← BioTransfer GEO Dataset Finder
GEO series

Docosahexaenoic Acid Enhances Dexamethasone-Mediated Suppression of Interferon-Regulated Genes in NZBWF1 Macrophages: Implications for Steroid-Sparing Lupus Treatment

GSE298247 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2025/10/28 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 36 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
36 samples: 36 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
Omega-3 fatty acid synergy with glucocorticoid in mouse lupus macrophage model: targeting pathogenic pathways to reduce steroid dependenceFrontiers in immunology · 2025
Summary (written by the submitters)
Introduction: Glucocorticoids (GC) are one of the most common anti-inflammatory treatments for autoimmune diseases such as systemic lupus erythematosus (lupus). Chronic use of GCs leads to several deleterious side-effects and diminished quality of life, inciting a need for steroid-sparing therapeutics.
Read the full summary ↓
Polyunsaturated fatty acids (PUFA), such as docosahexaenoic acid (DHA), have been shown to inhibit proinflammatory and Type I Interferon (IFN) gene signatures in macrophages, which are central to initiation of autoimmunity triggered by inhaled environmental toxicants such as bacterial lipopolysaccharide (LPS). The goal of the present study was to determine whether combination treatment with suboptimal concentrations of DHA and GC in LPS-primed alveolar-like macrophages could potentiate the anti-inflammatory effects of DHA and GC individual treatment. Methods: RNAseq was used to evaluate whether DHA could potentiate the anti-inflammatory effects of the GC dexamethasone (DEX) in LPS-stimulated fetal liver-derived alveolar-like macrophages (FLAMs) isolated from NZBWF1 lupus-prone mice. FLAMs were treated were treated with suboptimal concentrations of either DHA alone, DEX alone, or DHA+DEX in combination prior to LPS stimulation (20 ng/mL) for 4 or 8 hr. Results: LPS stimulation resulted in significant enrichment of innate immunity and Type I/II IFN pathways with corresponding upregulation of gene sets falling under these pathways at both timepoints. LPS-primed FLAMs treated in combination with suboptimal concentrations of DHA and DEX exhibited combinatorial affects greater than DHA and DEX treatment alone. DHA+DEX co-treatment resulted in downregulation of innate immunity and Type I/II IFN pathways initially altered by LPS treatment. Lastly, DHA+DEX co-treatment exhibited superior downregulation of individual Type I/II IFN genes compared to DHA and DEX treatment alone. Discussion: Our findings demonstrate that while DHA and DEX can significantly alter LPS-stimulated gene expression in alveolar-like macrophages when used in high enough concentrations alone, DHA can be used to potentiate the anti-inflammatory effects of DEX when both are used at lower concentrations. This study provides promising evidence that DHA can potentially be used to lower requisite amounts GCs required to reduce inflammation.
Published in
Omega-3 fatty acid synergy with glucocorticoid in mouse lupus macrophage model: targeting pathogenic pathways to reduce steroid dependence
Heine LK, Nault R, Jackson J et al. · Frontiers in immunology 2025 · PMID 41169395 · doi:10.3389/fimmu.2025.1646133
This dataset
Open GSE298247 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 GSE298247_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 PRJNA1268566 and SRA study SRP587995. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41169395) ↗
Study design
36 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 36 sample titles: 36 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

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.