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Matsuda F, Maeda K, Taniguchi T, Kondo Y, Yatabe F, Okahashi N, Shimizu H. mfapy: An open-source Python package for 13C-based metabolic flux analysis. Metab Eng Commun 2021; 13:e00177. [PMID: 34354925 PMCID: PMC8322459 DOI: 10.1016/j.mec.2021.e00177] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/01/2021] [Accepted: 07/05/2021] [Indexed: 11/28/2022] Open
Abstract
13C-based metabolic flux analysis (13C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In 13C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy). An open-source Python package, mfapy, is developed for 13C-MFA. mfapy enables users to write Python codes for data analysis procedures of 13C-MFA. mfapy has a flexibility and extensibility to support various data analysis procedures. Computer simulations of 13C-MFA experiments is supported for experimental design.
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Affiliation(s)
- Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kousuke Maeda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Takeo Taniguchi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yuya Kondo
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Futa Yatabe
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
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2
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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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Berry D, Loy A. Stable-Isotope Probing of Human and Animal Microbiome Function. Trends Microbiol 2018; 26:999-1007. [PMID: 30001854 PMCID: PMC6249988 DOI: 10.1016/j.tim.2018.06.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 06/10/2018] [Accepted: 06/20/2018] [Indexed: 12/30/2022]
Abstract
Humans and animals host diverse communities of microorganisms important to their physiology and health. Despite extensive sequencing-based characterization of host-associated microbiomes, there remains a dramatic lack of understanding of microbial functions. Stable-isotope probing (SIP) is a powerful strategy to elucidate the ecophysiology of microorganisms in complex host-associated microbiotas. Here, we suggest that SIP methodologies should be more frequently exploited as part of a holistic functional microbiomics approach. We provide examples of how SIP has been used to study host-associated microbes in vivo and ex vivo. We highlight recent developments in SIP technologies and discuss future directions that will facilitate deeper insights into the function of human and animal microbiomes.
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Affiliation(s)
- David Berry
- Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network Chemistry Meets Microbiology, University of Vienna, Althanstrasse 14, Vienna, Austria.
| | - Alexander Loy
- Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network Chemistry Meets Microbiology, University of Vienna, Althanstrasse 14, Vienna, Austria
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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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Maeda K, Okahashi N, Toya Y, Matsuda F, Shimizu H. Investigation of useful carbon tracers for 13C-metabolic flux analysis of Escherichia coli by considering five experimentally determined flux distributions. Metab Eng Commun 2016; 3:187-195. [PMID: 29142823 PMCID: PMC5678827 DOI: 10.1016/j.meteno.2016.06.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 05/12/2016] [Accepted: 06/01/2016] [Indexed: 12/25/2022] Open
Abstract
The 13C-MFA experiments require an optimal design since the precision or confidence intervals of the estimated flux levels depends on factors such as the composition of 13C-labeled carbon sources, as well as the metabolic flux distribution of interest. In this study, useful compositions of 13C-labeled glucose for 13C-metabolic flux analysis (13C-MFA) of Escherichia coli are investigated using a computer simulation of the stable isotope labeling experiment. Following the generation of artificial mass spectra datasets of amino acid fragments using five literature-reported flux distributions of E. coli, the best fitted flux distribution and the 95% confidence interval were estimated by the 13C-MFA procedure. A comparison of the precision scores showed that [1, 2-13C]glucose and a mixture of [1-13C] and [U-13C]glucose at 8:2 are one of the best carbon sources for a precise estimation of flux levels of the pentose phosphate pathway, glycolysis and the TCA cycle. Although the precision scores of the anaplerotic and glyoxylate pathway reactions were affected by both the carbon source and flux distribution, it was also shown that the mixture of non-labeled, [1-13C], and [U-13C]glucose at 4:1:5 was specifically effective for the flux estimation of the glyoxylate pathway reaction. These findings were confirmed by wet 13C-MFA experiments. Useful compositions of 13C-labeled glucose are investigated for 13C-MFA of E. coli. Computer simulations revealed that [1,2-13C] was one of the best first choices. Mixture of non-labeled, [1-13C] and [U-13C] at 0:8:2 was also suitable for 13C-MFA. Mixture at 4:1:5 was specifically effective for estimation of glyoxylate pathway. The wet 13C-MFA experiments of E. coli confirmed the findings.
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Affiliation(s)
- Kousuke Maeda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yoshihiro Toya
- 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
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
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Alberghina L, Gaglio D. Redox control of glutamine utilization in cancer. Cell Death Dis 2014; 5:e1561. [PMID: 25476909 PMCID: PMC4454159 DOI: 10.1038/cddis.2014.513] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Revised: 10/13/2014] [Accepted: 10/21/2014] [Indexed: 12/28/2022]
Abstract
Glutamine utilization promotes enhanced growth of cancer cells. We propose a new concept map of cancer metabolism in which mitochondrial NADH and NADPH, in the presence of a dysfunctional electron transfer chain, promote reductive carboxylation from glutamine. We also discuss why nicotinamide nucleotide transhydrogenase (NNT) is required in vivo for glutamine utilization by reductive carboxylation. Moreover, NADPH, generated by both the pentose phosphate pathway and the cancer-specific serine glycolytic diversion, appears to sustain glutamine utilization for amino-acid synthesis, lipid synthesis, and for ROS quenching. The fact that the supply of NAD+ precursors reduces tumor aggressiveness suggests experimental approaches to clarify the role of the NADH-driven redox network in cancer.
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Affiliation(s)
- L Alberghina
- 1] SYSBIO Center for Systems Biology, Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan and Rome, Italy [2] Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, Milan, Italy
| | - D Gaglio
- 1] SYSBIO Center for Systems Biology, Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan and Rome, Italy [2] Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Via F.lli Cervi 93, Segrate, Milan, Italy
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Cazzaniga P, Damiani C, Besozzi D, Colombo R, Nobile MS, Gaglio D, Pescini D, Molinari S, Mauri G, Alberghina L, Vanoni M. Computational strategies for a system-level understanding of metabolism. Metabolites 2014; 4:1034-87. [PMID: 25427076 PMCID: PMC4279158 DOI: 10.3390/metabo4041034] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 11/05/2014] [Accepted: 11/12/2014] [Indexed: 12/20/2022] Open
Abstract
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
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Affiliation(s)
- Paolo Cazzaniga
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Chiara Damiani
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Daniela Besozzi
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Marco S Nobile
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Sara Molinari
- Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
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Shupletsov MS, Golubeva LI, Rubina SS, Podvyaznikov DA, Iwatani S, Mashko SV. OpenFLUX2: (13)C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments. Microb Cell Fact 2014; 13:152. [PMID: 25408234 PMCID: PMC4263107 DOI: 10.1186/s12934-014-0152-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 10/18/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Steady-state (13)C-based metabolic flux analysis ((13)C-MFA) is the most powerful method available for the quantification of intracellular fluxes. These analyses include concertedly linked experimental and computational stages: (i) assuming the metabolic model and optimizing the experimental design; (ii) feeding the investigated organism using a chosen (13)C-labeled substrate (tracer); (iii) measuring the extracellular effluxes and detecting the (13)C-patterns of intracellular metabolites; and (iv) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages, (13)C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based (13)C-MFA is an important factor for PLE implementation. RESULTS The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2, in silico simulation confirmed that flux precision is improved when (13)C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes (i) the optimization of the experimental design, (ii) the computation of the flux parameters from LEs data, (iii) goodness-of-fit testing of the model's adequacy, (iv) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and (v) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method. CONCLUSIONS The developed open-source OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state (13)C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.
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Affiliation(s)
- Mikhail S Shupletsov
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Computational Mathematics and Cybernetics Department, Lomonosov Moscow State University, 119991, Moscow, Russian Federation.
| | - Lyubov I Golubeva
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation.
| | - Svetlana S Rubina
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation.
| | - Dmitry A Podvyaznikov
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Department of Theoretical and Experimental Physics, Moscow Physical Engineering Institute (Technical University), 115409, Moscow, Russian Federation.
| | - Shintaro Iwatani
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Present address: Fermentation Group, Process Industrialization Section, Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc., 840-2193, SAGA, Saga-shi, Morodomi-cho, 450 Morodomitsu, Japan.
| | - Sergey V Mashko
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Department of Theoretical and Experimental Physics, Moscow Physical Engineering Institute (Technical University), 115409, Moscow, Russian Federation. .,Biological Department, Lomonosov Moscow State University, 119991, Moscow, Russian Federation.
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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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10
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Villalpando S, Del Prado M, Cienfuego E, Morales P. (13)C enrichment of the CO2 in breast milk and in the breath is rapidly modified by changes in the (13)C content of the diet. ANNALS OF NUTRITION AND METABOLISM 2014; 64:44-9. [PMID: 24776965 DOI: 10.1159/000360405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 02/02/2014] [Indexed: 11/19/2022]
Abstract
BACKGROUND C4 plants (e.g. corn and sugar cane) have greater (13)C enrichment than C3 plants (e.g. wheat and sugar beet). OBJECTIVE To assess whether (13)C enrichment of CO2 in the breath and breast milk of women on diets based on C3 and C4 foods changes from one diet to the other. MATERIALS AND METHODS Six breast-feeding women were studied at 5-6 months postpartum. They ate a controlled C4 diet on days 1 and 2 followed by a C3 diet on days 3 and 4. Diet duplicates, breast milk on days 2 and 4 and hourly breath samples were collected over 4 days. (13)C enrichment was measured by isotope-ratio mass spectrometry. Values of δ(13)C were calculated from the international PDBV standard (δ(13)CPDBV). Differences between means were compared by paired t test or t test for repeated measurements. RESULTS δ(13)CPDBV values were significantly higher in the C4 diet than in the C3 diet composites (p < 0.01). In breath CO2, the δ(13)CPDBV value was greater on days 1 and 2 (range -15.4 to -13.2, respectively) and declined on days 3 and 4 (range -20.0 to -21.8, respectively, p < 0.01). The lipid and milk serum fractions of breast milk had significantly higher δ(13)CPDBV on the C3 diet than on the C4 diet (p < 0.01). CONCLUSIONS Subjects eating a C4 diet have a higher δ(13)CPDBV value in the breath and breast milk fractions, which diminish rapidly on a C3 diet. Further studies focusing on individual nutrients are warranted.
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Affiliation(s)
- Salvador Villalpando
- Center for Research on Nutrition and Health, Instituto Nacional de Salud Pública, Cuernavaca, Mexico
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11
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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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12
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13C metabolic flux analysis: optimal design of isotopic labeling experiments. Curr Opin Biotechnol 2013; 24:1116-21. [DOI: 10.1016/j.copbio.2013.02.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Revised: 01/15/2013] [Accepted: 02/01/2013] [Indexed: 11/22/2022]
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13
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Crown SB, Antoniewicz MR. Publishing 13C metabolic flux analysis studies: a review and future perspectives. Metab Eng 2013; 20:42-8. [PMID: 24025367 DOI: 10.1016/j.ymben.2013.08.005] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 08/14/2013] [Accepted: 08/29/2013] [Indexed: 11/17/2022]
Abstract
(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g., in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing (13)C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.
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Affiliation(s)
- Scott B Crown
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St., Newark, DE 19716, USA
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Leighty RW, Antoniewicz MR. COMPLETE-MFA: complementary parallel labeling experiments technique for metabolic flux analysis. Metab Eng 2013; 20:49-55. [PMID: 24021936 DOI: 10.1016/j.ymben.2013.08.006] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Revised: 08/13/2013] [Accepted: 08/29/2013] [Indexed: 12/17/2022]
Abstract
We have developed a novel approach for measuring highly accurate and precise metabolic fluxes in living cells, termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. The COMPLETE-MFA method is based on combined analysis of multiple isotopic labeling experiments, where the synergy of using complementary tracers greatly improves the precision of estimated fluxes. In this work, we demonstrate the COMPLETE-MFA approach using all singly labeled glucose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], [5-(13)C], and [6-(13)C]glucose to determine precise metabolic fluxes for wild-type Escherichia coli. Cells were grown in six parallel cultures on defined medium with glucose as the only carbon source. Mass isotopomers of biomass amino acids were measured by gas chromatography-mass spectrometry (GC-MS). The data from all six experiments were then fitted simultaneously to a single flux model to determine accurate intracellular fluxes. We obtained a statistically acceptable fit with more than 300 redundant measurements. The estimated flux map is the most precise flux result obtained thus far for E. coli cells. To our knowledge, this is the first time that six isotopic labeling experiments have been successfully integrated for high-resolution (13)C-flux analysis.
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Affiliation(s)
- Robert W Leighty
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St., Newark, DE 19716, USA
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