Document Type

Article

Publication Date

11-8-2024

Keywords

JMG, Animals, Encephalomyelitis, Autoimmune, Experimental, Mice, Phenotype, Female, Quantitative Trait Loci, Multiple Sclerosis, Male, Autoimmunity, Collaborative Cross Mice, Disease Models, Animal, Disease Progression, Central Nervous System, YAP-Signaling Proteins, Genetic Linkage

JAX Source

JCI Insight. 2024;9(21).

ISSN

2379-3708

PMID

39325545

DOI

https://doi.org/10.1172/jci.insight.184138

Grant

This work was supported by RG-1901-33309 from the NMSS as well as R21AI145306 and R01AI172166 from the NIH/NIAID to DNK. The authors acknowledge additional support by U19AI100625 from the NIH/NIAID to MTF. Additional support for EAN was provided by 5T32AI055402-08 from the NIH/ NIAID. The distribution of the CC mice used in this study was supported by U42OD010924 from the NIH to the MMRRC at UNC

Abstract

Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The 32 CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary-EAE (AR-EAE), accompanied by distinct immunopathology. Sex differences in EAE severity were observed in 6 strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity (Abcc4 and Gpc6) and AR-EAE (Yap1 and Dync2h1). This work expands the EAE phenotypic repertoire and identifies potentially novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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