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Transcriptomic profiling of baricitinib effects on host immune response to RVFV infection in A549 cells and organoids

GSE292720 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/03/28 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 8 conditions, mostly in triplicateGroups 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
JAK inhibitors remove innate immune barriers facilitating viral propagationNAR molecular medicine · 2025
Summary (written by the submitters)
This study investigates the modulatory effects of baricitinib on the host immune response to Rift Valley Fever Virus (RVFV) infection. A549 cells and organoids were treated with 5 µM baricitinib or vehicle control prior to infection with recombinant RVFV (rRVFV) or mock treatment. Total RNA was isolated at 8 and 24 hours post-infection, and mRNA was enriched for transcriptome sequencing.
Read the full summary ↓
We performed RNA-seq analysis to identify differentially expressed genes and pathways affected by baricitinib treatment during RVFV infection, providing insights into potential therapeutic mechanisms of JAK inhibition in viral infections.
Published in
JAK inhibitors remove innate immune barriers facilitating viral propagation
Ravlo E, Ianevski A, Skipperstøen MN et al. · NAR molecular medicine 2025 · PMID 41255708 · doi:10.1093/narmme/ugaf017
This dataset
Open GSE292720 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 GSE292720_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 PRJNA1241146 and SRA study SRP572789. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41255708) ↗
Study design
8 conditions, mostly in triplicate
A549 cells, baricitinib, RVFV, 8h, ×3 A549 cells, baricitinib, mock, 8h, ×3 A549 cells, vehicle, RVFV, 8h, ×3 A549 cells, vehicle, mock, 8h, ×3 Retinal organoids, baricitinib, RVFV, 24h, ×3 Retinal organoids, baricitinib, mock, 24h, ×3 Retinal organoids, vehicle, RVFV, 24h, ×3 Retinal organoids, vehicle, mock, 24h, ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 of them. 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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