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Cited by 164Drug-Induced Hepatotoxicity and ProtectionMetabolomics and Mass Spectrometry StudiesAnimal testing and alternatives

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Prediction of liver toxicity and mode of action using metabolomics in vitro in HepG2 cells

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Ramirez, Tzutzuy, Strigun, Alexander, Verlohner, Andreas, Huener, Hans-Albrecht, Peter, Erik, Herold, Michael, Bordag, Natalie, Mellert, Werner, Walk, Tilmann, Spitzer, Michael, Jiang, Xiaoqi, Sperber, Saskia, Hofmann, Thomas, Hartung, Thomas, Kamp, Hennicke, van Ravenzwaay, Ben·2018·Archives of Toxicology
DOI 10.1007/s00204-017-2079-6PMC PMC5818600
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Abstract

Liver toxicity is a leading systemic toxicity of drugs and chemicals demanding more human-relevant, high throughput, cost effective in vitro solutions. In addition to contributing to animal welfare, in vitro techniques facilitate exploring and understanding the molecular mechanisms underlying toxicity. New 'omics technologies can provide comprehensive information on the toxicological mode of action of compounds, as well as quantitative information about the multi-parametric metabolic response of cellular systems in normal and patho-physiological conditions. Here, we combined mass-spectroscopy metabolomics with an in vitro liver toxicity model. Metabolite profiles of HepG2 cells treated with 35 test substances resulted in 1114 cell supernatants and 3556 intracellular samples analyzed by metabolomics. Control samples showed relative standard deviations of about 10-15%, while the technical replicates were at 5-10%. Importantly, this procedure revealed concentration-response effects and patterns of metabolome changes that are consistent for different liver toxicity mechanisms (liver enzyme induction/inhibition, liver toxicity and peroxisome proliferation). Our findings provide evidence that identifying organ toxicity can be achieved in a robust, reliable, human-relevant system, representing a non-animal alternative for systemic toxicology.

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Crossref · 10.1007/s00204-017-2079-6

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Fig. 1 Metabolites distributed according to their metabolite class. Left: 89 metabolites found in the supernatant of HepG2 cells. Right: 194 metabolites found intracellular in HepG2 cells. The distribution and actual numbers of the different identified metabolite classes are depicted, where unknown represents metabolites undergoing chemical class identification
Fig. 1 Metabolites distributed according to their metabolite class. Left: 89 metabolites found in the supernatant of HepG2 cells. Right: 194 metabolites found intracellular in HepG2 cells. The distribution and actual numbers of the different identified metabolite classes are depicted, where unknown represents metabolites undergoing chemical class identificationCC BY · PubMed Central
Fig. 2 Variability of controls generated over 3 years. Displayed are the total relative standard deviations (RSD) of all control samples grouped per experiment (study) and corrected from the weak variability (depicted in red) and technical (depicted in blue) samples and grouped chronologically according to the time when the experiments were performed. The biological variability (purple) was estimating by subtracting the technical from the total variance on the log-scale and then transforming to RSD as described in the text
Fig. 2 Variability of controls generated over 3 years. Displayed are the total relative standard deviations (RSD) of all control samples grouped per experiment (study) and corrected from the weak variability (depicted in red) and technical (depicted in blue) samples and grouped chronologically according to the time when the experiments were performed. The biological variability (purple) was estimating by subtracting the technical from the total variance on the log-scale and then transforming to RSD as described in the textCC BY · PubMed Central
Fig. 3 Dose response effects. Principal component analysis (PCA) of all control samples (red) and all bezafibrate treated samples in low dose (LD, green) and high dose (HD, blue) treatments. a Supernatant and b intracellular metabolomics data (color figure online)
Fig. 3 Dose response effects. Principal component analysis (PCA) of all control samples (red) and all bezafibrate treated samples in low dose (LD, green) and high dose (HD, blue) treatments. a Supernatant and b intracellular metabolomics data (color figure online)CC BY · PubMed Central
Fig. 4 Dose response effects on metabolite changes. The number of changed metabolites induced by liver enzyme inducers and peroxisome proliferators at different doses (low dose in blue and high dose in red, specifications on the dose are depicted per test substance) (color figure online)
Fig. 4 Dose response effects on metabolite changes. The number of changed metabolites induced by liver enzyme inducers and peroxisome proliferators at different doses (low dose in blue and high dose in red, specifications on the dose are depicted per test substance) (color figure online)CC BY · PubMed Central
Fig. 5 Metabolomics of specific liver MoAs. Principal component analysis (PCA) of control samples (red squares) and peroxisome proliferator-treated samples [green crosses = low dose (LD), green dots = high dose (HD); including acifluorfen, bezafibrate, dichlorprop, dichlorprop-p, fluroglycofenethyl, mecoprop, mecoprop-p and Wy-14643] as well as liver enzyme inducer-treated samples (blue crosses = LD, blue dots = HD; including aroclor, β-naphthoflavone, fipronil, pendimethalin). a Supernatant and b intracellular metabolomics data; the latter more efficiently distinguish between different MoAs and dose-dependency (color figure online)
Fig. 5 Metabolomics of specific liver MoAs. Principal component analysis (PCA) of control samples (red squares) and peroxisome proliferator-treated samples [green crosses = low dose (LD), green dots = high dose (HD); including acifluorfen, bezafibrate, dichlorprop, dichlorprop-p, fluroglycofenethyl, mecoprop, mecoprop-p and Wy-14643] as well as liver enzyme inducer-treated samples (blue crosses = LD, blue dots = HD; including aroclor, β-naphthoflavone, fipronil, pendimethalin). a Supernatant and b intracellular metabolomics data; the latter more efficiently distinguish between different MoAs and dose-dependency (color figure online)CC BY · PubMed Central
Fig. 6 Heat map of metabolome changes induced by liver enzyme inducers and peroxisome proliferators in HepG2 cells. Yellow indicates statistically significant (p = 0.05) downregulation and magenta indicates statistical significant (p = 0.05) upregulation of the compound classes indicated; gray represent no statistical significant change. The metabolite classes are depicted in the vertical colored bars to the left of each heat map. a The metabolome changes induced by 3 liver enzyme inducers at HD, aroclor, dimethinamide and pendimethaline; the combination of metabolites that allow distinction of the liver enzyme inducers’ mode of action (pattern) is listed on the top left. For comparison, the metabolome changes induced by a peroxisome proliferator, acilfluorfen, are also displayed, clearly demonstrating a different pattern. b The metabolome changes induced by 3 peroxisome proliferators at HD, acylfluorfen, fluoroglycofen-ethyl and Wy-14643, including the list of metabolites that serve to distinguish the peroxisome proliferation mode of action (pattern). The metabolome pattern of changes induced by the liver enzyme inducer, β-naphthoflavone clearly differs from peroxisome proliferators
Fig. 6 Heat map of metabolome changes induced by liver enzyme inducers and peroxisome proliferators in HepG2 cells. Yellow indicates statistically significant (p = 0.05) downregulation and magenta indicates statistical significant (p = 0.05) upregulation of the compound classes indicated; gray represent no statistical significant change. The metabolite classes are depicted in the vertical colored bars to the left of each heat map. a The metabolome changes induced by 3 liver enzyme inducers at HD, aroclor, dimethinamide and pendimethaline; the combination of metabolites that allow distinction of the liver enzyme inducers’ mode of action (pattern) is listed on the top left. For comparison, the metabolome changes induced by a peroxisome proliferator, acilfluorfen, are also displayed, clearly demonstrating a different pattern. b The metabolome changes induced by 3 peroxisome proliferators at HD, acylfluorfen, fluoroglycofen-ethyl and Wy-14643, including the list of metabolites that serve to distinguish the peroxisome proliferation mode of action (pattern). The metabolome pattern of changes induced by the liver enzyme inducer, β-naphthoflavone clearly differs from peroxisome proliferatorsCC BY · PubMed Central
Fig. 7 Overview of a joint PCA analysis. The plot includes liver enzyme inducers, liver enzyme inhibitors, liver toxicants, peroxisome proliferators, steatogenic compounds, nephrotoxicants and verapamil. Benzylbutylphthalate and dimethylphthalate match close to controls, since metabolic conversion to the monoesters takes not place in HepG2 cells, which is necessary for the mode of action
Fig. 7 Overview of a joint PCA analysis. The plot includes liver enzyme inducers, liver enzyme inhibitors, liver toxicants, peroxisome proliferators, steatogenic compounds, nephrotoxicants and verapamil. Benzylbutylphthalate and dimethylphthalate match close to controls, since metabolic conversion to the monoesters takes not place in HepG2 cells, which is necessary for the mode of actionCC BY · PubMed Central

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  1. HSE (1992) Evaluation on: fluoroglycofen-ethyl. May 1992. Issue No. 50. Department for Environment, Food and Rural Affairs. Pesticides Safety Directorate, UK

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Metadata

Authors
Ramirez, T., Strigun, A., Verlohner, A., Huener, H.-A., Peter, E., Herold, M., Bordag, N., Mellert, W., Walk, T., Spitzer, M., Jiang, X., Sperber, S., Hofmann, T., Hartung, T., Kamp, H., & van Ravenzwaay, B.
Affiliations
BASF (Germany) (DE)
Corresponding author
Hofmann, Thomas
ORCID
Venue
Archives of Toxicology
Publisher
Springer Science and Business Media LLC
Published
2017-09-30
Volume
92
Issue
2
Pages
893-906
ISSN
  • 0340-5761 (ISSN-L)
  • 1432-0738
DOI
10.1007/s00204-017-2079-6
PMC
PMC5818600
OpenAlex
W2759884959
Cited by
164
References
39
Citations by year
2026: 14 · 2025: 13 · 2024: 14 · 2023: 26 · 2022: 25 · 2021: 25 · 2020: 23 · 2019: 13 · 2018: 10 · 2017: 1
FWCI
13.67
Type
journal article
Language
en
Open access
Licence
Locations
  • Archives of Toxicology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • KOPS (University of Konstanz) (submittedVersion, cc-by)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
Keywords
hepatotoxicity, metabolomics, HepG2 cells, in vitro toxicity models, mechanism of action, non-animal alternatives, liver enzyme induction, liver enzyme inhibition, peroxisome proliferation, concentration-dependent effects, intracellular metabolites, mass spectrometry metabolomics
MeSH terms
Animal Testing Alternatives, Enzyme Induction, Humans, Liver, Toxicity Tests, Metabolomics, Metabolome, Hep G2 Cells

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