GEO series

Trained immunity suppression determines kidney allograft survival [ChIP]

GSE280903 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2024/11/04 Platform GPL18573

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Samples: 24 samples: 24 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: Trained immunity suppression determines kidney allograft survivalAmerican journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2024

Summary (written by the submitters)

The innate immune system plays an essential role in regulating the immune responses to kidney transplantation, but the mechanisms through which innate immune cells influence long-term graft survival are unclear. The current study highlights the vital role of trained immunity in kidney allograft survival. Trained immunity describes the epigenetic and metabolic changes that innate immune cells undergo following an initial stimulus, allowing them have a stronger inflammatory response to subsequent stimuli.

Read the full summary ↓

We stimulated healthy peripheral blood mononuclear cells with pretransplant and posttransplant serum of kidney transplant patients and immunosuppressive drugs in an in vitro trained immunity assay and measured tumor necrosis factor and interleukin 6 cytokine levels in the supernatant as a readout for trained immunity. We show that the serum of kidney transplant recipients collected 1 week after transplantation can suppress trained immunity. Importantly, we found that kidney transplant recipients whose serum most strongly suppressed trained immunity rarely experienced graft loss. This suppressive effect of posttransplant serum is likely mediated by previously unreported effects of immunosuppressive drugs. Our findings provide mechanistic insights into the role of innate immunity in kidney allograft survival, uncovering trained immunity as a potential therapeutic target for improving graft survival.

Published in

Trained immunity suppression determines kidney allograft survival

Jonkman I, Jacobs MME, Negishi Y et al. · American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 2024 · PMID 39147201 · doi:10.1016/j.ajt.2024.08.006

This dataset

Open GSE280903 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE280903_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1181014 and SRA study SRP542673. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39147201) ↗

Study design

24 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 24 sample titles: 24 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

GSM8607362 PBMC, high, 196, H3K4me3
GSM8607363 PBMC, high, 197, H3K4me3
GSM8607364 PBMC, high, 220, H3K4me3
GSM8607365 PBMC, high, 226, H3K4me3
GSM8607366 PBMC, low, 229, H3K4me3
GSM8607367 PBMC, low, 230, H3K4me3
GSM8607368 PBMC, high, 233, H3K4me3
GSM8607369 PBMC, low, 242, H3K4me3
GSM8607370 PBMC, low, 243, H3K4me3
GSM8607371 PBMC, low, 244, H3K4me3
GSM8607372 PBMC, high, 250, H3K4me3
GSM8607373 PBMC, low, 378, H3K4me3
GSM8607374 PBMC, high, 196, H3K27ac
GSM8607375 PBMC, high, 197, H3K27ac
GSM8607376 PBMC, high, 220, H3K27ac
GSM8607377 PBMC, high, 226, H3K27ac
GSM8607378 PBMC, low, 229, H3K27ac
GSM8607379 PBMC, low, 230, H3K27ac
GSM8607380 PBMC, high, 233, H3K27ac
GSM8607381 PBMC, low, 242, H3K27ac
GSM8607382 PBMC, low, 243, H3K27ac
GSM8607383 PBMC, low, 244, H3K27ac
GSM8607384 PBMC, high, 250, H3K27ac
GSM8607385 PBMC, low, 378, H3K27ac

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GSE313845 RNA exonuclease REXO4 resolves m6A-marked R-loops and suppresses anti-tumor immunity [ChIP-Rx] 24 samples shares immunity, chip
GSE244944 NOTCH1 reverses immune suppression in small cell lung cancer through reactivation of STING [CHIP-seq] 18 samples shares suppression, chip
GSE328649 Lineage 2-Beijing Mycobacterium tuberculosis strains suppress BCG-trained innate immunity early after infection 39 samples shares immunity, trained
GSE262098 Transcriptomes during monocyte-to-macrophage differentiation and BCG induced trained immunity in vitro 21 samples shares immunity, trained
GSE303481 Spatio-temporal interaction of immune and renal cells determines glomerular crescent formation in autoimmune kidney disease 15 samples shares determines, kidney
GSE313850 RNA exonuclease REXO4 resolves m6A-marked R-loops and suppresses anti-tumor immunity [CUT&Tag-seq] 44 samples shares immunity

Search all human ChIP / ATAC / CUT&Tag 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.