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Dynamics of Th1/Th17 Responses and Antimicrobial Pathways in Leprosy Skin Lesions

GSE280021 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2024/12/21 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 45 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
45 samples: 45 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
Dynamics of Th1/Th17 responses and antimicrobial pathways in leprosy skin lesionsThe Journal of clinical investigation · 2025
Summary (written by the submitters)
Reversal reactions (RR) in leprosy provide a unique opportunity to study the dynamics of the immune response against intracellular bacteria in humans. These episodes are often severe and difficult to treat, frequently progressing to permanent disabilities. We performed RNA sequencing on paired skin biopsy specimens from nine leprosy patients before and during RR, identifying a 64-gene antimicrobial response signature that correlated with the concomitant decrease in Mycobacterium leprae bacilli in RR patients.
Read the full summary ↓
The upstream regulators of this signature included both innate (IL-1β, TNF) and adaptive (IFN-γ, IL-17) cytokines, indicating induction of both Th1 and Th17 responses. Using a machine learning classifier to identify proteins with predicted membrane-permeating activity, we identified 28 genes in RR with previously unknown direct antimicrobial activity, including S100A8. We validated the antimicrobial activity of four proteins (S100A7, S100A8, CCL17, CCL19) against M. leprae in infected macrophages and axenic culture. Scanning electron microscopy revealed distinct membrane damage in bacteria exposed to these antimicrobial proteins. Our findings reveal dynamic antimicrobial gene regulation during RR and identify new host defense effectors, supporting therapeutic strategies that boost Th1/Th17 function to improve outcomes in mycobacterial infections.
Published in
Dynamics of Th1/Th17 responses and antimicrobial pathways in leprosy skin lesions
Andrade PR, Ma F, Lu J et al. · The Journal of clinical investigation 2025 · PMID 40569678 · doi:10.1172/JCI190736
This dataset
Open GSE280021 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 GSE280021_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 PRJNA1175972 and SRA study SRP540119. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40569678) ↗
Study design
45 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 45 sample titles: 45 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

+ 5 more — browse all 45 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.