Characterizing the serum metabolome in multiple sclerosis

描述多发性硬化症的血清代谢组特征

• 批准号: 10597006
• 负责人: Farren B. S. Briggs
• 金额: $ 0.62万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2023-08-06
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10597006
• 关键词: Antibodies; Antigen Presentation; Autoimmune Diseases; Binding; Biochemical; Biological; Biological Markers; Biological Process; Central Nervous System; Chronic; Classification; Clinic; Complement; Demyelinating Diseases; Development; Diagnosis; Diagnostic; Diagnostic Sensitivity; Diagnostic Specificity; Discrimination; Disease; Disease remission; Early treatment; Evaluation; FDA approved; Financial cost; Free Will; Genes; Genetic; Goals; Guidelines; HLA-DRB1; Health; Inflammatory; Medical Care Costs; Metabolic; Metabolite Interaction; Migraine; Modeling; Molecular; Monitor; Multiple Sclerosis; Nerve Degeneration; Neurologic Symptoms; Neuromyelitis Optica; Outcome; Patients; Pattern; Peptides; Persons; Pharmaceutical Preparations; Phase; Process; Prognostic Marker; Relapse; Research; Resources; Risk; Risk Factors; Sampling; Serum; Serum Markers; Signal Transduction; Sjogren' s Syndrome; Specificity; Systemic Lupus Erythematosus; Testing; Time; Treatment outcome; Variant; autoreactive T cell; biomarker identification; clinical diagnosis; cohort; cost; diagnostic biomarker; disability; disorder control; immunomodulatory therapies; improved; improved outcome; medical specialties; metabolome; metabolomics; multiple sclerosis patient; multiple sclerosis treatment; novel; novel therapeutics; oligodendrocyte-myelin glycoprotein; side effect; supervised learning; targeted therapy trials; trait

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.

项目摘要和摘要 在过去的十年中，我们在理解基本机制方面取得了长足的进步 多发性硬化症（MS）风险和进展，但是许多变化仍无法解释。我们有 诊断时间大大减少了，我们提高了诊断敏感性，但是 特异性不是理想的。此外，大多数FDA批准的MS特异性免疫调节疗法（IMTS）聚焦 在复发阶段的炎症性疾病成分上，对改善结果几乎没有影响 一旦患者进入渐进阶段。药物试验面临的挑战是缺乏检测和 监视MS进程。当前应用程序的对象是：1。识别和表征 区分MS和其他中枢神经系统炎症性脱髓鞘疾病的生物标志物 （CNSIDDS）和非CNSIDD对照，以及2。确定疾病活动和生物标志物的生物标志物 与MS的渐进形式区分开来。我们建议对现有和良好的多阶段分析 从两个资源中定义了生物样品。 目标1。确定将MS与其他CNSIDD和健康对照区分开的生化特征。 监督的机器学习和分类模型将确定一种新陈代谢的签名，该签名与MS不同 其他CNSIDD和健康对照（HCS）在两个队列中。在第一个队列中，MS患者在他们的早期 将诊断（≤2年）和IMT幼稚/自由与HCS和其他CNSIDD病例进行比较。歧视 将代谢物测试在第二个队列中进行复制，将类似定义的MS患者与HCS和其他类似的MS患者进行了测试 CNSIDD和其他自身免疫性疾病患者。我们将确定复制MS-的方向 使用双向遗传仪器变量分析的代谢物关联。目标2。识别生化 MS疾病活动的特征。我们将确定与疾病活动相对应的代谢变异 在诊断的2年内与IMT幼稚/自由患者进行比较，以及最近退休的患者 在发现队列中使用监督分类的缓解≥3个月，然后使用监督分类 第二个队列中的复制分析。目标3。确定歧视进步的生化特征 复发MS。监督的机器学习和分类模型将确定相关的代谢模式 通过将具有复发形式的MS的IMT/自由患者与从 在一个学术专业诊所。目标4。确定与HLA-DRB1相互作用的代谢物*15：01 增加MS风险。在这个探索性目的中，我们将确定涉及的基因 - 金代谢物（GXM）相互作用 主要MS风险因素，HLA-DRB1*15：01。编码的肽参与抗原表现和 有效地与许多内源代谢产物结合，这表明了一种自动反应性T细胞的机制 可能被激活。我们将在MS-HC匹配的成对中进行GXM分析，以识别相关的代谢物 在HLA-DRB1的背景下具有MS风险。 拟议研究完成时，我们的预期结果将确定和 表征了血清衍生的代谢组学特征，该特征将MS与其他CNSIDD和非 - CNSIDD控件。我们还期望确定MS疾病活动的新型血清标记和 进展以及与HLA-DRB1*15：01相互作用的推定代谢产物来改变风险。这些结果将会 通过识别可用于改善的血清衍生的生化特征，从而产生重要的积极影响 MS的诊断特异性。也有识别MS的新分子过程的希望， 这将为开发和评估新疗法提供新的机会。