Yuan Keji, Nault Rance
Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, United States.
Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, United States.
Toxicol Sci. 2025 Jul 1;206(1):19-29. doi: 10.1093/toxsci/kfaf065.
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.
转录组分析一直是研究啮齿动物模型中环境污染物引起的生物学反应和代谢紊乱的有效方法。然而，众所周知，代谢网络高度连通且复杂，包括通路分析在内的传统基因表达分析方法捕捉这些复杂性的能力有限。鉴于代谢可以有效地表示为一个图，本研究旨在应用基于网络的图神经网络（GNN）来揭示对毒物的新的或隐藏的代谢紊乱。基于小鼠反应组通路的GNN模型在从Recount3中整理的来自26个小鼠组织的7689个转录组样本上进行了训练和验证。然后，该模型用于识别用环境污染物2,3,7,8-四氯二苯并对二恶英（TCDD）处理的小鼠肝脏的公开可用数据中的重要反应，在将单剂量与对照组进行比较时，性能达到100%。综合梯度和中心性分析确定了TCDD对SUMO化、细胞周期、P53信号传导和胶原蛋白生物合成通路的扰动，而使用通路分析方法未识别出这些扰动。总的来说，我们的结果表明，GNN可以揭示毒物介导的代谢破坏的新机制见解，提出了一种表征对毒物暴露的生物学反应的推定策略。我们的研究说明了基于反应的图神经网络的使用如何支持发现毒物诱导的代谢扰动，并突出了人工智能方法在环境健康研究应用中的优势和挑战。
