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Differential Effects of JAK1 vs. JAK2 Inhibition in Mouse Models of Hemophagocytic Lymphohistiocytosis

GSE230771 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2024/03/05 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 30 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
30 samples: 30 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
Differential effects of itacitinib, fedratinib, and ruxolitinib in mouse models of hemophagocytic lymphohistiocytosisBlood · 2024
Summary (written by the submitters)
Hemophagocytic lymphohistiocytosis (HLH) comprises a severe hyperinflammatory phenotype driven by the overproduction of cytokines, many of which signal via the JAK/STAT pathway. Indeed, the JAK1/2 inhibitor ruxolitinib has demonstrated efficacy in pre-clinical studies and early-phase clinical trials in HLH. Nevertheless, concerns remain for ruxolitinib-induced cytopenias, which are postulated to result from the blockade of JAK2-dependent hematopoietic growth factors.
Read the full summary ↓
To explore the therapeutic effects of selective JAK inhibition in mouse models of HLH, we carried out studies incorporating the JAK1 inhibitor itacitinib, the JAK2 inhibitor fedratinib and the JAK1/2 inhibitor ruxolitinib. All three drugs were well-tolerated and at the doses tested, they suppressed interferon-gamma (IFNg)-induced STAT1 phosphorylation in vitro and in vivo. Itacitinib, but not fedratinib, significantly improved survival and clinical scores in CpG-induced secondary HLH. Conversely, in primary HLH, where perforin-deficient (Prf1-/-) mice are infected with lymphocytic choriomeningitis virus (LCMV), itacitinib and fedratinib performed suboptimally. Ruxolitinib demonstrated excellent clinical efficacy in both HLH models. RNA-sequencing of splenocytes from LCMV-infected Prf1-/- mice revealed that itacitinib targeted inflammatory and metabolic pathway genes in CD8 T cells, while fedratinib targeted genes regulating cell proliferation and metabolism. In monocytes, neither drug conferred major transcriptional impacts. Consistent with its superior clinical effects, ruxolitinib exerted the greatest transcriptional changes in CD8 T cells and monocytes, targeting more genes across several biologic pathways, most notably JAK-dependent pro-inflammatory signaling. We conclude that JAK1 inhibition is sufficient to curtail CpG-induced disease, but combined inhibition of JAK1 and JAK2 is needed to best control LCMV-induced immunopathology.
Published in
Differential effects of itacitinib, fedratinib, and ruxolitinib in mouse models of hemophagocytic lymphohistiocytosis
Keenan C, Albeituni S, Oak N et al. · Blood 2024 · PMID 38446698 · doi:10.1182/blood.2023021046
This dataset
Open GSE230771 in finder → Find more mouse 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 GSE230771_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 PRJNA962548 and SRA study SRP434862. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38446698) ↗
Study design
30 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 30 sample titles: 30 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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