Abstract
The tremendous progress in the development of new technologies in the areas of molecular biology and bioinformatics enables interrogation of cellular responses to toxicant treatment at a global molecular level, allowing evaluation of toxic effects in the context of molecular pathways.
The major techniques currently employed, especially transcriptomics, but also proteomics and metabolomics, are being used and further evaluated in investigational toxicology. Since they already have been shown to provide increased insight into molecular mechanisms of toxicological effects, such data have been submitted to regulatory authorities to support regulatory assessment of new compounds in few cases. Still, such data could be used more broadly for hazard identification and even risk assessment, which is now being supported by recent initiatives though precompetitive collaborations.
Dr. Hans-Juergen Ahr is retired
Similar content being viewed by others
References
Afshari CA, Hamadeh HK, Bushel PR (2011) The evolution of bioinformatics in toxicology: advancing toxicogenomics. Toxicol Sci 120(Suppl 1):S225–S237
Bourdon JA, Williams A, Kuo B, Moffat I, White PA, Halappanavar S, Vogel U, Wallin H, Yauk CL (2013) Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure. Toxicology 303:83–93
Bushel PR, Paules RS, Auerbach SS (2018) A comparison of the TempO-Seq S1500+ platform to RNA-Seq and microarray using rat liver mode of action samples. Front Genet 9:485
Chen M, Zhang M, Borlak J, Tong W (2012) A decade of toxicogenomic research and its contribution to toxicological science. Toxicol Sci 130:217–228
Ellinger-Ziegelbauer H, Aubrecht J, Kleinjans JC, Ahr HJ (2009) Application of toxicogenomics to study mechanisms of genotoxicity and carcinogenicity. Toxicol Lett 186:36–44
Farmahin R, Williams A, Kuo B, Chepelev NL, Thomas RS, Barton-Maclaren TS, Curran IH, Nong A, Wade MG, Yauk CL (2017) Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment. Arch Toxicol 91:2045–2065
Ganter B, Tugendreich S, Pearson CI, Ayanoglu E, Baumhueter S, Bostian KA, Brady L, Browne LJ, Calvin JT, Day GJ, Breckenridge N, Dunlea S, Eynon BP, Furness LM, Ferng J, Fielden MR, Fujimoto SY, Gong L, Hu C, Idury R, Judo MS, Kolaja KL, Lee MD, McSorley C, Minor JM, Nair RV, Natsoulis G, Nguyen P, Nicholson SM, Pham H, Roter AH, Sun D, Tan S, Thode S, Tolley AM, Vladimirova A, Yang J, Zhou Z, Jarnagin K (2005) Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol 119:219–244
Goodsaid FM, Amur S, Aubrecht J, Burczynski ME, Carl K, Catalano J, Charlab R, Close S, Cornu-Artis C, Essioux L, Fornace AJ Jr, Hinman L, Hong H, Hunt I, Jacobson-Kram D, Jawaid A, Laurie D, Lesko L, Li HH, Lindpaintner K, Mayne J, Morrow P, Papaluca-Amati M, Robison TW, Roth J, Schuppe-Koistinen I, Shi L, Spleiss O, Tong W, Truter SL, Vonderscher J, Westelinck A, Zhang L, Zineh I (2010) Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact. Nat Rev Drug Discov 9:435–445
Greenwood C, Ruff D, Kirvell S, Johnson G, Dhillon HS, Bustin SA (2015) Proximity assays for sensitive quantification of proteins. Biomol Detect Quantif 4:10–16
Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, Hurban P, Phillips KL, Xu J, Deng X, Sun YA, Tong W, Dragan YP, Shi L (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol 24:1162–1169
Gusenleitner D, Auerbach SS, Melia T, Gomez HF, Sherr DH, Monti S (2014) Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action. PLoS One 9:e102579
Ly L, Wasinger VC (2011) Protein and peptide fractionation, enrichment and depletion: tools for the complex proteome. Proteomics 11:513–534
Peden WM (2016) Regulatory forum. Toxicol Pathol 44:1069–1071
Phillips JR, Svoboda DL, Tandon A, Patel S, Sedykh A, Mav D, Kuo B, Yauk CL, Yang L, Thomas RS, Gift JS, Davis JA, Olszyk L, Merrick BA, Paules RS, Parham F, Saddler T, Shah RR, Auerbach SS (2019) BMDExpress 2: enhanced transcriptomic dose-response analysis workflow. Bioinformatics 35:1780–1782
Podtelezhnikov AA, Monroe JJ, Aslamkhan AG, Pearson K, Qin C, Tamburino AM, Loboda AP, Glaab WE, Sistare FD, Tanis KQ (2020) Quantitative transcriptional biomarkers of xenobiotic receptor activation in rat liver for the early assessment of drug safety liabilities. Toxicol Sci 175:98–112
Ramaiahgari SC, Auerbach SS, Saddler TO, Rice JR, Dunlap PE, Sipes NS, DeVito MJ, Shah RR, Bushel PR, Merrick BA, Paules RS, Ferguson SS (2019) The power of resolution: contextualized understanding of biological responses to liver injury chemicals using high-throughput transcriptomics and benchmark concentration modeling. Toxicol Sci 169:553–566
Rooney J, Hill T III, Qin C, Sistare FD, Corton JC (2018) Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. Toxicol Appl Pharmacol 356:99–113
Sistare FD, Morton D, Alden C, Christensen J, Keller D, Jonghe SD, Storer RD, Reddy MV, Kraynak A, Trela B, Bienvenu JG, Bjurstrom S, Bosmans V, Brewster D, Colman K, Dominick M, Evans J, Hailey JR, Kinter L, Liu M, Mahrt C, Marien D, Myer J, Perry R, Potenta D, Roth A, Sherratt P, Singer T, Slim R, Soper K, Fransson-Steen R, Stoltz J, Turner O, Turnquist S, van Heerden M, Woicke J, DeGeorge JJ (2011) An analysis of pharmaceutical experience with decades of rat carcinogenicity testing: support for a proposal to modify current regulatory guidelines. Toxicol Pathol 39:716–744
Suter L, Schroeder S, Meyer K, Gautier JC, Amberg A, Wendt M, Gmuender H, Mally A, Boitier E, Ellinger-Ziegelbauer H, Matheis K, Pfannkuch F (2011) EU framework 6 project: predictive toxicology (PredTox) – overview and outcome. Toxicol Appl Pharmacol 252:73–84
Sutherland JJ, Webster YW, Willy JA, Searfoss GH, Goldstein KM, Irizarry AR, Hall DG, Stevens JL (2018) Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity. Pharmacogenomics J 18:377–390
Thomas RS, Wesselkamper SC, Wang NC, Zhao QJ, Petersen DD, Lambert JC, Cote I, Yang L, Healy E, Black MB, Clewell HJ III, Allen BC, Andersen ME (2013) Temporal concordance between apical and transcriptional points of departure for chemical risk assessment. Toxicol Sci 134:180–194
Uehara T, Minowa Y, Morikawa Y, Kondo C, Maruyama T, Kato I, Nakatsu N, Igarashi Y, Ono A, Hayashi H, Mitsumori K, Yamada H, Ohno Y, Urushidani T (2011) Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database. Toxicol Appl Pharmacol 255:297–306
Vinken M, Knapen D, Vergauwen L, Hengstler JG, Angrish M, Whelan M (2017) Adverse outcome pathways: a concise introduction for toxicologists. Arch Toxicol 91:3697–3707
Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Labaj PP, Kreil DP, Megherbi D, Gaj S, Caiment F, van Delft J, Kleinjans J, Scherer A, Devanarayan V, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hsieh JH, Svoboda DL, Fuscoe JC, Deng Y, Shi L, Paules RS, Auerbach SS, Tong W (2014) The concordance between RNA-Seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol 32:926–932
Williams SA, Kivimaki M, Langenberg C, Hingorani AD, Casas JP, Bouchard C, Jonasson C, Sarzynski MA, Shipley MJ, Alexander L, Ash J, Bauer T, Chadwick J, Datta G, DeLisle RK, Hagar Y, Hinterberg M, Ostroff R, Weiss S, Ganz P, Wareham NJ (2019) Plasma protein patterns as comprehensive indicators of health. Nat Med 25:1851–1857
Wolf DC, Cohen SM, Boobis AR, Dellarco VL, Fenner-Crisp PA, Moretto A, Pastoor TP, Schoeny RS, Seed JG, Doe JE (2019) Chemical carcinogenicity revisited 1: a unified theory of carcinogenicity based on contemporary knowledge. Regul Toxicol Pharmacol 103:86–92
Woollard PM, Mehta NA, Vamathevan JJ, Van Horn S, Bonde BK, Dow DJ (2011) The application of next-generation sequencing technologies to drug discovery and development. Drug Discov Today 16:512–519
Yauk CL, Harrill AH, Ellinger-Ziegelbauer H, van der Laan JW, Moggs J, Froetschl R, Sistare F, Pettit S (2020) A cross-sector call to improve carcinogenicity risk assessment through use of genomic methodologies. Regul Toxicol Pharmacol 110:104526
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer-Verlag GmbH Germany, part of Springer Nature
About this entry
Cite this entry
Ellinger-Ziegelbauer, H., Ahr, HJ. (2020). Omics in Toxicology. In: Reichl, FX., Schwenk, M. (eds) Regulatory Toxicology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36206-4_40-2
Download citation
DOI: https://doi.org/10.1007/978-3-642-36206-4_40-2
Received:
Accepted:
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36206-4
Online ISBN: 978-3-642-36206-4
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences