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Severe long-COVID is driven by monocyte reprogramming and broad immune dysregulation linked to herpesvirus activation

GSE339049 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 48 samples Submitted 2026/07/22 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 48 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
48 samples: 48 samples, each with a distinct title — no replicated groupsGroups 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)
Long-COVID (LC) continues to impose a significant burden on patients and society, yet its pathophysiology remains poorly understood. Here, we characterize the functional, transcriptomic, and epigenomic state of monocytes from 21 patients with severe LC (sLC) at a median of 3.4 years after primary infection, as well as the transcriptomic profiles of T and B-cells.
Read the full summary ↓
Plasma proteomics revealed elevated chemokines and type-I interferon-associated proteins, alongside a reduction in circulating monocytes. Monocytes from sLC patients demonstrated markedly reduced cytokine secretion upon ex vivo LPS stimulation, a finding reproduced in an in vitro model of LC. ATAC-sequencing revealed an epigenomic state of immune dysfunction in monocytes, partially driven by a loss of AP-1 transcription factor binding motifs. Single-cell transcriptomics using STAMP across 113,000 peripheral blood mononuclear cells (PBMCs) confirmed the downregulation of activator protein 1 (AP-1) family members and pro-inflammatory signaling in monocytes, along with impaired antigen presentation, dysregulated viral signaling, and immunosuppressive gene upregulation. T and B-cells demonstrated similar immune dysfunction at the transcriptome level. Epigenomic and transcriptomic evidence of herpesvirus activity, combined with elevated HHV-6 IgG serology in sLC patients, points toward viral reactivation as a candidate mechanism underlying persistent immune dysfunction. Together, these findings suggest that sLC is characterized by a long-lived, infection-induced interferon-priming state that drives monocyte hyporesponsiveness through AP-1-mediated epigenetic reprogramming and implicate HHV-6 reactivation as a potential therapeutic target.
This dataset
Open GSE339049 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE339049_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1496874. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
48 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 48 sample titles: 48 distinct titles with little repetition. 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.