Maresin-1 promotes neuroprotection and prevents disease progression in experimental models of multiple sclerosis through metabolic reprogramming and shaping innate and adaptive disease-associated cell types

Blood-based targeted metabolipidomics reveals altered omega fatty acid-derived lipid mediators in relapsing-remitting multiple sclerosis patients

Unresolved and uncontrolled inflammation is considered a hallmark of pathogenesis in chronic inflammatory diseases like multiple sclerosis (MS), suggesting a defective resolution process. Inflammatory resolution is an active process partially mediated by endogenous metabolites of dietary polyunsaturated fatty acids (PUFA), collectively termed specialized pro-resolving lipid mediators (SPMs). Altered levels of resolution mediators have been reported in several inflammatory diseases and may partly explain impaired inflammatory resolution. Performing LC-MS/MS-based targeted lipid mediator profiling, we observed distinct changes in fatty acid metabolites in serum from 30 relapsing-remitting MS (RRMS) patients relative to 30 matched healthy subjects (HS). Robust linear regression revealed 12 altered lipid mediators after adjusting for confounders (p <0.05). Of these, 15d-PGJ2, PGE3, and LTB5 were increased in MS while PGF2a, 8,9-DiHETrE, 5,6-DiHETrE, 20-HETE, 15-HETE, 12-HETE, 12-HEPE, 14-HDoHE, and DHEA were decreased in MS compared to HS. In addition, 12,13-DiHOME and 12,13-DiHODE were positively correlated with expanded disability status scale values (EDSS). Using Partial Least Squares, we identified several lipid mediators with high VIP scores (VIP > 1: 32% - 52%) of which POEA, PGE3, DHEA, LTB5, and 12-HETE were top predictors for distinguishing between RRMS and HS (AUC =0.75) based on the XGBoost Classifier algorithm. Collectively, these findings suggest an imbalance between inflammation and resolution. Altogether, lipid mediators appear to have potential as diagnostic and prognostic biomarkers for RRMS.

Profiling blood-based brain biomarkers and cytokines in experimental autoimmune encephalomyelitis model of multiple sclerosis using single-molecule array technology

Experimental autoimmune encephalomyelitis (EAE) remains a widely used pre-clinical animal model to study multiple sclerosis (MS). Blood-based cytokines and CNS biomarkers are increasingly used as predictors of neurodegeneration, disease activity, and disability in MS. However, there exists variation in animal model characterization and disease course across animal strains/studies due to understudied confounding factors, limiting the utility of these biomarkers to predict disease activity in EAE. In this study, we investigated the profile of blood-based analytes including, cytokines (IL6, IL17, IL12p70, IL10, and TNFα) and neural markers (NFL and GFAP) in the plasma of relapsing-remitting (RR) (SJL) and chronic (B6) models of EAE during different phases (acute, chronic, and progressive) of disease course using ultrasensitive single molecule array technology (SIMoA, Quanterix), which can detect ultra-low levels of a wide range of analytes. NFL showed a substantial increase during post-disease onset at peak, chronic, and progressive phases in both RR SJL and chronic B6 models of EAE. The increase was markedly pronounced in the chronic B6 model. The leakage of GFAP from CNS into the periphery was also higher after disease onset in EAE, however, it was highest during the acute phase in B6. Out of all cytokines, only IL10 showed consistently lower levels in both models of EAE along the disease duration. We report that NFL, GFAP, and IL10 may be more useful predictors of disease activity and neurological outcome in EAE, which would make them potential candidates for use as surrogate markers for preclinical testing of therapeutic interventions in MS.