Metabolism is intertwined with various cellular processes, including controlling cell fate, influencing tumorigenesis, participating in stress responses and more. Metabolism is a complex, interdependent network, and local perturbations can have indirect effects that are pervasive across the metabolic network. Current analytical and technical limitations have long created a bottleneck in metabolic data interpretation. To address these shortcomings, we developed Metaboverse, a user-friendly tool to facilitate data exploration and hypothesis generation. Here we introduce algorithms that leverage the metabolic network to extract complex reaction patterns from data. To minimize the impact of missing measurements within the network, we introduce methods that enable pattern recognition across multiple reactions. Using Metaboverse, we identify a previously undescribed metabolite signature that correlated with survival outcomes in early stage lung adenocarcinoma patients. Using a yeast model, we identify metabolic responses suggesting an adaptive role of citrate homeostasis during mitochondrial dysfunction facilitated by the citrate transporter, Ctp1. We demonstrate that Metaboverse augments the user’s ability to extract meaningful patterns from multi-omics datasets to develop actionable hypotheses.

新陈代谢与各种细胞过程交织在一起，包括控制细胞命运、影响肿瘤发生、参与应激反应等。代谢是一个复杂的、相互依赖的网络，局部扰动可能会产生普遍存在于整个代谢网络中的间接影响。目前的分析和技术限制长期以来一直是代谢数据解释的瓶颈。为了解决这些缺点，我们开发了 Metaboverse，这是一种用户友好的工具，可以促进数据探索和假设生成。在这里，我们介绍利用代谢网络从数据中提取复杂反应模式的算法。为了最大限度地减少网络中缺失测量的影响，我们引入了能够跨多个反应进行模式识别的方法。使用 Metaboverse，我们确定了一个先前未描述的代谢特征，该特征与早期肺腺癌患者的生存结果相关。使用酵母模型，我们确定了代谢反应，表明柠檬酸转运蛋白 Ctp1 促进线粒体功能障碍期间柠檬酸稳态的适应性作用。我们证明 Metaboverse 增强了用户从多组学数据集中提取有意义的模式以开发可行假设的能力。