1
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Kim J, Yeon GH, Kim MJ, Bae JH, Sohn JH, Sung BH. Systems Metabolic Engineering to Elucidate and Enhance Intestinal Metabolic Activities of Escherichia coli Nissle 1917. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:18234-18246. [PMID: 39087623 DOI: 10.1021/acs.jafc.4c00182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
Escherichia coli Nissle 1917 (EcN) is one of the most widely used probiotics to treat gastrointestinal diseases. Recently, many studies have engineered EcN to release therapeutic proteins to treat specific diseases. However, because EcN exhibits intestinal metabolic activities, it is difficult to predict outcomes after administration. In silico and fermentation profiles revealed mucin metabolism of EcN. Multiomics revealed that fucose metabolism contributes to the intestinal colonization of EcN by enhancing the synthesis of flagella and nutrient uptake. The multiomics results also revealed that excessive intracellular trehalose synthesis in EcN, which is responsible for galactose metabolism, acts as a metabolic bottleneck, adversely affecting growth. To improve the ability of EcN to metabolize galactose, otsAB genes for trehalose synthesis were deleted, resulting in the ΔotsAB strain; the ΔotsAB strain exhibited a 1.47-fold increase in the growth rate and a 1.37-fold increase in the substrate consumption rate relative to wild-type EcN.
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Affiliation(s)
- Jungyeon Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Graduate School of International Agricultural Technology, Seoul National University, Gangwon-do, Pyeongchang-gun 25354, Republic of Korea
- Institute of Food Industrialization, Institutes of Green Bioscience and Technology, Seoul National University, Gangwon-do 25354, Republic of Korea
| | - Gun-Hwi Yeon
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Department of Biosystems and Bioengineering, Korea National University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Mi-Jin Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Jung-Hoon Bae
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Jung-Hoon Sohn
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Department of Biosystems and Bioengineering, Korea National University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Bong Hyun Sung
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Department of Biosystems and Bioengineering, Korea National University of Science and Technology (UST), Daejeon 34113, Republic of Korea
- Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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2
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Shen D, Zhu Y, Mao J, Lin R, Jiang X, Liang L, Peng J, Cao Y, Dong S, He K, Wang N. Highly sensitive and accurate measurement of underivatized phosphoenolpyruvate in plasma and serum via EDTA-facilitated hydrophilic interaction liquid chromatography-tandem mass spectrometry. Talanta 2024; 275:126134. [PMID: 38692044 DOI: 10.1016/j.talanta.2024.126134] [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: 01/12/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024]
Abstract
Phosphoenolpyruvate (PEP) is an essential intermediate metabolite that is involved in various vital biochemical reactions. However, achieving the direct and accurate quantification of PEP in plasma or serum poses a significant challenge owing to its strong polarity and metal affinity. In this study, a sensitive method for the direct determination of PEP in plasma and serum based on ethylenediaminetetraacetic acid (EDTA)-facilitated hydrophilic interaction liquid chromatography-tandem mass spectrometry was developed. Superior chromatographic retention and peak shapes were achieved using a zwitterionic stationary-phase HILIC column with a metal-inert inner surface. Efficient dechelation of PEP-metal complexes in serum/plasma samples was achieved through the introduction of EDTA, resulting in a significant enhancement of the PEP signal. A PEP isotopically labelled standard was employed as a surrogate analyte for the determination of endogenous PEP, and validation assessments proved the sensitivity, selectivity, and reproducibility of this method. The method was applied to the comparative quantification of PEP in plasma and serum samples from mice and rats, as well as in HepG2 cells, HEK293T cells, and erythrocytes; the results confirmed its applicability in PEP-related biomedical research. The developed method can quantify PEP in diverse biological matrices, providing a feasible opportunity to investigate the role of PEP in relevant biomedical research.
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Affiliation(s)
- Danning Shen
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Yingjie Zhu
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Jie Mao
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Runfeng Lin
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Xin Jiang
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Longhui Liang
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Jing Peng
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Yanqing Cao
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Suhe Dong
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Kun He
- National Center of Biomedical Analysis, Beijing, 100850, China.
| | - Na Wang
- National Center of Biomedical Analysis, Beijing, 100850, China.
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3
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Holbrook-Smith D, Trouillon J, Sauer U. Metabolomics and Microbial Metabolism: Toward a Systematic Understanding. Annu Rev Biophys 2024; 53:41-64. [PMID: 38109374 DOI: 10.1146/annurev-biophys-030722-021957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.
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Affiliation(s)
| | - Julian Trouillon
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
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4
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Jasinska W, Dindo M, Cordoba SMC, Serohijos AWR, Laurino P, Brotman Y, Bershtein S. Non-consecutive enzyme interactions within TCA cycle supramolecular assembly regulate carbon-nitrogen metabolism. Nat Commun 2024; 15:5285. [PMID: 38902266 PMCID: PMC11189929 DOI: 10.1038/s41467-024-49646-7] [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: 11/29/2023] [Accepted: 06/14/2024] [Indexed: 06/22/2024] Open
Abstract
Enzymes of the central metabolism tend to assemble into transient supramolecular complexes. However, the functional significance of the interactions, particularly between enzymes catalyzing non-consecutive reactions, remains unclear. Here, by co-localizing two non-consecutive enzymes of the TCA cycle from Bacillus subtilis, malate dehydrogenase (MDH) and isocitrate dehydrogenase (ICD), in phase separated droplets we show that MDH-ICD interaction leads to enzyme agglomeration with a concomitant enhancement of ICD catalytic rate and an apparent sequestration of its reaction product, 2-oxoglutarate. Theory demonstrates that MDH-mediated clustering of ICD molecules explains the observed phenomena. In vivo analyses reveal that MDH overexpression leads to accumulation of 2-oxoglutarate and reduction of fluxes flowing through both the catabolic and anabolic branches of the carbon-nitrogen intersection occupied by 2-oxoglutarate, resulting in impeded ammonium assimilation and reduced biomass production. Our findings suggest that the MDH-ICD interaction is an important coordinator of carbon-nitrogen metabolism.
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Affiliation(s)
- Weronika Jasinska
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Mirco Dindo
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
- Department of Medicine and Surgery, Section of Physiology and Biochemistry, University of Perugia, Perugia, Italy
| | - Sandra M C Cordoba
- Max-Planck-Institut fur Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany
| | - Adrian W R Serohijos
- Departement de Biochimie, Universite de Montreal, Quebec, Canada
- Centre Robert-Cedergren en Bio-informatique et Genomique, Universite de Montreal, Quebec, Canada
| | - Paola Laurino
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.
- Institute for Protein Research, Osaka University, Suita, Japan.
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Shimon Bershtein
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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5
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Sato G, Kinoshita S, Yamada TG, Arai S, Kitaguchi T, Funahashi A, Doi N, Fujiwara K. Metabolic Tug-of-War between Glycolysis and Translation Revealed by Biochemical Reconstitution. ACS Synth Biol 2024; 13:1572-1581. [PMID: 38717981 DOI: 10.1021/acssynbio.4c00209] [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] [Indexed: 05/18/2024]
Abstract
Inside cells, various biological systems work cooperatively for homeostasis and self-replication. These systems do not work independently as they compete for shared elements like ATP and NADH. However, it has been believed that such competition is not a problem in codependent biological systems such as the energy-supplying glycolysis and the energy-consuming translation system. In this study, we biochemically reconstituted the coupling system of glycolysis and translation using purified elements and found that the competition for ATP between glycolysis and protein synthesis interferes with their coupling. Both experiments and simulations revealed that this interference is derived from a metabolic tug-of-war between glycolysis and translation based on their reaction rates, which changes the threshold of the initial substrate concentration for the success coupling. By the metabolic tug-of-war, translation energized by strong glycolysis is facilitated by an exogenous ATPase, which normally inhibits translation. These findings provide chemical insights into the mechanism of competition among biological systems in living cells and provide a framework for the construction of synthetic metabolism in vitro.
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Affiliation(s)
- Gaku Sato
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Saki Kinoshita
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Takahiro G Yamada
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
- Department of Molecular Biology, University of California San Diego, La Jolla, California 92093, United States
| | - Satoshi Arai
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Tetsuya Kitaguchi
- Institute of Innovative Research, Tokyo Institute of Technology, Nagatsuta-cho, Yokohama, Kanagawa 226-8503, Japan
| | - Akira Funahashi
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Nobuhide Doi
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Kei Fujiwara
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
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6
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Ben Nissan R, Milshtein E, Pahl V, de Pins B, Jona G, Levi D, Yung H, Nir N, Ezra D, Gleizer S, Link H, Noor E, Milo R. Autotrophic growth of Escherichia coli is achieved by a small number of genetic changes. eLife 2024; 12:RP88793. [PMID: 38381041 PMCID: PMC10942610 DOI: 10.7554/elife.88793] [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] [Indexed: 02/22/2024] Open
Abstract
Synthetic autotrophy is a promising avenue to sustainable bioproduction from CO2. Here, we use iterative laboratory evolution to generate several distinct autotrophic strains. Utilising this genetic diversity, we identify that just three mutations are sufficient for Escherichia coli to grow autotrophically, when introduced alongside non-native energy (formate dehydrogenase) and carbon-fixing (RuBisCO, phosphoribulokinase, carbonic anhydrase) modules. The mutated genes are involved in glycolysis (pgi), central-carbon regulation (crp), and RNA transcription (rpoB). The pgi mutation reduces the enzyme's activity, thereby stabilising the carbon-fixing cycle by capping a major branching flux. For the other two mutations, we observe down-regulation of several metabolic pathways and increased expression of native genes associated with the carbon-fixing module (rpiB) and the energy module (fdoGH), as well as an increased ratio of NADH/NAD+ - the cycle's electron-donor. This study demonstrates the malleability of metabolism and its capacity to switch trophic modes using only a small number of genetic changes and could facilitate transforming other heterotrophic organisms into autotrophs.
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Affiliation(s)
- Roee Ben Nissan
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Eliya Milshtein
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Vanessa Pahl
- Interfaculty Institute for Microbiology and Infection Medicine Tübingen, University of TübingenTübingenGermany
| | - Benoit de Pins
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Ghil Jona
- Department of Life Sciences Core Facilities, Weizmann Institute of ScienceRehovotIsrael
| | - Dikla Levi
- Department of Life Sciences Core Facilities, Weizmann Institute of ScienceRehovotIsrael
| | - Hadas Yung
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Noga Nir
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Dolev Ezra
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Shmuel Gleizer
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Hannes Link
- Interfaculty Institute for Microbiology and Infection Medicine Tübingen, University of TübingenTübingenGermany
| | - Elad Noor
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
| | - Ron Milo
- Department of Plant and Environmental Sciences, Weizmann Institute of ScienceRehovotIsrael
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7
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Hu XP, Schroeder S, Lercher MJ. Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow. mSystems 2023; 8:e0076023. [PMID: 37795991 PMCID: PMC10654084 DOI: 10.1128/msystems.00760-23] [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: 07/25/2023] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
IMPORTANCE Protein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in Escherichia coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, is near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.
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Affiliation(s)
- Xiao-Pan Hu
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Stefan Schroeder
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Martin J. Lercher
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
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8
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Faure L, Mollet B, Liebermeister W, Faulon JL. A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models. Nat Commun 2023; 14:4669. [PMID: 37537192 PMCID: PMC10400647 DOI: 10.1038/s41467-023-40380-0] [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: 12/01/2022] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Constraint-based metabolic models have been used for decades to predict the phenotype of microorganisms in different environments. However, quantitative predictions are limited unless labor-intensive measurements of media uptake fluxes are performed. We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions. We illustrate our hybrid models with growth rate predictions of Escherichia coli and Pseudomonas putida grown in different media and with phenotype predictions of gene knocked-out Escherichia coli mutants. Our neural-mechanistic models systematically outperform constraint-based models and require training set sizes orders of magnitude smaller than classical machine learning methods. Our hybrid approach opens a doorway to enhancing constraint-based modeling: instead of constraining mechanistic models with additional experimental measurements, our hybrid models grasp the power of machine learning while fulfilling mechanistic constrains, thus saving time and resources in typical systems biology or biological engineering projects.
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Affiliation(s)
- Léon Faure
- MICALIS Institute, INRAE, AgroParisTech, University of Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Bastien Mollet
- Ecole Normale Supérieure of Lyon, 69342, Lyon, France
- UMR MIA, INRAE, AgroParisTech, University of Paris-Saclay, 91120, Palaiseau, France
| | | | - Jean-Loup Faulon
- MICALIS Institute, INRAE, AgroParisTech, University of Paris-Saclay, 78350, Jouy-en-Josas, France.
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK.
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9
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Choi YM, Choi DH, Lee YQ, Koduru L, Lewis NE, Lakshmanan M, Lee DY. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations. Comput Struct Biotechnol J 2023; 21:3736-3745. [PMID: 37547082 PMCID: PMC10400880 DOI: 10.1016/j.csbj.2023.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.
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Affiliation(s)
- Yoon-Mi Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Yi Qing Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Lokanand Koduru
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Nathan E. Lewis
- Departments of Pediatrics and Bioengineering, University of California, La Jolla, San Diego, USA
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bitwinners Pte. Ltd., Singapore
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10
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Noell SE, Hellweger FL, Temperton B, Giovannoni SJ. A Reduction of Transcriptional Regulation in Aquatic Oligotrophic Microorganisms Enhances Fitness in Nutrient-Poor Environments. Microbiol Mol Biol Rev 2023; 87:e0012422. [PMID: 36995249 PMCID: PMC10304753 DOI: 10.1128/mmbr.00124-22] [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] [Indexed: 03/31/2023] Open
Abstract
In this review, we consider the regulatory strategies of aquatic oligotrophs, microbial cells that are adapted to thrive under low-nutrient concentrations in oceans, lakes, and other aquatic ecosystems. Many reports have concluded that oligotrophs use less transcriptional regulation than copiotrophic cells, which are adapted to high nutrient concentrations and are far more common subjects for laboratory investigations of regulation. It is theorized that oligotrophs have retained alternate mechanisms of regulation, such as riboswitches, that provide shorter response times and smaller amplitude responses and require fewer cellular resources. We examine the accumulated evidence for distinctive regulatory strategies in oligotrophs. We explore differences in the selective pressures copiotrophs and oligotrophs encounter and ask why, although evolutionary history gives copiotrophs and oligotrophs access to the same regulatory mechanisms, they might exhibit distinctly different patterns in how these mechanisms are used. We discuss the implications of these findings for understanding broad patterns in the evolution of microbial regulatory networks and their relationships to environmental niche and life history strategy. We ask whether these observations, which have emerged from a decade of increased investigation of the cell biology of oligotrophs, might be relevant to recent discoveries of many microbial cell lineages in nature that share with oligotrophs the property of reduced genome size.
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Affiliation(s)
- Stephen E. Noell
- Department of Microbiology, Oregon State University, Corvallis, Oregon, USA
| | | | - Ben Temperton
- School of Biosciences, University of Exeter, Exeter, United Kingdom
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11
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Wilkes RA, Waldbauer J, Carroll A, Nieto-Domínguez M, Parker DJ, Zhang L, Guss AM, Aristilde L. Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism. Nat Chem Biol 2023; 19:651-662. [PMID: 36747056 PMCID: PMC10154247 DOI: 10.1038/s41589-022-01237-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 11/29/2022] [Indexed: 02/08/2023]
Abstract
Critical to a sustainable energy future are microbial platforms that can process aromatic carbons from the largely untapped reservoir of lignin and plastic feedstocks. Comamonas species present promising bacterial candidates for such platforms because they can use a range of natural and xenobiotic aromatic compounds and often possess innate genetic constraints that avoid competition with sugars. However, the metabolic reactions of these species are underexplored, and the regulatory mechanisms are unknown. Here we identify multilevel regulation in the conversion of lignin-related natural aromatic compounds, 4-hydroxybenzoate and vanillate, and the plastics-related xenobiotic aromatic compound, terephthalate, in Comamonas testosteroni KF-1. Transcription-level regulation controls initial catabolism and cleavage, but metabolite-level thermodynamic regulation governs fluxes in central carbon metabolism. Quantitative 13C mapping of tricarboxylic acid cycle and cataplerotic reactions elucidates key carbon routing not evident from enzyme abundance changes. This scheme of transcriptional activation coupled with metabolic fine-tuning challenges outcome predictions during metabolic manipulations.
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Affiliation(s)
- Rebecca A Wilkes
- Department of Biological and Environmental Engineering, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
- Department of Civil and Environmental Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL, USA
| | - Jacob Waldbauer
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
| | - Austin Carroll
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Manuel Nieto-Domínguez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Darren J Parker
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Lichun Zhang
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
| | - Adam M Guss
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Ludmilla Aristilde
- Department of Biological and Environmental Engineering, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA.
- Department of Civil and Environmental Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL, USA.
- Northwestern Center for Synthetic Biology, Evanston, IL, USA.
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12
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Moreau PL. Regulation of phosphate starvation-specific responses in Escherichia coli. MICROBIOLOGY (READING, ENGLAND) 2023; 169. [PMID: 36972330 DOI: 10.1099/mic.0.001312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Toxic agents added into the medium of rapidly growing Escherichia coli induce specific stress responses through the activation of specialized transcription factors. Each transcription factor and downstream regulon (e.g. SoxR) are linked to a unique stress (e.g. superoxide stress). Cells starved of phosphate induce several specific stress regulons during the transition to stationary phase when the growth rate is steadily declining. Whereas the regulatory cascades leading to the expression of specific stress regulons are well known in rapidly growing cells stressed by toxic products, they are poorly understood in cells starved of phosphate. The intent of this review is to both describe the unique mechanisms of activation of specialized transcription factors and discuss signalling cascades leading to the induction of specific stress regulons in phosphate-starved cells. Finally, I discuss unique defence mechanisms that could be induced in cells starved of ammonium and glucose.
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Affiliation(s)
- Patrice L Moreau
- Laboratoire Chimie Bactérienne, LCB-UMR 7283, Institut Microbiologie Méditerranée, CNRS/Université Aix-Marseille, Marseille, France
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13
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Chen S, Hu M, Hu A, Xue Y, Wang S, Liu F, Li C, Zhou X, Zhou J. The integration host factor regulates multiple virulence pathways in bacterial pathogen Dickeya zeae MS2. MOLECULAR PLANT PATHOLOGY 2022; 23:1487-1507. [PMID: 35819797 PMCID: PMC9452768 DOI: 10.1111/mpp.13244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/12/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Dickeya zeae is an aggressive bacterial phytopathogen that infects a wide range of host plants. It has been reported that integration host factor (IHF), a nucleoid-associated protein consisting of IHFα and IHFβ subunits, regulates gene expression by influencing nucleoid structure and DNA bending. To define the role of IHF in the pathogenesis of D. zeae MS2, we deleted either and both of the IHF subunit encoding genes ihfA and ihfB, which significantly reduced the production of cell wall-degrading enzymes (CWDEs), an unknown novel phytotoxin and the virulence factor-modulating (VFM) quorum-sensing (QS) signal, cell motility, biofilm formation, and thereafter the infection ability towards both potato slices and banana seedlings. To characterize the regulatory pathways of IHF protein associated with virulence, IHF binding sites (consensus sequence 5'-WATCAANNNNTTR-3') were predicted and 272 binding sites were found throughout the genome. The expression of 110 tested genes was affected by IHF. Electrophoretic mobility shift assay (EMSA) showed direct interaction of IhfA protein with the promoters of vfmE, speA, pipR, fis, slyA, prtD, hrpL, hecB, hcp, indA, hdaA, flhD, pilT, gcpJ, arcA, arcB, and lysR. This study clarified the contribution of IHF in the pathogenic process of D. zeae by controlling the production of VFM and putrescine QS signals, phytotoxin, and indigoidine, the luxR-solo system, Fis, SlyA, and FlhD transcriptional regulators, and secretion systems from type I to type VI. Characterization of the regulatory networks of IHF in D. zeae provides a target for prevention and control of plant soft rot disease.
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Affiliation(s)
- Shanshan Chen
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Ming Hu
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Anqun Hu
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Yang Xue
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Si Wang
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Fan Liu
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Chuhao Li
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Xiaofan Zhou
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
| | - Jianuan Zhou
- Guangdong Province Key Laboratory of Microbial Signals and Disease ControlIntegrative Microbiology Research Center, South China Agricultural UniversityGuangzhouChina
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14
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Tian L, Yan X, Wang D, Du Q, Wan Y, Zhou L, Li T, Liao C, Li N, Wang X. Two key Geobacter species of wastewater-enriched electroactive biofilm respond differently to electric field. WATER RESEARCH 2022; 213:118185. [PMID: 35183018 DOI: 10.1016/j.watres.2022.118185] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
Electroactive biofilms have attracted increasing attention due to their unique ability to exchange electrons with electrodes. Geobacter spp. are widely found to be dominant in biofilms in acetate-rich environments when an appropriate voltage is applied, but it is still largely unknown how these bacteria are selectively enriched. Herein, two key Geobacter spp. that have been demonstrated predominant in wastewater-enriched electroactive biofilm after long-term operation, G. sulfurreducens and G. anodireducens, responded to electric field (EF) differently, leading to a higher abundance of EF-sensitive G. anodireducens in the strong EF region after cocultivation with G. sulfurreducens. Transcriptome analysis indicated that two-component systems containing sensor histidine kinases and response regulators were the key for EF sensing in G. anodireducens rather than in G. sulfurreducens, which are closely connected to chemotaxis, c-di-GMP, fatty acid metabolism, pilus, oxidative phosphorylation and transcription, resulting in an increase in extracellular polymeric substance secretion and rapid cell proliferation. Our data reveal the mechanism by which EF select specific Geobacter spp. over time, providing new insights into Geobacter biofilm formation regulated by electricity.
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Affiliation(s)
- Lili Tian
- MOE Key Laboratory of Pollution Processes and Environmental Criteria / Tianjin Key Laboratory of Environmental Remediation and Pollution Control / College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Xuejun Yan
- MOE Key Laboratory of Pollution Processes and Environmental Criteria / Tianjin Key Laboratory of Environmental Remediation and Pollution Control / College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Dongbin Wang
- School of Public Health, Guangdong Medical University, Xincheng Road, Dongguan 523000, China
| | - Qing Du
- School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Yuxuan Wan
- MOE Key Laboratory of Pollution Processes and Environmental Criteria / Tianjin Key Laboratory of Environmental Remediation and Pollution Control / College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Lean Zhou
- School of Hydraulic Engineering, Changsha University of Science and Technology, Changsha 410114, China
| | - Tian Li
- MOE Key Laboratory of Pollution Processes and Environmental Criteria / Tianjin Key Laboratory of Environmental Remediation and Pollution Control / College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Chengmei Liao
- MOE Key Laboratory of Pollution Processes and Environmental Criteria / Tianjin Key Laboratory of Environmental Remediation and Pollution Control / College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Nan Li
- School of Environmental Science and Engineering, Tianjin University, No. 35 Yaguan Road, Jinnan District, Tianjin 300350, China
| | - Xin Wang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria / Tianjin Key Laboratory of Environmental Remediation and Pollution Control / College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China.
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15
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Matsuda F, Maeda K, Taniguchi T, Kondo Y, Yatabe F, Okahashi N, Shimizu H. mfapy: An open-source Python package for 13C-based metabolic flux analysis. Metab Eng Commun 2021; 13:e00177. [PMID: 34354925 PMCID: PMC8322459 DOI: 10.1016/j.mec.2021.e00177] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/01/2021] [Accepted: 07/05/2021] [Indexed: 11/28/2022] Open
Abstract
13C-based metabolic flux analysis (13C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In 13C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy). An open-source Python package, mfapy, is developed for 13C-MFA. mfapy enables users to write Python codes for data analysis procedures of 13C-MFA. mfapy has a flexibility and extensibility to support various data analysis procedures. Computer simulations of 13C-MFA experiments is supported for experimental design.
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Affiliation(s)
- Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kousuke Maeda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Takeo Taniguchi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yuya Kondo
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Futa Yatabe
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
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16
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Jacobson TB, Callaghan MM, Amador-Noguez D. Hostile Takeover: How Viruses Reprogram Prokaryotic Metabolism. Annu Rev Microbiol 2021; 75:515-539. [PMID: 34348026 DOI: 10.1146/annurev-micro-060621-043448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
To reproduce, prokaryotic viruses must hijack the cellular machinery of their hosts and redirect it toward the production of viral particles. While takeover of the host replication and protein synthesis apparatus has long been considered an essential feature of infection, recent studies indicate that extensive reprogramming of host primary metabolism is a widespread phenomenon among prokaryotic viruses that is required to fulfill the biosynthetic needs of virion production. In this review we provide an overview of the most significant recent findings regarding virus-induced reprogramming of prokaryotic metabolism and suggest how quantitative systems biology approaches may be used to provide a holistic understanding of metabolic remodeling during lytic viral infection. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Tyler B Jacobson
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA; , , .,Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, Wisconsin 53726, USA.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Melanie M Callaghan
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA; , , .,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Daniel Amador-Noguez
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA; , , .,Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, Wisconsin 53726, USA.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
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17
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Shimada T, Nakazawa K, Tachikawa T, Saito N, Niwa T, Taguchi H, Tanaka K. Acetate overflow metabolism regulates a major metabolic shift after glucose depletion in Escherichia coli. FEBS Lett 2021; 595:2047-2056. [PMID: 34125966 DOI: 10.1002/1873-3468.14151] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/01/2021] [Accepted: 06/07/2021] [Indexed: 11/09/2022]
Abstract
Acetate overflow refers to the metabolism by which a large part of carbon incorporated as glucose into Escherichia coli cells is catabolized and excreted as acetate into the medium. We previously found that mutants for the acetate overflow pathway enzymes phosphoacetyltransferase (Pta) and acetate kinase (AckA) showed significant diauxic growth after glucose depletion in E. coli. Here, we analyzed the underlying mechanism in the pta mutant. Proteomic and other analyses revealed an increase in pyruvate dehydrogenase complex subunits and a decrease in glyoxylate shunt enzymes, which resulted from pyruvate accumulation. Since restoration of these enzyme levels by overexpressing PdhR (pyruvate-sensing transcription factor) or deleting iclR (gene encoding a pyruvate- and glyoxylate-sensing transcription factor) alleviated the growth lag of the pta mutant after glucose depletion, these changes were considered as the reason for the phenotype. Given the evidence for decreased coenzyme A (HS-CoA) levels in the pta mutant, the growth inhibition after glucose depletion was partly explained by limited availability of HS-CoA in the cell. The findings provide insights into the role of acetate overflow in metabolic regulation, which may be useful for biotechnological applications.
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Affiliation(s)
- Tomohiro Shimada
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Kohta Nakazawa
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.,School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
| | - Tomoyuki Tachikawa
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.,School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
| | - Natsumi Saito
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Department of Creative Engineering, Tsuruoka College, National Institute of Technology, Japan
| | - Tatsuya Niwa
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Hideki Taguchi
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Kan Tanaka
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
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18
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Fuentes DAF, Manfredi P, Jenal U, Zampieri M. Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli. Nat Commun 2021; 12:3204. [PMID: 34050162 PMCID: PMC8163773 DOI: 10.1038/s41467-021-23522-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/04/2021] [Indexed: 02/04/2023] Open
Abstract
Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.
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Affiliation(s)
| | | | - Urs Jenal
- Biozentrum, University of Basel, Basel, Switzerland
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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19
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Bergès C, Cahoreau E, Millard P, Enjalbert B, Dinclaux M, Heuillet M, Kulyk H, Gales L, Butin N, Chazalviel M, Palama T, Guionnet M, Sokol S, Peyriga L, Bellvert F, Heux S, Portais JC. Exploring the Glucose Fluxotype of the E. coli y-ome Using High-Resolution Fluxomics. Metabolites 2021; 11:metabo11050271. [PMID: 33926117 PMCID: PMC8145925 DOI: 10.3390/metabo11050271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/16/2021] [Accepted: 04/23/2021] [Indexed: 01/26/2023] Open
Abstract
We have developed a robust workflow to measure high-resolution fluxotypes (metabolic flux phenotypes) for large strain libraries under fully controlled growth conditions. This was achieved by optimizing and automating the whole high-throughput fluxomics process and integrating all relevant software tools. This workflow allowed us to obtain highly detailed maps of carbon fluxes in the central carbon metabolism in a fully automated manner. It was applied to investigate the glucose fluxotypes of 180 Escherichia coli strains deleted for y-genes. Since the products of these y-genes potentially play a role in a variety of metabolic processes, the experiments were designed to be agnostic as to their potential metabolic impact. The obtained data highlight the robustness of E. coli’s central metabolism to y-gene deletion. For two y-genes, deletion resulted in significant changes in carbon and energy fluxes, demonstrating the involvement of the corresponding y-gene products in metabolic function or regulation. This work also introduces novel metrics to measure the actual scope and quality of high-throughput fluxomics investigations.
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Affiliation(s)
- Cécilia Bergès
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Edern Cahoreau
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Pierre Millard
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Brice Enjalbert
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Mickael Dinclaux
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Maud Heuillet
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Hanna Kulyk
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Lara Gales
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Noémie Butin
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
- RESTORE, Université de Toulouse, Inserm U1031, CNRS 5070, UPS, EFS, 31100 Toulouse, France
| | - Maxime Chazalviel
- Toxalim (Research Centre in Food Toxicology), UMR1331, Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France;
| | - Tony Palama
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Matthieu Guionnet
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Sergueï Sokol
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Lindsay Peyriga
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Stéphanie Heux
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Jean-Charles Portais
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
- RESTORE, Université de Toulouse, Inserm U1031, CNRS 5070, UPS, EFS, 31100 Toulouse, France
- Correspondence:
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20
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Kochanowski K, Okano H, Patsalo V, Williamson J, Sauer U, Hwa T. Global coordination of metabolic pathways in Escherichia coli by active and passive regulation. Mol Syst Biol 2021; 17:e10064. [PMID: 33852189 PMCID: PMC8045939 DOI: 10.15252/msb.202010064] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 12/15/2022] Open
Abstract
Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.
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Affiliation(s)
- Karl Kochanowski
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Life Science Zurich PhD Program on Systems BiologyZurichSwitzerland
| | - Hiroyuki Okano
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
| | - Vadim Patsalo
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical BiologyThe Scripps Research InstituteLa JollaCAUSA
| | - James Williamson
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical BiologyThe Scripps Research InstituteLa JollaCAUSA
| | - Uwe Sauer
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Terence Hwa
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
- Institute for Theoretical ScienceETH ZurichZurichSwitzerland
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21
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Iyer MS, Pal A, Srinivasan S, Somvanshi PR, Venkatesh KV. Global Transcriptional Regulators Fine-Tune the Translational and Metabolic Efficiency for Optimal Growth of Escherichia coli. mSystems 2021; 6:e00001-21. [PMID: 33785570 PMCID: PMC8546960 DOI: 10.1128/msystems.00001-21] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 02/25/2021] [Indexed: 12/13/2022] Open
Abstract
Global transcriptional regulators coordinate complex genetic interactions that bestow better adaptability for an organism against external and internal perturbations. These transcriptional regulators are known to control an enormous array of genes with diverse functionalities. However, regulator-driven molecular mechanisms that underpin precisely tuned translational and metabolic processes conducive for rapid exponential growth remain obscure. Here, we comprehensively reveal the fundamental role of global transcriptional regulators FNR, ArcA, and IHF in sustaining translational and metabolic efficiency under glucose fermentative conditions in Escherichia coli By integrating high-throughput gene expression profiles and absolute intracellular metabolite concentrations, we illustrate that these regulators are crucial in maintaining nitrogen homeostasis, govern expression of otherwise unnecessary or hedging genes, and exert tight control on metabolic bottleneck steps. Furthermore, we characterize changes in expression and activity profiles of other coregulators associated with these dysregulated metabolic pathways, determining the regulatory interactions within the transcriptional regulatory network. Such systematic findings emphasize their importance in optimizing the proteome allocation toward metabolic enzymes as well as ribosomes, facilitating condition-specific phenotypic outcomes. Consequentially, we reveal that disruption of this inherent trade-off between ribosome and metabolic proteome economy due to the loss of regulators resulted in lowered growth rates. Moreover, our findings reinforce that the accumulations of intracellular metabolites in the event of proteome repartitions negatively affects the glucose uptake. Overall, by extending the three-partition proteome allocation theory concordant with multi-omics measurements, we elucidate the physiological consequences of loss of global regulators on central carbon metabolism restraining the organism to attain maximal biomass synthesis.IMPORTANCE Cellular proteome allocation in response to environmental or internal perturbations governs their eventual phenotypic outcome. This entails striking an effective balance between amino acid biosynthesis by metabolic proteins and its consumption by ribosomes. However, the global transcriptional regulator-driven molecular mechanisms that underpin their coordination remains unexplored. Here, we emphasize that global transcriptional regulators, known to control expression of a myriad of genes, are fundamental for precisely tuning the translational and metabolic efficiencies that define the growth optimality. Towards this, we systematically characterized the single deletion effect of FNR, ArcA, and IHF regulators of Escherichia coli on exponential growth under anaerobic glucose fermentative conditions. Their absence disrupts the stringency of proteome allocation, which manifests as impairment in key metabolic processes and an accumulation of intracellular metabolites. Furthermore, by incorporating an extension to the empirical growth laws, we quantitatively demonstrate the general design principles underlying the existence of these regulators in E. coli.
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Affiliation(s)
- Mahesh S Iyer
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ankita Pal
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Sumana Srinivasan
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Pramod R Somvanshi
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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22
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Abstract
It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes. IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today’s whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli. Being equally computationally lightweight as classical stoichiometric models and allowing for the application of established in silico tools, the PAM and related simulation approaches will foster the use of a model-driven metabolic research. Applications range from the investigation of mechanisms of microbial evolution to the determination of optimal strain design strategies in metabolic engineering, thus supporting basic scientists and engineers alike.
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23
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Reverchon S, Meyer S, Forquet R, Hommais F, Muskhelishvili G, Nasser W. The nucleoid-associated protein IHF acts as a 'transcriptional domainin' protein coordinating the bacterial virulence traits with global transcription. Nucleic Acids Res 2021; 49:776-790. [PMID: 33337488 PMCID: PMC7826290 DOI: 10.1093/nar/gkaa1227] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 11/30/2020] [Accepted: 12/07/2020] [Indexed: 02/04/2023] Open
Abstract
Bacterial pathogenic growth requires a swift coordination of pathogenicity function with various kinds of environmental stress encountered in the course of host infection. Among the factors critical for bacterial adaptation are changes of DNA topology and binding effects of nucleoid-associated proteins transducing the environmental signals to the chromosome and coordinating the global transcriptional response to stress. In this study, we use the model phytopathogen Dickeya dadantii to analyse the organisation of transcription by the nucleoid-associated heterodimeric protein IHF. We inactivated the IHFα subunit of IHF thus precluding the IHFαβ heterodimer formation and determined both phenotypic effects of ihfA mutation on D. dadantii virulence and the transcriptional response under various conditions of growth. We show that ihfA mutation reorganises the genomic expression by modulating the distribution of chromosomal DNA supercoils at different length scales, thus affecting many virulence genes involved in both symptomatic and asymptomatic phases of infection, including those required for pectin catabolism. Altogether, we propose that IHF heterodimer is a 'transcriptional domainin' protein, the lack of which impairs the spatiotemporal organisation of transcriptional stress-response domains harbouring various virulence traits, thus abrogating the pathogenicity of D. dadantii.
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Affiliation(s)
- Sylvie Reverchon
- Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, UMR5240 MAP, F-69622, France
| | - Sam Meyer
- Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, UMR5240 MAP, F-69622, France
| | - Raphaël Forquet
- Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, UMR5240 MAP, F-69622, France
| | - Florence Hommais
- Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, UMR5240 MAP, F-69622, France
| | - Georgi Muskhelishvili
- Agricultural University of Georgia, School of Natural Sciences, 0159 Tbilisi, Georgia
| | - William Nasser
- Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, UMR5240 MAP, F-69622, France
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24
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Rivas-Astroza M, Conejeros R. Metabolic flux configuration determination using information entropy. PLoS One 2020; 15:e0243067. [PMID: 33275628 PMCID: PMC7717585 DOI: 10.1371/journal.pone.0243067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 11/14/2020] [Indexed: 11/20/2022] Open
Abstract
Constraint-based models use steady-state mass balances to define a solution space of flux configurations, which can be narrowed down by measuring as many fluxes as possible. Due to loops and redundant pathways, this process typically yields multiple alternative solutions. To address this ambiguity, flux sampling can estimate the probability distribution of each flux, or a flux configuration can be singled out by further minimizing the sum of fluxes according to the assumption that cellular metabolism favors states where enzyme-related costs are economized. However, flux sampling is susceptible to artifacts introduced by thermodynamically infeasible cycles and is it not clear if the economy of fluxes assumption (EFA) is universally valid. Here, we formulated a constraint-based approach, MaxEnt, based on the principle of maximum entropy, which in this context states that if more than one flux configuration is consistent with a set of experimentally measured fluxes, then the one with the minimum amount of unwarranted assumptions corresponds to the best estimation of the non-observed fluxes. We compared MaxEnt predictions to Escherichia coli and Saccharomyces cerevisiae publicly available flux data. We found that the mean square error (MSE) between experimental and predicted fluxes by MaxEnt and EFA-based methods are three orders of magnitude lower than the median of 1,350,000 MSE values obtained using flux sampling. However, only MaxEnt and flux sampling correctly predicted flux through E. coli’s glyoxylate cycle, whereas EFA-based methods, in general, predict no flux cycles. We also tested MaxEnt predictions at increasing levels of overflow metabolism. We found that MaxEnt accuracy is not affected by overflow metabolism levels, whereas the EFA-based methods show a decreasing performance. These results suggest that MaxEnt is less sensitive than flux sampling to artifacts introduced by thermodynamically infeasible cycles and that its predictions are less susceptible to overfitting than EFA-based methods.
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Affiliation(s)
- Marcelo Rivas-Astroza
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
- * E-mail:
| | - Raúl Conejeros
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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25
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Antoniewicz MR. A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications. Metab Eng 2020; 63:2-12. [PMID: 33157225 DOI: 10.1016/j.ymben.2020.11.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 10/28/2020] [Accepted: 11/01/2020] [Indexed: 12/22/2022]
Abstract
The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. 13C-metabolic flux analysis (13C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.
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Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA.
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26
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Wu C, Cano M, Gao X, Lo J, Maness P, Xiong W. A quantitative lens on anaerobic life: leveraging the state-of-the-art fluxomics approach to explore clostridial metabolism. Curr Opin Biotechnol 2020; 64:47-54. [DOI: 10.1016/j.copbio.2019.09.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/02/2019] [Accepted: 09/12/2019] [Indexed: 10/25/2022]
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Diauxie and co-utilization of carbon sources can coexist during bacterial growth in nutritionally complex environments. Nat Commun 2020; 11:3135. [PMID: 32561713 PMCID: PMC7305145 DOI: 10.1038/s41467-020-16872-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 05/26/2020] [Indexed: 12/14/2022] Open
Abstract
It is commonly thought that when multiple carbon sources are available, bacteria metabolize them either sequentially (diauxic growth) or simultaneously (co-utilization). However, this view is mainly based on analyses in relatively simple laboratory settings. Here we show that a heterotrophic marine bacterium, Pseudoalteromonas haloplanktis, can use both strategies simultaneously when multiple possible nutrients are provided in the same growth experiment. The order of nutrient uptake is partially determined by the biomass yield that can be achieved when the same compounds are provided as single carbon sources. Using transcriptomics and time-resolved intracellular 1H-13C NMR, we reveal specific pathways for utilization of various amino acids. Finally, theoretical modelling indicates that this metabolic phenotype, combining diauxie and co-utilization of substrates, is compatible with a tight regulation that allows the modulation of assimilatory pathways. It is thought that when multiple carbon sources are available, bacteria metabolize them either sequentially or simultaneously. Here, the authors show that a marine bacterium can use a mixed strategy when multiple possible nutrients are provided, and analyse the metabolic pathways involved.
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28
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Abstract
Abstract
Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.
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29
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Long CP, Antoniewicz MR. Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of E. coli metabolism. Metab Eng 2019; 55:249-257. [DOI: 10.1016/j.ymben.2019.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 08/03/2019] [Accepted: 08/03/2019] [Indexed: 02/08/2023]
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30
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Bulović A, Fischer S, Dinh M, Golib F, Liebermeister W, Poirier C, Tournier L, Klipp E, Fromion V, Goelzer A. Automated generation of bacterial resource allocation models. Metab Eng 2019; 55:12-22. [PMID: 31189086 DOI: 10.1016/j.ymben.2019.06.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/09/2019] [Accepted: 06/08/2019] [Indexed: 11/30/2022]
Abstract
Resource Balance Analysis (RBA) is a computational method based on resource allocation, which performs accurate quantitative predictions of whole-cell states (i.e. growth rate, metabolic fluxes, abundances of molecular machines including enzymes) across growth conditions. We present an integrated workflow of RBA together with the Python package RBApy. RBApy builds bacterial RBA models from annotated genome-scale metabolic models by adding descriptions of cellular processes relevant for growth and maintenance. The package includes functions for model simulation and calibration and for interfacing to Escher maps and Proteomaps for visualization. We demonstrate that RBApy faithfully reproduces results obtained by a hand-curated and experimentally validated RBA model for Bacillus subtilis. We also present a calibrated RBA model of Escherichia coli generated from scratch, which obtained excellent fits to measured flux values and enzyme abundances. RBApy makes whole-cell modelling accessible for a wide range of bacterial wild-type and engineered strains, as illustrated with a CO2-fixing Escherichia coli strain. AVAILABILITY: RBApy is available at /https://github.com/SysBioInra/RBApy, under the licence GNU GPL version 3, and runs on Linux, Mac and Windows distributions.
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Affiliation(s)
- Ana Bulović
- Theoretische Biophysik, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stephan Fischer
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Marc Dinh
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Felipe Golib
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Wolfram Liebermeister
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France; Institut für Biochemie, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Poirier
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Laurent Tournier
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Edda Klipp
- Theoretische Biophysik, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vincent Fromion
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Anne Goelzer
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France.
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31
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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput Biol 2019; 15:e1006971. [PMID: 31009451 PMCID: PMC6497307 DOI: 10.1371/journal.pcbi.1006971] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 05/02/2019] [Accepted: 03/21/2019] [Indexed: 12/12/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
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Correia DM, Sargo CR, Silva AJ, Santos ST, Giordano RC, Ferreira EC, Zangirolami TC, Ribeiro MPA, Rocha I. Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis. Metab Eng 2019; 52:303-314. [PMID: 30529284 DOI: 10.1016/j.ymben.2018.11.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 11/26/2018] [Accepted: 11/28/2018] [Indexed: 12/20/2022]
Abstract
In the last years, Salmonella has been extensively studied not only due to its importance as a pathogen, but also as a host to produce pharmaceutical compounds. However, the full exploitation of Salmonella as a platform for bioproduct delivery has been hampered by the lack of information about its metabolism. Genome-scale metabolic models can be valuable tools to delineate metabolic engineering strategies as long as they closely represent the actual metabolism of the target organism. In the present study, a 13C-MFA approach was applied to map the fluxes at the central carbon pathways of S. typhimurium LT2 growing at glucose-limited chemostat cultures. The experiments were carried out in a 2L bioreactor, using defined medium enriched with 20% 13C-labeled glucose. Metabolic flux distributions in central carbon pathways of S. typhimurium LT2 were estimated using OpenFLUX2 based on the labeling pattern of biomass protein hydrolysates together with biomass composition. The results suggested that pentose phosphate is used to catabolize glucose, with minor fluxes through glycolysis. In silico simulations, using Optflux and pFBA as simulation method, allowed to study the performance of the genome-scale metabolic model. In general, the accuracy of in silico simulations was improved by the superimposition of estimated intracellular fluxes to the existing genome-scale metabolic model, showing a better fitting to the experimental extracellular fluxes, whereas the intracellular fluxes of pentose phosphate and anaplerotic reactions were poorly described.
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Affiliation(s)
- Daniela M Correia
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Cintia R Sargo
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Adilson J Silva
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Sophia T Santos
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal
| | - Roberto C Giordano
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Eugénio C Ferreira
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal
| | - Teresa C Zangirolami
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Marcelo P A Ribeiro
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Isabel Rocha
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal.
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33
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Yao R, Li J, Feng L, Zhang X, Hu H. 13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol. BIOTECHNOLOGY FOR BIOFUELS 2019; 12:29. [PMID: 30805028 PMCID: PMC6373095 DOI: 10.1186/s13068-019-1372-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 02/06/2019] [Indexed: 05/04/2023]
Abstract
BACKGROUND Bioprocessing offers a sustainable and green approach to manufacture various chemicals and materials. Development of bioprocesses requires transforming common producer strains to cell factories. 13C metabolic flux analysis (13C-MFA) can be applied to identify relevant targets to accomplish the desired phenotype, which has become one of the major tools to support systems metabolic engineering. In this research, we applied 13C-MFA to identify bottlenecks in the bioconversion of glycerol into acetol by Escherichia coli. Valorization of glycerol, the main by-product of biodiesel, has contributed to the viability of biofuel economy. RESULTS We performed 13C-MFA and measured intracellular pyridine nucleotide pools in a first-generation acetol producer strain (HJ06) and a non-producer strain (HJ06C), and identified that engineering the NADPH regeneration is a promising target. Based on this finding, we overexpressed nadK encoding NAD kinase or pntAB encoding membrane-bound transhydrogenase either individually or in combination with HJ06, obtaining HJ06N, HJ06P and HJ06PN. The step-wise approach resulted in increasing the acetol titer from 0.91 g/L (HJ06) to 2.81 g/L (HJ06PN). To systematically characterize and the effect of mutation(s) on the metabolism, we also examined the metabolomics and transcriptional levels of key genes in four strains. The pool sizes of NADPH, NADP+ and the NADPH/NADP+ ratio were progressively increased from HJ06 to HJ06PN, demonstrating that the sufficient NADPH supply is critical for acetol production. Flux distribution was optimized towards acetol formation from HJ06 to HJ06PN: (1) The carbon partitioning at the DHAP node directed gradually more carbon from the lower glycolytic pathway through the acetol biosynthetic pathway; (2) The transhydrogenation flux was constantly increased. In addition, 13C-MFA showed the rigidity of upper glycolytic pathway, PP pathway and the TCA cycle to support growth. The flux patterns were supported by most metabolomics data and gene expression profiles. CONCLUSIONS This research demonstrated how 13C-MFA can be applied to drive the cycles of design, build, test and learn implementation for strain development. This succeeding engineering strategy can also be applicable for rational design of other microbial cell factories.
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Affiliation(s)
- Ruilian Yao
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
| | - Jiawei Li
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
| | - Lei Feng
- Instrumental Analysis Center, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
| | - Xuehong Zhang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
| | - Hongbo Hu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
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Model metabolic strategy for heterotrophic bacteria in the cold ocean based on Colwellia psychrerythraea 34H. Proc Natl Acad Sci U S A 2018; 115:12507-12512. [PMID: 30446608 DOI: 10.1073/pnas.1807804115] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Colwellia psychrerythraea 34H is a model psychrophilic bacterium found in the cold ocean-polar sediments, sea ice, and the deep sea. Although the genomes of such psychrophiles have been sequenced, their metabolic strategies at low temperature have not been quantified. We measured the metabolic fluxes and gene expression of 34H at 4 °C (the mean global-ocean temperature and a normal-growth temperature for 34H), making comparative analyses at room temperature (above its upper-growth temperature of 18 °C) and with mesophilic Escherichia coli When grown at 4 °C, 34H utilized multiple carbon substrates without catabolite repression or overflow byproducts; its anaplerotic pathways increased flux network flexibility and enabled CO2 fixation. In glucose-only medium, the Entner-Doudoroff (ED) pathway was the primary glycolytic route; in lactate-only medium, gluconeogenesis and the glyoxylate shunt became active. In comparison, E. coli, cold stressed at 4 °C, had rapid glycolytic fluxes but no biomass synthesis. At their respective normal-growth temperatures, intracellular concentrations of TCA cycle metabolites (α-ketoglutarate, succinate, malate) were 4-17 times higher in 34H than in E. coli, while levels of energy molecules (ATP, NADH, NADPH) were 10- to 100-fold lower. Experiments with E. coli mutants supported the thermodynamic advantage of the ED pathway at cold temperature. Heat-stressed 34H at room temperature (2 hours) revealed significant down-regulation of genes associated with glycolytic enzymes and flagella, while 24 hours at room temperature caused irreversible cellular damage. We suggest that marine heterotrophic bacteria in general may rely upon simplified metabolic strategies to overcome thermodynamic constraints and thrive in the cold ocean.
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Evolution of gene knockout strains of E. coli reveal regulatory architectures governed by metabolism. Nat Commun 2018; 9:3796. [PMID: 30228271 PMCID: PMC6143558 DOI: 10.1038/s41467-018-06219-9] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 07/27/2018] [Indexed: 01/13/2023] Open
Abstract
Biological regulatory network architectures are multi-scale in their function and can adaptively acquire new functions. Gene knockout (KO) experiments provide an established experimental approach not just for studying gene function, but also for unraveling regulatory networks in which a gene and its gene product are involved. Here we study the regulatory architecture of Escherichia coli K-12 MG1655 by applying adaptive laboratory evolution (ALE) to metabolic gene KO strains. Multi-omic analysis reveal a common overall schema describing the process of adaptation whereby perturbations in metabolite concentrations lead regulatory networks to produce suboptimal states, whose function is subsequently altered and re-optimized through acquisition of mutations during ALE. These results indicate that metabolite levels, through metabolite-transcription factor interactions, have a dominant role in determining the function of a multi-scale regulatory architecture that has been molded by evolution. The function of metabolic genes in the context of regulatory networks is not well understood. Here, the authors investigate the adaptive responses of E. coli after knockout of metabolic genes and highlight the influence of metabolite levels in the evolution of regulatory function.
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Effect of global transcriptional regulators on kinetic behavior of Escherichia coli under anaerobic fermentation conditions. Arch Microbiol 2018; 200:979-987. [PMID: 29748694 DOI: 10.1007/s00203-018-1518-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 02/28/2018] [Accepted: 04/24/2018] [Indexed: 10/16/2022]
Abstract
The phenotype of Escherichia coli is governed by global transcriptional regulators under variable environmental conditions. Fnr, ArcA, IhfA-B, Crp, and Fis are amongst the major global transcription regulators that change their activity across the range of aeration, hence forming the core transcriptional network responsible for survival under changing aeration conditions. Effect of deletion of these global transcription factors on the kinetics of cell growth and mixed acid production under anaerobic fermentation conditions has not been characterized. To quantify the kinetic parameters in the absence of global transcription factors, experiments were performed using single deletion mutants of the above-mentioned global transcription regulators. The absence of global transcription regulators resulted in a relatively higher glucose uptake rate than that required for the observed growth rate. This further resulted in a higher yield of mixed acids per unit biomass in mutants as compared to the parent strain (E. coli BW25113). Thus, the increased channeling of carbon towards mixed acid secretion resulted in a lower growth rate in the mutants.
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Schwahn K, Nikoloski Z. Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism. FRONTIERS IN PLANT SCIENCE 2018; 9:538. [PMID: 29731765 PMCID: PMC5920133 DOI: 10.3389/fpls.2018.00538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 04/06/2018] [Indexed: 06/08/2023]
Abstract
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.
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Affiliation(s)
- Kevin Schwahn
- Systems Biology and Mathematical Modelling Group, Max Placnk Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modelling Group, Max Placnk Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
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A guide to 13C metabolic flux analysis for the cancer biologist. Exp Mol Med 2018; 50:1-13. [PMID: 29657327 PMCID: PMC5938039 DOI: 10.1038/s12276-018-0060-y] [Citation(s) in RCA: 162] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 12/21/2017] [Indexed: 01/15/2023] Open
Abstract
Cancer metabolism is significantly altered from normal cellular metabolism allowing cancer cells to adapt to changing microenvironments and maintain high rates of proliferation. In the past decade, stable-isotope tracing and network analysis have become powerful tools for uncovering metabolic pathways that are differentially activated in cancer cells. In particular, 13C metabolic flux analysis (13C-MFA) has emerged as the primary technique for quantifying intracellular fluxes in cancer cells. In this review, we provide a practical guide for investigators interested in getting started with 13C-MFA. We describe best practices in 13C-MFA, highlight potential pitfalls and alternative approaches, and conclude with new developments that can further enhance our understanding of cancer metabolism. Tracing tagged molecules can help researchers understand the altered metabolism of cancer cells. The abilities of cancer cells to multiply rapidly and invade new tissues are supported by metabolic alterations, which can be investigated by feeding tagged molecules to cells and tracing how they are metabolized. These techniques, such as 13C metabolic flux analysis (13C-MFA), have been perceived as difficult to use, but recent advances are making them more accessible. Maciek Antoniewicz, University of Delaware, Newark, USA, has published a practical guide for researchers wanting to use 13C-MFA. The review includes best practices, pitfalls, alternative approaches, and new developments, especially new user-friendly software that allows researchers without extensive training in mathematics, statistics, or coding to perform 13C-MFA. Broadening access to tools for investigating altered metabolic pathways may spur development of new cancer therapies targeting these pathways.
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Kargeti M, Venkatesh KV. The effect of global transcriptional regulators on the anaerobic fermentative metabolism of Escherichia coli. MOLECULAR BIOSYSTEMS 2018; 13:1388-1398. [PMID: 28573283 DOI: 10.1039/c6mb00721j] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Global transcription factors are known to regulate the anaerobic growth of Escherichia coli on glucose. These transcription factors help the organism to sense oxygen and accordingly regulate the synthesis of mixed acid producing enzymes. Five global transcription factors, namely ArcA, Fnr, IhfA-B, Crp and Fis, are known to play an important role in the growth phenotype of the organism in the transition from anaerobic to aerobic conditions. The effect of deletion of most of these global transcription factors on the growth phenotype has not been characterized under strict anaerobic fermentation conditions. In order to enumerate the role of global transcription factors in central carbon metabolism, experiments were performed using single deletion mutants of the above mentioned global transcription regulators. The mutants demonstrated lower growth rates, ranging from 3-75% lower growth as compared to the wild-type strain along with varying glucose uptake rates. Global transcription regulators help in lowering formate and acetate synthesis, thereby effectively channeling the carbon towards redox balance (through ethanol formation) and biomass synthesis. Flux analysis of mutant strains indicated that deletion of a single transcription factor alone does not play a significant role in the normalized flux distribution of the central carbon metabolism.
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Affiliation(s)
- Manika Kargeti
- Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai - 400076, India.
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40
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van der Stel AX, van de Lest CHA, Huynh S, Parker CT, van Putten JPM, Wösten MMSM. Catabolite repression in Campylobacter jejuni correlates with intracellular succinate levels. Environ Microbiol 2018; 20:1374-1388. [PMID: 29318721 DOI: 10.1111/1462-2920.14042] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 01/06/2018] [Indexed: 12/28/2022]
Abstract
Bacteria have evolved different mechanisms to catabolize carbon sources from nutrient mixtures. They first consume their preferred carbon source, before others are used. Regulatory mechanisms adapt the metabolism accordingly to maximize growth and to outcompete other organisms. The human pathogen Campylobacter jejuni is an asaccharolytic Gram-negative bacterium that catabolizes amino acids and organic acids for growth. It prefers serine and aspartate as carbon sources, however it lacks all regulators known to be involved in regulating carbon source utilization in other organisms. In which manner C. jejuni adapts its metabolism towards the presence or absence of preferred carbon sources is unknown. In this study, we show with transcriptomic analysis and enzyme assays how C. jejuni adapts its metabolism in response to its preferred carbon sources. In the presence of serine as well as lactate and pyruvate C. jejuni inhibits the utilization of other carbon sources, by repressing the expression of a number of central metabolic enzymes. The regulatory proteins RacR, Cj1000 and CsrA play a role in the regulation of these metabolic enzymes. This metabolism dependent transcriptional repression correlates with an accumulation of intracellular succinate. Hence, we propose a demand-based catabolite repression mechanism in C. jejuni, depended on intracellular succinate levels.
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Affiliation(s)
| | - Chris H A van de Lest
- Department of Biochemistry and Cell Biology, Utrecht University, Utrecht, The Netherlands
| | - Steven Huynh
- Agricultural Research Service, U.S. Department of Agriculture, Produce Safety and Microbiology Research Unit, Albany, CA, USA
| | - Craig T Parker
- Agricultural Research Service, U.S. Department of Agriculture, Produce Safety and Microbiology Research Unit, Albany, CA, USA
| | - Jos P M van Putten
- Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands
| | - Marc M S M Wösten
- Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands
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41
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Wortel MT, Noor E, Ferris M, Bruggeman FJ, Liebermeister W. Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield. PLoS Comput Biol 2018; 14:e1006010. [PMID: 29451895 PMCID: PMC5847312 DOI: 10.1371/journal.pcbi.1006010] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 03/12/2018] [Accepted: 01/30/2018] [Indexed: 11/25/2022] Open
Abstract
Microbes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off between growth rate and biomass yield has been proposed to explain this. We studied growth rate/yield trade-offs by using a novel modeling framework, Enzyme-Flux Cost Minimization (EFCM) and by assuming that the growth rate depends directly on the enzyme investment per rate of biomass production. In a comprehensive mathematical model of core metabolism in E. coli, we screened all elementary flux modes leading to cell synthesis, characterized them by the growth rates and yields they provide, and studied the shape of the resulting rate/yield Pareto front. By varying the model parameters, we found that the rate/yield trade-off is not universal, but depends on metabolic kinetics and environmental conditions. A prominent trade-off emerges under oxygen-limited growth, where yield-inefficient pathways support a 2-to-3 times higher growth rate than yield-efficient pathways. EFCM can be widely used to predict optimal metabolic states and growth rates under varying nutrient levels, perturbations of enzyme parameters, and single or multiple gene knockouts.
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Affiliation(s)
- Meike T. Wortel
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway
- Systems Bioinformatics Section, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit, Amsterdam, The Netherlands
| | - Elad Noor
- Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule, Zürich, Switzerland
| | - Michael Ferris
- Computer Sciences Department and Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Frank J. Bruggeman
- Systems Bioinformatics Section, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit, Amsterdam, The Netherlands
| | - Wolfram Liebermeister
- INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
- Institute of Biochemistry, Charité – Universitätsmedizin Berlin, Berlin, Germany
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Pyruvate cycle increases aminoglycoside efficacy and provides respiratory energy in bacteria. Proc Natl Acad Sci U S A 2018; 115:E1578-E1587. [PMID: 29382755 DOI: 10.1073/pnas.1714645115] [Citation(s) in RCA: 163] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The emergence and ongoing spread of multidrug-resistant bacteria puts humans and other species at risk for potentially lethal infections. Thus, novel antibiotics or alternative approaches are needed to target drug-resistant bacteria, and metabolic modulation has been documented to improve antibiotic efficacy, but the relevant metabolic mechanisms require more studies. Here, we show that glutamate potentiates aminoglycoside antibiotics, resulting in improved elimination of antibiotic-resistant pathogens. When exploring the metabolic flux of glutamate, it was found that the enzymes that link the phosphoenolpyruvate (PEP)-pyruvate-AcCoA pathway to the TCA cycle were key players in this increased efficacy. Together, the PEP-pyruvate-AcCoA pathway and TCA cycle can be considered the pyruvate cycle (P cycle). Our results show that inhibition or gene depletion of the enzymes in the P cycle shut down the TCA cycle even in the presence of excess carbon sources, and that the P cycle operates routinely as a general mechanism for energy production and regulation in Escherichia coli and Edwardsiella tarda These findings address metabolic mechanisms of metabolite-induced potentiation and fundamental questions about bacterial biochemistry and energy metabolism.
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43
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Ho WC, Zhang J. Evolutionary adaptations to new environments generally reverse plastic phenotypic changes. Nat Commun 2018; 9:350. [PMID: 29367589 PMCID: PMC5783951 DOI: 10.1038/s41467-017-02724-5] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 12/20/2017] [Indexed: 11/25/2022] Open
Abstract
Organismal adaptation to a new environment may start with plastic phenotypic changes followed by genetic changes, but whether the plastic changes are stepping stones to genetic adaptation is debated. Here we address this question by investigating gene expression and metabolic flux changes in the two-phase adaptation process using transcriptomic data from multiple experimental evolution studies and computational metabolic network analysis, respectively. We discover that genetic changes more frequently reverse than reinforce plastic phenotypic changes in virtually every adaptation. Metabolic network analysis reveals that, even in the presence of plasticity, organismal fitness drops after environmental shifts, but largely recovers through subsequent evolution. Such fitness trajectories explain why plastic phenotypic changes are genetically compensated rather than strengthened. In conclusion, although phenotypic plasticity may serve as an emergency response to a new environment that is necessary for survival, it does not generally facilitate genetic adaptation by bringing the organismal phenotype closer to the new optimum. Phenotypic plasticity has been suggested to facilitate survival in new environments and subsequent adaptation. Here, the authors reanalyze transcriptomic data from experimental evolution studies in combination with computational metabolic network analysis and show that genetic adaptation tends to reverse plastic changes in order to recover fitness.
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Affiliation(s)
- Wei-Chin Ho
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, 48109, USA.,Center for Mechanisms of Evolution, The Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, 48109, USA.
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44
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Matsuda F, Toya Y, Shimizu H. Learning from quantitative data to understand central carbon metabolism. Biotechnol Adv 2017; 35:971-980. [DOI: 10.1016/j.biotechadv.2017.09.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 09/01/2017] [Accepted: 09/14/2017] [Indexed: 12/23/2022]
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45
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Why Do Fast-Growing Bacteria Enter Overflow Metabolism? Testing the Membrane Real Estate Hypothesis. Cell Syst 2017; 5:95-104. [PMID: 28755958 DOI: 10.1016/j.cels.2017.06.005] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/13/2017] [Accepted: 06/09/2017] [Indexed: 11/22/2022]
Abstract
Bacteria and other cells show a puzzling behavior. At high growth rates, E. coli switch from respiration (which is ATP-efficient) to using fermentation for additional ATP (which is inefficient). This overflow metabolism results in a several-fold decrease in ATP produced per glucose molecule provided as food. By integrating diverse types of experimental data into a simple biophysical model, we give evidence that this onset is the result of the membrane real estate hypothesis: Fast growth drives cells to be bigger, reducing their surface-to-volume ratios. This decreases the membrane area available for respiratory proteins despite growing demand, causing increased crowding. Only when respiratory proteins reach their crowding limit does the cell activate fermentation, since fermentation allows faster ATP production per unit membrane area. Surface limitation thus creates a Pareto trade-off between membrane efficiency and ATP yield that links metabolic choice to the size and shape of a bacterial cell. By exploring the predictions that emerge from this trade-off, we show how consideration of molecular structures, energetics, rates, and equilibria can provide important insight into cellular behavior.
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46
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Ataman M, Hernandez Gardiol DF, Fengos G, Hatzimanikatis V. redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models. PLoS Comput Biol 2017; 13:e1005444. [PMID: 28727725 PMCID: PMC5519011 DOI: 10.1371/journal.pcbi.1005444] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 03/01/2017] [Indexed: 11/18/2022] Open
Abstract
Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models. Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology, fluxomics and metabolomics. Without consistent or clear criteria on how these reduced models are actually developed, it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models (GEMs). The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts. We applied redGEM for the construction of a core model for E. coli central carbon metabolism. We constructed the core model irJO1366 based on the latest genome-scale E. coli metabolic reconstruction (iJO1366). irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366. irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes. The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play”, synthetic biology approach. The algorithm is applicable to any compartmentalized or non-compartmentalized GEM.
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Affiliation(s)
- Meric Ataman
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), CH, Lausanne, Switzerland
| | - Daniel F. Hernandez Gardiol
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), CH, Lausanne, Switzerland
| | - Georgios Fengos
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), CH, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), CH, Lausanne, Switzerland
- * E-mail:
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47
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Lehning CE, Siedler S, Ellabaan MMH, Sommer MOA. Assessing glycolytic flux alterations resulting from genetic perturbations in E. coli using a biosensor. Metab Eng 2017; 42:194-202. [PMID: 28709932 PMCID: PMC5555440 DOI: 10.1016/j.ymben.2017.07.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 07/11/2017] [Indexed: 11/19/2022]
Abstract
We describe the development of an optimized glycolytic flux biosensor and its application in detecting altered flux in a production strain and in a mutant library. The glycolytic flux biosensor is based on the Cra-regulated ppsA promoter of E. coli controlling fluorescent protein synthesis. We validated the glycolytic flux dependency of the biosensor in a range of different carbon sources in six different E. coli strains and during mevalonate production. Furthermore, we studied the flux-altering effects of genome-wide single gene knock-outs in E. coli in a multiplex FlowSeq experiment. From a library consisting of 2126 knock-out mutants, we identified 3 mutants with high-flux and 95 mutants with low-flux phenotypes that did not have severe growth defects. This approach can improve our understanding of glycolytic flux regulation improving metabolic models and engineering efforts.
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Affiliation(s)
- Christina E Lehning
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800 Lyngby, Denmark
| | - Solvej Siedler
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800 Lyngby, Denmark
| | - Mostafa M H Ellabaan
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800 Lyngby, Denmark
| | - Morten O A Sommer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800 Lyngby, Denmark.
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48
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Jensen ED, Ferreira R, Jakočiūnas T, Arsovska D, Zhang J, Ding L, Smith JD, David F, Nielsen J, Jensen MK, Keasling JD. Transcriptional reprogramming in yeast using dCas9 and combinatorial gRNA strategies. Microb Cell Fact 2017; 16:46. [PMID: 28298224 PMCID: PMC5353793 DOI: 10.1186/s12934-017-0664-2] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 03/11/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Transcriptional reprogramming is a fundamental process of living cells in order to adapt to environmental and endogenous cues. In order to allow flexible and timely control over gene expression without the interference of native gene expression machinery, a large number of studies have focused on developing synthetic biology tools for orthogonal control of transcription. Most recently, the nuclease-deficient Cas9 (dCas9) has emerged as a flexible tool for controlling activation and repression of target genes, by the simple RNA-guided positioning of dCas9 in the vicinity of the target gene transcription start site. RESULTS In this study we compared two different systems of dCas9-mediated transcriptional reprogramming, and applied them to genes controlling two biosynthetic pathways for biobased production of isoprenoids and triacylglycerols (TAGs) in baker's yeast Saccharomyces cerevisiae. By testing 101 guide-RNA (gRNA) structures on a total of 14 different yeast promoters, we identified the best-performing combinations based on reporter assays. Though a larger number of gRNA-promoter combinations do not perturb gene expression, some gRNAs support expression perturbations up to ~threefold. The best-performing gRNAs were used for single and multiplex reprogramming strategies for redirecting flux related to isoprenoid production and optimization of TAG profiles. From these studies, we identified both constitutive and inducible multiplex reprogramming strategies enabling significant changes in isoprenoid production and increases in TAG. CONCLUSION Taken together, we show similar performance for a constitutive and an inducible dCas9 approach, and identify multiplex gRNA designs that can significantly perturb isoprenoid production and TAG profiles in yeast without editing the genomic context of the target genes. We also identify a large number of gRNA positions in 14 native yeast target pomoters that do not affect expression, suggesting the need for further optimization of gRNA design tools and dCas9 engineering.
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Affiliation(s)
- Emil D. Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
| | - Tadas Jakočiūnas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Dushica Arsovska
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Jie Zhang
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Ling Ding
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Justin D. Smith
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305 USA
- Stanford Genome Technology Center, Palo Alto, CA 94304 USA
| | - Florian David
- Department of Biology and Biological Engineering, Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
| | - Jens Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
- Department of Biology and Biological Engineering, Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
| | - Michael K. Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Jay D. Keasling
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
- Joint BioEnergy Institute, Emeryville, CA USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA
- Department of Chemical and Biomolecular Engineering & Department of Bioengineering, University of California, Berkeley, CA USA
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49
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Metabolic regulation is sufficient for global and robust coordination of glucose uptake, catabolism, energy production and growth in Escherichia coli. PLoS Comput Biol 2017; 13:e1005396. [PMID: 28187134 PMCID: PMC5328398 DOI: 10.1371/journal.pcbi.1005396] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 02/27/2017] [Accepted: 02/03/2017] [Indexed: 11/23/2022] Open
Abstract
The metabolism of microorganisms is regulated through two main mechanisms: changes of enzyme capacities as a consequence of gene expression modulation (“hierarchical control”) and changes of enzyme activities through metabolite-enzyme interactions. An increasing body of evidence indicates that hierarchical control is insufficient to explain metabolic behaviors, but the system-wide impact of metabolic regulation remains largely uncharacterized. To clarify its role, we developed and validated a detailed kinetic model of Escherichia coli central metabolism that links growth to environment. Metabolic control analyses confirm that the control is widely distributed across the network and highlight strong interconnections between all the pathways. Exploration of the model solution space reveals that several robust properties emerge from metabolic regulation, from the molecular level (e.g. homeostasis of total metabolite pool) to the overall cellular physiology (e.g. coordination of carbon uptake, catabolism, energy and redox production, and growth), while allowing a large degree of flexibility at most individual metabolic steps. These properties have important physiological implications for E. coli and significantly expand the self-regulating capacities of its metabolism. Metabolism is a fundamental biochemical process that enables cells to operate and grow by converting nutrients into ‘building blocks’ and energy. Metabolism happens through the work of enzymes, which are encoded by genes. Thus, genes and their regulation are often thought of controlling metabolism, somewhat at the top of a hierarchical control system. However, an increasing body of evidence indicates that metabolism plays an active role in the control of its own operation via a dense network of metabolite-enzyme interactions. The system-wide role of metabolic regulation is hard to dissect and so far remains largely uncharacterized. To better understand its role, we constructed a detailed kinetic model of the carbon and energy metabolism of the bacterium Escherichia coli, a model organism in Systems and Synthetic biology. Model simulations indicate that kinetic considerations of metabolism alone can explain data from hundreds of experiments, without needing to invoke regulation of gene expression. In particular, metabolic regulation is sufficient to coordinate carbon utilization, redox and energy production, and growth, while maintaining local flexibility at individual metabolic steps. These findings indicate that the self-regulating capacities of E. coli metabolism are far more significant than previously expected, and improve our understanding on how cells work.
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Kochanowski K, Gerosa L, Brunner SF, Christodoulou D, Nikolaev YV, Sauer U. Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli. Mol Syst Biol 2017; 13:903. [PMID: 28049137 PMCID: PMC5293157 DOI: 10.15252/msb.20167402] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Transcription networks consist of hundreds of transcription factors with thousands of often overlapping target genes. While we can reliably measure gene expression changes, we still understand relatively little why expression changes the way it does. How does a coordinated response emerge in such complex networks and how many input signals are necessary to achieve it? Here, we unravel the regulatory program of gene expression in Escherichia coli central carbon metabolism with more than 30 known transcription factors. Using a library of fluorescent transcriptional reporters, we comprehensively quantify the activity of central metabolic promoters in 26 environmental conditions. The expression patterns were dominated by growth rate‐dependent global regulation for most central metabolic promoters in concert with highly condition‐specific activation for only few promoters. Using an approximate mathematical description of promoter activity, we dissect the contribution of global and specific transcriptional regulation. About 70% of the total variance in promoter activity across conditions was explained by global transcriptional regulation. Correlating the remaining specific transcriptional regulation of each promoter with the cell's metabolome response across the same conditions identified potential regulatory metabolites. Remarkably, cyclic AMP, fructose‐1,6‐bisphosphate, and fructose‐1‐phosphate alone explained most of the specific transcriptional regulation through their interaction with the two major transcription factors Crp and Cra. Thus, a surprisingly simple regulatory program that relies on global transcriptional regulation and input from few intracellular metabolites appears to be sufficient to coordinate E. coli central metabolism and explain about 90% of the experimentally observed transcription changes in 100 genes.
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Affiliation(s)
- Karl Kochanowski
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Luca Gerosa
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Simon F Brunner
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Dimitris Christodoulou
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Yaroslav V Nikolaev
- Institute of Molecular Biology & Biophysics, ETH Zurich, Zurich, Switzerland
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
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