[Molecular biomarkers for prediction of multiple sclerosis relapse].
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system white matter mediated by an autoimmune process, whose development is triggered by a complex interplay of multiple genetic, infectious and environmental factors. MS is characterized by the relapsing-remitting clinical course. At present, molecular mechanisms underlying MS relapse remain unknown. If they are well clarified, we could predict the timing of relapses and start the earliest therapeutic and preventive interventions. DNA microarray is a novel technology that allows us to systematically monitor the expression of whole human genome in disease-affected tissues and cells. By using DNA microarray, we have recently studied gene expression profile of peripheral blood T cells isolated from clinically active MS patients and healthy controls, and from MS patients in relapse and during remission. We found a set of differentially expressed genes between MS and healthy subjects, and between acute relapse and complete remission. Hierarchical clustering analysis of the discriminator genes established classification of MS subgroups that exhibit distinct gene expression profiles and relapse-specific molecular signatures. By using KeyMolnet, a novel data-mining tool of bioinformatics, we identified the principal molecular network involved in development of MS and induction of acute relapse. Thus, DNA microarray technology is highly valuable to identify molecular mechanism-based biomarkers for classification of MS subgroups and prediction of MS relapse.
Satoh
Jun-ichi
J
Department of Bioinformatics, Meiji Pharmaceutical University.
D015415:Biomarkers / Q000032:analysis*; D006801:Humans; D009103:Multiple Sclerosis / Q000175:diagnosis* / Q000235:genetics*; D040081:Protein Array Analysis; D012008:Recurrence
D015415:Biomarkers