A guide to metabolic flux analysis in metabolic engineering: Methods,tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.     

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and ¹³C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.     

Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell’s biochemistry.We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.     

A kinetic model describes metabolic response to perturbations and distribution of flux control in the benzenoid network of Petunia hybrida

In recent years there has been much interest in the genetic enhancement of plant metabolism; however, attempts at genetic modification are often unsuccessful due to an incomplete understanding of network dynamics and their regulatory properties. Kinetic modeling of plant metabolic networks can provide predictive information on network control and response to genetic perturbations, which allow estimation of flux at any concentration of intermediate or enzyme in the system. In this research, a kinetic model of the benzenoid network was developed to simulate whole network responses to different concentrations of supplied phenylalanine (Phe) in petunia flowers and capture flux redistributions caused by genetic manipulations. Kinetic parameters were obtained by network decomposition and non‐linear least squares optimization of data from petunia flowers supplied with either 75 or 150 mm ²H₅‐Phe. A single set of kinetic parameters simultaneously accommodated labeling and pool size data obtained for all endogenous and emitted volatiles at the two concentrations of supplied ²H₅‐Phe. The generated kinetic model was validated using flowers from transgenic petunia plants in which benzyl CoA:benzyl alcohol/phenylethanol benzoyltransferase (BPBT) was down‐regulated via RNAi. The determined in vivo kinetic parameters were used for metabolic control analysis, in which flux control coefficients were calculated for fluxes around the key branch point at Phe and revealed that phenylacetaldehyde synthase activity is the primary controlling factor for the phenylacetaldehyde branch of the benzenoid network. In contrast, control of flux through the β‐oxidative and non‐β‐oxidative pathways is highly distributed.     

Stoichiometric analysis of self-maintaining metabolisms

This paper presents an extension of stoichiometric analysis in systems where the catalytic compounds (enzymes) are also intermediates of the metabolic network (dual property), so they are produced and degraded by the reaction network itself. To take this property into account, we introduce the definition of enzyme-maintaining mode, a set of reactions that produces its own catalyst and can operate at stationary state. Moreover, an enzyme-maintaining mode is defined as elementary with respect to a given reaction if the removal of any of the remaining reactions causes the cessation of any steady state flux through this reference reaction. These concepts are applied to determine the network structure of a simple self-maintaining system.     

Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity.
This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.     

碱蓬属植物耐盐机理研究进展

碱蓬属(Suaeda)植物是一类典型的真盐生植物,属于重要的盐生植物资源,全球广泛分布.人们已经对20种碱蓬属植物进行了观察和盐胁迫实验,研究了不同器官或组织的生理生化特征及其对盐胁迫的反应,并基于这些研究分析了盐胁迫的应答机制.叶片肉质化、细胞内离子区域化、渗透调节物质增加和抗氧化系统能力增强是碱蓬属植物响应和适应盐胁迫的重要方式和途径.但迄今为止的研究工作尚有一定的局限性,主要包括:研究工作主要集中在植物地上部分,而对植物地下部分的研究较少;多是少数生物学指标或生理学现象的单独观察,而缺乏对生理代谢过程的整体和综合分析;针对某种碱蓬的独立分析较多,而与近缘种的比较研究较少;植物对中性盐胁迫的反应研究较多,而对碱性盐的研究较少.为进一步系统阐明碱蓬属植物的耐盐机制,今后的工作应注重碱蓬属植物响应和适应盐胁迫的信号网络和调控机制研究,基于系统生物学研究思路,采用现代组学技术探索该属植物响应盐胁迫的由复杂信号网络调控的特殊生理特征和特异代谢途径.     

in silico analyses of metabolic pathway and protein interaction network for identification of next gen therapeutic targets in Chlamydophila pneumoniae

Chlamydophila pneumoniae, the causative agent of chronic obstructive pulmonary disease (COPD), is presently the fifth mortality causing chronic disease in the world. The understanding of disease and treatment options are limited represents a severe concern and a need for better therapeutics. With the advancements in the field of complete genome sequencing and computational approaches development have lead to metabolic pathway analysis and protein-protein interaction network which provides vital evidence to the protein function and has been appropriate to the fields such as systems biology and drug discovery. Protein interaction network analysis allows us to predict the most potential drug targets among large number of the non-homologous proteins involved in the unique metabolic pathway. A computational comparative metabolic pathway analysis of the host H. sapiens and the pathogen C pneumoniae AR39 has been carried out at three level analyses. Firstly, metabolic pathway analysis was performed to identify unique metabolic pathways and non-homologous proteins were identified. Secondly, essentiality of the proteins was checked, where these proteins contribute to the growth and survival of the organism. Finally these proteins were further subjected to predict protein interaction networks. Among the total 65 pathways in the C pneumoniae AR39 genome 10 were identified as the unique metabolic pathways which were not found in the human host, 32 enzymes were predicted as essential and these proteins were considered for protein interaction analysis, later using various criteria''s we have narrowed down to prioritize ribonucleotide-diphosphate reductase subunit beta as a potential drug target which facilitate for the successful entry into drug designing.     

Dissecting metabolic flux in C<Subscript>4</Subscript> plants: experimental and theoretical approaches

C₄ photosynthesis is the carbon fixation pathway in specific plant species, so called C₄ plants including maize, sorghum and sugarcane. It is characterized by the carboxylation reaction that forms four-carbon (C₄) molecules, which are then used to transport CO₂ to the proximity of RubisCO in the bundle sheath cells. Since C₄ photosynthesis confers high photosynthetic as well as water and nitrogen use efficiency on plants, worldwide efforts have been made to understand the mechanisms of C₄ photosynthesis and to properly introduce the pathway into C₃ crops. Metabolic flux analysis (MFA) is a research field trying to analyze the metabolic pathway structure and activity (i.e., flux) in vivo. Constraint-based reconstruction and analysis tools theoretically study the distribution of metabolic flux in genome-scale network-based models. Different types of MFA and model-based analyses have been contributing to the discovery of C₄ photosynthetic pathways and to analyze its operation in C₄ plant species. This article reviews the studies to dissect the operation of C₄ photosynthesis and adjacent pathways, from the pioneer studies using radioisotope-based MFA to the recent stable isotope-based MFA and the model-based approaches. These studies indicate complex interconnections among metabolic pathways and the importance of the integration of experimental and theoretical approaches. Perspectives on the integrative approach and major obstacles are also discussed.     

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. Radioisotope tracing is a major technique used to measure such fluxes, both within plants, and between plants and their environments. Flux data obtained with radiotracer protocols can help elucidate the capacity, mechanism, regulation, and energetics of transport systems for specific mineral nutrients or toxicants, and can provide insight into compartmentation and turnover rates of subcellular mineral and metabolite pools. Here, we describe two major radioisotope protocols used in plant biology: direct influx (DI) and compartmental analysis by tracer efflux (CATE). We focus on flux measurement of potassium (K⁺) as a nutrient, and ammonia/ammonium (NH₃/NH₄⁺) as a toxicant, in intact seedlings of the model species barley (Hordeum vulgare L.). These protocols can be readily adapted to other experimental systems (e.g., different species, excised plant material, and other nutrients/toxicants). Advantages and limitations of these protocols are discussed.     

A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism

Background

Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown.

Results

To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum.

Conclusions

In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.

     

Metabolic flux analysis of Arthrobacter sp. CGMCC 3584 for cAMP production based on C tracer experiments and gas chromatography–mass spectrometry

Arthrobacter sp. CGMCC 3584 are able to produce cAMP from glucose by the purine synthesis pathway via de novo or salvage biosynthesis. In order to gain an improved understanding of its metabolism, ¹³C-labeling experiment and gas chromatography–mass spectrometry (GC–MS) analysis were employed to determine the metabolic network structure and estimate the intracellular fluxes. GC–MS analysis helps to reflect the activity of the intracellular pathways and reactions. The metabolic network mainly contains glycolytic and pentose phosphate pathways, the tricarboxylic acid cycle, and the inactive glyoxylate shunt. Hypoxanthine as a precursor of cAMP and sodium fluoride as an inhibitor of glycolysis were found to increase the cAMP production, as well as the flux through the PP pathway. The effects of adding hypoxanthine and sodium fluoride are discussed based on the enzyme assays and metabolic flux analysis. In conclusion, our results provide quantitative insights into how cells manipulate the metabolic network under different culture conditions and this may be of value in metabolic regulation for desirable production.     

A generalized computational framework to streamline thermodynamics and kinetics analysis of metabolic pathways

Metabolic engineering is a critical biotechnological approach in addressing global energy and environment challenges. Most engineering efforts, however, consist of laborious and inefficient trial-and-error of target pathways, due in part to the lack of methodologies that can comprehensively assess pathway properties in thermodynamics and kinetics. Metabolic engineering can benefit from computational tools that evaluate feasibility, expense and stability of non-natural metabolic pathways. Such tools can also help us understand natural pathways and their regulation at systems level. Here we introduce a computational toolbox, PathParser, which, for the first time, integrates multiple important functions for pathway analysis including thermodynamics analysis, kinetics-based protein cost optimization and robustness analysis. Specifically, PathParser enables optimization of the driving force of a pathway by minimizing the Gibbs free energy of least thermodynamically favorable reaction. In addition, based on reaction thermodynamics and enzyme kinetics, it can compute the minimal enzyme protein cost that supports metabolic flux, and evaluate pathway stability and flux in response to enzyme concentration perturbations. In a demo analysis of the Calvin–Benson–Bassham cycle and photorespiration pathway in the model cyanobacterium Synechocystis PCC 6803, the computation results are corroborated by experimental proteomics data as well as metabolic engineering outcomes. This toolbox may have broad application in metabolic engineering and systems biology in other microbial systems.     

The metabolic flux phenotype of heterotrophic Arabidopsis cells reveals a complex response to changes in nitrogen supply

The extent to which individual plants utilise nitrate and ammonium, the two principal nitrogen sources in the rhizosphere, is variable and many species require a balance between the two forms for optimal growth. The effects of nitrate and ammonium on gene expression, enzyme activity and metabolite composition have been documented extensively with the aim of understanding the way in which plant cells respond to the different forms of nitrogen, but ultimately the impact of these changes on the organisation and operation of the central metabolic network can only be addressed by analysing the fluxes supported by the network. Accordingly steady‐state metabolic flux analysis was used to define the metabolic phenotype of a heterotrophic Arabidopsis thaliana cell culture grown in Murashige and Skoog and ammonium‐free media, treatments that influenced growth and biomass composition. Fluxes through the central metabolic network were deduced from the redistribution of label into metabolic intermediates and end products observed when cells were labelled with [1‐¹³C]‐, [2‐¹³C]‐ or [¹³C₆]glucose, in tandem with ¹⁴C‐measurements of the net accumulation of biomass. Analysis of the flux maps showed that: (i) flux through the oxidative pentose phosphate pathway varied independently of the reductant demand for biosynthesis, (ii) non‐plastidic processes made a significant and variable contribution to the provision of reducing power for the plastid, and (iii) the inclusion of ammonium in the growth medium increased cell maintenance costs, in agreement with the futile cycling model of ammonium toxicity. These conclusions highlight the complexity of the metabolic response to a change in nitrogen nutrition.     

Experimental and mathematical approaches to modeling plant metabolic networks

To support their sessile and autotrophic lifestyle higher plants have evolved elaborate networks of metabolic pathways. Dynamic changes in these metabolic networks are among the developmental forces underlying the functional differentiation of organs, tissues and specialized cell types. They are also important in the various interactions of a plant with its environment. Further complexity is added by the extensive compartmentation of the various interconnected metabolic pathways in plants. Thus, although being used widely for assessing the control of metabolic flux in microbes, mathematical modeling approaches that require steady-state approximations are of limited utility for understanding complex plant metabolic networks. However, considerable progress has been made when manageable metabolic subsystems were studied. In this article, we will explain in general terms and using simple examples the concepts underlying stoichiometric modeling (metabolic flux analysis and metabolic pathway analysis) and kinetic approaches to modeling (including metabolic control analysis as a special case). Selected studies demonstrating the prospects of these approaches, or combinations of them, for understanding the control of flux through particular plant pathways are discussed. We argue that iterative cycles of (dry) mathematical modeling and (wet) laboratory testing will become increasingly important for simulating the distribution of flux in plant metabolic networks and deriving rational experimental designs for metabolic engineering efforts.