1
|
Jeeves M, Roberts J, Ludwig C. Optimised collection of non-uniformly sampled 2D-HSQC NMR spectra for use in metabolic flux analysis. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2021; 59:287-299. [PMID: 32830359 DOI: 10.1002/mrc.5089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/06/2020] [Accepted: 08/18/2020] [Indexed: 06/11/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is integral to metabolic studies; yet, it can suffer from the long acquisition times required to collect data of sufficient signal strength and resolution. The use of non-uniform sampling (NUS) allows faster collection of NMR spectra without loss of spectral integrity. When planning experimental methodologies to perform metabolic flux analysis (MFA) of cell metabolism, a variety of options are available for the acquisition of NUS NMR data. Before beginning data collection, decisions have to be made regarding selection of pulse sequence, number of transients and NUS specific parameters such as the sampling level and sampling schedule. Poor choices will impact data quality, which may have a negative effect on the subsequent analysis and biological interpretation. Herein, we describe factors that should be considered when setting up non-uniformly sampled 2D-1 H,13 C HSQC NMR experiments for MFA and provide a standard protocol for users to follow.
Collapse
Affiliation(s)
- Mark Jeeves
- Henry Wellcome Building for Biomolecular NMR Spectroscopy, Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Jennie Roberts
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Christian Ludwig
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| |
Collapse
|
2
|
Zhang Z, Liu Z, Meng Y, Chen Z, Han J, Wei Y, Shen T, Yi Y, Xie X. Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale. BIOTECHNOLOGY FOR BIOFUELS 2020; 13:103. [PMID: 32523616 PMCID: PMC7278083 DOI: 10.1186/s13068-020-01737-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 05/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. RESULT Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. CONCLUSION This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.
Collapse
Affiliation(s)
- Zhengdong Zhang
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Zhentao Liu
- College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou China
| | - Yafei Meng
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
| | - Zhen Chen
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Jiayu Han
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Yimin Wei
- School of Mathematics Sciences and Key Laboratory of Mathematics for Nonlinear Sciences, Fudan University, Shanghai, China
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Yin Yi
- College of Life Science, Guizhou Normal University, Guiyang, Guizhou China
| | - Xiaoyao Xie
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| |
Collapse
|
3
|
Armstrong DW, Talebi M, Thakur N, Wahab MF, Mikhonin AV, Muckle MT, Neill JL. A Gas Chromatography‐Molecular Rotational Resonance Spectroscopy Based System of Singular Specificity. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201910507] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Daniel W. Armstrong
- Department of Chemistry & Biochemistry University of Texas at Arlington Arlington TX 76019 USA
- AZYP, LLC Arlington TX 76012 USA
| | | | - Nimisha Thakur
- Department of Chemistry & Biochemistry University of Texas at Arlington Arlington TX 76019 USA
| | - M. Farooq Wahab
- Department of Chemistry & Biochemistry University of Texas at Arlington Arlington TX 76019 USA
| | | | | | | |
Collapse
|
4
|
Armstrong DW, Talebi M, Thakur N, Wahab MF, Mikhonin AV, Muckle MT, Neill JL. A Gas Chromatography‐Molecular Rotational Resonance Spectroscopy Based System of Singular Specificity. Angew Chem Int Ed Engl 2019; 59:192-196. [DOI: 10.1002/anie.201910507] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/04/2019] [Indexed: 11/09/2022]
Affiliation(s)
- Daniel W. Armstrong
- Department of Chemistry & Biochemistry University of Texas at Arlington Arlington TX 76019 USA
- AZYP, LLC Arlington TX 76012 USA
| | | | - Nimisha Thakur
- Department of Chemistry & Biochemistry University of Texas at Arlington Arlington TX 76019 USA
| | - M. Farooq Wahab
- Department of Chemistry & Biochemistry University of Texas at Arlington Arlington TX 76019 USA
| | | | | | | |
Collapse
|
5
|
Beyß M, Azzouzi S, Weitzel M, Wiechert W, Nöh K. The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis. Front Microbiol 2019; 10:1022. [PMID: 31178829 PMCID: PMC6543931 DOI: 10.3389/fmicb.2019.01022] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/24/2019] [Indexed: 12/16/2022] Open
Abstract
13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.
Collapse
Affiliation(s)
- Martin Beyß
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Salah Azzouzi
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Michael Weitzel
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| |
Collapse
|
6
|
Tomàs-Gamisans M, Ødum ASR, Workman M, Ferrer P, Albiol J. Glycerol metabolism of Pichia pastoris (Komagataella spp.) characterised by 13C-based metabolic flux analysis. N Biotechnol 2019; 50:52-59. [DOI: 10.1016/j.nbt.2019.01.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 01/11/2019] [Accepted: 01/13/2019] [Indexed: 12/12/2022]
|
7
|
Advances in metabolic flux analysis toward genome-scale profiling of higher organisms. Biosci Rep 2018; 38:BSR20170224. [PMID: 30341247 PMCID: PMC6250807 DOI: 10.1042/bsr20170224] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 10/06/2018] [Accepted: 10/14/2018] [Indexed: 11/25/2022] Open
Abstract
Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.
Collapse
|
8
|
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]
|
9
|
Funk AM, Anderson BL, Wen X, Hever T, Khemtong C, Kovacs Z, Sherry AD, Malloy CR. The rate of lactate production from glucose in hearts is not altered by per-deuteration of glucose. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 284:86-93. [PMID: 28972888 PMCID: PMC5817885 DOI: 10.1016/j.jmr.2017.09.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 09/13/2017] [Accepted: 09/14/2017] [Indexed: 05/05/2023]
Abstract
This study was designed to determine whether perdeuterated glucose experiences a kinetic isotope effect (KIE) as glucose passes through glycolysis and is further oxidized in the tricarboxylic acid (TCA) cycle. Metabolism of deuterated glucose was investigated in two groups of perfused rat hearts. The control group was supplied with a 1:1 mixture of [U-13C6]glucose and [1,6-13C2]glucose, while the experimental group received [U-13C6,U-2H7]glucose and [1,6-13C2]glucose. Tissue extracts were analyzed by 1H, 2H and proton-decoupled 13C NMR spectroscopy. Extensive 2H-13C scalar coupling plus chemical shift isotope effects were observed in the proton-decoupled 13C NMR spectra of lactate, alanine and glutamate. A small but measureable (∼8%) difference in the rate of conversion of [U-13C6]glucose vs. [1,6-13C2]glucose to lactate, likely reflecting rates of CC bond breakage in the aldolase reaction, but conversion of [U-13C6]glucose versus [U-13C6,U-2H7]glucose to lactate did not differ. This shows that the presence of deuterium in glucose does not alter glycolytic flux. However, there were two distinct effects of deuteration on metabolism of glucose to alanine and oxidation of glucose in the TCA. First, alanine undergoes extensive exchange of methyl deuterons with solvent protons in the alanine amino transferase reaction. Second, there is a substantial kinetic isotope effect in metabolism of [U-13C6,U-2H7]glucose to alanine and glutamate. In the presence of [U-13C6,U-2H7]glucose, alanine and lactate are not in rapid exchange with the same pool of pyruvate. These studies indicate that the appearance of hyperpolarized 13C-lactate from hyperpolarized [U-13C6,U-2H7]glucose is not substantially influenced by a deuterium kinetic isotope effect.
Collapse
Affiliation(s)
- Alexander M Funk
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Brian L Anderson
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Xiaodong Wen
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Thomas Hever
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Chalermchai Khemtong
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Zoltan Kovacs
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - A Dean Sherry
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States; Department of Chemistry, University of Texas at Dallas, Richardson, TX, United States
| | - Craig R Malloy
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States; Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States; Veterans Affairs North Texas Healthcare System, Dallas, TX, United States.
| |
Collapse
|
10
|
Okahashi N, Matsuda F, Yoshikawa K, Shirai T, Matsumoto Y, Wada M, Shimizu H. Metabolic engineering of isopropyl alcohol-producingEscherichia colistrains with13C-metabolic flux analysis. Biotechnol Bioeng 2017; 114:2782-2793. [DOI: 10.1002/bit.26390] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 07/02/2017] [Accepted: 07/27/2017] [Indexed: 12/22/2022]
Affiliation(s)
- Nobuyuki Okahashi
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Osaka Japan
| | - Fumio Matsuda
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Osaka Japan
| | - Katsunori Yoshikawa
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Osaka Japan
| | - Tomokazu Shirai
- Synthetic Chemicals Laboratory; Mitsui Chemicals Inc.; Mobara Chiba Japan
| | - Yoshiko Matsumoto
- Synthetic Chemicals Laboratory; Mitsui Chemicals Inc.; Mobara Chiba Japan
| | - Mitsufumi Wada
- Synthetic Chemicals Laboratory; Mitsui Chemicals Inc.; Mobara Chiba Japan
| | - Hiroshi Shimizu
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Osaka Japan
| |
Collapse
|
11
|
Rollero S, Mouret JR, Bloem A, Sanchez I, Ortiz-Julien A, Sablayrolles JM, Dequin S, Camarasa C. Quantitative 13 C-isotope labelling-based analysis to elucidate the influence of environmental parameters on the production of fermentative aromas during wine fermentation. Microb Biotechnol 2017; 10:1649-1662. [PMID: 28695583 PMCID: PMC5658611 DOI: 10.1111/1751-7915.12749] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 04/28/2017] [Accepted: 05/24/2017] [Indexed: 02/02/2023] Open
Abstract
Nitrogen and lipids are key nutrients of grape must that influence the production of fermentative aromas by wine yeast, and we have previously shown that a strong interaction exists between these two nutrients. However, more than 90% of the acids and higher alcohols (and their acetate ester derivatives) were derived from intermediates produced by the carbon central metabolism (CCM). The objective of this study was to determine how variations in nitrogen and lipid resources can modulate the contribution of nitrogen and carbon metabolisms for the production of fermentative aromas. A quantitative analysis of metabolism using 13C‐labelled leucine and valine showed that nitrogen availability affected the part of the catabolism of N‐containing compounds, the formation of α‐ketoacids from CCM and the redistribution of fluxes around these precursors, explaining the optimum production of higher alcohols occurring at an intermediate nitrogen content. Moreover, nitrogen content modulated the total production of acids and higher alcohols differently, through variations in the redox state of cells. We also demonstrated that the phytosterol content, modifying the intracellular availability of acetyl‐CoA, can influence the flux distribution, especially the formation of higher alcohols and the conversion of α‐ketoisovalerate to α‐ketoisocaproate.
Collapse
Affiliation(s)
- Stéphanie Rollero
- UMR SPO: INRA, Universite Montpellier, Montpellier SupAgro, 34060, Montpellier, France.,Lallemand SAS, 31700, Blagnac, France
| | - Jean-Roch Mouret
- UMR SPO: INRA, Universite Montpellier, Montpellier SupAgro, 34060, Montpellier, France
| | - Audrey Bloem
- UMR SPO: INRA, Universite Montpellier, Montpellier SupAgro, 34060, Montpellier, France
| | - Isabelle Sanchez
- UMR SPO: INRA, Universite Montpellier, Montpellier SupAgro, 34060, Montpellier, France
| | | | | | - Sylvie Dequin
- UMR SPO: INRA, Universite Montpellier, Montpellier SupAgro, 34060, Montpellier, France
| | - Carole Camarasa
- UMR SPO: INRA, Universite Montpellier, Montpellier SupAgro, 34060, Montpellier, France
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Chong M, Jayaraman A, Marin S, Selivanov V, de Atauri Carulla PR, Tennant DA, Cascante M, Günther UL, Ludwig C. Combined Analysis of NMR and MS Spectra (CANMS). Angew Chem Int Ed Engl 2017. [DOI: 10.1002/ange.201611634] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Mei Chong
- Institute of Cancer and Genome Sciences; University of Birmingham; UK
| | - Anusha Jayaraman
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | - Silvia Marin
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | | | - Daniel A. Tennant
- Institute of Metabolism and Systems Research; University of Birmingham; IBR West Tower Birmingham UK B15 2TT
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | - Ulrich L. Günther
- Institute of Cancer and Genome Sciences; University of Birmingham; UK
| | - Christian Ludwig
- Institute of Metabolism and Systems Research; University of Birmingham; IBR West Tower Birmingham UK B15 2TT
| |
Collapse
|
14
|
Chong M, Jayaraman A, Marin S, Selivanov V, de Atauri Carulla PR, Tennant DA, Cascante M, Günther UL, Ludwig C. Combined Analysis of NMR and MS Spectra (CANMS). Angew Chem Int Ed Engl 2017; 56:4140-4144. [DOI: 10.1002/anie.201611634] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Indexed: 12/23/2022]
Affiliation(s)
- Mei Chong
- Institute of Cancer and Genome Sciences; University of Birmingham; UK
| | - Anusha Jayaraman
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | - Silvia Marin
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | | | - Daniel A. Tennant
- Institute of Metabolism and Systems Research; University of Birmingham; IBR West Tower Birmingham, B15 2TT UK
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology; Faculty of Biology; Universitat de Barcelona; Spain
| | - Ulrich L. Günther
- Institute of Cancer and Genome Sciences; University of Birmingham; UK
| | - Christian Ludwig
- Institute of Metabolism and Systems Research; University of Birmingham; IBR West Tower Birmingham, B15 2TT UK
| |
Collapse
|
15
|
Midani FS, Wynn ML, Schnell S. The importance of accurately correcting for the natural abundance of stable isotopes. Anal Biochem 2017; 520:27-43. [PMID: 27989585 PMCID: PMC5343595 DOI: 10.1016/j.ab.2016.12.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 11/18/2016] [Accepted: 12/13/2016] [Indexed: 11/26/2022]
Abstract
The use of isotopically labeled tracer substrates is an experimental approach for measuring in vivo and in vitro intracellular metabolic dynamics. Stable isotopes that alter the mass but not the chemical behavior of a molecule are commonly used in isotope tracer studies. Because stable isotopes of some atoms naturally occur at non-negligible abundances, it is important to account for the natural abundance of these isotopes when analyzing data from isotope labeling experiments. Specifically, a distinction must be made between isotopes introduced experimentally via an isotopically labeled tracer and the isotopes naturally present at the start of an experiment. In this tutorial review, we explain the underlying theory of natural abundance correction of stable isotopes, a concept not always understood by metabolic researchers. We also provide a comparison of distinct methods for performing this correction and discuss natural abundance correction in the context of steady state 13C metabolic flux, a method increasingly used to infer intracellular metabolic flux from isotope experiments.
Collapse
Affiliation(s)
- Firas S Midani
- Program in Computational Biology and Bioinformatics, Center for Genomic and Computational Biology & Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA.
| | - Michelle L Wynn
- Department of Molecular & Integrative Physiology, Department of Computational Medicine & Bioinformatics and Brehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Internal Medicine, Division of Hematology and Oncology and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Santiago Schnell
- Department of Molecular & Integrative Physiology, Department of Computational Medicine & Bioinformatics and Brehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, MI, USA.
| |
Collapse
|
16
|
Management of Multiple Nitrogen Sources during Wine Fermentation by Saccharomyces cerevisiae. Appl Environ Microbiol 2017; 83:AEM.02617-16. [PMID: 28115380 DOI: 10.1128/aem.02617-16] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 12/14/2016] [Indexed: 11/20/2022] Open
Abstract
During fermentative growth in natural and industrial environments, Saccharomyces cerevisiae must redistribute the available nitrogen from multiple exogenous sources to amino acids in order to suitably fulfill anabolic requirements. To exhaustively explore the management of this complex resource, we developed an advanced strategy based on the reconciliation of data from a set of stable isotope tracer experiments with labeled nitrogen sources. Thus, quantifying the partitioning of the N compounds through the metabolism network during fermentation, we demonstrated that, contrary to the generally accepted view, only a limited fraction of most of the consumed amino acids is directly incorporated into proteins. Moreover, substantial catabolism of these molecules allows for efficient redistribution of nitrogen, supporting the operative de novo synthesis of proteinogenic amino acids. In contrast, catabolism of consumed amino acids plays a minor role in the formation of volatile compounds. Another important feature is that the α-keto acid precursors required for the de novo syntheses originate mainly from the catabolism of sugars, with a limited contribution from the anabolism of consumed amino acids. This work provides a comprehensive view of the intracellular fate of consumed nitrogen sources and the metabolic origin of proteinogenic amino acids, highlighting a strategy of distribution of metabolic fluxes implemented by yeast as a means of adapting to environments with changing and scarce nitrogen resources.IMPORTANCE A current challenge for the wine industry, in view of the extensive competition in the worldwide market, is to meet consumer expectations regarding the sensory profile of the product while ensuring an efficient fermentation process. Understanding the intracellular fate of the nitrogen sources available in grape juice is essential to the achievement of these objectives, since nitrogen utilization affects both the fermentative activity of yeasts and the formation of flavor compounds. However, little is known about how the metabolism operates when nitrogen is provided as a composite mixture, as in grape must. Here we quantitatively describe the distribution through the yeast metabolic network of the N moieties and C backbones of these nitrogen sources. Knowledge about the management of a complex resource, which is devoted to improvement of the use of the scarce N nutrient for growth, will be useful for better control of the fermentation process and the sensory quality of wines.
Collapse
|
17
|
Guo W, Sheng J, Feng X. Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2017; 162:265-299. [PMID: 28424826 DOI: 10.1007/10_2017_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, 13C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.
Collapse
Affiliation(s)
- Weihua Guo
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Jiayuan Sheng
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Xueyang Feng
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA.
| |
Collapse
|
18
|
Martínez VS, Krömer JO. Quantification of Microbial Phenotypes. Metabolites 2016; 6:E45. [PMID: 27941694 PMCID: PMC5192451 DOI: 10.3390/metabo6040045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 12/05/2016] [Accepted: 12/06/2016] [Indexed: 11/16/2022] Open
Abstract
Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.
Collapse
Affiliation(s)
- Verónica S Martínez
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane 4072, Australia.
| | - Jens O Krömer
- Centre for Microbial Electrochemical Systems (CEMES), The University of Queensland, Brisbane 4072, Australia.
- Advanced Water Management Centre (AWMC), The University of Queensland, Brisbane 4072, Australia.
| |
Collapse
|
19
|
Fernández-Fernández M, Rodríguez-González P, García Alonso JI. A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression. JOURNAL OF MASS SPECTROMETRY : JMS 2016; 51:980-987. [PMID: 27388533 DOI: 10.1002/jms.3809] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 06/16/2016] [Accepted: 07/05/2016] [Indexed: 06/06/2023]
Abstract
We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms (13 C2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd.
Collapse
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
| | - J Ignacio García Alonso
- Department of Physical and Analytical Chemistry Faculty of Chemistry, University of Oviedo, Julián Clavería 8, 33006, Oviedo, Spain.
| |
Collapse
|
20
|
Hong M, Huang M, Chu J, Zhuang Y, Zhang S. Impacts of proline on the central metabolism of an industrial erythromycin-producing strain Saccharopolyspora erythraea via 13 C labeling experiments. J Biotechnol 2016; 231:1-8. [DOI: 10.1016/j.jbiotec.2016.05.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2015] [Revised: 05/18/2016] [Accepted: 05/19/2016] [Indexed: 10/21/2022]
|
21
|
DeGennaro CM, Savir Y, Springer M. Identifying Metabolic Subpopulations from Population Level Mass Spectrometry. PLoS One 2016; 11:e0151659. [PMID: 26986964 PMCID: PMC4795775 DOI: 10.1371/journal.pone.0151659] [Citation(s) in RCA: 4] [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: 11/11/2015] [Accepted: 03/02/2016] [Indexed: 12/25/2022] Open
Abstract
Metabolism underlies many important cellular decisions, such as the decisions to proliferate and differentiate, and defects in metabolic signaling can lead to disease and aging. In addition, metabolic heterogeneity can have biological consequences, such as differences in outcomes and drug susceptibilities in cancer and antibiotic treatments. Many approaches exist for characterizing the metabolic state of a population of cells, but technologies for measuring metabolism at the single cell level are in the preliminary stages and are limited. Here, we describe novel analysis methodologies that can be applied to established experimental methods to measure metabolic variability within a population. We use mass spectrometry to analyze amino acid composition in cells grown in a mixture of (12)C- and (13)C-labeled sugars; these measurements allow us to quantify the variability in sugar usage and thereby infer information about the behavior of cells within the population. The methodologies described here can be applied to a large range of metabolites and macromolecules and therefore have the potential for broad applications.
Collapse
Affiliation(s)
- Christine M. DeGennaro
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, 02115, United States of America
| | - Yonatan Savir
- Department of Physiology, Biophysics and Systems Biology, Faculty of Medicine, Technion, Haifa, 31096, Israel
| | - Michael Springer
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, 02115, United States of America
- * E-mail:
| |
Collapse
|
22
|
13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production. Bioengineering (Basel) 2015; 3:bioengineering3010003. [PMID: 28952565 PMCID: PMC5597161 DOI: 10.3390/bioengineering3010003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 12/10/2015] [Accepted: 12/18/2015] [Indexed: 12/15/2022] Open
Abstract
Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many 13C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms
Collapse
|
23
|
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.
Collapse
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.
| |
Collapse
|
24
|
White biotechnology: State of the art strategies for the development of biocatalysts for biorefining. Biotechnol Adv 2015; 33:1653-70. [PMID: 26303096 DOI: 10.1016/j.biotechadv.2015.08.004] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 07/31/2015] [Accepted: 08/17/2015] [Indexed: 12/31/2022]
Abstract
White biotechnology is a term that is now often used to describe the implementation of biotechnology in the industrial sphere. Biocatalysts (enzymes and microorganisms) are the key tools of white biotechnology, which is considered to be one of the key technological drivers for the growing bioeconomy. Biocatalysts are already present in sectors such as the chemical and agro-food industries, and are used to manufacture products as diverse as antibiotics, paper pulp, bread or advanced polymers. This review proposes an original and global overview of highly complementary fields of biotechnology at both enzyme and microorganism level. A certain number of state of the art approaches that are now being used to improve the industrial fitness of biocatalysts particularly focused on the biorefinery sector are presented. The first part deals with the technologies that underpin the development of industrial biocatalysts, notably the discovery of new enzymes and enzyme improvement using directed evolution techniques. The second part describes the toolbox available by the cell engineer to shape the metabolism of microorganisms. And finally the last part focuses on the 'omic' technologies that are vital for understanding and guide microbial engineering toward more efficient microbial biocatalysts. Altogether, these techniques and strategies will undoubtedly help to achieve the challenging task of developing consolidated bioprocessing (i.e. CBP) readily available for industrial purpose.
Collapse
|
25
|
Abstract
Stable isotopes have been used to trace atoms through metabolism and quantify metabolic fluxes for several decades. Only recently non-targeted stable isotope labeling approaches have emerged as a powerful tool to gain insights into metabolism. However, the manual detection of isotopic enrichment for a non-targeted analysis is tedious and time consuming. To overcome this limitation, the non-targeted tracer fate detection (NTFD) algorithm for the automated metabolome-wide detection of isotopic enrichment has been developed. NTFD detects and quantifies isotopic enrichment in the form of mass isotopomer distributions (MIDs) in an automated manner, providing the means to trace functional groups, determine MIDs for metabolic flux analysis, or detect tracer-derived molecules in general. Here, we describe the algorithmic background of NTFD, discuss practical considerations for the freely available NTFD software package, and present potential applications of non-targeted stable isotope labeling analysis.
Collapse
Affiliation(s)
- Daniel Weindl
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - André Wegner
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Karsten Hiller
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg.
| |
Collapse
|
26
|
|
27
|
Akanni GB, du Preez JC, Steyn L, Kilian SG. Protein enrichment of an Opuntia ficus-indica cladode hydrolysate by cultivation of Candida utilis and Kluyveromyces marxianus. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2015; 95:1094-1102. [PMID: 25371280 PMCID: PMC4402007 DOI: 10.1002/jsfa.6985] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 09/18/2014] [Accepted: 10/30/2014] [Indexed: 06/04/2023]
Abstract
BACKGROUND The cladodes of Opuntia ficus-indica (prickly pear cactus) have a low protein content; for use as a balanced feed, supplementation with other protein sources is therefore desirable. We investigated protein enrichment by cultivation of the yeasts Candida utilis and Kluyveromyces marxianus in an enzymatic hydrolysate of the cladode biomass. RESULTS Dilute acid pretreatment and enzymatic hydrolysis of sun-dried cladodes resulted in a hydrolysate containing (per litre) 45.5 g glucose, 6.3 g xylose, 9.1 g galactose, 10.8 g arabinose and 9.6 g fructose. Even though K. marxianus had a much higher growth rate and utilized l-arabinose and d-galactose more completely than C. utilis, its biomass yield coefficient was lower due to ethanol and ethyl acetate production despite aerobic cultivation. Yeast cultivation more than doubled the protein content of the hydrolysate, with an essential amino acid profile superior to sorghum and millet grains. CONCLUSIONS This K. marxianus strain was weakly Crabtree positive. Despite its low biomass yield, its performance compared well with C. utilis. This is the first report showing that the protein content and quality of O. ficus-indica cladode biomass could substantially be improved by yeast cultivation, including a comparative evaluation of C. utilis and K. marxianus.
Collapse
Affiliation(s)
| | - James C du Preez
- *Correspondence to: James du Preez, Department of Microbial, Biochemical and Food Biotechnology, University of the Free State, PO Box 339, Bloemfontein 9300, South Africa. E-mail:
| | | | | |
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
Nikel PI, Chavarría M. Quantitative Physiology Approaches to Understand and Optimize Reducing Power Availability in Environmental Bacteria. SPRINGER PROTOCOLS HANDBOOKS 2015. [DOI: 10.1007/8623_2015_84] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
|
30
|
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
| |
Collapse
|
31
|
CBFA: phenotype prediction integrating metabolic models with constraints derived from experimental data. BMC SYSTEMS BIOLOGY 2014; 8:123. [PMID: 25466481 PMCID: PMC4263207 DOI: 10.1186/s12918-014-0123-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 10/16/2014] [Indexed: 01/12/2023]
Abstract
Background Flux analysis methods lie at the core of Metabolic Engineering (ME), providing methods for phenotype simulation that allow the determination of flux distributions under different conditions. Although many constraint-based modeling software tools have been developed and published, none provides a free user-friendly application that makes available the full portfolio of flux analysis methods. Results This work presents Constraint-based Flux Analysis (CBFA), an open-source software application for flux analysis in metabolic models that implements several methods for phenotype prediction, allowing users to define constraints associated with measured fluxes and/or flux ratios, together with environmental conditions (e.g. media) and reaction/gene knockouts. CBFA identifies the set of applicable methods based on the constraints defined from user inputs, encompassing algebraic and constraint-based simulation methods. The integration of CBFA within the OptFlux framework for ME enables the utilization of different model formats and standards and the integration with complementary methods for phenotype simulation and visualization of results. Conclusions A general-purpose and flexible application is proposed that is independent of the origin of the constraints defined for a given simulation. The aim is to provide a simple to use software tool focused on the application of several flux prediction methods. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0123-1) contains supplementary material, which is available to authorized users.
Collapse
|
32
|
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.
Collapse
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.
| |
Collapse
|
33
|
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.
Collapse
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.
| |
Collapse
|
34
|
Jung JY, Oh MK. Isotope labeling pattern study of central carbon metabolites using GC/MS. J Chromatogr B Analyt Technol Biomed Life Sci 2014; 974:101-8. [PMID: 25463204 DOI: 10.1016/j.jchromb.2014.10.033] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 10/17/2014] [Accepted: 10/26/2014] [Indexed: 12/31/2022]
Abstract
Determination of fluxes by (13)C tracer experiments depends on monitoring the (13)C labeling pattern of metabolites during isotope experiments. In metabolome-based (13)C metabolic flux analysis, liquid chromatography combined with mass spectrometry or tandem mass spectrometry (LC/MS or LC/MS/MS, respectively) has been mainly used as an analytical platform for isotope pattern studies of central carbon metabolites. However, gas chromatography with mass spectrometry (GC/MS) has several advantages over LC/MS, such as high sensitivity, low cost, ease of operation, and availability of mass spectra databases for comparison. In this study, analysis of isotope pattern for central carbon metabolites using GC/MS was demonstrated. First, a proper set of mass ions for central carbon metabolites was selected based on carbon backbone information and structural isomers of mass fragment ions. A total of 34 mass fragment ions was selected and used for the quantification of 25 central carbon metabolites. Then, to quantify isotope fractions, a natural mass isotopomer library for selected mass fragment ions was constructed and subtracted from isotopomer mass spectra data. The results revealed a surprisingly high abundance of partially labeled (13)C intermediates, such as 56.4% of fructose 6-phosphate and 47.6% of dihydroxyacetone phosphate at isotopic steady state, which were generated in the pentose phosphate pathway. Finally, dynamic changes of isotope fragments of central metabolites were monitored with a U-(13)C glucose stimulus response experiment in Kluyveromyces marxianus. With a comprehensive study of isotope patterns of central carbon metabolites using GC/MS, 25 central carbon metabolites and their isotopic fractions were successfully quantified. Dynamic and precise acquisition of isotope pattern can then be used in combination with proper kinetic models to calculate metabolic fluxes.
Collapse
Affiliation(s)
- Joon-Young Jung
- Systems Bioengineering Laboratory, Department of Chemical and Biological Engineering, Korea University, Seoul 136-701, Republic of Korea
| | - Min-Kyu Oh
- Systems Bioengineering Laboratory, Department of Chemical and Biological Engineering, Korea University, Seoul 136-701, Republic of Korea.
| |
Collapse
|
35
|
Cho K, Mahieu N, Ivanisevic J, Uritboonthai W, Chen YJ, Siuzdak G, Patti GJ. isoMETLIN: a database for isotope-based metabolomics. Anal Chem 2014; 86:9358-61. [PMID: 25166490 DOI: 10.1021/ac5029177] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The METLIN metabolite database has become one of the most widely used resources in metabolomics for making metabolite identifications. However, METLIN is not designed to identify metabolites that have been isotopically labeled. As a result, unbiasedly tracking the transformation of labeled metabolites with isotope-based metabolomics is a challenge. Here, we introduce a new database, called isoMETLIN (http://isometlin.scripps.edu/), that has been developed specifically to identify metabolites incorporating isotopic labels. isoMETLIN enables users to search all computed isotopologues derived from METLIN on the basis of mass-to-charge values and specified isotopes of interest, such as (13)C or (15)N. Additionally, isoMETLIN contains experimental MS/MS data on hundreds of isotopomers. These data assist in localizing the position of isotopic labels within a metabolite. From these experimental MS/MS isotopomer spectra, precursor atoms can be mapped to fragments. The MS/MS spectra of additional isotopomers can then be computationally generated and included within isoMETLIN. Given that isobaric isotopomers cannot be separated chromatographically or by mass but are likely to occur simultaneously in a biological system, we have also implemented a spectral-mixing function in isoMETLIN. This functionality allows users to combine MS/MS spectra from various isotopomers in different ratios to obtain a theoretical MS/MS spectrum that matches the MS/MS spectrum from a biological sample. Thus, by searching MS and MS/MS experimental data, isoMETLIN facilitates the identification of isotopologues as well as isotopomers from biological samples and provides a platform to drive the next generation of isotope-based metabolomic studies.
Collapse
Affiliation(s)
- Kevin Cho
- Department of Chemistry, Washington University in St. Louis , St. Louis, Missouri 63130, United States
| | | | | | | | | | | | | |
Collapse
|
36
|
Ghosh A, Nilmeier J, Weaver D, Adams PD, Keasling JD, Mukhopadhyay A, Petzold CJ, Martín HG. A peptide-based method for 13C Metabolic Flux Analysis in microbial communities. PLoS Comput Biol 2014; 10:e1003827. [PMID: 25188426 PMCID: PMC4154649 DOI: 10.1371/journal.pcbi.1003827] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Accepted: 07/23/2014] [Indexed: 01/08/2023] Open
Abstract
The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species. Microbial communities underlie a variety of important biochemical processes ranging from underground cave formation to gold mining or the onset of obesity. Metabolic fluxes describe how carbon and energy flow through the microbial community and therefore provide insights that are rarely captured by other techniques, such as metatranscriptomics or metaproteomics. The most authoritative method to measure fluxes for pure cultures consists of feeding the cells a labeled carbon source and deriving the fluxes from the ensuing metabolite labeling pattern (typically amino acids). Since we cannot easily separate cells of metabolite for each species in a community, this approach is not generally applicable to microbial communities. Here we present a method to derive fluxes from the labeling of peptides, instead of amino acids. This approach has the advantage that peptides can be assigned to each species in a community in a high-throughput fashion through modern proteomic methods. We show that, by using this method, it is theoretically possible to recover the same amount of information as through the standard approach, if enough peptides are used. We computationally tested this method with a well-characterized simple microbial community consisting of two species.
Collapse
Affiliation(s)
- Amit Ghosh
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Jerome Nilmeier
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Daniel Weaver
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Paul D. Adams
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America
| | - Jay D. Keasling
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America
- Department of Chemical Engineering, University of California, Berkeley, Berkeley, United States of America
| | - Aindrila Mukhopadhyay
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Christopher J. Petzold
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Héctor García Martín
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
- * E-mail:
| |
Collapse
|
37
|
Pietzke M, Zasada C, Mudrich S, Kempa S. Decoding the dynamics of cellular metabolism and the action of 3-bromopyruvate and 2-deoxyglucose using pulsed stable isotope-resolved metabolomics. Cancer Metab 2014; 2:9. [PMID: 25035808 PMCID: PMC4101711 DOI: 10.1186/2049-3002-2-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Accepted: 06/23/2014] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Cellular metabolism is highly dynamic and continuously adjusts to the physiological program of the cell. The regulation of metabolism appears at all biological levels: (post-) transcriptional, (post-) translational, and allosteric. This regulatory information is expressed in the metabolome, but in a complex manner. To decode such complex information, new methods are needed in order to facilitate dynamic metabolic characterization at high resolution. RESULTS Here, we describe pulsed stable isotope-resolved metabolomics (pSIRM) as a tool for the dynamic metabolic characterization of cellular metabolism. We have adapted gas chromatography-coupled mass spectrometric methods for metabolomic profiling and stable isotope-resolved metabolomics. In addition, we have improved robustness and reproducibility and implemented a strategy for the absolute quantification of metabolites. CONCLUSIONS By way of examples, we have applied this methodology to characterize central carbon metabolism of a panel of cancer cell lines and to determine the mode of metabolic inhibition of glycolytic inhibitors in times ranging from minutes to hours. Using pSIRM, we observed that 2-deoxyglucose is a metabolic inhibitor, but does not directly act on the glycolytic cascade.
Collapse
Affiliation(s)
- Matthias Pietzke
- Integrative Metabolomics and Proteomics, Berlin Institute of Medical Systems Biology/Max-Delbrueck Center for Molecular Medicine, Robert Rossle Street 10, Berlin 13125, Germany
| | - Christin Zasada
- Integrative Metabolomics and Proteomics, Berlin Institute of Medical Systems Biology/Max-Delbrueck Center for Molecular Medicine, Robert Rossle Street 10, Berlin 13125, Germany
| | - Susann Mudrich
- Integrative Metabolomics and Proteomics, Berlin Institute of Medical Systems Biology/Max-Delbrueck Center for Molecular Medicine, Robert Rossle Street 10, Berlin 13125, Germany
- Present address: Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, 162 A, Avenue de la Faïencerie L-1511, Luxembourg, Luxembourg
| | - Stefan Kempa
- Integrative Metabolomics and Proteomics, Berlin Institute of Medical Systems Biology/Max-Delbrueck Center for Molecular Medicine, Robert Rossle Street 10, Berlin 13125, Germany
| |
Collapse
|
38
|
Okahashi N, Kajihata S, Furusawa C, Shimizu H. Reliable Metabolic Flux Estimation in Escherichia coli Central Carbon Metabolism Using Intracellular Free Amino Acids. Metabolites 2014; 4:408-20. [PMID: 24957033 PMCID: PMC4101513 DOI: 10.3390/metabo4020408] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 05/10/2014] [Accepted: 05/20/2014] [Indexed: 12/25/2022] Open
Abstract
13C metabolic flux analysis (MFA) is a tool of metabolic engineering for investigation of in vivo flux distribution. A direct 13C enrichment analysis of intracellular free amino acids (FAAs) is expected to reduce time for labeling experiments of the MFA. Measurable FAAs should, however, vary among the MFA experiments since the pool sizes of intracellular free metabolites depend on cellular metabolic conditions. In this study, minimal 13C enrichment data of FAAs was investigated to perform the FAAs-based MFA. An examination of a continuous culture of Escherichia coli using 13C-labeled glucose showed that the time required to reach an isotopically steady state for FAAs is rather faster than that for conventional method using proteinogenic amino acids (PAAs). Considering 95% confidence intervals, it was found that the metabolic flux distribution estimated using FAAs has a similar reliability to that of the PAAs-based method. The comparative analysis identified glutamate, aspartate, alanine and phenylalanine as the common amino acids observed in E. coli under different culture conditions. The results of MFA also demonstrated that the 13C enrichment data of the four amino acids is required for a reliable analysis of the flux distribution.
Collapse
Affiliation(s)
- Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Shuichi Kajihata
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Chikara Furusawa
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| |
Collapse
|
39
|
Abstract
Oxygen and oxidative stress have become relevant components in clarifying the mechanism that weakens bacterial cells in parallel to the mode of action of bactericidal antibiotics. Given the importance of oxidative stress in the overall defense mechanism of bacteria and their apparent role in the antimicrobial mode of action, it is important to understand how bacteria respond to this stress at a metabolic level. The aim of this study was to determine the impact of oxygen on the metabolism of the facultative anaerobe Enterococcus faecalis using continuous culture, metabolomics, and (13)C enrichment of metabolic intermediates. When E. faecalis was rapidly transitioned from anaerobic to aerobic growth, cellular metabolism was directed toward intracellular glutathione production and glycolysis was upregulated 2-fold, which increased the supply of critical metabolite precursors (e.g., glycine and glutamate) for sulfur metabolism and glutathione biosynthesis as well as reducing power for cellular respiration in the presence of hemin. The ultimate metabolic response of E. faecalis to an aerobic environment was the upregulation of fatty acid metabolism and benzoate degradation, which was linked to important changes in the bacterial membrane composition as evidenced by changes in membrane fatty acid composition and the reduction of membrane-associated demethylmenaquinone. These key metabolic pathways associated with the response of E. faecalis to oxygen may represent potential new targets to increase the susceptibility of this bacterium to bactericidal drugs.
Collapse
|
40
|
Abstract
The in vivo analysis of metabolic fluxes has become a valuable method for the investigation of microorganisms. It turned out especially useful in industrial biotechnology for the prediction of beneficial genetic targets for rational strain optimization. Here, we describe in detail the procedure for the state-of-the-art approach of (13)C metabolic flux analysis comprising steady-state cultivations in a mineral salt medium with (13)C-labeled substrates, GC-MS measurement for labeling analysis, as well as metabolic modeling using the open-source software OpenFlux.
Collapse
Affiliation(s)
- Judith Becker
- Institute of Systems Biotechnology, Saarland University, Campus A 1 5, 66123, Saarbrücken, Germany
| | | |
Collapse
|
41
|
Deregulation of feedback inhibition of phosphoenolpyruvate carboxylase for improved lysine production in Corynebacterium glutamicum. Appl Environ Microbiol 2013; 80:1388-93. [PMID: 24334667 DOI: 10.1128/aem.03535-13] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Allosteric regulation of phosphoenolpyruvate carboxylase (PEPC) controls the metabolic flux distribution of anaplerotic pathways. In this study, the feedback inhibition of Corynebacterium glutamicum PEPC was rationally deregulated, and its effect on metabolic flux redistribution was evaluated. Based on rational protein design, six PEPC mutants were designed, and all of them showed significantly reduced sensitivity toward aspartate and malate inhibition. Introducing one of the point mutations (N917G) into the ppc gene, encoding PEPC of the lysine-producing strain C. glutamicum LC298, resulted in ∼37% improved lysine production. In vitro enzyme assays and (13)C-based metabolic flux analysis showed ca. 20 and 30% increases in the PEPC activity and corresponding flux, respectively, in the mutant strain. Higher demand for NADPH in the mutant strain increased the flux toward pentose phosphate pathway, which increased the supply of NADPH for enhanced lysine production. The present study highlights the importance of allosteric regulation on the flux control of central metabolism. The strategy described here can also be implemented to improve other oxaloacetate-derived products.
Collapse
|
42
|
Fernie AR, Morgan JA. Analysis of metabolic flux using dynamic labelling and metabolic modelling. PLANT, CELL & ENVIRONMENT 2013; 36:1738-1750. [PMID: 23421750 DOI: 10.1111/pce.12083] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Revised: 02/05/2013] [Accepted: 02/11/2013] [Indexed: 06/01/2023]
Abstract
Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.
Collapse
Affiliation(s)
- A R Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.
| | | |
Collapse
|
43
|
Wasylenko TM, Stephanopoulos G. Kinetic isotope effects significantly influence intracellular metabolite (13) C labeling patterns and flux determination. Biotechnol J 2013; 8:1080-9. [PMID: 23828762 DOI: 10.1002/biot.201200276] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2013] [Revised: 06/06/2013] [Accepted: 07/01/2013] [Indexed: 12/21/2022]
Abstract
Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from (13) C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of (13) C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the (13) C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the (13) C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in (13) C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC-MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in (13) C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.
Collapse
Affiliation(s)
- Thomas M Wasylenko
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | |
Collapse
|
44
|
Ahmed Z, Zeeshan S, Huber C, Hensel M, Schomburg D, Münch R, Eisenreich W, Dandekar T. Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling. BMC Bioinformatics 2013; 14:218. [PMID: 23837681 PMCID: PMC3720290 DOI: 10.1186/1471-2105-14-218] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2012] [Accepted: 06/23/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. RESULTS The open-source software "Least Square Mass Isotopomer Analyzer" (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman's least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. CONCLUSIONS The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.
Collapse
Affiliation(s)
- Zeeshan Ahmed
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Saman Zeeshan
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hanover, Germany
| | - Claudia Huber
- Lehrstuhl für Biochemie, Technische Universität München, München, Germany
| | - Michael Hensel
- Department of Microbiology, University of Osnabrück, Osnabrück, Germany
| | - Dietmar Schomburg
- Department of Bioinformatics and Biochemistry, Technical University Braunschweig, Braunschweig, Germany
| | - Richard Münch
- Institute for Microbiology, Technical University Braunschweig, Braunschweig, Germany
| | | | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- EMBL, Structural and Computational Biology Unit, Heidelberg, Germany
| |
Collapse
|
45
|
Koubaa M, Cocuron JC, Thomasset B, Alonso AP. Highlighting the tricarboxylic acid cycle: liquid and gas chromatography-mass spectrometry analyses of (13)C-labeled organic acids. Anal Biochem 2013; 436:151-9. [PMID: 23399391 DOI: 10.1016/j.ab.2013.01.027] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Revised: 01/22/2013] [Accepted: 01/25/2013] [Indexed: 01/01/2023]
Abstract
The tricarboxylic acid (TCA) cycle is involved in the complete oxidation of organic acids to carbon dioxide in aerobic cells. It not only uses the acetyl-CoA derived from glycolysis but also uses breakdown products of proteins, fatty acids, and nucleic acids. Therefore, the TCA cycle involves numerous carbon fluxes through central metabolism to produce reductant power and transfer the generated electrons to the aerobic electron transport system where energy is formed by oxidative phosphorylation. Although the TCA cycle plays a crucial role in aerobic organisms and tissues, the lack of direct isotopic labeling information in its intermediates (organic acids) makes the quantification of its metabolic fluxes rather approximate. This is the major technical gap that this study intended to fill. In this work, we established and validated liquid and gas chromatography-mass spectrometry methods to determine (13)C labeling in organic acids involved in the TCA cycle using scheduled multiple reaction monitoring and single ion monitoring modes, respectively. Labeled samples were generated using maize embryos cultured with [(13)C]glucose or [(13)C]glutamine. Once steady-state labeling was reached, (13)C-labeled organic acids were extracted and purified. When applying our mass spectrometric methods to those extracts, mass isotopomer abundances of seven major organic acids were successfully determined.
Collapse
Affiliation(s)
- Mohamed Koubaa
- Department of Molecular Genetics, The Ohio State University, Columbus, OH 43210, USA.
| | | | | | | |
Collapse
|
46
|
Pathways at Work: Metabolic Flux Analysis of the Industrial Cell Factory Corynebacterium glutamicum. CORYNEBACTERIUM GLUTAMICUM 2013. [DOI: 10.1007/978-3-642-29857-8_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
47
|
Abstract
Isotope-based metabolic flux analysis is one of the emerging technologies applied to system level metabolic phenotype characterization in metabolic engineering. Among the developed approaches, (13)C-based metabolic flux analysis has been established as a standard tool and has been widely applied to quantitative pathway characterization of diverse biological systems. To implement (13)C-based metabolic flux analysis in practice, comprehending the underlying mathematical and computational modeling fundamentals is of importance along with carefully conducted experiments and analytical measurements. Such knowledge is also crucial when designing (13)C-labeling experiments and properly acquiring key data sets essential for in vivo flux analysis implementation. In this regard, the modeling fundamentals of (13)C-labeling systems and analytical data processing are the main topics we will deal with in this chapter. Along with this, the relevant numerical optimization techniques are addressed to help implementation of the entire computational procedures aiming at (13)C-based metabolic flux analysis in vivo.
Collapse
|
48
|
Nargund S, Sriram G. Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks. ACTA ACUST UNITED AC 2013; 9:99-112. [DOI: 10.1039/c2mb25253h] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
49
|
Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies. Metab Eng 2012; 16:21-32. [PMID: 23246523 DOI: 10.1016/j.ymben.2012.11.010] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2012] [Revised: 11/09/2012] [Accepted: 11/21/2012] [Indexed: 01/22/2023]
Abstract
Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for (13)C-metabolic flux analysis ((13)C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of (13)C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for (13)C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.
Collapse
|
50
|
Metabolic flux analysis of genetically engineered Saccharomyces cerevisiae that produces lactate under micro-aerobic conditions. Bioprocess Biosyst Eng 2012; 36:1261-5. [DOI: 10.1007/s00449-012-0870-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2012] [Accepted: 11/25/2012] [Indexed: 10/27/2022]
|