Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis
BackgroundMultiple sclerosis (MS) is a chronic neuroinflammatory disorder characterized by demyelination and immune dysregulation, and microglia play a central role in disease progression. Despite this, the specific microglial gene signatures contributing to MS remain inadequately characterized.Meth...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1581878/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849471444609662976 |
|---|---|
| author | Yan Wu Jianhong Wang Jianhong Wang Bo Chen Yuxue Guo Yuxue Guo Ping Gan Yanbing Han |
| author_facet | Yan Wu Jianhong Wang Jianhong Wang Bo Chen Yuxue Guo Yuxue Guo Ping Gan Yanbing Han |
| author_sort | Yan Wu |
| collection | DOAJ |
| description | BackgroundMultiple sclerosis (MS) is a chronic neuroinflammatory disorder characterized by demyelination and immune dysregulation, and microglia play a central role in disease progression. Despite this, the specific microglial gene signatures contributing to MS remain inadequately characterized.MethodsWe utilized an experimental autoimmune encephalomyelitis (EAE) mouse model and performed RNA sequencing to identify differentially expressed Messenger RNAs (DEmRNAs), Long Non-Coding RNAs (DElncRNAs), Circular RNAs (DEcircRNAs), and microRNAs (DEmiRNAs) in microglia. A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. The biological functions of the included genes were assessed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and validated by quantitative reverse transcription PCR (qRT-PCR). Additionally, molecular docking studies were conducted to explore potential interactions with approved MS therapeutics.ResultsSix DEmRNAs were identified as key microglia-associated biomarkers: Neutrophilic Granule Protein (NGP), Histone Cluster 1 H2B Family Member J (HIST1H2BJ), Phenazine Biosynthesis-Like Domain-Containing Protein 1 (PBLD1), Muscleblind-Like Protein 3 (MBNL3), Lymphocyte Antigen 180 (CD180), and Coagulation Factor X (F10). All six genes were found to be upregulated in EAE microglia compared to phosphate-buffered saline (PBS) treated mice. These genes are primarily involved in immune-related pathways, including Toll-like receptor (TLR) signaling, and interact with MS therapeutics such as teriflunomide. Among the identified DEcircRNAs, circGAS2 (mmu-circ-0001569) was significantly upregulated, suggesting its potential regulatory role in microglial function. The expression trends of these biomarkers were validated via quantitative reverse transcription PCR (qRT-PCR) and Western blot analysis.ConclusionsThis study provides a comprehensive microglial gene signature for EAE, highlighting the involvement of TLR pathways and circRNA-mediated regulation in MS pathogenesis. These findings provide a foundation for future research into microglia-targeted therapies and diagnostic tools for MS. |
| format | Article |
| id | doaj-art-db887f2f34b248d9bb16c683e06d6aa2 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-db887f2f34b248d9bb16c683e06d6aa22025-08-20T03:24:48ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-06-011610.3389/fimmu.2025.15818781581878Identification of the microglia-associated signature in experimental autoimmune encephalomyelitisYan Wu0Jianhong Wang1Jianhong Wang2Bo Chen3Yuxue Guo4Yuxue Guo5Ping Gan6Yanbing Han7Neurology Department, First Affiliated Hospital of Kunming Medical University, Kunming, ChinaNeurology Department, First Affiliated Hospital of Kunming Medical University, Kunming, ChinaKunming Medical University, Kunming, ChinaDepartment of Neurology, Tongji Hospital of Tongji Medical College, Huazhong University of Science of Technology, Wuhan, ChinaNeurology Department, First Affiliated Hospital of Kunming Medical University, Kunming, ChinaKunming Medical University, Kunming, ChinaBiochemistry and Molecular Department, College of Basic Medicine, Kunming Medical University, Kunming, Yunnan, ChinaNeurology Department, First Affiliated Hospital of Kunming Medical University, Kunming, ChinaBackgroundMultiple sclerosis (MS) is a chronic neuroinflammatory disorder characterized by demyelination and immune dysregulation, and microglia play a central role in disease progression. Despite this, the specific microglial gene signatures contributing to MS remain inadequately characterized.MethodsWe utilized an experimental autoimmune encephalomyelitis (EAE) mouse model and performed RNA sequencing to identify differentially expressed Messenger RNAs (DEmRNAs), Long Non-Coding RNAs (DElncRNAs), Circular RNAs (DEcircRNAs), and microRNAs (DEmiRNAs) in microglia. A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. The biological functions of the included genes were assessed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and validated by quantitative reverse transcription PCR (qRT-PCR). Additionally, molecular docking studies were conducted to explore potential interactions with approved MS therapeutics.ResultsSix DEmRNAs were identified as key microglia-associated biomarkers: Neutrophilic Granule Protein (NGP), Histone Cluster 1 H2B Family Member J (HIST1H2BJ), Phenazine Biosynthesis-Like Domain-Containing Protein 1 (PBLD1), Muscleblind-Like Protein 3 (MBNL3), Lymphocyte Antigen 180 (CD180), and Coagulation Factor X (F10). All six genes were found to be upregulated in EAE microglia compared to phosphate-buffered saline (PBS) treated mice. These genes are primarily involved in immune-related pathways, including Toll-like receptor (TLR) signaling, and interact with MS therapeutics such as teriflunomide. Among the identified DEcircRNAs, circGAS2 (mmu-circ-0001569) was significantly upregulated, suggesting its potential regulatory role in microglial function. The expression trends of these biomarkers were validated via quantitative reverse transcription PCR (qRT-PCR) and Western blot analysis.ConclusionsThis study provides a comprehensive microglial gene signature for EAE, highlighting the involvement of TLR pathways and circRNA-mediated regulation in MS pathogenesis. These findings provide a foundation for future research into microglia-targeted therapies and diagnostic tools for MS.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1581878/fullRNA sequencingmicrogliamultiple sclerosis (MS)experimental autoimmune encephalomyelitis (EAE)machine learning (ML) |
| spellingShingle | Yan Wu Jianhong Wang Jianhong Wang Bo Chen Yuxue Guo Yuxue Guo Ping Gan Yanbing Han Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis Frontiers in Immunology RNA sequencing microglia multiple sclerosis (MS) experimental autoimmune encephalomyelitis (EAE) machine learning (ML) |
| title | Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis |
| title_full | Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis |
| title_fullStr | Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis |
| title_full_unstemmed | Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis |
| title_short | Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis |
| title_sort | identification of the microglia associated signature in experimental autoimmune encephalomyelitis |
| topic | RNA sequencing microglia multiple sclerosis (MS) experimental autoimmune encephalomyelitis (EAE) machine learning (ML) |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1581878/full |
| work_keys_str_mv | AT yanwu identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis AT jianhongwang identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis AT jianhongwang identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis AT bochen identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis AT yuxueguo identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis AT yuxueguo identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis AT pinggan identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis AT yanbinghan identificationofthemicrogliaassociatedsignatureinexperimentalautoimmuneencephalomyelitis |