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IL-17 Signaling Mediates Inflammation-Induced Capillary Leakage in addition to TNFa

GSE326579 Homo sapiens (human) RNA-seq (gene expression) 42 samples Submitted 2026/04/06 Platform GPL18573
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: 14 conditions, mostly in triplicateGroups 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)
Capillary leak is a hallmark of acute and chronic inflammatory conditions and represents a critical driver of organ dysfunction and poor clinical outcomes. Despite its clinical relevance, the mechanisms governing endothelial destabilization under inflammatory conditions remain incompletely understood.
Read the full summary ↓
In this study, we investigated the endothelial response to inflammation using a dynamic human in vitro model, in which human umbilical vein endothelial cells (HUVECs) were cultured in the presence of lipopolysaccharide (LPS)–stimulated human peripheral blood mononuclear cells (PBMCs) to mimic systemic inflammation. Under these conditions, endothelial cells exhibited a pronounced inflammatory response characterized by loss of barrier integrity and the formation of capillary leak–like structures. Separated transcriptomic pathway analyses of HUVECs and PBMCs identified a strong activation of cytokine-driven signalling networks, with the IL-17 pathway emerging as a dominant regulator of endothelial dysfunction, alongside TNF and JAK–STAT signalling. Targeted pharmacological inhibition demonstrated that blockage of IL-17 signalling most effectively stabilized the endothelial layer and mitigated leak-like morphology compared to inhibition of other pathways. However, endothelial barrier function was only partially restored, indicating persistent dysfunction despite IL-17 pathway suppression. Together, these findings position IL-17 as a key driver of inflammation-induced capillary leak while highlighting the multifactorial nature of endothelial destabilization. This work underscores the necessity of addressing complex immune–endothelial interactions to develop effective strategies for preventing inflammation-induced vascular leakage.
This dataset
Open GSE326579 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 GSE326579_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 PRJNA1445956 and SRA study SRP688199. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 conditions, mostly in triplicate
HUVEC, Control, Dono ×3 HUVEC, LPS, Dono ×3 HUVEC, PBMC, Dono ×3 HUVEC, PBMC and LPS, Dono ×3 HUVEC, TNF, Dono ×3 HUVEC, PBMC and LPS and Abatacept, Dono ×3 HUVEC, PBMC and LPS and Adalimumab, Dono ×3 HUVEC, static, Dono ×3 +6 more

Supports a between-group comparison across 42 samples.

How we read this: 14 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

Search all human RNA-seq datasets in GEO →

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