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RBBP6 orchestrates diabetic endothelial dysfunction viadisrupting JUNB-centric chromatin topology

GSE291636 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 73 samples Submitted 2026/03/11 Platform GPL20301Platform GPL18573Platform GPL21103Platform GPL19057Platform GPL24247Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 73 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
73 samples: 22 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)
Diabetes mellitus, a chronic metabolic disease affecting over 536.6 million people globally, is closely associated with vascular endothelial dysfunction, an early hallmark of diabetic cardiovascular complications. This dysfunction is characterized by impaired endothelial nitric oxide synthase (eNOS) activity, reduced nitric oxide (NO) production, and diminished angiogenic capacity, ultimately contributing to tissue ischemia and complications such as diabetic nephropathy, coronary artery disease, and peripheral arterial disease.
Read the full summary ↓
These complications significantly increase morbidity and mortality in diabetic patients. Endothelial cells, which regulate vascular development and function, rely on the coordinated activity of cis-regulatory elements, including enhancers and promoters, which are influenced by the three-dimensional (3D) genome architecture and transcription factors (TFs) such as AP-1, ETS, and GATA families. However, the mechanisms by which these TFs mediate enhancer-promoter interactions and how these interactions are altered under diabetic conditions remain poorly understood. To address this, we employed multi-omics profiling to map the 3D genome architecture in endothelial cells under hyperglycemic conditions in both diabetic mice and human vein endothelial cells. Our findings identify the disruption of JUNB-centric genome topology as a key feature of hyperglycemic endothelial cells. Moreover, we demonstrate that RBBP6 mediates ubiquitin-dependent degradation of JUNB, leading to changes in enhancer-promoter interactions. This study provides novel mechanistic insights into the chromatin 3D structural basis of endothelial dysfunction in diabetes and highlights the RBBP6-JUNB axis as a potential therapeutic target for preventing diabetic cardiovascular complications.
This dataset
Open GSE291636 in finder → Find more 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 GSE291636_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 PRJNA1234557 and SRA study SRP579198. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
22 conditions, mostly in duplicate
STZ_control_ATAC ×4 C57BL6_db_ATAC ×3 C57BL6_control_ATAC ×3 STZ_db_ATAC ×3 BKS_db_H3K27ac ×2 BKS_control_H3K27ac ×2 C57BL6_db_H3K27ac ×2 C57BL6_control_H3K27ac ×2 +38 more

Supports a between-group comparison across 49 samples.

How we read this: 22 replicated groups read from 73 sample titles; they account for 49 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

+ 33 more — browse all 73 samples with per-sample file links →

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