A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells

Genotoxicity testing is a critical component of chemical assessment. The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the DNA damage response pathways involved in response, is becoming more common. In companion papers previously...

Full description

Bibliographic Details
Main Authors: Andrew Williams, Julie K. Buick, Ivy Moffat, Carol D. Swartz, Leslie Recio, Daniel R. Hyduke, Heng-Hong Li, Albert J. Fornace Jr., Jiri Aubrecht, Carole L. Yauk
Format: Article
Language:English
Published: Elsevier 2015-12-01
Series:Data in Brief
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340915001699
id doaj-3ac0ae68f5b14cd4b1a579f3b97fd151
record_format Article
spelling doaj-3ac0ae68f5b14cd4b1a579f3b97fd1512020-11-24T20:42:54ZengElsevierData in Brief2352-34092015-12-015C778310.1016/j.dib.2015.08.013A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cellsAndrew Williams0Julie K. Buick1Ivy Moffat2Carol D. Swartz3Leslie Recio4Daniel R. Hyduke5Heng-Hong Li6Albert J. Fornace Jr.7Jiri Aubrecht8Carole L. Yauk9Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada K1A 0K9Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada K1A 0K9Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada K1A 0K9Integrated Laboratory Systems Inc., Research Triangle Park, NC 27709, USAIntegrated Laboratory Systems Inc., Research Triangle Park, NC 27709, USABiological Engineering Department, Utah State University, Logan, UT 84322, USADepartment of Biochemistry and Molecular and Cellular Biology, and Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia 20057, USADepartment of Biochemistry and Molecular and Cellular Biology, and Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia 20057, USADrug Safety Research and Development, Pfizer Inc., Groton, CT 06340, USAEnvironmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada K1A 0K9Genotoxicity testing is a critical component of chemical assessment. The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the DNA damage response pathways involved in response, is becoming more common. In companion papers previously published in Environmental and Molecular Mutagenesis, Li et al. (2015) [6] developed a dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in human lymphoblastoid TK6 cells in culture. This optimization approach was applied to the analysis of TK6 cells exposed to one of 14 genotoxic or 14 non-genotoxic agents, with sampling 4 h post-exposure. Microarray-based transcriptomic analyses were then used to develop a classifier for genotoxicity using the nearest shrunken centroids method. A panel of 65 genes was identified that could accurately classify toxicants as genotoxic or non-genotoxic. In Buick et al. (2015) [1], the utility of the biomarker for chemicals that require metabolic activation was evaluated. In this study, TK6 cells were exposed to increasing doses of four chemicals (two genotoxic that require metabolic activation and two non-genotoxic chemicals) in the presence of rat liver S9 to demonstrate that S9 does not impair the ability to classify genotoxicity using this genomic biomarker in TK6cells.http://www.sciencedirect.com/science/article/pii/S2352340915001699
collection DOAJ
language English
format Article
sources DOAJ
author Andrew Williams
Julie K. Buick
Ivy Moffat
Carol D. Swartz
Leslie Recio
Daniel R. Hyduke
Heng-Hong Li
Albert J. Fornace Jr.
Jiri Aubrecht
Carole L. Yauk
spellingShingle Andrew Williams
Julie K. Buick
Ivy Moffat
Carol D. Swartz
Leslie Recio
Daniel R. Hyduke
Heng-Hong Li
Albert J. Fornace Jr.
Jiri Aubrecht
Carole L. Yauk
A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells
Data in Brief
author_facet Andrew Williams
Julie K. Buick
Ivy Moffat
Carol D. Swartz
Leslie Recio
Daniel R. Hyduke
Heng-Hong Li
Albert J. Fornace Jr.
Jiri Aubrecht
Carole L. Yauk
author_sort Andrew Williams
title A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells
title_short A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells
title_full A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells
title_fullStr A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells
title_full_unstemmed A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells
title_sort predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human tk6 cells
publisher Elsevier
series Data in Brief
issn 2352-3409
publishDate 2015-12-01
description Genotoxicity testing is a critical component of chemical assessment. The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the DNA damage response pathways involved in response, is becoming more common. In companion papers previously published in Environmental and Molecular Mutagenesis, Li et al. (2015) [6] developed a dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in human lymphoblastoid TK6 cells in culture. This optimization approach was applied to the analysis of TK6 cells exposed to one of 14 genotoxic or 14 non-genotoxic agents, with sampling 4 h post-exposure. Microarray-based transcriptomic analyses were then used to develop a classifier for genotoxicity using the nearest shrunken centroids method. A panel of 65 genes was identified that could accurately classify toxicants as genotoxic or non-genotoxic. In Buick et al. (2015) [1], the utility of the biomarker for chemicals that require metabolic activation was evaluated. In this study, TK6 cells were exposed to increasing doses of four chemicals (two genotoxic that require metabolic activation and two non-genotoxic chemicals) in the presence of rat liver S9 to demonstrate that S9 does not impair the ability to classify genotoxicity using this genomic biomarker in TK6cells.
url http://www.sciencedirect.com/science/article/pii/S2352340915001699
work_keys_str_mv AT andrewwilliams apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT juliekbuick apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT ivymoffat apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT caroldswartz apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT leslierecio apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT danielrhyduke apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT henghongli apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT albertjfornacejr apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT jiriaubrecht apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT carolelyauk apredictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT andrewwilliams predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT juliekbuick predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT ivymoffat predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT caroldswartz predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT leslierecio predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT danielrhyduke predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT henghongli predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT albertjfornacejr predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT jiriaubrecht predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
AT carolelyauk predictivetoxicogenomicssignaturetoclassifygenotoxicversusnongenotoxicchemicalsinhumantk6cells
_version_ 1716821312246120448