Literature DB >> 28903485

Editor's Highlight: Mechanistic Toxicity Tests Based on an Adverse Outcome Pathway Network for Hepatic Steatosis.

Michelle M Angrish1, Charlene A McQueen1, Elaine Cohen-Hubal1, Maribel Bruno1, Yue Ge1, Brian N Chorley1.   

Abstract

Risk assessors use liver endpoints in rodent toxicology studies to assess the safety of chemical exposures. Yet, rodent endpoints may not accurately reflect human responses. For this reason and others, human-based invitro models are being developed and anchored to adverse outcome pathways to better predict adverse human health outcomes. Here, a networked adverse outcome pathway-guided selection of biology-based assays for lipid uptake, lipid efflux, fatty acid oxidation, and lipid accumulation were developed. These assays were evaluated in a metabolically competent human hepatocyte cell model (HepaRG) exposed to compounds known to cause steatosis (amiodarone, cyclosporine A, and T0901317) or activate lipid metabolism pathways (troglitazone, Wyeth-14,643, and 22(R)-hydroxycholesterol). All of the chemicals activated at least one assay, however, only T0901317 and cyclosporin A dose-dependently increased lipid accumulation. T0901317 and cyclosporin A increased fatty acid uptake, decreased lipid efflux (inferred from apolipoprotein B100 levels), and increased fatty acid synthase protein levels. Using this biologically-based evaluation of key events regulating hepatic lipid levels, we demonstrated dysregulation of compensatory pathways that normally balance hepatic lipid levels. This approach may provide biological plausibility and data needed to increase confidence in linking invitro-based measurements to chemical effects on adverse human health outcomes. Published by Oxford University Press on behalf of the Society of Toxicology 2017. This work is written by US Government employees and is in the public domain in the US.

Entities:  

Keywords:  adverse outcome pathway; chemical risk assessment; hepatic steatosis; high-throughput toxicity testing; mechanistic toxicology; nonalcoholic fatty liver disease

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Year:  2017        PMID: 28903485      PMCID: PMC8130650          DOI: 10.1093/toxsci/kfx121

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  37 in total

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Authors:  K J Livak; T D Schmittgen
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Review 2.  Impaired mitochondrial function in microvesicular steatosis. Effects of drugs, ethanol, hormones and cytokines.

Authors:  B Fromenty; D Pessayre
Journal:  J Hepatol       Date:  1997       Impact factor: 25.083

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4.  Peroxisome proliferator-activated receptor-α agonist, Wy 14,643, improves metabolic indices, steatosis and ballooning in diabetic mice with non-alcoholic steatohepatitis.

Authors:  Claire Z Larter; Matthew M Yeh; Derrick M Van Rooyen; John Brooling; Kamaljit Ghatora; Geoffrey C Farrell
Journal:  J Gastroenterol Hepatol       Date:  2012-02       Impact factor: 4.029

5.  Advantageous use of HepaRG cells for the screening and mechanistic study of drug-induced steatosis.

Authors:  Laia Tolosa; M José Gómez-Lechón; Nuria Jiménez; David Hervás; Ramiro Jover; M Teresa Donato
Journal:  Toxicol Appl Pharmacol       Date:  2016-04-16       Impact factor: 4.219

Review 6.  Tipping the Balance: Hepatotoxicity and the 4 Apical Key Events of Hepatic Steatosis.

Authors:  Michelle M Angrish; Jonathan Phillip Kaiser; Charlene A McQueen; Brian N Chorley
Journal:  Toxicol Sci       Date:  2016-03-15       Impact factor: 4.849

7.  Induction of vesicular steatosis by amiodarone and tetracycline is associated with up-regulation of lipogenic genes in HepaRG cells.

Authors:  Sébastien Anthérieu; Alexandra Rogue; Bernard Fromenty; André Guillouzo; Marie-Anne Robin
Journal:  Hepatology       Date:  2011-05-02       Impact factor: 17.425

8.  Evaluation of HepaRG cells as an in vitro model for human drug metabolism studies.

Authors:  Kajsa P Kanebratt; Tommy B Andersson
Journal:  Drug Metab Dispos       Date:  2008-04-02       Impact factor: 3.922

9.  T0901317 is a dual LXR/FXR agonist.

Authors:  Keith A Houck; Kristen M Borchert; Christopher D Hepler; Jeffrey S Thomas; Kelli S Bramlett; Laura F Michael; Thomas P Burris
Journal:  Mol Genet Metab       Date:  2004 Sep-Oct       Impact factor: 4.797

10.  Stimulation of lipogenesis by pharmacological activation of the liver X receptor leads to production of large, triglyceride-rich very low density lipoprotein particles.

Authors:  Aldo Grefhorst; Baukje M Elzinga; Peter J Voshol; Torsten Plösch; Tineke Kok; Vincent W Bloks; Fjodor H van der Sluijs; Louis M Havekes; Johannes A Romijn; Henkjan J Verkade; Folkert Kuipers
Journal:  J Biol Chem       Date:  2002-07-03       Impact factor: 5.157

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2.  Adverse outcome pathway networks I: Development and applications.

Authors:  Dries Knapen; Michelle M Angrish; Marie C Fortin; Ioanna Katsiadaki; Marc Leonard; Luigi Margiotta-Casaluci; Sharon Munn; Jason M O'Brien; Nathan Pollesch; L Cody Smith; Xiaowei Zhang; Daniel L Villeneuve
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5.  Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

Authors:  Lyle D Burgoon; Michelle Angrish; Natalia Garcia-Reyero; Nathan Pollesch; Anze Zupanic; Edward Perkins
Journal:  Risk Anal       Date:  2019-11-13       Impact factor: 4.302

6.  Integrated transcriptomics and metabolomics reveal signatures of lipid metabolism dysregulation in HepaRG liver cells exposed to PCB 126.

Authors:  Robin Mesnage; Martina Biserni; Sucharitha Balu; Clément Frainay; Nathalie Poupin; Fabien Jourdan; Eva Wozniak; Theodoros Xenakis; Charles A Mein; Michael N Antoniou
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7.  Quizalofop-p-Ethyl Induces Adipogenesis in 3T3-L1 Adipocytes.

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Review 8.  Quantitative adverse outcome pathway (qAOP) models for toxicity prediction.

Authors:  Nicoleta Spinu; Mark T D Cronin; Steven J Enoch; Judith C Madden; Andrew P Worth
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9.  Combinations of LXR and RXR agonists induce triglyceride accumulation in human HepaRG cells in a synergistic manner.

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Review 10.  In Vitro Liver Toxicity Testing of Chemicals: A Pragmatic Approach.

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Journal:  Int J Mol Sci       Date:  2021-05-10       Impact factor: 5.923

  10 in total

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