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A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity.

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Title: A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity.
Authors: Sakhteman, A.1 (AUTHOR), Failli, M.1,2 (AUTHOR), Kublbeck, J.3,4 (AUTHOR), Levonen, A.L.3 (AUTHOR), Fortino, V.1 (AUTHOR) vittorio.fortino@uef.fi
Source: Environment International. Nov2021, Vol. 156, pN.PAG-N.PAG. 1p.
Subjects: Endocrine disruptors, Prediction models, Metabolic disorders, Machine learning, Forecasting, Toxicogenomics
Abstract: • Toxicogenomics and network analysis are used to inform on MoAs of EDCs. • Pathway-based predictive models can aid in the initial screening of potential EDs. • Network and machine learning-based analyses reveal novel EDC-gene associations. • A catalogue of genes and molecular pathways responsive to EDCs is provided. • Pathways linking MIEs of EDCs with metabolic diseases are identified through machine learning. Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo -based classifiers achieve accuracy greater than 75% when tested for in vitro to in vivo extrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDC evoked metabolic diseases. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• Toxicogenomics and network analysis are used to inform on MoAs of EDCs. • Pathway-based predictive models can aid in the initial screening of potential EDs. • Network and machine learning-based analyses reveal novel EDC-gene associations. • A catalogue of genes and molecular pathways responsive to EDCs is provided. • Pathways linking MIEs of EDCs with metabolic diseases are identified through machine learning. Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo -based classifiers achieve accuracy greater than 75% when tested for in vitro to in vivo extrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDC evoked metabolic diseases. [ABSTRACT FROM AUTHOR]
ISSN:01604120
DOI:10.1016/j.envint.2021.106751