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IFNg and IFNg mimetics prevent IFN-I-mediated TB susceptibility by regulating iron metabolism and lipid peroxidation.

GSE337186 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/07/06 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
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.
Summary (written by the submitters)
Type I interferons (IFN-I) and IFNγ exert divergent effects during tuberculosis, but the mechanisms that determine whether macrophage activation promotes host defense or inflammatory pathology remain incompletely understood. Here, we dissect the interplay between IFN-I and IFNγ in macrophage activation using genetically susceptible B6.Sst1S macrophages.
Read the full summary ↓
We show that, during tumor necrosis factor (TNF) stimulation, susceptible macrophages enter a persistent pathological activation state (pPAS) characterized by sustained lipid peroxidation and super-induction of IFN-I responses. This pathological state is maintained by autocrine IFN-I signaling. In contrast, IFNγ priming prevents pPAS development by enhancing macrophage resilience to oxidative stress, in part through regulation of iron metabolism and induction of ferritin expression. Computational cell state transition assessment and regulation (cSTAR) analysis identified pathways and small molecules predicted to promote the transition of susceptible macrophages toward an IFNγ-induced, Mtb-resistant state. Consistent with these predictions, the CDK4/6 inhibitor trilaciclib reduced lipid peroxidation by regulating iron metabolism, whereas retinoic acid signaling enhanced GPX4 expression and lipid biosynthesis programs. Combined CDK4/6 inhibition and retinoic acid receptor activation efficiently prevented the pathological activation state. Together, these findings delineate a mechanism of IFN-I/IFNγ crosstalk during macrophage activation and identify pharmacologic strategies to prevent IFN-I-dominant, lipid peroxidation-driven macrophage pathology.
This dataset
Open GSE337186 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 GSE337186_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 PRJNA1483334 and SRA study SRP713342. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
Untreated B6 WT ×3 TNFa treated B6 WT ×3 IFNg treated B6 WT ×3 TNFa + IFNg treated B6 WT ×3 Untreated B6.Sst1S ×3 TNFa treated B6.Sst1S ×3 IFNg treated B6.Sst1S ×3 TNFa + IFNg treated B6.Sst1S ×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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