Department of Biomedical and Health Sciences, University of Vermont (UVM), Burlington, Vermont, USA.
The Jackson Laboratory, Bar Harbor, Maine, USA.
JCI Insight. 2024 Nov 8;9(21):e184138. doi: 10.1172/jci.insight.184138.
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.
多发性硬化症 (MS) 是一种复杂的疾病，其病程和进展具有显著的异质性。遗传研究已经确定了许多与 MS 风险相关的基因座，但疾病进展的遗传基础仍然难以捉摸。为了解决这个问题，我们利用了遗传多样性的 CC（Collaborative Cross）小鼠品系和实验性自身免疫性脑脊髓炎 (EAE)。研究的 32 个 CC 株系捕捉到了广泛的 EAE 严重程度、轨迹和表现，包括严重进展型、单相型、复发缓解型和轴性旋转型 EAE (AR-EAE)，并伴有不同的免疫病理学。在 6 个株系中观察到 EAE 严重程度的性别差异。数量性状基因座分析显示，不同的 EAE 表型具有不同的遗传连锁模式，包括 EAE 严重程度和 AR-EAE 的发生率。基于机器学习的方法优先考虑了 EAE 严重程度（Abcc4 和 Gpc6）和 AR-EAE（Yap1 和 Dync2h1）的候选基因。这项工作扩展了 EAE 的表型谱，并确定了潜在的新基因座来控制独特的 EAE 表型，支持了 MS 疾病过程中的异质性是由遗传变异驱动的假设。
