Abstract
Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.
Highlights
The application of large-scale -omics data has revolutionized our understanding of how complex cellular, tissue, and organ systems respond to various perturbations
We demonstrate that Metabolomic Modularity Analysis (MMA), owing to a graph edge-weighting scheme derived from statistically significant metabolites (SSMs) based on metabolomics data, is advantageous over conventional pathway enrichment analysis (PEA) methodologies in its ability to elucidate network modules that span several canonical metabolic pathways
Metabolomic Modularity Analysis (MMA) as a novel approach to identify groups of reactions, or modules that elucidate the relationship between statistically significant metabolites (SSM) as a measure to evaluate the outcomes of machine perfusion on human livers
Summary
The application of large-scale -omics data has revolutionized our understanding of how complex cellular, tissue, and organ systems respond to various perturbations. Jha and coworkers have recently reported an elegant approach to identify sub-graphs in a bipartite metabolic network that maximized high-scoring nodes, where metabolite nodes with significant p-values (SSMs) received high scores [26] Another way to abstract metabolic networks is as a reaction-centric graph where metabolites are treated as a shared resource, which provides an intuitive framework for facilitating the discovery of reaction modules that highlight the utilization of energy cofactors [27]. Another major challenge in executing module detection algorithms is the poor scaling of computational run time for large-scale networks. Based on metabolomics data, is advantageous over conventional PEA methodologies in its ability to elucidate network modules that span several canonical metabolic pathways
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