AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks

Abstract

The Adverse Outcome Pathway (AOP) framework describes the progression of a toxicity pathway from molecular perturbation to population-level outcome in a series of measurable, mechanistic responses. The controlled, computer-readable vocabulary that defines an AOP has the ability to, automatically and on a large scale, integrate AOP knowledge with publically available sources of biological high-throughput data and its derived associations. To support the discovery and development of putative (existing) and potential AOPs, we introduce the AOP-DB, an exploratory database resource that aggregates association relationships between genes and their related chemicals, diseases, pathways, species orthology information, ontologies, and gene interactions. These associations are mined from publically available annotation databases and are integrated with the AOP information centralized in the AOP-Wiki, allowing for the automatic characterization of both putative and potential AOPs in the context of multiple areas of biological information, referred to here as "biological entities". The AOP-DB acts as a hypothesis-generation tool for the expansion of putative AOPs, as well as the characterization of potential AOPs, through the creation of association networks across these biological entities. Finally, the AOP-DB provides a useful interface between the AOP framework and existing chemical screening and prioritization efforts by the US Environmental Protection Agency.

Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies

“…Thus, the development of an analysis environment that exploits both canonical pathways and new extended network interactions may improve our understanding of the significance of highly regulated DEGs within the context of liver pathological processes (Cerami et al, 2010; Khatri et al, 2012). There has been tremendous progress in the pathway curation and integration process during the past few years and that progress has resulted in novel pathway tools (Fabregat et al, 2017; Huang et al, 2017; Uppal et al, 2017; Wang et al, 2017; Forsberg et al, 2018; Pittman et al, 2018; Ukmar et al, 2018). However, these tools are still highly fragmented and not integrated into a single framework for optimal DEGs analysis.…”

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

“…Other work on the linkage of data related to the AOP-Wiki is the development of the AOP-DataBase (AOP-DB) (Pittman et al, 2018). This database will soon be publicly available and will contain various types of information linked to gene IDs that is useful for AOPs to provide a standardized, systematic structure for AOP development.…”

“…This database will soon be publicly available and will contain various types of information linked to gene IDs that is useful for AOPs to provide a standardized, systematic structure for AOP development. Among a large amount of data, biological pathways from databases such as KEGG, Reactome, and ConsensusDB are included based on GO annotations of KEs in AOP-Wiki (Pittman et al, 2018). While the AOP-DB connects pathway databases based on the ontology annotations to of existing AOPs and assisting the identification of putative AOPs, we think that a direct link between KEs and molecular pathways would be valuable and more reliable.…”

Harnessing formal concepts of biological mechanism to analyze human disease

“…MecCog makes a clear distinction between MMs and biological entities, in contrast to other emerging mechanism-oriented representation projects such as Noctua (http://noctua.berkeleybop.org/). MecCog’s causal chain-related approach is shared by the Collaborative Adverse Outcome Pathway Wiki (AOP-Wiki) [8] initiative, a crowdsourced representation of toxicology pathways that integrates perturbed entity information from molecular to cellular to organ biological scales, although AOP does not explicitly represent mechanism. MecCog extends representation of evidence for mechanism components such as that provided by the Evidence and Conclusion Ontology (ECO) [9] to include levels of confidence in mechanism components, ambiguities in mechanism schemas, and different types of ignorance.…”