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Progress in toxicogenomics to protect human health

Nature Reviews Genetics (2024)Cite this article

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Abstract

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose–response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

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Fig. 1: Protecting human health through toxicogenomics.
Fig. 2: Predicting toxicity by pattern matching, transcriptomic biomarkers and weighted gene co-expression analysis.
Fig. 3: Traditional and transcriptomic points of departure in risk assessment.
Fig. 4: Single-cell RNA sequencing in toxicology.
Fig. 5: The role of multi-omics in systems toxicology.
Fig. 6: Machine learning and generative artificial intelligence in toxicogenomics.

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References

  1. Nuwaysir, E. F., Bittner, M., Trent, J., Barrett, J. C. & Afshari, C. A. Microarrays and toxicology: the advent of toxicogenomics. Mol. Carcinog. 24, 153 (1999). This was the original paper that introduced the concept of toxicogenomics and coined the term.

    Article  CAS  PubMed  Google Scholar 

  2. Waters, M. D. & Fostel, J. M. Toxicogenomics and systems toxicology: aims and prospects. Nat. Rev. 5, 936–948 (2004).

    Article  CAS  Google Scholar 

  3. Krewski, D. et al. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch. Toxicol. 94, 1–58 (2020). This paper summarizes key progress in moving regulatory toxicological testing away from vertebrate animal testing toward the use of in vitro and in silico models and mechanistic information.

    Article  CAS  PubMed  Google Scholar 

  4. Krewski, D. et al. Toxicity testing in the 21st century: a vision and a strategy. J. Toxicol. Environ. Health B Crit. Rev. 13, 51–138 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Souza, T. M., Kleinjans, J. C. S. & Jennen, D. G. J. In Big Data in Predictive Toxicology (eds Neagu, D. & Richarz, A.-N.) Ch. 7 https://doi.org/10.1039/9781782623656-00214 (Royal Society of Chemistry, 2019).

  6. Afshari, C. A., Hamadeh, H. K. & Bushel, P. R. The evolution of bioinformatics in toxicology: advancing toxicogenomics. Toxicol. Sci. 120, S225–S237 (2011).

    Article  CAS  PubMed  Google Scholar 

  7. Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017). This highly expanded Connectivity Map is a powerful tool to discover toxicological mechanism of action and predict chemical hazards.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. D’Agostino, N., Li, W. & Wang, D. High-throughput transcriptomics. Sci. Rep. 12, 20313 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Deshpande, D. et al. RNA-seq data science: from raw data to effective interpretation. Front. Genet. 14, 997383 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mav, D. et al. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS One 13, e0191105 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Balik-Meisner, M. R. et al. Development of a zebrafish S1500+ sentinel gene set for high-throughput transcriptomics. Zebrafish 16, 331–347 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Li, H., Qiu, J. & Fu, X.-D. RASL-seq for massively parallel and quantitative analysis of gene expression. Curr. Protoc. Mol. Biol. 98, 4.13.1–4.13.9 (2012).

    Article  Google Scholar 

  13. Ye, C. et al. DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery. Nat. Commun. 9, 4307 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Simon, J. M., Paranjape, S. R., Wolter, J. M., Salazar, G. & Zylka, M. J. High-throughput screening and classification of chemicals and their effects on neuronal gene expression using RASL-seq. Sci. Rep. 9, 4529 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Rowan-Carroll, A. et al. High-throughput transcriptomic analysis of human primary hepatocyte spheroids exposed to per- and polyfluoroalkyl substances as a platform for relative potency characterization. Toxicol. Sci. 181, 199–214 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Ramaiahgari, S. C. et al. 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 (2019). The power of concentration-response modelling of high-throughput transcriptomic data in this paper demonstrates its potential to effectively predict human liver injury from chemical exposures, showcasing the predictive capacity of toxicogenomics.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chin, K.-V. & Kong, A.-N. T. Application of DNA microarrays in pharmacogenomics and toxicogenomics. Pharm. Res. 19, 1773–1778 (2002).

    Article  CAS  PubMed  Google Scholar 

  18. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yeakley, J. M. et al. A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling. PLoS One 12, e0178302 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Soufan, O. et al. T1000: a reduced gene set prioritized for toxicogenomic studies. PeerJ 7, e7975 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Haider, S. et al. A qualitative modeling approach for whole genome prediction using high-throughput toxicogenomics data and pathway-based validation. Front. Pharmacol. 9, 1072 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550–558 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  24. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Verheijen, M. C. et al. R-ODAF: omics data analysis framework for regulatory application. Regul. Toxicol. Pharmacol. 131, 105143 (2022). R-ODAF provides a robust method designed to enhance the reproducibility and credibility of transcriptomics data in regulatory toxicology, thereby improving its acceptance by regulatory agencies for hazard assessment.

    Article  CAS  PubMed  Google Scholar 

  26. Liu, P. et al. ExpressAnalyst: a unified platform for RNA-sequencing analysis in non-model species. Nat. Commun. 14, 2995 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Martens, M. et al. WikiPathways: connecting communities. Nucleic Acids Res. 49, D613–D621 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Milacic, M. et al. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 52, D672–D678 (2024).

    Article  PubMed  Google Scholar 

  32. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Krämer, A., Green, J., Pollard, J. Jr & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).

    Article  PubMed  Google Scholar 

  34. Kamburov, A. & Herwig, R. ConsensusPathDB 2022: molecular interactions update as a resource for network biology. Nucleic Acids Res. 50, D587–D595 (2021).

    Article  PubMed Central  Google Scholar 

  35. Harrill, J. A. et al. Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology. Regul. Toxicol. Pharmacol. 125, 105020 (2021). Initiated by the OECD, this paper describes the first formal reporting framework for regulatory submission of omics data, aimed at enhancing transparency, standardization and regulatory acceptance of omics methodologies in toxicological research.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Das, S., McClain, C. J. & Rai, S. N. Fifteen years of gene set analysis for high-throughput genomic data: a review of statistical approaches and future challenges. Entropy 22, 427 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sewer, A., Talikka, M., Martin, F., Hoeng, J. & Peitsch, M. C. In Bioinformatics in the Era of Post Genomics and Big Data (ed. Abdurakhmonov, I. Y.) (InTech, 2018).

  38. Alexander-Dann, B. et al. Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data. Mol. Omics 14, 218–236 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Shah, I. et al. Navigating transcriptomic connectivity mapping workflows to link chemicals with bioactivities. Chem. Res. Toxicol. 35, 1929–1949 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Harrill, J. A. et al. High-throughput transcriptomics platform for screening environmental chemicals. Toxicol. Sci. 181, 68–89 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Caiment, F., Tsamou, M., Jennen, D. & Kleinjans, J. Assessing compound carcinogenicity in vitro using connectivity mapping. Carcinogenesis 35, 201–207 (2014).

    Article  CAS  PubMed  Google Scholar 

  42. De Abrew, K. N. et al. Use of connectivity mapping to support read across: a deeper dive using data from 186 chemicals, 19 cell lines and 2 case studies. Toxicology 423, 84–94 (2019). Expanded application of the CMap concept for predictive toxicology is described, demonstrating its utility in read-across by identifying biologically and functionally relevant chemical analogues for target structures and emphasizing the critical influence of factors, such as chemical promiscuity, concentration, cell line and time point, on outcomes.

    Article  PubMed  Google Scholar 

  43. Su, Z. et al. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol. 15, 523 (2014).

    Article  PubMed  Google Scholar 

  44. Fan, X. et al. Consistency of predictive signature genes and classifiers generated using different microarray platforms. Pharmacogenomics J. 10, 247–257 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Thienpont, A. et al. Unlocking the power of transcriptomic biomarkers in qualitative and quantitative genotoxicity assessment of chemicals. Chem. Res. Toxicol. 37, 465–475 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, Z., Walker, G. W., Muir, D. C. G. & Nagatani-Yoshida, K. Toward a global understanding of chemical pollution: a first comprehensive analysis of national and regional chemical inventories. Environ. Sci. Technol. 54, 2575–2584 (2020).

    Article  CAS  PubMed  Google Scholar 

  47. Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  CAS  PubMed  Google Scholar 

  48. Sutherland, J. J. et al. Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity. Pharmacogenomics J. 18, 377–390 (2018). Co-expression network analysis of rat liver gene expression data was applied for safety assessment, illustrating how this approach can enhance the prediction of toxicity phenotypes, complement traditional assessments, identify novel mechanisms and biomarkers of hepatotoxicity, and be used to bridge findings from rat models to humans.

    Article  CAS  PubMed  Google Scholar 

  49. De Abrew, K. N. et al. Grouping 34 chemicals based on mode of action using connectivity mapping. Toxicol. Sci. 151, 447–461 (2016).

    Article  PubMed  Google Scholar 

  50. Igarashi, Y. et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 43, 921 (2015).

    Article  Google Scholar 

  51. House, J. S. et al. Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays. ALTEX 39, 388–404 (2022). A demonstration of how transcriptomics can be used for grouping unknown or variable composition substances, revealing distinct transcriptional responses by manufacturing class and demonstrating value in supporting the regulatory grouping and characterization of complex chemical substances.

    PubMed  PubMed Central  Google Scholar 

  52. Koval, L. E. et al. Wildfire variable toxicity: identifying biomass smoke exposure groupings through transcriptomic similarity scoring. Environ. Sci. Technol. 56, 17131–17142 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Liu, Z., Huang, R., Roberts, R. & Tong, W. Toxicogenomics: a 2020 vision. Trends Pharmacol. Sci. 40, 92–103 (2019).

    Article  PubMed  Google Scholar 

  54. Forreryd, A. et al. The GARDTMskin assay: investigation of the applicability domain for metals. ALTEX 40, 425–438 (2023).

    PubMed  Google Scholar 

  55. Johansson, H. et al. Validation of the GARDskin assay for assessment of chemical skin sensitizers: ring trial results of predictive performance and reproducibility. Toxicol. Sci. 170, 374–381 (2019). Analysis of the GARDskin assay shows its high predictive accuracy and reproducibility across different laboratories, which supports its potential for regulatory validation and broader adoption in chemical safety assessment.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Gradin, R., Forreryd, A., Mattson, U., Jerre, A. & Johansson, H. Quantitative assessment of sensitizing potency using a dose–response adaptation of GARDskin. Sci. Rep. 11, 18904 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Organisation for Economic Co-operation and Development. Test No. 442E: In Vitro Skin Sensitisation: In Vitro Skin Sensitisation Assays Addressing the Key Event on Activation of Dendritic Cells on the Adverse Outcome Pathway for Skin Sensitisation (OECD, 2023).

  58. Lejal, V., Cerisier, N., Rouquié, D. & Taboureau, O. Assessment of drug-induced liver injury through cell morphology and gene expression analysis. Chem. Res. Toxicol. 36, 1456–1470 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Govaere, O. et al. Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis. Sci. Transl. Med. 12, eaba4448 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Kohonen, P. et al. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury. Nat. Commun. 8, 15932 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Li, H.-H. et al. Development and validation of a high-throughput transcriptomic biomarker to address 21st century genetic toxicology needs. Proc. Natl Acad. Sci. USA 114, E10881–E10889 (2017). This paper describes the development and validation of the TGx-DDI transcriptomic biomarker that is used to enhance the interpretation of in vitro genotoxicity tests.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Li, H.-H. et al. TGx-DDI, a transcriptomic biomarker for genotoxicity hazard assessment of pharmaceuticals and environmental chemicals. Front. Big Data 2, 36 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Thienpont, A. et al. Novel prediction models for genotoxicity based on biomarker genes in human HepaRGTM cells. ALTEX 40, 271–286 (2023).

    PubMed  Google Scholar 

  64. Ates, G. et al. A novel genotoxin-specific qPCR array based on the metabolically competent human HepaRGTM cell line as a rapid and reliable tool for improved in vitro hazard assessment. Arch. Toxicol. 92, 1593–1608 (2018).

    Article  CAS  PubMed  Google Scholar 

  65. Rooney, J., Hill, T., Qin, C., Sistare, F. D. & Corton, J. C. Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. Toxicol. Appl. Pharmacol. 356, 99–113 (2018). This paper describes the identification of transcriptomic biomarkers from short-term rat tests that predict tumorigenic potential of chemicals in 2-year bioassays with high accuracy, thereby offering a promising method for early identification of liver carcinogens in rodents.

    Article  CAS  PubMed  Google Scholar 

  66. Lewis, R. W., Hill, T. & Corton, J. C. A set of six gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays. Toxicology 443, 152547 (2020).

    Article  CAS  PubMed  Google Scholar 

  67. Corton, J. C., Hill, T., Sutherland, J. J., Stevens, J. L. & Rooney, J. A set of six gene expression biomarkers identify rat liver tumorigens in short-term assays. Toxicol. Sci. 177, 11–26 (2020).

    Article  CAS  PubMed  Google Scholar 

  68. Li, A. et al. The carcinogenome project: in vitro gene expression profiling of chemical perturbations to predict long-term carcinogenicity. Environ. Health Perspect. 127, 047002 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Oku, Y. et al. Analyses of transcriptomics cell signalling for pre-screening applications in the integrated approach for testing and assessment of non-genotoxic carcinogens. Int. J. Mol. Sci. 23, 12718 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Schaap, M. M. et al. A novel toxicogenomics-based approach to categorize (non-)genotoxic carcinogens. Arch. Toxicol. 89, 2413–2427 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Ayobahan, S. U. et al. Comprehensive identification of gene expression fingerprints and biomarkers of sexual endocrine disruption in zebrafish embryo. Ecotoxicol. Environ. Saf. 250, 114514 (2023).

    Article  CAS  PubMed  Google Scholar 

  72. Corton, J. C., Kleinstreuer, N. C. & Judson, R. S. Identification of potential endocrine disrupting chemicals using gene expression biomarkers. Toxicol. Appl. Pharmacol. 380, 114683 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Fortin, A.-M. V. et al. Application of a new approach methodology (NAM)-based strategy for genotoxicity assessment of data-poor compounds. Front. Toxicol. 5, 1098432 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Wang, X. et al. Toxicological mechanisms and potencies of organophosphate esters in KGN human ovarian granulosa cells as revealed by high-throughput transcriptomics. Toxicol. Sci. 197, 170–185 (2023).

    Article  PubMed  Google Scholar 

  75. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Vrijenhoek, N. G. et al. Application of high-throughput transcriptomics for mechanism-based biological read-across of short-chain carboxylic acid analogues of valproic acid. ALTEX 39, 207–220 (2022).

    PubMed  Google Scholar 

  77. Callegaro, G. et al. The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment. Arch. Toxicol. 95, 3745–3775 (2021). The TXG-MAPr webtool uses WGCNA to enhance the interpretation of toxicogenomic data, providing a mechanism-based approach for assessing chemical toxicity across species and experimental platforms.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Callegaro, G. et al. Identifying multiscale translational safety biomarkers using a network-based systems approach. iScience 26, 106094 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Pognan, F. et al. The evolving role of investigative toxicology in the pharmaceutical industry. Nat. Rev. Drug Discov. 22, 317–335 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wijaya, L. S. et al. A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury. Toxicol. Sci. 198, 14–30 (2024). Using HepG2 cells, researchers applied WGCNA to identify gene networks linked to drug-induced liver injury, revealing key modulators that may improve our understanding of drug-induced liver injury mechanisms and potentially aid in drug development.

    Article  PubMed  Google Scholar 

  81. Borzelleca, J. F. Paracelsus: herald of modern toxicology. Toxicol. Sci. 53, 2–4 (2000).

    Article  CAS  PubMed  Google Scholar 

  82. Johnson, K. J. et al. A transformative vision for an omics-based regulatory chemical testing paradigm. Toxicol. Sci. 190, 127–132 (2022). This paper proposes a logic framework to integrate tPODs into regulatory toxicity testing, outlining four principles to transform risk assessment and enhance the protection of human and ecological health.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Reardon, A. J. F. et al. From vision toward best practices: evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Front. Toxicol. 5, 1194895 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Thomas, R. S. et al. A method to integrate benchmark dose estimates with genomic data to assess the functional effects of chemical exposure. Toxicol. Sci. 98, 240–248 (2007).

    Article  CAS  PubMed  Google Scholar 

  85. Yang, L., Allen, B. C. & Thomas, R. S. BMDExpress: a software tool for the benchmark dose analyses of genomic data. BMC Genomics 8, 387 (2007). This paper conceptualized and built the first software tool for BMD analysis of transcriptomic data, marking a turning point leading to quantitative analyses of toxicogenomic data.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Farmahin, R. et al. Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment. Arch. Toxicol. 91, 2045–2065 (2017).

    Article  CAS  PubMed  Google Scholar 

  87. Pagé-Larivière, F., Crump, D. & O’Brien, J. M. Transcriptomic points-of-departure from short-term exposure studies are protective of chronic effects for fish exposed to estrogenic chemicals. Toxicol. Appl. Pharmacol. 378, 114634 (2019).

    Article  PubMed  Google Scholar 

  88. US Environmental Protection Agency. Scientific Studies Supporting Development of Transcriptomic Points of Departure for EPA Transcriptomic Assessment Products (ETAPs) (2024). This report reviewed the literature and finds a high degree of concordance between tPODs from rodent studies and PODs derived from conventional toxicology, supporting the use of tPODs from 5-day, repeated dose in vivo rodent studies for use in quantitative human health assessment.

  89. Gwinn, W. M. et al. Evaluation of 5-day in vivo rat liver and kidney with high-throughput transcriptomics for estimating benchmark doses of apical outcomes. Toxicol. Sci. 176, 343–354 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Thomas, R. S. et al. Temporal concordance between apical and transcriptional points of departure for chemical risk assessment. Toxicol. Sci. 134, 180–194 (2013).

    Article  CAS  PubMed  Google Scholar 

  91. Ingber, D. E. Human organs-on-chips for disease modelling, drug development and personalized medicine. Nat. Rev. Genet. 23, 467–491 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Sakolish, C. et al. Analysis of reproducibility and robustness of a renal proximal tubule microphysiological system OrganoPlate 3-lane 40 for in vitro studies of drug transport and toxicity. Toxicol. Sci. 196, 52–70 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Indolfo, N. et al. Combining a microphysiological system of three organ equivalents and transcriptomics to assess toxicological endpoints for cosmetic ingredients. Lab Chip 23, 5092–5106 (2023).

    Article  CAS  PubMed  Google Scholar 

  94. Kostrzewski, T. et al. Modelling human liver fibrosis in the context of non-alcoholic steatohepatitis using a microphysiological system. Commun. Biol. 4, 1080 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Parafati, M. et al. Human skeletal muscle tissue chip autonomous payload reveals changes in fiber type and metabolic gene expression due to spaceflight. NPJ Microgravity 9, 77 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Wetmore, B. A. et al. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicol. Sci. 125, 157–174 (2012).

    Article  CAS  PubMed  Google Scholar 

  97. Health Canada. Use of New Approach Methods (NAMs) in Risk Assessment https://www.canada.ca/en/health-canada/services/chemical-substances/fact-sheets/use-new-approach-methods-risk-assessment.html (2023).

  98. Dent, M. P. et al. Paving the way for application of next generation risk assessment to safety decision-making for cosmetic ingredients. Regul. Toxicol. Pharmacol. 125, 105026 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Organisation for Economic Co-operation and Development. Report on Considerations from Case Studies on Integrated Approaches for Testing and Assessment (IATA). https://one.oecd.org/document/env/cbc/mono(2022)36/en/pdf (OECD, 2022).

  100. US Environmental Protection Agency. Halauxifen-methyl — New Active Ingredient Human Health Risk Assessment for Proposed Uses on Cereal Grains (Barley, Wheat, and Triticale) (EPA, 2016).

  101. Brennan, A. et al. EPA Transcriptomic Assessment Product (ETAP) for perfluoro-3-methoxypropanoic acid. https://doi.org/10.23645/epacomptox.25352962 (2024).

  102. Thomas, R. S. et al. Incorporating new technologies into toxicity testing and risk assessment: moving from 21st century vision to a data-driven framework. Toxicol. Sci. 136, 4–18 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Hagiwara, S. et al. A value of information framework for assessing the trade-offs associated with uncertainty, duration, and cost of chemical toxicity testing. Risk Anal. 43, 498–515 (2023).

    Article  PubMed  Google Scholar 

  104. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  106. Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Xie, X. et al. Single-cell transcriptome profiling reveals neutrophil heterogeneity in homeostasis and infection. Nat. Immunol. 21, 1119–1133 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Travaglini, K. J. et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature 587, 619–625 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Umbaugh, D. S., Ramachandran, A. & Jaeschke, H. Spatial reconstruction of the early hepatic transcriptomic landscape after an acetaminophen overdose using single-cell RNA-sequencing. Toxicol. Sci. 182, 327–345 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Hsu, K. S. et al. Single-cell RNA-seq analysis reveals that prenatal arsenic exposure results in long-term, adverse effects on immune gene expression in response to influenza A infection. Toxicol. Sci. 176, 312–328 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Nault, R. et al. Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice. Toxicol. Sci. 191, 135–148 (2023).

    Article  CAS  PubMed  Google Scholar 

  112. Han, L. et al. 89 lung single cell transcriptomics to guide the development of AOP anchored cell-based assays in response to nanoparticle exposure. Ann. Work Expo. Health 67, i59–i60 (2023).

    Article  Google Scholar 

  113. No authors listed]. A call for spatial omics submissions. Nat. Genet. 56, 1 (2024).

    Article  Google Scholar 

  114. Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2019).

    Article  CAS  PubMed  Google Scholar 

  115. Quan, F. et al. Annotation of cell types (ACT): a convenient web server for cell type annotation. Genome Med. 15, 91 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Franzén, O., Gan, L.-M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, baz046 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Kim, S. H. & Cho, S. Y. Single-cell transcriptomics to understand the cellular heterogeneity in toxicology. Mol. Cell. Toxicol. 19, 223–228 (2023).

    Article  CAS  Google Scholar 

  118. Sturla, S. J. et al. Systems toxicology: from basic research to risk assessment. Chem. Res. Toxicol. 27, 314–329 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Ma, Y., Klein, H. & De Jager, P. L. Considerations for integrative multi‐omic approaches to explore Alzheimer’s disease mechanisms. Brain Pathol. 30, 984–991 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Mehrmohamadi, M., Sepehri, M. H., Nazer, N. & Norouzi, M. R. A comparative overview of epigenomic profiling methods. Front. Cell Dev. Biol. 9, 714687 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Wetmore, B. A. & Merrick, B. A. Invited Review: Toxicoproteomics: proteomics applied to toxicology and pathology. Toxicol. Pathol. 32, 619–642 (2004).

    Article  CAS  PubMed  Google Scholar 

  122. Alseekh, S. et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat. Methods 18, 747–756 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Züllig, T., Trötzmüller, M. & Köfeler, H. C. Lipidomics from sample preparation to data analysis: a primer. Anal. Bioanal. Chem. 412, 2191–2209 (2020).

    Article  PubMed  Google Scholar 

  124. Cheng, K. C., Katz, S. R., Lin, A. Y., Xin, X. & Ding, Y. Whole-organism cellular pathology: a systems approach to phenomics. Adv. Genet. 95, 89–115 (2016).

    Article  CAS  PubMed  Google Scholar 

  125. Bray, M.-A. et al. Cell painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. O’Connor, L. M., O’Connor, B. A., Lim, S. B., Zeng, J. & Lo, C. H. Integrative multi-omics and systems bioinformatics in translational neuroscience: a data mining perspective. J. Pharm. Anal. 13, 836–850 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Pilarczyk, M. et al. Connecting omics signatures and revealing biological mechanisms with iLINCS. Nat. Commun. 13, 4678 (2022). iLINCS is a web platform that enables integrative analysis of extensive omics data and pre-computed signatures for applications such as analysis of perturbagen signatures, signature-driven drug repositioning and mode of action analysis using a low-code environment.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Wang, C., Lye, X., Kaalia, R., Kumar, P. & Rajapakse, J. C. Deep learning and multi-omics approach to predict drug responses in cancer. BMC Bioinformatics 22, 632 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Norris, J. L. et al. Integrated, high-throughput, multiomics platform enables data-driven construction of cellular responses and reveals global drug mechanisms of action. J. Proteome Res. 16, 1364–1375 (2017).

    Article  CAS  PubMed  Google Scholar 

  130. Way, G. P. et al. Morphology and gene expression profiling provide complementary information for mapping cell state. Cell Syst. 13, 911–923.e9 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Haghighi, M., Caicedo, J., Cimini, B. A. & Carpenter, A. E. High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations. Nat. Methods 19, 1550–1557 (2022). Used data fusion analysis techniques to combine structural information, gene expression profiles and imaging-based phenotypic profiles to predict mitochondrial toxicity; greater sensitivity and accuracy in predictions were achieved when combining disparate omics data types.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Seal, S. et al. Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection. Commun. Biol. 5, 858 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Alfano, R. et al. A multi-omic analysis of birthweight in newborn cord blood reveals new underlying mechanisms related to cholesterol metabolism. Metabolism 110, 154292 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Wolters, J. E. J. et al. Integrated ‘omics analysis reveals new drug-induced mitochondrial perturbations in human hepatocytes. Toxicol. Lett. 289, 1–13 (2018).

    Article  CAS  PubMed  Google Scholar 

  135. Housman, D. & Ledley, F. D. Why pharmacogenomics? Why now? Nat. Biotechnol. 16, 2–3 (1998).

    Article  Google Scholar 

  136. Pirmohamed, M. Pharmacogenomics: current status and future perspectives. Nat. Rev. Genet. 24, 350–362 (2023).

    Article  CAS  PubMed  Google Scholar 

  137. Singh, A. V. et al. Integrative toxicogenomics: advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed. Pharmacother. 163, 114784 (2023).

    Article  CAS  PubMed  Google Scholar 

  138. Chung, M. K. et al. Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs). Exposome 4, osae001 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Chappell, G. A. & Rager, J. E. Epigenetics in chemical-induced genotoxic carcinogenesis. Curr. Opin. Toxicol. 6, 10–17 (2017).

    Article  Google Scholar 

  140. Louro, H. & Silva, M. J. Nanotoxicology in Safety Assessment of Nanomaterials Vol. 1357 (Springer International Publishing, 2022).

  141. Saarimäki, L. A. et al. Toxicogenomics analysis of dynamic dose-response in macrophages highlights molecular alterations relevant for multi-walled carbon nanotube-induced lung fibrosis. NanoImpact 20, 100274 (2020).

    Article  Google Scholar 

  142. Bannuscher, A. et al. A multi-omics approach reveals mechanisms of nanomaterial toxicity and structure–activity relationships in alveolar macrophages. Nanotoxicology 14, 181–195 (2020).

    Article  CAS  PubMed  Google Scholar 

  143. Canzler, S. et al. Prospects and challenges of multi-omics data integration in toxicology. Arch. Toxicol. 94, 371–388 (2020).

    Article  CAS  PubMed  Google Scholar 

  144. Kim, S., Hollinger, H. & Radke, E. G. ‘Omics in environmental epidemiological studies of chemical exposures: a systematic evidence map. Environ. Int. 164, 107243 (2022).

    Article  CAS  PubMed  Google Scholar 

  145. Rager, J. E. et al. Benchmark dose modeling estimates of the concentrations of inorganic arsenic that induce changes to the neonatal transcriptome, proteome, and epigenome in a pregnancy cohort. Chem. Res. Toxicol. 30, 1911–1920 (2017).

    Article  CAS  PubMed  Google Scholar 

  146. Mohammadi-Shemirani, P., Sood, T. & Paré, G. From ‘omics to multi-omics technologies: the discovery of novel causal mediators. Curr. Atheroscler. Rep. 25, 55–65 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  147. Kayalar, O., Rajabi, H., Mortazavi, D. & Bayram, H. In Oncology: Genomics, Precision Medicine and Therapeutic Targets (eds Tuli, H. S. & Yerer Aycan, M. B.) 237–263 (Springer Nature, 2023).

  148. Verheijen, M. et al. Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds. Sci. Data 9, 699 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Nyffeler, J. et al. Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: retinoic acid as an example. Toxicol. Appl. Pharmacol. 444, 116032 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Payton, A. et al. Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data. Front. Toxicol. 5, 1171175 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Ogris, C., Hu, Y., Arloth, J. & Müller, N. S. Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data. Sci. Rep. 11, 6806 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Uppal, K., Ma, C., Go, Y.-M. & Jones, D. P. xMWAS: a data-driven integration and differential network analysis tool. Bioinformatics 34, 701–702 (2018).

    Article  CAS  PubMed  Google Scholar 

  153. Tripodi, I. J. et al. Applying knowledge-driven mechanistic inference to toxicogenomics. Toxicol. Vitr. 66, 104877 (2020).

    Article  CAS  Google Scholar 

  154. Davis, A. P. et al. Comparative toxicogenomics database: a knowledgebase and discovery tool for chemical–gene–disease networks. Nucleic Acids Res. 37, D786–D792 (2009).

    Article  CAS  PubMed  Google Scholar 

  155. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Melis, J. P. M. et al. In vivo murine hepatic microRNA and mRNA expression signatures predicting the (non-)genotoxic carcinogenic potential of chemicals. Arch. Toxicol. 88, 1023–1034 (2014).

    Article  CAS  PubMed  Google Scholar 

  157. Shi, L. et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol. 28, 827–838 (2010).

    Article  CAS  PubMed  Google Scholar 

  158. Ng, S., Masarone, S., Watson, D. & Barnes, M. R. The benefits and pitfalls of machine learning for biomarker discovery. Cell Tissue Res. 394, 17–31 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  159. Svoboda, D. L., Saddler, T. & Auerbach, S. S. In Advances in Computational Toxicology: Methodologies and Applications in Regulatory Science (ed. Hong, H.) 141–157 (Springer International Publishing, 2019).

  160. Smith, B. P. et al. Identification of early liver toxicity gene biomarkers using comparative supervised machine learning. Sci. Rep. 10, 19128 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Jiang, J. et al. Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets. Arch. Toxicol. 97, 2969–2981 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Moein, M. et al. Chemistry-based modeling on phenotype-based drug-induced liver injury annotation: from public to proprietary data. Chem. Res. Toxicol. 36, 1238–1247 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Chen, X., Roberts, R., Liu, Z. & Tong, W. A generative adversarial network model alternative to animal studies for clinical pathology assessment. Nat. Commun. 14, 7141 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Chen, X., Roberts, R., Tong, W. & Liu, Z. Tox-GAN: an artificial intelligence approach alternative to animal studies — a case study with toxicogenomics. Toxicol. Sci. 186, 242–259 (2022).

    Article  CAS  PubMed  Google Scholar 

  165. Li, T., Roberts, R., Liu, Z. & Tong, W. TransOrGAN: an artificial intelligence mapping of rat transcriptomic profiles between organs, ages, and sexes. Chem. Res. Toxicol. 36, 916–925 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Brazma, A. et al. ArrayExpress — a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Kolesnikov, N. et al. ArrayExpress update — simplifying data submissions. Nucleic Acids Res. 43, D1113–D1116 (2015).

    Article  CAS  PubMed  Google Scholar 

  169. Clough, E. & Barrett, T. In Statistical Genomics: Methods and Protocols (eds Mathé, E. & Davis, S.) 93–110 (Springer, 2016).

  170. Waters, M. et al. Systems toxicology and the Chemical Effects in Biological Systems (CEBS) knowledge base. EHP Toxicogenomics 111, 15–28 (2003).

    CAS  PubMed  Google Scholar 

  171. Fostel, J. et al. Chemical effects in biological systems–data dictionary (CEBS-DD): a compendium of terms for the capture and integration of biological study design description, conventional phenotypes, and ’omics data. Toxicol. Sci. 88, 585–601 (2005).

    Article  CAS  PubMed  Google Scholar 

  172. Lea, I. A., Gong, H., Paleja, A., Rashid, A. & Fostel, J. CEBS: a comprehensive annotated database of toxicological data. Nucleic Acids Res. 45, D964–D971 (2017).

    Article  CAS  PubMed  Google Scholar 

  173. Martini, C. et al. CEBS update: curated toxicology database with enhanced tools for data integration. Nucleic Acids Res. 50, D1156–D1163 (2022).

    Article  CAS  PubMed  Google Scholar 

  174. Mattingly, C. J., Colby, G. T., Forrest, J. N. & Boyer, J. L. The comparative toxicogenomics database (CTD). Environ. Health Perspect. 111, 793–795 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Davis, A. P. et al. Comparative toxicogenomics database (CTD): update 2023. Nucleic Acids Res. 51, D1257–D1262 (2023).

    Article  CAS  PubMed  Google Scholar 

  176. Keenan, A. B. et al. The library of integrated network-based cellular signatures NIH program: system-level cataloging of human cells response to perturbations. Cell Syst. 6, 13–24 (2018).

    Article  CAS  PubMed  Google Scholar 

  177. Stathias, V. et al. LINCS Data Portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res. 48, D431–D439 (2020).

    Article  CAS  PubMed  Google Scholar 

  178. Evangelista, J. E. et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Res. 50, W697–W709 (2022). LINCS is a powerful resource to enhance understanding of drivers of human diseases by providing data that systematically catalogues diverse cellular responses to perturbations, including drugs and mutations, using advanced methods such as transcript profiling and mass spectrometry.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Hendrickx, D. M. et al. diXa: a data infrastructure for chemical safety assessment. Bioinformatics 31, 1505–1507 (2015).

    Article  CAS  PubMed  Google Scholar 

  180. Nair, S. K. et al. ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets. Nucleic Acids Res. 48, W455–W462 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. van der Zalm, A. J. et al. A framework for establishing scientific confidence in new approach methodologies. Arch. Toxicol. 96, 2865–2879 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  182. Stucki, A. O. et al. Use of new approach methodologies (NAMs) to meet regulatory requirements for the assessment of industrial chemicals and pesticides for effects on human health. Front. Toxicol. 4, 964553 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  183. Vinken, M. et al. Adverse outcome pathways: a concise introduction for toxicologists. Arch. Toxicol. 91, 3697–3707 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Ankley, G. T. et al. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 29, 730–741 (2010).

    Article  CAS  PubMed  Google Scholar 

  185. Organisation for Economic Co-operation and Development. Users’ Handbook Supplement to the Guidance Document for Developing and Assessing Adverse Outcome Pathways https://www.oecd-ilibrary.org/environment/users-handbook-supplement-to-the-guidance-document-for-developing-and-assessing-adverse-outcome-pathways_5jlv1m9d1g32-en (OECD, 2018).

  186. Gou, X. et al. Prediction of zebrafish embryonic developmental toxicity by integrating omics with adverse outcome pathway. J. Hazard. Mater. 448, 130958 (2023).

    Article  CAS  PubMed  Google Scholar 

  187. Xia, P., Wang, P., Fang, W. & Zhang, X. In Advances in Toxicology and Risk Assessment of Nanomaterials and Emerging Contaminants (eds Guo, L.-H. & Mortimer, M.) 307–324 (Springer, 2022).

  188. Jagiello, K. et al. Transcriptomics-based and AOP-informed structure–activity relationships to predict pulmonary pathology induced by multiwalled carbon nanotubes. Small 17, 2003465 (2021).

    Article  CAS  Google Scholar 

  189. Jin, Y. et al. High throughput data-based, toxicity pathway-oriented development of a quantitative adverse outcome pathway network linking AHR activation to lung damages. J. Hazard. Mater. 425, 128041 (2022).

    Article  CAS  PubMed  Google Scholar 

  190. Bakker, R. et al. Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology. Sci. Total Environ. 869, 161740 (2023).

    Article  CAS  PubMed  Google Scholar 

  191. Vinken, M. Omics-based input and output in the development and use of adverse outcome pathways. Curr. Opin. Toxicol. 18, 8–12 (2019).

    Article  Google Scholar 

  192. Ziemann, M., Poulain, P. & Bora, A. The five pillars of computational reproducibility: bioinformatics and beyond. Brief. Bioinform. 24, bbad375 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  193. Harrill, J. et al. Considerations for strategic use of high-throughput transcriptomics chemical screening data in regulatory decisions. Curr. Opin. Toxicol. 15, 64–75 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  194. Harrill, J. A. et al. Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates. Toxicology 501, 153694 (2024). Computational methods for calculating tPODs produced from high-throughput transcriptomic data are explored, underscoring the sensitivity of tPOD calculations to both computational approaches and experimental conditions, and emphasizing the need for diverse cell types and methodologies in chemical hazard screening.

    Article  CAS  PubMed  Google Scholar 

  195. SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014).

    Article  Google Scholar 

  196. Paul Friedman, K. et al. Reproducibility of organ-level effects in repeat dose animal studies. Comput. Toxicol. 28, 1–17 (2023). This paper finds variable concordance rates for chemical effects across organs and methods, substantial unexplained variance in treatment effects, and discrepancies in organ-level effects between chronic and subchronic dosing regimens for animal studies used in conventional toxicology, providing a benchmark for the performance of new methodologies.

    Google Scholar 

  197. US Environmental Protection Agency. Value of Information Case Study on the Human Health and Economic Trade-offs Associated with the Timeliness, Uncertainty, and Costs of the Draft EPA Transcriptomic Assessment Product (ETAP) (EPA, 2024).

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Acknowledgements

The authors’ work was conducted, in part, thanks to funding provided to C.L.Y. from the Canada Research Chairs Program (CRC-2020-00060). M.J.M. acknowledges funding from Health Canada’s Genomics Research and Development Initiative. The authors would like to acknowledge C. Carberry and E. Hickman for contributing to graphics generation, and A. Long, F. Marchetti and J. Bundy for insightful comments during the review of this manuscript. This manuscript has been reviewed by the Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, and the FDA and approved for publication. The article reflects the views of the authors and does not necessarily reflect those of the US Environmental Protection Agency or the FDA. Any mention of trade names or commercial products does not constitute endorsement or recommendation for use.

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Authors and Affiliations

  1. Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada

    Matthew J. Meier

  2. Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA

    Joshua Harrill & Russell S. Thomas

  3. Predictive Safety Center, Corteva Agriscience, Indianapolis, IN, USA

    Kamin Johnson

  4. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR, USA

    Weida Tong

  5. Curriculum in Toxicology & Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA

    Weida Tong & Julia E. Rager

  6. The Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina, Chapel Hill, NC, USA

    Julia E. Rager

  7. Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Julia E. Rager

  8. The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Julia E. Rager

  9. Department of Biology, University of Ottawa, Ottawa, Ontario, Canada

    Carole L. Yauk

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  1. Matthew J. Meier
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  3. Kamin Johnson
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  4. Russell S. Thomas
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Related links

Adverse Outcome Pathway Wiki (AOP-Wiki): https://aopwiki.org/

ArrayExpress: https://www.ebi.ac.uk/biostudies/arrayexpress

Chemical Effects in Biological Systems: https://cebs.niehs.nih.gov/cebs/

CMap LINCS: https://clue.io/

Comparative Toxicogenomics Database: https://ctdbase.org/

ConsensusPathDB: http://cpdb.molgen.mpg.de/

diXa: https://www.ebi.ac.uk/biostudies/diXa/studies/

Gene Expression Omnibus (GEO): https://www.ncbi.nlm.nih.gov/geo/

Gene Ontology: https://www.geneontology.org/

GENOMARK: https://livr.shinyapps.io/Genomark_Prediction/

Kyoto Encyclopedia of Genes and Genomes: https://www.genome.jp/kegg/

LINCS Data Portal 2.0: https://lincsportal.ccs.miami.edu/signatures/home

MSigDB: https://www.gsea-msigdb.org/gsea/msigdb

Omics technologies in chemical testing (OECD): https://www.oecd.org/chemicalsafety/testing/omics.htm

Qiagen Digital Insights: https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/

Reactome Pathway Database: https://reactome.org/

TGx-DDI: https://cebs.niehs.nih.gov/tgxddi/

ToxicoDB: https://www.toxicodb.ca/

WikiPathways: https://www.wikipathways.org/

Glossary

Cluster analysis

The statistical grouping of features or samples based on an underlying computational algorithm (for example, hierarchical clustering, based on a distance function and linkage method; k-means clustering, iterative refinement of data points into a predefined number of clusters based on minimization of within-cluster variance).

Connectivity mapping

A computational method used to identify compounds that cause similar changes in transcriptional patterns, which suggests that they may have related toxicological effects or mechanisms of action.

Generative artificial intelligence

A subset of artificial intelligence technologies and models that can generate new content that resembles human-like creativity. These models can create new data instances that do not simply replicate the input data but maintain a level of originality and relevance.

Imaging phenomics

Phenomics is the systematic study of traits (that is, features) that make up a phenotype. Imaging phenomics uses microscopy and computer-assisted image analysis to systematically measure a large variety of morphological features in cells or tissues.

In vitro-to-in vivo extrapolation

An approach that uses in vitro experimental data to predict the behaviour of a chemical in vivo, including estimating exposure doses that achieve specific tissue concentrations (for example, toxicokinetics) and predicting biological potential effects.

Mode of action

The sequence of biochemical events that describes the interaction of a toxicant with cellular molecules that lead through levels of biological organization to an adverse health effect.

Predictive toxicology

The process of training, testing and validating a model that can predict the toxicological properties of a chemical, given new experimental data. In the context of toxicogenomics, this could be predicting the class of a novel chemical given the transcriptional response it induces within a set of biomarker genes.

Reference dose

Also known as acceptable daily intake or tolerable daily intake. The dose of a chemical administered to humans that is determined to be without appreciable risk of adverse health effects over a lifetime.

Skin sensitization

The ability of chemicals to elicit an allergic response.

Toxicokinetics

The study of how (and the rate at which) a toxicant is absorbed, distributed, metabolized and excreted based on physicochemical characteristics. This is usually done using a mathematical model that includes compartments representing different physical segments (for example, blood) within the organism, and achieves the end goal of predicting concentrations of the chemical of interest in a target tissue.

Uncertainty factors

One of several factors applied to a point of departure to account for a lack of chemical-specific and/or species-specific data (for example, variability in susceptibility in a population, extrapolation from animals to humans, a limited number of studies).

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Meier, M.J., Harrill, J., Johnson, K. et al. Progress in toxicogenomics to protect human health. Nat Rev Genet (2024). https://doi.org/10.1038/s41576-024-00767-1

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