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Convergence of cytokine dysregulation and antibody deficiency in common variable immunodeficiency with inflammatory complications

GSE330223 Homo sapiens (human) RNA-seq (gene expression) 42 samples Submitted 2026/05/12 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 42 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
42 samples: 6 conditions, mostly with about 9 replicates eachGroups 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
Convergence of cytokine dysregulation and antibody deficiency in common variable immunodeficiency with inflammatory complicationsThe Journal of allergy and clinical immunology · 2022
Summary (written by the submitters)
Background: Noninfectious complications are the greatest cause of morbidity and mortality in common variable immunodeficiency (CVID), but their pathogenesis remains poorly defined. Objective: Using high-throughput approaches, we aimed to identify, correlate, and determine the significance of immunologic features of CVID with noninfectious complications (CVIDc).
Read the full summary ↓
Methods: We simultaneously applied proteomics, RNA sequencing, and mass cytometry to a large cohort with primary antibody deficiency. Results: CVIDc is differentiated from uncomplicated CVID, other forms of primary antibody deficiency, and healthy controls by a distinct plasma proteomic profile. In addition to confirming previously reported elevations of 4-1BB, IL-6, IL-18, and IFN-γ, we found elevations of colony-stimulating factor 1, IL-12p40, IL-18R, oncostatin M, TNF, and vascular endothelial growth factor A to differentiate CVIDc. This cytokine dysregulation correlated with deficiency of LPS-specific antibodies and increased soluble CD14, suggesting microbial translocation. Indicating potential significance of reduced LPS-specific antibodies and resultant microbial-induced inflammation, CVIDc had altered LPS-induced gene expression matching plasma proteomics and corresponding with increased CD14+CD16- monocytes, memory T cells, and tissue inflammation ameliorated by T-cell-targeted therapy. Unsupervised machine learning accurately differentiated subjects with CVIDc and supported cytokine dysregulation, antibody deficit, and T-cell activation as defining and convergent features. Conclusions: Our data expand understanding of CVIDc proteomics, establish its link with deficiency of IgA and LPS-specific antibodies, and implicate altered LPS-induced gene expression and elevated monocytes and T cells in this cytokine dysregulation. This work indicates that CVIDc results when insufficient antibody neutralization of pathogen-associated molecular patterns, like LPS, occurs in those with a heightened response to these inflammatory mediators, suggesting a 2-hit model of pathogenesis requiring further exploration.
Published in
Convergence of cytokine dysregulation and antibody deficiency in common variable immunodeficiency with inflammatory complications
Abyazi ML, Bell KA, Gyimesi G et al. · The Journal of allergy and clinical immunology 2022 · PMID 34146579 · doi:10.1016/j.jaci.2021.06.008
This dataset
Open GSE330223 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 GSE330223_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 PRJNA1463186 and SRA study SRP698318. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 34146579) ↗
Study design
6 conditions, mostly with about 9 replicates each
Complicated CVID 18 hr culture media only, bi… ×9 Complicated CVID 18 hr culture with LPS, biol ×9 HD 18 hr culture media only, biol ×7 HD 18 hr culture with LPS, biol ×7 Uncomplicated CVID 18 hr culture media only, … ×5 Uncomplicated CVID 18 hr culture with LPS, bi… ×5

Supports a between-group comparison across 42 samples.

How we read this: 6 replicated groups read from 42 sample titles; they account for 42 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

+ 2 more — browse all 42 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.