A predictive disease-specific protein biomarker fingerprint for clinical relapses and glucocorticoids treatment of multiple sclerosis

Abstract

Abstract Despite extensive research, multiple sclerosis (MS) remains a disease that lacks a definitive prognostic test to predict imminent disease relapses. Thus, patients may undergo years of unnecessary treatments. Additionally, current treatments for MS vary significantly in efficacy between individual patients, and thus there is a critical need to develop biomarkers for treatment efficacy and resistance. To address these issues, we recently developed a high-throughput quantitative proteomics method to measure changes in proteome expression over the course of the preclinical experimental autoimmune encephalomyelitis (EAE) model. Interestingly, using the EAE model we revealed characteristic CNS-specific protein expression waves prior to the onset of clinical symptoms. Moreover, we have identified key proteins with altered expression that correlated with the therapeutic efficacy of glucocorticoid treatment. Bioinformatics analysis revealed candidate protein biomarkers to predict treatment efficacy and clinical disease course. Importantly, these proteins could be detected in serum and expression trajectories analysis identified a strong correlation of the CNS proteome to their levels in serum. Prospective studies in the EAE model using these candidate protein biomarkers showed their effectiveness in predicting clinical disease and treatment responses. Our studies suggest the utility for establishing homologous protein biomarkers in human MS patients. Finally, our work investigating the CNS proteome over the course of EAE may provide novel insights and molecular targets for disease mechanisms and treatments of MS.