1
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Shen Y, Dinh HV, Cruz ER, Chen Z, Bartman CR, Xiao T, Call CM, Ryseck RP, Pratas J, Weilandt D, Baron H, Subramanian A, Fatma Z, Wu ZY, Dwaraknath S, Hendry JI, Tran VG, Yang L, Yoshikuni Y, Zhao H, Maranas CD, Wühr M, Rabinowitz JD. Mitochondrial ATP generation is more proteome efficient than glycolysis. Nat Chem Biol 2024:10.1038/s41589-024-01571-y. [PMID: 38448734 DOI: 10.1038/s41589-024-01571-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 02/05/2024] [Indexed: 03/08/2024]
Abstract
Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.
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Affiliation(s)
- Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Edward R Cruz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Zihong Chen
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA
| | - Caroline R Bartman
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA
| | - Tianxia Xiao
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Catherine M Call
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Rolf-Peter Ryseck
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Jimmy Pratas
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Daniel Weilandt
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Heide Baron
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Arjuna Subramanian
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Zia Fatma
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Zong-Yen Wu
- US Department of Energy Joint Genome Institute and Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sudharsan Dwaraknath
- US Department of Energy Joint Genome Institute and Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - John I Hendry
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Vinh G Tran
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Lifeng Yang
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yasuo Yoshikuni
- US Department of Energy Joint Genome Institute and Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Martin Wühr
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA.
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2
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Habibpour M, Razaghi-Moghadam Z, Nikoloski Z. Prediction and integration of metabolite-protein interactions with genome-scale metabolic models. Metab Eng 2024; 82:216-224. [PMID: 38367764 DOI: 10.1016/j.ymben.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/13/2024] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.
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Affiliation(s)
- Mahdis Habibpour
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
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3
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Kaste JA, Shachar-Hill Y. Model validation and selection in metabolic flux analysis and flux balance analysis. Biotechnol Prog 2024; 40:e3413. [PMID: 37997613 PMCID: PMC10922127 DOI: 10.1002/btpr.3413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/25/2023]
Abstract
13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.
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Affiliation(s)
- Joshua A.M. Kaste
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd, East Lansing, MI 48823
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
| | - Yair Shachar-Hill
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
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4
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Backman TWH, Schenk C, Radivojevic T, Ando D, Singh J, Czajka JJ, Costello Z, Keasling JD, Tang Y, Akhmatskaya E, Garcia Martin H. BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale. PLoS Comput Biol 2023; 19:e1011111. [PMID: 37948450 PMCID: PMC10664898 DOI: 10.1371/journal.pcbi.1011111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 11/22/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023] Open
Abstract
Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.
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Affiliation(s)
- Tyler W. H. Backman
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Christina Schenk
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - Tijana Radivojevic
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - David Ando
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Jahnavi Singh
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, United States of America
| | - Jeffrey J. Czajka
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Zak Costello
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - Jay D. Keasling
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California, United States of America
- Department of Bioengineering, University of California, Berkeley, California, United States of America
- QB3 Institute, University of California, Berkeley, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Yinjie Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Elena Akhmatskaya
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Hector Garcia Martin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- DOE Agile BioFoundry, Emeryville, California, United States of America
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5
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Wang B, Zuniga C, Guarnieri MT, Zengler K, Betenbaugh M, Young JD. Metabolic engineering of Synechococcus elongatus 7942 for enhanced sucrose biosynthesis. Metab Eng 2023; 80:12-24. [PMID: 37678664 DOI: 10.1016/j.ymben.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/28/2023] [Accepted: 09/03/2023] [Indexed: 09/09/2023]
Abstract
The capability of cyanobacteria to produce sucrose from CO2 and light has a remarkable societal and biotechnological impact since sucrose can serve as a carbon and energy source for a variety of heterotrophic organisms and can be converted into value-added products. However, most metabolic engineering efforts have focused on understanding local pathway alterations that drive sucrose biosynthesis and secretion in cyanobacteria rather than analyzing the global flux re-routing that occurs following induction of sucrose production by salt stress. Here, we investigated global metabolic flux alterations in a sucrose-secreting (cscB-overexpressing) strain relative to its wild-type Synechococcus elongatus 7942 parental strain. We used targeted metabolomics, 13C metabolic flux analysis (MFA), and genome-scale modeling (GSM) as complementary approaches to elucidate differences in cellular resource allocation by quantifying metabolic profiles of three cyanobacterial cultures - wild-type S. elongatus 7942 without salt stress (WT), wild-type with salt stress (WT/NaCl), and the cscB-overexpressing strain with salt stress (cscB/NaCl) - all under photoautotrophic conditions. We quantified the substantial rewiring of metabolic fluxes in WT/NaCl and cscB/NaCl cultures relative to WT and identified a metabolic bottleneck limiting carbon fixation and sucrose biosynthesis. This bottleneck was subsequently mitigated through heterologous overexpression of glyceraldehyde-3-phosphate dehydrogenase in an engineered sucrose-secreting strain. Our study also demonstrates that combining 13C-MFA and GSM is a useful strategy to both extend the coverage of MFA beyond central metabolism and to improve the accuracy of flux predictions provided by GSM.
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Affiliation(s)
- Bo Wang
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA; Department of Biology, San Diego State University, San Diego, CA, 92182, USA
| | - Michael T Guarnieri
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA; Department of Bioengineering, University of California, San Diego, CA, 92093, USA; Center for Microbiome Innovation, University of California, San Diego, CA, 92093, USA
| | - Michael Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37235, USA.
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6
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Kugler A, Stensjö K. Optimal energy and redox metabolism in the cyanobacterium Synechocystis sp. PCC 6803. NPJ Syst Biol Appl 2023; 9:47. [PMID: 37739963 PMCID: PMC10516873 DOI: 10.1038/s41540-023-00307-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 09/01/2023] [Indexed: 09/24/2023] Open
Abstract
Understanding energy and redox homeostasis and carbon partitioning is crucial for systems metabolic engineering of cell factories. Carbon metabolism alone cannot achieve maximal accumulation of metabolites in production hosts, since an efficient production of target molecules requires energy and redox balance, in addition to carbon flow. The interplay between cofactor regeneration and heterologous production in photosynthetic microorganisms is not fully explored. To investigate the optimality of energy and redox metabolism, while overproducing alkenes-isobutene, isoprene, ethylene and 1-undecene, in the cyanobacterium Synechocystis sp. PCC 6803, we applied stoichiometric metabolic modelling. Our network-wide analysis indicates that the rate of NAD(P)H regeneration, rather than of ATP, controls ATP/NADPH ratio, and thereby bioproduction. The simulation also implies that energy and redox balance is interconnected with carbon and nitrogen metabolism. Furthermore, we show that an auxiliary pathway, composed of serine, one-carbon and glycine metabolism, supports cellular redox homeostasis and ATP cycling. The study revealed non-intuitive metabolic pathways required to enhance alkene production, which are mainly driven by a few key reactions carrying a high flux. We envision that the presented comparative in-silico metabolic analysis will guide the rational design of Synechocystis as a photobiological production platform of target chemicals.
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Affiliation(s)
- Amit Kugler
- Microbial Chemistry, Department of Chemistry-Ångström Laboratory, Uppsala University, Box 523, SE-751 20, Uppsala, Sweden
| | - Karin Stensjö
- Microbial Chemistry, Department of Chemistry-Ångström Laboratory, Uppsala University, Box 523, SE-751 20, Uppsala, Sweden.
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7
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Hashemi S, Razaghi-Moghadam Z, Nikoloski Z. Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony. PLoS Comput Biol 2023; 19:e1011489. [PMID: 37721963 PMCID: PMC10538754 DOI: 10.1371/journal.pcbi.1011489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 09/28/2023] [Accepted: 09/04/2023] [Indexed: 09/20/2023] Open
Abstract
Intracellular fluxes represent a joint outcome of cellular transcription and translation and reflect the availability and usage of nutrients from the environment. While approaches from the constraint-based metabolic framework can accurately predict cellular phenotypes, such as growth and exchange rates with the environment, accurate prediction of intracellular fluxes remains a pressing problem. Parsimonious flux balance analysis (pFBA) has become an approach of choice to predict intracellular fluxes by employing the principle of efficient usage of protein resources. Nevertheless, comparative analyses of intracellular flux predictions from pFBA against fluxes estimated from labeling experiments remain scarce. Here, we posited that steady-state flux distributions derived from the principle of maximizing multi-reaction dependencies are of improved accuracy and precision than those resulting from pFBA. To this end, we designed a constraint-based approach, termed complex-balanced FBA (cbFBA), to predict steady-state flux distributions that support the given specific growth rate and exchange fluxes. We showed that the steady-state flux distributions resulting from cbFBA in comparison to pFBA show better agreement with experimentally measured fluxes from 17 Escherichia coli strains and are more precise, due to the smaller space of alternative solutions. We also showed that the same principle holds in eukaryotes by comparing the predictions of pFBA and cbFBA against experimentally derived steady-state flux distributions from 26 knock-out mutants of Saccharomyces cerevisiae. Furthermore, our results showed that intracellular fluxes predicted by cbFBA provide better support for the principle of minimizing metabolic adjustment between mutants and wild types. Together, our findings point that other principles that consider the dynamics and coordination of steady states may govern the distribution of intracellular fluxes.
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Affiliation(s)
- Seirana Hashemi
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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8
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Hu M, Dinh HV, Shen Y, Suthers PF, Foster CJ, Call CM, Ye X, Pratas J, Fatma Z, Zhao H, Rabinowitz JD, Maranas CD. Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale. Metab Eng 2023; 76:1-17. [PMID: 36603705 DOI: 10.1016/j.ymben.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/22/2022] [Accepted: 01/01/2023] [Indexed: 01/04/2023]
Abstract
The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.
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Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Catherine M Call
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Xuanjia Ye
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Jimmy Pratas
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Zia Fatma
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
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9
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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10
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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: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [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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11
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Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes. Curr Opin Biotechnol 2022; 75:102701. [DOI: 10.1016/j.copbio.2022.102701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/26/2022] [Accepted: 02/04/2022] [Indexed: 02/06/2023]
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12
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Zheng AO, Sher A, Fridman D, Musante CJ, Young JD. Pool size measurements improve precision of flux estimates but increase sensitivity to unmodeled reactions outside the core network in isotopically nonstationary metabolic flux analysis (INST-MFA). Biotechnol J 2022; 17:e2000427. [PMID: 35085426 DOI: 10.1002/biot.202000427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 11/08/2022]
Abstract
Metabolic flux analysis (MFA) involves model-based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified "core" models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST-MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST-MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large E. coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST-MFA datasets may reveal the activity of non-core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness-of-fit testing plays in assessing the quality of model fits obtained with INST-MFA. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Amy O Zheng
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA
| | - Anna Sher
- Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | | | - Cynthia J Musante
- Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA.,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
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13
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Wu C, Yu J, Guarnieri M, Xiong W. Computational Framework for Machine-Learning-Enabled 13C Fluxomics. ACS Synth Biol 2022; 11:103-115. [PMID: 34705423 DOI: 10.1021/acssynbio.1c00189] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
13C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.
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Affiliation(s)
- Chao Wu
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Jianping Yu
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Michael Guarnieri
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Wei Xiong
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
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14
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Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model. Metab Eng 2022; 69:286-301. [PMID: 34982997 DOI: 10.1016/j.ymben.2021.12.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/16/2021] [Accepted: 12/29/2021] [Indexed: 11/20/2022]
Abstract
Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via 13C-metabolic flux analysis (13C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated 13C labeling data were used with 13C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.
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15
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Sacco SA, Young JD. 13C metabolic flux analysis in cell line and bioprocess development. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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16
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Mack SG, Sriram G. NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks. Metab Eng Commun 2021; 12:e00154. [PMID: 33489751 PMCID: PMC7807149 DOI: 10.1016/j.mec.2020.e00154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/11/2020] [Accepted: 11/21/2020] [Indexed: 01/04/2023] Open
Abstract
Genome-scale stoichiometric models (GSMs) have been widely utilized to predict and understand cellular metabolism. GSMs and the flux predictions resulting from them have proven indispensable to fields ranging from metabolic engineering to human disease. Nonetheless, it is challenging to parse these flux predictions due to the inherent size and complexity of the GSMs. Several previous approaches have reduced this complexity by identifying key pathways contained within the genome-scale flux predictions. However, a reduction method that overlays carbon atom transitions on stoichiometry and flux predictions is lacking. To fill this gap, we developed NetFlow, an algorithm that leverages genome-scale carbon mapping to extract and quantitatively distinguish biologically relevant metabolic pathways from a given genome-scale flux prediction. NetFlow extends prior approaches by utilizing both full carbon mapping and context-specific flux predictions. Thus, NetFlow is uniquely able to quantitatively distinguish between biologically relevant pathways of carbon flow within the given flux map. NetFlow simulates 13C isotope labeling experiments to calculate the extent of carbon exchange, or carbon yield, between every metabolite in the given GSM. Based on the carbon yield, the carbon flow to or from any metabolite or between any pair of metabolites of interest can be isolated and readily visualized. The resulting pathways are much easier to interpret, which enables an in-depth mechanistic understanding of the metabolic phenotype of interest. Here, we first demonstrate NetFlow with a simple network. We then depict the utility of NetFlow on a model of central carbon metabolism in E. coli. Specifically, we isolated the production pathway for succinate synthesis in this model and the metabolic mechanism driving the predicted increase in succinate yield in a double knockout of E. coli. Finally, we describe the application of NetFlow to a GSM of lycopene-producing E. coli, which enabled the rapid identification of the mechanisms behind the measured increases in lycopene production following single, double, and triple knockouts.
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Affiliation(s)
- Sean G Mack
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
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17
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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18
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Metabolic flux analysis reaching genome wide coverage: lessons learned and future perspectives. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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19
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Gopalakrishnan S, Dash S, Maranas C. K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data. Metab Eng 2020; 61:197-205. [DOI: 10.1016/j.ymben.2020.03.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/31/2020] [Accepted: 03/02/2020] [Indexed: 12/12/2022]
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20
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Volkova S, Matos MRA, Mattanovich M, Marín de Mas I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites 2020; 10:E303. [PMID: 32722118 PMCID: PMC7465778 DOI: 10.3390/metabo10080303] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/08/2020] [Accepted: 07/22/2020] [Indexed: 01/05/2023] Open
Abstract
Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.
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Affiliation(s)
| | | | | | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; (S.V.); (M.R.A.M.); (M.M.)
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21
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Daletos G, Katsimpouras C, Stephanopoulos G. Novel Strategies and Platforms for Industrial Isoprenoid Engineering. Trends Biotechnol 2020; 38:811-822. [DOI: 10.1016/j.tibtech.2020.03.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 12/13/2022]
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22
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Suthers PF, Maranas CD. Challenges of cultivated meat production and applications of genome‐scale metabolic modeling. AIChE J 2020. [DOI: 10.1002/aic.16235] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Patrick F. Suthers
- Department of Chemical EngineeringThe Pennsylvania State University University Park Pennsylvania USA
| | - Costas D. Maranas
- Department of Chemical EngineeringThe Pennsylvania State University University Park Pennsylvania USA
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23
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Foguet C, Jayaraman A, Marin S, Selivanov VA, Moreno P, Messeguer R, de Atauri P, Cascante M. p13CMFA: Parsimonious 13C metabolic flux analysis. PLoS Comput Biol 2019; 15:e1007310. [PMID: 31490922 PMCID: PMC6750616 DOI: 10.1371/journal.pcbi.1007310] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 09/18/2019] [Accepted: 08/06/2019] [Indexed: 12/05/2022] Open
Abstract
Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2). 13C Metabolic Flux Analysis (13C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism. When a biological system is incubated with a 13C-labeled substrate, 13C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. 13C MFA integrates measurements of 13C enrichment in metabolites to identify the flux distributions consistent with the measured 13C propagation. However, there is often a range of flux values that can lead to the observed 13C distribution. Indeed, either when the metabolic network is large or a small set of measurements are integrated, the range of valid solutions can be too wide to accurately estimate part of the underlying flux distribution. Here we propose to use flux minimization to select the best flux solution in the13C MFA solution space. Furthermore, this approach can integrate gene expression data to give greater weight to the minimization of fluxes through enzymes with low gene expression evidence in order to ensure that the selected solution is biologically relevant. The concept of using flux minimization to select the best solution is widely used in flux balance analysis, but it had never been applied in the framework of 13C MFA. We have termed this new approach parsimonious 13C MFA (p13CMFA).
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Affiliation(s)
- Carles Foguet
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Anusha Jayaraman
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Silvia Marin
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Vitaly A. Selivanov
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Ramon Messeguer
- LEITAT Technological Center, Health & Biomedicine Unit, Barcelona, Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- * E-mail: (PdA); (MC)
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- * E-mail: (PdA); (MC)
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24
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Foster CJ, Gopalakrishnan S, Antoniewicz MR, Maranas CD. From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline. PLoS Comput Biol 2019; 15:e1007319. [PMID: 31504032 PMCID: PMC6759195 DOI: 10.1371/journal.pcbi.1007319] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 09/24/2019] [Accepted: 08/02/2019] [Indexed: 12/02/2022] Open
Abstract
Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.
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Affiliation(s)
- Charles J. Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Saratram Gopalakrishnan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Maciek R. Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware. Newark, Delaware, United States of America
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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25
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Long CP, Antoniewicz MR. High-resolution 13C metabolic flux analysis. Nat Protoc 2019; 14:2856-2877. [PMID: 31471597 DOI: 10.1038/s41596-019-0204-0] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 06/03/2019] [Indexed: 02/07/2023]
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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Lagziel S, Lee WD, Shlomi T. Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches. BMC Biol 2019; 17:51. [PMID: 31272436 PMCID: PMC6609376 DOI: 10.1186/s12915-019-0669-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
| | | | - Tomer Shlomi
- Faculty of Computer Science, Technion, Haifa, Israel. .,Faculty of Biology, Technion, Haifa, Israel. .,Lokey Center for Life Science and Engineering, Technion, Haifa, Israel.
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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] [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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Wu C, Chen CH, Lo J, Michener W, Maness P, Xiong W. EMUlator: An Elementary Metabolite Unit (EMU) Based Isotope Simulator Enabled by Adjacency Matrix. Front Microbiol 2019; 10:922. [PMID: 31114561 PMCID: PMC6503117 DOI: 10.3389/fmicb.2019.00922] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Accepted: 04/11/2019] [Indexed: 11/13/2022] Open
Abstract
Stable isotope based metabolic flux analysis is currently the unique methodology that allows the experimental study of the integrated responses of metabolic networks. This method primarily relies on isotope labeling and modeling, which could be a challenge in both experimental and computational biology. In particular, the algorithm implementation for isotope simulation is a critical step, limiting extensive usage of this powerful approach. Here, we introduce EMUlator a Python-based isotope simulator which is developed on Elementary Metabolite Unit (EMU) algorithm, an efficient and powerful algorithm for isotope modeling. We propose a novel adjacency matrix method to implement EMU modeling and exemplify it stepwise. This method is intuitively straightforward and can be conveniently mastered for various customized purposes. We apply this arithmetic pipeline to understand the phosphoketolase flux in the metabolic network of an industrial microbe Clostridium acetobutylicum. The resulting design enables a high-throughput and non-invasive approach for estimating phosphoketolase flux in vivo. Our computational insights allow the systematic design and prediction of isotope-based metabolic models and yield a comprehensive understanding of their limitations and potentials.
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Affiliation(s)
- Chao Wu
- National Renewable Energy Laboratory, Golden, CO, United States
| | | | - Jonathan Lo
- National Renewable Energy Laboratory, Golden, CO, United States
| | | | - PinChing Maness
- National Renewable Energy Laboratory, Golden, CO, United States
| | - Wei Xiong
- National Renewable Energy Laboratory, Golden, CO, United States
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29
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Tomàs-Gamisans M, Ødum ASR, Workman M, Ferrer P, Albiol J. Glycerol metabolism of Pichia pastoris (Komagataella spp.) characterised by 13C-based metabolic flux analysis. N Biotechnol 2019; 50:52-59. [DOI: 10.1016/j.nbt.2019.01.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 01/11/2019] [Accepted: 01/13/2019] [Indexed: 12/12/2022]
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St. John PC, Bomble YJ. Approaches to Computational Strain Design in the Multiomics Era. Front Microbiol 2019; 10:597. [PMID: 31024467 PMCID: PMC6461008 DOI: 10.3389/fmicb.2019.00597] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/08/2019] [Indexed: 01/29/2023] Open
Abstract
Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.
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Choi J, Antoniewicz MR. Tandem Mass Spectrometry for 13C Metabolic Flux Analysis: Methods and Algorithms Based on EMU Framework. Front Microbiol 2019; 10:31. [PMID: 30733712 PMCID: PMC6353858 DOI: 10.3389/fmicb.2019.00031] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/09/2019] [Indexed: 02/01/2023] Open
Abstract
In the past two decades, 13C metabolic flux analysis (13C-MFA) has matured into a powerful and widely used scientific tool in metabolic engineering and systems biology. Traditionally, metabolic fluxes have been determined from measurements of isotopic labeling by means of mass spectrometry (MS) or nuclear magnetic resonance (NMR). In recent years, tandem MS has emerged as a new analytical technique that can provide additional information for high-resolution quantification of metabolic fluxes in complex biological systems. In this paper, we present recent advances in methods and algorithms for incorporating tandem MS measurements into existing 13C-MFA approaches that are based on the elementary metabolite units (EMU) framework. Specifically, efficient EMU-based algorithms are presented for simulating tandem MS data, tracing isotopic labeling in biochemical network models and for correcting tandem MS data for natural isotope abundances.
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Affiliation(s)
- Jungik Choi
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, United States
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, United States
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32
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Serine synthesis through PHGDH coordinates nucleotide levels by maintaining central carbon metabolism. Nat Commun 2018; 9:5442. [PMID: 30575741 PMCID: PMC6303315 DOI: 10.1038/s41467-018-07868-6] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 12/04/2018] [Indexed: 12/22/2022] Open
Abstract
Phosphoglycerate dehydrogenase (PHGDH) catalyzes the committed step in de novo serine biosynthesis. Paradoxically, PHGDH and serine synthesis are required in the presence of abundant environmental serine even when serine uptake exceeds the requirements for nucleotide synthesis. Here, we establish a mechanism for how PHGDH maintains nucleotide metabolism. We show that inhibition of PHGDH induces alterations in nucleotide metabolism independent of serine utilization. These changes are not attributable to defects in serine-derived nucleotide synthesis and redox maintenance, another key aspect of serine metabolism, but result from disruption of mass balance within central carbon metabolism. Mechanistically, this leads to simultaneous alterations in both the pentose phosphate pathway and the tri-carboxylic acid cycle, as we demonstrate based on a quantitative model. These findings define a mechanism whereby disruption of one metabolic pathway induces toxicity by simultaneously affecting the activity of multiple related pathways. Serine synthesis from glucose is required even when serine is available from the environment. Here, the authors explain this paradox by showing that the enzyme PHGDH enables nucleotide synthesis by coordinating anabolic fluxes related to central carbon metabolism, independent of its role in serine production.
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Novel insights into global translational regulation through Pumilio family RNA-binding protein Puf3p revealed by ribosomal profiling. Curr Genet 2018; 65:201-212. [PMID: 29951697 DOI: 10.1007/s00294-018-0862-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/16/2018] [Accepted: 06/19/2018] [Indexed: 01/13/2023]
Abstract
RNA binding proteins (RBPs) can regulate the stability, localization, and translation of their target mRNAs. Among them, Puf3p is a well-known Pumilio family RBP whose biology has been intensively studied. Nevertheless, the impact of Puf3p on the translational regulation of its downstream genes still remains to be investigated at the genome-wide level. In this study, we combined ribosome profiling and RNA-Seq in budding yeast (Saccharomyces cerevisiae) to investigate Puf3p's functions in translational regulation. Comparison of translational efficiency (TE) between wild-type and puf3Δ strains demonstrates extensive translational modulation in the absence of Puf3p (over 27% genes are affected at the genome level). Besides confirming its known role in regulating mitochondrial metabolism, our data demonstrate that Puf3p serves as a key post-transcriptional regulator of downstream RBPs by regulating their translational efficiencies, indicating a network of interactions among RBPs at the post-transcriptional level. Furthermore, Puf3p switches the balance of translational flux between mitochondrial and cytosolic ribosome biogenesis to adapt to changes in cellular metabolism. In summary, our results indicate that TE can be utilized as an informative index to interrogate the mechanism underlying RBP functions, and provide novel insights into Puf3p's mode-of-action.
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34
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In silico reconstruction and experimental validation of Saccharopolyspora erythraea genome-scale metabolic model iZZ1342 that accounts for 1685 ORFs. BIORESOUR BIOPROCESS 2018. [DOI: 10.1186/s40643-018-0212-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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35
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Elucidation of photoautotrophic carbon flux topology in Synechocystis PCC 6803 using genome-scale carbon mapping models. Metab Eng 2018. [DOI: 10.1016/j.ymben.2018.03.008] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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36
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Hu G, Zhou J, Chen X, Qian Y, Gao C, Guo L, Xu P, Chen W, Chen J, Li Y, Liu L. Engineering synergetic CO2-fixing pathways for malate production. Metab Eng 2018; 47:496-504. [DOI: 10.1016/j.ymben.2018.05.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 05/10/2018] [Accepted: 05/10/2018] [Indexed: 12/11/2022]
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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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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38
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Backman TWH, Ando D, Singh J, Keasling JD, García Martín H. Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis. Metabolites 2018; 8:metabo8010003. [PMID: 29300340 PMCID: PMC5875993 DOI: 10.3390/metabo8010003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 12/23/2017] [Accepted: 01/02/2018] [Indexed: 12/19/2022] Open
Abstract
Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.
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Affiliation(s)
- Tyler W H Backman
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Agile BioFoundry, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
- QB3 Institute, University of California, Berkeley, CA 94720, USA.
| | - David Ando
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Agile BioFoundry, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
| | - Jahnavi Singh
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA.
- Department of Computer Science, University of California, Berkeley, CA 94720, USA.
| | - Jay D Keasling
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
- QB3 Institute, University of California, Berkeley, CA 94720, USA.
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA.
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2970 Horsholm, Denmark.
| | - Héctor García Martín
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Agile BioFoundry, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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39
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Badur MG, Metallo CM. Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease. Metab Eng 2018; 45:95-108. [PMID: 29199104 PMCID: PMC5927620 DOI: 10.1016/j.ymben.2017.11.013] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 10/11/2017] [Accepted: 11/29/2017] [Indexed: 12/16/2022]
Abstract
Metabolic dysfunction has reemerged as an essential hallmark of tumorigenesis, and metabolic phenotypes are increasingly being integrated into pre-clinical models of disease. The complexity of these metabolic networks requires systems-level interrogation, and metabolic flux analysis (MFA) with stable isotope tracing present a suitable conceptual framework for such systems. Here we review efforts to elucidate mechanisms through which metabolism influences tumor growth and survival, with an emphasis on applications using stable isotope tracing and MFA. Through these approaches researchers can now quantify pathway fluxes in various in vitro and in vivo contexts to provide mechanistic insights at molecular and physiological scales respectively. Knowledge and discoveries in cancer models are paving the way toward applications in other biological contexts and disease models. In turn, MFA approaches will increasingly help to uncover new therapeutic opportunities that enhance human health.
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Affiliation(s)
- Mehmet G Badur
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Christian M Metallo
- Department of Bioengineering, University of California, San Diego, La Jolla, USA; Moores Cancer Center, University of California, San Diego, La Jolla, USA; Diabetes and Endocrinology Research Center, University of California, San Diego, La Jolla, USA; Institute of Engineering in Medicine, University of California, San Diego, La Jolla, USA.
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40
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41
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Chae TU, Choi SY, Kim JW, Ko YS, Lee SY. Recent advances in systems metabolic engineering tools and strategies. Curr Opin Biotechnol 2017; 47:67-82. [DOI: 10.1016/j.copbio.2017.06.007] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 06/12/2017] [Indexed: 12/16/2022]
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42
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Huang Z, Lee DY, Yoon S. Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures. Biotechnol Bioeng 2017; 114:2717-2728. [DOI: 10.1002/bit.26384] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 06/07/2017] [Accepted: 07/12/2017] [Indexed: 12/23/2022]
Affiliation(s)
- Zhuangrong Huang
- Department of Chemical Engineering, University of Massachusetts Lowell; One University Avenue; Lowell Massachusetts
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering; National University of Singapore; Singapore Singapore
- Bioprocessing Technology Institute; Agency for Science, Technology and Research (A*STAR); Singapore Singapore
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell; One University Avenue; Lowell Massachusetts
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43
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Dai Z, Locasale JW. Understanding metabolism with flux analysis: From theory to application. Metab Eng 2017; 43:94-102. [PMID: 27667771 PMCID: PMC5362364 DOI: 10.1016/j.ymben.2016.09.005] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 09/06/2016] [Accepted: 09/19/2016] [Indexed: 12/27/2022]
Abstract
Quantitative and qualitative knowledge of metabolic rates (i.e. fluxes) over a metabolic network and in specific cellular compartments gives insights into the regulation of metabolism and helps to understand the contribution of metabolic alterations to pathology. In this review we introduce methodology to resolve metabolic fluxes from stable isotope labeling and relevant techniques in model development, model simplification, flux uncertainty analysis and experimental design that together is termed metabolic flux analysis. Finally we discuss applications using metabolic flux analysis to elucidate mechanisms pertinent to tumor cell metabolism. We hope that this review gives the readers a brief introduction of how flux analysis is conducted, how technical issues related to it are addressed, and how its application has contributed to our knowledge of tumor cell metabolism.
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Affiliation(s)
- Ziwei Dai
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Jason W Locasale
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC 27710, USA.
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44
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Theorell A, Leweke S, Wiechert W, Nöh K. To be certain about the uncertainty: Bayesian statistics for 13 C metabolic flux analysis. Biotechnol Bioeng 2017; 114:2668-2684. [PMID: 28695999 DOI: 10.1002/bit.26379] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 07/02/2017] [Indexed: 12/18/2022]
Abstract
13 C Metabolic Fluxes Analysis (13 C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of 13 C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to 13 C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli, we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in 13 C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi-square test, as a means of testing whether the model reproduces the data, is examined closer.
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Affiliation(s)
- Axel Theorell
- Forschungszentrum Jülich GmbH, Institute of Bio- und Geosciences, IBG-1: Biotechnology, Jülich, Germany
| | - Samuel Leweke
- Forschungszentrum Jülich GmbH, Institute of Bio- und Geosciences, IBG-1: Biotechnology, Jülich, Germany
| | - Wolfgang Wiechert
- Forschungszentrum Jülich GmbH, Institute of Bio- und Geosciences, IBG-1: Biotechnology, Jülich, Germany.,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Forschungszentrum Jülich GmbH, Institute of Bio- und Geosciences, IBG-1: Biotechnology, Jülich, Germany
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45
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Cook DJ, Nielsen J. Genome-scale metabolic models applied to human health and disease. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2017. [DOI: 10.1002/wsbm.1393] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Daniel J Cook
- Department of Biology and Biological Engineering; Chalmers University of Technology; Gothenburg Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering; Chalmers University of Technology; Gothenburg Sweden
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46
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Nilsson R, Roci I, Watrous J, Jain M. Estimation of flux ratios without uptake or release data: Application to serine and methionine metabolism. Metab Eng 2017; 43:137-146. [PMID: 28232235 DOI: 10.1016/j.ymben.2017.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 02/09/2017] [Accepted: 02/13/2017] [Indexed: 02/06/2023]
Abstract
Model-based metabolic flux analysis (MFA) using isotope-labeled substrates has provided great insight into intracellular metabolic activities across a host of organisms. One challenge with applying MFA in mammalian systems, however, is the need for absolute quantification of nutrient uptake, biomass composition, and byproduct release fluxes. Such measurements are often not feasible in complex culture systems or in vivo. One way to address this issue is to estimate flux ratios, the fractional contribution of a flux to a metabolite pool, which are independent of absolute measurements and yet informative for cellular metabolism. Prior work has focused on "local" estimation of a handful of flux ratios for specific metabolites and reactions. Here, we perform systematic, model-based estimation of all flux ratios in a metabolic network using isotope labeling data, in the absence of uptake/release data. In a series of examples, we investigate what flux ratios can be well estimated with reasonably tight confidence intervals, and contrast this with confidence intervals on normalized fluxes. We find that flux ratios can provide useful information on the metabolic state, and is complementary to normalized fluxes: for certain metabolic reactions, only flux ratios can be well estimated, while for others normalized fluxes can be obtained. Simulation studies of a large human metabolic network model suggest that estimation of flux ratios is technically feasible for complex networks, but additional studies on data from actual isotopomer labeling experiments are needed to validate these results. Finally, we experimentally study serine and methionine metabolism in cancer cells using flux ratios. We find that, in these cells, the methionine cycle is truncated with little remethylation from homocysteine, and polyamine synthesis in the absence of methionine salvage leads to loss of 5-methylthioadenosine, suggesting a new mode of overflow metabolism in cancer cells. This work highlights the potential for flux ratio analysis in the absence of absolute quantification, which we anticipate will be important for both in vitro and in vivo studies of cancer metabolism.
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Affiliation(s)
- Roland Nilsson
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Irena Roci
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Jeramie Watrous
- Departments of Medicine & Pharmacology, University of California, San Diego, USA
| | - Mohit Jain
- Departments of Medicine & Pharmacology, University of California, San Diego, USA
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Hadadi N, Hafner J, Soh KC, Hatzimanikatis V. Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites. Biotechnol J 2017; 12. [DOI: 10.1002/biot.201600464] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 12/13/2022]
Affiliation(s)
- Noushin Hadadi
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
| | - Jasmin Hafner
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
| | - Keng Cher Soh
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
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48
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Khodayari A, Maranas CD. A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Nat Commun 2016; 7:13806. [PMID: 27996047 PMCID: PMC5187423 DOI: 10.1038/ncomms13806] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 11/03/2016] [Indexed: 01/03/2023] Open
Abstract
Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).
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Affiliation(s)
- Ali Khodayari
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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49
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Recent advances in high-throughput 13C-fluxomics. Curr Opin Biotechnol 2016; 43:104-109. [PMID: 27838571 DOI: 10.1016/j.copbio.2016.10.010] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 10/21/2016] [Accepted: 10/25/2016] [Indexed: 12/11/2022]
Abstract
The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT 13C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT 13C-fluxomics workflows.
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50
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Ghosh A, Ando D, Gin J, Runguphan W, Denby C, Wang G, Baidoo EEK, Shymansky C, Keasling JD, García Martín H. 13C Metabolic Flux Analysis for Systematic Metabolic Engineering of S. cerevisiae for Overproduction of Fatty Acids. Front Bioeng Biotechnol 2016; 4:76. [PMID: 27761435 PMCID: PMC5050205 DOI: 10.3389/fbioe.2016.00076] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 09/20/2016] [Indexed: 11/24/2022] Open
Abstract
Efficient redirection of microbial metabolism into the abundant production of desired bioproducts remains non-trivial. Here, we used flux-based modeling approaches to improve yields of fatty acids in Saccharomyces cerevisiae. We combined 13C labeling data with comprehensive genome-scale models to shed light onto microbial metabolism and improve metabolic engineering efforts. We concentrated on studying the balance of acetyl-CoA, a precursor metabolite for the biosynthesis of fatty acids. A genome-wide acetyl-CoA balance study showed ATP citrate lyase from Yarrowia lipolytica as a robust source of cytoplasmic acetyl-CoA and malate synthase as a desirable target for downregulation in terms of acetyl-CoA consumption. These genetic modifications were applied to S. cerevisiae WRY2, a strain that is capable of producing 460 mg/L of free fatty acids. With the addition of ATP citrate lyase and downregulation of malate synthase, the engineered strain produced 26% more free fatty acids. Further increases in free fatty acid production of 33% were obtained by knocking out the cytoplasmic glycerol-3-phosphate dehydrogenase, which flux analysis had shown was competing for carbon flux upstream with the carbon flux through the acetyl-CoA production pathway in the cytoplasm. In total, the genetic interventions applied in this work increased fatty acid production by ~70%.
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Affiliation(s)
- Amit Ghosh
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA; Indian Institute of Technology (IIT), School of Energy Science and Engineering, Kharagpur, India
| | - David Ando
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA
| | - Jennifer Gin
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA
| | - Weerawat Runguphan
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA; National Center for Genetic Engineering and Biotechnology (BIOTEC), Pathum Thani, Thailand
| | - Charles Denby
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA
| | - George Wang
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA
| | - Edward E K Baidoo
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA
| | - Chris Shymansky
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA; Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA
| | - Jay D Keasling
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA; Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA; Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, Horsholm, Denmark
| | - Héctor García Martín
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA
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