A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity
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 s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412021003767 |
| _version_ | 1818594731521736704 |
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| author | A. Sakhteman M. Failli J. Kublbeck A.L. Levonen V. Fortino |
| author_facet | A. Sakhteman M. Failli J. Kublbeck A.L. Levonen V. Fortino |
| author_sort | A. Sakhteman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T11:04:47Z |
| format | Article |
| id | doaj.art-fc9ca2281a9b49ea922156b452e04122 |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-12-16T11:04:47Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-fc9ca2281a9b49ea922156b452e041222022-12-21T22:33:54ZengElsevierEnvironment International0160-41202021-11-01156106751A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicityA. Sakhteman0M. Failli1J. Kublbeck2A.L. Levonen3V. Fortino4Institute of Biomedicine, University of Eastern Finland, Kuopio 70210, FinlandDepartment of Chemical, Materials and Industrial Engineering, University of Naples, 'Federico II', Naples 80125, ItalyA.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70210, Finland; School of Pharmacy, University of Eastern Finland, Kuopio 70210, FinlandA.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70210, FinlandInstitute of Biomedicine, University of Eastern Finland, Kuopio 70210, Finland; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S0160412021003767Endocrine disrupting chemicalsMetabolic diseasesToxicogenomicsMachine learningIn silico toxicity prediction |
| spellingShingle | A. Sakhteman M. Failli J. Kublbeck A.L. Levonen V. Fortino A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity Environment International Endocrine disrupting chemicals Metabolic diseases Toxicogenomics Machine learning In silico toxicity prediction |
| title | A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity |
| title_full | A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity |
| title_fullStr | A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity |
| title_full_unstemmed | A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity |
| title_short | A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity |
| title_sort | toxicogenomic data space for system level understanding and prediction of edc induced toxicity |
| topic | Endocrine disrupting chemicals Metabolic diseases Toxicogenomics Machine learning In silico toxicity prediction |
| url | http://www.sciencedirect.com/science/article/pii/S0160412021003767 |
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