Literature DB >> 18619722

A toxicogenomics approach for early assessment of potential non-genotoxic hepatocarcinogenicity of chemicals in rats.

Takeki Uehara1, Mitsuhiro Hirode, Atsushi Ono, Naoki Kiyosawa, Ko Omura, Toshinobu Shimizu, Yumiko Mizukawa, Toshikazu Miyagishima, Taku Nagao, Tetsuro Urushidani.   

Abstract

For assessing carcinogenicity in animals, it is difficult and costly, an alternative strategy has been desired. We explored the possibility of applying a toxicogenomics approach by using comprehensive gene expression data in rat liver treated with various compounds. As prototypic non-genotoxic hepatocarcinogens, thioacetamide (TAA) and methapyrilene (MP) were selected and 349 commonly changed genes were extracted by statistical analysis. Taking both compounds as positive with six compounds, acetaminophen, aspirin, phenylbutazone, rifampicin, alpha-naphthylisothiocyanate, and amiodarone as negative, prediction analysis of microarray (PAM) was performed. By training and 10-fold cross validation, a classifier containing 112 probe sets that gave an overall success rate of 95% was obtained. The validity of the present discriminator was checked for 30 chemicals. The PAM score showed characteristic time-dependent increases by treatment with several non-genotoxic hepatocarcinogens, including TAA, MP, coumarin, ethionine and WY-14643, while almost all of the non-carcinogenic samples were correctly predicted. Measurement of hepatic glutathione content suggested that MP and TAA cause glutathione depletion followed by a protective increase, but the protective response is exhausted during repeated administration. Therefore, the presently obtained PAM classifier could predict potential non-genotoxic hepatocarcinogenesis within 24 h after single dose and the inevitable pseudo-positives could be eliminated by checking data of repeated administrations up to 28 days. Tests for carcinogenicity using rats takes at least 2 years, while the present work suggests the possibility of lowering the time to 28 days with high precision, at least for a category of non-genotoxic hepatocarcinogens causing oxidative stress.

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Year:  2008        PMID: 18619722     DOI: 10.1016/j.tox.2008.05.013

Source DB:  PubMed          Journal:  Toxicology        ISSN: 0300-483X            Impact factor:   4.221


  22 in total

1.  Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Authors:  Ivan Rusyn; Alexander Sedykh; Yen Low; Kathryn Z Guyton; Alexander Tropsha
Journal:  Toxicol Sci       Date:  2012-03-02       Impact factor: 4.849

Review 2.  The evolution of bioinformatics in toxicology: advancing toxicogenomics.

Authors:  Cynthia A Afshari; Hisham K Hamadeh; Pierre R Bushel
Journal:  Toxicol Sci       Date:  2010-12-22       Impact factor: 4.849

3.  A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays.

Authors:  J Christopher Corton; Thomas Hill; Jeffrey J Sutherland; James L Stevens; John Rooney
Journal:  Toxicol Sci       Date:  2020-09-01       Impact factor: 4.849

Review 4.  Use of transcriptomics in understanding mechanisms of drug-induced toxicity.

Authors:  Yuxia Cui; Richard S Paules
Journal:  Pharmacogenomics       Date:  2010-04       Impact factor: 2.533

5.  Human skin-derived stem cells as a novel cell source for in vitro hepatotoxicity screening of pharmaceuticals.

Authors:  Robim M Rodrigues; Joery De Kock; Steven Branson; Mathieu Vinken; Kesavan Meganathan; Umesh Chaudhari; Agapios Sachinidis; Olivier Govaere; Tania Roskams; Veerle De Boe; Tamara Vanhaecke; Vera Rogiers
Journal:  Stem Cells Dev       Date:  2013-09-21       Impact factor: 3.272

6.  Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

Authors:  Yen Low; Takeki Uehara; Yohsuke Minowa; Hiroshi Yamada; Yasuo Ohno; Tetsuro Urushidani; Alexander Sedykh; Eugene Muratov; Viktor Kuz'min; Denis Fourches; Hao Zhu; Ivan Rusyn; Alexander Tropsha
Journal:  Chem Res Toxicol       Date:  2011-07-21       Impact factor: 3.739

7.  Predicting environmental chemical factors associated with disease-related gene expression data.

Authors:  Chirag J Patel; Atul J Butte
Journal:  BMC Med Genomics       Date:  2010-05-06       Impact factor: 3.063

Review 8.  Practical application of toxicogenomics for profiling toxicant-induced biological perturbations.

Authors:  Naoki Kiyosawa; Sunao Manabe; Takashi Yamoto; Atsushi Sanbuissho
Journal:  Int J Mol Sci       Date:  2010-09-20       Impact factor: 5.923

9.  Toxicogenomic biomarkers for liver toxicity.

Authors:  Naoki Kiyosawa; Yosuke Ando; Sunao Manabe; Takashi Yamoto
Journal:  J Toxicol Pathol       Date:  2009-04-06       Impact factor: 1.628

10.  Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

Authors:  T Y Doktorova; Reha Yildirimman; Liesbeth Ceelen; Mireia Vilardell; Tamara Vanhaecke; Mathieu Vinken; Gamze Ates; Anja Heymans; Hans Gmuender; Roque Bort; Raffaella Corvi; Pascal Phrakonkham; Ruoya Li; Nicolas Mouchet; Christophe Chesne; Joost van Delft; Jos Kleinjans; Jose Castell; Ralf Herwig; Vera Rogiers
Journal:  EXCLI J       Date:  2014-05-28       Impact factor: 4.068

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