Multiple sclerosis (MS) is an inflammatory demyelinating disease of the nervous system that primarily affects young adults. Although the exact etiology of the disease remains obscure, it is clear that alterations in the metabolome contribute to this process. As such, defining a reliable and disease-specific metabolome has tremendous potential as a diagnostic and therapeutic strategy for MS. Here, we provide an overview of studies aimed at identifying the role of metabolomics in MS. These offer new insights into disease pathophysiology and the contributions of metabolic pathways to this process, identify unique markers indicative of treatment responses, and demonstrate the therapeutic effects of drug-like metabolites in cellular and animal models of MS. By and large, the commonly perturbed pathways in MS and its preclinical model include lipid metabolism involving alpha-linoleic acid pathway, nucleotide metabolism, amino acid metabolism, tricarboxylic acid cycle,d-ornithine andd-arginine pathways with collective role in signaling and energy supply. The metabolomics studies suggest that metabolic profiling of MS patient samples may uncover biomarkers that will advance our understanding of disease pathogenesis and progression, reduce delays and mistakes in diagnosis, monitor the course of disease, and detect better drug targets, all of which will improve early therapeutic interventions and improve evaluation of response to these treatments.

Metabolic aberrations impact the pathogenesis of multiple sclerosis (MS) and possibly can provide clues for new treatment strategies. Using untargeted metabolomics, we measured serum metabolites from 35 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy age-matched controls. Of 632 known metabolites detected, 60 were significantly altered in RRMS. Bioinformatics analysis identified an altered metabotype in patients with RRMS, represented by four changed metabolic pathways of glycerophospholipid, citrate cycle, sphingolipid, and pyruvate metabolism. Interestingly, the common upstream metabolic pathway feeding these four pathways is the glycolysis pathway. Real-time bioenergetic analysis of the patient-derived peripheral blood mononuclear cells showed enhanced glycolysis, supporting the altered metabolic state of immune cells. Experimental autoimmune encephalomyelitis mice treated with the glycolytic inhibitor 2-deoxy-D-glucose ameliorated the disease progression and inhibited the disease pathology significantly by promoting the antiinflammatory phenotype of monocytes/macrophage in the central nervous system. Our study provided a proof of principle for how a blood-based metabolomic approach using patient samples could lead to the identification of a therapeutic target for developing potential therapy.

Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system. The therapeutic landscape for MS has evolved significantly since the 1990s, with the development of more than 20 different disease-modifying therapies (DMTs). These therapies effectively manage relapses and inflammation, but most have failed to meaningfully prevent disease progression. While classically understood as a T cell-mediated condition, the most effective DMTs in slowing progression also target B cells. Novel classes of MS therapies in development, including anti-CD40L monoclonal antibodies, CD19 chimeric antigen receptor (CAR) T cells, and Bruton’s tyrosine kinase (BTK) inhibitors show greater capacity to target and eliminate B cells in the brain/CNS, as well as impacting T-cell and innate immune compartments. These approaches may help tackle the disease at its immunopathological core, addressing both peripheral and central immune responses that drive MS progression. Another emerging therapeutic strategy is to use bispecific antibodies, which have the potential for dual-targeting various disease aspects such as immune activation and neurodegeneration. As such, the next generation of MS therapies may be the first to reduce both inflammatory demyelination and disease progression in a clinically meaningful way. Their ability to target specific immune cell populations while minimizing broad immune suppression could also lead to better safety profiles. Here, we explore the biological rationale, advantages, limitations, and clinical progress of these emerging immunotherapies for relapsing–remitting and progressive forms of MS.

Experimental autoimmune encephalomyelitis (EAE) is a preclinical animal model widely used to study multiple sclerosis (MS). Blood-based analytes, including cytokines and neural biomarkers are the predictors of neurodegeneration, disease activity, and disability in patients with MS. However, understudied confounding factors cause variation in reports on EAE across animal strains/studies, limiting the utility of these biomarkers for predicting disease activity. In this study, we investigated blood-based analyte profiles, including neural markers (NFL and GFAP) and cytokines (IL-6, IL-17, IL-12p70, IL-10, and TNF-α), in two clinically distinct EAE models: relapsing-remitting (RR)-EAE and chronic-EAE. Ultrasensitive single-molecule array technology (SIMOA, Quanterix) was used to profile the analytes in the blood plasma of mice at the acute, chronic, and progressive phases of disease. In both models, NFL was substantially increased during post-disease onset across all phases, with a pronounced increase observed in chronic-EAE. The leakage of GFAP into peripheral blood was also greater after disease onset in both EAE models, especially in the acute phase of chronic-EAE. Among all cytokines, only IL-10 had consistently lower levels in both EAE models throughout the course of disease. This study suggests NFL, GFAP, and IL-10 as potential translational predictors of disease activity in EAE, making them potential candidates as surrogate markers for the preclinical testing of therapeutic interventions in animal models of MS.

Introduction Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease of the central nervous system (CNS) in young adults and results in progressive neurological defects. The relapsing-remitting phenotype (RRMS) is the most common disease course in MS and may progress to the progressive form (PPMS). Objectives There is a gap in knowledge regarding whether the relapsing form can be distinguished from the progressive course or healthy subjects (HS) based on an altered serum metabolite profile. In this study, we performed global untargeted metabolomics with the 2D GCxGC-MS platform to identify altered metabolites between RRMS, PPMS, and HS. Methods We profiled 235 metabolites in the serum of patients with RRMS (n=41), PPMS (n=31), and HS (n=91). A comparison of RRMS and HS patients revealed 22 significantly altered metabolites at p<0.05 (false discovery rate [FDR]=0.3). The PPMS and HS comparisons revealed 28 altered metabolites at p<0.05 (FDR=0.2). Results Pathway analysis using MetaboAnalyst revealed enrichment of four metabolic pathways in both RRMS and PPMS (hypergeometric test p<0.05): 1) galactose metabolism; 2) amino sugar and nucleotide sugar metabolism; 3) phenylalanine, tyrosine, and tryptophan biosynthesis; and 4) aminoacyl-tRNA biosynthesis. The Qiagen IPA enrichment test identified the sulfatase 2 (SULF2) (p=0.0033) and integrin subunit beta 1 binding protein 1 (ITGB1BP1) (p=0.0067) genes as upstream regulators of altered metabolites in the RRMS vs. HS groups. However, in the PPMS vs. HS comparison, valine was enriched in the neurodegeneration of brain cells (p=0.05), and heptadecanoic acid, alpha-ketoisocaproic acid, and glycerol participated in inflammation in the CNS (p=0.03). Conclusion Overall, our study suggested that RRMS and PPMS may contribute metabolic fingerprints in the form of unique altered metabolites for discriminating MS disease from HS, with the potential for constructing a metabolite panel for progressive autoimmune diseases such as MS.