Towards high throughput metabolic flux analysis in plants

Research on plant metabolism is currently experiencing the common use of various omics methods creating valuable information on the concentrations of the cell's constituents. However, little is known about in vivo reaction rates, which can be determined by Metabolic Flux Analysis (MFA), a combination of isotope labeling experiments and computer modeling of the metabolic network. Large-scale applications of this method so far have been hampered by tedious procedures of tissue culture, analytics, modeling and simulation. By streamlining the workflow of MFA, the throughput of the method could be significantly increased. We propose strategies for these improvements on various sub-steps which will move flux analysis to the medium-throughput range and closer to established methods such as metabolite profiling. Furthermore, this may enable novel applications of MFA, for example screening plant populations for traits related to the flux phenotype.     

Elucidating the role of copper in CHO cell energy metabolism using 13C metabolic flux analysis

¹³C‐metabolic flux analysis was used to understand copper deficiency‐related restructuring of energy metabolism, which leads to excessive lactate production in recombinant protein‐producing CHO cells. Stationary‐phase labeling experiments with U‐¹³C glucose were conducted on CHO cells grown under high and limiting copper in 3 L fed‐batch bioreactors. The resultant labeling patterns of soluble metabolites were measured by GC‐MS and used to estimate metabolic fluxes in the central carbon metabolism pathways using OpenFlux. Fluxes were evaluated 300 times from stoichiometrically feasible random guess values and their confidence intervals calculated by Monte Carlo simulations. Results from metabolic flux analysis exhibited significant carbon redistribution throughout the metabolic network in cells under Cu deficiency. Specifically, glycolytic fluxes increased (25%–79% relative to glucose uptake) whereas fluxes through the TCA and pentose phosphate pathway (PPP) were lower (15%–23% and 74%, respectively) compared with the Cu‐containing condition. Furthermore, under Cu deficiency, 33% of the flux entering TCA via the pyruvate node was redirected to lactate and malate production. Based on these results, we hypothesize that Cu deficiency disrupts the electron transport chain causing ATP deficiency, redox imbalance, and oxidative stress, which in turn drive copper‐deficient CHO cells to produce energy via aerobic glycolysis, which is associated with excessive lactate production, rather than the more efficient route of oxidative phosphorylation. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1179–1186, 2015     

Thermodynamics-based metabolic flux analysis

A new form of metabolic flux analysis (MFA) called thermodynamics-based metabolic flux analysis (TMFA) is introduced with the capability of generating thermodynamically feasible flux and metabolite activity profiles on a genome scale. TMFA involves the use of a set of linear thermodynamic constraints in addition to the mass balance constraints typically used in MFA. TMFA produces flux distributions that do not contain any thermodynamically infeasible reactions or pathways, and it provides information about the free energy change of reactions and the range of metabolite activities in addition to reaction fluxes. TMFA is applied to study the thermodynamically feasible ranges for the fluxes and the Gibbs free energy change, Delta(r)G', of the reactions and the activities of the metabolites in the genome-scale metabolic model of Escherichia coli developed by Palsson and co-workers. In the TMFA of the genome scale model, the metabolite activities and reaction Delta(r)G' are able to achieve a wide range of values at optimal growth. The reaction dihydroorotase is identified as a possible thermodynamic bottleneck in E. coli metabolism with a Delta(r)G' constrained close to zero while numerous reactions are identified throughout metabolism for which Delta(r)G' is always highly negative regardless of metabolite concentrations. As it has been proposed previously, these reactions with exclusively negative Delta(r)G' might be candidates for cell regulation, and we find that a significant number of these reactions appear to be the first steps in the linear portion of numerous biosynthesis pathways. The thermodynamically feasible ranges for the concentration ratios ATP/ADP, NAD(P)/NAD(P)H, and H(extracellular)(+)/H(intracellular)(+) are also determined and found to encompass the values observed experimentally in every case. Further, we find that the NAD/NADH and NADP/NADPH ratios maintained in the cell are close to the minimum feasible ratio and maximum feasible ratio, respectively.     

Parameter identification of a dynamic model of CHO cell cultures: an experimental case study

In this paper, we address the problem of parameter identification in dynamic models of animal cultures, and we propose a step-by-step procedure, which gradually considers more detailed models. This procedure allows subsets of parameters to be estimated at each step, which can be used in the initialization of the next identification step. Finally, the full parameter set can be re-estimated starting from the results of the last step. The efficiency of the procedure is illustrated with a simulation case study and with the identification of a dynamic model from experimental data collected in CHO cell culture.     

Genetically constrained metabolic flux analysis

Significant progress has been made in using existing metabolic databases to estimate metabolic fluxes. Traditional metabolic flux analysis generally starts with a predetermined metabolic network. This approach has been employed successfully to analyze the behaviors of recombinant strains by manually adding or removing the corresponding pathway(s) in the metabolic map. The current work focuses on the development of a new framework that utilizes genomic and metabolic databases, including available genetic/regulatory network structures and gene chip expression data, to constrain metabolic flux analysis. The genetic network consisting of the sensing/regulatory circuits will activate or deactivate a specific set of genes in response to external stimulus. The activation and/or repression of this set of genes will result in different gene expression levels that will in turn change the structure of the metabolic map. Hence, the metabolic map will automatically "adapt" to the external stimulus as captured by the genetic network. This adaptation selects a subnetwork from the pool of feasible reactions and so performs what we term "environmentally driven dimensional reduction." The Escherichia coli oxygen and redox sensing/regulatory system, which controls the metabolic patterns connected to glycolysis and the TCA cycle, was used as a model system to illustrate the proposed approach.     

Simple and efficient control of CHO cell cultures

Cell cultures must tightly be kept under control in order to guarantee a sufficiently small variability in the protein product quality. A simple and efficient technique for CHO-cell cultures is presented that allows keeping the viable cell count X(v) and the specific growth rate μ of the cells on predefined trajectories. As X(v) and μ cannot directly be measured online, they are controlled indirectly via the total mass of oxygen consumed. Online values of the latter can precisely be estimated from off gas analysis, i.e. from the O? volume ratio measured in the vent line and air flow rate measurements. In glutamine-limited fed-batch cultivations, the glutamine feed rate can be manipulated in such a way that the viable cell density and the specific growth rate are kept on predefined profiles for nearly the entire cultivation time. The viability of the cells is not affected by the closed loop control actions. The technique was validated with CHO-cells cultured in a 2.5-L fully instrumented stirred tank bioreactor. It is shown that the controller is able to run the process exactly on predefined tracks with a high batch-to-batch reproducibility. By means of six fed-batch cultivations of CHO cells it was shown that a remarkable reproducibility of viable cell concentration could be achieved throughout 140 h cultivation time.     

Automated flow cytometry for monitoring CHO cell cultures

Flow cytometry has been used to accurately monitor cell events that indicate the spatio-temporal state of a bioreactor culture. The introduction of process analytical technology (PAT) has led to process improvements using real-time or semi real-time monitoring systems. Integration of flow cytometry into an automated scheme for improved process monitoring can benefit PAT in bioreactor-based biopharmaceutical productions by establishing optimum process conditions and better quality protocols. Herein, we provide detailed protocols for establishing an automated flow cytometry system that can be used to investigate and monitor cell growth, viability, cell size, and cell cycle data. A method is described for the use of such a system primarily focused on CHO cell culture, although it is foreseen the information gathered from automated flow cytometry can be applied to a variety of cell lines to address both PAT requirements and gain further understanding of complex biological systems.     

Mass spectrometry for metabolic flux analysis

Mass spectrometry in combination with tracer experiments based on 13C substrates can serve as a powerful tool for the modeling and analysis of intracellular fluxes and the investigation of biochemical networks. The theoretical background for the application of mass spectrometry to metabolic flux analysis is discussed. Mass spectrometry methods are especially useful to determine mass distribution of metabolites. Additional information gained from fragmentation of metabolites, e.g., by electron impact ionization, allows further localization of labeling positions, up to complete resolution of isotopomer pools. To effectively handle mass distributions in simulation experiments, a matrix based general methodology is formulated. The natural isotope distribution of carbon, oxygen, hydrogen and nitrogen in the target metabolites is considered by introduction of correction matrices. It is shown by simulation results for the central carbon metabolism that neglecting natural isotope distributions causes significant errors in intracellular flux distributions. By varying relative fluxes into pentosephosphate pathway and pyruvate carboxylation reaction, marked changes in the mass distributions of metabolites result, which are illustrated for pyruvate, oxaloacetate, and alpha-ketoglutarate. In addition mass distributions of metabolites are significantly influenced over a broad range by the degree of reversibility of transaldolase and transketolase reactions in the pentosephosphate pathway. The mass distribution of metabolites is very sensitive towards intracellular flux patterns and can be measured with high accuracy by routine mass spectrometry methods. Copyright 1999 John Wiley & Sons, Inc.     

13C metabolic flux analysis.

Metabolic flux analysis using 13C-labeled substrates has become an important tool in metabolic engineering. It allows the detailed quantification of all intracellular fluxes in the central metabolism of a microorganism. The method has strongly evolved in recent years by the introduction of new experimental procedures, measurement techniques, and mathematical data evaluation methods. Many of these improvements require advanced skills in the application of nuclear magnetic resonance and mass spectrometry techniques on the one hand and computational and statistical experience on the other hand. This minireview summarizes these recent developments and sketches the major practical problems. An outlook to possible future developments concludes the text.     

Synergizing metabolic flux analysis and nucleotide sugar metabolism to understand the control of glycosylation of recombinant protein in CHO cells

Background

The photorespiratory nitrogen cycle in C₃ plants involves an extensive diversion of carbon and nitrogen away from the direct pathways of assimilation. The liberated ammonia is re-assimilated, but up to 25% of the carbon may be released into the atmosphere as CO₂. Because of the loss of CO₂ and high energy costs, there has been considerable interest in attempts to decrease the flux through the cycle in C₃ plants. Transgenic tobacco plants were generated that contained the genes gcl and hyi from E. coli encoding glyoxylate carboligase (EC 4.1.1.47) and hydroxypyruvate isomerase (EC 5.3.1.22) respectively, targeted to the peroxisomes. It was presumed that the two enzymes could work together and compete with the aminotransferases that convert glyoxylate to glycine, thus avoiding ammonia production in the photorespiratory nitrogen cycle.

Results

When grown in ambient air, but not in elevated CO₂, the transgenic tobacco lines had a distinctive phenotype of necrotic lesions on the leaves. Three of the six lines chosen for a detailed study contained single copies of the gcl gene, two contained single copies of both the gcl and hyi genes and one line contained multiple copies of both gcl and hyi genes. The gcl protein was detected in the five transgenic lines containing single copies of the gcl gene but hyi protein was not detected in any of the transgenic lines. The content of soluble amino acids including glycine and serine, was generally increased in the transgenic lines growing in air, when compared to the wild type. The content of soluble sugars, glucose, fructose and sucrose in the shoot was decreased in transgenic lines growing in air, consistent with decreased carbon assimilation.

Conclusions

Tobacco plants have been generated that produce bacterial glyoxylate carboligase but not hydroxypyruvate isomerase. The transgenic plants exhibit a stress response when exposed to air, suggesting that some glyoxylate is diverted away from conversion to glycine in a deleterious short-circuit of the photorespiratory nitrogen cycle. This diversion in metabolism gave rise to increased concentrations of amino acids, in particular glutamine and asparagine in the leaves and a decrease of soluble sugars.     

Towards dynamic metabolic network measurements by multi-dimensional NMR-based fluxomics

Novel technologies for measuring biological systems and methods for visualizing data have led to a revolution in the life sciences. Nuclear magnetic resonance (NMR) techniques can provide information on metabolite structure and metabolic dynamics at the atomic level. We have been developing a new method for measuring the dynamic metabolic network of crude extracts that combines [(13)C(6)]glucose stable isotope labeling of Arabidopsis thaliana and multi-dimensional heteronuclear NMR analysis, whereas most conventional metabolic flux analyses examine proteinogenic amino acids that are specifically labeled with partially labeled substrates such as [2-(13)C(1)]glucose or 10% [(13)C(6)]glucose. To show the validity of our method, we investigated how to obtain information about biochemical reactions, C-C bond formation, and the cleavage of the main metabolites, such as free amino acids, in crude extracts based on the analysis of the (13)C-(13)C coupling pattern in 2D-NMR spectra. For example, the combination of different extraction solvents allows one to distinguish complicated (13)C-(13)C fine couplings at the C2 position of amino acids. As another approach, f1-f3 projection of the HCACO spectrum also helps in the analysis of (13)C-(13)C connectivities. Using these new methods, we present an example that involves monitoring the incorporation profile of [(13)C(6)]glucose into A. thaliana and its metabolic dynamics, which change in a time-dependent manner with atmospheric (12)CO(2) assimilation.     

Apoptosis in CHO cell batch cultures: examination by flow cytometry

Chinese hamster ovary cells grown under conditions which are optimal for the production of a genetically engineered protein in batch culture, lose significant viability shortly after entering the stationary phase. This cell death was investigated morphologically and was found to be almost exclusively via apoptosi. Furthermore, cells were analyzed by flow cytometry using a fluorescent DNA end-labeling assay to label apoptotic cells, in conjunction with cell cycle analysis using propidium iodide. Apoptotic cells could be detected by this method, and by the radioactive end-labeling of extracted DNA, on all days of culture from day 1 to day 7; however, the degree of apoptotic cell death increased dramatically when the cells entered the stationary phase, rising to 50–60% of the total cell number at the termination of the culture. Flow cytometric analysis showed that the majority of cells underwent apoptosis whilst in G₁/G₀ and formed an apoptotic population with high DNA FITC end-labeling and hypodiploid propidium iodide binding. Additionally, the ability or inability to secrete specific protein products did not appear to interfere with the development of the apoptotic population with time.     

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.     

Insights into plant metabolic networks from steady-state metabolic flux analysis

Steady-state metabolic flux analysis (MFA) is an experimental approach that allows the measurement of multiple fluxes in the core network of primary carbon metabolism. It is based on isotopic labelling experiments, and although well established in the analysis of micro-organisms, and some mammalian systems, the extension of the method to plant cells has been challenging because of the extensive subcellular compartmentation of the metabolic network. Despite this difficulty there has been substantial progress in developing robust protocols for the analysis of heterotrophic plant metabolism by steady-state MFA, and flux maps have now been published that reflect the metabolic phenotypes of excised root tips, developing embryos and cotyledons, hairy root cultures, and cell suspensions under a variety of physiological conditions. There has been a steady improvement in the quality, extent and statistical reliability of these analyses, and new information is emerging on the performance of the plant metabolic network and the contributions of specific pathways.     

Increasing the flux in metabolic pathways: A metabolic control analysis perspective

The problems of engineering increased flux in metabolic pathways are analyzed in terms of the understanding provided by metabolic control analysis. Over-expression of a single enzyme is unlikely to be effective unless it is known to have a high flux control coefficient, which can be used as an approximate predictive tool. This is likely to rule out enzymes subject to feedback inhibition, because it transfers control downstream from the inhibited enzyme to the enzymes utilizing the feedback metabolite. Although abolishing feedback inhibition can restore flux control to an enzyme, it is also likely to cause large increases in the concentrations of metabolic intermediates. Simultaneous and coordinated over-expression of most of the enzymes in a pathway can, in principle, produce substantial flux increases without changes in metabolite levels, though technically it may be difficult to achieve. It is, however, closer to the method used by cells to change flux levels, where coordinated changes in the level of activity of pathway enzymes are the norm. Another option is to increase the demand for the pathway product, perhaps by increasing its rate of excretion or removal. Copyright 1998 John Wiley & Sons, Inc.     

Metabolic characterization of a CHO cell size increase phase in fed-batch cultures

Applied Microbiology and Biotechnology - Normally, the growth profile of a CHO cell fed-batch process can be divided into two main phases based on changes in cell concentration, being an...     

On‐line adaptive metabolic flux analysis: Application to PHB production by mixed microbial cultures

In this work, an algorithm for on‐line adaptive metabolic flux analysis (MFA) is proposed and applied to polyhydroxybutyrate (PHB) production by mixed microbial cultures (MMC). In this process, population dynamics constitutes an important source of perturbation to MFA calculations because some stoichiometric and energetic parameters of the underlying metabolic network are continuously changing over time. The proposed algorithm is based on the application of the observer‐based estimator (OBE) to the central MFA equation, whereby the role of the OBE is to force the accumulation of intracellular metabolites to converge to zero by adjusting the values of unknown network parameters. The algorithm was implemented in a reactor equipped with on‐line analyses of dissolved oxygen and carbon dioxide through respirometric and titrimetric measurements. The oxygen and carbon dioxide fluxes were measured directly, whereas acetate, PHB, and sludge production fluxes were estimated indirectly using a projection of latent structures model calibrated a priori with off‐line measurements. The algorithm was implemented in a way that the network parameters associated with biosynthesis were adjusted on‐line. The algorithm proofed to converge exponentially with the steady state error always below 1 mmol/L. The estimated fluxes passed the consistency index test for experimental error variances as low as 1%. The comparison of measured and estimated respiratory coefficient and of the theoretical and estimated yield of sludge on acetate further confirmed the metabolic consistency of the parameters that were estimated on‐line. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Evaluation of isotope discrimination in C-based metabolic flux analysis

In a ¹³C experiment for metabolic flux analysis (¹³C MFA), we examined isotope discrimination by measuring the labeling of glucose, amino acids, and hexose monophosphates via mass spectrometry. When Escherichia coli grew in a mix of 20% fully labeled and 80% naturally labeled glucose medium, the cell metabolism favored light isotopes and the measured isotopic ratios (δ¹³C) were in the range of −35 to −92. Glucose transporters might play an important role in such isotopic fractionation. Flux analysis showed that both isotopic discrimination and isotopic impurities in labeled substrates could affect the solution of ¹³C MFA.     

293SF metabolic flux analysis during cell growth and infection with an adenoviral vector

Metabolic flux quantification of cell culture is becoming a crucial means to improve cell growth as well as protein and vector productions. The technique allows rapid determination of cell culture status, thus providing a tool for further feeding improvements. Herein, we report on key results of a metabolic investigation using 293 cells adapted to suspension and serum-free medium (293SF) during growth and infection with an adenoviral vector encoding the green fluorescence protein (GFP). The model developed contains 35 fluxes, which include the main fluxes of glycolysis, glutaminolysis, and amino acids pathways. It requires specific consumption and production rate measurements of amino acids, glucose, lactate, NH(3), and O(2), as well as DNA and total proteins biosynthesis rate measurements. Also, it was found that extracellular protein concentration measurement is important for flux calculation accuracy. With this model, we are able to describe the 293SF cell metabolism, grown under different culture conditions in a 3-L controlled bioreactor for batch and fed-batch with low glucose. The metabolism is also investigated during infection under two different feeding strategies: a fed-batch starting at the end of the growth phase and extending during infection without medium change and a fed-batch after infection following medium renewal. Differences in metabolism are observed between growth and infection, as well as between the different feeding strategies, thus providing a better understanding of the general metabolism.