GEO series

KLF13 promotes SLE Pathogenesis by modifying chromatin accessibility of key proinflammatory cytokine genes [RNA-seq]

GSE278050 Mus musculus (mouse) RNA-seq (gene expression) 42 samples Submitted 2024/10/15 Platform GPL21626Platform GPL13112

At a glance

RNA-seq (gene expression) · mouse · 42 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: Mus musculus (mouse)
Samples: 42 samples: 42 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: KLF13 promotes SLE pathogenesis by modifying chromatin accessibility of key proinflammatory cytokine genesCommunications biology · 2024

Summary (written by the submitters)

Although significant progress has been achieved in elucidating the genetic architecture of systemic lupus erythematosus (SLE), identifying genes underlying the pathogenesis has been challenging. The NZM2410-derived lupus susceptibility Sle3 locus is associated with T cell hyperactivity and activated myeloid cells. However, candidate genes associated with these phenotypes have not been identified.

Read the full summary ↓

Here, we narrow the Sle3 locus to a smaller genomic segment (Sle3k) and show that mice carrying Sle3k and Sle1 loci developed lupus nephritis. We identify Klf13 as the primary candidate gene upregulated in Sle3k mice and demonstrate that Klf13 upregulation is associated with genome-wide transcription changes resulting in higher levels of proinflammatory cytokines, enhanced T cell activation, and hyperresponsiveness of myeloid cells. Correspondingly, Klf13 –/– mice display striking repression of genes involved in mediating immune activation, including key proinflammatory cytokines/chemokines. Furthermore, Klf13 –/– mice show profound dysregulation in cytokine signaling pathways in myeloid cells in response to toll receptor ligands, thus revealing a key role of KLF13 in modulating myeloid cell function. Klf13 upregulation is associated with increased production of RANTES, a key chemokine in lupus nephritis, in activated T cells and the kidneys of lupus-prone mice. In sum, our findings reveal Klf13 as a key gene in the Sle3 interval in mediating lupus pathogenesis that may have implications in the rational design of new therapies for SLE and other autoimmune/inflammatory diseases.

Published in

KLF13 promotes SLE pathogenesis by modifying chromatin accessibility of key proinflammatory cytokine genes

Wang A, Fairhurst AM, Liu K et al. · Communications biology 2024 · PMID 39506084 · doi:10.1038/s42003-024-07099-0

This dataset

Open GSE278050 in finder → Find more mouse 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 GSE278050_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 PRJNA1165281 and SRA study SRP534799. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39506084) ↗

Study design

42 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 42 sample titles: 42 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

GSM8538762 KLF13_B6_T0, RNA-seq
GSM8538763 KLF13_NZW_T0, RNA-seq
GSM8538764 B6_T1, RNA-seq
GSM8538765 Sle3_T1, RNA-seq
GSM8538766 B6_T2, RNA-seq
GSM8538767 Sle3_T2, RNA-seq
GSM8538768 B6_D0, RNA-seq
GSM8538769 Sle3_D0, RNA-seq
GSM8538770 B6_D1, RNA-seq
GSM8538771 Sle3_D1, RNA-seq
GSM8538772 WT_T0, replicate 1, RNA-seq
GSM8538773 WT_T0, replicate 2, RNA-seq
GSM8538774 KLF13-/-_T0, replicate 1, RNA-seq
GSM8538775 KLF13-/-_T0, replicate 2, RNA-seq
GSM8538776 WT_T1, replicate 1, RNA-seq
GSM8538777 WT_T1, replicate 2, RNA-seq
GSM8538778 KLF13-/-_T1, replicate 1, RNA-seq
GSM8538779 KLF13-/-_T1, replicate 2, RNA-seq
GSM8538780 WT_T2, replicate 1, RNA-seq
GSM8538781 WT_T2, replicate 2, RNA-seq
GSM8538782 KLF13-/-_T2, replicate 1, RNA-seq
GSM8538783 KLF13-/-_T2, replicate 2, RNA-seq
GSM8538784 WT_M0, RNA-seq
GSM8538785 KLF13-/-_M0, RNA-seq
GSM8538786 WT_M1, RNA-seq
GSM8538787 KLF13-/-_M1, RNA-seq
GSM8538788 WT_M2, RNA-seq
GSM8538789 KLF13-/-_M2, RNA-seq
GSM8538790 WT_M3, RNA-seq
GSM8538791 KLF13-/-_M3, RNA-seq
GSM8538792 WT_D0, replicate 1, RNA-seq
GSM8538793 WT_D0, replicate 2, RNA-seq
GSM8538794 KLF13-/-_D0, replicate 1, RNA-seq
GSM8538795 KLF13-/-_D0, replicate 2, RNA-seq
GSM8538796 WT_D1, replicate 1, RNA-seq
GSM8538797 WT_D1, replicate 2, RNA-seq
GSM8538798 KLF13-/-_D1, replicate 1, RNA-seq
GSM8538799 KLF13-/-_D1, replicate 2, RNA-seq
GSM8538800 WT_D2, replicate 1, RNA-seq
GSM8538801 WT_D2, replicate 2, RNA-seq

+ 2 more — browse all 42 samples with per-sample file links →

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GSE299933 The sleep–wake history contributes to rhythmic BMAL1 chromatin binding in the cerebral cortex but not in the liver 95 samples shares sle, chromatin
GSE290224 Intermittent Fasting Reprograms Chromatin Accessibility to Modulate Gene Expression in Brain and Muscle 96 samples shares accessibility, chromatin
GSE240857 Hmgb2 improves astrocyte to neuron conversion by increasing the chromatin accessibility of genes associated with neuronal maturation in a proneuronal factor-dependent manner[RNA-Seq] 29 samples shares accessibility, chromatin
GSE296085 E2F1 induces a G0-G1 re-entry transcriptional program without changing chromatin accessibility [RNA-seq] 22 samples shares accessibility, chromatin
GSE242943 Nuclear actin structure regulates chromatin accessibility [RNA-seq] 18 samples shares accessibility, chromatin

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