GEO series

Proteasome inhibition reprograms chromatin landscape in breast cancer (ChIP-Seq)

GSE241598 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 18 samples Submitted 2024/03/13 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 18 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)
Topic: Breast cancerThis series is part of our curated briefing on it.
Samples: 18 samples: 6 conditions, mostly in triplicateGroups 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 26S proteasome regulates general protein homeostasis and controls vital cellular processes, including cell division and transcriptional regulation. Cancer cells are dependent on the proper functioning of the proteasome to modulate gene expression networks that promote tumor growth. Proteasome inhibition has emerged as an effective therapeutic strategy, although mechanisms by which this approach achieves its outcomes remain unclear.

Read the full summary ↓

We performed an integrative analysis of the transcriptome and chromatin landscape of MCF-7 breast cancer cells treated with a model proteasome inhibitor, MG132, to examine the consequences of proteasome inhibition on gene regulation. MG132 treatment initiated dynamic changes in chromatin accessibility at specific loci termed Differentially Open Chromatin Regions (DOCRs). DOCRs with increased accessibility were primarily distal to transcription start sites (TSS), while those with decreased accessibility were promoter proximal and distal to TSS. Promoter proximal DOCRs showed a unique chromatin architecture associated with distinct divergent transcription patterns. Conversely, DOCRs distal to TSS were enriched in oncogenic super enhancers that are predominantly accessible in non-basal compared to basal breast tumor subtypes. These data define distinct chromatin states and RNAPII transcription patterns, revealing molecular mechanisms by which the proteasome modulates the expression of gene networks intrinsic to breast cancer biology.

This dataset

Open GSE241598 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Breast cancer landscape →

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 GSE241598_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 PRJNA1008754 and SRA study SRP456855. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

UNTR_H3K4me1 ×3 MG4H_H3K4me1 ×3 MG24H_H3K4me1 ×3 UNTR_Input ×3 MG4H_Input ×3 MG24H_Input ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM7731519 UNTR_H3K4me1_rep1 UNTR_H3K4me1
GSM7731520 UNTR_H3K4me1_rep2 UNTR_H3K4me1
GSM7731521 UNTR_H3K4me1_rep3 UNTR_H3K4me1
GSM7731522 MG4H_H3K4me1_rep1 MG4H_H3K4me1
GSM7731523 MG4H_H3K4me1_rep2 MG4H_H3K4me1
GSM7731524 MG4H_H3K4me1_rep3 MG4H_H3K4me1
GSM7731525 MG24H_H3K4me1_rep1 MG24H_H3K4me1
GSM7731526 MG24H_H3K4me1_rep2 MG24H_H3K4me1
GSM7731527 MG24H_H3K4me1_rep3 MG24H_H3K4me1
GSM7731528 UNTR_Input_rep1 UNTR_Input
GSM7731529 UNTR_Input_rep2 UNTR_Input
GSM7731530 UNTR_Input_rep3 UNTR_Input
GSM7731531 MG4H_Input_rep1 MG4H_Input
GSM7731532 MG4H_Input_rep2 MG4H_Input
GSM7731533 MG4H_Input_rep3 MG4H_Input
GSM7731534 MG24H_Input_rep1 MG24H_Input
GSM7731535 MG24H_Input_rep2 MG24H_Input
GSM7731536 MG24H_Input_rep3 MG24H_Input

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Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.