Compartment and hub definitions tune metabolic networks for metabolomic interpretations

• Published in: Gigascience 2020-01, Vol.9 (1)
• Main Authors: Waller, T Cameron, Berg, Jordan A, Lex, Alexander, Chapman, Brian E, Rutter, Jared
• Format: Article
• Language: English
• Subjects: Chemical reactions; Cluster analysis; Computational Biology - methods; Energy Metabolism; Gene regulation; Hubs; Human tissues; Humans; Metabolic networks; Metabolic Networks and Pathways; Metabolism; Metabolites; Metabolomics; Metabolomics - methods; Models, Biological; Modular structures; Modularity; Networks; Organelles; Software; User-Computer Interface; Web Browser
• ISSN: 2047-217X; 2047-217X
• DOI: 10.1093/gigascience/giz137

Abstract Background Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. Results We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. Conclusions Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.