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Molecular target sequence similarity as a basis for species extrapolation to assess the ecological risk of chemicals with known modes of action
Citation:
LaLone, C., Dan Villeneuve, L. Burgoon, C. Russom, H. Helgren, J. Berninger, J. Tietge, M. Severson, J. Cavallin, AND G. Ankley. Molecular target sequence similarity as a basis for species extrapolation to assess the ecological risk of chemicals with known modes of action. AQUATIC TOXICOLOGY. Elsevier Science Ltd, New York, NY, 144:141-154, (2013).
Impact/Purpose:
In practice, it is neither feasible nor ethical to conduct toxicity tests with all species that may be impacted by chemical exposures. Therefore, cross-species extrapolation is fundamental to human health and ecological risk assessment. The extensive chemical universe for which we lack empirical toxicity data upon which to evaluate potential environmental impacts has been an impetus driving the field of predictive toxicology. We propose a strategy that leverages expanding databases of molecular sequence information along with increased identification of specific molecular targets whose perturbation can lead to adverse outcomes (i.e., molecular initiating events) to predict and/or rank which species may be more or less susceptible to adverse effects following exposure to chemicals with known modes of action (e.g., pharmaceuticals, pesticides). Primary amino acid sequence alignments are combined with more detailed analyses of conserved functional domains to derive such predictions. This methodology employs bioinformatic approaches to automate, collate, and calculate quantitative metrics associated with sequence similarity to generate robust homology analyses. As an initial proof of concept, case examples focused on the action of 17α-ethinyl estradiol on the human (Homo sapiens) estrogen receptor; 17β-trenbolone on the bovine (Bos taraus) androgen receptor; and permethrin on the mosquito (Aedes aegypti) voltage gated para-like sodium channel, are presented to demonstrate the predictive utility of this species extrapolation strategy, by comparing homology rankings to empirical toxicity data. Through further refinement and selection of appropriate domains of applicability for the homology-based predictive method we envision a practical and routine use in chemical risk assessment and regulation.
Description:
In practice, it is neither feasible nor ethical to conduct toxicity tests with all species that may be impacted by chemical exposures. Therefore, cross-species extrapolation is fundamental to human health and ecological risk assessment. The extensive chemical universe for which we lack empirical toxicity data upon which to evaluate potential environmental impacts has been an impetus driving the field of predictive toxicology. We propose a strategy that leverages expanding databases of molecular sequence information along with increased identification of specific molecular targets whose perturbation can lead to adverse outcomes (i.e., molecular initiating events) to predict and/or rank which species may be more or less susceptible to adverse effects following exposure to chemicals with known modes of action (e.g., pharmaceuticals, pesticides). Primary amino acid sequence alignments are combined with more detailed analyses of conserved functional domains to derive such predictions. This methodology employs bioinformatic approaches to automate, collate, and calculate quantitative metrics associated with sequence similarity to generate robust homology analyses. As an initial proof of concept, case examples focused on the action of 17á-ethinyl estradiol on the human (Homo sapiens) estrogen receptor; 17â-trenbolone on the bovine (Bos taraus) androgen receptor; and permethrin on the mosquito (Aedes aegypti) voltage gated para-like sodium channel, are presented to demonstrate the predictive utility of this species extrapolation strategy, by comparing homology rankings to empirical toxicity data. Through further refinement and selection of appropriate domains of applicability for the homology-based predictive method we envision a practical and routine use in chemical risk assessment and regulation.
