← BioTransfer GEO Dataset Finder
GEO series

Clues to Drivers of Juvenile Dermatomyositis: Genetic Alterations after Type I Interferon and JAK Inhibitor Treatment in Pediatric Bioengineered Skeletal Muscle

GSE243339 Homo sapiens (human) RNA-seq (gene expression) 83 samples Submitted 2024/09/18 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 83 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
Homo sapiens (human)
Samples
83 samples: 83 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
Genetic changes from type I interferons and JAK inhibitors: clues to drivers of juvenile dermatomyositisRheumatology (Oxford, England) · 2024
Summary (written by the submitters)
To better understand the pathogenesis of juvenile dermatomyositis (JDM) we examined the genetic alterations that result from exogenous IFN I exposure in pediatric skeletal muscle. We compared untreated healthy pediatric donor-derived myobundles with those treated with IFN I as well as with JAKi baricitinib and tofacitinib. We used bulk mRNA sequencing to evaluate how IFN I and treatment with JAKi influence healthy and JDM skeletal muscle gene expression and show IFNβ leads to a greater pro-inflammatory gene response than IFNα in pediatric skeletal muscle.
Read the full summary ↓
As a type I interferon gene signature is established in JDM, downregulated oxidative phosphorylation, myogenesis, and contractile protein gene expression is implicated in JDM pathology. These genetic changes are partially reversed by JAK inhibitors, baricitinib and tofacitinib, with baricitinib leading to greater genetic alteration than tofacitinib. Our findings show that analysis of differential gene expression in bioengineered pediatric skeletal muscle can inform our knowledge of JDM pathogenesis and therapeutic effects.
Published in
Genetic changes from type I interferons and JAK inhibitors: clues to drivers of juvenile dermatomyositis
Covert LT, Prinz JA, Swain-Lenz D et al. · Rheumatology (Oxford, England) 2024 · PMID 38317053 · doi:10.1093/rheumatology/keae082
This dataset
Open GSE243339 in finder → Find more human RNA-seq 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 GSE243339_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: 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 PRJNA1017856 and SRA study SRP460771. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38317053) ↗
Study design
83 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 83 sample titles: 83 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

+ 43 more — browse all 83 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.