Gazing into the Metaboverse: Automated exploration and contextualization of metabolic data

Metabolism forms a complex, interdependent network, and perturbations can have indirect effects that are pervasive. Identifying these patterns and their consequences is difficult, particularly when the effects occur across canonical pathways, and these difficulties have long acted as a bottleneck in metabolic data analysis. This challenge is compounded by technical limitations in metabolomics approaches that garner incomplete datasets. Current network-based tools generally utilize pathway-level analysis lacking the granular resolution required to provide context into the effects of all perturbations, regardless of magnitude, across the metabolic network. To address these shortcomings, we introduce algorithms that allow for the real-time extraction of regulatory patterns and trends from user data. To minimize the impact of missing measurements within the metabolic network, we introduce methods that enable complex pattern recognition across multiple reactions. These tools are available interactively within the user-friendly Metaboverse app (https://github.com/Metaboverse) to facilitate exploration and hypothesis generation. We demonstrate that expected signatures are accurately captured by Metaboverse. Using public lung adenocarcinoma data, we identify a previously undescribed multi-dimensional signature that correlated with survival outcomes in lung adenocarcinoma patients. Using a model of respiratory deficiency, we identify relevant and previously unreported regulatory patterns that suggest an important compensatory role for citrate during mitochondrial dysfunction. This body of work thus demonstrates that Metaboverse can identify and decipher complex signals from data that have been otherwise difficult to identify with previous approaches.