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Shymansky CM, Wang G, Baidoo EEK, Gin J, Apel AR, Mukhopadhyay A, García Martín H, Keasling JD. Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism. Front Bioeng Biotechnol 2017; 5:31. [PMID: 28596955 PMCID: PMC5443151 DOI: 10.3389/fbioe.2017.00031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 04/25/2017] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Christopher M Shymansky
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA.,Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA
| | - George Wang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA
| | - Edward E K Baidoo
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA
| | - Jennifer Gin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA
| | - Amanda Reider Apel
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA
| | - Héctor García Martín
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA.,DOE Agile Biofoundry, Emeryville, CA, USA.,BCAM, Basque Center for Applied Mathematics, Mazarredo, Bilbao, Basque Country, Spain
| | - Jay D Keasling
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, USA.,Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA.,Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
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52
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Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii. PLoS One 2017; 12:e0177292. [PMID: 28542252 PMCID: PMC5443493 DOI: 10.1371/journal.pone.0177292] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 04/25/2017] [Indexed: 12/18/2022] Open
Abstract
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.
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53
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Lafontaine Rivera JG, Theisen MK, Chen PW, Liao JC. Kinetically accessible yield (KAY) for redirection of metabolism to produce exo-metabolites. Metab Eng 2017; 41:144-151. [PMID: 28389394 DOI: 10.1016/j.ymben.2017.03.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 02/15/2017] [Accepted: 03/31/2017] [Indexed: 01/17/2023]
Abstract
The product formation yield (product formed per unit substrate consumed) is often the most important performance indicator in metabolic engineering. Until now, the actual yield cannot be predicted, but it can be bounded by its maximum theoretical value. The maximum theoretical yield is calculated by considering the stoichiometry of the pathways and cofactor regeneration involved. Here we found that in many cases, dynamic stability becomes an issue when excessive pathway flux is drawn to a product. This constraint reduces the yield and renders the maximal theoretical yield too loose to be predictive. We propose a more realistic quantity, defined as the kinetically accessible yield (KAY) to predict the maximum accessible yield for a given flux alteration. KAY is either determined by the point of instability, beyond which steady states become unstable and disappear, or a local maximum before becoming unstable. Thus, KAY is the maximum flux that can be redirected for a given metabolic engineering strategy without losing stability. Strictly speaking, calculation of KAY requires complete kinetic information. With limited or no kinetic information, an Ensemble Modeling strategy can be used to determine a range of likely values for KAY, including an average prediction. We first apply the KAY concept with a toy model to demonstrate the principle of kinetic limitations on yield. We then used a full-scale E. coli model (193 reactions, 153 metabolites) and this approach was successful in E. coli for predicting production of isobutanol: the calculated KAY values are consistent with experimental data for three genotypes previously published.
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Affiliation(s)
| | - Matthew K Theisen
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, USA; Department of Bioengineering, University of California, Los Angeles, USA
| | - Po-Wei Chen
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, USA
| | - James C Liao
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, USA; UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, USA; Academia Sinica, Taiwan.
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54
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Salon C, Avice JC, Colombié S, Dieuaide-Noubhani M, Gallardo K, Jeudy C, Ourry A, Prudent M, Voisin AS, Rolin D. Fluxomics links cellular functional analyses to whole-plant phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:2083-2098. [PMID: 28444347 DOI: 10.1093/jxb/erx126] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.
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Affiliation(s)
- Christophe Salon
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Jean-Christophe Avice
- UNICAEN, UMR INRA 950 Ecophysiologie Végétale, Agronomie et nutritions N, C, S, Esplanade de la Paix, Université Caen Normandie, 14032 Caen Cedex 5, France
| | - Sophie Colombié
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
| | - Martine Dieuaide-Noubhani
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
| | - Karine Gallardo
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Christian Jeudy
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Alain Ourry
- UNICAEN, UMR INRA 950 Ecophysiologie Végétale, Agronomie et nutritions N, C, S, Esplanade de la Paix, Université Caen Normandie, 14032 Caen Cedex 5, France
| | - Marion Prudent
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Anne-Sophie Voisin
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Dominique Rolin
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
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55
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Mayers JR, Torrence ME, Danai LV, Papagiannakopoulos T, Davidson SM, Bauer MR, Lau AN, Ji BW, Dixit PD, Hosios AM, Muir A, Chin CR, Freinkman E, Jacks T, Wolpin BM, Vitkup D, Vander Heiden MG. Tissue of origin dictates branched-chain amino acid metabolism in mutant Kras-driven cancers. Science 2017; 353:1161-5. [PMID: 27609895 DOI: 10.1126/science.aaf5171] [Citation(s) in RCA: 409] [Impact Index Per Article: 58.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Accepted: 07/29/2016] [Indexed: 12/18/2022]
Abstract
Tumor genetics guides patient selection for many new therapies, and cell culture studies have demonstrated that specific mutations can promote metabolic phenotypes. However, whether tissue context defines cancer dependence on specific metabolic pathways is unknown. Kras activation and Trp53 deletion in the pancreas or the lung result in pancreatic ductal adenocarinoma (PDAC) or non-small cell lung carcinoma (NSCLC), respectively, but despite the same initiating events, these tumors use branched-chain amino acids (BCAAs) differently. NSCLC tumors incorporate free BCAAs into tissue protein and use BCAAs as a nitrogen source, whereas PDAC tumors have decreased BCAA uptake. These differences are reflected in expression levels of BCAA catabolic enzymes in both mice and humans. Loss of Bcat1 and Bcat2, the enzymes responsible for BCAA use, impairs NSCLC tumor formation, but these enzymes are not required for PDAC tumor formation, arguing that tissue of origin is an important determinant of how cancers satisfy their metabolic requirements.
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Affiliation(s)
- Jared R Mayers
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. These authors contributed equally to this work
| | - Margaret E Torrence
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. These authors contributed equally to this work
| | - Laura V Danai
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thales Papagiannakopoulos
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shawn M Davidson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Matthew R Bauer
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Allison N Lau
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Brian W Ji
- Center for Computational Biology and Bioinformatics and Department of Systems Biology, Columbia University, New York, NY 10027, USA
| | - Purushottam D Dixit
- Center for Computational Biology and Bioinformatics and Department of Systems Biology, Columbia University, New York, NY 10027, USA
| | - Aaron M Hosios
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alexander Muir
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christopher R Chin
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Elizaveta Freinkman
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. Broad Institute, Seven Cambridge Center, Cambridge, MA 02142, USA. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tyler Jacks
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Dennis Vitkup
- Center for Computational Biology and Bioinformatics and Department of Systems Biology, Columbia University, New York, NY 10027, USA
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Broad Institute, Seven Cambridge Center, Cambridge, MA 02142, USA. Dana-Farber Cancer Institute, Boston, MA 02115, USA.
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56
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Vercammen D, Telen D, Nimmegeers P, Janssens A, Akkermans S, Noriega Fernandez E, Logist F, Van Impe J. Application of a dynamic metabolic flux algorithm during a temperature-induced lag phase. FOOD AND BIOPRODUCTS PROCESSING 2017. [DOI: 10.1016/j.fbp.2016.10.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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57
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13C metabolite profiling to compare the central metabolic flux in two yeast strains. BIOTECHNOL BIOPROC E 2017. [DOI: 10.1007/s12257-016-0536-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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58
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Zhang Y, Liu D, Chen Z. Production of C2-C4 diols from renewable bioresources: new metabolic pathways and metabolic engineering strategies. BIOTECHNOLOGY FOR BIOFUELS 2017; 10:299. [PMID: 29255482 PMCID: PMC5727944 DOI: 10.1186/s13068-017-0992-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 12/05/2017] [Indexed: 05/17/2023]
Abstract
C2-C4 diols classically derived from fossil resource are very important bulk chemicals which have been used in a wide range of areas, including solvents, fuels, polymers, cosmetics, and pharmaceuticals. Production of C2-C4 diols from renewable resources has received significant interest in consideration of the reducing fossil resource and the increasing environmental issues. While bioproduction of certain diols like 1,3-propanediol has been commercialized in recent years, biosynthesis of many other important C2-C4 diol isomers is highly challenging due to the lack of natural synthesis pathways. Recent advances in synthetic biology have enabled the de novo design of completely new pathways to non-natural molecules from renewable feedstocks. In this study, we review recent advances in bioproduction of C2-C4 diols, focusing on new metabolic pathways and metabolic engineering strategies being developed. We also discuss the challenges and future trends toward the development of economically competitive processes for bio-based diol production.
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Affiliation(s)
- Ye Zhang
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084 China
- Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084 China
- Tsinghua Innovation Center in Dongguan, Dongguan, 523808 China
| | - Dehua Liu
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084 China
- Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084 China
- Tsinghua Innovation Center in Dongguan, Dongguan, 523808 China
- Center of Synthetic and Systems Biology, Tsinghua University, Beijing, 100084 China
| | - Zhen Chen
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084 China
- Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084 China
- Tsinghua Innovation Center in Dongguan, Dongguan, 523808 China
- Center of Synthetic and Systems Biology, Tsinghua University, Beijing, 100084 China
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59
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Gonzalez JE, Long CP, Antoniewicz MR. Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis. Metab Eng 2016; 39:9-18. [PMID: 27840237 DOI: 10.1016/j.ymben.2016.11.003] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 10/09/2016] [Accepted: 11/07/2016] [Indexed: 12/13/2022]
Abstract
Glucose and xylose are the two most abundant sugars derived from the breakdown of lignocellulosic biomass. While aerobic glucose metabolism is relatively well understood in E. coli, until now there have been only a handful of studies focused on anaerobic glucose metabolism and no 13C-flux studies on xylose metabolism. In the absence of experimentally validated flux maps, constraint-based approaches such as MOMA and RELATCH cannot be used to guide new metabolic engineering designs. In this work, we have addressed this critical gap in current understanding by performing comprehensive characterizations of glucose and xylose metabolism under aerobic and anaerobic conditions, using recent state-of-the-art techniques in 13C metabolic flux analysis (13C-MFA). Specifically, we quantified precise metabolic fluxes for each condition by performing parallel labeling experiments and analyzing the data through integrated 13C-MFA using the optimal tracers [1,2-13C]glucose, [1,6-13C]glucose, [1,2-13C]xylose and [5-13C]xylose. We also quantified changes in biomass composition and confirmed turnover of macromolecules by applying [U-13C]glucose and [U-13C]xylose tracers. We demonstrated that under anaerobic growth conditions there is significant turnover of lipids and that a significant portion of CO2 originates from biomass turnover. Using knockout strains, we also demonstrated that β-oxidation is critical for anaerobic growth on xylose. Quantitative analysis of co-factor balances (NADH/FADH2, NADPH, and ATP) for different growth conditions provided new insights regarding the interplay of energy and redox metabolism and the impact on E. coli cell physiology.
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Affiliation(s)
- Jacqueline E Gonzalez
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
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60
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Crown SB, Kelleher JK, Rouf R, Muoio DM, Antoniewicz MR. Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts. Am J Physiol Heart Circ Physiol 2016; 311:H881-H891. [PMID: 27496880 DOI: 10.1152/ajpheart.00428.2016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 07/25/2016] [Indexed: 11/22/2022]
Abstract
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.
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Affiliation(s)
- Scott B Crown
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Duke University, Durham, North Carolina;
| | - Joanne K Kelleher
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Rosanne Rouf
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Deborah M Muoio
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Duke University, Durham, North Carolina; Department of Medicine, Duke University, Durham, North Carolina; Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina; and
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delware
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61
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Mareš J, Kukal J, Hrnčiřík P, Náhlík J. Process state classification of fed-batch fermentation based on process variables analysis. Biochem Eng J 2016. [DOI: 10.1016/j.bej.2016.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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62
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Okahashi N, Kawana S, Iida J, Shimizu H, Matsuda F. GC-MS/MS survey of collision-induced dissociation of tert-butyldimethylsilyl-derivatized amino acids and its application to (13)C-metabolic flux analysis of Escherichia coli central metabolism. Anal Bioanal Chem 2016; 408:6133-40. [PMID: 27342798 DOI: 10.1007/s00216-016-9724-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 06/13/2016] [Accepted: 06/14/2016] [Indexed: 12/31/2022]
Abstract
Stable isotope labeling experiments using mass spectrometry have been employed to investigate carbon flow levels (metabolic flux) in mammalian, plant, and microbial cells. To achieve a more precise (13)C-metabolic flux analysis ((13)C-MFA), novel fragmentations of tert-butyldimethylsilyl (TBDMS)-amino acids were investigated by gas chromatography-tandem mass spectrometry (GC-MS/MS). The product ion scan analyses of 15 TBDMS-amino acids revealed 24 novel fragment ions. The amino acid-derived carbons included in the five fragment ions were identified by the analyses of (13)C-labeled authentic standards. The identification of the fragment ion at m/z 170 indicated that the isotopic abundance of S-methyl carbon in methionine could be determined from the cleavage of C5 in the precursor of [M-159](+) (m/z 218). It was also confirmed that the precision of (13)C-MFA in Escherichia coli central carbon metabolism could be improved by introducing (13)C-labeling data derived from novel fragmentations. Graphical Abstract Novel collision-induced dissociation fragmentations of tert-butyldimethylsilyl amino acids were investigated and identified by GC-MS/MS.
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Affiliation(s)
- Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shuichi Kawana
- Analytical and Measuring Instruments Division, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Junko Iida
- Analytical and Measuring Instruments Division, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan.,Osaka University Shimadzu Analytical Innovation Research Laboratory, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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63
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Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system. Metab Eng 2016; 38:10-18. [PMID: 27267409 DOI: 10.1016/j.ymben.2016.06.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 06/01/2016] [Accepted: 06/03/2016] [Indexed: 12/11/2022]
Abstract
13C-Metabolic flux analysis (13C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13C-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 13C-MFA. The best single tracers were doubly 13C-labeled glucose tracers, including [1,6-13C]glucose, [5,6-13C]glucose and [1,2-13C]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 [1,6-13C]glucose and [1,2-13C]glucose. Combined analysis of [1,6-13C]glucose and [1,2-13C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-13C]glucose +20% [U-13C]glucose.
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64
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Long CP, Gonzalez JE, Sandoval NR, Antoniewicz MR. Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism. Metab Eng 2016; 37:102-113. [PMID: 27212692 DOI: 10.1016/j.ymben.2016.05.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2016] [Revised: 05/09/2016] [Accepted: 05/16/2016] [Indexed: 11/17/2022]
Abstract
Understanding the impact of gene knockouts on cellular physiology, and metabolism in particular, is centrally important to quantitative systems biology and metabolic engineering. Here, we present a comprehensive physiological characterization of wild-type Escherichia coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including the PTS system, glycolysis, pentose phosphate pathway and Entner-Doudoroff pathway. Our results reveal significant metabolic changes that are affected by specific gene knockouts. Analysis of collective trends and correlations in the data using principal component analysis (PCA) provide new, and sometimes surprising, insights into E. coli physiology. Additionally, by comparing the data-to-model predictions from constraint-based approaches such as FBA, MOMA and RELATCH we demonstrate the important role of less well-understood kinetic and regulatory effects in central carbon metabolism.
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Affiliation(s)
- Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Jacqueline E Gonzalez
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Nicholas R Sandoval
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA.
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65
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Cordova LT, Lu J, Cipolla RM, Sandoval NR, Long CP, Antoniewicz MR. Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by (13)C metabolic flux analysis and whole genome sequencing. Metab Eng 2016; 37:63-71. [PMID: 27164561 DOI: 10.1016/j.ymben.2016.05.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 04/04/2016] [Accepted: 05/05/2016] [Indexed: 01/20/2023]
Abstract
We evolved Thermus thermophilus to efficiently co-utilize glucose and xylose, the two most abundant sugars in lignocellulosic biomass, at high temperatures without carbon catabolite repression. To generate the strain, T. thermophilus HB8 was first evolved on glucose to improve its growth characteristics, followed by evolution on xylose. The resulting strain, T. thermophilus LC113, was characterized in growth studies, by whole genome sequencing, and (13)C-metabolic flux analysis ((13)C-MFA) with [1,6-(13)C]glucose, [5-(13)C]xylose, and [1,6-(13)C]glucose+[5-(13)C]xylose as isotopic tracers. Compared to the starting strain, the evolved strain had an increased growth rate (~2-fold), increased biomass yield, increased tolerance to high temperatures up to 90°C, and gained the ability to grow on xylose in minimal medium. At the optimal growth temperature of 81°C, the maximum growth rate on glucose and xylose was 0.44 and 0.46h(-1), respectively. In medium containing glucose and xylose the strain efficiently co-utilized the two sugars. (13)C-MFA results provided insights into the metabolism of T. thermophilus LC113 that allows efficient co-utilization of glucose and xylose. Specifically, (13)C-MFA revealed that metabolic fluxes in the upper part of metabolism adjust flexibly to sugar availability, while fluxes in the lower part of metabolism remain relatively constant. Whole genome sequence analysis revealed two large structural changes that can help explain the physiology of the evolved strain: a duplication of a chromosome region that contains many sugar transporters, and a 5x multiplication of a region on the pVV8 plasmid that contains xylose isomerase and xylulokinase genes, the first two enzymes of xylose catabolism. Taken together, (13)C-MFA and genome sequence analysis provided complementary insights into the physiology of the evolved strain.
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Affiliation(s)
- Lauren T Cordova
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Jing Lu
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Robert M Cipolla
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Nicholas R Sandoval
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Christopher P Long
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
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66
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Wu SG, Wang Y, Jiang W, Oyetunde T, Yao R, Zhang X, Shimizu K, Tang YJ, Bao FS. Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming. PLoS Comput Biol 2016; 12:e1004838. [PMID: 27092947 PMCID: PMC4836714 DOI: 10.1371/journal.pcbi.1004838] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Accepted: 03/01/2016] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Stephen Gang Wu
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Yuxuan Wang
- Department of Computer Science and Engineering, Ohio State University, Columbus, Ohio, United States of America
| | - Wu Jiang
- Boxed Wholesale, Edison, New Jersey, United States of America
| | - Tolutola Oyetunde
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Ruilian Yao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, People’s Republic of China
| | - Xuehong Zhang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, People’s Republic of China
| | - Kazuyuki Shimizu
- Institute of Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Yinjie J. Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
- * E-mail: (YJT); (FSB)
| | - Forrest Sheng Bao
- Department of Electrical and Computer Engineering, University of Akron, Akron, Ohio, United States of America
- * E-mail: (YJT); (FSB)
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McCloskey D, Young JD, Xu S, Palsson BO, Feist AM. Modeling Method for Increased Precision and Scope of Directly Measurable Fluxes at a Genome-Scale. Anal Chem 2016; 88:3844-52. [DOI: 10.1021/acs.analchem.5b04914] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Douglas McCloskey
- Department
of Bioengineering, University of California, San Diego, California 92093, United States
| | | | - Sibei Xu
- Department
of Bioengineering, University of California, San Diego, California 92093, United States
| | - Bernhard O. Palsson
- Department
of Bioengineering, University of California, San Diego, California 92093, United States
- Novo
Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Adam M. Feist
- Department
of Bioengineering, University of California, San Diego, California 92093, United States
- Novo
Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
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68
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Sandberg TE, Long CP, Gonzalez JE, Feist AM, Antoniewicz MR, Palsson BO. Evolution of E. coli on [U-13C]Glucose Reveals a Negligible Isotopic Influence on Metabolism and Physiology. PLoS One 2016; 11:e0151130. [PMID: 26964043 PMCID: PMC4786092 DOI: 10.1371/journal.pone.0151130] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 02/24/2016] [Indexed: 12/16/2022] Open
Abstract
13C-Metabolic flux analysis (13C-MFA) traditionally assumes that kinetic isotope effects from isotopically labeled compounds do not appreciably alter cellular growth or metabolism, despite indications that some biochemical reactions can be non-negligibly impacted. Here, populations of Escherichia coli were adaptively evolved for ~1000 generations on uniformly labeled 13C-glucose, a commonly used isotope for 13C-MFA. Phenotypic characterization of these evolved strains revealed ~40% increases in growth rate, with no significant difference in fitness when grown on either labeled (13C) or unlabeled (12C) glucose. The evolved strains displayed decreased biomass yields, increased glucose and oxygen uptake, and increased acetate production, mimicking what is observed after adaptive evolution on unlabeled glucose. Furthermore, full genome re-sequencing revealed that the key genetic changes underlying these phenotypic alterations were essentially the same as those acquired during adaptive evolution on unlabeled glucose. Additionally, glucose competition experiments demonstrated that the wild-type exhibits no isotopic preference for unlabeled glucose, and the evolved strains have no preference for labeled glucose. Overall, the results of this study indicate that there are no significant differences between 12C and 13C-glucose as a carbon source for E. coli growth.
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Affiliation(s)
- Troy E. Sandberg
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, United States of America
| | - Christopher P. Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, 19716, United States of America
| | - Jacqueline E. Gonzalez
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, 19716, United States of America
| | - Adam M. Feist
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Maciek R. Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, 19716, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
- * E-mail:
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69
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Fernandes de Sousa S, Bastin G, Jolicoeur M, Vande Wouwer A. Dynamic metabolic flux analysis using a convex analysis approach: Application to hybridoma cell cultures in perfusion. Biotechnol Bioeng 2015; 113:1102-12. [DOI: 10.1002/bit.25879] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 10/30/2015] [Accepted: 11/01/2015] [Indexed: 12/22/2022]
Affiliation(s)
| | - Georges Bastin
- Department of Mathematical Engineering; ICTEAM; Catholic University of Louvain; Louvain-La-Neuve Belgium
| | - Mario Jolicoeur
- Department of Chemical Engineering; Laboratory in Applied Metabolic Engineering; Polytechnic University of Montreal; Montréal Canada
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70
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Recent advances in microbial production of fuels and chemicals using tools and strategies of systems metabolic engineering. Biotechnol Adv 2015; 33:1455-66. [DOI: 10.1016/j.biotechadv.2014.11.006] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Revised: 10/23/2014] [Accepted: 11/09/2014] [Indexed: 11/22/2022]
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71
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Adebiyi AO, Jazmin LJ, Young JD. 13 C flux analysis of cyanobacterial metabolism. PHOTOSYNTHESIS RESEARCH 2015; 126:19-32. [PMID: 25280933 DOI: 10.1007/s11120-014-0045-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 09/22/2014] [Indexed: 06/03/2023]
Abstract
(13)C metabolic flux analysis (MFA) has made important contributions to our understanding of the physiology of model strains of E. coli and yeast, and it has been widely used to guide metabolic engineering efforts in these microorganisms. Recent advancements in (13)C MFA methodology combined with publicly available software tools are creating new opportunities to extend this approach to examine less characterized microbes. In particular, growing interest in the use of cyanobacteria as industrial hosts for photosynthetic production of biofuels and biochemicals has led to a critical need to better understand how cyanobacterial metabolic fluxes are regulated in response to changes in growth conditions or introduction of heterologous pathways. In this contribution, we review several prior studies that have applied isotopic steady-state (13)C MFA to examine heterotrophic or mixotrophic growth of cyanobacteria, as well as recent studies that have pioneered the use of isotopically nonstationary MFA (INST-MFA) to study autotrophic cultures. We also provide recommendations for the design and analysis of INST-MFA experiments in cyanobacteria, based on our previous experience and a series of simulation studies used to assess the selection of measurements and sample time points. We anticipate that this emerging knowledgebase of prior (13)C MFA studies, optimized experimental protocols, and public software tools will catalyze increasing use of (13)C MFA techniques by the cyanobacteria research community.
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Affiliation(s)
- Adeola O Adebiyi
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Lara J Jazmin
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA.
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37235, USA.
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García Martín H, Kumar VS, Weaver D, Ghosh A, Chubukov V, Mukhopadhyay A, Arkin A, Keasling JD. A Method to Constrain Genome-Scale Models with 13C Labeling Data. PLoS Comput Biol 2015; 11:e1004363. [PMID: 26379153 PMCID: PMC4574858 DOI: 10.1371/journal.pcbi.1004363] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 05/29/2015] [Indexed: 01/31/2023] Open
Abstract
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. While metabolic fluxes constitute the most direct window into a cell’s metabolism, their accurate measurement is non trivial. The gold standard for flux measurement involves providing a labeled feed where some of the carbon atoms have been substituted by isotopes with higher atomic mass (13C instead of 12C). The ensuing labeling found in intracellular metabolites is then used to computationally infer the metabolic fluxes that produced the observed pattern. However, this procedure is typically performed with small metabolic models encompassing only central carbon metabolism. The genomic revolution has afforded us easily available genomes and, with them, comprehensive genome-scale models of cellular metabolism. It would be desirable to use the 13C labeling experimental data to constrain genome-scale models: these data constrain fluxes very effectively and provide in the labeling data fit an obvious proof that the underlying model correctly explains measured quantities. Here, we introduce a rigorous, self-consistent method that uses the full amount of information contained in 13C labeling data to constrain fluxes for a genome-scale model where underlying assumptions are explicitly stated.
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Affiliation(s)
- Héctor García Martín
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
- * E-mail:
| | - Vinay Satish Kumar
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Daniel Weaver
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Amit Ghosh
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Victor Chubukov
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Aindrila Mukhopadhyay
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Adam Arkin
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkely, United States of America
| | - Jay D. Keasling
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkely, United States of America
- Department of Chemical Engineering, University of California, Berkeley, Berkeley, United States of America
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73
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13C metabolic flux analysis at a genome-scale. Metab Eng 2015; 32:12-22. [PMID: 26358840 DOI: 10.1016/j.ymben.2015.08.006] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Revised: 08/10/2015] [Accepted: 08/20/2015] [Indexed: 11/21/2022]
Abstract
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.
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Abstract
In this work, we present a novel approach for performing (13)C metabolic flux analysis ((13)C-MFA) of co-culture systems. We demonstrate for the first time that it is possible to determine metabolic flux distributions in multiple species simultaneously without the need for physical separation of cells or proteins, or overexpression of species-specific products. Instead, metabolic fluxes for each species in a co-culture are estimated directly from isotopic labeling of total biomass obtained using conventional mass spectrometry approaches such as GC-MS. In addition to determining metabolic fluxes, this approach estimates the relative population size of each species in a mixed culture and inter-species metabolite exchange. As such, it enables detailed studies of microbial communities including species dynamics and interactions between community members. The methodology is experimentally validated here using a co-culture of two E. coli knockout strains. Taken together, this work greatly extends the scope of (13)C-MFA to a large number of multi-cellular systems that are of significant importance in biotechnology and medicine.
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Affiliation(s)
- Nikodimos A Gebreselassie
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
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75
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Holmes WE, Angel TE, Li KW, Hellerstein MK. Dynamic Proteomics: In Vivo Proteome-Wide Measurement of Protein Kinetics Using Metabolic Labeling. Methods Enzymol 2015; 561:219-76. [PMID: 26358907 DOI: 10.1016/bs.mie.2015.05.018] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Control of biosynthetic and catabolic rates of polymers, including proteins, stands at the center of phenotype, physiologic adaptation, and disease pathogenesis. Advances in stable isotope-labeling concepts and mass spectrometric instrumentation now allow accurate in vivo measurement of protein synthesis and turnover rates, both for targeted proteins and for unbiased screening across the proteome. We describe here the underlying principles and operational protocols for measuring protein dynamics, focusing on metabolic labeling with (2)H2O (heavy water) combined with tandem mass spectrometric analysis of mass isotopomer abundances in trypsin-generated peptides. The core principles of combinatorial analysis (mass isotopomer distribution analysis or MIDA) are reviewed in detail, including practical advantages, limitations, and technical procedures to ensure optimal kinetic results. Technical factors include heavy water labeling protocols, optimal duration of labeling, clean up and simplification of sample matrices, accurate quantitation of mass isotopomer abundances in peptides, criteria for adequacy of mass spectrometric abundance measurements, and calculation algorithms. Some applications are described, including the noninvasive "virtual biopsy" strategy for measuring molecular flux rates in tissues through measurements in body fluids. In addition, application of heavy water labeling to measure flux lipidomics is noted. In summary, the combination of stable isotope labeling, particularly from (2)H2O, with tandem mass spectrometric analysis of mass isotopomer abundances in peptides, provides a powerful approach for characterizing the dynamics of proteins across the global proteome. Many applications in research and clinical medicine have been achieved and many others can be envisioned.
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Affiliation(s)
- W E Holmes
- KineMed Inc., Emeryville, California, USA
| | - T E Angel
- KineMed Inc., Emeryville, California, USA
| | - K W Li
- KineMed Inc., Emeryville, California, USA
| | - M K Hellerstein
- KineMed Inc., Emeryville, California, USA; Department of Nutritional Sciences and Toxicology, University of California, Berkeley, Berkeley, California, USA.
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76
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Cordova LT, Antoniewicz MR. (13)C metabolic flux analysis of the extremely thermophilic, fast growing, xylose-utilizing Geobacillus strain LC300. Metab Eng 2015; 33:148-157. [PMID: 26100076 DOI: 10.1016/j.ymben.2015.06.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 05/24/2015] [Accepted: 06/12/2015] [Indexed: 01/24/2023]
Abstract
Thermophiles are increasingly used as versatile hosts in the biotechnology industry. One of the key advantages of thermophiles is the potential to achieve high rates of feedstock conversion at elevated temperatures. The recently isolated Geobacillus strain LC300 grows extremely fast on xylose, with a doubling time of less than 30 min. In the accompanying paper, the genome of Geobacillus LC300 was sequenced and annotated. In this work, we have experimentally validated the metabolic network model using parallel (13)C-labeling experiments and applied (13)C-metabolic flux analysis to quantify precise metabolic fluxes. Specifically, the complete set of singly labeled xylose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], and [5-(13)C]xylose, was used for the first time. Isotopic labeling of biomass amino acids was measured by gas chromatography mass spectrometry (GC-MS). Isotopic labeling of carbon dioxide in the off-gas was also measured by an on-line mass spectrometer. The (13)C-labeling data was then rigorously integrated for flux elucidation using the COMPLETE-MFA approach. The results provided important new insights into the metabolism of Geobacillus LC300, its efficient xylose utilization pathways, and the balance between carbon, redox and energy fluxes. The pentose phosphate pathway, glycolysis and TCA cycle were found to be highly active in Geobacillus LC300. The oxidative pentose phosphate pathway was also active and contributed significantly to NADPH production. No transhydrogenase activity was detected. Results from this work provide a solid foundation for future studies of this strain and its metabolic engineering and biotechnological applications.
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Affiliation(s)
- Lauren T Cordova
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
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77
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Martínez VS, Buchsteiner M, Gray P, Nielsen LK, Quek LE. Dynamic metabolic flux analysis using B-splines to study the effects of temperature shift on CHO cell metabolism. Metab Eng Commun 2015; 2:46-57. [PMID: 34150508 PMCID: PMC8193249 DOI: 10.1016/j.meteno.2015.06.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 04/12/2015] [Accepted: 06/03/2015] [Indexed: 01/19/2023] Open
Abstract
Metabolic flux analysis (MFA) is widely used to estimate intracellular fluxes. Conventional MFA, however, is limited to continuous cultures and the mid-exponential growth phase of batch cultures. Dynamic MFA (DMFA) has emerged to characterize time-resolved metabolic fluxes for the entire culture period. Here, the linear DMFA approach was extended using B-spline fitting (B-DMFA) to estimate mass balanced fluxes. Smoother fits were achieved using reduced number of knots and parameters. Additionally, computation time was greatly reduced using a new heuristic algorithm for knot placement. B-DMFA revealed that Chinese hamster ovary cells shifted from 37 °C to 32 °C maintained a constant IgG volume-specific productivity, whereas the productivity for the controls peaked during mid-exponential growth phase and declined afterward. The observed 42% increase in product titer at 32 °C was explained by a prolonged cell growth with high cell viability, a larger cell volume and a more stable volume-specific productivity. New dynamic MFA framework using B-spline (B-DMFA) generates smooth fit. B-DMFA performs better than linear DMFA when fitting fast dynamic changes. Heuristic algorithm for knot placement dramatically reduced computation time. Temperature shifted cultures maintain a constant IgG volume specific productivity. CHO cells shifted to 32 °C have a 42% higher IgG titer due to larger cell volume.
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Affiliation(s)
- Verónica S Martínez
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Maria Buchsteiner
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Peter Gray
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Lars K Nielsen
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Lake-Ee Quek
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
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78
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Antoniewicz MR. Methods and advances in metabolic flux analysis: a mini-review. J Ind Microbiol Biotechnol 2015; 42:317-25. [PMID: 25613286 DOI: 10.1007/s10295-015-1585-x] [Citation(s) in RCA: 145] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 01/09/2015] [Indexed: 01/12/2023]
Abstract
Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.
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Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE, 19716, USA,
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79
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Crown SB, Long CP, Antoniewicz MR. Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli. Metab Eng 2015; 28:151-158. [PMID: 25596508 DOI: 10.1016/j.ymben.2015.01.001] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 12/29/2014] [Accepted: 01/05/2015] [Indexed: 01/19/2023]
Abstract
The use of parallel labeling experiments for (13)C metabolic flux analysis ((13)C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-(13)C]glucose and mixtures of [1-(13)C]glucose and [U-(13)C]glucose, four novel tracers were applied in this study: [2,3-(13)C]glucose, [4,5,6-(13)C]glucose, [2,3,4,5,6-(13)C]glucose and a mixture of [1-(13)C]glucose and [4,5,6-(13)C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 80% [1-(13)C]glucose+20% [U-(13)C]glucose, while [4,5,6-(13)C]glucose and [5-(13)C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.
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Affiliation(s)
- Scott B Crown
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
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80
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Niedenführ S, Wiechert W, Nöh K. How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics. Curr Opin Biotechnol 2014; 34:82-90. [PMID: 25531408 DOI: 10.1016/j.copbio.2014.12.003] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Revised: 11/28/2014] [Accepted: 12/01/2014] [Indexed: 12/24/2022]
Abstract
Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.
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Affiliation(s)
| | - Wolfgang Wiechert
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Katharina Nöh
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
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81
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Young JD. (13)C metabolic flux analysis of recombinant expression hosts. Curr Opin Biotechnol 2014; 30:238-45. [PMID: 25456032 DOI: 10.1016/j.copbio.2014.10.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 10/10/2014] [Accepted: 10/11/2014] [Indexed: 12/11/2022]
Abstract
Identifying host cell metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. (13)C metabolic flux analysis (MFA) provides a rigorous approach to quantify these metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from (13)C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of (13)C MFA within the biotech research community. A number of recent (13)C MFA studies have identified increased citric acid cycle and pentose phosphate pathway fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.
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Affiliation(s)
- Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, PMB 351604, Nashville, TN 37235-1604, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, PMB 351604, Nashville, TN 37235-1604, USA.
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82
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Mayers JR, Wu C, Clish CB, Kraft P, Torrence ME, Fiske BP, Yuan C, Bao Y, Townsend MK, Tworoger SS, Davidson SM, Papagiannakopoulos T, Yang A, Dayton TL, Ogino S, Stampfer MJ, Giovannucci EL, Qian ZR, Rubinson DA, Ma J, Sesso HD, Gaziano JM, Cochrane BB, Liu S, Wactawski–Wende J, Manson JE, Pollak MN, Kimmelman AC, Souza A, Pierce K, Wang TJ, Gerszten RE, Fuchs CS, Heiden MGV, Wolpin BM. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat Med 2014; 20:1193-1198. [PMID: 25261994 PMCID: PMC4191991 DOI: 10.1038/nm.3686] [Citation(s) in RCA: 453] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 08/05/2014] [Indexed: 12/12/2022]
Abstract
Most patients with pancreatic ductal adenocarcinoma (PDAC) are diagnosed with advanced disease and survive less than 12 months. PDAC has been linked with obesity and glucose intolerance, but whether changes in circulating metabolites are associated with early cancer progression is unknown. To better understand metabolic derangements associated with early disease, we profiled metabolites in prediagnostic plasma from individuals with pancreatic cancer (cases) and matched controls from four prospective cohort studies. We find that elevated plasma levels of branched-chain amino acids (BCAAs) are associated with a greater than twofold increased risk of future pancreatic cancer diagnosis. This elevated risk was independent of known predisposing factors, with the strongest association observed among subjects with samples collected 2 to 5 years before diagnosis, when occult disease is probably present. We show that plasma BCAAs are also elevated in mice with early-stage pancreatic cancers driven by mutant Kras expression but not in mice with Kras-driven tumors in other tissues, and that breakdown of tissue protein accounts for the increase in plasma BCAAs that accompanies early-stage disease. Together, these findings suggest that increased whole-body protein breakdown is an early event in development of PDAC.
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Affiliation(s)
- Jared R. Mayers
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Chen Wu
- Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA
| | - Margaret E. Torrence
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Brian P. Fiske
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Chen Yuan
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Ying Bao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Mary K. Townsend
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Shelley S. Tworoger
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Shawn M. Davidson
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Thales Papagiannakopoulos
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Annan Yang
- Division of Genomic Stability and DNA repair, Department of Radiation Oncology, Dana– Farber Cancer Institute, Boston, MA 02215
| | - Talya L. Dayton
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Shuji Ogino
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Meir J. Stampfer
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Department of Nutrition, Harvard School of Public Health, Boston, MA
| | - Edward L. Giovannucci
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Department of Nutrition, Harvard School of Public Health, Boston, MA
| | - Zhi Rong Qian
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Douglas A. Rubinson
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Jing Ma
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Howard D. Sesso
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
| | - John Michael Gaziano
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System
| | | | - Simin Liu
- Departments of Epidemiology and Medicine, Brown University, Providence, RI
| | - Jean Wactawski–Wende
- Department of Social and Preventive Medicine, University at Buffalo, SUNY, Buffalo, NY
| | - JoAnn E. Manson
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
| | - Michael N. Pollak
- Departments of Oncology and Medicine, McGill University, Montreal, QC, Canada
| | - Alec C. Kimmelman
- Division of Genomic Stability and DNA repair, Department of Radiation Oncology, Dana– Farber Cancer Institute, Boston, MA 02215
| | - Amanda Souza
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Kerry Pierce
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Thomas J. Wang
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN
| | - Robert E. Gerszten
- Broad Institute of MIT and Harvard University, Cambridge, MA
- Cardiology Division, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
| | - Charles S. Fuchs
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Matthew G. Vander Heiden
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Brian M. Wolpin
- Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
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83
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Kim HU, Kim B, Seung DY, Lee SY. Effects of introducing heterologous pathways on microbial metabolism with respect to metabolic optimality. BIOTECHNOL BIOPROC E 2014. [DOI: 10.1007/s12257-014-0137-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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84
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He L, Xiao Y, Gebreselassie N, Zhang F, Antoniewiez MR, Tang YJ, Peng L. Central metabolic responses to the overproduction of fatty acids in Escherichia coli based on 13C-metabolic flux analysis. Biotechnol Bioeng 2014; 111:575-85. [PMID: 24122357 DOI: 10.1002/bit.25124] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 09/25/2013] [Accepted: 09/25/2013] [Indexed: 01/12/2023]
Abstract
We engineered a fatty acid overproducing Escherichia coli strain through overexpressing tesA (“pull”) and fadR (“push”) and knocking out fadE (“block”). This “pull-push-block” strategy yielded 0.17 g of fatty acids (C12–C18) per gram of glucose (equivalent to 48% of the maximum theoretical yield) in batch cultures during the exponential growth phase under aerobic conditions. Metabolic fluxes were determined for the engineered E. coli and its control strain using tracer ([1,2-13C]glucose) experiments and 13C-metabolic flux analysis. Cofactor (NADPH) and energy (ATP) balances were also investigated for both strains based on estimated fluxes. Compared to the control strain, fatty acid overproduction led to significant metabolic responses in the central metabolism: (1) Acetic acid secretion flux decreased 10-fold; (2) Pentose phosphate pathway and Entner–Doudoroff pathway fluxes increased 1.5- and 2.0-fold, respectively; (3) Biomass synthesis flux was reduced 1.9-fold; (4) Anaplerotic phosphoenolpyruvate carboxylation flux decreased 1.7-fold; (5) Transhydrogenation flux converting NADH to NADPH increased by 1.7-fold. Real-time quantitative RT-PCR analysis revealed the engineered strain increased the transcription levels of pntA (encoding the membrane-bound transhydrogenase) by 2.1-fold and udhA (encoding the soluble transhydrogenase) by 1.4-fold, which is in agreement with the increased transhydrogenation flux. Cofactor and energy balances analyses showed that the fatty acid overproducing E. coli consumed significantly higher cellular maintenance energy than the control strain. We discussed the strategies to future strain development and process improvements for fatty acid production in E. coli.
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85
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Templeton N, Lewis A, Dorai H, Qian EA, Campbell MP, Smith KD, Lang SE, Betenbaugh MJ, Young JD. The impact of anti-apoptotic gene Bcl-2∆ expression on CHO central metabolism. Metab Eng 2014; 25:92-102. [DOI: 10.1016/j.ymben.2014.06.010] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 06/24/2014] [Accepted: 06/27/2014] [Indexed: 11/27/2022]
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86
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Au J, Choi J, Jones SW, Venkataramanan KP, Antoniewicz MR. Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for (13)C metabolic flux analysis. Metab Eng 2014; 26:23-33. [PMID: 25183671 DOI: 10.1016/j.ymben.2014.08.002] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Revised: 07/27/2014] [Accepted: 08/15/2014] [Indexed: 12/18/2022]
Abstract
In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and (13)C-metabolic flux analysis ((13)C-MFA). Here, cells were grown in parallel cultures with [1-(13)C]glucose and [U-(13)C]glucose as tracers and (13)C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of (13)C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for (13)C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased (13)C-flux measurements in C. acetobutylicum.
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Affiliation(s)
- Jennifer Au
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA
| | - Jungik Choi
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA
| | - Shawn W Jones
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA
| | - Keerthi P Venkataramanan
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA.
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87
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Impacts of high β-galactosidase expression on central metabolism of recombinant Pichia pastoris GS115 using glucose as sole carbon source via (13)C metabolic flux analysis. J Biotechnol 2014; 187:124-34. [PMID: 25058396 DOI: 10.1016/j.jbiotec.2014.07.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Revised: 06/20/2014] [Accepted: 07/08/2014] [Indexed: 11/22/2022]
Abstract
The yeast Pichia pastoris GS115 is a widely used microbial cell factory for the production of heterologous protein. In order to reveal the impacts of high heterologous protein expression on the central metabolism of Pichia pastoris GS115 using glucose as sole carbon source, we engineered a high β-galactosidase expression strain P. pastoris G1HL and a low expression control strain P. pastoris GHL through controlling the initiation strength of constitutive promoter pGAP. The carbon flux distributions in these two strains were quantified via (13)C metabolic flux analysis. Compared to the control strain, G1HL showed a lower growth rate, a higher flux through glycolysis pathway, a higher flux through pentose phosphate pathway, and a lower flux through by-products secretion pathway. The metabolic flux redistribution in G1HL was thought to compensate the increased redox cofactors and energy demands caused by the high protein expression. Although the fluxes through Krebs cycle in two engineered strains were almost the same, they were significantly lower than those in wild strain. The enhanced expression of β-galactosidase by glutamate supplementation demonstrated the potential of P. pastoris GS115 to catabolize more carbon through the Krebs cycle for even higher protein expression. In conclusion, our work indicates that P. pastoris GS115 can readjusts the central metabolism for higher heterologous protein expression and provides strategies for strain development or process optimization for enhancing production of heterologous protein.
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88
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Heise R, Arrivault S, Szecowka M, Tohge T, Nunes-Nesi A, Stitt M, Nikoloski Z, Fernie AR. Flux profiling of photosynthetic carbon metabolism in intact plants. Nat Protoc 2014; 9:1803-24. [DOI: 10.1038/nprot.2014.115] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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89
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Swarup A, Lu J, DeWoody KC, Antoniewicz MR. Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8. Metab Eng 2014; 24:173-80. [DOI: 10.1016/j.ymben.2014.05.013] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Revised: 05/03/2014] [Accepted: 05/20/2014] [Indexed: 12/21/2022]
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90
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Okahashi N, Kajihata S, Furusawa C, Shimizu H. Reliable Metabolic Flux Estimation in Escherichia coli Central Carbon Metabolism Using Intracellular Free Amino Acids. Metabolites 2014; 4:408-20. [PMID: 24957033 PMCID: PMC4101513 DOI: 10.3390/metabo4020408] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 05/10/2014] [Accepted: 05/20/2014] [Indexed: 12/25/2022] Open
Abstract
13C metabolic flux analysis (MFA) is a tool of metabolic engineering for investigation of in vivo flux distribution. A direct 13C enrichment analysis of intracellular free amino acids (FAAs) is expected to reduce time for labeling experiments of the MFA. Measurable FAAs should, however, vary among the MFA experiments since the pool sizes of intracellular free metabolites depend on cellular metabolic conditions. In this study, minimal 13C enrichment data of FAAs was investigated to perform the FAAs-based MFA. An examination of a continuous culture of Escherichia coli using 13C-labeled glucose showed that the time required to reach an isotopically steady state for FAAs is rather faster than that for conventional method using proteinogenic amino acids (PAAs). Considering 95% confidence intervals, it was found that the metabolic flux distribution estimated using FAAs has a similar reliability to that of the PAAs-based method. The comparative analysis identified glutamate, aspartate, alanine and phenylalanine as the common amino acids observed in E. coli under different culture conditions. The results of MFA also demonstrated that the 13C enrichment data of the four amino acids is required for a reliable analysis of the flux distribution.
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Affiliation(s)
- Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Shuichi Kajihata
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Chikara Furusawa
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
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91
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Nargund S, Misra A, Zhang X, Coleman GD, Sriram G. Flux and reflux: metabolite reflux in plant suspension cells and its implications for isotope-assisted metabolic flux analysis. MOLECULAR BIOSYSTEMS 2014; 10:1496-508. [PMID: 24675729 DOI: 10.1039/c3mb70348g] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Isotope-assisted metabolic flux analysis (MFA) is a powerful methodology to quantify intracellular fluxes via isotope labeling experiments (ILEs). In batch cultures, which are often convenient, inexpensive or inevitable especially for eukaryotic systems, MFA is complicated by the presence of the initially present biomass. This unlabeled biomass may either mix with the newly synthesized labeled biomass or reflux into the metabolic network, thus masking the true labeling patterns in the newly synthesized biomass. Here, we report a detailed investigation of such metabolite reflux in cell suspensions of the tree poplar. In ILEs supplying 28% or 98% U-(13)C glucose as the sole organic carbon source, biomass components exhibited lower (13)C enrichments than the supplied glucose as well as anomalous isotopomers not explainable by simple mixing of the initial and newly synthesized biomass. These anomalous labeling patterns were most prominent in a 98% U-(13)C glucose ILE. By comparing the performance of light- and dark-grown cells as well as by analyzing the isotope labeling patterns in aspartic and glutamic acids, we eliminated photosynthetic or anaplerotic fixation of extracellular (12)CO2 as explanations for the anomalous labeling patterns. We further investigated four different metabolic models for interpreting the labeling patterns and evaluating fluxes: (i) a carbon source (glucose) dilution model, (ii) an isotopomer correction model with uniform dilution for all amino acids, (iii) an isotopomer correction model with variable dilution for different amino acids, and (iv) a comprehensive metabolite reflux model. Of these, the metabolite reflux model provided a substantially better fit for the observed labeling patterns (sum of squared residues: 538) than the other three models whose sum of squared residues were (i) 4626, (ii) 4983, and (iii) 1748, respectively. We compared fluxes determined using the metabolite reflux model to those determined using an independent methodology involving an excessively long ILE to wash out initial biomass and a minimal reflux model. This comparison showed identical or similar distributions for a majority of fluxes, thus validating our comprehensive reflux model. In summary, we have demonstrated the need for quantifying interactions between initially present biomass and newly synthesized biomass in batch ILEs, especially through the use of ≈100% U-(13)C carbon sources. Our ILEs reveal a high amount of metabolite reflux in poplar cell suspensions, which is well explained by a comprehensive metabolite reflux model.
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Affiliation(s)
- Shilpa Nargund
- Department of Chemical and Biomolecular Engineering, University of Maryland, 1208D, Chemical and Nuclear Engineering Building 090, College Park, MD 20742, USA.
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92
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Elucidation of intrinsic biosynthesis yields using 13C-based metabolism analysis. Microb Cell Fact 2014; 13:42. [PMID: 24642094 PMCID: PMC3994946 DOI: 10.1186/1475-2859-13-42] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Accepted: 03/12/2014] [Indexed: 11/10/2022] Open
Abstract
This paper discusses the use of 13C-based metabolism analysis for the assessment of intrinsic product yields - the actual carbon contribution from a single carbon substrate to the final product via a specific biosynthesis route - in the following four cases. First, undefined nutrients (such as yeast extract) in fermentation may contribute significantly to product synthesis, which can be quantified through an isotopic dilution method. Second, product and biomass synthesis may be dependent on the co-metabolism of multiple-carbon sources. 13C labeling experiments can track the fate of each carbon substrate in the cell metabolism and identify which substrate plays a main role in product synthesis. Third, 13C labeling can validate and quantify the contribution of the engineered pathway (versus the native pathway) to the product synthesis. Fourth, the loss of catabolic energy due to cell maintenance (energy used for functions other than production of new cell components) and low P/O ratio (Phosphate/Oxygen Ratio) significantly reduces product yields. Therefore, 13C-metabolic flux analysis is needed to assess the influence of suboptimal energy metabolism on microbial productivity, and determine how ATP/NAD(P)H are partitioned among various cellular functions. Since product yield is a major determining factor in the commercialization of a microbial cell factory, we foresee that 13C-isotopic labeling experiments, even without performing extensive flux calculations, can play a valuable role in the development and verification of microbial cell factories.
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93
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Mandy DE, Goldford JE, Yang H, Allen DK, Libourel IGL. Metabolic flux analysis using ¹³C peptide label measurements. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2014; 77:476-86. [PMID: 24279886 DOI: 10.1111/tpj.12390] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 11/08/2013] [Accepted: 11/15/2013] [Indexed: 05/09/2023]
Abstract
¹³C metabolic flux analysis (MFA) has become the experimental method of choice to investigate the cellular metabolism of microbes, cell cultures and plant seeds. Conventional steady-state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. To retain spatial information in conventional steady-state MFA, tissues or subcellular fractions must be dissected or biochemically purified. In contrast, peptides retain their identity in complex protein extracts, and may therefore be associated with a specific time of expression, tissue type and subcellular compartment. To enable 'single-sample' spatially and temporally resolved steady-state flux analysis, we investigated the suitability of peptide mass distributions (PMDs) as an alternative to amino acid label measurements. PMDs are the discrete convolution of the mass distributions of the constituent amino acids of a peptide. We investigated the requirements for the unique deconvolution of PMDs into amino acid mass distributions (AAMDs), the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates that are determined through deconvolution compare to estimates from a conventional GC-MS measurement-based approach. Deconvolution of PMDs of the storage protein β-conglycinin of soybean (Glycine max) resulted in good AAMD and flux estimates if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD and flux estimates. PMD measurements do not include amino acid backbone fragments, which increase the information content in GC-MS-derived analyses. Nonetheless, the resulting flux maps were of comparable quality due to the precision of Orbitrap quantification and the larger number of peptide measurements.
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Affiliation(s)
- Dominic E Mandy
- Department of Plant Biology, University of Minnesota, 1500 Gortner Avenue, St Paul, MN, 55108, USA
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94
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Shlomi T, Fan J, Tang B, Kruger WD, Rabinowitz JD. Quantitation of cellular metabolic fluxes of methionine. Anal Chem 2014; 86:1583-91. [PMID: 24397525 DOI: 10.1021/ac4032093] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Methionine is an essential proteogenic amino acid. In addition, it is a methyl donor for DNA and protein methylation and a propylamine donor for polyamine biosynthesis. Both the methyl and propylamine donation pathways involve metabolic cycles, and methods are needed to quantitate these cycles. Here, we describe an analytical approach for quantifying methionine metabolic fluxes that accounts for the mixing of intracellular and extracellular methionine pools. We observe that such mixing prevents isotope tracing experiments from reaching the steady state due to the large size of the media pools and hence precludes the use of standard stationary metabolic flux analysis. Our approach is based on feeding cells with (13)C methionine and measuring the isotope-labeling kinetics of both intracellular and extracellular methionine by liquid chromatography-mass spectrometry (LC-MS). We apply this method to quantify methionine metabolism in a human fibrosarcoma cell line and study how methionine salvage pathway enzyme methylthioadenosine phosphorylase (MTAP), frequently deleted in cancer, affects methionine metabolism. We find that both transmethylation and propylamine transfer fluxes amount to roughly 15% of the net methionine uptake, with no major changes due to MTAP deletion. Our method further enables the quantification of flux through the pro-tumorigenic enzyme ornithine decarboxylase, and this flux increases 2-fold following MTAP deletion. The analytical approach used to quantify methionine metabolic fluxes is applicable for other metabolic systems affected by mixing of intracellular and extracellular metabolite pools.
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Affiliation(s)
- Tomer Shlomi
- Dept. of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel
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95
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Quek LE, Nielsen LK. Customization of ¹³C-MFA strategy according to cell culture system. Methods Mol Biol 2014; 1191:81-90. [PMID: 25178785 DOI: 10.1007/978-1-4939-1170-7_5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
(13)C-MFA is far from being a simple assay for quantifying metabolic activity. It requires considerable up-front experimental planning and familiarity with the cell culture system in question, as well as optimized analytics and adequate computation frameworks. The success of a (13)C-MFA experiment is ultimately rated by the ability to accurately quantify the flux of one or more reactions of interest. In this chapter, we describe the different (13)C-MFA strategies that have been developed for the various fermentation or cell culture systems, as well as the limitations of the respective strategies. The strategies are affected by many factors and the (13)C-MFA modeling and experimental strategy must be tailored to conditions. The prevailing philosophy in the computation process is that any metabolic processes that produce significant systematic bias in the labeling pattern of the metabolites being measured must be described in the model. It is equally important to plan a labeling strategy by analytical screening or by heuristics.
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Affiliation(s)
- Lake-Ee Quek
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Building 75, Corner of College and Cooper Road, Brisbane, QLD, 4072, Australia
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96
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Wiechert W, Nöh K. Isotopically non-stationary metabolic flux analysis: complex yet highly informative. Curr Opin Biotechnol 2013; 24:979-86. [DOI: 10.1016/j.copbio.2013.03.024] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Revised: 03/28/2013] [Accepted: 03/30/2013] [Indexed: 12/16/2022]
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97
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Antoniewicz MR. Dynamic metabolic flux analysis—tools for probing transient states of metabolic networks. Curr Opin Biotechnol 2013; 24:973-8. [DOI: 10.1016/j.copbio.2013.03.018] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2012] [Revised: 03/21/2013] [Accepted: 03/22/2013] [Indexed: 12/16/2022]
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98
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13C metabolic flux analysis: optimal design of isotopic labeling experiments. Curr Opin Biotechnol 2013; 24:1116-21. [DOI: 10.1016/j.copbio.2013.02.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Revised: 01/15/2013] [Accepted: 02/01/2013] [Indexed: 11/22/2022]
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99
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Crown SB, Antoniewicz MR. Publishing 13C metabolic flux analysis studies: a review and future perspectives. Metab Eng 2013; 20:42-8. [PMID: 24025367 DOI: 10.1016/j.ymben.2013.08.005] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 08/14/2013] [Accepted: 08/29/2013] [Indexed: 11/17/2022]
Abstract
(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g., in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing (13)C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.
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Affiliation(s)
- Scott B Crown
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St., Newark, DE 19716, USA
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100
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Leighty RW, Antoniewicz MR. COMPLETE-MFA: complementary parallel labeling experiments technique for metabolic flux analysis. Metab Eng 2013; 20:49-55. [PMID: 24021936 DOI: 10.1016/j.ymben.2013.08.006] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Revised: 08/13/2013] [Accepted: 08/29/2013] [Indexed: 12/17/2022]
Abstract
We have developed a novel approach for measuring highly accurate and precise metabolic fluxes in living cells, termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. The COMPLETE-MFA method is based on combined analysis of multiple isotopic labeling experiments, where the synergy of using complementary tracers greatly improves the precision of estimated fluxes. In this work, we demonstrate the COMPLETE-MFA approach using all singly labeled glucose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], [5-(13)C], and [6-(13)C]glucose to determine precise metabolic fluxes for wild-type Escherichia coli. Cells were grown in six parallel cultures on defined medium with glucose as the only carbon source. Mass isotopomers of biomass amino acids were measured by gas chromatography-mass spectrometry (GC-MS). The data from all six experiments were then fitted simultaneously to a single flux model to determine accurate intracellular fluxes. We obtained a statistically acceptable fit with more than 300 redundant measurements. The estimated flux map is the most precise flux result obtained thus far for E. coli cells. To our knowledge, this is the first time that six isotopic labeling experiments have been successfully integrated for high-resolution (13)C-flux analysis.
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Affiliation(s)
- Robert W Leighty
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St., Newark, DE 19716, USA
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