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Kuriya Y, Murata M, Yamamoto M, Watanabe N, Araki M. Prediction of Metabolic Flux Distribution by Flux Sampling: As a Case Study, Acetate Production from Glucose in Escherichia coli. Bioengineering (Basel) 2023; 10:636. [PMID: 37370567 DOI: 10.3390/bioengineering10060636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
Omics data was acquired, and the development and research of metabolic simulation and analysis methods using them were also actively carried out. However, it was a laborious task to acquire such data each time the medium composition, culture conditions, and target organism changed. Therefore, in this study, we aimed to extract and estimate important variables and necessary numbers for predicting metabolic flux distribution as the state of cell metabolism by flux sampling using a genome-scale metabolic model (GSM) and its analysis. Acetic acid production from glucose in Escherichia coli with GSM iJO1366 was used as a case study. Flux sampling obtained by OptGP using 1000 pattern constraints on substrate, product, and growth fluxes produced a wider sample than the default case. The analysis also suggested that the fluxes of iron ions, O2, CO2, and NH4+, were important for predicting the metabolic flux distribution. Additionally, the comparison with the literature value of 13C-MFA using CO2 emission flux as an example of an important flux suggested that the important flux obtained by this method was valid for the prediction of flux distribution. In this way, the method of this research was useful for extracting variables that were important for predicting flux distribution, and as a result, the possibility of contributing to the reduction of measurement variables in experiments was suggested.
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Affiliation(s)
- Yuki Kuriya
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Masahiro Murata
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
| | - Masaki Yamamoto
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Naoki Watanabe
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Michihiro Araki
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
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2
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An N, Xie C, Zhou S, Wang J, Sun X, Yan Y, Shen X, Yuan Q. Establishing a growth-coupled mechanism for high-yield production of β-arbutin from glycerol in Escherichia coli. BIORESOURCE TECHNOLOGY 2023; 369:128491. [PMID: 36529444 DOI: 10.1016/j.biortech.2022.128491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Biodiesel production has increased significantly in recent years, leading to an increase in the production of crude glycerol. In this study, a novel growth-coupled erythrose 4-phosphate (E4P) formation strategy that can be used to produce high levels of β-arbutin using engineered Escherichia coli and glycerol as the carbon source was developed. In the strategy, E4P formation was coupled with cell growth, and a growth-driving force made the E4P formation efficient. By applying this strategy, efficient microbial synthesis of β-arbutin was achieved, with 7.91 g/L β-arbutin produced in shaking flask, and 28.1 g/L produced in a fed batch fermentation with a yield of 0.20 g/g glycerol and a productivity of 0.39 g/L/h. This is the highest β-arbutin production through microbial fermentation ever reported to date. This study may have significant implications in the large-scale production of β-arbutin as well as other aromatic compounds of importance.
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Affiliation(s)
- Ning An
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Chong Xie
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Shubin Zhou
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jia Wang
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinxiao Sun
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yajun Yan
- School of Chemical, Materials and Biomedical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Xiaolin Shen
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
| | - Qipeng Yuan
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
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3
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Noguchi S, Wakita K, Matsuda F, Shimizu H. 13C metabolic flux analysis clarifies distinct metabolic phenotypes of cancer cell spheroid mimicking tumor hypoxia. Metab Eng 2022; 73:192-200. [DOI: 10.1016/j.ymben.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/09/2022] [Accepted: 07/25/2022] [Indexed: 11/30/2022]
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4
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Eylem CC, Reçber T, Waris M, Kır S, Nemutlu E. State-of-the-art GC-MS approaches for probing central carbon metabolism. Microchem J 2022. [DOI: 10.1016/j.microc.2021.106892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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5
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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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6
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Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models. Metab Eng 2021; 67:133-144. [PMID: 34174426 DOI: 10.1016/j.ymben.2021.06.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 03/04/2021] [Accepted: 06/21/2021] [Indexed: 12/23/2022]
Abstract
Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.
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Direct and quantitative analysis of altered metabolic flux distributions and cellular ATP production pathway in fumarate hydratase-diminished cells. Sci Rep 2020; 10:13065. [PMID: 32747645 PMCID: PMC7400513 DOI: 10.1038/s41598-020-70000-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 07/20/2020] [Indexed: 01/22/2023] Open
Abstract
Fumarate hydratase (FH) is an enzyme in the tricarboxylic acid (TCA) cycle, biallelic loss-of-function mutations of which are associated with hereditary leiomyomatosis and renal cell cancer. However, how FH defect modulates intracellular metabolic fluxes in human cells has remained unclear. This study aimed to reveal metabolic flux alterations induced by reduced FH activity. We applied 13C metabolic flux analysis (13C-MFA) to an established cell line with diminished FH activity (FHdim) and parental HEK293 cells. FHdim cells showed reduced pyruvate import flux into mitochondria and subsequent TCA cycle fluxes. Interestingly, the diminished FH activity decreased FH flux only by about 20%, suggesting a very low need for FH to maintain the oxidative TCA cycle. Cellular ATP production from the TCA cycle was dominantly suppressed compared with that from glycolysis in FHdim cells. Consistently, FHdim cells exhibited higher glucose dependence for ATP production and higher resistance to an ATP synthase inhibitor. In summary, using FHdim cells we demonstrated that FH defect led to suppressed pyruvate import into mitochondria, followed by downregulated TCA cycle activity and altered ATP production pathway balance from the TCA cycle to glycolysis. We confirmed that 13C-MFA can provide direct and quantitative information on metabolic alterations induced by FH defect.
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8
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Matsuda F, Maeda K, Okahashi N. Computational data mining method for isotopomer analysis in the quantitative assessment of metabolic reprogramming. Sci Rep 2020; 10:286. [PMID: 31937835 PMCID: PMC6959353 DOI: 10.1038/s41598-019-57146-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 12/18/2019] [Indexed: 12/12/2022] Open
Abstract
Measurement of metabolic flux levels using stable isotope labeling has been successfully used to investigate metabolic redirection and reprogramming in living cells or tissues. The metabolic flux ratio between two reactions can be estimated from the 13C-labeling patterns of a few metabolites combined with the knowledge of atom mapping in the complicated metabolic network. However, it remains unclear whether an observed change in the labeling pattern of the metabolites is sufficient evidence of a shift in flux ratio between two metabolic states. In this study, a data analysis method was developed for the quantitative assessment of metabolic reprogramming. The Metropolis-Hastings algorithm was used with an in silico metabolic model to generate a probability distribution of metabolic flux levels under a condition in which the 13C-labeling pattern was observed. Reanalysis of literature data demonstrated that the developed method enables analysis of metabolic redirection using whole 13C-labeling pattern data. Quantitative assessment by Cohen’s effect size (d) enables a more detailed read-out of metabolic reprogramming information. The developed method will enable future applications of the metabolic isotopomer analysis to various targets, including cultured cells, whole tissues, and organs.
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Affiliation(s)
- Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.
| | - Kousuke Maeda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
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9
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Correia DM, Sargo CR, Silva AJ, Santos ST, Giordano RC, Ferreira EC, Zangirolami TC, Ribeiro MPA, Rocha I. Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis. Metab Eng 2019; 52:303-314. [PMID: 30529284 DOI: 10.1016/j.ymben.2018.11.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 11/26/2018] [Accepted: 11/28/2018] [Indexed: 12/20/2022]
Abstract
In the last years, Salmonella has been extensively studied not only due to its importance as a pathogen, but also as a host to produce pharmaceutical compounds. However, the full exploitation of Salmonella as a platform for bioproduct delivery has been hampered by the lack of information about its metabolism. Genome-scale metabolic models can be valuable tools to delineate metabolic engineering strategies as long as they closely represent the actual metabolism of the target organism. In the present study, a 13C-MFA approach was applied to map the fluxes at the central carbon pathways of S. typhimurium LT2 growing at glucose-limited chemostat cultures. The experiments were carried out in a 2L bioreactor, using defined medium enriched with 20% 13C-labeled glucose. Metabolic flux distributions in central carbon pathways of S. typhimurium LT2 were estimated using OpenFLUX2 based on the labeling pattern of biomass protein hydrolysates together with biomass composition. The results suggested that pentose phosphate is used to catabolize glucose, with minor fluxes through glycolysis. In silico simulations, using Optflux and pFBA as simulation method, allowed to study the performance of the genome-scale metabolic model. In general, the accuracy of in silico simulations was improved by the superimposition of estimated intracellular fluxes to the existing genome-scale metabolic model, showing a better fitting to the experimental extracellular fluxes, whereas the intracellular fluxes of pentose phosphate and anaplerotic reactions were poorly described.
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Affiliation(s)
- Daniela M Correia
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Cintia R Sargo
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Adilson J Silva
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Sophia T Santos
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal
| | - Roberto C Giordano
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Eugénio C Ferreira
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal
| | - Teresa C Zangirolami
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Marcelo P A Ribeiro
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Isabel Rocha
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal.
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10
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Walvekar A, Rashida Z, Maddali H, Laxman S. A versatile LC-MS/MS approach for comprehensive, quantitative analysis of central metabolic pathways. Wellcome Open Res 2018; 3:122. [PMID: 30345389 PMCID: PMC6171562 DOI: 10.12688/wellcomeopenres.14832.1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2018] [Indexed: 11/23/2022] Open
Abstract
Liquid chromatography-mass spectrometry (LC-MS/MS) based approaches are widely used for the identification and quantitation of specific metabolites, and are a preferred approach towards analyzing cellular metabolism. Most methods developed come with specific requirements such as unique columns, ion-pairing reagents and pH conditions, and typically allow measurements in a specific pathway alone. Here, we present a single column-based set of methods for simultaneous coverage of multiple pathways, primarily focusing on central carbon, amino acid, and nucleotide metabolism. We further demonstrate the use of this method for quantitative, stable isotope-based metabolic flux experiments, expanding its use beyond steady-state level measurements of metabolites. The expected kinetics of label accumulation pertinent to the pathway under study are presented with some examples. The methods discussed here are broadly applicable, minimize the need for multiple chromatographic resolution methods, and highlight how simple labeling experiments can be valuable in facilitating a comprehensive understanding of the metabolic state of cells.
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Affiliation(s)
- Adhish Walvekar
- Institute for Stem Cell biology and Regenerative Medicine (inStem), Bangalore, Karnataka, 560065, India
| | - Zeenat Rashida
- Institute for Stem Cell biology and Regenerative Medicine (inStem), Bangalore, Karnataka, 560065, India
- Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Hemanth Maddali
- Institute for Stem Cell biology and Regenerative Medicine (inStem), Bangalore, Karnataka, 560065, India
| | - Sunil Laxman
- Institute for Stem Cell biology and Regenerative Medicine (inStem), Bangalore, Karnataka, 560065, India
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11
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Araki C, Okahashi N, Maeda K, Shimizu H, Matsuda F. Mass Spectrometry-Based Method to Study Inhibitor-Induced Metabolic Redirection in the Central Metabolism of Cancer Cells. Mass Spectrom (Tokyo) 2018; 7:A0067. [PMID: 29922569 PMCID: PMC6002601 DOI: 10.5702/massspectrometry.a0067] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 04/24/2018] [Indexed: 11/23/2022] Open
Abstract
Cancer cells often respond to chemotherapeutic inhibitors by redirecting carbon flow in the central metabolism. To understand the metabolic redirections of inhibitor treatment on cancer cells, this study established a 13C-metabolic flux analysis (13C-MFA)-based method to evaluate metabolic redirection in MCF-7 breast cancer cells using mass spectrometry. A metabolic stationary state necessary for accurate 13C-MFA was confirmed during an 8-24 h window using low-dose treatments of various metabolic inhibitors. Further 13C-labeling experiments using [1-13C]glucose and [U-13C]glutamine, combined with gas chromatography-mass spectrometry (GC-MS) analysis of mass isotopomer distributions (MIDs), confirmed that an isotopic stationary state of intracellular metabolites was reached 24 h after treatment with paclitaxel (Taxol), an inhibitor of mitosis used for cancer treatment. Based on these metabolic and isotopic stationary states, metabolic flux distribution in the central metabolism of paclitaxel-treated MCF-7 cells was determined by 13C-MFA. Finally, estimations of the 95% confidence intervals showed that tricarboxylic acid cycle metabolic flux increased after paclitaxel treatment. Conversely, anaerobic glycolysis metabolic flux decreased, revealing metabolic redirections by paclitaxel inhibition. The gap between total regeneration and consumption of ATP in paclitaxel-treated cells was also found to be 1.2 times greater than controls, suggesting ATP demand was increased by paclitaxel treatment, likely due to increased microtubule polymerization. These data confirm that 13C-MFA can be used to investigate inhibitor-induced metabolic redirection in cancer cells. This will contribute to future pharmaceutical developments and understanding variable patient response to treatment.
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Affiliation(s)
- Chie Araki
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Kousuke Maeda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
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12
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Hayakawa K, Matsuda F, Shimizu H. 13C-metabolic flux analysis of ethanol-assimilating Saccharomyces cerevisiae for S-adenosyl-L-methionine production. Microb Cell Fact 2018; 17:82. [PMID: 29855316 PMCID: PMC5977476 DOI: 10.1186/s12934-018-0935-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 05/22/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Saccharomyces cerevisiae is a host for the industrial production of S-adenosyl-L-methionine (SAM), which has been widely used in pharmaceutical and nutritional supplement industries. It has been reported that the intracellular SAM content in S. cerevisiae can be improved by the addition of ethanol during cultivation. However, the metabolic state in ethanol-assimilating S. cerevisiae remains unclear. In this study, 13C-metabolic flux analysis (13C-MFA) was conducted to investigate the metabolic regulation responsible for the high SAM production from ethanol. RESULTS The comparison between the metabolic flux distributions of central carbon metabolism showed that the metabolic flux levels of the tricarboxylic acid cycle and glyoxylate shunt in the ethanol culture were significantly higher than that of glucose. Estimates of the ATP balance from the 13C-MFA data suggested that larger amounts of excess ATP was produced from ethanol via increased oxidative phosphorylation. The finding was confirmed by the intracellular ATP level under ethanol-assimilating condition being similarly higher than glucose. CONCLUSIONS These results suggest that the enhanced ATP regeneration due to ethanol assimilation was critical for the high SAM accumulation.
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Affiliation(s)
- Kenshi Hayakawa
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan.,KANEKA Fundamental Technology Research Alliance Laboratories, Graduate School of Engineering, Osaka University, 2-8 Yamadaoka, Suita, Osaka, 565-0871, Japan.,Biotechnology Development Laboratories, Health Care Solutions Research Institute, Kaneka Corporation, 1-8 Miyamae-cho, Takasago-cho, Takasago, Hyogo, 676-8688, 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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13
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Matsuda F, Toya Y, Shimizu H. Learning from quantitative data to understand central carbon metabolism. Biotechnol Adv 2017; 35:971-980. [DOI: 10.1016/j.biotechadv.2017.09.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 09/01/2017] [Accepted: 09/14/2017] [Indexed: 12/23/2022]
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14
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Toya Y, Ohashi S, Shimizu H. Optimal 13C-labeling of glycerol carbon source for precise flux estimation in Escherichia coli. J Biosci Bioeng 2017; 125:301-305. [PMID: 29107627 DOI: 10.1016/j.jbiosc.2017.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 09/01/2017] [Accepted: 09/26/2017] [Indexed: 10/18/2022]
Abstract
Glycerol is a promising carbon source for bio-production and is particularly attractive because it is produced in excess as a biodiesel byproduct. Elucidating the flux distribution of glycerol catabolism would greatly aid metabolic engineering, but 13C-labeling of glycerol has not yet been optimized for precise flux estimations. In this study, an Escherichia coli wild type strain was aerobically cultured using glycerol as the sole carbon source. [1,3-13C], [2-13C], and [U-13C] glycerols were independently mixed with an equal amount of naturally labeled glycerol; these mixtures were used as 13C-labeled substrates, and flux distributions during exponential growth were estimated based on 13C-enrichment of proteinogenic amino acids. The glycerol catabolism pathway in E. coli has four branches: the oxidative pentose phosphate pathway (PP), Entner-Doudoroff pathway (ED), and malic enzyme (ME) pathways, and the glyoxylate shunt (GX). The 95% confidence intervals of these fluxes were compared across the 13C-labeling experiments. The [2-13C] and [U-13C] glycerols, but not [1,3-13C] glycerol allowed precise characterization of the PP, ED, and ME pathway fluxes. All three types of 13C-labeling aided in successfully determining the GX flux. Based on the above estimated flux distribution, various patterns of 13C-labeling of glycerol were computationally generated. These in silico experiments revealed that the sole use of [2-13C] glycerol or [1,3-13C] glycerol is optimal for precise flux estimation, where simultaneous using glycerols with different types of 13C-labeling failed to improve flux estimation as assessed by confidence intervals.
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Affiliation(s)
- Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Shugo Ohashi
- 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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15
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Azizan KA, Ressom HW, Mendoza ER, Baharum SN. 13C based proteinogenic amino acid (PAA) and metabolic flux ratio analysis of Lactococcus lactis reveals changes in pentose phosphate (PP) pathway in response to agitation and temperature related stresses. PeerJ 2017; 5:e3451. [PMID: 28695065 PMCID: PMC5501154 DOI: 10.7717/peerj.3451] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 05/19/2017] [Indexed: 11/20/2022] Open
Abstract
Lactococcus lactis subsp. cremoris MG1363 is an important starter culture for dairy fermentation. During industrial fermentations, L. lactis is constantly exposed to stresses that affect the growth and performance of the bacterium. Although the response of L. lactis to several stresses has been described, the adaptation mechanisms at the level of in vivo fluxes have seldom been described. To gain insights into cellular metabolism, 13C metabolic flux analysis and gas chromatography mass spectrometry (GC-MS) were used to measure the flux ratios of active pathways in the central metabolism of L. lactis when subjected to three conditions varying in temperature (30°C, 37°C) and agitation (with and without agitation at 150 rpm). Collectively, the concentrations of proteinogenic amino acids (PAAs) and free fatty acids (FAAs) were compared, and Pearson correlation analysis (r) was calculated to measure the pairwise relationship between PAAs. Branched chain and aromatic amino acids, threonine, serine, lysine and histidine were correlated strongly, suggesting changes in flux regulation in glycolysis, the pentose phosphate (PP) pathway, malic enzyme and anaplerotic reaction catalysed by pyruvate carboxylase (pycA). Flux ratio analysis revealed that glucose was mainly converted by glycolysis, highlighting the stability of L. lactis’ central carbon metabolism despite different conditions. Higher flux ratios through oxaloacetate (OAA) from pyruvate (PYR) reaction in all conditions suggested the activation of pyruvate carboxylate (pycA) in L. lactis, in response to acid stress during exponential phase. Subsequently, more significant flux ratio differences were seen through the oxidative and non-oxidative pentose phosphate (PP) pathways, malic enzyme, and serine and C1 metabolism, suggesting NADPH requirements in response to environmental stimuli. These reactions could play an important role in optimization strategies for metabolic engineering in L. lactis. Overall, the integration of systematic analysis of amino acids and flux ratio analysis provides a systems-level understanding of how L. lactis regulates central metabolism under various conditions.
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Affiliation(s)
- Kamalrul Azlan Azizan
- Metabolomics Research Laboratory, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
| | - Habtom W Ressom
- Departments of Oncology, Georgetown Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, D.C., United States of America
| | - Eduardo R Mendoza
- Institute of Mathematics, University of the Philippines Diliman, Quezon City, Metro Manila, Philippines.,Membrane Biochemistry Group, Max Planck Institute of Biochemistry, Planegg, Germany
| | - Syarul Nataqain Baharum
- Metabolomics Research Laboratory, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
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