3 vWATmE2_ERa_ChIPseq ×2 vWATpE2_H3K27ac_ChIPseq ×2 vWATmE2_H3K27ac_ChIPseq ×2 LiverpE2_ERa_ChIPseq ×2 vWATmE2_RNAseq ×2 +1 more

Supports a between-group comparison across 20 samples.

How we read this: 8 replicated groups read from 21 sample titles; they account for 20 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
Datasets on a similar topic

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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.