Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data.
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Authors
Reynolds, Joe
Houghton, Jade
Malcomber, Sophie
Chambers, Bryant
Liddell, Mark
Muller, Iris
White, Andrew
Everett, Logan J
Middleton, Alistair
Publication Date
2022-04-18Journal Title
Chem Res Toxicol
ISSN
0893-228X
Publisher
American Chemical Society (ACS)
Volume
35
Issue
4
Pages
670-683
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Basili, D., Reynolds, J., Houghton, J., Malcomber, S., Chambers, B., Liddell, M., Muller, I., et al. (2022). Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data.. Chem Res Toxicol, 35 (4), 670-683. https://doi.org/10.1021/acs.chemrestox.1c00444
Description
Funder: Unilever
Abstract
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.
Keywords
Estrogens, Bayes Theorem, Risk Assessment, Toxicogenetics, Transcriptome
Identifiers
35333521, PMC9019810
External DOI: https://doi.org/10.1021/acs.chemrestox.1c00444
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336443
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
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