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Identification of reactive CpGs and RNA expression in early COVID-19 through cis-eQTM analysis reflecting disease severity and recovery [RNA-seq]

GSE300696 Homo sapiens (human) RNA-seq (gene expression) 346 samples Submitted 2025/07/21 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 346 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
346 samples: 71 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.
Paper
Identification of reactive CpGs and RNA expression in early COVID-19 through cis-eQTM analysis reflecting disease severity and recoveryCommunications biology · 2025
Summary (written by the submitters)
Multi-omics analyses of severe COVID-19 cases are crucial in deciphering the complex interplay between genetic and epigenetic factors. Here, we present an analysis of Expression Quantitative Trait Methylation (eQTM) to investigate the complex interplay of methylation and gene expression pattern during the acute phase of severe COVID-19.
Read the full summary ↓
We identified 16 differentially expressed genes and 30 nearby differentially methylated CpG sites. Six key genes—SRXN1, FURIN, IL18RAP, FOXO3, GCNT4, and FKBP5—were either up-regulated or down-regulated near hypomethylated CpG sites. These genes are associated with viral infiltration, immune activation, lung damage, and oxidative stress-related multi-organ failure, which are the hallmarks of severe COVID-19. Interestingly, during the recovery phase, methylation and gene expression levels returned to baseline, underscoring the rapid and reversible nature of these molecular changes. These findings provide insight into the dynamics of epigenetic and transcriptomic shifts according to the infectious stage, supporting potential prognostic and therapeutic approaches for severe COVID-19.
Published in
Identification of reactive CpGs and RNA expression in early COVID-19 through cis-eQTM analysis reflecting disease severity and recovery
Ryu H, An K, Kwon Y et al. · Communications biology 2025 · PMID 40775517 · doi:10.1038/s42003-025-08609-4
This dataset
Open GSE300696 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 GSE300696_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 PRJNA1292728 and SRA study SRP601487. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40775517) ↗
Study design
71 conditions, mostly in triplicate
COVID-19,Hospitalized,patient002,Visit ×5 COVID-19,Hospitalized,patient010,Visit ×5 COVID-19,Hospitalized,patient017,Visit ×5 COVID-19,Hospitalized,patient001,Visit ×4 COVID-19,Hospitalized,patient003,Visit ×4 COVID-19,Hospitalized,patient004,Visit ×4 COVID-19,Hospitalized,patient005,Visit ×4 COVID-19,Hospitalized,patient006,Visit ×4 +201 more

Supports a between-group comparison across 208 samples.

How we read this: 71 replicated groups read from 346 sample titles; they account for 208 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

+ 306 more — browse all 346 samples with per-sample file links →

Datasets on a similar topic

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