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Targeted and untargeted metabolomics for deriving benchmark doses (BMDs) in fathead minnows
Citation:
Henderson, W., D. Glinski, C. Coleman, A. Biales, R. Flick, M. Bell, O. Torano, J. Martinson, W. Huang, S. Purucker, AND D. Bencic. Targeted and untargeted metabolomics for deriving benchmark doses (BMDs) in fathead minnows. SETAC North America 44th Annual Meeting, Louisville, KY, November 12 - 16, 2023.
Impact/Purpose:
A multi-omics integration approach can offer multiple-layer evidence of exposure effects, and also can provide more comprehensive understanding of toxicities of environmental contaminants. This is particularly important to understand transgenerational and multigenerational effects of some environmental chemicals of emerging concern (e.g., phthalates). The application of omics/multi-omics based new approach methods (NAMs) for environmental exposure studies, however, is almost impossible without high-quality omics reference data of relevant model/non-model organisms.
Description:
Metabolomics is an emerging tool to predict changes in an organism’s pathophysiological state prior to the onset of overt toxicity. Although useful for providing insight into biochemical changes in ecological toxicity studies, the use of metabolomics data in the regulatory arena has been limited. Cited pitfalls include, but are not limited to, the inability to derive accepted ‘regulatory’ benchmarks from untargeted metabolomics data such as LOAELs or NOAELs. In the current study, we utilized both untargeted and targeted gas chromatography high resolution mass spectrometry (GC-HRMS) data to derive benchmark doses (BMDs) in fathead minnows exposed to several contaminants of emerging concern (i.e., pesticides, plasticizers, and pharmaceuticals). Across all contaminants, fathead minnow larvae (5 dpf) were exposed to 10 concentrations identified in range finding studies for 24 hrs. In the non-targeted analyses, the spectral features were used, in the absence of putative metabolite identification, as the input data and using BMDExpress 2 to establish chemical-specific points of departure (POD). All spectral features were then annotated post-statistical identification and the top metabolites subjected to biochemical pathway analysis. For targeted approaches, an in-house library containing >200 metabolites was developed and identified metabolite peak areas were used in a similar method as described above. The metabolites in the curated library spanned >20 biochemical pathways including the urea cycle, gluconeogenesis, glycolysis, and amino acid metabolism. In the targeted approach, BMDs were calculated based on the top biological pathways impacted containing at least three altered metabolite signatures. BMDs calculated across the studied contaminants were all compared to their aquatic life benchmarks, based on traditional organismal endpoints, collected from the US EPA’s Ecotoxicology (ECOTOX) Knowledgebase as well as species-specific transcriptomic-based PODs when available. Further, current efforts in our laboratory are aimed at comparing BMDs derived from transcriptomics, metabolomics and behavioral assays. These data highlight the potential integration and use of GC-HRMS untargeted and targeted metabolomics data in ecological risk assessment practices.
URLs/Downloads:
https://www.setac.org/discover-events/global-meetings/setac-north-america-44th-annual-meeting.html