A high-throughput proteomics approach for biomarker discovery and the investigation of mechanisms of autoimmunity

Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, mechanisms that drive MS disease processes are largely unknown and consequently hinders the discovery of biomarker for monitoring relapses and progression in MS. This has prompted the development of novel data science techniques for these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we perform high throughput shotgun proteomics of the brain in the mouse model of MS, experimental autoimmune encephalomyelitis (EAE) and use a systems biology approach to identify genes and pathways associated with relapse and progression with the aim of elucidating disease mechanism and discovery of biomarkers. We applied this proteomics technique to another autoimmune model, Kawasaki Disease and successfully identified STAT3 and Vimentin as markers of disease severity. Lastly, we characterized glucan particles as a novel adjuvant for the induction of EAE that minimized the pain and swelling that results from the more traditional Complete Freund's Adjuvant.