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Transcription factor occupancy is affected by genetic variation in the white adipose tissue of C57BL6/j and 129S1/SvImJ mice [RNA-seq]

GSE281686 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/11/12 Platform GPL19057

At a glance

RNA-seq (gene expression) · mouse · 24 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: 24 samples: 7 × RNAseq_B6_HFD vs 6 × RNAseq_B6_Chow vs 6 × RNAseq_SV_HFD vs 5 × RNAseq_SV_ChowGroups 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)

Most of the disease associated genetic variants identified in genome wide association studies have been mapped to the non-coding regions of the genome. One of the leading mechanisms by which these variants are thought to affect disease susceptibility is by altering transcription factor (TF) binding. Even though inbred mouse strains have been commonly used to investigate polygenic diseases, less is known on how their genetic differences translate to the level of gene regulation and chromatin landscape.

Read the full summary ↓

Here, we investigated how genetic variation affects chromatin accessibility, a commonly used proxy for TF binding, and how this relates to gene regulation in the epididymal white adipose tissue (eWAT) of C57BL/6J and 129S1/SvImJ mice fed either chow or high-fat diet. We show that differences in chromatin accessibility are almost exclusively strain-specific and driven by genetic variation. In addition, we integrate ATAC-seq (chromatin accessibility) and H3K27ac ChIP-seq (active regulatory regions) data to show that tissue-specific TFs are commonly found in the active regulatory regions hosting TF binding motif altering variants in eWAT. By incorporating footprint analysis, we also show that TF occupancy is consistent with TF binding motif scores at the genetically altered loci. In addition, we validate these findings by extending the footprint analysis to ATAC-seq and H3K27ac ChIP-seq data obtained from the liver. Also, we employ RNA-seq to show that differentially expressed genes are collocated with differentially accessible regions hosting genetic variants. Overall, our findings highlight the connection between differential chromatin accessibility, TF binding and genetic variation and their role in the gene regulation across metabolically central tissues of a mouse model for polygenic obesity.

This dataset

Open GSE281686 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 GSE281686_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 PRJNA1185225 and SRA study SRP544759. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

7 × RNAseq_B6_HFD vs 6 × RNAseq_B6_Chow vs 6 × RNAseq_SV_HFD vs 5 × RNAseq_SV_Chow

Supports a between-group comparison across 24 samples.

How we read this: 4 replicated groups read from 24 sample titles; they account for 24 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

GSM8625677 RNAseq_B6_Chow_1 RNAseq_B6_Chow
GSM8625678 RNAseq_B6_Chow_2 RNAseq_B6_Chow
GSM8625679 RNAseq_B6_Chow_3 RNAseq_B6_Chow
GSM8625680 RNAseq_B6_Chow_4 RNAseq_B6_Chow
GSM8625681 RNAseq_B6_Chow_5 RNAseq_B6_Chow
GSM8625682 RNAseq_B6_Chow_6 RNAseq_B6_Chow
GSM8625683 RNAseq_SV_Chow_1 RNAseq_SV_Chow
GSM8625684 RNAseq_SV_Chow_2 RNAseq_SV_Chow
GSM8625685 RNAseq_SV_Chow_3 RNAseq_SV_Chow
GSM8625686 RNAseq_SV_Chow_4 RNAseq_SV_Chow
GSM8625687 RNAseq_SV_Chow_5 RNAseq_SV_Chow
GSM8625688 RNAseq_B6_HFD_1 RNAseq_B6_HFD
GSM8625689 RNAseq_B6_HFD_2 RNAseq_B6_HFD
GSM8625690 RNAseq_B6_HFD_3 RNAseq_B6_HFD
GSM8625691 RNAseq_B6_HFD_4 RNAseq_B6_HFD
GSM8625692 RNAseq_B6_HFD_5 RNAseq_B6_HFD
GSM8625693 RNAseq_B6_HFD_6 RNAseq_B6_HFD
GSM8625694 RNAseq_B6_HFD_7 RNAseq_B6_HFD
GSM8625695 RNAseq_SV_HFD_1 RNAseq_SV_HFD
GSM8625696 RNAseq_SV_HFD_2 RNAseq_SV_HFD
GSM8625697 RNAseq_SV_HFD_3 RNAseq_SV_HFD
GSM8625698 RNAseq_SV_HFD_4 RNAseq_SV_HFD
GSM8625699 RNAseq_SV_HFD_5 RNAseq_SV_HFD
GSM8625700 RNAseq_SV_HFD_6 RNAseq_SV_HFD

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GSE281685 Transcription factor occupancy is affected by genetic variation in the white adipose tissue of C57BL6/j and 129S1/SvImJ mice [ChIP-seq] 16 samples shares c57bl6, 129s1, transcription, occupancy, variation, affected, genetic, adipose
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GSE157232 The CD8+ T cell tolerance checkpoint triggers a transcriptionally and epigenetically distinct differentiation state defined by protein translation defects 30 samples shares transcription, genetic
GSE271884 GFI1-driven transcriptional and epigenetic programs maintain memory CD8+ T cell persistence [ATAC-seq]. 30 samples shares transcription, genetic
GSE226609 Mouse lung tissue RNA-Seq from C57Bl6 wild type and ALOX15 KO mice with and without house dust mite (HDM) exposure. 20 samples shares c57bl6

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