Characterizing the serum metabolome in multiple sclerosis

项目摘要和摘要 在过去的十年里，我们在对潜在机制的理解上取得了长足的进步 多发性硬化症(MS)的风险和进展，然而，许多变异仍然无法解释。我们有 大大缩短了诊断时间，但我们提高了诊断灵敏度 特异性不是很理想。此外，FDA批准的大多数MS特异性免疫调节疗法(IMTS)都集中在 复发阶段的炎症性疾病成分，对改善预后影响不大 一旦病人进入进展期。药物试验的挑战是缺乏生物标记物来检测和 监控MS进展情况。当前应用程序的目标是：1.识别和表征 鉴别MS和其他中枢神经系统炎性脱髓鞘疾病的生物标志物 (CNSIDD)和非CNSIDD对照，以及2.确定疾病活动的生物标记物和 区分复发性和进行性形式的多发性硬化症我们提出了一个多阶段分析的预先存在和良好- 来自两个来源的已定义生物样本。 目的1.确定MS区别于其他CNSIDD和健康对照的生化特征。 有监督的机器学习和分类模型将识别区分多发性硬化症和多发性硬化的代谢特征 其他CNSIDD和健康对照(HCS)在两个队列中。在第一个队列中，早期的多发性硬化症患者 诊断(≤2年)和IMT幼稚/免费将与肥厚性脑病和其他CNSIDD病例进行比较。辨别性 代谢产物将在第二个队列中进行复制测试，将相似定义的MS患者与HCS和其他患者进行比较 CNSIDDS和其他自身免疫性疾病患者。我们将确定复制MS的方向- 使用双向遗传工具变量分析的代谢物关联。目标2.确定生化 多发性硬化症疾病活动特点。我们将通过以下方式确定与疾病活动相对应的代谢变化 比较确诊2年内IMT幼稚/无IMT的患者和最近复发的IMT患者与 在≥3个月内缓解，并在发现队列中使用监督分类进入HC，随后 第二个队列中的复制分析。目标3.确定区分进行性和进行性的生化特征 有监督的机器学习和分类模型将识别相关的代谢模式 通过比较IMT幼稚/无症状的复发性多发性硬化症患者和进展性多发性硬化症患者的MS进展 在一家学术专科诊所。目的4.确定与人类白细胞抗原-DRB1*15：01相互作用的代谢物 增加多发性硬化症的风险。在这个探索性目标中，我们将确定基因-代谢物(GxM)相互作用，涉及 主要多发性硬化危险因素，人类白细胞抗原-DRB1*15：01。编码的多肽参与抗原提呈和 有效地与许多内源性代谢物结合，提示了一种自体反应性T细胞 可能会被激活。我们将在MS-HC配对中进行GxM分析，以确定相关的代谢物 在HLA-DRB1的背景下有MS的风险。 在拟议的研究完成时，我们的预期结果将确定和 描述了一种血清来源的代谢特征，将MS与其他CNSIDD和非CNSIDD区分开来 CNSIDD控件。我们还希望能识别出MS疾病活动性的新血清标志物，并 进展，以及与人类白细胞抗原-DRB1*15：01相互作用以修改风险的推定代谢物。这些结果将 通过确定可以用来改进的血清衍生生化特征，产生重要的积极影响 MS的诊断特异性也有望识别MS潜在的新的分子过程， 这将为新疗法的开发和评估提供新的机会。

PROJECT SUMMARY AND ABSTRACT Within the last decade, we have made great strides in our understanding of the mechanisms underlying multiple sclerosis (MS) risk and progression, however much of the variation remains unexplained. We have achieved significant reductions in the time to diagnosis and we have improved diagnostic sensitivity, however specificity is not ideal. Further, most of the FDA-approved MS-specific immunomodulatory therapies (IMTs) focus on the inflammatory disease component in the relapsing phase and have little effect on improving outcomes once a patient enters the progressive phase. The challenge for drug trials is the lack biomarkers to detect and monitor MS progression. The objectives of the current application are: 1. To identify and characterize biomarkers that discriminate MS and from other central nervous system inflammatory demyelinating diseases (CNSIDDs) and non-CNSIDD controls, and 2. To identify biomarkers of disease activity and biomarkers that distinguish relapsing from progressive forms of MS. We propose a multi-stage analysis of pre-existing and well- defined biological samples from two resources. Aim 1. Identify biochemical traits that discriminate MS from other CNSIDDs and healthy controls. Supervised machine learning and classification models will identify a metabolic signature discriminating MS from other CNSIDDs and healthy controls (HCs) in two cohorts. In the 1st cohort, MS patients who are early in their diagnosis (≤ 2 years) and IMT naïve/free will be compared to HCs and other CNSIDD cases. Discriminating metabolites will be tested for replication in a 2nd cohort comparing similarly defined MS patients to HCs and other CNSIDDs, and other autoimmune disease patients. We will determine the direction of the replicating MS- metabolite associations using bidirectional genetic instrumental variable analyses. Aim 2. Identify biochemical features of MS disease activity. We will identify metabolic variation corresponding to disease activity by comparing IMT naïve/free patients within 2 years of diagnosis and with a recent relapse to those who have been in remission for ≥3 months and to HCs using supervised classification in a discovery cohort followed by replication analyses in a 2nd cohort. Aim 3. Identify biochemical traits that discriminate progressive from relapsing MS. Supervised machine learning and classification models will identify metabolic patterns associated with MS progression by comparing IMT naïve/free patients with relapsing forms of MS to progressive MS from at a single academic specialty clinic. Aim 4. Identify metabolites that interact with HLA-DRB1*15:01 to increase MS risk. In this exploratory aim we will identify gene-metabolite (GxM) interactions involving the primary MS risk factor, HLA-DRB1*15:01. The encoded peptide is involved in antigen presentation and effectively binds to many endogenous metabolites, suggesting a mechanism through which autoreactive T cells may be activated. We will conduct GxM analyses in MS-HC matched pairs to identify metabolites associated with MS risk in the context of HLA-DRB1. At the completion of the proposed research, our expected outcomes are to have identified and characterized a serum-derived metabolomic signature that discriminates MS from other CNSIDDs and non- CNSIDD controls. We also expect to have identified novel serum markers of MS disease activity and progression, as well as putative metabolites that interact with HLA-DRB1*15:01 to modify risk. These results will have an important positive impact by identifying serum-derived biochemical traits that could be used to improve diagnostic specificity in MS. There is also the promise of discerning novel molecular processes underlying MS, which will provide new opportunities for the development and evaluation of novel therapies.