Epstein–Barr virus and multiple sclerosis: lesson learned to develop better nonhuman primate models
Abstract Multiple sclerosis (MS) is a chronic autoimmune disorder with a complex etiology, and Epstein–Barr virus (EBV) is considered the leading cause. While understanding the role of EBV infection in the pathogenesis of MS in human subjects is crucial, animal models, particularly nonhuman primates...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-06-01
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| Series: | Experimental and Molecular Medicine |
| Online Access: | https://doi.org/10.1038/s12276-025-01482-5 |
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| Summary: | Abstract Multiple sclerosis (MS) is a chronic autoimmune disorder with a complex etiology, and Epstein–Barr virus (EBV) is considered the leading cause. While understanding the role of EBV infection in the pathogenesis of MS in human subjects is crucial, animal models, particularly nonhuman primates (NHPs), would provide an ideal controlled environment for testing EBV hypotheses and identifying potential therapeutic targets. Here in this Review we address clinically relevant questions regarding the link between EBV infection and MS to inform the development and refinement of virally induced NHP models. We focus on integrating known EBV-related risk factors for MS, including age at infection, infectious mononucleosis, genetic predispositions such as the human leukocyte antigen (HLA)-DR15 haplotype, sex-specific susceptibility, low vitamin D levels and CD8+ T cell deficiency. We also explore the application of these risk factors in model development, investigate why most EBV-infected individuals do not develop MS and propose potential disease-modifying therapeutic options and vaccines. Integrating these approaches into NHP models will improve our understanding of MS pathogenesis and guide the development of targeted strategies for disease management and prevention. We propose to develop a refined EBV infection NHP model of MS coupled with CD8+ cell depletion and other inclusion and exclusion criteria. |
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| ISSN: | 2092-6413 |