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IFNg shapes macrophage inflammatory responses by STAT1 isoform-specific epigenetic and transcriptional mechanisms [RNA-Seq]

GSE309564 Mus musculus (mouse) RNA-seq (gene expression) 72 samples Submitted 2025/09/30 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 72 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
72 samples: 24 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
IFNγ shapes macrophage inflammatory responses by STAT1 isoform-specific epigenetic and transcriptional mechanismsBMC genomics · 2026
Summary (written by the submitters)
Interferon gamma (IFNg) is a pivotal cytokine that activates macrophages and shapes their response to subsequent inflammatory stimuli. Here, we investigated the distinct roles of STAT1 isoforms, STAT1alpha and STAT1beta, in IFNg-induced epigenetic remodelling and subsequent responses to lipopolysaccharide (LPS) in primary macrophages.
Read the full summary ↓
We show that the STAT1 C-terminal TAD, which is missing in the STAT1beta isoform, is required for both H3K27 acetylation and deacetylation in response to IFNg. Notably, H3K27 deacetylation was associated with AP-1 motifs rather than STAT1 binding, suggesting indirect regulatory mechanisms. Functionally, IFNg pretreatment suppressed the induction of anti-inflammatory and virus response genes, including a subset of IFN-stimulated genes (ISGs), in response to LPS, while enhancing the expression of a distinct set of ISGs. Mechanistically, the STAT1 C-terminal TAD was critical for IFNg-mediated inhibition of LPS-induced enhancer activation at key regulatory genes, such as Il10 and Irak3, which are involved in negative feedback of toll-like receptor (TLR) signalling. Conversely, cooperative gene activation by IFNg and LPS was largely independent of the STAT1 C-terminal TAD, with notable exceptions, such as Ido1 and Tgtp1. Genome-wide analysis indicated that IRF1 and IRF8, rather than STAT1 homodimers, predominantly mediate in IFNg-induced H3K27 acetylation and transcription factor network analysis identified additional regulators integrating IFNg and LPS responses. Together, these findings reveal gene- and isoform-specific roles of STAT1 in coordinating IFNg-dependent chromatin remodelling and transcriptional cross-regulation, providing new insights into the control of inflammation and innate immunity.
Published in
IFNγ shapes macrophage inflammatory responses by STAT1 isoform-specific epigenetic and transcriptional mechanisms
Akagha MJ, Georgolopoulos G, Martin D et al. · BMC genomics 2026 · PMID 41645061 · doi:10.1186/s12864-026-12601-5
This dataset
Open GSE309564 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 GSE309564_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 PRJNA1336261 and SRA study SRP629701. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41645061) ↗
Study design
24 conditions, mostly in triplicate
RNA, Macrophage, STAT1alpha KI, Interferon ga… ×3 RNA, Macrophage, STAT1alpha KI, IFNg+LPS, 3h, ×3 RNA, Macrophage, STAT1alpha KI, IFNg+LPS, 6h, ×3 RNA, Macrophage, STAT1alpha KI, LPS, 3h, ×3 RNA, Macrophage, STAT1alpha KI, LPS, 6h, ×3 RNA, Macrophage, STAT1alpha KI, Untreated, 14… ×3 RNA, Macrophage, STAT1beta KI, Interferon gam… ×3 RNA, Macrophage, STAT1beta KI, IFNg+LPS, 3h, ×3 +16 more

Supports a between-group comparison across 72 samples.

How we read this: 24 replicated groups read from 72 sample titles; they account for 72 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

+ 32 more — browse all 72 samples with per-sample file links →

Datasets on a similar topic

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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.