DOCSLIB.ORG

13C Metabolic Flux Analysis of Recombinant Expression Hosts

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
13C Metabolic Flux Analysis of Recombinant Expression Hosts Available online at www.sciencedirect.com ScienceDirect 13 C metabolic flux analysis of recombinant expression hosts 1,2 Jamey D Young Identifying host cell metabolic phenotypes that promote high since metabolic enzymes are largely regulated by allo- recombinant protein titer is a major goal of the biotech industry. steric feedback and substrate availability, and mRNA or 13 C metabolic flux analysis (MFA) provides a rigorous approach protein abundances often do not correlate strongly with 13 to quantify these metabolic phenotypes by applying isotope pathway fluxes [3,4]. On the other hand, C MFA relies tracers to map the flow of carbon through intracellular on least-squares regression of direct metabolite measure- metabolic pathways. Recent advances in tracer theory and ments (isotope labeling patterns and extracellular measurements are enabling more information to be extracted exchange rates) to determine the adjustable flux 13 from C labeling experiments. Sustained development of parameters in a mathematical model of cellular metab- publicly available software tools and standardization of olism (Figure 1). The resulting output is a comprehensive experimental workflows is simultaneously encouraging flux map that depicts the flow of carbon throughout 13 increased adoption of C MFA within the biotech research intracellular metabolism under the experimental con- 13 community. A number of recent C MFA studies have ditions of interest. Recent theoretical and experimental 13 identified increased citric acid cycle and pentose phosphate advances are pushing the boundaries of C MFA to new pathway fluxes as consistent markers of high recombinant heights of precision and flexibility, while increased avail- protein expression, both in mammalian and microbial hosts. ability of public software tools is streamlining MFA Further work is needed to determine whether redirecting flux workflows and providing improved access to these tech- into these pathways can effectively enhance protein titers while nologies within the biotech research community. In this maintaining acceptable glycan profiles. article, we provide an overview of these advances and Addresses highlight some important findings that have been 1 13 Department of Chemical and Biomolecular Engineering, Vanderbilt obtained through the application of C MFA to industrial University, PMB 351604, Nashville, TN 37235-1604, USA 2 expression hosts. We also point to emerging areas where Department of Molecular Physiology and Biophysics, Vanderbilt 13 C MFA is likely to provide new insights for improving University, PMB 351604, Nashville, TN 37235-1604, USA the quantity and quality of recombinant proteins pro- Corresponding author: Young, Jamey D ([email protected]) duced by these hosts. 13 Current Opinion in Biotechnology 2014, 30:238–245 Recent advances in C MFA tools and methodologies This review comes from a themed issue on Pharmaceutical biotechnology Optimal experiment design (OED) 13 The earliest C MFA experiments involved feeding Edited by Beth Junker and Jamey D Young 13 13 either [1- C] or [U- C6]glucose as a single tracer. Nowa- days, the increased precision obtainable from combining multiple tracers, whether fed simultaneously or in parallel http://dx.doi.org/10.1016/j.copbio.2014.10.004 experiments, is widely understood and accepted. How- 0958-1669/# 2014 Elsevier Ltd. All right reserved. ever, it is not always appreciated that the optimal tracer combination can depend strongly on the network top- ology and the available measurements, which vary from system to system. This implies that there is a need to tailor the labeling strategy to the system of interest rather than naı¨vely following prior conventions. Furthermore, 2 15 Introduction the use of H or N tracers to probe redox or nitrogen Industrial bioprocesses place high demands on the inter- metabolism, respectively, has proven to be a powerful 13 mediary metabolism of host cells to meet the biosynthetic complement to C MFA. For example, the Metallo and requirements for maximal growth and protein expression. Rabinowitz labs have recently applied an arsenal of 13 2 13 C metabolic flux analysis (MFA) has become the pre- different H and C tracers to assess the contributions mier approach to quantitatively assess the metabolic from multiple pathways to compartment-specific phenotypes of cultured cells and is now playing a growing NADPH production in the cytosol and mitochondria of role in host cell engineering and bioprocess optimization. mammalian cells [5 ,6 ]. Although transcriptomics [1] and proteomics [2] can be used to infer metabolic pathway alterations indirectly from The first systematic treatment of optimal design of iso- changes in enzyme expression, this can be misleading tope labeling experiments was introduced by Mo¨llney Current Opinion in Biotechnology 2014, 30:238–245 www.sciencedirect.com 13 C MFA of recombinant expression hosts Young 239 Figure 1 (1) Develop metabolic 100 network model (1) A 20 80 C G B 40 140 F D E (2) (2) Optimize tracers 70 30 and measurements H (3) Perform isotope (3) labeling experiment (4) (4) Measure extracellular exchange rates and 13C isotopomer abundances 13C MFA INST-MFA OR M0 M1 M2 M3 MID (5) Apply 13C MFA (5) Simulated to estimate fluxes Measured Time Current Opinion in Biotechnology 13 Typical workflow of C MFA studies. (Step 1) Development of a mathematical model comprising a system of mass balances and isotopomer balances on all metabolites in the biochemical reaction network is the initial step of MFA. (Step 2) This model can be applied to perform simulation studies and optimal experiment design (OED) computations to select tracer/measurement combinations that maximize flux resolution. (Step 3) A series of isotope labeling experiments is then performed by introducing the selected tracer(s) into culture and collecting cell and media samples at various time points during the growth phase of interest. (Step 4) MS and/or NMR is used to analyze the isotope labeling of target metabolites in these samples, while a parallel set of biochemical analyses is applied to profile the exchange rates of extracellular metabolites, as 13 well as cell growth and product formation rates. (Step 5) These data sets are regressed using either stationary C MFA or INST-MFA, depending on whether the labeling reaches equilibrium, which enables the adjustable flux parameters in the model to be uniquely determined. A series of statistical tests can be performed to assess the goodness-of-fit of the model and to calculate uncertainties for each of the estimated parameters. Recent theoretical advances and improvements in the availability of MFA software tools have enhanced the efficiency of this workflow, especially steps 1,2, and 5. At the same time, better analytical methods for obtaining isotopomer measurements have provided increased flexibility and precision in steps 3 and 4. et al. [7]. Their approach was based on the classical OED [9] to compute accurate nonlinear confidence intervals on 13 formulation of minimizing a scalar objective function all fluxes. They identified [1,2- C2]glucose and 13 computed from the flux covariance matrix of a reference [U- C5]glutamine as the most useful single tracers for flux map. Recent studies have applied more sophisticated flux determination in glycolysis and citric acid cycle 13 OED algorithms to tailor C MFA experiments to mam- (CAC) pathways, respectively. Walther et al. [10] later malian cell cultures, which pose a greater challenge due to extended this approach to examine mixtures of tracers their use of complex media containing multiple carbon identified using a genetic algorithm. sources. For example, Metallo et al. [8] examined flux identifiability of a carcinoma cell line using a variety of Another exciting development was the introduction of single tracers. Rather than using the parameter covariance the EMU basis vector (EMU-BV) approach, which can be matrix as a local estimate of flux uncertainty, they applied used to express the labeling of any metabolite in the the parameter continuation method of Antoniewicz et al. network as a linear combination of labeled substrate www.sciencedirect.com Current Opinion in Biotechnology 2014, 30:238–245 240 Pharmaceutical biotechnology atoms [11 ]. This allows the influence of fluxes on the expected to become an indispensible tool for extending 13 isotopomer measurements to be decoupled from sub- C MFA approaches to studies of mammalian systems strate labeling, and thereby enables a fully a priori and industrial bioprocesses. Finally, INST-MFA pro- approach to tracer selection that does not depend on vides increased measurement sensitivity to estimate the choice of a reference flux map. Crown et al. [12] reversible exchange fluxes and metabolite pool sizes subsequently applied the EMU-BV method to identify [32,33], which represents a potential framework for inte- 13 two novel tracers for quantifying oxidative pentose phos- grating metabolomic analysis with C MFA. phate pathway (OPPP) flux and anaplerotic pyruvate carboxylase flux using a metabolic model of HEK-293 New measurement strategies 13 cell metabolism. The EMU-BV approach also suggests In addition to theoretical advances, the field of C MFA that maximal flux information can be obtained by inte- is also rapidly expanding due to new isotopomer measure- grating data sets from multiple parallel tracer exper- ment capabilities. Traditionally, these measurements iments, each of which is designed to elucidate specific have been obtained from mass spectrometry (MS) [34] reactions in the
Load more

Recommended publications
  • In Vivo Metabolism of [1,6-13C2]Glucose Reveals Distinct
    H OH metabolites OH Article 13 In Vivo Metabolism of [1,6- C2]Glucose Reveals Distinct Neuroenergetic Functionality between Mouse Hippocampus and Hypothalamus Antoine Cherix 1,†, Rajesh Sonti 1,‡, Bernard Lanz 1 and Hongxia Lei 2,3,*,§ 1 Laboratory of Functional and Metabolic Imaging (LIFMET), Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; [email protected] (A.C.); [email protected] (R.S.); bernard.lanz@epfl.ch (B.L.) 2 Animal Imaging and Technology (AIT), Center for Biomedical Imaging (CIBM), Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland 3 Faculty of Medicine, University of Geneva, CH-1206 Geneva, Switzerland * Correspondence: [email protected] † Current address: Wellcome Centre for Integrative Neuroimaging (WIN), Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK. ‡ Current address: Department of Pharmaceutical analysis, National Institute of Pharmaceutical Education and Research (NIPER) Hyderabad- NH 65, Hyderabad 500037, India. § Current address: Wuhan United Imaging Life Science Instruments Ltd., Wuhan 430206, China. Abstract: Glucose is a major energy fuel for the brain, however, less is known about specificities of its metabolism in distinct cerebral areas. Here we examined the regional differences in glucose utilization between the hypothalamus and hippocampus using in vivo indirect 13C magnetic res- 1 13 13 onance spectroscopy ( H-[ C]-MRS) upon infusion of [1,6- C2]glucose. Using a metabolic flux analysis with a 1-compartment mathematical model of brain metabolism, we report that compared to hippocampus, hypothalamus shows higher levels of aerobic glycolysis associated with a marked Citation: Cherix, A.; Sonti, R.; Lanz, gamma-aminobutyric acid-ergic (GABAergic) and astrocytic metabolic dependence.
    [Show full text]
  • Quantitative Analysis of Amino Acid Metabolism in Liver Cancer Links Glutamate Excretion to Nucleotide Synthesis
    Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis Avlant Nilssona,1, Jurgen R. Haanstrab,1, Martin Engqvista, Albert Gerdingc,d, Barbara M. Bakkerb,c, Ursula Klingmüllere, Bas Teusinkb, and Jens Nielsena,f,2 aDepartment of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden; bSystems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, NL1081HZ Amsterdam, The Netherlands; cLaboratory of Pediatrics, Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, NL-9713AV Groningen, The Netherlands dDepartment of Laboratory Medicine, University of Groningen, University Medical Center Groningen, NL-9713AV Groningen, The Netherlands; eDivision of Systems Biology and Signal Transduction, German Cancer Research Center, D-69120 Heidelberg, Germany; and fNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, DK2800, Denmark Contributed by Jens Nielsen, March 9, 2020 (sent for review November 4, 2019; reviewed by Eytan Ruppin and Matthew G. Vander Heiden) Many cancer cells consume glutamine at high rates; counterintu- the excretion of lactate, glutamate, alanine, and glycine (5). How- itively, they simultaneously excrete glutamate, the first interme- ever, a full genome-wide modeling approach is required to expand diate in glutamine metabolism. Glutamine consumption has been our knowledge of how metabolism functions beyond the canonical linked to replenishment of tricarboxylic acid cycle (TCA) interme- pathways and, in particular, to understand the intricate effects of diates and synthesis of adenosine triphosphate (ATP), but the metabolic compartmentalization and the interplay between ex- reason for glutamate excretion is unclear. Here, we dynamically change fluxes and synthesis of biomass.
    [Show full text]
  • Nicotinamide Adenine Dinucleotide Is Transported Into Mammalian
    RESEARCH ARTICLE Nicotinamide adenine dinucleotide is transported into mammalian mitochondria Antonio Davila1,2†, Ling Liu3†, Karthikeyani Chellappa1, Philip Redpath4, Eiko Nakamaru-Ogiso5, Lauren M Paolella1, Zhigang Zhang6, Marie E Migaud4,7, Joshua D Rabinowitz3, Joseph A Baur1* 1Department of Physiology, Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States; 2PARC, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States; 3Lewis-Sigler Institute for Integrative Genomics, Department of Chemistry, Princeton University, Princeton, United States; 4School of Pharmacy, Queen’s University Belfast, Belfast, United Kingdom; 5Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States; 6College of Veterinary Medicine, Northeast Agricultural University, Harbin, China; 7Mitchell Cancer Institute, University of South Alabama, Mobile, United States Abstract Mitochondrial NAD levels influence fuel selection, circadian rhythms, and cell survival under stress. It has alternately been argued that NAD in mammalian mitochondria arises from import of cytosolic nicotinamide (NAM), nicotinamide mononucleotide (NMN), or NAD itself. We provide evidence that murine and human mitochondria take up intact NAD. Isolated mitochondria preparations cannot make NAD from NAM, and while NAD is synthesized from NMN, it does not localize to the mitochondrial matrix or effectively support oxidative phosphorylation. Treating cells *For correspondence: with nicotinamide riboside that is isotopically labeled on the nicotinamide and ribose moieties [email protected] results in the appearance of doubly labeled NAD within mitochondria. Analogous experiments with †These authors contributed doubly labeled nicotinic acid riboside (labeling cytosolic NAD without labeling NMN) demonstrate equally to this work that NAD(H) is the imported species.
    [Show full text]
  • Metabolic Flux Analysis—Linking Isotope Labeling and Metabolic Fluxes
    H OH metabolites OH Review Metabolic Flux Analysis—Linking Isotope Labeling and Metabolic Fluxes Yujue Wang 1,2, Fredric E. Wondisford 1, Chi Song 3, Teng Zhang 4 and Xiaoyang Su 1,2,* 1 Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA; [email protected] (Y.W.); [email protected] (F.E.W.) 2 Metabolomics Shared Resource, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA 3 Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA; [email protected] 4 Department of Mathematics, University of Central Florida, Orlando, FL 32816, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-732-235-5447 Received: 14 September 2020; Accepted: 4 November 2020; Published: 6 November 2020 Abstract: Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns.
    [Show full text]
  • 1 Quantitative Determinants of Aerobic Glycolysis Identify Flux Through the Enzyme 1 GAPDH As a Limiting Step 2 3 Alexander a Sh
    1 Quantitative determinants of aerobic glycolysis identify flux through the enzyme 2 GAPDH as a limiting step 3 4 Alexander A Shestov1*, Xiaojing Liu1*, Zheng Ser1, Ahmad Cluntun2, Yin P Hung3, Lei 5 Huang4, Dongsung Kim2, Anne Le5, Gary Yellen3, John G Albeck6, Jason W 6 Locasale1,2,4** 7 8 1Division of Nutritional Sciences, Cornell University, Ithaca NY 14850 9 2Field of Biochemistry and Molecular Cell Biology, Department of Molecular Biology 10 and Genetics Cornell University, Ithaca NY 14850 11 3Department of Neurobiology, Harvard Medical School, Boston MA 02115 12 4Field of Computational Biology, Department of Biological Statistics and Computational 13 Biology, Cornell University, Ithaca NY 14850 14 5Department of Pathology and Oncology, Johns Hopkins University School of Medicine, 15 Baltimore, MD 21231 16 6Department of Molecular Cell Biology, University of California Davis, Davis CA 95616 17 * Equal contribution 18 ** Correspondence: [email protected] 19 20 Abstract 21 Aerobic glycolysis or the Warburg Effect (WE) is characterized by the increased 22 metabolism of glucose to lactate. It remains unknown what quantitative changes to the 23 activity of metabolism are necessary and sufficient for this phenotype. We developed a 24 computational model of glycolysis and an integrated analysis using metabolic control 25 analysis (MCA), metabolomics data, and statistical simulations. We identified and 26 confirmed a novel mode of regulation specific to aerobic glycolysis where flux through 27 GAPDH, the enzyme separating lower and upper glycolysis, is the rate-limiting step in 28 the pathway and the levels of fructose (1,6) bisphosphate (FBP), are predictive of the rate 29 and control points in glycolysis.
    [Show full text]
  • Prediction of Metabolic Fluxes by Incorporating Genomic Context And
    Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses Jong Myoung Parka,b, Tae Yong Kima,b, and Sang Yup Leea,b,c,1 aMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Program), KAIST, Daejeon 305-701, Republic of Korea; bCenter for Systems and Synthetic Biotechnology, Institute for the BioCentury, KAIST, Daejeon 305-701, Republic of Korea; and cDepartment of Bio and Brain Engineering, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon 305-701, Republic of Korea Edited* by Bernhard Ø. Palsson, University of California at San Diego, La Jolla, CA, and accepted by the Editorial Board July 6, 2010 (received for review March 26, 2010) Flux balance analysis (FBA) of a genome-scale metabolic model However, this approach still works with a flux solution space that allows calculation of intracellular fluxes by optimizing an objective is too large compared with the biologically feasible flux solution function, such as maximization of cell growth, under given con- space of a real organism (15–17). Thus, additional information straints, and has found numerous applications in the field of systems and procedures, such as providing more constraints, are needed to biology and biotechnology. Due to the underdetermined nature of narrow down the set of candidate solutions. the system, however, it has limitations such as inaccurate prediction One example of a suitable constraint that can be applied is the of fluxes and existence of multiple solutions for an optimal objective experimentally measured metabolic flux data obtained from 13C- value. Here, we report a strategy for accurate prediction of meta- based flux analysis and fermentation data (18–21).
    [Show full text]
  • Glucose Flux in Relation to Energy Expenditure in Malnourished Patients with and Without Cancer During Periods of Fasting and Feeding1
    [CANCER RESEARCH 44, 1718-1724, April 1984] Glucose Flux in Relation to Energy Expenditure in Malnourished Patients with and without Cancer during Periods of Fasting and Feeding1 Elisabeth Edén,Staffar) Edström,Klas Bennegárd, Tore Scherstén,and Kent Lundholm2 Surgical Metabolic Research Laboratory, Department of Surgery I [T. S., K. L], Anaesthasiology l-ll [E. E., K. B.] and Otolaryngology [S. £.],Sahlgrenska Hospital, University of Gothenburg, Gothenburg, Sweden ABSTRACT fuel economy in progressive neoplasia (12). Our recent evidence suggests that elevated lactate turnover in cancer patients may Glucose dynamics, energy metabolism, and nitrogen balance be counteracted by depressed recycling of carbon in the glucose- were studied in eight malnourished cancer patients and seven alanine cycle (2), supported by the findings that a decreased malnourished patients without cancer. Glucose flux was mea alanine release occurred in combination with a diminished glu sured by single injection of [6-3H]glucose and [U-14C]glucose. cose uptake across the leg in cancer patients after an overnight Energy expenditure was measured by indirect calorimetry. Each fast (2), and that intrahepatic cycling of glucose carbon was patient was studied after an overnight fast and during constant decreased in sarcoma-bearing mice (21). Thus, the evidence gastric infusion of a formula diet. points to the possibility that net recycling of glucose in cancer Cancer patients had elevated glucose flux when fasting, cor cachexia may not influence the overall fuel economy, even if the responding to 42% of their spontaneous daily intake of glucose. Cori cycle activity is highly elevated (16). Instead, it is rather the At least one-half of the elevated flux in cancer patients compared sum of whole-body gluconeogenesis and glycogenolysis, mea with controls was due to increased recycling of glucose carbon sured as glucose turnover, that may be of quantitative impor after an overnight fast.
    [Show full text]
  • 13C-Metabolic Flux Analysis: an Accurate Approach to Demystify Microbial Metabolism for Biochemical Production
    Review 13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production Weihua Guo, Jiayuan Sheng and Xueyang Feng * Received: 1 October 2015; Accepted: 18 December 2015; Published: 25 December 2015 Academic Editors: Mark Blenner and Michael D. Lynch Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA; [email protected] (W.G.); [email protected] (J.S.) * Correspondence: [email protected]; Tel.: +1-540-231-2974 Abstract: 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 Keywords: Bottleneck; isotope; cofactor imbalance; cell metabolism; synthetic biology; biofuels 1.
    [Show full text]
  • Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia Coli
    H OH metabolites OH Article Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli Yuki Kuriya 1 and Michihiro Araki 1,2,3,* 1 Graduate School of Medicine, Kyoto University, 54 ShogoinKawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan; [email protected] 2 National Institutes of Biomedical Innovation, Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo 162-8636, Japan 3 Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan * Correspondence: [email protected]; Tel.: +81-3-3203-5721 Received: 17 April 2020; Accepted: 12 May 2020; Published: 15 May 2020 Abstract: Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation.
    [Show full text]
  • A Genetically Encoded Tool for Manipulation of NADP+/NADPH in Living Cells
    HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nat Chem Manuscript Author Biol. Author Manuscript Author manuscript; available in PMC 2018 February 07. Published in final edited form as: Nat Chem Biol. 2017 October ; 13(10): 1088–1095. doi:10.1038/nchembio.2454. A genetically encoded tool for manipulation of NADP+/NADPH in living cells Valentin Cracan1,2,3,†, Denis V. Titov1,2,3,†, Hongying Shen1,2,3, Zenon Grabarek1,*, and Vamsi K. Mootha1,2,3,* 1Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA 2Department of Systems Biology, Harvard Medical School, Boston, MA, USA 3Broad Institute, Cambridge, MA, USA Abstract NADH and NADPH are redox coenzymes broadly required for energy metabolism, biosynthesis and detoxification. Despite detailed knowledge of specific enzymes and pathways that utilize these coenzymes, a holistic understanding of the regulation and compartmentalization of NADH and NADPH-dependent pathways is lacking, in part because of a lack of tools with which to investigate them in living cells. We previously reported the use of the naturally occurring Lactobacillus brevis H2O-forming NADH oxidase (LbNOX) as a genetic tool for manipulation of the NAD+/NADH ratio in human cells. Here we present TPNOX (triphosphopyridine nucleotide oxidase), a rationally designed and engineered mutant of LbNOX that is strictly specific towards NADPH. We characterize the effects of TPNOX expression on cellular metabolism and use it in combination with LbNOX to show how the redox states of mitochondrial NADPH and NADH pools are connected. Graphical Abstract Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms *Corresponding authors: Vamsi K.
    [Show full text]
  • Capacity for Instantaneous Catabolism of Preferred and Non-Preferred
    www.nature.com/scientificreports OPEN Capacity for instantaneous catabolism of preferred and non-preferred carbon sources in Received: 6 April 2018 Accepted: 26 July 2018 Escherichia coli and Bacillus subtilis Published: xx xx xxxx Marieke F. Bufng1,2, Hannes Link3, Dimitris Christodoulou 1,2 & Uwe Sauer1 Making the right choice for nutrient consumption in an ever-changing environment is a key factor for evolutionary success of bacteria. Here we investigate the regulatory mechanisms that enable dynamic adaptation between non-preferred and preferred carbon sources for the model Gram- negative and -positive species Escherichia coli and Bacillus subtilis, respectively. We focus on the ability for instantaneous catabolism of a gluconeogenic carbon source upon growth on a glycolytic carbon source and vice versa. By following isotopic tracer dynamics on a 1–2 minute scale, we show that fux reversal from the preferred glucose to non-preferred pyruvate as the sole carbon source is primarily transcriptionally regulated. In the opposite direction, however, E. coli can reverse its fux instantaneously by means of allosteric regulation, whereas in B. subtilis this fux reversal is transcriptionally regulated. Upon removal of transcriptional regulation, B. subtilis assumes the ability of instantaneous glucose catabolism. Using an approach that combines quantitative metabolomics and kinetic modelling, we then identify the additionally necessary key metabolite-enzyme interactions that implement the instantaneous fux reversal in the transcriptionally deregulated B. subtilis, and validate the most relevant allosteric interactions. Evolutionary adaptation enables bacterial growth and survival in a large range of habitats, as is evident from the ability to exploit diverse and frequently changing energy and nutrient sources1.
    [Show full text]
  • Cofactor Engineering for Advancing Chemical Biotechnology. Yipeng
    Cofactor engineering for advancing chemical biotechnology. Yipeng Wanga, Ka-Yiu Sanb,c and George N. Bennetta a. Department of Biochemistry and Cell Biology, Rice University, Houston, TX USA 77005 b. Department of Bioengineering, Rice University, Houston, TX USA 77005 c. Department of Chemical and Biomolecular Engineering, Rice University, Houston, USA TX 77251 Corresponding author George N. Bennett Telephone:713 348 4920 e-mail: [email protected] fax: 713 348 5154 Abstract Cofactors provide redox carriers for biosynthetic reactions, catabolic reactions and act as important agents in transfer of energy for the cell. Recent advances in manipulating cofactors include culture conditions or additive alterations, genetic modification of host pathways for increased availability of desired cofactor, changes in enzyme cofactor specificity, and introduction of novel redox partners to form effective circuits for biochemical processes and biocatalysts. Genetic strategies to employ ferredoxin, NADH and NADPH most effectively in natural or novel pathways have improved yield and efficiency of large-scale processes for fuels and chemicals and have been demonstrated with a variety of microbial organisms. Introduction Cofactors provide redox carriers for biosynthetic reactions, catabolic reactions and act as important agents in transfer of energy for the cell. Some examples illustrating the requirement for cofactor balance and availability include: the conversion of biomass feedstocks containing xylose to ethanol where the formation of xylitol is a problem [1-7]; as a driving force for more effective production of reduced compounds such as biofuels [8]; in using cytochrome P450s in specific oxidation reactions where the recycling of active enzyme is required [9-11]; and the production of chiral pharmaceutical intermediates where specific reductions require a certain cofactor [12,13].
    [Show full text]