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Triptolide clears Staphylococcus aureus infection by targeting XIAP to induce host apoptosis while preserving gut microbiota homeostasis [mouse lung macrophages]

GSE320244 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2026/05/18 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 18 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.
Paper
Triptolide clears Staphylococcus aureus infection by targeting XIAP to induce host apoptosis while maintaining gut microbiota homeostasisFrontiers in pharmacology · 2026
Summary (written by the submitters)
Staphylococcus aureus (SA) remains a global health threat due to its increasing drug resistance and intracellular persistence, which compromise the efficacy of conventional antibiotics. Host-directed therapy (HDT) has emerged as a promising alternative by modulating host immunity. With the multi-targeting and immunomodulatory properties, Traditional Chinese Medicine (TCM) monomers represent ideal candidates for HDT.
Read the full summary ↓
However, their ability to promote host immunity-mediated SA clearance remains largely unexplored. Here, we identified triptolide as a compound that facilitates intracellular clearance of SA and MRSA. Mechanistic studies revealed that triptolide directly binds to the X‑linked inhibitor of apoptosis protein (XIAP) to inhibit XIAP-caspase interaction, thereby releasing caspase inhibition and inducing apoptosis. Moreover, treatment with triptolide reduced bacterial loads and alleviated lung inflammation in mice infected with SA and MRSA, concurrently maintaining microbiota homeostasis and improving metabolic function. Taken together, these findings establish triptolide as a promising lead compound for the development of HDT against SA and MRSA infections.
Published in
Triptolide clears Staphylococcus aureus infection by targeting XIAP to induce host apoptosis while maintaining gut microbiota homeostasis
Qiu X, Qiang L, Wang Y et al. · Frontiers in pharmacology 2026 · PMID 42272841 · doi:10.3389/fphar.2026.1834558
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 GSE320244_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 PRJNA1422487 and SRA study SRP676945. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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
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