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. 2024 Jun 17;37(6):923-934.
doi: 10.1021/acs.chemrestox.4c00002. Epub 2024 Jun 6.

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay

Elena Sostare  1 , Tara J Bowen  2 , Thomas N Lawson  1 , Anne Freier  2 , Xiaojing Li  2 , Gavin R Lloyd  3 , Lukáš Najdekr  3 , Andris Jankevics  3 , Thomas Smith  3 , Dorsa Varshavi  3 , Christian Ludwig  3 , John K Colbourne  1   2 , Ralf J M Weber  1   2   3 , David M Crizer  4 , Scott S Auerbach  4 , John R Bucher  4 , Mark R Viant  1   2   3
Affiliations

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay

Elena Sostare et al. Chem Res Toxicol. .

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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Conflict of interest statement

The authors declare the following competing financial interest(s): Professors Mark Viant and John Colbourne are employees of the University of Birmingham. They are also Founders and Directors of Michabo Health Science Ltd., a spin-out company of the University of Birmingham that provides scientific consultancy services in New Approach Methodologies (NAMs) specialising in omics technologies and computational toxicology.

Figures

Figure 1
Figure 1
Dose–response curves of putatively annotated endogenous metabolites, (a) UHPLC–MS feature annotated as PE(18:4/18:2); LIPIDS negative assay, and (b) NMR feature annotated as either glucose or maltose; NMR assay. Both features fit the Exponential 4 model. Vertical black lines mark BMDL (BMD lower confidence limit, left) and BMD (right) and a green line shows BMDU (BMD upper confidence limit). Red points indicate mean ± 1 standard deviation for each dose.
Figure 2
Figure 2
Accumulation plot from the BMD analysis of the rat liver metabolome following TPhP exposure, showing the BMD value for every endogenous metabolic feature that has a viable dose–response model, derived from all five metabolomics assays (four UHPLC–MS and one NMR assay).
Figure 3
Figure 3
IPA core analysis revealed which (a) canonical pathways and (b) toxicity functions were perturbed following TPhP exposure, utilizing both transcriptomics (left) and metabolomics (right, annotated endogenous features from all five assays) data sets. Orange squares represent activation, and blue squares inhibition, of the pathways and functions as a function of escalating TPhP dose (mg/kg). Only significantly perturbed pathways and functions (with z-scores greater than 2 and less than −2 for at least one of the doses) are shown. Pathways and functions are sorted based on maximum absolute z-score for each ‘omics approach.
Figure 4
Figure 4
TPhP biotransformation map in rat liver discovered using UHPLC–MS metabolomics assays, with Phase I transformations depicted in blue and Phase II transformations in orange.
Figure 5
Figure 5
Correlation of 12 representative xenobiotic-related features (measured using UHPLC–MS and corresponding to TPhP, DPhP, and 10 other biotransformation products) with 58 genes participating in xenobiotic metabolism and oxidative stress pathways (xenobiotic metabolism CAR signaling pathway, xenobiotic metabolism general signaling pathway, xenobiotic metabolism PXR signaling pathway and NRF2-mediated oxidative stress response). Filled blue radar plot shows the number of correlated metabolic features for each gene (p-value < 0.05). Orange radar plot corresponds to median absolute correlation coefficient (across the 12 features) for the respective genes. Four out of 58 genes exhibited no correlation and are not shown.
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