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MSCs can alleviate ferroptosis and improve DKD through the Smad2/3/METTL3/S1PR1 axis.

GSE285781 Mus musculus (mouse) DNA methylation 15 samples Submitted 2025/09/01 Platform GPL24247
At a glance

DNA methylation · mouse · 15 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Mus musculus (mouse)
Samples
15 samples: 6 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.
Summary (written by the submitters)
Diabetic kidney disease (DKD) is a significant cause of end-stage renal disease (ESRD), markedly increasing the risk of cardiovascular disease and all-cause mortality. Although mesenchymal stem cells (MSCs) have demonstrated potential in mitigating DKD through various pathways, their specific mechanisms remain unclear. In this study, we observed increased Smad2 phosphorylation and ferroptosis in DKD models, which were alleviated by MSC intervention.
Read the full summary ↓
Dot blot analysis and the EpiQuik M6A RNA Methylation Quantification Kit revealed elevated m6A modification levels in DKD. Bioinformatics analysis, receiver operating characteristic (ROC) curves, and Western blot (WB) assays indicated a correlation between METTL3 expression and changes in m6A modification in DKD. Knockdown of METTL3 and Smad2 reduced m6A modification and ferroptosis levels. Furthermore, knockdown of METTL3 reversed the increase in m6A modification caused by Smad2 overexpression. Notably, transcriptome and m6A-seq analysis identified S1pr1 as a target gene. Using the GEPIA database, we found a close association between S1pr1 expression and both Smad2 and METTL3 in kidney tissues. The SRAMP tool predicted nine m6A modification sites on S1pr1 mRNA, eight with over 95% credibility and one at position 688 with over 90% credibility. Knockdown of S1pr1 reversed the protective effect of METTL3 knockdown against ferroptosis in DKD, increasing Fe2+, ROS, and ACSL4 levels while decreasing GPX4 and SLC7A11 expression. Finally, MSC intervention and shRNA-mediated METTL3 knockdown increased the reduced S1pr1 levels in DKD kidney tissue. Our findings suggest that upon nuclear translocation, Smad2 promotes S1pr1 gene m6A modification by binding to METTL3, thereby affecting S1pr1 expression and positively regulating cellular ferroptosis in DKD models. MSC intervention can reduce this process, further elucidating the mechanism by which MSCs regulate podocyte ferroptosis in DKD.
This dataset
Open GSE285781 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 GSE285781_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 beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1206080 and SRA study SRP555096. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
MPC5 cells,control_input RNA, ×2 MPC5 cells,control_IP RNA, ×2 MPC5 cells,sh_METTL3_input RNA, ×2 MPC5 cells,sh_METTL3_IP RNA, ×2 MPC5 cells,sh_METTL3_G_input RNA, ×2 MPC5 cells,sh_METTL3_G_IP RNA, ×2 MPC5 cells,control ×1 MPC5 cells,sh_METTL ×1 +1 more

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 15 sample titles; they account for 12 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
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