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Archives of Toxicology

, Volume 92, Issue 10, pp 3007–3029 | Cite as

In vitro assessment of hepatotoxicity by metabolomics: a review

  • Matthias CuykxEmail author
  • Robim M. Rodrigues
  • Kris Laukens
  • Tamara Vanhaecke
  • Adrian CovaciEmail author
Review Article

Abstract

Omics technologies, and in particular metabolomics, have received an increasing attention during the assessment of hepatotoxicity in vitro. However, at present, a consensus on good metabolomics practices has yet to be reached. Therefore, in this review, a range of experimental approaches, applied methodologies, and data processing workflows are compared and critically evaluated. Experimental designs among the studies are similar, reporting the use of primary hepatocytes or hepatic cell lines as the most frequently used cell sources. Experiments are usually conducted in short time-frames (< 48 h) at sub-toxic dosages. Applied sample preparations are protein precipitation or Bligh-and-Dyer extraction. Most analytical platforms rely on chromatographic separations with mass spectrometric detection using high-resolution instruments. Untargeted metabolomics was typically used to allow the simultaneous detection of several classes of the metabolome, including endogenous metabolites that are not initially linked to toxicity. This non-biased detection platform is a valuable tool for generating hypothesis-based mechanistic research. The most frequently reported metabolites that are altered under toxicological impulses are alanine, lactate, and proline, which are often correlated. Other unspecific biomarkers of hepatotoxicity in vitro are the down-regulation of choline, glutathione, and 3-phospho-glycerate. Disruptions on the Krebs cycle are associated with increased glutamate, tryptophan, and valine. Phospholipid alterations are described in steatosis, lipo-apoptosis, and oxidative stress. Although there is a growing trend towards quality control, data analysis procedures do often not follow good contemporary metabolomics practices, which include feature filtering, false-discovery rate correction, and reporting the confidence of metabolite annotation. The currently annotated biomarkers can be used to identify hepatotoxicity in general and provide, to a certain extent, a tool for mechanistic distinction.

Keywords

Metabolomics In vitro Liver Hepatotoxicity Drug-induced liver injury (DILI) 

Abbreviations

2D

Two-dimensional

3D

Three-dimensional

3-PG

3-Phospho-glycerate

3R

Replace/reduce/refine-principle for animal testing

AA

Amino acids

AM

Accurate mass

AMP

Adenosine monophosphate

ANOVA

Analysis of variance

AOP

Adverse outcome pathway

ATP

Adenosine triphosphate

BPA

Bisphenol A

CE

Capillary electrophoresis

DG

Diacyl glycerol

DILI

Drug-induced liver injury

DMBA

Dimethylbenz(a)anthracene

DNA

Deoxyribosyl nucleic acids

EtOH

Ethanol

FDR

False-discovery rate

FT-ICR

Fourier-transform ion cyclotron resonance mass spectrometry

GC

Gas chromatography

GSH

Reduced glutathione

GSSG

Oxidised glutathione

HBCD

Hexabromocyclododecane

HCA

Hierarchical clustering analysis

HCV

Hepatitis C virus

HILIC

Hydrophilic liquid interaction chromatography

HR

High resolution

IC

Inhibitory concentration

IP

Ion pairing

LC

Liquid chromatography

LIT

Linear ion trap mass spectrometer

LPC

Lysophosphatidylcholine

MeOH

Methanol

MIE

Molecular initiating event

MOA

Mode of action

MS

Mass spectrometry

MSI

Metabolomics Standards Initiative

MTT

3-(4,5-Dimethylthiazol-2-yl)- 2,5-diphenyltetrazolium bromide

MV

Missing values

MWU

Mann–Whitney U

N/A

Not applicable

N/R

Not reported

NADPH

Nicotinamide adenine dinucleotide phosphate

NMR

Nucleic magnetic resonance

NOAEL

No observed adverse effect level

NRU

Neutral red uptake

PAH

Poly-aromatic hydrocarbons

PBS

Phosphate buffer saline

PC

Phosphatidyl choline

PCA

Principal component analysis

PE

Phosphatidyl ethanolamine

PEP

Phospho-enol pyruvate

PG

Prostaglandins

PLS

Partial least-squares analysis

PQN

Probabilistic quotient normalisation

Q

Quadrupole mass spectrometer

QC

Quality control

QMTLS

Quantum mechanical total line shape fitting

RNA

Ribosyl nucleic acids

ROS

Reactive oxygen species

RP

Reversed phase

SAM

s-Adenosyl methionine

TCA

Tricarboxylic acid cycle

TCDD

Tetrachlorodibenzo-p-dioxin

TG

Triacyl glycerol

TOF

Time-of-flight mass spectrometer

UPLC

Ultra-performance liquid chromatography

VIP

Variable importance of projection

Notes

Acknowledgements

Matthias Cuykx and Dr. Robim M. Rodrigues have been funded by individual fellowships of Research Foundation Flanders (FWO, fellowships 12H2216N and 11Z3318N). Further funding has been available from the Vrije Universiteit Brussel (VUB, Brussels, Belgium) and the University of Antwerp (UA, Antwerp, Belgium).

Compliance with ethical standards

Conflict of interest

The authors declare to have no conflicts of interests.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Toxicological CentreUniversity of AntwerpWilrijkBelgium
  2. 2.Department of In Vitro Toxicology and Dermato-CosmetologyVrije Universiteit BrusselJetteBelgium
  3. 3.Department of Mathematics and Computer ScienceUniversity of AntwerpAntwerpBelgium
  4. 4.Biomedical Informatics Network Antwerpen (Biomina)University of AntwerpAntwerpBelgium

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