| Autor: |
Nelson EA; Department of Biomedical and Health Sciences, University of Vermont (UVM), Burlington, Vermont, USA., Tyler AL; The Jackson Laboratory, Bar Harbor, Maine, USA., Lakusta-Wong T; Department of Neurological Sciences and., Lahue KG; Department of Biomedical and Health Sciences, University of Vermont (UVM), Burlington, Vermont, USA., Hankes KC; Department of Biomedical and Health Sciences, University of Vermont (UVM), Burlington, Vermont, USA., Teuscher C; Department of Medicine, UVM, Larner College of Medicine, Burlington, Vermont, USA., Lynch RM; Department of Genetics, University of North Carolina at Chapel Hill (UNC), Chapel Hill, North Carolina, USA., Ferris MT; Department of Genetics, University of North Carolina at Chapel Hill (UNC), Chapel Hill, North Carolina, USA., Mahoney JM; The Jackson Laboratory, Bar Harbor, Maine, USA.; Department of Neurological Sciences and., Krementsov DN; Department of Biomedical and Health Sciences, University of Vermont (UVM), Burlington, Vermont, USA. |
| Abstrakt: |
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. |
