Metabolic pathway analysis of Scheffersomyces (Pichia) stipitis: effect of oxygen availability on ethanol synthesis and flux distributions

Engineering Scheffersomyces segobiensis for palmitoleic acid-rich lipid production

Palmitoleic acid (POA; C16:1) is an essential high-value ω-7-conjugated fatty acid with beneficial bioactivities and potential applications in the nutraceutical and pharmaceutical industries. Previously, the oleaginous yeast Scheffersomyces segobiensis DSM27193 has been identified as a promising production host as an alternative for POA extraction from plant or animal sources. Here, the POA-producing capacity of this host was further expanded by optimizing the fermentation process and molecular strain engineering. Specifically, a dual fermentation strategy (O-S dynamic regulation strategy) focused on the substrate and dissolved oxygen concentration was designed to eliminate ethanol and pyruvate accumulation during fermentation. Key genes influencing POA production, such as jen, dgat, ole were identified on the transcriptional level and were subsequently over-expressed. Furthermore, the phosphoketolase (Xpk)/phosphotransacetylase (Pta) pathway was introduced to improve the yield of the precursor acetyl-CoA from glucose. The resulting cell factory SS-12 produced 7.3 g/L of POA, corresponding to an 11-fold increase compared to the wild type, presenting the highest POA titre reported using oleaginous yeast to date. An economic evaluation based on the raw materials, utilities and facility-dependent costs showed that microbial POA production using S. segobiensis can supersede the current extraction method from plant oil and marine fish. This study reports the construction of a promising cell factory and an effective microbial fermentation strategy for commercial POA production.

Metabolic flux analysis for metabolome data validation of naturally xylose-fermenting yeasts

BACKGROUND

Efficient xylose fermentation still demands knowledge regarding xylose catabolism. In this study, metabolic flux analysis (MFA) and metabolomics were used to improve our understanding of xylose metabolism. Thus, a stoichiometric model was constructed to simulate the intracellular carbon flux and used to validate the metabolome data collected within xylose catabolic pathways of non-Saccharomyces xylose utilizing yeasts.

RESULTS

A metabolic flux model was constructed using xylose fermentation data from yeasts Scheffersomyces stipitis, Spathaspora arborariae, and Spathaspora passalidarum. In total, 39 intracellular metabolic reactions rates were utilized validating the measurements of 11 intracellular metabolites, acquired by mass spectrometry. Among them, 80% of total metabolites were confirmed with a correlation above 90% when compared to the stoichiometric model. Among the intracellular metabolites, fructose-6-phosphate, glucose-6-phosphate, ribulose-5-phosphate, and malate are validated in the three studied yeasts. However, the metabolites phosphoenolpyruvate and pyruvate could not be confirmed in any yeast. Finally, the three yeasts had the metabolic fluxes from xylose to ethanol compared. Xylose catabolism occurs at twice-higher flux rates in S. stipitis than S. passalidarum and S. arborariae. Besides, S. passalidarum present 1.5 times high flux rate in the xylose reductase reaction NADH-dependent than other two yeasts.

CONCLUSIONS

This study demonstrated a novel strategy for metabolome data validation and brought insights about naturally xylose-fermenting yeasts. S. stipitis and S. passalidarum showed respectively three and twice higher flux rates of XR with NADH cofactor, reducing the xylitol production when compared to S. arborariae. Besides then, the higher flux rates directed to pentose phosphate pathway (PPP) and glycolysis pathways resulted in better ethanol production in S. stipitis and S. passalidarum when compared to S. arborariae.

Systematic development of biomass overproducing Scheffersomyces stipitis for high-cell-density fermentations

The development of economically feasible bio-based process requires efficient cell factories capable of producing the desired product at high titer under high-cell-density fermentation. Herein we present a combinatorial approach based on systems metabolic engineering and metabolic evolution for the development of efficient biomass-producing strain. Systems metabolic engineering guided by flux balance analysis (FBA) was first employed to rationally design mutant strains of Scheffersomyces stipitis with high biomass yield. By experimentally implementing these mutations, the biomass yield was improved by 30% in GPD1, 25% in TKL1, 30% in CIT1, and 44% in ZWF1 overexpressed mutants compared to wild-type. These designed mutants were further fine-tuned through metabolic evolution resulting in the maximal biomass yield of 0.49 g-cdw/g-glucose, which matches well with predicted yield phenotype. The constructed mutants are beneficial for biotechnology applications dealing with high cell titer cultivations. This work demonstrates a solid confirmation of systems metabolic engineering in combination with metabolic evolution approach for efficient strain development, which could assist in rapid optimization of cell factory for an economically viable and sustainable bio-based process.

Reconstruction and analysis of a genome-scale metabolic network of Corynebacterium glutamicum S9114

Corynebacterium glutamicum S9114 is commonly used for industrial glutamate production. Therefore, a comprehensive understanding of the physiological and metabolic characteristics of C. glutamicum is important for developing its potential for industrial production. A genome-scale metabolic model, iJM658, was reconstructed based on genome annotation and literature mining. The model consists of 658 genes, 984 metabolites and 1065 reactions. The model quantitatively predicted C. glutamicum growth on different carbon and nitrogen sources and determined 129 genes to be essential for cell growth. The iJM658 model predicted that C. glutamicum had two glutamate biosynthesis pathways and lacked eight key genes in biotin synthesis. Robustness analysis indicated a relative low oxygen level (1.21mmol/gDW/h) would improve glutamate production rate. Potential metabolic engineering targets for improving γ-aminobutyrate and isoleucine production rate were predicted by in silico deletion or overexpression of some genes. The iJM658 model is a useful tool for understanding and optimizing the metabolism of C. glutamicum and a valuable resource for future metabolic and physiological research.

Engineering Scheffersomyces stipitis for fumaric acid production from xylose

In this work, Scheffersomyces stipitis, the yeast with excellent xylose-utilizing ability, was firstly engineered for fumaric acid production from xylose with the heterologous reductive pathway from Rhizopus oryzae FM19, and 1.86g/L fumaric acid was produced by the initial strain PSRPMF under the oxygen-limited condition. Furthermore, three strategies were performed to improve the fumaric acid production, including increasing the reductive pathway activity by codon optimization, blocking the fumaric acid conversion in tricarboxylic acid cycle by knocking out the native fumarases, and improving the fumaric acid transportation by overexpressing heterologous transporter. Finally, the strain PSYPMFfS was obtained and the fumaric acid titer reached to 4.67g/L, significantly increased by 37.92-fold than that of the control strain PSPYSS. It was indicated that the S. stipitis was a promising platform for fumaric acid production from xylose.

Genome-scale NAD(H/(+)) availability patterns as a differentiating feature between Saccharomyces cerevisiae and Scheffersomyces stipitis in relation to fermentative metabolism

Scheffersomyces stipitis is a yeast able to ferment pentoses to ethanol, unlike Saccharomyces cerevisiae, it does not present the so-called overflow phenomenon. Metabolic features characterizing the presence or not of this phenomenon have not been fully elucidated. This work proposes that genome-scale metabolic response to variations in NAD(H/⁺) availability characterizes fermentative behavior in both yeasts. Thus, differentiating features in S. stipitis and S. cerevisiae were determined analyzing growth sensitivity response to changes in available reducing capacity in relation to ethanol production capacity and overall metabolic flux span. Using genome-scale constraint-based metabolic models, phenotypic phase planes and shadow price analyses, an excess of available reducing capacity for growth was found in S. cerevisiae at every metabolic phenotype where growth is limited by oxygen uptake, while in S. stipitis this was observed only for a subset of those phenotypes. Moreover, by using flux variability analysis, an increased metabolic flux span was found in S. cerevisiae at growth limited by oxygen uptake, while in S. stipitis flux span was invariant. Therefore, each yeast can be characterized by a significantly different metabolic response and flux span when growth is limited by oxygen uptake, both features suggesting a higher metabolic flexibility in S. cerevisiae. By applying an optimization-based approach on the genome-scale models, three single reaction deletions were found to generate in S. stipitis the reducing capacity availability pattern found in S. cerevisiae, two of them correspond to reactions involved in the overflow phenomenon. These results show a close relationship between the growth sensitivity response given by the metabolic network and fermentative behavior.

¹³C-based metabolic flux analysis of recombinant Pichia pastoris

Overexpression of a foreign protein may negatively affect several cell growth parameters, as well as cause cellular stress. Central (or core) metabolism plays a crucial role since it supplies energy, reduction equivalents, and precursor molecules for the recombinant product, cell's maintenance, and growth needs. However, the number of quantitative physiology studies of the impact of recombinant protein production on the central metabolic pathways of yeast cell factories has been traditionally rather limited, thereby hampering the application of rational strain engineering strategies targeting central metabolism.The development and application of quantitative physiology and modelling tools and methodologies is allowing for a systems-level understanding of the effect of bioprocess parameters such as growth rate, temperature, oxygen availability, and substrate(s) choice on metabolism, and its subsequent interactions with recombinant protein synthesis, folding, and secretion.Here, we review the recent developments and applications of (13)C-based metabolic flux analysis ((13)C-MFA) of Pichia pastoris and the gained understanding of the metabolic behavior of this yeast in recombinant protein production bioprocesses. We also discuss the potential of multilevel studies integrating (13)C-MFA with other omics analyses, as well as future perspectives on the metabolic modelling approaches to study and design metabolic engineering strategies for improved protein production.

Pathway analysis of Pichia pastoris to elucidate methanol metabolism and its regulation for production of recombinant proteins

This research rationally analyzes metabolic pathways of Pichia pastoris to study the metabolic flux responses of this yeast under methanol metabolism. A metabolic model of P. pastoris was constructed and analyzed by elementary mode analysis (EMA). EMA was used to comprehensively identify the cell's metabolic flux profiles and its underlying regulation mechanisms for the production of recombinant proteins from methanol. Change in phenotypes and flux profiles during methanol adaptation with varying feed mixture of glycerol and methanol was examined. EMA identified increasing and decreasing fluxes during the glycerol-methanol metabolic shift, which well agreed with experimental observations supporting the validity of the metabolic network model. Analysis of all the identified pathways also led to the determination of the metabolic capacities as well as the optimum metabolic pathways for recombinant protein synthesis during methanol induction. The network sensitivity analysis revealed that the production of proteins can be improved by manipulating the flux ratios at the pyruvate branch point. In addition, EMA suggested that protein synthesis is optimum under hypoxic culture conditions. The metabolic modeling and analysis presented in this study could potentially form a valuable knowledge base for future research on rational design and optimization of P. pastoris by determining target genes, pathways, and culture conditions for enhanced recombinant protein synthesis. The metabolic pathway analysis is also of considerable value for production of therapeutic proteins by P. pastoris in biopharmaceutical applications.

Metabolic reconstruction and flux analysis of industrial Pichia yeasts

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.

A constraint-based model of Scheffersomyces stipitis for improved ethanol production

BACKGROUND

As one of the best xylose utilization microorganisms, Scheffersomyces stipitis exhibits great potential for the efficient lignocellulosic biomass fermentation. Therefore, a comprehensive understanding of its unique physiological and metabolic characteristics is required to further improve its performance on cellulosic ethanol production.

RESULTS

A constraint-based genome-scale metabolic model for S. stipitis CBS 6054 was developed on the basis of its genomic, transcriptomic and literature information. The model iTL885 consists of 885 genes, 870 metabolites, and 1240 reactions. During the reconstruction process, 36 putative sugar transporters were reannotated and the metabolisms of 7 sugars were illuminated. Essentiality study was conducted to predict essential genes on different growth media. Key factors affecting cell growth and ethanol formation were investigated by the use of constraint-based analysis. Furthermore, the uptake systems and metabolic routes of xylose were elucidated, and the optimization strategies for the overproduction of ethanol were proposed from both genetic and environmental perspectives.

CONCLUSIONS

Systems biology modelling has proven to be a powerful tool for targeting metabolic changes. Thus, this systematic investigation of the metabolism of S. stipitis could be used as a starting point for future experiment designs aimed at identifying the metabolic bottlenecks of this important yeast.