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The IL-33 and IL-4Rα blocking antibodies, itepekimab and dupilumab, modulate distinct and common inflammatory mediators in asthma - human mast cell and eosinophil data

GSE282860 Homo sapiens (human) RNA-seq (gene expression) 63 samples Submitted 2025/12/03 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 63 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
Homo sapiens (human)
Samples
63 samples: 63 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.
Summary (written by the submitters)
Biologics that target IL-4Ra or IL-33 pathways have demonstrated clinical efficacy in asthma and chronic obstructive pulmonary disease (COPD), highlighting the importance of IL-4, IL-13 and IL-33 in respiratory diseases. Despite this, there are limited studies that link preclinical models to human disease and evaluate disease biology in the setting of clinical trials.
Read the full summary ↓
To address these gaps, we evaluated transcriptional, cellular and pathophysiological processes driven by IL-4/ IL-13 and IL-33 using human innate cells in vitro, a mouse model of airway inflammation and a Bronchial Allergen Challenge (BAC) in house dust mite (HDM)-sensitized individuals with mild asthma. Using the HDM mouse model of airway inflammation, we show that blockade of either IL-4Ra or IL-33 at the peak of type 2 (T2) inflammation leads to inhibition of airway inflammation and remodeling. While the combination of dupilumab and itepekimab was not additive, blockade of both IL-33 and IL-4Ra in a severe, late-phase mixed T1/T2 inflammation reduced all measured parameters. Consistent with some of these observations in mice, blocking either IL-33 or IL-4Ra in the context of T2 airway inflammation suppressed sputum gene expression in individuals with mild persistent asthma post-BAC. Overall, these results highlight that IL-33 and IL-4/IL-13 are key drivers of airway inflammation and remodeling and provide insight into the differences in targeting IL-4Ra or IL-33 pathways in asthma independently or in combination.
This dataset
Open GSE282860 in finder → Find more human 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 GSE282860_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 PRJNA1190900 and SRA study SRP547676. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
63 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 63 sample titles: 63 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

+ 23 more — browse all 63 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.