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Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling
All-trans retinoic acid (ATRA) gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors during pregnancy, leading to fetal skeleton defects. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow fo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511990/ https://www.ncbi.nlm.nih.gov/pubmed/36172177 http://dx.doi.org/10.3389/fphar.2022.971296 |
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author | Pierro, Jocylin D. Ahir, Bhavesh K. Baker, Nancy C. Kleinstreuer, Nicole C. Xia, Menghang Knudsen, Thomas B. |
author_facet | Pierro, Jocylin D. Ahir, Bhavesh K. Baker, Nancy C. Kleinstreuer, Nicole C. Xia, Menghang Knudsen, Thomas B. |
author_sort | Pierro, Jocylin D. |
collection | PubMed |
description | All-trans retinoic acid (ATRA) gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors during pregnancy, leading to fetal skeleton defects. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow for the development of new approach methods (NAMs) for predictive toxicology with less reliance on animal testing. Here, a data-driven model was constructed to identify chemicals associated with both ATRA pathway bioactivity and prenatal skeletal defects. The phenotype data was culled from ToxRefDB prenatal developmental toxicity studies and produced a list of 363 ToxRefDB chemicals with altered skeletal observations. Defects were classified regionally as cranial, post-cranial axial, appendicular, and other (unspecified) features based on ToxRefDB descriptors. To build a multivariate statistical model, high-throughput screening bioactivity data from >8,070 chemicals in ToxCast/Tox21 across 10 in vitro assays relevant to the retinoid signaling system were evaluated and compared to literature-based candidate reference chemicals in the dataset. There were 48 chemicals identified for effects on both in vivo skeletal defects and in vitro ATRA pathway targets for computational modeling. The list included 28 chemicals with prior evidence of skeletal defects linked to retinoid toxicity and 20 chemicals without prior evidence. The combination of thoracic cage defects and DR5 (direct repeats of 5 nucleotides for RAR/RXR transactivation) disruption was the most frequently occurring phenotypic and target disturbance, respectively. This data model provides valuable AOP elucidation and validates current mechanistic understanding. These findings also shed light on potential avenues for new mechanistic discoveries related to ATRA pathway disruption and associated skeletal dysmorphogenesis due to environmental exposures. |
format | Online Article Text |
id | pubmed-9511990 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95119902022-09-27 Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling Pierro, Jocylin D. Ahir, Bhavesh K. Baker, Nancy C. Kleinstreuer, Nicole C. Xia, Menghang Knudsen, Thomas B. Front Pharmacol Pharmacology All-trans retinoic acid (ATRA) gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors during pregnancy, leading to fetal skeleton defects. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow for the development of new approach methods (NAMs) for predictive toxicology with less reliance on animal testing. Here, a data-driven model was constructed to identify chemicals associated with both ATRA pathway bioactivity and prenatal skeletal defects. The phenotype data was culled from ToxRefDB prenatal developmental toxicity studies and produced a list of 363 ToxRefDB chemicals with altered skeletal observations. Defects were classified regionally as cranial, post-cranial axial, appendicular, and other (unspecified) features based on ToxRefDB descriptors. To build a multivariate statistical model, high-throughput screening bioactivity data from >8,070 chemicals in ToxCast/Tox21 across 10 in vitro assays relevant to the retinoid signaling system were evaluated and compared to literature-based candidate reference chemicals in the dataset. There were 48 chemicals identified for effects on both in vivo skeletal defects and in vitro ATRA pathway targets for computational modeling. The list included 28 chemicals with prior evidence of skeletal defects linked to retinoid toxicity and 20 chemicals without prior evidence. The combination of thoracic cage defects and DR5 (direct repeats of 5 nucleotides for RAR/RXR transactivation) disruption was the most frequently occurring phenotypic and target disturbance, respectively. This data model provides valuable AOP elucidation and validates current mechanistic understanding. These findings also shed light on potential avenues for new mechanistic discoveries related to ATRA pathway disruption and associated skeletal dysmorphogenesis due to environmental exposures. Frontiers Media S.A. 2022-09-06 /pmc/articles/PMC9511990/ /pubmed/36172177 http://dx.doi.org/10.3389/fphar.2022.971296 Text en Copyright © 2022 Pierro, Ahir, Baker, Kleinstreuer, Xia and Knudsen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Pierro, Jocylin D. Ahir, Bhavesh K. Baker, Nancy C. Kleinstreuer, Nicole C. Xia, Menghang Knudsen, Thomas B. Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling |
title | Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling |
title_full | Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling |
title_fullStr | Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling |
title_full_unstemmed | Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling |
title_short | Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling |
title_sort | computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511990/ https://www.ncbi.nlm.nih.gov/pubmed/36172177 http://dx.doi.org/10.3389/fphar.2022.971296 |
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