1
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Hudson EP. The Calvin Benson cycle in bacteria: New insights from systems biology. Semin Cell Dev Biol 2024; 155:71-83. [PMID: 37002131 DOI: 10.1016/j.semcdb.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/21/2023] [Accepted: 03/16/2023] [Indexed: 03/31/2023]
Abstract
The Calvin Benson cycle in phototrophic and chemolithoautotrophic bacteria has ecological and biotechnological importance, which has motivated study of its regulation. I review recent advances in our understanding of how the Calvin Benson cycle is regulated in bacteria and the technologies used to elucidate regulation and modify it, and highlight differences between and photoautotrophic and chemolithoautotrophic models. Systems biology studies have shown that in oxygenic phototrophic bacteria, Calvin Benson cycle enzymes are extensively regulated at post-transcriptional and post-translational levels, with multiple enzyme activities connected to cellular redox status through thioredoxin. In chemolithoautotrophic bacteria, regulation is primarily at the transcriptional level, with effector metabolites transducing cell status, though new methods should now allow facile, proteome-wide exploration of biochemical regulation in these models. A biotechnological objective is to enhance CO2 fixation in the cycle and partition that carbon to a product of interest. Flux control of CO2 fixation is distributed over multiple enzymes, and attempts to modulate gene Calvin cycle gene expression show a robust homeostatic regulation of growth rate, though the synthesis rates of products can be significantly increased. Therefore, de-regulation of cycle enzymes through protein engineering may be necessary to increase fluxes. Non-canonical Calvin Benson cycles, if implemented with synthetic biology, could have reduced energy demand and enzyme loading, thus increasing the attractiveness of these bacteria for industrial applications.
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Affiliation(s)
- Elton P Hudson
- Department of Protein Science, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
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2
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de Leeuw M, Matos MRA, Nielsen LK. Omics data for sampling thermodynamically feasible kinetic models. Metab Eng 2023; 78:41-47. [PMID: 37209863 DOI: 10.1016/j.ymben.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/03/2023] [Accepted: 05/07/2023] [Indexed: 05/22/2023]
Abstract
Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.
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Affiliation(s)
- Marina de Leeuw
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Marta R A Matos
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Lars Keld Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark; Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, 4072, Brisbane QLD, Australia.
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3
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Hurbain J, Thommen Q, Anquez F, Pfeuty B. Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy. iScience 2022; 25:104681. [PMID: 35856027 PMCID: PMC9287814 DOI: 10.1016/j.isci.2022.104681] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/12/2022] [Accepted: 06/23/2022] [Indexed: 01/22/2023] Open
Abstract
Living cells use signaling and regulatory mechanisms to adapt to environmental stresses. Adaptation to oxidative stress involves the regulation of many enzymes in both glycolysis and pentose phosphate pathways (PPP), so as to support PPP-driven NADPH recycling for antioxidant defense. The underlying regulatory logic is investigated by developing a kinetic modeling approach fueled with metabolomics and 13C-fluxomics datasets from human fibroblast cells. Bayesian parameter estimation and phenotypic analysis of models highlight complementary roles for several metabolite-enzyme regulations. Specifically, carbon flux rerouting into PPP involves a tight coordination between the upregulation of G6PD activity concomitant to a decreased NADPH/NADP+ ratio and the differential control of downward and upward glycolytic fluxes through the joint inhibition of PGI and GAPD enzymes. Such functional interplay between distinct regulatory feedbacks promotes efficient detoxification and homeostasis response over a broad range of stress level, but can also explain paradoxical pertubation phenotypes for instance reported for 6PGD modulation in mammalian cells.
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Affiliation(s)
- Julien Hurbain
- CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, University of Lille, 59000 Lille, France
| | - Quentin Thommen
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277 - CANTHER - Cancer Heterogeneity Plasticity and Resistance to Therapies, University of Lille, 59000 Lille, France
| | - Francois Anquez
- CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, University of Lille, 59000 Lille, France
| | - Benjamin Pfeuty
- CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, University of Lille, 59000 Lille, France
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4
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Janasch M, Crang N, Asplund-Samuelsson J, Sporre E, Bruch M, Gynnå A, Jahn M, Hudson EP. Thermodynamic limitations of PHB production from formate and fructose in Cupriavidus necator. Metab Eng 2022; 73:256-269. [PMID: 35987434 DOI: 10.1016/j.ymben.2022.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/10/2022] [Accepted: 08/06/2022] [Indexed: 11/30/2022]
Abstract
The chemolithotroph Cupriavidus necator H16 is known as a natural producer of the bioplastic-polymer PHB, as well as for its metabolic versatility to utilize different substrates, including formate as the sole carbon and energy source. Depending on the entry point of the substrate, this versatility requires adjustment of the thermodynamic landscape to maintain sufficiently high driving forces for biological processes. Here we employed a model of the core metabolism of C. necator H16 to analyze the thermodynamic driving forces and PHB yields from formate for different metabolic engineering strategies. For this, we enumerated elementary flux modes (EFMs) of the network and evaluated their PHB yields as well as thermodynamics via Max-min driving force (MDF) analysis and random sampling of driving forces. A heterologous ATP:citrate lyase reaction was predicted to increase driving force for producing acetyl-CoA. A heterologous phosphoketolase reaction was predicted to increase maximal PHB yields as well as driving forces. These enzymes were then verified experimentally to enhance PHB titers between 60 and 300% in select conditions. The EFM analysis also revealed that PHB production from formate may be limited by low driving forces through citrate lyase and aconitase, as well as cofactor balancing, and identified additional reactions associated with low and high PHB yield. Proteomics analysis of the engineered strains confirmed an increased abundance of aconitase and cofactor balancing. The findings of this study aid in understanding metabolic adaptation. Furthermore, the outlined approach will be useful in designing metabolic engineering strategies in other non-model bacteria.
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Affiliation(s)
- Markus Janasch
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden.
| | - Nick Crang
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden.
| | - Johannes Asplund-Samuelsson
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden
| | - Emil Sporre
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden
| | - Manuel Bruch
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden
| | - Arvid Gynnå
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden
| | - Michael Jahn
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden
| | - Elton P Hudson
- Science for Life Laboratory, School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, P-Box 1031, 171 21, Solna, Sweden.
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5
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Kawai R, Toya Y, Shimizu H. Metabolic pathway design for growth-associated phenylalanine production using synthetically designed mutualism. Bioprocess Biosyst Eng 2022; 45:1539-1546. [PMID: 35930086 DOI: 10.1007/s00449-022-02762-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/21/2022] [Indexed: 11/02/2022]
Abstract
Combination of growth-associated pathway engineering based on flux balance analysis (FBA) and adaptive laboratory evolution (ALE) is a powerful approach to enhance the production of useful compounds. However, the feasibility of such growth-associated pathway designs depends on the type of target compound. In the present study, FBA predicted a set of gene deletions (pykA, pykF, ppc, zwf, and adhE) that leads to growth-associated phenylalanine production in Escherichia coli. The knockout strain is theoretically enforced to produce phenylalanine only at high growth yields, and could not be applied to the ALE experiment because of a severe growth defect. To overcome this challenge, we propose a novel approach for ALE based on mutualistic co-culture for coupling growth and production, regardless of the growth rate. We designed a synthetic mutualism of a phenylalanine-producing leucine-auxotrophic strain (KF strain) and a leucine-producing phenylalanine-auxotrophic strain (KL strain) and performed an ALE experiment for approximately 160 generations. The evolved KF strain (KF-E strain) grew in a synthetic medium (with glucose as the main carbon source) supplemented with leucine, while severe growth defects were observed in the parental KF strain. The phenylalanine yield of the KF-E strain was 2.3 times higher than that of the KF strain.
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Affiliation(s)
- Ryutaro Kawai
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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6
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Chung CH, Lin DW, Eames A, Chandrasekaran S. Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms. Metabolites 2021; 11:606. [PMID: 34564422 PMCID: PMC8470976 DOI: 10.3390/metabo11090606] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 12/18/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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Affiliation(s)
- Carolina H. Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Da-Wei Lin
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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7
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Lee JY, Nguyen B, Orosco C, Styczynski MP. SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions. BMC Bioinformatics 2021; 22:365. [PMID: 34238207 PMCID: PMC8268592 DOI: 10.1186/s12859-021-04281-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/30/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. RESULTS We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. CONCLUSIONS SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.
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Affiliation(s)
- Justin Y Lee
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Britney Nguyen
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Carlos Orosco
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Mark P Styczynski
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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8
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Luzarowski M, Vicente R, Kiselev A, Wagner M, Schlossarek D, Erban A, de Souza LP, Childs D, Wojciechowska I, Luzarowska U, Górka M, Sokołowska EM, Kosmacz M, Moreno JC, Brzezińska A, Vegesna B, Kopka J, Fernie AR, Willmitzer L, Ewald JC, Skirycz A. Global mapping of protein-metabolite interactions in Saccharomyces cerevisiae reveals that Ser-Leu dipeptide regulates phosphoglycerate kinase activity. Commun Biol 2021; 4:181. [PMID: 33568709 PMCID: PMC7876005 DOI: 10.1038/s42003-021-01684-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 01/08/2021] [Indexed: 01/30/2023] Open
Abstract
Protein-metabolite interactions are of crucial importance for all cellular processes but remain understudied. Here, we applied a biochemical approach named PROMIS, to address the complexity of the protein-small molecule interactome in the model yeast Saccharomyces cerevisiae. By doing so, we provide a unique dataset, which can be queried for interactions between 74 small molecules and 3982 proteins using a user-friendly interface available at https://promis.mpimp-golm.mpg.de/yeastpmi/ . By interpolating PROMIS with the list of predicted protein-metabolite interactions, we provided experimental validation for 225 binding events. Remarkably, of the 74 small molecules co-eluting with proteins, 36 were proteogenic dipeptides. Targeted analysis of a representative dipeptide, Ser-Leu, revealed numerous protein interactors comprising chaperones, proteasomal subunits, and metabolic enzymes. We could further demonstrate that Ser-Leu binding increases activity of a glycolytic enzyme phosphoglycerate kinase (Pgk1). Consistent with the binding analysis, Ser-Leu supplementation leads to the acute metabolic changes and delays timing of a diauxic shift. Supported by the dipeptide accumulation analysis our work attests to the role of Ser-Leu as a metabolic regulator at the interface of protein degradation and central metabolism.
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Affiliation(s)
- Marcin Luzarowski
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Rubén Vicente
- grid.418390.70000 0004 0491 976XDepartment of Metabolic Networks, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Andrei Kiselev
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany ,grid.503344.50000 0004 0445 6769Laboratoire de Recherche en Sciences Végétales (LRSV), UPS/CNRS, UMR, Castanet Tolosan, France
| | - Mateusz Wagner
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany ,grid.8505.80000 0001 1010 5103University of Wrocław, Faculty of Biotechnology, Laboratory of Medical Biology, Wrocław, Poland
| | - Dennis Schlossarek
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Alexander Erban
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Leonardo Perez de Souza
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Dorothee Childs
- grid.4709.a0000 0004 0495 846XDepartment of Genome Biology, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Izabela Wojciechowska
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Urszula Luzarowska
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany ,grid.7489.20000 0004 1937 0511Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michał Górka
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Ewelina M. Sokołowska
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Monika Kosmacz
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany ,grid.45672.320000 0001 1926 5090Center for Desert Agriculture, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Juan C. Moreno
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany ,grid.45672.320000 0001 1926 5090Center for Desert Agriculture, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Aleksandra Brzezińska
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Bhavana Vegesna
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Joachim Kopka
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Alisdair R. Fernie
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Lothar Willmitzer
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Jennifer C. Ewald
- grid.10392.390000 0001 2190 1447Interfaculty Institute of Cell Biology, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Aleksandra Skirycz
- grid.418390.70000 0004 0491 976XDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany ,grid.5386.8000000041936877XBoyce Thompson Institute, Ithaca, NY USA
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9
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A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation. Biochem Soc Trans 2020; 48:1889-1903. [DOI: 10.1042/bst20190840] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/16/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.
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10
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Volkova S, Matos MRA, Mattanovich M, Marín de Mas I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites 2020; 10:E303. [PMID: 32722118 PMCID: PMC7465778 DOI: 10.3390/metabo10080303] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/08/2020] [Accepted: 07/22/2020] [Indexed: 01/05/2023] Open
Abstract
Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.
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Affiliation(s)
| | | | | | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; (S.V.); (M.R.A.M.); (M.M.)
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11
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Shen F, Sun R, Yao J, Li J, Liu Q, Price ND, Liu C, Wang Z. OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling. PLoS Comput Biol 2019; 15:e1006835. [PMID: 30849073 PMCID: PMC6426274 DOI: 10.1371/journal.pcbi.1006835] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 03/20/2019] [Accepted: 02/01/2019] [Indexed: 02/07/2023] Open
Abstract
The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.
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Affiliation(s)
- Fangzhou Shen
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Renliang Sun
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Yao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Li
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Qian Liu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Chenguang Liu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhuo Wang
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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12
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Integrating -omics data into genome-scale metabolic network models: principles and challenges. Essays Biochem 2018; 62:563-574. [PMID: 30315095 DOI: 10.1042/ebc20180011] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/30/2018] [Accepted: 08/31/2018] [Indexed: 12/13/2022]
Abstract
At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available -omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of -omics data into CBMs focussing on the methods' assumptions and limitations. We argue that key assumptions - often derived from single-enzyme kinetics - do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for -omics data integration in a common framework to provide more accurate predictions.
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13
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Tokuyama K, Toya Y, Horinouchi T, Furusawa C, Matsuda F, Shimizu H. Application of adaptive laboratory evolution to overcome a flux limitation in anEscherichia coliproduction strain. Biotechnol Bioeng 2018; 115:1542-1551. [DOI: 10.1002/bit.26568] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 02/13/2018] [Accepted: 02/14/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Kento Tokuyama
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Suita Osaka Japan
| | - Yoshihiro Toya
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Suita Osaka Japan
| | | | - Chikara Furusawa
- Quantitative Biology Center; RIKEN; Suita Osaka Japan
- Universal Biology Institute; The University of Tokyo; Hongo Tokyo Japan
| | - Fumio Matsuda
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Suita Osaka Japan
- RIKEN Center for Sustainable Resource Science; Tsurumi-ku Yokohama Japan
| | - Hiroshi Shimizu
- Department of Bioinfomatic Engineering; Graduate School of Information Science and Technology; Osaka University; Suita Osaka Japan
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14
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Zuñiga C, Levering J, Antoniewicz MR, Guarnieri MT, Betenbaugh MJ, Zengler K. Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris. PLANT PHYSIOLOGY 2018; 176:450-462. [PMID: 28951490 PMCID: PMC5761767 DOI: 10.1104/pp.17.00605] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 09/21/2017] [Indexed: 06/02/2023]
Abstract
Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.
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Affiliation(s)
- Cristal Zuñiga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0760
| | - Jennifer Levering
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0760
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy Street, Newark, Delaware 19716
| | - Michael T Guarnieri
- National Bioenergy Center, National Renewable Energy Laboratory, Golden, Colorado 80401
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0760
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15
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McNerney MP, Styczynski MP. Small molecule signaling, regulation, and potential applications in cellular therapeutics. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2017; 10. [PMID: 28960879 DOI: 10.1002/wsbm.1405] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 07/20/2017] [Accepted: 08/14/2017] [Indexed: 12/19/2022]
Abstract
Small molecules have many important roles across the tree of life: they regulate processes from metabolism to transcription, they enable signaling within and between species, and they serve as the biochemical building blocks for cells. They also represent valuable phenotypic endpoints that are promising for use as biomarkers of disease states. In the context of engineering cell-based therapeutics, they hold particularly great promise for enabling finer control over the therapeutic cells and allowing them to be responsive to extracellular cues. The natural signaling and regulatory functions of small molecules can be harnessed and rewired to control cell activity and delivery of therapeutic payloads, potentially increasing efficacy while decreasing toxicity. To that end, this review considers small molecule-mediated regulation and signaling in bacteria. We first discuss some of the most prominent applications and aspirations for responsive cell-based therapeutics. We then describe the transport, signaling, and regulation associated with three classes of molecules that may be exploited in the engineering of therapeutic bacteria: amino acids, fatty acids, and quorum-sensing signaling molecules. We also present examples of existing engineering efforts to generate cells that sense and respond to levels of different small molecules. Finally, we discuss future directions for how small molecule-mediated regulation could be harnessed for therapeutic applications, as well as some critical considerations for the ultimate success of such endeavors. WIREs Syst Biol Med 2018, 10:e1405. doi: 10.1002/wsbm.1405 This article is categorized under: Biological Mechanisms > Cell Signaling Biological Mechanisms > Metabolism Translational, Genomic, and Systems Medicine > Therapeutic Methods.
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Affiliation(s)
- Monica P McNerney
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Mark P Styczynski
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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16
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Matsuoka Y, Kurata H. Modeling and simulation of the redox regulation of the metabolism in Escherichia coli at different oxygen concentrations. BIOTECHNOLOGY FOR BIOFUELS 2017; 10:183. [PMID: 28725263 PMCID: PMC5512849 DOI: 10.1186/s13068-017-0867-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Microbial production of biofuels and biochemicals from renewable feedstocks has received considerable recent attention from environmental protection and energy production perspectives. Many biofuels and biochemicals are produced by fermentation under oxygen-limited conditions following initiation of aerobic cultivation to enhance the cell growth rate. Thus, it is of significant interest to investigate the effect of dissolved oxygen concentration on redox regulation in Escherichia coli, a particularly popular cellular factory due to its high growth rate and well-characterized physiology. For this, the systems biology approach such as modeling is powerful for the analysis of the metabolism and for the design of microbial cellular factories. RESULTS Here, we developed a kinetic model that describes the dynamics of fermentation by taking into account transcription factors such as ArcA/B and Fnr, respiratory chain reactions and fermentative pathways, and catabolite regulation. The hallmark of the kinetic model is its ability to predict the dynamics of metabolism at different dissolved oxygen levels and facilitate the rational design of cultivation methods. The kinetic model was verified based on the experimental data for a wild-type E. coli strain. The model reasonably predicted the metabolic characteristics and molecular mechanisms of fnr and arcA gene-knockout mutants. Moreover, an aerobic-microaerobic dual-phase cultivation method for lactate production in a pfl-knockout mutant exhibited promising yield and productivity. CONCLUSIONS It is quite important to understand metabolic regulation mechanisms from both scientific and engineering points of view. In particular, redox regulation in response to oxygen limitation is critically important in the practical production of biofuel and biochemical compounds. The developed model can thus be used as a platform for designing microbial factories to produce a variety of biofuels and biochemicals.
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Affiliation(s)
- Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
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17
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The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization. Nat Commun 2017; 8:16018. [PMID: 28691704 PMCID: PMC5508129 DOI: 10.1038/ncomms16018] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Accepted: 05/23/2017] [Indexed: 01/03/2023] Open
Abstract
Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite–enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism. Metabolites act as enzyme inhibitors, but their global impact on metabolism has scarcely been considered. Here, the authors generate a human genome-wide metabolite-enzyme inhibition network, and find that inhibition occurs largely due to limited structural diversity of metabolites, leading to a global constraint on metabolism which subcellular compartmentalization minimizes.
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18
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Mori M, Ponce-de-León M, Peretó J, Montero F. Metabolic Complementation in Bacterial Communities: Necessary Conditions and Optimality. Front Microbiol 2016; 7:1553. [PMID: 27774085 PMCID: PMC5054487 DOI: 10.3389/fmicb.2016.01553] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 09/16/2016] [Indexed: 11/13/2022] Open
Abstract
Bacterial communities may display metabolic complementation, in which different members of the association partially contribute to the same biosynthetic pathway. In this way, the end product of the pathway is synthesized by the community as a whole. However, the emergence and the benefits of such complementation are poorly understood. Herein, we present a simple model to analyze the metabolic interactions among bacteria, including the host in the case of endosymbiotic bacteria. The model considers two cell populations, with both cell types encoding for the same linear biosynthetic pathway. We have found that, for metabolic complementation to emerge as an optimal strategy, both product inhibition and large permeabilities are needed. In the light of these results, we then consider the patterns found in the case of tryptophan biosynthesis in the endosymbiont consortium hosted by the aphid Cinara cedri. Using in-silico computed physicochemical properties of metabolites of this and other biosynthetic pathways, we verified that the splitting point of the pathway corresponds to the most permeable intermediate.
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Affiliation(s)
- Matteo Mori
- Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de MadridMadrid, Spain; Department of Physics, University of California, San DiegoLa Jolla, CA, USA
| | - Miguel Ponce-de-León
- Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid Madrid, Spain
| | - Juli Peretó
- Department of Biochemistry and Molecular Biology and Institute for Integrative Systems Biology, Universitat de València-CSIC Valencia, Spain
| | - Francisco Montero
- Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid Madrid, Spain
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19
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Jahan N, Maeda K, Matsuoka Y, Sugimoto Y, Kurata H. Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli. Microb Cell Fact 2016; 15:112. [PMID: 27329289 PMCID: PMC4915146 DOI: 10.1186/s12934-016-0511-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Accepted: 06/08/2016] [Indexed: 01/17/2023] Open
Abstract
Background A kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters. Results In this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture. Conclusions This model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes. Electronic supplementary material The online version of this article (doi:10.1186/s12934-016-0511-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nusrat Jahan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Kazuhiro Maeda
- Frontier Research Academy for Young Researchers, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata, Kitakyushu, Fukuoka, 804-8550, Japan.,Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yurie Sugimoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. .,Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
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20
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Morrison ES, Badyaev AV. The Landscape of Evolution: Reconciling Structural and Dynamic Properties of Metabolic Networks in Adaptive Diversifications. Integr Comp Biol 2016; 56:235-46. [PMID: 27252203 DOI: 10.1093/icb/icw026] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The network of the interactions among genes, proteins, and metabolites delineates a range of potential phenotypic diversifications in a lineage, and realized phenotypic changes are the result of differences in the dynamics of the expression of the elements and interactions in this deterministic network. Regulatory mechanisms, such as hormones, mediate the relationship between the structural and dynamic properties of networks by determining how and when the elements are expressed and form a functional unit or state. Changes in regulatory mechanisms lead to variable expression of functional states of a network within and among generations. Functional properties of network elements, and the magnitude and direction of evolutionary change they determine, depend on their location within a network. Here, we examine the relationship between network structure and the dynamic mechanisms that regulate flux through a metabolic network. We review the mechanisms that control metabolic flux in enzymatic reactions and examine structural properties of the network locations that are targets of flux control. We aim to establish a predictive framework to test the contributions of structural and dynamic properties of deterministic networks to evolutionary diversifications.
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Affiliation(s)
- Erin S Morrison
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721-0001, USA
| | - Alexander V Badyaev
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721-0001, USA
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21
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Dash S, Ng CY, Maranas CD. Metabolic modeling of clostridia: current developments and applications. FEMS Microbiol Lett 2016; 363:fnw004. [PMID: 26755502 DOI: 10.1093/femsle/fnw004] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2016] [Indexed: 12/12/2022] Open
Abstract
Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.
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Affiliation(s)
- Satyakam Dash
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802-1503, USA
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802-1503, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802-1503, USA
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22
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Machado D, Zhuang KH, Sonnenschein N, Herrgård MJ. Editorial: Current Challenges in Modeling Cellular Metabolism. Front Bioeng Biotechnol 2015; 3:193. [PMID: 26636080 PMCID: PMC4659907 DOI: 10.3389/fbioe.2015.00193] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 11/09/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Daniel Machado
- Centre of Biological Engineering, University of Minho , Braga , Portugal
| | - Kai H Zhuang
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
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