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Central role of SUMOylation in the regulation of chromatin interactions and transcriptional outputs of androgen receptor in prostate cancer cells (ChIP-seq)

GSE211138 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 46 samples Submitted 2024/07/09 Platform GPL30173Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 46 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
Homo sapiens (human)
Samples
46 samples: 17 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
Central role of SUMOylation in the regulation of chromatin interactions and transcriptional outputs of the androgen receptor in prostate cancer cellsNucleic acids research · 2024
Summary (written by the submitters)
The androgen receptor (AR) is pivotal in prostate cancer (PCa) progression and represents a critical therapeutic target. AR-mediated gene regulation involves intricate interactions with nuclear proteins, with many mediating and undergoing post-translational modifications that present alternative therapeutic avenues. Through chromatin proteomics in PCa cells, we identified SUMO ligases together with nuclear receptor coregulators and pioneer transcription factors within the AR’s protein interaction network.
Read the full summary ↓
Intriguingly, this network displayed a significant association with SUMO2/3. To elucidate the influence of SUMOylation on AR chromatin interactions and subsequent gene regulation, we inhibited SUMOylation using ML-792 (SUMOi). While androgens generally facilitated the co-occupancy of SUMO2/3 and AR on chromatin, SUMOi induced divergent effects dependent on the enhancer type. SUMOi augmented AR’s pioneer-like binding on inaccessible chromatin regions abundant in androgen response elements (AREs) and diminished its interaction with accessible chromatin regions sparse in AREs yet rich in pioneer transcription factor motifs. The SUMOi-impacted AR-binding sites divergently influenced AR-regulated genes; those associated with AR-mediated activation played roles in negative regulation of cell proliferation, while those with AR-mediated repression were involved in pattern formation. In conclusion, our findings underscore the pervasive influence of SUMOylation in shaping AR's role in PCa cells, potentially unveiling new therapeutic strategies.
Published in
Central role of SUMOylation in the regulation of chromatin interactions and transcriptional outputs of the androgen receptor in prostate cancer cells
Launonen KM, Varis V, Aaltonen N et al. · Nucleic acids research 2024 · PMID 39106160 · doi:10.1093/nar/gkae653
This dataset
Open GSE211138 in finder → Find more human 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 GSE211138_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 PRJNA869082 and SRA study SRP391889. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39106160) ↗
Study design
17 conditions, mostly in duplicate
VCaP_input_DMSO-DHT ×3 VCaP_input_ML792-DHT ×3 VCaP_FOXA1_DMSO_DHT ×3 VCaP_FOXA1_ML792_DHT ×3 VCaP_HOXB13_DMSO_DHT ×3 VCaP_HOXB13_ML792_DHT ×3 VCaP_AR_DMSO-DHT ×2 VCaP_AR_ML792-DHT ×2 +15 more

Supports a between-group comparison across 40 samples.

How we read this: 17 replicated groups read from 46 sample titles; they account for 40 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

+ 6 more — browse all 46 samples with per-sample file links →

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.