Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.Plants are of vital significance as a source of food (Grusak and DellaPenna, 1999; Rogalski and Carrer, 2011), feed (Lu et al., 2011), energy (Tilman et al., 2006; Parmar et al., 2011), and feedstocks for the chemical industry (Metzger and Bornscheuer, 2006; Kinghorn et al., 2011). Given the close connection between plant metabolism and the usability of plant products, there is a growing interest in understanding and predicting the behavior and regulation of plant metabolic processes. In order to increase crop quality and yield, there is a need for methods guiding the rational redesign of the plant metabolic network (Schwender, 2009).Mathematical modeling of plant metabolism offers new approaches to understand, predict, and modify complex plant metabolic processes. In plant research, the issue of metabolic modeling is constantly gaining attention, and different modeling approaches applied to plant metabolism exist, ranging from highly detailed quantitative to less complex qualitative approaches (for review, see Giersch, 2000; Morgan and Rhodes, 2002; Poolman et al., 2004; Rios-Estepa and Lange, 2007).A widely used modeling approach is flux balance analysis (FBA), which allows the prediction of metabolic capabilities and steady-state fluxes under different environmental and genetic backgrounds using (non)linear optimization (Orth et al., 2010). Assuming steady-state conditions, FBA has the advantage of not requiring the knowledge of kinetic parameters and, therefore, can be applied to model detailed, large-scale systems. In recent years, the FBA approach has been applied to several different plant species, such as maize (Zea mays; Dal’Molin et al., 2010; Saha et al., 2011), barley (Hordeum vulgare; Grafahrend-Belau et al., 2009b; Melkus et al., 2011; Rolletschek et al., 2011), rice (Oryza sativa; Lakshmanan et al., 2013), Arabidopsis (Arabidopsis thaliana; Poolman et al., 2009; de Oliveira Dal’Molin et al., 2010; Radrich et al., 2010; Williams et al., 2010; Mintz-Oron et al., 2012; Cheung et al., 2013), and rapeseed (Brassica napus; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011), as well as algae (Boyle and Morgan, 2009; Cogne et al., 2011; Dal’Molin et al., 2011) and photoautotrophic bacteria (Knoop et al., 2010; Montagud et al., 2010; Boyle and Morgan, 2011). These models have been used to study different aspects of metabolism, including the prediction of optimal metabolic yields and energy efficiencies (Dal’Molin et al., 2010; Boyle and Morgan, 2011), changes in flux under different environmental and genetic backgrounds (Grafahrend-Belau et al., 2009b; Dal’Molin et al., 2010; Melkus et al., 2011), and nonintuitive metabolic pathways that merit subsequent experimental investigations (Poolman et al., 2009; Knoop et al., 2010; Rolletschek et al., 2011). Although FBA of plant metabolic models was shown to be capable of reproducing experimentally determined flux distributions (Williams et al., 2010; Hay and Schwender, 2011b) and generating new insights into metabolic behavior, capacities, and efficiencies (Sweetlove and Ratcliffe, 2011), challenges remain to advance the utility and predictive power of the models.Given that many plant metabolic functions are based on interactions between different subcellular compartments, cell types, tissues, and organs, the reconstruction of organ-specific models and the integration of these models into interacting multiorgan and/or whole-plant models is a prerequisite to get insight into complex plant metabolic processes organized on a whole-plant scale (e.g. source-sink interactions). Almost all FBA models of plant metabolism are restricted to one cell type (Boyle and Morgan, 2009; Knoop et al., 2010; Montagud et al., 2010; Cogne et al., 2011; Dal’Molin et al., 2011), one tissue or one organ (Grafahrend-Belau et al., 2009b; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011; Mintz-Oron et al., 2012), and only one model exists taking into account the interaction between two cell types by specifying the interaction between mesophyll and bundle sheath cells in C4 photosynthesis (Dal’Molin et al., 2010). So far, no model representing metabolism at the whole-plant scale exists.Considering whole-plant metabolism raises the problem of taking into account temporal and environmental changes in metabolism during plant development and growth. Although classical static FBA is unable to predict the dynamics of metabolic processes, as the network analysis is based on steady-state solutions, time-dependent processes can be taken into account by extending the classical static FBA to a dynamic flux balance analysis (dFBA), as proposed by Mahadevan et al. (2002). The static (SOA) and dynamic optimization approaches introduced in this work provide a framework for analyzing the transience of metabolism by integrating kinetic expressions to dynamically constrain exchange fluxes. Due to the requirement of knowing or estimating a large number of kinetic parameters, so far dFBA has only been applied to a plant metabolic model once, to study the photosynthetic metabolism in the chloroplasts of C3 plants by a simplified model of five biochemical reactions (Luo et al., 2009). Integrating a dynamic model into a static FBA model is an alternative approach to perform dFBA.In this study, a multiscale metabolic modeling (MMM) approach was applied with the aim of achieving a spatiotemporal resolution of cereal crop plant metabolism. To provide a framework for the in silico analysis of the metabolic dynamics of barley on a whole-plant scale, the MMM approach integrates a static multiorgan FBA model and a dynamic whole-plant multiscale functional plant model (FPM) to perform dFBA. The performance of the novel whole-plant MMM approach was tested by studying source-sink interactions during the seed developmental phase of barley plants.     

Analysis of Metabolic Subnetworks by Flux Cone Projection

Background

Analysis of elementary modes (EMs) is proven to be a powerful constraint-based method in the study of metabolic networks. However, enumeration of EMs is a hard computational task. Additionally, due to their large number, EMs cannot be simply used as an input for subsequent analysis. One possibility is to limit the analysis to a subset of interesting reactions. However, analysing an isolated subnetwork can result in finding incorrect EMs which are not part of any steady-state flux distribution of the original network. The ideal set to describe the reaction activity in a subnetwork would be the set of all EMs projected to the reactions of interest. Recently, the concept of "elementary flux patterns" (EFPs) has been proposed. Each EFP is a subset of the support (i.e., non-zero elements) of at least one EM.

Results

We introduce the concept of ProCEMs (Projected Cone Elementary Modes). The ProCEM set can be computed by projecting the flux cone onto a lower-dimensional subspace and enumerating the extreme rays of the projected cone. In contrast to EFPs, ProCEMs are not merely a set of reactions, but projected EMs. We additionally prove that the set of EFPs is included in the set of ProCEM supports. Finally, ProCEMs and EFPs are compared for studying substructures of biological networks.

Conclusions

We introduce the concept of ProCEMs and recommend its use for the analysis of substructures of metabolic networks for which the set of EMs cannot be computed.     

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.     

大肠杆菌L-色氨酸合成的代谢流分析简

目的：从代谢流的层面研究育种过程中基因操作对色氨酸积累的影响,为色氨酸菌种选育的设计思路提供理论指导和验证。方法：根据实验菌株的代谢特点构建￡一色氨酸代谢网络图,对出发菌株TRTH0709,及其重组菌株TRTH1013、TRTH1105和TRTH1107在30L发酵罐中进行分批流加发酵试验,在发酵进入稳定期后的26．28h,分别检测主要胞外代谢物的浓度并计算变化速率。结果和结论：得到了各菌株在拟稳态下的代谢流分布图。转酮酶基因（tktA）和磷酸烯醇式丙酮酸合成酶基因（ppsA）过表达能显著影响中心代谢途径,使代谢流向有利于色氨酸合成的方向改变,贮碳因子基因（csrA）敲除的影响较小,但在tktA和ppsA过表达质粒存在的情况下对色氨酸合成的代谢流有明显的促进作用。进一步的菌种改造仍有待进行,葡萄糖转运系统的替代和三羧酸循环的减弱是主要方向。     

L-缬氨酸产生菌的选育及其代谢流分析

以谷氨酸棒杆菌(Corynebacterium glutamicum)AS1.495(Leu^-)为出发菌株,通过多次亚硝基胍(NTG)诱变,给AS1．495(Leu^-)依次叠加L-AAH^as,2-TA^r,Vd^-的遗传标记,得到突变株AATV341(Leu^-,L-AAH^as,2-TA^r,Vd^-),可在8％的葡萄糖培养基积累L-缬氨酸24．5g／L,比出发菌株提高了5．13倍。同时运用代谢流量分析理论,测定出发菌株AS1．495及其突变株AATV341在L-缬氨酸合成阶段的代谢流量,并初步进行比较和分析,发现遗传标记的引入使流量分配发生了重大变化,流量分配朝着有利于L-缬氨酸合成的方向改变。     

Structural Control of Metabolic Flux

Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions'' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign “share of control” to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions.     

A Peptide-Based Method for 13C Metabolic Flux Analysis in Microbial Communities

The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, ¹³C Metabolic Flux Analysis (¹³C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during ¹³C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of ¹³C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based ¹³C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species.     

Automated Protein Turnover Calculations from 15N Partial Metabolic Labeling LC/MS Shotgun Proteomics Data

Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS ¹⁵N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (¹⁵N) to the intensity sum of all light (¹⁴N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.     

L-谷氨酰胺产生菌的选育和代谢流量分析

目的:建立并完善嗜乙酰乙酸棒杆菌YL012及其突变株LCHA0082合成L-谷氨酰胺的中心代谢网络.方法:分别测定了它们在特定培养时段(48h～50h)L-谷氨酰胺等代谢物的胞外浓度,由此计算这一时段这些代谢物在发酵液中积累(或消耗)的速率,分别作出这两株菌在拟稳态下的代谢流量分布图,进而研究诱变育种过程中不同诱变标记对代谢网络中L-谷氨酰胺合成流量分布的影响.结果:育种操作使流量分配朝着有利于L-谷氨酰胺合成的方向改变,流入谷氨酸节点的流量由29.198mmol/L·h上升到44.854mmol/L·h,提高到原来的1.5倍左右,合成L-谷氨酰胺的流量由18.138mmol/L·h上升至31.065mmol/L·h,效果明显.结论:从代谢流量分析角度上,证明诱变育种对代谢流量的改变起到明显的作用,代谢流量分析也为新的设计育种提供了思路.     

Metabolic fingerprinting of rat urine by LC/MS Part 2. Data pretreatment methods for handling of complex data

Metabolic fingerprinting of biofluids like urine is a useful technique for detecting differences between individuals. With this approach, it might be possible to classify samples according to their biological relevance. In Part 1 of this work a method for the comprehensive screening of metabolites was described, using two different liquid chromatography (LC) column set-ups and detection by electrospray ionization mass spectrometry (ESI-MS). Data pretreatment of the resulting data described in is needed to reduce the complexity of the data and to obtain useful metabolic fingerprints. Three different approaches, i.e., reduced dimensionality (RD), MarkerLynx, and MS Resolver, were compared for the extraction of information. The pretreated data were then subjected to multivariate data analysis by partial least squares discriminant analysis (PLS-DA) for classification. By combining two different chromatographic procedures and data analysis, the detection of metabolites was enhanced as well as the finding of metabolic fingerprints that govern classification. Additional potential biomarkers or xenobiotic metabolites were detected in the fraction containing highly polar compounds that are normally discarded when using reversed-phase liquid chromatography.     

Modeling contaminants in AP-MS/MS experiments

Identification of protein-protein interactions (PPI) by affinity purification (AP) coupled with tandem mass spectrometry (AP-MS/MS) produces large data sets with high rates of false positives. This is in part because of contamination at the AP level (due to gel contamination, nonspecific binding to the TAP columns in the context of tandem affinity purification, insufficient purification, etc.). In this paper, we introduce a Bayesian approach to identify false-positive PPIs involving contaminants in AP-MS/MS experiments. Specifically, we propose a confidence assessment algorithm (called Decontaminator) that builds a model of contaminants using a small number of representative control experiments. It then uses this model to determine whether the Mascot score of a putative prey is significantly larger than what was observed in control experiments and assigns it a p-value and a false discovery rate. We show that our method identifies contaminants better than previously used approaches and results in a set of PPIs with a larger overlap with databases of known PPIs. Our approach will thus allow improved accuracy in PPI identification while reducing the number of control experiments required.     

Quantification of Metabolic Rearrangements During Neural Stem Cells Differentiation into Astrocytes by Metabolic Flux Analysis

Proliferation and differentiation of neural stem cells (NSCs) have a crucial role to ensure neurogenesis and gliogenesis in the mammalian brain throughout life. As there is growing evidence for the significance of metabolism in regulating cell fate, knowledge on the metabolic programs in NSCs and how they evolve during differentiation into somatic cells may provide novel therapeutic approaches to address brain diseases. In this work, we applied a quantitative analysis to assess how the central carbon metabolism evolves upon differentiation of NSCs into astrocytes. Murine embryonic stem cell (mESC)-derived NSCs and astrocytes were incubated with labelled [1-¹³C]glucose and the label incorporation into intracellular metabolites was followed by GC-MS. The obtained ¹³C labelling patterns, together with uptake/secretion rates determined from supernatant analysis, were integrated into an isotopic non-stationary metabolic flux analysis (¹³C-MFA) model to estimate intracellular flux maps. Significant metabolic differences between NSCs and astrocytes were identified, with a general downregulation of central carbon metabolism during astrocytic differentiation. While glucose uptake was 1.7-fold higher in NSCs (on a per cell basis), a high lactate-secreting phenotype was common to both cell types. Furthermore, NSCs consumed glutamine from the medium; the highly active reductive carboxylation of alpha-ketoglutarate indicates that this was converted to citrate and used for biosynthetic purposes. In astrocytes, pyruvate entered the TCA cycle mostly through pyruvate carboxylase (81%). This pathway supported glutamine and citrate secretion, recapitulating well described metabolic features of these cells in vivo. Overall, this fluxomics study allowed us to quantify the metabolic rewiring accompanying astrocytic lineage specification from NSCs.     

Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.     

Phosphoketolase Pathway for Xylose Catabolism in Clostridium acetobutylicum Revealed by 13C Metabolic Flux Analysis

Solvent-producing clostridia are capable of utilizing pentose sugars, including xylose and arabinose; however, little is known about how pentose sugars are catabolized through the metabolic pathways in clostridia. In this study, we identified the xylose catabolic pathways and quantified their fluxes in Clostridium acetobutylicum based on [1-(13)C]xylose labeling experiments. The phosphoketolase pathway was found to be active, which contributed up to 40% of the xylose catabolic flux in C. acetobutylicum. The split ratio of the phosphoketolase pathway to the pentose phosphate pathway was markedly increased when the xylose concentration in the culture medium was increased from 10 to 20 g liter(-1). To our knowledge, this is the first time that the in vivo activity of the phosphoketolase pathway in clostridia has been revealed. A phosphoketolase from C. acetobutylicum was purified and characterized, and its activity with xylulose-5-P was verified. The phosphoketolase was overexpressed in C. acetobutylicum, which resulted in slightly increased xylose consumption rates during the exponential growth phase and a high level of acetate accumulation.     

13C Metabolic Flux Analysis of acetate conversion to lipids by Yarrowia lipolytica

Volatile fatty acids (VFAs) are an inexpensive and renewable carbon source that can be generated from gas fermentation and anaerobic digestion of fermentable wastes. The oleaginous yeast Yarrowia lipolytica is a promising biocatalyst that can utilize VFAs and convert them into triacylglycerides (TAGs). However, currently there is limited knowledge on the metabolism of Y. lipolytica when cultured on VFAs. To develop a better understanding, we used acetate as the sole carbon source to culture two strains, a control strain and a previously engineered strain for lipid overaccumulation. For both strains, metabolism during the growth phase and lipid production phase were investigated by metabolic flux analysis using two parallel sodium acetate tracers. The resolved flux distributions demonstrate that the glyoxylate shunt pathway is constantly active and the flux through gluconeogenesis varies depending on strain and phase. In particular, by regulating the activities of malate transport and pyruvate kinase, the cells divert only a portion of the glyoxylate shunt flux required to satisfy the needs for anaplerotic reactions and NADPH production through gluconeogenesis and the oxidative pentose phosphate pathway (PPP). Excess flux flows back to the tricarboxylic acid (TCA) cycle for energy production. As with the case of glucose as the substrate, the primary source for lipogenic NADPH is derived from the oxidative PPP.     

Metabolic Flux Analysis of Mitochondrial Uncoupling in 3T3-L1 Adipocytes

Background

Increasing energy expenditure at the cellular level offers an attractive option to limit adiposity and improve whole body energy balance. In vivo and in vitro observations have correlated mitochondrial uncoupling protein-1 (UCP1) expression with reduced white adipose tissue triglyceride (TG) content. The metabolic basis for this correlation remains unclear.

Methodology/Principal Findings

This study tested the hypothesis that mitochondrial uncoupling requires the cell to compensate for the decreased oxidation phosphorylation efficiency by up-regulating lactate production, thus redirecting carbon flux away from TG synthesis. Metabolic flux analysis was used to characterize the effects of non-lethal, long-term mitochondrial uncoupling (up to 18 days) on the pathways of intermediary metabolism in differentiating 3T3-L1 adipocytes. Uncoupling was induced by forced expression of UCP1 and chemical (FCCP) treatment. Chemical uncoupling significantly decreased TG content by ca. 35%. A reduction in the ATP level suggested diminished oxidative phosphorylation efficiency in the uncoupled adipocytes. Flux analysis estimated significant up-regulation of glycolysis and down-regulation of fatty acid synthesis, with chemical uncoupling exerting quantitatively larger effects.

Conclusions/Significance

The results of this study support our hypothesis regarding uncoupling-induced redirection of carbon flux into glycolysis and lactate production, and suggest mitochondrial proton translocation as a potential target for controlling adipocyte lipid metabolism.     

Comparative Metabolic Flux Analysis of Lysine-Producing Corynebacterium glutamicum Cultured on Glucose or Fructose

A comprehensive approach to ¹³C tracer studies, labeling measurements by gas chromatography-mass spectrometry, metabolite balancing, and isotopomer modeling, was applied for comparative metabolic network analysis of lysine-producing Corynebacterium glutamicum on glucose or fructose. Significantly reduced yields of lysine and biomass and enhanced formation of dihydroxyacetone, glycerol, and lactate in comparison to those for glucose resulted on fructose. Metabolic flux analysis revealed drastic differences in intracellular flux depending on the carbon source applied. On fructose, flux through the pentose phosphate pathway (PPP) was only 14.4% of the total substrate uptake flux and therefore markedly decreased compared to that for glucose (62.0%). This result is due mainly to (i) the predominance of phosphoenolpyruvate-dependent phosphotransferase systems for fructose uptake (PTS_Fructose) (92.3%), resulting in a major entry of fructose via fructose 1,6-bisphosphate, and (ii) the inactivity of fructose 1,6-bisphosphatase (0.0%). The uptake of fructose during flux via PTS_Mannose was only 7.7%. In glucose-grown cells, the flux through pyruvate dehydrogenase (70.9%) was much less than that in fructose-grown cells (95.2%). Accordingly, flux through the tricarboxylic acid cycle was decreased on glucose. Normalized to that for glucose uptake, the supply of NADPH during flux was only 112.4% on fructose compared to 176.9% on glucose, which might explain the substantially lower lysine yield of C. glutamicum on fructose. Balancing NADPH levels even revealed an apparent deficiency of NADPH on fructose, which is probably overcome by in vivo activity of malic enzyme. Based on these results, potential targets could be identified for optimization of lysine production by C. glutamicum on fructose, involving (i) modification of flux through the two PTS for fructose uptake, (ii) amplification of fructose 1,6-bisphosphatase to increase flux through the PPP, and (iii) knockout of a not-yet-annotated gene encoding dihydroxyacetone phosphatase or kinase activity to suppress overflow metabolism. Statistical evaluation revealed high precision of the estimates of flux, so the observed differences for metabolic flux are clearly substrate specific.     

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims toanalyze all metabolites in a biological sample comprehensively.The detailed metabolite profiling of thousands of plant sampleshas great potential for directly elucidating plant metabolicprocesses. However, both a comprehensive analysis and a highthroughput are difficult to achieve at the same time due tothe wide diversity of metabolites in plants. Here, we have establisheda novel and practical metabolomics methodology for quantifyinghundreds of targeted metabolites in a high-throughput manner.Multiple reaction monitoring (MRM) using tandem quadrupole massspectrometry (TQMS), which monitors both the specific precursorions and product ions of each metabolite, is a standard techniquein targeted metabolomics, as it enables high sensitivity, reproducibilityand a broad dynamic range. In this study, we optimized the MRMconditions for specific compounds by performing automated flowinjection analyses with TQMS. Based on a total of 61,920 spectrafor 860 authentic compounds, the MRM conditions of 497 compoundswere successfully optimized. These were applied to high-throughputautomated analysis of biological samples using TQMS coupledwith ultra performance liquid chromatography (UPLC). By thisanalysis, approximately 100 metabolites were quantified in eachof 14 plant accessions from Brassicaceae, Gramineae and Fabaceae.A hierarchical cluster analysis based on the metabolite accumulationpatterns clearly showed differences among the plant families,and family-specific metabolites could be predicted using a batch-learningself-organizing map analysis. Thus, the automated widely targetedmetabolomics approach established here should pave the way forlarge-scale metabolite profiling and comparative metabolomics.