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How infection impacts regulation of adipocyte metabolism and does IFNγ directly targets adipocytes and mediates lipid metabolism in vivo following infection

GSE290553 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2025/04/11 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 16 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
NK cell-derived IFNγ mobilizes free fatty acids from adipose tissue to promote early B cell activation during viral infectionNature metabolism · 2025
Summary (written by the submitters)
Viral infection often causes us to lose fat, but how and why this happens is unclear. The immune system plays a major role in the regulation of adipose tissue biology during homeostasis and in context of metabolic disease. Here, we investigated whether immune cells also modulate adipocyte metabolism during viral infection. We find that visceral adipose tissue transiently decreases adiposity following viral infection.
Read the full summary ↓
Upon pathogen encounter, adipose tissue upregulates surface expression of ligands for activating receptors on NK cells, which drives their secretion of IFNγ. This cytokine directly stimulates adipocytes to shift their balance from lipogenesis to lipolysis, which leads to their release of lipids in circulation, most notably of free fatty acids (FFAs). The FFA oleic acid stimulates early activated B cells by promoting oxidative phosphorylation. Oleic acid promoted expression of costimulatory B7 molecules on B cells and promoted their ability to prime CD8+ T cells. Inhibiting lipid uptake by activated B cells impaired CD8+ T cell responses, causing an increase of viral replication in vivo. Our findings uncover a new mechanism of metabolic adaptation to infection and provide better understanding of the interactions between immune cells and adipose tissue in response to inflammation.
Published in
NK cell-derived IFNγ mobilizes free fatty acids from adipose tissue to promote early B cell activation during viral infection
Krapić M, Kavazović I, Mikašinović S et al. · Nature metabolism 2025 · PMID 40217117 · doi:10.1038/s42255-025-01273-2
This dataset
Open GSE290553 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 GSE290553_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 PRJNA1228619 and SRA study SRP566576. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40217117) ↗
Study design
16 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 16 sample titles: 16 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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