Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis

Abstract

Background: Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes.

Results: We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation.

Conclusion: The genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.

Figure 1

Iterative procedure for reconstruction of the genome scale metabolic network of Scheffersomyces stipitis.

Figure 2

Characteristics of the genome scale metabolic network. A) Statistics. B) Functional classification of metabolic reactions in the model. C) Functional classification of the non-gene associated metabolic reactions in the model. D) Functional classification of enzyme classes in the model.

Figure 3

Reaction essentiality, gene essentiality and amino acid production capability. A) Functional distribution of the essential reactions in the model. B) Functional distribution of the essential genes in the model. C) Comparison of the amino acid production capability of S. stipitis and S. cerevisiae metabolic network on a carbon mole basis.

Figure 4

Network expansion and metabolic gap analysis based on high-throughput substrate utilization data. A) Comparison of experimental data from Biolog phenotype micro-arrays to model predictions across different substrate categories. Results are scored as + or - meaning growth or no growth determined from in vivo/in silico data. The n represents that corresponding pathway could not be included in the S. stipitis network due to unknown pathway enzymes. B) Improvement of prediction accuracy C) Comparison of incorrect predictions (+/- and -/+ cases in (A)) with published experimental results. (D) Comparison of in silico predictions with published experimental results for the Biolog substrates identified as low-confidence data. The Biolog data was considered as low confidence growth when the inference of growth/no-growth was difficult from the absorbance measurements. In vivo¹ from Biolog phenotyping, in vivo² from literature.

Figure 5

In silico analysis of xylose uptake. Dependence of xylose uptake rate on oxygen uptake rate for various NADPH/NADH ratios for xylose reductase.

Figure 6

In silico analysis of ethanol production. Ethanol production as function of oxygen uptake rate for various NADPH/NADH ratios for xylose reductase. The NADPH/NADH ratio was varied from zero to a very high value (1000000).

Figure 7

Cofactor balancing pathway. Enzymatic reactions which were reported to convert NADH to NADPH in S. stipitis. GDH2--NAD-dependent Glutamate dehydrogenase, GAD2--Glutamate decarboxylase, UGA1--4-aminobutyrate aminotransferase (UGA1.1 or UGA1.2) and UGA2--Succinate semialdehyde dehydrogenase (UGA2 or UGA2.2).