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Clazakizumab treatment effects on peripheral blood and kidney transcriptomes in chronic antibody-mediated rejection

GSE287697 Homo sapiens (human) RNA-seq (gene expression) 58 samples Submitted 2025/12/09 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 58 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
58 samples: 18 conditions, mostly in duplicateGroups 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
The Effects of Clazakizumab on Peripheral Blood and Kidney Transcriptomes in Patients With Late Antibody-Mediated RejectionKidney international reports · 2025
Summary (written by the submitters)
There are currently no licenced treatments for antibody-mediated rejection (ABMR), a major cause of late kidney allograft loss. IL-6 is a cytokine that promotes B cell differentiation to plasma cells, T follicular cell (Tfh) polarisation, and monocyte activation, key processes for antibody generation or effector functions. Clazakizumab (CLZ), an IL-6-neutralising antibody, has been used in a phase 2 trial in patients with ABMR, showing potential efficacy in terms of a reduction in donor-specific antibodies and kidney molecular ABMR (MMDx) score.
Read the full summary ↓
Here we studied the transcriptional changes occurring in paired blood and kidney samples taken following CLZ treatment during this clinical trial. We observed a reduction in ‘FcγR mediated phagocytosis’, ‘complement and coagulation cascades’ and ‘natural killer (NK) cell mediated cytotoxicity’ gene sets in peripheral blood with short-term CLZ treatment, with variable individual responses and some patients showing a rebound in these pathways in longer-term despite ongoing CLZ treatment. We identified a gene module in blood that showed a significant positive correlation with kidney ABMR MMDx score and was down-regulated with CLZ-treatment, potentially identifying a peripheral blood gene set that can be used to non-invasively assess treatment efficacy. In the kidney, CLZ-treatment was associated with a reduction in a “damaged tubule” gene signature and preservation of podocyte signatures, providing further evidence of its therapeutic efficacy in ABMR.
Published in
The Effects of Clazakizumab on Peripheral Blood and Kidney Transcriptomes in Patients With Late Antibody-Mediated Rejection
Zhang R, Lee CYC, Schatzl M et al. · Kidney international reports 2025 · PMID 41278310 · doi:10.1016/j.ekir.2025.08.027
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 GSE287697_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 PRJNA1214260 and SRA study SRP559103. Searching any of these in the dataset finder brings you back here.

Study design
18 conditions, mostly in duplicate
01_002_week ×2 01_003_week ×2 01_004_week ×2 01_005_week ×2 01_008_week ×2 01_009_week ×2 01_010_week ×2 01_011_week ×2 +32 more

Supports a between-group comparison across 36 samples.

How we read this: 18 replicated groups read from 58 sample titles; they account for 36 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

+ 18 more — browse all 58 samples with per-sample file links →

Datasets on a similar topic

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