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Benchmark Concentrations for Untargeted Metabolomics vs. Transcriptomics for Liver Injury Compounds in In Vitro Liver Models

David M Crizer, Sreenivasa C Ramaiahgari, Stephen S Ferguson, Julie R Rice, Paul E Dunlap, Nisha S Sipes, Scott S Auerbach, B Alex Merrick, Michael J DeVito

Toxicological Science (2021) DOI: https://doi.org/10.1093/toxsci/kfab036 PMID: 33749773
DOI: https://doi.org/10.22427/NTP-DATA-002-00058-0003-0000-8


Publication


Abstract

Interpretation of untargeted metabolomics data from both in vivo and physiologically-relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted LCMS analysis. The analyte features observed (identity of metabolites not determined) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (i.e., sucrose, KCl). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Figures


Figure 1A - B: 2D HepaRG Cells

A.A. Representative photomicrograph of two-dimensional (2D) HepaRG cultures from vehicle control-treatment over 96h exposure. Photomicrograph (10X magnification) of culture well was captured using an Incucyte Zoom (Essen BioSciences).
B.HepaRG cells differentiate into hepatocyte-like and cholangiocyte-like cells

Figure 2A - G: LCMS untargeted metabolomics run of 2D HepaRG cells exposed with ritonavir

A.Cloud plot representation of LCMS untargeted metabolomics run of 2D HepaRG cells exposed with ritonavir in positive mode using reverse-phase C18 separation method. Samples were spent culture media samples at 96 hours. Data shows statistically significant differences between the low and high exposure (without overt cytotoxicity) samples.
B.Box plot comparing low exposure and high exposure of compounds involved in steroid synthesis
C.Box plot comparing low exposure and high exposure of Carnitines
D.Box plot comparing low exposure and high exposure of Glycerphospholipids
E.Box plot comparing low exposure and high exposure of Eicosanoids
F.Box plot comparing low exposure and high exposure of compounds involved in tryptophan metabolism
G.Box plot comparing low exposure and high exposure of Bile Acids

Figure 3A - D: LCMS untargeted metabolomics run of 2D HepaRG cells exposed with tamoxifen

A. Cloud plot representation of LCMS untargeted metabolomics run of 2D HepaRG cells exposed with tamoxifen in positive mode using reverse-phase C18 separation method. Samples are spent media samples at 96 hours. Data shows statistically significant differences between the low and high exposure (without overt cytotoxicity) samples
B-D. Box plot comparison of the intensity of lipid metabolites between the low and high exposures (without overt cytotoxicity) of tamoxifen treated samples

Figure 4A - B: Representative dose response curves for tamoxifen exposed 2D HepaRG cells

A.Dose response curves for feature at m/z 406.237
B.Dose response curves for feature at m/z 613.181

Figure 5A - B: Benchmark Concentrations (BMCs) derived from metabolomics and transcriptomics data

A.BMC distribution scatter plots showing BMC values for metabolomic features (blue squares), metabolomic LCRD values (green circles), transcriptomic features (red diamonds), transcriptomic LCRD values (light yellow triangles), therapeutic/normal blood levels (if available; orange sideways triangles) and toxic blood levels (if available; light blue sideways triangles)
B.Metabolomic and transcriptomic LCRD range plot showing the 95% lower (BMCL) (circles) and upper bound (BMCU) (diamonds) around the BMC that corresponds to the LCRD (squares)

Tables


Table 1. Compounds studied with exposure concentrations and liver injury classification category

Table 2. Significantly changed metabolite features and metabolite identification challenges

Table 3. Comparison of metabolomics and transcriptomics data

Table 4. Cmax/LCRD ratios for metabolomics and transcriptomics experiments

Additional Materials


Supplementary Data

FASTQ Files

The SRA fastq data files for this study (SRA study number SRP166108 are available for download from the NCBI SRA website using the SRA Toolkit. Links to the Toolkit and supporting documentation are provided below. Details describing configuration, fastq file download, and other functions in Toolkit are available in the Installation and Configuration Guide. The application is run in the command prompt from the directory containing the Toolkit.exe file and does not have a user-interface. Instructions and descriptions of the commands used by Toolkit are given in the Toolkit Documentation.

SRA Toolkit: Download
Toolkit Installation and Configuration Guide: Download
Toolkit Documentation: Download

Raw Read Count

2D RG Plat1 093016 cor
2D RG Plat2 093016 cor

SRA Metadata

2D RG PLATE1 REPLICATE1 SN
2D RG PLATE2 REPLICATE1 SN

LDH Data

TGMX 1-24 2D Plate1 Rep1 96h
TGMX 1-24 2D Plate2 Rep1 96h

LCMS Metabolomics Raw Data

XCMS Metabolomics Data Analysis Output Files

BMD Analysis Files

BMD Analysis File: BMDExpress Analysis File.zip
BMD Input Files: BMDExpress Input Files.zip
BMD Express Program