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Systematic Proteomic Approach to Characterize the Impacts of Chemical Interactions on Protein and Cytotoxicity Responses to Metal Mixture Exposures
Citation:
Ge, Y., M. Bruno, K. Wallace, S. Levitt, D. Andrews, M. Spassova, M. Xi, A. Roy, N. Haykal-Coates, W. Lefew, A. Swank, W. Winnik, C. Chen, J. Woodard, A. Farraj, K. Teichman, AND J. Ross. Systematic Proteomic Approach to Characterize the Impacts of Chemical Interactions on Protein and Cytotoxicity Responses to Metal Mixture Exposures. Journal of Proteome Research . American Chemical Society, Washington, DC, 14(1):183-92, (2015).
Impact/Purpose:
EPA is developing methods for utilizing novel and high throughput technologies including various of toxicoproteomics technologies to screen, categorize, and prioritize environmental chemicals that likely represent the greatest hazard to human health and the environment. One of the major goals of these chemical prioritization research programs is to develop the ability for sensitive, selective and accurate prediction of chemical toxicity and potential adverse health outcomes based on bioactivity and gene expression profiling. Protein based toxicity signatures indicating "later" biological effects are in fact stronger predictors of chemical toxicity as compared to other biomolecules such as DNA and RNA indictors. Therefore, proteomic-based toxicity studies and biomarkers are more relevant to human health risk assessment and hold the key for the success of human health risk assessment of environmental chemicals and mixtures. The strategy of this project encompasses a diverse range of data types including toxicity data, protein toxicity signature and biomarkers, protein activity, mass spectrum fingerprints for each chemical mixtures, toxicity pathways and potential adverse health outcomes. It will be the overall pattern across many proteomic assays and data types that will be the predictor of toxicity of used for prioritizing chemical mixtures. The main goal of this project was taking advantage of high throughput proteomic technologies and other system biology tools for large-scale toxicity signature profiling of environmental chemical mixtures to screen, categorize, and prioritize environmental chemicals that likely represent the greatest hazard to human health and the environment. According to the NRC released report on toxicity testing in the 21st century,regulatory agencies should enhance efforts to incorporate toxicogenomic data into risk assessment. One of the important tasks to do is to substantially enhance agency capability to integrate toxicogenomic approaches including toxicoproteomics into risk assessment practice such as the assessment of chemical mixtures.
Description:
Chemical interactions have posed a big challenge in toxicity characterization and human health risk assessment of environmental mixtures. To characterize the impacts of chemical interactions on protein and cytotoxicity responses to environmental mixtures, we established a systems biology approach integrating proteomics, bioinformatics, statistics, and computational toxicology to measure expression or phosphorylation levels of 21 critical toxicity pathway regulators and 445 downstream proteins in human BEAS-28 cells treated with 4 concentrations of nickel, 2 concentrations each of cadmium and chromium, as well as 12 defined binary and 8 defined ternary mixtures of these metals in vitro. Multivariate statistical analysis and mathematical modeling of the metal-mediated proteomic response patterns showed a high correlation between changes in protein expression or phosphorylation and cellular toxic responses to both individual metals and metal mixtures. Of the identified correlated proteins, only a small set of proteins including HIF-1a is likely to be responsible for selective cytotoxic responses to different metals and metals mixtures. Furthermore, support vector machine learning was utilized to computationally predict protein responses to uncharacterized metal mixtures using experimentally generated protein response profiles corresponding to known metal mixtures. This study provides a novel proteomic approach for characterization and prediction of toxicities of metal and other chemical mixtures.
