Identifying changes to cellular physiology that drive multiple sclerosis risk

多发性硬化症（MS）是一种中枢神经系统（CNS）的慢性自身免疫性炎症性疾病。多发性硬化症的病理生理是多因素的，很可能主要是由不适当的免疫反应引起的，这些免疫反应针对中枢神经系统中的髓磷脂成分，导致脱髓鞘，并在后期导致神经退行性变。在过去的二十年里，研究主要集中在T辅助细胞驱动MS病理，但最近，实验和临床证据都表明B细胞是病理的额外驱动因素。多发性硬化症的风险是可遗传的，在整个人类基因组中，已经确定了200多个编码这种风险的遗传因素。驱动MS风险的细胞机制尚不清楚，现在的核心挑战是将遗传发现转化为对疾病风险病理生理学的理解。先前的研究表明，大多数与疾病相关的基因座干扰基因调控而不是基因结构，并且许多基因座编码主要与免疫反应和细胞因子信号传导相关的有限数量的信号级联的成分。该项目的目的是将MS风险的遗传因素与不同免疫细胞类型（包括B细胞和辅助T细胞）的细胞生理学改变联系起来。这将使用联合似然映射（JLIM）进行调查，JLIM是一种最近开发的强大方法，用于确定两个不同的性状是否由相同的潜在遗传效应驱动。我们将使用JLIM和已经被证明具有遗传基础的免疫细胞表型数据，来自T细胞，B细胞和单核细胞的基因表达数据以及MS风险数据来揭示与MS病理生理相关的免疫细胞表型。这可以让我们更好地了解这种复杂的疾病，并可能促进新的治疗策略的发展。

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disorder of the central nervous system (CNS). The pathophysiology of MS is multifactorial and most likely driven primarily by an inappropriate immune response mounted against components of myelin in the CNS leading to demyelination and at later stages to neurodegeneration. Over the last twenty years, research focused on T helper cells as driving MS pathology but more recently, both experimental and clinical evidence has implicated B cells as additional drivers of pathology. MS risk is heritable and over 200 of genetic factors across the entire human genome encoding this risk have been identified. The cellular mechanisms driving MS risk are yet to be understood and a central challenge now is to translate the genetic findings into an understanding of the pathophysiology of disease risk. It has previously been shown that most of disease-associated loci perturb gene regulation rather than gene structure and many of the loci encode components of a limited number of signaling cascades primarily associated with immune responses and cytokine signaling. The aim of this project is to link genetic factors for MS risk with alterations in the cellular physiology of different immune cell types including B cells and helper T cells. This will be investigated using Joint likelihood mapping (JLIM), a recently developed robust method to determine whether two different traits are driven by the the same underlying genetic effect. We will use JLIM and data of immune cell phenotypes that have already been shown to have a genetic basis, gene expression data from T cells, B cells and monocytes and MS risk data to uncover immune cell phenotypes relevant to MS pathophysiology. This could give us a better understanding of this complex disease and could potentially facilitate the development of new treatment strategies.