This project will utilize 5-chemical mixture, which models groundwater contamination, to explore interactions among metals and organics in the induction of stress response proteins such as metallothionein, heme oxygenase, and nitric oxide synthase, and the induction of inflammatory cytokines (i.e. tumor necrosis factor-alpha, interleukin-`1, and interleukin-6). These cytokines ar associated with both liver injury and induciton of these same stress response proteins. The focus of the project is on delineating mechanisms of interactions. The rationale for the experimental plan is based upon the hypothesis that induction of these genes will influence the toxicity of components int he mixture and can be used as sensitive indicators of chemical exposure. We postulate that interactions betweeen chemicals which occur in the stress response will mimic toxic interactions. However, alterations in gene expression will occur at lower doses and with shorter duration exposure than overt toxicity is detected. The chemical mixture used for these studies is also being investigated in Projects 1 and 2 and the mechanistic information obtained in this project 3 will pertain to all 3 projects while the mixture contains metals (chromium, lead, arsenic) and organics benzene, and phenol, we will initially concern are on the metals. Chemical interactions and interactions with endogenous mediators (cytokines) will be determined in liver cells obtained from rats treated in vivo (Specific Aim 1) and in ok 49 (Specific Aim 2).
In Specific Aim 3, Differential Display Polymerase Chain Reaction technology and transcription factor activation studies will be conducted in order to determine the profile of stress response genes which are differentially expressed in liver cells from rats treated with chemical mixtures. Throughout this project, both a TOP-DOWN and a BOTTOM- UP approach will be utilized. That is, single chemicals will be investigated as well as the chemical mixtures. This will be followed by combining single chemicals into 2-[component, 3-component mixtures, and also fractionating the mixture. By simultaneously pursuing both ends of the spectrum, we expect the data to converge in a way which will limit the number of combinations which must be tested. The data generated in this project will be used in Project 1 for mathematical modeling by Response Surface Methodology. With this modeling approach it is expected that the data will be more predictive of interactions between chemicals in other mixtures and more effectively utilized in risk assessment procedures.
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