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Multi-omics analysis of the epigenetic effects of lipopolysaccharide-induced inflammation in Type II Pneumocytes

GSE275769 Mus musculus (mouse) DNA methylation 36 samples Submitted 2025/06/04 Platform GPL21626Platform GPL17021
At a glance

DNA methylation · mouse · 36 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
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
Multi-Omics Analysis of the Epigenetic Effects of Inflammation in Murine Type II PneumocytesInternational journal of molecular sciences · 2025
Summary (written by the submitters)
Chronic inflammation plays a central role in the pathogenesis of many lung diseases including asthma, SARS-COVID19, obstructive pulmonary disease (COPD), and lung cancer. Lipopolysaccharide (LPS) is an inflammatory agent produced by most Gram-negative bacteria and found in cigarette smoke. In a mouse model of inflammation, inhalation exposure to LPS induced COPD and increased the size and the multiplicity of lung tumors induced by tobacco-specific nitrosamines.
Read the full summary ↓
In the present work, we employed a comprehensive multi-omics approach that monitored changes in DNA methylation/hydroxymethylation, gene expression, and global protein abundances in Type II pneumocytes of A/J mice following sub-chronic exposure to LPS.
Published in
Multi-Omics Analysis of the Epigenetic Effects of Inflammation in Murine Type II Pneumocytes
Fernandez JA, Han Q, Rajczewski AT et al. · International journal of molecular sciences 2025 · PMID 40429836 · doi:10.3390/ijms26104692
This dataset
Open GSE275769 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 GSE275769_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1152971 and SRA study SRP528928. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40429836) ↗
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 →

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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.