Ustilago maydis is an important plant pathogen causing corn-smut disease and an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of environmental adaptation and a full use of the organism’s metabolic potential. Here, we report the first genome scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% of annotated genes in the sequenced genome. Substrate utilization was corrected by Biolog-Phenotype arrays and exponential batch cultivations were used to test growth predictions. A pan-genome of four different U. maydis strains revealed missing metabolic pathways in iUma22. The majority of metabolic differences between iUma22 and the pan-genome occurs in the inositol, purine and starch metabolic pathways. The new model allows studies of metabolic adaptations to different environmental niches as well as for biotechnological applications.
Liebal et al. (2022), Ustilago maydis Metabolic Characterization and Growth Quantification with a Genome-Scale Metabolic Model. J. Fungi 2022, 8(5), 524; https://doi.org/10.3390/jof8050524
Simulations for Review Renewable carbon sources to biochemicals and -fuels: contributions of the smut fungi Ustilaginaceae by Becker et al., 2022
Simulations on the genome scale model have been performed to test the yields of different products on various substrates. The associated Jupyter Notebook to reproduce the simulations is available as code/Becker22_UmaySubProdYields.ipynb
Utilisation: experimental data reconstruction
Field: metabolic-network reconstruction
Type of model: curated
Model source: PathwayTools
Taxonomic name: Ustilago maydis
Strain: 521
**Metabolic system: **general metabolism
**Condition: **aerobic; glucose limited; glycerol limited; defined media
Online simulation via Binder:
