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Emerging Bioinformatic Tools in Toxicogenomics

Hypothesis and Theory ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2018.00661

Introducing WikiPathways as a data-source to support Adverse Outcome Pathways for regulatory risk assessment of chemicals and nanomaterials.

 Marvin Martens1*, Tim Verbruggen1,  Penny Nymark2, 3, Roland Grafström2, 3, Lyle D. Burgoon4, Hristo Aladjov5,  Fernando Torres Andón6, 7,  Chris T. Evelo1, 8 and  Egon L. Willighagen1*
  • 1Department of Bioinformatics (BiGCaT), Maastricht University, Netherlands
  • 2Institute of Environmental Medicine, Karolinska Institutet, Sweden
  • 3Department of Toxicology, Misvik Biology Ltd, Finland
  • 4Engineer Research and Development Center (ERDC), United States
  • 5Organisation For Economic Co-Operation and Development, France
  • 6Humanitas Research Hospital, Italy
  • 7Centro de Investigación en Medicina Molecular y Enfermedades Crónicas (CiMUS), Spain
  • 8Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Netherlands

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. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of Key Events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70% and 71% respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki.

Keywords: Adverse outcome pathway (AOP), Risk assessement, Omics data integration, WikiPathways, interoperability among databases, Omics data analysis

Received: 07 Jul 2018; Accepted: 04 Dec 2018.

Edited by:

Paul Jennings, VU University Amsterdam, Netherlands

Reviewed by:

Frederic Y. Bois, French National Instiute for Industrial Environment and Risks, France
Mark Cronin, Liverpool John Moores University, United Kingdom  

Copyright: © 2018 Martens, Verbruggen, Nymark, Grafström, Burgoon, Aladjov, Torres Andón, Evelo and Willighagen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mr. Marvin Martens, Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, Netherlands,
Dr. Egon L. Willighagen, Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, Netherlands,