Foraging theory predicts predator-prey energy fluxes.

PubMed

Brose, U; Ehnes, R B; Rall, B C; Vucic-Pestic, O; Berlow, E L; Scheu, S

2008-09-01

1. In natural communities, populations are linked by feeding interactions that make up complex food webs. The stability of these complex networks is critically dependent on the distribution of energy fluxes across these feeding links. 2. In laboratory experiments with predatory beetles and spiders, we studied the allometric scaling (body-mass dependence) of metabolism and per capita consumption at the level of predator individuals and per link energy fluxes at the level of feeding links. 3. Despite clear power-law scaling of the metabolic and per capita consumption rates with predator body mass, the per link predation rates on individual prey followed hump-shaped relationships with the predator-prey body mass ratios. These results contrast with the current metabolic paradigm, and find better support in foraging theory. 4. This suggests that per link energy fluxes from prey populations to predator individuals peak at intermediate body mass ratios, and total energy fluxes from prey to predator populations decrease monotonically with predator and prey mass. Surprisingly, contrary to predictions of metabolic models, this suggests that for any prey species, the per link and total energy fluxes to its largest predators are smaller than those to predators of intermediate body size. 5. An integration of metabolic and foraging theory may enable a quantitative and predictive understanding of energy flux distributions in natural food webs.

Bidirectional reaction steps in metabolic networks: I. Modeling and simulation of carbon isotope labeling experiments.

PubMed

Wiechert, W; de Graaf, A A

1997-07-05

The extension of metabolite balancing with carbon labeling experiments, as described by Marx et al. (Biotechnol. Bioeng. 49: 11-29), results in a much more detailed stationary metabolic flux analysis. As opposed to basic metabolite flux balancing alone, this method enables both flux directions of bidirectional reaction steps to be quantitated. However, the mathematical treatment of carbon labeling systems is much more complicated, because it requires the solution of numerous balance equations that are bilinear with respect to fluxes and fractional labeling. In this study, a universal modeling framework is presented for describing the metabolite and carbon atom flux in a metabolic network. Bidirectional reaction steps are extensively treated and their impact on the system's labeling state is investigated. Various kinds of modeling assumptions, as usually made for metabolic fluxes, are expressed by linear constraint equations. A numerical algorithm for the solution of the resulting linear constrained set of nonlinear equations is developed. The numerical stability problems caused by large bidirectional fluxes are solved by a specially developed transformation method. Finally, the simulation of carbon labeling experiments is facilitated by a flexible software tool for network synthesis. An illustrative simulation study on flux identifiability from available flux and labeling measurements in the cyclic pentose phosphate pathway of a recombinant strain of Zymomonas mobilis concludes this contribution.

A Method to Constrain Genome-Scale Models with 13C Labeling Data

PubMed Central

García Martín, Héctor; Kumar, Vinay Satish; Weaver, Daniel; Ghosh, Amit; Chubukov, Victor; Mukhopadhyay, Aindrila; Arkin, Adam; Keasling, Jay D.

2015-01-01

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems. PMID:26379153

The Inheritance of Metabolic Flux: Expressions for the within-Sibship Mean and Variance Given the Parental Genotypes

PubMed Central

Ward, P. J.

1990-01-01

Recent developments have related quantitative trait expression to metabolic flux. The present paper investigates some implications of this for statistical aspects of polygenic inheritance. Expressions are derived for the within-sibship genetic mean and genetic variance of metabolic flux given a pair of parental, diploid, n-locus genotypes. These are exact and hold for arbitrary numbers of gene loci, arbitrary allelic values at each locus, and for arbitrary recombination fractions between adjacent gene loci. The within-sibship, genetic variance is seen to be simply a measure of parental heterozygosity plus a measure of the degree of linkage coupling within the parental genotypes. Approximations are given for the within-sibship phenotypic mean and variance of metabolic flux. These results are applied to the problem of attaining adequate statistical power in a test of association between allozymic variation and inter-individual variation in metabolic flux. Simulations indicate that statistical power can be greatly increased by augmenting the data with predictions and observations on progeny statistics in relation to parental allozyme genotypes. Adequate power may thus be attainable at small sample sizes, and when allozymic variation is scored at a only small fraction of the total set of loci whose catalytic products determine the flux. PMID:2379825

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.

PubMed

Birkel, Garrett W; Ghosh, Amit; Kumar, Vinay S; Weaver, Daniel; Ando, David; Backman, Tyler W H; Arkin, Adam P; Keasling, Jay D; Martín, Héctor García

2017-04-05

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13 C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

DOE PAGES

Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.; ...

2017-04-05

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics,more » proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S- 13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.« less

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics,more » proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S- 13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.« less

Rerouting of carbon flux in a glycogen mutant of cyanobacteria assessed via isotopically non-stationary 13C metabolic flux analysis

USDA-ARS?s Scientific Manuscript database

Cyanobacteria, which constitute a quantitatively dominant phylum, are well known for their ability to carry out oxygenic photosynthesis. This prokaryotic group has attracted attention in biofuel applications due to favourable physiological characteristics, photosynthetic efficiency and amenability t...

Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism

PubMed Central

Fleming, R.M.T.; Thiele, I.; Provan, G.; Nasheuer, H.P.

2010-01-01

The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in E. coli and compare favourably with in silico prediction by flux balance analysis. PMID:20230840

Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation

PubMed Central

Ma, Fangfang; Jazmin, Lara J.; Young, Jamey D.; Allen, Doug K.

2014-01-01

Improving plant productivity is an important aim for metabolic engineering. There are few comprehensive methods that quantitatively describe leaf metabolism, although such information would be valuable for increasing photosynthetic capacity, enhancing biomass production, and rerouting carbon flux toward desirable end products. Isotopically nonstationary metabolic flux analysis (INST-MFA) has been previously applied to map carbon fluxes in photoautotrophic bacteria, which involves model-based regression of transient 13C-labeling patterns of intracellular metabolites. However, experimental and computational difficulties have hindered its application to terrestrial plant systems. We performed in vivo isotopic labeling of Arabidopsis thaliana rosettes with 13CO2 and estimated fluxes throughout leaf photosynthetic metabolism by INST-MFA. Plants grown at 200 µmol m-2s−1 light were compared with plants acclimated for 9 d at an irradiance of 500 µmol⋅m−2⋅s−1. Approximately 1,400 independent mass isotopomer measurements obtained from analysis of 37 metabolite fragment ions were regressed to estimate 136 total fluxes (54 free fluxes) under each condition. The results provide a comprehensive description of changes in carbon partitioning and overall photosynthetic flux after long-term developmental acclimation of leaves to high light. Despite a doubling in the carboxylation rate, the photorespiratory flux increased from 17 to 28% of net CO2 assimilation with high-light acclimation (Vc/Vo: 3.5:1 vs. 2.3:1, respectively). This study highlights the potential of 13C INST-MFA to describe emergent flux phenotypes that respond to environmental conditions or plant physiology and cannot be obtained by other complementary approaches. PMID:25368168

Two-Scale 13C Metabolic Flux Analysis for Metabolic Engineering.

PubMed

Ando, David; Garcia Martin, Hector

2018-01-01

Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a 13 C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, 13 C Metabolic Flux Analysis, and it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is illustrated through a step-by-step demonstration, which is also contained in a digital Jupyter Notebook format that enhances reproducibility and provides the capability to be adopted to the user's specific needs. As an open-source software project, users can modify and extend the code base and make improvements at will, providing a base for future modeling efforts.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hay, J.; Schwender, J.

Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganicmore » nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.« less

Metabolic network flux analysis for engineering plant systems.

PubMed

Shachar-Hill, Yair

2013-04-01

Metabolic network flux analysis (NFA) tools have proven themselves to be powerful aids to metabolic engineering of microbes by providing quantitative insights into the flows of material and energy through cellular systems. The development and application of NFA tools to plant systems has advanced in recent years and are yielding significant insights and testable predictions. Plants present substantial opportunities for the practical application of NFA but they also pose serious challenges related to the complexity of plant metabolic networks and to deficiencies in our knowledge of their structure and regulation. By considering the tools available and selected examples, this article attempts to assess where and how NFA is most likely to have a real impact on plant biotechnology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation

DOE PAGES

Ma, Fangfang; Jazmin, Lara J.; Young, Jamey D.; ...

2014-11-03

Improving plant productivity is an important aim for metabolic engineering. There are few comprehensive methods that quantitatively describe leaf metabolism, although such information would be valuable for increasing photosynthetic capacity, enhancing biomass production, and rerouting carbon flux toward desirable end products. Isotopically nonstationary metabolic flux analysis (INST-MFA) has been previously applied to map carbon fluxes in photoautotrophic bacteria, which involves model-based regression of transient 13C-labeling patterns of intracellular metabolites. However, experimental and computational difficulties have hindered its application to terrestrial plant systems. Here, we performed in vivo isotopic labeling of Arabidopsis thaliana rosettes with 13CO 2 and estimated fluxes throughoutmore » leaf photosynthetic metabolism by INST-MFA. Plants grown at 200 µmol m $-$2s $-$1 light were compared with plants acclimated for 9 d at an irradiance of 500 µmol∙m $-$2∙s $-$1. Approximately 1,400 independent mass isotopomer measurements obtained from analysis of 37 metabolite fragment ions were regressed to estimate 136 total fluxes (54 free fluxes) under each condition. The results provide a comprehensive description of changes in carbon partitioning and overall photosynthetic flux after long-term developmental acclimation of leaves to high light. Despite a doubling in the carboxylation rate, the photorespiratory flux increased from 17 to 28% of net CO 2 assimilation with high-light acclimation (Vc/Vo: 3.5:1 vs. 2.3:1, respectively). In conclusion, this study highlights the potential of 13C INST-MFA to describe emergent flux phenotypes that respond to environmental conditions or plant physiology and cannot be obtained by other complementary approaches.« less

Soil Fluxomics: Disentangling Microbial Group Specific Metabolism by Modeling of 13C-Incorporation into PLFAs

NASA Astrophysics Data System (ADS)

Apostel, C.; Kuzyakov, Y.; Dippold, M. A.

2016-12-01

Soils are the largest terrestrial C sinks and microorganisms are the most important drivers of organic matter (OM) dynamics in soils: C allocation to ana- or catabolism in microbial cells is the decisive step, whether C gets oxidized to CO2 or whether it is allocated to microbial biomass, which, after cell death can be stabilized in soils. The metabolic parameter describing the ratio between the two fluxes is the carbon use efficiency (CUE), which can be assessed by position-specific labeling followed by metabolic flux modelling. However, to disentangle the single microbial groups' contribution to the bulk soil CUE, a tracing of individual groups metabolism is necessary. We assessed short-term (3 and 10 days) transformations of monosaccharides by adding position-specifically 13C labeled glucose to soil in a field experiment. Incorporation of 13C in the microbial PLFAs enabled us to distinguish individual microbial groups metabolic fluxes and compare their C-utilization efficiency using a quantitative C-flux model. The position-specific pattern in PLFAs revealed two sets of microorganisms: one metabolized glucose mainly by glycolysis and the other mainly by the pentose-phosphate pathway, which results in a higher CUE. Both of those sets included prokaryotic as well as eukaryotic microorganisms. This demonstrates that phylogenetic grouping is not decisive for the metabolic behavior of a microbial group and that the contribution of individual group members to the soil C fluxes cannot be concluded from their phylogeny.

A detailed view on sulphur metabolism at the cellular and whole-plant level illustrates challenges in metabolite flux analyses.

PubMed

Rennenberg, Heinz; Herschbach, Cornelia

2014-11-01

Understanding the dynamics of physiological process in the systems biology era requires approaches at the genome, transcriptome, proteome, and metabolome levels. In this context, metabolite flux experiments have been used in mapping metabolite pathways and analysing metabolic control. In the present review, sulphur metabolism was taken to illustrate current challenges of metabolic flux analyses. At the cellular level, restrictions in metabolite flux analyses originate from incomplete knowledge of the compartmentation network of metabolic pathways. Transport of metabolites through membranes is usually not considered in flux experiments but may be involved in controlling the whole pathway. Hence, steady-state and snapshot readings need to be expanded to time-course studies in combination with compartment-specific metabolite analyses. Because of species-specific differences, differences between tissues, and stress-related responses, the quantitative significance of different sulphur sinks has to be elucidated; this requires the development of methods for whole-sulphur metabolome approaches. Different cell types can contribute to metabolite fluxes to different extents at the tissue and organ level. Cell type-specific analyses are needed to characterize these contributions. Based on such approaches, metabolite flux analyses can be expanded to the whole-plant level by considering long-distance transport and, thus, the interaction of roots and the shoot in metabolite fluxes. However, whole-plant studies need detailed empirical and mathematical modelling that have to be validated by experimental analyses. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

PubMed Central

Chang, Roger L; Ghamsari, Lila; Manichaikul, Ani; Hom, Erik F Y; Balaji, Santhanam; Fu, Weiqi; Shen, Yun; Hao, Tong; Palsson, Bernhard Ø; Salehi-Ashtiani, Kourosh; Papin, Jason A

2011-01-01

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. PMID:21811229

Quiescent Fibroblasts Exhibit High Metabolic Activity

PubMed Central

Lemons, Johanna M. S.; Feng, Xiao-Jiang; Bennett, Bryson D.; Legesse-Miller, Aster; Johnson, Elizabeth L.; Raitman, Irene; Pollina, Elizabeth A.; Rabitz, Herschel A.; Rabinowitz, Joshua D.; Coller, Hilary A.

2010-01-01

Many cells in mammals exist in the state of quiescence, which is characterized by reversible exit from the cell cycle. Quiescent cells are widely reported to exhibit reduced size, nucleotide synthesis, and metabolic activity. Much lower glycolytic rates have been reported in quiescent compared with proliferating lymphocytes. In contrast, we show here that primary human fibroblasts continue to exhibit high metabolic rates when induced into quiescence via contact inhibition. By monitoring isotope labeling through metabolic pathways and quantitatively identifying fluxes from the data, we show that contact-inhibited fibroblasts utilize glucose in all branches of central carbon metabolism at rates similar to those of proliferating cells, with greater overflow flux from the pentose phosphate pathway back to glycolysis. Inhibition of the pentose phosphate pathway resulted in apoptosis preferentially in quiescent fibroblasts. By feeding the cells labeled glutamine, we also detected a “backwards” flux in the tricarboxylic acid cycle from α-ketoglutarate to citrate that was enhanced in contact-inhibited fibroblasts; this flux likely contributes to shuttling of NADPH from the mitochondrion to cytosol for redox defense or fatty acid synthesis. The high metabolic activity of the fibroblasts was directed in part toward breakdown and resynthesis of protein and lipid, and in part toward excretion of extracellular matrix proteins. Thus, reduced metabolic activity is not a hallmark of the quiescent state. Quiescent fibroblasts, relieved of the biosynthetic requirements associated with generating progeny, direct their metabolic activity to preservation of self integrity and alternative functions beneficial to the organism as a whole. PMID:21049082

Interaction of storage carbohydrates and other cyclic fluxes with central metabolism: A quantitative approach by non-stationary 13C metabolic flux analysis.

PubMed

Suarez-Mendez, C A; Hanemaaijer, M; Ten Pierick, Angela; Wolters, J C; Heijnen, J J; Wahl, S A

2016-12-01

13 C labeling experiments in aerobic glucose limited cultures of Saccharomyces cerevisiae at four different growth rates (0.054; 0.101, 0.207, 0.307 h -1 ) are used for calculating fluxes that include intracellular cycles (e.g., storage carbohydrate cycles, exchange fluxes with amino acids), which are rearranged depending on the growth rate. At low growth rates the impact of the storage carbohydrate recycle is relatively more significant than at high growth rates due to a higher concentration of these materials in the cell (up to 560-fold) and higher fluxes relative to the glucose uptake rate (up to 16%). Experimental observations suggest that glucose can be exported to the extracellular space, and that its source is related to storage carbohydrates, most likely via the export and subsequent extracellular breakdown of trehalose. This hypothesis is strongly supported by 13 C-labeling experimental data, measured extracellular trehalose, and the corresponding flux estimations.

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

DOE PAGES

Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant; ...

2017-03-08

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We appliedmore » GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.« less

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We appliedmore » GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.« less

Enzyme clustering accelerates processing of intermediates through metabolic channeling

PubMed Central

Castellana, Michele; Wilson, Maxwell Z.; Xu, Yifan; Joshi, Preeti; Cristea, Ileana M.; Rabinowitz, Joshua D.; Gitai, Zemer; Wingreen, Ned S.

2015-01-01

We present a quantitative model to demonstrate that coclustering multiple enzymes into compact agglomerates accelerates the processing of intermediates, yielding the same efficiency benefits as direct channeling, a well-known mechanism in which enzymes are funneled between enzyme active sites through a physical tunnel. The model predicts the separation and size of coclusters that maximize metabolic efficiency, and this prediction is in agreement with previously reported spacings between coclusters in mammalian cells. For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can accelerate the processing of a shared intermediate by one branch, and thus regulate steady-state flux division. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation. PMID:25262299

Methods for the Determination of Rates of Glucose and Fatty Acid Oxidation in the Isolated Working Rat Heart

PubMed Central

Bakrania, Bhavisha; Granger, Joey P.; Harmancey, Romain

2016-01-01

The mammalian heart is a major consumer of ATP and requires a constant supply of energy substrates for contraction. Not surprisingly, alterations of myocardial metabolism have been linked to the development of contractile dysfunction and heart failure. Therefore, unraveling the link between metabolism and contraction should shed light on some of the mechanisms governing cardiac adaptation or maladaptation in disease states. The isolated working rat heart preparation can be used to follow, simultaneously and in real time, cardiac contractile function and flux of energy providing substrates into oxidative metabolic pathways. The present protocol aims to provide a detailed description of the methods used in the preparation and utilization of buffers for the quantitative measurement of the rates of oxidation for glucose and fatty acids, the main energy providing substrates of the heart. The methods used for sample analysis and data interpretation are also discussed. In brief, the technique is based on the supply of 14C- radiolabeled glucose and a 3H- radiolabeled long-chain fatty acid to an ex vivo beating heart via normothermic crystalloid perfusion. 14CO2 and 3H2O, end byproducts of the enzymatic reactions involved in the utilization of these energy providing substrates, are then quantitatively recovered from the coronary effluent. With knowledge of the specific activity of the radiolabeled substrates used, it is then possible to individually quantitate the flux of glucose and fatty acid in the oxidation pathways. Contractile function of the isolated heart can be determined in parallel with the appropriate recording equipment and directly correlated to metabolic flux values. The technique is extremely useful to study the metabolism/contraction relationship in response to various stress conditions such as alterations in pre and after load and ischemia, a drug or a circulating factor, or following the alteration in the expression of a gene product. PMID:27768055

Quantitative Assessment of Thermodynamic Constraints on the Solution Space of Genome-Scale Metabolic Models

PubMed Central

Hamilton, Joshua J.; Dwivedi, Vivek; Reed, Jennifer L.

2013-01-01

Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. PMID:23870272

Determination of the fluxes in the central metabolism of Corynebacterium glutamicum by nuclear magnetic resonance spectroscopy combined with metabolite balancing.

PubMed

Marx, A; de Graaf, A A; Wiechert, W; Eggeling, L; Sahm, H

1996-01-20

To determine the in vivo fluxes of the central metabolism we have developed a comprehensive approach exclusively based on the fundamental enzyme reactions known to be present, the fate of the carbon atoms of individual reactions, and the metabolite balance of the culture. No information on the energy balance is required, nor information on enzyme activities, or the directionalities of reactions. Our approach combines the power of (1)H-detected (13)C nuclear magnetic resonance spectroscopy to follow individual carbons with the simplicity of establishing carbon balances of bacterial cultures. We grew a lysine-producing strain of Corynebacterium glutamicum to the metabolic and isotopic steady state with [1-(13)C]glucose and determined the fractional enrichments in 27 carbon atoms of 11 amino acids isolated from the cell. Since precursor metabolites of the central metabolism are incorporated in an exactly defined manner in the carbon skeleton of amino acids, the fractional enrichments in carbons of precursor metabolites (oxaloacetate, glyceraldehyde 3-phosphate, erythrose 4-phosphate, etc.) became directly accessible. A concise and generally applicable mathematical model was established using matrix calculus to express all metabolite mass and carbon labeling balances. An appropriate all-purpose software for the iterative solution of the equations is supplied. Applying this comprehensive methodology to C. glutamicum, all major fluxes within the central metabolism were determined. The result is that the flux through the pentose phosphate pathway is 66.4% (relative to the glucose input flux of 1.49 mmol/g dry weight h), that of entry into the tricarboxylic acid cycle 62.2%, and the contribution of the succinylase pathway of lysine synthesis 13.7%. Due to the large amount and high quality of measured data in vivo exchange reactions could also be quantitated with particularly high exchange rates within the pentose phosphate pathway for the ribose 5-phosphate transketolase reaction. Moreover, the total net flux of the anaplerotic reactions was quantitated as 38.0%. Most importantly, we found that in vivo one component within these anaplerotic reactions is a back flux from the carbon 4 units of the tricarboxylic acid cycle to the carbon 3 units of glycolysis of 30.6%. (c) 1996 John Wiley & Sons, Inc.

Metabolic flux profiling of recombinant protein secreting Pichia pastoris growing on glucose:methanol mixtures

PubMed Central

2012-01-01

Background The methylotrophic yeast Pichia pastoris has emerged as one of the most promising yeast hosts for the production of heterologous proteins. Mixed feeds of methanol and a multicarbon source instead of methanol as sole carbon source have been shown to improve product productivities and alleviate metabolic burden derived from protein production. Nevertheless, systematic quantitative studies on the relationships between the central metabolism and recombinant protein production in P. pastoris are still rather limited, particularly when growing this yeast on mixed carbon sources, thus hampering future metabolic network engineering strategies for improved protein production. Results The metabolic flux distribution in the central metabolism of P. pastoris growing on a mixed feed of glucose and methanol was analyzed by Metabolic Flux Analysis (MFA) using 13C-NMR-derived constraints. For this purpose, we defined new flux ratios for methanol assimilation pathways in P. pastoris cells growing on glucose:methanol mixtures. By using this experimental approach, the metabolic burden caused by the overexpression and secretion of a Rhizopus oryzae lipase (Rol) in P. pastoris was further analyzed. This protein has been previously shown to trigger the unfolded protein response in P. pastoris. A series of 13C-tracer experiments were performed on aerobic chemostat cultivations with a control and two different Rol producing strains growing at a dilution rate of 0.09 h−1 using a glucose:methanol 80:20 (w/w) mix as carbon source. The MFA performed in this study reveals a significant redistristribution of carbon fluxes in the central carbon metabolism when comparing the two recombinant strains vs the control strain, reflected in increased glycolytic, TCA cycle and NADH regeneration fluxes, as well as higher methanol dissimilation rates. Conclusions Overall, a further 13C-based MFA development to characterise the central metabolism of methylotrophic yeasts when growing on mixed methanol:multicarbon sources has been implemented, thus providing a new tool for the investigation of the relationships between central metabolism and protein production. Specifically, the study points at a limited but significant impact of the conformational stress associated to secretion of recombinant proteins on the central metabolism, occurring even at modest production levels. PMID:22569166

Metabolic flux analysis of Cyanothece sp. ATCC 51142 under mixotrophic conditions.

PubMed

Alagesan, Swathi; Gaudana, Sandeep B; Sinha, Avinash; Wangikar, Pramod P

2013-11-01

Cyanobacteria are a group of photosynthetic prokaryotes capable of utilizing solar energy to fix atmospheric carbon dioxide to biomass. Despite several "proof of principle" studies, low product yield is an impediment in commercialization of cyanobacteria-derived biofuels. Estimation of intracellular reaction rates by (13)C metabolic flux analysis ((13)C-MFA) would be a step toward enhancing biofuel yield via metabolic engineering. We report (13)C-MFA for Cyanothece sp. ATCC 51142, a unicellular nitrogen-fixing cyanobacterium, known for enhanced hydrogen yield under mixotrophic conditions. Rates of reactions in the central carbon metabolism under nitrogen-fixing and -non-fixing conditions were estimated by monitoring the competitive incorporation of (12)C and (13)C from unlabeled CO2 and uniformly labeled glycerol, respectively, into terminal metabolites such as amino acids. The observed labeling patterns suggest mixotrophic growth under both the conditions, with a larger fraction of unlabeled carbon in nitrate-sufficient cultures asserting a greater contribution of carbon fixation by photosynthesis and an anaplerotic pathway. Indeed, flux analysis complements the higher growth observed under nitrate-sufficient conditions. On the other hand, the flux through the oxidative pentose phosphate pathway and tricarboxylic acid cycle was greater in nitrate-deficient conditions, possibly to supply the precursors and reducing equivalents needed for nitrogen fixation. In addition, an enhanced flux through fructose-6-phosphate phosphoketolase possibly suggests the organism's preferred mode under nitrogen-fixing conditions. The (13)C-MFA results complement the reported predictions by flux balance analysis and provide quantitative insight into the organism's distinct metabolic features under nitrogen-fixing and -non-fixing conditions.

Constrained Allocation Flux Balance Analysis

PubMed Central

Mori, Matteo; Hwa, Terence; Martin, Olivier C.

2016-01-01

New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an “ensemble averaging” procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws. PMID:27355325

Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models.

PubMed

Hamilton, Joshua J; Dwivedi, Vivek; Reed, Jennifer L

2013-07-16

Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Temporal Expression-based Analysis of Metabolism

PubMed Central

Segrè, Daniel

2012-01-01

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques. PMID:23209390

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

PubMed Central

2014-01-01

Background Saccharomyces cerevisiae is the most relevant yeast species conducting the alcoholic fermentation that takes place during winemaking. Although the physiology of this model organism has been extensively studied, systematic quantitative physiology studies of this yeast under winemaking conditions are still scarce, thus limiting the understanding of fermentative metabolism of wine yeast strains and the systematic description, modelling and prediction of fermentation processes. In this study, we implemented and validated the use of chemostat cultures as a tool to simulate different stages of a standard wine fermentation, thereby allowing to implement metabolic flux analyses describing the sequence of metabolic states of S. cerevisae along the wine fermentation. Results Chemostat cultures mimicking the different stages of standard wine fermentations of S. cerevisiae EC1118 were performed using a synthetic must and strict anaerobic conditions. The simulated stages corresponded to the onset of the exponential growth phase, late exponential growth phase and cells just entering stationary phase, at dilution rates of 0.27, 0.04, 0.007 h−1, respectively. Notably, measured substrate uptake and product formation rates at each steady state condition were generally within the range of corresponding conversion rates estimated during the different batch fermentation stages. Moreover, chemostat data were further used for metabolic flux analysis, where biomass composition data for each condition was considered in the stoichiometric model. Metabolic flux distributions were coherent with previous analyses based on batch cultivations data and the pseudo-steady state assumption. Conclusions Steady state conditions obtained in chemostat cultures reflect the environmental conditions and physiological states of S. cerevisiae corresponding to the different growth stages of a typical batch wine fermentation, thereby showing the potential of this experimental approach to systematically study the effect of environmental relevant factors such as temperature, sugar concentration, C/N ratio or (micro) oxygenation on the fermentative metabolism of wine yeast strains. PMID:24928139

In vivo 13C MRS in the mouse brain at 14.1 Tesla and metabolic flux quantification under infusion of [1,6-13C2]glucose.

PubMed

Lai, Marta; Lanz, Bernard; Poitry-Yamate, Carole; Romero, Jackeline F; Berset, Corina M; Cudalbu, Cristina; Gruetter, Rolf

2017-01-01

In vivo 13 C magnetic resonance spectroscopy (MRS) enables the investigation of cerebral metabolic compartmentation while, e.g. infusing 13 C-labeled glucose. Metabolic flux analysis of 13 C turnover previously yielded quantitative information of glutamate and glutamine metabolism in humans and rats, while the application to in vivo mouse brain remains exceedingly challenging. In the present study, 13 C direct detection at 14.1 T provided highly resolved in vivo spectra of the mouse brain while infusing [1,6- 13 C 2 ]glucose for up to 5 h. 13 C incorporation to glutamate and glutamine C4, C3, and C2 and aspartate C3 were detected dynamically and fitted to a two-compartment model: flux estimation of neuron-glial metabolism included tricarboxylic acid cycle (TCA) flux in astrocytes (V g  = 0.16 ± 0.03 µmol/g/min) and neurons (V TCA n  = 0.56 ± 0.03 µmol/g/min), pyruvate carboxylase activity (V PC  = 0.041 ± 0.003 µmol/g/min) and neurotransmission rate (V NT  = 0.084 ± 0.008 µmol/g/min), resulting in a cerebral metabolic rate of glucose (CMR glc ) of 0.38 ± 0.02 µmol/g/min, in excellent agreement with that determined with concomitant 18 F-fluorodeoxyglucose positron emission tomography ( 18 FDG PET).We conclude that modeling of neuron-glial metabolism in vivo is accessible in the mouse brain from 13 C direct detection with an unprecedented spatial resolution under [1,6- 13 C 2 ]glucose infusion.

Metabolite recycling and bidirectional C fluxes: Revolutionizing our view on microbial C cycling in soils

NASA Astrophysics Data System (ADS)

Dippold, M. A.; Apostel, C.; Kuzyakov, Y.

2016-12-01

Biogeochemists' view on microbial C transformation in soil has rarely exceed a strongly simplified concept assuming that C gets either oxidized to CO2 via the microbial catabolism or incorporated into biomass via the anabolism. However, life in a C limited environment as challenging as soil requires microbial adaptation strategies at all levels of metabolism. By coupling of position-specific labeling of core metabolites with compound-specific isotope analysis we demonstrated that catabolic oxidation of these metabolites exists in parallel to reductive, energy consuming pathways, reducing them for anabolic purposes. Up to 55% of glucose, incorporated into the glucose derivative glucosamine, first passed glycolysis before allocated back via gluconeogenesis. Similarly, glutamate-derived C is allocated via anaplerotic pathways towards fatty acid synthesis and in parallel to its oxidation in the citric acid cycle. Furthermore, position-specific labeling of rather `cost-intensive' biomass compounds such as fatty acids revealed that intact recycling of metabolites is a crucial microbial adaptation to C scarcity in soils. Both processes are unlikely to occur in pure cultures, where constant growth conditions under high C supply allow a straight unidirectional regulation of C metabolism. However, unstable environmental conditions, C scarcity and interactions between a still unknown diversity of microorganisms in soils are likely to induce the observed metabolic diversity. To understand how microorganisms catalyze the biogeochemical fluxes in soil, a profound understanding of their metabolic adaptation strategies such as recycling or switching between bidirectional fluxes is crucial. Metabolic flux models adapted to soil microbial communities and their regulatory strategies will not only deepen our understanding on the microorganims' reactions to environmental changes but also create the prerequisits for a quantitative prediction of biogeochemical fluxes based on the underlying microbial processes.

Design principles of autocatalytic cycles constrain enzyme kinetics and force low substrate saturation at flux branch points

PubMed Central

Barenholz, Uri; Davidi, Dan; Reznik, Ed; Bar-On, Yinon; Antonovsky, Niv; Noor, Elad; Milo, Ron

2017-01-01

A set of chemical reactions that require a metabolite to synthesize more of that metabolite is an autocatalytic cycle. Here, we show that most of the reactions in the core of central carbon metabolism are part of compact autocatalytic cycles. Such metabolic designs must meet specific conditions to support stable fluxes, hence avoiding depletion of intermediate metabolites. As such, they are subjected to constraints that may seem counter-intuitive: the enzymes of branch reactions out of the cycle must be overexpressed and the affinity of these enzymes to their substrates must be relatively weak. We use recent quantitative proteomics and fluxomics measurements to show that the above conditions hold for functioning cycles in central carbon metabolism of E. coli. This work demonstrates that the topology of a metabolic network can shape kinetic parameters of enzymes and lead to seemingly wasteful enzyme usage. DOI: http://dx.doi.org/10.7554/eLife.20667.001 PMID:28169831

Metabolism of Citrate and Other Carboxylic Acids in Erythrocytes As a Function of Oxygen Saturation and Refrigerated Storage.

PubMed

Nemkov, Travis; Sun, Kaiqi; Reisz, Julie A; Yoshida, Tatsuro; Dunham, Andrew; Wen, Edward Y; Wen, Alexander Q; Roach, Rob C; Hansen, Kirk C; Xia, Yang; D'Alessandro, Angelo

2017-01-01

State-of-the-art proteomics technologies have recently helped to elucidate the unanticipated complexity of red blood cell metabolism. One recent example is citrate metabolism, which is catalyzed by cytosolic isoforms of Krebs cycle enzymes that are present and active in mature erythrocytes and was determined using quantitative metabolic flux analysis. In previous studies, we reported significant increases in glycolytic fluxes in red blood cells exposed to hypoxia in vitro or in vivo , an observation relevant to transfusion medicine owing to the potential benefits associated with hypoxic storage of packed red blood cells. Here, using a combination of steady state and quantitative tracing metabolomics experiments with 13 C 1,2,3 -glucose, 13 C 6 -citrate, 13 C 5 15 N 2 -glutamine, and 13 C 1 -aspartate via ultra-high performance liquid chromatography coupled on line with mass spectrometry, we observed that hypoxia in vivo and in vitro promotes consumption of citrate and other carboxylates. These metabolic reactions are theoretically explained by the activity of cytosolic malate dehydrogenase 1 and isocitrate dehydrogenase 1 (abundantly represented in the red blood cell proteome), though moonlighting functions of additional enzymes cannot be ruled out. These observations enhance understanding of red blood cell metabolic responses to hypoxia, which could be relevant to understand systemic physiological and pathological responses to high altitude, ischemia, hemorrhage, sepsis, pulmonary hypertension, or hemoglobinopathies. Results from this study will also inform the design and testing of novel additive solutions that optimize red blood cell storage under oxygen-controlled conditions.

Metabolism of Citrate and Other Carboxylic Acids in Erythrocytes As a Function of Oxygen Saturation and Refrigerated Storage

PubMed Central

Nemkov, Travis; Sun, Kaiqi; Reisz, Julie A.; Yoshida, Tatsuro; Dunham, Andrew; Wen, Edward Y.; Wen, Alexander Q.; Roach, Rob C.; Hansen, Kirk C.; Xia, Yang; D’Alessandro, Angelo

2017-01-01

State-of-the-art proteomics technologies have recently helped to elucidate the unanticipated complexity of red blood cell metabolism. One recent example is citrate metabolism, which is catalyzed by cytosolic isoforms of Krebs cycle enzymes that are present and active in mature erythrocytes and was determined using quantitative metabolic flux analysis. In previous studies, we reported significant increases in glycolytic fluxes in red blood cells exposed to hypoxia in vitro or in vivo, an observation relevant to transfusion medicine owing to the potential benefits associated with hypoxic storage of packed red blood cells. Here, using a combination of steady state and quantitative tracing metabolomics experiments with 13C1,2,3-glucose, 13C6-citrate, 13C515N2-glutamine, and 13C1-aspartate via ultra-high performance liquid chromatography coupled on line with mass spectrometry, we observed that hypoxia in vivo and in vitro promotes consumption of citrate and other carboxylates. These metabolic reactions are theoretically explained by the activity of cytosolic malate dehydrogenase 1 and isocitrate dehydrogenase 1 (abundantly represented in the red blood cell proteome), though moonlighting functions of additional enzymes cannot be ruled out. These observations enhance understanding of red blood cell metabolic responses to hypoxia, which could be relevant to understand systemic physiological and pathological responses to high altitude, ischemia, hemorrhage, sepsis, pulmonary hypertension, or hemoglobinopathies. Results from this study will also inform the design and testing of novel additive solutions that optimize red blood cell storage under oxygen-controlled conditions. PMID:29090212

Separation of lipoproteins for quantitative analysis of 14C-labeled lipid soluble compounds

USDA-ARS?s Scientific Manuscript database

Carbon-14 tracer studies using accelerator mass spectrometry (AMS) have provided novel insights into nutrient metabolism and whole body metabolite flux. In addition to a baseline separation of analytes, a critical requirement specific to the AMS analysis was a stable carbon baseline within the anal...

iSCHRUNK--In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks.

PubMed

Andreozzi, Stefano; Miskovic, Ljubisa; Hatzimanikatis, Vassily

2016-01-01

Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

(13)C metabolic flux analysis in neurons utilizing a model that accounts for hexose phosphate recycling within the pentose phosphate pathway.

PubMed

Gebril, Hoda M; Avula, Bharathi; Wang, Yan-Hong; Khan, Ikhlas A; Jekabsons, Mika B

2016-02-01

Glycolysis, mitochondrial substrate oxidation, and the pentose phosphate pathway (PPP) are critical for neuronal bioenergetics and oxidation-reduction homeostasis, but quantitating their fluxes remains challenging, especially when processes such as hexose phosphate (i.e., glucose/fructose-6-phosphate) recycling in the PPP are considered. A hexose phosphate recycling model was developed which exploited the rates of glucose consumption, lactate production, and mitochondrial respiration to infer fluxes through the major glucose consuming pathways of adherent cerebellar granule neurons by replicating [(13)C]lactate labeling from metabolism of [1,2-(13)C2]glucose. Flux calculations were predicated on a steady-state system with reactions having known stoichiometries and carbon atom transitions. Non-oxidative PPP activity and consequent hexose phosphate recycling, as well as pyruvate production by cytoplasmic malic enzyme, were optimized by the model and found to account for 28 ± 2% and 7.7 ± 0.2% of hexose phosphate and pyruvate labeling, respectively. From the resulting fluxes, 52 ± 6% of glucose was metabolized by glycolysis, compared to 19 ± 2% by the combined oxidative/non-oxidative pentose cycle that allows for hexose phosphate recycling, and 29 ± 8% by the combined oxidative PPP/de novo nucleotide synthesis reactions. By extension, 62 ± 6% of glucose was converted to pyruvate, the metabolism of which resulted in 16 ± 1% of glucose oxidized by mitochondria and 46 ± 6% exported as lactate. The results indicate a surprisingly high proportion of glucose utilized by the pentose cycle and the reactions synthesizing nucleotides, and exported as lactate. While the in vitro conditions to which the neurons were exposed (high glucose, no lactate or other exogenous substrates) limit extrapolating these results to the in vivo state, the approach provides a means of assessing a number of metabolic fluxes within the context of hexose phosphate recycling in the PPP from a minimal set of measurements. Copyright © 2015 Elsevier Ltd. All rights reserved.

Stepwise inference of likely dynamic flux distributions from metabolic time series data.

PubMed

Faraji, Mojdeh; Voit, Eberhard O

2017-07-15

Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution. We applied the proposed method to the lignin biosynthesis pathway in switchgrass. The system consists of 16 metabolites and 23 enzymatic reactions. It has seven degrees of freedom and therefore admits a large space of dynamic flux distributions that all fit a set of metabolic time series data equally well. The proposed method reduces this space in a systematic and biologically reasonable manner and converges to a likely dynamic flux distribution in just a few iterations. The estimated solution and the true flux distribution, which is known in this case, show excellent agreement and thereby lend support to the method. The computational model was implemented in MATLAB (version R2014a, The MathWorks, Natick, MA). The source code is available at https://github.gatech.edu/VoitLab/Stepwise-Inference-of-Likely-Dynamic-Flux-Distributions and www.bst.bme.gatech.edu/research.php . mojdeh@gatech.edu or eberhard.voit@bme.gatech.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Understanding Regulation of Metabolism through Feasibility Analysis

PubMed Central

Nikerel, Emrah; Berkhout, Jan; Hu, Fengyuan; Teusink, Bas; Reinders, Marcel J. T.; de Ridder, Dick

2012-01-01

Understanding cellular regulation of metabolism is a major challenge in systems biology. Thus far, the main assumption was that enzyme levels are key regulators in metabolic networks. However, regulation analysis recently showed that metabolism is rarely controlled via enzyme levels only, but through non-obvious combinations of hierarchical (gene and enzyme levels) and metabolic regulation (mass action and allosteric interaction). Quantitative analyses relating changes in metabolic fluxes to changes in transcript or protein levels have revealed a remarkable lack of understanding of the regulation of these networks. We study metabolic regulation via feasibility analysis (FA). Inspired by the constraint-based approach of Flux Balance Analysis, FA incorporates a model describing kinetic interactions between molecules. We enlarge the portfolio of objectives for the cell by defining three main physiologically relevant objectives for the cell: function, robustness and temporal responsiveness. We postulate that the cell assumes one or a combination of these objectives and search for enzyme levels necessary to achieve this. We call the subspace of feasible enzyme levels the feasible enzyme space. Once this space is constructed, we can study how different objectives may (if possible) be combined, or evaluate the conditions at which the cells are faced with a trade-off among those. We apply FA to the experimental scenario of long-term carbon limited chemostat cultivation of yeast cells, studying how metabolism evolves optimally. Cells employ a mixed strategy composed of increasing enzyme levels for glucose uptake and hexokinase and decreasing levels of the remaining enzymes. This trade-off renders the cells specialized in this low-carbon flux state to compete for the available glucose and get rid of over-overcapacity. Overall, we show that FA is a powerful tool for systems biologists to study regulation of metabolism, interpret experimental data and evaluate hypotheses. PMID:22808034

In vivo nuclear magnetic resonance studies of hepatic methoxyflurane metabolism. II. A reevaluation of hepatic metabolic pathways.

PubMed

Selinsky, B S; Perlman, M E; London, R E

1988-05-01

Methoxyflurane (2,2-dichloro-1,1-difluoro-ethyl methyl ether) is believed to be metabolized via two convergent metabolic pathways. The relative flux through these two metabolic pathways has been investigated using a combination of in vivo surface coil NMR techniques and in vitro analyses of urinary metabolites. Analysis of the measured concentrations of inorganic fluoride, oxalate, and methoxydifluoroacetate in the urine of methoxyflurane-treated rats for 4 days after anesthesia indicates that the anesthetic is metabolized primarily via dechlorination to yield methoxydifluoroacetate. The methoxydifluoroacetate is largely excreted without further metabolism, although a small percentage of this metabolite is broken down to yield fluoride and oxalate, as determined by urine analysis of rats dosed with synthetic methoxydifluoroacetate. At early times after methoxyflurane exposure, the relative concentrations of methoxyflurane metabolites indicate that a significant fraction of the metabolic flux occurs via a different pathway, presumably demethylation, to yield dichloroacetate as an intermediate. Direct analysis of dichloroacetate in the urine using water-suppressed proton NMR indicates that the level of this metabolite is below the detection threshold of the method. Measurements made on the urine of rats dosed directly with dichloroacetate indicate that this compound is quickly metabolized, and dichloroacetate levels in urine are again found to be below the detection threshold. These results demonstrate the quantitative importance of the dechlorination pathway in the metabolism of methoxyflurane in rats.

Metabolic Flux Analysis during the Exponential Growth Phase of Saccharomyces cerevisiae in Wine Fermentations

PubMed Central

Quirós, Manuel; Martínez-Moreno, Rubén; Albiol, Joan; Morales, Pilar; Vázquez-Lima, Felícitas; Barreiro-Vázquez, Antonio; Ferrer, Pau; Gonzalez, Ramon

2013-01-01

As a consequence of the increase in global average temperature, grapes with the adequate phenolic and aromatic maturity tend to be overripe by the time of harvest, resulting in increased sugar concentrations and imbalanced C/N ratios in fermenting musts. This fact sets obvious additional hurdles in the challenge of obtaining wines with reduced alcohols levels, a new trend in consumer demands. It would therefore be interesting to understand Saccharomyces cerevisiae physiology during the fermentation of must with these altered characteristics. The present study aims to determine the distribution of metabolic fluxes during the yeast exponential growth phase, when both carbon and nitrogen sources are in excess, using continuous cultures. Two different sugar concentrations were studied under two different winemaking temperature conditions. Although consumption and production rates for key metabolites were severely affected by the different experimental conditions studied, the general distribution of fluxes in central carbon metabolism was basically conserved in all cases. It was also observed that temperature and sugar concentration exerted a higher effect on the pentose phosphate pathway and glycerol formation than on glycolysis and ethanol production. Additionally, nitrogen uptake, both quantitatively and qualitatively, was strongly influenced by environmental conditions. This work provides the most complete stoichiometric model used for Metabolic Flux Analysis of S. cerevisiae in wine fermentations employed so far, including the synthesis and release of relevant aroma compounds and could be used in the design of optimal nitrogen supplementation of wine fermentations. PMID:23967264

Modeling and experimental design for metabolic flux analysis of lysine-producing Corynebacteria by mass spectrometry.

PubMed

Wittmann, C; Heinzle, E

2001-04-01

Experimental design of (13)C-tracer studies for metabolic flux analysis with mass spectrometric determination of labeling patterns was performed for the central metabolism of Corynebacterium glutamicum comprising various flux scenarios. Ratio measurement of mass isotopomer pools of Corynebacterium products lysine, alanine, and trehalose is sufficient to quantify the flux partitioning ratios (i) between glycolysis and pentose phosphate pathways (Phi(PPP)), (ii) between the split pathways in the lysine biosynthesis (Phi(DH)), (iii) at the pyruvate node (Phi(PC)), and reversibilities of (iv) glucose 6-phosphate isomerase (zeta(PGI)), (v) at the pyruvate node (zeta(PC/PEPCK)), and (vi) of transaldolase and transketolases in the PPP. Weighted sensitivities for flux parameters were derived from partial derivatives to quantitatively evaluate experimental approaches and predict precision for estimated flux parameters. Deviation of intensity ratios from ideal values of 1 was used as weighting function. Weighted flux sensitivities can be used to identify optimal type and degree of tracer labeling or potential intensity ratios to be measured. Experimental design for lysine-producing strain C. glutamicum MH 20-22B (Marx et al., Biotechnol. Bioeng. 49, 111-129, 1996) and various potential mutants with different alterations in the flux pattern showed that specific tracer labelings are optimal to quantify a certain flux parameter uninfluenced by the overall flux situation. Identified substrates of choice are [1-(13)C]glucose for the estimation of Phi(PPP) and zeta(PGI) and a 1 : 1 mixture of [U-(12)C/U-(13)C]glucose for the determination of zeta(PC/PEPCK). Phi(PC) can be quantified by feeding [4-(13)C]glucose or [U-(12)C/U-(13)C]glucose (1 : 1), whereas Phi(DH) is accessible via [4-(13)C]glucose. The sensitivity for the quantification of a certain flux parameter can be influenced by superposition through other flux parameters in the network, but substrate and measured mass isotopomers of choice remain the same. In special cases, reduced labeling degree of the tracer substrate can increase the precision of flux analysis. Enhanced precision and flux information can be achieved via multiply labeled substrates. The presented approach can be applied for effective experimental design of (13)C tracer studies for metabolic flux analysis. Intensity ratios of other products such as glutamate, valine, phenylalanine, and riboflavin also sensitively reflect flux parameters, which underlines the great potential of mass spectrometry for flux analysis. Copyright 2001 Academic Press.

Metabolic dynamics in skeletal muscle during acute reduction in blood flow and oxygen supply to mitochondria: in-silico studies using a multi-scale, top-down integrated model.

PubMed

Dash, Ranjan K; Li, Yanjun; Kim, Jaeyeon; Beard, Daniel A; Saidel, Gerald M; Cabrera, Marco E

2008-09-09

Control mechanisms of cellular metabolism and energetics in skeletal muscle that may become evident in response to physiological stresses such as reduction in blood flow and oxygen supply to mitochondria can be quantitatively understood using a multi-scale computational model. The analysis of dynamic responses from such a model can provide insights into mechanisms of metabolic regulation that may not be evident from experimental studies. For the purpose, a physiologically-based, multi-scale computational model of skeletal muscle cellular metabolism and energetics was developed to describe dynamic responses of key chemical species and reaction fluxes to muscle ischemia. The model, which incorporates key transport and metabolic processes and subcellular compartmentalization, is based on dynamic mass balances of 30 chemical species in both capillary blood and tissue cells (cytosol and mitochondria) domains. The reaction fluxes in cytosol and mitochondria are expressed in terms of a general phenomenological Michaelis-Menten equation involving the compartmentalized energy controller ratios ATP/ADP and NADH/NAD(+). The unknown transport and reaction parameters in the model are estimated simultaneously by minimizing the differences between available in vivo experimental data on muscle ischemia and corresponding model outputs in coupled with the resting linear flux balance constraints using a robust, nonlinear, constrained-based, reduced gradient optimization algorithm. With the optimal parameter values, the model is able to simulate dynamic responses to reduced blood flow and oxygen supply to mitochondria associated with muscle ischemia of several key metabolite concentrations and metabolic fluxes in the subcellular cytosolic and mitochondrial compartments, some that can be measured and others that can not be measured with the current experimental techniques. The model can be applied to test complex hypotheses involving dynamic regulation of cellular metabolism and energetics in skeletal muscle during physiological stresses such as ischemia, hypoxia, and exercise.

Systematic inference of functional phosphorylation events in yeast metabolism.

PubMed

Chen, Yu; Wang, Yonghong; Nielsen, Jens

2017-07-01

Protein phosphorylation is a post-translational modification that affects proteins by changing their structure and conformation in a rapid and reversible way, and it is an important mechanism for metabolic regulation in cells. Phosphoproteomics enables high-throughput identification of phosphorylation events on metabolic enzymes, but identifying functional phosphorylation events still requires more detailed biochemical characterization. Therefore, development of computational methods for investigating unknown functions of a large number of phosphorylation events identified by phosphoproteomics has received increased attention. We developed a mathematical framework that describes the relationship between phosphorylation level of a metabolic enzyme and the corresponding flux through the enzyme. Using this framework, it is possible to quantitatively estimate contribution of phosphorylation events to flux changes. We showed that phosphorylation regulation analysis, combined with a systematic workflow and correlation analysis, can be used for inference of functional phosphorylation events in steady and dynamic conditions, respectively. Using this analysis, we assigned functionality to phosphorylation events of 17 metabolic enzymes in the yeast Saccharomyces cerevisiae , among which 10 are novel. Phosphorylation regulation analysis cannot only be extended for inference of other functional post-translational modifications but also be a promising scaffold for multi-omics data integration in systems biology. Matlab codes for flux balance analysis in this study are available in Supplementary material. yhwang@ecust.edu.cn or nielsenj@chalmers.se. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Dynamic flux balancing elucidates NAD(P)H production as limiting response to furfural inhibition in Saccharomyces cerevisiae.

PubMed

Pornkamol, Unrean; Franzen, Carl J

2015-08-01

Achieving efficient and economical lignocellulose-based bioprocess requires a robust organism tolerant to furfural, a major inhibitory compound present in lignocellulosic hydrolysate. The aim of this study was to develop a model that could generate quantitative descriptions of cell metabolism for elucidating the cell's adaptive response to furfural. Such a modelling tool could provide strategies for the design of more robust cells. A dynamic flux balance (dFBA) model of Saccharomyces cerevisiae was created by coupling a kinetic fermentation model with a previously published genome-scale stoichiometric model. The dFBA model was used for studying intracellular and extracellular flux responses to furfural perturbations under steady state and dynamic conditions. The predicted effects of furfural on dynamic flux profiles agreed well with previously published experimental results. The model showed that the yeast cell adjusts its metabolism in response to furfural challenge by increasing fluxes through the pentose phosphate pathway, TCA cycle, and proline and serine biosynthesis in order to meet the high demand of NAD(P)H cofactors. The model described here can be used to aid in systematic optimization of the yeast, as well as of the fermentation process, for efficient lignocellulosic ethanol production. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Omics markers of the red cell storage lesion and metabolic linkage

PubMed Central

D’Alessandro, Angelo; Nemkov, Travis; Reisz, Julie; Dzieciatkowska, Monika; Wither, Matthew J.; Hansen, Kirk C.

2017-01-01

The introduction of omics technologies in the field of Transfusion Medicine has significantly advanced our understanding of the red cell storage lesion. While the clinical relevance of such a lesion is still a matter of debate, quantitative and redox proteomics approaches, as well quantitative metabolic flux analysis and metabolic tracing experiments promise to revolutionise our understanding of the role of blood processing strategies, inform the design and testing of novel additives or technologies (such as pathogen reduction), and evaluate the clinical relevance of donor and recipient biological variability with respect to red cell storability and transfusion outcomes. By reviewing existing literature in this rapidly expanding research endeavour, we highlight for the first time a correlation between metabolic markers of the red cell storage age and protein markers of haemolysis. Finally, we introduce the concept of metabolic linkage, i.e. the appreciation of a network of highly correlated small molecule metabolites which results from biochemical constraints of erythrocyte metabolic enzyme activities. For the foreseeable future, red cell studies will advance Transfusion Medicine and haematology by addressing the alteration of metabolic linkage phenotypes in response to stimuli, including, but not limited to, storage additives, enzymopathies (e.g. glucose 6-phosphate dehydrogenase deficiency), hypoxia, sepsis or haemorrhage. PMID:28263171

Role of Central Metabolism in the Osmoadaptation of the Halophilic Bacterium Chromohalobacter salexigens*

PubMed Central

Pastor, José M.; Bernal, Vicente; Salvador, Manuel; Argandoña, Montserrat; Vargas, Carmen; Csonka, Laszlo; Sevilla, Ángel; Iborra, José L.; Nieto, Joaquín J.; Cánovas, Manuel

2013-01-01

Bacterial osmoadaptation involves the cytoplasmic accumulation of compatible solutes to counteract extracellular osmolarity. The halophilic and highly halotolerant bacterium Chromohalobacter salexigens is able to grow up to 3 m NaCl in a minimal medium due to the de novo synthesis of ectoines. This is an osmoregulated pathway that burdens central metabolic routes by quantitatively drawing off TCA cycle intermediaries. Consequently, metabolism in C. salexigens has adapted to support this biosynthetic route. Metabolism of C. salexigens is more efficient at high salinity than at low salinity, as reflected by lower glucose consumption, lower metabolite overflow, and higher biomass yield. At low salinity, by-products (mainly gluconate, pyruvate, and acetate) accumulate extracellularly. Using [1-13C]-, [2-13C]-, [6-13C]-, and [U-13C6]glucose as carbon sources, we were able to determine the main central metabolic pathways involved in ectoines biosynthesis from glucose. C. salexigens uses the Entner-Doudoroff pathway rather than the standard glycolytic pathway for glucose catabolism, and anaplerotic activity is high to replenish the TCA cycle with the intermediaries withdrawn for ectoines biosynthesis. Metabolic flux ratios at low and high salinity were similar, revealing a certain metabolic rigidity, probably due to its specialization to support high biosynthetic fluxes and partially explaining why metabolic yields are so highly affected by salinity. This work represents an important contribution to the elucidation of specific metabolic adaptations in compatible solute-accumulating halophilic bacteria. PMID:23615905

Decrease of energy spilling in Escherichia coli continuous cultures with rising specific growth rate and carbon wasting

PubMed Central

2011-01-01

Background Growth substrates, aerobic/anaerobic conditions, specific growth rate (μ) etc. strongly influence Escherichia coli cell physiology in terms of cell size, biomass composition, gene and protein expression. To understand the regulation behind these different phenotype properties, it is useful to know carbon flux patterns in the metabolic network which are generally calculated by metabolic flux analysis (MFA). However, rarely is biomass composition determined and carbon balance carefully measured in the same experiments which could possibly lead to distorted MFA results and questionable conclusions. Therefore, we carried out both detailed carbon balance and biomass composition analysis in the same experiments for more accurate quantitative analysis of metabolism and MFA. Results We applied advanced continuous cultivation methods (A-stat and D-stat) to continuously monitor E. coli K-12 MG1655 flux and energy metabolism dynamic responses to change of μ and glucose-acetate co-utilisation. Surprisingly, a 36% reduction of ATP spilling was detected with increasing μ and carbon wasting to non-CO2 by-products under constant biomass yield. The apparent discrepancy between constant biomass yield and decline of ATP spilling could be explained by the rise of carbon wasting from 3 to 11% in the carbon balance which was revealed by the discovered novel excretion profile of E. coli pyrimidine pathway intermediates carbamoyl-phosphate, dihydroorotate and orotate. We found that carbon wasting patterns are dependent not only on μ, but also on glucose-acetate co-utilisation capability. Accumulation of these compounds was coupled to the two-phase acetate accumulation profile. Acetate overflow was observed in parallel with the reduction of TCA cycle and glycolysis fluxes, and induction of pentose phosphate pathway. Conclusions It can be concluded that acetate metabolism is one of the major regulating factors of central carbon metabolism. More importantly, our model calculations with actual biomass composition and detailed carbon balance analysis in steady state conditions with -omics data comparison demonstrate the importance of a comprehensive systems biology approach for more advanced understanding of metabolism and carbon re-routing mechanisms potentially leading to more successful metabolic engineering. PMID:21726468

Quantitative Analysis of Energy Metabolic Pathways in MCF-7 Breast Cancer Cells by Selected Reaction Monitoring Assay*

PubMed Central

Drabovich, Andrei P.; Pavlou, Maria P.; Dimitromanolakis, Apostolos; Diamandis, Eleftherios P.

2012-01-01

To investigate the quantitative response of energy metabolic pathways in human MCF-7 breast cancer cells to hypoxia, glucose deprivation, and estradiol stimulation, we developed a targeted proteomics assay for accurate quantification of protein expression in glycolysis/gluconeogenesis, TCA cycle, and pentose phosphate pathways. Cell growth conditions were selected to roughly mimic the exposure of cells in the cancer tissue to the intermittent hypoxia, glucose deprivation, and hormonal stimulation. Targeted proteomics assay allowed for reproducible quantification of 76 proteins in four different growth conditions after 24 and 48 h of perturbation. Differential expression of a number of control and metabolic pathway proteins in response to the change of growth conditions was found. Elevated expression of the majority of glycolytic enzymes was observed in hypoxia. Cancer cells, as opposed to near-normal MCF-10A cells, exhibited significantly increased expression of key energy metabolic pathway enzymes (FBP1, IDH2, and G6PD) that are known to redirect cellular metabolism and increase carbon flux through the pentose phosphate pathway. Our quantitative proteomic protocol is based on a mass spectrometry-compatible acid-labile detergent and is described in detail. Optimized parameters of a multiplex selected reaction monitoring (SRM) assay for 76 proteins, 134 proteotypic peptides, and 401 transitions are included and can be downloaded and used with any SRM-compatible mass spectrometer. The presented workflow is an integrated tool for hypothesis-driven studies of mammalian cells as well as functional studies of proteins, and can greatly complement experimental methods in systems biology, metabolic engineering, and metabolic transformation of cancer cells. PMID:22535206

A Prochlorococcus proving ground for constraint-based metabolic modeling and multi-`omics data integration

NASA Astrophysics Data System (ADS)

Casey, J.; Ji, B.; Shaoie, S.; Mardinoglu, A.; Sarathi Sen, P.; Jahn, O.; Reda, K.; Leigh, J.; Follows, M. J.; Nielsen, J.; Karl, D. M.

2016-02-01

Representatives of the oligotrophic marine cyanobacterium Prochlorococcus marinus are the smallest free-living photosynthetic organisms, both in terms of physical size and genome size, yet are the most abundant photoautotrophic microbes in the oceans and profoundly influence global biogeochemical cycles. Physiological and regulatory control of nutrient and light stress has been observed in MED4 in culture and in its closely related `ecotype' eMED4 in the field, however its metabolism has not been investigated in detail. We present a genome-scale metabolic network reconstruction of the high-light adapted axenic strain MED4ax ("iJCMED4") for the quantitative analysis of a range of its metabolic phenotypes. The resulting structure is a proving ground for the incorporation of enzyme kinetics, biochemical and elemental compositional data, transcriptomic, proteomic, metabolomic, and fluxomic datasets which can be implemented within a constraint-based metabolic modeling environment. The iJCMED4 stoichiometric model consists of 523 metabolic genes encoding 787 reactions with 673 unique metabolites distributed in 5 sub-cellular compartments and is mass, charge, and thermodynamically balanced. Several variants of flux balance analysis were used to simulate growth and metabolic fluxes over the diel cycle, under various stress conditions (e.g., nitrogen, phosphorus, light), and within the framework of a global biogeochemical model (DARWIN). Model simulations accurately predicted growth rates in culture under a variety of defined medium compositions and there was close agreement of photosynthetic performance, biomass and energy yields and efficiencies, and transporter fluxes for iJCMED4 and culture experiments. In addition to a nearly optimal photosynthetic quotient and central carbon metabolism efficiency, MED4 has made dramatic alterations to redox and phosphorus metabolism across biosynthetic and intermediate pathways. We propose that reductions in phosphate reaction participation reflects the P-deplete environment where MED4 was originally isolated.

Estimating the size of the solution space of metabolic networks

PubMed Central

Braunstein, Alfredo; Mulet, Roberto; Pagnani, Andrea

2008-01-01

Background Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion We propose a novel efficient distributed algorithmic strategy to estimate the size and shape of the affine space of a non full-dimensional convex polytope in high dimensions. The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time. The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods. PMID:18489757

The importance of alcohol dehydrogenase in regulation of ethanol metabolism in rat liver cells.

PubMed Central

Page, R A; Kitson, K E; Hardman, M J

1991-01-01

We used titration with the inhibitors tetramethylene sulphoxide and isobutyramide to assess quantitatively the importance of alcohol dehydrogenase in regulation of ethanol oxidation in rat hepatocytes. In hepatocytes isolated from starved rats the apparent Flux Control Coefficient (calculated assuming a single-substrate irreversible reaction with non-competitive inhibition) of alcohol dehydrogenase is 0.3-0.5. Adjustment of this coefficient to allow for alcohol dehydrogenase being a two-substrate reversible enzyme increases the value by 1.3-1.4-fold. The final value of the Flux Control Coefficient of 0.5-0.7 indicates that alcohol dehydrogenase is a major rate-determining enzyme, but that other factors also have a regulatory role. In hepatocytes from fed rats the Flux Control Coefficient for alcohol dehydrogenase decreases with increasing acetaldehyde concentration. This suggests that, as acetaldehyde concentrations rise, control of the pathway shifts from alcohol dehydrogenase to other enzymes, particularly aldehyde dehydrogenase. There is not a single rate-determining step for the ethanol metabolism pathway and control is shared among several steps. PMID:1898355

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

PubMed Central

Ando, David; Singh, Jahnavi; Keasling, Jay D.; García Martín, Héctor

2018-01-01

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore. PMID:29300340

A bottom-up approach to urban metabolism: the perspective of BRIDGE

NASA Astrophysics Data System (ADS)

Chrysoulakis, N.; Borrego, C.; San Josè, R.; Grimmond, S. B.; Jones, M. B.; Magliulo, V.; Klostermann, J.; Santamouris, M.

2011-12-01

Urban metabolism considers a city as a system and usually distinguishes between energy and material flows as its components. "Metabolic" studies are usually top-down approaches that assess the inputs and outputs of food, water, energy, and pollutants from a city, or that compare the changing metabolic process of several cities. In contrast, bottom-up approaches are based on quantitative estimates of urban metabolism components at local to regional scales. Such approaches consider the urban metabolism as the 3D exchange and transformation of energy and matter between a city and its environment. The city is considered as a system and the physical flows between this system and its environment are quantitatively estimated. The transformation of landscapes from primarily agricultural and forest uses to urbanized landscapes can greatly modify energy and material exchanges and it is, therefore, an important aspect of an urban area. Here we focus on the exchanges and transformation of energy, water, carbon and pollutants. Recent advances in bio-physical sciences have led to new methods and models to estimate local scale energy, water, carbon and pollutant fluxes. However, there is often poor communication of new knowledge and its implications to end-users, such as planners, architects and engineers. The FP7 Project BRIDGE (SustainaBle uRban plannIng Decision support accountinG for urban mEtabolism) aims at bridging this gap and at illustrating the advantages of considering environmental issues in urban planning. BRIDGE does not perform a complete life cycle analysis or calculate whole system urban metabolism, but rather focuses on specific metabolism components (energy, water, carbon and pollutants). Its main goal is the development of a Decision Suport System (DSS) with the potential to select planning actions which better fit the goal of changing the metabolism of urban systems towards sustainability. BRIDGE evaluates how planning alternatives can modify the physical flows of the above urban metabolism components under consideration in five European cities: Helsinki, Athens, London, Firenze and Gliwice. A Multi-Criteria Evaluation approach has been adopted. To cope with the complexity of urban metabolism issues, objectives are defined in relation to the interactions between the environmental elements (fluxes of energy, water, carbon and pollutants) and socio-economic components (investment costs, housing, employment, etc.) of urban sustainability.

Metabolic-flux dependent regulation of microbial physiology.

PubMed

Litsios, Athanasios; Ortega, Álvaro D; Wit, Ernst C; Heinemann, Matthias

2018-04-01

According to the most prevalent notion, changes in cellular physiology primarily occur in response to altered environmental conditions. Yet, recent studies have shown that changes in metabolic fluxes can also trigger phenotypic changes even when environmental conditions are unchanged. This suggests that cells have mechanisms in place to assess the magnitude of metabolic fluxes, that is, the rate of metabolic reactions, and use this information to regulate their physiology. In this review, we describe recent evidence for metabolic flux-sensing and flux-dependent regulation. Furthermore, we discuss how such sensing and regulation can be mechanistically achieved and present a set of new candidates for flux-signaling metabolites. Similar to metabolic-flux sensing, we argue that cells can also sense protein translation flux. Finally, we elaborate on the advantages that flux-based regulation can confer to cells. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm.

PubMed

Xie, Zhengwei; Zhang, Tianyu; Ouyang, Qi

2018-02-01

One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models, like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks. In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporate the enzymatic properties into the FBA model by introducing a pseudo reaction V constrained by enzymatic parameters. Our algorithm uses the enzymatic information quantitatively rather than qualitatively. We first demonstrate the concept by applying HCSA to a simple three-node network, whereby we obtained a good correlation between flux and enzyme abundance. We then validate its prediction by comparison with ODE and with a synthetic network producing voilacein and analogues in Saccharomyces cerevisiae. We show that HCSA can mimic the state-state results of ODE. Finally, we show its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast. We show the ability of HCSA to operate without biomass flux and perform MCA to determine rate-limiting reactions. Algorithm was implemented by Matlab and C ++. The code is available at https://github.com/kekegg/HCSA. xiezhengwei@hsc.pku.edu.cn or qi@pku.edu.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

OpenMebius: an open source software for isotopically nonstationary 13C-based metabolic flux analysis.

PubMed

Kajihata, Shuichi; Furusawa, Chikara; Matsuda, Fumio; Shimizu, Hiroshi

2014-01-01

The in vivo measurement of metabolic flux by (13)C-based metabolic flux analysis ((13)C-MFA) provides valuable information regarding cell physiology. Bioinformatics tools have been developed to estimate metabolic flux distributions from the results of tracer isotopic labeling experiments using a (13)C-labeled carbon source. Metabolic flux is determined by nonlinear fitting of a metabolic model to the isotopic labeling enrichment of intracellular metabolites measured by mass spectrometry. Whereas (13)C-MFA is conventionally performed under isotopically constant conditions, isotopically nonstationary (13)C metabolic flux analysis (INST-(13)C-MFA) has recently been developed for flux analysis of cells with photosynthetic activity and cells at a quasi-steady metabolic state (e.g., primary cells or microorganisms under stationary phase). Here, the development of a novel open source software for INST-(13)C-MFA on the Windows platform is reported. OpenMebius (Open source software for Metabolic flux analysis) provides the function of autogenerating metabolic models for simulating isotopic labeling enrichment from a user-defined configuration worksheet. Analysis using simulated data demonstrated the applicability of OpenMebius for INST-(13)C-MFA. Confidence intervals determined by INST-(13)C-MFA were less than those determined by conventional methods, indicating the potential of INST-(13)C-MFA for precise metabolic flux analysis. OpenMebius is the open source software for the general application of INST-(13)C-MFA.

An integrated approach to characterize genetic interaction networks in yeast metabolism

PubMed Central

Szappanos, Balázs; Kovács, Károly; Szamecz, Béla; Honti, Frantisek; Costanzo, Michael; Baryshnikova, Anastasia; Gelius-Dietrich, Gabriel; Lercher, Martin J.; Jelasity, Márk; Myers, Chad L.; Andrews, Brenda J.; Boone, Charles; Oliver, Stephen G.; Pál, Csaba; Papp, Balázs

2011-01-01

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments. PMID:21623372

Metabolic cartography: experimental quantification of metabolic fluxes from isotopic labelling studies.

PubMed

O'Grady, John; Schwender, Jörg; Shachar-Hill, Yair; Morgan, John A

2012-03-01

For the past decade, flux maps have provided researchers with an in-depth perspective on plant metabolism. As a rapidly developing field, significant headway has been made recently in computation, experimentation, and overall understanding of metabolic flux analysis. These advances are particularly applicable to the study of plant metabolism. New dynamic computational methods such as non-stationary metabolic flux analysis are finding their place in the toolbox of metabolic engineering, allowing more organisms to be studied and decreasing the time necessary for experimentation, thereby opening new avenues by which to explore the vast diversity of plant metabolism. Also, improved methods of metabolite detection and measurement have been developed, enabling increasingly greater resolution of flux measurements and the analysis of a greater number of the multitude of plant metabolic pathways. Methods to deconvolute organelle-specific metabolism are employed with increasing effectiveness, elucidating the compartmental specificity inherent in plant metabolism. Advances in metabolite measurements have also enabled new types of experiments, such as the calculation of metabolic fluxes based on (13)CO(2) dynamic labelling data, and will continue to direct plant metabolic engineering. Newly calculated metabolic flux maps reveal surprising and useful information about plant metabolism, guiding future genetic engineering of crops to higher yields. Due to the significant level of complexity in plants, these methods in combination with other systems biology measurements are necessary to guide plant metabolic engineering in the future.

The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism

PubMed Central

den Besten, Gijs; van Eunen, Karen; Groen, Albert K.; Venema, Koen; Reijngoud, Dirk-Jan; Bakker, Barbara M.

2013-01-01

Short-chain fatty acids (SCFAs), the end products of fermentation of dietary fibers by the anaerobic intestinal microbiota, have been shown to exert multiple beneficial effects on mammalian energy metabolism. The mechanisms underlying these effects are the subject of intensive research and encompass the complex interplay between diet, gut microbiota, and host energy metabolism. This review summarizes the role of SCFAs in host energy metabolism, starting from the production by the gut microbiota to the uptake by the host and ending with the effects on host metabolism. There are interesting leads on the underlying molecular mechanisms, but there are also many apparently contradictory results. A coherent understanding of the multilevel network in which SCFAs exert their effects is hampered by the lack of quantitative data on actual fluxes of SCFAs and metabolic processes regulated by SCFAs. In this review we address questions that, when answered, will bring us a great step forward in elucidating the role of SCFAs in mammalian energy metabolism. PMID:23821742

Insights from quantitative metaproteomics and protein-stable isotope probing into microbial ecology.

PubMed

von Bergen, Martin; Jehmlich, Nico; Taubert, Martin; Vogt, Carsten; Bastida, Felipe; Herbst, Florian-Alexander; Schmidt, Frank; Richnow, Hans-Hermann; Seifert, Jana

2013-10-01

The recent development of metaproteomics has enabled the direct identification and quantification of expressed proteins from microbial communities in situ, without the need for microbial enrichment. This became possible by (1) significant increases in quality and quantity of metagenome data and by improvements of (2) accuracy and (3) sensitivity of modern mass spectrometers (MS). The identification of physiologically relevant enzymes can help to understand the role of specific species within a community or an ecological niche. Beside identification, relative and absolute quantitation is also crucial. We will review label-free and label-based methods of quantitation in MS-based proteome analysis and the contribution of quantitative proteome data to microbial ecology. Additionally, approaches of protein-based stable isotope probing (protein-SIP) for deciphering community structures are reviewed. Information on the species-specific metabolic activity can be obtained when substrates or nutrients are labeled with stable isotopes in a protein-SIP approach. The stable isotopes ((13)C, (15)N, (36)S) are incorporated into proteins and the rate of incorporation can be used for assessing the metabolic activity of the corresponding species. We will focus on the relevance of the metabolic and phylogenetic information retrieved with protein-SIP studies and for detecting and quantifying the carbon flux within microbial consortia. Furthermore, the combination of protein-SIP with established tools in microbial ecology such as other stable isotope probing techniques are discussed.

Temperature effects on the fractionation of multiple sulfur isotopes by Thermodesulfobacterium and Desulfovibrio strains

NASA Astrophysics Data System (ADS)

Wang, P.; Sun, C.; Ono, S.; Lin, L.

2012-12-01

Microbial dissimilatory sulfate reduction is one of the major mechanisms driving anaerobic mineralization of organic matter in global ocean. While sulfate-reducing prokaryotes are well known to fractionate sulfur isotopes during dissimilatory sulfate reduction, unraveling the isotopic compositions of sulfur-bearing minerals preserved in sedimentary records could provide invaluable constraints on the evolution of seawater chemistry and metabolic pathways. Variations in the sulfur isotope fractionations are partly due to inherent differences among species and also affected by environmental conditions. The isotope fractionations caused by microbial sulfate reduction have been interpreted to be a sequence of enzyme-catalyzed isotope fractionation steps. Therefore, the fractionation factor depends on (1) the sulfate flux into and out of the cell, and (2) the flux of sulfur transformation between the internal pools. Whether the multiple sulfur isotope effect could be quantitatively predicted using such a metabolic flux model would provide insights into the cellular machinery catalyzing with sulfate reduction. This study examined the multiple sulfur isotope fractionation patterns associated with a thermophilic Thermodesulfobacterium-related strain and a mesophilic Desulfovibrio gigas over a wide temperature range. The Thermodesulfobacterium-related strain grew between 34 and 79°C with an optimal temperature at 72°C and the highest cell-specific sulfate reduction rate at 77°C. The 34ɛ values ranged between 8.2 and 31.6‰ with a maximum at 68°C. The D. gigas grew between 10 and 45 °C with an optimal temperature at 30°C and the highest cell-specific sulfate reduction rate at 41°C. The 34ɛ values ranged between 10.3 and 29.7‰ with higher magnitude at both lower and higher temperatures. The results of multiple sulfur isotope measurements expand the previously reported range and cannot be described by a solution field of the metabolic flux model, which calculates the Δ33S and 34ɛ values assuming equilibrium fractionation among internal steps. Either larger isotope effects or kinetic fractionation has to be considered in the metabolic flux model to explain the multiple sulfur isotope effect produced by these two strains. Overall, the metabolic flux model warrants further revision and further studies regarding physiological responses to growth conditions may probably offer a linkage between multiple sulfur isotope effects and environmental factors for microbial dissimilatory sulfate reduction.

Quantitative imaging for discovery and assembly of the metabo-regulome

PubMed Central

Okumoto, Sakiko; Takanaga, Hitomi; Frommer, Wolf B.

2009-01-01

Summary Little is known about regulatory networks that control metabolic flux in plant cells. Detailed understanding of regulation is crucial for synthetic biology. The difficulty of measuring metabolites with cellular and subcellular precision is a major roadblock. New tools have been developed for monitoring extracellular, cytosolic, organellar and vacuolar ion and metabolite concentrations with a time resolution of milliseconds to hours. Genetically encoded sensors allow quantitative measurement of steady-state concentrations of ions, signaling molecules and metabolites and their respective changes over time. Fluorescence resonance energy transfer (FRET) sensors exploit conformational changes in polypeptides as a proxy for analyte concentrations. Subtle effects of analyte binding on the conformation of the recognition element are translated into a FRET change between two fused green fluorescent protein (GFP) variants, enabling simple monitoring of analyte concentrations using fluorimetry or fluorescence microscopy. Fluorimetry provides information averaged over cell populations, while microscopy detects differences between cells or populations of cells. The genetically encoded sensors can be targeted to subcellular compartments or the cell surface. Confocal microscopy ultimately permits observation of gradients or local differences within a compartment. The FRET assays can be adapted to high-throughput analysis to screen mutant populations in order to systematically identify signaling networks that control individual steps in metabolic flux. PMID:19138219

Determination of ¹⁵N-incorporation into plant proteins and their absolute quantitation: a new tool to study nitrogen flux dynamics and protein pool sizes elicited by plant-herbivore interactions.

PubMed

Ullmann-Zeunert, Lynn; Muck, Alexander; Wielsch, Natalie; Hufsky, Franziska; Stanton, Mariana A; Bartram, Stefan; Böcker, Sebastian; Baldwin, Ian T; Groten, Karin; Svatoš, Aleš

2012-10-05

Herbivory leads to changes in the allocation of nitrogen among different pools and tissues; however, a detailed quantitative analysis of these changes has been lacking. Here, we demonstrate that a mass spectrometric data-independent acquisition approach known as LC-MS(E), combined with a novel algorithm to quantify heavy atom enrichment in peptides, is able to quantify elicited changes in protein amounts and (15)N flux in a high throughput manner. The reliable identification/quantitation of rabbit phosphorylase b protein spiked into leaf protein extract was achieved. The linear dynamic range, reproducibility of technical and biological replicates, and differences between measured and expected (15)N-incorporation into the small (SSU) and large (LSU) subunits of ribulose-1,5-bisphosphate-carboxylase/oxygenase (RuBisCO) and RuBisCO activase 2 (RCA2) of Nicotiana attenuata plants grown in hydroponic culture at different known concentrations of (15)N-labeled nitrate were used to further evaluate the procedure. The utility of the method for whole-plant studies in ecologically realistic contexts was demonstrated by using (15)N-pulse protocols on plants growing in soil under unknown (15)N-incorporation levels. Additionally, we quantified the amounts of lipoxygenase 2 (LOX2) protein, an enzyme important in antiherbivore defense responses, demonstrating that the approach allows for in-depth quantitative proteomics and (15)N flux analyses of the metabolic dynamics elicited during plant-herbivore interactions.

Quantitative assessment of brain glucose metabolic rates using in vivo deuterium magnetic resonance spectroscopy.

PubMed

Lu, Ming; Zhu, Xiao-Hong; Zhang, Yi; Mateescu, Gheorghe; Chen, Wei

2017-11-01

Quantitative assessment of cerebral glucose consumption rate (CMR glc ) and tricarboxylic acid cycle flux (V TCA ) is crucial for understanding neuroenergetics under physiopathological conditions. In this study, we report a novel in vivo Deuterium ( 2 H) MRS (DMRS) approach for simultaneously measuring and quantifying CMR glc and V TCA in rat brains at 16.4 Tesla. Following a brief infusion of deuterated glucose, dynamic changes of isotope-labeled glucose, glutamate/glutamine (Glx) and water contents in the brain can be robustly monitored from their well-resolved 2 H resonances. Dynamic DMRS glucose and Glx data were employed to determine CMR glc and V TCA concurrently. To test the sensitivity of this method in response to altered glucose metabolism, two brain conditions with different anesthetics were investigated. Increased CMR glc (0.46 vs. 0.28 µmol/g/min) and V TCA (0.96 vs. 0.6 µmol/g/min) were found in rats under morphine as compared to deeper anesthesia using 2% isoflurane. This study demonstrates the feasibility and new utility of the in vivo DMRS approach to assess cerebral glucose metabolic rates at high/ultrahigh field. It provides an alternative MRS tool for in vivo study of metabolic coupling relationship between aerobic and anaerobic glucose metabolisms in brain under physiopathological states.

IsoCor: correcting MS data in isotope labeling experiments.

PubMed

Millard, Pierre; Letisse, Fabien; Sokol, Serguei; Portais, Jean-Charles

2012-05-01

Mass spectrometry (MS) is widely used for isotopic labeling studies of metabolism and other biological processes. Quantitative applications-e.g. metabolic flux analysis-require tools to correct the raw MS data for the contribution of all naturally abundant isotopes. IsoCor is a software that allows such correction to be applied to any chemical species. Hence it can be used to exploit any isotopic tracer, from well-known ((13)C, (15)N, (18)O, etc) to unusual ((57)Fe, (77)Se, etc) isotopes. It also provides new features-e.g. correction for the isotopic purity of the tracer-to improve the accuracy of quantitative isotopic studies, and implements an efficient algorithm to process large datasets. Its user-friendly interface makes isotope labeling experiments more accessible to a wider biological community. IsoCor is distributed under OpenSource license at http://metasys.insa-toulouse.fr/software/isocor/

Inference and Prediction of Metabolic Network Fluxes

PubMed Central

Nikoloski, Zoran; Perez-Storey, Richard; Sweetlove, Lee J.

2015-01-01

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping. PMID:26392262

Effects of Storage Time on Glycolysis in Donated Human Blood Units

PubMed Central

Qi, Zhen; Roback, John D.; Voit, Eberhard O.

2017-01-01

Background: Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms accounting for such biochemical changes, qualitatively and quantitatively; Study Design and Methods: Based on the integration of metabolic time series data, kinetic models, and a stoichiometric model of the glycolytic pathway, a customized inference method was developed and used to quantify the dynamic changes in glycolytic fluxes during the storage of donated blood units. The method provides a proof of principle for the feasibility of inferences regarding flux characteristics from metabolomics data; Results: Several glycolytic reaction steps change substantially during storage time and vary among different fluxes and donors. The quantification of these storage time effects, which are possibly irreversible, allows for predictions of the transfusion outcome of individual blood units; Conclusion: The improved mechanistic understanding of blood storage, obtained from this computational study, may aid the identification of blood units that age quickly or more slowly during storage, and may ultimately improve transfusion management in clinics. PMID:28353627

Effects of Storage Time on Glycolysis in Donated Human Blood Units.

PubMed

Qi, Zhen; Roback, John D; Voit, Eberhard O

2017-03-29

Background : Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms accounting for such biochemical changes, qualitatively and quantitatively; Study Design and Methods : Based on the integration of metabolic time series data, kinetic models, and a stoichiometric model of the glycolytic pathway, a customized inference method was developed and used to quantify the dynamic changes in glycolytic fluxes during the storage of donated blood units. The method provides a proof of principle for the feasibility of inferences regarding flux characteristics from metabolomics data; Results : Several glycolytic reaction steps change substantially during storage time and vary among different fluxes and donors. The quantification of these storage time effects, which are possibly irreversible, allows for predictions of the transfusion outcome of individual blood units; Conclusion : The improved mechanistic understanding of blood storage, obtained from this computational study, may aid the identification of blood units that age quickly or more slowly during storage, and may ultimately improve transfusion management in clinics.

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

PubMed Central

2011-01-01

Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

PubMed

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Cellular metabolic network analysis: discovering important reactions in Treponema pallidum.

PubMed

Chen, Xueying; Zhao, Min; Qu, Hong

2015-01-01

T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pallidum, critical reactions are identified. As a comparison, we also apply the analytical approaches to the metabolic network of H. pylori to find coregulated drug targets and unique drug targets for different microorganisms. Based on the clustering results, all reactions are further classified into various roles. Therefore, the general picture of their metabolic network is obtained and two types of reactions, both of which are involved in nucleic acid metabolism, are found to be essential for T. pallidum. It is also discovered that both hubs of reactions and the isolated reactions in purine and pyrimidine metabolisms play important roles in T. pallidum. These reactions could be potential drug targets for treating syphilis.

Metabolic flux analysis using 13C peptide label measurements

USDA-ARS?s Scientific Manuscript database

13C metabolic flux analysis (MFA) has become the experimental method of choice to investigate cellular metabolism. MFA has established flux maps of central metabolism for dozens of microbes, cell cultures, and plant seeds. Steady-state MFA utilizes isotopic labeling measurements of amino acids obtai...

A quantitative study of methanol/sorbitol co-feeding process of a Pichia pastoris Mut+/pAOX1-lacZ strain

PubMed Central

2013-01-01

Background One of the main challenges for heterologous protein production by the methylotrophic yeast Pichia pastoris at large-scale is related to its high oxygen demand. A promising solution is a co-feeding strategy based on a methanol/sorbitol mixture during the induction phase. Nonetheless, a deep understanding of the cellular physiology and the regulation of the AOX1 promoter, used to govern heterologous protein production, during this co-feeding strategy is still scarce. Results Transient continuous cultures with a dilution rate of 0.023 h-1 at 25°C were performed to quantitatively assess the benefits of a methanol/sorbitol co-feeding process with a Mut+ strain in which the pAOX1-lacZ construct served as a reporter gene. Cell growth and metabolism, including O2 consumption together with CO2 and heat production were analyzed with regard to a linear change of methanol fraction in the mixed feeding media. In addition, the regulation of the promoter AOX1 was investigated by means of β-galactosidase measurements. Our results demonstrated that the cell-specific oxygen consumption (qO2) could be reduced by decreasing the methanol fraction in the feeding media. More interestingly, maximal β-galactosidase cell-specific activity (>7500 Miller unit) and thus, optimal pAOX1 induction, was achieved and maintained in the range of 0.45 ~ 0.75 C-mol/C-mol of methanol fraction. In addition, the qO2 was reduced by 30% at most in those conditions. Based on a simplified metabolic network, metabolic flux analysis (MFA) was performed to quantify intracellular metabolic flux distributions during the transient continuous cultures, which further shed light on the advantages of methanol/sorbitol co-feeding process. Finally, our observations were further validated in fed-batch cultures. Conclusion This study brings quantitative insight into the co-feeding process, which provides valuable data for the control of methanol/sorbitol co-feeding, aiming at enhancing biomass and heterologous protein productivities under given oxygen supply. According to our results, β-galactosidase productivity could be improved about 40% using the optimally mixed feed. PMID:23565774

A quantitative study of methanol/sorbitol co-feeding process of a Pichia pastoris Mut⁺/pAOX1-lacZ strain.

PubMed

Niu, Hongxing; Jost, Laurent; Pirlot, Nathalie; Sassi, Hosni; Daukandt, Marc; Rodriguez, Christian; Fickers, Patrick

2013-04-08

One of the main challenges for heterologous protein production by the methylotrophic yeast Pichia pastoris at large-scale is related to its high oxygen demand. A promising solution is a co-feeding strategy based on a methanol/sorbitol mixture during the induction phase. Nonetheless, a deep understanding of the cellular physiology and the regulation of the AOX1 promoter, used to govern heterologous protein production, during this co-feeding strategy is still scarce. Transient continuous cultures with a dilution rate of 0.023 h(-1) at 25°C were performed to quantitatively assess the benefits of a methanol/sorbitol co-feeding process with a Mut+ strain in which the pAOX1-lacZ construct served as a reporter gene. Cell growth and metabolism, including O2 consumption together with CO2 and heat production were analyzed with regard to a linear change of methanol fraction in the mixed feeding media. In addition, the regulation of the promoter AOX1 was investigated by means of β-galactosidase measurements. Our results demonstrated that the cell-specific oxygen consumption (qO2) could be reduced by decreasing the methanol fraction in the feeding media. More interestingly, maximal β-galactosidase cell-specific activity (>7500 Miller unit) and thus, optimal pAOX1 induction, was achieved and maintained in the range of 0.45 ~ 0.75 C-mol/C-mol of methanol fraction. In addition, the qO2 was reduced by 30% at most in those conditions. Based on a simplified metabolic network, metabolic flux analysis (MFA) was performed to quantify intracellular metabolic flux distributions during the transient continuous cultures, which further shed light on the advantages of methanol/sorbitol co-feeding process. Finally, our observations were further validated in fed-batch cultures. This study brings quantitative insight into the co-feeding process, which provides valuable data for the control of methanol/sorbitol co-feeding, aiming at enhancing biomass and heterologous protein productivities under given oxygen supply. According to our results, β-galactosidase productivity could be improved about 40% using the optimally mixed feed.

Metabolic flux estimation using particle swarm optimization with penalty function.

PubMed

Long, Hai-Xia; Xu, Wen-Bo; Sun, Jun

2009-01-01

Metabolic flux estimation through 13C trace experiment is crucial for quantifying the intracellular metabolic fluxes. In fact, it corresponds to a constrained optimization problem that minimizes a weighted distance between measured and simulated results. In this paper, we propose particle swarm optimization (PSO) with penalty function to solve 13C-based metabolic flux estimation problem. The stoichiometric constraints are transformed to an unconstrained one, by penalizing the constraints and building a single objective function, which in turn is minimized using PSO algorithm for flux quantification. The proposed algorithm is applied to estimate the central metabolic fluxes of Corynebacterium glutamicum. From simulation results, it is shown that the proposed algorithm has superior performance and fast convergence ability when compared to other existing algorithms.

Mapping the landscape of metabolic goals of a cell

DOE PAGES

Zhao, Qi; Stettner, Arion I.; Reznik, Ed; ...

2016-05-23

Here, genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. We introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptationmore » trajectories.« less

Integrated metabolic flux and omics analysis of Synechocystis sp. PCC 6803 under mixotrophic and photoheterotrophic conditions.

PubMed

Nakajima, Tsubasa; Kajihata, Shuichi; Yoshikawa, Katsunori; Matsuda, Fumio; Furusawa, Chikara; Hirasawa, Takashi; Shimizu, Hiroshi

2014-09-01

Cyanobacteria have flexible metabolic capability that enables them to adapt to various environments. To investigate their underlying metabolic regulation mechanisms, we performed an integrated analysis of metabolic flux using transcriptomic and metabolomic data of a cyanobacterium Synechocystis sp. PCC 6803, under mixotrophic and photoheterotrophic conditions. The integrated analysis indicated drastic metabolic flux changes, with much smaller changes in gene expression levels and metabolite concentrations between the conditions, suggesting that the flux change was not caused mainly by the expression levels of the corresponding genes. Under photoheterotrophic conditions, created by the addition of the photosynthesis inhibitor atrazine in mixotrophic conditions, the result of metabolic flux analysis indicated the significant repression of carbon fixation and the activation of the oxidative pentose phosphate pathway (PPP). Moreover, we observed gluconeogenic activity of upstream of glycolysis, which enhanced the flux of the oxidative PPP to compensate for NADPH depletion due to the inhibition of the light reaction of photosynthesis. 'Omics' data suggested that these changes were probably caused by the repression of the gap1 gene, which functions as a control valve in the metabolic network. Since metabolic flux is the outcome of a complicated interplay of cellular components, integrating metabolic flux with other 'omics' layers can identify metabolic changes and narrow down these regulatory mechanisms more effectively. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

An objective function exploiting suboptimal solutions in metabolic networks

PubMed Central

2013-01-01

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221

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

PubMed

Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

2013-12-01

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.

Beyond topology: coevolution of structure and flux in metabolic networks.

PubMed

Morrison, E S; Badyaev, A V

2017-10-01

Interactions between the structure of a metabolic network and its functional properties underlie its evolutionary diversification, but the mechanism by which such interactions arise remains elusive. Particularly unclear is whether metabolic fluxes that determine the concentrations of compounds produced by a metabolic network, are causally linked to a network's structure or emerge independently of it. A direct empirical study of populations where both structural and functional properties vary among individuals' metabolic networks is required to establish whether changes in structure affect the distribution of metabolic flux. In a population of house finches (Haemorhous mexicanus), we reconstructed full carotenoid metabolic networks for 442 individuals and uncovered 11 structural variants of this network with different compounds and reactions. We examined the consequences of this structural diversity for the concentrations of plumage-bound carotenoids produced by flux in these networks. We found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure. Flux was partitioned similarly among compounds in individuals of the same network structure: within each network, compound concentrations were closely correlated. The highest among-individual variation in flux occurred in networks with the strongest among-compound correlations, suggesting that changes in the magnitude, but not the distribution of flux, underlie individual differences in compound concentrations on a static network structure. These findings indicate that the distribution of flux in carotenoid metabolism closely follows network structure. Thus, evolutionary diversification and local adaptations in carotenoid metabolism may depend more on the gain or loss of enzymatic reactions than on changes in flux within a network structure. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise.

PubMed

Chi, Baofang; Tao, Shiheng; Liu, Yanlin

2015-01-01

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C13-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.

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

PubMed

Nargund, Shilpa; Qiu, Jinshu; Goudar, Chetan T

2015-01-01

(13)C-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-(13)C 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.

Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations.

PubMed

Fiévet, Julie B; Nidelet, Thibault; Dillmann, Christine; de Vienne, Dominique

2018-01-01

Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding as well as evolutionary biology. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP) relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of "parental" tubes resulted in "hybrids," whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the concavity of the GP relationship, which is commonly observed at various levels of the phenotypic hierarchy, and could account for the pervasiveness of heterosis.

Natural isotope correction of MS/MS measurements for metabolomics and (13)C fluxomics.

PubMed

Niedenführ, Sebastian; ten Pierick, Angela; van Dam, Patricia T N; Suarez-Mendez, Camilo A; Nöh, Katharina; Wahl, S Aljoscha

2016-05-01

Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of (13)C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS-type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC-MS(/MS) requires derivatization for the usually non-volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full use of LC- and GC-MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; (13)C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation. © 2015 Wiley Periodicals, Inc.

13C metabolic flux analysis at a genome-scale.

PubMed

Gopalakrishnan, Saratram; Maranas, Costas D

2015-11-01

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Inferred ranges for 81% of the reactions in the genome-scale metabolic (GSM) model varied less than one-tenth of the basis glucose uptake rate (95% confidence test). This is because as many as 411 reactions in the GSM are growth coupled meaning that the single measurement of biomass formation rate locks the reaction flux values. This implies that accurate biomass formation rate and composition are critical for resolving metabolic fluxes away from central metabolism and suggests the importance of biomass composition (re)assessment under different genetic and environmental backgrounds. In addition, the loss of information associated with mapping fluxes from MFA on a core model to a GSM model is quantified. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Reconstruction and flux analysis of coupling between metabolic pathways of astrocytes and neurons: application to cerebral hypoxia

PubMed Central

Çakιr, Tunahan; Alsan, Selma; Saybaşιlι, Hale; Akιn, Ata; Ülgen, Kutlu Ö

2007-01-01

Background It is a daunting task to identify all the metabolic pathways of brain energy metabolism and develop a dynamic simulation environment that will cover a time scale ranging from seconds to hours. To simplify this task and make it more practicable, we undertook stoichiometric modeling of brain energy metabolism with the major aim of including the main interacting pathways in and between astrocytes and neurons. Model The constructed model includes central metabolism (glycolysis, pentose phosphate pathway, TCA cycle), lipid metabolism, reactive oxygen species (ROS) detoxification, amino acid metabolism (synthesis and catabolism), the well-known glutamate-glutamine cycle, other coupling reactions between astrocytes and neurons, and neurotransmitter metabolism. This is, to our knowledge, the most comprehensive attempt at stoichiometric modeling of brain metabolism to date in terms of its coverage of a wide range of metabolic pathways. We then attempted to model the basal physiological behaviour and hypoxic behaviour of the brain cells where astrocytes and neurons are tightly coupled. Results The reconstructed stoichiometric reaction model included 217 reactions (184 internal, 33 exchange) and 216 metabolites (183 internal, 33 external) distributed in and between astrocytes and neurons. Flux balance analysis (FBA) techniques were applied to the reconstructed model to elucidate the underlying cellular principles of neuron-astrocyte coupling. Simulation of resting conditions under the constraints of maximization of glutamate/glutamine/GABA cycle fluxes between the two cell types with subsequent minimization of Euclidean norm of fluxes resulted in a flux distribution in accordance with literature-based findings. As a further validation of our model, the effect of oxygen deprivation (hypoxia) on fluxes was simulated using an FBA-derivative approach, known as minimization of metabolic adjustment (MOMA). The results show the power of the constructed model to simulate disease behaviour on the flux level, and its potential to analyze cellular metabolic behaviour in silico. Conclusion The predictive power of the constructed model for the key flux distributions, especially central carbon metabolism and glutamate-glutamine cycle fluxes, and its application to hypoxia is promising. The resultant acceptable predictions strengthen the power of such stoichiometric models in the analysis of mammalian cell metabolism. PMID:18070347

The oxidative TCA cycle operates during methanotrophic growth of the Type I methanotroph Methylomicrobium buryatense 5GB1.

PubMed

Fu, Yanfen; Li, Yi; Lidstrom, Mary

2017-07-01

Methanotrophs are a group of bacteria that use methane as sole carbon and energy source. Type I methanotrophs are gamma-proteobacterial methanotrophs using the ribulose monophosphate cycle (RuMP) cycle for methane assimilation. In order to facilitate metabolic engineering in the industrially promising Type I methanotroph Methylomicrobium buryatense 5GB1, flux analysis of cellular metabolism is needed and 13 C tracer analysis is a foundational tool for such work. This biological system has a single-carbon input and a special network topology that together pose challenges to the current well-established methodology for 13 C tracer analysis using a multi-carbon input such as glucose, and to date, no 13 C tracer analysis of flux in a Type I methanotroph has been reported. In this study, we showed that by monitoring labeling patterns of several key intermediate metabolites in core metabolism, it is possible to quantitate the relative flux ratios for important branch points, such as the malate node. In addition, it is possible to assess the operation of the TCA cycle, which has been thought to be incomplete in Type I methanotrophs. Surprisingly, our analysis provides direct evidence of a complete, oxidative TCA cycle operating in M. buryatense 5GB1 using methane as sole carbon and energy substrate, contributing about 45% of the total flux for de novo malate production. Combined with mutant analysis, this method was able to identify fumA (METBUDRAFT_1453/MBURv2__60244) as the primary fumarase involved in the oxidative TCA cycle, among 2 predicted fumarases, supported by 13 C tracer analysis on both fumA and fumC single knockouts. Interrupting the oxidative TCA cycle leads to a severe growth defect, suggesting that the oxidative TCA cycle functions to not only provide precursors for de novo biomass synthesis, but also to provide reducing power to the system. This information provides new opportunities for metabolic engineering of M. buryatense for the production of industrially relevant products. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Systems analysis of iron metabolism: the network of iron pools and fluxes

PubMed Central

2010-01-01

Background Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole. Results Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime. Conclusions This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place. PMID:20704761

CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism.

PubMed

Karlstädt, Anja; Fliegner, Daniela; Kararigas, Georgios; Ruderisch, Hugo Sanchez; Regitz-Zagrosek, Vera; Holzhütter, Hermann-Georg

2012-08-29

Availability of oxygen and nutrients in the coronary circulation is a crucial determinant of cardiac performance. Nutrient composition of coronary blood may significantly vary in specific physiological and pathological conditions, for example, administration of special diets, long-term starvation, physical exercise or diabetes. Quantitative analysis of cardiac metabolism from a systems biology perspective may help to a better understanding of the relationship between nutrient supply and efficiency of metabolic processes required for an adequate cardiac output. Here we present CardioNet, the first large-scale reconstruction of the metabolic network of the human cardiomyocyte comprising 1793 metabolic reactions, including 560 transport processes in six compartments. We use flux-balance analysis to demonstrate the capability of the network to accomplish a set of 368 metabolic functions required for maintaining the structural and functional integrity of the cell. Taking the maintenance of ATP, biosynthesis of ceramide, cardiolipin and further important phospholipids as examples, we analyse how a changed supply of glucose, lactate, fatty acids and ketone bodies may influence the efficiency of these essential processes. CardioNet is a functionally validated metabolic network of the human cardiomyocyte that enables theorectical studies of cellular metabolic processes crucial for the accomplishment of an adequate cardiac output.

Pseudomonas putida KT2440 Strain Metabolizes Glucose through a Cycle Formed by Enzymes of the Entner-Doudoroff, Embden-Meyerhof-Parnas, and Pentose Phosphate Pathways.

PubMed

Nikel, Pablo I; Chavarría, Max; Fuhrer, Tobias; Sauer, Uwe; de Lorenzo, Víctor

2015-10-23

The soil bacterium Pseudomonas putida KT2440 lacks a functional Embden-Meyerhof-Parnas (EMP) pathway, and glycolysis is known to proceed almost exclusively through the Entner-Doudoroff (ED) route. To investigate the raison d'être of this metabolic arrangement, the distribution of periplasmic and cytoplasmic carbon fluxes was studied in glucose cultures of this bacterium by using (13)C-labeled substrates, combined with quantitative physiology experiments, metabolite quantification, and in vitro enzymatic assays under both saturating and non-saturating, quasi in vivo conditions. Metabolic flux analysis demonstrated that 90% of the consumed sugar was converted into gluconate, entering central carbon metabolism as 6-phosphogluconate and further channeled into the ED pathway. Remarkably, about 10% of the triose phosphates were found to be recycled back to form hexose phosphates. This set of reactions merges activities belonging to the ED, the EMP (operating in a gluconeogenic fashion), and the pentose phosphate pathways to form an unforeseen metabolic architecture (EDEMP cycle). Determination of the NADPH balance revealed that the default metabolic state of P. putida KT2440 is characterized by a slight catabolic overproduction of reducing power. Cells growing on glucose thus run a biochemical cycle that favors NADPH formation. Because NADPH is required not only for anabolic functions but also for counteracting different types of environmental stress, such a cyclic operation may contribute to the physiological heftiness of this bacterium in its natural habitats. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Imaging mitochondrial flux in single cells with a FRET sensor for pyruvate.

PubMed

San Martín, Alejandro; Ceballo, Sebastián; Baeza-Lehnert, Felipe; Lerchundi, Rodrigo; Valdebenito, Rocío; Contreras-Baeza, Yasna; Alegría, Karin; Barros, L Felipe

2014-01-01

Mitochondrial flux is currently accessible at low resolution. Here we introduce a genetically-encoded FRET sensor for pyruvate, and methods for quantitative measurement of pyruvate transport, pyruvate production and mitochondrial pyruvate consumption in intact individual cells at high temporal resolution. In HEK293 cells, neurons and astrocytes, mitochondrial pyruvate uptake was saturated at physiological levels, showing that the metabolic rate is determined by intrinsic properties of the organelle and not by substrate availability. The potential of the sensor was further demonstrated in neurons, where mitochondrial flux was found to rise by 300% within seconds of a calcium transient triggered by a short theta burst, while glucose levels remained unaltered. In contrast, astrocytic mitochondria were insensitive to a similar calcium transient elicited by extracellular ATP. We expect the improved resolution provided by the pyruvate sensor will be of practical interest for basic and applied researchers interested in mitochondrial function.

Imaging Mitochondrial Flux in Single Cells with a FRET Sensor for Pyruvate

PubMed Central

Baeza-Lehnert, Felipe; Lerchundi, Rodrigo; Valdebenito, Rocío; Contreras-Baeza, Yasna; Alegría, Karin; Barros, L. Felipe

2014-01-01

Mitochondrial flux is currently accessible at low resolution. Here we introduce a genetically-encoded FRET sensor for pyruvate, and methods for quantitative measurement of pyruvate transport, pyruvate production and mitochondrial pyruvate consumption in intact individual cells at high temporal resolution. In HEK293 cells, neurons and astrocytes, mitochondrial pyruvate uptake was saturated at physiological levels, showing that the metabolic rate is determined by intrinsic properties of the organelle and not by substrate availability. The potential of the sensor was further demonstrated in neurons, where mitochondrial flux was found to rise by 300% within seconds of a calcium transient triggered by a short theta burst, while glucose levels remained unaltered. In contrast, astrocytic mitochondria were insensitive to a similar calcium transient elicited by extracellular ATP. We expect the improved resolution provided by the pyruvate sensor will be of practical interest for basic and applied researchers interested in mitochondrial function. PMID:24465702

Experimental Definition and Validation of Protein Coding Transcripts in Chlamydomonas reinhardtii

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kourosh Salehi-Ashtiani; Jason A. Papin

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnectedmore » metabolic networks to optimize production of the compounds of interest. Using Chlamydomonas reinhardtii as a model, we developed a systems-level methodology bridging metabolic network reconstruction with annotation and experimental verification of enzyme encoding open reading frames. We reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. Our approach to generate a predictive metabolic model integrated with cloned open reading frames, provides a cost-effective platform to generate metabolic engineering resources. While the generated resources are specific to algal systems, the approach that we have developed is not specific to algae and can be readily expanded to other microbial systems as well as higher plants and animals.« less

From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints.

PubMed

Klamt, Steffen; Regensburger, Georg; Gerstl, Matthias P; Jungreuthmayer, Christian; Schuster, Stefan; Mahadevan, Radhakrishnan; Zanghellini, Jürgen; Müller, Stefan

2017-04-01

Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [FBA]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks.

From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints

PubMed Central

Klamt, Steffen; Gerstl, Matthias P.; Jungreuthmayer, Christian; Mahadevan, Radhakrishnan; Müller, Stefan

2017-01-01

Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [FBA]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks. PMID:28406903

Quantitative Relationships between Photosynthetic, Nitrogen Fixing, and Fermentative H2 Metabolism in a Photosynthetic Microbial Mat

NASA Technical Reports Server (NTRS)

Hoehler, Tori M.; Albert, Daniel B.; Bebout, Brad M.; Turk, Kendra A.; DesMarais, David J.

2004-01-01

The ultimate potential of any microbial ecosystem to contribute chemically to its environment - and therefore, to impact planetary biogeochemistry or to generate recognizable biosignatures - depends not only on the individual metabolic capabilities of constituent organisms, but also on how those capabilities are expressed through interactions with neighboring organisms. This is particularly important for microbial mats, which compress an extremely broad range of metabolic potential into a small and dynamic system. H2 participates in many of these metabolic processes, including the major elemental cycling processes of photosynthesis, nitrogen fixation, sulfate reduction, and fermentation, and may therefore serve as a mediator of microbial interactions within the mat system. Collectively, the requirements of energy, electron transfer, and biomass element stoichiometry suggest quantitative relationships among the major element cycling processes, as regards H2 metabolism We determined experimentally the major contributions to 32 cycling in hypersaline microbial mats from Baja California, Mexico, and compared them to predicted relationships. Fermentation under dark, anoxic conditions is quantitatively the most important mechanism of H2 production, consistent with expectations for non-heterocystous mats such as those under study. Up to 16% of reducing equivalents fixed by photosynthesis during the day may be released by this mechanism. The direct contribution of nitrogen fixation to H2 production is small in comparison, but this process may indirectly stimulate substantial H2 generation, by requiring higher rates of fermentation. Sulfate reduction, aerobic consumption, diffusive and ebulitive loss, and possibly H2-based photoreduction of CO2 serve as the principal H2 sinks. Collectively, these processes interact to create an orders-of-magnitude daily variation in H2 concentrations and fluxes, and thereby in the oxidation-reduction potential that is imposed on microbial processes occuring within the mat matrix.

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

PubMed

Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

2004-05-05

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques. Copyright 2004 Wiley Periodicals, Inc.

Reducing Recon 2 for steady-state flux analysis of HEK cell culture.

PubMed

Quek, Lake-Ee; Dietmair, Stefanie; Hanscho, Michael; Martínez, Verónica S; Borth, Nicole; Nielsen, Lars K

2014-08-20

A representative stoichiometric model is essential to perform metabolic flux analysis (MFA) using experimentally measured consumption (or production) rates as constraints. For Human Embryonic Kidney (HEK) cell culture, there is the opportunity to use an extremely well-curated and annotated human genome-scale model Recon 2 for MFA. Performing MFA using Recon 2 without any modification would have implied that cells have access to all functionality encoded by the genome, which is not realistic. The majority of intracellular fluxes are poorly determined as only extracellular exchange rates are measured. This is compounded by the fact that there is no suitable metabolic objective function to suppress non-specific fluxes. We devised a heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions. This implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways. The reduced metabolic model becomes a functionalized version of Recon 2 used for identifying significant metabolic changes in cells by flux analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

A metabolite-centric view on flux distributions in genome-scale metabolic models

PubMed Central

2013-01-01

Background Genome-scale metabolic models are important tools in systems biology. They permit the in-silico prediction of cellular phenotypes via mathematical optimisation procedures, most importantly flux balance analysis. Current studies on metabolic models mostly consider reaction fluxes in isolation. Based on a recently proposed metabolite-centric approach, we here describe a set of methods that enable the analysis and interpretation of flux distributions in an integrated metabolite-centric view. We demonstrate how this framework can be used for the refinement of genome-scale metabolic models. Results We applied the metabolite-centric view developed here to the most recent metabolic reconstruction of Escherichia coli. By compiling the balance sheets of a small number of currency metabolites, we were able to fully characterise the energy metabolism as predicted by the model and to identify a possibility for model refinement in NADPH metabolism. Selected branch points were examined in detail in order to demonstrate how a metabolite-centric view allows identifying functional roles of metabolites. Fructose 6-phosphate aldolase and the sedoheptulose bisphosphate bypass were identified as enzymatic reactions that can carry high fluxes in the model but are unlikely to exhibit significant activity in vivo. Performing a metabolite essentiality analysis, unconstrained import and export of iron ions could be identified as potentially problematic for the quality of model predictions. Conclusions The system-wide analysis of split ratios and branch points allows a much deeper insight into the metabolic network than reaction-centric analyses. Extending an earlier metabolite-centric approach, the methods introduced here establish an integrated metabolite-centric framework for the interpretation of flux distributions in genome-scale metabolic networks that can complement the classical reaction-centric framework. Analysing fluxes and their metabolic context simultaneously opens the door to systems biological interpretations that are not apparent from isolated reaction fluxes. Particularly powerful demonstrations of this are the analyses of the complete metabolic contexts of energy metabolism and the folate-dependent one-carbon pool presented in this work. Finally, a metabolite-centric view on flux distributions can guide the refinement of metabolic reconstructions for specific growth scenarios. PMID:23587327

Bidirectionality and compartmentation of metabolic fluxes are revealed in the dynamics of isotopomer networks.

PubMed

Schryer, David W; Peterson, Pearu; Paalme, Toomas; Vendelin, Marko

2009-04-17

Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth.

E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data.

PubMed

Kim, Min Kyung; Lane, Anatoliy; Kelley, James J; Lun, Desmond S

2016-01-01

Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes. We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by 13C metabolic flux analysis (13C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/). Our method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.

Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics

PubMed Central

Costenoble, Roeland; Picotti, Paola; Reiter, Lukas; Stallmach, Robert; Heinemann, Matthias; Sauer, Uwe; Aebersold, Ruedi

2011-01-01

Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes. PMID:21283140

Linear analysis near a steady-state of biochemical networks: control analysis, correlation metrics and circuit theory.

PubMed

Heuett, William J; Beard, Daniel A; Qian, Hong

2008-05-15

Several approaches, including metabolic control analysis (MCA), flux balance analysis (FBA), correlation metric construction (CMC), and biochemical circuit theory (BCT), have been developed for the quantitative analysis of complex biochemical networks. Here, we present a comprehensive theory of linear analysis for nonequilibrium steady-state (NESS) biochemical reaction networks that unites these disparate approaches in a common mathematical framework and thermodynamic basis. In this theory a number of relationships between key matrices are introduced: the matrix A obtained in the standard, linear-dynamic-stability analysis of the steady-state can be decomposed as A = SRT where R and S are directly related to the elasticity-coefficient matrix for the fluxes and chemical potentials in MCA, respectively; the control-coefficients for the fluxes and chemical potentials can be written in terms of RTBS and STBS respectively where matrix B is the inverse of A; the matrix S is precisely the stoichiometric matrix in FBA; and the matrix eAt plays a central role in CMC. One key finding that emerges from this analysis is that the well-known summation theorems in MCA take different forms depending on whether metabolic steady-state is maintained by flux injection or concentration clamping. We demonstrate that if rate-limiting steps exist in a biochemical pathway, they are the steps with smallest biochemical conductances and largest flux control-coefficients. We hypothesize that biochemical networks for cellular signaling have a different strategy for minimizing energy waste and being efficient than do biochemical networks for biosynthesis. We also discuss the intimate relationship between MCA and biochemical systems analysis (BSA).

Metabolic networks evolve towards states of maximum entropy production.

PubMed

Unrean, Pornkamol; Srienc, Friedrich

2011-11-01

A metabolic network can be described by a set of elementary modes or pathways representing discrete metabolic states that support cell function. We have recently shown that in the most likely metabolic state the usage probability of individual elementary modes is distributed according to the Boltzmann distribution law while complying with the principle of maximum entropy production. To demonstrate that a metabolic network evolves towards such state we have carried out adaptive evolution experiments with Thermoanaerobacterium saccharolyticum operating with a reduced metabolic functionality based on a reduced set of elementary modes. In such reduced metabolic network metabolic fluxes can be conveniently computed from the measured metabolite secretion pattern. Over a time span of 300 generations the specific growth rate of the strain continuously increased together with a continuous increase in the rate of entropy production. We show that the rate of entropy production asymptotically approaches the maximum entropy production rate predicted from the state when the usage probability of individual elementary modes is distributed according to the Boltzmann distribution. Therefore, the outcome of evolution of a complex biological system can be predicted in highly quantitative terms using basic statistical mechanical principles. Copyright © 2011 Elsevier Inc. All rights reserved.

Investigation of metabolic objectives in cultured hepatocytes.

PubMed

Uygun, Korkut; Matthew, Howard W T; Huang, Yinlun

2007-06-15

Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms.

PubMed

Toya, Yoshihiro; Shimizu, Hiroshi

2013-11-01

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 (13)C-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. Copyright © 2013 Elsevier Inc. All rights reserved.

SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis

PubMed Central

Kogadeeva, Maria

2016-01-01

Metabolic fluxes are a cornerstone of cellular physiology that emerge from a complex interplay of enzymes, carriers, and nutrients. The experimental assessment of in vivo intracellular fluxes using stable isotopic tracers is essential if we are to understand metabolic function and regulation. Flux estimation based on 13C or 2H labeling relies on complex simulation and iterative fitting; processes that necessitate a level of expertise that ordinarily preclude the non-expert user. To overcome this, we have developed SUMOFLUX, a methodology that is broadly applicable to the targeted analysis of 13C-metabolic fluxes. By combining surrogate modeling and machine learning, we trained a predictor to specialize in estimating flux ratios from measurable 13C-data. SUMOFLUX targets specific flux features individually, which makes it fast, user-friendly, applicable to experimental design and robust in terms of experimental noise and exchange flux magnitude. Collectively, we predict that SUMOFLUX's properties realistically pave the way to high-throughput flux analyses. PMID:27626798

An optimized method for measuring fatty acids and cholesterol in stable isotope-labeled cells

PubMed Central

Argus, Joseph P.; Yu, Amy K.; Wang, Eric S.; Williams, Kevin J.; Bensinger, Steven J.

2017-01-01

Stable isotope labeling has become an important methodology for determining lipid metabolic parameters of normal and neoplastic cells. Conventional methods for fatty acid and cholesterol analysis have one or more issues that limit their utility for in vitro stable isotope-labeling studies. To address this, we developed a method optimized for measuring both fatty acids and cholesterol from small numbers of stable isotope-labeled cultured cells. We demonstrate quantitative derivatization and extraction of fatty acids from a wide range of lipid classes using this approach. Importantly, cholesterol is also recovered, albeit at a modestly lower yield, affording the opportunity to quantitate both cholesterol and fatty acids from the same sample. Although we find that background contamination can interfere with quantitation of certain fatty acids in low amounts of starting material, our data indicate that this optimized method can be used to accurately measure mass isotopomer distributions for cholesterol and many fatty acids isolated from small numbers of cultured cells. Application of this method will facilitate acquisition of lipid parameters required for quantifying flux and provide a better understanding of how lipid metabolism influences cellular function. PMID:27974366

Metabolic Flux Between Unsaturated and Saturated Fatty Acids is Controlled by the FabA:FabB Ratio in the Fully Reconstituted Fatty Acid Biosynthetic Pathway of E. coli#

PubMed Central

Xiao, Xirui; Yu, Xingye; Khosla, Chaitan

2013-01-01

The entire fatty acid biosynthetic pathway from Escherichia coli, starting from the acetyl-CoA carboxylase, has been reconstituted in vitro from fourteen purified protein components. Radiotracer analysis verified stoichiometric conversion of acetyl-CoA and NAD(P)H into the free fatty acid product, allowing implementation of a facile spectrophotometric assay for kinetic analysis of this multi-enzyme system. At steady state, a maximum turnover rate of 0.5 s−1 was achieved. Under optimal turnover conditions, the predominant products were C16 and C18 saturated as well as monounsaturated fatty acids. The reconstituted system allowed us to quantitatively interrogate the factors that influence metabolic flux toward unsaturated versus saturated fatty acids. In particular, the concentrations of the dehydratase FabA and the β-ketoacyl synthase FabB were found to be crucial for controlling this property. By altering these variables, the percentage of unsaturated fatty acid produced could be adjusted between 10 and 50% without significantly affecting the maximum turnover rate of the pathway. Our reconstituted system provides a powerful tool to understand and engineer rate-limiting and regulatory steps in this complex and practically significant metabolic pathway. PMID:24147979

Fluxomics - connecting 'omics analysis and phenotypes.

PubMed

Winter, Gal; Krömer, Jens O

2013-07-01

In our modern 'omics era, metabolic flux analysis (fluxomics) represents the physiological counterpart of its siblings transcriptomics, proteomics and metabolomics. Fluxomics integrates in vivo measurements of metabolic fluxes with stoichiometric network models to allow the determination of absolute flux through large networks of the central carbon metabolism. There are many approaches to implement fluxomics including flux balance analysis (FBA), (13) C fluxomics and (13) C-constrained FBA as well as many experimental settings for flux measurement including dynamic, stationary and semi-stationary. Here we outline the principles of the different approaches and their relative advantages. We demonstrate the unique contribution of flux analysis for phenotype elucidation using a thoroughly studied metabolic reaction as a case study, the microbial aerobic/anaerobic shift, highlighting the importance of flux analysis as a single layer of data as well as interlaced in multi-omics studies. © 2012 John Wiley & Sons Ltd and Society for Applied Microbiology.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

PubMed

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

Stoichiometric modelling of assimilatory and dissimilatory biomass utilisation in a microbial community

PubMed Central

Hunt, Kristopher A.; Jennings, Ryan deM.; Inskeep, William P.; Carlson, Ross P.

2017-01-01

Summary Assimilatory and dissimilatory utilisation of autotroph biomass by heterotrophs is a fundamental mechanism for the transfer of nutrients and energy across trophic levels. Metagenome data from a tractable, thermoacidophilic microbial community in Yellowstone National Park was used to build an in silico model to study heterotrophic utilisation of autotroph biomass using elementary flux mode analysis and flux balance analysis. Assimilatory and dissimilatory biomass utilisation was investigated using 29 forms of biomass-derived dissolved organic carbon (DOC) including individual monomer pools, individual macromolecular pools and aggregate biomass. The simulations identified ecologically competitive strategies for utilizing DOC under conditions of varying electron donor, electron acceptor or enzyme limitation. The simulated growth environment affected which form of DOC was the most competitive use of nutrients; for instance, oxygen limitation favoured utilisation of less reduced and fermentable DOC while carbon-limited environments favoured more reduced DOC. Additionally, metabolism was studied considering two encompassing metabolic strategies: simultaneous versus sequential use of DOC. Results of this study bound the transfer of nutrients and energy through microbial food webs, providing a quantitative foundation relevant to most microbial ecosystems. PMID:27387069

Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803.

PubMed

Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio; de Córdoba, Pedro Fernández; Urchueguía, Javier F; Patil, Kiran Raosaheb

2011-03-01

Synechocystis sp. PCC6803 is a model cyanobacterium capable of producing biofuels with CO(2) as carbon source and with its metabolism fueled by light, for which it stands as a potential production platform of socio-economic importance. Compilation and characterization of Synechocystis genome-scale metabolic model is a pre-requisite toward achieving a proficient photosynthetic cell factory. To this end, we report iSyn811, an upgraded genome-scale metabolic model of Synechocystis sp. PCC6803 consisting of 956 reactions and accounting for 811 genes. To gain insights into the interplay between flux activities and metabolic physiology, flux coupling analysis was performed for iSyn811 under four different growth conditions, viz., autotrophy, mixotrophy, heterotrophy, and light-activated heterotrophy (LH). Initial steps of carbon acquisition and catabolism formed the versatile center of the flux coupling networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production platform. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

PubMed Central

Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

2013-01-01

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

Experimental determination of group flux control coefficients in metabolic networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, T.W.; Shimizu, Hiroshi; Stephanopoulos, G.

1998-04-20

Grouping of reactions around key metabolite branch points can facilitate the study of metabolic control of complex metabolic networks. This top-down Metabolic Control Analysis is exemplified through the introduction of group control coefficients whose magnitudes provide a measure of the relative impact of each reaction group on the overall network flux, as well as on the overall network stability, following enzymatic amplification. In this article, the authors demonstrate the application of previously developed theory to the determination of group flux control coefficients. Experimental data for the changes in metabolic fluxes obtained in response to the introduction of six different environmentalmore » perturbations are used to determine the group flux control coefficients for three reaction groups formed around the phosphoenolpyruvate/pyruvate branch point. The consistency of the obtained group flux control coefficient estimates is systematically analyzed to ensure that all necessary conditions are satisfied. The magnitudes of the determined control coefficients suggest that the control of lysine production flux in Corynebacterium glutamicum cells at a growth base state resides within the lysine biosynthetic pathway that begins with the PEP/PYR carboxylation anaplorotic pathway.« less

Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts.

PubMed

Crown, Scott B; Kelleher, Joanne K; Rouf, Rosanne; Muoio, Deborah M; Antoniewicz, Maciek R

2016-10-01

In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using 13 C-metabolic flux analysis ( 13 C-MFA) aided by multiple tracer experiments and integrated data analysis. Using 13 C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different 13 C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of 13 C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems. Copyright © 2016 the American Physiological Society.

Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts

PubMed Central

Kelleher, Joanne K.; Rouf, Rosanne; Muoio, Deborah M.; Antoniewicz, Maciek R.

2016-01-01

In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using 13C-metabolic flux analysis (13C-MFA) aided by multiple tracer experiments and integrated data analysis. Using 13C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different 13C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of 13C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems. PMID:27496880

Metabolic analysis of the synthesis of high levels of intracellular human SOD in Saccharomyces cerevisiae rhSOD 2060 411 SGA122.

PubMed

Gonzalez, Ramon; Andrews, Barbara A; Molitor, Julia; Asenjo, Juan A

2003-04-20

The synthesis of human superoxide dismutase (SOD) in batch cultures of a Saccharomyces cerevisiae strain using a glucose-limited minimal medium was studied through metabolic flux analysis. A stoichiometric model was built, which included 78 reactions, according to metabolic pathways operative in these strains during respirofermentative and oxidative metabolism. It allowed calculation of the distribution of metabolic fluxes during diauxic growth on glucose and ethanol. Fermentation profiles and metabolic fluxes were analyzed at different phases of diauxic growth for the recombinant strain (P+) and for its wild type (P-). The synthesis of SOD by the strain P+ resulted in a decrease in specific growth rate of 34 and 54% (growth on glucose and ethanol respectively) in comparison to the wild type. Both strains exhibited similar flux of glucose consumption and ethanol synthesis but important differences in carbon distribution with biomass/substrate yields and ATP production 50% higher in P-. A higher contribution of fermentative metabolism, with 64% of the energy produced at the phosphorylation level, was observed during SOD production. The flux of precursors to amino acids and nucleotides was higher in the recombinant strain, in agreement with the higher total RNA and protein levels. Lower specific growth rates in strain P+ appear to be related to the decrease in the rate of synthesis of nonrecombinant protein, as well as a decrease in the activities of the pentose phosphate (PP) pathway and TCA cycle. A very different way of entry into the stationary phase was observed for each strain: in the wild-type strain most metabolic fluxes decreased and fluxes related to energy reserve synthesis increased, while in the P+ strain the flux of 22 reactions (including PP pathway and amino acids biosynthesis) related to SOD production increased their fluxes. Changes in SOD production rates at different physiological states appear to be related to the differences in building blocks availability between respirofermentative and oxidative metabolism. Using the present expression system, ideal conditions for SOD synthesis are represented by either active growth during respirofermentative metabolism or transition from a growing to a nongrowing state. An increase in SOD flux could be achieved using an expression system nonassociated to growth and potentially eliminating part of the metabolic burden. Copyright 2003 Wiley Periodicals, Inc.

Modeling Central Carbon Metabolic Processes in Soil Microbial Communities: Comparing Measured With Modeled

NASA Astrophysics Data System (ADS)

Dijkstra, P.; Fairbanks, D.; Miller, E.; Salpas, E.; Hagerty, S.

2013-12-01

Understanding the mechanisms regulating C cycling is hindered by our inability to directly observe and measure the biochemical processes of glycolysis, pentose phosphate pathway, and TCA cycle in intact and complex microbial communities. Position-specific 13C labeled metabolic tracer probing is proposed as a new way to study microbial community energy production, biosynthesis, C use efficiency (the proportion of substrate incorporated into microbial biomass), and enables the quantification of C fluxes through the central C metabolic network processes (Dijkstra et al 2011a,b). We determined the 13CO2 production from U-13C, 1-13C, 2-13C, 3-13C, 4-13C, 5-13C, and 6-13C labeled glucose and 1-13C and 2,3-13C pyruvate in parallel incubations in three soils along an elevation gradient. Qualitative and quantitative interpretation of the results indicate a high pentose phosphate pathway activity in soils. Agreement between modeled and measured CO2 production rates for the six C-atoms of 13C-labeled glucose indicate that the metabolic model used is appropriate for soil community processes, but that improvements can be made. These labeling and modeling techniques may improve our ability to analyze the biochemistry and (eco)physiology of intact microbial communities. Dijkstra, P., Blankinship, J.C., Selmants, P.C., Hart, S.C., Koch, G.W., Schwartz, E., Hungate, B.A., 2011a. Probing C flux patterns of soil microbial metabolic networks using parallel position-specific tracer labeling. Soil Biology & Biochemistry 43, 126-132. Dijkstra, P., Dalder, J.J., Selmants, P.C., Hart, S.C., Koch, G.W., Schwartz, E., Hungate, B.A., 2011b. Modeling soil metabolic processes using isotopologue pairs of position-specific 13C-labeled glucose and pyruvate. Soil Biology & Biochemistry 43, 1848-1857.

Energetic and metabolic transient response of Saccharomyces cerevisiae to benzoic acid.

PubMed

Kresnowati, M T A P; van Winden, W A; van Gulik, W M; Heijnen, J J

2008-11-01

Saccharomyces cerevisiae is known to be able to adapt to the presence of the commonly used food preservative benzoic acid with a large energy expenditure. Some mechanisms for the adaptation process have been suggested, but its quantitative energetic and metabolic aspects have rarely been discussed. This study discusses use of the stimulus response approach to quantitatively study the energetic and metabolic aspects of the transient adaptation of S. cerevisiae to a shift in benzoic acid concentration, from 0 to 0.8 mM. The information obtained also serves as the basis for further utilization of benzoic acid as a tool for targeted perturbation of the energy system, which is important in studying the kinetics and regulation of central carbon metabolism in S. cerevisiae. Using this experimental set-up, we found significant fast-transient (< 3000 s) increases in O(2) consumption and CO(2) production rates, of approximately 50%, which reflect a high energy requirement for the adaptation process. We also found that with a longer exposure time to benzoic acid, S. cerevisiae decreases the cell membrane permeability for this weak acid by a factor of 10 and decreases the cell size to approximately 80% of the initial value. The intracellular metabolite profile in the new steady-state indicates increases in the glycolytic and tricarboxylic acid cycle fluxes, which are in agreement with the observed increases in specific glucose and O(2) uptake rates.

A global resource allocation strategy governs growth transition kinetics of Escherichia coli

PubMed Central

Erickson, David W; Schink, Severin J.; Patsalo, Vadim; Williamson, James R.; Gerland, Ulrich; Hwa, Terence

2018-01-01

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms1–3 and they remain a topic of active investigation4–11. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations12–17, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models15,18–20 from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions. PMID:29072300

Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii.

PubMed

Boyle, Nanette R; Sengupta, Neelanjan; Morgan, John A

2017-01-01

Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, 13C-metabolic flux analysis (13C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with 13C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.

Non-stationary (13)C-metabolic flux ratio analysis.

PubMed

Hörl, Manuel; Schnidder, Julian; Sauer, Uwe; Zamboni, Nicola

2013-12-01

(13)C-metabolic flux analysis ((13)C-MFA) has become a key method for metabolic engineering and systems biology. In the most common methodology, fluxes are calculated by global isotopomer balancing and iterative fitting to stationary (13)C-labeling data. This approach requires a closed carbon balance, long-lasting metabolic steady state, and the detection of (13)C-patterns in a large number of metabolites. These restrictions mostly reduced the application of (13)C-MFA to the central carbon metabolism of well-studied model organisms grown in minimal media with a single carbon source. Here we introduce non-stationary (13)C-metabolic flux ratio analysis as a novel method for (13)C-MFA to allow estimating local, relative fluxes from ultra-short (13)C-labeling experiments and without the need for global isotopomer balancing. The approach relies on the acquisition of non-stationary (13)C-labeling data exclusively for metabolites in the proximity of a node of converging fluxes and a local parameter estimation with a system of ordinary differential equations. We developed a generalized workflow that takes into account reaction types and the availability of mass spectrometric data on molecular ions or fragments for data processing, modeling, parameter and error estimation. We demonstrated the approach by analyzing three key nodes of converging fluxes in central metabolism of Bacillus subtilis. We obtained flux estimates that are in agreement with published results obtained from steady state experiments, but reduced the duration of the necessary (13)C-labeling experiment to less than a minute. These results show that our strategy enables to formally estimate relative pathway fluxes on extremely short time scale, neglecting cellular carbon balancing. Hence this approach paves the road to targeted (13)C-MFA in dynamic systems with multiple carbon sources and towards rich media. © 2013 Wiley Periodicals, Inc.

Linear analysis near a steady-state of biochemical networks: Control analysis, correlation metrics and circuit theory

PubMed Central

Heuett, William J; Beard, Daniel A; Qian, Hong

2008-01-01

Background Several approaches, including metabolic control analysis (MCA), flux balance analysis (FBA), correlation metric construction (CMC), and biochemical circuit theory (BCT), have been developed for the quantitative analysis of complex biochemical networks. Here, we present a comprehensive theory of linear analysis for nonequilibrium steady-state (NESS) biochemical reaction networks that unites these disparate approaches in a common mathematical framework and thermodynamic basis. Results In this theory a number of relationships between key matrices are introduced: the matrix A obtained in the standard, linear-dynamic-stability analysis of the steady-state can be decomposed as A = SRT where R and S are directly related to the elasticity-coefficient matrix for the fluxes and chemical potentials in MCA, respectively; the control-coefficients for the fluxes and chemical potentials can be written in terms of RTBS and STBS respectively where matrix B is the inverse of A; the matrix S is precisely the stoichiometric matrix in FBA; and the matrix eAt plays a central role in CMC. Conclusion One key finding that emerges from this analysis is that the well-known summation theorems in MCA take different forms depending on whether metabolic steady-state is maintained by flux injection or concentration clamping. We demonstrate that if rate-limiting steps exist in a biochemical pathway, they are the steps with smallest biochemical conductances and largest flux control-coefficients. We hypothesize that biochemical networks for cellular signaling have a different strategy for minimizing energy waste and being efficient than do biochemical networks for biosynthesis. We also discuss the intimate relationship between MCA and biochemical systems analysis (BSA). PMID:18482450

Structural Control of Metabolic Flux

PubMed Central

Sajitz-Hermstein, Max; Nikoloski, Zoran

2013-01-01

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. PMID:24367246

13C labeling analysis of sugars by high resolution-mass spectrometry for metabolic flux analysis.

PubMed

Acket, Sébastien; Degournay, Anthony; Merlier, Franck; Thomasset, Brigitte

2017-06-15

Metabolic flux analysis is particularly complex in plant cells because of highly compartmented metabolism. Analysis of free sugars is interesting because it provides data to define fluxes around hexose, pentose, and triose phosphate pools in different compartment. In this work, we present a method to analyze the isotopomer distribution of free sugars labeled with carbon 13 using a liquid chromatography-high resolution mass spectrometry, without derivatized procedure, adapted for Metabolic flux analysis. Our results showed a good sensitivity, reproducibility and better accuracy to determine isotopic enrichments of free sugars compared to our previous methods [5, 6]. Copyright © 2017 Elsevier Inc. All rights reserved.

Metabolic flux ratio analysis and cell staining suggest the existence of C4 photosynthesis in Phaeodactylum tricornutum.

PubMed

Huang, A; Liu, L; Zhao, P; Yang, C; Wang, G C

2016-03-01

Mechanisms for carbon fixation via photosynthesis in the diatom Phaeodactylum tricornutum Bohlin were studied recently but there remains a long-standing debate concerning the occurrence of C4 photosynthesis in this species. A thorough investigation of carbon metabolism and the evidence for C4 photosynthesis based on organelle partitioning was needed. In this study, we identified the flux ratios between C3 and C4 compounds in P. tricornutum using (13)C-labelling metabolic flux ratio analysis, and stained cells with various cell-permeant fluorescent probes to investigate the likely organelle partitioning required for single-cell C4 photosynthesis. Metabolic flux ratio analysis indicated the C3/C4 exchange ratios were high. Cell staining indicated organelle partitioning required for single-cell C4 photosynthesis might exist in P. tricornutum. The results of (13)C-labelling metabolic flux ratio analysis and cell staining suggest single-cell C4 photosynthesis exists in P. tricornutum. This study provides insights into photosynthesis patterns of P. tricornutum and the evidence for C4 photosynthesis based on (13)C-labelling metabolic flux ratio analysis and organelle partitioning. © 2015 The Society for Applied Microbiology.

Study of AMPK-Regulated Metabolic Fluxes in Neurons Using the Seahorse XFe Analyzer.

PubMed

Marinangeli, Claudia; Kluza, Jérome; Marchetti, Philippe; Buée, Luc; Vingtdeux, Valérie

2018-01-01

AMP-activated protein kinase (AMPK) is the intracellular master energy sensor and metabolic regulator. AMPK is involved in cell energy homeostasis through the regulation of glycolytic flux and mitochondrial biogenesis. Interestingly, metabolic dysfunctions and AMPK deregulations are observed in many neurodegenerative diseases, including Alzheimer's. While these deregulations could play a key role in the development of these diseases, the study of metabolic fluxes has remained quite challenging and time-consuming. In this chapter, we describe the Seahorse XFe respirometry assay as a fundamental experimental tool to investigate the role of AMPK in controlling and modulating cell metabolic fluxes in living and intact differentiated primary neurons. The Seahorse XFe respirometry assay allows the real-time monitoring of glycolytic flux and mitochondrial respiration from different kind of cells, tissues, and isolated mitochondria. Here, we specify a protocol optimized for primary neuronal cells using several energy substrates such as glucose, pyruvate, lactate, glutamine, and ketone bodies. Nevertheless, this protocol can easily be adapted to monitor metabolic fluxes from other types of cells, tissues, or isolated mitochondria by taking into account the notes proposed for each key step of this assay.

Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii.

PubMed

Boyle, Nanette R; Morgan, John A

2009-01-07

Photosynthetic organisms convert atmospheric carbon dioxide into numerous metabolites along the pathways to make new biomass. Aquatic photosynthetic organisms, which fix almost half of global inorganic carbon, have great potential: as a carbon dioxide fixation method, for the economical production of chemicals, or as a source for lipids and starch which can then be converted to biofuels. To harness this potential through metabolic engineering and to maximize production, a more thorough understanding of photosynthetic metabolism must first be achieved. A model algal species, C. reinhardtii, was chosen and the metabolic network reconstructed. Intracellular fluxes were then calculated using flux balance analysis (FBA). The metabolic network of primary metabolism for a green alga, C. reinhardtii, was reconstructed using genomic and biochemical information. The reconstructed network accounts for the intracellular localization of enzymes to three compartments and includes 484 metabolic reactions and 458 intracellular metabolites. Based on BLAST searches, one newly annotated enzyme (fructose-1,6-bisphosphatase) was added to the Chlamydomonas reinhardtii database. FBA was used to predict metabolic fluxes under three growth conditions, autotrophic, heterotrophic and mixotrophic growth. Biomass yields ranged from 28.9 g per mole C for autotrophic growth to 15 g per mole C for heterotrophic growth. The flux balance analysis model of central and intermediary metabolism in C. reinhardtii is the first such model for algae and the first model to include three metabolically active compartments. In addition to providing estimates of intracellular fluxes, metabolic reconstruction and modelling efforts also provide a comprehensive method for annotation of genome databases. As a result of our reconstruction, one new enzyme was annotated in the database and several others were found to be missing; implying new pathways or non-conserved enzymes. The use of FBA to estimate intracellular fluxes also provides flux values that can be used as a starting point for rational engineering of C. reinhardtii. From these initial estimates, it is clear that aerobic heterotrophic growth on acetate has a low yield on carbon, while mixotrophically and autotrophically grown cells are significantly more carbon efficient.

Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli.

PubMed

Chen, Xuewen; Alonso, Ana P; Allen, Doug K; Reed, Jennifer L; Shachar-Hill, Yair

2011-01-01

Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and ¹³C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes. © 2010 Elsevier Inc. All rights reserved.

Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system.

PubMed

Crown, Scott B; Long, Christopher P; Antoniewicz, Maciek R

2016-11-01

13 C-Metabolic flux analysis ( 13 C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13 C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for 13 C-MFA. The best single tracers were doubly 13 C-labeled glucose tracers, including [1,6- 13 C]glucose, [5,6- 13 C]glucose and [1,2- 13 C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6- 13 C]glucose and [1,2- 13 C]glucose. Combined analysis of [1,6- 13 C]glucose and [1,2- 13 C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1- 13 C]glucose +20% [U- 13 C]glucose. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Inhibition effect of zinc in wastewater on the N2O emission from coastal loam soils.

PubMed

Huang, Yan; Ou, Danyun; Chen, Shunyang; Chen, Bin; Liu, Wenhua; Bai, Renao; Chen, Guangcheng

2017-03-15

The effects of zinc (Zn) on nitrous oxide (N 2 O) fluxes from coastal loam soil and the abundances of soil nitrifier and denitrifier were studied in a tidal microcosm receiving livestock wastewater with different Zn levels. Soil N 2 O emission significantly increased due to discharge of wastewater rich in ammonia (NH 4 + -N) while the continuous measurements of gas flux showed a durative reduction in N 2 O flux by high Zn input (40mgL -1 ) during the low tide period. Soil inorganic nitrogen concentrations increased at the end of the experiment and even more soil NH 4 + -N was measured in the high-Zn-level treatment, indicating an inhibition of ammonia oxidation by Zn input. Quantitative PCR of soil amoA, narG and nirK genes encoding ammonia monooxygenase, nitrate reductase and nitrite reductase, respectively, showed that the microbial abundances involved in these metabolisms were neither affected by wastewater discharge nor Zn contamination. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metabolite-balancing techniques vs. 13C tracer experiments to determine metabolic fluxes in hybridoma cells.

PubMed

Bonarius, H P; Timmerarends, B; de Gooijer, C D; Tramper, J

The estimation of intracellular fluxes of mammalian cells using only mass balances of the relevant metabolites is not possible because the set of linear equations defined by these mass balances is underdetermined. In order to quantify fluxes in cyclic pathways the mass balance equations can be complemented with several constraints: (1) the mass balances of co-metabolites, such as ATP or NAD(P)H, (2) linear objective functions, (3) flux data obtained by isotopic-tracer experiments. Here, these three methods are compared for the analysis of fluxes in the primary metabolism of continuously cultured hybridoma cells. The significance of different theoretical constraints and different objective functions is discussed after comparing their resulting flux distributions to the fluxes determined using 13CO2 and 13C-lactate measurements of 1 - 13C-glucose-fed hybridoma cells. Metabolic fluxes estimated using the objective functions "maximize ATP" and "maximize NADH" are relatively similar to the experimentally determined fluxes. This is consistent with the observation that cancer cells, such as hybridomas, are metabolically hyperactive, and produce ATP and NADH regardless of the need for these cofactors. Copyright 1998 John Wiley & Sons, Inc.

Radiopharmaceuticals for Assessment of Altered Metabolism and Biometal Fluxes in Brain Aging and Alzheimer's Disease with Positron Emission Tomography.

PubMed

Xie, Fang; Peng, Fangyu

2017-01-01

Aging is a risk factor for Alzheimer's disease (AD). There are changes of brain metabolism and biometal fluxes due to brain aging, which may play a role in pathogenesis of AD. Positron emission tomography (PET) is a versatile tool for tracking alteration of metabolism and biometal fluxes due to brain aging and AD. Age-dependent changes in cerebral glucose metabolism can be tracked with PET using 2-deoxy-2-[18F]-fluoro-D-glucose (18F-FDG), a radiolabeled glucose analogue, as a radiotracer. Based on different patterns of altered cerebral glucose metabolism, 18F-FDG PET was clinically used for differential diagnosis of AD and Frontotemporal dementia (FTD). There are continued efforts to develop additional radiopharmaceuticals or radiotracers for assessment of age-dependent changes of various metabolic pathways and biometal fluxes due to brain aging and AD with PET. Elucidation of age-dependent changes of brain metabolism and altered biometal fluxes is not only significant for a better mechanistic understanding of brain aging and the pathophysiology of AD, but also significant for identification of new targets for the prevention, early diagnosis, and treatment of AD.

13 C Flux Analysis Reveals that Rebalancing Medium Amino Acid Composition can Reduce Ammonia Production while Preserving Central Carbon Metabolism of CHO Cell Cultures.

PubMed

McAtee Pereira, Allison G; Walther, Jason L; Hollenbach, Myles; Young, Jamey D

2018-02-06

13 C metabolic flux analysis (MFA) provides a rigorous approach to quantify intracellular metabolism of industrial cell lines. In this study, 13 C MFA was used to characterize the metabolic response of Chinese hamster ovary (CHO) cells to a novel medium variant designed to reduce ammonia production. Ammonia inhibits growth and viability of CHO cell cultures, alters glycosylation of recombinant proteins, and enhances product degradation. Ammonia production was reduced by manipulating the amino acid composition of the culture medium; specifically, glutamine, glutamate, asparagine, aspartate, and serine levels were adjusted. Parallel 13 C flux analysis experiments determined that, while ammonia production decreased by roughly 40%, CHO cell metabolic phenotype, growth, viability, and monoclonal antibody (mAb) titer were not significantly altered by the changes in media composition. This study illustrates how 13 C flux analysis can be applied to assess the metabolic effects of media manipulations on mammalian cell cultures. The analysis revealed that adjusting the amino acid composition of CHO cell culture media can effectively reduce ammonia production while preserving fluxes throughout central carbon metabolism. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Recent Carbon Cycle Dynamics in an Ombrotrophic Peatland: Implications From Warming and eCO2 Treatments and the Role of Vegetation Layers in the Flux of CO2 and CH4

NASA Astrophysics Data System (ADS)

Hanson, P. J.; Phillips, J. R.; Nettles, W. R., IV; Heiderman, R.

2017-12-01

Following 2 years of sustained whole-ecosystem warming treatments spanning a range from 0 to +9 °C (SPRUCE experiment), the net fluxes of CO2 and CH4 from a raised-bog peatland in northern Minnesota show increased emissions of both gases from the community of woody ericaceous shrubs, forbs and Sphagnum moss. Increased emissions for CO2 and CH4 are primarily driven by sustaining temperature conditions for metabolic activity throughout the growing season. Seasonal temperature relationships for each gas suggest that warming affected growth and metabolic processes in a consistent manner across a wide range of temperature treatments. Elevated CO2 treatments (eCO2) have not yet shown anticipated increases in the input and processing of recent carbon. Quantitative annual estimates of the amount of net C and greenhouse gas flux increases will be calculated and presented for all treatments. A mid-season deconstruction of the contribution of vegetation layers to net ecosystem exchange of C and community respiration processes was also completed for replicate ambient shrub communities. The deconstruction data demonstrate the fractional contribution of wood shrubs, forbs/sedges and moss to the community to the flux of C and provide further evidence that the current C cycle of the bog is driven primarily by surface phenomenon fed be recently fixed C. These results should be considered early results from the SPRUCE experiment anticipated to operate through 2025. Affiliated studies will add mechanisms to these observations and long-term cumulative effects may differ.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Interstrain Variability in Trichloroethylene Metabolism in the Mouse

PubMed Central

Campbell, Jerry L.; Clewell, Harvey J.; Zhou, Yi-Hui; Wright, Fred A.; Guyton, Kathryn Z.

2014-01-01

Background: Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, interindividual differences in the population are accounted for by default assumptions or, in rare cases, are based on human toxicokinetic data. Objectives: We evaluated the utility of genetically diverse mouse strains for estimating toxicokinetic population variability for risk assessment, using trichloroethylene (TCE) metabolism as a case study. Methods: We used data on oxidative and glutathione conjugation metabolism of TCE in 16 inbred and 1 hybrid mouse strains to calibrate and extend existing physiologically based pharmacokinetic (PBPK) models. We added one-compartment models for glutathione metabolites and a two-compartment model for dichloroacetic acid (DCA). We used a Bayesian population analysis of interstrain variability to quantify variability in TCE metabolism. Results: Concentration–time profiles for TCE metabolism to oxidative and glutathione conjugation metabolites varied across strains. Median predictions for the metabolic flux through oxidation were less variable (5-fold range) than that through glutathione conjugation (10-fold range). For oxidative metabolites, median predictions of trichloroacetic acid production were less variable (2-fold range) than DCA production (5-fold range), although the uncertainty bounds for DCA exceeded the predicted variability. Conclusions: Population PBPK modeling of genetically diverse mouse strains can provide useful quantitative estimates of toxicokinetic population variability. When extrapolated to lower doses more relevant to environmental exposures, mouse population-derived variability estimates for TCE metabolism closely matched population variability estimates previously derived from human toxicokinetic studies with TCE, highlighting the utility of mouse interstrain metabolism studies for addressing toxicokinetic variability. Citation: Chiu WA, Campbell JL Jr, Clewell HJ III, Zhou YH, Wright FA, Guyton KZ, Rusyn I. 2014. Physiologically based pharmacokinetic (PBPK) modeling of interstrain variability in trichloroethylene metabolism in the mouse. Environ Health Perspect 122:456–463; http://dx.doi.org/10.1289/ehp.1307623 PMID:24518055

The Interrelationship between Promoter Strength, Gene Expression, and Growth Rate

PubMed Central

Klesmith, Justin R.; Detwiler, Emily E.; Tomek, Kyle J.; Whitehead, Timothy A.

2014-01-01

In exponentially growing bacteria, expression of heterologous protein impedes cellular growth rates. Quantitative understanding of the relationship between expression and growth rate will advance our ability to forward engineer bacteria, important for metabolic engineering and synthetic biology applications. Recently, a work described a scaling model based on optimal allocation of ribosomes for protein translation. This model quantitatively predicts a linear relationship between microbial growth rate and heterologous protein expression with no free parameters. With the aim of validating this model, we have rigorously quantified the fitness cost of gene expression by using a library of synthetic constitutive promoters to drive expression of two separate proteins (eGFP and amiE) in E. coli in different strains and growth media. In all cases, we demonstrate that the fitness cost is consistent with the previous findings. We expand upon the previous theory by introducing a simple promoter activity model to quantitatively predict how basal promoter strength relates to growth rate and protein expression. We then estimate the amount of protein expression needed to support high flux through a heterologous metabolic pathway and predict the sizable fitness cost associated with enzyme production. This work has broad implications across applied biological sciences because it allows for prediction of the interplay between promoter strength, protein expression, and the resulting cost to microbial growth rates. PMID:25286161

13C tracer experiments and metabolite balancing for metabolic flux analysis: comparing two approaches

PubMed

Schmidt; Marx; de Graaf AA; Wiechert; Sahm; Nielsen; Villadsen

1998-04-05

Conventional metabolic flux analysis uses the information gained from determination of measurable fluxes and a steady-state assumption for intracellular metabolites to calculate the metabolic fluxes in a given metabolic network. The determination of intracellular fluxes depends heavily on the correctness of the assumed stoichiometry including the presence of all reactions with a noticeable impact on the model metabolite balances. Determination of fluxes in complex metabolic networks often requires the inclusion of NADH and NADPH balances, which are subject to controversial debate. Transhydrogenation reactions that transfer reduction equivalents from NADH to NADPH or vice versa can usually not be included in the stoichiometric model, because they result in singularities in the stoichiometric matrix. However, it is the NADPH balance that, to a large extent, determines the calculated flux through the pentose phosphate pathway. Hence, wrong assumptions on the presence or activity of transhydrogenation reactions will result in wrong estimations of the intracellular flux distribution. Using 13C tracer experiments and NMR analysis, flux analysis can be performed on the basis of only well established stoichiometric equations and measurements of the labeling state of intracellular metabolites. Neither NADH/NADPH balancing nor assumptions on energy yields need to be included to determine the intracellular fluxes. Because metabolite balancing methods and the use of 13C labeling measurements are two different approaches to the determination of intracellular fluxes, both methods can be used to verify each other or to discuss the origin and significance of deviations in the results. Flux analysis based entirely on metabolite balancing and flux analysis, including labeling information, have been performed independently for a wild-type strain of Aspergillus oryzae producing alpha-amylase. Two different nitrogen sources, NH4+ and NO3-, have been used to investigate the influence of the NADPH requirements on the intracellular flux distribution. The two different approaches to the calculation of fluxes are compared and deviations in the results are discussed. Copyright 1998 John Wiley & Sons, Inc.

Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks

PubMed Central

Hoppe, Andreas; Hoffmann, Sabrina; Holzhütter, Hermann-Georg

2007-01-01

Background In recent years, constrained optimization – usually referred to as flux balance analysis (FBA) – has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions. Results To augment the reliability of FBA-based flux calculations we propose an additional side constraint which assures thermodynamic realizability, i.e. that the flux directions are consistent with the corresponding changes of Gibb's free energies. The latter depend on metabolite levels for which plausible ranges can be inferred from experimental data. Computationally, our method results in the solution of a mixed integer linear optimization problem with quadratic scoring function. An optimal flux distribution together with a metabolite profile is determined which assures thermodynamic realizability with minimal deviations of metabolite levels from their expected values. We applied our novel approach to two exemplary metabolic networks of different complexity, the metabolic core network of erythrocytes (30 reactions) and the metabolic network iJR904 of Escherichia coli (931 reactions). Our calculations show that increasing network complexity entails increasing sensitivity of predicted flux distributions to variations of standard Gibb's free energy changes and metabolite concentration ranges. We demonstrate the usefulness of our method for assessing critical concentrations of external metabolites preventing attainment of a metabolic steady state. Conclusion Our method incorporates the thermodynamic link between flux directions and metabolite concentrations into a practical computational algorithm. The weakness of conventional FBA to rely on intuitive assumptions about the reversibility of biochemical reactions is overcome. This enables the computation of reliable flux distributions even under extreme conditions of the network (e.g. enzyme inhibition, depletion of substrates or accumulation of end products) where metabolite concentrations may be drastically altered. PMID:17543097

OptFlux: an open-source software platform for in silico metabolic engineering.

PubMed

Rocha, Isabel; Maia, Paulo; Evangelista, Pedro; Vilaça, Paulo; Soares, Simão; Pinto, José P; Nielsen, Jens; Patil, Kiran R; Ferreira, Eugénio C; Rocha, Miguel

2010-04-19

Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.

OptFlux: an open-source software platform for in silico metabolic engineering

PubMed Central

2010-01-01

Background Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. Results OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. Conclusions The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models. PMID:20403172

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

PubMed Central

Ghosh, Amit; Nilmeier, Jerome; Weaver, Daniel; Adams, Paul D.; Keasling, Jay D.; Mukhopadhyay, Aindrila; Petzold, Christopher J.; Martín, Héctor García

2014-01-01

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. PMID:25188426

Quantitative flux analysis reveals folate-dependent NADPH production

NASA Astrophysics Data System (ADS)

Fan, Jing; Ye, Jiangbin; Kamphorst, Jurre J.; Shlomi, Tomer; Thompson, Craig B.; Rabinowitz, Joshua D.

2014-06-01

ATP is the dominant energy source in animals for mechanical and electrical work (for example, muscle contraction or neuronal firing). For chemical work, there is an equally important role for NADPH, which powers redox defence and reductive biosynthesis. The most direct route to produce NADPH from glucose is the oxidative pentose phosphate pathway, with malic enzyme sometimes also important. Although the relative contribution of glycolysis and oxidative phosphorylation to ATP production has been extensively analysed, similar analysis of NADPH metabolism has been lacking. Here we demonstrate the ability to directly track, by liquid chromatography-mass spectrometry, the passage of deuterium from labelled substrates into NADPH, and combine this approach with carbon labelling and mathematical modelling to measure NADPH fluxes. In proliferating cells, the largest contributor to cytosolic NADPH is the oxidative pentose phosphate pathway. Surprisingly, a nearly comparable contribution comes from serine-driven one-carbon metabolism, in which oxidation of methylene tetrahydrofolate to 10-formyl-tetrahydrofolate is coupled to reduction of NADP+ to NADPH. Moreover, tracing of mitochondrial one-carbon metabolism revealed complete oxidation of 10-formyl-tetrahydrofolate to make NADPH. As folate metabolism has not previously been considered an NADPH producer, confirmation of its functional significance was undertaken through knockdown of methylenetetrahydrofolate dehydrogenase (MTHFD) genes. Depletion of either the cytosolic or mitochondrial MTHFD isozyme resulted in decreased cellular NADPH/NADP+ and reduced/oxidized glutathione ratios (GSH/GSSG) and increased cell sensitivity to oxidative stress. Thus, although the importance of folate metabolism for proliferating cells has been long recognized and attributed to its function of producing one-carbon units for nucleic acid synthesis, another crucial function of this pathway is generating reducing power.

Use of CellNetAnalyzer in biotechnology and metabolic engineering.

PubMed

von Kamp, Axel; Thiele, Sven; Hädicke, Oliver; Klamt, Steffen

2017-11-10

Mathematical models of the cellular metabolism have become an essential tool for the optimization of biotechnological processes. They help to obtain a systemic understanding of the metabolic processes in the used microorganisms and to find suitable genetic modifications maximizing the production performance. In particular, methods of stoichiometric and constraint-based modeling are frequently used in the context of metabolic and bioprocess engineering. Since metabolic networks can be complex and comprise hundreds or even thousands of metabolites and reactions, dedicated software tools are required for an efficient analysis. One such software suite is CellNetAnalyzer, a MATLAB package providing, among others, various methods for analyzing stoichiometric and constraint-based metabolic models. CellNetAnalyzer can be used via command-line based operations or via a graphical user interface with embedded network visualizations. Herein we will present key functionalities of CellNetAnalyzer for applications in biotechnology and metabolic engineering and thereby review constraint-based modeling techniques such as metabolic flux analysis, flux balance analysis, flux variability analysis, metabolic pathway analysis (elementary flux modes) and methods for computational strain design. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Metabolic flux and nodes control analysis of brewer's yeasts under different fermentation temperature during beer brewing.

PubMed

Yu, Zhimin; Zhao, Haifeng; Zhao, Mouming; Lei, Hongjie; Li, Huiping

2012-12-01

The aim of this work was to further investigate the glycolysis performance of lager and ale brewer's yeasts under different fermentation temperature using a combined analysis of metabolic flux, glycolytic enzyme activities, and flux control. The results indicated that the fluxes through glycolytic pathway decreased with the change of the fermentation temperature from 15 °C to 10 °C, which resulted in the prolonged fermentation times. The maximum activities (V (max)) of hexokinase (HK), phosphofructokinase (PFK), and pyruvate kinase (PK) at key nodes of glycolytic pathway decreased with decreasing fermentation temperature, which was estimated to have different control extent (22-84 %) on the glycolytic fluxes in exponential or flocculent phase. Moreover, the decrease of V (max) of PFK or PK displayed the crucial role in down-regulation of flux in flocculent phase. In addition, the metabolic state of ale strain was more sensitive to the variation of temperature than that of lager strain. The results of the metabolic flux and nodes control analysis in brewer's yeasts under different fermentation temperature may provide an alternative approach to regulate glycolytic flux by changing V (max) and improve the production efficiency and beer quality.

Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

NASA Astrophysics Data System (ADS)

Tartakovsky, G. D.; Tartakovsky, A. M.; Scheibe, T. D.; Fang, Y.; Mahadevan, R.; Lovley, D. R.

2013-09-01

Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparison to prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under conditions in which one or more nutrients were limiting. The fitted Monod kinetic model was also applied at the Darcy scale; that is, to simulate average reaction processes at the scale of the entire pore-scale model domain. As we expected, even under excess nutrient conditions for which the Monod and genome-scale models predicted equal reaction rates at the pore scale, the Monod model over-predicted the rates of biomass growth and iron and acetate utilization when applied at the Darcy scale. This discrepancy is caused by an inherent assumption of perfect mixing over the Darcy-scale domain, which is clearly violated in the pore-scale models. These results help to explain the need to modify the flux constraint parameters in order to match observations in previous applications of the genome-scale model at larger scales. These results also motivate further investigation of quantitative multi-scale relationships between fundamental behavior at the pore scale (where genome-scale models are appropriately applied) and observed behavior at larger scales (where predictions of reactive transport phenomena are needed).

Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

NASA Astrophysics Data System (ADS)

Scheibe, T. D.; Tartakovsky, G.; Tartakovsky, A. M.; Fang, Y.; Mahadevan, R.; Lovley, D. R.

2012-12-01

Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparison to prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under conditions in which one or more nutrients were limiting. The fitted Monod kinetic model was also applied at the Darcy scale; that is, to simulate average reaction processes at the scale of the entire pore-scale model domain. As we expected, even under excess nutrient conditions for which the Monod and genome-scale models predicted equal reaction rates at the pore scale, the Monod model over-predicted the rates of biomass growth and iron and acetate utilization when applied at the Darcy scale. This discrepancy is caused by an inherent assumption of perfect mixing over the Darcy-scale domain, which is clearly violated in the pore-scale models. These results help to explain the need to modify the flux constraint parameters in order to match observations in previous applications of the genome-scale model at larger scales. These results also motivate further investigation of quantitative multi-scale relationships between fundamental behavior at the pore scale (where genome-scale models are appropriately applied) and observed behavior at larger scales (where predictions of reactive transport phenomena are needed).

Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tartakovsky, Guzel D.; Tartakovsky, Alexandre M.; Scheibe, Timothy D.

2013-09-07

Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated withmore » microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparisonto prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under conditions in which one or more nutrients were limiting. The fitted Monod kinetic model was also applied at the Darcy scale; that is, to simulate average reaction processes at the scale of the entire pore-scale model domain. As we expected, even under excess nutrient conditions for which the Monod and genome-scale models predicted equal reaction rates at the pore scale, the Monod model over-predicted the rates of biomass growth and iron and acetate utilization when applied at the Darcy scale. This discrepancy is caused by an inherent assumption of perfect mixing over the Darcy-scale domain, which is clearly violated in the pore-scale models. These results help to explain the need to modify the flux constraint parameters in order to match observations in previous applications of the genome-scale model at larger scales. These results also motivate further investigation of quantitative multi-scale relationships between fundamental behavior at the pore scale (where genome-scale models are appropriately applied) and observed behavior at larger scales (where predictions of reactive transport phenomena are needed).« less

13C based proteinogenic amino acid (PAA) and metabolic flux ratio analysis of Lactococcus lactis reveals changes in pentose phosphate (PP) pathway in response to agitation and temperature related stresses.

PubMed

Azizan, Kamalrul Azlan; Ressom, Habtom W; Mendoza, Eduardo R; Baharum, Syarul Nataqain

2017-01-01

Lactococcus lactis subsp. cremoris MG1363 is an important starter culture for dairy fermentation. During industrial fermentations, L. lactis is constantly exposed to stresses that affect the growth and performance of the bacterium. Although the response of L. lactis to several stresses has been described, the adaptation mechanisms at the level of in vivo fluxes have seldom been described. To gain insights into cellular metabolism, 13 C metabolic flux analysis and gas chromatography mass spectrometry (GC-MS) were used to measure the flux ratios of active pathways in the central metabolism of L. lactis when subjected to three conditions varying in temperature (30°C, 37°C) and agitation (with and without agitation at 150 rpm). Collectively, the concentrations of proteinogenic amino acids (PAAs) and free fatty acids (FAAs) were compared, and Pearson correlation analysis ( r ) was calculated to measure the pairwise relationship between PAAs. Branched chain and aromatic amino acids, threonine, serine, lysine and histidine were correlated strongly, suggesting changes in flux regulation in glycolysis, the pentose phosphate (PP) pathway, malic enzyme and anaplerotic reaction catalysed by pyruvate carboxylase (pycA). Flux ratio analysis revealed that glucose was mainly converted by glycolysis, highlighting the stability of L. lactis' central carbon metabolism despite different conditions. Higher flux ratios through oxaloacetate (OAA) from pyruvate (PYR) reaction in all conditions suggested the activation of pyruvate carboxylate (pycA) in L. lactis , in response to acid stress during exponential phase. Subsequently, more significant flux ratio differences were seen through the oxidative and non-oxidative pentose phosphate (PP) pathways, malic enzyme, and serine and C1 metabolism, suggesting NADPH requirements in response to environmental stimuli. These reactions could play an important role in optimization strategies for metabolic engineering in L. lactis . Overall, the integration of systematic analysis of amino acids and flux ratio analysis provides a systems-level understanding of how L. lactis regulates central metabolism under various conditions.

Analysis of optimality in natural and perturbed metabolic networks

PubMed Central

Segrè, Daniel; Vitkup, Dennis; Church, George M.

2002-01-01

An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism. PMID:12415116

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions.

PubMed

Cheung, C Y Maurice; Williams, Thomas C R; Poolman, Mark G; Fell, David A; Ratcliffe, R George; Sweetlove, Lee J

2013-09-01

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance. © 2013 The Authors The Plant Journal © 2013 John Wiley & Sons Ltd.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

PubMed

Megchelenbrink, Wout; Katzir, Rotem; Lu, Xiaowen; Ruppin, Eytan; Notebaart, Richard A

2015-09-29

Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

Synthetic metabolic bypass for a metabolic toggle switch enhances acetyl-CoA supply for isopropanol production by Escherichia coli.

PubMed

Soma, Yuki; Yamaji, Taiki; Matsuda, Fumio; Hanai, Taizo

2017-05-01

Almost all synthetic pathways for biofuel production are designed to require endogenous metabolites in glycolysis, such as phosphoenolpyruvate, pyruvate, and acetyl-CoA. However, such metabolites are also required for bacterial cell growth. To reduce the metabolic imbalance between cell growth and target chemical production, we previously constructed a metabolic toggle switch (MTS) as a conditional flux redirection tool controlling metabolic flux of TCA cycle toward isopropanol production. This approach succeeded to improve the isopropanol production titer and yield while ensuring sufficient cell growth. However, excess accumulation of pyruvate, the precursor for acetyl-CoA synthesis, was also observed. In this study, for efficient conversation of pyruvate to acetyl-CoA (pyruvate oxidation), we designed a synthetic metabolic bypass composed of poxB and acs with the MTS for acetyl-CoA supply from the excess pyruvate. When this designed bypass was expressed at the appropriate expression level associated with the conditional metabolic flux redirection, pyruvate accumulation was prevented, and the isopropanol production titer and yield were improved. Final isopropanol production titer of strain harboring MTS with the synthetic metabolic bypass improved 4.4-fold compared with strain without metabolic flux regulation, and it was 1.3-fold higher than that of strain harboring the conventional MTS alone. Additionally, glucose consumption was also improved 1.7-fold compared with strain without metabolic flux regulation. On the other hand, introduction of the synthetic metabolic bypass alone showed no improvement in isopropanol production and glucose consumption. These results showed that the improvement in bio-production process caused by synergy between the MTS and the synthetic metabolic bypass. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis.

PubMed

Pitkänen, Esa; Akerlund, Arto; Rantanen, Ari; Jouhten, Paula; Ukkonen, Esko

2008-08-25

ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitate (13)C metabolic flux analysis. The construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced in (13)C metabolic flux analysis, where the mappings of carbon atoms from substrates into products in the model are required. ReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool. The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.

PubMed

Kruger, Nicholas J; Ratcliffe, R George

2015-01-01

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

PubMed Central

Hawley, Alyse K.; Katsev, Sergei; Torres-Beltran, Monica; Bhatia, Maya P.; Kheirandish, Sam; Michiels, Céline C.; Capelle, David; Lavik, Gaute; Doebeli, Michael; Crowe, Sean A.; Hallam, Steven J.

2016-01-01

Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet—a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite “leakage” during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales. PMID:27655888

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone.

PubMed

Louca, Stilianos; Hawley, Alyse K; Katsev, Sergei; Torres-Beltran, Monica; Bhatia, Maya P; Kheirandish, Sam; Michiels, Céline C; Capelle, David; Lavik, Gaute; Doebeli, Michael; Crowe, Sean A; Hallam, Steven J

2016-10-04

Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet-a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite "leakage" during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.

Exploring the quantitative relationship between metabolism and enzymatic phenotype by physiological modeling of glucose metabolism and lactate oxidation in solid tumors

NASA Astrophysics Data System (ADS)

Wang, Qian; Vaupel, Peter; Ziegler, Sibylle I.; Shi, Kuangyu

2015-03-01

Molecular imaging using PET or hyperpolarized MRI can characterize tumor phenotypes by assessing the related metabolism of certain substrates. However, the interpretation of the substrate turnover in terms of a pathophysiological understanding is not straightforward and only semiquantitative. The metabolism of imaging probes is influenced by a number of factors, such as the microvascular structure or the expression of key enzymes. This study aims to use computational simulation to investigate the relationship between the metabolism behind molecular imaging and the underlying tumor phenotype. The study focused on the pathways of glucose metabolism and lactate oxidation in order to establish the quantitative relationship between the expression of several transporters (GLUT, MCT1 and MCT4), expression of the enzyme hexokinase (HK), microvasculature and the metabolism of glucose or lactate and the extracellular pH distribution. A computational model for a 2D tumor tissue phantom was constructed and the spatio-temporal evolution of related species (e.g. oxygen, glucose, lactate, protons, bicarbonate ions) was estimated by solving reaction-diffusion equations. The proposed model was tested by the verification of the simulation results using in vivo and in vitro literature data. The influences of different expression levels of GLUT, MCT1, MCT4, HK and microvessel distribution on substrate concentrations were analyzed. The major results are consistent with experimental data (e.g. GLUT is more influential to glycolytic flux than HK; extracellular pH is not correlated with MCT expressions) and provide theoretical interpretation of the co-influence of multiple factors of the tumor microenvironment. This computational simulation may assist the generation of hypotheses to bridge the discrepancy between tumor metabolism and the functions of transporters and enzymes. It has the potential to accelerate the development of multi-modal imaging strategies for assessment of tumor phenotypes.

Computational Platform for Flux Analysis Using 13C-Label Tracing- Phase I SBIR Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Dien, Stephen J.

Isotopic label tracing is a powerful experimental technique that can be combined with metabolic models to quantify metabolic fluxes in an organism under a particular set of growth conditions. In this work we constructed a genome-scale metabolic model of Methylobacterium extorquens, a facultative methylotroph with potential application in the production of useful chemicals from methanol. A series of labeling experiments were performed using 13C-methanol, and the resulting distribution of labeled carbon in the proteinogenic amino acids was determined by mass spectrometry. Algorithms were developed to analyze this data in context of the metabolic model, yielding flux distributions for wild-type andmore » several engineered strains of M. extorquens. These fluxes were compared to those predicted by model simulation alone, and also integrated with microarray data to give an improved understanding of the metabolic physiology of this organism.« less

Differential Regulation of Macrophage Glucose Metabolism by Macrophage Colony-stimulating Factor and Granulocyte-Macrophage Colony-stimulating Factor: Implications for 18F FDG PET Imaging of Vessel Wall Inflammation

PubMed Central

Tavakoli, Sina; Short, John D.; Downs, Kevin; Nguyen, Huynh Nga; Lai, Yanlai; Zhang, Wei; Jerabek, Paul; Goins, Beth; Sadeghi, Mehran M.

2017-01-01

Purpose To determine the divergence of immunometabolic phenotypes of macrophages stimulated with macrophage colony-stimulating factor (M-CSF) and granulocyte-M-CSF (GM-CSF) and its implications for fluorine 18 (18F) fluorodeoxyglucose (FDG) imaging of atherosclerosis. Materials and Methods This study was approved by the animal care committee. Uptake of 2-deoxyglucose and various indexes of oxidative and glycolytic metabolism were evaluated in nonactivated murine peritoneal macrophages (MΦ0) and macrophages stimulated with M-CSF (MΦM-CSF) or GM-CSF (MΦGM-CSF). Intracellular glucose flux was measured by using stable isotope tracing of glycolytic and tricyclic acid intermediary metabolites. 18F-FDG uptake was evaluated in murine atherosclerotic aortas after stimulation with M-CSF or GM-CSF by using quantitative autoradiography. Results Despite inducing distinct activation states, GM-CSF and M-CSF stimulated progressive but similar levels of increased 2-deoxyglucose uptake in macrophages that reached up to sixfold compared with MΦ0. The expression of glucose transporters, oxidative metabolism, and mitochondrial biogenesis were induced to similar levels in MΦM-CSF and MΦGM-CSF. Unexpectedly, there was a 1.7-fold increase in extracellular acidification rate, a 1.4-fold increase in lactate production, and overexpression of several critical glycolytic enzymes in MΦM-CSF compared with MΦGM-CSF with associated increased glucose flux through glycolytic pathway. Quantitative autoradiography demonstrated a 1.6-fold induction of 18F-FDG uptake in murine atherosclerotic plaques by both M-CSF and GM-CSF. Conclusion The proinflammatory and inflammation-resolving activation states of macrophages induced by GM-CSF and M-CSF in either cell culture or atherosclerotic plaques may not be distinguishable by the assessment of glucose uptake. © RSNA, 2016 Online supplemental material is available for this article. PMID:27849433

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production.

PubMed

Guo, Weihua; Sheng, Jiayuan; Feng, Xueyang

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, 13 C Metabolic Flux Analysis ( 13 C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, 13 C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of 13 C-MFA and illustrate how 13 C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

Glucose metabolism in different regions of the rat brain under hypokinetic stress influence

NASA Technical Reports Server (NTRS)

Konitzer, K.; Voigt, S.

1980-01-01

Glucose metabolism in rats kept under long term hypokinetic stress was studied in 7 brain regions. Determination was made of the regional levels of glucose, lactate, glutamate, glutamine, aspartate, gamma-aminobutyrate and the incorporation of C-14 from plasma glucose into these metabolites, in glycogen and protein. From the content and activity data the regional glucose flux was approximated quantitatively. Under normal conditions the activity gradient cortex and frontal pole cerebellum, thalamus and mesencephalon, hypothalamus and pons and medulla is identical with that of the regional blood supply (measured with I131 serum albumin as the blood marker). Within the first days of immobilization a functional hypoxia occurred in all brain regions and the utilization of cycle amino acids for protein synthesis was strongly diminished. After the first week of stress the capillary volumes of all regions increased, aerobic glucose metabolism was enhanced (factors 1.3 - 2.0) and the incorporation of glucose C-14 via cycle amino acids into protein was considerably potentiated. The metabolic parameters normalized between the 7th and 11th week of stress. Blood supply and metabolic rate increased most in the hypothalamus.

Physiologically Based Pharmacokinetic (PBPK) Modeling of ...

EPA Pesticide Factsheets

Background: Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, inter-individual differences in the population are accounted for by default assumptions or, in rare cases, are based on human toxicokinetic data.Objectives: To evaluate the utility of genetically diverse mouse strains for estimating toxicokinetic population variability for risk assessment, using trichloroethylene (TCE) metabolism as a case study. Methods: We used data on oxidative and glutathione conjugation metabolism of TCE in 16 inbred and one hybrid mouse strains to calibrate and extend existing physiologically-based pharmacokinetic (PBPK) models. We added one-compartment models for glutathione metabolites and a two-compartment model for dichloroacetic acid (DCA). A Bayesian population analysis of inter-strain variability was used to quantify variability in TCE metabolism. Results: Concentration-time profiles for TCE metabolism to oxidative and glutathione conjugation metabolites varied across strains. Median predictions for the metabolic flux through oxidation was less variable (5-fold range) than that through glutathione conjugation (10-fold range). For oxidative metabolites, median predictions of trichloroacetic acid production was less variable (2-fold range) than DCA production (5-fold range), although uncertainty bounds for DCA exceeded the predicted variability. Conclusions:

Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

PubMed Central

Hay, Jordan O.; Shi, Hai; Heinzel, Nicolas; Hebbelmann, Inga; Rolletschek, Hardy; Schwender, Jorg

2014-01-01

The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content. PMID:25566296

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

PubMed

Wu, Stephen Gang; Wang, Yuxuan; Jiang, Wu; Oyetunde, Tolutola; Yao, Ruilian; Zhang, Xuehong; Shimizu, Kazuyuki; Tang, Yinjie J; Bao, Forrest Sheng

2016-04-01

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

PubMed Central

Wu, Stephen Gang; Wang, Yuxuan; Jiang, Wu; Oyetunde, Tolutola; Yao, Ruilian; Zhang, Xuehong; Shimizu, Kazuyuki; Tang, Yinjie J.; Bao, Forrest Sheng

2016-01-01

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species. PMID:27092947

Difference in C3-C4 metabolism underlies tradeoff between growth rate and biomass yield in Methylobacterium extorquens AM1.

PubMed

Fu, Yanfen; Beck, David A C; Lidstrom, Mary E

2016-07-19

Two variants of Methylobacterium extorquens AM1 demonstrated a trade-off between growth rate and biomass yield. In addition, growth rate and biomass yield were also affected by supplementation of growth medium with different amounts of cobalt. The metabolism changes relating to these growth phenomena as well as the trade-off were investigated in this study. (13)C metabolic flux analysis was used to generate a detailed central carbon metabolic flux map with both absolute and normalized flux values. The major differences between the two variants occurred at the formate node as well as within C3-C4 inter-conversion pathways. Higher relative fluxes through formyltetrahydrofolate ligase, phosphoenolpyruvate carboxylase, and malic enzyme led to higher biomass yield, while higher relative fluxes through pyruvate kinase and pyruvate dehydrogenase led to higher growth rate. These results were then tested by phenotypic studies on three mutants (null pyk, null pck mutant and null dme mutant) in both variants, which agreed with the model prediction. In this study, (13)C metabolic flux analysis for two strain variants of M. extorquens AM1 successfully identified metabolic pathways contributing to the trade-off between cell growth and biomass yield. Phenotypic analysis of mutants deficient in corresponding genes supported the conclusion that C3-C4 inter-conversion strategies were the major response to the trade-off.

Influence of 2,3-diphosphoglycerate metabolism on sodium-potassium permeability in human red blood cells: studies with bisulfite and other redox agents

PubMed Central

Parker, John C.

1969-01-01

It is known that bisulfite ions can selectively deplete red blood cells of 2,3-diphosphoglycerate (2,3-DPG). Studies of the effects of bisulfite on sodium-potassium permeability and metabolism were undertaken to clarify the physiologic role of the abundant quantities of 2,3-DPG in human erythrocytes. Treatment of cells with bisulfite results in a reversible increase in the passive permeability to Na and K ions. Metabolism of glucose to lactate is increased, with a rise in the intracellular ratio of fructose diphosphate to hexose monophosphate. Cell 2,3-DPG is quantitatively converted to pyruvate and inorganic phosphate. The permeability effects of bisulfite are countered by ethacrynic acid and by such oxidizing agents as pyruvate and methylene blue. Taken together, the results suggest that the effects on Na-K flux of bisulfite are related more to the reducing potential of this anion than to its capacity to deplete cells of 2,3-DPG. PMID:5765015

Isotopically nonstationary metabolic flux analysis (INST-MFA) of photosynthesis and photorespiration in plants

USDA-ARS?s Scientific Manuscript database

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary 13C metabolic flux analysis (INST...

Metabolic Flux Analysis of the Synechocystis sp. PCC 6803 ΔnrtABCD Mutant Reveals a Mechanism for Metabolic Adaptation to Nitrogen-Limited Conditions.

PubMed

Nakajima, Tsubasa; Yoshikawa, Katsunori; Toya, Yoshihiro; Matsuda, Fumio; Shimizu, Hiroshi

2017-03-01

Metabolic flux redirection during nitrogen-limited growth was investigated in the Synechocystis sp. PCC 6803 glucose-tolerant (GT) strain under photoautotrophic conditions by isotopically non-stationary metabolic flux analysis (INST-MFA). A ΔnrtABCD mutant of Synechocystis sp. PCC 6803 was constructed to reproduce phenotypes arising during nitrogen starvation. The ΔnrtABCD mutant and the wild-type GT strain were cultured under photoautotrophic conditions by a photobioreactor. Intracellular metabolites were labeled over a time course using NaH13CO3 as a carbon source. Based on these data, the metabolic flux distributions in the wild-type and ΔnrtABCD cells were estimated by INST-MFA. The wild-type GT and ΔnrtABCD strains displayed similar distribution patterns, although the absolute levels of metabolic flux were lower in ΔnrtABCD. Furthermore, the relative flux levels for glycogen metabolism, anaplerotic reactions and the oxidative pentose phosphate pathway were increased in ΔnrtABCD. This was probably due to the increased expression of enzyme genes that respond to nitrogen depletion. Additionally, we found that the ratio of ATP/NADPH demand increased slightly in the ΔnrtABCD mutant. These results indicated that futile ATP consumption increases under nitrogen-limited conditions because the Calvin-Benson cycle and the oxidative pentose phosphate pathway form a metabolic futile cycle that consumes ATP without CO2 fixation and NADPH regeneration. © The Author 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

PFA toolbox: a MATLAB tool for Metabolic Flux Analysis.

PubMed

Morales, Yeimy; Bosque, Gabriel; Vehí, Josep; Picó, Jesús; Llaneras, Francisco

2016-07-11

Metabolic Flux Analysis (MFA) is a methodology that has been successfully applied to estimate metabolic fluxes in living cells. However, traditional frameworks based on this approach have some limitations, particularly when measurements are scarce and imprecise. This is very common in industrial environments. The PFA Toolbox can be used to face those scenarios. Here we present the PFA (Possibilistic Flux Analysis) Toolbox for MATLAB, which simplifies the use of Interval and Possibilistic Metabolic Flux Analysis. The main features of the PFA Toolbox are the following: (a) It provides reliable MFA estimations in scenarios where only a few fluxes can be measured or those available are imprecise. (b) It provides tools to easily plot the results as interval estimates or flux distributions. (c) It is composed of simple functions that MATLAB users can apply in flexible ways. (d) It includes a Graphical User Interface (GUI), which provides a visual representation of the measurements and their uncertainty. (e) It can use stoichiometric models in COBRA format. In addition, the PFA Toolbox includes a User's Guide with a thorough description of its functions and several examples. The PFA Toolbox for MATLAB is a freely available Toolbox that is able to perform Interval and Possibilistic MFA estimations.

Genome-Based Metabolic Mapping and 13C Flux Analysis Reveal Systematic Properties of an Oleaginous Microalga Chlorella protothecoides

DOE PAGES

Wu, Chao; Xiong, Wei; Dai, Junbiao; ...

2014-12-15

We report that integrated and genome-based flux balance analysis, metabolomics, and 13C-label profiling of phototrophic and heterotrophic metabolism in Chlorella protothecoides, an oleaginous green alga for biofuel. The green alga Chlorella protothecoides, capable of autotrophic and heterotrophic growth with rapid lipid synthesis, is a promising candidate for biofuel production. Based on the newly available genome knowledge of the alga, we reconstructed the compartmentalized metabolic network consisting of 272 metabolic reactions, 270 enzymes, and 461 encoding genes and simulated the growth in different cultivation conditions with flux balance analysis. Phenotype-phase plane analysis shows conditions achieving theoretical maximum of the biomass andmore » corresponding fatty acid-producing rate for phototrophic cells (the ratio of photon uptake rate to CO 2 uptake rate equals 8.4) and heterotrophic ones (the glucose uptake rate to O 2 consumption rate reaches 2.4), respectively. Isotope-assisted liquid chromatography-mass spectrometry/mass spectrometry reveals higher metabolite concentrations in the glycolytic pathway and the tricarboxylic acid cycle in heterotrophic cells compared with autotrophic cells. We also observed enhanced levels of ATP, nicotinamide adenine dinucleotide (phosphate), reduced, acetyl-Coenzyme A, and malonyl-Coenzyme A in heterotrophic cells consistently, consistent with a strong activity of lipid synthesis. To profile the flux map in experimental conditions, we applied nonstationary 13C metabolic flux analysis as a complementing strategy to flux balance analysis. We found that the result reveals negligible photorespiratory fluxes and a metabolically low active tricarboxylic acid cycle in phototrophic C. protothecoides. In comparison, high throughput of amphibolic reactions and the tricarboxylic acid cycle with no glyoxylate shunt activities were measured for heterotrophic cells. Lastly, taken together, the metabolic network modeling assisted by experimental metabolomics and 13C labeling better our understanding on global metabolism of oleaginous alga, paving the way to the systematic engineering of the microalga for biofuel production.« less

Glucose metabolic flux distribution of Lactobacillus amylophilus during lactic acid production using kitchen waste saccharified solution.

PubMed

Liu, Jianguo; Wang, Qunhui; Zou, Hui; Liu, Yingying; Wang, Juan; Gan, Kemin; Xiang, Juan

2013-11-01

The (13) C isotope tracer method was used to investigate the glucose metabolic flux distribution and regulation in Lactobacillus amylophilus to improve lactic acid production using kitchen waste saccharified solution (KWSS). The results demonstrate that L. amylophilus is a homofermentative bacterium. In synthetic medium, 60.6% of the glucose entered the Embden-Meyerhof-Parnas (EMP) to produce lactic acid, whereas 36.4% of the glucose entered the pentose phosphate metabolic pathway (HMP). After solid-liquid separation of the KWSS, the addition of Fe(3+) during fermentation enhanced the NADPH production efficiency and increased the NADH content. The flux to the EMP was also effectively increased. Compared with the control (60.6% flux to EMP without Fe(3+) addition), the flux to the EMP with the addition of Fe(3+) (74.3%) increased by 23.8%. In the subsequent pyruvate metabolism, Fe(3+) also increased lactate dehydrogenase activity, and inhibited alcohol dehydrogenase, pyruvate dehydrogenase and pyruvate carboxylase, thereby increasing the lactic acid production to 9.03 g l(-1) , an increase of 8% compared with the control. All other organic acid by-products were lower than in the control. However, the addition of Zn(2+) showed an opposite effect, decreasing the lactic acid production. In conclusion it is feasible and effective means using GC-MS, isotope experiment and MATLAB software to integrate research the metabolic flux distribution of lactic acid bacteria, and the results provide the theoretical foundation for similar metabolic flux distribution. © 2013 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Metabolic profiling of PPARalpha-/- mice reveals defects in carnitine and amino acid homeostasis that are partially reversed by oral carnitine supplementation.

PubMed

Makowski, Liza; Noland, Robert C; Koves, Timothy R; Xing, Weibing; Ilkayeva, Olga R; Muehlbauer, Michael J; Stevens, Robert D; Muoio, Deborah M

2009-02-01

Peroxisome proliferator-activated receptor-alpha (PPARalpha) is a master transcriptional regulator of beta-oxidation and a prominent target of hypolipidemic drugs. To gain deeper insights into the systemic consequences of impaired fat catabolism, we used quantitative, mass spectrometry-based metabolic profiling to investigate the fed-to-fasted transition in PPARalpha(+/+) and PPARalpha(-/-) mice. Compared to PPARalpha(+/+) animals, acylcarnitine profiles of PPARalpha(-/-) mice revealed 2- to 4-fold accumulation of long-chain species in the plasma, whereas short-chain species were reduced by as much as 69% in plasma, liver, and skeletal muscle. These results reflect a metabolic bottleneck downstream of carnitine palmitoyltransferase-1, a mitochondrial enzyme that catalyzes the first step in beta-oxidation. Organic and amino acid profiles of starved PPARalpha(-/-) mice suggested compromised citric acid cycle flux, enhanced urea cycle activity, and increased amino acid catabolism. PPARalpha(-/-) mice had 40-50% lower plasma and tissue levels of free carnitine, corresponding with diminished hepatic expression of genes involved in carnitine biosynthesis and transport. One week of oral carnitine supplementation conferred partial metabolic recovery in the PPARalpha(-/-) mice. In summary, comprehensive metabolic profiling revealed novel biomarkers of defective fat oxidation, while also highlighting the potential value of supplemental carnitine as a therapy and diagnostic tool for metabolic disorders.

Deregulation of Feedback Inhibition of Phosphoenolpyruvate Carboxylase for Improved Lysine Production in Corynebacterium glutamicum

PubMed Central

Chen, Zhen; Bommareddy, Rajesh Reddy; Frank, Doinita; Rappert, Sugima

2014-01-01

Allosteric regulation of phosphoenolpyruvate carboxylase (PEPC) controls the metabolic flux distribution of anaplerotic pathways. In this study, the feedback inhibition of Corynebacterium glutamicum PEPC was rationally deregulated, and its effect on metabolic flux redistribution was evaluated. Based on rational protein design, six PEPC mutants were designed, and all of them showed significantly reduced sensitivity toward aspartate and malate inhibition. Introducing one of the point mutations (N917G) into the ppc gene, encoding PEPC of the lysine-producing strain C. glutamicum LC298, resulted in ∼37% improved lysine production. In vitro enzyme assays and 13C-based metabolic flux analysis showed ca. 20 and 30% increases in the PEPC activity and corresponding flux, respectively, in the mutant strain. Higher demand for NADPH in the mutant strain increased the flux toward pentose phosphate pathway, which increased the supply of NADPH for enhanced lysine production. The present study highlights the importance of allosteric regulation on the flux control of central metabolism. The strategy described here can also be implemented to improve other oxaloacetate-derived products. PMID:24334667

Deregulation of feedback inhibition of phosphoenolpyruvate carboxylase for improved lysine production in Corynebacterium glutamicum.

PubMed

Chen, Zhen; Bommareddy, Rajesh Reddy; Frank, Doinita; Rappert, Sugima; Zeng, An-Ping

2014-02-01

Allosteric regulation of phosphoenolpyruvate carboxylase (PEPC) controls the metabolic flux distribution of anaplerotic pathways. In this study, the feedback inhibition of Corynebacterium glutamicum PEPC was rationally deregulated, and its effect on metabolic flux redistribution was evaluated. Based on rational protein design, six PEPC mutants were designed, and all of them showed significantly reduced sensitivity toward aspartate and malate inhibition. Introducing one of the point mutations (N917G) into the ppc gene, encoding PEPC of the lysine-producing strain C. glutamicum LC298, resulted in ∼37% improved lysine production. In vitro enzyme assays and (13)C-based metabolic flux analysis showed ca. 20 and 30% increases in the PEPC activity and corresponding flux, respectively, in the mutant strain. Higher demand for NADPH in the mutant strain increased the flux toward pentose phosphate pathway, which increased the supply of NADPH for enhanced lysine production. The present study highlights the importance of allosteric regulation on the flux control of central metabolism. The strategy described here can also be implemented to improve other oxaloacetate-derived products.

Metabolic flux analysis of recombinant Pichia pastoris growing on different glycerol/methanol mixtures by iterative fitting of NMR-derived (13)C-labelling data from proteinogenic amino acids.

PubMed

Jordà, Joel; de Jesus, Sérgio S; Peltier, Solenne; Ferrer, Pau; Albiol, Joan

2014-01-25

The yeast Pichia pastoris has emerged as one of the most promising yeast cell factories for the production of heterologous proteins. The readily available genetic tools and the ease of high-cell density cultivations using methanol or glycerol/methanol mixtures are among the key factors for this development. Previous studies have shown that the use of mixed feeds of glycerol and methanol seem to alleviate the metabolic burden derived from protein production, allowing for higher specific and volumetric process productivities. However, initial studies of glycerol/methanol co-metabolism in P. pastoris by classical metabolic flux analyses using (13)C-derived Metabolic Flux Ratio (METAFoR) constraints were hampered by the reduced labelling information obtained when using C3:C1 substrate mixtures in relation to the conventional C6 substrate, that is, glucose. In this study, carbon flux distributions through the central metabolic pathways in glycerol/methanol co-assimilation conditions have been further characterised using biosynthetically directed fractional (13)C labelling. In particular, metabolic flux distributions were obtained under 3 different glycerol/methanol ratios and growth rates by iterative fitting of NMR-derived (13)C-labelling data from proteinogenic amino acids using the software tool (13)CFlux2. Specifically, cells were grown aerobically in chemostat cultures fed with 80:20, 60:40 and 40:60 (w:w) glycerol/methanol mixtures at two dilutions rates (0.05 hour(-1) and 0.16 hour(-1)), allowing to obtain additional data (biomass composition and extracellular fluxes) to complement pre-existing datasets. The performed (13)C-MFA reveals a significant redistribution of carbon fluxes in the central carbon metabolism as a result of the shift in the dilution rate, while the ratio of carbon sources has a lower impact on carbon flux distribution in cells growing at the same dilution rate. At low growth rate, the percentage of methanol directly dissimilated to CO2 ranges between 50% and 70%. At high growth rate the methanol is completely dissimilated to CO2 by the direct pathway, in the two conditions of highest methanol content. Copyright © 2013 Elsevier B.V. All rights reserved.

Disturbed hepatic carbohydrate management during high metabolic demand in medium-chain acyl-CoA dehydrogenase (MCAD)-deficient mice.

PubMed

Herrema, Hilde; Derks, Terry G J; van Dijk, Theo H; Bloks, Vincent W; Gerding, Albert; Havinga, Rick; Tietge, Uwe J F; Müller, Michael; Smit, G Peter A; Kuipers, Folkert; Reijngoud, Dirk-Jan

2008-06-01

Medium-chain acyl-coenzyme A (CoA) dehydrogenase (MCAD) catalyzes crucial steps in mitochondrial fatty acid oxidation, a process that is of key relevance for maintenance of energy homeostasis, especially during high metabolic demand. To gain insight into the metabolic consequences of MCAD deficiency under these conditions, we compared hepatic carbohydrate metabolism in vivo in wild-type and MCAD(-/-) mice during fasting and during a lipopolysaccharide (LPS)-induced acute phase response (APR). MCAD(-/-) mice did not become more hypoglycemic on fasting or during the APR than wild-type mice did. Nevertheless, microarray analyses revealed increased hepatic peroxisome proliferator-activated receptor gamma coactivator-1alpha (Pgc-1alpha) and decreased peroxisome proliferator-activated receptor alpha (Ppar alpha) and pyruvate dehydrogenase kinase 4 (Pdk4) expression in MCAD(-/-) mice in both conditions, suggesting altered control of hepatic glucose metabolism. Quantitative flux measurements revealed that the de novo synthesis of glucose-6-phosphate (G6P) was not affected on fasting in MCAD(-/-) mice. During the APR, however, this flux was significantly decreased (-20%) in MCAD(-/-) mice compared with wild-type mice. Remarkably, newly formed G6P was preferentially directed toward glycogen in MCAD(-/-) mice under both conditions. Together with diminished de novo synthesis of G6P, this led to a decreased hepatic glucose output during the APR in MCAD(-/-) mice; de novo synthesis of G6P and hepatic glucose output were maintained in wild-type mice under both conditions. APR-associated hypoglycemia, which was observed in wild-type mice as well as MCAD(-/-) mice, was mainly due to enhanced peripheral glucose uptake. Our data demonstrate that MCAD deficiency in mice leads to specific changes in hepatic carbohydrate management on exposure to metabolic stress. This deficiency, however, does not lead to reduced de novo synthesis of G6P during fasting alone, which may be due to the existence of compensatory mechanisms or limited rate control of MCAD in murine mitochondrial fatty acid oxidation.

Assessment of energetic costs of AhR activation by β-naphthoflavone in rainbow trout (Oncorhynchus mykiss) hepatocytes using metabolic flux analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nault, Rance, E-mail: naultran@msu.edu; Abdul-Fattah, Hiba; Mironov, Gleb G.

2013-08-15

Exposure to environmental contaminants such as activators of the aryl hydrocarbon receptor (AhR) leads to the induction of defense and detoxification mechanisms. While these mechanisms allow organisms to metabolize and excrete at least some of these environmental contaminants, it has been proposed that these mechanisms lead to significant energetic challenges. This study tests the hypothesis that activation of the AhR by the model agonist β-naphthoflavone (βNF) results in increased energetic costs in rainbow trout (Oncorhynchus mykiss) hepatocytes. To address this hypothesis, we employed traditional biochemical approaches to examine energy allocation and metabolism including the adenylate energy charge (AEC), protein synthesismore » rates, Na{sup +}/K{sup +}-ATPase activity, and enzyme activities. Moreover, we have used for the first time in a fish cell preparation, metabolic flux analysis (MFA) an in silico approach for the estimation of intracellular metabolic fluxes. Exposure of trout hepatocytes to 1 μM βNF for 48 h did not alter hepatocyte AEC, protein synthesis, or Na{sup +}/K{sup +}-ATPase activity but did lead to sparing of glycogen reserves and changes in activities of alanine aminotransferase and citrate synthase suggesting altered metabolism. Conversely, MFA did not identify altered metabolic fluxes, although we do show that the dynamic metabolism of isolated trout hepatocytes poses a significant challenge for this type of approach which should be considered in future studies. - Highlights: • Energetic costs of AhR activation by βNF was examined in rainbow trout hepatocytes. • Metabolic flux analysis was performed on a fish cell preparation for the first time. • Exposure to βNF led to sparing of glycogen reserves and altered enzyme activities. • Adenylate energy charge was maintained despite temporal changes in metabolism.« less

Second Law of Thermodynamics Applied to Metabolic Networks

NASA Technical Reports Server (NTRS)

Nigam, R.; Liang, S.

2003-01-01

We present a simple algorithm based on linear programming, that combines Kirchoff's flux and potential laws and applies them to metabolic networks to predict thermodynamically feasible reaction fluxes. These law's represent mass conservation and energy feasibility that are widely used in electrical circuit analysis. Formulating the Kirchoff's potential law around a reaction loop in terms of the null space of the stoichiometric matrix leads to a simple representation of the law of entropy that can be readily incorporated into the traditional flux balance analysis without resorting to non-linear optimization. Our technique is new as it can easily check the fluxes got by applying flux balance analysis for thermodynamic feasibility and modify them if they are infeasible so that they satisfy the law of entropy. We illustrate our method by applying it to the network dealing with the central metabolism of Escherichia coli. Due to its simplicity this algorithm will be useful in studying large scale complex metabolic networks in the cell of different organisms.

Application of isotope labeling experiments and (13)C flux analysis to enable rational pathway engineering.

PubMed

McAtee, Allison G; Jazmin, Lara J; Young, Jamey D

2015-12-01

Isotope labeling experiments (ILEs) and (13)C flux analysis provide actionable information for metabolic engineers to identify knockout, overexpression, and/or media optimization targets. ILEs have been used in both academic and industrial labs to increase product formation, discover novel metabolic functions in previously uncharacterized organisms, and enhance the metabolic efficiency of host cell factories. This review highlights specific examples of how ILEs have been used in conjunction with enzyme or metabolic engineering to elucidate host cell metabolism and improve product titer, rate, or yield in a directed manner. We discuss recent progress and future opportunities involving the use of ILEs and (13)C flux analysis to characterize non-model host organisms and to identify and subsequently eliminate wasteful byproduct pathways or metabolic bottlenecks. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Metabolic flux analysis of L-serine synthesis by Corynebacterium glutamicum SYPS-062].

PubMed

Zhang, Xiaomei; Dou, Wenfang; Xu, Hongyu; Xu, Zhenghong

2010-10-01

Corynebacterium glutamicum SYPS-062 was an L-serine producing strain stored at our lab and could produce L-serine directly from sugar. We studied the effects of cofactors in one carbon unit metabolism-folate and VB12 on the cell growth, sucrose consumption and L-serine production by SYPS-062. In the same time, the metabolic flux distribution was determined in different conditions. The supplementation of folate or VB12 enhanced the cell growth, energy synthesis, and finally increased the flux of pentose phosphate pathway (HMP), whereas the carbon flux to L-serine was decreased. The addition of VB12 not only increased the ratio of L-serine synthesis pathway on G3P joint, but also caused the insufficiency of tricarboxylic acid cycle (TCA) flux, which needed more anaplerotic reaction flux to replenish TCA cycle, that was an important limiting factor for the further increasing of the L-serine productivity.

Isotopically Nonstationary Metabolic Flux Analysis (INST-MFA) of Photosynthesis and Photorespiration in Plants.

PubMed

Ma, Fangfang; Jazmin, Lara J; Young, Jamey D; Allen, Doug K

2017-01-01

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary 13 C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO 2 over a diurnal period implying that the metabolic steady state is limited to less than 12 h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient 13 CO 2 labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.

Difference in C3–C4 metabolism underlies tradeoff between growth rate and biomass yield in Methylobacterium extorquens AM1

DOE PAGES

Fu, Yanfen; Beck, David A. C.; Lidstrom, Mary E.

2016-07-19

In this study, two variants of Methylobacterium extorquens AM1 demonstrated a trade-off between growth rate and biomass yield. In addition, growth rate and biomass yield were also affected by supplementation of growth medium with different amounts of cobalt. The metabolism changes relating to these growth phenomena as well as the trade-off were investigated in this study. 13C metabolic flux analysis was used to generate a detailed central carbon metabolic flux map with both absolute and normalized flux values. As a result, the major differences between the two variants occurred at the formate node as well as within C3-C4 inter-conversion pathways.more » Higher relative fluxes through formyltetrahydrofolate ligase, phosphoenolpyruvate carboxylase, and malic enzyme led to higher biomass yield, while higher relative fluxes through pyruvate kinase and pyruvate dehydrogenase led to higher growth rate. These results were then tested by phenotypic studies on three mutants (null pyk, null pck mutant and null dme mutant) in both variants, which agreed with the model prediction. In this study, 13C metabolic flux analysis for two strain variants of M. extorquens AM1 successfully identified metabolic pathways contributing to the trade-off between cell growth and biomass yield. Phenotypic analysis of mutants deficient in corresponding genes supported the conclusion that C3-C4 inter-conversion strategies were the major response to the trade-off.« less

Difference in C3–C4 metabolism underlies tradeoff between growth rate and biomass yield in Methylobacterium extorquens AM1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fu, Yanfen; Beck, David A. C.; Lidstrom, Mary E.

In this study, two variants of Methylobacterium extorquens AM1 demonstrated a trade-off between growth rate and biomass yield. In addition, growth rate and biomass yield were also affected by supplementation of growth medium with different amounts of cobalt. The metabolism changes relating to these growth phenomena as well as the trade-off were investigated in this study. 13C metabolic flux analysis was used to generate a detailed central carbon metabolic flux map with both absolute and normalized flux values. As a result, the major differences between the two variants occurred at the formate node as well as within C3-C4 inter-conversion pathways.more » Higher relative fluxes through formyltetrahydrofolate ligase, phosphoenolpyruvate carboxylase, and malic enzyme led to higher biomass yield, while higher relative fluxes through pyruvate kinase and pyruvate dehydrogenase led to higher growth rate. These results were then tested by phenotypic studies on three mutants (null pyk, null pck mutant and null dme mutant) in both variants, which agreed with the model prediction. In this study, 13C metabolic flux analysis for two strain variants of M. extorquens AM1 successfully identified metabolic pathways contributing to the trade-off between cell growth and biomass yield. Phenotypic analysis of mutants deficient in corresponding genes supported the conclusion that C3-C4 inter-conversion strategies were the major response to the trade-off.« less

Comprehensive analysis of a Metabolic Model for lipid production in Rhodosporidium toruloides.

PubMed

Castañeda, María Teresita; Nuñez, Sebastián; Garelli, Fabricio; Voget, Claudio; Battista, Hernán De

2018-05-19

The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with Flux Balance Analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism. Copyright © 2018 Elsevier B.V. All rights reserved.

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler

The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

DOE PAGES

Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler; ...

2016-07-02

The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

Hyaluronan Production Regulates Metabolic and Cancer Stem-like Properties of Breast Cancer Cells via Hexosamine Biosynthetic Pathway-coupled HIF-1 Signaling*

PubMed Central

Chanmee, Theerawut; Ontong, Pawared; Izumikawa, Tomomi; Higashide, Miho; Mochizuki, Nobutoshi; Chokchaitaweesuk, Chatchadawalai; Khansai, Manatsanan; Nakajima, Kazuki; Kakizaki, Ikuko; Kongtawelert, Prachya; Taniguchi, Naoyuki; Itano, Naoki

2016-01-01

Cancer stem cells (CSCs) represent a small subpopulation of self-renewing oncogenic cells. As in many other stem cells, metabolic reprogramming has been implicated to be a key characteristic of CSCs. However, little is known about how the metabolic features of cancer cells are controlled to orchestrate their CSC-like properties. We recently demonstrated that hyaluronan (HA) overproduction allowed plastic cancer cells to revert to stem cell states. Here, we adopted stable isotope-assisted tracing and mass spectrometry profiling to elucidate the metabolic features of HA-overproducing breast cancer cells. These integrated approaches disclosed an acceleration of metabolic flux in the hexosamine biosynthetic pathway (HBP). A metabolic shift toward glycolysis was also evident by quantitative targeted metabolomics, which was validated by the expression profiles of key glycolytic enzymes. Forced expression of glutamine:fructose-6-phosphate amidotransferase 1 (GFAT1), an HBP rate-limiting enzyme, resembled the results of HA overproduction with regard to HIF-1α accumulation and glycolytic program, whereas GFAT1 inhibition significantly decreased HIF-1α protein level in HA-overproducing cancer cells. Moreover, inhibition of the HBP-HIF-1 axis abrogated HA-driven glycolytic enhancement and reduced the CSC-like subpopulation. Taken together, our results provide compelling evidence that HA production regulates the metabolic and CSC-like properties of breast cancer cells via HBP-coupled HIF-1 signaling. PMID:27758869

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

PubMed

Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

2016-09-01

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

PubMed Central

Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

2016-01-01

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

Metabolic remodeling triggered by salivation and diabetes in major salivary glands.

PubMed

Nogueira, Fernando N; Carvalho, Rui A

2017-02-01

The metabolic profile of major salivary glands was evaluated by 13 C nuclear magnetic resonance isotopomer analysis ( 13 C NMR-IA) following the infusion of [U- 13 C]glucose in order to define the true metabolic character of submandibular (SM) and parotid (PA) glands at rest and during salivary stimulation, and to determine the metabolic remodeling driven by diabetes. In healthy conditions, the SM gland is characterized at rest by a glycolytic metabolic profile and extensive pyruvate cycling. On the contrary, the PA gland, although also dominated by glycolysis, also possesses significant Krebs' cycle activity and does not sustain extensive pyruvate cycling. Under stimulation, both glands increase their glycolytic and Krebs' cycle fluxes, but the metabolic coupling between the two pathways is further compromised to account for the much increased biosynthetic anaplerotic fluxes. In diabetes, the responsiveness of the PA gland to a salivary stimulus is seriously hindered, mostly as a result of the incapacity to burst glycolytic activity and also an inability to improve the Krebs' cycle flux to compensate. The Krebs' cycle activity in the SM gland is also consistently compromised, but the glycolytic flux in this gland is more resilient. This diabetes-induced metabolic remodeling in SM and PA salivary glands illustrates the metabolic need to sustain adequate saliva production, and identifies glycolytic and oxidative pathways as key players in the metabolic dynamism of salivary glands. Copyright © 2016 John Wiley & Sons, Ltd.

The role of metabolism in modulating CO2 fluxes in boreal lakes

NASA Astrophysics Data System (ADS)

Bogard, Matthew J.; del Giorgio, Paul A.

2016-10-01

Lake CO2 emissions are increasingly recognized as an important component of the global CO2 cycle, yet the origin of these emissions is not clear, as specific contributions from metabolism and in-lake cycling, versus external inputs, are not well defined. To assess the coupling of lake metabolism with CO2 concentrations and fluxes, we estimated steady state ratios of gross primary production to respiration (GPP:R) and rates of net ecosystem production (NEP = GPP-R) from surface water O2 dynamics (concentration and stable isotopes) in 187 boreal lakes spanning long environmental gradients. Our findings suggest that internal metabolism plays a dominant role in regulating CO2 fluxes in most lakes, but this pattern only emerges when examined at a resolution that accounts for the vastly differing relationships between lake metabolism and CO2 fluxes. Fluxes of CO2 exceeded those from NEP in over half the lakes, but unexpectedly, these effects were most common and typically largest in a subset ( 30% of total) of net autotrophic lakes that nevertheless emitted CO2. Equally surprising, we found no environmental characteristics that distinguished this category from the more common net heterotrophic, CO2 outgassing lakes. Excess CO2 fluxes relative to NEP were best predicted by catchment structure and hydrologic properties, and we infer from a combination of methods that both catchment inputs and internal anaerobic processes may have contributed this excess CO2. Together, our findings show that the link between lake metabolism and CO2 fluxes is often strong but can vary widely across the boreal biome, having important implications for catchment-wide C budgets.

A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf.

PubMed

Cheung, C Y Maurice; Ratcliffe, R George; Sweetlove, Lee J

2015-11-01

Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing flux-weighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the Calvin-Benson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the light-dependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture. © 2015 American Society of Plant Biologists. All Rights Reserved.

Bonded Cumomer Analysis of Human Melanoma Metabolism Monitored by 13C NMR Spectroscopy of Perfused Tumor Cells*

PubMed Central

Shestov, Alexander A.; Mancuso, Anthony; Lee, Seung-Cheol; Guo, Lili; Nelson, David S.; Roman, Jeffrey C.; Henry, Pierre-Gilles; Leeper, Dennis B.; Blair, Ian A.; Glickson, Jerry D.

2016-01-01

A network model for the determination of tumor metabolic fluxes from 13C NMR kinetic isotopomer data has been developed and validated with perfused human DB-1 melanoma cells carrying the BRAF V600E mutation, which promotes oxidative metabolism. The model generated in the bonded cumomer formalism describes key pathways of tumor intermediary metabolism and yields dynamic curves for positional isotopic enrichment and spin-spin multiplets. Cells attached to microcarrier beads were perfused with 26 mm [1,6-13C2]glucose under normoxic conditions at 37 °C and monitored by 13C NMR spectroscopy. Excellent agreement between model-predicted and experimentally measured values of the rates of oxygen and glucose consumption, lactate production, and glutamate pool size validated the model. ATP production by glycolytic and oxidative metabolism were compared under hyperglycemic normoxic conditions; 51% of the energy came from oxidative phosphorylation and 49% came from glycolysis. Even though the rate of glutamine uptake was ∼50% of the tricarboxylic acid cycle flux, the rate of ATP production from glutamine was essentially zero (no glutaminolysis). De novo fatty acid production was ∼6% of the tricarboxylic acid cycle flux. The oxidative pentose phosphate pathway flux was 3.6% of glycolysis, and three non-oxidative pentose phosphate pathway exchange fluxes were calculated. Mass spectrometry was then used to compare fluxes through various pathways under hyperglycemic (26 mm) and euglycemic (5 mm) conditions. Under euglycemic conditions glutamine uptake doubled, but ATP production from glutamine did not significantly change. A new parameter measuring the Warburg effect (the ratio of lactate production flux to pyruvate influx through the mitochondrial pyruvate carrier) was calculated to be 21, close to upper limit of oxidative metabolism. PMID:26703469

Detection of Metabolic Fluxes of O and H Atoms into Intracellular Water in Mammalian Cells

PubMed Central

Kreuzer, Helen W.; Quaroni, Luca; Podlesak, David W.; Zlateva, Theodora; Bollinger, Nikki; McAllister, Aaron; Lott, Michael J.; Hegg, Eric L.

2012-01-01

Metabolic processes result in the release and exchange of H and O atoms from organic material as well as some inorganic salts and gases. These fluxes of H and O atoms into intracellular water result in an isotopic gradient that can be measured experimentally. Using isotope ratio mass spectroscopy, we revealed that slightly over 50% of the H and O atoms in the intracellular water of exponentially-growing cultured Rat-1 fibroblasts were isotopically distinct from growth medium water. We then employed infrared spectromicroscopy to detect in real time the flux of H atoms in these same cells. Importantly, both of these techniques indicate that the H and O fluxes are dependent on metabolic processes; cells that are in lag phase or are quiescent exhibit a much smaller flux. In addition, water extracted from the muscle tissue of rats contained a population of H and O atoms that were isotopically distinct from body water, consistent with the results obtained using the cultured Rat-1 fibroblasts. Together these data demonstrate that metabolic processes produce fluxes of H and O atoms into intracellular water, and that these fluxes can be detected and measured in both cultured mammalian cells and in mammalian tissue. PMID:22848359

Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes.

PubMed

Pey, Jon; Rubio, Angel; Theodoropoulos, Constantinos; Cascante, Marta; Planes, Francisco J

2012-07-01

Constraints-based modeling is an emergent area in Systems Biology that includes an increasing set of methods for the analysis of metabolic networks. In order to refine its predictions, the development of novel methods integrating high-throughput experimental data is currently a key challenge in the field. In this paper, we present a novel set of constraints that integrate tracer-based metabolomics data from Isotope Labeling Experiments and metabolic fluxes in a linear fashion. These constraints are based on Elementary Carbon Modes (ECMs), a recently developed concept that generalizes Elementary Flux Modes at the carbon level. To illustrate the effect of our ECMs-based constraints, a Flux Variability Analysis approach was applied to a previously published metabolic network involving the main pathways in the metabolism of glucose. The addition of our ECMs-based constraints substantially reduced the under-determination resulting from a standard application of Flux Variability Analysis, which shows a clear progress over the state of the art. In addition, our approach is adjusted to deal with combinatorial explosion of ECMs in genome-scale metabolic networks. This extension was applied to infer the maximum biosynthetic capacity of non-essential amino acids in human metabolism. Finally, as linearity is the hallmark of our approach, its importance is discussed at a methodological, computational and theoretical level and illustrated with a practical application in the field of Isotope Labeling Experiments. Copyright © 2012 Elsevier Inc. All rights reserved.

The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis

PubMed Central

2014-01-01

Background In human vaccine manufacturing some pathogens such as Modified Vaccinia Virus Ankara, measles, mumps virus as well as influenza viruses are still produced on primary material derived from embryonated chicken eggs. Processes depending on primary cell culture, however, are difficult to adapt to modern vaccine production. Therefore, we derived previously a continuous suspension cell line, AGE1.CR.pIX, from muscovy duck and established chemically-defined media for virus propagation. Results To better understand vaccine production processes, we developed a stoichiometric model of the central metabolism of AGE1.CR.pIX cells and applied flux variability and metabolic flux analysis. Results were compared to literature dealing with mammalian and insect cell culture metabolism focusing on the question whether cultured avian cells differ in metabolism. Qualitatively, the observed flux distribution of this avian cell line was similar to distributions found for mammalian cell lines (e.g. CHO, MDCK cells). In particular, glucose was catabolized inefficiently and glycolysis and TCA cycle seem to be only weakly connected. Conclusions A distinguishing feature of the avian cell line is that glutaminolysis plays only a minor role in energy generation and production of precursors, resulting in low extracellular ammonia concentrations. This metabolic flux study is the first for a continuous avian cell line. It provides a basis for further metabolic analyses to exploit the biotechnological potential of avian and vertebrate cell lines and to develop specific optimized cell culture processes, e.g. vaccine production processes. PMID:25077436

The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis.

PubMed

Lohr, Verena; Hädicke, Oliver; Genzel, Yvonne; Jordan, Ingo; Büntemeyer, Heino; Klamt, Steffen; Reichl, Udo

2014-07-30

In human vaccine manufacturing some pathogens such as Modified Vaccinia Virus Ankara, measles, mumps virus as well as influenza viruses are still produced on primary material derived from embryonated chicken eggs. Processes depending on primary cell culture, however, are difficult to adapt to modern vaccine production. Therefore, we derived previously a continuous suspension cell line, AGE1.CR.pIX, from muscovy duck and established chemically-defined media for virus propagation. To better understand vaccine production processes, we developed a stoichiometric model of the central metabolism of AGE1.CR.pIX cells and applied flux variability and metabolic flux analysis. Results were compared to literature dealing with mammalian and insect cell culture metabolism focusing on the question whether cultured avian cells differ in metabolism. Qualitatively, the observed flux distribution of this avian cell line was similar to distributions found for mammalian cell lines (e.g. CHO, MDCK cells). In particular, glucose was catabolized inefficiently and glycolysis and TCA cycle seem to be only weakly connected. A distinguishing feature of the avian cell line is that glutaminolysis plays only a minor role in energy generation and production of precursors, resulting in low extracellular ammonia concentrations. This metabolic flux study is the first for a continuous avian cell line. It provides a basis for further metabolic analyses to exploit the biotechnological potential of avian and vertebrate cell lines and to develop specific optimized cell culture processes, e.g. vaccine production processes.

Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hay, Jordan O.; Shi, Hai; Heinzel, Nicolas

The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) modelmore » and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis ( 13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). In conclusion, using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.« less

Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

DOE PAGES

Hay, Jordan O.; Shi, Hai; Heinzel, Nicolas; ...

2014-12-19

The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) modelmore » and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis ( 13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). In conclusion, using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.« less

Regulation of pyruvate metabolism and human disease.

PubMed

Gray, Lawrence R; Tompkins, Sean C; Taylor, Eric B

2014-07-01

Pyruvate is a keystone molecule critical for numerous aspects of eukaryotic and human metabolism. Pyruvate is the end-product of glycolysis, is derived from additional sources in the cellular cytoplasm, and is ultimately destined for transport into mitochondria as a master fuel input undergirding citric acid cycle carbon flux. In mitochondria, pyruvate drives ATP production by oxidative phosphorylation and multiple biosynthetic pathways intersecting the citric acid cycle. Mitochondrial pyruvate metabolism is regulated by many enzymes, including the recently discovered mitochondria pyruvate carrier, pyruvate dehydrogenase, and pyruvate carboxylase, to modulate overall pyruvate carbon flux. Mutations in any of the genes encoding for proteins regulating pyruvate metabolism may lead to disease. Numerous cases have been described. Aberrant pyruvate metabolism plays an especially prominent role in cancer, heart failure, and neurodegeneration. Because most major diseases involve aberrant metabolism, understanding and exploiting pyruvate carbon flux may yield novel treatments that enhance human health.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

PubMed Central

Guo, Weihua; Sheng, Jiayuan; Feng, Xueyang

2015-01-01

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many 13C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms PMID:28952565

Protein-protein interactions and metabolite channelling in the plant tricarboxylic acid cycle

PubMed Central

Zhang, Youjun; Beard, Katherine F. M.; Swart, Corné; Bergmann, Susan; Krahnert, Ina; Nikoloski, Zoran; Graf, Alexander; Ratcliffe, R. George; Sweetlove, Lee J.; Fernie, Alisdair R.; Obata, Toshihiro

2017-01-01

Protein complexes of sequential metabolic enzymes, often termed metabolons, may permit direct channelling of metabolites between the enzymes, providing increased control over metabolic pathway fluxes. Experimental evidence supporting their existence in vivo remains fragmentary. In the present study, we test binary interactions of the proteins constituting the plant tricarboxylic acid (TCA) cycle. We integrate (semi-)quantitative results from affinity purification-mass spectrometry, split-luciferase and yeast-two-hybrid assays to generate a single reliability score for assessing protein–protein interactions. By this approach, we identify 158 interactions including those between catalytic subunits of sequential enzymes and between subunits of enzymes mediating non-adjacent reactions. We reveal channelling of citrate and fumarate in isolated potato mitochondria by isotope dilution experiments. These results provide evidence for a functional TCA cycle metabolon in plants, which we discuss in the context of contemporary understanding of this pathway in other kingdoms. PMID:28508886

FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

PubMed

Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

2014-10-01

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring.

PubMed

Long, Christopher P; Gonzalez, Jacqueline E; Feist, Adam M; Palsson, Bernhard O; Antoniewicz, Maciek R

2017-11-01

Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13 C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Metabolism of 7-ethyoxycoumarin by Isolated Perfused Rainbow Trout Livers

EPA Science Inventory

Isolated trout livers were perfused using methods designed to preserve tissue viability and function. Liver performance was evaluated by measuring O2 consumption, vascular resistance, K+ leakage, glucose flux, lactate flux, alanine aminotransferase leakage, and metabolic clearanc...

Analyzing Clonal Variation of Monoclonal Antibody-Producing CHO Cell Lines Using an In Silico Metabolomic Platform

PubMed Central

Ghorbaniaghdam, Atefeh; Chen, Jingkui; Henry, Olivier; Jolicoeur, Mario

2014-01-01

Monoclonal antibody producing Chinese hamster ovary (CHO) cells have been shown to undergo metabolic changes when engineered to produce high titers of recombinant proteins. In this work, we have studied the distinct metabolism of CHO cell clones harboring an efficient inducible expression system, based on the cumate gene switch, and displaying different expression levels, high and low productivities, compared to that of the parental cells from which they were derived. A kinetic model for CHO cell metabolism was further developed to include metabolic regulation. Model calibration was performed using intracellular and extracellular metabolite profiles obtained from shake flask batch cultures. Model simulations of intracellular fluxes and ratios known as biomarkers revealed significant changes correlated with clonal variation but not to the recombinant protein expression level. Metabolic flux distribution mostly differs in the reactions involving pyruvate metabolism, with an increased net flux of pyruvate into the tricarboxylic acid (TCA) cycle in the high-producer clone, either being induced or non-induced with cumate. More specifically, CHO cell metabolism in this clone was characterized by an efficient utilization of glucose and a high pyruvate dehydrogenase flux. Moreover, the high-producer clone shows a high rate of anaplerosis from pyruvate to oxaloacetate, through pyruvate carboxylase and from glutamate to α-ketoglutarate, through glutamate dehydrogenase, and a reduced rate of cataplerosis from malate to pyruvate, through malic enzyme. Indeed, the increase of flux through pyruvate carboxylase was not driven by an increased anabolic demand. It is in fact linked to an increase of the TCA cycle global flux, which allows better regulation of higher redox and more efficient metabolic states. To the best of our knowledge, this is the first time a dynamic in silico platform is proposed to analyze and compare the metabolomic behavior of different CHO clones. PMID:24632968

Principal elementary mode analysis (PEMA).

PubMed

Folch-Fortuny, Abel; Marques, Rodolfo; Isidro, Inês A; Oliveira, Rui; Ferrer, Alberto

2016-03-01

Principal component analysis (PCA) has been widely applied in fluxomics to compress data into a few latent structures in order to simplify the identification of metabolic patterns. These latent structures lack a direct biological interpretation due to the intrinsic constraints associated with a PCA model. Here we introduce a new method that significantly improves the interpretability of the principal components with a direct link to metabolic pathways. This method, called principal elementary mode analysis (PEMA), establishes a bridge between a PCA-like model, aimed at explaining the maximum variance in flux data, and the set of elementary modes (EMs) of a metabolic network. It provides an easy way to identify metabolic patterns in large fluxomics datasets in terms of the simplest pathways of the organism metabolism. The results using a real metabolic model of Escherichia coli show the ability of PEMA to identify the EMs that generated the different simulated flux distributions. Actual flux data of E. coli and Pichia pastoris cultures confirm the results observed in the simulated study, providing a biologically meaningful model to explain flux data of both organisms in terms of the EM activation. The PEMA toolbox is freely available for non-commercial purposes on http://mseg.webs.upv.es.

31P-Magnetization Transfer Magnetic Resonance Spectroscopy Measurements of In Vivo Metabolism

PubMed Central

Befroy, Douglas E.; Rothman, Douglas L.; Petersen, Kitt Falk; Shulman, Gerald I.

2012-01-01

Magnetic resonance spectroscopy offers a broad range of noninvasive analytical methods for investigating metabolism in vivo. Of these, the magnetization-transfer (MT) techniques permit the estimation of the unidirectional fluxes associated with metabolic exchange reactions. Phosphorus (31P) MT measurements can be used to examine the bioenergetic reactions of the creatine-kinase system and the ATP synthesis/hydrolysis cycle. Observations from our group and others suggest that the inorganic phosphate (Pi) → ATP flux in skeletal muscle may be modulated by certain conditions, including aging, insulin resistance, and diabetes, and may reflect inherent alterations in mitochondrial metabolism. However, such effects on the Pi → ATP flux are not universally observed under conditions in which mitochondrial function, assessed by other techniques, is impaired, and recent articles have raised concerns about the absolute magnitude of the measured reaction rates. As the application of 31P-MT techniques becomes more widespread, this article reviews the methodology and outlines our experience with its implementation in a variety of models in vivo. Also discussed are potential limitations of the technique, complementary methods for assessing oxidative metabolism, and whether the Pi → ATP flux is a viable biomarker of metabolic function in vivo. PMID:23093656

Fusarium oxysporum mediates systems metabolic reprogramming of chickpea roots as revealed by a combination of proteomics and metabolomics.

PubMed

Kumar, Yashwant; Zhang, Limin; Panigrahi, Priyabrata; Dholakia, Bhushan B; Dewangan, Veena; Chavan, Sachin G; Kunjir, Shrikant M; Wu, Xiangyu; Li, Ning; Rajmohanan, Pattuparambil R; Kadoo, Narendra Y; Giri, Ashok P; Tang, Huiru; Gupta, Vidya S

2016-07-01

Molecular changes elicited by plants in response to fungal attack and how this affects plant-pathogen interaction, including susceptibility or resistance, remain elusive. We studied the dynamics in root metabolism during compatible and incompatible interactions between chickpea and Fusarium oxysporum f. sp. ciceri (Foc), using quantitative label-free proteomics and NMR-based metabolomics. Results demonstrated differential expression of proteins and metabolites upon Foc inoculations in the resistant plants compared with the susceptible ones. Additionally, expression analysis of candidate genes supported the proteomic and metabolic variations in the chickpea roots upon Foc inoculation. In particular, we found that the resistant plants revealed significant increase in the carbon and nitrogen metabolism; generation of reactive oxygen species (ROS), lignification and phytoalexins. The levels of some of the pathogenesis-related proteins were significantly higher upon Foc inoculation in the resistant plant. Interestingly, results also exhibited the crucial role of altered Yang cycle, which contributed in different methylation reactions and unfolded protein response in the chickpea roots against Foc. Overall, the observed modulations in the metabolic flux as outcome of several orchestrated molecular events are determinant of plant's role in chickpea-Foc interactions. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains.

PubMed

Hua, Qiang; Joyce, Andrew R; Fong, Stephen S; Palsson, Bernhard Ø

2006-12-05

Experimental evolution is now frequently applied to many biological systems to achieve desired objectives. To obtain optimized performance for metabolite production, a successful strategy has been recently developed that couples metabolic engineering techniques with laboratory evolution of microorganisms. Previously, we reported the growth characteristics of three lactate-producing, adaptively evolved Escherichia coli mutant strains designed by the OptKnock computational algorithm. Here, we describe the use of (13)C-labeled experiments and mass distribution measurements to study the evolutionary effects on the fluxome of these differently designed strains. Metabolic flux ratios and intracellular flux distributions as well as physiological data were used to elucidate metabolic responses over the course of adaptive evolution and metabolic differences among strains. The study of 3 unevolved and 12 evolved engineered strains as well as a wild-type strain suggests that evolution resulted in remarkable improvements in both substrate utilization rate and the proportion of glycolytic flux to total glucose utilization flux. Among three strain designs, the most significant increases in the fraction of glucose catabolized through glycolysis (>50%) and the glycolytic fluxes (>twofold) were observed in phosphotransacetylase and phosphofructokinase 1 (PFK1) double deletion (pta- pfkA) strains, which were likely attributed to the dramatic evolutionary increase in gene expression and catalytic activity of the minor PFK encoded by pfkB. These fluxomic studies also revealed the important role of acetate synthetic pathway in anaerobic lactate production. Moreover, flux analysis suggested that independent of genetic background, optimal relative flux distributions in cells could be achieved faster than physiological parameters such as nutrient utilization rate. (c) 2006 Wiley Periodicals, Inc.

Conservation of high-flux backbone in alternate optimal and near-optimal flux distributions of metabolic networks.

PubMed

Samal, Areejit

2008-12-01

Constraint-based flux balance analysis (FBA) has proven successful in predicting the flux distribution of metabolic networks in diverse environmental conditions. FBA finds one of the alternate optimal solutions that maximizes the biomass production rate. Almaas et al. have shown that the flux distribution follows a power law, and it is possible to associate with most metabolites two reactions which maximally produce and consume a given metabolite, respectively. This observation led to the concept of high-flux backbone (HFB) in metabolic networks. In previous work, the HFB has been computed using a particular optima obtained using FBA. In this paper, we investigate the conservation of HFB of a particular solution for a given medium across different alternate optima and near-optima in metabolic networks of E. coli and S. cerevisiae. Using flux variability analysis (FVA), we propose a method to determine reactions that are guaranteed to be in HFB regardless of alternate solutions. We find that the HFB of a particular optima is largely conserved across alternate optima in E. coli, while it is only moderately conserved in S. cerevisiae. However, the HFB of a particular near-optima shows a large variation across alternate near-optima in both organisms. We show that the conserved set of reactions in HFB across alternate near-optima has a large overlap with essential reactions and reactions which are both uniquely consuming (UC) and uniquely producing (UP). Our findings suggest that the structure of the metabolic network admits a high degree of redundancy and plasticity in near-optimal flow patterns enhancing system robustness for a given environmental condition.

Soil CO2 Flux in the Amargosa Desert, Nevada, during El Nino 1998 and La Nina 1999

USGS Publications Warehouse

Riggs, Alan C.; Stannard, David I.; Maestas, Florentino B.; Karlinger, Michael R.; Striegl, Robert G.

2009-01-01

Mean annual soil CO2 fluxes from normally bare mineral soil in the Amargosa Desert in southern Nevada, United States, measured with clear and opaque soil CO2-flux chambers (autochambers) were small - <5 millimoles per square meter per day - during both El Nino 1998 and La Nina 1999. The 1998 opaque-chamber flux exceeded 1999 opaque-chamber flux by an order of magnitude, whereas the 1998 clear-chamber flux exceeded 1999 clear-chamber flux by less than a factor of two. These data suggest that above-normal soil moisture stimulated increased metabolic activity, but that much of the extra CO2 produced was recaptured by plants. Fluxes from warm moist soil were the largest sustained fluxes measured, and their hourly pattern is consistent with enhanced soil metabolic activity at some depth in the soil and photosynthetic uptake of a substantial portion of the CO2 released. Flux from cool moist soil was smaller than flux from warm moist soil. Flux from hot dry soil was intermediate between warm-moist and cool-moist fluxes, and clear-chamber flux was more than double the opaque-chamber flux, apparently due to a chamber artifact stemming from a thermally controlled CO2 reservoir near the soil surface. There was no demonstrable metabolic contribution to the very small flux from cool dry soil, which was dominated by diffusive up-flux of CO2 from the water table and temperature-controlled CO2-reservoir up- and down-fluxes. These flux patterns suggest that transfer of CO2 across the land surface is a complex process that is difficult to accurately measure.

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

PubMed

Kiparissides, A; Hatzimanikatis, V

2017-01-01

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs

PubMed Central

Jungreuthmayer, Christian; Ruckerbauer, David E.; Gerstl, Matthias P.; Hanscho, Michael; Zanghellini, Jürgen

2015-01-01

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle. PMID:26091045

Improvements in metabolic flux analysis using carbon bond labeling experiments: bondomer balancing and Boolean function mapping.

PubMed

Sriram, Ganesh; Shanks, Jacqueline V

2004-04-01

The biosynthetically directed fractional (13)C labeling method for metabolic flux evaluation relies on performing a 2-D [(13)C, (1)H] NMR experiment on extracts from organisms cultured on a uniformly labeled carbon substrate. This article focuses on improvements in the interpretation of data obtained from such an experiment by employing the concept of bondomers. Bondomers take into account the natural abundance of (13)C; therefore many bondomers in a real network are zero, and can be precluded a priori--thus resulting in fewer balances. Using this method, we obtained a set of linear equations which can be solved to obtain analytical formulas for NMR-measurable quantities in terms of fluxes in glycolysis and the pentose phosphate pathways. For a specific case of this network with four degrees of freedom, a priori identifiability of the fluxes was shown possible for any set of fluxes. For a more general case with five degrees of freedom, the fluxes were shown identifiable for a representative set of fluxes. Minimal sets of measurements which best identify the fluxes are listed. Furthermore, we have delineated Boolean function mapping, a new method to iteratively simulate bondomer abundances or efficiently convert carbon skeleton rearrangement information to mapping matrices. The efficiency of this method is expected to be valuable while analyzing metabolic networks which are not completely known (such as in plant metabolism) or while implementing iterative bondomer balancing methods.

Integrated analysis of gene expression and metabolic fluxes in PHA-producing Pseudomonas putida grown on glycerol.

PubMed

Beckers, Veronique; Poblete-Castro, Ignacio; Tomasch, Jürgen; Wittmann, Christoph

2016-05-03

Given its high surplus and low cost, glycerol has emerged as interesting carbon substrate for the synthesis of value-added chemicals. The soil bacterium Pseudomonas putida KT2440 can use glycerol to synthesize medium-chain-length poly(3-hydroxyalkanoates) (mcl-PHA), a class of biopolymers of industrial interest. Here, glycerol metabolism in P. putida KT2440 was studied on the level of gene expression (transcriptome) and metabolic fluxes (fluxome), using precisely adjusted chemostat cultures, growth kinetics and stoichiometry, to gain a systematic understanding of the underlying metabolic and regulatory network. Glycerol-grown P. putida KT2440 has a maintenance energy requirement [0.039 (mmolglycerol (gCDW h)(-1))] that is about sixteen times lower than that of other bacteria, such as Escherichia coli, which provides a great advantage to use this substrate commercially. The shift from carbon (glycerol) to nitrogen (ammonium) limitation drives the modulation of specific genes involved in glycerol metabolism, transport electron chain, sensors to assess the energy level of the cell, and PHA synthesis, as well as changes in flux distribution to increase the precursor availability for PHA synthesis (Entner-Doudoroff pathway and pyruvate metabolism) and to reduce respiration (glyoxylate shunt). Under PHA-producing conditions (N-limitation), a higher PHA yield was achieved at low dilution rate (29.7 wt% of CDW) as compared to a high rate (12.8 wt% of CDW). By-product formation (succinate, malate) was specifically modulated under these regimes. On top of experimental data, elementary flux mode analysis revealed the metabolic potential of P. putida KT2440 to synthesize PHA and identified metabolic engineering targets towards improved production performance on glycerol. This study revealed the complex interplay of gene expression levels and metabolic fluxes under PHA- and non-PHA producing conditions using the attractive raw material glycerol as carbon substrate. This knowledge will form the basis for the development of future metabolically engineered hyper-PHA-producing strains derived from the versatile bacterium P. putida KT2440.

Metstoich--Teaching Quantitative Metabolism and Energetics in Biochemical Engineering

ERIC Educational Resources Information Center

Wong, Kelvin W. W.; Barford, John P.

2010-01-01

Metstoich, a metabolic calculator developed for teaching, can provide a novel way to teach quantitative metabolism to biochemical engineering students. It can also introduce biochemistry/life science students to the quantitative aspects of life science subjects they have studied. Metstoich links traditional biochemistry-based metabolic approaches…

Metabolic Analysis of Adaptation to Short-Term Changes in Culture Conditions of the Marine Diatom Thalassiosira pseudonana

PubMed Central

Bromke, Mariusz A.; Giavalisco, Patrick; Willmitzer, Lothar; Hesse, Holger

2013-01-01

This report describes the metabolic and lipidomic profiling of 97 low-molecular weight compounds from the primary metabolism and 124 lipid compounds of the diatom Thalassiosira pseudonana. The metabolic profiles were created for diatoms perturbed for 24 hours with four different treatments: (I) removal of nitrogen, (II) lower iron concentration, (III) addition of sea salt, (IV) addition of carbonate to their growth media. Our results show that as early as 24 hours after nitrogen depletion significant qualitative and quantitative change in lipid composition as well as in the primary metabolism of Thalassiosira pseudonana occurs. So we can observe the accumulation of several storage lipids, namely triacylglycerides, and TCA cycle intermediates, of which citric acid increases more than 10-fold. These changes are positively correlated with expression of TCA enzymes genes. Next to the TCA cycle intermediates and storage lipid changes, we have observed decrease in N-containing lipids and primary metabolites such as amino acids. As a measure of counteracting nitrogen starvation, we have observed elevated expression levels of nitrogen uptake and amino acid biosynthetic genes. This indicates that diatoms can fast and efficiently adapt to changing environment by altering the metabolic fluxes and metabolite abundances. Especially, the accumulation of proline and the decrease of dimethylsulfoniopropionate suggest that the proline is the main osmoprotectant for the diatom in nitrogen rich conditions. PMID:23799147

Modeling Neisseria meningitidis metabolism: from genome to metabolic fluxes

PubMed Central

Baart, Gino JE; Zomer, Bert; de Haan, Alex; van der Pol, Leo A; Beuvery, E Coen; Tramper, Johannes; Martens, Dirk E

2007-01-01

Background Neisseria meningitidis is a human pathogen that can infect diverse sites within the human host. The major diseases caused by N. meningitidis are responsible for death and disability, especially in young infants. In general, most of the recent work on N. meningitidis focuses on potential antigens and their functions, immunogenicity, and pathogenicity mechanisms. Very little work has been carried out on Neisseria primary metabolism over the past 25 years. Results Using the genomic database of N. meningitidis serogroup B together with biochemical and physiological information in the literature we constructed a genome-scale flux model for the primary metabolism of N. meningitidis. The validity of a simplified metabolic network derived from the genome-scale metabolic network was checked using flux-balance analysis in chemostat cultures. Several useful predictions were obtained from in silico experiments, including substrate preference. A minimal medium for growth of N. meningitidis was designed and tested succesfully in batch and chemostat cultures. Conclusion The verified metabolic model describes the primary metabolism of N. meningitidis in a chemostat in steady state. The genome-scale model is valuable because it offers a framework to study N. meningitidis metabolism as a whole, or certain aspects of it, and it can also be used for the purpose of vaccine process development (for example, the design of growth media). The flux distribution of the main metabolic pathways (that is, the pentose phosphate pathway and the Entner-Douderoff pathway) indicates that the major part of pyruvate (69%) is synthesized through the ED-cleavage, a finding that is in good agreement with literature. PMID:17617894

Post photosynthetic carbon partitioning to sugar alcohols and consequences for plant growth.

PubMed

Dumschott, Kathryn; Richter, Andreas; Loescher, Wayne; Merchant, Andrew

2017-12-01

The occurrence of sugar alcohols is ubiquitous among plants. Physiochemical properties of sugar alcohols suggest numerous primary and secondary functions in plant tissues and are often well documented. In addition to functions arising from physiochemical properties, the synthesis of sugar alcohols may have significant influence over photosynthetic, respiratory, and developmental processes owing to their function as a large sink for photosynthates. Sink strength is demonstrated by the high concentrations of sugar alcohols found in plant tissues and their ability to be readily transported. The plant scale distribution and physiochemical function of these compounds renders them strong candidates for functioning as stress metabolites. Despite this, several aspects of sugar alcohol biosynthesis and function are poorly characterised namely: 1) the quantitative characterisation of carbon flux into the sugar alcohol pool; 2) the molecular control governing sugar alcohol biosynthesis on a quantitative basis; 3) the role of sugar alcohols in plant growth and ecology; and 4) consequences of sugar alcohol synthesis for yield production and yield quality. We highlight the need to adopt new approaches to investigating sugar alcohol biosynthesis using modern technologies in gene expression, metabolic flux analysis and agronomy. Combined, these approaches will elucidate the impact of sugar alcohol biosynthesis on growth, stress tolerance, yield and yield quality. Copyright © 2017 Elsevier Ltd. All rights reserved.

A hybrid approach identifies metabolic signatures of high-producers for chinese hamster ovary clone selection and process optimization.

PubMed

Popp, Oliver; Müller, Dirk; Didzus, Katharina; Paul, Wolfgang; Lipsmeier, Florian; Kirchner, Florian; Niklas, Jens; Mauch, Klaus; Beaucamp, Nicola

2016-09-01

In-depth characterization of high-producer cell lines and bioprocesses is vital to ensure robust and consistent production of recombinant therapeutic proteins in high quantity and quality for clinical applications. This requires applying appropriate methods during bioprocess development to enable meaningful characterization of CHO clones and processes. Here, we present a novel hybrid approach for supporting comprehensive characterization of metabolic clone performance. The approach combines metabolite profiling with multivariate data analysis and fluxomics to enable a data-driven mechanistic analysis of key metabolic traits associated with desired cell phenotypes. We applied the methodology to quantify and compare metabolic performance in a set of 10 recombinant CHO-K1 producer clones and a host cell line. The comprehensive characterization enabled us to derive an extended set of clone performance criteria that not only captured growth and product formation, but also incorporated information on intracellular clone physiology and on metabolic changes during the process. These criteria served to establish a quantitative clone ranking and allowed us to identify metabolic differences between high-producing CHO-K1 clones yielding comparably high product titers. Through multivariate data analysis of the combined metabolite and flux data we uncovered common metabolic traits characteristic of high-producer clones in the screening setup. This included high intracellular rates of glutamine synthesis, low cysteine uptake, reduced excretion of aspartate and glutamate, and low intracellular degradation rates of branched-chain amino acids and of histidine. Finally, the above approach was integrated into a workflow that enables standardized high-content selection of CHO producer clones in a high-throughput fashion. In conclusion, the combination of quantitative metabolite profiling, multivariate data analysis, and mechanistic network model simulations can identify metabolic traits characteristic of high-performance clones and enables informed decisions on which clones provide a good match for a particular process platform. The proposed approach also provides a mechanistic link between observed clone phenotype, process setup, and feeding regimes, and thereby offers concrete starting points for subsequent process optimization. Biotechnol. Bioeng. 2016;113: 2005-2019. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Metabolic network modeling with model organisms.

PubMed

Yilmaz, L Safak; Walhout, Albertha Jm

2017-02-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. Published by Elsevier Ltd.

Metabolic network modeling with model organisms

PubMed Central

Yilmaz, L. Safak; Walhout, Albertha J.M.

2017-01-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. PMID:28088694

High rates of energy expenditure and water flux in free-ranging Point Reyes mountain beavers Aplodontia rufa phaea

USGS Publications Warehouse

Crocker, D.E.; Kofahl, N.; Fellers, G.D.; Gates, N.B.; Houser, D.S.

2007-01-01

We measured water flux and energy expenditure in free-ranging Point Reyes mountain beavers Aplodontia rufa phaea by using the doubly labeled water method. Previous laboratory investigations have suggested weak urinary concentrating ability, high rates of water flux, and low basal metabolic rates in this species. However, free-ranging measurements from hygric mammals are rare, and it is not known how these features interact in the environment. Rates of water flux (210 ?? 32 mL d-1) and field metabolic rates (1,488 ?? 486 kJ d-1) were 159% and 265%, respectively, of values predicted by allometric equations for similar-sized herbivores. Mountain beavers can likely meet their water needs through metabolic water production and preformed water in food and thus remain in water balance without access to free water. Arginine-vasopressin levels were strongly correlated with rates of water flux and plasma urea : creatinine ratios, suggesting an important role for this hormone in regulating urinary water loss in mountain beavers. High field metabolic rates may result from cool burrow temperatures that are well below lower critical temperatures measured in previous laboratory studies and suggest that thermoregulation costs may strongly influence field energetics and water flux in semifossorial mammals. ?? 2007 by The University of Chicago. All rights reserved.

The role of the mitochondrial pyruvate carrier in substrate regulation

PubMed Central

Vacanti, Nathaniel M.; Divakaruni, Ajit S.; Green, Courtney R.; Parker, Seth J.; Henry, Robert R.; Ciaraldi, Theodore P.; Murphy, Anne N.; Metallo, Christian M.

2014-01-01

SUMMARY Pyruvate lies at a central biochemical node connecting carbohydrate, amino acid, and fatty acid metabolism, and the regulation of pyruvate flux into mitochondria represents a critical step in intermediary metabolism impacting numerous diseases. To characterize changes in mitochondrial substrate utilization in the context of compromised mitochondrial pyruvate transport, we applied 13C metabolic flux analysis (MFA) to cells after transcriptional or pharmacological inhibition of the mitochondrial pyruvate carrier (MPC). Despite profound suppression of both glucose and pyruvate oxidation, cell growth, oxygen consumption, and tricarboxylic acid (TCA) metabolism were surprisingly maintained. Oxidative TCA flux was achieved through enhanced reliance on glutaminolysis through malic enzyme and pyruvate dehydrogenase (PDH) as well as fatty acid and branched chain amino acid oxidation. Thus, in contrast to inhibition of complex I or PDH, suppression of pyruvate transport induces a form of metabolic flexibility associated with use of lipids and amino acids as catabolic and anabolic fuels. PMID:25458843

13C metabolic flux analysis: optimal design of isotopic labeling experiments.

PubMed

Antoniewicz, Maciek R

2013-12-01

Measuring fluxes by 13C metabolic flux analysis (13C-MFA) has become a key activity in chemical and pharmaceutical biotechnology. Optimal design of isotopic labeling experiments is of central importance to 13C-MFA as it determines the precision with which fluxes can be estimated. Traditional methods for selecting isotopic tracers and labeling measurements did not fully utilize the power of 13C-MFA. Recently, new approaches were developed for optimal design of isotopic labeling experiments based on parallel labeling experiments and algorithms for rational selection of tracers. In addition, advanced isotopic labeling measurements were developed based on tandem mass spectrometry. Combined, these approaches can dramatically improve the quality of 13C-MFA results with important applications in metabolic engineering and biotechnology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures-Systems biology-based interpretation using genome-scale metabolic flux balance model and multivariate data analysis.

PubMed

Reimonn, Thomas M; Park, Seo-Young; Agarabi, Cyrus D; Brorson, Kurt A; Yoon, Seongkyu

2016-09-01

Genome-scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745-753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome-scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163-1173, 2016. © 2016 American Institute of Chemical Engineers.

Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit

PubMed Central

Gupta, Apoorv; Brockman Reizman, Irene M.; Reisch, Christopher R.; Prather, Kristala L. J.

2017-01-01

Metabolic engineering of microorganisms to produce desirable products on an industrial scale can result in unbalanced cellular metabolic networks that reduce productivity and yield. Metabolic fluxes can be rebalanced using dynamic pathway regulation, but few broadly applicable tools are available to achieve this. We present a pathway-independent genetic control module that can be used to dynamically regulate the expression of target genes. We applied our module to identify the optimal point to redirect glycolytic flux into heterologous engineered pathways in Escherichia coli, resulting in 5.5-fold increased titres of myo-inositol and titers of glucaric acid that improved from unmeasurable quantities to >0.8 g/L. Scaled-up production in benchtop bioreactors resulted in almost 10-fold and 5-fold increases in titers of myo-inositol and glucaric acid. We also used our module to control flux into aromatic amino acid biosynthesis to increase titers of shikimate in E. coli from unmeasurable quantities to >100 mg/L. PMID:28191902

Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation.

PubMed

Meadows, Adam L; Karnik, Rahi; Lam, Harry; Forestell, Sean; Snedecor, Brad

2010-03-01

We have developed a reactor-scale model of Escherichia coli metabolism and growth in a 1000 L process for the production of a recombinant therapeutic protein. The model consists of two distinct parts: (1) a dynamic, process specific portion that describes the time evolution of 37 process variables of relevance and (2) a flux balance based, 123-reaction metabolic model of E. coli metabolism. This model combines several previously reported modeling approaches including a growth rate-dependent biomass composition, maximum growth rate objective function, and dynamic flux balancing. In addition, we introduce concentration-dependent boundary conditions of transport fluxes, dynamic maintenance demands, and a state-dependent cellular objective. This formulation was able to describe specific runs with high-fidelity over process conditions including rich media, simultaneous acetate and glucose consumption, glucose minimal media, and phosphate depleted media. Furthermore, the model accurately describes the effect of process perturbations--such as glucose overbatching and insufficient aeration--on growth, metabolism, and titer. (c) 2009 Elsevier Inc. All rights reserved.

Redirection of pyruvate flux toward desired metabolic pathways through substrate channeling between pyruvate kinase and pyruvate-converting enzymes in Saccharomyces cerevisiae.

PubMed

Kim, Sujin; Bae, Sang-Jeong; Hahn, Ji-Sook

2016-04-07

Spatial organization of metabolic enzymes allows substrate channeling, which accelerates processing of intermediates. Here, we investigated the effect of substrate channeling on the flux partitioning at a metabolic branch point, focusing on pyruvate metabolism in Saccharomyces cerevisiae. As a platform strain for the channeling of pyruvate flux, PYK1-Coh-Myc strain was constructed in which PYK1 gene encoding pyruvate kinase is tagged with cohesin domain. By using high-affinity cohesin-dockerin interaction, the pyruvate-forming enzyme Pyk1 was tethered to heterologous pyruvate-converting enzymes, lactate dehydrogenase and α-acetolactate synthase, to produce lactic acid and 2,3-butanediol, respectively. Pyruvate flux was successfully redirected toward desired pathways, with a concomitant decrease in ethanol production even without genetic attenuation of the ethanol-producing pathway. This pyruvate channeling strategy led to an improvement of 2,3-butanediol production by 38%, while showing a limitation in improving lactic acid production due to a reduced activity of lactate dehydrogenase by dockerin tagging.

Effects of drugs in subtoxic concentrations on the metabolic fluxes in human hepatoma cell line Hep G2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niklas, Jens; Noor, Fozia, E-mail: fozia.noor@mx.uni-saarland.d; Heinzle, Elmar

2009-11-01

Commonly used cytotoxicity assays assess the toxicity of a compound by measuring certain parameters which directly or indirectly correlate to the viability of the cells. However, the effects of a given compound at concentrations considerably below EC{sub 50} values are usually not evaluated. These subtoxic effects are difficult to identify but may eventually cause severe and costly long term problems such as idiosyncratic hepatotoxicity. We determined the toxicity of three hepatotoxic compounds, namely amiodarone, diclofenac and tacrine on the human hepatoma cell line Hep G2 using an online kinetic respiration assay and analysed the effects of subtoxic concentrations of thesemore » drugs on the cellular metabolism by using metabolic flux analysis. Several changes in the metabolism could be detected upon exposure to subtoxic concentrations of the test compounds. Upon exposure to diclofenac and tacrine an increase in the TCA-cycle activity was observed which could be a signature of an uncoupling of the oxidative phosphorylation. The results indicate that metabolic flux analysis could serve as an invaluable novel tool for the investigation of the effects of drugs. The described methodology enables tracking the toxicity of compounds dynamically using the respiration assay in a range of concentrations and the metabolic flux analysis permits interesting insights into the changes in the central metabolism of the cell upon exposure to drugs.« less

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models.

PubMed

Cotten, Cameron; Reed, Jennifer L

2013-01-30

Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets.

Reconstructing metabolic flux vectors from extreme pathways: defining the alpha-spectrum.

PubMed

Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

2003-10-07

The move towards genome-scale analysis of cellular functions has necessitated the development of analytical (in silico) methods to understand such large and complex biochemical reaction networks. One such method is extreme pathway analysis that uses stoichiometry and thermodynamic irreversibly to define mathematically unique, systemic metabolic pathways. These extreme pathways form the edges of a high-dimensional convex cone in the flux space that contains all the attainable steady state solutions, or flux distributions, for the metabolic network. By definition, any steady state flux distribution can be described as a nonnegative linear combination of the extreme pathways. To date, much effort has been focused on calculating, defining, and understanding these extreme pathways. However, little work has been performed to determine how these extreme pathways contribute to a given steady state flux distribution. This study represents an initial effort aimed at defining how physiological steady state solutions can be reconstructed from a network's extreme pathways. In general, there is not a unique set of nonnegative weightings on the extreme pathways that produce a given steady state flux distribution but rather a range of possible values. This range can be determined using linear optimization to maximize and minimize the weightings of a particular extreme pathway in the reconstruction, resulting in what we have termed the alpha-spectrum. The alpha-spectrum defines which extreme pathways can and cannot be included in the reconstruction of a given steady state flux distribution and to what extent they individually contribute to the reconstruction. It is shown that accounting for transcriptional regulatory constraints can considerably shrink the alpha-spectrum. The alpha-spectrum is computed and interpreted for two cases; first, optimal states of a skeleton representation of core metabolism that include transcriptional regulation, and second for human red blood cell metabolism under various physiological, non-optimal conditions.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models

PubMed Central

2013-01-01

Background Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. Results In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. Conclusions This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets. PMID:23360254

Development of Computational Tools for Metabolic Model Curation, Flux Elucidation and Strain Design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maranas, Costas D

An overarching goal of the Department of Energy mission is the efficient deployment and engineering of microbial and plant systems to enable biomass conversion in pursuit of high energy density liquid biofuels. This has spurred the pace at which new organisms are sequenced and annotated. This torrent of genomic information has opened the door to understanding metabolism in not just skeletal pathways and a handful of microorganisms but for truly genome-scale reconstructions derived for hundreds of microbes and plants. Understanding and redirecting metabolism is crucial because metabolic fluxes are unique descriptors of cellular physiology that directly assess the current cellularmore » state and quantify the effect of genetic engineering interventions. At the same time, however, trying to keep pace with the rate of genomic data generation has ushered in a number of modeling and computational challenges related to (i) the automated assembly, testing and correction of genome-scale metabolic models, (ii) metabolic flux elucidation using labeled isotopes, and (iii) comprehensive identification of engineering interventions leading to the desired metabolism redirection.« less

Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis

NASA Astrophysics Data System (ADS)

De Martino, D.

2016-02-01

In this article the notion of metabolic turnover is revisited in the light of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo methods we perform an exact sampling of the enzymatic fluxes in a genome scale metabolic network of E. Coli in stationary growth conditions from which we infer the metabolites turnover times. However the latter are inferred from net fluxes, and we argue that this approximation is not valid for enzymes working nearby thermodynamic equilibrium. We recalculate turnover times from total fluxes by performing an energy balance analysis of the network and recurring to the fluctuation theorem. We find in many cases values one of order of magnitude lower, implying a faster picture of intermediate metabolism.

Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism

DOE PAGES

Shymansky, Christopher M.; Wang, George; Baidoo, Edward E. K.; ...

2017-05-24

13C metabolic flux analysis ( 13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference Saccharomyces cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose-repressing media and/or knockout of SIP1 using a multi-scale variant ofmore » 13C MFA known as 2-Scale 13C metabolic flux analysis (2S- 13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1Δ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1Δ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria toward cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1Δ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium.« less

Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shymansky, Christopher M.; Wang, George; Baidoo, Edward E. K.

13C metabolic flux analysis ( 13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference Saccharomyces cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose-repressing media and/or knockout of SIP1 using a multi-scale variant ofmore » 13C MFA known as 2-Scale 13C metabolic flux analysis (2S- 13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1Δ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1Δ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria toward cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1Δ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium.« less

Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism

PubMed Central

Shymansky, Christopher M.; Wang, George; Baidoo, Edward E. K.; Gin, Jennifer; Apel, Amanda Reider; Mukhopadhyay, Aindrila; García Martín, Héctor; Keasling, Jay D.

2017-01-01

13C metabolic flux analysis (13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference Saccharomyces cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose-repressing media and/or knockout of SIP1 using a multi-scale variant of 13C MFA known as 2-Scale 13C metabolic flux analysis (2S-13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1Δ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1Δ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria toward cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1Δ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium. PMID:28596955

Characterizability of metabolic pathway systems from time series data.

PubMed

Voit, Eberhard O

2013-12-01

Over the past decade, the biomathematical community has devoted substantial effort to the complicated challenge of estimating parameter values for biological systems models. An even more difficult issue is the characterization of functional forms for the processes that govern these systems. Most parameter estimation approaches tacitly assume that these forms are known or can be assumed with some validity. However, this assumption is not always true. The recently proposed method of Dynamic Flux Estimation (DFE) addresses this problem in a genuinely novel fashion for metabolic pathway systems. Specifically, DFE allows the characterization of fluxes within such systems through an analysis of metabolic time series data. Its main drawback is the fact that DFE can only directly be applied if the pathway system contains as many metabolites as unknown fluxes. This situation is unfortunately rare. To overcome this roadblock, earlier work in this field had proposed strategies for augmenting the set of unknown fluxes with independent kinetic information, which however is not always available. Employing Moore-Penrose pseudo-inverse methods of linear algebra, the present article discusses an approach for characterizing fluxes from metabolic time series data that is applicable even if the pathway system is underdetermined and contains more fluxes than metabolites. Intriguingly, this approach is independent of a specific modeling framework and unaffected by noise in the experimental time series data. The results reveal whether any fluxes may be characterized and, if so, which subset is characterizable. They also help with the identification of fluxes that, if they could be determined independently, would allow the application of DFE. Copyright © 2013 Elsevier Inc. All rights reserved.

Characterizability of Metabolic Pathway Systems from Time Series Data

PubMed Central

Voit, Eberhard O.

2013-01-01

Over the past decade, the biomathematical community has devoted substantial effort to the complicated challenge of estimating parameter values for biological systems models. An even more difficult issue is the characterization of functional forms for the processes that govern these systems. Most parameter estimation approaches tacitly assume that these forms are known or can be assumed with some validity. However, this assumption is not always true. The recently proposed method of Dynamic Flux Estimation (DFE) addresses this problem in a genuinely novel fashion for metabolic pathway systems. Specifically, DFE allows the characterization of fluxes within such systems through an analysis of metabolic time series data. Its main drawback is the fact that DFE can only directly be applied if the pathway system contains as many metabolites as unknown fluxes. This situation is unfortunately rare. To overcome this roadblock, earlier work in this field had proposed strategies for augmenting the set of unknown fluxes with independent kinetic information, which however is not always available. Employing Moore-Penrose pseudo-inverse methods of linear algebra, the present article discusses an approach for characterizing fluxes from metabolic time series data that is applicable even if the pathway system is underdetermined and contains more fluxes than metabolites. Intriguingly, this approach is independent of a specific modeling framework and unaffected by noise in the experimental time series data. The results reveal whether any fluxes may be characterized and, if so, which subset is characterizable. They also help with the identification of fluxes that, if they could be determined independently, would allow the application of DFE. PMID:23391489

YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities

PubMed Central

Schwarz, Roland; Musch, Patrick; von Kamp, Axel; Engels, Bernd; Schirmer, Heiner; Schuster, Stefan; Dandekar, Thomas

2005-01-01

Background A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. Results YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at . Conclusion A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts. PMID:15929789

13C Metabolic Flux Analysis for Systematic Metabolic Engineering of S. cerevisiae for Overproduction of Fatty Acids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghosh, Amit; Ando, David; Gin, Jennifer

Efficient redirection of microbial metabolism into the abundant production of desired bioproducts remains non-trivial. Here, we used flux-based modeling approaches to improve yields of fatty acids in Saccharomyces cerevisiae. We combined 13C labeling data with comprehensive genome-scale models to shed light onto microbial metabolism and improve metabolic engineering efforts. We concentrated on studying the balance of acetyl-CoA, a precursor metabolite for the biosynthesis of fatty acids. A genome-wide acetyl-CoA balance study showed ATP citrate lyase from Yarrowia lipolytica as a robust source of cytoplasmic acetyl-CoA and malate synthase as a desirable target for downregulation in terms of acetyl-CoA consumption. Thesemore » genetic modifications were applied to S. cerevisiae WRY2, a strain that is capable of producing 460 mg/L of free fatty acids. With the addition of ATP citrate lyase and downregulation of malate synthase, the engineered strain produced 26% more free fatty acids. Further increases in free fatty acid production of 33% were obtained by knocking out the cytoplasmic glycerol-3-phosphate dehydrogenase, which flux analysis had shown was competing for carbon flux upstream with the carbon flux through the acetyl-CoA production pathway in the cytoplasm. In total, the genetic interventions applied in this work increased fatty acid production by ~70%.« less

13C Metabolic Flux Analysis for Systematic Metabolic Engineering of S. cerevisiae for Overproduction of Fatty Acids

DOE PAGES

Ghosh, Amit; Ando, David; Gin, Jennifer; ...

2016-10-05

Efficient redirection of microbial metabolism into the abundant production of desired bioproducts remains non-trivial. Here, we used flux-based modeling approaches to improve yields of fatty acids in Saccharomyces cerevisiae. We combined 13C labeling data with comprehensive genome-scale models to shed light onto microbial metabolism and improve metabolic engineering efforts. We concentrated on studying the balance of acetyl-CoA, a precursor metabolite for the biosynthesis of fatty acids. A genome-wide acetyl-CoA balance study showed ATP citrate lyase from Yarrowia lipolytica as a robust source of cytoplasmic acetyl-CoA and malate synthase as a desirable target for downregulation in terms of acetyl-CoA consumption. Thesemore » genetic modifications were applied to S. cerevisiae WRY2, a strain that is capable of producing 460 mg/L of free fatty acids. With the addition of ATP citrate lyase and downregulation of malate synthase, the engineered strain produced 26% more free fatty acids. Further increases in free fatty acid production of 33% were obtained by knocking out the cytoplasmic glycerol-3-phosphate dehydrogenase, which flux analysis had shown was competing for carbon flux upstream with the carbon flux through the acetyl-CoA production pathway in the cytoplasm. In total, the genetic interventions applied in this work increased fatty acid production by ~70%.« less

¹³C-based metabolic flux analysis of Saccharomyces cerevisiae with a reduced Crabtree effect.

PubMed

Kajihata, Shuichi; Matsuda, Fumio; Yoshimi, Mika; Hayakawa, Kenshi; Furusawa, Chikara; Kanda, Akihisa; Shimizu, Hiroshi

2015-08-01

Saccharomyces cerevisiae shows a Crabtree effect that produces ethanol in a high glucose concentration even under fully aerobic condition. For efficient production of cake yeast or compressed yeast for baking, ethanol by-production is not desired since glucose limited chemostat or fed-batch cultivations are performed to suppress the Crabtree effect. In this study, the (13)C-based metabolic flux analysis ((13)C-MFA) was performed for the S288C derived S. cerevisiae strain to characterize a metabolic state under the reduced Crabtree effect. S. cerevisiae cells were cultured at a low dilution rate (0.1 h(-1)) under the glucose-limited chemostat condition. The estimated metabolic flux distribution showed that the acetyl-CoA in mitochondria was mainly produced from pyruvate by pyruvate dehydrogenase (PDH) reaction and that the level of the metabolic flux through the pentose phosphate pathway was much higher than that of the Embden-Meyerhof-Parnas pathway, which contributes to high biomass yield at low dilution rate by supplying NADPH required for cell growth. Copyright © 2015 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Pathway Thermodynamics Highlights Kinetic Obstacles in Central Metabolism

PubMed Central

Flamholz, Avi; Reznik, Ed; Liebermeister, Wolfram; Milo, Ron

2014-01-01

In metabolism research, thermodynamics is usually used to determine the directionality of a reaction or the feasibility of a pathway. However, the relationship between thermodynamic potentials and fluxes is not limited to questions of directionality: thermodynamics also affects the kinetics of reactions through the flux-force relationship, which states that the logarithm of the ratio between the forward and reverse fluxes is directly proportional to the change in Gibbs energy due to a reaction (ΔrG′). Accordingly, if an enzyme catalyzes a reaction with a ΔrG′ of -5.7 kJ/mol then the forward flux will be roughly ten times the reverse flux. As ΔrG′ approaches equilibrium (ΔrG′ = 0 kJ/mol), exponentially more enzyme counterproductively catalyzes the reverse reaction, reducing the net rate at which the reaction proceeds. Thus, the enzyme level required to achieve a given flux increases dramatically near equilibrium. Here, we develop a framework for quantifying the degree to which pathways suffer these thermodynamic limitations on flux. For each pathway, we calculate a single thermodynamically-derived metric (the Max-min Driving Force, MDF), which enables objective ranking of pathways by the degree to which their flux is constrained by low thermodynamic driving force. Our framework accounts for the effect of pH, ionic strength and metabolite concentration ranges and allows us to quantify how alterations to the pathway structure affect the pathway's thermodynamics. Applying this methodology to pathways of central metabolism sheds light on some of their features, including metabolic bypasses (e.g., fermentation pathways bypassing substrate-level phosphorylation), substrate channeling (e.g., of oxaloacetate from malate dehydrogenase to citrate synthase), and use of alternative cofactors (e.g., quinone as an electron acceptor instead of NAD). The methods presented here place another arrow in metabolic engineers' quiver, providing a simple means of evaluating the thermodynamic and kinetic quality of different pathway chemistries that produce the same molecules. PMID:24586134

Quantifying plant phenotypes with isotopic labeling and metabolic flux analysis

USDA-ARS?s Scientific Manuscript database

Analyses of metabolic flux using stable isotopes in plants have traditionally been restricted to tissues with presumed homogeneous cell populations such as developing seeds, cell suspensions, or cultured roots and root tips. It is now possible to describe these and other more complex tissues such a...

Bridging the gap between fluxomics and industrial biotechnology.

PubMed

Feng, Xueyang; Page, Lawrence; Rubens, Jacob; Chircus, Lauren; Colletti, Peter; Pakrasi, Himadri B; Tang, Yinjie J

2010-01-01

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

The photospheric magnetic flux budget

NASA Technical Reports Server (NTRS)

Schrijver, C. J.; Harvey, K. L.

1994-01-01

The ensemble of bipolar regions and the magnetic network both contain a substantial and strongly variable part of the photospheric magnetic flux at any phase in the solar cycle. The time-dependent distribution of the magnetic flux over and within these components reflects the action of the dynamo operating in the solar interior. We perform a quantitative comparison of the flux emerging in the ensemble of magnetic bipoles with the observed flux content of the solar photosphere. We discuss the photospheric flux budget in terms of flux appearance and disappearance, and argue that a nonlinear dependence exists between the flux present in the photosphere and the rate of flux appearance and disappearance. In this context, we discuss the problem of making quantitative statements about dynamos in cool stars other than the Sun.

Sequential Metabolic Phases as a Means to Optimize Cellular Output in a Constant Environment

PubMed Central

Bockmayr, Alexander; Holzhütter, Hermann-Georg

2015-01-01

Temporal changes of gene expression are a well-known regulatory feature of all cells, which is commonly perceived as a strategy to adapt the proteome to varying external conditions. However, temporal (rhythmic and non-rhythmic) changes of gene expression are also observed under virtually constant external conditions. Here we hypothesize that such changes are a means to render the synthesis of the metabolic output more efficient than under conditions of constant gene activities. In order to substantiate this hypothesis, we used a flux-balance model of the cellular metabolism. The total time span spent on the production of a given set of target metabolites was split into a series of shorter time intervals (metabolic phases) during which only selected groups of metabolic genes are active. The related flux distributions were calculated under the constraint that genes can be either active or inactive whereby the amount of protein related to an active gene is only controlled by the number of active genes: the lower the number of active genes the more protein can be allocated to the enzymes carrying non-zero fluxes. This concept of a predominantly protein-limited efficiency of gene expression clearly differs from other concepts resting on the assumption of an optimal gene regulation capable of allocating to all enzymes and transporters just that fraction of protein necessary to prevent rate limitation. Applying this concept to a simplified metabolic network of the central carbon metabolism with glucose or lactate as alternative substrates, we demonstrate that switching between optimally chosen stationary flux modes comprising different sets of active genes allows producing a demanded amount of target metabolites in a significantly shorter time than by a single optimal flux mode at fixed gene activities. Our model-based findings suggest that temporal expression of metabolic genes can be advantageous even under conditions of constant external substrate supply. PMID:25786979

Investigate the Metabolic Reprogramming of Saccharomyces cerevisiae for Enhanced Resistance to Mixed Fermentation Inhibitors via 13C Metabolic Flux Analysis

PubMed Central

Guo, Weihua; Chen, Yingying; Wei, Na; Feng, Xueyang

2016-01-01

The fermentation inhibitors from the pretreatment of lignocellulosic materials, e.g., acetic acid and furfural, are notorious due to their negative effects on the cell growth and chemical production. However, the metabolic reprogramming of the cells under these stress conditions, especially metabolic response for resistance to mixed inhibitors, has not been systematically investigated and remains mysterious. Therefore, in this study, 13C metabolic flux analysis (13C-MFA), a powerful tool to elucidate the intracellular carbon flux distributions, has been applied to two Saccharomyces cerevisiae strains with different tolerances to the inhibitors under acetic acid, furfural, and mixed (i.e., acetic acid and furfural) stress conditions to unravel the key metabolic responses. By analyzing the intracellular carbon fluxes as well as the energy and cofactor utilization under different conditions, we uncovered varied metabolic responses to different inhibitors. Under acetate stress, ATP and NADH production was slightly impaired, while NADPH tended towards overproduction. Under furfural stress, ATP and cofactors (including both NADH and NADPH) tended to be overproduced. However, under dual-stress condition, production of ATP and cofactors was severely impaired due to synergistic stress caused by the simultaneous addition of two fermentation inhibitors. Such phenomenon indicated the pivotal role of the energy and cofactor utilization in resisting the mixed inhibitors of acetic acid and furfural. Based on the discoveries, valuable insights are provided to improve the tolerance of S. cerevisiae strain and further enhance lignocellulosic fermentation. PMID:27532329

Metabolic control analysis of integrated energy metabolism in permeabilized cardiomyocytes - experimental study.

PubMed

Tepp, Kersti; Timohhina, Natalja; Chekulayev, Vladimir; Shevchuk, Igor; Kaambre, Tuuli; Saks, Valdur

2010-01-01

The main focus of this research was to apply Metabolic Control Analysis to quantitative investigation of the regulation of respiration by components of the Mitochondrial Interactosome (MI, a supercomplex consisting of ATP Synthasome, mitochondrial creatine kinase (MtCK), voltage dependent anion channel (VDAC), and tubulin) in permeabilized cardiomyocytes. Flux control coefficients (FCC) were measured using two protocols: 1) with direct ADP activation, and 2) with MtCK activation by creatine (Cr) in the presence of ATP and pyruvate kinase-phosphoenolpyruvate system. The results show that the metabolic control is much stronger in the latter case: the sum of the measured FCC is 2.7 versus 0.74 (ADP activation). This is consistent with previous data showing recycling of ADP and ATP inside the MI due to the functional coupling between MtCK and ANT and limited permeability of VDAC for these compounds, PCr being the major energy carrier between the mitochondria and ATPases. In physiological conditions, when the MI is activated, the key sites of regulation of respiration in mitochondria are MtCK (FCC = 0.93), adenine nucleotide translocase ANT (FCC = 0.95) and CoQ cytochrome c oxidoreductase (FCC = 0.4). These results show clearly that under the physiological conditions the energy transfer from mitochondria to the cytoplasm is regulated by the MI supercomplex and is very sensitive to metabolic signals.

The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.

PubMed

Noor, Elad; Flamholz, Avi; Bar-Even, Arren; Davidi, Dan; Milo, Ron; Liebermeister, Wolfram

2016-11-01

Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.

Correlating two-photon excited fluorescence imaging of breast cancer cellular redox state with seahorse flux analysis of normalized cellular oxygen consumption

NASA Astrophysics Data System (ADS)

Hou, Jue; Wright, Heather J.; Chan, Nicole; Tran, Richard; Razorenova, Olga V.; Potma, Eric O.; Tromberg, Bruce J.

2016-06-01

Two-photon excited fluorescence (TPEF) imaging of the cellular cofactors nicotinamide adenine dinucleotide and oxidized flavin adenine dinucleotide is widely used to measure cellular metabolism, both in normal and pathological cells and tissues. When dual-wavelength excitation is used, ratiometric TPEF imaging of the intrinsic cofactor fluorescence provides a metabolic index of cells-the "optical redox ratio" (ORR). With increased interest in understanding and controlling cellular metabolism in cancer, there is a need to evaluate the performance of ORR in malignant cells. We compare TPEF metabolic imaging with seahorse flux analysis of cellular oxygen consumption in two different breast cancer cell lines (MCF-7 and MDA-MB-231). We monitor metabolic index in living cells under both normal culture conditions and, for MCF-7, in response to cell respiration inhibitors and uncouplers. We observe a significant correlation between the TPEF-derived ORR and the flux analyzer measurements (R=0.7901, p<0.001). Our results confirm that the ORR is a valid dynamic index of cell metabolism under a range of oxygen consumption conditions relevant for cancer imaging.

Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring

PubMed Central

Long, Christopher P.; Gonzalez, Jacqueline E.; Feist, Adam M.; Palsson, Bernhard O.; Antoniewicz, Maciek R.

2018-01-01

Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution. PMID:28951266

Probing de novo sphingolipid metabolism in mammalian cells utilizing mass spectrometry.

PubMed

Snider, Justin M; Snider, Ashley J; Obeid, Lina M; Luberto, Chiara; Hannun, Yusuf A

2018-06-01

Sphingolipids constitute a dynamic metabolic network that interconnects several bioactive molecules, including ceramide (Cer), sphingosine (Sph), Sph 1-phosphate, and Cer 1-phosphate. The interconversion of these metabolites is controlled by a cohort of at least 40 enzymes, many of which respond to endogenous or exogenous stimuli. Typical probing of the sphingolipid pathway relies on sphingolipid mass levels or determination of the activity of individual enzymes. Either approach is unable to provide a complete analysis of flux through sphingolipid metabolism, which, given the interconnectivity of the sphingolipid pathway, is critical information to identify nodes of regulation. Here, we present a one-step in situ assay that comprehensively probes the flux through de novo sphingolipid synthesis, post serine palmitoyltransferase, by monitoring the incorporation and metabolism of the 17 carbon dihydrosphingosine precursor with LC/MS. Pulse labeling and analysis of precursor metabolism identified sequential well-defined phases of sphingolipid synthesis, corresponding to the activity of different enzymes in the pathway, further confirmed by the use of specific inhibitors and modulators of sphingolipid metabolism. This work establishes precursor pulse labeling as a practical tool for comprehensively studying metabolic flux through de novo sphingolipid synthesis and complex sphingolipid generation.

Easy regulation of metabolic flux in Escherichia coli using an endogenous type I-E CRISPR-Cas system.

PubMed

Chang, Yizhao; Su, Tianyuan; Qi, Qingsheng; Liang, Quanfeng

2016-11-15

Clustered regularly interspaced short palindromic repeats interference (CRISPRi) is a recently developed powerful tool for gene regulation. In Escherichia coli, the type I CRISPR system expressed endogenously shall be easy for internal regulation without causing metabolic burden in compared with the widely used type II system, which expressed dCas9 as an additional plasmid. By knocking out cas3 and activating the expression of CRISPR-associated complex for antiviral defense (Cascade), we constructed a native CRISPRi system in E. coli. Downregulation of the target gene from 6 to 82% was demonstrated using green fluorescent protein. Regulation of the citrate synthase gene (gltA) in the TCA cycle affected host metabolism. The effect of metabolic flux regulation was demonstrated by the poly-3-hydroxbutyrate (PHB) accumulation in vivo. By regulating native gltA in E. coli using an engineered endogenous type I-E CRISPR system, we redirected metabolic flux from the central metabolic pathway to the PHB synthesis pathway. This study demonstrated that the endogenous type I-E CRISPR-Cas system is an easy and effective method for regulating internal metabolic pathways, which is useful for product synthesis.

Engineering microorganisms to increase ethanol production by metabolic redirection

DOEpatents

Deng, Yu; Olson, Daniel G.; van Dijken, Johannes Pieter; Shaw, IV, Arthur J.; Argyros, Aaron; Barrett, Trisha; Caiazza, Nicky; Herring, Christopher D.; Rogers, Stephen R.; Agbogbo, Frank

2017-10-31

The present invention provides for the manipulation of carbon flux in a recombinant host cell to increase the formation of desirable products. The invention relates to cellulose-digesting organisms that have been genetically modified to allow the production of ethanol at a high yield by redirecting carbon flux at key steps of central metabolism.

Modeling the flux of metabolites in the juvenile hormone biosynthesis pathway using generalized additive models and ordinary differential equations.

PubMed

Martínez-Rincón, Raúl O; Rivera-Pérez, Crisalejandra; Diambra, Luis; Noriega, Fernando G

2017-01-01

Juvenile hormone (JH) regulates development and reproductive maturation in insects. The corpora allata (CA) from female adult mosquitoes synthesize fluctuating levels of JH, which have been linked to the ovarian development and are influenced by nutritional signals. The rate of JH biosynthesis is controlled by the rate of flux of isoprenoids in the pathway, which is the outcome of a complex interplay of changes in precursor pools and enzyme levels. A comprehensive study of the changes in enzymatic activities and precursor pool sizes have been previously reported for the mosquito Aedes aegypti JH biosynthesis pathway. In the present studies, we used two different quantitative approaches to describe and predict how changes in the individual metabolic reactions in the pathway affect JH synthesis. First, we constructed generalized additive models (GAMs) that described the association between changes in specific metabolite concentrations with changes in enzymatic activities and substrate concentrations. Changes in substrate concentrations explained 50% or more of the model deviances in 7 of the 13 metabolic steps analyzed. Addition of information on enzymatic activities almost always improved the fitness of GAMs built solely based on substrate concentrations. GAMs were validated using experimental data that were not included when the model was built. In addition, a system of ordinary differential equations (ODE) was developed to describe the instantaneous changes in metabolites as a function of the levels of enzymatic catalytic activities. The results demonstrated the ability of the models to predict changes in the flux of metabolites in the JH pathway, and can be used in the future to design and validate experimental manipulations of JH synthesis.

Membrane biofouling process correlated to the microbial community succession in an A/O MBR.

PubMed

Chen, Chun-Hong; Fu, Yuan; Gao, Da-Wen

2015-12-01

The microbial community succession of the biofouling layer in a submerged anoxic/oxic membrane biological reactor (A/O MBR) that fed with synthesized domestic wastewater was investigated under three different flux conditions without the changing of the nutrient load. The noticeable microbial community succession and its significant correlation with the metabolic products were observed under the subcritical flux condition. Under the supercritical flux condition, the microbial community shift was in a different pattern compared with that under the subcritical flux condition and it was affected by the increased permeable suction more than the metabolic products. The most abundant microorganisms in the foulants were β-proteobacteria and γ-proteobacteria which can reach more than 20% of the microbial community. However the microorganisms which had significant correlation with the metabolic products were in lower abundance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Towards an Understanding of Energy Impairment in Huntington’s Disease Brain

PubMed Central

Dubinsky, Janet M.

2017-01-01

This review systematically examines the evidence for shifts in flux through energy generating biochemical pathways in Huntington’s disease (HD) brains from humans and model systems. Compromise of the electron transport chain (ETC) appears not to be the primary or earliest metabolic change in HD pathogenesis. Rather, compromise of glucose uptake facilitates glucose flux through glycolysis and may possibly decrease flux through the pentose phosphate pathway (PPP), limiting subsequent NADPH and GSH production needed for antioxidant protection. As a result, oxidative damage to key glycolytic and tricarboxylic acid (TCA) cycle enzymes further restricts energy production so that while basal needs may be met through oxidative phosphorylation, those of excessive stimulation cannot. Energy production may also be compromised by deficits in mitochondrial biogenesis, dynamics or trafficking. Restrictions on energy production may be compensated for by glutamate oxidation and/or stimulation of fatty acid oxidation. Transcriptional dysregulation generated by mutant huntingtin also contributes to energetic disruption at specific enzymatic steps. Many of the alterations in metabolic substrates and enzymes may derive from normal regulatory feedback mechanisms and appear oscillatory. Fine temporal sequencing of the shifts in metabolic flux and transcriptional and expression changes associated with mutant huntingtin expression remain largely unexplored and may be model dependent. Differences in disease progression among HD model systems at the time of experimentation and their varying states of metabolic compensation may explain conflicting reports in the literature. Progressive shifts in metabolic flux represent homeostatic compensatory mechanisms that maintain the model organism through presymptomatic and symptomatic stages. PMID:29125492

Metabolic changes associated with active water vapour absorption in the mealworm Tenebrio molitor L. (Coleoptera, Tenebrionidae): a microcalorimetric study.

PubMed

Hansen, Lars L; Westh, Peter; Wright, Jonathan C; Ramløv, Hans

2006-03-01

Water vapour absorption (WVA) is an important mechanism for water gain in several xeric insects. Theoretical calculations indicate that the energetic cost of WVA should be small (5-10% of standard metabolic rate) assuming realistic efficiencies. In this study we explored the relationship between WVA, metabolic heat flux (HFmet.) and CO2 release in larvae of Tenebrio molitor using microcalorimetry. By comparing metabolic heat flux with the catabolic rate estimated from VCO2 , we were able to differentiate anabolic and catabolic rates prior to and during WVA, while simultaneously monitoring water exchange. Three to four hours before the onset of WVA, larvae showed clear increases in HFmet. and catabolic flux, and a simultaneous decrease in anabolic flux. Following the onset of WVA, HFmet. decreased again until indistinguishable from control (non-absorbing) values. Possible factors contributing to the "preparatory phase" are discussed, including mobilization of Malpighian tubule transporters and muscular activity in the rectum. Absorbing larvae reduced the water activity of the calorimetric cell to 0.906, agreeing with gravimetric estimates of the critical equilibrium activity. Periods of movement during WVA coincided with decreased uptake fluxes, consistent with the animal's hydrostatic skeleton and the need to close the anus to generate pressure increases in the haemocoel.

Metabolic modelling and flux analysis of microorganisms from the Atacama Desert used in biotechnological processes.

PubMed

Razmilic, Valeria; Castro, Jean Franco; Marchant, Francisca; Asenjo, Juan A; Andrews, Barbara

2018-02-02

Metabolic modelling is a useful tool that enables the rational design of metabolic engineering experiments and the study of the unique capabilities of biotechnologically important microorganisms. The extreme abiotic conditions of the Atacama Desert have selected microbial diversity with exceptional characteristics that can be applied in the mining industry for bioleaching processes and for production of specialised metabolites with antimicrobial, antifungal, antiviral, antitumoral, among other activities. In this review we summarise the scientific data available of the use of metabolic modelling and flux analysis to improve the performance of Atacama Desert microorganisms in biotechnological applications.

Carbon and Nitrogen Provisions Alter the Metabolic Flux in Developing Soybean Embryos1[W][OA

PubMed Central

Allen, Doug K.; Young, Jamey D.

2013-01-01

Soybean (Glycine max) seeds store significant amounts of their biomass as protein, levels of which reflect the carbon and nitrogen received by the developing embryo. The relationship between carbon and nitrogen supply during filling and seed composition was examined through a series of embryo-culturing experiments. Three distinct ratios of carbon to nitrogen supply were further explored through metabolic flux analysis. Labeling experiments utilizing [U-13C5]glutamine, [U-13C4]asparagine, and [1,2-13C2]glucose were performed to assess embryo metabolism under altered feeding conditions and to create corresponding flux maps. Additionally, [U-14C12]sucrose, [U-14C6]glucose, [U-14C5]glutamine, and [U-14C4]asparagine were used to monitor differences in carbon allocation. The analyses revealed that: (1) protein concentration as a percentage of total soybean embryo biomass coincided with the carbon-to-nitrogen ratio; (2) altered nitrogen supply did not dramatically impact relative amino acid or storage protein subunit profiles; and (3) glutamine supply contributed 10% to 23% of the carbon for biomass production, including 9% to 19% of carbon to fatty acid biosynthesis and 32% to 46% of carbon to amino acids. Seed metabolism accommodated different levels of protein biosynthesis while maintaining a consistent rate of dry weight accumulation. Flux through ATP-citrate lyase, combined with malic enzyme activity, contributed significantly to acetyl-coenzyme A production. These fluxes changed with plastidic pyruvate kinase to maintain a supply of pyruvate for amino and fatty acids. The flux maps were independently validated by nitrogen balancing and highlight the robustness of primary metabolism. PMID:23314943

Cyanobacterial carbon metabolism: Fluxome plasticity and oxygen dependence: Cyanobacterial Carbon Metabolism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan, Ni; DeLorenzo, Drew M.; He, Lian

Synechocystis sp. strain PCC 6803 has been widely used as a photo-biorefinery chassis. Based on its genome annotation, this species contains a complete TCA cycle, an Embden-Meyerhof-Parnas pathway (EMPP), an oxidative pentose phosphate pathway (OPPP), and an Entner–Doudoroff pathway (EDP). To evaluate how Synechocystis 6803 catabolizes glucose under heterotrophic conditions, we performed 13C metabolic flux analysis, metabolite pool size analysis, gene knockouts, and heterologous expressions. The results revealed a cyclic mode of flux through the OPPP. Small, but non-zero, fluxes were observed through the TCA cycle and the malic shunt. Independent knockouts of 6-phosphogluconate dehydrogenase (gnd) and malic enzyme (me)more » corroborated these results, as neither mutant could grow under dark heterotrophic conditions. Our data also indicate that Synechocystis 6803 metabolism relies upon oxidative phosphorylation to generate ATP from NADPH under dark or insufficient light conditions. The pool sizes of intermediates in the TCA cycle, particularly acetyl-CoA, were found to be several fold lower in Synechocystis 6803 (compared to E. coli metabolite pool sizes), while its sugar phosphate intermediates were several-fold higher. Moreover, negligible flux was detected through the native, or heterologous, EDP in the wild type or Δgnd strains under heterotrophic conditions. Comparing photoautotrophic, photomixotrophic, and heterotrophic conditions, the Calvin cycle, OPPP, and EMPP in Synechocystis 6803 possess the ability to regulate their fluxes under various growth conditions (plastic), whereas its TCA cycle always maintains at low levels (rigid). This work also demonstrates how genetic profiles do not always reflect actual metabolic flux through native or heterologous pathways. Biotechnol. Bioeng. 2017;114: 1593–1602. © 2017 Wiley Periodicals, Inc.« less

The Molecular and Metabolic Influence of Long Term Agmatine Consumption*

PubMed Central

Nissim, Itzhak; Horyn, Oksana; Daikhin, Yevgeny; Chen, Pan; Li, Changhong; Wehrli, Suzanne L.; Nissim, Ilana; Yudkoff, Marc

2014-01-01

Agmatine (AGM), a product of arginine decarboxylation, influences multiple physiologic and metabolic functions. However, the mechanism(s) of action, the impact on whole body gene expression and metabolic pathways, and the potential benefits and risks of long term AGM consumption are still a mystery. Here, we scrutinized the impact of AGM on whole body metabolic profiling and gene expression and assessed a plausible mechanism(s) of AGM action. Studies were performed in rats fed a high fat diet or standard chow. AGM was added to drinking water for 4 or 8 weeks. We used 13C or 15N tracers to assess metabolic reactions and fluxes and real time quantitative PCR to determine gene expression. The results demonstrate that AGM elevated the synthesis and tissue level of cAMP. Subsequently, AGM had a widespread impact on gene expression and metabolic profiling including (a) activation of peroxisomal proliferator-activated receptor-α and its coactivator, PGC1α, and (b) increased expression of peroxisomal proliferator-activated receptor-γ and genes regulating thermogenesis, gluconeogenesis, and carnitine biosynthesis and transport. The changes in gene expression were coupled with improved tissue and systemic levels of carnitine and short chain acylcarnitine, increased β-oxidation but diminished incomplete fatty acid oxidation, decreased fat but increased protein mass, and increased hepatic ureagenesis and gluconeogenesis but decreased glycolysis. These metabolic changes were coupled with reduced weight gain and a curtailment of the hormonal and metabolic derangements associated with high fat diet-induced obesity. The findings suggest that AGM elevated the synthesis and levels of cAMP, thereby mimicking the effects of caloric restriction with respect to metabolic reprogramming. PMID:24523404

Thermodynamics-Based Metabolic Flux Analysis

PubMed Central

Henry, Christopher S.; Broadbelt, Linda J.; Hatzimanikatis, Vassily

2007-01-01

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, ΔrG′, 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 ΔrG′ 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 ΔrG′ constrained close to zero while numerous reactions are identified throughout metabolism for which ΔrG′ is always highly negative regardless of metabolite concentrations. As it has been proposed previously, these reactions with exclusively negative ΔrG′ 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 \\documentclass[10pt]{article} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{pmc} \\pagestyle{empty} \\oddsidemargin -1.0in \\begin{document} \\begin{equation*}{\\mathrm{H}}_{{\\mathrm{extracellular}}}^{+}/{\\mathrm{H}}_{{\\mathrm{intracellular}}}^{+}\\end{equation*}\\end{document} 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. PMID:17172310

In vivo stationary flux analysis by 13C labeling experiments.

PubMed

Wiechert, W; de Graaf, A A

1996-01-01

Stationary flux analysis is an invaluable tool for metabolic engineering. In the last years the metabolite balancing technique has become well established in the bioengineering community. On the other hand metabolic tracer experiments using 13C isotopes have long been used for intracellular flux determination. Only recently have both techniques been fully combined to form a considerably more powerful flux analysis method. This paper concentrates on modeling and data analysis for the evaluation of such stationary 13C labeling experiments. After reviewing recent experimental developments, the basic equations for modeling carbon labeling in metabolic systems, i.e. metabolite, carbon label and isotopomer balances, are introduced and discussed in some detail. Then the basics of flux estimation from measured extracellular fluxes combined with carbon labeling data are presented and, finally, this method is illustrated by using an example from C. glutamicum. The main emphasis is on the investigation of the extra information that can be obtained with tracer experiments compared with the metabolite balancing technique alone. As a principal result it is shown that the combined flux analysis method can dispense with some rather doubtful assumptions on energy balancing and that the forward and backward flux rates of bidirectional reaction steps can be simultaneously determined in certain situations. Finally, it is demonstrated that the variant of fractional isotopomer measurement is even more powerful than fractional labeling measurement but requires much higher numerical effort to solve the balance equations.

Neuronal differentiation is associated with a redox-regulated increase of copper flow to the secretory pathway

PubMed Central

Hatori, Yuta; Yan, Ye; Schmidt, Katharina; Furukawa, Eri; Hasan, Nesrin M.; Yang, Nan; Liu, Chin-Nung; Sockanathan, Shanthini; Lutsenko, Svetlana

2016-01-01

Brain development requires a fine-tuned copper homoeostasis. Copper deficiency or excess results in severe neuro-pathologies. We demonstrate that upon neuronal differentiation, cellular demand for copper increases, especially within the secretory pathway. Copper flow to this compartment is facilitated through transcriptional and metabolic regulation. Quantitative real-time imaging revealed a gradual change in the oxidation state of cytosolic glutathione upon neuronal differentiation. Transition from a broad range of redox states to a uniformly reducing cytosol facilitates reduction of the copper chaperone Atox1, liberating its metal-binding site. Concomitantly, expression of Atox1 and its partner, a copper transporter ATP7A, is upregulated. These events produce a higher flux of copper through the secretory pathway that balances copper in the cytosol and increases supply of the cofactor to copper-dependent enzymes, expression of which is elevated in differentiated neurons. Direct link between glutathione oxidation and copper compartmentalization allows for rapid metabolic adjustments essential for normal neuronal function. PMID:26879543

Neuronal differentiation is associated with a redox-regulated increase of copper flow to the secretory pathway.

PubMed

Hatori, Yuta; Yan, Ye; Schmidt, Katharina; Furukawa, Eri; Hasan, Nesrin M; Yang, Nan; Liu, Chin-Nung; Sockanathan, Shanthini; Lutsenko, Svetlana

2016-02-16

Brain development requires a fine-tuned copper homoeostasis. Copper deficiency or excess results in severe neuro-pathologies. We demonstrate that upon neuronal differentiation, cellular demand for copper increases, especially within the secretory pathway. Copper flow to this compartment is facilitated through transcriptional and metabolic regulation. Quantitative real-time imaging revealed a gradual change in the oxidation state of cytosolic glutathione upon neuronal differentiation. Transition from a broad range of redox states to a uniformly reducing cytosol facilitates reduction of the copper chaperone Atox1, liberating its metal-binding site. Concomitantly, expression of Atox1 and its partner, a copper transporter ATP7A, is upregulated. These events produce a higher flux of copper through the secretory pathway that balances copper in the cytosol and increases supply of the cofactor to copper-dependent enzymes, expression of which is elevated in differentiated neurons. Direct link between glutathione oxidation and copper compartmentalization allows for rapid metabolic adjustments essential for normal neuronal function.

A Minimal Set of Glycolytic Genes Reveals Strong Redundancies in Saccharomyces cerevisiae Central Metabolism

PubMed Central

Solis-Escalante, Daniel; Kuijpers, Niels G. A.; Barrajon-Simancas, Nuria; van den Broek, Marcel; Pronk, Jack T.

2015-01-01

As a result of ancestral whole-genome and small-scale duplication events, the genomes of Saccharomyces cerevisiae and many eukaryotes still contain a substantial fraction of duplicated genes. In all investigated organisms, metabolic pathways, and more particularly glycolysis, are specifically enriched for functionally redundant paralogs. In ancestors of the Saccharomyces lineage, the duplication of glycolytic genes is purported to have played an important role leading to S. cerevisiae's current lifestyle favoring fermentative metabolism even in the presence of oxygen and characterized by a high glycolytic capacity. In modern S. cerevisiae strains, the 12 glycolytic reactions leading to the biochemical conversion from glucose to ethanol are encoded by 27 paralogs. In order to experimentally explore the physiological role of this genetic redundancy, a yeast strain with a minimal set of 14 paralogs was constructed (the “minimal glycolysis” [MG] strain). Remarkably, a combination of a quantitative systems approach and semiquantitative analysis in a wide array of growth environments revealed the absence of a phenotypic response to the cumulative deletion of 13 glycolytic paralogs. This observation indicates that duplication of glycolytic genes is not a prerequisite for achieving the high glycolytic fluxes and fermentative capacities that are characteristic of S. cerevisiae and essential for many of its industrial applications and argues against gene dosage effects as a means of fixing minor glycolytic paralogs in the yeast genome. The MG strain was carefully designed and constructed to provide a robust prototrophic platform for quantitative studies and has been made available to the scientific community. PMID:26071034

Metabolic modeling of dynamic brain 13C NMR multiplet data: Concepts and simulations with a two-compartment neuronal-glial model

PubMed Central

Shestov, Alexander A.; Valette, Julien; Deelchand, Dinesh K.; Uğurbil, Kâmil; Henry, Pierre-Gilles

2016-01-01

Metabolic modeling of dynamic 13C labeling curves during infusion of 13C-labeled substrates allows quantitative measurements of metabolic rates in vivo. However metabolic modeling studies performed in the brain to date have only modeled time courses of total isotopic enrichment at individual carbon positions (positional enrichments), not taking advantage of the additional dynamic 13C isotopomer information available from fine-structure multiplets in 13C spectra. Here we introduce a new 13C metabolic modeling approach using the concept of bonded cumulative isotopomers, or bonded cumomers. The direct relationship between bonded cumomers and 13C multiplets enables fitting of the dynamic multiplet data. The potential of this new approach is demonstrated using Monte-Carlo simulations with a brain two-compartment neuronal-glial model. The precision of positional and cumomer approaches are compared for two different metabolic models (with and without glutamine dilution) and for different infusion protocols ([1,6-13C2]glucose, [1,2-13C2]acetate, and double infusion [1,6-13C2]glucose + [1,2-13C2]acetate). In all cases, the bonded cumomer approach gives better precision than the positional approach. In addition, of the three different infusion protocols considered here, the double infusion protocol combined with dynamic bonded cumomer modeling appears the most robust for precise determination of all fluxes in the model. The concepts and simulations introduced in the present study set the foundation for taking full advantage of the available dynamic 13C multiplet data in metabolic modeling. PMID:22528840

The Crc/CrcZ-CrcY global regulatory system helps the integration of gluconeogenic and glycolytic metabolism in Pseudomonas putida.

PubMed

La Rosa, Ruggero; Nogales, Juan; Rojo, Fernando

2015-09-01

In metabolically versatile bacteria, carbon catabolite repression (CCR) facilitates the preferential assimilation of the most efficient carbon sources, improving growth rates and fitness. In Pseudomonas putida, the Crc and Hfq proteins and the CrcZ and CrcY small RNAs, which are believed to antagonize Crc/Hfq, are key players in CCR. Unlike that seen in other bacterial species, succinate and glucose elicit weak CCR in this bacterium. In the present work, metabolic, transcriptomic and constraint-based metabolic flux analyses were combined to clarify whether P. putida prefers succinate or glucose, and to identify the role of the Crc protein in the metabolism of these compounds. When provided simultaneously, succinate was consumed faster than glucose, although both compounds were metabolized. CrcZ and CrcY levels were lower when both substrates were present than when only one was provided, suggesting a role for Crc in coordinating metabolism of these compounds. Flux distribution analysis suggested that, when both substrates are present, Crc works to organize a metabolism in which carbon compounds flow in opposite directions: from glucose to pyruvate, and from succinate to pyruvate. Thus, our results support that Crc not only favours the assimilation of preferred compounds, but balances carbon fluxes, optimizing metabolism and growth. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

Identifying model error in metabolic flux analysis - a generalized least squares approach.

PubMed

Sokolenko, Stanislav; Quattrociocchi, Marco; Aucoin, Marc G

2016-09-13

The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range). The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

Construction and completion of flux balance models from pathway databases.

PubMed

Latendresse, Mario; Krummenacker, Markus; Trupp, Miles; Karp, Peter D

2012-02-01

Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models can be time consuming and tedious because of the difficulty in assembling completely accurate descriptions of these sets, and in identifying errors in the composition of these sets. For example, the presence of a single non-producible metabolite in the biomass will make the entire model infeasible. Other difficulties in FBA modeling are that model distributions, and predicted fluxes, can be cryptic and difficult to understand. We present a multiple gap-filling method to accelerate the development of FBA models using a new tool, called MetaFlux, based on mixed integer linear programming (MILP). The method suggests corrections to the sets of reactions, biomass metabolites, nutrients and secretions. The method generates FBA models directly from Pathway/Genome Databases. Thus, FBA models developed in this framework are easily queried and visualized using the Pathway Tools software. Predicted fluxes are more easily comprehended by visualizing them on diagrams of individual metabolic pathways or of metabolic maps. MetaFlux can also remove redundant high-flux loops, solve FBA models once they are generated and model the effects of gene knockouts. MetaFlux has been validated through construction of FBA models for Escherichia coli and Homo sapiens. Pathway Tools with MetaFlux is freely available to academic users, and for a fee to commercial users. Download from: biocyc.org/download.shtml. mario.latendresse@sri.com Supplementary data are available at Bioinformatics online.

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.

PubMed

Chaudhary, Neha; Tøndel, Kristin; Bhatnagar, Rakesh; dos Santos, Vítor A P Martins; Puchałka, Jacek

2016-03-01

Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve them.

Impaired in vivo mitochondrial Krebs cycle activity after myocardial infarction assessed using hyperpolarized magnetic resonance spectroscopy.

PubMed

Dodd, Michael S; Atherton, Helen J; Carr, Carolyn A; Stuckey, Daniel J; West, James A; Griffin, Julian L; Radda, George K; Clarke, Kieran; Heather, Lisa C; Tyler, Damian J

2014-11-01

Myocardial infarction (MI) is one of the leading causes of heart failure. An increasing body of evidence links alterations in cardiac metabolism and mitochondrial function with the progression of heart disease. The aim of this work was to, therefore, follow the in vivo mitochondrial metabolic alterations caused by MI, thereby allowing a greater understanding of the interplay between metabolic and functional abnormalities. Using hyperpolarized carbon-13 ((13)C)-magnetic resonance spectroscopy, in vivo alterations in mitochondrial metabolism were assessed for 22 weeks after surgically induced MI with reperfusion in female Wister rats. One week after MI, there were no detectable alterations in in vivo cardiac mitochondrial metabolism over the range of ejection fractions observed (from 28% to 84%). At 6 weeks after MI, in vivo mitochondrial Krebs cycle activity was impaired, with decreased (13)C-label flux into citrate, glutamate, and acetylcarnitine, which correlated with the degree of cardiac dysfunction. These changes were independent of alterations in pyruvate dehydrogenase flux. By 22 weeks, alterations were also seen in pyruvate dehydrogenase flux, which decreased at lower ejection fractions. These results were confirmed using in vitro analysis of enzyme activities and metabolomic profiles of key intermediates. The in vivo decrease in Krebs cycle activity in the 6-week post-MI heart may represent an early maladaptive phase in the metabolic alterations after MI in which reductions in Krebs cycle activity precede a reduction in pyruvate dehydrogenase flux. Changes in mitochondrial metabolism in heart disease are progressive and proportional to the degree of cardiac impairment. © 2014 American Heart Association, Inc.

Impaired In Vivo Mitochondrial Krebs Cycle Activity After Myocardial Infarction Assessed Using Hyperpolarized Magnetic Resonance Spectroscopy

PubMed Central

Carr, Carolyn A.; Stuckey, Daniel J.; West, James A.; Griffin, Julian L.; Radda, George K.; Clarke, Kieran; Heather, Lisa C.; Tyler, Damian J.

2015-01-01

Background Myocardial infarction (MI) is one of the leading causes of heart failure. An increasing body of evidence links alterations in cardiac metabolism and mitochondrial function with the progression of heart disease. The aim of this work was to, therefore, follow the in vivo mitochondrial metabolic alterations caused by MI, thereby allowing a greater understanding of the interplay between metabolic and functional abnormalities. Methods and Results Using hyperpolarized carbon-13 (13C)-magnetic resonance spectroscopy, in vivo alterations in mitochondrial metabolism were assessed for 22 weeks after surgically induced MI with reperfusion in female Wister rats. One week after MI, there were no detectable alterations in in vivo cardiac mitochondrial metabolism over the range of ejection fractions observed (from 28% to 84%). At 6 weeks after MI, in vivo mitochondrial Krebs cycle activity was impaired, with decreased 13C-label flux into citrate, glutamate, and acetylcarnitine, which correlated with the degree of cardiac dysfunction. These changes were independent of alterations in pyruvate dehydrogenase flux. By 22 weeks, alterations were also seen in pyruvate dehydrogenase flux, which decreased at lower ejection fractions. These results were confirmed using in vitro analysis of enzyme activities and metabolomic profiles of key intermediates. Conclusions The in vivo decrease in Krebs cycle activity in the 6-week post-MI heart may represent an early maladaptive phase in the metabolic alterations after MI in which reductions in Krebs cycle activity precede a reduction in pyruvate dehydrogenase flux. Changes in mitochondrial metabolism in heart disease are progressive and proportional to the degree of cardiac impairment. PMID:25201905

Dynamic elementary mode modelling of non-steady state flux data.

PubMed

Folch-Fortuny, Abel; Teusink, Bas; Hoefsloot, Huub C J; Smilde, Age K; Ferrer, Alberto

2018-06-18

A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling. This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks.

PubMed

Ruppin, Eytan; Papin, Jason A; de Figueiredo, Luis F; Schuster, Stefan

2010-08-01

With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well. Copyright © 2010 Elsevier Ltd. All rights reserved.

Towards high resolution analysis of metabolic flux in cells and tissues.

PubMed

Sims, James K; Manteiga, Sara; Lee, Kyongbum

2013-10-01

Metabolism extracts chemical energy from nutrients, uses this energy to form building blocks for biosynthesis, and interconverts between various small molecules that coordinate the activities of cellular pathways. The metabolic state of a cell is increasingly recognized to determine the phenotype of not only metabolically active cell types such as liver, muscle, and adipose, but also other specialized cell types such as neurons and immune cells. This review focuses on methods to quantify intracellular reaction flux as a measure of cellular metabolic activity, with emphasis on studies involving cells of mammalian tissue. Two key areas are highlighted for future development, single cell metabolomics and noninvasive imaging, which could enable spatiotemporally resolved analysis and thereby overcome issues of heterogeneity, a distinctive feature of tissue metabolism. Copyright © 2013 Elsevier Ltd. All rights reserved.

Towards High Resolution Analysis of Metabolic Flux in Cells and Tissues

PubMed Central

Sims, James K; Manteiga, Sara; Lee, Kyongbum

2013-01-01

Metabolism extracts chemical energy from nutrients, uses this energy to form building blocks for biosynthesis, and interconverts between various small molecules that coordinate the activities of cellular pathways. The metabolic state of a cell is increasingly recognized to determine the phenotype of not only metabolically active cell types such as liver, muscle, and adipose, but also other specialized cell types such as neurons and immune cells. This review focuses on methods to quantify intracellular reaction flux as a measure of cellular metabolic activity, with emphasis on studies involving cells of mammalian tissue. Two key areas are highlighted for future development, single cell metabolomics and noninvasive imaging, which could enable spatiotemporally resolved analysis and thereby overcome issues of heterogeneity, a distinctive feature of tissue metabolism. PMID:23906926

Fluorescence resonance energy transfer sensors for quantitative monitoring of pentose and disaccharide accumulation in bacteria

PubMed Central

Kaper, Thijs; Lager, Ida; Looger, Loren L; Chermak, Diane; Frommer, Wolf B

2008-01-01

Background Engineering microorganisms to improve metabolite flux requires detailed knowledge of the concentrations and flux rates of metabolites and metabolic intermediates in vivo. Fluorescence resonance energy transfer sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. These sensors have been applied successfully in mammalian and plant cells but potentially could also be used to monitor steady-state levels of metabolites in microorganisms using fluorimetric assays. Sensors for hexose and pentose carbohydrates could help in the development of fermentative microorganisms, for example, for biofuels applications. Arabinose is one of the carbohydrates to be monitored during biofuels production from lignocellulose, while maltose is an important degradation product of starch that is relevant for starch-derived biofuels production. Results An Escherichia coli expression vector compatible with phage λ recombination technology was constructed to facilitate sensor construction and was used to generate a novel fluorescence resonance energy transfer sensor for arabinose. In parallel, a strategy for improving the sensor signal was applied to construct an improved maltose sensor. Both sensors were expressed in the cytosol of E. coli and sugar accumulation was monitored using a simple fluorimetric assay of E. coli cultures in microtiter plates. In the case of both nanosensors, the addition of the respective ligand led to concentration-dependent fluorescence resonance energy transfer responses allowing quantitative analysis of the intracellular sugar levels at given extracellular supply levels as well as accumulation rates. Conclusion The nanosensor destination vector combined with the optimization strategy for sensor responses should help to accelerate the development of metabolite sensors. The new carbohydrate fluorescence resonance energy transfer sensors can be used for in vivo monitoring of sugar levels in prokaryotes, demonstrating the potential of such sensors as reporter tools in the development of metabolically engineered microbial strains or for real-time monitoring of intracellular metabolite during fermentation. PMID:18522753

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Lian; Xiu, Yu; Jones, J. Andrew

Microbial fermentation conditions are dynamic, due to transcriptional induction, nutrient consumption, or changes to incubation conditions. In this paper, 13C-metabolic flux analysis was used to characterize two violacein-producing E. coli strains with vastly different productivities, and to profile their metabolic adjustments resulting from external perturbations during fermentation. The two strains were first grown at 37 °C in stage 1, and then the temperature was transitioned to 20 °C in stage 2 for the optimal expression of the violacein synthesis pathway. After induction, violacein production was minimal in stage 3, but accelerated in stage 4 (early production phase) and 5 (latemore » production phase) in the high producing strain, reaching a final concentration of 1.5 mmol/L. On the contrary, ~0.02 mmol/L of violacein was obtained from the low producing strain. To have a snapshot of the temporal metabolic changes in each stage, we performed 13C-MFA via isotopomer analysis of fast-turnover free metabolites. The results indicate strikingly stable flux ratios in the central metabolism throughout the early growth stages. In the late stages, however, the high producer rewired its flux distribution significantly, which featured an upregulated pentose phosphate pathway and TCA cycle, reflux from acetate utilization, negligible anabolic fluxes, and elevated maintenance loss, to compensate for nutrient depletion and drainage of some building blocks due to violacein overproduction. The low producer with stronger promoters shifted its relative fluxes in stage 5 by enhancing the flux through the TCA cycle and acetate overflow, while exhibiting a reduced biomass growth and a minimal flux towards violacein synthesis. Finally, interestingly, the addition of the violacein precursor (tryptophan) in the medium inhibited high producer but enhanced low producer's productivity, leading to hypotheses of unknown pathway regulations (such as metabolite channeling).« less

Streptomyces clavuligerus shows a strong association between TCA cycle intermediate accumulation and clavulanic acid biosynthesis.

PubMed

Ramirez-Malule, Howard; Junne, Stefan; Nicolás Cruz-Bournazou, Mariano; Neubauer, Peter; Ríos-Estepa, Rigoberto

2018-05-01

Clavulanic acid (CA) is produced by Streptomyces clavuligerus (S. clavuligerus) as a secondary metabolite. Knowledge about the carbon flux distribution along the various routes that supply CA precursors would certainly provide insights about metabolic performance. In order to evaluate metabolic patterns and the possible accumulation of tricarboxylic acid (TCA) cycle intermediates during CA biosynthesis, batch and subsequent continuous cultures with steadily declining feed rates were performed with glycerol as the main substrate. The data were used to in silico explore the metabolic capabilities and the accumulation of metabolic intermediates in S. clavuligerus. While clavulanic acid accumulated at glycerol excess, it steadily decreased at declining dilution rates; CA synthesis stopped when glycerol became the limiting substrate. A strong association of succinate, oxaloacetate, malate, and acetate accumulation with CA production in S. clavuligerus was observed, and flux balance analysis (FBA) was used to describe the carbon flux distribution in the network. This combined experimental and numerical approach also identified bottlenecks during the synthesis of CA in a batch and subsequent continuous cultivation and demonstrated the importance of this type of methodologies for a more advanced understanding of metabolism; this potentially derives valuable insights for future successful metabolic engineering studies in S. clavuligerus.

Dynamic modeling of lactic acid fermentation metabolism with Lactococcus lactis.

PubMed

Oh, Euhlim; Lu, Mingshou; Park, Changhun; Park, Changhun; Oh, Han Bin; Lee, Sang Yup; Lee, Jinwon

2011-02-01

A dynamic model of lactic acid fermentation using Lactococcus lactis was constructed, and a metabolic flux analysis (MFA) and metabolic control analysis (MCA) were performed to reveal an intensive metabolic understanding of lactic acid bacteria (LAB). The parameter estimation was conducted with COPASI software to construct a more accurate metabolic model. The experimental data used in the parameter estimation were obtained from an LC-MS/ MS analysis and time-course simulation study. The MFA results were a reasonable explanation of the experimental data. Through the parameter estimation, the metabolic system of lactic acid bacteria can be thoroughly understood through comparisons with the original parameters. The coefficients derived from the MCA indicated that the reaction rate of L-lactate dehydrogenase was activated by fructose 1,6-bisphosphate and pyruvate, and pyruvate appeared to be a stronger activator of L-lactate dehydrogenase than fructose 1,6-bisphosphate. Additionally, pyruvate acted as an inhibitor to pyruvate kinase and the phosphotransferase system. Glucose 6-phosphate and phosphoenolpyruvate showed activation effects on pyruvate kinase. Hexose transporter was the strongest effector on the flux through L-lactate dehydrogenase. The concentration control coefficient (CCC) showed similar results to the flux control coefficient (FCC).

Hormonal regulation of the alpha-ketoglutarate dehydrogenase complex in the isolated perfused rat liver.

PubMed

Rashed, H M; Waller, F M; Patel, T B

1988-04-25

The metabolic flux through the alpha-ketoglutarate dehydrogenase reaction in perfused livers was monitored by measuring the rate of 14CO2 production from [1-14C]alpha-ketoglutarate. The rates of 14CO2 production and glucose production from [1-14C]alpha-ketoglutarate were increased with increasing perfusate alpha-ketoglutarate concentrations. Vasopressin, angiotensin II, and the alpha 1-adrenergic agonist phenylephrine stimulated transiently by 2.5-fold the metabolic flux through the alpha-ketoglutarate dehydrogenase reaction in the presence and absence of Ca2+ in the perfusion medium. High concentrations of glucagon (1 x 10(-8) M) and 8-p-chlorophenylthio-cAMP (100 microM) (data not shown) also stimulated transiently the metabolic flux through the alpha-ketoglutarate dehydrogenase reaction. However, lower glucagon concentrations (1 x 10(-9) M) stimulated the rate of 14CO2 production from [1-14C]alpha-ketoglutarate only under conditions optimized to fix the cellular oxidation-reduction state at an intermediate level, when glucagon (1 x 10(-9) M)-mediated elevation of cAMP content was greater than that observed under highly oxidizing and reducing conditions. These data indicate that agonists which increase cytosolic free Ca2+ levels stimulate the metabolic flux through the alpha-ketoglutarate dehydrogenase complex. Furthermore, the data presented here demonstrate for the first time that physiological glucagon concentrations stimulate the metabolic flux through the alpha-ketoglutarate dehydrogenase reaction only under conditions known to be optimal for glucagon-mediated Ca2+ mobilization in the isolated perfused rat liver.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hay, J.; Schwender, J.

Plant oils are an important renewable resource, and seed oil content is a key agronomical trait that is in part controlled by the metabolic processes within developing seeds. A large-scale model of cellular metabolism in developing embryos of Brassica napus (bna572) was used to predict biomass formation and to analyze metabolic steady states by flux variability analysis under different physiological conditions. Predicted flux patterns are highly correlated with results from prior 13C metabolic flux analysis of B. napus developing embryos. Minor differences from the experimental results arose because bna572 always selected only one sugar and one nitrogen source from themore » available alternatives, and failed to predict the use of the oxidative pentose phosphate pathway. Flux variability, indicative of alternative optimal solutions, revealed alternative pathways that can provide pyruvate and NADPH to plastidic fatty acid synthesis. The nutritional values of different medium substrates were compared based on the overall carbon conversion efficiency (CCE) for the biosynthesis of biomass. Although bna572 has a functional nitrogen assimilation pathway via glutamate synthase, the simulations predict an unexpected role of glycine decarboxylase operating in the direction of NH4+ assimilation. Analysis of the light-dependent improvement of carbon economy predicted two metabolic phases. At very low light levels small reductions in CO2 efflux can be attributed to enzymes of the tricarboxylic acid cycle (oxoglutarate dehydrogenase, isocitrate dehydrogenase) and glycine decarboxylase. At higher light levels relevant to the 13C flux studies, ribulose-1,5-bisphosphate carboxylase activity is predicted to account fully for the light-dependent changes in carbon balance.« less

Regulation of yeast central metabolism by enzyme phosphorylation

PubMed Central

Oliveira, Ana Paula; Ludwig, Christina; Picotti, Paola; Kogadeeva, Maria; Aebersold, Ruedi; Sauer, Uwe

2012-01-01

As a frequent post-translational modification, protein phosphorylation regulates many cellular processes. Although several hundred phosphorylation sites have been mapped to metabolic enzymes in Saccharomyces cerevisiae, functionality was demonstrated for few of them. Here, we describe a novel approach to identify in vivo functionality of enzyme phosphorylation by combining flux analysis with proteomics and phosphoproteomics. Focusing on the network of 204 enzymes that constitute the yeast central carbon and amino-acid metabolism, we combined protein and phosphoprotein levels to identify 35 enzymes that change their degree of phosphorylation during growth under five conditions. Correlations between previously determined intracellular fluxes and phosphoprotein abundances provided first functional evidence for five novel phosphoregulated enzymes in this network, adding to nine known phosphoenzymes. For the pyruvate dehydrogenase complex E1 α subunit Pda1 and the newly identified phosphoregulated glycerol-3-phosphate dehydrogenase Gpd1 and phosphofructose-1-kinase complex β subunit Pfk2, we then validated functionality of specific phosphosites through absolute peptide quantification by targeted mass spectrometry, metabolomics and physiological flux analysis in mutants with genetically removed phosphosites. These results demonstrate the role of phosphorylation in controlling the metabolic flux realised by these three enzymes. PMID:23149688

A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

PubMed

Yuan, Huili; Cheung, C Y Maurice; Poolman, Mark G; Hilbers, Peter A J; van Riel, Natal A W

2016-01-01

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses. © 2015 The Authors.The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Multi-scale modeling of Arabidopsis thaliana response to different CO2 conditions: From gene expression to metabolic flux.

PubMed

Liu, Lin; Shen, Fangzhou; Xin, Changpeng; Wang, Zhuo

2016-01-01

Multi-scale investigation from gene transcript level to metabolic activity is important to uncover plant response to environment perturbation. Here we integrated a genome-scale constraint-based metabolic model with transcriptome data to explore Arabidopsis thaliana response to both elevated and low CO2 conditions. The four condition-specific models from low to high CO2 concentrations show differences in active reaction sets, enriched pathways for increased/decreased fluxes, and putative post-transcriptional regulation, which indicates that condition-specific models are necessary to reflect physiological metabolic states. The simulated CO2 fixation flux at different CO2 concentrations is consistent with the measured Assimilation-CO2intercellular curve. Interestingly, we found that reactions in primary metabolism are affected most significantly by CO2 perturbation, whereas secondary metabolic reactions are not influenced a lot. The changes predicted in key pathways are consistent with existing knowledge. Another interesting point is that Arabidopsis is required to make stronger adjustment on metabolism to adapt to the more severe low CO2 stress than elevated CO2 . The challenges of identifying post-transcriptional regulation could also be addressed by the integrative model. In conclusion, this innovative application of multi-scale modeling in plants demonstrates potential to uncover the mechanisms of metabolic response to different conditions. © 2015 Institute of Botany, Chinese Academy of Sciences.

Metabolic pathway analysis of Scheffersomyces (Pichia) stipitis: effect of oxygen availability on ethanol synthesis and flux distributions.

PubMed

Unrean, Pornkamol; Nguyen, Nhung H A

2012-06-01

Elementary mode analysis (EMA) identifies all possible metabolic states of the cell metabolic network. Investigation of these states can provide a detailed insight into the underlying metabolism in the cell. In this study, the flux states of Scheffersomyces (Pichia) stipitis metabolism were examined. It was shown that increasing oxygen levels led to a decrease of ethanol synthesis. This trend was confirmed by experimental evaluation of S. stipitis in glucose-xylose fermentation. The oxygen transfer rate for an optimal ethanol production was 1.8 mmol/l/h, which gave the ethanol yield of 0.40 g/g and the ethanol productivity of 0.25 g/l/h. For a better understanding of the cell's regulatory mechanism in response to oxygenation levels, EMA was used to examine metabolic flux patterns under different oxygen levels. Up- and downregulation of enzymes in the network during the change of culturing condition from oxygen limitation to oxygen sufficiency were identified. The results indicated the flexibility of S. stipitis metabolism to cope with oxygen availability. In addition, relevant genetic targets towards improved ethanol-producing strains under all oxygenation levels were identified. These targeted genes limited the metabolic functionality of the cell to function according to the most efficient ethanol synthesis pathways. The presented approach is promising and can contribute to the development of culture optimization and strain engineers for improved lignocellulosic ethanol production by S. stipitis.

Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils

PubMed Central

Álvarez-Yela, Astrid Catalina; Gómez-Cano, Fabio; Zambrano, María Mercedes; Husserl, Johana; Danies, Giovanna; Restrepo, Silvia; González-Barrios, Andrés Fernando

2017-01-01

Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community. PMID:28767679

Batch culture characterization and metabolic flux analysis of succinate-producing Escherichia coli strains.

PubMed

Sánchez, Ailen M; Bennett, George N; San, Ka-Yiu

2006-05-01

This study presents an in-depth analysis of the anaerobic metabolic fluxes of various mutant strains of Escherichia coli overexpressing the Lactococcus lactis pyruvate carboxylase (PYC) for the production of succinate. Previously, a metabolic network design that includes an active glyoxylate pathway implemented in vivo increased succinate yield from glucose in an E. coli mutant to 1.6 mol/mol under fully anaerobic conditions. The design consists of a dual succinate synthesis route, which diverts required quantities of NADH through the traditional fermentative pathway and maximizes the carbon converted to succinate by balancing the carbon flux through the fermentative pathway and the glyoxylate pathway (which has a lower NADH requirement). Mutant strains previously constructed during the development of high-yield succinate-producing strains were selected for further characterization to understand their metabolic response as a result of several genetic manipulations and to determine the significance of the fermentative and the glyoxylate pathways in the production of succinate. Measured fluxes obtained under batch cultivation conditions were used to estimate intracellular fluxes and identify critical branch point flux split ratios. The comparison of changes in branch point flux split ratios to the glyoxylate pathway and the fermentative pathway at the oxaloacetate (OAA) node as a result of different mutations revealed the sensitivity of succinate yield to these manipulations. The most favorable split ratio to obtain the highest succinate yield was the fractional partition of OAA to glyoxylate of 0.32 and 0.68 to the fermentative pathway obtained in strains SBS550MG (pHL413) and SBS990MG (pHL413). The succinate yields achieved in these two strains were 1.6 and 1.7 mol/mol, respectively. In addition, an active glyoxylate pathway in an ldhA, adhE, ack-pta mutant strain is shown to be responsible for the high succinate yields achieved anaerobically. Furthermore, in vitro activity measurements of seven crucial enzymes involved in the pathways studied and intracellular measurements of key intermediate metabolite pools provided additional insights on the physiological perturbations caused by these mutations. The characterization of these recombinant mutant strains in terms of flux distribution pattern, in vitro enzyme activity and intracellular metabolite pools provides useful information for the rational modification of metabolic fluxes to improve succinate production.

DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems.

PubMed

Smith, Robert W; van Rosmalen, Rik P; Martins Dos Santos, Vitor A P; Fleck, Christian

2018-06-19

Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.

Mixomics analysis of Bacillus subtilis: effect of oxygen availability on riboflavin production.

PubMed

Hu, Junlang; Lei, Pan; Mohsin, Ali; Liu, Xiaoyun; Huang, Mingzhi; Li, Liang; Hu, Jianhua; Hang, Haifeng; Zhuang, Yingping; Guo, Meijin

2017-09-12

Riboflavin, an intermediate of primary metabolism, is one kind of important food additive with high economic value. The microbial cell factory Bacillus subtilis has already been proven to possess significant importance for the food industry and have become one of the most widely used riboflavin-producing strains. In the practical fermentation processes, a sharp decrease in riboflavin production is encountered along with a decrease in the dissolved oxygen (DO) tension. Influence of this oxygen availability on riboflavin biosynthesis through carbon central metabolic pathways in B. subtilis is unknown so far. Therefore the unveiled effective metabolic pathways were still an unaccomplished task till present research work. In this paper, the microscopic regulation mechanisms of B. subtilis grown under different dissolved oxygen tensions were studied by integrating 13 C metabolic flux analysis, metabolomics and transcriptomics. It was revealed that the glucose metabolic flux through pentose phosphate (PP) pathway was lower as being confirmed by smaller pool sizes of metabolites in PP pathway and lower expression amount of ykgB at transcriptional level. The latter encodes 6-phosphogluconolactonase (6-PGL) under low DO tension. In response to low DO tension in broth, the glucose metabolic flux through Embden-Meyerhof-Parnas (EMP) pathway was higher and the gene, alsS, encoding for acetolactate synthase was significantly activated that may result due to lower ATP concentration and higher NADH/NAD + ratio. Moreover, ResE, a membrane-anchored protein that is capable of oxygen regulated phosphorylase activity, and ResD, a regulatory protein that can be phosphorylated and dephosphorylated by ResE, were considered as DO tension sensor and transcriptional regulator respectively. This study shows that integration of transcriptomics, 13 C metabolic flux analysis and metabolomics analysis provides a comprehensive understanding of biosynthesized riboflavin's regulatory mechanisms in B. subtilis grown under different dissolved oxygen tension conditions. The two-component system, ResD-ResE, was considered as the signal receiver of DO tension and gene regulator that led to differences between biomass and riboflavin production after triggering the shifts in gene expression, metabolic flux distributions and metabolite pool sizes.

Fundamental Escherichia coli biochemical pathways for biomass and energy production: creation of overall flux states.

PubMed

Carlson, Ross; Srienc, Friedrich

2004-04-20

We have previously shown that the metabolism for most efficient cell growth can be realized by a combination of two types of elementary modes. One mode produces biomass while the second mode generates only energy. The identity of the four most efficient biomass and energy pathway pairs changes, depending on the degree of oxygen limitation. The identification of such pathway pairs for different growth conditions offers a pathway-based explanation of maintenance energy generation. For a given growth rate, experimental aerobic glucose consumption rates can be used to estimate the contribution of each pathway type to the overall metabolic flux pattern. All metabolic fluxes are then completely determined by the stoichiometries of involved pathways defining all nutrient consumption and metabolite secretion rates. We present here equations that permit computation of network fluxes on the basis of unique pathways for the case of optimal, glucose-limited Escherichia coli growth under varying levels of oxygen stress. Predicted glucose and oxygen uptake rates and some metabolite secretion rates are in remarkable agreement with experimental observations supporting the validity of the presented approach. The entire most efficient, steady-state, metabolic rate structure is explicitly defined by the developed equations without need for additional computer simulations. The approach should be generally useful for analyzing and interpreting genomic data by predicting concise, pathway-based metabolic rate structures. Copyright 2004 Wiley Periodicals, Inc.

Kruppel-like factor 15 regulates skeletal muscle lipid flux and exercise adaptation

PubMed Central

Haldar, Saptarsi M.; Jeyaraj, Darwin; Anand, Priti; Zhu, Han; Lu, Yuan; Prosdocimo, Domenick A.; Eapen, Betty; Kawanami, Daiji; Okutsu, Mitsuharu; Brotto, Leticia; Fujioka, Hisashi; Kerner, Janos; Rosca, Mariana G.; McGuinness, Owen P.; Snow, Rod J.; Russell, Aaron P.; Gerber, Anthony N.; Bai, Xiaodong; Yan, Zhen; Nosek, Thomas M.; Brotto, Marco; Hoppel, Charles L.; Jain, Mukesh K.

2012-01-01

The ability of skeletal muscle to enhance lipid utilization during exercise is a form of metabolic plasticity essential for survival. Conversely, metabolic inflexibility in muscle can cause organ dysfunction and disease. Although the transcription factor Kruppel-like factor 15 (KLF15) is an important regulator of glucose and amino acid metabolism, its endogenous role in lipid homeostasis and muscle physiology is unknown. Here we demonstrate that KLF15 is essential for skeletal muscle lipid utilization and physiologic performance. KLF15 directly regulates a broad transcriptional program spanning all major segments of the lipid-flux pathway in muscle. Consequently, Klf15-deficient mice have abnormal lipid and energy flux, excessive reliance on carbohydrate fuels, exaggerated muscle fatigue, and impaired endurance exercise capacity. Elucidation of this heretofore unrecognized role for KLF15 now implicates this factor as a central component of the transcriptional circuitry that coordinates physiologic flux of all three basic cellular nutrients: glucose, amino acids, and lipids. PMID:22493257

influx_s: increasing numerical stability and precision for metabolic flux analysis in isotope labelling experiments.

PubMed

Sokol, Serguei; Millard, Pierre; Portais, Jean-Charles

2012-03-01

The problem of stationary metabolic flux analysis based on isotope labelling experiments first appeared in the early 1950s and was basically solved in early 2000s. Several algorithms and software packages are available for this problem. However, the generic stochastic algorithms (simulated annealing or evolution algorithms) currently used in these software require a lot of time to achieve acceptable precision. For deterministic algorithms, a common drawback is the lack of convergence stability for ill-conditioned systems or when started from a random point. In this article, we present a new deterministic algorithm with significantly increased numerical stability and accuracy of flux estimation compared with commonly used algorithms. It requires relatively short CPU time (from several seconds to several minutes with a standard PC architecture) to estimate fluxes in the central carbon metabolism network of Escherichia coli. The software package influx_s implementing this algorithm is distributed under an OpenSource licence at http://metasys.insa-toulouse.fr/software/influx/. Supplementary data are available at Bioinformatics online.

The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization

PubMed Central

Noor, Elad; Flamholz, Avi; Bar-Even, Arren; Davidi, Dan; Milo, Ron; Liebermeister, Wolfram

2016-01-01

Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified. PMID:27812109

Optimal design of isotope labeling experiments.

PubMed

Yang, Hong; Mandy, Dominic E; Libourel, Igor G L

2014-01-01

Stable isotope labeling experiments (ILE) constitute a powerful methodology for estimating metabolic fluxes. An optimal label design for such an experiment is necessary to maximize the precision with which fluxes can be determined. But often, precision gained in the determination of one flux comes at the expense of the precision of other fluxes, and an appropriate label design therefore foremost depends on the question the investigator wants to address. One could liken ILE to shadows that metabolism casts on products. Optimal label design is the placement of the lamp; creating clear shadows for some parts of metabolism and obscuring others.An optimal isotope label design is influenced by: (1) the network structure; (2) the true flux values; (3) the available label measurements; and, (4) commercially available substrates. The first two aspects are dictated by nature and constrain any optimal design. The second two aspects are suitable design parameters. To create an optimal label design, an explicit optimization criterion needs to be formulated. This usually is a property of the flux covariance matrix, which can be augmented by weighting label substrate cost. An optimal design is found by using such a criterion as an objective function for an optimizer. This chapter uses a simple elementary metabolite units (EMU) representation of the TCA cycle to illustrate the process of experimental design of isotope labeled substrates.

Molecules in motion: influences of diffusion on metabolic structure and function in skeletal muscle.

PubMed

Kinsey, Stephen T; Locke, Bruce R; Dillaman, Richard M

2011-01-15

Metabolic processes are often represented as a group of metabolites that interact through enzymatic reactions, thus forming a network of linked biochemical pathways. Implicit in this view is that diffusion of metabolites to and from enzymes is very fast compared with reaction rates, and metabolic fluxes are therefore almost exclusively dictated by catalytic properties. However, diffusion may exert greater control over the rates of reactions through: (1) an increase in reaction rates; (2) an increase in diffusion distances; or (3) a decrease in the relevant diffusion coefficients. It is therefore not surprising that skeletal muscle fibers have long been the focus of reaction-diffusion analyses because they have high and variable rates of ATP turnover, long diffusion distances, and hindered metabolite diffusion due to an abundance of intracellular barriers. Examination of the diversity of skeletal muscle fiber designs found in animals provides insights into the role that diffusion plays in governing both rates of metabolic fluxes and cellular organization. Experimental measurements of metabolic fluxes, diffusion distances and diffusion coefficients, coupled with reaction-diffusion mathematical models in a range of muscle types has started to reveal some general principles guiding muscle structure and metabolic function. Foremost among these is that metabolic processes in muscles do, in fact, appear to be largely reaction controlled and are not greatly limited by diffusion. However, the influence of diffusion is apparent in patterns of fiber growth and metabolic organization that appear to result from selective pressure to maintain reaction control of metabolism in muscle.

Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models

PubMed Central

Saa, Pedro A.; Nielsen, Lars K.

2016-01-01

Motivation: Computation of steady-state flux solutions in large metabolic models is routinely performed using flux balance analysis based on a simple LP (Linear Programming) formulation. A minimal requirement for thermodynamic feasibility of the flux solution is the absence of internal loops, which are enforced using ‘loopless constraints’. The resulting loopless flux problem is a substantially harder MILP (Mixed Integer Linear Programming) problem, which is computationally expensive for large metabolic models. Results: We developed a pre-processing algorithm that significantly reduces the size of the original loopless problem into an easier and equivalent MILP problem. The pre-processing step employs a fast matrix sparsification algorithm—Fast- sparse null-space pursuit (SNP)—inspired by recent results on SNP. By finding a reduced feasible ‘loop-law’ matrix subject to known directionalities, Fast-SNP considerably improves the computational efficiency in several metabolic models running different loopless optimization problems. Furthermore, analysis of the topology encoded in the reduced loop matrix enabled identification of key directional constraints for the potential permanent elimination of infeasible loops in the underlying model. Overall, Fast-SNP is an effective and simple algorithm for efficient formulation of loop-law constraints, making loopless flux optimization feasible and numerically tractable at large scale. Availability and Implementation: Source code for MATLAB including examples is freely available for download at http://www.aibn.uq.edu.au/cssb-resources under Software. Optimization uses Gurobi, CPLEX or GLPK (the latter is included with the algorithm). Contact: lars.nielsen@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27559155

Flux Imbalance Analysis and the Sensitivity of Cellular Growth to Changes in Metabolite Pools

PubMed Central

Reznik, Ed; Mehta, Pankaj; Segrè, Daniel

2013-01-01

Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell. PMID:24009492

A Diel Flux Balance Model Captures Interactions between Light and Dark Metabolism during Day-Night Cycles in C3 and Crassulacean Acid Metabolism Leaves.

PubMed

Cheung, C Y Maurice; Poolman, Mark G; Fell, David A; Ratcliffe, R George; Sweetlove, Lee J

2014-06-01

Although leaves have to accommodate markedly different metabolic flux patterns in the light and the dark, models of leaf metabolism based on flux-balance analysis (FBA) have so far been confined to consideration of the network under continuous light. An FBA framework is presented that solves the two phases of the diel cycle as a single optimization problem and, thus, provides a more representative model of leaf metabolism. The requirement to support continued export of sugar and amino acids from the leaf during the night and to meet overnight cellular maintenance costs forces the model to set aside stores of both carbon and nitrogen during the day. With only minimal constraints, the model successfully captures many of the known features of C 3 leaf metabolism, including the recently discovered role of citrate synthesis and accumulation in the night as a precursor for the provision of carbon skeletons for amino acid synthesis during the day. The diel FBA model can be applied to other temporal separations, such as that which occurs in Crassulacean acid metabolism (CAM) photosynthesis, allowing a system-level analysis of the energetics of CAM. The diel model predicts that there is no overall energetic advantage to CAM, despite the potential for suppression of photorespiration through CO 2 concentration. Moreover, any savings in enzyme machinery costs through suppression of photorespiration are likely to be offset by the higher flux demand of the CAM cycle. It is concluded that energetic or nitrogen use considerations are unlikely to be evolutionary drivers for CAM photosynthesis. © 2014 American Society of Plant Biologists. All Rights Reserved.

A Diel Flux Balance Model Captures Interactions between Light and Dark Metabolism during Day-Night Cycles in C3 and Crassulacean Acid Metabolism Leaves1[C][W][OPEN

PubMed Central

Cheung, C.Y. Maurice; Poolman, Mark G.; Fell, David. A.; Ratcliffe, R. George; Sweetlove, Lee J.

2014-01-01

Although leaves have to accommodate markedly different metabolic flux patterns in the light and the dark, models of leaf metabolism based on flux-balance analysis (FBA) have so far been confined to consideration of the network under continuous light. An FBA framework is presented that solves the two phases of the diel cycle as a single optimization problem and, thus, provides a more representative model of leaf metabolism. The requirement to support continued export of sugar and amino acids from the leaf during the night and to meet overnight cellular maintenance costs forces the model to set aside stores of both carbon and nitrogen during the day. With only minimal constraints, the model successfully captures many of the known features of C3 leaf metabolism, including the recently discovered role of citrate synthesis and accumulation in the night as a precursor for the provision of carbon skeletons for amino acid synthesis during the day. The diel FBA model can be applied to other temporal separations, such as that which occurs in Crassulacean acid metabolism (CAM) photosynthesis, allowing a system-level analysis of the energetics of CAM. The diel model predicts that there is no overall energetic advantage to CAM, despite the potential for suppression of photorespiration through CO2 concentration. Moreover, any savings in enzyme machinery costs through suppression of photorespiration are likely to be offset by the higher flux demand of the CAM cycle. It is concluded that energetic or nitrogen use considerations are unlikely to be evolutionary drivers for CAM photosynthesis. PMID:24596328

Dynamic optimization of metabolic networks coupled with gene expression.

PubMed

Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

2015-01-21

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright © 2014 Elsevier Ltd. All rights reserved.

Creating metabolic demand as an engineering strategy in Pseudomonas putida - Rhamnolipid synthesis as an example.

PubMed

Tiso, Till; Sabelhaus, Petra; Behrens, Beate; Wittgens, Andreas; Rosenau, Frank; Hayen, Heiko; Blank, Lars Mathias

2016-12-01

Metabolic engineering of microbial cell factories for the production of heterologous secondary metabolites implicitly relies on the intensification of intracellular flux directed toward the product of choice. Apart from reactions following peripheral pathways, enzymes of the central carbon metabolism are usually targeted for the enhancement of precursor supply. In Pseudomonas putida , a Gram-negative soil bacterium, central carbon metabolism, i.e., the reactions required for the synthesis of all 12 biomass precursors, was shown to be regulated at the metabolic level and not at the transcriptional level. The bacterium's central carbon metabolism appears to be driven by demand to react rapidly to ever-changing environmental conditions. In contrast, peripheral pathways that are only required for growth under certain conditions are regulated transcriptionally. In this work, we show that this regulation regime can be exploited for metabolic engineering. We tested this driven-by-demand metabolic engineering strategy using rhamnolipid production as an example. Rhamnolipid synthesis relies on two pathways, i.e., fatty acid de novo synthesis and the rhamnose pathway, providing the required precursors hydroxyalkanoyloxy-alkanoic acid (HAA) and activated (dTDP-)rhamnose, respectively. In contrast to single-pathway molecules, rhamnolipid synthesis causes demand for two central carbon metabolism intermediates, i.e., acetyl-CoA for HAA and glucose-6-phosphate for rhamnose synthesis. Following the above-outlined strategy of driven by demand, a synthetic promoter library was developed to identify the optimal expression of the two essential genes ( rhlAB ) for rhamnolipid synthesis. The best rhamnolipid-synthesizing strain had a yield of 40% rhamnolipids on sugar [Cmol RL /Cmol Glc ], which is approximately 55% of the theoretical yield. The rate of rhamnolipid synthesis of this strain was also high. Compared to an exponentially growing wild type, the rhamnose pathway increased its flux by 300%, whereas the flux through de novo fatty acid synthesis increased by 50%. We show that the central carbon metabolism of P. putida is capable of meeting the metabolic demand generated by engineering transcription in peripheral pathways, thereby enabling a significant rerouting of carbon flux toward the product of interest, in this case, rhamnolipids of industrial interest.

Red blood cell storage in additive solution-7 preserves energy and redox metabolism: a metabolomics approach.

PubMed

D'Alessandro, Angelo; Nemkov, Travis; Hansen, Kirk C; Szczepiorkowski, Zbigniew M; Dumont, Larry J

2015-12-01

Storage and transfusion of red blood cells (RBCs) has a huge medical and economic impact. Routine storage practices can be ameliorated through the implementation of novel additive solutions (ASs) that tackle the accumulation of biochemical and morphologic lesion during routine cold liquid storage in the blood bank, such as the recently introduced alkaline solution AS-7. Here we hypothesize that AS-7 might exert its beneficial effects through metabolic modulation during routine storage. Apheresis RBCs were resuspended either in control AS-3 or experimental AS-7, before ultrahigh-performance liquid chromatography-mass spectrometry metabolomics analysis. Unambiguous assignment and relative quantitation was achieved for 229 metabolites. AS-3 and AS-7 results in many similar metabolic trends over storage, with AS-7 RBCs being more metabolically active in the first storage week. AS-7 units had faster fueling of the pentose phosphate pathway, higher total glutathione pools, and increased flux through glycolysis as indicated by higher levels of pathway intermediates. Metabolite differences are especially observed at 7 days of storage, but were still maintained throughout 42 days. AS-7 formulation (chloride free and bicarbonate loading) appears to improve energy and redox metabolism in stored RBCs in the early storage period, and the differences, though diminished, are still appreciable by Day 42. Energy metabolism and free fatty acids should be investigated as potentially important determinants for preservation of RBC structure and function. Future studies will be aimed at identifying metabolites that correlate with in vitro and in vivo circulation times. © 2015 AABB.

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khodayari, Ali; Maranas, Costas D.

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimentalmore » data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.« less

Phototransduction Influences Metabolic Flux and Nucleotide Metabolism in Mouse Retina.

PubMed

Du, Jianhai; Rountree, Austin; Cleghorn, Whitney M; Contreras, Laura; Lindsay, Ken J; Sadilek, Martin; Gu, Haiwei; Djukovic, Danijel; Raftery, Dan; Satrústegui, Jorgina; Kanow, Mark; Chan, Lawrence; Tsang, Stephen H; Sweet, Ian R; Hurley, James B

2016-02-26

Production of energy in a cell must keep pace with demand. Photoreceptors use ATP to maintain ion gradients in darkness, whereas in light they use it to support phototransduction. Matching production with consumption can be accomplished by coupling production directly to consumption. Alternatively, production can be set by a signal that anticipates demand. In this report we investigate the hypothesis that signaling through phototransduction controls production of energy in mouse retinas. We found that respiration in mouse retinas is not coupled tightly to ATP consumption. By analyzing metabolic flux in mouse retinas, we also found that phototransduction slows metabolic flux through glycolysis and through intermediates of the citric acid cycle. We also evaluated the relative contributions of regulation of the activities of α-ketoglutarate dehydrogenase and the aspartate-glutamate carrier 1. In addition, a comprehensive analysis of the retinal metabolome showed that phototransduction also influences steady-state concentrations of 5'-GMP, ribose-5-phosphate, ketone bodies, and purines. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

DOE PAGES

Khodayari, Ali; Maranas, Costas D.

2016-12-20

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimentalmore » data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.« less

Probing soil C metabolism in response to temperature: results from experiments and modeling

NASA Astrophysics Data System (ADS)

Dijkstra, P.; Dalder, J.; Blankinship, J.; Selmants, P. C.; Schwartz, E.; Koch, G. W.; Hart, S.; Hungate, B. A.

2010-12-01

C use efficiency (CUE) is one of the least understood aspects of soil C cycling, has a very large effect on soil respiration and C sequestration, and decreases with elevated temperature. CUE is directly related to substrate partitioning over energy production and biosynthesis. The production of energy and metabolic precursors occurs in well-known processes such as glycolysis and Krebs cycle. We have developed a new stable isotope approach using position-specific 13C-labeled metabolic tracers to measure these fundamental metabolic processes in intact soil communities (1). We use this new approach, combined with models of soil metabolic flux patterns, to analyze the response of microbial energy production, biosynthesis, and CUE to temperature. The method consists of adding small but precise amounts of position-specific 13C -labeled metabolic tracers to parallel soil incubations, in this case 1-13C and 2,3-13C pyruvate and 1-13C and U-13C glucose. The measurement of CO2 released from the labeled tracers is used to calculate the C flux rates through various metabolic pathways. A simplified metabolic model consisting of 23 reactions is iteratively solved using results of the metabolic tracer experiments and information on microbial precursor demand under different temperatures. This new method enables direct study of fundamental aspects of microbial energy production, C use efficiency, and soil organic matter formation in response to temperature. (1) Dijkstra P, Blankinship JC, Selmants PC, Hart SC, Koch GW, Schwarz E and Hungate BA. Probing metabolic flux patterns of soil microbial communities using parallel position-specific tracer labeling. Soil Biology and Biochemistry (accepted)

Modeling Phenotypic Metabolic Adaptations of Mycobacterium tuberculosis H37Rv under Hypoxia

DTIC Science & Technology

2012-09-13

experimentally measured reaction fluxes from two separate laboratories. These studies examined metabolic fluxes in yeast grown on four different carbon...Boshoff and coworkers recently confirmed the importance of fermentation in latent hypoxic M. tuberculosis by analyzing metabolite isotopes in the...relative changes in mRNA levels have been shown to be correlated to protein abundance in several studies of Saccharomyces cerevisiae ( yeast ) and

Metabolism estimates in small boreal lakes: the importance of accounting for vertical fluxes of oxygen

NASA Astrophysics Data System (ADS)

Klaus, M.; MacIntyre, S.; Hotchkiss, E. R.; Bergström, A. K.; Karlsson, J.

2015-12-01

Lake metabolism models based on the diel oxygen technique often assume that oxygen dynamics are mainly controlled by metabolic processes, only accounting for wind-driven atmospheric gas exchange. However, oxygen dynamics can also be affected by abiotic mass fluxes across oxygen gradients within lakes and atmospheric gas exchange driven by convection. Here, we quantify how much vertical fluxes of oxygen modify epilimnetic metabolism estimates for three pairs of small Swedish boreal lakes, one of each fertilized with nitrate, with dissolved organic carbon (DOC) concentrations of 7 to 22 mg l-1. Oxygen concentrations were measured every 10 min at 50 cm depth and biweekly across depths profiles during one full open water period. Based on additional two weeks of ten-minute oxygen profiling we calculated vertical fluxes of oxygen using equations for atmospheric gas exchange caused by wind shear (F1) and convection (F2), and lake-internal gas exchange caused by diffusion and mixed layer deepening (F3). We ran three inverse Bayesian models to estimate daily metabolism: (M1) accounting for F1, (M2) accounting for F1 and F2, and (M3) accounting for F1 and F3. Initial results suggest that gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) ranged from 0.1 to 0.2, -0.3 to -0.5 and -0.2 to -0.4 g C m-2 d-1, respectively. GPP and R were higher in fertilized lakes and at the lower end of previous worldwide estimates. Accounting for convection-driven gas exchange increased ER estimates by 10-40% (M2 vs. M1). This bias increased with DOC concentration but was not affected by fertilization. Including lake-internal vertical oxygen fluxes changed GPP and ER estimates by up to ±40% (M3 vs. M1), with inconsistent trends along the DOC-gradient. We conclude that vertical fluxes of oxygen can significantly affect diel oxygen dynamics in oligotrophic humic systems and should therefore be included in metabolism models applied to small boreal lakes.

The Glyoxylate Cycle in an Arbuscular Mycorrhizal Fungus. Carbon Flux and Gene Expression

PubMed Central

Lammers, Peter J.; Jun, Jeongwon; Abubaker, Jehad; Arreola, Raul; Gopalan, Anjali; Bago, Berta; Hernandez-Sebastia, Cinta; Allen, James W.; Douds, David D.; Pfeffer, Philip E.; Shachar-Hill, Yair

2001-01-01

The arbuscular mycorrhizal (AM) symbiosis is responsible for huge fluxes of photosynthetically fixed carbon from plants to the soil. Lipid, which is the dominant form of stored carbon in the fungal partner and which fuels spore germination, is made by the fungus within the root and is exported to the extraradical mycelium. We tested the hypothesis that the glyoxylate cycle is central to the flow of carbon in the AM symbiosis. The results of 13C labeling of germinating spores and extraradical mycelium with 13C2-acetate and 13C2-glycerol and analysis by nuclear magnetic resonance spectroscopy indicate that there are very substantial fluxes through the glyoxylate cycle in the fungal partner. Full-length sequences obtained by polymerase chain reaction from a cDNA library from germinating spores of the AM fungus Glomus intraradices showed strong homology to gene sequences for isocitrate lyase and malate synthase from plants and other fungal species. Quantitative real-time polymerase chain reaction measurements show that these genes are expressed at significant levels during the symbiosis. Glyoxysome-like bodies were observed by electron microscopy in fungal structures where the glyoxylate cycle is expected to be active, which is consistent with the presence in both enzyme sequences of motifs associated with glyoxysomal targeting. We also identified among several hundred expressed sequence tags several enzymes of primary metabolism whose expression during spore germination is consistent with previous labeling studies and with fluxes into and out of the glyoxylate cycle. PMID:11706207

DOSIMETRY MODELING OF INHALED FORMALDEHYDE: BINNING NASAL FLUX PREDICTIONS FOR QUANTITATIVE RISK ASSESSMENT

EPA Science Inventory

Dosimetry Modeling of Inhaled Formaldehyde: Binning Nasal Flux Predictions for Quantitative Risk Assessment. Kimbell, J.S., Overton, J.H., Subramaniam, R.P., Schlosser, P.M., Morgan, K.T., Conolly, R.B., and Miller, F.J. (2001). Toxicol. Sci. 000, 000:000.

Interspecies e...

(13)C-metabolic flux analysis in S-adenosyl-L-methionine production by Saccharomyces cerevisiae.

PubMed

Hayakawa, Kenshi; Kajihata, Shuichi; Matsuda, Fumio; Shimizu, Hiroshi

2015-11-01

S-Adenosyl-L-methionine (SAM) is a major biological methyl group donor, and is used as a nutritional supplement and prescription drug. Yeast is used for the industrial production of SAM owing to its high intracellular SAM concentrations. To determine the regulation mechanisms responsible for such high SAM production, (13)C-metabolic flux analysis ((13)C-MFA) was conducted to compare the flux distributions in the central metabolism between Kyokai no. 6 (high SAM-producing) and S288C (control) strains. (13)C-MFA showed that the levels of tricarboxylic acid (TCA) cycle flux in SAM-overproducing strain were considerably increased compared to those in the S228C strain. Analysis of ATP balance also showed that a larger amount of excess ATP was produced in the Kyokai 6 strain because of increased oxidative phosphorylation. These results suggest that high SAM production in Kyokai 6 strains could be attributed to enhanced ATP regeneration with high TCA cycle fluxes and respiration activity. Thus, maintaining high respiration efficiency during cultivation is important for improving SAM production. Copyright © 2015 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Metabolic flux phenotype of tobacco hairy roots engineered for increased geraniol production.

PubMed

Masakapalli, Shyam K; Ritala, Anneli; Dong, Lemeng; van der Krol, Alexander R; Oksman-Caldentey, Kirsi-Marja; Ratcliffe, R George; Sweetlove, Lee J

2014-03-01

The goal of this study was to characterise the metabolic flux phenotype of transgenic tobacco (Nicotiana tabacum) hairy roots engineered for increased biosynthesis of geraniol, an intermediate of the terpenoid indole alkaloid pathway. Steady state, stable isotope labelling was used to determine flux maps of central carbon metabolism for transgenic lines over-expressing (i) plastid-targeted geraniol synthase (pGES) from Valeriana officinalis, and (ii) pGES in combination with plastid-targeted geranyl pyrophosphate synthase from Arabidopsis thaliana (pGES+pGPPS), as well as for wild type and control-vector-transformed roots. Fluxes were constrained by the redistribution of label from [1-¹³C]-, [2-¹³C]- or [¹³C6]glucose into amino acids, sugars and organic acids at isotopic steady state, and by biomass output fluxes determined from the fractionation of [U-¹⁴C]glucose into insoluble polymers. No significant differences in growth and biomass composition were observed between the lines. The pGES line accumulated significant amounts of geraniol/geraniol glycosides (151±24 ng/mg dry weight) and the de novo synthesis of geraniol in pGES was confirmed by ¹³C labelling analysis. The pGES+pGPPS also accumulated geraniol and geraniol glycosides, but to lower levels than the pGES line. Although there was a distinct impact of the transgenes at the level of geraniol synthesis, other network fluxes were unaffected, reflecting the capacity of central metabolism to meet the relatively modest demand for increased precursors in the transgenic lines. It is concluded that re-engineering of the terpenoid indole alkaloid pathway will only require simultaneous manipulation of the steps producing the pathway precursors that originate in central metabolism in tissues engineered to produce at least an order of magnitude more geraniol than has been achieved so far. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multi-site modulation of flux during monolignol formation in loblolly pine (Pinus taeda)

NASA Technical Reports Server (NTRS)

Anterola, A. M.; van Rensburg, H.; van Heerden, P. S.; Davin, L. B.; Lewis, N. G.

1999-01-01

Loblolly pine (Pinus taeda L.) cell suspension cultures secrete monolignols when placed in 8% sucrose/20 mM KI solution, and these were used to identify phenylpropanoid pathway flux-modulating steps. When cells were provided with increasing amounts of either phenylalanine (Phe) or cinnamic acid, cellular concentrations of immediate downstream products (cinnamic and p-coumaric acids, respectively) increased, whereas caffeic and ferulic acid pool sizes were essentially unaffected. Increasing Phe concentrations resulted in increased amounts of p-coumaryl alcohol relative to coniferyl alcohol. However, exogenously supplied cinnamic, p-coumaric, caffeic, and ferulic acids resulted only in increases in their intercellular concentrations, but not that of downstream cinnamyl aldehydes and monolignols. Supplying p-coumaryl and coniferyl aldehydes up to 40, 000-320,000-fold above the detection limits resulted in rapid, quantitative conversion into the monolignols. Only at nonphysiological concentrations was transient accumulation of intracellular aldehydes observed. These results indicate that cinnamic and p-coumaric acid hydroxylations assume important regulatory positions in phenylpropanoid metabolism, whereas cinnamyl aldehyde reduction does not serve as a control point. Copyright 1999 Academic Press.

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses.

PubMed

Desai, Trunil S; Srivastava, Shireesh

2018-01-01

13 C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13 C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13 C-MFA software that works in various operating systems will enable more researchers to perform 13 C-MFA and to further modify and develop the package.

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses

PubMed Central

Desai, Trunil S.

2018-01-01

13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package. PMID:29736347

Metabolic profiling of PPARα−/− mice reveals defects in carnitine and amino acid homeostasis that are partially reversed by oral carnitine supplementation

PubMed Central

Makowski, Liza; Noland, Robert C.; Koves, Timothy R.; Xing, Weibing; Ilkayeva, Olga R.; Muehlbauer, Michael J.; Stevens, Robert D.; Muoio, Deborah M.

2009-01-01

Peroxisome proliferator-activated receptor-α (PPARα) is a master transcriptional regulator of β-oxidation and a prominent target of hypolipidemic drugs. To gain deeper insights into the systemic consequences of impaired fat catabolism, we used quantitative, mass spectrometry-based metabolic profiling to investigate the fed-to-fasted transition in PPARα+/+ and PPARα−/− mice. Compared to PPARα+/+ animals, acylcarnitine profiles of PPARα−/− mice revealed 2- to 4-fold accumulation of long-chain species in the plasma, whereas short-chain species were reduced by as much as 69% in plasma, liver, and skeletal muscle. These results reflect a metabolic bottleneck downstream of carnitine palmitoyltransferase-1, a mitochondrial enzyme that catalyzes the first step in β-oxidation. Organic and amino acid profiles of starved PPARα−/− mice suggested compromised citric acid cycle flux, enhanced urea cycle activity, and increased amino acid catabolism. PPARα−/− mice had 40–50% lower plasma and tissue levels of free carnitine, corresponding with diminished hepatic expression of genes involved in carnitine biosynthesis and transport. One week of oral carnitine supplementation conferred partial metabolic recovery in the PPARα−/− mice. In summary, comprehensive metabolic profiling revealed novel biomarkers of defective fat oxidation, while also highlighting the potential value of supplemental carnitine as a therapy and diagnostic tool for metabolic disorders.—Makowski, L., Noland, R. C., Koves, T. R., Xing, W., Ilkayeva, O. R., Muehlbauer, M. J., Stevens, R. D., Muoio, D. M. Metabolic profiling of PPARα−/− mice reveals defects in carnitine and amino acid homeostasis that are partially reversed by oral carnitine supplementation. PMID:18945875

Fermentation of Xylose Causes Inefficient Metabolic State Due to Carbon/Energy Starvation and Reduced Glycolytic Flux in Recombinant Industrial Saccharomyces cerevisiae

PubMed Central

Matsushika, Akinori; Nagashima, Atsushi; Goshima, Tetsuya; Hoshino, Tamotsu

2013-01-01

In the present study, comprehensive, quantitative metabolome analysis was carried out on the recombinant glucose/xylose-cofermenting S. cerevisiae strain MA-R4 during fermentation with different carbon sources, including glucose, xylose, or glucose/xylose mixtures. Capillary electrophoresis time-of-flight mass spectrometry was used to determine the intracellular pools of metabolites from the central carbon pathways, energy metabolism pathways, and the levels of twenty amino acids. When xylose instead of glucose was metabolized by MA-R4, glycolytic metabolites including 3- phosphoglycerate, 2- phosphoglycerate, phosphoenolpyruvate, and pyruvate were dramatically reduced, while conversely, most pentose phosphate pathway metabolites such as sedoheptulose 7- phosphate and ribulose 5-phosphate were greatly increased. These results suggest that the low metabolic activity of glycolysis and the pool of pentose phosphate pathway intermediates are potential limiting factors in xylose utilization. It was further demonstrated that during xylose fermentation, about half of the twenty amino acids declined, and the adenylate/guanylate energy charge was impacted due to markedly decreased adenosine triphosphate/adenosine monophosphate and guanosine triphosphate/guanosine monophosphate ratios, implying that the fermentation of xylose leads to an inefficient metabolic state where the biosynthetic capabilities and energy balance are severely impaired. In addition, fermentation with xylose alone drastically increased the level of citrate in the tricarboxylic acid cycle and increased the aromatic amino acids tryptophan and tyrosine, strongly supporting the view that carbon starvation was induced. Interestingly, fermentation with xylose alone also increased the synthesis of the polyamine spermidine and its precursor S-adenosylmethionine. Thus, differences in carbon substrates, including glucose and xylose in the fermentation medium, strongly influenced the dynamic metabolism of MA-R4. These results provide a metabolic explanation for the low ethanol productivity on xylose compared to glucose. PMID:23874849

Multifaceted plant responses to circumvent Phe hyperaccumulation by downregulation of flux through the shikimate pathway and by vacuolar Phe sequestration.

PubMed

Lynch, Joseph H; Orlova, Irina; Zhao, Chengsong; Guo, Longyun; Jaini, Rohit; Maeda, Hiroshi; Akhtar, Tariq; Cruz-Lebron, Junellie; Rhodes, David; Morgan, John; Pilot, Guillaume; Pichersky, Eran; Dudareva, Natalia

2017-12-01

Detrimental effects of hyperaccumulation of the aromatic amino acid phenylalanine (Phe) in animals, known as phenylketonuria, are mitigated by excretion of Phe derivatives; however, how plants endure Phe accumulating conditions in the absence of an excretion system is currently unknown. To achieve Phe hyperaccumulation in a plant system, we simultaneously decreased in petunia flowers expression of all three Phe ammonia lyase (PAL) isoforms that catalyze the non-oxidative deamination of Phe to trans-cinnamic acid, the committed step for the major pathway of Phe metabolism. A total decrease in PAL activity by 81-94% led to an 18-fold expansion of the internal Phe pool. Phe accumulation had multifaceted intercompartmental effects on aromatic amino acid metabolism. It resulted in a decrease in the overall flux through the shikimate pathway, and a redirection of carbon flux toward the shikimate-derived aromatic amino acids tyrosine and tryptophan. Accumulation of Phe did not lead to an increase in flux toward phenylacetaldehyde, for which Phe is a direct precursor. Metabolic flux analysis revealed this to be due to the presence of a distinct metabolically inactive pool of Phe, likely localized in the vacuole. We have identified a vacuolar cationic amino acid transporter (PhCAT2) that contributes to sequestering excess of Phe in the vacuole. In vitro assays confirmed PhCAT2 can transport Phe, and decreased PhCAT2 expression in PAL-RNAi transgenic plants resulted in 1.6-fold increase in phenylacetaldehyde emission. These results demonstrate mechanisms by which plants maintain intercompartmental aromatic amino acid homeostasis, and provide critical insight for future phenylpropanoid metabolic engineering strategies. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Glutamatergic and GABAergic neurotransmitter cycling and energy metabolism in rat cerebral cortex during postnatal development.

PubMed

Chowdhury, Golam M I; Patel, Anant B; Mason, Graeme F; Rothman, Douglas L; Behar, Kevin L

2007-12-01

The contribution of glutamatergic and gamma-aminobutyric acid (GABA)ergic neurons to oxidative energy metabolism and neurotransmission in the developing brain is not known. Glutamatergic and GABAergic fluxes were assessed in neocortex of postnatal day 10 (P10) and 30 (P30) urethane-anesthetized rats infused intravenously with [1,6-(13)C(2)]glucose for different time intervals (time course) or with [2-(13)C]acetate for 2 to 3 h (steady state). Amino acid levels and (13)C enrichments were determined in tissue extracts ex vivo using (1)H-[(13)C]-NMR spectroscopy. Metabolic fluxes were estimated from the best fits of a three-compartment metabolic model (glutamatergic neurons, GABAergic neurons, and astroglia) to the (13)C-enrichment time courses of amino acids from [1,6-(13)C(2)]glucose, constrained by the ratios of neurotransmitter cycling (V(cyc))-to-tricarboxylic acid (TCA) cycle flux (V(TCAn)) calculated from the steady-state [2-(13)C]acetate enrichment data. From P10 to P30 increases in total neuronal (glutamate plus GABA) TCA cycle flux (3 x ; 0.24+/-0.05 versus 0.71+/-0.07 micromol per g per min, P<0.0001) and total neurotransmitter cycling flux (3.1 to 5 x ; 0.07 to 0.11 (+/-0.03) versus 0.34+/-0.03 micromol per g per min, P<0.0001) were approximately proportional. Incremental changes in total cycling (DeltaV(cyc(tot))) and neuronal TCA cycle flux (DeltaV(TCAn(tot))) between P10 and P30 were 0.23 to 0.27 and 0.47 micromol per g per min, respectively, similar to the approximately 1:2 relationship previously reported for adult cortex. For the individual neurons, increases in V(TCAn) and V(cyc) were similar in magnitude (glutamatergic neurons, 2.7 x versus 2.8 to 4.6 x ; GABAergic neurons, approximately 5 x versus approximately 7 x), although GABAergic flux changes were larger. The findings show that glutamate and GABA neurons undergo large and approximately proportional increases in neurotransmitter cycling and oxidative energy metabolism during this major postnatal growth spurt.

Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast

PubMed Central

Misra, Ashish; Conway, Matthew F.; Johnnie, Joseph; Qureshi, Tabish M.; Lige, Bao; Derrick, Anne M.; Agbo, Eddy C.; Sriram, Ganesh

2013-01-01

Synthetic biology enables metabolic engineering of industrial microbes to synthesize value-added molecules. In this, a major challenge is the efficient redirection of carbon to the desired metabolic pathways. Pinpointing strategies toward this goal requires an in-depth investigation of the metabolic landscape of the organism, particularly primary metabolism, to identify precursor and cofactor availability for the target compound. The potent antimalarial therapeutic artemisinin and its precursors are promising candidate molecules for production in microbial hosts. Recent advances have demonstrated the production of artemisinin precursors in engineered yeast strains as an alternative to extraction from plants. We report the application of in silico and in vivo metabolic pathway analyses to identify metabolic engineering targets to improve the yield of the direct artemisinin precursor dihydroartemisinic acid (DHA) in yeast. First, in silico extreme pathway (ExPa) analysis identified NADPH-malic enzyme and the oxidative pentose phosphate pathway (PPP) as mechanisms to meet NADPH demand for DHA synthesis. Next, we compared key DHA-synthesizing ExPas to the metabolic flux distributions obtained from in vivo 13C metabolic flux analysis of a DHA-synthesizing strain. This comparison revealed that knocking out ethanol synthesis and overexpressing glucose-6-phosphate dehydrogenase in the oxidative PPP (gene YNL241C) or the NADPH-malic enzyme ME2 (YKL029C) are vital steps toward overproducing DHA. Finally, we employed in silico flux balance analysis and minimization of metabolic adjustment on a yeast genome-scale model to identify gene knockouts for improving DHA yields. The best strategy involved knockout of an oxaloacetate transporter (YKL120W) and an aspartate aminotransferase (YKL106W), and was predicted to improve DHA yields by 70-fold. Collectively, our work elucidates multiple non-trivial metabolic engineering strategies for improving DHA yield in yeast. PMID:23898325

Fast flux module detection using matroid theory.

PubMed

Reimers, Arne C; Bruggeman, Frank J; Olivier, Brett G; Stougie, Leen

2015-05-01

Flux balance analysis (FBA) is one of the most often applied methods on genome-scale metabolic networks. Although FBA uniquely determines the optimal yield, the pathway that achieves this is usually not unique. The analysis of the optimal-yield flux space has been an open challenge. Flux variability analysis is only capturing some properties of the flux space, while elementary mode analysis is intractable due to the enormous number of elementary modes. However, it has been found by Kelk et al. (2012) that the space of optimal-yield fluxes decomposes into flux modules. These decompositions allow a much easier but still comprehensive analysis of the optimal-yield flux space. Using the mathematical definition of module introduced by Müller and Bockmayr (2013b), we discovered useful connections to matroid theory, through which efficient algorithms enable us to compute the decomposition into modules in a few seconds for genome-scale networks. Using that every module can be represented by one reaction that represents its function, in this article, we also present a method that uses this decomposition to visualize the interplay of modules. We expect the new method to replace flux variability analysis in the pipelines for metabolic networks.

On the effects of alternative optima in context-specific metabolic model predictions

PubMed Central

Nikoloski, Zoran

2017-01-01

The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed—generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative models. Finally, our findings indicate that alternative optima must be taken into account for rendering the context-specific metabolic model predictions less ambiguous. PMID:28557990

On the effects of alternative optima in context-specific metabolic model predictions.

PubMed

Robaina-Estévez, Semidán; Nikoloski, Zoran

2017-05-01

The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed-generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative models. Finally, our findings indicate that alternative optima must be taken into account for rendering the context-specific metabolic model predictions less ambiguous.

An ancient Chinese wisdom for metabolic engineering: Yin-Yang

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Stephen G.; He, Lian; Wang, Qingzhao

In ancient Chinese philosophy, Yin-Yang describes two contrary forces that are interconnected and interdependent. This concept also holds true in microbial cell factories, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes. On the other hand, microbial metabolism loses significant free energy as heat when converting sugar into ATP; while maintenance energy expenditures further aggravate ATP shortage. The limitation of cell “powerhouse” prevents hosts from achieving high carbon yields and rates. Via an Escherichia coli flux balance analysismore » model, we further demonstrate the penalty of ATP cost on biofuel synthesis. To ensure cell powerhouse being sufficient in microbial cell factories, we propose five principles: 1. Take advantage of native pathways for product synthesis. 2. Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3. Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4. Create “minimal cells” or use non-model microbial hosts with higher energy fitness. 5. Develop a photosynthesis chassis that can utilize light energy and cheap carbon feedstocks. Meanwhile, metabolic flux analysis can be used to quantify both carbon and energy metabolisms. The fluxomics results are essential to evaluate the industrial potential of laboratory strains, avoiding false starts and dead ends during metabolic engineering« less

An ancient Chinese wisdom for metabolic engineering: Yin-Yang

DOE PAGES

Wu, Stephen G.; He, Lian; Wang, Qingzhao; ...

2015-03-20

In ancient Chinese philosophy, Yin-Yang describes two contrary forces that are interconnected and interdependent. This concept also holds true in microbial cell factories, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes. On the other hand, microbial metabolism loses significant free energy as heat when converting sugar into ATP; while maintenance energy expenditures further aggravate ATP shortage. The limitation of cell “powerhouse” prevents hosts from achieving high carbon yields and rates. Via an Escherichia coli flux balance analysismore » model, we further demonstrate the penalty of ATP cost on biofuel synthesis. To ensure cell powerhouse being sufficient in microbial cell factories, we propose five principles: 1. Take advantage of native pathways for product synthesis. 2. Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3. Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4. Create “minimal cells” or use non-model microbial hosts with higher energy fitness. 5. Develop a photosynthesis chassis that can utilize light energy and cheap carbon feedstocks. Meanwhile, metabolic flux analysis can be used to quantify both carbon and energy metabolisms. The fluxomics results are essential to evaluate the industrial potential of laboratory strains, avoiding false starts and dead ends during metabolic engineering« less

Quantitative role of splanchnic region in leucine metabolism: L-(1-13C,15N)leucine and substrate balance studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Y.M.; Wagner, D.A.; Tredget, E.E.

1990-07-01

The role of the splanchnic region (Sp) in whole body leucine metabolism was assessed in six chronically catheterized fasting mongrel dogs and in eight dogs during constant enteral feeding of a complete amino acid solution (0.24 g.kg-1.h-1). We used primed continuous intravenous infusions of L-(1-13C,15N)leucine and L-(1-14C)leucine and measurements of arteriovenous isotope and leucine balance across the gut, liver, and Sp. In the fasted condition, 3.5% of arterial leucine supply was oxidized in the Sp, accounting for 13% of total body leucine oxidation, with 10% by liver. With amino acid feeding (1) leucine carbon and nitrogen fluxes and oxidation weremore » increased (P less than 0.01) at the whole body level; (2) the percent of whole body leucine oxidation occurring in the Sp and liver increased (P less than 0.01) to 41 and 27%, respectively; (3) fractional metabolic utilization of leucine delivered to the Sp was reduced (P less than 0.01) from 47 to 35%; (4) the deamination rate of leucine in the gut was increased (P less than 0.05), along with an increased reamination rate of alpha-ketoisocaproic acid in the Sp (P less than 0.05). These findings reveal that the Sp accounts for a small fraction of whole body leucine oxidation during the fasting condition, but it plays a quantitatively important role in total body leucine oxidation during amino acid feeding; the gut and liver play cooperative roles in controlling leucine supply to peripheral tissues.« less

Quantitative Proteomic Profiling of Early and Late Responses to Salicylic Acid in Cucumber Leaves

PubMed Central

Li, Liang; Shang, Qing-Mao

2016-01-01

Salicylic acid (SA) is an important phytohormone that plays vital regulatory roles in plant growth, development, and stress responses. However, studies on the molecular mechanism of SA, especially during the early SA responses, are lagging behind. In this study, we initiated a comprehensive isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis to explore the early and late SA-responsive proteins in leaves of cucumber (Cucumis sativus L.) seedlings. Upon SA application through the roots, endogenous SA accumulated in cucumber leaves. By assaying the changes in marker gene expression and photosynthetic rate, we collected samples at 12 h and 72 h post treatment (hpt) to profile the early and late SA responsiveness, respectively. The iTRAQ assay followed by tandem mass spectrometry revealed 135 differentially expressed proteins (DEPs) at 12 hpt and 301 DEPs at 72 hpt. The functional categories for these SA-responsive proteins included in a variety of biochemical processes, including photosynthesis, redox homeostasis, carbohydrate and energy metabolism, lipid metabolism, transport, protein folding and modification, proteolysis, cell wall organization, and the secondary phenylpropanoid pathway. Conclusively, based on the abundant changes of these DEPs, together with their putative functions, we proposed a possible SA-responsive protein network. It appears that SA could elicit reactive oxygen species (ROS) production via enhancing the photosynthetic electron transferring, and then confer some growth-promoting and stress-priming effects on cells during the late phase, including enhanced photosynthesis and ROS scavenging, altered carbon metabolic flux for the biosynthesis of amino acids and nucleotides, and cell wall reorganization. Overall, the present iTRAQ assay provides higher proteome coverage and deepened our understanding of the molecular basis of SA-responses. PMID:27551830

A mathematical model of liver metabolism: from steady state to dynamic

NASA Astrophysics Data System (ADS)

Calvetti, D.; Kuceyeski, A.; Somersalo, E.

2008-07-01

The increase in Type 2 diabetes and other metabolic disorders has led to an intense focus on the areas of research related to metabolism. Because the liver is essential in regulating metabolite concentrations that maintain life, it is especially important to have good knowledge of the functions within this organ. In silico mathematical models that can adequately describe metabolite concentrations, flux and transport rates in the liver in vivo can be a useful predictive tool. Fully dynamic models, which contain expressions for Michaelis-Menten reaction kinetics can be utilized to investigate different metabolic states, for example exercise, fed or starved state. In this paper we describe a two compartment (blood and tissue) spatially lumped liver metabolism model. First, we use Bayesian Flux Balance Analysis (BFBA) to estimate the values of flux and transport rates at steady state, which agree closely with values from the literature. These values are then used to find a set of Michaelis-Menten parameters and initial concentrations which identify a dynamic model that can be used for exploring different metabolic states. In particular, we investigate the effect of doubling the concentration of lactate entering the system via the hepatic artery and portal vein. This change in lactate concentration forces the system to a new steady state, where glucose production is increased.

Enhanced uridine 5'-monophosphate production by whole cell of Saccharomyces cerevisiae through rational redistribution of metabolic flux.

PubMed

Liu, Dong; Chen, Yong; Li, An; Xie, Jingjing; Xiong, Jian; Bai, Jianxin; Chen, Xiaochun; Niu, Huanqing; Zhou, Tao; Ying, Hanjie

2012-06-01

A whole-cell biocatalytic process for uridine 5'-monophosphate (UMP) production from orotic acid by Saccharomyces cerevisiae was developed. To rationally redistribute the metabolic flux between glycolysis and pentose phosphate pathway, statistical methods were employed first to find out the critical factors in the process. NaH(2)PO(4), MgCl(2) and pH were found to be the important factors affecting UMP production significantly. The levels of these three factors required for the maximum production of UMP were determined: NaH(2)PO(4) 22.1 g/L; MgCl(2) 2.55 g/L; pH 8.15. An enhancement of UMP production from 6.12 to 8.13 g/L was achieved. A significant redistribution of metabolic fluxes was observed and the underlying mechanism was discussed.

Cold adaptation increases rates of nutrient flow and metabolic plasticity during cold exposure in Drosophila melanogaster

PubMed Central

McCue, Marshall D.; Sunny, Nishanth E.; Szejner-Sigal, Andre; Morgan, Theodore J.; Allison, David B.; Hahn, Daniel A.

2016-01-01

Metabolic flexibility is an important component of adaptation to stressful environments, including thermal stress and latitudinal adaptation. A long history of population genetic studies suggest that selection on core metabolic enzymes may shape life histories by altering metabolic flux. However, the direct relationship between selection on thermal stress hardiness and metabolic flux has not previously been tested. We investigated flexibility of nutrient catabolism during cold stress in Drosophila melanogaster artificially selected for fast or slow recovery from chill coma (i.e. cold-hardy or -susceptible), specifically testing the hypothesis that stress adaptation increases metabolic turnover. Using 13C-labelled glucose, we first showed that cold-hardy flies more rapidly incorporate ingested carbon into amino acids and newly synthesized glucose, permitting rapid synthesis of proline, a compound shown elsewhere to improve survival of cold stress. Second, using glucose and leucine tracers we showed that cold-hardy flies had higher oxidation rates than cold-susceptible flies before cold exposure, similar oxidation rates during cold exposure, and returned to higher oxidation rates during recovery. Additionally, cold-hardy flies transferred compounds among body pools more rapidly during cold exposure and recovery. Increased metabolic turnover may allow cold-adapted flies to better prepare for, resist and repair/tolerate cold damage. This work illustrates for the first time differences in nutrient fluxes associated with cold adaptation, suggesting that metabolic costs associated with cold hardiness could invoke resource-based trade-offs that shape life histories. PMID:27605506

The Short-Chain Fatty Acid Uptake Fluxes by Mice on a Guar Gum Supplemented Diet Associate with Amelioration of Major Biomarkers of the Metabolic Syndrome

PubMed Central

den Besten, Gijs; Havinga, Rick; Bleeker, Aycha; Rao, Shodhan; Gerding, Albert; van Eunen, Karen; Groen, Albert K.; Reijngoud, Dirk-Jan; Bakker, Barbara M.

2014-01-01

Studies with dietary supplementation of various types of fibers have shown beneficial effects on symptoms of the metabolic syndrome. Short-chain fatty acids (SCFAs), the main products of intestinal bacterial fermentation of dietary fiber, have been suggested to play a key role. Whether the concentration of SCFAs or their metabolism drives these beneficial effects is not yet clear. In this study we investigated the SCFA concentrations and in vivo host uptake fluxes in the absence or presence of the dietary fiber guar gum. C57Bl/6J mice were fed a high-fat diet supplemented with 0%, 5%, 7.5% or 10% of the fiber guar gum. To determine the effect on SCFA metabolism, 13C-labeled acetate, propionate or butyrate were infused into the cecum of mice for 6 h and the isotopic enrichment of cecal SCFAs was measured. The in vivo production, uptake and bacterial interconversion of acetate, propionate and butyrate were calculated by combining the data from the three infusion experiments in a single steady-state isotope model. Guar gum treatment decreased markers of the metabolic syndrome (body weight, adipose weight, triglycerides, glucose and insulin levels and HOMA-IR) in a dose-dependent manner. In addition, hepatic mRNA expression of genes involved in gluconeogenesis and fatty acid synthesis decreased dose-dependently by guar gum treatment. Cecal SCFA concentrations were increased compared to the control group, but no differences were observed between the different guar gum doses. Thus, no significant correlation was found between cecal SCFA concentrations and metabolic markers. In contrast, in vivo SCFA uptake fluxes by the host correlated linearly with metabolic markers. We argue that in vivo SCFA fluxes, and not concentrations, govern the protection from the metabolic syndrome by dietary fibers. PMID:25203112

Metabolic and transcriptional regulatory mechanisms underlying the anoxic adaptation of rice coleoptile

PubMed Central

Lakshmanan, Meiyappan; Mohanty, Bijayalaxmi; Lim, Sun-Hyung; Ha, Sun-Hwa; Lee, Dong-Yup

2014-01-01

The ability of rice to germinate under anoxia by extending the coleoptile is a highly unusual characteristic and a key feature underpinning the ability of rice seeds to establish in such a stressful environment. The process has been a focal point for research for many years. However, the molecular mechanisms underlying the anoxic growth of the coleoptile still remain largely unknown. To unravel the key regulatory mechanisms of rice germination under anoxic stress, we combined in silico modelling with gene expression data analysis. Our initial modelling analysis via random flux sampling revealed numerous changes in rice primary metabolism in the absence of oxygen. In particular, several reactions associated with sucrose metabolism and fermentation showed a significant increase in flux levels, whereas reaction fluxes across oxidative phosphorylation, the tricarboxylic acid cycle and the pentose phosphate pathway were down-regulated. The subsequent comparative analysis of the differences in calculated fluxes with previously published gene expression data under air and anoxia identified at least 37 reactions from rice central metabolism that are transcriptionally regulated. Additionally, cis-regulatory content analyses of these transcriptionally controlled enzymes indicate a regulatory role for transcription factors such as MYB, bZIP, ERF and ZnF in transcriptional control of genes that are up-regulated during rice germination and coleoptile elongation under anoxia. PMID:24894389

Molecules in motion: influences of diffusion on metabolic structure and function in skeletal muscle

PubMed Central

Kinsey, Stephen T.; Locke, Bruce R.; Dillaman, Richard M.

2011-01-01

Metabolic processes are often represented as a group of metabolites that interact through enzymatic reactions, thus forming a network of linked biochemical pathways. Implicit in this view is that diffusion of metabolites to and from enzymes is very fast compared with reaction rates, and metabolic fluxes are therefore almost exclusively dictated by catalytic properties. However, diffusion may exert greater control over the rates of reactions through: (1) an increase in reaction rates; (2) an increase in diffusion distances; or (3) a decrease in the relevant diffusion coefficients. It is therefore not surprising that skeletal muscle fibers have long been the focus of reaction–diffusion analyses because they have high and variable rates of ATP turnover, long diffusion distances, and hindered metabolite diffusion due to an abundance of intracellular barriers. Examination of the diversity of skeletal muscle fiber designs found in animals provides insights into the role that diffusion plays in governing both rates of metabolic fluxes and cellular organization. Experimental measurements of metabolic fluxes, diffusion distances and diffusion coefficients, coupled with reaction–diffusion mathematical models in a range of muscle types has started to reveal some general principles guiding muscle structure and metabolic function. Foremost among these is that metabolic processes in muscles do, in fact, appear to be largely reaction controlled and are not greatly limited by diffusion. However, the influence of diffusion is apparent in patterns of fiber growth and metabolic organization that appear to result from selective pressure to maintain reaction control of metabolism in muscle. PMID:21177946

Analysis of Metabolic Pathways and Fluxes in a Newly Discovered Thermophilic and Ethanol-Tolerant Geobacillus Strain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Yinjie J.; Sapra, Rajat; Joyner, Dominique

2009-01-20

A recently discovered thermophilic bacterium, Geobacillus thermoglucosidasius M10EXG, ferments a range of C5 (e.g., xylose) and C6 sugars (e.g., glucose) and istolerant to high ethanol concentrations (10percent, v/v). We have investigated the central metabolism of this bacterium using both in vitro enzyme assays and 13C-based flux analysis to provide insights into the physiological properties of this extremophile and explore its metabolism for bio-ethanol or other bioprocess applications. Our findings show that glucose metabolism in G. thermoglucosidasius M10EXG proceeds via glycolysis, the pentose phosphate pathway, and the TCA cycle; the Entner?Doudoroff pathway and transhydrogenase activity were not detected. Anaplerotic reactions (includingmore » the glyoxylate shunt, pyruvate carboxylase, and phosphoenolpyruvate carboxykinase) were active, but fluxes through those pathways could not be accuratelydetermined using amino acid labeling. When growth conditions were switched from aerobic to micro-aerobic conditions, fluxes (based on a normalized glucose uptake rate of 100 units (g DCW)-1 h-1) through the TCA cycle and oxidative pentose phosphate pathway were reduced from 64+-3 to 25+-2 and from 30+-2 to 19+-2, respectively. The carbon flux under micro-aerobic growth was directed formate. Under fully anerobic conditions, G. thermoglucosidasius M10EXG used a mixed acid fermentation process and exhibited a maximum ethanol yield of 0.38+-0.07 mol mol-1 glucose. In silico flux balance modeling demonstrates that lactate and acetate production from G. thermoglucosidasius M10EXG reduces the maximum ethanol yieldby approximately threefold, thus indicating that both pathways should be modified to maximize ethanol production.« less

FLUXNET to MODIS: Connecting the dots to capture heterogenious biosphere metabolism

NASA Astrophysics Data System (ADS)

Woods, K. D.; Schwalm, C.; Huntzinger, D. N.; Massey, R.; Poulter, B.; Kolb, T.

2015-12-01

Eddy co-variance flux towers provide our most widely distributed network of direct observations for land-atmosphere carbon exchange. Carbon flux sensitivity analysis is a method that uses in situ networks to understand how ecosystems respond to changes in climatic variables. Flux towers concurrently observe key ecosystem metabolic processes (e..g. gross primary productivity) and micrometeorological variation, but only over small footprints. Remotely sensed vegetation indices from MODIS offer continuous observations of the vegetated land surface, but are less direct, as they are based on light use efficiency algorithms, and not on the ground observations. The marriage of these two data products offers an opportunity to validate remotely sensed indices with in situ observations and translate information derived from tower sites to globally gridded products. Here we provide correlations between Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and MODIS gross primary production with FLUXNET derived estimates of gross primary production, respiration and net ecosystem exchange. We demonstrate remotely sensed vegetation products which have been transformed to gridded estimates of terrestrial biosphere metabolism on a regional-to-global scale. We demonstrate anomalies in gross primary production, respiration, and net ecosystem exchange as predicted by both MODIS-carbon flux sensitivities and meteorological driver-carbon flux sensitivities. We apply these sensitivities to recent extreme climatic events and demonstrate both our ability to capture changes in biosphere metabolism, and differences in the calculation of carbon flux anomalies based on method. The quantification of co-variation in these two methods of observation is important as it informs both how remotely sensed vegetation indices are correlated with on the ground tower observations, and with what certainty we can expand these observations and relationships.

Metabolic flux profiling of MDCK cells during growth and canine adenovirus vector production.

PubMed

Carinhas, Nuno; Pais, Daniel A M; Koshkin, Alexey; Fernandes, Paulo; Coroadinha, Ana S; Carrondo, Manuel J T; Alves, Paula M; Teixeira, Ana P

2016-03-23

Canine adenovirus vector type 2 (CAV2) represents an alternative to human adenovirus vectors for certain gene therapy applications, particularly neurodegenerative diseases. However, more efficient production processes, assisted by a greater understanding of the effect of infection on producer cells, are required. Combining [1,2-(13)C]glucose and [U-(13)C]glutamine, we apply for the first time (13)C-Metabolic flux analysis ((13)C-MFA) to study E1-transformed Madin-Darby Canine Kidney (MDCK) cells metabolism during growth and CAV2 production. MDCK cells displayed a marked glycolytic and ammoniagenic metabolism, and (13)C data revealed a large fraction of glutamine-derived labelling in TCA cycle intermediates, emphasizing the role of glutamine anaplerosis. (13)C-MFA demonstrated the importance of pyruvate cycling in balancing glycolytic and TCA cycle activities, as well as occurrence of reductive alphaketoglutarate (AKG) carboxylation. By turn, CAV2 infection significantly upregulated fluxes through most central metabolism, including glycolysis, pentose-phosphate pathway, glutamine anaplerosis and, more prominently, reductive AKG carboxylation and cytosolic acetyl-coenzyme A formation, suggestive of increased lipogenesis. Based on these results, we suggest culture supplementation strategies to stimulate nucleic acid and lipid biosynthesis for improved canine adenoviral vector production.

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles.

PubMed

Kitayama, Tomoya; Kinoshita, Ayako; Sugimoto, Masahiro; Nakayama, Yoichi; Tomita, Masaru

2006-07-17

In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems. The results of two case studies modelling a straight and a branched pathway, respectively, showed that our method reduced the number of unknown parameters needing to be estimated. The time-courses simulated by conventional kinetic models and those described by our method behaved similarly under a wide range of perturbations of metabolite concentrations. The proposed method reduces calculation complexity and facilitates the construction of large-scale S-system models of metabolic pathways, realizing a practical application of reverse engineering of dynamic simulation models from the Jacobian of the targeted system and steady-state flux profiles.

Energetics of glucose metabolism: a phenomenological approach to metabolic network modeling.

PubMed

Diederichs, Frank

2010-08-12

A new formalism to describe metabolic fluxes as well as membrane transport processes was developed. The new flux equations are comparable to other phenomenological laws. Michaelis-Menten like expressions, as well as flux equations of nonequilibrium thermodynamics, can be regarded as special cases of these new equations. For metabolic network modeling, variable conductances and driving forces are required to enable pathway control and to allow a rapid response to perturbations. When applied to oxidative phosphorylation, results of simulations show that whole oxidative phosphorylation cannot be described as a two-flux-system according to nonequilibrium thermodynamics, although all coupled reactions per se fulfill the equations of this theory. Simulations show that activation of ATP-coupled load reactions plus glucose oxidation is brought about by an increase of only two different conductances: a [Ca(2+)] dependent increase of cytosolic load conductances, and an increase of phosphofructokinase conductance by [AMP], which in turn becomes increased through [ADP] generation by those load reactions. In ventricular myocytes, this feedback mechanism is sufficient to increase cellular power output and O(2) consumption several fold, without any appreciable impairment of energetic parameters. Glucose oxidation proceeds near maximal power output, since transformed input and output conductances are nearly equal, yielding an efficiency of about 0.5. This conductance matching is fulfilled also by glucose oxidation of β-cells. But, as a price for the metabolic mechanism of glucose recognition, β-cells have only a limited capability to increase their power output.

Modeling the optimal central carbon metabolic pathways under feedback inhibition using flux balance analysis.

PubMed

De, Rajat K; Tomar, Namrata

2012-12-01

Metabolism is a complex process for energy production for cellular activity. It consists of a cascade of reactions that form a highly branched network in which the product of one reaction is the reactant of the next reaction. Metabolic pathways efficiently produce maximal amount of biomass while maintaining a steady-state behavior. The steady-state activity of such biochemical pathways necessarily incorporates feedback inhibition of the enzymes. This observation motivates us to incorporate feedback inhibition for modeling the optimal activity of metabolic pathways using flux balance analysis (FBA). We demonstrate the effectiveness of the methodology on a synthetic pathway with and without feedback inhibition. Similarly, for the first time, the Central Carbon Metabolic (CCM) pathways of Saccharomyces cerevisiae and Homo sapiens have been modeled and compared based on the above understanding. The optimal pathway, which maximizes the amount of the target product(s), is selected from all those obtained by the proposed method. For this, we have observed the concentration of the product inhibited enzymes of CCM pathway and its influence on its corresponding metabolite/substrate. We have also studied the concentration of the enzymes which are responsible for the synthesis of target products. We further hypothesize that an optimal pathway would opt for higher flux rate reactions. In light of these observations, we can say that an optimal pathway should have lower enzyme concentration and higher flux rates. Finally, we demonstrate the superiority of the proposed method by comparing it with the extreme pathway analysis.

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection.

PubMed

Yoon, Jeongah; Si, Yaguang; Nolan, Ryan; Lee, Kyongbum

2007-09-15

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism. Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism. Supplementary data are available at Bioinformatics online.

Obesity, metabolic syndrome and adipocytes

USDA-ARS?s Scientific Manuscript database

Obesity and metabolic syndrome are examples whereby excess energy consumption and energy flux disruptions are causative agents of increased fatness. Because other, as yet elucidated, cellular factors may be involved and because potential treatments of these metabolic problems involve systemic agents...

Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp. PCC 6803

PubMed Central

Knoop, Henning; Gründel, Marianne; Zilliges, Yvonne; Lehmann, Robert; Hoffmann, Sabrina; Lockau, Wolfgang; Steuer, Ralf

2013-01-01

Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism. PMID:23843751

The Yeast Cyclin-Dependent Kinase Routes Carbon Fluxes to Fuel Cell Cycle Progression.

PubMed

Ewald, Jennifer C; Kuehne, Andreas; Zamboni, Nicola; Skotheim, Jan M

2016-05-19

Cell division entails a sequence of processes whose specific demands for biosynthetic precursors and energy place dynamic requirements on metabolism. However, little is known about how metabolic fluxes are coordinated with the cell division cycle. Here, we examine budding yeast to show that more than half of all measured metabolites change significantly through the cell division cycle. Cell cycle-dependent changes in central carbon metabolism are controlled by the cyclin-dependent kinase (Cdk1), a major cell cycle regulator, and the metabolic regulator protein kinase A. At the G1/S transition, Cdk1 phosphorylates and activates the enzyme Nth1, which funnels the storage carbohydrate trehalose into central carbon metabolism. Trehalose utilization fuels anabolic processes required to reliably complete cell division. Thus, the cell cycle entrains carbon metabolism to fuel biosynthesis. Because the oscillation of Cdk activity is a conserved feature of the eukaryotic cell cycle, we anticipate its frequent use in dynamically regulating metabolism for efficient proliferation. Copyright © 2016 Elsevier Inc. All rights reserved.

The plasticity of cyanobacterial carbon metabolism

DOE PAGES

Xiong, Wei; Cano, Melissa; Wang, Bo; ...

2017-09-29

This opinion article aims to raise awareness of a fundamental issue which governs sustainable production of biofuels and bio-chemicals from photosynthetic cyanobacteria. Discussed is the plasticity of carbon metabolism, by which the cyanobacterial cells flexibly distribute intracellular carbon fluxes towards target products and adapt to environmental/genetic alterations. This intrinsic feature in cyanobacterial metabolism is being understood through recent identification of new biochemical reactions and engineering on low-throughput pathways. We focus our discussion on new insights into the nature of metabolic plasticity in cyanobacteria and its impact on hydrocarbons (e.g. ethylene and isoprene) production. Here, we discuss approaches that need tomore » be developed to rationally rewire photosynthetic carbon fluxes throughout primary metabolism. We also outline open questions about the regulatory mechanisms of the metabolic network that remain to be answered, which might shed light on photosynthetic carbon metabolism and help optimize design principles in order to improve the production of fuels and chemicals in cyanobacteria.« less

The plasticity of cyanobacterial carbon metabolism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiong, Wei; Cano, Melissa; Wang, Bo

This opinion article aims to raise awareness of a fundamental issue which governs sustainable production of biofuels and bio-chemicals from photosynthetic cyanobacteria. Discussed is the plasticity of carbon metabolism, by which the cyanobacterial cells flexibly distribute intracellular carbon fluxes towards target products and adapt to environmental/genetic alterations. This intrinsic feature in cyanobacterial metabolism is being understood through recent identification of new biochemical reactions and engineering on low-throughput pathways. We focus our discussion on new insights into the nature of metabolic plasticity in cyanobacteria and its impact on hydrocarbons (e.g. ethylene and isoprene) production. Here, we discuss approaches that need tomore » be developed to rationally rewire photosynthetic carbon fluxes throughout primary metabolism. We also outline open questions about the regulatory mechanisms of the metabolic network that remain to be answered, which might shed light on photosynthetic carbon metabolism and help optimize design principles in order to improve the production of fuels and chemicals in cyanobacteria.« less

Metabolic networks in motion: 13C-based flux analysis

PubMed Central

Sauer, Uwe

2006-01-01

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism. PMID:17102807

Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation.

PubMed

Flahaut, Nicolas A L; Wiersma, Anne; van de Bunt, Bert; Martens, Dirk E; Schaap, Peter J; Sijtsma, Lolke; Dos Santos, Vitor A Martins; de Vos, Willem M

2013-10-01

Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.

Elucidating central metabolic redox obstacles hindering ethanol production in Clostridium thermocellum

DOE PAGES

Thompson, R. Adam; Layton, Donovan S.; Guss, Adam M.; ...

2015-10-21

Clostridium thermocellum is an anaerobic, Gram-positive, thermophilic bacterium that has generated great interest due to its ability to ferment lignocellulosic biomass to ethanol. However, ethanol production is low due to the complex and poorly understood branched metabolism of C. thermocellum, and in some cases overflow metabolism as well. In this work, we developed a predictive stoichiometric metabolic model for C. thermocellum which incorporates the current state of understanding, with particular attention to cofactor specificity in the atypical glycolytic enzymes and the complex energy, redox, and fermentative pathways with the goal of aiding metabolic engineering efforts. We validated the model smore » capability to encompass experimentally observed phenotypes for the parent strain and derived mutants designed for significant perturbation of redox and energy pathways. Metabolic flux distributions revealed significant alterations in key metabolic branch points (e.g., phosphoenol pyruvate, pyruvate, acetyl-CoA, and cofactor nodes) in engineered strains for channeling electron and carbon fluxes for enhanced ethanol synthesis, with the best performing strain doubling ethanol yield and titer compared to the parent strain. In silico predictions of a redox-imbalanced genotype incapable of growth were confirmed in vivo, and a mutant strain was used as a platform to probe redox bottlenecks in the central metabolism that hinder efficient ethanol production. The results highlight the robustness of the redox metabolism of C. thermocellum and the necessity of streamlined electron flux from reduced ferredoxin to NAD(P)H for high ethanol production. The model was further used to design a metabolic engineering strategy to phenotypically constrain C. thermocellum to achieve high ethanol yields while requiring minimal genetic manipulations. Furthermore, the model can be applied to design C. thermocellum as a platform microbe for consolidated bioprocessing to produce ethanol and other reduced metabolites.« less

Metabolic Flux Increases Glycoprotein Sialylation: Implications for Cell Adhesion and Cancer Metastasis*

PubMed Central

Almaraz, Ruben T.; Tian, Yuan; Bhattarcharya, Rahul; Tan, Elaine; Chen, Shih-Hsun; Dallas, Matthew R.; Chen, Li; Zhang, Zhen; Zhang, Hui; Konstantopoulos, Konstantinos; Yarema, Kevin J.

2012-01-01

This study reports a global glycoproteomic analysis of pancreatic cancer cells that describes how flux through the sialic acid biosynthetic pathway selectively modulates a subset of N-glycosylation sites found within cellular proteins. These results provide evidence that sialoglycoprotein patterns are not determined exclusively by the transcription of biosynthetic enzymes or the availability of N-glycan sequons; instead, bulk metabolic flux through the sialic acid pathway has a remarkable ability to increase the abundance of certain sialoglycoproteins while having a minimal impact on others. Specifically, of 82 glycoproteins identified through a mass spectrometry and bioinformatics approach, ∼31% showed no change in sialylation, ∼29% exhibited a modest increase, whereas ∼40% experienced an increase of greater than twofold. Increased sialylation of specific glycoproteins resulted in changes to the adhesive properties of SW1990 pancreatic cancer cells (e.g. increased CD44-mediated adhesion to selectins under physiological flow and enhanced integrin-mediated cell mobility on collagen and fibronectin). These results indicate that cancer cells can become more aggressively malignant by controlling the sialylation of proteins implicated in metastatic transformation via metabolic flux. PMID:22457533

Primary fatty acid amide metabolism: conversion of fatty acids and an ethanolamine in N18TG2 and SCP cells.

PubMed

Farrell, Emma K; Chen, Yuden; Barazanji, Muna; Jeffries, Kristen A; Cameroamortegui, Felipe; Merkler, David J

2012-02-01

Primary fatty acid amides (PFAM) are important signaling molecules in the mammalian nervous system, binding to many drug receptors and demonstrating control over sleep, locomotion, angiogenesis, and many other processes. Oleamide is the best-studied of the primary fatty acid amides, whereas the other known PFAMs are significantly less studied. Herein, quantitative assays were used to examine the endogenous amounts of a panel of PFAMs, as well as the amounts produced after incubation of mouse neuroblastoma N(18)TG(2) and sheep choroid plexus (SCP) cells with the corresponding fatty acids or N-tridecanoylethanolamine. Although five endogenous primary amides were discovered in the N(18)TG(2) and SCP cells, a different pattern of relative amounts were found between the two cell lines. Higher amounts of primary amides were found in SCP cells, and the conversion of N-tridecanoylethanolamine to tridecanamide was observed in the two cell lines. The data reported here show that the N(18)TG(2) and SCP cells are excellent model systems for the study of PFAM metabolism. Furthermore, the data support a role for the N-acylethanolamines as precursors for the PFAMs and provide valuable new kinetic results useful in modeling the metabolic flux through the pathways for PFAM biosynthesis and degradation.

Primary fatty acid amide metabolism: conversion of fatty acids and an ethanolamine in N18TG2 and SCP cells1[S

PubMed Central

Farrell, Emma K.; Chen, Yuden; Barazanji, Muna; Jeffries, Kristen A.; Cameroamortegui, Felipe; Merkler, David J.

2012-01-01

Primary fatty acid amides (PFAM) are important signaling molecules in the mammalian nervous system, binding to many drug receptors and demonstrating control over sleep, locomotion, angiogenesis, and many other processes. Oleamide is the best-studied of the primary fatty acid amides, whereas the other known PFAMs are significantly less studied. Herein, quantitative assays were used to examine the endogenous amounts of a panel of PFAMs, as well as the amounts produced after incubation of mouse neuroblastoma N18TG2 and sheep choroid plexus (SCP) cells with the corresponding fatty acids or N-tridecanoylethanolamine. Although five endogenous primary amides were discovered in the N18TG2 and SCP cells, a different pattern of relative amounts were found between the two cell lines. Higher amounts of primary amides were found in SCP cells, and the conversion of N-tridecanoylethanolamine to tridecanamide was observed in the two cell lines. The data reported here show that the N18TG2 and SCP cells are excellent model systems for the study of PFAM metabolism. Furthermore, the data support a role for the N-acylethanolamines as precursors for the PFAMs and provide valuable new kinetic results useful in modeling the metabolic flux through the pathways for PFAM biosynthesis and degradation. PMID:22095832

Coordinated regulation of growth, activity and transcription in natural populations of the unicellular nitrogen-fixing cyanobacterium Crocosphaera.

PubMed

Wilson, Samuel T; Aylward, Frank O; Ribalet, Francois; Barone, Benedetto; Casey, John R; Connell, Paige E; Eppley, John M; Ferrón, Sara; Fitzsimmons, Jessica N; Hayes, Christopher T; Romano, Anna E; Turk-Kubo, Kendra A; Vislova, Alice; Armbrust, E Virginia; Caron, David A; Church, Matthew J; Zehr, Jonathan P; Karl, David M; DeLong, Edward F

2017-07-31

The temporal dynamics of phytoplankton growth and activity have large impacts on fluxes of matter and energy, yet obtaining in situ metabolic measurements of sufficient resolution for even dominant microorganisms remains a considerable challenge. We performed Lagrangian diel sampling with synoptic measurements of population abundances, dinitrogen (N 2 ) fixation, mortality, productivity, export and transcription in a bloom of Crocosphaera over eight days in the North Pacific Subtropical Gyre (NPSG). Quantitative transcriptomic analyses revealed clear diel oscillations in transcript abundances for 34% of Crocosphaera genes identified, reflecting a systematic progression of gene expression in diverse metabolic pathways. Significant time-lagged correspondence was evident between nifH transcript abundance and maximal N 2 fixation, as well as sepF transcript abundance and cell division, demonstrating the utility of transcriptomics to predict the occurrence and timing of physiological and biogeochemical processes in natural populations. Indirect estimates of carbon fixation by Crocosphaera were equivalent to 11% of net community production, suggesting that under bloom conditions this diazotroph has a considerable impact on the wider carbon cycle. Our cross-scale synthesis of molecular, population and community-wide data underscores the tightly coordinated in situ metabolism of the keystone N 2 -fixing cyanobacterium Crocosphaera, as well as the broader ecosystem-wide implications of its activities.

An Oral Load of [13C3]Glycerol and Blood NMR Analysis Detect Fatty Acid Esterification, Pentose Phosphate Pathway, and Glycerol Metabolism through the Tricarboxylic Acid Cycle in Human Liver.

PubMed

Jin, Eunsook S; Sherry, A Dean; Malloy, Craig R

2016-09-02

Drugs and other interventions for high impact hepatic diseases often target biochemical pathways such as gluconeogenesis, lipogenesis, or the metabolic response to oxidative stress. However, traditional liver function tests do not provide quantitative data about these pathways. In this study, we developed a simple method to evaluate these processes by NMR analysis of plasma metabolites. Healthy subjects ingested [U-(13)C3]glycerol, and blood was drawn at multiple times. Each subject completed three visits under differing nutritional states. High resolution (13)C NMR spectra of plasma triacylglycerols and glucose provided new insights into a number of hepatic processes including fatty acid esterification, the pentose phosphate pathway, and gluconeogenesis through the tricarboxylic acid cycle. Fasting stimulated pentose phosphate pathway activity and metabolism of [U-(13)C3]glycerol in the tricarboxylic acid cycle prior to gluconeogenesis or glyceroneogenesis. Fatty acid esterification was transient in the fasted state but continuous under fed conditions. We conclude that a simple NMR analysis of blood metabolites provides an important biomarker of pentose phosphate pathway activity, triacylglycerol synthesis, and flux through anaplerotic pathways in mitochondria of human liver. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Growth control of the eukaryote cell: a systems biology study in yeast.

PubMed

Castrillo, Juan I; Zeef, Leo A; Hoyle, David C; Zhang, Nianshu; Hayes, Andrew; Gardner, David Cj; Cornell, Michael J; Petty, June; Hakes, Luke; Wardleworth, Leanne; Rash, Bharat; Brown, Marie; Dunn, Warwick B; Broadhurst, David; O'Donoghue, Kerry; Hester, Svenja S; Dunkley, Tom Pj; Hart, Sarah R; Swainston, Neil; Li, Peter; Gaskell, Simon J; Paton, Norman W; Lilley, Kathryn S; Kell, Douglas B; Oliver, Stephen G

2007-01-01

Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.

Metabolic and morphological differences between rapidly proliferating cancerous and normal breast epithelial cells.

PubMed

Meadows, Adam L; Kong, Becky; Berdichevsky, Marina; Roy, Siddhartha; Rosiva, Rosiva; Blanch, Harvey W; Clark, Douglas S

2008-01-01

The metabolic and morphological characteristics of two human epithelial breast cell populations--MCF7 cells, a cancerous cell line, and 48R human mammary epithelial cells (48R HMECs), a noncancerous, finite lifespan cell strain--were compared at identical growth rates. Both cell types were induced to grow rapidly in nutrient-rich media containing 13C-labeled glucose, and the isotopic enrichment of cellular metabolites was quantified to calculate metabolic fluxes in key pathways. Despite their similar growth rates, the cells exhibited distinctly different metabolic and morphological profiles. MCF7 cells have an 80% smaller exposed surface area and contain 26% less protein per cell than the 48R cells. Surprisingly, rapidly proliferating 48R cells exhibited a 225% higher per-cell glucose consumption rate, a 250% higher per-cell lactate production rate, and a nearly identical per-cell glutamine consumption rate relative to the cancer cell line. However, when fluxes were considered on the basis of exposed area, the cancer cells were observed to have higher glucose, lactate, and glutamine fluxes, demonstrating superior transport capabilities per unit area of cell membrane. MCF7 cells also consumed amino acids at rates much higher than are generally required for protein synthesis, whereas 48R cells generally did not. Pentose phosphate pathway activity was higher in MCF7 cells, and the flux of glutamine to glutamate was less reversible. Energy efficiency was significantly higher in MCF7 cells, as a result of a combination of their smaller size and greater reliance on the TCA cycle than the 48R cells. These observations support evolutionary models of cancer cell metabolism and suggest targets for metabolic drugs in metastatic breast cancers.

Growth control of the eukaryote cell: a systems biology study in yeast

PubMed Central

Castrillo, Juan I; Zeef, Leo A; Hoyle, David C; Zhang, Nianshu; Hayes, Andrew; Gardner, David CJ; Cornell, Michael J; Petty, June; Hakes, Luke; Wardleworth, Leanne; Rash, Bharat; Brown, Marie; Dunn, Warwick B; Broadhurst, David; O'Donoghue, Kerry; Hester, Svenja S; Dunkley, Tom PJ; Hart, Sarah R; Swainston, Neil; Li, Peter; Gaskell, Simon J; Paton, Norman W; Lilley, Kathryn S; Kell, Douglas B; Oliver, Stephen G

2007-01-01

Background Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. Results Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. Conclusion This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell. PMID:17439666

Mapping the diatom redox-sensitive proteome provides insight into response to nitrogen stress in the marine environment.

PubMed

Rosenwasser, Shilo; Graff van Creveld, Shiri; Schatz, Daniella; Malitsky, Sergey; Tzfadia, Oren; Aharoni, Asaph; Levin, Yishai; Gabashvili, Alexandra; Feldmesser, Ester; Vardi, Assaf

2014-02-18

Diatoms are ubiquitous marine photosynthetic eukaryotes responsible for approximately 20% of global photosynthesis. Little is known about the redox-based mechanisms that mediate diatom sensing and acclimation to environmental stress. Here we used a quantitative mass spectrometry-based approach to elucidate the redox-sensitive signaling network (redoxome) mediating the response of diatoms to oxidative stress. We quantified the degree of oxidation of 3,845 cysteines in the Phaeodactylum tricornutum proteome and identified approximately 300 redox-sensitive proteins. Intriguingly, we found redox-sensitive thiols in numerous enzymes composing the nitrogen assimilation pathway and the recently discovered diatom urea cycle. In agreement with this finding, the flux from nitrate into glutamine and glutamate, measured by the incorporation of (15)N, was strongly inhibited under oxidative stress conditions. Furthermore, by targeting the redox-sensitive GFP sensor to various subcellular localizations, we mapped organelle-specific oxidation patterns in response to variations in nitrogen quota and quality. We propose that redox regulation of nitrogen metabolism allows rapid metabolic plasticity to ensure cellular homeostasis, and thus is essential for the ecological success of diatoms in the marine ecosystem.

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria

PubMed Central

Hui, Sheng; Silverman, Josh M; Chen, Stephen S; Erickson, David W; Basan, Markus; Wang, Jilong; Hwa, Terence; Williamson, James R

2015-01-01

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other ‘omics’ studies. PMID:25678603

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

PubMed

Laniau, Julie; Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien; Siegel, Anne

2017-01-01

The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests , which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

PubMed

Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen

2018-05-01

The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small example and demonstrate their relevance for metabolic engineering with realistic models of E. coli. We develop a comprehensive mathematical framework for yield optimization in metabolic models. Our theory is particularly useful for the study and rational modification of cell factories designed under given yield and/or rate requirements. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

PubMed Central

Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien

2017-01-01

Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype. PMID:29038751

Measuring and modeling C flux rates through the central metabolic pathways in microbial communities using position-specific 13C-labeled tracers

NASA Astrophysics Data System (ADS)

Dijkstra, P.; van Groenigen, K.; Hagerty, S.; Salpas, E.; Fairbanks, D. E.; Hungate, B. A.; KOCH, G. W.; Schwartz, E.

2012-12-01

The production of energy and metabolic precursors occurs in well-known processes such as glycolysis and Krebs cycle. We use position-specific 13C-labeled metabolic tracers, combined with models of microbial metabolic organization, to analyze the response of microbial community energy production, biosynthesis, and C use efficiency (CUE) in soils, decomposing litter, and aquatic communities. The method consists of adding position-specific 13C -labeled metabolic tracers to parallel soil incubations, in this case 1-13C and 2,3-13C pyruvate and 1-13C and U-13C glucose. The measurement of CO2 released from the labeled tracers is used to calculate the C flux rates through the various metabolic pathways. A simplified metabolic model consisting of 23 reactions is solved using results of the metabolic tracer experiments and assumptions of microbial precursor demand. This new method enables direct estimation of fundamental aspects of microbial energy production, CUE, and soil organic matter formation in relatively undisturbed microbial communities. We will present results showing the range of metabolic patterns observed in these communities and discuss results from testing metabolic models.

Advanced Stoichiometric Analysis of Metabolic Networks of Mammalian Systems

PubMed Central

Orman, Mehmet A.; Berthiaume, Francois; Androulakis, Ioannis P.; Ierapetritou, Marianthi G.

2013-01-01

Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted. PMID:22196224

Principles for circadian orchestration of metabolic pathways.

PubMed

Thurley, Kevin; Herbst, Christopher; Wesener, Felix; Koller, Barbara; Wallach, Thomas; Maier, Bert; Kramer, Achim; Westermark, Pål O

2017-02-14

Circadian rhythms govern multiple aspects of animal metabolism. Transcriptome-, proteome- and metabolome-wide measurements have revealed widespread circadian rhythms in metabolism governed by a cellular genetic oscillator, the circadian core clock. However, it remains unclear if and under which conditions transcriptional rhythms cause rhythms in particular metabolites and metabolic fluxes. Here, we analyzed the circadian orchestration of metabolic pathways by direct measurement of enzyme activities, analysis of transcriptome data, and developing a theoretical method called circadian response analysis. Contrary to a common assumption, we found that pronounced rhythms in metabolic pathways are often favored by separation rather than alignment in the times of peak activity of key enzymes. This property holds true for a set of metabolic pathway motifs (e.g., linear chains and branching points) and also under the conditions of fast kinetics typical for metabolic reactions. By circadian response analysis of pathway motifs, we determined exact timing separation constraints on rhythmic enzyme activities that allow for substantial rhythms in pathway flux and metabolite concentrations. Direct measurements of circadian enzyme activities in mouse skeletal muscle confirmed that such timing separation occurs in vivo.

Principles for circadian orchestration of metabolic pathways

PubMed Central

Thurley, Kevin; Herbst, Christopher; Wesener, Felix; Koller, Barbara; Wallach, Thomas; Maier, Bert; Kramer, Achim

2017-01-01

Circadian rhythms govern multiple aspects of animal metabolism. Transcriptome-, proteome- and metabolome-wide measurements have revealed widespread circadian rhythms in metabolism governed by a cellular genetic oscillator, the circadian core clock. However, it remains unclear if and under which conditions transcriptional rhythms cause rhythms in particular metabolites and metabolic fluxes. Here, we analyzed the circadian orchestration of metabolic pathways by direct measurement of enzyme activities, analysis of transcriptome data, and developing a theoretical method called circadian response analysis. Contrary to a common assumption, we found that pronounced rhythms in metabolic pathways are often favored by separation rather than alignment in the times of peak activity of key enzymes. This property holds true for a set of metabolic pathway motifs (e.g., linear chains and branching points) and also under the conditions of fast kinetics typical for metabolic reactions. By circadian response analysis of pathway motifs, we determined exact timing separation constraints on rhythmic enzyme activities that allow for substantial rhythms in pathway flux and metabolite concentrations. Direct measurements of circadian enzyme activities in mouse skeletal muscle confirmed that such timing separation occurs in vivo. PMID:28159888

IsoDesign: a software for optimizing the design of 13C-metabolic flux analysis experiments.

PubMed

Millard, Pierre; Sokol, Serguei; Letisse, Fabien; Portais, Jean-Charles

2014-01-01

The growing demand for (13) C-metabolic flux analysis ((13) C-MFA) in the field of metabolic engineering and systems biology is driving the need to rationalize expensive and time-consuming (13) C-labeling experiments. Experimental design is a key step in improving both the number of fluxes that can be calculated from a set of isotopic data and the precision of flux values. We present IsoDesign, a software that enables these parameters to be maximized by optimizing the isotopic composition of the label input. It can be applied to (13) C-MFA investigations using a broad panel of analytical tools (MS, MS/MS, (1) H NMR, (13) C NMR, etc.) individually or in combination. It includes a visualization module to intuitively select the optimal label input depending on the biological question to be addressed. Applications of IsoDesign are described, with an example of the entire (13) C-MFA workflow from the experimental design to the flux map including important practical considerations. IsoDesign makes the experimental design of (13) C-MFA experiments more accessible to a wider biological community. IsoDesign is distributed under an open source license at http://metasys.insa-toulouse.fr/software/isodes/ © 2013 Wiley Periodicals, Inc.

TNA4OptFlux – a software tool for the analysis of strain optimization strategies

PubMed Central

2013-01-01

Background Rational approaches for Metabolic Engineering (ME) deal with the identification of modifications that improve the microbes’ production capabilities of target compounds. One of the major challenges created by strain optimization algorithms used in these ME problems is the interpretation of the changes that lead to a given overproduction. Often, a single gene knockout induces changes in the fluxes of several reactions, as compared with the wild-type, and it is therefore difficult to evaluate the physiological differences of the in silico mutant. This is aggravated by the fact that genome-scale models per se are difficult to visualize, given the high number of reactions and metabolites involved. Findings We introduce a software tool, the Topological Network Analysis for OptFlux (TNA4OptFlux), a plug-in which adds to the open-source ME platform OptFlux the capability of creating and performing topological analysis over metabolic networks. One of the tool’s major advantages is the possibility of using these tools in the analysis and comparison of simulated phenotypes, namely those coming from the results of strain optimization algorithms. We illustrate the capabilities of the tool by using it to aid the interpretation of two E. coli strains designed in OptFlux for the overproduction of succinate and glycine. Conclusions Besides adding new functionalities to the OptFlux software tool regarding topological analysis, TNA4OptFlux methods greatly facilitate the interpretation of non-intuitive ME strategies by automating the comparison between perturbed and non-perturbed metabolic networks. The plug-in is available on the web site http://www.optflux.org, together with extensive documentation. PMID:23641878

Core Fluxome and Metafluxome of Lactic Acid Bacteria under Simulated Cocoa Pulp Fermentation Conditions

PubMed Central

Adler, Philipp; Bolten, Christoph Josef; Dohnt, Katrin; Hansen, Carl Erik

2013-01-01

In the present work, simulated cocoa fermentation was investigated at the level of metabolic pathway fluxes (fluxome) of lactic acid bacteria (LAB), which are typically found in the microbial consortium known to convert nutrients from the cocoa pulp into organic acids. A comprehensive 13C labeling approach allowed to quantify carbon fluxes during simulated cocoa fermentation by (i) parallel 13C studies with [13C6]glucose, [1,2-13C2]glucose, and [13C6]fructose, respectively, (ii) gas chromatography-mass spectrometry (GC/MS) analysis of secreted acetate and lactate, (iii) stoichiometric profiling, and (iv) isotopomer modeling for flux calculation. The study of several strains of L. fermentum and L. plantarum revealed major differences in their fluxes. The L. fermentum strains channeled only a small amount (4 to 6%) of fructose into central metabolism, i.e., the phosphoketolase pathway, whereas only L. fermentum NCC 575 used fructose to form mannitol. In contrast, L. plantarum strains exhibited a high glycolytic flux. All strains differed in acetate flux, which originated from fractions of citrate (25 to 80%) and corresponding amounts of glucose and fructose. Subsequent, metafluxome studies with consortia of different L. fermentum and L. plantarum strains indicated a dominant (96%) contribution of L. fermentum NCC 575 to the overall flux in the microbial community, a scenario that was not observed for the other strains. This highlights the idea that individual LAB strains vary in their metabolic contribution to the overall fermentation process and opens up new routes toward streamlined starter cultures. L. fermentum NCC 575 might be one candidate due to its superior performance in flux activity. PMID:23851099

Genealogy profiling through strain improvement by using metabolic network analysis: metabolic flux genealogy of several generations of lysine-producing corynebacteria.

PubMed

Wittmann, Christoph; Heinzle, Elmar

2002-12-01

A comprehensive approach of metabolite balancing, (13)C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the fact that the observed differences were clearly strain specific.

Genealogy Profiling through Strain Improvement by Using Metabolic Network Analysis: Metabolic Flux Genealogy of Several Generations of Lysine-Producing Corynebacteria

PubMed Central

Wittmann, Christoph; Heinzle, Elmar

2002-01-01

A comprehensive approach of metabolite balancing, 13C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C3 carboxylation and C4 decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C3 carboxylation and C4 decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the fact that the observed differences were clearly strain specific. PMID:12450803

System-Wide Quantitative Proteomics of the Metabolic Syndrome in Mice: Genotypic and Dietary Effects.

PubMed

Terfve, Camille; Sabidó, Eduard; Wu, Yibo; Gonçalves, Emanuel; Choi, Meena; Vaga, Stefania; Vitek, Olga; Saez-Rodriguez, Julio; Aebersold, Ruedi

2017-02-03

Advances in mass spectrometry have made the quantitative measurement of proteins across multiple samples a reality, allowing for the study of complex biological systems such as the metabolic syndrome. Although the deregulation of lipid metabolism and increased hepatic storage of triacylglycerides are known to play a part in the onset of the metabolic syndrome, its molecular basis and dependency on dietary and genotypic factors are poorly characterized. Here, we used an experimental design with two different mouse strains and dietary and metabolic perturbations to generate a compendium of quantitative proteome data using three mass spectrometric techniques. The data reproduce known properties of the metabolic system and indicate differential molecular adaptation of the two mouse strains to perturbations, contributing to a better understanding of the metabolic syndrome. We show that high-quality, high-throughput proteomic data sets provide an unbiased broad overview of the behavior of complex systems after perturbation.

Theoretical Basis for Dynamic Label Propagation in Stationary Metabolic Networks under Step and Periodic Inputs

PubMed Central

Sokol, Serguei; Portais, Jean-Charles

2015-01-01

The dynamics of label propagation in a stationary metabolic network during an isotope labeling experiment can provide highly valuable information on the network topology, metabolic fluxes, and on the size of metabolite pools. However, major issues, both in the experimental set-up and in the accompanying numerical methods currently limit the application of this approach. Here, we propose a method to apply novel types of label inputs, sinusoidal or more generally periodic label inputs, to address both the practical and numerical challenges of dynamic labeling experiments. By considering a simple metabolic system, i.e. a linear, non-reversible pathway of arbitrary length, we develop mathematical descriptions of label propagation for both classical and novel label inputs. Theoretical developments and computer simulations show that the application of rectangular periodic pulses has both numerical and practical advantages over other approaches. We applied the strategy to estimate fluxes in a simulated experiment performed on a complex metabolic network (the central carbon metabolism of Escherichia coli), to further demonstrate its value in conditions which are close to those in real experiments. This study provides a theoretical basis for the rational interpretation of label propagation curves in real experiments, and will help identify the strengths, pitfalls and limitations of such experiments. The cases described here can also be used as test cases for more general numerical methods aimed at identifying network topology, analyzing metabolic fluxes or measuring concentrations of metabolites. PMID:26641860

A systems biology approach uncovers cellular strategies used by Methylobacterium extorquens AM1 during the switch from multi- to single-carbon growth.

PubMed

Skovran, Elizabeth; Crowther, Gregory J; Guo, Xiaofeng; Yang, Song; Lidstrom, Mary E

2010-11-24

When organisms experience environmental change, how does their metabolic network reset and adapt to the new condition? Methylobacterium extorquens is a bacterium capable of growth on both multi- and single-carbon compounds. These different modes of growth utilize dramatically different central metabolic pathways with limited pathway overlap. This study focused on the mechanisms of metabolic adaptation occurring during the transition from succinate growth (predicted to be energy-limited) to methanol growth (predicted to be reducing-power-limited), analyzing changes in carbon flux, gene expression, metabolites and enzymatic activities over time. Initially, cells experienced metabolic imbalance with excretion of metabolites, changes in nucleotide levels and cessation of cell growth. Though assimilatory pathways were induced rapidly, a transient block in carbon flow to biomass synthesis occurred, and enzymatic assays suggested methylene tetrahydrofolate dehydrogenase as one control point. This "downstream priming" mechanism ensures that significant carbon flux through these pathways does not occur until they are fully induced, precluding the buildup of toxic intermediates. Most metabolites that are required for growth on both carbon sources did not change significantly, even though transcripts and enzymatic activities required for their production changed radically, underscoring the concept of metabolic setpoints. This multi-level approach has resulted in new insights into the metabolic strategies carried out to effect this shift between two dramatically different modes of growth and identified a number of potential flux control and regulatory check points as a further step toward understanding metabolic adaptation and the cellular strategies employed to maintain metabolic setpoints.

Signaling Pathways Regulating Redox Balance in Cancer Metabolism

PubMed Central

De Santis, Maria Chiara; Porporato, Paolo Ettore; Martini, Miriam; Morandi, Andrea

2018-01-01

The interplay between rewiring tumor metabolism and oncogenic driver mutations is only beginning to be appreciated. Metabolic deregulation has been described for decades as a bystander effect of genomic aberrations. However, for the biology of malignant cells, metabolic reprogramming is essential to tackle a harsh environment, including nutrient deprivation, reactive oxygen species production, and oxygen withdrawal. Besides the well-investigated glycolytic metabolism, it is emerging that several other metabolic fluxes are relevant for tumorigenesis in supporting redox balance, most notably pentose phosphate pathway, folate, and mitochondrial metabolism. The relationship between metabolic rewiring and mutant genes is still unclear and, therefore, we will discuss how metabolic needs and oncogene mutations influence each other to satisfy cancer cells’ demands. Mutations in oncogenes, i.e., PI3K/AKT/mTOR, RAS pathway, and MYC, and tumor suppressors, i.e., p53 and liver kinase B1, result in metabolic flexibility and may influence response to therapy. Since metabolic rewiring is shaped by oncogenic driver mutations, understanding how specific alterations in signaling pathways affect different metabolic fluxes will be instrumental for the development of novel targeted therapies. In the era of personalized medicine, the combination of driver mutations, metabolite levels, and tissue of origins will pave the way to innovative therapeutic interventions. PMID:29740540

Signaling Pathways Regulating Redox Balance in Cancer Metabolism.

PubMed

De Santis, Maria Chiara; Porporato, Paolo Ettore; Martini, Miriam; Morandi, Andrea

2018-01-01

The interplay between rewiring tumor metabolism and oncogenic driver mutations is only beginning to be appreciated. Metabolic deregulation has been described for decades as a bystander effect of genomic aberrations. However, for the biology of malignant cells, metabolic reprogramming is essential to tackle a harsh environment, including nutrient deprivation, reactive oxygen species production, and oxygen withdrawal. Besides the well-investigated glycolytic metabolism, it is emerging that several other metabolic fluxes are relevant for tumorigenesis in supporting redox balance, most notably pentose phosphate pathway, folate, and mitochondrial metabolism. The relationship between metabolic rewiring and mutant genes is still unclear and, therefore, we will discuss how metabolic needs and oncogene mutations influence each other to satisfy cancer cells' demands. Mutations in oncogenes, i.e., PI3K/AKT/mTOR, RAS pathway, and MYC, and tumor suppressors, i.e., p53 and liver kinase B1, result in metabolic flexibility and may influence response to therapy. Since metabolic rewiring is shaped by oncogenic driver mutations, understanding how specific alterations in signaling pathways affect different metabolic fluxes will be instrumental for the development of novel targeted therapies. In the era of personalized medicine, the combination of driver mutations, metabolite levels, and tissue of origins will pave the way to innovative therapeutic interventions.

13C-flux analysis reveals NADPH-balancing transhydrogenation cycles in stationary phase of nitrogen-starving Bacillus subtilis.

PubMed

Rühl, Martin; Le Coq, Dominique; Aymerich, Stéphane; Sauer, Uwe

2012-08-10

In their natural habitat, microorganisms are typically confronted with nutritional limitations that restrict growth and force them to persevere in a stationary phase. Despite the importance of this phase, little is known about the metabolic state(s) that sustains it. Here, we investigate metabolically active but non-growing Bacillus subtilis during nitrogen starvation. In the absence of biomass formation as the major NADPH sink, the intracellular flux distribution in these resting B. subtilis reveals a large apparent catabolic NADPH overproduction of 5.0 ± 0.6 mmol g(-1)h(-1) that was partly caused by high pentose phosphate pathway fluxes. Combining transcriptome analysis, stationary (13)C-flux analysis in metabolic deletion mutants, (2)H-labeling experiments, and kinetic flux profiling, we demonstrate that about half of the catabolic excess NADPH is oxidized by two transhydrogenation cycles, i.e. isoenzyme pairs of dehydrogenases with different cofactor specificities that operate in reverse directions. These transhydrogenation cycles were constituted by the combined activities of the glyceraldehyde 3-phosphate dehydrogenases GapA/GapB and the malic enzymes MalS/YtsJ. At least an additional 6% of the overproduced NADPH is reoxidized by continuous cycling between ana- and catabolism of glutamate. Furthermore, in vitro enzyme data show that a not yet identified transhydrogenase could potentially reoxidize ∼20% of the overproduced NADPH. Overall, we demonstrate the interplay between several metabolic mechanisms that concertedly enable network-wide NADPH homeostasis under conditions of high catabolic NADPH production in the absence of cell growth in B. subtilis.

13C-flux Analysis Reveals NADPH-balancing Transhydrogenation Cycles in Stationary Phase of Nitrogen-starving Bacillus subtilis *

PubMed Central

Rühl, Martin; Le Coq, Dominique; Aymerich, Stéphane; Sauer, Uwe

2012-01-01

In their natural habitat, microorganisms are typically confronted with nutritional limitations that restrict growth and force them to persevere in a stationary phase. Despite the importance of this phase, little is known about the metabolic state(s) that sustains it. Here, we investigate metabolically active but non-growing Bacillus subtilis during nitrogen starvation. In the absence of biomass formation as the major NADPH sink, the intracellular flux distribution in these resting B. subtilis reveals a large apparent catabolic NADPH overproduction of 5.0 ± 0.6 mmol·g−1·h−1 that was partly caused by high pentose phosphate pathway fluxes. Combining transcriptome analysis, stationary 13C-flux analysis in metabolic deletion mutants, 2H-labeling experiments, and kinetic flux profiling, we demonstrate that about half of the catabolic excess NADPH is oxidized by two transhydrogenation cycles, i.e. isoenzyme pairs of dehydrogenases with different cofactor specificities that operate in reverse directions. These transhydrogenation cycles were constituted by the combined activities of the glyceraldehyde 3-phosphate dehydrogenases GapA/GapB and the malic enzymes MalS/YtsJ. At least an additional 6% of the overproduced NADPH is reoxidized by continuous cycling between ana- and catabolism of glutamate. Furthermore, in vitro enzyme data show that a not yet identified transhydrogenase could potentially reoxidize ∼20% of the overproduced NADPH. Overall, we demonstrate the interplay between several metabolic mechanisms that concertedly enable network-wide NADPH homeostasis under conditions of high catabolic NADPH production in the absence of cell growth in B. subtilis. PMID:22740702

Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling.

PubMed

Ahmed, Zeeshan; Zeeshan, Saman; Huber, Claudia; Hensel, Michael; Schomburg, Dietmar; Münch, Richard; Eisenreich, Wolfgang; Dandekar, Thomas

2013-07-09

The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. The open-source software "Least Square Mass Isotopomer Analyzer" (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman's least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.

A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

PubMed Central

2014-01-01

Background Constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology. Results We present EcoCyc–18.0–GEM, a genome-scale model of the E. coli K–12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc–18.0–GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc–18.0–GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model’s derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc–18.0–GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization. Conclusion Significant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database. PMID:24974895

A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect

PubMed Central

Damiani, Chiara; Colombo, Riccardo; Gaglio, Daniela; Mastroianni, Fabrizia; Westerhoff, Hans Victor; Vanoni, Marco; Alberghina, Lilia

2017-01-01

Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth. PMID:28957320

Assessing compartmentalized flux in lipid metabolism with isotopes

DOE PAGES

Allen, Doug K.

2016-03-18

Metabolism in plants takes place across multiple cell types and within distinct organelles. The distributions equate to spatial heterogeneity; though the limited means to experimentally assess metabolism frequently involve homogenizing tissues and mixing metabolites from different locations.Most current isotope investigations of metabolism therefore lack the ability to resolve spatially distinct events. Recognition of this limitation has resulted in inspired efforts to advance metabolic flux analysis and isotopic labeling techniques. Though a number of these efforts have been applied to studies in central metabolism; recent advances in instrumentation and techniques present an untapped opportunity to make similar progress in lipid metabolismmore » where the use of stable isotopes has been more limited. These efforts will benefit from sophisticated radiolabeling reports that continue to enrich our knowledge on lipid biosynthetic pathways and provide some direction for stable isotope experimental design and extension of MFA. Evidence for this assertion is presented through the review of several elegant stable isotope studies and by taking stock of what has been learned from radioisotope investigations when spatial aspects of metabolism were considered. The studies emphasize that glycerolipid production occurs across several locations with assembly of lipids in the ER or plastid, fatty acid biosynthesis occurring in the plastid, and the generation of acetyl-CoA and glycerol-3-phosphate taking place at multiple sites. Considering metabolism in this context underscores the cellular and subcellular organization that is important to enhanced production of glycerolipids in plants. An attempt is made to unify salient features from a number of reports into a diagrammatic model of lipid metabolism and propose where stable isotope labeling experiments and further flux analysis may help address questions in the field.« less

MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

PubMed Central

Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko

2012-01-01

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads. PMID:23272111

Assessing compartmentalized flux in lipid metabolism with isotopes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allen, Doug K.

Metabolism in plants takes place across multiple cell types and within distinct organelles. The distributions equate to spatial heterogeneity; though the limited means to experimentally assess metabolism frequently involve homogenizing tissues and mixing metabolites from different locations.Most current isotope investigations of metabolism therefore lack the ability to resolve spatially distinct events. Recognition of this limitation has resulted in inspired efforts to advance metabolic flux analysis and isotopic labeling techniques. Though a number of these efforts have been applied to studies in central metabolism; recent advances in instrumentation and techniques present an untapped opportunity to make similar progress in lipid metabolismmore » where the use of stable isotopes has been more limited. These efforts will benefit from sophisticated radiolabeling reports that continue to enrich our knowledge on lipid biosynthetic pathways and provide some direction for stable isotope experimental design and extension of MFA. Evidence for this assertion is presented through the review of several elegant stable isotope studies and by taking stock of what has been learned from radioisotope investigations when spatial aspects of metabolism were considered. The studies emphasize that glycerolipid production occurs across several locations with assembly of lipids in the ER or plastid, fatty acid biosynthesis occurring in the plastid, and the generation of acetyl-CoA and glycerol-3-phosphate taking place at multiple sites. Considering metabolism in this context underscores the cellular and subcellular organization that is important to enhanced production of glycerolipids in plants. An attempt is made to unify salient features from a number of reports into a diagrammatic model of lipid metabolism and propose where stable isotope labeling experiments and further flux analysis may help address questions in the field.« less

Metabolic Profiling and Flux Analysis of MEL-2 Human Embryonic Stem Cells during Exponential Growth at Physiological and Atmospheric Oxygen Concentrations

PubMed Central

Titmarsh, Drew; Krömer, Jens O.; Kao, Li-Pin; Nielsen, Lars; Wolvetang, Ernst; Cooper-White, Justin

2014-01-01

As human embryonic stem cells (hESCs) steadily progress towards regenerative medicine applications there is an increasing emphasis on the development of bioreactor platforms that enable expansion of these cells to clinically relevant numbers. Surprisingly little is known about the metabolic requirements of hESCs, precluding the rational design and optimisation of such platforms. In this study, we undertook an in-depth characterisation of MEL-2 hESC metabolic behaviour during the exponential growth phase, combining metabolic profiling and flux analysis tools at physiological (hypoxic) and atmospheric (normoxic) oxygen concentrations. To overcome variability in growth profiles and the problem of closing mass balances in a complex environment, we developed protocols to accurately measure uptake and production rates of metabolites, cell density, growth rate and biomass composition, and designed a metabolic flux analysis model for estimating internal rates. hESCs are commonly considered to be highly glycolytic with inactive or immature mitochondria, however, whilst the results of this study confirmed that glycolysis is indeed highly active, we show that at least in MEL-2 hESC, it is supported by the use of oxidative phosphorylation within the mitochondria utilising carbon sources, such as glutamine to maximise ATP production. Under both conditions, glycolysis was disconnected from the mitochondria with all of the glucose being converted to lactate. No difference in the growth rates of cells cultured under physiological or atmospheric oxygen concentrations was observed nor did this cause differences in fluxes through the majority of the internal metabolic pathways associated with biogenesis. These results suggest that hESCs display the conventional Warburg effect, with high aerobic activity despite high lactate production, challenging the idea of an anaerobic metabolism with low mitochondrial activity. The results of this study provide new insight that can be used in rational bioreactor design and in the development of novel culture media for hESC maintenance and expansion. PMID:25412279

Metabolic pathway rewiring in engineered cyanobacteria for solar-to-chemical and solar-to-fuel production from CO2.

PubMed

Woo, Han Min

2018-01-01

Photoautotrophic cyanobacteria have been developed to convert CO 2 to valuable chemicals and fuels as solar-to-chemical (S2C) and solar-to-fuel (S2F) platforms. Here, I describe the rewiring of the metabolic pathways in cyanobacteria to better understand the endogenous carbon flux and to enhance the yield of heterologous products. The plasticity of the cyanobacterial metabolism has been proposed to be advantageous for the development of S2C and S2F processes. The rewiring of the sugar catabolism and of the phosphoketolase pathway in the central cyanobacterial metabolism allowed for an enhancement in the level of target products by redirecting the carbon fluxes. Thus, metabolic pathway rewiring can promote the development of more efficient cyanobacterial cell factories for the generation of feasible S2C and S2F platforms.

Quantitative method for measuring heat flux emitted from a cryogenic object

DOEpatents

Duncan, Robert V.

1993-01-01

The present invention is a quantitative method for measuring the total heat flux, and of deriving the total power dissipation, of a heat-fluxing object which includes the steps of placing an electrical noise-emitting heat-fluxing object in a liquid helium bath and measuring the superfluid transition temperature of the bath. The temperature of the liquid helium bath is thereafter reduced until some measurable parameter, such as the electrical noise, exhibited by the heat-fluxing object or a temperature-dependent resistive thin film in intimate contact with the heat-fluxing object, becomes greatly reduced. The temperature of the liquid helum bath is measured at this point. The difference between the superfluid transition temperature of the liquid helium bath surrounding the heat-fluxing object, and the temperature of the liquid helium bath when the electrical noise emitted by the heat-fluxing object becomes greatly reduced, is determined. The total heat flux from the heat-fluxing object is determined as a function of this difference between these temperatures. In certain applications, the technique can be used to optimize thermal design parameters of cryogenic electronics, for example, Josephson junction and infra-red sensing devices.

Quantitative method for measuring heat flux emitted from a cryogenic object

DOEpatents

Duncan, R.V.

1993-03-16

The present invention is a quantitative method for measuring the total heat flux, and of deriving the total power dissipation, of a heat-fluxing object which includes the steps of placing an electrical noise-emitting heat-fluxing object in a liquid helium bath and measuring the superfluid transition temperature of the bath. The temperature of the liquid helium bath is thereafter reduced until some measurable parameter, such as the electrical noise, exhibited by the heat-fluxing object or a temperature-dependent resistive thin film in intimate contact with the heat-fluxing object, becomes greatly reduced. The temperature of the liquid helum bath is measured at this point. The difference between the superfluid transition temperature of the liquid helium bath surrounding the heat-fluxing object, and the temperature of the liquid helium bath when the electrical noise emitted by the heat-fluxing object becomes greatly reduced, is determined. The total heat flux from the heat-fluxing object is determined as a function of this difference between these temperatures. In certain applications, the technique can be used to optimize thermal design parameters of cryogenic electronics, for example, Josephson junction and infrared sensing devices.

13C-MFA delineates the photomixotrophic metabolism of Synechocystis sp. PCC 6803 under light- and carbon-sufficient conditions.

PubMed

You, Le; Berla, Bert; He, Lian; Pakrasi, Himadri B; Tang, Yinjie J

2014-05-01

The central carbon metabolism of cyanobacteria is under debate. For over 50 years, the lack of α-ketoglutarate dehydrogenase has led to the belief that cyanobacteria have an incomplete TCA cycle. Recent in vitro enzymatic experiments suggest that this cycle may in fact be closed. The current study employed (13) C isotopomers to delineate pathways in the cyanobacterium Synechocystis sp. PCC 6803. By tracing the incorporation of supplemented glutamate into the downstream metabolites in the TCA cycle, we observed a direct in vivo transformation of α-ketoglutarate to succinate. Additionally, isotopic tracing of glyoxylate did not show a functional glyoxylate shunt and glyoxylate was used for glycine synthesis. The photomixotrophic carbon metabolism was then profiled with (13) C-MFA under light and carbon-sufficient conditions. We observed that: (i) the in vivo flux through the TCA cycle reactions (α-ketoglutarate → succinate) was minimal (<2%); (ii) the flux ratio of CO2 fixation was six times higher than that of glucose utilization; (iii) the relative flux through the oxidative pentose phosphate pathway was low (<2%); (iv) high flux through malic enzyme served as a main route for pyruvate synthesis. Our results improve the understanding of the versatile metabolism in cyanobacteria and shed light on their application for photo-biorefineries. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A dynamic metabolic flux analysis of ABE (acetone-butanol-ethanol) fermentation by Clostridium acetobutylicum ATCC 824, with riboflavin as a by-product.

PubMed

Zhao, Xinhe; Kasbi, Mayssa; Chen, Jingkui; Peres, Sabine; Jolicoeur, Mario

2017-12-01

The present study reveals that supplementing sodium acetate (NaAc) strongly stimulates riboflavin production in acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum ATCC 824 with xylose as carbon source. Riboflavin production increased from undetectable concentrations to ∼0.2 g L -1 (0.53 mM) when supplementing 60 mM NaAc. Of interest, solvents production and biomass yield were also promoted with fivefold acetone, 2.6-fold butanol, and 2.4-fold biomass adding NaAc. A kinetic metabolic model, developed to simulate ABE biosystem, with riboflavin production, revealed from a dynamic metabolic flux analysis (dMFA) simultaneous increase of riboflavin (ribA) and GTP (precursor of riboflavin) (PurM) synthesis flux rates under NaAc supplementation. The model includes 23 fluxes, 24 metabolites, and 72 kinetic parameters. It also suggested that NaAc condition has first stimulated the accumulation of intracellular metabolite intermediates during the acidogenic phase, which have then fed the solventogenic phase leading to increased ABE production. In addition, NaAc resulted in higher intracellular levels of NADH during the whole culture. Moreover, lower GTP-to-adenosine phosphates (ATP, ADP, AMP) ratio under NaAc supplemented condition suggests that GTP may have a minor role in the cell energetic metabolism compared to its contribution to riboflavin synthesis. © 2017 Wiley Periodicals, Inc.

Engineering of metabolic control

DOEpatents

Liao, James C.

2006-10-17

The invention features a method of producing heterologous molecules in cells under the regulatory control of a metabolite and metabolic flux. The method can enhance the synthesis of heterologous polypeptides and metabolites.

Engineering of metabolic control

DOEpatents

Liao, James C.

2004-03-16

The invention features a method of producing heterologous molecules in cells under the regulatory control of a metabolite and metabolic flux. The method can enhance the synthesis of heterologous polypeptides and metabolites.

Consequences of C4 differentiation for chloroplast membrane proteomes in maize mesophyll and bundle sheath cells.

PubMed

Majeran, Wojciech; Zybailov, Boris; Ytterberg, A Jimmy; Dunsmore, Jason; Sun, Qi; van Wijk, Klaas J

2008-09-01

Chloroplasts of maize leaves differentiate into specific bundle sheath (BS) and mesophyll (M) types to accommodate C(4) photosynthesis. Chloroplasts contain thylakoid and envelope membranes that contain the photosynthetic machineries and transporters but also proteins involved in e.g. protein homeostasis. These chloroplast membranes must be specialized within each cell type to accommodate C(4) photosynthesis and regulate metabolic fluxes and activities. This quantitative study determined the differentiated state of BS and M chloroplast thylakoid and envelope membrane proteomes and their oligomeric states using innovative gel-based and mass spectrometry-based protein quantifications. This included native gels, iTRAQ, and label-free quantification using an LTQ-Orbitrap. Subunits of Photosystems I and II, the cytochrome b(6)f, and ATP synthase complexes showed average BS/M accumulation ratios of 1.6, 0.45, 1.0, and 1.33, respectively, whereas ratios for the light-harvesting complex I and II families were 1.72 and 0.68, respectively. A 1000-kDa BS-specific NAD(P)H dehydrogenase complex with associated proteins of unknown function containing more than 15 proteins was observed; we speculate that this novel complex possibly functions in inorganic carbon concentration when carboxylation rates by ribulose-bisphosphate carboxylase/oxygenase are lower than decarboxylation rates by malic enzyme. Differential accumulation of thylakoid proteases (Egy and DegP), state transition kinases (STN7,8), and Photosystem I and II assembly factors was observed, suggesting that cell-specific photosynthetic electron transport depends on post-translational regulatory mechanisms. BS/M ratios for inner envelope transporters phosphoenolpyruvate/P(i) translocator, Dit1, Dit2, and Mex1 were determined and reflect metabolic fluxes in carbon metabolism. A wide variety of hundreds of other proteins showed differential BS/M accumulation. Mass spectral information and functional annotations are available through the Plant Proteome Database. These data are integrated with previous data, resulting in a model for C(4) photosynthesis, thereby providing new rationales for metabolic engineering of C(4) pathways and targeted analysis of genetic networks that coordinate C(4) differentiation.

Differential retention of metabolic genes following whole-genome duplication.

PubMed

Gout, Jean-François; Duret, Laurent; Kahn, Daniel

2009-05-01

Classical studies in Metabolic Control Theory have shown that metabolic fluxes usually exhibit little sensitivity to changes in individual enzyme activity, yet remain sensitive to global changes of all enzymes in a pathway. Therefore, little selective pressure is expected on the dosage or expression of individual metabolic genes, yet entire pathways should still be constrained. However, a direct estimate of this selective pressure had not been evaluated. Whole-genome duplications (WGDs) offer a good opportunity to address this question by analyzing the fates of metabolic genes during the massive gene losses that follow. Here, we take advantage of the successive rounds of WGD that occurred in the Paramecium lineage. We show that metabolic genes exhibit different gene retention patterns than nonmetabolic genes. Contrary to what was expected for individual genes, metabolic genes appeared more retained than other genes after the recent WGD, which was best explained by selection for gene expression operating on entire pathways. Metabolic genes also tend to be less retained when present at high copy number before WGD, contrary to other genes that show a positive correlation between gene retention and preduplication copy number. This is rationalized on the basis of the classical concave relationship relating metabolic fluxes with enzyme expression.

Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism

NASA Astrophysics Data System (ADS)

Zielinski, Daniel C.; Jamshidi, Neema; Corbett, Austin J.; Bordbar, Aarash; Thomas, Alex; Palsson, Bernhard O.

2017-01-01

Malignant transformation is often accompanied by significant metabolic changes. To identify drivers underlying these changes, we calculated metabolic flux states for the NCI60 cell line collection and correlated the variance between metabolic states of these lines with their other properties. The analysis revealed a remarkably consistent structure underlying high flux metabolism. The three primary uptake pathways, glucose, glutamine and serine, are each characterized by three features: (1) metabolite uptake sufficient for the stoichiometric requirement to sustain observed growth, (2) overflow metabolism, which scales with excess nutrient uptake over the basal growth requirement, and (3) redox production, which also scales with nutrient uptake but greatly exceeds the requirement for growth. We discovered that resistance to chemotherapeutic drugs in these lines broadly correlates with the amount of glucose uptake. These results support an interpretation of the Warburg effect and glutamine addiction as features of a growth state that provides resistance to metabolic stress through excess redox and energy production. Furthermore, overflow metabolism observed may indicate that mitochondrial catabolic capacity is a key constraint setting an upper limit on the rate of cofactor production possible. These results provide a greater context within which the metabolic alterations in cancer can be understood.

Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability.

PubMed

Hu, Xiao-Pan; Yang, Yi; Ma, Bin-Guang

2015-06-09

Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.

Mitochondrial metabolism mediates oxidative stress and inflammation in fatty liver

PubMed Central

Satapati, Santhosh; Kucejova, Blanka; Duarte, Joao A.G.; Fletcher, Justin A.; Reynolds, Lacy; Sunny, Nishanth E.; He, Tianteng; Nair, L. Arya; Livingston, Kenneth; Fu, Xiaorong; Merritt, Matthew E.; Sherry, A. Dean; Malloy, Craig R.; Shelton, John M.; Lambert, Jennifer; Parks, Elizabeth J.; Corbin, Ian; Magnuson, Mark A.; Browning, Jeffrey D.; Burgess, Shawn C.

2015-01-01

Mitochondria are critical for respiration in all tissues; however, in liver, these organelles also accommodate high-capacity anaplerotic/cataplerotic pathways that are essential to gluconeogenesis and other biosynthetic activities. During nonalcoholic fatty liver disease (NAFLD), mitochondria also produce ROS that damage hepatocytes, trigger inflammation, and contribute to insulin resistance. Here, we provide several lines of evidence indicating that induction of biosynthesis through hepatic anaplerotic/cataplerotic pathways is energetically backed by elevated oxidative metabolism and hence contributes to oxidative stress and inflammation during NAFLD. First, in murine livers, elevation of fatty acid delivery not only induced oxidative metabolism, but also amplified anaplerosis/cataplerosis and caused a proportional rise in oxidative stress and inflammation. Second, loss of anaplerosis/cataplerosis via genetic knockdown of phosphoenolpyruvate carboxykinase 1 (Pck1) prevented fatty acid–induced rise in oxidative flux, oxidative stress, and inflammation. Flux appeared to be regulated by redox state, energy charge, and metabolite concentration, which may also amplify antioxidant pathways. Third, preventing elevated oxidative metabolism with metformin also normalized hepatic anaplerosis/cataplerosis and reduced markers of inflammation. Finally, independent histological grades in human NAFLD biopsies were proportional to oxidative flux. Thus, hepatic oxidative stress and inflammation are associated with elevated oxidative metabolism during an obesogenic diet, and this link may be provoked by increased work through anabolic pathways. PMID:26571396

Perspectives for a better understanding of the metabolic integration of photorespiration within a complex plant primary metabolism network

USDA-ARS?s Scientific Manuscript database

Photorespiration is an important high flux metabolic pathway that is found in all oxygen-producing photosynthetic organisms. It is often viewed as a closed loop that recycles carbon to fuel the Calvin cycle. However, the photorespiratory cycle is known to interact with several primary metabolic path...

Mitochondrial and Morphologic Alterations in Native Human Corneal Endothelial Cells Associated With Diabetes Mellitus.

PubMed

Aldrich, Benjamin T; Schlötzer-Schrehardt, Ursula; Skeie, Jessica M; Burckart, Kimberlee A; Schmidt, Gregory A; Reed, Cynthia R; Zimmerman, M Bridget; Kruse, Friedrich E; Greiner, Mark A

2017-04-01

To characterize changes in the energy-producing metabolic activity and morphologic ultrastructure of corneal endothelial cells associated with diabetes mellitus. Transplant suitable corneoscleral tissue was obtained from donors aged 50 to 75 years. We assayed 3-mm punches of endothelium-Descemet membrane for mitochondrial respiration and glycolysis activity using extracellular flux analysis of oxygen and pH, respectively. Transmission electron microscopy was used to assess qualitative and quantitative ultrastructural changes in corneal endothelial cells and associated Descemet membrane. For purposes of analysis, samples were divided into four groups based on a medical history of diabetes regardless of type: (1) nondiabetic, (2) noninsulin-dependent diabetic, (3) insulin-dependent diabetic, and (4) insulin-dependent diabetic with specified complications due to diabetes (advanced diabetic). In total, 229 corneas from 159 donors were analyzed. Insulin-dependent diabetic samples with complications due to diabetes displayed the lowest spare respiratory values compared to all other groups (P ≤ 0.002). The remaining mitochondrial respiration and glycolysis metrics did not differ significantly among groups. Compared to nondiabetic controls, the endothelium from advanced diabetic samples had alterations in mitochondrial morphology, pronounced Golgi bodies associated with abundant vesicles, accumulation of lysosomal bodies/autophagosomes, and focal production of abnormal long-spacing collagen. Extracellular flux analysis suggests that corneal endothelial cells of donors with advanced diabetes have impaired mitochondrial function. Metabolic findings are supported by observed differences in mitochondrial morphology of advanced diabetic samples but not controls. Additional studies are needed to determine the precise mechanism(s) by which mitochondria become impaired in diabetic corneal endothelial cells.

Elucidating central metabolic redox obstacles hindering ethanol production in Clostridium thermocellum.

PubMed

Thompson, R Adam; Layton, Donovan S; Guss, Adam M; Olson, Daniel G; Lynd, Lee R; Trinh, Cong T

2015-11-01

Clostridium thermocellum is an anaerobic, Gram-positive, thermophilic bacterium that has generated great interest due to its ability to ferment lignocellulosic biomass to ethanol. However, ethanol production is low due to the complex and poorly understood branched metabolism of C. thermocellum, and in some cases overflow metabolism as well. In this work, we developed a predictive stoichiometric metabolic model for C. thermocellum which incorporates the current state of understanding, with particular attention to cofactor specificity in the atypical glycolytic enzymes and the complex energy, redox, and fermentative pathways with the goal of aiding metabolic engineering efforts. We validated the model's capability to encompass experimentally observed phenotypes for the parent strain and derived mutants designed for significant perturbation of redox and energy pathways. Metabolic flux distributions revealed significant alterations in key metabolic branch points (e.g., phosphoenol pyruvate, pyruvate, acetyl-CoA, and cofactor nodes) in engineered strains for channeling electron and carbon fluxes for enhanced ethanol synthesis, with the best performing strain doubling ethanol yield and titer compared to the parent strain. In silico predictions of a redox-imbalanced genotype incapable of growth were confirmed in vivo, and a mutant strain was used as a platform to probe redox bottlenecks in the central metabolism that hinder efficient ethanol production. The results highlight the robustness of the redox metabolism of C. thermocellum and the necessity of streamlined electron flux from reduced ferredoxin to NAD(P)H for high ethanol production. The model was further used to design a metabolic engineering strategy to phenotypically constrain C. thermocellum to achieve high ethanol yields while requiring minimal genetic manipulations. The model can be applied to design C. thermocellum as a platform microbe for consolidated bioprocessing to produce ethanol and other reduced metabolites. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Rates of insulin secretion in INS-1 cells are enhanced by coupling to anaplerosis and Kreb's cycle flux independent of ATP synthesis.

PubMed

Cline, Gary W; Pongratz, Rebecca L; Zhao, Xiaojian; Papas, Klearchos K

2011-11-11

Mechanistic models of glucose stimulated insulin secretion (GSIS) established in minimal media in vitro, may not accurately describe the complexity of coupling metabolism with insulin secretion that occurs in vivo. As a first approximation, we have evaluated metabolic pathways in a typical growth media, DMEM as a surrogate in vivo medium, for comparison to metabolic fluxes observed under the typical experimental conditions using the simple salt-buffer of KRB. Changes in metabolism in response to glucose and amino acids and coupling to insulin secretion were measured in INS-1 832/13 cells. Media effects on mitochondrial function and the coupling efficiency of oxidative phosphorylation were determined by fluorometrically measured oxygen consumption rates (OCRs) combined with (31)P NMR measured rates of ATP synthesis. Substrate preferences and pathways into the TCA cycle, and the synthesis of mitochondrial 2nd messengers by anaplerosis were determined by (13)C NMR isotopomer analysis of the fate of [U-(13)C] glucose metabolism. Despite similar incremental increases in insulin secretion, the changes of OCR in response to increasing glucose from 2.5 to 15mM were blunted in DMEM relative to KRB. Basal and stimulated rates of insulin secretion rates were consistently higher in DMEM, while ATP synthesis rates were identical in both DMEM and KRB, suggesting greater mitochondrial uncoupling in DMEM. The relative rates of anaplerosis, and hence synthesis and export of 2nd messengers from the mitochondria were found to be similar in DMEM to those in KRB. And, the correlation of total PC flux with insulin secretion rates in DMEM was found to be congruous with the correlation in KRB. Together, these results suggest that signaling mechanisms associated with both TCA cycle flux and with anaplerotic flux, but not ATP production, may be responsible for the enhanced rates of insulin secretion in more complex, and physiologically-relevant media. Copyright © 2011 Elsevier Inc. All rights reserved.

(13)C-metabolic flux analysis of lipid accumulation in the oleaginous fungus Mucor circinelloides.

PubMed

Zhao, Lina; Zhang, Huaiyuan; Wang, Liping; Chen, Haiqin; Chen, Yong Q; Chen, Wei; Song, Yuanda

2015-12-01

The oleaginous fungus Mucor circinelloides is of industrial interest because it can produce high levels of polyunsaturated fatty acid γ-linolenic acid. M. circinelloides CBS 277.49 is able to accumulate less than 15% of cell dry weight as lipids, while M. circinelloides WJ11 can accumulate lipid up to 36%. In order to better understand the mechanisms behind the differential lipid accumulation in these two strains, tracer experiments with (13)C-glucose were performed with the growth of M. circinelloides and subsequent gas chromatography-mass spectrometric detection of (13)C-patterns in proteinogenic amino acids was carried out to identify the metabolic network topology and estimate intracellular fluxes. Our results showed that the high oleaginous strain WJ11 had higher flux of pentose phosphate pathway and malic enzyme, lower flux in tricarboxylic acid cycle, higher flux in glyoxylate cycle and ATP: citrate lyase, together, it might provide more NADPH and substrate acetyl-CoA for fatty acid synthesis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Metabolite concentrations, fluxes and free energies imply efficient enzyme usage

DOE PAGES

Park, Junyoung O.; Rubin, Sara A.; Xu, Yi -Fan; ...

2016-05-02

In metabolism, available free energy is limited and must be divided across pathway steps to maintain a negative Δ G throughout. For each reaction, Δ G is log proportional both to a concentration ratio (reaction quotient to equilibrium constant) and to a flux ratio (backward to forward flux). In this paper, we use isotope labeling to measure absolute metabolite concentrations and fluxes in Escherichia coli, yeast and a mammalian cell line. We then integrate this information to obtain a unified set of concentrations and Δ G for each organism. In glycolysis, we find that free energy is partitioned so asmore » to mitigate unproductive backward fluxes associated with Δ G near zero. Across metabolism, we observe that absolute metabolite concentrations and Δ G are substantially conserved and that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site dissociation constant ( K m or K i). Finally, the observed conservation of metabolite concentrations is consistent with an evolutionary drive to utilize enzymes efficiently given thermodynamic and osmotic constraints.« less

Mapping human brain capillary water lifetime: high‐resolution metabolic neuroimaging

PubMed Central

Li, Xin; Sammi, Manoj K.; Bourdette, Dennis N.; Neuwelt, Edward A.

2015-01-01

Shutter‐speed analysis of dynamic‐contrast‐agent (CA)‐enhanced normal, multiple sclerosis (MS), and glioblastoma (GBM) human brain data gives the mean capillary water molecule lifetime (τ b) and blood volume fraction (v b; capillary density–volume product (ρ † V)) in a high‐resolution 1H2O MRI voxel (40 μL) or ROI. The equilibrium water extravasation rate constant, k po (τ b −1), averages 3.2 and 2.9 s−1 in resting‐state normal white matter (NWM) and gray matter (NGM), respectively (n = 6). The results (italicized) lead to three major conclusions. (A) k po differences are dominated by capillary water permeability (P W †), not size, differences. NWM and NGM voxel k po and vb values are independent. Quantitative analyses of concomitant population‐averaged k po, vb variations in normal and normal‐appearing MS brain ROIs confirm PW † dominance. (B) P W † is dominated (>95%) by a trans(endothelial)cellular pathway, not the P CA † paracellular route. In MS lesions and GBM tumors, PCA † increases but PW † decreases. (C) k po tracks steady‐state ATP production/consumption flux per capillary. In normal, MS, and GBM brain, regional k po correlates with literature MRSI ATP (positively) and Na + (negatively) tissue concentrations. This suggests that the PW † pathway is metabolically active. Excellent agreement of the relative NGM/NWM k po vb product ratio with the literature 31PMRSI‐MT CMRoxphos ratio confirms the flux property. We have previously shown that the cellular water molecule efflux rate constant (k io) is proportional to plasma membrane P‐type ATPase turnover, likely due to active trans‐membrane water cycling. With synaptic proximities and synergistic metabolic cooperativities, polar brain endothelial, neuroglial, and neuronal cells form “gliovascular units.” We hypothesize that a chain of water cycling processes transmits brain metabolic activity to k po, letting it report neurogliovascular unit Na+,K+‐ATPase activity. Cerebral k po maps represent metabolic (functional) neuroimages. The NGM 2.9 s−1 k po means an equilibrium unidirectional water efflux of ~1015 H2O molecules s−1 per capillary (in 1 μL tissue): consistent with the known ATP consumption rate and water co‐transporting membrane symporter stoichiometries. © 2015 The Authors NMR in Biomedicine Published by John Wiley & Sons Ltd. PMID:25914365

The Pentose Phosphate Pathway as a Potential Target for Cancer Therapy

PubMed Central

Cho, Eunae Sandra; Cha, Yong Hoon; Kim, Hyun Sil; Kim, Nam Hee; Yook, Jong In

2018-01-01

During cancer progression, cancer cells are repeatedly exposed to metabolic stress conditions in a resource-limited environment which they must escape. Increasing evidence indicates the importance of nicotinamide adenine dinucleotide phosphate (NADPH) homeostasis in the survival of cancer cells under metabolic stress conditions, such as metabolic resource limitation and therapeutic intervention. NADPH is essential for scavenging of reactive oxygen species (ROS) mainly derived from oxidative phosphorylation required for ATP generation. Thus, metabolic reprogramming of NADPH homeostasis is an important step in cancer progression as well as in combinational therapeutic approaches. In mammalian, the pentose phosphate pathway (PPP) and one-carbon metabolism are major sources of NADPH production. In this review, we focus on the importance of glucose flux control towards PPP regulated by oncogenic pathways and the potential therein for metabolic targeting as a cancer therapy. We also summarize the role of Snail (Snai1), an important regulator of the epithelial mesenchymal transition (EMT), in controlling glucose flux towards PPP and thus potentiating cancer cell survival under oxidative and metabolic stress. PMID:29212304

Control analysis of lipid biosynthesis in tissue cultures from oil crops shows that flux control is shared between fatty acid synthesis and lipid assembly.

PubMed Central

Ramli, Umi S; Baker, Darren S; Quant, Patti A; Harwood, John L

2002-01-01

Top-Down (Metabolic) Control Analysis (TDCA) was used to examine, quantitatively, lipid biosynthesis in tissue cultures from two commercially important oil crops, olive (Olea europaea L.) and oil palm (Elaeis guineensis Jacq.). A conceptually simplified system was defined comprising two blocks of reactions: fatty acid synthesis (Block A) and lipid assembly (Block B), which produced and consumed, respectively, a common and unique system intermediate, cytosolic acyl-CoA. We manipulated the steady-state levels of the system intermediate by adding exogenous oleic acid and, using two independent assays, measured the effect of the addition on the system fluxes (J(A) and J(B)). These were the rate of incorporation of radioactivity: (i) through Block A from [1-(14)C]acetate into fatty acids and (ii) via Block B from [U-(14)C]glycerol into complex lipids respectively. The data showed that fatty acid formation (Block A) exerted higher control than lipid assembly (Block B) in both tissues with the following group flux control coefficients (C):(i) Oil palm: *C(J(TL))(BlkA)=0.64+/-0.05 and *C(J(TL))(BlkB)=0.36+/-0.05(ii) Olive: *C(J(TL))(BlkA)=0.57+/-0.10 and *C(J(TL))(BlkB)=0.43+/-0.10where *C indicates the group flux control coefficient over the lipid biosynthesis flux (J(TL)) and the subscripts BlkA and BlkB refer to defined blocks of the system, Block A and Block B. Nevertheless, because both parts of the lipid biosynthetic pathway exert significant flux control, we suggest strongly that manipulation of single enzyme steps will not affect product yield appreciably. The present study represents the first use of TDCA to examine the overall lipid biosynthetic pathway in any tissue, and its findings are of immediate academic and economic relevance to the yield and nutritional quality of oil crops. PMID:12023882

Quantitative Analysis of Glycerol Accumulation, Glycolysis and Growth under Hyper Osmotic Stress

PubMed Central

Nordlander, Bodil; Klein, Dagmara; Hong, Kuk-Ki; Jacobson, Therese; Dahl, Peter; Schaber, Jörg; Nielsen, Jens; Hohmann, Stefan; Klipp, Edda

2013-01-01

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies. PMID:23762021

A scientific workflow framework for (13)C metabolic flux analysis.

PubMed

Dalman, Tolga; Wiechert, Wolfgang; Nöh, Katharina

2016-08-20

Metabolic flux analysis (MFA) with (13)C labeling data is a high-precision technique to quantify intracellular reaction rates (fluxes). One of the major challenges of (13)C MFA is the interactivity of the computational workflow according to which the fluxes are determined from the input data (metabolic network model, labeling data, and physiological rates). Here, the workflow assembly is inevitably determined by the scientist who has to consider interacting biological, experimental, and computational aspects. Decision-making is context dependent and requires expertise, rendering an automated evaluation process hardly possible. Here, we present a scientific workflow framework (SWF) for creating, executing, and controlling on demand (13)C MFA workflows. (13)C MFA-specific tools and libraries, such as the high-performance simulation toolbox 13CFLUX2, are wrapped as web services and thereby integrated into a service-oriented architecture. Besides workflow steering, the SWF features transparent provenance collection and enables full flexibility for ad hoc scripting solutions. To handle compute-intensive tasks, cloud computing is supported. We demonstrate how the challenges posed by (13)C MFA workflows can be solved with our approach on the basis of two proof-of-concept use cases. Copyright © 2015 Elsevier B.V. All rights reserved.

A Systems Biology Approach Uncovers Cellular Strategies Used by Methylobacterium extorquens AM1 During the Switch from Multi- to Single-Carbon Growth

PubMed Central

Skovran, Elizabeth; Crowther, Gregory J.; Guo, Xiaofeng; Yang, Song; Lidstrom, Mary E.

2010-01-01

Background When organisms experience environmental change, how does their metabolic network reset and adapt to the new condition? Methylobacterium extorquens is a bacterium capable of growth on both multi- and single-carbon compounds. These different modes of growth utilize dramatically different central metabolic pathways with limited pathway overlap. Methodology/Principal Findings This study focused on the mechanisms of metabolic adaptation occurring during the transition from succinate growth (predicted to be energy-limited) to methanol growth (predicted to be reducing-power-limited), analyzing changes in carbon flux, gene expression, metabolites and enzymatic activities over time. Initially, cells experienced metabolic imbalance with excretion of metabolites, changes in nucleotide levels and cessation of cell growth. Though assimilatory pathways were induced rapidly, a transient block in carbon flow to biomass synthesis occurred, and enzymatic assays suggested methylene tetrahydrofolate dehydrogenase as one control point. This “downstream priming” mechanism ensures that significant carbon flux through these pathways does not occur until they are fully induced, precluding the buildup of toxic intermediates. Most metabolites that are required for growth on both carbon sources did not change significantly, even though transcripts and enzymatic activities required for their production changed radically, underscoring the concept of metabolic setpoints. Conclusions/Significance This multi-level approach has resulted in new insights into the metabolic strategies carried out to effect this shift between two dramatically different modes of growth and identified a number of potential flux control and regulatory check points as a further step toward understanding metabolic adaptation and the cellular strategies employed to maintain metabolic setpoints. PMID:21124828

Lake Metabolism: Comparison of Lake Metabolic Rates Estimated from a Diel CO2- and the Common Diel O2-Technique.

PubMed

Peeters, Frank; Atamanchuk, Dariia; Tengberg, Anders; Encinas-Fernández, Jorge; Hofmann, Hilmar

2016-01-01

Lake metabolism is a key factor for the understanding of turnover of energy and of organic and inorganic matter in lake ecosystems. Long-term time series on metabolic rates are commonly estimated from diel changes in dissolved oxygen. Here we present long-term data on metabolic rates based on diel changes in total dissolved inorganic carbon (DIC) utilizing an open-water diel CO2-technique. Metabolic rates estimated with this technique and the traditional diel O2-technique agree well in alkaline Lake Illmensee (pH of ~8.5), although the diel changes in molar CO2 concentrations are much smaller than those of the molar O2 concentrations. The open-water diel CO2- and diel O2-techniques provide independent measures of lake metabolic rates that differ in their sensitivity to transport processes. Hence, the combination of both techniques can help to constrain uncertainties arising from assumptions on vertical fluxes due to gas exchange and turbulent diffusion. This is particularly important for estimates of lake respiration rates because these are much more sensitive to assumptions on gradients in vertical fluxes of O2 or DIC than estimates of lake gross primary production. Our data suggest that it can be advantageous to estimate respiration rates assuming negligible gradients in vertical fluxes rather than including gas exchange with the atmosphere but neglecting vertical mixing in the water column. During two months in summer the average lake net production was close to zero suggesting at most slightly autotrophic conditions. However, the lake emitted O2 and CO2 during the entire time period suggesting that O2 and CO2 emissions from lakes can be decoupled from the metabolism in the near surface layer.

Lake Metabolism: Comparison of Lake Metabolic Rates Estimated from a Diel CO2- and the Common Diel O2-Technique

PubMed Central

Peeters, Frank; Atamanchuk, Dariia; Tengberg, Anders; Encinas-Fernández, Jorge; Hofmann, Hilmar

2016-01-01

Lake metabolism is a key factor for the understanding of turnover of energy and of organic and inorganic matter in lake ecosystems. Long-term time series on metabolic rates are commonly estimated from diel changes in dissolved oxygen. Here we present long-term data on metabolic rates based on diel changes in total dissolved inorganic carbon (DIC) utilizing an open-water diel CO2-technique. Metabolic rates estimated with this technique and the traditional diel O2-technique agree well in alkaline Lake Illmensee (pH of ~8.5), although the diel changes in molar CO2 concentrations are much smaller than those of the molar O2 concentrations. The open-water diel CO2- and diel O2-techniques provide independent measures of lake metabolic rates that differ in their sensitivity to transport processes. Hence, the combination of both techniques can help to constrain uncertainties arising from assumptions on vertical fluxes due to gas exchange and turbulent diffusion. This is particularly important for estimates of lake respiration rates because these are much more sensitive to assumptions on gradients in vertical fluxes of O2 or DIC than estimates of lake gross primary production. Our data suggest that it can be advantageous to estimate respiration rates assuming negligible gradients in vertical fluxes rather than including gas exchange with the atmosphere but neglecting vertical mixing in the water column. During two months in summer the average lake net production was close to zero suggesting at most slightly autotrophic conditions. However, the lake emitted O2 and CO2 during the entire time period suggesting that O2 and CO2 emissions from lakes can be decoupled from the metabolism in the near surface layer. PMID:28002477

Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction

PubMed Central

Yoshikawa, Katsunori; Aikawa, Shimpei; Kojima, Yuta; Toya, Yoshihiro; Furusawa, Chikara; Kondo, Akihiko; Shimizu, Hiroshi

2015-01-01

Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source. PMID:26640947

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens Using Proteomic Data from a Field Biostimulation Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Yilin; Wilkins, Michael J.; Yabusaki, Steven B.

2012-12-12

Biomass and shotgun global proteomics data that reflected relative protein abundances from samples collected during the 2008 experiment at the U.S. Department of Energy Integrated Field-Scale Subsurface Research Challenge site in Rifle, Colorado, provided an unprecedented opportunity to validate a genome-scale metabolic model of Geobacter metallireducens and assess its performance with respect to prediction of metal reduction, biomass yield, and growth rate under dynamic field conditions. Reconstructed from annotated genomic sequence, biochemical, and physiological data, the constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes.more » Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low fluxes through amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.« less

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

PubMed Central

Ranganathan, Sridhar; Suthers, Patrick F.; Maranas, Costas D.

2010-01-01

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis. PMID:20419153

Metabolism of branched-chain amino acids in leg muscles from tail-cast suspended intact and adrenalectomized rats

NASA Technical Reports Server (NTRS)

Jaspers, Stephen R.; Henriksen, Erik; Jacob, Stephan; Tischler, Marc E.

1989-01-01

The effects of muscle unloading, adrenalectomy, and cortisol treatment on the metabolism of branched-chain amino acids in the soleus and extensor digitorum longus of tail-cast suspended rats were investigated using C-14-labeled lucine, isoleucine, and valine in incubation studies. It was found that, compared to not suspended controls, the degradation of branched-chain amino acids in hind limb muscles was accelerated in tail-cast suspended rats. Adrenalectomy was found to abolish the aminotransferase flux and to diminish the dehydrogenase flux in the soleus. The data also suggest that cortisol treatment increases the rate of metabolism of branched-chain amino acids at the dehydrogenase step.

A review of standardized metabolic phenotyping of animal models.

PubMed

Rozman, Jan; Klingenspor, Martin; Hrabě de Angelis, Martin

2014-10-01

Metabolic phenotyping of genetically modified animals aims to detect new candidate genes and related metabolic pathways that result in dysfunctional energy balance regulation and predispose for diseases such as obesity or type 2 diabetes mellitus. In this review, we provide a comprehensive overview on the technologies available to monitor energy flux (food uptake, bomb calorimetry of feces and food, and indirect calorimetry) and body composition (qNMR, DXA, and MRI) in animal models for human diseases with a special focus on phenotyping methods established in genetically engineered mice. We use an energy flux model to illustrate the principles of energy allocation, describe methodological aspects how to monitor energy balance, and introduce strategies for data analysis and presentation.

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

PubMed

Feng, Xueyang; Tang, Kuo-Hsiang; Blankenship, Robert E; Tang, Yinjie J

2010-12-10

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

Revealing Differences in Metabolic Flux Distributions between a Mutant Strain and Its Parent Strain Gluconacetobacter xylinus CGMCC 2955

PubMed Central

Liu, Miao; Yang, Xiao-Ning; Zhu, Hui-Xia; Jia, Yuan-Yuan; Jia, Shi-Ru; Piergiovanni, Luciano

2014-01-01

A better understanding of metabolic fluxes is important for manipulating microbial metabolism toward desired end products, or away from undesirable by-products. A mutant strain, Gluconacetobacter xylinus AX2-16, was obtained by combined chemical mutation of the parent strain (G. xylinus CGMCC 2955) using DEC (diethyl sulfate) and LiCl. The highest bacterial cellulose production for this mutant was obtained at about 11.75 g/L, which was an increase of 62% compared with that by the parent strain. In contrast, gluconic acid (the main byproduct) concentration was only 5.71 g/L for mutant strain, which was 55.7% lower than that of parent strain. Metabolic flux analysis indicated that 40.1% of the carbon source was transformed to bacterial cellulose in mutant strain, compared with 24.2% for parent strain. Only 32.7% and 4.0% of the carbon source were converted into gluconic acid and acetic acid in mutant strain, compared with 58.5% and 9.5% of that in parent strain. In addition, a higher flux of tricarboxylic acid (TCA) cycle was obtained in mutant strain (57.0%) compared with parent strain (17.0%). It was also indicated from the flux analysis that more ATP was produced in mutant strain from pentose phosphate pathway (PPP) and TCA cycle. The enzymatic activity of succinate dehydrogenase (SDH), which is one of the key enzymes in TCA cycle, was 1.65-fold higher in mutant strain than that in parent strain at the end of culture. It was further validated by the measurement of ATPase that 3.53–6.41 fold higher enzymatic activity was obtained from mutant strain compared with parent strain. PMID:24901455

Lipotoxicity in steatohepatitis occurs despite an increase in tricarboxylic acid cycle activity

PubMed Central

Patterson, Rainey E.; Kalavalapalli, Srilaxmi; Williams, Caroline M.; Nautiyal, Manisha; Mathew, Justin T.; Martinez, Janie; Reinhard, Mary K.; McDougall, Danielle J.; Rocca, James R.; Yost, Richard A.; Cusi, Kenneth; Garrett, Timothy J.

2016-01-01

The hepatic tricarboxylic acid (TCA) cycle is central to integrating macronutrient metabolism and is closely coupled to cellular respiration, free radical generation, and inflammation. Oxidative flux through the TCA cycle is induced during hepatic insulin resistance, in mice and humans with simple steatosis, reflecting early compensatory remodeling of mitochondrial energetics. We hypothesized that progressive severity of hepatic insulin resistance and the onset of nonalcoholic steatohepatitis (NASH) would impair oxidative flux through the hepatic TCA cycle. Mice (C57/BL6) were fed a high-trans-fat high-fructose diet (TFD) for 8 wk to induce simple steatosis and NASH by 24 wk. In vivo fasting hepatic mitochondrial fluxes were determined by 13C-nuclear magnetic resonance (NMR)-based isotopomer analysis. Hepatic metabolic intermediates were quantified using mass spectrometry-based targeted metabolomics. Hepatic triglyceride accumulation and insulin resistance preceded alterations in mitochondrial metabolism, since TCA cycle fluxes remained normal during simple steatosis. However, mice with NASH had a twofold induction (P < 0.05) of mitochondrial fluxes (μmol/min) through the TCA cycle (2.6 ± 0.5 vs. 5.4 ± 0.6), anaplerosis (9.1 ± 1.2 vs. 16.9 ± 2.2), and pyruvate cycling (4.9 ± 1.0 vs. 11.1 ± 1.9) compared with their age-matched controls. Induction of the TCA cycle activity during NASH was concurrent with blunted ketogenesis and accumulation of hepatic diacylglycerols (DAGs), ceramides (Cer), and long-chain acylcarnitines, suggesting inefficient oxidation and disposal of excess free fatty acids (FFA). Sustained induction of mitochondrial TCA cycle failed to prevent accretion of “lipotoxic” metabolites in the liver and could hasten inflammation and the metabolic transition to NASH. PMID:26814015

Application of the principles of systems biology and Wiener's cybernetics for analysis of regulation of energy fluxes in muscle cells in vivo.

PubMed

Guzun, Rita; Saks, Valdur

2010-03-08

The mechanisms of regulation of respiration and energy fluxes in the cells are analyzed based on the concepts of systems biology, non-equilibrium steady state kinetics and applications of Wiener's cybernetic principles of feedback regulation. Under physiological conditions cardiac function is governed by the Frank-Starling law and the main metabolic characteristic of cardiac muscle cells is metabolic homeostasis, when both workload and respiration rate can be changed manifold at constant intracellular level of phosphocreatine and ATP in the cells. This is not observed in skeletal muscles. Controversies in theoretical explanations of these observations are analyzed. Experimental studies of permeabilized fibers from human skeletal muscle vastus lateralis and adult rat cardiomyocytes showed that the respiration rate is always an apparent hyperbolic but not a sigmoid function of ADP concentration. It is our conclusion that realistic explanations of regulation of energy fluxes in muscle cells require systemic approaches including application of the feedback theory of Wiener's cybernetics in combination with detailed experimental research. Such an analysis reveals the importance of limited permeability of mitochondrial outer membrane for ADP due to interactions of mitochondria with cytoskeleton resulting in quasi-linear dependence of respiration rate on amplitude of cyclic changes in cytoplasmic ADP concentrations. The system of compartmentalized creatine kinase (CK) isoenzymes functionally coupled to ANT and ATPases, and mitochondrial-cytoskeletal interactions separate energy fluxes (mass and energy transfer) from signalling (information transfer) within dissipative metabolic structures - intracellular energetic units (ICEU). Due to the non-equilibrium state of CK reactions, intracellular ATP utilization and mitochondrial ATP regeneration are interconnected by the PCr flux from mitochondria. The feedback regulation of respiration occurring via cyclic fluctuations of cytosolic ADP, Pi and Cr/PCr ensures metabolic stability necessary for normal function of cardiac cells.

Metabolic regulation and maximal reaction optimization in the central metabolism of a yeast cell

NASA Astrophysics Data System (ADS)

Kasbawati, Gunawan, A. Y.; Hertadi, R.; Sidarto, K. A.

2015-03-01

Regulation of fluxes in a metabolic system aims to enhance the production rates of biotechnologically important compounds. Regulation is held via modification the cellular activities of a metabolic system. In this study, we present a metabolic analysis of ethanol fermentation process of a yeast cell in terms of continuous culture scheme. The metabolic regulation is based on the kinetic formulation in combination with metabolic control analysis to indicate the key enzymes which can be modified to enhance ethanol production. The model is used to calculate the intracellular fluxes in the central metabolism of the yeast cell. Optimal control is then applied to the kinetic model to find the optimal regulation for the fermentation system. The sensitivity results show that there are external and internal control parameters which are adjusted in enhancing ethanol production. As an external control parameter, glucose supply should be chosen in appropriate way such that the optimal ethanol production can be achieved. For the internal control parameter, we find three enzymes as regulation targets namely acetaldehyde dehydrogenase, pyruvate decarboxylase, and alcohol dehydrogenase which reside in the acetaldehyde branch. Among the three enzymes, however, only acetaldehyde dehydrogenase has a significant effect to obtain optimal ethanol production efficiently.

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale1[W][OPEN

PubMed Central

Grafahrend-Belau, Eva; Junker, Astrid; Eschenröder, André; Müller, Johannes; Schreiber, Falk; Junker, Björn H.

2013-01-01

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. PMID:23926077

Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

PubMed Central

Bogart, Eli; Myers, Christopher R.

2016-01-01

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, we suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data, and we demonstrate that our method predicts fluxes that achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. PMID:26990967

Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies.

PubMed

Crown, Scott B; Antoniewicz, Maciek R

2013-03-01

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for (13)C-metabolic flux analysis ((13)C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of (13)C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for (13)C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection. Copyright © 2012 Elsevier Inc. All rights reserved.

Understanding alternative fluxes/effluxes through comparative metabolic pathway analysis of phylum actinobacteria using a simplified approach.

PubMed

Verma, Mansi; Lal, Devi; Saxena, Anjali; Anand, Shailly; Kaur, Jasvinder; Kaur, Jaspreet; Lal, Rup

2013-12-01

Actinobacteria are known for their diverse metabolism and physiology. Some are dreadful human pathogens whereas some constitute the natural flora for human gut. Therefore, the understanding of metabolic pathways is a key feature for targeting the pathogenic bacteria without disturbing the symbiotic ones. A big challenge faced today is multiple drug resistance by Mycobacterium and other pathogens that utilize alternative fluxes/effluxes. With the availability of genome sequence, it is now feasible to conduct the comparative in silico analysis. Here we present a simplified approach to compare metabolic pathways so that the species specific enzyme may be traced and engineered for future therapeutics. The analyses of four key carbohydrate metabolic pathways, i.e., glycolysis, pyruvate metabolism, tri carboxylic acid cycle and pentose phosphate pathway suggest the presence of alternative fluxes. It was found that the upper pathway of glycolysis was highly variable in the actinobacterial genomes whereas lower glycolytic pathway was highly conserved. Likewise, pentose phosphate pathway was well conserved in contradiction to TCA cycle, which was found to be incomplete in majority of actinobacteria. The clustering based on presence and absence of genes of these metabolic pathways clearly revealed that members of different genera shared identical pathways and, therefore, provided an easy method to identify the metabolic similarities/differences between pathogenic and symbiotic organisms. The analyses could identify isoenzymes and some key enzymes that were found to be missing in some pathogenic actinobacteria. The present work defines a simple approach to explore the effluxes in four metabolic pathways within the phylum actinobacteria. The analysis clearly reflects that actinobacteria exhibit diverse routes for metabolizing substrates. The pathway comparison can help in finding the enzymes that can be used as drug targets for pathogens without effecting symbiotic organisms within the same host. This may help to prevail over the multiple drug resistance, for designing broad spectrum drugs, in food industries and other clinical research areas. © 2013.

Carbon Metabolism of Prochlorococcus sp. Under Nitrogen Limitation

NASA Astrophysics Data System (ADS)

Szul, M.

2016-02-01

Phytoplankton growth rates are limited by nutrient availability in the world's euphotic oligotrophic oceans. In these vast biomes, convergent evolutions of the dominant planktonic populations suggest traits such as small genome and cell size provide selective advantages. While these traits have been shown to improve both thrift and competition for scarce nutrients, how fitness is manifest through reductive evolution on metabolisms remains poorly understood. To develop a better understanding of carbon fate and flux under nutrient limitation, we grew axenic Prochlorococcus under nitrogen-limited and nitrogen-replete conditions and measured metabolite pools, the flux of carbon through these pools as well as photosynthesis, photosystem health and efficiency. Our data show cells under nitrogen limitation reduce rates of both metabolite flux and total carbon fixation while maintaining elevated metabolite pool levels and releasing a larger proportion of total fixed carbon to the environment. Accounting for these observations, potential metabolic mechanisms that contribute to the fitness of Prochlorococcus in the nutrient limited oceans will be discussed.

Updates to a 13C metabolic flux analysis model for evaluating energy metabolism in cultured cerebellar granule neurons from neonatal rats.

PubMed

Jekabsons, Mika B; Gebril, Hoda M; Wang, Yan-Hong; Avula, Bharathi; Khan, Ikhlas A

2017-10-01

A hexose phosphate recycling model previously developed to infer fluxes through the major glucose consuming pathways in cultured cerebellar granule neurons (CGNs) from neonatal rats metabolizing [1,2- 13 C 2 ]glucose was revised by considering reverse flux through the non-oxidative pentose phosphate pathway (PPP) and symmetrical succinate oxidation within the tricarboxylic acid (TCA) cycle. The model adjusts three flux ratios to effect 13 C distribution in the hexose, pentose, and triose phosphate pools, and in TCA cycle malate to minimize the error between predicted and measured 13 C labeling in exported lactate (i.e., unlabeled, single-, double-, and triple-labeled; M, M1, M2, and M3, respectively). Inclusion of reverse non-oxidative PPP flux substantially increased the number of calculations but ultimately had relatively minor effects on the labeling of glycolytic metabolites. From the error-minimized solution in which the predicted M-M3 lactate differed by 0.49% from that measured by liquid chromatography-triple quadrupole mass spectrometry, the neurons exhibited negligible forward non-oxidative PPP flux. Thus, no glucose was used by the pentose cycle despite explicit consideration of hexose phosphate recycling. Mitochondria consumed only 16% of glucose while 45% was exported as lactate by aerobic glycolysis. The remaining 39% of glucose was shunted to pentose phosphates presumably for de novo nucleotide synthesis, but the proportion metabolized through the oxidative PPP vs. the reverse non-oxidative PPP could not be determined. The lactate exported as M1 (2.5%) and M3 (1.2%) was attributed to malic enzyme, which was responsible for 7.8% of pyruvate production (vs. 92.2% by glycolysis). The updated model is more broadly applicable to different cell types by considering bi-directional flux through the non-oxidative PPP. Its application to cultured neurons utilizing glucose as the sole exogenous substrate has demonstrated substantial oxygen-independent glucose utilization by aerobic glycolysis as well as the oxidative PPP and/or reverse non-oxidative PPP, but negligible glucose consumption by the pentose cycle. Copyright © 2017 Elsevier Ltd. All rights reserved.

The nutritional status of Methanosarcina acetivorans regulates glycogen metabolism and gluconeogenesis and glycolysis fluxes.

PubMed

Santiago-Martínez, Michel Geovanni; Encalada, Rusely; Lira-Silva, Elizabeth; Pineda, Erika; Gallardo-Pérez, Juan Carlos; Reyes-García, Marco Antonio; Saavedra, Emma; Moreno-Sánchez, Rafael; Marín-Hernández, Alvaro; Jasso-Chávez, Ricardo

2016-05-01

Gluconeogenesis is an essential pathway in methanogens because they are unable to use exogenous hexoses as carbon source for cell growth. With the aim of understanding the regulatory mechanisms of central carbon metabolism in Methanosarcina acetivorans, the present study investigated gene expression, the activities and metabolic regulation of key enzymes, metabolite contents and fluxes of gluconeogenesis, as well as glycolysis and glycogen synthesis/degradation pathways. Cells were grown with methanol as a carbon source. Key enzymes were kinetically characterized at physiological pH/temperature. Active consumption of methanol during exponential cell growth correlated with significant methanogenesis, gluconeogenic flux and steady glycogen synthesis. After methanol exhaustion, cells reached the stationary growth phase, which correlated with the rise in glycogen consumption and glycolytic flux, decreased methanogenesis, negligible acetate production and an absence of gluconeogenesis. Elevated activities of carbon monoxide dehydrogenase/acetyl-CoA synthetase complex and pyruvate: ferredoxin oxidoreductase suggested the generation of acetyl-CoA and pyruvate for glycogen synthesis. In the early stationary growth phase, the transcript contents and activities of pyruvate phosphate dikinase, fructose 1,6-bisphosphatase and glycogen synthase decreased, whereas those of glycogen phosphorylase, ADP-phosphofructokinase and pyruvate kinase increased. Therefore, glycogen and gluconeogenic metabolites were synthesized when an external carbon source was provided. Once such a carbon source became depleted, glycolysis and methanogenesis fed by glycogen degradation provided the ATP supply. Weak inhibition of key enzymes by metabolites suggested that the pathways evaluated were mainly transcriptionally regulated. Because glycogen metabolism and glycolysis/gluconeogenesis are not present in all methanogens, the overall data suggest that glycogen storage might represent an environmental advantage for methanosarcinales when carbon sources are scarce. Also, the understanding of the central carbohydrate metabolism in methanosarcinales may help to optimize methane production. © 2016 Federation of European Biochemical Societies.

Distinct Mechanisms of the ORANGE Protein in Controlling Carotenoid Flux1[OPEN

PubMed Central

Ohali, Shachar; Meir, Ayala; Sa’ar, Uzi; Mazourek, Michael; Lewinsohn, Efraim; Schaffer, Arthur A.; Burger, Joseph

2017-01-01

β-Carotene adds nutritious value and determines the color of many fruits, including melon (Cucumis melo). In melon mesocarp, β-carotene accumulation is governed by the Orange gene (CmOr) golden single-nucleotide polymorphism (SNP) through a yet to be discovered mechanism. In Arabidopsis (Arabidopsis thaliana), OR increases carotenoid levels by posttranscriptionally regulating phytoene synthase (PSY). Here, we identified a CmOr nonsense mutation (Cmor-lowβ) that lowered fruit β-carotene levels with impaired chromoplast biogenesis. Cmor-lowβ exerted a minimal effect on PSY transcripts but dramatically decreased PSY protein levels and enzymatic activity, leading to reduced carotenoid metabolic flux and accumulation. However, the golden SNP was discovered to not affect PSY protein levels and carotenoid metabolic flux in melon fruit, as shown by carotenoid and immunoblot analyses of selected melon genotypes and by using chemical pathway inhibitors. The high β-carotene accumulation in golden SNP melons was found to be due to a reduced further metabolism of β-carotene. This was revealed by genetic studies with double mutants including carotenoid isomerase (yofi), a carotenoid-isomerase nonsense mutant, which arrests the turnover of prolycopene. The yofi F2 segregants accumulated prolycopene independently of the golden SNP. Moreover, Cmor-lowβ was found to inhibit chromoplast formation and chloroplast disintegration in fruits from 30 d after anthesis until ripening, suggesting that CmOr regulates the chloroplast-to-chromoplast transition. Taken together, our results demonstrate that CmOr is required to achieve PSY protein levels to maintain carotenoid biosynthesis metabolic flux but that the mechanism of the CmOr golden SNP involves an inhibited metabolism downstream of β-carotene to dramatically affect both carotenoid content and plastid fate. PMID:27837090

Experimental Systems-Biology Approaches for Clostridia-Based Bioenergy Production

DOE Office of Scientific and Technical Information (OSTI.GOV)

Papoutsakis, Elefterios

This is the final project report for project "Experimental Systems-Biology Approaches for Clostridia-Based Bioenergy Production" for the funding period of 9/1/12 to 2/28/2015 (three years with a 6-month no-cost extension) OVERVIEW AND PROJECT GOALS The bottleneck of achieving higher rates and titers of toxic metabolites (such as solvents and carboxylic acids that can used as biofuels or biofuel precursors) can be overcome by engineering the stress response system. Thus, understanding and modeling the response of cells to toxic metabolites is a problem of great fundamental and practical significance. In this project, our goal is to dissect at the molecular systemsmore » level and build models (conceptual and quantitative) for the stress response of C. acetobutylicum (Cac) to its two toxic metabolites: butanol (BuOH) and butyrate (BA). Transcriptional (RNAseq and microarray based), proteomic and fluxomic data and their analysis are key requirements for this goal. Transcriptional data from mid-exponential cultures of Cac under 4 different levels of BuOH and BA stress was obtained using both microarrays (Papoutsakis group) and deep sequencing (RNAseq; Meyers and Papoutsakis groups). These two sets of data do not only serve to validate each other, but are also used for identification of stress-induced changes in transcript levels, small regulatory RNAs, & in transcriptional start sites. Quantitative proteomic data (Lee group), collected using the iTRAQ technology, are essential for understanding of protein levels and turnover under stress and the various protein-protein interactions that orchestrate the stress response. Metabolic flux changes (Antoniewicz group) of core pathways, which provide important information on the re-allocation of energy and carbon resources under metabolite stress, were examined using 13C-labelled chemicals. Omics data are integrated at different levels and scales. At the metabolic-pathway level, omics data are integrated into a 2nd generation genome-scale model (GSM) (Maranas group). Omics data are also integrated using bioinformatics (Wu and Huang group), whereby regulatory details of gene and protein expression, protein-protein interactions and metabolic flux regulation are incorporated. The PI (Papoutsakis) facilitated project integration through monthly meeting and reports, conference calls, and collaborative manuscript preparation. The five groups collaborated extensively and made a large number of presentations in national and international meetings. It has also published several papers, with several more in the preparation stage. Several PhD, MS and postdoctoral students were trained as part of this collaborative and interdisciplinary project.« less

Quantitative evaluation of high-energy O- ion particle flux in a DC magnetron sputter plasma with an indium-tin-oxide target

NASA Astrophysics Data System (ADS)

Suyama, Taku; Bae, Hansin; Setaka, Kenta; Ogawa, Hayato; Fukuoka, Yushi; Suzuki, Haruka; Toyoda, Hirotaka

2017-11-01

O- ion flux from the indium tin oxide (ITO) sputter target under Ar ion bombardment is quantitatively evaluated using a calorimetry method. Using a mass spectrometer with an energy analyzer, O- energy distribution is measured with spatial dependence. Directional high-energy O- ion ejected from the target surface is observed. Using a calorimetry method, localized heat flux originated from high-energy O- ion is measured. From absolute evaluation of the heat flux from O- ion, O- particle flux in order of 1018 m-2 s-1 is evaluated at a distance of 10 cm from the target. Production yield of O- ion on the ITO target by one Ar+ ion impingement at a kinetic energy of 244 eV is estimated to be 3.3  ×  10-3 as the minimum value.

Hypoxanthine is a checkpoint stress metabolite in colonic epithelial energy modulation and barrier function.

PubMed

Lee, J Scott; Wang, Ruth X; Alexeev, Erica E; Lanis, Jordi M; Battista, Kayla D; Glover, Louise E; Colgan, Sean P

2018-04-20

Intestinal epithelial cells form a selectively permeable barrier to protect colon tissues from luminal microbiota and antigens and to mediate nutrient, fluid, and waste flux in the intestinal tract. Dysregulation of the epithelial cell barrier coincides with profound shifts in metabolic energy, especially in the colon, which exists in an energetically depleting state of physiological hypoxia. However, studies that systematically examine energy flux and adenylate metabolism during intestinal epithelial barrier development and restoration after disruption are lacking. Here, to delineate barrier-related energy flux, we developed an HPLC-based profiling method to track changes in energy flux and adenylate metabolites during barrier development and restoration. Cultured epithelia exhibited pooling of phosphocreatine and maintained ATP during barrier development. EDTA-induced epithelial barrier disruption revealed that hypoxanthine levels correlated with barrier resistance. Further studies uncovered that hypoxanthine supplementation improves barrier function and wound healing and that hypoxanthine appears to do so by increasing intracellular ATP, which improved cytoskeletal G- to F-actin polymerization. Hypoxanthine supplementation increased the adenylate energy charge in the murine colon, indicating potential to regulate adenylate energy charge-mediated metabolism in intestinal epithelial cells. Moreover, experiments in a murine colitis model disclosed that hypoxanthine loss during active inflammation correlates with markers of disease severity. In summary, our results indicate that hypoxanthine modulates energy metabolism in intestinal epithelial cells and is critical for intestinal barrier function. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

In vivo and in silico dynamics of the development of Metabolic Syndrome.

PubMed

Rozendaal, Yvonne J W; Wang, Yanan; Paalvast, Yared; Tambyrajah, Lauren L; Li, Zhuang; Willems van Dijk, Ko; Rensen, Patrick C N; Kuivenhoven, Jan A; Groen, Albert K; Hilbers, Peter A J; van Riel, Natal A W

2018-06-01

The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.

Metabolic Mapping: Quantitative Enzyme Cytochemistry and Histochemistry to Determine the Activity of Dehydrogenases in Cells and Tissues.

PubMed

Molenaar, Remco J; Khurshed, Mohammed; Hira, Vashendriya V V; Van Noorden, Cornelis J F

2018-05-26

Altered cellular metabolism is a hallmark of many diseases, including cancer, cardiovascular diseases and infection. The metabolic motor units of cells are enzymes and their activity is heavily regulated at many levels, including the transcriptional, mRNA stability, translational, post-translational and functional level. This complex regulation means that conventional quantitative or imaging assays, such as quantitative mRNA experiments, Western Blots and immunohistochemistry, yield incomplete information regarding the ultimate activity of enzymes, their function and/or their subcellular localization. Quantitative enzyme cytochemistry and histochemistry (i.e., metabolic mapping) show in-depth information on in situ enzymatic activity and its kinetics, function and subcellular localization in an almost true-to-nature situation. We describe a protocol to detect the activity of dehydrogenases, which are enzymes that perform redox reactions to reduce cofactors such as NAD(P) + and FAD. Cells and tissue sections are incubated in a medium that is specific for the enzymatic activity of one dehydrogenase. Subsequently, the dehydrogenase that is the subject of investigation performs its enzymatic activity in its subcellular site. In a chemical reaction with the reaction medium, this ultimately generates blue-colored formazan at the site of the dehydrogenase's activity. The formazan's absorbance is therefore a direct measure of the dehydrogenase's activity and can be quantified using monochromatic light microscopy and image analysis. The quantitative aspect of this protocol enables researchers to draw statistical conclusions from these assays. Besides observational studies, this technique can be used for inhibition studies of specific enzymes. In this context, studies benefit from the true-to-nature advantages of metabolic mapping, giving in situ results that may be physiologically more relevant than in vitro enzyme inhibition studies. In all, metabolic mapping is an indispensable technique to study metabolism at the cellular or tissue level. The technique is easy to adopt, provides in-depth, comprehensive and integrated metabolic information and enables rapid quantitative analysis.

Quantification of functional genes from procaryotes in soil by PCR.

PubMed

Sharma, Shilpi; Radl, Viviane; Hai, Brigitte; Kloos, Karin; Fuka, Mirna Mrkonjic; Engel, Marion; Schauss, Kristina; Schloter, Michael

2007-03-01

Controlling turnover processes and fluxes in soils and other environments requires information about the gene pool and possibilities for its in situ induction. Therefore in the recent years there has been a growing interest in genes and transcripts coding for metabolic enzymes. Besides questions addressing redundancy and diversity, more and more attention is given on the abundance of specific DNA and mRNA in the different habitats. This review will describe several PCR techniques that are suitable for quantification of functional genes and transcripts such as MPN-PCR, competitive PCR and real-time PCR. The advantages and disadvantages of the mentioned methods are discussed. In addition, the problems of quantitative extraction of nucleic acid and substances that inhibit polymerase are described. Finally, some examples from recent papers are given to demonstrate the applicability and usefulness of the different approaches.

Integrating Proteomics and Enzyme Kinetics Reveals Tissue-Specific Types of the Glycolytic and Gluconeogenic Pathways.

PubMed

Wiśniewski, Jacek R; Gizak, Agnieszka; Rakus, Dariusz

2015-08-07

Glycolysis is the core metabolic pathway supplying energy to cells. Whereas the vast majority of studies focus on specific aspects of the process, global analyses characterizing simultaneously all enzymes involved in the process are scarce. Here, we demonstrate that quantitative label- and standard-free proteomics allows accurate determination of titers of metabolic enzymes and enables simultaneous measurements of titers and maximal enzymatic activities (Amax) of all glycolytic enzymes and the gluconeogenic fructose 1,6-bisphosphatase in mouse brain, liver and muscle. Despite occurrence of tissue-specific isoenzymes bearing different kinetic properties, the enzyme titers often correlated well with the Amax values. To provide a more general picture of energy metabolism, we analyzed titers of the enzymes in additional 7 mouse organs and in human cells. Across the analyzed samples, we identified two basic profiles: a "fast glucose uptake" one in brain and heart, and a "gluconeogenic rich" one occurring in liver. In skeletal muscles and other organs, we found intermediate profiles. Obtained data highlighted the glucose-flux-limiting role of hexokinase which activity was always 10- to 100-fold lower than the average activity of all other glycolytic enzymes. A parallel determination of enzyme titers and maximal enzymatic activities allowed determination of kcat values without enzyme purification. Results of our in-depth proteomic analysis of the mouse organs did not support the concepts of regulation of glycolysis by lysine acetylation.

Metabolomic Analysis of Key Central Carbon Metabolism Carboxylic Acids as Their 3-Nitrophenylhydrazones by UPLC/ESI-MS

PubMed Central

Han, Jun; Gagnon, Susannah; Eckle, Tobias; Borchers, Christoph H.

2014-01-01

Multiple hydroxy-, keto-, di-, and tri-carboxylic acids are among the cellular metabolites of central carbon metabolism (CCM). Sensitive and reliable analysis of these carboxylates is important for many biological and cell engineering studies. In this work, we examined 3-nitrophenylhydrazine as a derivatizing reagent and optimized the reaction conditions for the measurement of ten CCM related carboxylic compounds, including glycolate, lactate, malate, fumarate, succinate, citrate, isocitrate, pyruvate, oxaloacetate, and α-ketoglutarate as their 3-nitrophenylhydrazones using LC/MS with electrospray ionization. With the derivatization protocol which we have developed, and using negative-ion multiple reaction monitoring on a triple-quadrupole instrument, all of the carboxylates showed good linearity within a dynamic range of ca. 200 to more than 2000. The on-column limits of detection and quantitation were from high femtomoles to low picomoles. The analytical accuracies for eight of the ten analytes were determined to be between 89.5 to 114.8% (CV≤7.4%, n=6). Using a quadrupole time-of-flight instrument, the isotopic distribution patterns of these carboxylates, extracted from a 13C-labeled mouse heart, were successfully determined by UPLC/MS with full-mass detection, indicating the possible utility of this analytical method for metabolic flux analysis. In summary, this work demonstrates an efficient chemical derivatization LC/MS method for metabolomic analysis of these key CCM intermediates in a biological matrix. PMID:23580203

Target Organ Metabolism, Toxicity, and Mechanisms of Trichloroethylene and Perchloroethylene: Key Similarities, Differences, and Data Gaps

PubMed Central

Cichocki, Joseph A.; Guyton, Kathryn Z.; Guha, Neela; Chiu, Weihsueh A.

2016-01-01

Trichloroethylene (TCE) and perchloroethylene or tetrachloroethylene (PCE) are high–production volume chemicals with numerous industrial applications. As a consequence of their widespread use, these chemicals are ubiquitous environmental contaminants to which the general population is commonly exposed. It is widely assumed that TCE and PCE are toxicologically similar; both are simple olefins with three (TCE) or four (PCE) chlorines. Nonetheless, despite decades of research on the adverse health effects of TCE or PCE, few studies have directly compared these two toxicants. Although the metabolic pathways are qualitatively similar, quantitative differences in the flux and yield of metabolites exist. Recent human health assessments have uncovered some overlap in target organs that are affected by exposure to TCE or PCE, and divergent species- and sex-specificity with regard to cancer and noncancer hazards. The objective of this minireview is to highlight key similarities, differences, and data gaps in target organ metabolism and mechanism of toxicity. The main anticipated outcome of this review is to encourage research to 1) directly compare the responses to TCE and PCE using more sensitive biochemical techniques and robust statistical comparisons; 2) more closely examine interindividual variability in the relationship between toxicokinetics and toxicodynamics for TCE and PCE; 3) elucidate the effect of coexposure to these two toxicants; and 4) explore new mechanisms for target organ toxicity associated with TCE and/or PCE exposure. PMID:27511820

PEPCK Coordinates the Regulation of Central Carbon Metabolism to Promote Cancer Cell Growth.

PubMed

Montal, Emily D; Dewi, Ruby; Bhalla, Kavita; Ou, Lihui; Hwang, Bor Jang; Ropell, Ashley E; Gordon, Chris; Liu, Wan-Ju; DeBerardinis, Ralph J; Sudderth, Jessica; Twaddel, William; Boros, Laszlo G; Shroyer, Kenneth R; Duraisamy, Sekhar; Drapkin, Ronny; Powers, R Scott; Rohde, Jason M; Boxer, Matthew B; Wong, Kwok-Kin; Girnun, Geoffrey D

2015-11-19

Phosphoenolpyruvate carboxykinase (PEPCK) is well known for its role in gluconeogenesis. However, PEPCK is also a key regulator of TCA cycle flux. The TCA cycle integrates glucose, amino acid, and lipid metabolism depending on cellular needs. In addition, biosynthetic pathways crucial to tumor growth require the TCA cycle for the processing of glucose and glutamine derived carbons. We show here an unexpected role for PEPCK in promoting cancer cell proliferation in vitro and in vivo by increasing glucose and glutamine utilization toward anabolic metabolism. Unexpectedly, PEPCK also increased the synthesis of ribose from non-carbohydrate sources, such as glutamine, a phenomenon not previously described. Finally, we show that the effects of PEPCK on glucose metabolism and cell proliferation are in part mediated via activation of mTORC1. Taken together, these data demonstrate a role for PEPCK that links metabolic flux and anabolic pathways to cancer cell proliferation. Copyright © 2015 Elsevier Inc. All rights reserved.

Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans

PubMed Central

Watson, Emma; Olin-Sandoval, Viridiana; Hoy, Michael J; Li, Chi-Hua; Louisse, Timo; Yao, Victoria; Mori, Akihiro; Holdorf, Amy D; Troyanskaya, Olga G; Ralser, Markus; Walhout, Albertha JM

2016-01-01

Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild. DOI: http://dx.doi.org/10.7554/eLife.17670.001 PMID:27383050

An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models

PubMed Central

Chindelevitch, Leonid; Trigg, Jason; Regev, Aviv; Berger, Bonnie

2014-01-01

Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations. PMID:25291352

Exploring metabolic pathways in genome-scale networks via generating flux modes.

PubMed

Rezola, A; de Figueiredo, L F; Brock, M; Pey, J; Podhorski, A; Wittmann, C; Schuster, S; Bockmayr, A; Planes, F J

2011-02-15

The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. The source code in C++ is available upon request.

Integration of C₁ and C₂ Metabolism in Trees.

PubMed

Jardine, Kolby J; Fernandes de Souza, Vinicius; Oikawa, Patty; Higuchi, Niro; Bill, Markus; Porras, Rachel; Niinemets, Ülo; Chambers, Jeffrey Q

2017-09-23

C₁ metabolism in plants is known to be involved in photorespiration, nitrogen and amino acid metabolism, as well as methylation and biosynthesis of metabolites and biopolymers. Although the flux of carbon through the C₁ pathway is thought to be large, its intermediates are difficult to measure and relatively little is known about this potentially ubiquitous pathway. In this study, we evaluated the C₁ pathway and its integration with the central metabolism using aqueous solutions of 13 C-labeled C₁ and C₂ intermediates delivered to branches of the tropical species Inga edulis via the transpiration stream. Delivery of [ 13 C]methanol and [ 13 C]formaldehyde rapidly stimulated leaf emissions of [ 13 C]methanol, [ 13 C]formaldehyde, [ 13 C]formic acid, and 13 CO₂, confirming the existence of the C1 pathway and rapid interconversion between methanol and formaldehyde. However, while [ 13 C]formate solutions stimulated emissions of 13 CO₂, emissions of [ 13 C]methanol or [ 13 C]formaldehyde were not detected, suggesting that once oxidation to formate occurs it is rapidly oxidized to CO₂ within chloroplasts. 13 C-labeling of isoprene, a known photosynthetic product, was linearly related to 13 CO₂ across C₁ and C₂ ([ 13 C₂]acetate and [2- 13 C]glycine) substrates, consistent with reassimilation of C₁, respiratory, and photorespiratory CO₂. Moreover, [ 13 C]methanol and [ 13 C]formaldehyde induced a quantitative labeling of both carbon atoms of acetic acid emissions, possibly through the rapid turnover of the chloroplastic acetyl-CoA pool via glycolate oxidation. The results support a role of the C₁ pathway to provide an alternative carbon source for glycine methylation in photorespiration, enhance CO₂ concentrations within chloroplasts, and produce key C₂ intermediates (e.g., acetyl-CoA) central to anabolic and catabolic metabolism.

Improvement of constraint-based flux estimation during L-phenylalanine production with Escherichia coli using targeted knock-out mutants.

PubMed

Weiner, Michael; Tröndle, Julia; Albermann, Christoph; Sprenger, Georg A; Weuster-Botz, Dirk

2014-07-01

Fed-batch production of the aromatic amino acid L-phenylalanine was studied with recombinant Escherichia coli strains on a 15 L-scale using glycerol as carbon source. Flux Variability Analysis (FVA) was applied for intracellular flux estimation to obtain an insight into intracellular flux distribution during L-phenylalanine production. Variability analysis revealed great flux uncertainties in the central carbon metabolism, especially concerning malate consumption. Due to these results two recombinant strains were genetically engineered differing in the ability of malate degradation and anaplerotic reactions (E. coli FUS4.11 ΔmaeA pF81kan and E. coli FUS4.11 ΔmaeA ΔmaeB pF81kan). Applying these malic enzyme knock-out mutants in the standardized L-phenylalanine production process resulted in almost identical process performances (e.g., L-phenylalanine concentration, production rate and byproduct formation). This clearly highlighted great redundancies in central metabolism in E. coli. Uncertainties of intracellular flux estimations by constraint-based analyses during fed-batch production of L-phenylalanine were drastically reduced by application of the malic enzyme knock-out mutants. © 2014 Wiley Periodicals, Inc.

Water Velocity as a Driver of Stream Metabolism: a Parallel Application of the Open Water and Eddy Correlation Techniques

NASA Astrophysics Data System (ADS)

Koopmans, D.; Berg, P.

2013-12-01

Inland waters respire or store a large portion of net terrestrial ecosystem production. As a result their metabolism is significant to the global carbon budget. The proximal drivers of aquatic respiration are organic matter availability, temperature, nutrients, and water velocity. Among these water velocity may be the least quantified. A partial explanation is that the footprint of the open water technique is typically hundreds of meters of river length, while the effect of a change in velocity may be specific to a local benthic environment, e.g., a riffle. With the eddy correlation technique oxygen flux is calculated from the turbulent fluctuation of vertical velocity and the oxygen concentration at a point in the water column. The footprint of the technique scales with the height of the point of measurement allowing an investigation of the in situ oxygen flux at the scale of a riffle. The combination of techniques, then, can be used to investigate the coupling of hydrodynamic conditions and benthic environments in driving aquatic ecosystem metabolism. This parallel approach was applied seasonally to examine the drivers of metabolism in a nutrient-rich, sand-bed coastal stream on the Eastern Shore of Virginia. An ecosystem-scale oxygen flux was calculated with the open water technique while pool-, run-, riffle-, and freshwater tidal-scale oxygen fluxes were calculated with the eddy correlation technique. At the ecosystem scale the stream bed functioned as an effective biocatalytic filter with an average annual net oxygen consumption of 300 mmol m^-2 d^-1. Prior to a stage-discharge shift water velocity explained 90% of the variance in ecosystem respiration (n = 63 days). After the stage-discharge shift water velocity explained 96 % of it (n = 40 days). Hyporheic exchange supported respiration in this system, contributing to its close correlation with water velocity. Among the physically similar benthic environments of the run, riffle, and freshwater tidal sites, however, similar water velocities generated order of magnitude differences in oxygen flux. The smallest oxygen fluxes were observed at the tidal site followed by the riffle and pool. The patterns were consistent with the site-specific suppression of hyporheic exchange by pore water clogging. An uneven distribution of sediment organic matter may also contribute. These results demonstrate that ecosystem metabolism in this stream is hydrodynamically controlled and suggest mechanisms by which that control may be undermined. Oxygen flux measured at a stream riffle with the eddy correlation technique.

Combining position-specific 13C labeling with compound-specific isotope analysis: first steps towards soil fluxomics

NASA Astrophysics Data System (ADS)

Dippold, Michaela; Kuzyakov, Yakov

2015-04-01

Understanding the soil organic matter (SOM) dynamics is one of the most important challenges in soil science. Transformation of low molecular weight organic substances (LMWOS) is a key step in biogeochemical cycles because 1) all high molecular substances pass this stage during their decomposition and 2) only LMWOS will be taken up by microorganisms. Previous studies on LMWOS were focused on determining net fluxes through the LMWOS pool, but they rarely identified transformations. As LMWOS are the preferred C and energy source for microorganisms, the transformations of LMWOS are dominated by biochemical pathways of the soil microorganisms. Thus, understanding fluxes and transformations in soils requires a detailed knowledge on the biochemical pathways and its controlling factors. Tracing C fate in soil by isotopes became on of the most applied and promising biogeochemistry tools. Up to now, studies on LMWOS were nearly exclusively based on uniformly labeled organic substances i.e. all C atoms in the molecules were labeled with 13C or 14C. However, this classical approach did not allow the differentiation between use of intact initial substances in any process, or whether they were transformed to metabolites. The novel tool of position-specific labeling enables to trace molecule atoms separately and thus to determine the cleavage of molecules - a prerequisite for metabolic tracing. Position-specific labeling of LMWOS and quantification of 13CO2 and 13C in bulk soil enabled following the basic metabolic pathways of soil microorganisms. However, only the combination of position-specific 13C labeling with compound-specific isotope analysis of microbial biomarkers and metabolites allowed 1) tracing specific anabolic pathways in diverse microbial communities in soils and 2) identification of specific pathways of individual functional microbial groups. So, these are the prerequisites for soil fluxomics. Our studies combining position-specific labeled glucose with amino sugar 13C analysis showed that oxidizing catabolic pathways and anabolic pathways, i.e. building-up new cellular compounds, occurred in soils simultaneously. This involved an intensive C recycling within the microorganisms that was observed not only for cytosolic compounds but also for cell wall polymers. Fungal metabolism and fluxes were slower than bacterial intracellular C recycling and turnover. Furthermore, position-specific labeling of glutamate and subsequent 13C analysis of microbial phospholipid fatty acids (PLFA) revealed starvation pathways, which were only active in specific microbial groups in soils. These studies revealed that position-specific labeling enables the reconstruction of metabolic pathways of LMWOS within diverse microbial communities in complex media such as soil. Processes occurring simultaneously in soil i.e. 1) within individual, reversible metabolic pathways and 2) in various microbial groups could be traced by position-specific labeling in soils in situ. Tracing these pathways and understanding their regulating factors are crucial for soil C fluxomics, the extremely complex network of transformations towards mineralization versus the formation of microbial biomass compounds. Quantitative models to assess microbial group specific metabolic networks can be generated and parameterized by this approach. The submolecular knowledge of transformation steps and biochemical pathways in soils and their regulating factors is essential for understanding C cycling and long-term C storage in soils.

Elucidation of new condition-dependent roles for fructose-1,6-bisphosphatase linked to cofactor balances

PubMed Central

Kilian, Stephanus G.; du Preez, James C.

2017-01-01

The cofactor balances in metabolism is of paramount importance in the design of a metabolic engineering strategy and understanding the regulation of metabolism in general. ATP, NAD+ and NADP+ balances are central players linking the various fluxes in central metabolism as well as biomass formation. NADP+ is especially important in the metabolic engineering of yeasts for xylose fermentation, since NADPH is required by most yeasts in the initial step of xylose utilisation, including the fast-growing Kluyveromyces marxianus. In this simulation study of yeast metabolism, the complex interplay between these cofactors was investigated; in particular, how they may affect the possible roles of fructose-1,6-bisphosphatase, the pentose phosphate pathway, glycerol production and the pyruvate dehydrogenase bypass. Using flux balance analysis, it was found that the potential role of fructose-1,6-bisphosphatase was highly dependent on the cofactor specificity of the oxidative pentose phosphate pathway and on the carbon source. Additionally, the excessive production of ATP under certain conditions might be involved in some of the phenomena observed, which may have been overlooked to date. Based on these findings, a strategy is proposed for the metabolic engineering of a future xylose-fermenting yeast for biofuel production. PMID:28542187

Preserved pontine glucose metabolism in Alzheimer disease: A reference region for functional brain image (PET) analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Minoshima, Satoshi; Frey, K.A.; Foster, N.L.

1995-07-01

Our goal was to examine regional preservation of energy metabolism in Alzheimer disease (AD) and to evaluate effects of PET data normalization to reference regions. Regional metabolic rates in the pons, thalamus, putamen, sensorimotor cortex, visual cortex, and cerebellum (reference regions) were determined stereotaxically and examined in 37 patients with probable AD and 22 normal controls based on quantitative {sup 18}FDG-PET measurements. Following normalization of metabolic rates of the parietotemporal association cortex and whole brain to each reference region, distinctions of the two groups were assessed. The pons showed the best preservation of glucose metabolism in AD. Other reference regionsmore » showed relatively preserved metabolism compared with the parietotemporal association cortex and whole brain, but had significant metabolic reduction. Data normalization to the pons not only enhanced statistical significance of metabolic reduction in the parietotemporal association cortex, but also preserved the presence of global cerebral metabolic reduction indicated in analysis of the quantitative data. Energy metabolism in the pons in probable AD is well preserved. The pons is a reliable reference for data normalization and will enhance diagnostic accuracy and efficiency of quantitative and nonquantitative functional brain imaging. 39 refs., 2 figs., 3 tabs.« less

Quantitative comparison of in situ soil CO2 flux measurement methods

Treesearch

Jennifer D. Knoepp; James M. Vose

2002-01-01

Development of reliable regional or global carbon budgets requires accurate measurement of soil CO2 flux. We conducted laboratory and field studies to determine the accuracy and comparability of methods commonly used to measure in situ soil CO2 fluxes. Methods compared included CO2...

A proof for loop-law constraints in stoichiometric metabolic networks

PubMed Central

2012-01-01

Background Constraint-based modeling is increasingly employed for metabolic network analysis. Its underlying assumption is that natural metabolic phenotypes can be predicted by adding physicochemical constraints to remove unrealistic metabolic flux solutions. The loopless-COBRA approach provides an additional constraint that eliminates thermodynamically infeasible internal cycles (or loops) from the space of solutions. This allows the prediction of flux solutions that are more consistent with experimental data. However, it is not clear if this approach over-constrains the models by removing non-loop solutions as well. Results Here we apply Gordan’s theorem from linear algebra to prove for the first time that the constraints added in loopless-COBRA do not over-constrain the problem beyond the elimination of the loops themselves. Conclusions The loopless-COBRA constraints can be reliably applied. Furthermore, this proof may be adapted to evaluate the theoretical soundness for other methods in constraint-based modeling. PMID:23146116

WEbcoli: an interactive and asynchronous web application for in silico design and analysis of genome-scale E.coli model.

PubMed

Jung, Tae-Sung; Yeo, Hock Chuan; Reddy, Satty G; Cho, Wan-Sup; Lee, Dong-Yup

2009-11-01

WEbcoli is a WEb application for in silico designing, analyzing and engineering Escherichia coli metabolism. It is devised and implemented using advanced web technologies, thereby leading to enhanced usability and dynamic web accessibility. As a main feature, the WEbcoli system provides a user-friendly rich web interface, allowing users to virtually design and synthesize mutant strains derived from the genome-scale wild-type E.coli model and to customize pathways of interest through a graph editor. In addition, constraints-based flux analysis can be conducted for quantifying metabolic fluxes and charactering the physiological and metabolic states under various genetic and/or environmental conditions. WEbcoli is freely accessible at http://webcoli.org. cheld@nus.edu.sg.

Combinatorial complexity of pathway analysis in metabolic networks.

PubMed

Klamt, Steffen; Stelling, Jörg

2002-01-01

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.

Constraint-Based Modeling Highlights Cell Energy, Redox Status and α-Ketoglutarate Availability as Metabolic Drivers for Anthocyanin Accumulation in Grape Cells Under Nitrogen Limitation

PubMed Central

Soubeyrand, Eric; Colombié, Sophie; Beauvoit, Bertrand; Dai, Zhanwu; Cluzet, Stéphanie; Hilbert, Ghislaine; Renaud, Christel; Maneta-Peyret, Lilly; Dieuaide-Noubhani, Martine; Mérillon, Jean-Michel; Gibon, Yves; Delrot, Serge; Gomès, Eric

2018-01-01

Anthocyanin biosynthesis is regulated by environmental factors (such as light, temperature, and water availability) and nutrient status (such as carbon, nitrogen, and phosphate nutrition). Previous reports show that low nitrogen availability strongly enhances anthocyanin accumulation in non carbon-limited plant organs or cell suspensions. It has been hypothesized that high carbon-to-nitrogen ratio would lead to an energy excess in plant cells, and that an increase in flavonoid pathway metabolic fluxes would act as an “energy escape valve,” helping plant cells to cope with energy and carbon excess. However, this hypothesis has never been tested directly. To this end, we used the grapevine Vitis vinifera L. cultivar Gamay Teinturier (syn. Gamay Freaux or Freaux Tintorier, VIVC #4382) cell suspension line as a model system to study the regulation of anthocyanin accumulation in response to nitrogen supply. The cells were sub-cultured in the presence of either control (25 mM) or low (5 mM) nitrate concentration. Targeted metabolomics and enzyme activity determinations were used to parametrize a constraint-based model describing both the central carbon and nitrogen metabolisms and the flavonoid (phenylpropanoid) pathway connected by the energy (ATP) and reducing power equivalents (NADPH and NADH) cofactors. The flux analysis (2 flux maps generated, for control and low nitrogen in culture medium) clearly showed that in low nitrogen-fed cells all the metabolic fluxes of central metabolism were decreased, whereas fluxes that consume energy and reducing power, were either increased (upper part of glycolysis, shikimate, and flavonoid pathway) or maintained (pentose phosphate pathway). Also, fluxes of flavanone 3β-hydroxylase, flavonol synthase, and anthocyanidin synthase were strongly increased, advocating for a regulation of the flavonoid pathway by alpha-ketoglutarate levels. These results strongly support the hypothesis of anthocyanin biosynthesis acting as an energy escape valve in plant cells, and they open new possibilities to manipulate flavonoid production in plant cells. They do not, however, support a role of anthocyanins as an effective mechanism for coping with carbon excess in high carbon to nitrogen ratio situations in grape cells. Instead, constraint-based modeling output and biomass analysis indicate that carbon excess is dealt with by vacuolar storage of soluble sugars. PMID:29868039

Constraint-Based Modeling Highlights Cell Energy, Redox Status and α-Ketoglutarate Availability as Metabolic Drivers for Anthocyanin Accumulation in Grape Cells Under Nitrogen Limitation.

PubMed

Soubeyrand, Eric; Colombié, Sophie; Beauvoit, Bertrand; Dai, Zhanwu; Cluzet, Stéphanie; Hilbert, Ghislaine; Renaud, Christel; Maneta-Peyret, Lilly; Dieuaide-Noubhani, Martine; Mérillon, Jean-Michel; Gibon, Yves; Delrot, Serge; Gomès, Eric

2018-01-01

Anthocyanin biosynthesis is regulated by environmental factors (such as light, temperature, and water availability) and nutrient status (such as carbon, nitrogen, and phosphate nutrition). Previous reports show that low nitrogen availability strongly enhances anthocyanin accumulation in non carbon-limited plant organs or cell suspensions. It has been hypothesized that high carbon-to-nitrogen ratio would lead to an energy excess in plant cells, and that an increase in flavonoid pathway metabolic fluxes would act as an "energy escape valve," helping plant cells to cope with energy and carbon excess. However, this hypothesis has never been tested directly. To this end, we used the grapevine Vitis vinifera L. cultivar Gamay Teinturier (syn. Gamay Freaux or Freaux Tintorier, VIVC #4382) cell suspension line as a model system to study the regulation of anthocyanin accumulation in response to nitrogen supply. The cells were sub-cultured in the presence of either control (25 mM) or low (5 mM) nitrate concentration. Targeted metabolomics and enzyme activity determinations were used to parametrize a constraint-based model describing both the central carbon and nitrogen metabolisms and the flavonoid (phenylpropanoid) pathway connected by the energy (ATP) and reducing power equivalents (NADPH and NADH) cofactors. The flux analysis (2 flux maps generated, for control and low nitrogen in culture medium) clearly showed that in low nitrogen-fed cells all the metabolic fluxes of central metabolism were decreased, whereas fluxes that consume energy and reducing power, were either increased (upper part of glycolysis, shikimate, and flavonoid pathway) or maintained (pentose phosphate pathway). Also, fluxes of flavanone 3β-hydroxylase, flavonol synthase, and anthocyanidin synthase were strongly increased, advocating for a regulation of the flavonoid pathway by alpha-ketoglutarate levels. These results strongly support the hypothesis of anthocyanin biosynthesis acting as an energy escape valve in plant cells, and they open new possibilities to manipulate flavonoid production in plant cells. They do not, however, support a role of anthocyanins as an effective mechanism for coping with carbon excess in high carbon to nitrogen ratio situations in grape cells. Instead, constraint-based modeling output and biomass analysis indicate that carbon excess is dealt with by vacuolar storage of soluble sugars.

Quantitative Validation of the Presto Blue Metabolic Assay for Online Monitoring of Cell Proliferation in a 3D Perfusion Bioreactor System.

PubMed

Sonnaert, Maarten; Papantoniou, Ioannis; Luyten, Frank P; Schrooten, Jan Ir

2015-06-01

As the fields of tissue engineering and regenerative medicine mature toward clinical applications, the need for online monitoring both for quantitative and qualitative use becomes essential. Resazurin-based metabolic assays are frequently applied for determining cytotoxicity and have shown great potential for monitoring 3D bioreactor-facilitated cell culture. However, no quantitative correlation between the metabolic conversion rate of resazurin and cell number has been defined yet. In this work, we determined conversion rates of Presto Blue, a resazurin-based metabolic assay, for human periosteal cells during 2D and 3D static and 3D perfusion cultures. Our results showed that for the evaluated culture systems there is a quantitative correlation between the Presto Blue conversion rate and the cell number during the expansion phase with no influence of the perfusion-related parameters, that is, flow rate and shear stress. The correlation between the cell number and Presto Blue conversion subsequently enabled the definition of operating windows for optimal signal readouts. In conclusion, our data showed that the conversion of the resazurin-based Presto Blue metabolic assay can be used as a quantitative readout for online monitoring of cell proliferation in a 3D perfusion bioreactor system, although a system-specific validation is required.

Quantitative Validation of the Presto Blue™ Metabolic Assay for Online Monitoring of Cell Proliferation in a 3D Perfusion Bioreactor System

PubMed Central

Sonnaert, Maarten; Papantoniou, Ioannis; Luyten, Frank P.

2015-01-01

As the fields of tissue engineering and regenerative medicine mature toward clinical applications, the need for online monitoring both for quantitative and qualitative use becomes essential. Resazurin-based metabolic assays are frequently applied for determining cytotoxicity and have shown great potential for monitoring 3D bioreactor-facilitated cell culture. However, no quantitative correlation between the metabolic conversion rate of resazurin and cell number has been defined yet. In this work, we determined conversion rates of Presto Blue™, a resazurin-based metabolic assay, for human periosteal cells during 2D and 3D static and 3D perfusion cultures. Our results showed that for the evaluated culture systems there is a quantitative correlation between the Presto Blue conversion rate and the cell number during the expansion phase with no influence of the perfusion-related parameters, that is, flow rate and shear stress. The correlation between the cell number and Presto Blue conversion subsequently enabled the definition of operating windows for optimal signal readouts. In conclusion, our data showed that the conversion of the resazurin-based Presto Blue metabolic assay can be used as a quantitative readout for online monitoring of cell proliferation in a 3D perfusion bioreactor system, although a system-specific validation is required. PMID:25336207

Redistribution of carbon flux in Torulopsis glabrata by altering vitamin and calcium level.

PubMed

Liu, Liming; Li, Yin; Zhu, Yang; Du, Guocheng; Chen, Jian

2007-01-01

Manipulation of cofactor (thiamine, biotin and Ca(2+)) levels as a potential tool to redistribute carbon flux was studied in Torulopsis glabrata. With sub-optimization of vitamin in fermentation medium, the carbon flux was blocked at the key node of pyruvate, and 69 g/L pyruvate was accumulated. Increasing the concentrations of thiamine and biotin could selectively open the valve of carbon flux from pyruvate to pyruvate dehydrogenase complex, the pyruvate carboxylase (PC) pathway and the channel into the TCA cycle, leading to the over-production of alpha-ketoglutarate. In addition, the activity of PC was enhanced with Ca(2+) present in fermentation medium. By combining high concentration's vitamins and CaCO(3) as the pH buffer, a batch culture was conducted in a 7-L fermentor, with the pyruvate concentration decreased to 21.8 g/L while alpha-ketoglutarate concentration increased to 43.7 g/L. Our study indicated that the metabolic flux could be redistributed to overproduce desired metabolites with manipulating the cofactor levels. Furthermore, the manipulation of vitamin level provided an alternative tool to realize metabolic engineering goals.

What do magnetic resonance-based measurements of Pi→ATP flux tell us about skeletal muscle metabolism?

PubMed

Kemp, Graham J; Brindle, Kevin M

2012-08-01

Magnetic resonance spectroscopy (MRS) methods offer a potentially valuable window into cellular metabolism. Measurement of flux between inorganic phosphate (Pi) and ATP using (31)P MRS magnetization transfer has been used in resting muscle to assess what is claimed to be mitochondrial ATP synthesis and has been particularly popular in the study of insulin effects and insulin resistance. However, the measured Pi→ATP flux in resting skeletal muscle is far higher than the true rate of oxidative ATP synthesis, being dominated by a glycolytically mediated Pi↔ATP exchange reaction that is unrelated to mitochondrial function. Furthermore, even if measured accurately, the ATP production rate in resting muscle has no simple relationship to mitochondrial capacity as measured either ex vivo or in vivo. We summarize the published measurements of Pi→ATP flux, concentrating on work relevant to diabetes and insulin, relate it to current understanding of the physiology of mitochondrial ATP synthesis and glycolytic Pi↔ATP exchange, and discuss some possible implications of recently reported correlations between Pi→ATP flux and other physiological measures.

Nuclear magnetic resonance studies of the regulation of the pentose phosphate pathway

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolo, N.R.

1991-11-01

The goal of this work is to investigate the potential for and limitations of in vivo nuclear magnetic resonance (NMR) spectroscopy for quantitation of glucose flux through the pentose phosphate pathway (shunt). Interest in the shunt is motivated by the possibility that its activity may be greatly increased in cancer and in the pathological states of cardiac and cerebral ischemia. The ability to dynamically monitor flux through the pentose shunt can give new knowledge about metabolism in pathological states. {sup 13}C NMR spectroscopy was used to monitor shunt activity by determination of the ratios of ({sup 13}C-4) to ({sup 13}C-5)-glutamate,more » ({sup 13}C-3) to ({sup 13}C-2)-alanine or ({sup 13}C-3) to ({sup 13}C-2)-lactate produced when ({sup 13}C-2)-glucose is infused. These methods provide measures of the effect of oxidative stresses on shunt activity in systems ranging from cell free enzyme-substrate preparations to cell suspensions and whole animals. In anaerobic cell free preparations, the fraction of glucose flux through the shunt was monitored with a time resolution of 3 minutes. This work predicts the potential for in vivo human studies of pentose phosphate pathway activity based on the mathematical simulation of the {sup 13}C fractional enrichments of C4 and C5-glutamate as a function of shunt activity and on the signal-to- noise ratio acquired in {sup 13}C NMR human studies from the current literature.« less

Nuclear magnetic resonance studies of the regulation of the pentose phosphate pathway

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolo, Nicolas Robin

1991-11-01

The goal of this work is to investigate the potential for and limitations of in vivo nuclear magnetic resonance (NMR) spectroscopy for quantitation of glucose flux through the pentose phosphate pathway (shunt). Interest in the shunt is motivated by the possibility that its activity may be greatly increased in cancer and in the pathological states of cardiac and cerebral ischemia. The ability to dynamically monitor flux through the pentose shunt can give new knowledge about metabolism in pathological states. 13C NMR spectroscopy was used to monitor shunt activity by determination of the ratios of [ 13C-4] to [ 13C-5]-glutamate, [more » 13C-3] to [ 13C-2]-alanine or [ 13C-3] to [ 13C-2]-lactate produced when [ 13C-2]-glucose is infused. These methods provide measures of the effect of oxidative stresses on shunt activity in systems ranging from cell free enzyme-substrate preparations to cell suspensions and whole animals. In anaerobic cell free preparations, the fraction of glucose flux through the shunt was monitored with a time resolution of 3 minutes. This work predicts the potential for in vivo human studies of pentose phosphate pathway activity based on the mathematical simulation of the 13C fractional enrichments of C4 and C5-glutamate as a function of shunt activity and on the signal-to- noise ratio acquired in 13C NMR human studies from the current literature.« less

Radial diffusion, vertical transport, and refixation of labeled bicarbonate in scots pine stems

NASA Astrophysics Data System (ADS)

Marshall, J. D.; Tarvainen, L.; Wallin, G.

2016-12-01

The CO2 produced by a respiring stem provides an index of metabolic activity in the stem and a quantitative estimate of an important component of the forest carbon budget. Production of CO2 by a given stem volume is lost by three competing processes. First, some diffuses radially outward through the bark. Second, some is dissolved and vertically transported upward out of the control volume by the xylem stream. Third, some is refixed by photosynthesis under the bark. The relative balance among these pathways was quantified in 17-m Scots pine trees by 13C-bicarbonate labeling of the xylem stream and monitoring of the 13CO2 in the xylem water, along with continuous monitoring of the radial diffusive flux at four canopy heights and in transpiration from leaves. Most of the label diffused out radially, as 13CO2, immediately above the labeling site, over about a week. The pulse was weakly and briefly detected 4 m above that height. Further up the stem it was not detected at all. We detected significant refixation of CO2 in the stems at all heights above 4 m, where the bark becomes papery and thin, but the label was so weak at this height that refixation had little influence on the pulse chase. We conclude that the vertical flux is negligible in Scots pine, but that the refixation flux must be accounted for in estimates of whole-stem CO2 efflux.

Modulating autophagy in cancer therapy: Advancements and challenges for cancer cell death sensitization.

PubMed

Bhat, Punya; Kriel, Jurgen; Shubha Priya, Babu; Basappa; Shivananju, Nanjunda Swamy; Loos, Ben

2018-01-01

Autophagy is a major protein degradation pathway capable of upholding cellular metabolism under nutrient limiting conditions, making it a valuable resource to highly proliferating tumour cells. Although the regulatory machinery of the autophagic pathway has been well characterized, accurate modulation of this pathway remains complex in the context of clinical translatability for improved cancer therapies. In particular, the dynamic relationship between the rate of protein degradation through autophagy, i.e. autophagic flux, and the susceptibility of tumours to undergo apoptosis remains largely unclear. Adding to inefficient clinical translation is the lack of measurement techniques that accurately depict autophagic flux. Paradoxically, both increased autophagic flux as well as autophagy inhibition have been shown to sensitize cancer cells to undergo cell death, indicating the highly context dependent nature of this pathway. In this article, we aim to disentangle the role of autophagy modulation in tumour suppression by assessing existing literature in the context of autophagic flux and cellular metabolism at the interface of mitochondrial function. We highlight the urgency to not only assess autophagic flux more accurately, but also to center autophagy manipulation within the unique and inherent metabolic properties of cancer cells. Lastly, we discuss the challenges faced when targeting autophagy in the clinical setting. In doing so, it is hoped that a better understanding of autophagy in cancer therapy is revealed in order to overcome tumour chemoresistance through more controlled autophagy modulation in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting the accumulation of storage compounds by Rhodococcus jostii RHA1 in the feast-famine growth cycles using genome-scale flux balance analysis

PubMed Central

Tajparast, Mohammad

2018-01-01

Feast-famine cycles in biological wastewater resource recovery systems select for bacterial species that accumulate intracellular storage compounds such as poly-β-hydroxybutyrate (PHB), glycogen, and triacylglycerols (TAG). These species survive better the famine phase and resume rapid substrate uptake at the beginning of the feast phase faster than microorganisms unable to accumulate storage. However, ecophysiological conditions favouring the accumulation of either storage compounds remain to be clarified, and predictive capabilities need to be developed to eventually rationally design reactors producing these compounds. Using a genome-scale metabolic modelling approach, the storage metabolism of Rhodococcus jostii RHA1 was investigated for steady-state feast-famine cycles on glucose and acetate as the sole carbon sources. R. jostii RHA1 is capable of accumulating the three storage compounds (PHB, TAG, and glycogen) simultaneously. According to the experimental observations, when glucose was the substrate, feast phase chemical oxygen demand (COD) accumulation was similar for the three storage compounds; when acetate was the substrate, however, PHB accumulation was 3 times higher than TAG accumulation and essentially no glycogen was accumulated. These results were simulated using the genome-scale metabolic model of R. jostii RHA1 (iMT1174) by means of flux balance analysis (FBA) to determine the objective functions capable of predicting these behaviours. Maximization of the growth rate was set as the main objective function, while minimization of total reaction fluxes and minimization of metabolic adjustment (environmental MOMA) were considered as the sub-objective functions. The environmental MOMA sub-objective performed better than the minimization of total reaction fluxes sub-objective function at predicting the mixture of storage compounds accumulated. Additional experiments with 13C-labelled bicarbonate (HCO3−) found that the fluxes through the central metabolism reactions during the feast phases were similar to the ones during the famine phases on acetate due to similarity in the carbon sources in the feast and famine phases (i.e., acetate and poly-β-hydroxybutyrate, respectively); this suggests that the environmental MOMA sub-objective function could be used to analyze successive environmental conditions such as the feast and famine cycles while the metabolically similar carbon sources are taken up by microorganisms. PMID:29494607

Predicting the accumulation of storage compounds by Rhodococcus jostii RHA1 in the feast-famine growth cycles using genome-scale flux balance analysis.

PubMed

Tajparast, Mohammad; Frigon, Dominic

2018-01-01

Feast-famine cycles in biological wastewater resource recovery systems select for bacterial species that accumulate intracellular storage compounds such as poly-β-hydroxybutyrate (PHB), glycogen, and triacylglycerols (TAG). These species survive better the famine phase and resume rapid substrate uptake at the beginning of the feast phase faster than microorganisms unable to accumulate storage. However, ecophysiological conditions favouring the accumulation of either storage compounds remain to be clarified, and predictive capabilities need to be developed to eventually rationally design reactors producing these compounds. Using a genome-scale metabolic modelling approach, the storage metabolism of Rhodococcus jostii RHA1 was investigated for steady-state feast-famine cycles on glucose and acetate as the sole carbon sources. R. jostii RHA1 is capable of accumulating the three storage compounds (PHB, TAG, and glycogen) simultaneously. According to the experimental observations, when glucose was the substrate, feast phase chemical oxygen demand (COD) accumulation was similar for the three storage compounds; when acetate was the substrate, however, PHB accumulation was 3 times higher than TAG accumulation and essentially no glycogen was accumulated. These results were simulated using the genome-scale metabolic model of R. jostii RHA1 (iMT1174) by means of flux balance analysis (FBA) to determine the objective functions capable of predicting these behaviours. Maximization of the growth rate was set as the main objective function, while minimization of total reaction fluxes and minimization of metabolic adjustment (environmental MOMA) were considered as the sub-objective functions. The environmental MOMA sub-objective performed better than the minimization of total reaction fluxes sub-objective function at predicting the mixture of storage compounds accumulated. Additional experiments with 13C-labelled bicarbonate (HCO3-) found that the fluxes through the central metabolism reactions during the feast phases were similar to the ones during the famine phases on acetate due to similarity in the carbon sources in the feast and famine phases (i.e., acetate and poly-β-hydroxybutyrate, respectively); this suggests that the environmental MOMA sub-objective function could be used to analyze successive environmental conditions such as the feast and famine cycles while the metabolically similar carbon sources are taken up by microorganisms.

Flux balance analysis predicts Warburg-like effects of mouse hepatocyte deficient in miR-122a

PubMed Central

Wu, Hsuan-Hui; Chen, Meng-Chun; Liu, Wen-Huan; Wu, Wu-Hsiung; Chang, Peter Mu-Hsin; Huang, Chi-Ying F.; Tsou, Ann-Ping; Shiao, Ming-Shi

2017-01-01

The liver is a vital organ involving in various major metabolic functions in human body. MicroRNA-122 (miR-122) plays an important role in the regulation of liver metabolism, but its intrinsic physiological functions require further clarification. This study integrated the genome-scale metabolic model of hepatocytes and mouse experimental data with germline deletion of Mir122a (Mir122a–/–) to infer Warburg-like effects. Elevated expression of MiR-122a target genes in Mir122a–/–mice, especially those encoding for metabolic enzymes, was applied to analyze the flux distributions of the genome-scale metabolic model in normal and deficient states. By definition of the similarity ratio, we compared the flux fold change of the genome-scale metabolic model computational results and metabolomic profiling data measured through a liquid-chromatography with mass spectrometer, respectively, for hepatocytes of 2-month-old mice in normal and deficient states. The Ddc gene demonstrated the highest similarity ratio of 95% to the biological hypothesis of the Warburg effect, and similarity of 75% to the experimental observation. We also used 2, 6, and 11 months of mir-122 knockout mice liver cell to examined the expression pattern of DDC in the knockout mice livers to show upregulated profiles of DDC from the data. Furthermore, through a bioinformatics (LINCS program) prediction, BTK inhibitors and withaferin A could downregulate DDC expression, suggesting that such drugs could potentially alter the early events of metabolomics of liver cancer cells. PMID:28686599

Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

PubMed

Yang, Yi; Hu, Xiao-Pan; Ma, Bin-Guang

2017-02-28

Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

Measurement of air and VOC vapor fluxes during gas-driven soil remediation: bench-scale experiments.

PubMed

Kim, Heonki; Kim, Taeyun; Shin, Seungyeop; Annable, Michael D

2012-09-04

In this laboratory study, an experimental method was developed for the quantitative analyses of gas fluxes in soil during advective air flow. One-dimensional column and two- and three-dimensional flow chamber models were used in this study. For the air flux measurement, n-octane vapor was used as a tracer, and it was introduced in the air flow entering the physical models. The tracer (n-octane) in the gas effluent from the models was captured for a finite period of time using a pack of activated carbon, which then was analyzed for the mass of n-octane. The air flux was calculated based on the mass of n-octane captured by the activated carbon and the inflow concentration. The measured air fluxes are in good agreement with the actual values for one- and two-dimensional model experiments. Using both the two- and three-dimensional models, the distribution of the air flux at the soil surface was measured. The distribution of the air flux was found to be affected by the depth of the saturated zone. The flux and flux distribution of a volatile contaminant (perchloroethene) was also measured by using the two-dimensional model. Quantitative information of both air and contaminant flux may be very beneficial for analyzing the performance of gas-driven subsurface remediation processes including soil vapor extraction and air sparging.