← BioTransfer GEO Dataset Finder
GEO series

Metabolomic Profiling Reveals the Potential of Fatty Acids as Regulators of Exhausted CD8 T Cells During Chronic Viral Infection

GSE312809 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2025/12/10 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 30 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
30 samples: 10 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.
Summary (written by the submitters)
Chronic infections induce CD8 T cell exhaustion, marked by impaired effector function. While intrinsic drivers are well studied, the role of the surrounding metabolic environment in shaping exhausted CD8 T cells (Tex) is less understood. Using untargeted metabolomics and the murine lymphocytic choriomeningitis virus infection model we investigated systemic metabolite changes following acute versus chronic viral infections.
Read the full summary ↓
We identified distinct short-term and persistent metabolite shifts, with the most significant differences occurring transiently during the early phase of the sustained infection. This included nutrient changes that were partially associated with CD8 T cell-induced anorexia and lipolysis. One remarkable observation was the elevation of medium- and long-chain fatty acid (FA) and acylcarnitines during the first week after chronic infection. Consistently, virus-specific CD8 T cells from chronic infection exhibited increased lipid accumulation and uptake compared to their counterparts from acute infection, particularly the stem-like Tex (TexSTEM), which generates TexINT that directly limit viral replication. Notably, only TexSTEM increased oxidative metabolism upon ex-vivo FA exposure, while short-term administration of FA during late chronic infection exclusively increased TexSTEM and their mitochondrial potential. The last-mentioned treatment also led to reduced TexINT and enhanced PD-1 across all Tex subsets, which coincided with compromised viral control. Our study offers a valuable resource for investigating the regulatory role of specific metabolites during acute and chronic viral infections and highlights the potential of FA to fine-tune Tex subsets during protracted infections.
This dataset
Open GSE312809 in finder → Find more mouse 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 GSE312809_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 PRJNA1377450 and SRA study SRP653258. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly in triplicate
TexSTEM, untreated, in-vivo, d21 p.i., ×3 TexINT, untreated, in-vivo, d21 p.i., ×3 TexTERM, untreated, in-vivo, d21 p.i., ×3 TexSTEM, FA-treated, in-vivo, d21 p.i., ×3 TexINT, FA-treated, in-vivo, d21 p.i., ×3 TexTERM, FA-treated, in-vivo, d21 p.i., ×3 CX3CR1+, BSA only, ex-vivo, d8 p.i., ×3 CX3CR1-, BSA only, ex-vivo, d8 p.i., ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.