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RNA-seq in wild type and EGR2 knock-out mouse alveolar macrophages upon vehicle and zymosan treatment

GSE181086 Mus musculus (mouse) RNA-seq (gene expression) 22 samples Submitted 2024/08/29 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 22 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
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
EGR2 is an epigenomic regulator of phagocytosis and antifungal immunity in alveolar macrophagesJCI insight · 2024
Summary (written by the submitters)
The alveolar macrophages (AMs) as a component of the innate immunity of the lung play important role in the elimination of inhaled microbes and harmful agents. The transcription factor EGR2 is a known marker transcription factor of AMs but its exact epigenetic and transcriptomic effects have not been examined. In our study, we performed ATAC-seq and RNA-seq in WT and EGR2 deficient alveolar macrophages to describe the mechanism of action and to predict potential direct target genes of EGR2.
Read the full summary ↓
Clec7a is one of the targets of this transcription factor which is essential in the zymosan-induced inflammatory response. We further analyzed this process by applying in vivo mouse model. Our findings demonstrate that EGR2 is a key transcriptional activator, responsible for the intact protective program against different pathogens, especially fungi in AMs.
Published in
EGR2 is an epigenomic regulator of phagocytosis and antifungal immunity in alveolar macrophages
Kolostyak Z, Bojcsuk D, Baksa V et al. · JCI insight 2024 · PMID 39042472 · doi:10.1172/jci.insight.164009
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 GSE181086_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA750657 and SRA study SRP330417. Searching any of these in the dataset finder brings you back here.

Study design
22 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 8 samples read as cases, 6 as controls.

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
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