Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived C-labelling data

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Implications of glycerol metabolism for lipid production

Triacylglycerol (TAG) is an important product in oil-producing organisms. Biosynthesis of TAG can be completed through either esterification of fatty acids to glycerol backbone, or through esterification of 2-monoacylglycerol. This review will focus on the former pathway in which two precursors, fatty acid and glycerol-3-phosphate (G3P), are required for TAG formation. Tremendous progress has been made about the enzymes or genes that regulate the biosynthetic pathway of TAG. However, much attention has been paid to the fatty acid provision and the esterification process, while the possible role of G3P is largely neglected. Glycerol is extensively studied on its usage as carbon source for value-added products, but the modification of glycerol metabolism, which is directly associated with G3P synthesis, is seldom recognized in lipid investigations. The relevance among glycerol metabolism, G3P synthesis and lipid production is described, and the role of G3P in glycerol metabolism and lipid production are discussed in detail with an emphasis on how G3P affects lipid production through the modulation of glycerol metabolism. Observations of lipid metabolic changes due to glycerol related disruption in mammals, plants, and microorganisms are introduced. Altering glycerol metabolism results in the changes of final lipid content. Possible regulatory mechanisms concerning the relationship between glycerol metabolism and lipid production are summarized.

Integrating transcriptomics and metabolomics for the analysis of the aroma profiles of Saccharomyces cerevisiae strains from diverse origins

Background

During must fermentation thousands of volatile aroma compounds are formed, with higher alcohols, acetate esters and ethyl esters being the main aromatic compounds contributing to floral and fruity aromas. The action of yeast, in particular Saccharomyces cerevisiae, on the must components will build the architecture of the wine flavour and its fermentation bouquet. The objective of the present work was to better understand the molecular and metabolic bases of aroma production during a fermentation process. For such, comparative transcriptomic and metabolic analysis was performed at two time points (5 and 50 g/L of CO₂ released) in fermentations conducted by four yeast strains from different origins and/or technological applications (cachaça, sake, wine, and laboratory), and multivariate factorial analyses were used to rationally identify new targets for improving aroma production.

Results

Results showed that strains from cachaça, sake and wine produced higher amounts of acetate esters, ethyl esters, acids and higher alcohols, in comparison with the laboratory strain. At fermentation time T1 (5 g/L CO₂ released), comparative transcriptomics of the three S. cerevisiae strains from different fermentative environments in comparison with the laboratory yeast S288c, showed an increased expression of genes related with tetracyclic and pentacyclic triterpenes metabolism, involved in sterol synthesis. Sake strain also showed upregulation of genes ADH7 and AAD6, involved in the formation of higher alcohols in the Ehrlich pathway. For fermentation time point T2 (50 g/L CO₂ released), again sake strain, but also VL1 strain, showed an increased expression of genes involved in formation of higher alcohols in the Ehrlich pathway, namely ADH7, ADH6 and AAD6, which is in accordance with the higher levels of methionol, isobutanol, isoamyl alcohol and phenylethanol observed.

Conclusions

Our approach revealed successful to integrate data from several technologies (HPLC, GC-MS, microarrays) and using different data analysis methods (PCA, MFA). The results obtained increased our knowledge on the production of wine aroma and flavour, identifying new gene in association to the formation of flavour active compounds, mainly in the production of fatty acids, and ethyl and acetate esters.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3816-1) contains supplementary material, which is available to authorized users.

Accurate and sensitive determination of molar fractions of 13C-Labeled intracellular metabolites in cell cultures grown in the presence of isotopically-labeled glucose

This work describes a methodology based on multiple linear regression and GC-MS for the determination of molar fractions of isotopically-labeled intracellular metabolites in cell cultures. Novel aspects of this work are: i) the calculation of theoretical isotopic distributions of the different isotopologues from an experimentally measured value of % 13C enrichment of the labeled precursor ii) the calculation of the contribution of lack of mass resolution of the mass spectrometer and different fragmentation mechanism such as the loss or gain of hydrogen atoms in the EI source to measure the purity of the selected cluster for each metabolite and iii) the validation of the methodology not only by the analysis of gravimetrically prepared mixtures of isotopologues but also by the comparison of the obtained molar fractions with experimental values obtained by GC-Combustion-IRMS based on ¹³C/¹²C isotope ratio measurements. The method is able to measure molar fractions for twenty-eight intracellular metabolites derived from glucose metabolism in cell cultures grown in the presence of ¹³C-labeled Glucose. The validation strategies demonstrate a satisfactory accuracy and precision of the proposed procedure. Also, our results show that the minimum value of ¹³C incorporation that can be accurately quantified is significantly influenced by the calculation of the spectral purity of the measured cluster and the number of ¹³C atoms of the labeled precursor. The proposed procedure was able to accurately quantify gravimetrically prepared mixtures of natural and labeled glucose molar fractions of 0.07% and mixtures of natural and labeled glycine at molar fractions down to 0.7%. The method was applied to initial studies of glucose metabolism of different prostate cancer cell lines.

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.

Metabolomic method: UPLC-q-ToF polar and non-polar metabolites in the healthy rat cerebellum using an in-vial dual extraction

Unbiased metabolomic analysis of biological samples is a powerful and increasingly commonly utilised tool, especially for the analysis of bio-fluids to identify candidate biomarkers. To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites. The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification. Here we describe an in-vial dual extraction (IVDE) method, with reversed phase and hydrophilic liquid interaction chromatography (HILIC) which reproducibly measured over 4,000 metabolite features from as little as 3mg of brain tissue. The aqueous phase was analysed in positive and negative modes following HILIC separation in which 2,838 metabolite features were consistently measured including amino acids, sugars and purine bases. The non-aqueous phase was also analysed in positive and negative modes following reversed phase separation gradients respectively from which 1,183 metabolite features were consistently measured representing metabolites such as phosphatidylcholines, sphingolipids and triacylglycerides. The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum.

Use of chemostat cultures mimicking different phases of wine fermentations as a tool for quantitative physiological analysis

Background

Saccharomyces cerevisiae is the most relevant yeast species conducting the alcoholic fermentation that takes place during winemaking. Although the physiology of this model organism has been extensively studied, systematic quantitative physiology studies of this yeast under winemaking conditions are still scarce, thus limiting the understanding of fermentative metabolism of wine yeast strains and the systematic description, modelling and prediction of fermentation processes. In this study, we implemented and validated the use of chemostat cultures as a tool to simulate different stages of a standard wine fermentation, thereby allowing to implement metabolic flux analyses describing the sequence of metabolic states of S. cerevisae along the wine fermentation.

Results

Chemostat cultures mimicking the different stages of standard wine fermentations of S. cerevisiae EC1118 were performed using a synthetic must and strict anaerobic conditions. The simulated stages corresponded to the onset of the exponential growth phase, late exponential growth phase and cells just entering stationary phase, at dilution rates of 0.27, 0.04, 0.007 h⁻¹, respectively. Notably, measured substrate uptake and product formation rates at each steady state condition were generally within the range of corresponding conversion rates estimated during the different batch fermentation stages.

Moreover, chemostat data were further used for metabolic flux analysis, where biomass composition data for each condition was considered in the stoichiometric model. Metabolic flux distributions were coherent with previous analyses based on batch cultivations data and the pseudo-steady state assumption.

Conclusions

Steady state conditions obtained in chemostat cultures reflect the environmental conditions and physiological states of S. cerevisiae corresponding to the different growth stages of a typical batch wine fermentation, thereby showing the potential of this experimental approach to systematically study the effect of environmental relevant factors such as temperature, sugar concentration, C/N ratio or (micro) oxygenation on the fermentative metabolism of wine yeast strains.

¹³C-based metabolic flux analysis in yeast: the Pichia pastoris case

Metabolic flux analysis based on tracing patterns of stable isotopes, particularly (13)C, comprises a set of methodologies to experimentally quantify intracellular biochemical reaction rates, i.e., to measure carbon flux distributions through a metabolic network. This allows quantifying the response of a metabolic network to an environmental or genetic perturbation (i.e., the metabolic phenotype). Here, we describe a protocol based on growing yeast on a (13)C-labelled substrate and subsequent NMR detection of (13)C-patterns in proteinogenic amino acids. To calculate metabolic fluxes, we describe two complementary mathematical approaches using available software; namely, an approach based on the estimation of local ratios in network nodes, and a method based on a global iterative fitting approach. Furthermore, we consider specificities of these protocols for their application to the yeast Pichia pastoris growing on multicarbon substrates other than glucose (glycerol), as well as the case when methanol is used as co-substrate in combination with glucose or glycerol.

An integrated computational approach for metabolic flux analysis coupled with inference of tandem-MS collisional fragments

MOTIVATION

Metabolic flux analysis (MFA) is a commonly used approach for quantifying metabolic fluxes based on tracking isotope labeling of metabolite within cells. Tandem mass-spectrometry (MS/MS) has been recently shown to be especially useful for MFA by providing rich information on metabolite positional labeling, measuring isotopic labeling patterns of collisional fragments. However, a major limitation in this approach is the requirement that the positional origin of atoms in a collisional fragment would be known a priori, which in many cases is difficult to determine.

RESULTS

Here we show that MS/MS data could also be used to improve flux inference even when the positional origin of fragments is unknown. We develop a novel method, metabolic flux analysis/unknown fragments, that extends on standard MFA and jointly searches for the most likely metabolic fluxes together with the most plausible position of collisional fragments that would optimally match measured MS/MS data. MFA/UF is shown to markedly improve flux prediction accuracy in a simulation model of gluconeogenesis and using experimental MS/MS data in Bacillus subtilis.

Analysis of metabolic flux using dynamic labelling and metabolic modelling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

Quantitative quenching evaluation and direct intracellular metabolite analysis in Penicillium chrysogenum

Sustained progress in metabolic engineering methodologies has stimulated new efforts toward optimizing fungal production strains such as through metabolite analysis of Penicillium chrysogenum industrial-scale processes. Accurate intracellular metabolite quantification requires sampling procedures that rapidly stop metabolism (quenching) and avoid metabolite loss via the cell membrane (leakage). When sampling protocols are validated, the quenching efficiency is generally not quantitatively assessed. For fungal metabolomics, quantitative biomass separation using centrifugation is a further challenge. In this study, P. chrysogenum intracellular metabolites were quantified directly from biomass extracts using automated sampling and fast filtration. A master/slave bioreactor concept was applied to provide industrial production conditions. Metabolic activity during sampling was monitored by 13C tracing. Enzyme activities were efficiently stopped and metabolite leakage was absent. This work provides a reliable method for P. chrysogenum metabolomics and will be an essential base for metabolic engineering of industrial processes.

Systematic applications of metabolomics in metabolic engineering

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Exploring the metabolic state of microorganisms using metabolomics

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes

One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.

Fluxes in metabolic pathways are a highly informative aspect of an organism's phenotype. The experimental determination of such fluxes is well established and has proven very useful. To address some of the limitations of experimental flux analysis, such as when the cell is divided in multiple compartments, stoichiometric modeling provides a valuable addition. The approach that we take is based on stoichiometric modeling where we consider the thermodynamic feasibility of many different possible routes through the metabolic network of Saccharomyces cerevisiae using experimentally determined metabolite concentrations. We show that next to conclusions on single biochemical reactions in the metabolic network, we obtain system-level insights on thermodynamically infeasible flux patterns. We found that the compartmental concentrations of and NADH are the causes for the system-level infeasibilities. With the current advances in quantitative metabolomics and biochemical thermodynamics, we envision that the presented method will help gaining more insight into complex metabolic systems.

Strategies for investigating the plant metabolic network with steady-state metabolic flux analysis: lessons from an Arabidopsis cell culture and other systems

Steady-state (13)C metabolic flux analysis (MFA) is currently the experimental method of choice for generating flux maps of the compartmented network of primary metabolism in heterotrophic and mixotrophic plant tissues. While statistically robust protocols for the application of steady-state MFA to plant tissues have been developed by several research groups, the implementation of the method is still far from routine. The effort required to produce a flux map is more than justified by the information that it contains about the metabolic phenotype of the system, but it remains the case that steady-state MFA is both analytically and computationally demanding. This article provides an overview of principles that underpin the implementation of steady-state MFA, focusing on the definition of the metabolic network responsible for redistribution of the label, experimental considerations relating to data collection, the modelling process that allows a set of metabolic fluxes to be deduced from the labelling data, and the interpretation of flux maps. The article draws on published studies of Arabidopsis cell cultures and other systems, including developing oilseeds, with the aim of providing practical guidance and strategies for handling the issues that arise when applying steady-state MFA to the complex metabolic networks encountered in plants.

Collisional fragmentation of central carbon metabolites in LC-MS/MS increases precision of ¹³C metabolic flux analysis

Experimental determination of fluxes by (13)C-tracers relies on detection of (13)C-patterns in metabolites or by-products. In the field of (13)C metabolic flux analysis, the most recent developments point toward recording labeling patterns by liquid chromatography (LC)-mass spectrometry (MS)/MS directly in intermediates in central carbon metabolism (CCM) to increase temporal resolution. Surprisingly, the flux studies published so far with LC-MS measurements were based on intact metabolic intermediates-thus neglected the potential benefits of using positional information to improve flux estimation. For the first time, we exploit collisional fragmentation to obtain more fine-grained (13)C-data on intermediates of CCM and investigate their impact in (13)C metabolic flux analysis. For the case study of Bacillus subtilis grown in mineral medium with (13)C-labeled glucose, we compare the flux estimates obtained by iterative isotopologue balancing of (13)C-data obtained either by LC-MS/MS for solely intact intermediates or LC-MS/MS for intact and fragmented intermediates of CCM. We show that with LC-MS/MS data, fragment information leads to more precise estimates of fluxes in pentose phosphate pathway, glycolysis, and to the tricarboxylic acid cycle. Additionally, we present an efficient analytical strategy to rapidly acquire large sets of (13)C-patterns by tandem MS, and an in-depth analysis of the collisional fragmentation of primary intermediates. In the future, this catalogue will enable comprehensive in silico calculability analyses to identify the most sensitive measurements and direct experimental design.

Redox regulation in respiring Saccharomyces cerevisiae

BACKGROUND

In biological systems, redox reactions are central to most cellular processes and the redox potential of the intracellular compartment dictates whether a particular reaction can or cannot occur. Indeed the widespread use of redox reactions in biological systems makes their detailed description outside the scope of one review.

SCOPE OF THE REVIEW

Here we will focus on how system-wide redox changes can alter the reaction and transcriptional landscape of Saccharomyces cerevisiae. To understand this we explore the major determinants of cellular redox potential, how these are sensed by the cell and the dynamic responses elicited.

MAJOR CONCLUSIONS

Redox regulation is a large and complex system that has the potential to rapidly and globally alter both the reaction and transcription landscapes. Although we have a basic understanding of many of the sub-systems and a partial understanding of the transcriptional control, we are far from understanding how these systems integrate to produce coherent responses. We argue that this non-linear system self-organises, and that the output in many cases is temperature-compensated oscillations that may temporally partition incompatible reactions in vivo.

GENERAL SIGNIFICANCE

Redox biochemistry impinges on most of cellular processes and has been shown to underpin ageing and many human diseases. Integrating the complexity of redox signalling and regulation is perhaps one of the most challenging areas of biology. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Pyruvate kinase triggers a metabolic feedback loop that controls redox metabolism in respiring cells

In proliferating cells, a transition from aerobic to anaerobic metabolism is known as the Warburg effect, whose reversal inhibits cancer cell proliferation. Studying its regulator pyruvate kinase (PYK) in yeast, we discovered that central metabolism is self-adapting to synchronize redox metabolism when respiration is activated. Low PYK activity activated yeast respiration. However, levels of reactive oxygen species (ROS) did not increase, and cells gained resistance to oxidants. This adaptation was attributable to accumulation of the PYK substrate phosphoenolpyruvate (PEP). PEP acted as feedback inhibitor of the glycolytic enzyme triosephosphate isomerase (TPI). TPI inhibition stimulated the pentose phosphate pathway, increased antioxidative metabolism, and prevented ROS accumulation. Thus, a metabolic feedback loop, initiated by PYK, mediated by its substrate and acting on TPI, stimulates redox metabolism in respiring cells. Originating from a single catalytic step, this autonomous reconfiguration of central carbon metabolism prevents oxidative stress upon shifts between fermentation and respiration.

Eukaryotic metabolism: measuring compartment fluxes

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.

Integration of metabolic reactions and gene regulation

Metabolic reactions and gene regulation are two primary processes of cells. In response to environmental changes cells often adjust the regulatory programs and shift the metabolic states. An integrative investigation and modeling of these two processes would improve our understanding about the cellular systems and may generate substantial impacts in medicine, agriculture, environmental protection, and energy production. We review the studies of the various aspects of the crosstalk between metabolic reactions and gene regulation, including models, empirical evidence, and available databases.

Linking energy metabolism to dysfunctions in mitochondrial respiration--a metabolomics in vitro approach

The study presented here describes the application of metabolite profiling of highly polar, intracellular metabolites after incubation of a mammalian fibroblast cell line with inhibitors of mitochondrial function. A metabolomics approach was used to assess the complex response of the cellular energy metabolism. Metabolic profiles of phosphorylated and carboxylated intracellular metabolites were assessed by UPLC-MS/MS and used to predict the mode of mitochondrial toxicity. Based on distinct metabolic patterns, multivariate data analysis allowed for the discrimination of two groups of toxins: inhibitors of the electron transport in mitochondrial membranes (complex IV inhibitors) and uncouplers of oxidative phosphorylation. Beyond these known interferences, metabolic profiling was able to reveal additional inhibitory effects on the cellular energy metabolism. Most prominently, for three of the toxins, metabolic patterns also disclosed an enhanced activity of the glycerol phosphate shuttle inferring the inhibition of NADH dehydrogenase at complex I. Secondly, inhibition of the electron transport was accompanied by a limiting availability of citric acid cycle intermediates and aspartate. Concomitantly, specific perturbations of the purine nucleotide cycle were observed. We have shown here that metabolomic approaches may assist to predict complex modes of action of toxic compounds on cellular level as well as to unravel specific dysfunctions in the energy metabolism.

Bridging the gap between fluxomics and industrial biotechnology

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. ¹³C-based metabolic flux analysis (¹³C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both ¹³C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

Metabolic flux analysis of the mixotrophic metabolisms in the green sulfur bacterium Chlorobaculum tepidum

The photosynthetic green sulfur bacterium Chlorobaculum tepidum assimilates CO(2) and organic carbon sources (acetate or pyruvate) during mixotrophic growth conditions through a unique carbon and energy metabolism. Using a (13)C-labeling approach, this study examined biosynthetic pathways and flux distributions in the central metabolism of C. tepidum. The isotopomer patterns of proteinogenic amino acids revealed an alternate pathway for isoleucine synthesis (via citramalate synthase, CimA, CT0612). A (13)C-assisted flux analysis indicated that carbons in biomass were mostly derived from CO(2) fixation via three key routes: the reductive tricarboxylic acid (RTCA) cycle, the pyruvate synthesis pathway via pyruvate:ferredoxin oxidoreductase, and the CO(2)-anaplerotic pathway via phosphoenolpyruvate carboxylase. During mixotrophic growth with acetate or pyruvate as carbon sources, acetyl-CoA was mainly produced from acetate (via acetyl-CoA synthetase) or citrate (via ATP citrate lyase). Pyruvate:ferredoxin oxidoreductase converted acetyl-CoA and CO(2) to pyruvate, and this growth-rate control reaction is driven by reduced ferredoxin generated during phototrophic growth. Most reactions in the RTCA cycle were reversible. The relative fluxes through the RTCA cycle were 80∼100 units for mixotrophic cultures grown on acetate and 200∼230 units for cultures grown on pyruvate. Under the same light conditions, the flux results suggested a trade-off between energy-demanding CO(2) fixation and biomass growth rate; C. tepidum fixed more CO(2) and had a higher biomass yield (Y(X/S), mole carbon in biomass/mole substrate) in pyruvate culture (Y(X/S) = 9.2) than in acetate culture (Y(X/S) = 6.4), but the biomass growth rate was slower in pyruvate culture than in acetate culture.

Modeling with a view to target identification in metabolic engineering: a critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy.

Dynamic flux responses in riboflavin overproducing Bacillus subtilis to increasing glucose limitation in fed-batch culture

How do intracellular fluxes respond to dynamically increasing glucose limitation when the physiology changes from strong overflow metabolism near to exclusively maintenance metabolism? Here we investigate this question in a typical industrial, glucose-limited fed-batch cultivation with a riboflavin overproducing Bacillus subtilis strain. To resolve dynamic flux changes, a novel approach to (13)C flux analysis was developed that is based on recording (13)C labeling patterns in free intracellular amino acids. Fluxes are then estimated with stationary flux ratio and iterative isotopomer balancing methods, for which a decomposition of the process into quasi-steady states and estimation of isotopic steady state (13)C labeling patterns was necessary. By this approach, we achieve a temporal resolution of 30-60 min that allows us to resolve the slow metabolic transients that typically occur in such cultivations. In the late process phase we found, most prominently, almost exclusive respiratory metabolism, significantly increased pentose phosphate pathway contribution and a strongly decreased futile cycle through the PEP carboxykinase. As a consequence, higher catabolic NADPH formation occurred than was necessary to satisfy the anabolic demands, suggesting a transhydrogenase-like mechanism to close the balance of reducing equivalents.

Metabolic flux analysis in eukaryotes

Metabolic flux analysis (MFA) represents a powerful tool for systems biology research on eukaryotic cells. This review describes recent advances, the challenges as well as applications of metabolic flux analysis comprising fungi, mammalian cells and plants. While MFA is widely established and applied in microorganisms, it remains still a challenge to adapt these methods to eukaryotic cell systems having a higher complexity particularly concerning compartmentation or media composition. In fungi MFA was used in the past few years to analyze a variety of conditions and factors and their effects on cellular metabolism. In mammalian cells MFA was applied mainly in cell culture technology and in medical and toxicological research. (13)C metabolic studies on native whole plants are additionally challenging by the fact that CO(2) is usually the only carbon source.

Systems biology of industrial microorganisms

The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

Metabolic fluxes during strong carbon catabolite repression by malate in Bacillus subtilis

Commonly glucose is considered to be the only preferred substrate in Bacillus subtilis whose presence represses utilization of other alternative substrates. Because recent data indicate that malate might be an exception, we quantify here the carbon source utilization hierarchy. Based on physiology and transcriptional data during co-utilization experiments with eight carbon substrates, we demonstrate that malate is a second preferred carbon source for B. subtilis, which is rapidly co-utilized with glucose and strongly represses the uptake of alternative substrates. From the different hierarchy and degree of catabolite repression exerted by glucose and malate, we conclude that both substrates might act through different molecular mechanisms. To obtain a quantitative and functional network view of how malate is (co)metabolized, we developed a novel approach to metabolic flux analysis that avoids error-prone, intuitive, and ad hoc decisions on (13)C rearrangements. In particular, we developed a rigorous approach for deriving reaction reversibilities by combining in vivo intracellular metabolite concentrations with a thermodynamic feasibility analysis. The thus-obtained analytical model of metabolism was then used for network-wide isotopologue balancing to estimate the intracellular fluxes. These (13)C-flux data revealed an extraordinarily high malate influx that is primarily catabolized via the gluconeogenic reactions and toward overflow metabolism. Furthermore, a considerable NADPH-producing malic enzyme flux is required to supply the biosynthetically required NADPH in the presence of malate. Co-utilization of glucose and malate resulted in a synergistic decrease of the respiratory tricarboxylic acid cycle flux.

Evaluation of 13C isotopic tracers for metabolic flux analysis in mammalian cells

(13)C metabolic flux analysis (MFA) is the most comprehensive means of characterizing cellular metabolic states. Uniquely labeled isotopic tracers enable more focused analyses to probe specific reactions within the network. As a result, the choice of tracer largely determines the precision with which one can estimate metabolic fluxes, especially in complex mammalian systems that require multiple substrates. Here we have experimentally determined metabolic fluxes in a tumor cell line, successfully recapitulating the hallmarks of cancer cell metabolism. Using these data, we computationally evaluated specifically labeled (13)C glucose and glutamine tracers for their ability to precisely and accurately estimate fluxes in central carbon metabolism. These methods enabled us to identify the optimal tracer for analyzing individual fluxes, specific pathways, and central carbon metabolism as a whole. [1,2-(13)C(2)]glucose provided the most precise estimates for glycolysis, the pentose phosphate pathway, and the overall network. Tracers such as [2-(13)C]glucose and [3-(13)C]glucose also outperformed the more commonly used [1-(13)C]glucose. [U-(13)C(5)]glutamine emerged as the preferred isotopic tracer for the analysis of the tricarboxylic acid (TCA) cycle. These results provide valuable, quantitative information on the performance of (13)C-labeled substrates and can aid in the design of more informative MFA experiments in mammalian cell culture.

(13)C-based metabolic flux analysis

Stable isotope, and in particular (13)C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on (13)C-labeled glucose and subsequent gas chromatography mass spectrometric detection of (13)C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on (13)C-pattern in free intracellular metabolites. The presented protocols take 5-10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.

Advances in analysis of microbial metabolic fluxes via (13)C isotopic labeling

Metabolic flux analysis via (13)C labeling ((13)C MFA) quantitatively tracks metabolic pathway activity and determines overall enzymatic function in cells. Three core techniques are necessary for (13)C MFA: (1) a steady state cell culture in a defined medium with labeled-carbon substrates; (2) precise measurements of the labeling pattern of targeted metabolites; and (3) evaluation of the data sets obtained from mass spectrometry measurements with a computer model to calculate the metabolic fluxes. In this review, we summarize recent advances in the (13)C-flux analysis technologies, including mini-bioreactor usage for tracer experiments, isotopomer analysis of metabolites via high resolution mass spectrometry (such as GC-MS, LC-MS, or FT-ICR), high performance and large-scale isotopomer modeling programs for flux analysis, and the integration of fluxomics with other functional genomics studies. It will be shown that there is a significant value for (13)C-based metabolic flux analysis in many biological research fields.

Control of lipid accumulation in the yeast Yarrowia lipolytica

A genomic comparison of Yarrowia lipolytica and Saccharomyces cerevisiae indicates that the metabolism of Y. lipolytica is oriented toward the glycerol pathway. To redirect carbon flux toward lipid synthesis, the GUT2 gene, which codes for the glycerol-3-phosphate dehydrogenase isomer, was deleted in Y. lipolytica in this study. This Delta gut2 mutant strain demonstrated a threefold increase in lipid accumulation compared to the wild-type strain. However, mobilization of lipid reserves occurred after the exit from the exponential phase due to beta-oxidation. Y. lipolytica contains six acyl-coenzyme A oxidases (Aox), encoded by the POX1 to POX6 genes, that catalyze the limiting step of peroxisomal beta-oxidation. Additional deletion of the POX1 to POX6 genes in the Delta gut2 strain led to a fourfold increase in lipid content. The lipid composition of all of the strains tested demonstrated high proportions of FFA. The size and number of the lipid bodies in these strains were shown to be dependent on the lipid composition and accumulation ratio.

Nectar formation and floral nectary anatomy of Anigozanthos flavidus: a combined magnetic resonance imaging and spectroscopy study

Metabolic processes underlying the formation of floral nectar carbohydrates, especially the generation of the proportions of fructose, glucose, and sucrose, are important for understanding ecological plant-pollinator interactions. The ratio of sucrose-derived hexoses, fructose and glucose, in the floral nectar of Anigozanthos flavidus (Haemodoraceae) was observed to be different from 1:1, which cannot be explained by the simple action of invertases. Various NMR techniques were used to investigate how such an unbalanced ratio of the two nectar hexoses can be formed. High-resolution (13)C NMR spectroscopy in solution was used to determine the proportion of carbohydrates in vascular bundles of excised inflorescences fed with (13)C-labelled carbohydrates. These experiments verified that feeding did not affect the metabolic processes involved in nectar formation. In vivo magnetic resonance imaging (e.g. cyclic J cross-polarization) was used to detect carbohydrates in vascular bundles and (1)H spin echo imaging non-invasively displayed the architecture of tepal nectaries and showed how they are connected to the vascular bundles. A model of the carbohydrate metabolism involved in forming A. flavidus floral nectar was established. Sucrose from the vascular bundles is not directly secreted into the lumen of the nectary but, either before or after invertase-catalysed hydrolyses, taken up by nectary cells and cycled at least partly through glycolysis, gluconeogenesis, and the pentose phosphate pathway. Secretion of the two hexoses in the cytosolic proportion could elegantly explain the observed fructose:glucose ratio of the nectar.

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

Background

Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³C isotopomer data are typically needed.

Results

We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³C isotopomer measurement techniques, substrates and substrate labelling patterns.

By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.

Conclusion

The core of ¹³C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.

Metabolomics for plant stress response

Stress in plants could be defined as any change in growth condition(s) that disrupts metabolic homeostasis and requires an adjustment of metabolic pathways in a process that is usually referred to as acclimation. Metabolomics could contribute significantly to the study of stress biology in plants and other organisms by identifying different compounds, such as by-products of stress metabolism, stress signal transduction molecules or molecules that are part of the acclimation response of plants. These could be further tested by direct measurements, correlated with changes in transcriptome and proteome expression and confirmed by mutant analysis. In this review, we will discuss recent application of metabolomics and system biology to the area of plant stress response. We will describe approaches such as metabolic profiling and metabolic fingerprinting as well as combination of different 'omics' platforms to achieve a holistic view of the plant response stress and conduct detailed pathway analysis.