A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity.
Saved in:
| 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] |
| Copyright of Environment International is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 152100159 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sakhteman%2C+A%2E%22">Sakhteman, A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Failli%2C+M%2E%22">Failli, M.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kublbeck%2C+J%2E%22">Kublbeck, J.</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Levonen%2C+A%2EL%2E%22">Levonen, A.L.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fortino%2C+V%2E%22">Fortino, V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vittorio.fortino@uef.fi</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment+International%22">Environment International</searchLink>. Nov2021, Vol. 156, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Endocrine+disruptors%22">Endocrine disruptors</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Metabolic+disorders%22">Metabolic disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Toxicogenomics%22">Toxicogenomics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environment International is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=152100159 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.envint.2021.106751 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Endocrine disruptors Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Metabolic disorders Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Toxicogenomics Type: general Titles: – TitleFull: A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sakhteman, A. – PersonEntity: Name: NameFull: Failli, M. – PersonEntity: Name: NameFull: Kublbeck, J. – PersonEntity: Name: NameFull: Levonen, A.L. – PersonEntity: Name: NameFull: Fortino, V. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 01604120 Numbering: – Type: volume Value: 156 Titles: – TitleFull: Environment International Type: main |
| ResultId | 1 |
