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Nicotinamide mononucleotide mitigates neuroinflammation by enhancing GPX4-mediated ferroptosis defense in microglia

GSE262303 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/03/01 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 3 × BV-2 cells Control vs 3 × BV-2 cells LPS vs 3 × BV-2 cells NMN vs 3 × BV-2 cells LPS+NMNGroups 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)
Numerous neurological diseases involve neuroinflammation, a process in which immune cells, particularly microglia, contribute to neuronal death. Ferroptosis, a recently identified form of regulated cell death, is implicated in various diseases characterized by neuronal injury. Nicotinamide mononucleotide (NMN), a potent NAD+ precursor supplement, has been found to inhibit neuroinflammation and ferroptosis.
Read the full summary ↓
However, the mechanisms of NMN in both ferroptosis and neuroinflammation remains unclear. The present study aimed to investigate the impact of NMN on neuroinflammation and the susceptibility of microglia to ferroptosis. Ferroptosis markers in microglia exposed to lipopolysaccharide (LPS) were analyzed using CCK8, flow cytometry, ELISA, and RT-qPCR. The effects of NMN on LPS-induced ferroptosis in microglia were evaluated through flow cytometry, western blotting, and immunofluorescence staining. RT-qPCR analysis assessed the inflammatory cytokine production of microglia subjected to ferrostatin-1-regulated ferroptosis. RNA sequencing elucidated the underlying mechanisms of NMN-associated microglia ferroptosis under LPS induction. In BV2 microglia, an inhibitor of Glutathione Peroxidase 4(GPX4), RSL3, was employed to suppress GPX4 expression. Intracerebroventricular injection of LPS was performed to evaluate neuroinflammation and microglia activation in vivo. LPS treatment resulted in decreased cell viability, accompanied by upregulation of ferroptosis markers SLC7A11 and GPX4, and elevated levels of malondialdehyde (MDA), 4-hydroxynonenal (4-HNE), and total iron in a dose-dependent manner. NMN effectively rescued LPS-induced ferroptosis and improved cell viability in microglia. Co-administration of NMN and ferrostatin-1 significantly reduced proinflammatory cytokine production in microglia following the introduction of LPS stimuli. Mechanistically, NMN facilitated glutathione (GSH) production, and promoted resistance to lipid peroxidation in a GPX4-dependent manner, repressing cytokine transcription and protecting cells from ferroptosis. RNA sequencing elucidated the underlying mechanism of NMN-associated microglia ferroptosis under LPS induction. Furthermore, simultaneous injection of NMN ameliorated LPS-induced ferroptosis and neuroinflammation in mouse brains. The data from the present study indicated that NMN enhances GPX4-mediated ferroptosis defense against LPS-induced ferroptosis in microglia by recruiting GSH, thereby inhibiting neuroinflammation. Therefore, therapeutic approaches targeting ferroptosis in diseases using NMN should consider both its anti-ferroptosis and anti-inflammatory effects to achieve optimal outcomes, presenting promising strategies for treating neuroinflammation-related diseases or disorders.
This dataset
Open GSE262303 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 GSE262303_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 PRJNA1091191 and SRA study SRP497432. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × BV-2 cells Control vs 3 × BV-2 cells LPS vs 3 × BV-2 cells NMN vs 3 × BV-2 cells LPS+NMN

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 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
Datasets on a similar topic

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