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Endogenous retroviruses promote aberrant T cell differentiation in systemic lupus erythematosus via the RIG-I pathway [SLE]

GSE297006 Homo sapiens (human) RNA-seq (gene expression) 10 samples Submitted 2025/11/18 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 10 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
10 samples: 4 × CD4+T cells, N vs 4 × CD4+T cells, SLE vs 2 × CD4+T cells, SLE_TGroups 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
DNA demethylation mediated endogenous retroviruses transcription promotes aberrant T cell differentiation in systemic lupus erythematosus via RIG-I pathwayEBioMedicine · 2025
Summary (written by the submitters)
The dysregulated differentiation of T lymphocytes play an important role in systemic lupus erythematosus (SLE). However, the underlying mechanism remains unclear. Here, we screened and identified many overexpressed HERVs in CD4+ T cells from SLE patients by RNA-seq and found that some HERVs formed dsRNAs to induce the activation of the RIG-I signaling pathway. And our findings reveal the roles and mechanism of HERVs and RIG-I in regulating the aberrant differentiation of T cells in SLE patients.
Published in
DNA demethylation mediated endogenous retroviruses transcription promotes aberrant T cell differentiation in systemic lupus erythematosus via RIG-I pathway
Min X, Yu Y, Hu Z et al. · EBioMedicine 2025 · PMID 40992212 · doi:10.1016/j.ebiom.2025.105934
This dataset
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.

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 GSE297006_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1262359 and SRA study SRP584860. Searching any of these in the dataset finder brings you back here.

Study design
4 × CD4+T cells, N vs 4 × CD4+T cells, SLE vs 2 × CD4+T cells, SLE_T

Supports a between-group comparison across 10 samples.

How we read this: 3 replicated groups read from 10 sample titles; they account for 10 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
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