A Novel Data Integration Framework to Assess the Health Impacts of Pesticides in Food

Abstract

The assessment of crop protection chemicals for human safety is currently achieved using toxicity studies performed on vertebrate models as surrogates for humans. These studies are resource intensive, slow and are not fully representative of the risks to human health. Many of these chemicals are endocrine disruptors (EDCs) whose molecular mechanisms of action are not well understood. Correlating the effects of EDC exposures with disease incidence is a global priority, and currently proposed frameworks for toxicity assessments typically include some form of broad coverage assay that aims to identify any potential off-target effects, and this usually incorporates multiple cell lines and transcriptomics. The main goal is to develop a novel statistical model for identification of key events as gene subnetworks and hub genes by integrating toxicogenomics data with interactome data across compounds, exposure durations, and dose levels. The long-term goal of this project is to develop a robust toolkit for integration into the adverse outcome pathway (AOP) conceptual framework, whereby existing knowledge linking molecular-level perturbation of a biological system and an adverse biological outcome with predictive or regulatory relevance is generated. The AOP comprises a Molecular Initiating Event (MIE), Key Events (KE) and an Adverse Outcome (AO), causally linked together. The project will promote molecular-level understanding of complex cellular and organismal responses to various food contaminants by identifying causal networks and associating them with pathological endpoints.