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METTL3-Mediated N6-methyladenosine mRNA Modification and cGAS-STING Pathway Activity in Kidney Fibrosis

GSE271143 Homo sapiens (human) RNA-seq (gene expression) 127 samples Submitted 2024/07/03 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 127 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
127 samples: 127 × kidney sampleGroups 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.
Summary (written by the submitters)
Background Chemical modifications on RNA profoundly impact RNA function and regulation. m6A, the most abundant RNA modification in eukaryotes, plays a pivotal role in diverse cellular processes and disease mechanisms. However, its importance is understudied in human chronic kidney disease (CKD) samples regarding its influence on pathological mechanisms.
Read the full summary ↓
Methods LC-MS/MS and Methylated RNA Immunoprecipitation (MeRIP) sequencing were utilized to examine alterations in m6A levels and patterns in CKD samples. Overexpression of the m6A writer METTL3 in cultured kidney tubular cells was performed to confirm the impact of m6A in tubular cells and explore the biological functions of m6A modification on target genes. Additionally, tubule-specific deletion of Mettl3 (Ksp-Cre Mettl3f/f) mice and the use of anti-sense oligonucleotides inhibiting Mettl3 expression were utilized to reduce m6A modification in an animal kidney disease model. Results By examining 127 human CKD samples, we observed a significant increase in m6A modification and METTL3 expression in diseased kidneys. Epitranscriptomic analysis unveiled an enrichment of m6A modifications in transcripts associated with the activation of inflammatory signaling pathways, particularly the cGAS-STING pathway. m6A hypermethylation increased mRNA stability in cGAS and STING1, as well as elevated the expression of key proteins within the cGAS-STING pathway. Both the tubule-specific deletion of Mettl3 and the use of anti-sense oligonucleotides to inhibit Mettl3 expression protected mice from inflammation, reduced cytokine expression, decreased immune cell recruitment, and attenuated kidney fibrosis. Conclusions Our research revealed heightened METTL3-mediated m6A modification in fibrotic kidneys, particularly enriching the cGAS-STING pathway. This hypermethylation increased mRNA stability for cGAS and STING1, leading to sterile inflammation and fibrosis.
This dataset
Open GSE271143 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 GSE271143_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 PRJNA1130049 and SRA study SRP517119. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
127 × kidney sample

Supports a between-group comparison across 127 samples.

How we read this: 1 replicated groups read from 127 sample titles; they account for 127 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

+ 87 more — browse all 127 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.