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Unraveling Epigenetic and Immune Mechanisms Underlying Cystogenesis in Autosomal Dominant Polycystic Kidney Disease [ChIP-Seq]

GSE244998 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 15 samples Submitted 2024/10/10 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 15 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
15 samples: 13 distinct titles, 2 of them repeatedGroups 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
Epigenomic, cistromic, and transcriptomic profiling of primary kidney tubular cellsJournal of biological methods · 2024
Summary (written by the submitters)
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a prevalent, life-threatening monogenetic disorder that affects millions worldwide. Currently, therapeutic options are limited and often associated with complications. To find more effective treatments, this study aimed to understand the molecular mechanisms driving cystogenesis in ADPKD.
Read the full summary ↓
By analysing various epigenetic factors, we identified a set of master transcription factors that guided a core regulatory circuitry and remodeled chromatin accessibility in ADPKD. We further explored the role of immune cells, notably macrophages, in the disease’s progression using single-cell RNA sequencing. A subgroup of Lhfpl2+/Fat3+ macrophages, which reactivated the Hgf-Met-Nfkb1-Runx1 pathways, was found essential for cystic cell fate transition. Targeting the identified epigenetic regulators, H3K4me3 and TF Runx1, effectively halted disease progression in a mouse model. These findings may lead to innovative treatments for ADPKD in humans.
Published in
Epigenomic, cistromic, and transcriptomic profiling of primary kidney tubular cells
Liu Z, Zhang L, Chen Y · Journal of biological methods 2024 · PMID 39323486 · doi:10.14440/jbm.2024.0009
This dataset
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.

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 GSE244998_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1026595 and SRA study SRP465466. Searching any of these in the dataset finder brings you back here.

Study design
13 distinct titles, 2 of them repeated
ADPKD_H3K4me ×2 WT_H3K4me ×2

How we read this: Read from 15 sample titles: 13 distinct titles with little repetition. 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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