54th Congress of the European Societies of Toxicology (EUROTOX)

Abstract:
OpenRiskNet (https://openrisknet.org/) is an EU funded infrastructure project with the main objective to develop an open e-infrastructure providing resources and services to a variety of industries requiring risk assessment, including chemicals, cosmetic ingredients, drugs and nanomaterials. The OpenRiskNet approach is to work on different case studies to test and evaluate requirements to overcome the fragmentation of data and tools and to provide solutions improving the harmonization of data, usability and interoperability of application programming interfaces (APIs) and the deployment into virtual infrastructure. The cases present real-world settings such as systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based only on alternative methods approaches. We discuss our progress on the OpenRiskNet goal of harmonizing data and metadata within APIs that can be added to already existing analysis and modeling services and data warehouses. We also demonstrate how these APIs can easily be used towards the generation of full risk assessment workflows either using scripting languages or workflow managers. Finally, we show the first approaches to make these APIs semantically rich by annotating data with human- and computer-readable data schemata. OpenRiskNet has initiated the Associated Partners Programme strengthening the working ties between the OpenRiskNet members and other organizations within the scientific community.

Abstract:
Whole genome transcriptional profiling allows global measurement of gene expression changes induced by particular experimental conditions. Toxic treatments of biological systems, such as cell models, may perturb interactions among genes and, in toxicogenomics, such perturbations assessed by transcriptional profiling are used to predict impact of toxic compounds. Form early days on, this toxicogenomics-based approach for predicting apical toxicities, has been dedicated to the purpose of improving predictions of genotoxicity and carcinogenicity in vivo. Over the past decade large amounts of transcriptional profiling data have been generated for in vitro study models using various chemical compounds, across different doses and time points as well as different organisms. As part of the H2020 EU project OpenRiskNet, we propose a large-scale integrative analysis approach using these data sets for predicting genotoxicity and carcinogenicity in vivo. From the diXa Data Warehouse, NCBI GEO, and EBI ArrayExpress we collected gene expression data for human in vitro liver cell models exposed to 125 compounds with known genotoxicity information at different time points and dosages resulting in 822 experiments. We analyzed these data sets using ten different classification algorithms, thereby using 80% of the data for training and 20% for testing. Support Vector Machines algorithm had the best accuracy for predicting genotoxicity in vivo at 92.5% with 95% specificity and 87% sensitivity. Upon identifying deregulated gene-gene interaction networks by applying ConsensusPathDB, the top 5 of affected pathways are related to p53-centered pathways. The results from our meta-analysis demonstrate both high accuracy and robustness of transcriptomic profiling of genotoxicity hazards across a large set of genotoxicants and across multiple human liver cell models. We propose that these assays can be used for regulatory purposes, certainly when applied in combination with the traditional genotoxicity in vitro test battery. Next, we want to perform similar analyses on rat and mouse data and identify core orthologous genes among the three different species that are potential predictive targets for assessing genotoxicity and carcinogenicity across different biological systems.

Abstract:
In the last decade, omics-based approaches such as transcriptomics, proteomics and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. A 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 after exposure to a chemical or nanomaterial. However, the implementation of omics-based approaches in the AOPs and acceptance by the risk assessment community is still a challenge. Therefore, tools are required for omics-based data analysis and visualization, and to link the data to the traditional AOPs. Here we show how WikiPathways, an open science pathway database, can serve as a viable tool for this purpose. Therefore, an AOP Portal (aop.wikipathways.org)has been created with a rapidly growing collection of molecular-level AOPs on which omics datasets can be mapped an analyzed, currently consisting of 15 pathways by 14 authors that are structured in various ways. Besides that, we are making WikiPathways more interoperable with aopwiki.org, the main knowledge-base that collects and stores AOPs. The open and collaborative nature makes WikiPathways a fast growing platform that is applicable in a wide range of biomedical research fields in which omics-based approaches are used. Also, its use of ontologies, OpenAPI documentation and FAIR (Findable, Accessible, Interoperable, Reusable) approaches makes WikiPathways interoperable with many other data sources. By introducing AOPs in WikiPathways and linking these with the AOPs in aopwiki.org, we aimed to make WikiPathways a useful tool for the regulatory toxicity community and for toxicological research in general. Eventually this could lead to implementation of WikiPathways as a data-source for decision-making in REACH (Registration, Evaluation, Authorization, and restriction of Chemicals) dossiers for risk assessment of chemicals. This project has received funding from the European Union’s Horizon 2020 research and innovation programme project EU-ToxRisk under grant agreement No. 681002 and EINFRA-22-2016 programme project OpenRiskNet under grant agreement No. 731075.