Developing the Ecotoxicological - Predictive - Information - Connectivity Map (EPIC-map)

A fundamental challenge within the environmental sciences is to establish the relationship between exposure to a chemical stressor, its molecular effect and the resulting adverse outcome. Particularly in risk assessment it is desired to identify or predict the adverse outcome before its manifestation. To this effect, computational (predictive) biology aims at developing techniques and methodologies to identify molecular pathways and physiological endpoints that can be directly linked to the adverse outcome. The Environmental Protection Agency (US EPA) following a White House Mandate in 2003 has placed computational predictive ecotoxicology at the centre of their R&D strategy and since then has been heavily investing into this technology. In the UK, the NERC has also recognised the importance of a computational approach to integrating multi-level complex datasets by establishing the Environmental Omics Synthesis (EOS) initiative and by including Systems Biology as one of the core research priorities.
An essential part of the these programs is the adverse outcome pathway (AOP) framework, developed by G. Ankley et al at the US EPA.This was designed to link available knowledge on key events in a logical chain of events, from the molecular initiating event to organism molecular and physiology response and leading up to and including the adverse outcome. The resulting pathways could then be interrogated and used to support risk assessment and environmental monitoring. As the number of AOPs increase more robust predictions can be made on the effect a novel compound may have on a given organism.
At the core of the AOPs is the reconstruction of molecular pathways leading to adverse outcomes. This requires extensive and highly complex datasets allowing for an unbiased approach. The underpinning computational tools required to develop an AOP from such data should therefore be able to
1) reconstruct the underlying regulatory network,
2) link the molecular response to the adverse outcome,
3) link structural features of compounds to molecular response, and
4) provide an interpretive output for AOP and hypothesis generation.
Finally, the results developed from a set of known compounds forms the basis for deriving predictions on the potential molecular effect of novel chemicals on a given organism.
This project proposal addresses this important need by developing the necessary computational framework in collaboration with the US EPA which directly integrates with their FY2015-17 research plan to develop and validate computational (eco)toxicology techniques to integrate and eventually transition away from traditional assays. To achieve its goals the project will start by integrating various publicly available databases with a purpose built standardized dataset of 200 compounds into a single database. Links between the genes and compounds will form the basis of the networking approach, while the standardized dataset will be used for identification purposes and predictive modelling. To interrogate the database the project will utilize a number of tools currently used by many computational biologists and in addition incorporate a set of predictive modelling approaches. This will allow any bench user to identify compounds linked to their molecular response, gene-gene-chemical-adverse outcome networks for AOP development and hypothetical prediction of molecular effect of yet untested compounds via chemical structure. This will provide a basis for risk assessors such as the US EPA, UK Environmental Agency or European Commission Joint Research Center and many other high level and academic organisations to develop hypotheses on the potential risk and effects a chemical may have on an organism.

The primary objective of this project is to develop a simple to use web-service that allows any bench user to identify and compare their observed molecular response to a database containing an environmentally-relevant standardized dataset and an integration of a number of databases to provide hypotheses on gene-gene-compound interactions and potential adverse outcomes. As such this project has great value in environmental monitoring, risk assessment and identification of adverse outcome pathways (AOPs).

My Fellowship proposal is formally supported by a number of advisory and regulatory bodies within the UK (Cefas - Centre for Environmental, Fisheries and Aquaculture Science - see letter of support (LoS) Ioanna Katsiadaki) and US EPA (US Environmental Protection Agency - see LoS Rong-Lin Wang), as well as other leading environmental institutes US ERDC (US Engineer Research and Development Center, Corps of Engineers, see LoS Ed Perkins), NIVA (Norwegian Institute for Water Research, see LoS Knut-Erik Tollefsen), Nanjing University (see LoS Xiaowei Zhang), IGBB (Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University - see LoS Natalia Reyero Vinas), University of Florida (see LoS Nancy Denslow) and the Consortium for Environmental Omics and Technology (see LoS John Colbourne) to develop and advance these techniques in the environmental sciences. Upon successful validation of the tool through the use of data developed at the supporting institutes, access to the wider scientific community will be provided. Additional stakeholders from the following list may also be highly interested in the success of this project:
1. Regulatory and monitoring bodies (US EPA, OECD, UK Environment Agency, Cefas, Defra, Public Health England, European commission Joint Research Centre, ECVAM, Umweltbundesamt (UBA)
2. Industry such as Unilever, AstraZeneca, UK Water and Sewerage Companies, European Chemical Council (CEFIC), Waternet
3. Academia, Groups involved in Ecotoxicology, AOP development and monitoring, NC3Rs
4. General Public
The EPIC-map will provide the international community with the computational tools and hypothesis-generating abilities of state-of-the-art computational biology approaches driving the identification of AOPs and compounds in the environment. In addition, animal usage could be drastically reduced through the use of the zFET. To facilitate the update of this platform, and its incorporation into future risk assessment and environmental monitoring protocols, I will assemble an expert steering panel from international stakeholders and collaborators to this Fellowship. They will consult periodically on a range of issues focused on its relevance to their sector; the selection of compounds, the development of the EPIC web presence, delivery of AOP compliant outputs, coordination of standards for lab protocols and informatics processing, and the need for validation to meet the needs of regulatory authorities etc. Due to the time and distance limitations, this discussion will typically occur via videoconferencing (AccessGrid/Skype). Part of the impact workshop in Year 3, which is described in the next section, will be a key advisory meeting of representatives of stakeholders listed above to focus on the means of seeking regulatory approval and further developmental direction.