Integration of Metabolic Modeling and Phenotypic Data in Evaluation and Improvement of Ethanol Production Using Respiration-Deficient Mutants of Saccharomyces cerevisiae

Abstract

Flux balance analysis and phenotypic data were used to provide clues to the relationships between the activities of gene products and the phenotypes resulting from the deletion of genes involved in respiratory function in Saccharomyces cerevisiae. The effect of partial or complete respiratory deficiency on the ethanol production and growth characteristics of hap4⌬/hap4⌬, mig1⌬/mig1⌬, qdr3⌬/qdr3⌬, pdr3⌬/pdr3⌬, qcr7⌬/ qcr7⌬, cyt1⌬/cyt1⌬, and rip1⌬/rip1⌬ mutants grown in microaerated chemostats was investigated. The study provided additional evidence for the importance of the selection of a physiologically relevant objective function, and it may improve quantitative predictions of exchange fluxes, as well as qualitative estimations of changes in intracellular fluxes. Ethanol production was successfully predicted by flux balance analysis in the case of the qdr3⌬/qdr3⌬ mutant, with maximization of ethanol production as the objective function, suggesting an additional role for Qdr3p in respiration. The absence of similar changes in estimated intracellular fluxes in the qcr7⌬/qcr7⌬ mutant compared to the rip1⌬/rip1⌬ and cyt1⌬/cyt1⌬ mutants indicated that the effect of the deletion of this subunit of complex III was somehow compensated for. Analysis of predicted flux distributions indicated self-organization of intracellular fluxes to avoid NAD ؉ /NADH imbalance in rip1⌬/rip1⌬ and cyt1⌬/ cyt1⌬ mutants, but not the qcr7⌬/qcr7⌬ mutant. The flux through the glycerol efflux channel, Fps1p, was estimated to be zero in all strains under the investigated conditions. This indicates that previous strategies for improving ethanol production, such as the overexpression of the glutamate synthase gene GLT1 in a GDH1 deletion background or deletion of the glycerol efflux channel gene FPS1 and overexpression of GLT1, are unnecessary in a respiration-deficient background.

Increased ethanol production by deletion of HAP4 in recombinant xylose-assimilating Saccharomyces cerevisiae

“…Biomass yields calculated from the cell densities of B42-DHAP4 were 11 % lower than those of MA-B42 in cultivation using MD medium (Table 1). Thus, lower biomass concentrations and biomass yields from glucose were observed with B42-DHAP4 compared to MA-B42, which is in good agreement with previous studies using hap4Δ strains [16,23]. MA-B42 depleted the available glucose within 48 h (Fig.…”

“…As shown in Table 1, the maximum specific glucose consumption rate was not statistically distinguishable between the two strains, while the ethanol production rate was 14 % higher in B42-DHAP4 than in MA-B42. The ethanol yield of B42-DHAP4 on glucose was 10 % higher than that of MA-B42 (Table 1), consistent with a previous finding that hap4Δ mutants displayed increased ethanol yields on glucose [16,23]. The pyruvate and glycerol yields of B42-DHAP4 on glucose were higher than those of MA-B42 (Table 1).…”

“…Earlier work demonstrated increased ethanol production in S. cerevisiae hap4 mutants grown in microaerated chemostats with glucose as the sole carbon source [16]. Further elevation of ethanol yield was reported in a hap4Δ mutant of Pichia guilliermondii during aerobic fermentation using glucose [17].…”

Repeated-batch fermentations of xylose and glucose–xylose mixtures using a respiration-deficient Saccharomyces cerevisiae engineered for xylose metabolism

“…Fermenting glucose, a respiration-deficient mutant of S. cerevisiae has already known for showing higher ethanol productivity and/or yield than that of the respiration-sufficient parental strain (Dikicioglu et al, 2008;Hutter and Oliver, 1998;Oner et al, 2005;Panoutsopoulou et al, 2001). A respirationdeficient mutant of engineered stain also produced more ethanol (0.25 g ethanol/g xylose) from xylose as compared to the parental strain (0.15 g g −1 ) (Jin et al, 2004).…”

Applications of genome-scale metabolic network model in metabolic engineering

“…In addition to the prediction of gene knockout targets, the algorithms have been expanded to predict the up-and down-regulation gene targets, allowing more exhaustive search of phenotypic space [28,55,65,68,72]. These computational tools have been successfully employed for the metabolic engineering of Escherichia coli, Saccharomyces cerevisiae, Corynebacterium glutamicum, and many others for the production of a range of natural and non-natural compounds including ethanol [22,49], butanol [48,71], succinic acid [46], lactic acid [26], lycopene [15], amino acids [64], vanillin [8], and 1,4-butanediol [94].…”