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Strategically selecting test species using the sequence alignment to predict across Species Susceptibility (SeqAPASS) tool.
Citation:
Doering, J., G. Ankley, B. Blackwell, K. Dean, C. Finnegan, C. LaLone, S. Poole, AND Dan Villeneuve. Strategically selecting test species using the sequence alignment to predict across Species Susceptibility (SeqAPASS) tool. SETAC EUROPE, Helsinki, N/A, FINLAND, May 26 - 30, 2019.
Impact/Purpose:
This is a presentation showing how a computational tool developed by the US EPA, the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, can be used to strategically select test species towards characterizing species differences in sensitivity to chemicals. Specifically, differences in sensitivity to chemicals can range by a few fold to more than a thousand-fold among species, but toxicity information used in risk assessment is based on only a few model test species which might not accurately represent all species. This presentation shows that SeqAPASS can be used to predict which species are most likely to differ in their sensitivity to chemicals and can be used to strategically select species for further investigation in order to more comprehensively and cost effectively characterize species differences in sensitivity to chemicals. This work supports aims of CSS project 17.01 towards cross-species extrapolation of adverse effects towards guiding more objective ecological risk assessments of native species of ecological and economic importance in the US.
Description:
Chemicals in the environment can affect the health of wildlife with the potential for vastly different sensitivities among species. However, toxicity data used in risk assessment is based on a small number of model test species which might not represent the diversity of species sensitivities. This uncertainty is particularly true for many contaminants of emerging concern (CECs), including agonists of the peroxisome proliferator-activated receptor y (PPARy), for which species-sensitivity information might only be available for one or a couple species. However, performing toxicity testing for many species to determine the range in sensitivity is not practical. Therefore, the goal of this study was to demonstrate a means of selecting test species in a strategic fashion to determine the extent of differences in sensitivity by using the least number of species possible. Specifically, this study used the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool to rapidly and computationally predict species-specific chemical susceptibility across phylogenetically diverse species through evaluation of structural similarities and differences in the protein target of a chemical. The public literature was used to identify 17 key amino acid residues in the ligand binding domain of PPARy that interact with chemicals, along with knowledge of amino acid differences that cause differences in binding affinity. Differences in these amino acids were investigated among sequences of 246 phylogenetically diverse species from the NCBI database using SeqAPASS. Four amino acid positions had differences across species that were predicted by SeqAPASS to potentially result in differences in binding of chemicals and therefore potentially result in differences in sensitivity among species. Five PPARy-types were proposed that likely differ in their sensitivity to agonists, namely Type 1 (mammals), Type 2 (birds, reptiles, amphibians, ancient fish), Type 3 (most fish), Type 4 (salmonids), and Type 5 (zebrafish). Based on these results, Xenopus (Type 2), fathead minnow (Type 3), rainbow trout (Type 4), and zebrafish (Type 5) were strategically selected as being representative of the diversity of species sensitivities to agonists of PPARy for ongoing investigation. This study demonstrates how SeqAPASS can be used to computationally predict species most likely to differ in sensitivity to chemicals for the strategic characterization of species differences in sensitivity. The content of this presentation neither constitute nor necessarily reflect US EPA policy.