← BioTransfer GEO Dataset Finder
GEO series

Diabetes mellitus is associated with a shared hyper-inflammatory immune response in melioidosis and tuberculosis patients: an observational case-control study

GSE296546 Homo sapiens (human) RNA-seq (gene expression) 110 samples Submitted 2026/06/24 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 110 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
110 samples: 110 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
Association of Diabetes Mellitus With a Shared Hyperinflammatory Immune Response in Patients With Melioidosis and Patients With Tuberculosis: An Observational Case-Control StudyOpen forum infectious diseases · 2026
Summary (written by the submitters)
Melioidosis is a serious infection caused by the bacterium Burkholderia pseudomallei (Bp) with a case fatality rate of up to 40% in Northeast Thailand. Diabetes increases the risk of developing melioidosis by 12-fold. A similar, but less marked, relationship with diabetes is seen in tuberculosis (TB) patients, with a 3-fold increased risk of developing TB in people with diabetes.
Read the full summary ↓
However, the mechanisms underlying increased susceptibility are not fully understood. Whole blood samples of 81 acute melioidosis patients from Northeast Thailand and 151 TB patients from South Africa, Indonesia, Romania and Peru alongside uninfected control cohorts were studied by whole blood RNA sequencing. We identified that having diabetes is associated with a hyper-inflammatory response to both melioidosis and TB, with increased neutrophil and platelet degranulation, and exaggerated activation of coagulation and scavenger activation pathways. The impact is more subtle in melioidosis but pronounced in tuberculosis with stunted interferon responses
Published in
Association of Diabetes Mellitus With a Shared Hyperinflammatory Immune Response in Patients With Melioidosis and Patients With Tuberculosis: An Observational Case-Control Study
Rongkard P, Kronsteiner B, Eckold C et al. · Open forum infectious diseases 2026 · PMID 42318305 · doi:10.1093/ofid/ofag286
This dataset
Open GSE296546 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 GSE296546_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 PRJNA1259949 and SRA study SRP583620. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42318305) ↗
Study design
110 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 110 sample titles: 110 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

+ 70 more — browse all 110 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.