Characterizing the serum metabolome in multiple sclerosis

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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.