GEO series

Distinct endothelial pathomechanisms drive arterio-venous malformation in Alk1 or Smad4 loss-of-function conditions

GSE282952 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2025/02/18 Platform GPL34284

At a glance

RNA-seq (gene expression) · human · 45 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: 45 samples
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: Divergent endothelial mechanisms drive arteriovenous malformations in Alk1 and SMAD4 loss-of-functionbioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

Hereditary hemorrhagic telangiectasia (HHT) is an inherited autosomal dominant disorder associated with mutations in the bone morphogenetic protein (BMP) pathway. Inherited heterozygous loss of function mutations and acquired loss of heterozygosity in either Alk1, Eng, or Smad4 lead to the development of arteriovenous malformations (AVMs), which trigger local vessel instability, hypoxia and vessel leakage or rupture.

Read the full summary ↓

Current models assume common cellular pathomechanisms culminating in altered endothelial cell (EC) shape regulation, directional migration and proliferation control as a consequence of deficient BMP pathway signaling in ECs under the influence of blood-flow mediated shear stress. Here we report that loss of Alk1 or Smad4 surprisingly triggers very distinct endothelial phenotypes, signaling dynamics and transcriptional changes in ECs, both in vitro and in vivo. EC behavior in both in vivo and mosaic flow cultures illustrate that cells deficient in Smad4 effectively migrate against the direction of fluid shear, from veins to arteries, whereas cells lacking Alk1 fail to polarize and migrate against flow. Our data suggest that AVMs triggered by Smad4 mutations occur through hyperpruning of capillaries, thus precipitating flow in a single shunt, whereas AVMs caused by Alk1 mutation grow by cell accumulations close to the vein, as well as the persistence of a hyperdense plexus that drives nidus formation. We propose that the cellular pathomechanisms leading to AVM formation are not the same if the upstream BMP receptor Alk1, or the downstream common transcription factor Smad4 are mutated, raising the prospect for urgently needed, mechanism-based, therapeutic avenues that need to be tailored to correcting the specific pathomechanism.

Published in

Divergent endothelial mechanisms drive arteriovenous malformations in Alk1 and SMAD4 loss-of-function

Oppenheim O, Giese W, Park H et al. · bioRxiv : the preprint server for biology 2025 · PMID 39829872 · doi:10.1101/2025.01.03.631070

This dataset

Open GSE282952 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 GSE282952_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 PRJNA1191349 and SRA study SRP547889. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39829872) ↗

Samples in this study

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+ 45 more — browse all 45 samples with per-sample file links →

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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.