Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.

Transcriptomic analyses have been an effective approach to investigate the biological responses and metabolic perturbations by environmental contaminants in rodent models. However, it is well recognized that metabolic networks are highly connected and complex, and that traditional gene expression analysis methods, including pathway analyses, have a limited ability to capture these complexities. Given that metabolism can be effectively represented as a graph, this study aims to apply a network-based graph neural network (GNN) to uncover novel or hidden metabolic perturbations in response to a toxicant. A GNN model based on the mouse Reactome pathways was trained and validated on 7,689 transcriptomic samples from 26 mouse tissues curated from Recount3. This model was then used to identify important reactions in publicly available data from livers of mice treated with the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) achieving a performance of 100% when comparing a single dose to a control group. Integrated gradients and centrality analyses identified perturbation of the SUMOylation, cell cycle, P53 signaling, and collagen biosynthesis pathways by TCDD which were not identified using a pathway analysis approach. Collectively, our results demonstrate that GNNs can reveal novel mechanistic insights into toxicant-mediated metabolic disruption, presenting a putative strategy to characterize biological responses to toxicant exposures. Our studies illustrate how the use of a reaction-based graph neural network can support the discovery of toxicant-induced metabolic perturbations, and highlight strengths and challenges in the application of artificial intelligence methods for environmental health research.