The Adverse Outcome Pathway (AOP) framework is increasingly used to integrate data based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms. Herein, we present a comprehensive annotation of 172 AOPs housed in the AOP-Wiki as of December 4, 2016, using terms from existing biological ontologies. AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from 14 ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components. This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high-throughput genomics becomes a more important part of the high-throughput toxicity testing landscape, the new approaches described here for annotating KEs will also promote the visualization and analysis of genomics data in an AOP context.
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References
1.
KrewskiD, AcostaDJr., AndersenM, et al.Toxicity testing in the 21st century: A vision and a strategy. J Toxicol Environ Health B Crit Rev, 2010:13; 51–138.
2.
National Research Council.. Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: The National Academies Press; 2007, 216 p.
3.
AnkleyGT, BennettRS, EricksonRJ, et al.Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem, 2010:29; 730–741.
4.
GrohKJ, CarvalhoRN, ChipmanJK, et al.Development and application of the Adverse outcome pathway framework for understanding and predicting chronic toxicity: I. challenges and research needs in ecotoxicology. Chemosphere, 2015:120; 764–777.
5.
VilleneuveDL, CrumpD, Garcia-ReyeroN, et al.Adverse outcome pathway (AOP) development I: strategies and principles. Toxicol Sci, 2014a:142; 312–320.
6.
VilleneuveDL, CrumpD, Garcia-ReyeroN, et al.adverse outcome pathway development II: best practices. Toxicol Sci, 2014b:142; 321–330.
7.
TollefsenKE, ScholzS, CroninMT, et al.Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA). Regul Toxicol Pharmacol, 2014:70; 629–640.
8.
OECD.. Guidance Document on Developing and Assessing Adverse Outcome Pathways. Paris: OECD; 2013, ENV/JM/MONO(2013)6.
9.
OECD. The Guidance Document for Using the OECD (Q)SAR Application Toolbox to Develop Chemical Categories According to the OECD Guidance on Grouping Chemicals. Paris: OECD Publishing; 2014.
10.
BerggrenE, AmcoffP, BenigniR, et al.Chemical safety assessment using read-across: assessing the use of novel testing methods to strengthen the evidence base for decision making. Environ Health Perspect, 2015:123; 1232–1240.
11.
PatlewiczG, BallN, BoogaardPJ, et al.Building scientific confidence in the development and evaluation of read-across. Regul Toxicol Pharmacol, 2015:72; 117–133.
12.
VinkenM. Adverse outcome pathways as tools to assess drug-induced toxicity. Methods Mol Biol, 2016:1425; 325–337.
13.
PerkinsEJ, AntczakP, BurgoonL, et al.Adverse outcome pathways for regulatory applications: examination of four case studies with different degrees of completeness and scientific confidence. Toxicol Sci, 2015:148; 14–25.
14.
OkiNO, NelmsMD, BellSM, et al.Accelerating adverse outcome pathway development using publicly available data sources. Curr Environ Health Rep, 2016:3; 1–11.
15.
BardJB, RheeSY. Ontologies in biology: design, applications and future challenges. Nat Rev Genet, 2004:5; 213–222.
16.
McMurryJA, KohlerS, WashingtonNL, et al.Navigating the phenotype frontier: the Monarch Initiative. Genetics, 2016:203; 1491–1495.
17.
MungallCJ, McMurryJA, KohlerS, et al.The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic acids research, 2017:45; D712–D722.
18.
SmithB, AshburnerM, RosseC, et al.The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007:25; 1251–1255.
19.
ZhengJ, ManduchiE, StoeckertC. Development of an application ontology for beta cell genomics based on the ontology for biomedical investigations. International conference on biomedical ontologies (ICBO 2013), Montreal, Canada, July 8th – 9th, 2013, pp. 1–6.
20.
FuG, BatchelorC, DumontierM, et al.PubChemRDF: towards the semantic annotation of PubChem compound and substance databases. J Cheminform, 2015:7; 34.
21.
NelsonSJ, ed. Medical terminologies that work: the example of MeSH. Proceedings of the 10th international symposium on pervasive systems, algorithms, and networks (ISPAN 2009), held on 14–16 December 2009 in Kaohsiung, Taiwan.
BurgoonLD. The AOPOntology: a semantic artificial intelligence tool for predictive toxicology. Appl In Vitro Toxicol, 2017:3; 278–281.
24.
RobinsonPN, BauerS. Introduction to Bio-Ontologies. HallC, ed. Boca Raton, FL: Taylor & Francis Group, LLC; 2011.
25.
SmithB, ed. On classifying material entities in basic formal ontology. Proceedings of the third interdisciplinary ontology meeting held on February 27–28, 2010 in Tokyo, Keio University Press: 2012.
OngE, XiangZ, ZhaoB, et al.Ontobee: a linked ontology data server to support ontology term dereferencing, linkage, query and integration. Nucleic Acids Res, 2017:45; D347–D352.
28.
WhetzelPL, NoyNF, ShahNH, et al.BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011:39; W541–W545.
29.
JuppS, ed. A new ontology lookup service at EMBL-EBI. SWAT4LS International Conference, held on 7–10 December 2015 in Cambridge, England.
MungallCJ, TorniaiC, GkoutosGV, et al.Uberon, an integrative multi-species anatomy ontology. Genome Biol, 2012:13; R5.
38.
OECD. Users' Handbook Supplement to the Guidance Document for Developing and Assessing Adverse Outcome Pathways. Paris: OECD Publishing; 2016.
39.
Adverse outcome pathway wiki 2016 (updated Mar. 7, 2017). https://aopwiki.org (last accessed August15, 2017).
40.
VilleneuveD. Aromatase inhibition leading to reproductive dysfunction (updated Apr. 18, 2017). https://aopwiki.org/aops/25 (last accessed August15, 2017).
41.
VilleneuveD. Adverse Outcome Pathway on Aromatase Inhibition Leading to Reproductive Dysfunction (in Fish). Paris: OECD Publishing; 2016.
42.
HastingsJ, de MatosP, DekkerA, et al.The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res, 2013:41; D456–D463.
SachanaM, MunnS, Bal-PriceA. Chronic binding of antagonist to N-methyl-D-aspartate receptors (NMDARs) during brain development induces impairment of learning and memory abilities 2016 (updated May 23, 2017; May 5, 2017). https://aopwiki.org/wiki/index.php/Aop:13 (last accessed August15, 2017).
45.
SachanaM, MunnS, Bal-PriceA. Adverse Outcome Pathway on Chronic Binding of Antagonist to N-methyl-D-Aspartate Receptors (NMDARs) During Brain Development Induces Impairment of Learning and Memory Abilities. Paris: OECD Publishing; 2016.
46.
NepelskaM, GrignardE, MunnS. PPARalpha activation in utero leading to imparied fertility in males (updated May 20, 2017; May 5, 2017). https://aopwiki.org/wiki/index.php/Aop:18 (last accessed August15, 2017).
47.
NepelskaM, OdumJ, MunnS. Adverse Outcome Pathway (AOP): PPARα activation and reproductive toxicity. Development and application in assessment of endocrine disruptors/reproductive toxicants. Appl In Vitro Toxicol, 2017:3; 234–249.
48.
CroftonKM, GilbertMG, FriedmanKP, et al.Inhibition of thyroperoxidase and subsequent adverse neurodevelopmental outcomes in mammals (updated June 06, 2017; May 5, 2017). https://aopwiki.org/aops/42 (last accessed August15, 2017).
49.
LandesmannB. Adverse Outcome Pathway on Protein Alkylation Leading to Liver Fibrosis. Paris: OECD Publishing; 2016.
50.
LandesmannB. Protein alkylation leading to liver fibrosis (updated May 23, 2017; May 5, 2017). https://aopwiki.org/aops/38 (last accessed August15, 2017).
SlukaJP, ShirinifardA, SwatM, et al.The cell behavior ontology: describing the intrinsic biological behaviors of real and model cells seen as active agents. Bioinformatics, 2014:30; 2367–2374.
54.
HastingsJ, JeliazkovaN, OwenG, et al.eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment. J Biomed Semantics, 2015:6; 10.
55.
LandesmannB. Covalent protein binding leading to skin sensitisation 2016 (updated Dec 03, 2016; May 5, 2017). https://aopwiki.org/wiki/index.php/Aop:40 (last accessed August15, 2017).
56.
MungallC, GkoutosG, SmithC, et al.Integrating phenotype ontologies across multiple species. Genome Biol, 2010:11; R2.
57.
LoebeF, StumpfF, HoehndorfR, et al.Towards improving phenotype representation in OWL. J Biomed Semantics, 2012:3(Suppl 2);S5.
58.
ScheuermannRH, CeustersW, SmithB. Toward an ontological treatment of disease and diagnosis. Summit Transl Bioinform, 2009:2009; 116–120.
WittwehrC, AladjovH, AnkleyG, et al.How Adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology. Toxicol Sci, 2017:155; 326–336.
62.
PittmanM, EdwardsS, IvesC, et al.AOP-DB: a database resource for the exploration of Adverse Outcome Pathways through integrated association networks. Toxicol Appl Pharmacol, 2017 (in press).
63.
ConollyRB, AnkleyGT, ChengW, et al.Quantitative adverse outcome pathways and their application to predictive toxicology. Environ Sci Technol, 2017:51; 4661–4672.
64.
MattinglyCJ, BoylesR, LawlerCP, et al.Laying a community-based foundation for data-driven semantic standards in environmental health sciences. Environ Health Perspect, 2016:124; 1136–1140.
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