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From Single Metal to Metal Mixtures: Systematic Proteomic Approach to Characterize the Impacts of Chemical Interactions on Protein and Cytotoxicity Responses to Environmental Mixture Exposures
Citation:
Ge, Y. AND M. Bruno. From Single Metal to Metal Mixtures: Systematic Proteomic Approach to Characterize the Impacts of Chemical Interactions on Protein and Cytotoxicity Responses to Environmental Mixture Exposures. SETAC, Denver, Colorado, September 06 - 08, 2017.
Impact/Purpose:
For risk assessment purpose, there is an urgent need to develop novel and biology-focused methodologies and approaches for efficient analysis of chemical interactions in mixtures and toxic mechanisms associated with exposure to the chemical mixtures.This study provides a novel proteomic approach for characterization and prediction of toxicities of metal and other 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-2B 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. The results obtained from 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. In addition, support vector machine learning was utilized to computationally predict protein responses for metal mixtures not in the experimental set, and the average percentage differences between the predicted and experimental values of protein expression changes were generally below 10% for the unknown mixtures. The present study therefore provides an efficient proteomics approach for generating scientific data to characterize and predict metal mixture toxicity and to support risk assessment of environmental mixtures.