Steady-state Metabolic Flux Research Articles

High Growth Rate of Diatoms Explained by Reduced Carbon Requirement and Low Energy Cost of Silica Deposition.

The rapid growth of diatoms makes them one of the most pervasive and productive types of plankton in the world's ocean, but the physiological basis for their high growth rates remains poorly understood. Here, we evaluate the factors that elevate diatom growth rates, relative to other plankton, using a steady-state metabolic flux model that computes the photosynthetic C source from intracellular light attenuation and the carbon cost of growth from empirical cell C quotas, across a wide range of cell sizes. For both diatoms and other phytoplankton, growth rates decline with increased cell volume, consistent with observations, because the C cost of division increases with size faster than photosynthesis. However, the model predicts overall higher growth rates for diatoms due to reduced C requirements and the low energetic cost of Si deposition. The C savings from the silica frustule are supported by metatranscriptomic data from Tara Oceans, which show that the abundance of transcripts for cytoskeleton components in diatoms is lower than in other phytoplankton. Our results highlight the importance of understanding the origins of phylogenetic differences in cellular C quotas, and suggest that the evolution of silica frustules may play a critical role in the global dominance of marine diatoms. IMPORTANCE This study addresses a longstanding issue regarding diatoms, namely, their fast growth. Diatoms, which broadly are phytoplankton with silica frustules, are the world's most productive microorganisms and dominate in polar and upwelling regions. Their dominance is largely supported by their high growth rate, but the physiological reasoning behind that characteristic has been obscure. In this study, we combine a quantitative model and metatranscriptomic approaches and show that diatoms' low carbon requirements and low energy costs for silica frustule production are the key factors supporting their fast growth. Our study suggests that the effective use of energy-efficient silica as a cellular structure, instead of carbon, enables diatoms to be the most productive organisms in the global ocean.

Expression of malic enzyme reveals subcellular carbon partitioning for storage reserve production in soybeans.

Central metabolism produces amino and fatty acids for protein and lipids that establish seed value. Biosynthesis of storage reserves occurs in multiple organelles that exchange central intermediates including two essential metabolites, malate, and pyruvate that are linked by malic enzyme. Malic enzyme can be active in multiple subcellular compartments, partitioning carbon and reducing equivalents for anabolic and catabolic requirements. Prior studies based on isotopic labeling and steady-state metabolic flux analyses indicated malic enzyme provides carbon for fatty acid biosynthesis in plants, though genetic evidence confirming this role is lacking. We hypothesized that increasing malic enzyme flux would alter carbon partitioning and result in increased lipid levels in soybeans. Homozygous transgenic soybean plants expressing Arabidopsis malic enzyme alleles, targeting the translational products to plastid or outside the plastid during seed development, were verified by transcript and enzyme activity analyses, organelle proteomics, and transient expression assays. Protein, oil, central metabolites, cofactors, and acyl-acyl carrier protein (ACPs) levels were quantified overdevelopment. Amino and fatty acid levels were altered resulting in an increase in lipids by 0.5-2% of seed biomass (i.e. 2-9% change in oil). Subcellular targeting of a single gene product in central metabolism impacts carbon and reducing equivalent partitioning for seed storage reserves in soybeans.

Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures.

Fluxes are the ultimate phenotype of metabolism and their accurate quantification is fundamental to any understanding of metabolic networks. Steady state metabolic flux analysis has been the method of choice for quantifying fluxes in heterotrophic cells, but it is unable to measure fluxes during short-lived metabolic states, such as a transient oxidative load. Isotopically non-stationary metabolic flux analysis (INST-MFA) can be performed over shorter timescales (minutes - hours) and might overcome this limitation. INST-MFA has recently been applied to photosynthesising leaves, but agriculturally important tissues such as roots and storage organs, or plants during the night are heterotrophic. Here we outline the application of INST-MFA to heterotrophic plant cells. Using INST-MFA we were able to identify changes in the fluxes supported by phosphoenolpyruvate carboxylase and malic enzyme under oxidative load, highlighting the potential of INST-MFA to measure fluxes during short-lived metabolic states. We discuss the challenges in applying INST-MFA, and highlight further development required before it can be routinely used to quantify fluxes in heterotrophic plant cells.

TMET-28. A METHOD FOR EX VIVO STABLE ISOTOPE TRACING IN SURGICALLY EXPLANTED GLIOMA ORGANOIDS

Abstract Stable isotope tracing is a powerful method for elucidating tumor metabolism. While in vivo stable isotope tracing is one of the most faithful approaches for studying metabolic activity under pathophysiologically relevant conditions, this method is technically challenging and costly to perform. We therefore sought to optimize an ex vivo stable isotope tracing approach under conditions that recapitulate the in vivo tumor microenvironment (TME), focusing on three key features: cellular heterogeneity, nutrient availability, and oxygenation. Surgically eXplanted Organoids (SXOs) are 3-dimensional glioma models that retain the diverse cell populations in human brain tumors. We tested whether glioma SXOs can be cultured in Human Plasma-Like Medium (HPLM) under brain-relevant oxygen levels (5%) for the purpose of stable isotope tracing. We cultured SXOs in conventional media, HPLM for 24 hours, or HPLM for 120 hours under 5% oxygen. We observed no significant differences in markers of tumor architecture, cellular proliferation rates, or stemness profiles. We then performed stable isotope tracing in SXOs cultured in HLPM at 5% oxygen. We acclimated SXOs to HPLM for 24 hours or 120 hours before replacing media with either HPLM (unlabeled) or HPLM with 15N2 glutamine (labeled). 15N isotopic labeling patterns in various metabolites were compared between the 24- and 120-hour conditions by linear regression. These patterns were strongly concordant over time (r2 = 0.9544), indicating that steady-state metabolic fluxes are stable in these cultures. We then analyzed intermediates that occupy key nodes in amino acid metabolism, redox homeostasis, and nucleotide synthesis pathways and found similarly strong concordance between labeling patterns at the individual metabolite level. Our findings outline an effective approach for conducting ex vivo stable isotope tracing in heterocellular glioma models under conditions that mimic the TME. This approach may complement and enhance analogous in vivo methods to provide new insights into glioma metabolism.

Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment.

Simple SummaryImmune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. In this battle, tumor cells reprogram their metabolism for a high demand of building blocks and energy and to gain advantages over immune cells. To study these mechanisms, we require the quantification of metabolic fluxes, which can be estimated at the genome-scale, with constraint-based or kinetic modeling.The tumor’s physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.

A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa

Understanding the mechanisms behind lignin formation is an important research area with significant implications for the bioenergy and biomaterial industries. Computational models are indispensable tools for understanding this complex process. Models of the monolignol pathway in Populus trichocarpa and other plants have been developed to explore how transgenic modifications affect important bioenergy traits. Many of these models, however, only capture one level of biological organization and are unable to capture regulation across multiple biological scales. This limits their ability to predict how gene modification strategies will impact lignin and other wood properties. While the first multiscale model of lignin biosynthesis in P. trichocarpa spanned the transcript, protein, metabolic, and phenotypic layers, it did not account for cross-regulatory influences that could impact abundances of untargeted monolignol transcripts and proteins. Here, we present a multiscale model incorporating these cross-regulatory influences for predicting lignin and wood traits from transgenic knockdowns of the monolignol genes. The three main components of this multiscale model are (1) a transcript-protein model capturing cross-regulatory influences, (2) a kinetic-based metabolic model, and (3) random forest models relating the steady state metabolic fluxes to 25 physical traits. We demonstrate that including the cross-regulatory behavior results in smaller predictive error for 23 of the 25 traits. We use this multiscale model to explore the predicted impact of novel combinatorial knockdowns on key bioenergy traits, and identify the perturbation of PtrC3H3 and PtrCAld5H1&2 monolignol genes as a candidate strategy for increasing saccharification efficiencies while reducing negative impacts on wood density and height.

Simulation of multicellular populations with Petri nets and genome scale intracellular networks

• A distributed architecture for simulation of populations of living cells is proposed. • Individual cells are represented with the Quasi-Steady State Petri Net model. • Spark Big data framework and Pregel computation model are used. • Liver cells during homeostatic response to cholesterol burst are simulated. We present a new distributed architecture allowing simulation of living cells in spatial structures. Each cell is represented with a Quasi-Steady State Petri Net that integrates dynamic regulatory network expressed with a Petri net and Genome Scale Metabolic Network (GSMN) where linear programming is used to explore the steady-state metabolic flux distributions in the whole-cell model. The combination of Petri net and GSMN has already been used in simulations of single cells, but we present an extension to the model and an architecture to simulate populations of millions of interacting cells organised in spatial structures which can be used to model tumour growth or formation of tuberculosis lesions. The crucial element of this solution is the ability of cells to communicate by producing and detecting substances such as cytokines and chemokines. This ability is modeled by allowing cells to share tokens in places called communicators. To make the simulation of such a huge model possible we use the Spark framework and organise the computation in an agent-based “think like a vertex” fashion as in Pregel-like systems. In the cluster we introduce a special kind of per node caching to speed up computation of the steady-state metabolic flux. We demonstrate capabilities of the new architecture by simulating communication of liver cells through FGF19 cytokine during the homeostatic response to cholesterol burst. Our approach can be used for mechanistic modelling of the emergence of multicellular system behaviour from interaction between genome and environment.

The EcoCyc Database.

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Metabolic flux network analysis of hydrogen production from crude glycerol by Clostridium pasteurianum

The present study has attempted to get insight into ultrasound induced enhancement in biohydrogen production from glycerol fermentation using metabolic flux analysis (MFA). A pseudo steady state metabolic flux network model was constructed and analyzed using experimentally measured glycerol uptake rate and fluxes of four metabolites, viz. acetate, butyrate, succinate and 1,3-PDO. Glycerol consumption increased by ∼50% under sonication. Biohydrogen yield showed marked rise of ∼40% with application of ultrasound. Butyrate and 1,3-PDO were the major products of glycerol metabolism. Sonication had major influence on carbon fluxes at the acetyl-CoA node. MFA results revealed enhanced flux towards butyrate under sonication, which was manifested in higher butyrate to acetate (B/A) ratio in products and greater H2 generation. Biohydrogen production was also a microbial growth associated process. Finally, two theoretical alternatives for further enhancement of biohydrogen production were assessed with MFA, viz. enhancement of glycerol uptake and blocking of butyrate pathway.

Steady-state metabolism of ethanol in perfused rat livers treated with cyanamide: quantitative analysis of acetaldehyde effects on the metabolic flux rates.

Alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) are principal enzymes responsible for metabolism of ethanol in mammals. The steady-state metabolic flux of ethanol has been poorly understood. We investigated flux rates of the individual steps of ethanol metabolism in perfused rat livers treated with ALDH inactivator cyanamide as an attempt to mimic human ALDH2 deficiency commonly seen in East Asians. The net rates of ethanol oxidation, acetaldehyde oxidation, and acetate activation were determined with a set of defined equations, based on the set influx rates of ethanol and the measured efflux rates of ethanol, acetaldehyde, and acetate. After intraperitoneal injections of 0.2 and 1.5mg/kg cyanamide, hepatic activities of mitochondrial ALDH2 and cytoplasmic ALDH1A1 decreased to a similar degree, that is, 51 to 57% and 69 to 74%, compared with the corresponding controls, respectively, whereas cytoplasmic ADH1 activity remained unchanged. At infusing 2mM ethanol, acetaldehyde oxidation rate well matched (99%) the net ethanol oxidation rate in control liver. Both the ethanol and acetaldehyde oxidation rates were significantly decreased after cyanamide treatments. At 10mM ethanol, the efflux acetaldehyde was significantly higher than that infusing 2mM ethanol in both control and cyanamide groups. Seventy-eight percent of the oxidized ethanol released as efflux acetate. At 2mM ethanol, the apparent flux control coefficients of ADH1 were assessed to be 0.78, 0.54, and 0.39, respectively, in control, low, and high cyanamide-treated livers. Kinetic simulations revealed that inhibition by acetaldehyde may largely account for the observed reduction of ADH1 oxidation rates after cyanamide treatment. Our results provide the first flux evidence that ADH and ALDH are steps influencing steady-state metabolism of ethanol in rat livers with inactivated ALDHs.

The EcoCyc Database.

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review provides a detailed description of the data content of EcoCyc and of the procedures by which this content is generated.

Complementary elementary modes for fast and efficient analysis of metabolic networks

Metabolic pathway analysis facilitates understanding or designing a complex metabolic system and enables prediction of steady-state metabolic flux distributions. A serious problem of elementary mode (EM) or extreme pathway (Expa) analysis is that the computational time increases exponentially with an increase in network sizes, which makes the computation of the EMs/Expas expensive and infeasible for large-scale networks. To overcome such problems, we proposed a fast and efficient algorithm named complementary EM (cEM) analysis. To achieve the computational time improvement, we employ the EM decomposition method that explores EMs or linear combinations of them which are responsible for the metabolic flux distributions. Flux balance analysis (FBA) is used to determine possible ranges of metabolic flux distributions as the input data necessary for the EM decomposition method. The maximum entropy principle (MEP) is employed as an objective function for estimating the coefficients of cEMs. To demonstrate the feasibility of cEM analysis, we compared it with EM/Expa analysis by using two medium-scale metabolic networks of Escherichia coli and a genome-scale metabolic network of head and neck cancer cells. The cEM analysis greatly reduces the computational time and memory cost, exposing a new window for large-scale metabolic network analysis.

The metabolic flux phenotype of heterotrophic Arabidopsis cells reveals a flexible balance between the cytosolic and plastidic contributions to carbohydrate oxidation in response to phosphate limitation

Understanding the mechanisms that allow plants to respond to variable and reduced availability of inorganic phosphate is of increasing agricultural importance because of the continuing depletion of the rock phosphate reserves that are used to combat inadequate phosphate levels in the soil. Changes in gene expression, protein levels, enzyme activities and metabolite levels all point to a reconfiguration of the central metabolic network in response to reduced availability of inorganic phosphate, but the metabolic significance of these changes can only be assessed in terms of the fluxes supported by the network. Steady-state metabolic flux analysis was used to define the metabolic phenotype of a heterotrophic Arabidopsis thaliana cell culture grown on a Murashige and Skoog medium containing 0, 1.25 or 5mm inorganic phosphate. Fluxes through the central metabolic network were deduced from the redistribution of (13) C into metabolic intermediates and end products when cells were labelled with [1-(13) C], [2-(13) C], or [(13) C6 ]glucose, in combination with (14) C measurements of the rates of biomass accumulation. Analysis of the flux maps showed that reduced levels of phosphate in the growth medium stimulated flux through phosphoenolpyruvate carboxylase and malic enzyme, altered the balance between cytosolic and plastidic carbohydrate oxidation in favour of the plastid, and increased cell maintenance costs. We argue that plant cells respond to phosphate deprivation by reconfiguring the flux distribution through the pathways of carbohydrate oxidation to take advantage of better phosphate homeostasis in the plastid.

Continuous succinic acid production by Actinobacillus succinogenes in a biofilm reactor: Steady-state metabolic flux variation

Continuous anaerobic fermentations were performed in a novel external-recycle, biofilm reactor using d-glucose and CO2 as carbon substrates. Succinic acid (SA) yields were found to be an increasing function of glucose consumption with the succinic acid to acetic acid ratio increasing from 2.4gg−1 at a glucose consumption of 10gL−1, to 5.7gg−1 at a glucose consumption of 50gL−1. The formic acid to acetic acid ratio decreased from an equimolar value (0.77gg−1) at a glucose consumption of 10gL−1 to a value close to zero at 50gL−1. The highest SA yield on glucose and highest SA titre obtained were 0.91gg−1 and 48.5gL−1 respectively. Metabolic flux analysis based on the established C3 and C4 metabolic pathways of Actinobacillus succinogenes revealed that the increase in the succinate to acetate ratio could not be attributed to the decrease in formic acid and that an additional source of NADH was present. The fraction of unaccounted NADH increased with glucose consumption, suggesting that additional reducing power is present in the medium or is provided by the activation of an alternative metabolic pathway.

INCA: a computational platform for isotopically non-stationary metabolic flux analysis

13C flux analysis studies have become an essential component of metabolic engineering research. The scope of these studies has gradually expanded to include both isotopically steady-state and transient labeling experiments, the latter of which are uniquely applicable to photosynthetic organisms and slow-to-label mammalian cell cultures. Isotopomer network compartmental analysis (INCA) is the first publicly available software package that can perform both steady-state metabolic flux analysis and isotopically non-stationary metabolic flux analysis. The software provides a framework for comprehensive analysis of metabolic networks using mass balances and elementary metabolite unit balances. The generation of balance equations and their computational solution is completely automated and can be performed on networks of arbitrary complexity.

Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

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-(13) C]-, [2-(13) C]- or [(13) C6 ]glucose, in tandem with (14) C-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.

Abstract 5164: Using metabolomic flux to uncover new targets for the modulation of breast cancer metastasis

Abstract Inflammatory breast cancer (IBC) is the most deadly breast cancer because of its ability to rapidly metastasize, often before the primary lesion is detected. To better understand what drives this highly aggressive form of cancer, we studied central carbon metabolism in IBC. The Warburg effect, which is characterized by high rates of glucose uptake and glycolysis even under aerobic conditions, occurs in most cancer cells and plays an important role in the tumorigenic capacity of breast cancer. Specifically, we examined metabolic changes that occur when breast cancer cells switch from a proliferating phenotype to a highly motile phenotype. In primary tumor formation rapid proliferation is crucial, while in metastasis motility to new sites is critical. We hypothesize that alterations in the regulation of metabolic pathways leads to the direction of energy from proliferation to motility, and that this change is important in the progression of breast cancer towards metastases. Understanding if a coincident metabolic shift occurs when malignant cells switch from a proliferative to a more aggressive motile form may help identify new therapeutics for highly aggressive breast cancers. For our studies, we are using “normal-like” MCF-10A breast cells, which proliferate quickly and move slower than cancer cells, and two highly metastatic breast cancer cell lines (MDA-MB-231 and the IBC-derived line SUM 149), which have the ability to move quickly and proliferate relatively slowly. Using these cell lines as a model, we have examined metabolic changes as cancer progresses. We are conducting targeted metabolomic studies to determine relative concentrations of metabolites in the glycolysis, tricarboxylic acid (TCA) cycle, and mevalonate pathway. In addition, we are using 13C-labeled pyruvate and glucose to measure the transient and steady state flux through these metabolic pathways. Our preliminary results indicate interesting and unexpected changes in both the transient and steady state metabolic fluxes across the different cell lines. In addition, the metabolic flux through the TCA cycle is very active in the metastatic cancer cells despite an increase in glycolysis. In agreement with flux studies of other cancer cell lines, our results suggest that the cancer cells are likely using intermediates from the TCA cycle to generate nucleotides and fatty acids needed for replication. The results from this work, once validated, can be used to identify new therapeutic metabolic targets to modulate breast cancer metastasis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5164. doi:1538-7445.AM2012-5164

The Ethylmalonyl-CoA Pathway Is Used in Place of the Glyoxylate Cycle by Methylobacterium extorquens AM1 during Growth on Acetate

Acetyl-CoA assimilation was extensively studied in organisms harboring the glyoxylate cycle. In this study, we analyzed the metabolism of the facultative methylotroph Methylobacterium extorquens AM1, which lacks isocitrate lyase, the key enzyme in the glyoxylate cycle, during growth on acetate. MS/MS-based proteomic analysis revealed that the protein repertoire of M. extorquens AM1 grown on acetate is similar to that of cells grown on methanol and includes enzymes of the ethylmalonyl-CoA (EMC) pathway that were recently shown to operate during growth on methanol. Dynamic 13C labeling experiments indicate the presence of distinct entry points for acetate: the EMC pathway and the TCA cycle. 13C steady-state metabolic flux analysis showed that oxidation of acetyl-CoA occurs predominantly via the TCA cycle and that assimilation occurs via the EMC pathway. Furthermore, acetyl-CoA condenses with the EMC pathway product glyoxylate, resulting in malate formation. The latter, also formed by the TCA cycle, is converted to phosphoglycerate by a reaction sequence that is reversed with respect to the serine cycle. Thus, the results obtained in this study reveal the utilization of common pathways during the growth of M. extorquens AM1 on C1 and C2 compounds, but with a major redirection of flux within the central metabolism. Furthermore, our results indicate that the metabolic flux distribution is highly complex in this model methylotroph during growth on acetate and is fundamentally different from organisms using the glyoxylate cycle.

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.