1
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Stern A, Fokra M, Sarvin B, Alrahem AA, Lee WD, Aizenshtein E, Sarvin N, Shlomi T. Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution. Nat Commun 2023; 14:7525. [PMID: 37980339 PMCID: PMC10657349 DOI: 10.1038/s41467-023-42824-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/19/2023] [Indexed: 11/20/2023] Open
Abstract
The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.
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Affiliation(s)
- Alon Stern
- Department of Computer Science, Technion-Israel Institute of Technology, 32000, Haifa, Israel
| | - Mariam Fokra
- Department of Biology, Technion-Israel Institute of Technology, 32000, Haifa, Israel
| | - Boris Sarvin
- Department of Biology, Technion-Israel Institute of Technology, 32000, Haifa, Israel
| | - Ahmad Abed Alrahem
- Department of Biology, Technion-Israel Institute of Technology, 32000, Haifa, Israel
| | - Won Dong Lee
- Department of Biology, Technion-Israel Institute of Technology, 32000, Haifa, Israel
| | - Elina Aizenshtein
- Department of Biology, Technion-Israel Institute of Technology, 32000, Haifa, Israel
| | - Nikita Sarvin
- Department of Biology, Technion-Israel Institute of Technology, 32000, Haifa, Israel
| | - Tomer Shlomi
- Department of Computer Science, Technion-Israel Institute of Technology, 32000, Haifa, Israel.
- Department of Biology, Technion-Israel Institute of Technology, 32000, Haifa, Israel.
- Lokey Center for Life Science and Engineering, Technion-Israel Institute of Technology, 32000, Haifa, Israel.
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2
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Mirveis Z, Howe O, Cahill P, Patil N, Byrne HJ. Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives. Metabolomics 2023; 19:67. [PMID: 37482587 PMCID: PMC10363518 DOI: 10.1007/s11306-023-02031-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge. AIM This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway. KEY SCIENTIFIC CONCEPTS Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.
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Affiliation(s)
- Zohreh Mirveis
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland.
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological, Health and Sport Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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3
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Richter C, Grafahrend-Belau E, Ziegler J, Raorane ML, Junker BH. Improved 13C metabolic flux analysis in Escherichia coli metabolism: application of a high-resolution MS (GC-EI-QTOF) for comprehensive assessment of MS/MS fragments. J Ind Microbiol Biotechnol 2023; 50:kuad039. [PMID: 37960978 PMCID: PMC10716738 DOI: 10.1093/jimb/kuad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/10/2023] [Indexed: 11/15/2023]
Abstract
Gas chromatography-tandem mass spectrometry with electron ionization (GC-EI-MS/MS) provides rich information on stable-isotope labeling for 13C-metabolic flux analysis (13C-MFA). To pave the way for the routine application of tandem MS data for metabolic flux quantification, we aimed to compile a comprehensive library of GC-EI-MS/MS fragments of tert-butyldimethylsilyl (TBDMS) derivatized proteinogenic amino acids. First, we established an analytical workflow that combines high-resolution gas chromatography-quadrupole time-of-flight mass spectrometry and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments. Application of the high-mass accuracy MS procedure resulted into the identification of 129 validated precursor-product ion pairs of 13 amino acids with 30 fragments being accepted for 13C-MFA. The practical benefit of the novel tandem MS data was demonstrated by a proof-of-concept study, which confirmed the importance of the compiled library for high-resolution 13C-MFA. ONE SENTENCE SUMMARY An analytical workflow that combines high-resolution mass spectrometry (MS) and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments, which provide positional information and therefore offering significant advantages over traditional MS to improve 13C-metabolic flux analysis.
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Affiliation(s)
- Chris Richter
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Hoher Weg 8, D-06120Halle (Saale), Germany
| | - Eva Grafahrend-Belau
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Hoher Weg 8, D-06120Halle (Saale), Germany
| | - Jörg Ziegler
- Leibniz Institute of Plant Biochemistry, Weinberg 3, D-06120Halle (Saale), Germany
| | - Manish L Raorane
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Hoher Weg 8, D-06120Halle (Saale), Germany
| | - Björn H Junker
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Hoher Weg 8, D-06120Halle (Saale), Germany
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4
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Huang L, Drouin N, Causon J, Wegrzyn A, Castro-Perez J, Fleming R, Harms A, Hankemeier T. Reconstruction of Glutathione Metabolism in the Neuronal Model of Rotenone-Induced Neurodegeneration Using Mass Isotopologue Analysis with Hydrophilic Interaction Liquid Chromatography-Zeno High-Resolution Multiple Reaction Monitoring. Anal Chem 2023; 95:3255-3266. [PMID: 36735349 PMCID: PMC9933045 DOI: 10.1021/acs.analchem.2c04231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Accurate reconstruction of metabolic pathways is an important prerequisite for interpreting metabolomics changes and understanding the diverse biological processes in disease models. A tracer-based metabolomics strategy utilizes stable isotope-labeled precursors to resolve complex pathways by tracing the labeled atom(s) to downstream metabolites through enzymatic reactions. Isotope enrichment analysis is informative and achieved by counting total labeled atoms and acquiring the mass isotopologue distribution (MID) of the intact metabolite. However, quantitative analysis of labeled metabolite substructures/moieties (MS2 fragments) can offer more valuable insights into the reaction connections through measuring metabolite transformation. In order to acquire the isotopic labeling information at the intact metabolite and moiety level simultaneously, we developed a method that couples hydrophilic interaction liquid chromatography (HILIC) with Zeno trap-enabled high-resolution multiple reaction monitoring (MRMHR). The method enabled accurate and reproducible MID quantification for intact metabolites as well as their fragmented moieties, with notably high sensitivity in the MS2 fragmentation mode based on the measurement of 13C- or 15N-labeled cellular samples. The method was applied to human-induced pluripotent stem cell-derived neurons to trace the fate of 13C/15N atoms from D-13C6-glucose/L-15N2-glutamine added to the media. With the MID analysis of both intact metabolites and fragmented moieties, we validated the pathway reconstruction of de novo glutathione synthesis in mid-brain neurons. We discovered increased glutathione oxidization from both basal and newly synthesized glutathione pools under neuronal oxidative stress. Furthermore, the significantly decreased de novo glutathione synthesis was investigated and associated with altered activities of several key enzymes, as evidenced by suppressed glutamate supply via glucose metabolism and a diminished flux of glutathione synthetic reaction in the neuronal model of rotenone-induced neurodegeneration.
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Affiliation(s)
- Luojiao Huang
- Metabolomics
and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2333 CC, Netherlands
| | - Nicolas Drouin
- Metabolomics
and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2333 CC, Netherlands
| | | | - Agnieszka Wegrzyn
- Metabolomics
and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2333 CC, Netherlands
| | | | - Ronan Fleming
- Metabolomics
and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2333 CC, Netherlands,School
of Medicine, National University of Ireland, University Rd, Galway H91 TK33, Ireland
| | - Amy Harms
- Metabolomics
and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2333 CC, Netherlands
| | - Thomas Hankemeier
- Metabolomics
and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2333 CC, Netherlands,
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5
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Sheng Q, Yi L, Zhong B, Wu X, Liu L, Zhang B. Shikimic acid biosynthesis in microorganisms: Current status and future direction. Biotechnol Adv 2023; 62:108073. [PMID: 36464143 DOI: 10.1016/j.biotechadv.2022.108073] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/03/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022]
Abstract
Shikimic acid (SA), a hydroaromatic natural product, is used as a chiral precursor for organic synthesis of oseltamivir (Tamiflu®, an antiviral drug). The process of microbial production of SA has recently undergone vigorous development. Particularly, the sustainable construction of recombinant Corynebacterium glutamicum (141.2 g/L) and Escherichia coli (87 g/L) laid a solid foundation for the microbial fermentation production of SA. However, its industrial application is restricted by limitations such as the lack of fermentation tests for industrial-scale and the requirement of growth-limiting factors, antibiotics, and inducers. Therefore, the development of SA biosensors and dynamic molecular switches, as well as genetic modification strategies and optimization of the fermentation process based on omics technology could improve the performance of SA-producing strains. In this review, recent advances in the development of SA-producing strains, including genetic modification strategies, metabolic pathway construction, and biosensor-assisted evolution, are discussed and critically reviewed. Finally, future challenges and perspectives for further reinforcing the development of robust SA-producing strains are predicted, providing theoretical guidance for the industrial production of SA.
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Affiliation(s)
- Qi Sheng
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Lingxin Yi
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Bin Zhong
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Xiaoyu Wu
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
| | - Bin Zhang
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China.
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6
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Zhang R, Chen B, Zhang H, Tu L, Luan T. Stable isotope-based metabolic flux analysis: A robust tool for revealing toxicity pathways of emerging contaminants. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2022.116909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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7
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de Falco B, Giannino F, Carteni F, Mazzoleni S, Kim DH. Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas. RSC Adv 2022; 12:25528-25548. [PMID: 36199351 PMCID: PMC9449821 DOI: 10.1039/d2ra03326g] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/26/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.
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Affiliation(s)
- Bruna de Falco
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
| | - Francesco Giannino
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Fabrizio Carteni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Stefano Mazzoleni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Dong-Hyun Kim
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
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8
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Isotope Calculation Gadgets: A Series of Software for Isotope-Tracing Experiments in Garuda Platform. Metabolites 2022; 12:metabo12070646. [PMID: 35888770 PMCID: PMC9318330 DOI: 10.3390/metabo12070646] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/12/2022] [Indexed: 02/06/2023] Open
Abstract
Isotope tracing is a powerful technique for elucidating intracellular metabolism. Experiments utilizing this technique involve various processes, such as the correction of natural isotopes. Although some previously developed software are available for these procedures, there are still time-consuming steps in isotope tracing including the creation of an isotope measurement method in mass spectrometry (MS) and the interpretation of obtained labeling data. Additionally, these multi-step tasks often require data format conversion, which is also time-consuming. In this study, the Isotope Calculation Gadgets, a series of software that supports an entire workflow of isotope-tracing experiments, was developed in the Garuda platform, an open community. Garuda is a graphical user interface-based platform that allows individual operations to be sequentially performed, without data format conversion, which significantly reduces the required time and effort. The developed software includes new features that construct channels for isotopomer measurements, as well as conventional functions such as natural isotope correction, the calculation of fractional labeling and split ratio, and data mapping, thus facilitating an overall workflow of isotope-tracing experiments through smooth functional integration.
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9
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Lugar DJ, Sriram G. Isotope-assisted metabolic flux analysis as an equality-constrained nonlinear program for improved scalability and robustness. PLoS Comput Biol 2022; 18:e1009831. [PMID: 35324890 PMCID: PMC8947808 DOI: 10.1371/journal.pcbi.1009831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 01/12/2022] [Indexed: 01/11/2023] Open
Abstract
Stable isotope-assisted metabolic flux analysis (MFA) is a powerful method to estimate carbon flow and partitioning in metabolic networks. At its core, MFA is a parameter estimation problem wherein the fluxes and metabolite pool sizes are model parameters that are estimated, via optimization, to account for measurements of steady-state or isotopically-nonstationary isotope labeling patterns. As MFA problems advance in scale, they require efficient computational methods for fast and robust convergence. The structure of the MFA problem enables it to be cast as an equality-constrained nonlinear program (NLP), where the equality constraints are constructed from the MFA model equations, and the objective function is defined as the sum of squared residuals (SSR) between the model predictions and a set of labeling measurements. This NLP can be solved by using an algebraic modeling language (AML) that offers state-of-the-art optimization solvers for robust parameter estimation and superior scalability to large networks. When implemented in this manner, the optimization is performed with no distinction between state variables and model parameters. During each iteration of such an optimization, the system state is updated instead of being calculated explicitly from scratch, and this occurs concurrently with improvement in the model parameter estimates. This optimization approach starkly contrasts with traditional “shooting” methods where the state variables and model parameters are kept distinct and the system state is computed afresh during each iteration of a stepwise optimization. Our NLP formulation uses the MFA modeling framework of Wiechert et al. [1], which is amenable to incorporation of the model equations into an NLP. The NLP constraints consist of balances on either elementary metabolite units (EMUs) or cumomers. In this formulation, both the steady-state and isotopically-nonstationary MFA (inst-MFA) problems may be solved as an NLP. For the inst-MFA case, the ordinary differential equation (ODE) system describing the labeling dynamics is transcribed into a system of algebraic constraints for the NLP using collocation. This large-scale NLP may be solved efficiently using an NLP solver implemented on an AML. In our implementation, we used the reduced gradient solver CONOPT, implemented in the General Algebraic Modeling System (GAMS). The NLP framework is particularly advantageous for inst-MFA, scaling well to large networks with many free parameters, and having more robust convergence properties compared to the shooting methods that compute the system state and sensitivities at each iteration. Additionally, this NLP approach supports the use of tandem-MS data for both steady-state and inst-MFA when the cumomer framework is used. We assembled a software, eiFlux, written in Python and GAMS that uses the NLP approach and supports both steady-state and inst-MFA. We demonstrate the effectiveness of the NLP formulation on several examples, including a genome-scale inst-MFA model, to highlight the scalability and robustness of this approach. In addition to typical inst-MFA applications, we expect that this framework and our associated software, eiFlux, will be particularly useful for applying inst-MFA to complex MFA models, such as those developed for eukaryotes (e.g. algae) and co-cultures with multiple cell types. Isotope-assisted metabolic flux analysis (MFA) is a computationally intensive parameter estimation problem. Isotopically nonstationary MFA (inst-MFA) represents the most computationally burdensome MFA application. We present the formulation of the steady-state and inst-MFA problems as equality-constrained nonlinear programs (NLPs), solved by a state-of-the-art solver implemented in an algebraic modeling language. We show that this formulation leads to robust convergence properties compared to traditional approaches, particularly for inst-MFA. We developed a software, eiFlux that uses the NLP formulation to perform both steady-state and inst-MFA. We demonstrate the application of eiFlux on several examples, including a genome-scale inst-MFA model, and show that it has robust optimal convergence even when started from a very poor initial guess for the parameters. eiFlux is implemented using the Python programming language and the General Algebraic Modeling System (GAMS), using the CONOPT solver. eiFlux is available upon request, pending institutional approval, and is free for academic use.
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Affiliation(s)
- Daniel J. Lugar
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland, United States of America
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland, United States of America
- * E-mail:
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10
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Millard P, Sokol S, Kohlstedt M, Wittmann C, Létisse F, Lippens G, Portais JC. IsoSolve: An Integrative Framework to Improve Isotopic Coverage and Consolidate Isotopic Measurements by Mass Spectrometry and/or Nuclear Magnetic Resonance. Anal Chem 2021; 93:9428-9436. [PMID: 34197087 DOI: 10.1021/acs.analchem.1c01064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Stable-isotope labeling experiments are widely used to investigate the topology and functioning of metabolic networks. Label incorporation into metabolites can be quantified using a broad range of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy methods, but in general, no single approach can completely cover isotopic space, even for small metabolites. The number of quantifiable isotopic species could be increased and the coverage of isotopic space improved by integrating measurements obtained by different methods; however, this approach has remained largely unexplored because no framework able to deal with partial, heterogeneous isotopic measurements has yet been developed. Here, we present a generic computational framework based on symbolic calculus that can integrate any isotopic data set by connecting measurements to the chemical structure of the molecules. As a test case, we apply this framework to isotopic analyses of amino acids, which are ubiquitous to life, central to many biological questions, and can be analyzed by a broad range of MS and NMR methods. We demonstrate how this integrative framework helps to (i) clarify and improve the coverage of isotopic space, (ii) evaluate the complementarity and redundancy of different techniques, (iii) consolidate isotopic data sets, (iv) design experiments, and (v) guide future analytical developments. This framework, which can be applied to any labeled element, isotopic tracer, metabolite, and analytical platform, has been implemented in IsoSolve (available at https://github.com/MetaSys-LISBP/IsoSolve and https://pypi.org/project/IsoSolve), an open-source software that can be readily integrated into data analysis pipelines.
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Affiliation(s)
- Pierre Millard
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse 31077, France.,MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse 31077, France
| | - Serguei Sokol
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse 31077, France.,MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse 31077, France
| | - Michael Kohlstedt
- Institute of Systems Biotechnology, Saarland University, Saarbrücken 66123, Germany
| | - Christoph Wittmann
- Institute of Systems Biotechnology, Saarland University, Saarbrücken 66123, Germany
| | - Fabien Létisse
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse 31077, France.,Université Toulouse III - Paul Sabatier, Toulouse 31077, France
| | - Guy Lippens
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse 31077, France
| | - Jean-Charles Portais
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse 31077, France.,MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse 31077, France.,Université Toulouse III - Paul Sabatier, Toulouse 31077, France.,RESTORE, Université de Toulouse, INSERM U1031, CNRS 5070, Université Toulouse III - Paul Sabatier, EFS, Toulouse 31077, France
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11
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Wang Y, Hui S, Wondisford FE, Su X. Utilizing tandem mass spectrometry for metabolic flux analysis. J Transl Med 2021; 101:423-429. [PMID: 32994481 PMCID: PMC7987671 DOI: 10.1038/s41374-020-00488-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/01/2020] [Accepted: 09/01/2020] [Indexed: 12/23/2022] Open
Abstract
Metabolic flux analysis (MFA) aims at revealing the metabolic reaction rates in a complex biochemical network. To do so, MFA uses the input of stable isotope labeling patterns of the intracellular metabolites. Elementary metabolic unit (EMU) is the computational framework to simulate the metabolite labeling patterns in a network, which was originally designed for simulating mass isotopomer distributions (MIDs) at the MS1 level. Recently, the EMU framework is expanded to simulate tandem mass spectrometry data. Tandem mass spectrometry has emerged as a new experimental approach to provide information on the positional isotope labeling of metabolites and therefore greatly improves the precision of MFA. In this review, we will discuss the new EMU framework that can accommodate the tandem mass isotopomer distributions (TMIDs) data. We will also analyze the improvement on the MFA precision by using TMID. Our analysis shows that combining the MIDs of the parent and daughter ions and the TMID for the MFA is more powerful than using TMID alone.
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Affiliation(s)
- Yujue Wang
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Sheng Hui
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Fredric E Wondisford
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Xiaoyang Su
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
- Metabolomics Shared Resource, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
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12
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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13
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Antoniewicz MR. A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications. Metab Eng 2020; 63:2-12. [PMID: 33157225 DOI: 10.1016/j.ymben.2020.11.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 10/28/2020] [Accepted: 11/01/2020] [Indexed: 12/22/2022]
Abstract
The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. 13C-metabolic flux analysis (13C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.
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Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA.
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14
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Tivendale ND, Hanson AD, Henry CS, Hegeman AD, Millar AH. Enzymes as Parts in Need of Replacement - and How to Extend Their Working Life. TRENDS IN PLANT SCIENCE 2020; 25:661-669. [PMID: 32526171 DOI: 10.1016/j.tplants.2020.02.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 02/11/2020] [Accepted: 02/14/2020] [Indexed: 06/11/2023]
Abstract
Enzymes catalyze reactions in vivo at different rates and each enzyme molecule has a lifetime limit before it is degraded and replaced to enable catalysis to continue. Considering these rates together as a unitless ratio of catalytic cycles until replacement (CCR) provides a new quantitative tool to assess the replacement schedule of and energy investment into enzymes as they relate to function. Here, we outline the challenges of determining CCRs and new approaches to overcome them and then assess the CCRs of selected enzymes in bacteria and plants to reveal a range of seven orders of magnitude for this ratio. Modifying CCRs in plants holds promise to lower cellular costs, to tailor enzymes for particular environments, and to breed enzyme improvements for crop productivity.
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Affiliation(s)
- Nathan D Tivendale
- ARC Centre for Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, M316, Perth, WA 6009, Australia
| | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, PO Box 110690, Gainesville, FL 32611-0690, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA; Computation Institute, The University of Chicago, Chicago, IL 60637, USA
| | - Adrian D Hegeman
- Department of Horticultural Science, Department of Plant and Microbial Biology, and The Microbial and Plant Genomics Institute, University of Minnesota, 1970 Folwell Avenue, Saint Paul, MN 55108-6007, USA
| | - A Harvey Millar
- ARC Centre for Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, M316, Perth, WA 6009, Australia.
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15
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Dange MC, Mishra V, Mukherjee B, Jaiswal D, Merchant MS, Prasannan CB, Wangikar PP. Evaluation of freely available software tools for untargeted quantification of 13C isotopic enrichment in cellular metabolome from HR-LC/MS data. Metab Eng Commun 2019; 10:e00120. [PMID: 31908925 PMCID: PMC6940703 DOI: 10.1016/j.mec.2019.e00120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 12/21/2019] [Accepted: 12/21/2019] [Indexed: 12/31/2022] Open
Abstract
13C Metabolic Flux Analysis (13C-MFA) involves the quantification of isotopic enrichment in cellular metabolites and fitting the resultant data to the metabolic network model of the organism. Coverage and resolution of the resultant flux map depends on the total number of metabolites and fragments in which 13C enrichment can be quantified accurately. Experimental techniques for tracking 13C enrichment are evolving rapidly and large volumes of data are now routinely generated through the use of Liquid Chromatography coupled with High-Resolution Mass Spectrometry (HR-LC/MS). Therefore, the current manuscript is focused on the challenges in high-throughput analyses of such large datasets. Current 13C-MFA studies often have to rely on the targeted quantification of a small subset of metabolites, thereby leaving a large fraction of the data unexplored. A number of public domain software tools have been reported in recent years for the untargeted quantitation of isotopic enrichment. However, the suitability of their application across diverse datasets has not been investigated. Here, we test the software tools X13CMS, DynaMet, geoRge, and HiResTEC with three diverse datasets. The tools provided a global, untargeted view of 13C enrichment in metabolites in all three datasets and a much-needed automation in data analysis. Some inconsistencies were observed in results obtained from the different tools, which could be partially ascribed to the lack of baseline separation and potential mass conflicts. After removing the false positives manually, isotopic enrichment could be quantified reliably in a large repertoire of metabolites. Of the software tools explored, geoRge and HiResTEC consistently performed well for the untargeted analysis of all datasets tested.
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Affiliation(s)
- Manohar C Dange
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 40076, India
| | - Vivek Mishra
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 40076, India
| | - Bratati Mukherjee
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 40076, India.,DBT-Pan IIT Center for Bioenergy, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Damini Jaiswal
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 40076, India
| | - Murtaza S Merchant
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 40076, India
| | - Charulata B Prasannan
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 40076, India.,DBT-Pan IIT Center for Bioenergy, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Pramod P Wangikar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 40076, India.,DBT-Pan IIT Center for Bioenergy, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India.,Wadhwani Research Center for Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
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16
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Kappelmann J, Beyß M, Nöh K, Noack S. Separation of 13C- and 15N-Isotopologues of Amino Acids with a Primary Amine without Mass Resolution by Means of O-Phthalaldehyde Derivatization and Collision Induced Dissociation. Anal Chem 2019; 91:13407-13417. [PMID: 31577133 DOI: 10.1021/acs.analchem.9b01788] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Computational and experimental advances of recent years have culminated in establishing 13C-Metabolic Flux Analysis (13C-MFA) as a routine methodology to unravel the fluxome. As the acronym suggests, 13C-MFA has relied on the relative abundance of 13C-isotopes in metabolites for flux inference, most commonly measured by mass spectrometry. In this manuscript we expand the scope of labeling measurements to the case of simultaneous 13C- and 15N-labeling of amino acids. Analytically, the separation of isotopologues of this metabolite class can only be achieved at resolving power beyond 65,000. In this manuscript we harvest an overlooked property of the collision induced dissociation of amino acid adducts to discern 13C- and 15N- isotopologues of amino acids with a primary amine without separating them in the m/z domain.
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Affiliation(s)
- Jannick Kappelmann
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
| | - Martin Beyß
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany.,Bioeconomy Science Center (BioSC) , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
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17
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Sun X, Heinrich P, Berger RS, Oefner PJ, Dettmer K. Quantification and 13C-Tracer analysis of total reduced glutathione by HPLC-QTOFMS/MS. Anal Chim Acta 2019; 1080:127-137. [DOI: 10.1016/j.aca.2019.07.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 06/18/2019] [Accepted: 07/01/2019] [Indexed: 12/21/2022]
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18
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Choi J, Antoniewicz MR. Tandem Mass Spectrometry for 13C Metabolic Flux Analysis: Methods and Algorithms Based on EMU Framework. Front Microbiol 2019; 10:31. [PMID: 30733712 PMCID: PMC6353858 DOI: 10.3389/fmicb.2019.00031] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/09/2019] [Indexed: 02/01/2023] Open
Abstract
In the past two decades, 13C metabolic flux analysis (13C-MFA) has matured into a powerful and widely used scientific tool in metabolic engineering and systems biology. Traditionally, metabolic fluxes have been determined from measurements of isotopic labeling by means of mass spectrometry (MS) or nuclear magnetic resonance (NMR). In recent years, tandem MS has emerged as a new analytical technique that can provide additional information for high-resolution quantification of metabolic fluxes in complex biological systems. In this paper, we present recent advances in methods and algorithms for incorporating tandem MS measurements into existing 13C-MFA approaches that are based on the elementary metabolite units (EMU) framework. Specifically, efficient EMU-based algorithms are presented for simulating tandem MS data, tracing isotopic labeling in biochemical network models and for correcting tandem MS data for natural isotope abundances.
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Affiliation(s)
- Jungik Choi
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, United States
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, United States
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19
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Jaiswal D, Prasannan CB, Hendry JI, Wangikar PP. SWATH Tandem Mass Spectrometry Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic 13C Carbon. Anal Chem 2018; 90:6486-6493. [PMID: 29712418 DOI: 10.1021/acs.analchem.7b05329] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Accurate quantification of mass isotopologue distribution (MID) of metabolites is a prerequisite for 13C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run. Moreover, the deconvolution step results in amplification of error. Here, we demonstrate that SWATH MS/MS, a data independent acquisition (DIA) technique allows quantification of a large number of precursor and product MIDs in a single run. SWATH sequentially fragments all precursor ions in stacked mass isolation windows. Co-fragmentation of all precursor isotopologues in a single SWATH window yields higher sensitivity enabling quantification of MIDs of fragments with low abundance and lower systematic and random errors. We quantify the MIDs of 53 precursor and product ions corresponding to 19 intracellular metabolites from a dynamic 13C-labeling of a model cyanobacterium, Synechococcus sp. PCC 7002. The use of product MIDs resulted in an improved precision of many measured fluxes compared to when only precursor MIDs were used for flux analysis. The approach is truly untargeted and allows additional metabolites to be quantified from the same data.
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Affiliation(s)
- Damini Jaiswal
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
| | - Charulata B Prasannan
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India.,DBT-Pan IIT Center for Bioenergy , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
| | - John I Hendry
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
| | - Pramod P Wangikar
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India.,DBT-Pan IIT Center for Bioenergy , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India.,Wadhwani Research Center for Bioengineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
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20
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Golubeva LI, Shupletsov MS, Mashko SV. Metabolic Flux Analysis using 13C Isotopes: III. Significance for Systems Biology and Metabolic Engineering. APPL BIOCHEM MICRO+ 2018. [DOI: 10.1134/s0003683817090058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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21
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Li H, Liang C, Chen W, Jin JM, Tang SY, Tao Y. Monitoring in vivo metabolic flux with a designed whole-cell metabolite biosensor of shikimic acid. Biosens Bioelectron 2017; 98:457-465. [DOI: 10.1016/j.bios.2017.07.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 06/01/2017] [Accepted: 07/08/2017] [Indexed: 01/24/2023]
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22
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Pandey R, Caflisch L, Lodi A, Brenner AJ, Tiziani S. Metabolomic signature of brain cancer. Mol Carcinog 2017; 56:2355-2371. [PMID: 28618012 DOI: 10.1002/mc.22694] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 06/01/2017] [Accepted: 06/13/2017] [Indexed: 12/17/2022]
Abstract
Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.
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Affiliation(s)
- Renu Pandey
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas
| | - Laura Caflisch
- Department of Hematology and Medical oncology, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Alessia Lodi
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas
| | - Andrew J Brenner
- Department of Hematology and Medical oncology, University of Texas Health Science Center at San Antonio, San Antonio, Texas.,Department of Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Stefano Tiziani
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas.,Dell Pediatric Research Institute, The University of Texas at Austin, Austin, Texas
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23
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Mairinger T, Hann S. Implementation of data-dependent isotopologue fragmentation in 13C-based metabolic flux analysis. Anal Bioanal Chem 2017; 409:3713-3718. [PMID: 28389915 PMCID: PMC5427153 DOI: 10.1007/s00216-017-0339-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 03/23/2017] [Indexed: 12/30/2022]
Abstract
A novel analytical approach based on liquid chromatography coupled to quadrupole time of flight mass spectrometry, employing data-dependent triggering for analysis of isotopologue and tandem mass isotopomer fractions of metabolites of the primary carbon metabolism was developed. The implemented QTOFMS method employs automated MS/MS triggering of higher abundant, biologically relevant isotopologues for generating positional information of the respective metabolite. Using this advanced isotopologue selective fragmentation approach enables the generation of significant tandem mass isotopomer data within a short cycle time without compromising sensitivity. Due to a lack of suitable reference material certified for isotopologue ratios, a Pichia pastoris cell extract with a defined 13C distribution as well as a cell extract from a 13C-based metabolic flux experiment were employed for proof of concept. Moreover, a method inter-comparison with an already established GC-CI-(Q)TOFMS approach was conducted. Both methods showed good agreement on isotopologue and tandem mass isotopomer distributions for the two different cell extracts. Graphical abstract Schematic overview of data-dependent isotopologue fragmentation for acquisition of isotopologue and tandem mass isotopomer fractions.
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Affiliation(s)
- Teresa Mairinger
- Department of Chemistry, University of Natural Resources and Life Sciences - BOKU Vienna, Muthgasse 18, 1190, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Muthgasse 18, 1190, Vienna, Austria
| | - Stephan Hann
- Department of Chemistry, University of Natural Resources and Life Sciences - BOKU Vienna, Muthgasse 18, 1190, Vienna, Austria.
- Austrian Centre of Industrial Biotechnology, Muthgasse 18, 1190, Vienna, Austria.
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24
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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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25
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Fernández-Fernández M, Rodríguez-González P, Hevia Sánchez D, González-Menéndez P, Sainz Menéndez RM, García Alonso JI. Accurate and sensitive determination of molar fractions of 13C-Labeled intracellular metabolites in cell cultures grown in the presence of isotopically-labeled glucose. Anal Chim Acta 2017; 969:35-48. [PMID: 28411628 DOI: 10.1016/j.aca.2017.03.019] [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: 10/28/2016] [Revised: 03/09/2017] [Accepted: 03/16/2017] [Indexed: 01/27/2023]
Abstract
This work describes a methodology based on multiple linear regression and GC-MS for the determination of molar fractions of isotopically-labeled intracellular metabolites in cell cultures. Novel aspects of this work are: i) the calculation of theoretical isotopic distributions of the different isotopologues from an experimentally measured value of % 13C enrichment of the labeled precursor ii) the calculation of the contribution of lack of mass resolution of the mass spectrometer and different fragmentation mechanism such as the loss or gain of hydrogen atoms in the EI source to measure the purity of the selected cluster for each metabolite and iii) the validation of the methodology not only by the analysis of gravimetrically prepared mixtures of isotopologues but also by the comparison of the obtained molar fractions with experimental values obtained by GC-Combustion-IRMS based on 13C/12C isotope ratio measurements. The method is able to measure molar fractions for twenty-eight intracellular metabolites derived from glucose metabolism in cell cultures grown in the presence of 13C-labeled Glucose. The validation strategies demonstrate a satisfactory accuracy and precision of the proposed procedure. Also, our results show that the minimum value of 13C incorporation that can be accurately quantified is significantly influenced by the calculation of the spectral purity of the measured cluster and the number of 13C atoms of the labeled precursor. The proposed procedure was able to accurately quantify gravimetrically prepared mixtures of natural and labeled glucose molar fractions of 0.07% and mixtures of natural and labeled glycine at molar fractions down to 0.7%. The method was applied to initial studies of glucose metabolism of different prostate cancer cell lines.
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Affiliation(s)
- Mario Fernández-Fernández
- Department of Physical and Analytical Chemistry, Faculty of Chemistry, University of Oviedo, Julián Clavería 8, 33006 Oviedo, Spain
| | - Pablo Rodríguez-González
- Department of Physical and Analytical Chemistry, Faculty of Chemistry, University of Oviedo, Julián Clavería 8, 33006 Oviedo, Spain.
| | - David Hevia Sánchez
- University Institute of Oncology (IUOPA), University of Oviedo, Julián Clavería 6, 33006 Oviedo, Spain
| | - Pedro González-Menéndez
- University Institute of Oncology (IUOPA), University of Oviedo, Julián Clavería 6, 33006 Oviedo, Spain
| | - Rosa M Sainz Menéndez
- University Institute of Oncology (IUOPA), University of Oviedo, Julián Clavería 6, 33006 Oviedo, Spain
| | - J Ignacio García Alonso
- Department of Physical and Analytical Chemistry, Faculty of Chemistry, University of Oviedo, Julián Clavería 8, 33006 Oviedo, Spain
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26
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Kappelmann J, Klein B, Geilenkirchen P, Noack S. Comprehensive and accurate tracking of carbon origin of LC-tandem mass spectrometry collisional fragments for 13C-MFA. Anal Bioanal Chem 2017; 409:2309-2326. [PMID: 28116490 PMCID: PMC5477699 DOI: 10.1007/s00216-016-0174-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 12/03/2016] [Accepted: 12/21/2016] [Indexed: 01/20/2023]
Abstract
In recent years the benefit of measuring positionally resolved 13C-labeling enrichment from tandem mass spectrometry (MS/MS) collisional fragments for improved precision of 13C-Metabolic Flux Analysis (13C-MFA) has become evident. However, the usage of positional labeling information for 13C-MFA faces two challenges: (1) The mass spectrometric acquisition of a large number of potentially interfering mass transitions may hamper accuracy and sensitivity. (2) The positional identity of carbon atoms of product ions needs to be known. The present contribution addresses the latter challenge by deducing the maximal positional labeling information contained in LC-ESI-MS/MS spectra of product anions of central metabolism as well as product cations of amino acids. For this purpose, we draw on accurate mass spectrometry, selectively labeled standards, and published fragmentation pathways to structurally annotate all dominant mass peaks of a large collection of metabolites, some of which with a complete fragmentation pathway. Compiling all available information, we arrive at the most detailed map of carbon atom fate of LC-ESI-MS/MS collisional fragments yet, comprising 170 intense and structurally annotated product ions with unique carbon origin from 76 precursor ions of 72 metabolites. Our 13C-data proof that heuristic fragmentation rules often fail to yield correct fragment structures and we expose common pitfalls in the structural annotation of product ions. We show that the positionally resolved 13C-label information contained in the product ions that we structurally annotated allows to infer the entire isotopomer distribution of several central metabolism intermediates, which is experimentally demonstrated for malate using quadrupole-time-of-flight MS technology. Finally, the inclusion of the label information from a subset of these fragments improves flux precision in a Corynebacterium glutamicum model of the central carbon metabolism.
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Affiliation(s)
- Jannick Kappelmann
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Bianca Klein
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Petra Geilenkirchen
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.
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27
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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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28
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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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29
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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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30
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Allen DK. Assessing compartmentalized flux in lipid metabolism with isotopes. Biochim Biophys Acta Mol Cell Biol Lipids 2016; 1861:1226-1242. [PMID: 27003250 DOI: 10.1016/j.bbalip.2016.03.017] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2016] [Revised: 03/13/2016] [Accepted: 03/14/2016] [Indexed: 12/28/2022]
Abstract
Metabolism in plants takes place across multiple cell types and within distinct organelles. The distributions equate to spatial heterogeneity; though the limited means to experimentally assess metabolism frequently involve homogenizing tissues and mixing metabolites from different locations. Most current isotope investigations of metabolism therefore lack the ability to resolve spatially distinct events. Recognition of this limitation has resulted in inspired efforts to advance metabolic flux analysis and isotopic labeling techniques. Though a number of these efforts have been applied to studies in central metabolism; recent advances in instrumentation and techniques present an untapped opportunity to make similar progress in lipid metabolism where the use of stable isotopes has been more limited. These efforts will benefit from sophisticated radiolabeling reports that continue to enrich our knowledge on lipid biosynthetic pathways and provide some direction for stable isotope experimental design and extension of MFA. Evidence for this assertion is presented through the review of several elegant stable isotope studies and by taking stock of what has been learned from radioisotope investigations when spatial aspects of metabolism were considered. The studies emphasize that glycerolipid production occurs across several locations with assembly of lipids in the ER or plastid, fatty acid biosynthesis occurring in the plastid, and the generation of acetyl-CoA and glycerol-3-phosphate taking place at multiple sites. Considering metabolism in this context underscores the cellular and subcellular organization that is important to enhanced production of glycerolipids in plants. An attempt is made to unify salient features from a number of reports into a diagrammatic model of lipid metabolism and propose where stable isotope labeling experiments and further flux analysis may help address questions in the field. This article is part of a Special Issue entitled: Plant Lipid Biology edited by Kent D. Chapman and Ivo Feussner.
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Affiliation(s)
- Doug K Allen
- United States Department of Agriculture, Agricultural Research Service, 975 North Warson Road, St. Louis, MO 63132, United States; Donald Danforth Plant Science Center, 975 North Warson Road, St. Louis, MO 63132, United States.
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31
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Reardon PN, Marean-Reardon CL, Bukovec MA, Coggins BE, Isern NG. 3D TOCSY-HSQC NMR for Metabolic Flux Analysis Using Non-Uniform Sampling. Anal Chem 2016; 88:2825-31. [PMID: 26849182 DOI: 10.1021/acs.analchem.5b04535] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
(13)C-Metabolic Flux Analysis ((13)C-MFA) is rapidly being recognized as the authoritative method for determining fluxes through metabolic networks. Site-specific (13)C enrichment information obtained using NMR spectroscopy is a valuable input for (13)C-MFA experiments. Chemical shift overlaps in the 1D or 2D NMR experiments typically used for (13)C-MFA frequently hinder assignment and quantitation of site-specific (13)C enrichment. Here we propose the use of a 3D TOCSY-HSQC experiment for (13)C-MFA. We employ Non-Uniform Sampling (NUS) to reduce the acquisition time of the experiment to a few hours, making it practical for use in (13)C-MFA experiments. Our data show that the NUS experiment is linear and quantitative. Identification of metabolites in complex mixtures, such as a biomass hydrolysate, is simplified by virtue of the (13)C chemical shift obtained in the experiment. In addition, the experiment reports (13)C-labeling information that reveals the position specific labeling of subsets of isotopomers. The information provided by this technique will enable more accurate estimation of metabolic fluxes in large metabolic networks.
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Affiliation(s)
- P N Reardon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , 3335 Innovation Boulevard, Richland, Washington 99352, United States
| | - C L Marean-Reardon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , 3335 Innovation Boulevard, Richland, Washington 99352, United States.,Department of Environmental Sciences, Washington State University , Richland, Washington 99354, United States
| | - M A Bukovec
- Department of Chemical, Paper and Biomedical Engineering, Miami University , Oxford, Ohio 45056, United States
| | - B E Coggins
- Department of Biochemistry, Duke University Medical Center , Durham, North Carolina 27710, United States
| | - N G Isern
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , 3335 Innovation Boulevard, Richland, Washington 99352, United States
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32
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McCloskey D, Young JD, Xu S, Palsson BO, Feist AM. MID Max: LC-MS/MS Method for Measuring the Precursor and Product Mass Isotopomer Distributions of Metabolic Intermediates and Cofactors for Metabolic Flux Analysis Applications. Anal Chem 2015; 88:1362-70. [PMID: 26666286 DOI: 10.1021/acs.analchem.5b03887] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The analytical challenges to acquire accurate isotopic data of intracellular metabolic intermediates for stationary, nonstationary, and dynamic metabolic flux analysis (MFA) are numerous. This work presents MID Max, a novel LC-MS/MS workflow, acquisition, and isotopomer deconvolution method for MFA that takes advantage of additional scan types that maximizes the number of mass isotopomer distributions (MIDs) that can be acquired in a given experiment. The analytical method was found to measure the MIDs of 97 metabolites, corresponding to 74 unique metabolite-fragment pairs (32 precursor spectra and 42 product spectra) with accuracy and precision. The compounds measured included metabolic intermediates in central carbohydrate metabolism and cofactors of peripheral metabolism (e.g., ATP). Using only a subset of the acquired MIDs, the method was found to improve the precision of flux estimations and number of resolved exchange fluxes for wild-type E. coli compared to traditional methods and previously published data sets.
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Affiliation(s)
- Douglas McCloskey
- Department of Bioengineering, University of California, San Diego , 9500 Gilman Drive, La Jolla, California 92093, United States
| | | | - Sibei Xu
- Department of Bioengineering, University of California, San Diego , 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego , 9500 Gilman Drive, La Jolla, 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 , 9500 Gilman Drive, La Jolla, California 92093, United States.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , 2800 Lyngby, Denmark
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33
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Niedenführ S, ten Pierick A, van Dam PTN, Suarez-Mendez CA, Nöh K, Wahl SA. Natural isotope correction of MS/MS measurements for metabolomics and (13)C fluxomics. Biotechnol Bioeng 2015; 113:1137-47. [PMID: 26479486 DOI: 10.1002/bit.25859] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 09/08/2015] [Accepted: 10/12/2015] [Indexed: 11/09/2022]
Abstract
Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of (13)C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS-type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC-MS(/MS) requires derivatization for the usually non-volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full use of LC- and GC-MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; (13)C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation.
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Affiliation(s)
- Sebastian Niedenführ
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Angela ten Pierick
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands
| | - Patricia T N van Dam
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands
| | - Camilo A Suarez-Mendez
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands. .,Departamento de Procesos y Energia, Universidad Nacional de Colombia, Carrera 80 No. 65-223, Blq. M3, Medellin, Colombia.
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
| | - S Aljoscha Wahl
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands.
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34
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Efficient Modeling of MS/MS Data for Metabolic Flux Analysis. PLoS One 2015; 10:e0130213. [PMID: 26230524 PMCID: PMC4521746 DOI: 10.1371/journal.pone.0130213] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 05/16/2015] [Indexed: 01/25/2023] Open
Abstract
Metabolic flux analysis (MFA) is a widely used method for quantifying intracellular metabolic fluxes. It works by feeding cells with isotopic labeled nutrients, measuring metabolite isotopic labeling, and computationally interpreting the measured labeling data to estimate flux. Tandem mass-spectrometry (MS/MS) has been shown to be useful for MFA, providing positional isotopic labeling data. Specifically, MS/MS enables the measurement of a metabolite tandem mass-isotopomer distribution, representing the abundance in which certain parent and product fragments of a metabolite have different number of labeled atoms. However, a major limitation in using MFA with MS/MS data is the lack of a computationally efficient method for simulating such isotopic labeling data. Here, we describe the tandemer approach for efficiently computing metabolite tandem mass-isotopomer distributions in a metabolic network, given an estimation of metabolic fluxes. This approach can be used by MFA to find optimal metabolic fluxes, whose induced metabolite labeling patterns match tandem mass-isotopomer distributions measured by MS/MS. The tandemer approach is applied to simulate MS/MS data in a small-scale metabolic network model of mammalian methionine metabolism and in a large-scale metabolic network model of E. coli. It is shown to significantly improve the running time by between two to three orders of magnitude compared to the state-of-the-art, cumomers approach. We expect the tandemer approach to promote broader usage of MS/MS technology in metabolic flux analysis. Implementation is freely available at www.cs.technion.ac.il/~tomersh/methods.html
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35
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Allen DK, Bates PD, Tjellström H. Tracking the metabolic pulse of plant lipid production with isotopic labeling and flux analyses: Past, present and future. Prog Lipid Res 2015; 58:97-120. [PMID: 25773881 DOI: 10.1016/j.plipres.2015.02.002] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Revised: 01/30/2015] [Accepted: 02/11/2015] [Indexed: 11/25/2022]
Abstract
Metabolism is comprised of networks of chemical transformations, organized into integrated biochemical pathways that are the basis of cellular operation, and function to sustain life. Metabolism, and thus life, is not static. The rate of metabolites transitioning through biochemical pathways (i.e., flux) determines cellular phenotypes, and is constantly changing in response to genetic or environmental perturbations. Each change evokes a response in metabolic pathway flow, and the quantification of fluxes under varied conditions helps to elucidate major and minor routes, and regulatory aspects of metabolism. To measure fluxes requires experimental methods that assess the movements and transformations of metabolites without creating artifacts. Isotopic labeling fills this role and is a long-standing experimental approach to identify pathways and quantify their metabolic relevance in different tissues or under different conditions. The application of labeling techniques to plant science is however far from reaching it potential. In light of advances in genetics and molecular biology that provide a means to alter metabolism, and given recent improvements in instrumentation, computational tools and available isotopes, the use of isotopic labeling to probe metabolism is becoming more and more powerful. We review the principal analytical methods for isotopic labeling with a focus on seminal studies of pathways and fluxes in lipid metabolism and carbon partitioning through central metabolism. Central carbon metabolic steps are directly linked to lipid production by serving to generate the precursors for fatty acid biosynthesis and lipid assembly. Additionally some of the ideas for labeling techniques that may be most applicable for lipid metabolism in the future were originally developed to investigate other aspects of central metabolism. We conclude by describing recent advances that will play an important future role in quantifying flux and metabolic operation in plant tissues.
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Affiliation(s)
- Doug K Allen
- United States Department of Agriculture, Agricultural Research Service, 975 North Warson Road, St. Louis, MO 63132, United States; Donald Danforth Plant Science Center, 975 North Warson Road, St. Louis, MO 63132, United States.
| | - Philip D Bates
- Department of Chemistry and Biochemistry, University of Southern Mississippi, Hattiesburg, MS 39406, United States
| | - Henrik Tjellström
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, United States; Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824, United States
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36
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Abstract
Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.
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Affiliation(s)
- Kelli M Sas
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | | | - Subramaniam Pennathur
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
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37
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Lien SK, Niedenführ S, Sletta H, Nöh K, Bruheim P. Fluxome study of Pseudomonas fluorescens reveals major reorganisation of carbon flux through central metabolic pathways in response to inactivation of the anti-sigma factor MucA. BMC SYSTEMS BIOLOGY 2015; 9:6. [PMID: 25889900 PMCID: PMC4351692 DOI: 10.1186/s12918-015-0148-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 01/27/2015] [Indexed: 11/25/2022]
Abstract
Background The bacterium Pseudomonas fluorescens switches to an alginate-producing phenotype when the pleiotropic anti-sigma factor MucA is inactivated. The inactivation is accompanied by an increased biomass yield on carbon sources when grown under nitrogen-limited chemostat conditions. A previous metabolome study showed significant changes in the intracellular metabolite concentrations, especially of the nucleotides, in mucA deletion mutants compared to the wild-type. In this study, the P. fluorescens SBW25 wild-type and an alginate non-producing mucA- ΔalgC double-knockout mutant are investigated through model-based 13C-metabolic flux analysis (13C-MFA) to explore the physiological consequences of MucA inactivation at the metabolic flux level. Intracellular metabolite extracts from three carbon labelling experiments using fructose as the sole carbon source are analysed for 13C-label incorporation in primary metabolites by gas and liquid chromatography tandem mass spectrometry. Results From mass isotopomer distribution datasets, absolute intracellular metabolic reaction rates for the wild type and the mutant are determined, revealing extensive reorganisation of carbon flux through central metabolic pathways in response to MucA inactivation. The carbon flux through the Entner-Doudoroff pathway was reduced in the mucA- ΔalgC mutant, while flux through the pentose phosphate pathway was increased. Our findings also indicated flexibility of the anaplerotic reactions through down-regulation of the pyruvate shunt in the mucA- ΔalgC mutant and up-regulation of the glyoxylate shunt. Conclusions Absolute metabolic fluxes and metabolite levels give detailed, integrated insight into the physiology of this industrially, medically and agriculturally important bacterial species and suggest that the most efficient way of using a mucA- mutant as a cell factory for alginate production would be to use non-growing conditions and nitrogen deprivation. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0148-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Stina K Lien
- Department of Biotechnology, Norwegian University of Science and Technology, Sem Sælands vei 6/8, N-7491, Trondheim, Norway.
| | - Sebastian Niedenführ
- Institute of Bio- and Geosciences IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425, Jülich, Germany.
| | - Håvard Sletta
- Department of Bioprocess technology, SINTEF Materials and Chemistry, Sem Sælands vei 2a, N-7465, Trondheim, Norway.
| | - Katharina Nöh
- Institute of Bio- and Geosciences IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425, Jülich, Germany.
| | - Per Bruheim
- Department of Biotechnology, Norwegian University of Science and Technology, Sem Sælands vei 6/8, N-7491, Trondheim, Norway.
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38
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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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39
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Kerkhoven EJ, Lahtvee PJ, Nielsen J. Applications of computational modeling in metabolic engineering of yeast. FEMS Yeast Res 2015; 15:1-13. [PMID: 25156867 DOI: 10.1111/1567-1364.12199] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 05/28/2014] [Accepted: 08/19/2014] [Indexed: 12/13/2022] Open
Abstract
Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.
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Affiliation(s)
- Eduard J Kerkhoven
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Petri-Jaan Lahtvee
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden .,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
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40
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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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41
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Sá JV, Duarte TM, Carrondo MJT, Alves PM, Teixeira AP. Metabolic Flux Analysis: A Powerful Tool in Animal Cell Culture. CELL ENGINEERING 2015. [DOI: 10.1007/978-3-319-10320-4_16] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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42
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Miranda-Santos I, Gramacho S, Pineiro M, Martinez-Gomez K, Fritz M, Hollemeyer K, Salvador A, Heinzle E. Mass Isotopomer Analysis of Nucleosides Isolated from RNA and DNA Using GC/MS. Anal Chem 2014; 87:617-23. [DOI: 10.1021/ac503305w] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Ines Miranda-Santos
- Department
of Life Sciences, Faculty of Science and Technology, University of Coimbra, Coimbra 3000-456, Portugal
- CNC
− Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3004-504, Portugal
- Biochemical
Engineering Institute, University of Saarland, Saarbrücken, Saarland 66123, Germany
| | - Silvia Gramacho
- Department
of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra 3004-535, Portugal
| | - Marta Pineiro
- Department
of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra 3004-535, Portugal
| | - Karla Martinez-Gomez
- Biochemical
Engineering Institute, University of Saarland, Saarbrücken, Saarland 66123, Germany
| | - Michel Fritz
- Biochemical
Engineering Institute, University of Saarland, Saarbrücken, Saarland 66123, Germany
| | - Klaus Hollemeyer
- Biochemical
Engineering Institute, University of Saarland, Saarbrücken, Saarland 66123, Germany
| | - Armindo Salvador
- CNC
− Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3004-504, Portugal
| | - Elmar Heinzle
- Biochemical
Engineering Institute, University of Saarland, Saarbrücken, Saarland 66123, Germany
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43
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Rennenberg H, Herschbach C. A detailed view on sulphur metabolism at the cellular and whole-plant level illustrates challenges in metabolite flux analyses. JOURNAL OF EXPERIMENTAL BOTANY 2014; 65:5711-24. [PMID: 25124317 DOI: 10.1093/jxb/eru315] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Understanding the dynamics of physiological process in the systems biology era requires approaches at the genome, transcriptome, proteome, and metabolome levels. In this context, metabolite flux experiments have been used in mapping metabolite pathways and analysing metabolic control. In the present review, sulphur metabolism was taken to illustrate current challenges of metabolic flux analyses. At the cellular level, restrictions in metabolite flux analyses originate from incomplete knowledge of the compartmentation network of metabolic pathways. Transport of metabolites through membranes is usually not considered in flux experiments but may be involved in controlling the whole pathway. Hence, steady-state and snapshot readings need to be expanded to time-course studies in combination with compartment-specific metabolite analyses. Because of species-specific differences, differences between tissues, and stress-related responses, the quantitative significance of different sulphur sinks has to be elucidated; this requires the development of methods for whole-sulphur metabolome approaches. Different cell types can contribute to metabolite fluxes to different extents at the tissue and organ level. Cell type-specific analyses are needed to characterize these contributions. Based on such approaches, metabolite flux analyses can be expanded to the whole-plant level by considering long-distance transport and, thus, the interaction of roots and the shoot in metabolite fluxes. However, whole-plant studies need detailed empirical and mathematical modelling that have to be validated by experimental analyses.
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Affiliation(s)
- Heinz Rennenberg
- Institute of Forest Sciences, Chair of Tree Physiology, University of Freiburg, Georges-Koehler-Allee 53, 79110 Freiburg, Germany Centre for Biosystems Analysis (ZBSA), University of Freiburg, Habsburgerstrasse 49, 79104 Freiburg, Germany
| | - Cornelia Herschbach
- Institute of Forest Sciences, Chair of Tree Physiology, University of Freiburg, Georges-Koehler-Allee 53, 79110 Freiburg, Germany
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44
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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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45
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Nöh K, Droste P, Wiechert W. Visual workflows for 13 C-metabolic flux analysis. Bioinformatics 2014; 31:346-54. [DOI: 10.1093/bioinformatics/btu585] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
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46
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Kummitha CM, Kalhan SC, Saidel GM, Lai N. Relating tissue/organ energy expenditure to metabolic fluxes in mouse and human: experimental data integrated with mathematical modeling. Physiol Rep 2014; 2:2/9/e12159. [PMID: 25263208 PMCID: PMC4270223 DOI: 10.14814/phy2.12159] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Mouse models of human diseases are used to study the metabolic and physiological processes leading to altered whole-body energy expenditure (EE), which is the sum of EE of all body organs and tissues. Isotopic techniques, arterio-venous difference of substrates, oxygen, and blood flow measurements can provide essential information to quantify tissue/organ EE and substrate oxidation. To complement and integrate experimental data, quantitative mathematical model analyses have been applied in the design of experiments and evaluation of metabolic fluxes. In this study, a method is presented to quantify the energy expenditure of the main mouse organs using metabolic flux measurements. The metabolic fluxes and substrate utilization of the main metabolic pathways of energy metabolism in the mouse tissue/organ systems and the whole body are quantified using a mathematical model based on mass and energy balances. The model is composed of six organ/tissue compartments: brain, heart, liver, gastrointestinal tract, muscle, and adipose tissue. Each tissue/organ is described with a distinct system of metabolic reactions. This model quantifies metabolic and energetic characteristics of mice under overnight fasting conditions. The steady-state mass balances of metabolites and energy balances of carbohydrate and fat are integrated with available experimental data to calculate metabolic fluxes, substrate utilization, and oxygen consumption in each tissue/organ. The model serves as a paradigm for designing experiments with the minimal reliable measurements necessary to quantify tissue/organs fluxes and to quantify the contributions of tissue/organ EE to whole-body EE that cannot be easily determined currently.
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Affiliation(s)
- China M Kummitha
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Satish C Kalhan
- Department of Pathobiology, Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio, USA
| | - Gerald M Saidel
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Nicola Lai
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio, USA
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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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48
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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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49
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Nargund S, Sriram G. Mathematical modeling of isotope labeling experiments for metabolic flux analysis. Methods Mol Biol 2014; 1083:109-131. [PMID: 24218213 DOI: 10.1007/978-1-62703-661-0_8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Isotope labeling experiments (ILEs) offer a powerful methodology to perform metabolic flux analysis. However, the task of interpreting data from these experiments to evaluate flux values requires significant mathematical modeling skills. Toward this, this chapter provides background information and examples to enable the reader to (1) model metabolic networks, (2) simulate ILEs, and (3) understand the optimization and statistical methods commonly used for flux evaluation. A compartmentalized model of plant glycolysis and pentose phosphate pathway illustrates the reconstruction of a typical metabolic network, whereas a simpler example network illustrates the underlying metabolite and isotopomer balancing techniques. We also discuss the salient features of commonly used flux estimation software 13CFLUX2, Metran, NMR2Flux+, FiatFlux, and OpenFLUX. Furthermore, we briefly discuss methods to improve flux estimates. A graphical checklist at the end of the chapter provides a reader a quick reference to the mathematical modeling concepts and resources.
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50
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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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