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RORA proximal enhancer regulates its expression during early human Th17 cell differentiation [ATAC]

GSE243058 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 22 samples Submitted 2024/05/23 Platform GPL16791
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 22 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
22 samples: 22 samples, each with a distinct title — no replicated groupsGroups 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
A proximal enhancer regulates RORA expression during early human Th17 cell differentiationClinical immunology (Orlando, Fla.) · 2024
Summary (written by the submitters)
Distal regulatory elements, such as enhancers, play a pivotal role in dictating cell identity by controlling the transcriptional status of specific cells. The comprehensive understanding of the epigenetic landscape, encompassing enhancer establishment and chromatin accessibility, during the differentiation of human Th17 cells remains incomplete.
Read the full summary ↓
Leveraging ATAC-seq based chromatin accessibility profile along with the profiling of key histone marks we identified potential enhancers crucial for fate specification of Th17 cells. We found that 24 single nucleotide polymorphisms (SNPs) associated with autoimmune diseases were located near Th17-enhancers. Interestingly, these SNPs overlapped the binding sites of transcription factor active in Th17 cells. Among the Th17 specific enhancers, we identified an enhancer in the intron of RAR-related orphan receptor alpha (RORA) that was earlier predicted to regulate RORA expression. Functional validation through luciferase reporter assays confirmed that this enhancer positively regulate the transcription. Moreover, employing CRISPR-Cas9-mediated deletion of a transcription factor binding site-rich region within the identified RORA enhancer, we demonstrated its role in regulating RORA transcription. These findings provide insights into the potential mechanism by which the RORA enhancer modulates Th17 differentiation and into the role of regulatory SNPs within noncoding regions in conferring resistance or susceptibility to Th17 cell-mediated autoimmune pathologies.
Published in
A proximal enhancer regulates RORA expression during early human Th17 cell differentiation
Kalim UU, Biradar R, Junttila S et al. · Clinical immunology (Orlando, Fla.) 2024 · PMID 38788884 · doi:10.1016/j.clim.2024.110261
This dataset
Open GSE243058 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 GSE243058_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 PRJNA1016305 and SRA study SRP460182. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38788884) ↗
Study design
22 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 22 sample titles: 22 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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