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Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data
[Image: see text] Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019810/ https://www.ncbi.nlm.nih.gov/pubmed/35333521 http://dx.doi.org/10.1021/acs.chemrestox.1c00444 |
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author | Basili, Danilo Reynolds, Joe Houghton, Jade Malcomber, Sophie Chambers, Bryant Liddell, Mark Muller, Iris White, Andrew Shah, Imran Everett, Logan J. Middleton, Alistair Bender, Andreas |
author_facet | Basili, Danilo Reynolds, Joe Houghton, Jade Malcomber, Sophie Chambers, Bryant Liddell, Mark Muller, Iris White, Andrew Shah, Imran Everett, Logan J. Middleton, Alistair Bender, Andreas |
author_sort | Basili, Danilo |
collection | PubMed |
description | [Image: see text] Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration–response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs’ concentration–response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration–response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration–response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies. |
format | Online Article Text |
id | pubmed-9019810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-90198102022-04-20 Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data Basili, Danilo Reynolds, Joe Houghton, Jade Malcomber, Sophie Chambers, Bryant Liddell, Mark Muller, Iris White, Andrew Shah, Imran Everett, Logan J. Middleton, Alistair Bender, Andreas Chem Res Toxicol [Image: see text] Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration–response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs’ concentration–response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration–response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration–response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies. American Chemical Society 2022-03-25 2022-04-18 /pmc/articles/PMC9019810/ /pubmed/35333521 http://dx.doi.org/10.1021/acs.chemrestox.1c00444 Text en © 2022 American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Basili, Danilo Reynolds, Joe Houghton, Jade Malcomber, Sophie Chambers, Bryant Liddell, Mark Muller, Iris White, Andrew Shah, Imran Everett, Logan J. Middleton, Alistair Bender, Andreas Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data |
title | Latent Variables
Capture Pathway-Level Points of Departure
in High-Throughput Toxicogenomic Data |
title_full | Latent Variables
Capture Pathway-Level Points of Departure
in High-Throughput Toxicogenomic Data |
title_fullStr | Latent Variables
Capture Pathway-Level Points of Departure
in High-Throughput Toxicogenomic Data |
title_full_unstemmed | Latent Variables
Capture Pathway-Level Points of Departure
in High-Throughput Toxicogenomic Data |
title_short | Latent Variables
Capture Pathway-Level Points of Departure
in High-Throughput Toxicogenomic Data |
title_sort | latent variables
capture pathway-level points of departure
in high-throughput toxicogenomic data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019810/ https://www.ncbi.nlm.nih.gov/pubmed/35333521 http://dx.doi.org/10.1021/acs.chemrestox.1c00444 |
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