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Hierarchical, non-additive MARTX effector interplay rewires host transcription to drive Vibrio vulnificus invasion and lethal sepsis

GSE327326 Homo sapiens (human) RNA-seq (gene expression) 51 samples Submitted 2026/04/15 Platform GPL34284Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 51 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
51 samples: 17 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)
Multifunctional-autoprocessing repeats-in-toxin (MARTX) toxins deliver multiple effectors into host cells, yet how distinct effector combinations integrate to shape virulence remains poorly defined. Here, we systematically dissect effector interplay in the highly virulent pathogen Vibrio vulnificus. In the M-type MARTX toxin strain MO6-24/O, which delivers four effectors, we generated catalytically-inactivating point mutations in all combinations in situ.
Read the full summary ↓
We found that effector composition influences virulence potential, revealing both virulence and anti-virulence factors. Integrated transcriptomics revealed that coordinated effector activity governs the magnitude and coherence of host response. Two functional effector modules define a regulatory–metabolic axis and a silencing–execution axis, each partially contributing to virulence. When combined into the holotoxin, these modules reprogram host transcription toward a highly activated state, supporting a model in which virulence emerges from non-additive effector crosstalk that rewires host transcriptional landscapes.
This dataset
Open GSE327326 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 GSE327326_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 PRJNA1450517 and SRA study SRP690098. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
17 conditions, mostly in triplicate
DAM ×3 DRM ×3 DAR ×3 AM ×3 AR ×3 MR ×3 DM ×3 DA ×3 +9 more

Supports a between-group comparison across 51 samples.

How we read this: 17 replicated groups read from 51 sample titles; they account for 51 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

+ 11 more — browse all 51 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.