ABSTRACT: Parkinson's disease is a prevalent neurodegenerative disorder for which there is no cure. The cause of PD symptoms is loss of dopamine neurons in the midbrain, but it is not known why these neurons die. Pesticide exposure is epidemiologically associated with PD, and administration of the organic pesticide rotenone to rats recapitulates most of the behavioral, neurochemical, and neuropathological findings in PD, including specific death of dopamine neurons. We have developed an in vitro model of rotenone toxicity using a dopaminergic cell line (SK-N-MC neuroblastoma cells) that mimics many of the cellular changes seen with in vivo rotenone toxicity and with PD, such as alpha-synuclein aggregation and oxidative damage. We are currently using this simple model to explore mechanisms of dopaminergic neurodegeneration, with our ultimate goal being the discovery of novel mechanisms for dopaminergic neuroprotection in PD. We will examine gene expression profiles of cultured SK-N-MC cells at several time points during rotenone exposure to determine pathways involved in rotenone toxicity and dopaminergic degeneration. We will compare these profiles to baseline profiles of rodent dopaminergic neurons that we have already obtained, as well as to profiles of dopamine neurons from rotenone-treated rats that we will obtain in the near future. We will also compare these data to published results from SN neurons from human PD patients. This technique will not only help us to detect gene expression changes relevant to dopaminergic neurodegeneration in PD, but it will allow us to determine if the SK-N-MC system can be reliably used to screen for neuroprotective therapies for PD. We anticipate that SK-N-MC cells will show a relevant subset of the gene changes seen in dopamine neurons in vivo and that this will guide us in the sorts of mechanisms and drugs that can be screened in this system. Chronic exposure to low levels of rotenone causes changes in gene expression in SK-N-MC cells that sensitize the cells to toxic insults. We also hypothesize that there are several compensatory protective pathways that are stimulated by chronic rotenone, although these pathways are ultimately ineffective at preventing damage. We anticipate that gene expression profiling of rotenone-treated cells over time will suggest several novel strategies for neuroprotective intervention. SK-N-MC cells will be grown in three different media: media only, vehicle (EtOH), and rotenone (5 nM). All current experimental evidence in our lab indicates that vehicle-treated cells are indistinguishable from media-only cells. Rotenone-treated cellls have a stereotypical response in culture. At one week, the only noticed change is an increase in alph-synuclein aggregation. At two weeks, evidence of increased oxidative stress appears (increased protein carbonyls and lipid peroxidation). At four weeks, the cells are markedly sensitized to oxidative challenge with H2O2. Therefore, we will examine gene expression at baseline, and during 1, 2, and 4 weeks of rotenone treatment. Three experiments will be performed, each lasting 4 weeks. For each experiment, three separate dishes of vehicle-treated, and rotenone-treated cells will be harvested at 1, 2, and 4 weeks (18 independent samples). Untreated, media-only cells will be harvested after 1 week in vitro to serve as baseline cells. Total RNA will be isolated. An equal amount of RNA from one dish per experiment per group will be used to compose the final samples. Therefore, each independent sample will consist of RNA from 3 separate experiments. This will allow us to take advantage of a pooling strategy, yet not sacrifice technical and biological replication. 21 samples will be sent to the Consortium. Three will be from untreated cells. Nine will be vehicle-treated at 1, 2, and 4 weeks (3 each). Nine will be rotenone-treated at 1, 2 , and 4 weeks (3 each). Each sample will be labeled and hybridized to one Affymetrix Human Genome U133 Plus 2.0 Gene Chip. With assistance of the consortium, we will analyze the data using the Signifiance Analysis of Microarrays (SAM) program and self-organizing map algorithms.
