Toxicogenomics: A 2020 Vision

Overview

Journal Trends Pharmacol Sci

Specialty Pharmacology

Date 2018 Dec 31

PMID 30594306

Citations 43

Authors

Zhichao Liu

Ruili Huang

Ruth Roberts

Weida Tong

Affiliations

Soon will be listed here.

Abstract

Toxicogenomics (TGx) has contributed significantly to toxicology and now has great potential to support moves towards animal-free approaches in regulatory decision making. Here, we discuss in vitro TGx systems and their potential impact on risk assessment. We raise awareness of the rapid advancement of genomics technologies, which generates novel genomics features essential for enhanced risk assessment. We specifically emphasize the importance of reproducibility in utilizing TGx in the regulatory setting. We also highlight the role of machine learning (particularly deep learning) in developing TGx-based predictive models. Lastly, we touch on the topics of how TGx approaches could facilitate adverse outcome pathways (AOP) development and enhance read-across strategies to further regulatory application. Finally, we summarize current efforts to develop TGx for risk assessment and set out remaining challenges.

Citing Articles

Four functional genotoxic marker genes (Bax, Btg2, Ccng1, and Cdkn1a) discriminate genotoxic hepatocarcinogens from non-genotoxic hepatocarcinogens and non-genotoxic non-hepatocarcinogens in rat public toxicogenomics data, Open TG-GATEs.

Furihata C, Suzuki T Genes Environ. 2024; 46(1):28.

PMID: 39702344 PMC: 11661030. DOI: 10.1186/s41021-024-00322-8.

Comprehensive insights into mechanism of nanotoxicity, assessment methods and regulatory challenges of nanomedicines.

Havelikar U, Ghorpade K, Kumar A, Patel A, Singh M, Banjare N Discov Nano. 2024; 19(1):165.

PMID: 39365367 PMC: 11452581. DOI: 10.1186/s11671-024-04118-1.

Progress in toxicogenomics to protect human health.

Meier M, Harrill J, Johnson K, Thomas R, Tong W, Rager J Nat Rev Genet. 2024; 26(2):105-122.

PMID: 39223311 DOI: 10.1038/s41576-024-00767-1.

Incorporating Tissue-Specific Gene Expression Data to Improve Chemical-Disease Inference of in Silico Toxicogenomics Methods.

Wang S, Wang C, Wang C, Lin Y, Tung C J Xenobiot. 2024; 14(3):1023-1035.

PMID: 39189172 PMC: 11348041. DOI: 10.3390/jox14030057.

Use of Transcriptomics to Reveal the Joint Immunotoxicity Mechanism Initiated by Difenoconazole and Chlorothalonil in the Human Jurkat T-Cell Line.

Li Y, Liu S, Li H, Meng F, Qiu J, Qian Y Foods. 2024; 13(1).

PMID: 38201063 PMC: 10778019. DOI: 10.3390/foods13010034.

References

Nan A, Chen L, Zhang N, Liu Z, Yang T, Wang Z . A novel regulatory network among LncRpa, CircRar1, MiR-671 and apoptotic genes promotes lead-induced neuronal cell apoptosis. Arch Toxicol. 2016; 91(4):1671-1684. PMC: 5364257. DOI: 10.1007/s00204-016-1837-1. View

Patlewicz G, Ball N, Booth E, Hulzebos E, Zvinavashe E, Hennes C . Use of category approaches, read-across and (Q)SAR: general considerations. Regul Toxicol Pharmacol. 2013; 67(1):1-12. DOI: 10.1016/j.yrtph.2013.06.002. View

Libbrecht M, Noble W . Machine learning applications in genetics and genomics. Nat Rev Genet. 2015; 16(6):321-32. PMC: 5204302. DOI: 10.1038/nrg3920. View

Attene-Ramos M, Miller N, Huang R, Michael S, Itkin M, Kavlock R . The Tox21 robotic platform for the assessment of environmental chemicals--from vision to reality. Drug Discov Today. 2013; 18(15-16):716-23. PMC: 3771082. DOI: 10.1016/j.drudis.2013.05.015. View

Guo L, Lobenhofer E, Wang C, Shippy R, Harris S, Zhang L . Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol. 2006; 24(9):1162-9. DOI: 10.1038/nbt1238. View

Boess F, Kamber M, Romer S, Gasser R, Muller D, Albertini S . Gene expression in two hepatic cell lines, cultured primary hepatocytes, and liver slices compared to the in vivo liver gene expression in rats: possible implications for toxicogenomics use of in vitro systems. Toxicol Sci. 2003; 73(2):386-402. DOI: 10.1093/toxsci/kfg064. View

Liu Z, Fang H, Borlak J, Roberts R, Tong W . In vitro to in vivo extrapolation for drug-induced liver injury using a pair ranking method. ALTEX. 2017; 34(3):399-407. DOI: 10.14573/altex.1610201. View

Huang J, Shi W, Zhang J, Chou J, Paules R, Gerrish K . Genomic indicators in the blood predict drug-induced liver injury. Pharmacogenomics J. 2010; 10(4):267-77. PMC: 3180890. DOI: 10.1038/tpj.2010.33. View

Dix D, Houck K, Martin M, Richard A, Setzer R, Kavlock R . The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicol Sci. 2006; 95(1):5-12. DOI: 10.1093/toxsci/kfl103. View

10.

Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T . Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 2014; 43(Database issue):D921-7. PMC: 4384023. DOI: 10.1093/nar/gku955. View

11.

Wilkes M, Repellin C, Sakamoto K . Beyond mRNA: The role of non-coding RNAs in normal and aberrant hematopoiesis. Mol Genet Metab. 2017; 122(3):28-38. PMC: 5722683. DOI: 10.1016/j.ymgme.2017.07.008. View

12.

Mav D, Shah R, Howard B, Auerbach S, Bushel P, Collins J . A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS One. 2018; 13(2):e0191105. PMC: 5819766. DOI: 10.1371/journal.pone.0191105. View

13.

Lauschke V, Hendriks D, Bell C, Andersson T, Ingelman-Sundberg M . Novel 3D Culture Systems for Studies of Human Liver Function and Assessments of the Hepatotoxicity of Drugs and Drug Candidates. Chem Res Toxicol. 2016; 29(12):1936-1955. DOI: 10.1021/acs.chemrestox.6b00150. View

14.

Chen Y, Li Y, Narayan R, Subramanian A, Xie X . Gene expression inference with deep learning. Bioinformatics. 2016; 32(12):1832-9. PMC: 4908320. DOI: 10.1093/bioinformatics/btw074. View

15.

Qin C, Tanis K, Podtelezhnikov A, Glaab W, Sistare F, DeGeorge J . Toxicogenomics in drug development: a match made in heaven?. Expert Opin Drug Metab Toxicol. 2016; 12(8):847-9. DOI: 10.1080/17425255.2016.1175437. View

16.

Harrill A, McCullough S, Wood C, Kahle J, Chorley B . MicroRNA Biomarkers of Toxicity in Biological Matrices. Toxicol Sci. 2016; 152(2):264-72. DOI: 10.1093/toxsci/kfw090. View

17.

Fatehullah A, Tan S, Barker N . Organoids as an in vitro model of human development and disease. Nat Cell Biol. 2016; 18(3):246-54. DOI: 10.1038/ncb3312. View

18.

Hamburg M . Advancing regulatory science. Science. 2011; 331(6020):987. DOI: 10.1126/science.1204432. View

19.

Shi L, Kusko R, Wolfinger R, Haibe-Kains B, Fischer M, Sansone S . The international MAQC Society launches to enhance reproducibility of high-throughput technologies. Nat Biotechnol. 2017; 35(12):1127-1128. DOI: 10.1038/nbt.4029. View

20.

Caiment F, Gaj S, Claessen S, Kleinjans J . High-throughput data integration of RNA-miRNA-circRNA reveals novel insights into mechanisms of benzo[a]pyrene-induced carcinogenicity. Nucleic Acids Res. 2015; 43(5):2525-34. PMC: 4357716. DOI: 10.1093/nar/gkv115. View