AOP-Wiki SPARQL Endpoint

Abstract:
The Adverse Outcome Pathway (AOP) concept has been introduced to support risk assessment (Ankley et al., 2010). An AOP is initiated upon exposure to a stressor that causes a Molecular Initiating Event (MIE), followed by a series of Key Events (KEs) on increasing levels of biological organization. Eventually, the chain of KEs ends with the Adverse Outcome (AO), which describes the phenotypic outcome, disease, or the effect on the population. In general, an AOP captures mechanistic knowledge of a sequence of toxicological responses after exposure to a stressor. While starting with molecular information, for example, the initial interaction of a chemical with a cell, the AOPs contain information of downstream responses of the tissue, organ, individual and population. Currently, AOPs are stored in the AOP-Wiki, a collaborative platform to exchange mechanistic toxicological knowledge as a part of the AOP-KB, an initiative by the OECD. Normally, AOP development starts with a thorough literature search for existing knowledge, describing the sequence of KEs that form the AOP. However, the use of AOPs for regulatory purposes also requires detailed validation and linking to existing knowledge (Knapen et al., 2015; Burgdorf et al., 2017). Part of the development of AOPs is the search for data that supports the occurrence and biological plausibility of KEs and their relationships (KERs). This type of data can be found in literature, and increasingly in public databases. The main goal of this case study is to establish the links between AOPs of the AOP-Wiki and experimental data to support a particular AOP. This will allow finding AOPs related to experimental data, and finding data related to a particular AOP.

Abstract:
1. Introduction With the ever-growing number of chemicals that require toxicological risk assessment, there is a need for faster, more efficient use of existing data to assemble effective assessment strategies [1]. Therefore, the concept of Adverse Outcome Pathways (AOPs) was introduced [2], a framework to organize existing mechanistic information about toxicological processes into a chain of smaller pieces of knowledge, called Key Events (KEs). These allow the structuring of toxicological knowledge and reduce the effort needed to capture all information before performing risk assessment [2, 3]. In order to facilitate a community effort in gathering toxicological knowledge, the AOP-Wiki was created by the European Commission JRC and the US EPA. To integrate this knowledge base more easily with other resources, we explored the use of semantic web technologies to link AOP-Wiki with other chemical and biological databases. 2. Approach The AOP-Wiki provides quarterly permanent downloads for the full database XML (https://aopwiki.org/downloads/). We parsed the AOP-Wiki knowledge with Python 3.5 and the ElementTree XML API and converted it into a semantic web RDF format, which allows for accurate description with ontological annotations, including the AOPO, CHEMINF, and Dublin Core. Chemical compounds are identified in the AOP-Wiki with CAS numbers and biological processes with a variety of ontologies, e.g. GO, Mammalian Phenotype Ontology, and Molecular Interactions ontology. These annotations are used to create Internationalized Resource Identifiers. To integrate and test the RDF, a variety of federated SPARQL queries were written and executed in Blazegraph (build version 2.1.4). 3. Results We created an AOP-Wiki RDF scheme and converted the XML into Turtle syntax. The RDF was tested with a variety of SPARQL queries to answer biological question relevant to risk assessment, such as: - What measurement / test-method information is available for a given AOP? - Which of the stressor chemicals on the AOP-Wiki can be linked molecular pathways on WikiPathways? 4. Discussion The RDF transformation of AOP-Wiki content can assist in the accessibility and expansion of toxicological knowledge by allowing semantic interoperability. The created RDF of the AOP-Wiki allows the querying and providing of additional information for stressor chemicals, genes, and proteins involved in KEs, the underlying molecular pathways, but also for the applicability of AOPs by cell types or species. This semantic approach allows novel ways to explore the rapidly growing AOP knowledge with every new publication related to toxicological studies. There is work in progress on a Virtuoso SPARQL endpoint Docker image to simplify the use of the data, and integrate the database in the OpenRiskNet e-infrastructure to provide AOP knowledge useful for automated risk assessment workflows. Funding This project has received funding from the European Union’s Horizon 2020 (EU 2020) research and innovation program under grant agreement no. 681002 (EU-ToxRisk) and EINFRA-22-2016 program under grant agreement no. 731075 (OpenRiskNet).

Abstract:
A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of Adverse Outcome Pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level towards an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge.