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Theorell A, Jadebeck JF, Wiechert W, McFadden J, Nöh K. Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference. Metab Eng 2024; 83:137-149. [PMID: 38582144 DOI: 10.1016/j.ymben.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/08/2024]
Abstract
Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.
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Affiliation(s)
- Axel Theorell
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Johnjoe McFadden
- Department of Microbial and Cellular Sciences, University of Surrey, GU2 7XH Guildford, United Kingdom
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
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2
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Wang K, Xie W, Harcum SW. Metabolic regulatory network kinetic modeling with multiple isotopic tracers for iPSCs. Biotechnol Bioeng 2024; 121:1336-1354. [PMID: 38037741 DOI: 10.1002/bit.28609] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/26/2023] [Accepted: 11/15/2023] [Indexed: 12/02/2023]
Abstract
The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.
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Affiliation(s)
- Keqi Wang
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts, USA
| | - Wei Xie
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts, USA
| | - Sarah W Harcum
- Department of Bioengineering, Clemson University, Clemson, South Carolina, USA
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3
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Uy ALT, Yamamoto A, Matsuda M, Arae T, Hasunuma T, Demura T, Ohtani M. The Carbon Flow Shifts from Primary to Secondary Metabolism during Xylem Vessel Cell Differentiation in Arabidopsis thaliana. Plant Cell Physiol 2023; 64:1563-1575. [PMID: 37875012 PMCID: PMC10734892 DOI: 10.1093/pcp/pcad130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 10/12/2023] [Accepted: 10/23/2023] [Indexed: 10/26/2023]
Abstract
Xylem vessel cell differentiation is characterized by the deposition of a secondary cell wall (SCW) containing cellulose, hemicellulose and lignin. VASCULAR-RELATED NAC-DOMAIN7 (VND7), a plant-specific NAC (NAM, ATAF1/2, and CUC2) transcription factor, is a master regulator of xylem vessel cell differentiation in Arabidopsis (Arabidopsis thaliana). Previous metabolome analysis using the VND7-inducible system in tobacco BY-2 cells successfully revealed significant quantitative changes in primary metabolites during xylem vessel cell differentiation. However, the flow of primary metabolites is not yet well understood. Here, we performed a metabolomic analysis of VND7-inducible Arabidopsis T87 suspension cells. Capillary electrophoresis-time-of-flight mass spectrometry quantified 57 metabolites, and subsequent data analysis highlighted active changes in the levels of UDP-glucose and phenylalanine, which are building blocks of cellulose and lignin, respectively. In a metabolic flow analysis using stable carbon 13 (13C) isotope, the 13C-labeling ratio specifically increased in 3-phosphoglycerate after 12 h of VND7 induction, followed by an increase in shikimate after 24 h of induction, while the inflow of 13C into lactate from pyruvate was significantly inhibited, indicating an active shift of carbon flow from glycolysis to the shikimate pathway during xylem vessel cell differentiation. In support of this notion, most glycolytic genes involved in the downstream of glyceraldehyde 3-phosphate were downregulated following the induction of xylem vessel cell differentiation, whereas genes for the shikimate pathway and phenylalanine biosynthesis were upregulated. These findings provide evidence for the active shift of carbon flow from primary metabolic pathways to the SCW polymer biosynthetic pathway at specific points during xylem vessel cell differentiation.
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Affiliation(s)
| | - Atsushi Yamamoto
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8562 Japan
| | - Mami Matsuda
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo, 657-8501 Japan
| | - Toshihiro Arae
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8562 Japan
| | - Tomohisa Hasunuma
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo, 657-8501 Japan
- Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo, 657-8501 Japan
| | - Taku Demura
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, 630-0192 Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-Cho, Tsurumi-Ku, Yokohama, Kanagawa, 230-0045 Japan
| | - Misato Ohtani
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, 630-0192 Japan
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8562 Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-Cho, Tsurumi-Ku, Yokohama, Kanagawa, 230-0045 Japan
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4
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McFadden J. Razor sharp: The role of Occam's razor in science. Ann N Y Acad Sci 2023; 1530:8-17. [PMID: 38018886 PMCID: PMC10952609 DOI: 10.1111/nyas.15086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Occam's razor-the principle of simplicity-has recently been attacked as a cultural bias without rational foundation. Increasingly, belief in pseudoscience and mysticism is growing. I argue that inclusion of Occam's razor is an essential factor that distinguishes science from superstition and pseudoscience. I also describe how the razor is embedded in Bayesian inference and argue that science is primarily the means to discover the simplest descriptions of our world.
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Affiliation(s)
- Johnjoe McFadden
- Leverhulme Quantum Biology Doctoral Training CentreUniversity of SurreyGuildfordUK
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5
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Law RC, O’Keeffe S, Nurwono G, Ki R, Lakhani A, Lai PK, Park JO. Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns. bioRxiv 2023:2023.11.06.565907. [PMID: 37986781 PMCID: PMC10659294 DOI: 10.1101/2023.11.06.565907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.
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Affiliation(s)
- Richard C. Law
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
| | - Samantha O’Keeffe
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
| | - Glenn Nurwono
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095
| | - Rachel Ki
- Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA 90095
| | - Aliya Lakhani
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
| | - Pin-Kuang Lai
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ 07030
| | - Junyoung O. Park
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
- California NanoSystems Institute and Jonsson Comprehensive Cancer Center at UCLA, Los Angeles, CA 90095
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6
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Wendering P, Nikoloski Z. Model-driven insights into the effects of temperature on metabolism. Biotechnol Adv 2023; 67:108203. [PMID: 37348662 DOI: 10.1016/j.biotechadv.2023.108203] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/22/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023]
Abstract
Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
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Jadebeck JF, Wiechert W, Nöh K. Practical sampling of constraint-based models: Optimized thinning boosts CHRR performance. PLoS Comput Biol 2023; 19:e1011378. [PMID: 37566638 PMCID: PMC10446239 DOI: 10.1371/journal.pcbi.1011378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 08/23/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling constraint-based models, a highly relevant use-case in systems biology, we here demonstrate that thinning boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. By benchmarking CHRR with thinning with simplices and genome-scale metabolic networks of up to thousands of dimensions, we find a substantial increase in computational efficiency compared to unthinned CHRR, in our examples by orders of magnitude, as measured by the effective sample size per time (ESS/t), with performance gains growing with polytope (effective network) dimension. Using a set of benchmark models we derive a ready-to-apply guideline for tuning thinning to efficient and effective use of compute resources without requiring additional coding effort. Our guideline is validated using three (out-of-sample) large-scale networks and we show that it allows sampling convex polytopes uniformly to convergence in a fraction of time, thereby unlocking the rigorous investigation of hitherto intractable models. The derivation of our guideline is explained in detail, allowing future researchers to update it as needed as new model classes and more training data becomes available. CHRR with deliberate utilization of thinning thereby paves the way to keep pace with progressing model sizes derived with the constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.
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Affiliation(s)
- Johann F. Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
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Fina A, Millard P, Albiol J, Ferrer P, Heux S. High throughput 13C-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux. Microb Cell Fact 2023; 22:117. [PMID: 37380999 DOI: 10.1186/s12934-023-02123-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/27/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput 13C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris. RESULTS We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5. CONCLUSIONS This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris's central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes.
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Affiliation(s)
- Albert Fina
- Department of Chemical, Biological and Environmental Engineering, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, 08193, Spain
| | - Pierre Millard
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France
| | - Joan Albiol
- Department of Chemical, Biological and Environmental Engineering, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, 08193, Spain
| | - Pau Ferrer
- Department of Chemical, Biological and Environmental Engineering, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, 08193, Spain.
| | - Stephanie Heux
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France
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Sagun JV, Yadav UP, Alonso AP. Progress in understanding and improving oil content and quality in seeds. Front Plant Sci 2023; 14:1116894. [PMID: 36778708 PMCID: PMC9909563 DOI: 10.3389/fpls.2023.1116894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
The world's population is projected to increase by two billion by 2050, resulting in food and energy insecurity. Oilseed crops have been identified as key to address these challenges: they produce and store lipids in the seeds as triacylglycerols that can serve as a source of food/feed, renewable fuels, and other industrially-relevant chemicals. Therefore, improving seed oil content and composition has generated immense interest. Research efforts aiming to unravel the regulatory pathways involved in fatty acid synthesis and to identify targets for metabolic engineering have made tremendous progress. This review provides a summary of the current knowledge of oil metabolism and discusses how photochemical activity and unconventional pathways can contribute to high carbon conversion efficiency in seeds. It also highlights the importance of 13C-metabolic flux analysis as a tool to gain insights on the pathways that regulate oil biosynthesis in seeds. Finally, a list of key genes and regulators that have been recently targeted to enhance seed oil production are reviewed and additional possible targets in the metabolic pathways are proposed to achieve desirable oil content and quality.
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Khattri RB, Puglise J, Ryan TE, Walter GA, Merritt ME, Barton ER. Isolated murine skeletal muscles utilize pyruvate over glucose for oxidation. Metabolomics 2022; 18:105. [PMID: 36480060 PMCID: PMC9732067 DOI: 10.1007/s11306-022-01948-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/29/2022] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Fuel sources for skeletal muscle tissue include carbohydrates and fatty acids, and utilization depends upon fiber type, workload, and substrate availability. The use of isotopically labeled substrate tracers combined with nuclear magnetic resonance (NMR) enables a deeper examination of not only utilization of substrates by a given tissue, but also their contribution to tricarboxylic acid (TCA) cycle intermediates. OBJECTIVES The goal of this study was to determine the differential utilization of substrates in isolated murine skeletal muscle, and to evaluate how isopotomer anlaysis provided insight into skeletal muscle metabolism. METHODS Isolated C57BL/6 mouse hind limb muscles were incubated in oxygenated solution containing uniformly labeled 13C6 glucose, 13C3 pyruvate, or 13C2 acetate at room temperature. Isotopomer analysis of 13C labeled glutamate was performed on pooled extracts of isolated soleus and extensor digitorum longus (EDL) muscles. RESULTS Pyruvate and acetate were more avidly consumed than glucose with resultant increases in glutamate labeling in both muscle groups. Glucose incubation resulted in glutamate labeling, but with high anaplerotic flux in contrast to the labeling by pyruvate. Muscle fiber type distinctions were evident by differences in lactate enrichment and extent of substrate oxidation. CONCLUSION Isotope tracing experiments in isolated muscles reveal that pyruvate and acetate are avidly oxidized by isolated soleus and EDL muscles, whereas glucose labeling of glutamate is accompanied by high anaplerotic flux. We believe our results may set the stage for future examination of metabolic signatures of skeletal muscles from pre-clinical models of aging, type-2 diabetes and neuromuscular disease.
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Affiliation(s)
- Ram B Khattri
- Department of Applied Physiology and Kinesiology, College of Health & Human Performance, University of Florida, 124 Florida Gym, 1864 Stadium Road, Gainesville, FL, 32611, USA
| | - Jason Puglise
- Department of Applied Physiology and Kinesiology, College of Health & Human Performance, University of Florida, 124 Florida Gym, 1864 Stadium Road, Gainesville, FL, 32611, USA
| | - Terence E Ryan
- Department of Applied Physiology and Kinesiology, College of Health & Human Performance, University of Florida, 124 Florida Gym, 1864 Stadium Road, Gainesville, FL, 32611, USA
- Myology Institute, University of Florida, Gainesville, USA
- Center for Exercise Science, University of Florida, Gainesville, FL, USA
| | - Glenn A Walter
- Department of Physiology and Functional Genomics, University of Florida, Gainesville, USA
- Myology Institute, University of Florida, Gainesville, USA
| | - Matthew E Merritt
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, USA
| | - Elisabeth R Barton
- Department of Applied Physiology and Kinesiology, College of Health & Human Performance, University of Florida, 124 Florida Gym, 1864 Stadium Road, Gainesville, FL, 32611, USA.
- Myology Institute, University of Florida, Gainesville, USA.
- Center for Exercise Science, University of Florida, Gainesville, FL, USA.
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Moiz B, Li A, Padmanabhan S, Sriram G, Clyne AM. Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease. Metabolites 2022; 12:1066. [PMID: 36355149 PMCID: PMC9694183 DOI: 10.3390/metabo12111066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 04/28/2024] Open
Abstract
Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.
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Affiliation(s)
- Bilal Moiz
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Andrew Li
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Surya Padmanabhan
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
| | - Alisa Morss Clyne
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
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12
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de Falco B, Giannino F, Carteni F, Mazzoleni S, Kim DH. Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas. RSC Adv 2022; 12:25528-25548. [PMID: 36199351 PMCID: PMC9449821 DOI: 10.1039/d2ra03326g] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/26/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.
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Affiliation(s)
- Bruna de Falco
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
| | - Francesco Giannino
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Fabrizio Carteni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Stefano Mazzoleni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Dong-Hyun Kim
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
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13
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Abstract
INTRODUCTION Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking. OBJECTIVES To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism. METHODS The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of 13C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism. RESULTS The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time. CONCLUSION The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.
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Affiliation(s)
- Noémie Butin
- RESTORE, CNRS ERL5311, EFS, ENVT, Inserm U1031, UPS, Université de Toulouse, Toulouse, France
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Cécilia Bergès
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Jean-Charles Portais
- RESTORE, CNRS ERL5311, EFS, ENVT, Inserm U1031, UPS, Université de Toulouse, Toulouse, France
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France.
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France.
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14
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Fingerhut L, Yücel L, Strutzberg-Minder K, von Köckritz-Blickwede M, Ohnesorge B, de Buhr N. Ex Vivo and In Vitro Analysis Identify a Detrimental Impact of Neutrophil Extracellular Traps on Eye Structures in Equine Recurrent Uveitis. Front Immunol 2022; 13:830871. [PMID: 35251020 PMCID: PMC8896353 DOI: 10.3389/fimmu.2022.830871] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/24/2022] [Indexed: 01/21/2023] Open
Abstract
Equine recurrent uveitis (ERU) is a common ocular disease of horses and described as a model for human autoimmune uveitis. This immune-mediated, inflammatory condition progressively destroys the eye, ultimately leading to blindness. Genetic and autoimmune factors, next to infections with Leptospira, are discussed as key factors in the pathogenesis. Furthermore, a release of neutrophil extracellular traps (NETs) by activated neutrophils is involved. NETs are composed of decondensed chromatin and proteins that can immobilize invading pathogens. However, if NETs accumulate, they can contribute to detrimental autoimmune processes. Thus, we aimed to investigate the impact of NETs in ERU patients. Therefore, we quantified several NET-markers (cell-free DNA, nucleosomes, citrullinated histone H3, histone-myeloperoxidase complexes, interleukin-17, equine cathelicidin 1 and DNase I activity) and NET-autoantibodies in sera and vitreous body fluids (VBF) of ERU-diseased horses and correlated the data with the disease status (signalment, ERU scores and Leptospira infection status). NET markers were detected to varying degrees in VBF of diseased horses, and partially correlated to disease severity and the presence of Leptospira spp. Cell-free DNA and nucleosomes as NET markers correlate with ERU severity in total and VBF scores, despite the presence of active DNases. Additionally, a significant correlation between fundus affection in the eye and NET autoantibodies was detectable. Therefore, we further investigated the influence of VBF samples from equine patients and isolated NETs on the blood-retina barrier in a cell culture model. VBF of diseased horses significantly induced cytotoxicity in retinal pigment epithelial cells. Moreover, partially digested NETs also resulted in cytotoxic effects. In the presence of lipopolysaccharide (LPS), the main component of the leptospiral surface, both undigested and completely digested NETs were cytotoxic. Correlations between the ERU-scores and Leptospira were also calculated. Detection of leptospiral DNA, and antibody titers of the serovar Grippotyphosa correlated with disease severity. In addition, a correlation between Leptospira and several NET markers was observed in VBF. Altogether, our findings suggest a positive correlation between NET markers with disease severity and involvement of Leptospira in the VBF of ERU-diseased horses, as well as a cytotoxic effect of NETs in eyes.
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Affiliation(s)
- Leonie Fingerhut
- Department of Biochemistry, University of Veterinary Medicine Hannover, Hannover, Germany.,Research Center for Emerging Infections and Zoonoses (RIZ), University of Veterinary Medicine Hannover, Hannover, Germany.,Clinic for Horses, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Leyla Yücel
- Clinic for Horses, University of Veterinary Medicine Hannover, Hannover, Germany.,Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Hannover, Germany
| | | | - Maren von Köckritz-Blickwede
- Department of Biochemistry, University of Veterinary Medicine Hannover, Hannover, Germany.,Research Center for Emerging Infections and Zoonoses (RIZ), University of Veterinary Medicine Hannover, Hannover, Germany
| | - Bernhard Ohnesorge
- Clinic for Horses, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Nicole de Buhr
- Department of Biochemistry, University of Veterinary Medicine Hannover, Hannover, Germany.,Research Center for Emerging Infections and Zoonoses (RIZ), University of Veterinary Medicine Hannover, Hannover, Germany
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15
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Emwas AH, Szczepski K, Al-Younis I, Lachowicz JI, Jaremko M. Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways. Front Pharmacol 2022; 13:805782. [PMID: 35387341 PMCID: PMC8977530 DOI: 10.3389/fphar.2022.805782] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 01/24/2022] [Indexed: 12/18/2022] Open
Abstract
Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.
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Affiliation(s)
- Abdul-Hamid Emwas
- King Abdullah University of Science and Technology, Core Labs, Thuwal, Saudi Arabia
| | - Kacper Szczepski
- Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Inas Al-Younis
- King Abdullah University of Science and Technology (KAUST), Biological and Environmental Sciences & Engineering Division (BESE), Thuwal, Saudi Arabia
| | - Joanna Izabela Lachowicz
- Department of Medical Sciences and Public Health, University of Cagliari, Cittadella Universitaria, Monserrato, Italy
| | - Mariusz Jaremko
- Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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16
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Abstract
Although metabolic remodeling during cardiovascular diseases has been well-recognized for decades, the recent development of analytical platforms and mathematical tools has driven the emergence of assessing cardiac metabolism using tracers. Metabolism is a critical component of cellular functions and adaptation to stress. The pathogenesis of cardiovascular disease involves metabolic adaptation to maintain cardiac contractile function even in advanced disease stages. Stable-isotope tracer measurements are a powerful tool for measuring flux distributions at the whole organism level and assessing metabolic changes at a systems level in vivo. The goal of this review is to summarize techniques and concepts for in vivo or ex vivo stable isotope labeling in cardiovascular research, to highlight mathematical concepts and their limitations, to describe analytical methods at the tissue and single-cell level, and to discuss opportunities to leverage metabolic models to address important mechanistic questions relevant to all patients with cardiovascular disease.
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Affiliation(s)
- Anja Karlstaedt
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.,Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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17
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Abstract
Tuberculosis (TB) is a devastating infectious disease that kills over a million people every year. There is an increasing burden of multi drug resistance (MDR) and extensively drug resistance (XDR) TB. New and improved therapies are urgently needed to overcome the limitations of current treatment. The causative agent, Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens that can manipulate host cell environment for adaptation, evading immune defences, virulence, and pathogenesis of TB infection. Host-pathogen interaction is important to establish infection and it involves a complex set of processes. Metabolic cross talk between the host and pathogen is a facet of TB infection and has been an important topic of research where there is growing interest in developing therapies and drugs that target these interactions and metabolism of the pathogen in the host. Mtb scavenges multiple nutrient sources from the host and has adapted its metabolism to survive in the intracellular niche. Advancements in systems-based omic technologies have been successful to unravel host-pathogen interactions in TB. In this review we discuss the application and usefulness of omics in TB research that provides promising interventions for developing anti-TB therapies.
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Affiliation(s)
- Khushboo Borah
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | | | - Johnjoe McFadden
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
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18
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Shimizu H, Toya Y. Recent advances in metabolic engineering-integration of in silico design and experimental analysis of metabolic pathways. J Biosci Bioeng 2021; 132:429-436. [PMID: 34509367 DOI: 10.1016/j.jbiosc.2021.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/07/2021] [Indexed: 11/29/2022]
Abstract
Microorganisms are widely used to produce valuable compounds. Because thousands of metabolic reactions occur simultaneously and many metabolic reactions are related to target production and cell growth, the development of a rational design method for metabolic pathway modification to optimize target production is needed. In this paper, recent advances in metabolic engineering are reviewed, specifically considering computational pathway modification design and experimental evaluation of metabolic fluxes by 13C-metabolic flux analysis. Computational tools for seeking effective gene deletion targets and recruiting heterologous genes are described in flux balance analysis approaches. A kinetic model and adaptive laboratory evolution were applied to identify and eliminate the rate-limiting step in metabolic pathways. Data science-based approaches for process monitoring and control are described to maximize the performance of engineered cells in bioreactors.
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Affiliation(s)
- Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
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19
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Wiechert W, Nöh K. Quantitative Metabolic Flux Analysis Based on Isotope Labeling. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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20
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Beyß M, Parra-Peña VD, Ramirez-Malule H, Nöh K. Robustifying Experimental Tracer Design for 13C-Metabolic Flux Analysis. Front Bioeng Biotechnol 2021; 9:685323. [PMID: 34239861 PMCID: PMC8258161 DOI: 10.3389/fbioe.2021.685323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/19/2021] [Indexed: 11/25/2022] Open
Abstract
13C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the13C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for13C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies.
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Affiliation(s)
- Martin Beyß
- 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
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21
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Zelle E, Pfelzer N, Oldiges M, Koch-Koerfges A, Bott M, Nöh K, Wiechert W. An energetic profile of Corynebacterium glutamicum underpinned by measured biomass yield on ATP. Metab Eng 2021; 65:66-78. [PMID: 33722651 DOI: 10.1016/j.ymben.2021.03.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 02/17/2021] [Accepted: 03/06/2021] [Indexed: 11/17/2022]
Abstract
The supply and usage of energetic cofactors in metabolism is a central concern for systems metabolic engineering, particularly in case of energy intensive products. One of the most important parameters for systems wide balancing of energetic cofactors is the ATP requirement for biomass formation YATP/Biomass. Despite its fundamental importance, YATP/Biomass values for non-fermentative organisms are still rough estimates deduced from theoretical considerations. For the first time, we present an approach for the experimental determination of YATP/Biomass using comparative 13C metabolic flux analysis (13C MFA) of a wild type strain and an ATP synthase knockout mutant. We show that the energetic profile of a cell can then be deduced from a genome wide stoichiometric model and experimental maintenance data. Particularly, the contributions of substrate level phosphorylation (SLP) and electron transport phosphorylation (ETP) to ATP generation become available which enables the overall energetic efficiency of a cell to be characterized. As a model organism, the industrial platform organism Corynebacterium glutamicum is used. C. glutamicum uses a respiratory type of energy metabolism, implying that ATP can be synthesized either by SLP or by ETP with the membrane-bound F1FO-ATP synthase using the proton motive force (pmf) as driving force. The presence of two terminal oxidases, which differ in their proton translocation efficiency by a factor of three, further complicates energy balancing for this organism. By integration of experimental data and network models, we show that in the wild type SLP and ETP contribute equally to ATP generation. Thus, the role of ETP in respiring bacteria may have been overrated in the past. Remarkably, in the genome wide setting 65% of the pmf is actually not used for ATP synthesis. However, it turns out that, compared to other organisms C. glutamicum still uses its energy budget rather efficiently.
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Affiliation(s)
- E Zelle
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, D, 52425, Jülich, Germany
| | - N Pfelzer
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, D, 52425, Jülich, Germany
| | - M Oldiges
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, D, 52425, Jülich, Germany
| | - A Koch-Koerfges
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, D, 52425, Jülich, Germany
| | - M Bott
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, D, 52425, Jülich, Germany
| | - K Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, D, 52425, Jülich, Germany
| | - W Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, D, 52425, Jülich, Germany.
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22
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He Y, Wu B, Xia W, Zhao KY, Qin Y, Tan Q, Yu QH, Liu PT, Hu GQ, He MX. Metabolic engineering of Zymomonas moblis for ethylene production from straw hydrolysate. Appl Microbiol Biotechnol 2021; 105:1709-20. [PMID: 33512573 DOI: 10.1007/s00253-021-11091-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 10/27/2020] [Accepted: 01/03/2021] [Indexed: 10/22/2022]
Abstract
Biological ethylene production is a promising sustainable alternative approach for fossil-based ethylene production. The high glucose utilization of Z. mobilis makes it as a promising bioethylene producer. In this study, Zymomonas mobilis has been engineered to produce ethylene through the introduction of the synthetic ethylene-forming enzyme (EFE). We also investigated the effect of systematically knocking out the competitive metabolic pathway of pyruvate in an effort to improve the availability of pyruvate for ethylene production in Z. mobilis expressing EFE. Guided by these results, we tested a number of conjectures that could improve the α-ketoglutarate supply. Optimization of these pathways and different substrate supplies resulted in a greater production of ethylene (from 1.36 to 12.83 nmol/OD600/mL), which may guide future engineering work on ethylene production using other organisms. Meanwhile, we achieved an ethylene production of 5.8 nmol/OD600/mL in the ZM532-efe strain using enzymatic straw hydrolysate of corn straw as the sole carbon source. As a preferred host in biorefinery technologies using lignocellulosic biomass as feedstock, heterologous expression of EFE in Z. mobilis converts the non-ethylene producing strain into an ethylene-producing one using a metabolic engineering approach, which is of great significance for the utilization of cellulosic biomass in the future. KEY POINTS: • Heterologous expression of EFE in Z. mobilis successfully converted the non-ethylene producing strain into an ethylene producer (1.36 nmol/OD600/mL). Targeted modifications of the central carbon metabolism can effectively improve ethylene production (peak production: 8.3 nmol/OD600/mL). • The addition of nutrients to the medium can further increase the production of ethylene (peak production: 12.8 nmol/OD600/mL). • The ZM532-efe strain achieved an ethylene production of 5.8 nmol/OD600/mL when enzymatic hydrolysate of corn straw was used as the sole carbon source.
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23
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Capellades J, Junza A, Samino S, Brunner JS, Schabbauer G, Vinaixa M, Yanes O. Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labeling in Metabolomics. Anal Chem 2021; 93:1242-1248. [PMID: 33369389 DOI: 10.1021/acs.analchem.0c02998] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Isotopic-labeling experiments have been valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode, or selected ion clusters using optimized narrow windows (∼10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as a reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway, and urea cycle, from LRMS and HRMS data.
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Affiliation(s)
- Jordi Capellades
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
| | - Alexandra Junza
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara Samino
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Julia S Brunner
- Institute for Vascular Biology, Centre for Physiology and Pharmacology, Medical University Vienna, 1090 Vienna, Austria.,Christian Doppler Laboratory for Arginine Metabolism in Rheumatoid Arthritis and Multiple Sclerosis, 1090 Vienna, Austria
| | - Gernot Schabbauer
- Institute for Vascular Biology, Centre for Physiology and Pharmacology, Medical University Vienna, 1090 Vienna, Austria.,Christian Doppler Laboratory for Arginine Metabolism in Rheumatoid Arthritis and Multiple Sclerosis, 1090 Vienna, Austria
| | - Maria Vinaixa
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
| | - Oscar Yanes
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
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24
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Baslam M, Mitsui T, Sueyoshi K, Ohyama T. Recent Advances in Carbon and Nitrogen Metabolism in C3 Plants. Int J Mol Sci 2020; 22:E318. [PMID: 33396811 PMCID: PMC7795015 DOI: 10.3390/ijms22010318] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/23/2020] [Accepted: 12/23/2020] [Indexed: 12/19/2022] Open
Abstract
C and N are the most important essential elements constituting organic compounds in plants. The shoots and roots depend on each other by exchanging C and N through the xylem and phloem transport systems. Complex mechanisms regulate C and N metabolism to optimize plant growth, agricultural crop production, and maintenance of the agroecosystem. In this paper, we cover the recent advances in understanding C and N metabolism, regulation, and transport in plants, as well as their underlying molecular mechanisms. Special emphasis is given to the mechanisms of starch metabolism in plastids and the changes in responses to environmental stress that were previously overlooked, since these changes provide an essential store of C that fuels plant metabolism and growth. We present general insights into the system biology approaches that have expanded our understanding of core biological questions related to C and N metabolism. Finally, this review synthesizes recent advances in our understanding of the trade-off concept that links C and N status to the plant's response to microorganisms.
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Affiliation(s)
- Marouane Baslam
- Laboratory of Biochemistry, Faculty of Agriculture, Niigata University, Niigata 950-2181, Japan; (M.B.); (T.M.)
| | - Toshiaki Mitsui
- Laboratory of Biochemistry, Faculty of Agriculture, Niigata University, Niigata 950-2181, Japan; (M.B.); (T.M.)
- Department of Life and Food Sciences, Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan;
| | - Kuni Sueyoshi
- Department of Life and Food Sciences, Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan;
| | - Takuji Ohyama
- Department of Life and Food Sciences, Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan;
- Faculty of Applied Biosciences, Tokyo University of Agriculture, Tokyo 156-8502, Japan
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25
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Nilsson R. Validity of natural isotope abundance correction for metabolic flux analysis. Math Biosci 2020; 330:108481. [PMID: 33007317 DOI: 10.1016/j.mbs.2020.108481] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/18/2020] [Accepted: 09/18/2020] [Indexed: 01/22/2023]
Abstract
A pervasive issue in stable isotope tracing and metabolic flux analysis is the presence of naturally occurring isotopes such as 13C. For mass isotopomer distributions (MIDs) measured by mass spectrometry, it is common practice to correct for natural occurrence of isotopes within metabolites of interest using a linear transform based on binomial distributions. The resulting corrected MIDs are often used to fit metabolic network models and infer metabolic fluxes, which implicitly assumes that corrected MIDs will yield the same flux solution as the actual observed MIDs. Although this assumption can be empirically verified in special cases by simulation studies, there seems to be no published proof of this important property for the general case. In this paper, we prove that this property holds for the case of noise-free MID data obtained at steady state. On the other hand, for noisy MID data, the flux solution will generally differ between the two representations. These results provide a theoretical foundation for the common practice of MID correction in metabolic flux analysis.
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Affiliation(s)
- Roland Nilsson
- Cardiovascular Medicine Unit, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden; Division of Cardiovascular Medicine, Karolinska University Hospital, Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
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26
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Correa SM, Fernie AR, Nikoloski Z, Brotman Y. Towards model-driven characterization and manipulation of plant lipid metabolism. Prog Lipid Res 2020; 80:101051. [PMID: 32640289 DOI: 10.1016/j.plipres.2020.101051] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/20/2020] [Accepted: 06/21/2020] [Indexed: 01/09/2023]
Abstract
Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel; Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany.
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
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27
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Quek LE, Krycer JR, Ohno S, Yugi K, Fazakerley DJ, Scalzo R, Elkington SD, Dai Z, Hirayama A, Ikeda S, Shoji F, Suzuki K, Locasale JW, Soga T, James DE, Kuroda S. Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin. iScience 2020; 23:100855. [PMID: 32058966 PMCID: PMC7005519 DOI: 10.1016/j.isci.2020.100855] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 11/26/2019] [Accepted: 01/15/2020] [Indexed: 12/22/2022] Open
Abstract
Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.
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Affiliation(s)
- Lake-Ee Quek
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.
| | - James R Krycer
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Satoshi Ohno
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Katsuyuki Yugi
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; YCI Laboratory for Trans-Omics, Young Chief Investigator Program, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Richard Scalzo
- Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
| | - Sarah D Elkington
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Ziwei Dai
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Duke University, Durham, NC 27710, USA
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; AMED-CREST, AMED, 1-7-1 Otemachi, Chiyoda-Ku, Tokyo 100-0004, Japan
| | - Satsuki Ikeda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Futaba Shoji
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Kumi Suzuki
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Jason W Locasale
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Duke University, Durham, NC 27710, USA
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; AMED-CREST, AMED, 1-7-1 Otemachi, Chiyoda-Ku, Tokyo 100-0004, Japan
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia.
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan; CREST, Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan.
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28
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Abstract
Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. 13C metabolic flux analysis (13C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for 13C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with 13C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for 13C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.
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Affiliation(s)
- Christopher P Long
- Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA.,Ginkgo Bioworks, Boston, MA, USA
| | - Maciek R Antoniewicz
- Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA.
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29
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Thomas SC, Tamadonfar KO, Seymour CO, Lai D, Dodsworth JA, Murugapiran SK, Eloe-Fadrosh EA, Dijkstra P, Hedlund BP. Position-Specific Metabolic Probing and Metagenomics of Microbial Communities Reveal Conserved Central Carbon Metabolic Network Activities at High Temperatures. Front Microbiol 2019; 10:1427. [PMID: 31333598 PMCID: PMC6624737 DOI: 10.3389/fmicb.2019.01427] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 06/05/2019] [Indexed: 12/02/2022] Open
Abstract
Temperature is a primary driver of microbial community composition and taxonomic diversity; however, it is unclear to what extent temperature affects characteristics of central carbon metabolic pathways (CCMPs) at the community level. In this study, 16S rRNA gene amplicon and metagenome sequencing were combined with 13C-labeled metabolite probing of the CCMPs to assess community carbon metabolism along a temperature gradient (60–95°C) in Great Boiling Spring, NV. 16S rRNA gene amplicon diversity was inversely proportional to temperature, and Archaea were dominant at higher temperatures. KO richness and diversity were also inversely proportional to temperature, yet CCMP genes were similarly represented across the temperature gradient and many individual metagenome-assembled genomes had complete pathways. In contrast, genes encoding cellulosomes and many genes involved in plant matter degradation and photosynthesis were absent at higher temperatures. In situ13C-CO2 production from labeled isotopomer pairs of glucose, pyruvate, and acetate suggested lower relative oxidative pentose phosphate pathway activity and/or fermentation at 60°C, and a stable or decreased maintenance energy demand at higher temperatures. Catabolism of 13C-labeled citrate, succinate, L-alanine, L-serine, and L-cysteine was observed at 85°C, demonstrating broad heterotrophic activity and confirming functioning of the TCA cycle. Together, these results suggest that temperature-driven losses in biodiversity and gene content in geothermal systems may not alter CCMP function or maintenance energy demands at a community level.
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Affiliation(s)
- Scott C Thomas
- School of Life Sciences, University of Nevada, Las Vegas, NV, United States
| | - Kevin O Tamadonfar
- School of Life Sciences, University of Nevada, Las Vegas, NV, United States
| | - Cale O Seymour
- School of Life Sciences, University of Nevada, Las Vegas, NV, United States
| | - Dengxun Lai
- School of Life Sciences, University of Nevada, Las Vegas, NV, United States
| | - Jeremy A Dodsworth
- Department of Biology, California State University, San Bernardino, CA, United States
| | | | - Emiley A Eloe-Fadrosh
- Department of Energy Joint Genome Institute, Joint Genome Institute, Walnut Creek, CA, United States
| | - Paul Dijkstra
- Department of Biological Sciences, Center of Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, United States
| | - Brian P Hedlund
- School of Life Sciences, University of Nevada, Las Vegas, NV, United States.,Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV, United States
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30
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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31
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Yuan M, Kremer DM, Huang H, Breitkopf SB, Ben-Sahra I, Manning BD, Lyssiotis CA, Asara JM. Ex vivo and in vivo stable isotope labelling of central carbon metabolism and related pathways with analysis by LC-MS/MS. Nat Protoc 2019; 14:313-30. [PMID: 30683937 DOI: 10.1038/s41596-018-0102-x] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Targeted tandem mass spectrometry (LC-MS/MS) has been extremely useful for profiling small molecules extracted from biological sources, such as cells, bodily fluids and tissues. Here, we present a protocol for analysing incorporation of the non-radioactive stable isotopes carbon-13 (13C) and nitrogen-15 (15N) into polar metabolites in central carbon metabolism and related pathways. Our platform utilizes selected reaction monitoring (SRM) with polarity switching and amide hydrophilic interaction liquid chromatography (HILIC) to capture transitions for carbon and nitrogen incorporation into selected metabolites using a hybrid triple quadrupole (QQQ) mass spectrometer. This protocol represents an extension of a previously published protocol for targeted metabolomics of unlabeled species and has been used extensively in tracing the metabolism of nutrients such as 13C-labeled glucose, 13C-glutamine and 15N-glutamine in a variety of biological settings (e.g., cell culture experiments and in vivo mouse labelling via i.p. injection). SRM signals are integrated to produce an array of peak areas for each labelling form that serve as the output for further analysis. The processed data are then used to obtain the degree and distribution of labelling of the targeted molecules (termed fluxomics). Each method can be customized on the basis of known unlabeled Q1/Q3 SRM transitions and adjusted to account for the corresponding 13C or 15N incorporation. The entire procedure takes ~6-7 h for a single sample from experimental labelling and metabolite extraction to peak integration.
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32
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Sharma J, Larkin J. Therapeutic Implication of SOCS1 Modulation in the Treatment of Autoimmunity and Cancer. Front Pharmacol 2019; 10:324. [PMID: 31105556 PMCID: PMC6499178 DOI: 10.3389/fphar.2019.00324] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 03/18/2019] [Indexed: 12/14/2022] Open
Abstract
The suppressor of cytokine signaling (SOCS) family of intracellular proteins has a vital role in the regulation of the immune system and resolution of inflammatory cascades. SOCS1, also called STAT-induced STAT inhibitor (SSI) or JAK-binding protein (JAB), is a member of the SOCS family with actions ranging from immune modulation to cell cycle regulation. Knockout of SOCS1 leads to perinatal lethality in mice and increased vulnerability to cancer, while several SNPs associated with the SOCS1 gene have been implicated in human inflammation-mediated diseases. In this review, we describe the mechanism of action of SOCS1 and its potential therapeutic role in the prevention and treatment of autoimmunity and cancer. We also provide a brief outline of the other JAK inhibitors, both FDA-approved and under investigation.
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Affiliation(s)
- Jatin Sharma
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, United States
| | - Joseph Larkin
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, United States
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33
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Abstract
13C-assisted metabolism analysis provides rigorous calculations of the intracellular reaction rates (i.e., fluxes) within the central metabolism of microbial hosts. This mapping of the intracellular network within microbes has proven to be essential for understanding the cell physiology. The approach is also a key to identifying central metabolic nodes, probing the rigidity of a metabolic network, revealing cofactor balances, and delineating hidden pathways. Here we present the methodology of using stable isotopic carbon substrates for both qualitative (13C-fingerprinting of functional pathways) and quantitative (Metabolic Flux Analysis) metabolism studies on bacterial species. In this methodology, we include step-by-step instructions to use the open source WUflux software for the steady-state flux calculations based on labeling information of amino acids or free metabolites.
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Affiliation(s)
- Whitney Hollinshead
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA
| | - Lian He
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA
| | - Yinjie J Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA.
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34
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Triebl A, Wenk MR. Analytical Considerations of Stable Isotope Labelling in Lipidomics. Biomolecules 2018; 8:biom8040151. [PMID: 30453585 PMCID: PMC6315579 DOI: 10.3390/biom8040151] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 11/12/2018] [Accepted: 11/13/2018] [Indexed: 12/26/2022] Open
Abstract
Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage, and are now also being implicated to play important roles in a variety of diseases, with lipid biomarker research one of the most widespread applications of lipidomic techniques both in research and in clinical settings. Stable isotope labelling has become a staple technique in the analysis of small molecule metabolism and dynamics, as it is the only experimental setup by which biosynthesis, remodelling and degradation of biomolecules can be directly measured. Using state-of-the-art analytical technologies such as chromatography-coupled high resolution tandem mass spectrometry, the stable isotope label can be precisely localized and quantified within the biomolecules. The application of stable isotope labelling to lipidomics is however complicated by the diversity of lipids and the complexity of the necessary data analysis. This article discusses key experimental aspects of stable isotope labelling in the field of mass spectrometry-based lipidomics, summarizes current applications and provides an outlook on future developments and potential.
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Affiliation(s)
- Alexander Triebl
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore; Singapore 117596, Singapore.
| | - Markus R Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore; Singapore 117596, Singapore.
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35
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Desai TS, Srivastava S. FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses. PeerJ 2018; 6:e4716. [PMID: 29736347 PMCID: PMC5933345 DOI: 10.7717/peerj.4716] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 04/13/2018] [Indexed: 02/02/2023] Open
Abstract
13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package.
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Affiliation(s)
- Trunil S Desai
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.,DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India
| | - Shireesh Srivastava
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.,DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India
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36
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Mairinger T, Wegscheider W, Peña DA, Steiger MG, Koellensperger G, Zanghellini J, Hann S. Comprehensive assessment of measurement uncertainty in 13C-based metabolic flux experiments. Anal Bioanal Chem 2018; 410:3337-3348. [PMID: 29654338 PMCID: PMC5937919 DOI: 10.1007/s00216-018-1017-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 02/28/2018] [Accepted: 03/09/2018] [Indexed: 11/30/2022]
Abstract
In the field of metabolic engineering 13C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a 13C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation. ᅟ ![]()
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Affiliation(s)
- Teresa Mairinger
- Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 18, 1190, Vienna, Austria
| | - Wolfhard Wegscheider
- Department of General, Analytical and Physical Chemistry, University of Leoben, Franz-Josef-Strasse 18, 8700, Leoben, Austria
| | - David Alejandro Peña
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria.,Department of Biotechnology, University of Natural Resources and Life Sciences, Muthgasse 18, 1190, Vienna, Austria
| | - Matthias G Steiger
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria.,Department of Biotechnology, University of Natural Resources and Life Sciences, Muthgasse 18, 1190, Vienna, Austria
| | - Gunda Koellensperger
- Institute of Analytical Chemistry, University of Vienna, Währinger Strasse 38, 1090, Vienna, Austria
| | - Jürgen Zanghellini
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria.,Department of Biotechnology, University of Natural Resources and Life Sciences, Muthgasse 18, 1190, Vienna, Austria
| | - Stephan Hann
- Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria.
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37
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Golubeva LI, Shupletsov MS, Mashko SV. Metabolic Flux Analysis Using 13C Isotopes (13C-MFA). 1. Experimental Basis of the Method and the Present State of Investigations. APPL BIOCHEM MICRO+ 2018. [DOI: 10.1134/s0003683817070031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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38
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Zhang SW, Gou WL, Li Y. Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function. Mol Biosyst 2018; 13:901-909. [PMID: 28338129 DOI: 10.1039/c6mb00811a] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
As one of the critical parameters of a metabolic pathway, the metabolic flux in a metabolic network serves as an essential role in physiology and pathology. Constraint-based metabolic models are the widely used frameworks for predicting metabolic fluxes in genome-scale metabolic networks. Integrating the transcriptomic data into the constraint-based metabolic models can effectively predict context-specific fluxes across different conditions. However, these methods always need user-defined thresholds to identify the expression levels of metabolic genes or restrain the rate of biomass production, and the predictive results are sensitive to the thresholds. In this work, we present the Huber penalty convex optimization function (HPCOF) combined with the flux minimization principle to predict metabolic fluxes. Our HPCOF method integrates gene expression profiles into the genome-scale metabolic models (GEMs) to reduce the sensitivity to outliers, and uses continuous expression data to avoid selection of arbitrary threshold parameters. In the case studies of Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) strains under different conditions, the results show that our HPCOF method has a better fit to the experimentally measured values, and has a higher Pearson correlation coefficient, a smaller P-value and a lower sum of squared error than other methods. The HPCOF code can be freely downloaded from for academic users.
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Affiliation(s)
- Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
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39
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Abstract
Engineering biological systems that are capable of overproducing products of interest is the ultimate goal of any biotechnology application. To this end, stoichiometric (or steady state) and kinetic models are increasingly becoming available for a variety of organisms including prokaryotes, eukaryotes, and microbial communities. This ever-accelerating pace of such model reconstructions has also spurred the development of optimization-based strain design techniques. This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering. In particular, three major methods are covered in detail including two methods for designing strains (i.e., one stoichiometric model-based and the other by integrating kinetic information into a stoichiometric model) and one method for analyzing microbial communities.
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Abd Algfoor Z, Shahrizal Sunar M, Abdullah A, Kolivand H. Identification of metabolic pathways using pathfinding approaches: a systematic review. Brief Funct Genomics 2017; 16:87-98. [PMID: 26969656 DOI: 10.1093/bfgp/elw002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.
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Affiliation(s)
- Zeyad Abd Algfoor
- MaGIC-X (Media and Games Innovation Centre of Excellence), UTM-IRDA Digital Media Centre, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Mohd Shahrizal Sunar
- MaGIC-X (Media and Games Innovation Centre of Excellence), UTM-IRDA Digital Media Centre, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Afnizanfaizal Abdullah
- Boston University School of Medicine, Boston Medical Center, Boston, MA, USA.,Duke Global Health Institute, Duke University, Durham, NC, USA.,Global Health Program, Duke Kunshan University, Jiangsu, China
| | - Hoshang Kolivand
- Department of Computer Science, Liverpool John Moores University, Liverpool, UK
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Affiliation(s)
- Nisarg Gohil
- Synthetic Biology Laboratory, Department of Microbiology, School of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
| | - Happy Panchasara
- Synthetic Biology Laboratory, Department of Microbiology, School of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
| | - Shreya Patel
- Synthetic Biology Laboratory, Department of Microbiology, School of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
| | - Robert Ramírez-García
- Synthetic Biology Laboratory, Department of Microbiology, School of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
| | - Vijai Singh
- Synthetic Biology Laboratory, Department of Microbiology, School of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
- *Correspondence: Vijai Singh, ,
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Rossi MT, Kalde M, Srisakvarakul C, Kruger NJ, Ratcliffe RG. Cell-Type Specific Metabolic Flux Analysis: A Challenge for Metabolic Phenotyping and a Potential Solution in Plants. Metabolites 2017; 7:E59. [PMID: 29137184 DOI: 10.3390/metabo7040059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 11/09/2017] [Accepted: 11/10/2017] [Indexed: 12/22/2022] Open
Abstract
Stable isotope labelling experiments are used routinely in metabolic flux analysis (MFA) to determine the metabolic phenotype of cells and tissues. A complication arises in multicellular systems because single cell measurements of transcriptomes, proteomes and metabolomes in multicellular organisms suggest that the metabolic phenotype will differ between cell types. In silico analysis of simulated metabolite isotopomer datasets shows that cellular heterogeneity confounds conventional MFA because labelling data averaged over multiple cell types does not necessarily yield averaged flux values. A potential solution to this problem—the use of cell-type specific reporter proteins as a source of cell-type specific labelling data—is proposed and the practicality of implementing this strategy in the roots of Arabidopsis thaliana seedlings is explored. A protocol for the immunopurification of ectopically expressed green fluorescent protein (GFP) from Arabidopsis thaliana seedlings using a GFP-binding nanobody is developed, and through GC-MS analysis of protein hydrolysates it is established that constitutively expressed GFP reports accurately on the labelling of total protein in root tissues. It is also demonstrated that the constitutive expression of GFP does not perturb metabolism. The principal obstacle to the implementation of the method in tissues with cell-type specific GFP expression is the sensitivity of the GC-MS system.
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Voit EO. The best models of metabolism. Wiley Interdiscip Rev Syst Biol Med 2017; 9:10.1002/wsbm.1391. [PMID: 28544810 PMCID: PMC5643013 DOI: 10.1002/wsbm.1391] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 03/31/2017] [Accepted: 04/01/2017] [Indexed: 12/25/2022]
Abstract
Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Eberhard O Voit
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Abstract
Stable isotope tracing is a powerful method for interrogating metabolic enzyme activities across the metabolic network of living cells. However, most studies of mammalian cells have used (13)C-labeled tracers only and focused on reactions in central carbon metabolism. Cellular metabolism, however, involves other biologically important elements, including nitrogen, hydrogen, oxygen, phosphate and sulfur. Tracing stable isotopes of such elements may help shed light on poorly understood metabolic pathways. Here, we demonstrate the use of high-resolution mass spectrometry to simultaneously trace carbon and nitrogen metabolism in human cells cultured with (13)C- and (15)N-labeled glucose and glutamine. To facilitate interpretation of the complex isotopomer data generated, we extend current methods for metabolic flux analysis to handle multivariate mass isotopomer distributions (MMIDs). We find that observed MMIDs are broadly consistent with known biochemical pathways. Whereas measured (13)C MIDs were informative for central carbon metabolism, (15)N isotopes provided evidence for nitrogen-carrying reactions in amino acid and nucleotide metabolism. This computational and experimental methodology expands the scope of metabolic flux analysis beyond carbon metabolism, and may prove important to understanding metabolic phenotypes in health and disease.
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Affiliation(s)
- Roland Nilsson
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden. and Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Mohit Jain
- Departments of Medicine and Pharmacology, University of California, San Diego, USA.
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Lehnen M, Ebert BE, Blank LM. A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis. Metab Eng Commun 2017; 5:34-44. [PMID: 29188182 PMCID: PMC5699530 DOI: 10.1016/j.meteno.2017.07.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 05/29/2017] [Accepted: 07/05/2017] [Indexed: 11/18/2022] Open
Abstract
Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for 13C-based metabolic flux analysis of central yeast carbon metabolism was developed that is universally valid and does not depend on localization information regarding amino acid anabolism. The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells. Compartmentation influences computed intracellular fluxes. Improper localization constraints potentially produce false flux solutions. Minimal compartmentation constraints result in high-quality flux computations.
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Key Words
- 13C-metabolic flux analysis
- ACCOA, acetyl-CoA
- Compartmented metabolism
- Eukaryotes
- GLY, glycine
- H. polymorpha
- ILE, isoleucine
- LEU, leucine
- MDV, mass distribution vector
- MFA, metabolic flux analysis
- Non-conventional yeast
- PYR, pyruvate
- S. cerevisiae
- SER, serine
- Sd, flux solution from a fully constrained model
- Sdmin, flux solution from a model with minimal constraints
- Sf, flux solution from an unconstrained model
- THR, threonine
- TP, TargetP 1.1
- WP, WoLF PSORT
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Affiliation(s)
- Mathias Lehnen
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, D-52074 Aachen, Germany
| | - Birgitta E Ebert
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, D-52074 Aachen, Germany
| | - Lars M Blank
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, D-52074 Aachen, Germany
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Boyle NR, Sengupta N, Morgan JA. Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii. PLoS One 2017; 12:e0177292. [PMID: 28542252 DOI: 10.1371/journal.pone.0177292] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 04/25/2017] [Indexed: 12/18/2022] Open
Abstract
Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, 13C-metabolic flux analysis (13C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with 13C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.
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Salon C, Avice JC, Colombié S, Dieuaide-Noubhani M, Gallardo K, Jeudy C, Ourry A, Prudent M, Voisin AS, Rolin D. Fluxomics links cellular functional analyses to whole-plant phenotyping. J Exp Bot 2017; 68:2083-2098. [PMID: 28444347 DOI: 10.1093/jxb/erx126] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.
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Affiliation(s)
- Christophe Salon
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Jean-Christophe Avice
- UNICAEN, UMR INRA 950 Ecophysiologie Végétale, Agronomie et nutritions N, C, S, Esplanade de la Paix, Université Caen Normandie, 14032 Caen Cedex 5, France
| | - Sophie Colombié
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
| | - Martine Dieuaide-Noubhani
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
| | - Karine Gallardo
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Christian Jeudy
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Alain Ourry
- UNICAEN, UMR INRA 950 Ecophysiologie Végétale, Agronomie et nutritions N, C, S, Esplanade de la Paix, Université Caen Normandie, 14032 Caen Cedex 5, France
| | - Marion Prudent
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Anne-Sophie Voisin
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Dominique Rolin
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
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Guo W, Sheng J, Feng X. Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production. Adv Biochem Eng Biotechnol 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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.
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