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Hutchinson Gilford Progeria Syndrome alters the endothelial genetic response to laminar shear stress

GSE311520 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/12/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 11 conditions, mostly in duplicateGroups 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)
Hutchinson-Gilford Progeria Syndrome (HGPS) is a fatal, accelerated-aging disease caused by a mutation in the nuclear envelope protein Lamin A. The resulting mutant protein, progerin, accumulates on the nuclear envelope, causing nuclear blebbing, altered gene expression, and other cellular defects. The primary pathology of HGPS is atherosclerosis, leading to stroke or heart attack.
Read the full summary ↓
Given that atherosclerosis begins with endothelial dysfunction in non-HGPS individuals, we examined whether the HGPS endothelium has an altered genetic response to shear stress, contributing to atherogenesis. We examined morphology and gene expression of HGPS and healthy iPSC-derived endothelial cells (viECs) after exposure to steady laminar shear stress (12 dynes/cm^2 for 24 hours) in a parallel-plate flow channel. We found that elongation in response to flow is impaired in HGPS viECs compared with healthy viECs. Differential expression (DE) analysis showed fewer significant differentially expressed genes and a lower magnitude of gene expression change after flow in HGPS compared with healthy viECs. Gene Set Enrichment Analysis identified differences in the gene sets altered by flow-induced DE, including Cholesterol Homeostasis, which was overrepresented in HGPS viECs. LGALS3, encoding the atherosclerosis marker galectin-3, was a main driver of this overrepresentation. RT-PCR confirmed LGALS3 as a shear-sensitive gene robustly upregulated in HGPS viECs compared with healthy viECs after flow. Treatment with an adenine base editor correcting the HGPS mutation restored LGALS3 expression to healthy levels. These observations indicate that HGPS ECs have an aberrant molecular response to atheroprotective shear stress, contributing to atherogenesis in HGPS patients.
This dataset
Open GSE311520 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 GSE311520_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 PRJNA1369589 and SRA study SRP648991. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
11 conditions, mostly in duplicate
Healthy viECs, 168CL2, Flow, ×4 Healthy viECs, 168CL2, Static, ×2 Healthy viECs, 168CL2, Static ×2 Healthy viECs, 168CL3, Static, ×2 Healthy viECs, 168CL3, Flow, ×2 Healthy viECs, 0901B, Static, ×2 Healthy viECs, 0901B, Flow, ×2 HGPS viECs, 167CL2, Static, ×2 +3 more

Supports a between-group comparison across 24 samples.

How we read this: 11 replicated groups read from 24 sample titles; they account for 24 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
Datasets on a similar topic

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