Generation of a Genome-Scale Metabolic Model of Ustilago Maydis, Causative Agent of Corn Smut

A genome-scale network reconstruction (GENRE) for Ustilago maydis, a plant pathogen of maize, was compiled by combining genomic, physiological and experimental information available for this fungus. The network was built using the automatic reconstruction software Pathway Tools, followed by extensive manual curation on the initial draft. The reconstruction serves as an easy-to-navigate, offline knowledgebase for biochemical processes as well as a means of accessing and maintaining genome annotations. Using the GENRE as the basis, a genome-scale stoichiometric model (GEM) was generated, which includes 1987 metabolites across 2218 reactions. 1658 of these reactions can be classified as conversion reactions, whereas 560 reactions describe transport processes between the 4 included compartments (Extracellular, Cytosol, Mitochondrial Lumen, Mitochondrial Intermembrane Space). Since the model contains exactly 1079 genes that are specifically assigned to about 86% of the reactions, it was termed iCL1079. Using the stoichiometry of all reactions and assuming a steady-state equilibrium, cellular metabolic fluxes could be determined using Flux Balance Analysis (FBA). After in-silico characterization, iCL1079 predicted a growth rate of 0.397 h^-1 on glucose and 0.227 h^-1 on glycerol. Moreover, product yields of malate on glucose (1.276 g_malate g^-1_glucose) and on glycerol (1.456 g_malate g^-1_glyerol) could be calculated. The presented GENRE and GEM are comprehensive, extendable, up-to-date resources and thus valuable tools for system-wide discoveries regarding the biochemistry, metabolism and genome of U. maydis.