Proteomics in neurological disease

Abstract: Neurodegenerative and neuroinflammatory diseases are conditions affecting the central nervous system that in the end have dramatic impacts on the affected individuals and their families. Today, large efforts are made to understand the disease origin and progression. This thesis focuses on Multiple Sclerosis (MS), which is the most common neurological disease among young adults. The diagnosis of MS is based on a series of clinical and neuroimaging criteria, and at present no reliable prognostic tools are available. We have aimed at developing a solid technical platform for investigation of potential biomarkers in MS. New purification methods to remove abundant proteins from the cerebrospinal fluid and tissue from individuals with MS and controls were developed. The samples were further analyzed using proteomic techniques, two-dimensional gel electrophoresis, mass spectrometry and bioinformatics. We demonstrated that proteins in MS plaques, adjacent tissue and non-affected brain tissue were differentially expressed. Cerebrospinal fluid samples of individuals affected with MS encompassing its different disease phases and control individuals were analyzed. A reference disease, Post-Polio Syndrome (PPS), was included and regarded as a non-inflammatory condition. Prediction models were constructed and univarate analysis of the protein expression was performed. The results revealed that there was a large heterogeneity in protein profiles between the MS subgroups. In PPS individuals, a protein profile based on three proteins could predict the disease with a high sensitivity and specificity. Interestingly, in contrast to the prevailing assumption, the identification of these proteins indicated that there is an ongoing neuroinflammation and neurodegeneration in PPS. To further evaluate the MS results we developed a multiplex quantitative immunoassay based on the expression pattern of ten proteins. A new cohort comprising individuals affected with MS and control individuals was assembled, and the expression of the proteins was analyzed. Classification models based on the biomarker panel could identify 70% of the relapsing-remitting MS and 80% of the secondary progressive MS individuals and controls correctly. Thus, protein expression profiles differ between the different forms of MS. In conclusion, we have developed a proteomic platform that has enabled the discovery of potential biochemical biomarkers of diagnostic and prognostic value in MS and the PPS. Further analysis of the protein expression patterns has also added biological information, which may prove useful for the understanding of etiology and disease course