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Pathogen-derived glyoxylate inhibits Tet2 DNA dioxygenase to facilitate bacterial persister formation [II]

GSE281880 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/11/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 3 × RNA-seq_WT_0h vs 3 × RNA-seq_KO_0h vs 3 × RNA-seq_WT_6h vs 3 × RNA-seq_KO_6hGroups 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)
Resistance, tolerance and persistence enable pathogenic bacteria to survive antibiotic treatment and are associated with an elevated risk of treatment failure and relapsing infections. The mechanism underlying bacterial antibiotic persister formation is not well understood. Here, we show that glyoxylate, a metabolite originally evolved to allow bacteria to utilize alternative carbon sources for optimal growth and pathogenicity, also serves as a signaling molecule to alter host gene expression and support persister formation.
Read the full summary ↓
Specifically, we discovered glyoxylate interacting with TET2 DNA dioxygenase through an in-silico screen of human metabolome. We further show that Salmonella-produced glyoxylate inhibits the activity of Tet2 to suppress the expression of pro-inflammatory genes and attenuate host immune defense. Catalytic inactivation of Tet2, by genetic knock-in mutation, glyoxylate production by Salmonella, or exogenous glyoxylate treatment, facilitates bacterial persister formation in both murine and human macrophages. Conversely, stimulating TET2 with vitamin C or blocking Salmonella production of glyoxylate counters bacterial antibiotic resistance and improves the infection treatment outcomes. In conclusion, our study reveals a novel signaling function of glyoxylate in gene regulation beyond energetic and biosynthetic metabolism. Our findings also suggest that stimulating TET activity within host cells represents a potential therapeutic strategy to combat bacterial persistence.
This dataset
Open GSE281880 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 GSE281880_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 PRJNA1186121 and SRA study SRP545228. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × RNA-seq_WT_0h vs 3 × RNA-seq_KO_0h vs 3 × RNA-seq_WT_6h vs 3 × RNA-seq_KO_6h

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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

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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.