Network identification and flux quantification in the central metabolism of Saccharomyces cerevisiae under different conditions of glucose repression

Management of Multiple Nitrogen Sources during Wine Fermentation by Saccharomyces cerevisiae

During fermentative growth in natural and industrial environments, Saccharomyces cerevisiae must redistribute the available nitrogen from multiple exogenous sources to amino acids in order to suitably fulfill anabolic requirements. To exhaustively explore the management of this complex resource, we developed an advanced strategy based on the reconciliation of data from a set of stable isotope tracer experiments with labeled nitrogen sources. Thus, quantifying the partitioning of the N compounds through the metabolism network during fermentation, we demonstrated that, contrary to the generally accepted view, only a limited fraction of most of the consumed amino acids is directly incorporated into proteins. Moreover, substantial catabolism of these molecules allows for efficient redistribution of nitrogen, supporting the operative de novo synthesis of proteinogenic amino acids. In contrast, catabolism of consumed amino acids plays a minor role in the formation of volatile compounds. Another important feature is that the α-keto acid precursors required for the de novo syntheses originate mainly from the catabolism of sugars, with a limited contribution from the anabolism of consumed amino acids. This work provides a comprehensive view of the intracellular fate of consumed nitrogen sources and the metabolic origin of proteinogenic amino acids, highlighting a strategy of distribution of metabolic fluxes implemented by yeast as a means of adapting to environments with changing and scarce nitrogen resources.IMPORTANCE A current challenge for the wine industry, in view of the extensive competition in the worldwide market, is to meet consumer expectations regarding the sensory profile of the product while ensuring an efficient fermentation process. Understanding the intracellular fate of the nitrogen sources available in grape juice is essential to the achievement of these objectives, since nitrogen utilization affects both the fermentative activity of yeasts and the formation of flavor compounds. However, little is known about how the metabolism operates when nitrogen is provided as a composite mixture, as in grape must. Here we quantitatively describe the distribution through the yeast metabolic network of the N moieties and C backbones of these nitrogen sources. Knowledge about the management of a complex resource, which is devoted to improvement of the use of the scarce N nutrient for growth, will be useful for better control of the fermentation process and the sensory quality of wines.

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, ¹³C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.

Interaction of storage carbohydrates and other cyclic fluxes with central metabolism: A quantitative approach by non-stationary 13C metabolic flux analysis

13C labeling experiments in aerobic glucose limited cultures of Saccharomyces cerevisiae at four different growth rates (0.054; 0.101, 0.207, 0.307 h-1) are used for calculating fluxes that include intracellular cycles (e.g., storage carbohydrate cycles, exchange fluxes with amino acids), which are rearranged depending on the growth rate. At low growth rates the impact of the storage carbohydrate recycle is relatively more significant than at high growth rates due to a higher concentration of these materials in the cell (up to 560-fold) and higher fluxes relative to the glucose uptake rate (up to 16%). Experimental observations suggest that glucose can be exported to the extracellular space, and that its source is related to storage carbohydrates, most likely via the export and subsequent extracellular breakdown of trehalose. This hypothesis is strongly supported by 13C-labeling experimental data, measured extracellular trehalose, and the corresponding flux estimations.

Improving the flux distributions simulated with genome-scale metabolic models of Saccharomyces cerevisiae

Genome-scale metabolic models (GEMs) can be used to evaluate genotype-phenotype relationships and their application to microbial strain engineering is increasing in popularity. Some of the algorithms used to simulate the phenotypes of mutant strains require the determination of a wild-type flux distribution. However, the accuracy of this reference, when calculated with flux balance analysis, has not been studied in detail before.

Here, the wild-type simulations of selected GEMs for Saccharomyces cerevisiae have been analysed and most of the models tested predicted erroneous fluxes in central pathways, especially in the pentose phosphate pathway. Since the problematic fluxes were mostly related to areas of the metabolism consuming or producing NADPH/NADH, we have manually curated all reactions including these cofactors by forcing the use of NADPH/NADP⁺ in anabolic reactions and NADH/NAD⁺ for catabolic reactions. The curated models predicted more accurate flux distributions and performed better in the simulation of mutant phenotypes.

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The flux distributions of the genome-scale models of Saccharomyces cerevisiae were evaluated

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Most of the tested models showed fluxes inconsistent with experimental data

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A manual curation process was performed on all reactions including NADH or NADPH

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The curated models showed flux distributions more consistent with experimental data

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Phenotype simulations improved when the curated flux distributions were used

Reduced Histone Expression or a Defect in Chromatin Assembly Induces Respiration

Regulation of mitochondrial biogenesis and respiration is a complex process that involves several signaling pathways and transcription factors as well as communication between the nuclear and mitochondrial genomes. Here we show that decreased expression of histones or a defect in nucleosome assembly in the yeast Saccharomyces cerevisiae results in increased mitochondrial DNA (mtDNA) copy numbers, oxygen consumption, ATP synthesis, and expression of genes encoding enzymes of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS). The metabolic shift from fermentation to respiration induced by altered chromatin structure is associated with the induction of the retrograde (RTG) pathway and requires the activity of the Hap2/3/4/5p complex as well as the transport and metabolism of pyruvate in mitochondria. Together, our data indicate that altered chromatin structure relieves glucose repression of mitochondrial respiration by inducing transcription of the TCA cycle and OXPHOS genes carried by both nuclear and mitochondrial DNA.

Inhibitory Role of Greatwall-Like Protein Kinase Rim15p in Alcoholic Fermentation via Upregulating the UDP-Glucose Synthesis Pathway in Saccharomyces cerevisiae

The high fermentation rate of Saccharomyces cerevisiae sake yeast strains is attributable to a loss-of-function mutation in the RIM15 gene, which encodes a Greatwall-family protein kinase that is conserved among eukaryotes. In the present study, we performed intracellular metabolic profiling analysis and revealed that deletion of the RIM15 gene in a laboratory strain impaired glucose-anabolic pathways through the synthesis of UDP-glucose (UDPG). Although Rim15p is required for the synthesis of trehalose and glycogen from UDPG upon entry of cells into the quiescent state, we found that Rim15p is also essential for the accumulation of cell wall β-glucans, which are also anabolic products of UDPG. Furthermore, the impairment of UDPG or 1,3-β-glucan synthesis contributed to an increase in the fermentation rate. Transcriptional induction of PGM2 (phosphoglucomutase) and UGP1 (UDPG pyrophosphorylase) was impaired in Rim15p-deficient cells in the early stage of fermentation. These findings demonstrate that the decreased anabolism of glucose into UDPG and 1,3-β-glucan triggered by a defect in the Rim15p-mediated upregulation of PGM2 and UGP1 redirects the glucose flux into glycolysis. Consistent with this, sake yeast strains with defective Rim15p exhibited impaired expression of PGM2 and UGP1 and decreased levels of β-glucans, trehalose, and glycogen during sake fermentation. We also identified a sake yeast-specific mutation in the glycogen synthesis-associated glycogenin gene GLG2, supporting the conclusion that the glucose-anabolic pathway is impaired in sake yeast. These findings demonstrate that downregulation of the UDPG synthesis pathway is a key mechanism accelerating alcoholic fermentation in industrially utilized S. cerevisiae sake strains.

Carboxylic Acids Plasma Membrane Transporters in Saccharomyces cerevisiae

This chapter covers the functionally characterized plasma membrane carboxylic acids transporters Jen1, Ady2, Fps1 and Pdr12 in the yeast Saccharomyces cerevisiae, addressing also their homologues in other microorganisms, as filamentous fungi and bacteria. Carboxylic acids can either be transported into the cells, to be used as nutrients, or extruded in response to acid stress conditions. The secondary active transporters Jen1 and Ady2 can mediate the uptake of the anionic form of these substrates by a H(+)-symport mechanism. The undissociated form of carboxylic acids is lipid-soluble, crossing the plasma membrane by simple diffusion. Furthermore, acetic acid can also be transported by facilitated diffusion via Fps1 channel. At the cytoplasmic physiological pH, the anionic form of the acid prevails and it can be exported by the Pdr12 pump. This review will highlight the mechanisms involving carboxylic acids transporters, and the way they operate according to the yeast cell response to environmental changes, as carbon source availability, extracellular pH and acid stress conditions.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

Triacetic acid lactone production in industrial Saccharomyces yeast strains

Triacetic acid lactone (TAL) is a potential platform chemical that can be produced in yeast. To evaluate the potential for industrial yeast strains to produce TAL, the g2ps1 gene encoding 2-pyrone synthase was transformed into 13 industrial yeast strains of varied genetic background. TAL production varied 63-fold between strains when compared in batch culture with glucose. Ethanol, acetate, and glycerol were also tested as potential carbon sources. Batch cultures with ethanol medium produced the highest titers. Therefore, fed-batch cultivation with ethanol feed was assayed for TAL production in bioreactors, producing our highest TAL titer, 5.2 g/L. Higher feed rates resulted in a loss of TAL and subsequent production of additional TAL side products. Finally, TAL efflux was measured and TAL is actively exported from S. cerevisiae cells. Percent yield for all strains was low, indicating that further metabolic engineering of the strains is required.

(13)C-metabolic flux analysis in S-adenosyl-L-methionine production by Saccharomyces cerevisiae

S-Adenosyl-L-methionine (SAM) is a major biological methyl group donor, and is used as a nutritional supplement and prescription drug. Yeast is used for the industrial production of SAM owing to its high intracellular SAM concentrations. To determine the regulation mechanisms responsible for such high SAM production, (13)C-metabolic flux analysis ((13)C-MFA) was conducted to compare the flux distributions in the central metabolism between Kyokai no. 6 (high SAM-producing) and S288C (control) strains. (13)C-MFA showed that the levels of tricarboxylic acid (TCA) cycle flux in SAM-overproducing strain were considerably increased compared to those in the S228C strain. Analysis of ATP balance also showed that a larger amount of excess ATP was produced in the Kyokai 6 strain because of increased oxidative phosphorylation. These results suggest that high SAM production in Kyokai 6 strains could be attributed to enhanced ATP regeneration with high TCA cycle fluxes and respiration activity. Thus, maintaining high respiration efficiency during cultivation is important for improving SAM production.

¹³C-based metabolic flux analysis of Saccharomyces cerevisiae with a reduced Crabtree effect

Saccharomyces cerevisiae shows a Crabtree effect that produces ethanol in a high glucose concentration even under fully aerobic condition. For efficient production of cake yeast or compressed yeast for baking, ethanol by-production is not desired since glucose limited chemostat or fed-batch cultivations are performed to suppress the Crabtree effect. In this study, the (13)C-based metabolic flux analysis ((13)C-MFA) was performed for the S288C derived S. cerevisiae strain to characterize a metabolic state under the reduced Crabtree effect. S. cerevisiae cells were cultured at a low dilution rate (0.1 h(-1)) under the glucose-limited chemostat condition. The estimated metabolic flux distribution showed that the acetyl-CoA in mitochondria was mainly produced from pyruvate by pyruvate dehydrogenase (PDH) reaction and that the level of the metabolic flux through the pentose phosphate pathway was much higher than that of the Embden-Meyerhof-Parnas pathway, which contributes to high biomass yield at low dilution rate by supplying NADPH required for cell growth.

Applications of computational modeling in metabolic engineering of yeast

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.

Deletion of D-ribulose-5-phosphate 3-epimerase (RPE1) induces simultaneous utilization of xylose and glucose in xylose-utilizing Saccharomyces cerevisiae

Simultaneous co-utilization of xylose and glucose is a key issue in engineering microbes for cellulosic ethanol production. We coupled xylose utilization with glucose metabolism by deletion of D-ribulose-5-phosphate 3-epimerase (RPE1) through pentose phosphate pathway flux. Simultaneous utilization of xylose and glucose then occurred in the engineered Saccharomyces cerevisiae strain with the xylose utilization pathway. Xylose consumption occurred at the beginning of glucose consumption by the engineered yeast without RPE1 in a mixed sugar fermentation. About 3.2 g xylose l(-1) was utilized simultaneously with consumption of 40.2 g glucose l(-1) under O2-limited conditions. In addition, an approximate ratio (~1:10) for xylose and glucose consumption was observed in the fermentation with different sugar concentration by the engineered strain without RPE1. Simultaneous utilization of xylose is realized by the coupling of glucose metabolism and xylose utilization through RPE1 deletion in xylose-utilizing S. cerevisiae.

Storage lipid synthesis is necessary for autophagy induced by nitrogen starvation

Nitrogen starvation is a universal stimulus of autophagy. At present, little is known about the relationship between carbon metabolism and autophagy under nitrogen starvation. Here, we show that yeast cells continue to consume glucose and downregulate fermentation under nitrogen starvation. Storage lipid production is increased, with concurrent proliferation of lipid droplets. Furthermore, we provide evidence that triacylglycerol synthesis is crucial for autophagosome biogenesis. It is involved in a step downstream of PAS (phagophore assembly site) scaffold assembly, and upstream of the recruitment of Atg1, Atg14, Atg5 and Atg8. Finally, we demonstrate that lipid droplets transiently interact with Atg8-containing membranes. Our study reveals a novel connection linking neutral lipid metabolism, lipid droplets and autophagy.

Metabolomic and (13)C-metabolic flux analysis of a xylose-consuming Saccharomyces cerevisiae strain expressing xylose isomerase

Over the past two decades, significant progress has been made in the engineering of xylose-consuming Saccharomyces cerevisiae strains for production of lignocellulosic biofuels. However, the ethanol productivities achieved on xylose are still significantly lower than those observed on glucose for reasons that are not well understood. We have undertaken an analysis of central carbon metabolite pool sizes and metabolic fluxes on glucose and on xylose under aerobic and anaerobic conditions in a strain capable of rapid xylose assimilation via xylose isomerase in order to investigate factors that may limit the rate of xylose fermentation. We find that during xylose utilization the flux through the non-oxidative Pentose Phosphate Pathway (PPP) is high but the flux through the oxidative PPP is low, highlighting an advantage of the strain employed in this study. Furthermore, xylose fails to elicit the full carbon catabolite repression response that is characteristic of glucose fermentation in S. cerevisiae. We present indirect evidence that the incomplete activation of the fermentation program on xylose results in a bottleneck in lower glycolysis, leading to inefficient re-oxidation of NADH produced in glycolysis.

Protein acetylation and acetyl coenzyme a metabolism in budding yeast

Cells sense and appropriately respond to the physical conditions and availability of nutrients in their environment. This sensing of the environment and consequent cellular responses are orchestrated by a multitude of signaling pathways and typically involve changes in transcription and metabolism. Recent discoveries suggest that the signaling and transcription machineries are regulated by signals which are derived from metabolism and reflect the metabolic state of the cell. Acetyl coenzyme A (CoA) is a key metabolite that links metabolism with signaling, chromatin structure, and transcription. Acetyl-CoA is produced by glycolysis as well as other catabolic pathways and used as a substrate for the citric acid cycle and as a precursor in synthesis of fatty acids and steroids and in other anabolic pathways. This central position in metabolism endows acetyl-CoA with an important regulatory role. Acetyl-CoA serves as a substrate for lysine acetyltransferases (KATs), which catalyze the transfer of acetyl groups to the epsilon-amino groups of lysines in histones and many other proteins. Fluctuations in the concentration of acetyl-CoA, reflecting the metabolic state of the cell, are translated into dynamic protein acetylations that regulate a variety of cell functions, including transcription, replication, DNA repair, cell cycle progression, and aging. This review highlights the synthesis and homeostasis of acetyl-CoA and the regulation of transcriptional and signaling machineries in yeast by acetylation.

Mass-spectrometry-based microbial metabolomics: recent developments and applications

Metabolomics is an omics technique aiming at qualitatively and quantitatively describing a metabolome by various analytical platforms. It is an indispensable component of modern systems biology. Microbial metabolomics can be roughly classified as metabolic footprint analysis and metabolic fingerprint analysis depending on the analyte origins. Both of them have been beneficial to microbiological research for different reasons. Mass spectrometry and nuclear magnetic resonance spectroscopy techniques are popular analytical strategies prevailing in the metabolomics field. In this review, chromatography-mass-spectrometry-based microbial metabolomic analysis steps are summarized, including sample collection, metabolite extraction, instrument analysis, and data analysis. Moreover, their applications in some representative fields are discussed as examples. The aim of this review is to present briefly recent technical advances in mass-spectrometry-based analysis, and to highlight the value of modern applications of microbial metabolomics.

Comparative metabolic flux analysis of an Ashbya gossypii wild type strain and a high riboflavin-producing mutant strain

In the present study, we analyzed the central metabolic pathway of an Ashbya gossypii wild type strain and a riboflavin over-producing mutant strain developed in a previous study in order to characterize the riboflavin over-production pathway. (13)C-Metabolic flux analysis ((13)C-MFA) was carried out in both strains, and the resulting data were fit to a steady-state flux isotopomer model using OpenFLUX. Flux to pentose-5-phosphate (P5P) via the pentose phosphate pathway (PPP) was 9% higher in the mutant strain compared to the wild type strain. The flux from purine synthesis to riboflavin in the mutant strain was 1.6%, while that of the wild type strain was only 0.1%, a 16-fold difference. In addition, the flux from the cytoplasmic pyruvate pool to the extracellular metabolites, pyruvate, lactate, and alanine, was 2-fold higher in the mutant strain compared to the wild type strain. This result demonstrates that increased guanosine triphosphate (GTP) flux through the PPP and purine synthesis pathway (PSP) increased riboflavin production in the mutant strain. The present study provides the first insight into metabolic flux through the central carbon pathway in A. gossypii and sets the foundation for development of a quantitative and functional model of the A. gossypii metabolic network.

Continuous-time Markov chain-based flux analysis in metabolism

Metabolic flux analysis (MFA), a key technology in bioinformatics, is an effective way of analyzing the entire metabolic system by measuring fluxes. Many existing MFA approaches are based on differential equations, which are complicated to be solved mathematically. So MFA requires some simple approaches to investigate metabolism further. In this article, we applied continuous-time Markov chain to MFA, called MMFA approach, and transformed the MFA problem into a set of quadratic equations by analyzing the transition probability of each carbon atom in the entire metabolic system. Unlike the other methods, MMFA analyzes the metabolic model only through the transition probability. This approach is very generic and it could be applied to any metabolic system if all the reaction mechanisms in the system are known. The results of the MMFA approach were compared with several chemical reaction equilibrium constants from early experiments by taking pentose phosphate pathway as an example.

Comparison and analysis of objective functions in flux balance analysis

Flux balance analysis (FBA) is currently one of the most important and used techniques for estimation of metabolic reaction rates (fluxes). This mathematical approach utilizes an optimization criterion in order to select a distribution of fluxes from the feasible space delimited by the metabolic reactions and some restrictions imposed over them, assuming that cellular metabolism is in steady state. Therefore, the obtained flux distribution depends on the specific objective function used. Multiple studies have been aimed to compare distinct objective functions at given conditions, in order to determine which of those functions produces values of fluxes closer to real data when used as objective in the FBA; in other words, what is the best objective function for modeling cell metabolism at a determined environmental condition. However, these comparative studies have been designed in very dissimilar ways, and in general, several factors that can change the ideal objective function in a cellular condition have not been adequately considered. Additionally, most of them have used only one dataset for representing one condition of cell growth, and different measuring techniques have been used. For these reasons, a rigorous study on the effect of factors such as the quantity of used data, the number and type of fluxes utilized as input data, and the selected classification of growth conditions, are required in order to obtain useful conclusions for these comparative studies, allowing limiting clearly the application range on any of those results.

k-OptForce: integrating kinetics with flux balance analysis for strain design

Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.

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.

Labelling analysis for ¹³C MFA using NMR spectroscopy

NMR spectroscopy is an efficient method for analyzing (13)C labelling of cellular metabolites. The strength of it is especially the ability to provide direct quantitative positional information on the (13)C labelling status of carbon atoms in metabolites. NMR spectroscopic methods allow also for detection of contiguously (13)C-labelled fragments in the carbon backbones of the metabolites. Furthermore, the recent developments of NMR spectroscopy hardware have substantially improved the sensitivity of the methods. In this chapter we describe a method for analyzing the (13)C labelling of the biomass amino acids for metabolic flux analysis, sample preparation for NMR spectroscopy, acquiring and processing the NMR spectra, and extracting the (13)C labelling information from the NMR data. Different NMR methods are applied depending on the (13)C labelling strategy chosen. These strategies include uniform (13)C labelling, positional (13)C labelling, or a combination of both. Not only the preparation of sample for analysis of (13)C labelling in proteinogenic amino acids in biomass is described, but also the necessary modifications to the method when analysis of (13)C labelling in free metabolic intermediates is of interest. Finally the strategies for using the different NMR-detected (13)C labelling data in (13)C MFA are discussed.

Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae

Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design. Correlation analysis across eight environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.

The yeast AMPK homolog SNF1 regulates acetyl coenzyme A homeostasis and histone acetylation

Acetyl coenzyme A (acetyl-CoA) is a key metabolite at the crossroads of metabolism, signaling, chromatin structure, and transcription. Concentration of acetyl-CoA affects histone acetylation and links intermediary metabolism and transcriptional regulation. Here we show that SNF1, the budding yeast ortholog of the mammalian AMP-activated protein kinase (AMPK), plays a role in the regulation of acetyl-CoA homeostasis and global histone acetylation. SNF1 phosphorylates and inhibits acetyl-CoA carboxylase, which catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, the first and rate-limiting reaction in the de novo synthesis of fatty acids. Inactivation of SNF1 results in a reduced pool of cellular acetyl-CoA, globally decreased histone acetylation, and reduced fitness and stress resistance. The histone acetylation and transcriptional defects can be partially suppressed and the overall fitness improved in snf1Δ mutant cells by increasing the cellular concentration of acetyl-CoA, indicating that the regulation of acetyl-CoA homeostasis represents another mechanism in the SNF1 regulatory repertoire.

The 2-oxoglutarate supply exerts significant control on the lysine synthesis flux in Saccharomyces cerevisiae

To determine the extent to which the supply of the precursor 2-oxoglutarate (2-OG) controls the synthesis of lysine in Saccharomyces cerevisiae growing exponentially in high glucose, top-down elasticity analysis was used. Three groups of reactions linked by 2-OG were defined. The 2-OG supply group comprised all metabolic steps leading to its formation, and the two 2-OG consumer groups comprised the enzymes and transporters involved in 2-OG transformation into lysine and glutamate and their further utilization for protein synthesis and storage. Various 2-OG steady-state concentrations that produced different fluxes to lysine and glutamate were attained using yeast mutants with increasing activities of Krebs cycle enzymes and decreased activities of Lys synthesis enzymes. The elasticity coefficients of the three enzyme groups were determined from the dependence of the amino acid fluxes on the 2-OG concentration. The respective degrees of control on the flux towards lysine (flux control coefficients) were determined from their elasticities, and were 1.1, 0.41 and -0.52 for the 2-OG producer group and the Lys and Glu branches, respectively. Thus, the predominant control exerted by the 2-OG supply on the rate of lysine synthesis suggests that over-expression of 2-OG producer enzymes may be a highly effective strategy to enhance Lys production.

Nitrogen regulation involved in the accumulation of urea in Saccharomyces cerevisiae

Rice wine is a popular traditional alcoholic drink with a long history in China. However, the presence of the potential carcinogen ethyl carbamate (EC) raises a series of food safety concerns. Although the metabolic pathway of urea (the major precusor of EC) has been characterized in Saccharomyces cerevisiae, the regulation of urea accumulation remains unclear, making the efficient elimination of urea difficult. To demonstrate the regulatory mechanisms governing urea accumulation, three key nitrogen sources that can inhibit urea utilization for a commercial S. cerevisiae strain were identified. In addition, regulators of nitrogen catabolite repression (NCR) and target of rapamycin (TOR) pathways were identified as being involved in urea accumulation by real-time quantitative PCR. Based on these results, preferred nitrogen sources were found to repress urea utilization by converting them to glutamine or glutamate. Moreover, the results indicated that the manner of urea metabolism regulation was different for two positive regulators involved in NCR; Gln3p can be retained in the cytoplasm by glutamine, while Gat1p can be retained by glutamine and glutamate. Furthermore, this was confirmed by fluorescence location detection. These new findings provide new targets for eliminating EC and other harmful nitrogen-containing compounds in fermented foods.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Kinetic isotope effects significantly influence intracellular metabolite (13) C labeling patterns and flux determination

Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from (13) C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of (13) C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the (13) C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the (13) C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in (13) C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC-MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in (13) C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.

Yeast phospholipase C is required for normal acetyl-CoA homeostasis and global histone acetylation

Phospholipase C (Plc1p) is required for the initial step of inositol polyphosphate (InsP) synthesis, and yeast cells with deletion of the PLC1 gene are completely devoid of any InsPs and display aberrations in transcriptional regulation. Here we show that Plc1p is required for a normal level of histone acetylation; plc1Δ cells that do not synthesize any InsPs display decreased acetylation of bulk histones and global hypoacetylation of chromatin histones. In accordance with the role of Plc1p in supporting histone acetylation, plc1Δ mutation is synthetically lethal with mutations in several subunits of SAGA and NuA4 histone acetyltransferase (HAT) complexes. Conversely, the growth rate, sensitivity to multiple stresses, and the transcriptional defects of plc1Δ cells are partially suppressed by deletion of histone deacetylase HDA1. The histone hypoacetylation in plc1Δ cells is due to the defect in degradation of repressor Mth1p, and consequently lower expression of HXT genes and reduced conversion of glucose to acetyl-CoA, a substrate for HATs. The histone acetylation and transcriptional defects can be partially suppressed and the overall fitness improved in plc1Δ cells by increasing the cellular concentration of acetyl-CoA. Together, our data indicate that Plc1p and InsPs are required for normal acetyl-CoA homeostasis, which, in turn, regulates global histone acetylation.

Isolation of a high malic and low acetic acid-producing sake yeast Saccharomyces cerevisiae strain screened from respiratory inhibitor 2,4-dinitrophenol (DNP)-resistant strains

We isolated 2,4-dinitrophenol (DNP)-resistant sake yeast strains by UV mutagenesis. Among the DNP-resistant mutants, we focused on strains exhibiting high malic acid and low acetic acid production. The improved organic acid composition is unlikely to be under the control of enzyme activities related to malic and acetic acid synthesis pathways. Instead, low mitochondrial activity was observed in DNP-resistant mutants, indicating that the excess pyruvic acid generated during glycolysis is not metabolized in the mitochondria but converted to malic acid in the cytosol. In addition, the NADH/NAD(+) ratio of the DNP-resistant strains was higher than that of the parental strain K901. These results suggest that the increased NADH/NAD(+) ratio together with the low mitochondrial activity alter the organic acid composition because malic acid synthesis requires NADH, while acetic acid uses NAD(+).

Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

Background

The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network.

Results

Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold for integrating transcriptomics, and flux data from four different conditions in order to identify transcriptionally controlled reactions, i.e. reactions that change both in flux and transcription between the compared conditions.

Conclusion

We present a new yeast model that represents a comprehensive up-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition-dependent changes of metabolism between different environmental conditions.

The metabolic costs of improving ethanol yield by reducing glycerol formation capacity under anaerobic conditions in Saccharomyces cerevisiae

BACKGROUND

Finely regulating the carbon flux through the glycerol pathway by regulating the expression of the rate controlling enzyme, glycerol-3-phosphate dehydrogenase (GPDH), has been a promising approach to redirect carbon from glycerol to ethanol and thereby increasing the ethanol yield in ethanol production. Here, strains engineered in the promoter of GPD1 and deleted in GPD2 were used to investigate the possibility of reducing glycerol production of Saccharomyces cerevisiae without jeopardising its ability to cope with process stress during ethanol production. For this purpose, the mutant strains TEFmut7 and TEFmut2 with different GPD1 residual expression were studied in Very High Ethanol Performance (VHEP) fed-batch process under anaerobic conditions.

RESULTS

Both strains showed a drastic reduction of the glycerol yield by 44 and 61% while the ethanol yield improved by 2 and 7% respectively. TEFmut2 strain showing the highest ethanol yield was accompanied by a 28% reduction of the biomass yield. The modulation of the glycerol formation led to profound redox and energetic changes resulting in a reduction of the ATP yield (YATP) and a modulation of the production of organic acids (acetate, pyruvate and succinate). Those metabolic rearrangements resulted in a loss of ethanol and stress tolerance of the mutants, contrarily to what was previously observed under aerobiosis.

CONCLUSIONS

This work demonstrates the potential of fine-tuned pathway engineering, particularly when a compromise has to be found between high product yield on one hand and acceptable growth, productivity and stress resistance on the other hand. Previous study showed that, contrarily to anaerobiosis, the resulting gain in ethanol yield was accompanied with no loss of ethanol tolerance under aerobiosis. Moreover those mutants were still able to produce up to 90 gl-1 ethanol in an anaerobic SSF process. Fine tuning metabolic strategy may then open encouraging possibilities for further developing robust strains with improved ethanol yield.

Metabolic engineering with plants for a sustainable biobased economy

Plants are bona fide sustainable organisms because they accumulate carbon and synthesize beneficial metabolites from photosynthesis. To meet the challenges to food security and health threatened by increasing population growth and depletion of nonrenewable natural resources, recent metabolic engineering efforts have shifted from single pathways to holistic approaches with multiple genes owing to integration of omics technologies. Successful engineering of plants results in the high yield of biomass components for primary food sources and biofuel feedstocks, pharmaceuticals, and platform chemicals through synthetic biology and systems biology strategies. Further discovery of undefined biosynthesis pathways in plants, integrative analysis of discrete omics data, and diversified process developments for production of platform chemicals are essential to overcome the hurdles for sustainable production of value-added biomolecules from plants.

Improved polyhydroxybutyrate production by Saccharomyces cerevisiae through the use of the phosphoketolase pathway

The metabolic pathways of the central carbon metabolism in Saccharomyces cerevisiae are well studied and consequently S. cerevisiae has been widely evaluated as a cell factory for many industrial biological products. In this study, we investigated the effect of engineering the supply of precursor, acetyl-CoA, and cofactor, NADPH, on the biosynthesis of the bacterial biopolymer polyhydroxybutyrate (PHB), in S. cerevisiae. Supply of acetyl-CoA was engineered by over-expression of genes from the ethanol degradation pathway or by heterologous expression of the phophoketolase pathway from Aspergillus nidulans. Both strategies improved the production of PHB. Integration of gapN encoding NADP(+) -dependent glyceraldehyde-3-phosphate dehydrogenase from Streptococcus mutans into the genome enabled an increased supply of NADPH resulting in a decrease in glycerol production and increased production of PHB. The strategy that resulted in the highest PHB production after 100 h was with a strain harboring the phosphoketolase pathway to supply acetyl-CoA without the need of increased NADPH production by gapN integration. The results from this study imply that during the exponential growth on glucose, the biosynthesis of PHB in S. cerevisiae is likely to be limited by the supply of NADPH whereas supply of acetyl-CoA as precursor plays a more important role in the improvement of PHB production during growth on ethanol.

A universal fixation method based on quaternary ammonium salts (RNAlater) for omics-technologies: Saccharomyces cerevisiae as a case study

Genomics, transcriptomics, proteomics and fluxomics are powerful omics-technologies that play a major role in today's research. For each of these techniques good sample quality is crucial. Major factors contributing to the quality of a sample is the actual sampling procedure itself and the way the sample is stored directly after sampling. It has already been described that RNAlater can be used to store tissues and cells in a way that the RNA quality and quantity are preserved. In this paper, we demonstrate that quaternary ammonium salts (RNAlater) are also suitable to preserve and store samples from Saccharomyces cerevisiae for later use with the four major omics-technologies. Moreover, it is shown that RNAlater also preserves the cell morphology and the potential to recover growth, permitting microscopic analysis and yeast cell culturing at a later stage.

Quantitative 1H-NMR-metabolomics reveals extensive metabolic reprogramming and the effect of the aquaglyceroporin FPS1 in ethanol-stressed yeast cells

A metabolomic analysis using high resolution ¹H NMR spectroscopy coupled with multivariate statistical analysis was used to characterize the alterations in the endo- and exo-metabolome of S. cerevisiae BY4741 during the exponential phase of growth in minimal medium supplemented with different ethanol concentrations (0, 2, 4 and 6% v/v). This study provides evidence that supports the notion that ethanol stress induces reductive stress in yeast cells, which, in turn, appears to be counteracted by the increase in the rate of NAD⁺ regenerating bioreactions. Metabolomics data also shows increased intra- and extra-cellular accumulation of most amino acids and TCA cycle intermediates in yeast cells growing under ethanol stress suggesting a state of overflow metabolism in turn of the pyruvate branch-point. Given its previous implication in ethanol stress resistance in yeast, this study also focused on the effect of the expression of the aquaglyceroporin encoded by FPS1 in the yeast metabolome, in the absence or presence of ethanol stress. The metabolomics data collected herein shows that the deletion of the FPS1 gene in the absence of ethanol stress partially mimics the effect of ethanol stress in the parental strain. Moreover, the results obtained suggest that the reported action of Fps1 in mediating the passive diffusion of glycerol is a key factor in the maintenance of redox balance, an important feature for ethanol stress resistance, and may interfere with the ability of the yeast cell to accumulate trehalose. Overall, the obtained results corroborate the idea that metabolomic approaches may be crucial tools to understand the function and/or the effect of membrane transporters/porins, such as Fps1, and may be an important tool for the clear-cut design of improved process conditions and more robust yeast strains aiming to optimize industrial fermentation performance.

Metabolic fluxes in Schizosaccharomyces pombe grown on glucose and mixtures of glycerol and acetate

Growth on glycerol has already been a topic of research for several yeast species, and recent publications deal with the regulatory mechanisms of glycerol assimilation by the fission yeast Schizosaccharomyces pombe. We investigated glycerol metabolism of S. pombe from a physiological point of view, characterizing growth and metabolism on a mixture of glycerol and acetate and comparing it to growth on glucose under respirative growth conditions in chemostat experiments. On glycerol/acetate mixtures, the cells grew with a maximum specific growth rate of 0.11 h(-1) where 46 % of the carbon was channeled into biomass and the key fermentation product ethanol was not detectable. (13)C-assisted metabolic flux analysis resolved substrate distributions through central carbon metabolism, proving that glycerol is used as a precursor for glycolysis, gluconeogenesis, and the pentose phosphate pathway, while acetate enters the tricarboxylic acid cycle via acetyl-CoA. Considering compartmentalization between cytosol and mitochondria in the metabolic model, we found compartmentalization of biosynthesis for the amino acids aspartate and leucine. Balancing of redox cofactors revealed an abundant production of cytosolic NADPH that must be finally regenerated via the respiratory chain shown by the simulated and measured CO2 production and oxygen consumption rates which were in good agreement.

Yeast and cancer cells - common principles in lipid metabolism

One of the paradigms in cancer pathogenesis is the requirement of a cell to undergo transformation from respiration to aerobic glycolysis - the Warburg effect - to become malignant. The demands of a rapidly proliferating cell for carbon metabolites for the synthesis of biomass, energy and redox equivalents, are fundamentally different from the requirements of a differentiated, quiescent cell, but it remains open whether this metabolic switch is a cause or a consequence of malignant transformation. One of the major requirements is the synthesis of lipids for membrane formation to allow for cell proliferation, cell cycle progression and cytokinesis. Enzymes involved in lipid metabolism were indeed found to play a major role in cancer cell proliferation, and most of these enzymes are conserved in the yeast, Saccharomyces cerevisiae. Most notably, cancer cell physiology and metabolic fluxes are very similar to those in the fermenting and rapidly proliferating yeast. Both types of cells display highly active pathways for the synthesis of fatty acids and their incorporation into complex lipids, and imbalances in synthesis or turnover of lipids affect growth and viability of both yeast and cancer cells. Thus, understanding lipid metabolism in S. cerevisiae during cell cycle progression and cell proliferation may complement recent efforts to understand the importance and fundamental regulatory mechanisms of these pathways in cancer.

Genome-based kinetic modeling of cytosolic glucose metabolism in industrially relevant cell lines: Saccharomyces cerevisiae and Chinese hamster ovary cells

Model-based analysis of cellular metabolism can facilitate our understanding of intracellular kinetics and aid the improvement of cell growth and biological product manufacturing. In this paper, a model-based kinetic study of cytosolic glucose metabolism for two industrially relevant cell lines, Saccharomyces cerevisiae and Chinese hamster ovary (CHO) cells, based on enzyme genetic presence and expression information is described. We have reconstructed the cytosolic glucose metabolism map for S. cerevisiae and CHO cells, containing 18 metabolites and 18 enzymes using information from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Based on this map, we have developed akinetic mathematical model for the pathways involved,considering regulation and/or inhibition by products orco-substrates. The values of the maximum rates of reactions(V(max)) were estimated based on kinetic parameter information and metabolic flux analysis results available in literature, and the resulting simulation results for steady-state metabolite concentrations are in good agreement with published experimental data. Finally, the model was used to analyse how the production of DHAP, an important intermediate in fine chemicals synthesis, could be increased using gene knockout.

Scheffersomyces stipitis: a comparative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae

BACKGROUND

Scheffersomyces stipitis is a Crabtree negative yeast, commonly known for its capacity to ferment pentose sugars. Differently from Crabtree positive yeasts such as Saccharomyces cerevisiae, the onset of fermentation in S. stipitis is not dependent on the sugar concentration, but is regulated by a decrease in oxygen levels. Even though S. stipitis has been extensively studied due to its potential application in pentoses fermentation, a limited amount of information is available about its metabolism during aerobic growth on glucose. Here, we provide a systems biology based comparison between the two yeasts, uncovering the metabolism of S. stipitis during aerobic growth on glucose under batch and chemostat cultivations.

RESULTS

Starting from the analysis of physiological data, we confirmed through 13C-based flux analysis the fully respiratory metabolism of S. stipitis when growing both under glucose limited or glucose excess conditions. The patterns observed showed similarity to the fully respiratory metabolism observed for S. cerevisiae under chemostat cultivations however, intracellular metabolome analysis uncovered the presence of several differences in metabolite patterns. To describe gene expression levels under the two conditions, we performed RNA sequencing and the results were used to quantify transcript abundances of genes from the central carbon metabolism and compared with those obtained with S. cerevisiae. Interestingly, genes involved in central pathways showed different patterns of expression, suggesting different regulatory networks between the two yeasts. Efforts were focused on identifying shared and unique families of transcription factors between the two yeasts through in silico transcription factors analysis, suggesting a different regulation of glycolytic and glucoenogenic pathways.

CONCLUSIONS

The work presented addresses the impact of high-throughput methods in describing and comparing the physiology of Crabtree positive and Crabtree negative yeasts. Based on physiological data and flux analysis we identified the presence of one metabolic condition for S. stipitis under aerobic batch and chemostat cultivations, which shows similarities to the oxidative metabolism observed for S. cerevisiae under chemostat cultivations. Through metabolome analysis and genome-wide transcriptomic analysis several differences were identified. Interestingly, in silico analysis of transciption factors was useful to address a different regulation of mRNAs of genes involved in the central carbon metabolism. To our knowledge, this is the first time that the metabolism of S. stiptis is investigated in details and is compared to S. cerevisiae. Our study provides useful results and allows for the possibility to incorporate these data into recently developed genome-scaled metabolic, thus contributing to improve future industrial applications of S. stipitis as cell factory.