Genomic approach to neurodegenerative diseases

All neurodegenerative diseases are characterized by progressive degeneration of neurons which cannot be monitered in vivo. The degeneration itself begins long before the symptoms develop and this symptomless time period of the disease gives us an important opportunity for therapeutic intervention in the sense of delaying the progress of the disease. To evaluate the stage of neurodegeneration, more detailed knowledge of pathogenesis is needed and specific and sensitive biomarkers, also. A neurodegenerative disease where we are able to identify the patients before the symptoms start, is Huntington's disease (HD). One of the recently widely accepted hypothesis explains HD pathogenesis in relation to transcriptional deregulation, which has been shown in central nervous system and muscle tissue in cell and animal models of HD. Since mutant huntigtin and related transcriptional factors show ubiquitous distribution in all tissues, we postulate that transcriptional impairments in HD may also exist in peripheral blood and that we will find differences in expression profiles of carriers of HD mutation compared to healthy control subjects. With use of statistics and bioinformatics to analyze the differences in global gene expression of HD mutation carriers compared to healthy control subject, we expect to find specific genetic biomarkers. Biomarkers would enable us to better understand pathogenesis of HD, to speculate about start and progression of HD and in the future also to evaluate response to potential new treatments. In relation to overlap of clinical symptoms and some known pathogenetic pathways in neurodegenerative disorders, we speculate that it is possible to find more specific genetic biomarkers characteristic of different neurodegenerative disorders. In order to test this hypothesis we plan to generate database of expression changes in selected neurodegenerative disorders (HD, Alzheimer's disease, Parkinson's disease, spinocerecellar ataxia). We will try to identify common biomarkers with use of data mining and literature-based discovery.