Characterizing the serum metabolome in multiple sclerosis

项目总结和摘要 在过去的十年里，我们在理解其背后的机制方面取得了很大的进步。 多发性硬化症（MS）的风险和进展，但许多变化仍然无法解释。我们有 在诊断时间上取得了显著的缩短，我们提高了诊断的敏感性，然而， 特异性并不理想。此外，大多数FDA批准的MS特异性免疫调节疗法（IMT）集中于 对复发期炎症性疾病成分的影响， 一旦病人进入了进展期。药物试验面临的挑战是缺乏可检测的生物标志物， 监测MS进展。本申请的目的是：1.以鉴定和表征 区分MS和其他中枢神经系统炎性脱髓鞘疾病的生物标志物 （CNSIDDs）和非CNSIDD对照，以及2.为了鉴定疾病活动的生物标志物和 区分MS的复发和进展形式。我们提出了一个多阶段的分析预先存在的和良好的， 从两个资源中定义生物样品。 目标1.确定区分MS与其他CNSIDD和健康对照的生化特征。 受监督的机器学习和分类模型将识别区分MS和MS的代谢特征。 其他CNSIDD和健康对照（HC）。在第一个队列中，早期MS患者 诊断（≤ 2年）和IMT初治/无IMT的病例将与HC和其他CNSIDD病例进行比较。判别 将在第2个队列中检测代谢物的复制，将相似定义的MS患者与HC和其他代谢物进行比较。 CNSIDDs和其他自身免疫性疾病患者。我们将确定复制的MS的方向- 代谢物协会使用双向遗传工具变量分析。目标2.鉴定生化 MS疾病活动的特征。我们将通过以下方法确定与疾病活动相对应的代谢变化： 比较2年内诊断为IMT初治/无IMT且近期复发的患者与 在发现队列中使用监督分类的缓解≥3个月和HC患者，随后 在第二队列中进行重复分析。目标3。识别区分进行性和 有监督的机器学习和分类模型将识别与MS复发相关的代谢模式。 通过比较复发型MS的IMT初治/无IMT患者与 在一个学术专业诊所。目标4。鉴定与HLA-DRB 1 *15：01相互作用的代谢物， 增加MS风险。在这个探索性的目标，我们将确定基因代谢物（GXM）的相互作用，涉及 原发性MS危险因素，HLA-DRB 1 *15：01。所编码的肽参与抗原呈递， 有效地结合许多内源性代谢物，这表明自身反应性T细胞 可以被激活。我们将在MS-HC匹配对中进行GxM分析，以确定相关代谢物 HLA-DRB 1背景下的MS风险。 在拟议的研究完成后，我们的预期成果是已经确定， 其特征在于血清衍生的代谢组学特征，其将MS与其他CNSIDD和非CNSIDD区分开。 CNSIDD控制。我们还期望鉴定MS疾病活动的新血清标志物， 进展，以及与HLA-DRB 1 *15：01相互作用以改变风险的推定代谢物。这些结果将 具有重要的积极影响，通过鉴定血清衍生的生化性状，可用于改善 MS中的诊断特异性。也有希望辨别MS潜在的新分子过程， 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.