Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model of Danio rerio

Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum-infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.