The long-term objectives of this project are to use clinical metabolomics to understand environmental contributions to Parkinson's Disease and develop novel interventional strategies to reduce risk and manage outcome. PD is a debilitating disease affecting elderly which is of growing concern because of the aging of the American population. PD appears to have multiple causes with environmental exposures, such as pesticides, being of particular concern. These agents induce oxidative stress and inhibit mitochondrial respiratory complex I, thereby contributing to two biochemical processes associated with PD risk. We have assembled a team of experts in analytical biochemistry, neurodegenerative disease and bioinformatics to use a new high-throughput metabolic profiling method coupled to bioinformatics to identify unique metabolic signatures of pesticide exposures and PD development. This powerful methodology is coupled with capabilities for precise analytical determinations of pesticide contents and oxidative stress so that metabolic signatures can provide a direct link between exposure and disease symptoms. Identification of such metabolic signatures could have considerable impact in providing a means for early detection of risk and suggesting novel targets for intervention to prevent or manage disease progression. The metabolomics platform allows detection of 2000 metabolic features in a 10 min analysis of human plasma.
Specific Aims are to use this assay to 1) profile the metabolic changes in plasma, cerebrospinal fluid and substantia nigra of mouse models of PD in which chemicals (MPTP, dieldrin) are used to elicit symptoms in wildtype mice, a sensitive mouse line with reduced ability to properly store dopamine (VMAT2-deficient), and a resistant mouse line with increased content of mitochondrial thioredoxin-2;2) identify metabolic features associated with PD and with pesticide levels in PD and non-PD controls;and 3) develop standardization procedures to allow creation of a cumulative data library for human metabolomic data. Successful completion of these aims will provide key new information concerning the hypothesis that exposure to environmental chemicals create a metabolic platform for PD risk which can be evaluated by clinical metabolomics and used as a basis for early interventions to prevent disease and therapeutic approaches to delay progression.
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