1
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Sostare E, Bowen TJ, Lawson TN, Freier A, Li X, Lloyd GR, Najdekr L, Jankevics A, Smith T, Varshavi D, Ludwig C, Colbourne JK, Weber RJM, Crizer DM, Auerbach SS, Bucher JR, Viant MR. Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay. Chem Res Toxicol 2024; 37:923-934. [PMID: 38842447 PMCID: PMC11187623 DOI: 10.1021/acs.chemrestox.4c00002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/07/2024]
Abstract
Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).
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Affiliation(s)
- Elena Sostare
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
| | - Tara J. Bowen
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Thomas N. Lawson
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
| | - Anne Freier
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Xiaojing Li
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Gavin R. Lloyd
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Lukáš Najdekr
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Andris Jankevics
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Thomas Smith
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Dorsa Varshavi
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Christian Ludwig
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - John K. Colbourne
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Ralf J. M. Weber
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - David M. Crizer
- Division
of Translational Toxicology, National Institute
of Environmental Health Sciences, Research Triangle Park NC 27709, North Carolina, United
States
| | - Scott S. Auerbach
- Division
of Translational Toxicology, National Institute
of Environmental Health Sciences, Research Triangle Park NC 27709, North Carolina, United
States
| | - John R. Bucher
- Division
of Translational Toxicology, National Institute
of Environmental Health Sciences, Research Triangle Park NC 27709, North Carolina, United
States
| | - Mark R. Viant
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
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Ewald JD, Basu N, Crump D, Boulanger E, Head J. Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15960-15968. [PMID: 36268973 DOI: 10.1021/acs.est.2c04665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Transcriptomics dose-response analysis (TDRA) has emerged as a promising approach for integrating toxicogenomics data into a risk assessment context; however, variability and uncertainty associated with experimental design are not well understood. Here, we evaluated n = 55 RNA-seq profiles derived from Japanese quail liver tissue following exposure to chlorpyrifos (0, 0.04, 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, and 40 μg/g; n = 5 replicates per group) via egg injection. The full dataset was subsampled 637 times to generate smaller datasets with different dose ranges and spacing (designs A-E) and number of replicates (n = 2-5). TDRA of the 637 datasets revealed substantial variability in the gene and pathway benchmark doses, but relative stability in overall transcriptomic point-of-departure (tPOD) values when tPODs were calculated with the "pathway" and "mode" methods. Further, we found that tPOD values were more dependent on the dose range and spacing than on the number of replicates, suggesting that optimal experimental designs should use fewer replicates (n = 2 or 3) and more dose groups to reduce uncertainty in the results. Finally, tPOD values ranged by over ten times for all surveyed experimental designs and tPOD types, suggesting that tPODs should be interpreted as order-of-magnitude estimates.
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Affiliation(s)
- Jessica D Ewald
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
| | - Doug Crump
- Ecotoxicology and Wildlife Health Division, National Wildlife Research Centre, Environment and Climate Change Canada, Ottawa K1A 0H3, Canada
| | - Emily Boulanger
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
| | - Jessica Head
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
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3
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Yu J, Tu W, Payne A, Rudyk C, Cuadros Sanchez S, Khilji S, Kumarathasan P, Subedi S, Haley B, Wong A, Anghel C, Wang Y, Chauhan V. Adverse Outcome Pathways and Linkages to Transcriptomic Effects Relevant to Ionizing Radiation Injury. Int J Radiat Biol 2022; 98:1789-1801. [PMID: 35939063 DOI: 10.1080/09553002.2022.2110313] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
BACKGROUND In the past three decades, a large body of data on the effects of exposure to ionizing radiation and the ensuing changes in gene expression has been generated. These data have allowed for an understanding of molecular-level events and shown a level of consistency in response despite the vast formats and experimental procedures being used across institutions. However, clarity on how this information may inform strategies for health risk assessment needs to be explored. An approach to bridge this gap is the adverse outcome pathway (AOP) framework. AOPs represent an illustrative framework characterizing a stressor associated with a sequential set of causally linked key events (KEs) at different levels of biological organization, beginning with a molecular initiating event (MIE) and culminating in an adverse outcome (AO). Here, we demonstrate the interpretation of transcriptomic datasets in the context of the AOP framework within the field of ionizing radiation by using a lung cancer AOP (AOP 272: https://www.aopwiki.org/aops/272) as a case example. METHODS Through the mining of the literature, radiation exposure-related transcriptomic studies in line with AOP 272 related to lung cancer, DNA damage response, and repair were identified. The differentially expressed genes within relevant studies were collated and subjected to the pathway and network analysis using Reactome and GeneMANIA platforms. Identified pathways were filtered (p < 0.001, ≥ 3 genes) and categorized based on relevance to KEs in the AOP. Gene connectivities were identified and further grouped by gene expression-informed associated events (AEs). Relevant quantitative dose-response data were used to inform the directionality in the expression of the genes in the network across AEs. RESULTS Reactome analyses identified 7 high-level biological processes with multiple pathways and associated genes that mapped to potential KEs in AOP 272. The gene connectivities were further represented as a network of AEs with associated expression profiles that highlighted patterns of gene expression levels. CONCLUSIONS This study demonstrates the application of transcriptomics data in AOP development and provides information on potential data gaps. Although the approach is new and anticipated to evolve, it shows promise for improving the understanding of underlying mechanisms of disease progression with a long-term vision to be predictive of adverse outcomes.
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Affiliation(s)
- Jihang Yu
- Canadian Nuclear Laboratories, Chalk River, Ontario, Canada
| | - Wangshu Tu
- Carleton University, Ottawa, Ontario, Canada
| | | | - Chris Rudyk
- Carleton University, Ottawa, Ontario, Canada
| | | | | | | | | | - Brittany Haley
- Canadian Nuclear Laboratories, Chalk River, Ontario, Canada
| | - Alicia Wong
- Canadian Nuclear Laboratories, Chalk River, Ontario, Canada.,McMaster University, Hamilton, Ontario, Canada
| | | | - Yi Wang
- Canadian Nuclear Laboratories, Chalk River, Ontario, Canada.,University of Ottawa, Ottawa, Ontario, Canada
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4
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Qin Y, Yi D, Chen X, Guan Y. Deep learning identifies erroneous microarray-based, gene-level conclusions in literature. NAR Genom Bioinform 2021; 3:lqab089. [PMID: 34617014 PMCID: PMC8489595 DOI: 10.1093/nargab/lqab089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 08/25/2021] [Accepted: 09/17/2021] [Indexed: 11/14/2022] Open
Abstract
More than 110 000 publications have used microarrays to decipher phenotype-associated genes, clinical biomarkers and gene functions. Microarrays rely on digital assaying the fluorescence signals of arrays. In this study, we retrospectively constructed raw images for 37 724 published microarray data, and developed deep learning algorithms to automatically detect systematic defects. We report that an alarming amount of 26.73% of the microarray-based studies are affected by serious imaging defects. By literature mining, we found that publications associated with these affected microarrays have reported disproportionately more biological discoveries on the genes in the contaminated areas compared to other genes. 28.82% of the gene-level conclusions reported in these publications were based on measurements falling into the contaminated area, indicating severe, systematic problems caused by such contaminations. We provided the identified published, problematic datasets, affected genes and the imputed arrays as well as software tools for scanning such contamination that will become essential to future studies to scrutinize and critically analyze microarray data.
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Affiliation(s)
- Yanan Qin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Daiyao Yi
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Xianghao Chen
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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Chang Y, Huynh CTT, Bastin KM, Rivera BN, Siddens LK, Tilton SC. Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures. Toxicol In Vitro 2020; 69:104991. [PMID: 32890658 PMCID: PMC7572825 DOI: 10.1016/j.tiv.2020.104991] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/15/2020] [Accepted: 08/29/2020] [Indexed: 01/26/2023]
Abstract
One of the most difficult challenges for risk assessment is evaluation of chemicals that predominately co-occur in mixtures like polycyclic aromatic hydrocarbons (PAHs). We previously developed a classification model in which systems biology data collected from mice short-term after chemical exposure accurately predict tumor outcome. The present study demonstrates translation of this approach into a human in vitro model in which chemical-specific bioactivity profiles from 3D human bronchial epithelial cells (HBEC) classify PAHs by carcinogenic potency. Gene expression profiles were analyzed from HBEC exposed to carcinogenic and non-carcinogenic PAHs and classification accuracies were identified for individual pathway-based gene sets. Posterior probabilities of best performing gene sets were combined via Bayesian integration resulting in a classifier with four gene sets, including aryl hydrocarbon receptor signaling, regulation of epithelial mesenchymal transition, regulation of angiogenesis, and cell cycle G2-M. In addition, transcriptional benchmark dose modeling of benzo[a]pyrene (BAP) showed that the most sensitive gene sets to BAP regulation were largely dissimilar from those that best classified PAH carcinogenicity challenging current assumptions that BAP carcinogenicity (and subsequent mode of action) is reflective of overall PAH carcinogenicity. These results illustrate utility of using systems toxicology approaches to analyze global gene expression towards carcinogenic hazard assessment.
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Affiliation(s)
- Yvonne Chang
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA
| | - Celine Thanh Thu Huynh
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA
| | - Kelley M Bastin
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA
| | - Brianna N Rivera
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA
| | - Lisbeth K Siddens
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA; Linus Pauling Institute, Oregon State University, Corvallis, OR, USA
| | - Susan C Tilton
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA; Superfund Research Program, Oregon State University, Corvallis, OR, USA.
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6
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Shockley KR, Cora MC, Malarkey DE, Jackson-Humbles D, Vallant M, Collins BJ, Mutlu E, Robinson VG, Waidyanatha S, Zmarowski A, Machesky N, Richey J, Harbo S, Cheng E, Patton K, Sparrow B, Dunnick JK. Comparative toxicity and liver transcriptomics of legacy and emerging brominated flame retardants following 5-day exposure in the rat. Toxicol Lett 2020; 332:222-234. [PMID: 32679240 PMCID: PMC7903589 DOI: 10.1016/j.toxlet.2020.07.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/06/2020] [Accepted: 07/11/2020] [Indexed: 12/13/2022]
Abstract
The relative toxicity of three legacy and six emerging brominated flame retardants* was studied in the male Harlan Sprague Dawley rat. The hepatocellular and thyroid toxicity of each flame retardant was evaluated following five-day exposure to each of the nine flame retardants (oral gavage in corn oil) at 0.1-1000 μmol/kg body weight per day. Histopathology and transcriptomic analysis were performed on the left liver lobe. Centrilobular hypertrophy of hepatocytes and increases in liver weight were seen following exposure to two legacy (PBDE-47, HBCD) and to one emerging flame retardant (HCDBCO). Total thyroxine (TT4) concentrations were reduced to the greatest extent after PBDE-47 exposure. The PBDE-47, decaBDE, and HBCD liver transcriptomes were characterized by upregulation of liver disease-related and/or metabolic transcripts. Fewer liver disease or metabolic transcript changes were detected for the other flame retardants studied (TBB, TBPH, TBBPA-DBPE, BTBPE, DBDPE, or HCDBCO). PBDE-47 exhibited the most disruption of hepatocellular toxic endpoints, with the Nrf2 antioxidant pathway transcripts upregulated to the greatest extent, although some activation of this pathway also occurred after decaBDE, HBCD, TBB, and HCBCO exposure. These studies provide information that can be used for prioritizing the need for more in-depth brominated flame retardant toxicity studies.
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Affiliation(s)
- Keith R Shockley
- Biostatistics & Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - Michelle C Cora
- Cellular & Molecular Pathology Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - David E Malarkey
- Cellular & Molecular Pathology Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - Daven Jackson-Humbles
- Cellular & Molecular Pathology Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - Molly Vallant
- Program Operations Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - Brad J Collins
- Program Operations Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - Esra Mutlu
- Program Operations Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - Veronica G Robinson
- Program Operations Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | - Surayma Waidyanatha
- Program Operations Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States
| | | | | | | | - Sam Harbo
- Battelle, Columbus, Ohio, 43210, United States
| | - Emily Cheng
- Battelle, Columbus, Ohio, 43210, United States
| | | | | | - June K Dunnick
- Toxicology Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, United States.
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