GEO series

SIRT2 deacylase modulators control B cell metabolic reprogramming in EBV infection and mitogenic activation

GSE342301 Homo sapiens (human) RNA-seq (gene expression) 8 samples Submitted 2026/08/06 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 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: Homo sapiens (human)
Samples: 8 samples: 8 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)

Sirtuin-2 (SIRT2) is a cellular deacylase, regulating cell cycle progression and metabolic homeostasis. Recently, SIRT2 has emerged as a target with both anticancer and antiviral potential. However, the role and targetability of SIRT2 in viral-driven cancers remains unexplored. Epstein-Barr virus (EBV) is a ubiquitous herpesvirus with oncogenic potential that establishes latency in B lymphocytes and is typically controlled by a robust T cell immune response.

Read the full summary ↓

In settings that compromise this response, such as immune suppression following transplant, EBV can cause B cell lymphomas. With broad immunosuppression and varying response rates limiting the effectiveness of existing lymphoma therapeutics, new strategies are necessary. SIRT2 modulation significantly alters gene expression and metabolism of EBV-infected B cells. Treatment with SIRT2 modulators drives hyperacetylation of targets involved in lipid metabolism, central carbon metabolism, and oxidative phosphorylation. EBV-positive and EBV-negative B cell lymphomas rely on glycolysis to avoid cell death after SIRT2 modulation, revealing a metabolic vulnerability that can be harnessed to kill lymphoma cells. Overall, we have identified how SIRT2 could be implicated as a novel target of therapeutic potential for B cell lymphomas, while also defining fundamental roles for extranuclear lysine acetylation in regulating B cell proliferation and metabolism.

This dataset

Open GSE342301 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 GSE342301_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 PRJNA1507326. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 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 8 sample titles: 8 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

GSM9927674 LCL 44 treated with DMSO 24 hours
GSM9927675 LCL 44 treated with DMSO 48 hours
GSM9927676 LCL 45 treated with DMSO 24 hours
GSM9927677 LCL 45 treated with DMSO 48 hours
GSM9927678 LCL 44 treated with FLS-359 24 hours
GSM9927679 LCL 44 treated with FLS-359 48 hours
GSM9927680 LCL 45 treated with FLS-359 24 hours
GSM9927681 LCL 45 treated with FLS-359 48 hours

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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.