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Archives of Toxicology

, Volume 92, Issue 4, pp 1593–1608 | Cite as

A novel genotoxin-specific qPCR array based on the metabolically competent human HepaRG cell line as a rapid and reliable tool for improved in vitro hazard assessment

  • Gamze Ates
  • Birgit Mertens
  • Anja Heymans
  • Luc Verschaeve
  • Dimiter Milushev
  • Philippe Vanparys
  • Nancy H. C. Roosens
  • Sigrid C. J. De Keersmaecker
  • Vera Rogiers
  • Tatyana Y. Doktorova
Genotoxicity and Carcinogenicity

Abstract

Although the value of the regulatory accepted batteries for in vitro genotoxicity testing is recognized, they result in a high number of false positives. This has a major impact on society and industries developing novel compounds for pharmaceutical, chemical, and consumer products, as afflicted compounds have to be (prematurely) abandoned or further tested on animals. Using the metabolically competent human HepaRG cell line and toxicogenomics approaches, we have developed an upgraded, innovative, and proprietary gene classifier. This gene classifier is based on transcriptomic changes induced by 12 genotoxic and 12 non-genotoxic reference compounds tested at sub-cytotoxic concentrations, i.e., IC10 concentrations as determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The resulting gene classifier was translated into an easy-to-handle qPCR array that, as shown by pathway analysis, covers several different cellular processes related to genotoxicity. To further assess the predictivity of the tool, a set of 5 known positive and 5 known negative test compounds for genotoxicity was evaluated. In addition, 2 compounds with debatable genotoxicity data were tested to explore how the qPCR array would classify these. With an accuracy of 100%, when equivocal results were considered positive, the results showed that combining HepaRG cells with a genotoxin-specific qPCR array can improve (geno)toxicological hazard assessment. In addition, the developed qPCR array was able to provide additional information on compounds for which so far debatable genotoxicity data are available. The results indicate that the new in vitro tool can improve human safety assessment of chemicals in general by basing predictions on mechanistic toxicogenomics information.

Keywords

Genotoxicity Mutagenicity qPCR array Toxicogenomics HepaRG In vitro screening 

Abbreviations

2NF

2-nitrofluorene

AFB

Aflatoxin B1

AMP

Ampicillin trihydrate

ANT

Anthranilic acid

B[α]P

Benzo[α]pyrene

BLE

Bleomycin sulfate

BOR

Basic orange 31

CAP

Caprolactam

CAvit

In vitro chromosome aberration test

CdCl2

Cadmium chloride

CHF

Chloramphenicol

CIS

Cisplatin

CLI

Climbazole

CLR

4chlororesorcinol

CND

Clonidine

CYC

Cyclophosphamide

DAT

2,4-diaminotoluene

DMN

Dimethylnitrosamine

EMS

Ethyl methanesulphonate

ENU

1-ethyl-1-nitrosourea

ETO

Etoposide

FAM

6-carboxyfluorescein

FDR

False discovery rate

HBM

Hydroxybenzomorpholine

HCA

Hierarchical cluster analysis

HKG

Housekeeping genes

MAN

d-mannitol

MAP

m-aminophenol

MELA

Melamine

MMS

Methyl methanesulphonate

MNvit

In vitro micronucleus test

MNviv

In vivo micronucleus test

MTT

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

NAC

No amplification control

NaCl

Sodium chloride

NAP

1-naphthol

NIF

Nifedipine

NNK

4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone

NTC

No template control

OECD

Organisation for economic co-operation and development

PAM

Prediction analysis of micorarrays

pCA

p-chloroaniline

PCA

Principal component analysis

RIN

RNA integrity number

SCCS

Scientific Committee on Consumer Safety

SDF

Sodium diclofenac

SOR

Sorbitol

SVM

Support vector machine

TAMRA

Tetramethylrhodamine

TOL

Tolbutamide

TRI

Triclosan

VIN

Vinblastine sulfate

WHO

World Health Organization

ZID

Zidovudine

Notes

Acknowledgements

This study was supported by the Vrije Universiteit Brussel (VUB) and the Scientific Institute of Public Health (WIV-ISP), Belgium.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Ates G, Doktorova TY, Pauwels M, Rogiers V (2014) Retrospective analysis of the mutagenicity/genotoxicity data of the cosmetic ingredients present on the Annexes of the Cosmetic EU legislation (2000-12). Mutagenesis 29:115–121.  https://doi.org/10.1093/mutage/get068 CrossRefPubMedGoogle Scholar
  2. Ates G, Raitano G, Heymans A et al (2016) In silico tools and transcriptomics analyses in the mutagenicity assessment of cosmetic ingredients: a proof-of-principle on how to add weight to the evidence. Mutagenesis 31:1–9.  https://doi.org/10.1093/mutage/gew008 CrossRefGoogle Scholar
  3. Aubrecht J, Caba E (2005) Gene expression profile analysis: an emerging approach to investigate mechanisms of genotoxicity. Pharmacogenomics 6:419–428.  https://doi.org/10.1517/14622416.6.4.419 CrossRefPubMedGoogle Scholar
  4. Bakhtyari NG, Raitano G, Benfenati E et al (2013) Comparison of in silico models for prediction of mutagenicity. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 31:45–66.  https://doi.org/10.1080/10590501.2013.763576 CrossRefPubMedGoogle Scholar
  5. Boehme K, Dietz Y, Hewitt P, Mueller SO (2011) Genomic profiling uncovers a molecular pattern for toxicological characterization of mutagens and promutagens in vitro. Toxicol Sci 122:185–197.  https://doi.org/10.1093/toxsci/kfr090 CrossRefPubMedGoogle Scholar
  6. Boehncke DA, Kielhorn J, Könnecker G et al (2003) Concise international chemical assessment document 48: 4-Chloroaniline, WHO, ISSN 1020–6167Google Scholar
  7. Buick JK, Moffat I, Williams A et al (2015) Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells. Environ Mol Mutagen 56:520–534. https://doi.org/10.1002/emCrossRefPubMedPubMedCentralGoogle Scholar
  8. Cahill PA, Knight AW, Billinton N et al (2004) The GreenScreen(R) genotoxicity assay: a screening validation programme. Mutagenesis 19:105–119.  https://doi.org/10.1093/mutage/geh015 CrossRefPubMedGoogle Scholar
  9. Ceelen L, De Spiegelaere W, David M et al (2011) Critical selection of reliable reference genes for gene expression study in the HepaRG cell line. Biochem Pharmacol 81:1255–1261.  https://doi.org/10.1016/j.bcp.2011.03.004 CrossRefPubMedGoogle Scholar
  10. COM (2011) Guidance on a strategy for genotoxicity testing of chemical substances guidance on a strategy for genotoxicity testing of chemical substances. pp 1–84Google Scholar
  11. Corvi R, Madia F, Worth A, Whelan M (2013) EURL ECVAM strategy to avoid and reduce animal use in genotoxicity testing. JRC, LuxemburgGoogle Scholar
  12. Doktorova TY, Yildirimman R, Vinken M et al (2013) Transcriptomic responses generated by hepatocarcinogens in a battery of liver-based in vitro models. Carcinogenesis 34:1393–1402.  https://doi.org/10.1093/carcin/bgt054 CrossRefPubMedGoogle Scholar
  13. Doktorova TY, Ates G, Vinken M et al (2014a) Way forward in case of a false positive in vitro genotoxicity result for a cosmetic substance? Toxicol Vitr 28:54–59.  https://doi.org/10.1016/j.tiv.2013.09.022 CrossRefGoogle Scholar
  14. Doktorova TY, Yildirimman R, Ceelen L et al (2014b) Testing chemical carcinogenicity by using a transcriptomics heparg-based model? EXCLI J 13:623–637PubMedPubMedCentralGoogle Scholar
  15. EC (2009) Regulation (EC) No 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products. Off J Eur Communion L342:59–209Google Scholar
  16. Garcia-Canton C, Anadon A, Meredith C (2013) Assessment of the in vitro yH2AX assay by High Content Screening as a novel genotoxicity test. Mutat Res Genet Toxicol Environ Mutagen 757:158–166.  https://doi.org/10.1016/j.mrgentox.2013.08.002 CrossRefGoogle Scholar
  17. Greene EJ, Friedman MA, Sherrod JA (1979) In vitro mutagenicity and cell transformation screening of caprolactam. Environ Mutagen 407:399–407CrossRefGoogle Scholar
  18. Hastwell PW, Chai LL, Roberts KJ et al (2006) High-specificity and high-sensitivity genotoxicity assessment in a human cell line: validation of the GreenScreen HC GADD45a-GFP genotoxicity assay. Mutat Res - Genet Toxicol Environ Mutagen 607:160–175.  https://doi.org/10.1016/j.mrgentox.2006.04.011 CrossRefGoogle Scholar
  19. Hendriks G, Derr RS, Misovic B et al (2016) The extended ToxTracker assay discriminates between induction of DNA damage, oxidative stress and protein misfolding. Toxicol Sci 150:190–203.  https://doi.org/10.1093/toxsci/kfv323 CrossRefPubMedGoogle Scholar
  20. Hughes C, Rabinowitz A, Tate M et al (2012) Development of a high-throughput Gaussia luciferase reporter assay for the activation of the GADD45a gene by mutagens, promutagens, clastogens, and aneugens. J Biomol Screen 17:1302–1315.  https://doi.org/10.1177/1087057112453312 CrossRefPubMedGoogle Scholar
  21. Kamber M, Fluckiger-Isler S, Engelhardt G et al (2009) Comparison of the Ames II and traditional Ames test responses with respect to mutagenicity, strain specificities, need for metabolism and correlation with rodent carcinogenicity. Mutagenesis 24:359–366.  https://doi.org/10.1093/mutage/gep017 CrossRefPubMedGoogle Scholar
  22. Khoury L, Zalko D, Audebert M (2013) Validation of high-throughput genotoxicity assay screening using yH2AX in-cell western assay on HepG2 cells. Environ Mol Mutagen 54:737–746.  https://doi.org/10.1002/em.21817 CrossRefPubMedGoogle Scholar
  23. Kirkland D (2011) Improvements in the reliability of in vitro genotoxicity testing. Toxicol Lett 205:S7–S7.  https://doi.org/10.1016/j.toxlet.2011.05.029 CrossRefGoogle Scholar
  24. Kirkland D, Speit G (2008) Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens. III. Appropriate follow-up testing in vivo. Mutat Res - Genet Toxicol Environ Mutagen 654:114–132.  https://doi.org/10.1016/j.mrgentox.2008.05.002 CrossRefGoogle Scholar
  25. Kirkland D, Aardema M, Henderson L, Müller L (2005) Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens. I. Sensitivity, specificity and relative predicticity. Mutat Res - Genet Toxicol Environ Mutagen 584:1–256.  https://doi.org/10.1016/j.mrgentox.2008.05.002 CrossRefGoogle Scholar
  26. Kirkland D, Aardema M, Banduhn N, Carmichael P, Fautz R, Meunier JR, Pfuhler S (2007) In vitro approaches to develop weight of evidence (WoE) and mode of action (MoA) discussions with positive in vitro genotoxicity results. Mutagenesis 22:161–175.  https://doi.org/10.1093/mutage/gem006 CrossRefPubMedGoogle Scholar
  27. Kirkland D, Kasper P, Müller L et al (2008) Recommended lists of genotoxic and non-genotoxic chemicals for assessment of the performance of new or improved genotoxicity tests: A follow-up to an ECVAM workshop. Mutat Res - Genet Toxicol Environ Mutagen 653:99–108.  https://doi.org/10.1016/j.mrgentox.2008.03.008 CrossRefGoogle Scholar
  28. Kirkland D, Reeve L, Gatehouse D, Vanparys P (2011) A core in vitro genotoxicity battery comprising the Ames test plus the in vitro micronucleus test is sufficient to detect rodent carcinogens and in vivo genotoxins. Mutat Res - Genet Toxicol Environ Mutagen 721:27–73.  https://doi.org/10.1016/j.mrgentox.2010.12.015 CrossRefGoogle Scholar
  29. Kirkland D, Kasper P, Martus HJ et al (2016) Updated recommended lists of genotoxic and non-genotoxic chemicals for assessment of the performance of new or improved genotoxicity tests. Mutat Res - Genet Toxicol Environ Mutagen 795:7–30.  https://doi.org/10.1016/j.mrgentox.2015.10.006 CrossRefPubMedGoogle Scholar
  30. Lee SJ, Yum YN, Kim SC et al (2013) Distinguishing between genotoxic and non-genotoxic hepatocarcinogens by gene expression profiling and bioinformatic pathway analysis. Sci Rep 3:34–41.  https://doi.org/10.1038/srep02783 Google Scholar
  31. Li H-H, Hyduke DR, Chen R et al (2015) Development of a toxicogenomics signature for genotoxicity using a dose-optimization and informatics strategy in human cells. Environ Mol Mutagen 56:229–262.  https://doi.org/10.1007/978-1-4614-5915-6 Google Scholar
  32. Mathijs K, Brauers KJJ, Jennen DGJ et al (2010) Gene expression profiling in primary mouse hepatocytes discriminates true from false-positive genotoxic compounds. Mutagenesis 25:561–568.  https://doi.org/10.1093/mutage/geq040 CrossRefPubMedGoogle Scholar
  33. Mi H, Huang X, Muruganujan A et al (2017) PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res 45:D183–D189.  https://doi.org/10.1093/nar/gkw1138 CrossRefPubMedGoogle Scholar
  34. Miller K (1991) Clastogenic effects of bleomycin, cyclophosphamide, and ethyl methanesulfonate on resting and proliferating human B- and T-lymphocytes. Mutat Res Mol Mech Mutagen 251:241–251.  https://doi.org/10.1016/0027-5107(91)90079-4 CrossRefGoogle Scholar
  35. Mizota T, Ohno K, Yamada T (2011) Validation of a genotoxicity test based on p53R2 gene expression in human lymphoblastoid cells. Mutat Res 724:76–85.  https://doi.org/10.1016/j.mrgentox.2011.06.003 CrossRefPubMedGoogle Scholar
  36. Mozdarani H, Saberi AH (1994) Induction of cytogenetic adaptive response of mouse bone marrow cells to radiation by therapeutic doses of bleomycin sulfate and actinomycin D as assayed by the micronucleus test. Cancer Lett 78:141–150.  https://doi.org/10.1016/0304-3835(94)90043-4 CrossRefPubMedGoogle Scholar
  37. Phipson B, Lee S, Majewski IJ et al (2016) Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Ann Appl Stat 10:946–963.  https://doi.org/10.1214/16-AOAS920 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Pottenger LH, Bus JS, Gollapudi BB (2007) Genetic Toxicity Assessment: Employing the Best Science for Human Safety Evaluation Part VI: When Salt and Sugar and Vegetables Are Positive, How Can Genotoxicity Data Serve to Inform Risk Assessment? Toxicol Sci 98:327–331.  https://doi.org/10.1093/toxsci/kfm068 CrossRefPubMedGoogle Scholar
  39. Provenzano M, Mocellin S (2007) Complementary techniques: Validation of gene expression data by quantitative real time PCR. Adv Exp Med Biol 593:66–73.  https://doi.org/10.1007/978-0-387-39978-2_7 CrossRefPubMedGoogle Scholar
  40. Rieswijk L, Brauers KJJ, Coonen MLJ et al (2016) Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity. Mutagenesis 31:603–615.  https://doi.org/10.1093/mutage/gew027 CrossRefPubMedGoogle Scholar
  41. SCCP (2005) Opinion on hydroxybenzomorpholine. 0965/05:pp 1–39Google Scholar
  42. SCCP (2008a) Opinion on triclosan. 1192/08:p 136Google Scholar
  43. SCCP (2008b) Opinion on 1-Naphthol. 1123/07:pp 1–26Google Scholar
  44. SCCP (2008c) Opinion on Climbazole. 1204/08:pp 1–33Google Scholar
  45. SCCS (2006) Opinion on m-aminophenol. 0978/06:p 26Google Scholar
  46. SCCS (2010a) Opinion on Basic Orange 31. 1334/10:31Google Scholar
  47. SCCS (2010b) Opinion on 4-chlororesorcinol. 1224/09:p 26Google Scholar
  48. SCCS (2016) The SCCS Notes of Guidance for the testing of cosmetic ingredients and their safety evaluation 9th revision. 1564/15:pp 1–151Google Scholar
  49. Suenaga K, Takasawa H, Watanabe T et al (2013) Differential gene expression profiling between genotoxic and non-genotoxic hepatocarcinogens in young rat liver determined by quantitative real-time PCR and principal component analysis. Mutat Res Toxicol Environ Mutagen 751:73–83.  https://doi.org/10.1016/j.mrgentox.2012.11.003 CrossRefGoogle Scholar
  50. Teixeira do Amaral R, Ansell J, Aptula N et al (2014) In silico approaches for safety assessment of cosmetic ingredients. In: Rep. Int. Coop. Cosmet. Regul. http://www.iccrnet.org/topics/. Accessed 9 Sep 2016
  51. Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 99:6567–6572.  https://doi.org/10.1073/pnas.082099299 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Tinker A, Boussioutas A, Bowtell D (2006) The challenges of gene expression microarrays for the study of human cancer. Cancer Cell 9(5):333–339.  https://doi.org/10.1016/j.ccr.2006.05.001 CrossRefPubMedGoogle Scholar
  53. Vandesompele J, De Preter K, Pattyn I et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:34–31.  https://doi.org/10.1186/gb-2002-3-7-research0034 CrossRefGoogle Scholar
  54. Vinken M, Doktorova T, Ellinger-Ziegelbauer H et al (2008) The carcinoGENOMICS project: critical selection of model compounds for the development of omics-based in vitro carcinogenicity screening assays. Mutat Res - Rev Mutat Res 659:202–210.  https://doi.org/10.1016/j.mrrev.2008.04.006 CrossRefGoogle Scholar
  55. Watanabe T, Tanaka G, Hamada S et al (2009) Dose-dependent alterations in gene expression in mouse liver induced by diethylnitrosamine and ethylnitrosourea and determined by quantitative real-time PCR. Mutat Res Toxicol Environ Mutagen 673:9–20.  https://doi.org/10.1016/j.mrgentox.2008.11.004 CrossRefGoogle Scholar
  56. Westerink WM, Schirris TJJ, Horbach GJ, Schoonen WGEJ. (2011) Development and validation of a high-content screening in vitro micronucleus assay in CHO-k1 and HepG2 cells. Mutat Res Toxicol Environ Mutagen 724:7–21.  https://doi.org/10.1016/j.mrgentox.2011.05.007 CrossRefGoogle Scholar
  57. Williams A, Buick JK, Moffat I et al (2015) A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells. Data Br 5:77–83.  https://doi.org/10.1016/j.dib.2015.08.013 CrossRefGoogle Scholar
  58. Zeiger E, Gollapudi B, Aardema MJ et al (2015) Opportunities to integrate new approaches in genetic toxicology: An ILSI-HESI workshop report. Environ Mol Mutagen 56:277–285.  https://doi.org/10.1002/em.21923 CrossRefPubMedGoogle Scholar
  59. Zhou X, Zhang T, Song D et al (2017) Comparison and evaluation of conventional RT-PCR, SYBR green I and TaqMan real-time RT-PCR assays for the detection of porcine epidemic diarrhea virus. Mol Cell Probes 33:36–41.  https://doi.org/10.1016/j.mcp.2017.02.002 CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Gamze Ates
    • 1
  • Birgit Mertens
    • 2
  • Anja Heymans
    • 1
  • Luc Verschaeve
    • 2
  • Dimiter Milushev
    • 3
  • Philippe Vanparys
    • 4
  • Nancy H. C. Roosens
    • 5
  • Sigrid C. J. De Keersmaecker
    • 5
  • Vera Rogiers
    • 1
  • Tatyana Y. Doktorova
    • 2
  1. 1.Department of In Vitro Toxicology and Dermato-CosmetologyVrije Universiteit Brussel (VUB)BrusselsBelgium
  2. 2.Department of Food, Medicines and Consumer Safety, Unit of ToxicologyScientific Institute of Public Health (WIV-ISP)BrusselsBelgium
  3. 3.Super Stellar SolutionsBrusselsBelgium
  4. 4.Gentoxicon BVBAVosselaarBelgium
  5. 5.Platform Biotechnology and Molecular BiologyScientific Institute of Public Health (WIV-ISP)BrusselsBelgium

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