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HIV-1 reprograms CD4 T cell responses by impairing antigen-specific communication with dendritic cells

GSE304995 Mus musculus (mouse) RNA-seq (gene expression) 8 samples Submitted 2026/08/07 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 8 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
Mus musculus (mouse)
Samples
8 samples: 8 × OT-II.2 CD4 T cell-BMDC co-culture_sublibGroups 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)
HIV-1 infection causes general dysfunction of adaptive immune cells that persists even under therapy but the underlaying mechanisms remain elusive. Antigen-specific interactions of the main target cells of HIV, CD4 T cells, with dendritic cells (DCs) orchestrate global T cell responses and convey help to CD8 T cells. Here we report that HIV-1, by virtue of its pathogenesis factor Nef, impairs activation and transcriptionally reprograms CD4 T cells to dampen Th1 differentiation in response to antigen-specific stimulation by DCs.
Read the full summary ↓
These alterations also disrupt functional communication to DCs to reduce DC activation and limit Th1 helper cytokine production. Mechanistically, Nef achieves this modulation of antigen-specific CD4 T cell function by reducing T cell surface levels of CD4. These results define modulation of CD4 T cell-DC communication as pathogenic principle by which HIV-1 disrupts adaptive immunity and emphasize the direct role of CD4 in immune cell communication.
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 GSE304995_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 PRJNA1303660 and SRA study SRP607663. Searching any of these in the dataset finder brings you back here.

Study design
8 × OT-II.2 CD4 T cell-BMDC co-culture_sublib

Supports a between-group comparison across 8 samples.

How we read this: 1 replicated groups read from 8 sample titles; they account for 8 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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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.