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Epithelial Cell Membrane Perforation Induces Allergic Airway Inflammation

GSE276386 Homo sapiens (human) RNA-seq (gene expression) 99 samples Submitted 2025/06/18 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · human · 99 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
99 samples: 99 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)
Allergens that induce allergic airway inflammation are highly diverse, yet they commonly activate type 2 immune responses1,2. Airway epithelial cells are crucial in the immune sensing of allergens3-5. However, what shared features among diverse allergens lead to their similar innate immune sensing, and how epithelial cells detect these features, remain poorly defined1,2,6-9.
Read the full summary ↓
Here, we show that pore-forming proteins represent one of the common stimuli of allergic airway inflammation and elucidate their immune activation mechanisms. Using the prevalent mold allergen Alternaria alternata (A. alternata) as a model, we established a unique in vitro system to investigate type 2 innate immune sensing. Through a six-step biochemical fractionation, we identified its core immune-stimulatory components as Aeg-S and Aeg-L. Biochemical reconstitution and cryo-electron microscopy demonstrate that these proteins form 16-20-mer transmembrane pore complexes. Their cooperative perforation acts as a bona fide type 2 immune adjuvant to support antigen-specific TH2 and IgE responses. Genetically engineered A. alternata strains lacking such pore-forming activity fail to induce allergic responses in mice. Moreover, pore-forming proteins from various species, despite structural and membrane target differences, are sufficient to trigger respiratory allergies. Perforations in airway epithelial cells initiate allergic responses through two mechanisms: one triggers IL-33 release; another involves Ca2+ influx, which induces MAPK pathway activation and type 2 inflammatory gene expression. These findings provide insight into how type 2 immune responses detect common perturbations caused by structurally diverse stimuli. Targeting downstream signaling of epithelial cell perforation may open new avenues for treating respiratory allergies.
This dataset
Open GSE276386 in finder → Find more 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 GSE276386_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 PRJNA1156895 and SRA study SRP530690. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
99 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 99 sample titles: 99 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

+ 59 more — browse all 99 samples with per-sample file links →

Datasets on a similar topic

Search all 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.