1
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Casini I, McCubbin T, Esquivel-Elizondo S, Luque GG, Evseeva D, Fink C, Beblawy S, Youngblut ND, Aristilde L, Huson DH, Dräger A, Ley RE, Marcellin E, Angenent LT, Molitor B. An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter. iScience 2023; 26:108016. [PMID: 37854702 PMCID: PMC10579436 DOI: 10.1016/j.isci.2023.108016] [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: 07/27/2023] [Revised: 08/29/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023] Open
Abstract
Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.
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Affiliation(s)
- Isabella Casini
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Tim McCubbin
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Metabolomics and Proteomics (Q-MAP), The University of Queensland, Brisbane, QLD 4072, Australia
- ARC Centre of Excellence in Synthetic Biology (COESB), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Sofia Esquivel-Elizondo
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Guillermo G. Luque
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Daria Evseeva
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
| | - Christian Fink
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Sebastian Beblawy
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Nicholas D. Youngblut
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Ludmilla Aristilde
- Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Daniel H. Huson
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Andreas Dräger
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Ruth E. Ley
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Metabolomics and Proteomics (Q-MAP), The University of Queensland, Brisbane, QLD 4072, Australia
- ARC Centre of Excellence in Synthetic Biology (COESB), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Largus T. Angenent
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
- AG Angenent, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Department of Biological and Chemical Engineering, Aarhus University, Gustav Wieds Vej 10D, 8000 Aarhus C, Denmark
- The Novo Nordisk Foundation CO2 Research Center (CORC), Aarhus University, Gustav Wieds Vej 10C, 8000 Aarhus C, Denmark
| | - Bastian Molitor
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
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2
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Basile A, Zampieri G, Kovalovszki A, Karkaria B, Treu L, Patil KR, Campanaro S. Modelling of microbial interactions in anaerobic digestion: from black to glass box. Curr Opin Microbiol 2023; 75:102363. [PMID: 37542746 DOI: 10.1016/j.mib.2023.102363] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/20/2023] [Accepted: 07/10/2023] [Indexed: 08/07/2023]
Abstract
Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.
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Affiliation(s)
- Arianna Basile
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
| | - Guido Zampieri
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
| | - Adam Kovalovszki
- Department of Environmental and Resource Engineering, Technical University of Denmark, Building 115, Bygningstorvet, 2800 Kgs. Lyngby, Denmark
| | - Behzad Karkaria
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Laura Treu
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy.
| | - Kiran Raosaheb Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Stefano Campanaro
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
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3
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Esser SP, Rahlff J, Zhao W, Predl M, Plewka J, Sures K, Wimmer F, Lee J, Adam PS, McGonigle J, Turzynski V, Banas I, Schwank K, Krupovic M, Bornemann TLV, Figueroa-Gonzalez PA, Jarett J, Rattei T, Amano Y, Blaby IK, Cheng JF, Brazelton WJ, Beisel CL, Woyke T, Zhang Y, Probst AJ. A predicted CRISPR-mediated symbiosis between uncultivated archaea. Nat Microbiol 2023; 8:1619-1633. [PMID: 37500801 DOI: 10.1038/s41564-023-01439-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/23/2023] [Indexed: 07/29/2023]
Abstract
CRISPR-Cas systems defend prokaryotic cells from invasive DNA of viruses, plasmids and other mobile genetic elements. Here, we show using metagenomics, metatranscriptomics and single-cell genomics that CRISPR systems of widespread, uncultivated archaea can also target chromosomal DNA of archaeal episymbionts of the DPANN superphylum. Using meta-omics datasets from Crystal Geyser and Horonobe Underground Research Laboratory, we find that CRISPR spacers of the hosts Candidatus Altiarchaeum crystalense and Ca. A. horonobense, respectively, match putative essential genes in their episymbionts' genomes of the genus Ca. Huberiarchaeum and that some of these spacers are expressed in situ. Metabolic interaction modelling also reveals complementation between host-episymbiont systems, on the basis of which we propose that episymbionts are either parasitic or mutualistic depending on the genotype of the host. By expanding our analysis to 7,012 archaeal genomes, we suggest that CRISPR-Cas targeting of genomes associated with symbiotic archaea evolved independently in various archaeal lineages.
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Affiliation(s)
- Sarah P Esser
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Janina Rahlff
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
- Centre for Ecology and Evolution in Microbial Model Systems (EEMiS), Department of Biology and Environmental Science, Linnaeus University, Kalmar, Sweden
| | - Weishu Zhao
- Department of Cell and Molecular Biology, College of the Environment and Life Sciences, University of Rhode Island, Kingston, RI, USA
- Shanghai Jiao Tong University, School of Life Sciences and Biotechnology, International Center for Deep Life Investigation (IC-DLI), Shanghai Jiao Tong University, Shanghai, China
| | - Michael Predl
- Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
- Doctoral School in Microbiology and Environmental Science, University of Vienna, Vienna, Austria
| | - Julia Plewka
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Katharina Sures
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Franziska Wimmer
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Centre for Infection Research (HZI), Würzburg, Germany
| | - Janey Lee
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Panagiotis S Adam
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Julia McGonigle
- School of Biological Sciences, University of Utah, Salt Lake City, UT, USA
| | - Victoria Turzynski
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Indra Banas
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Katrin Schwank
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
- University of Regensburg, Biochemistry III, Regensburg, Germany
| | - Mart Krupovic
- Institut Pasteur, Université Paris Cité, CNRS UMR6047, Archaeal Virology Unit, Paris, France
| | - Till L V Bornemann
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Perla Abigail Figueroa-Gonzalez
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Jessica Jarett
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Thomas Rattei
- Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
- Doctoral School in Microbiology and Environmental Science, University of Vienna, Vienna, Austria
| | - Yuki Amano
- Nuclear Fuel Cycle Engineering Laboratories, Japan Atomic Energy Agency, Tokai, Japan
| | - Ian K Blaby
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jan-Fang Cheng
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Chase L Beisel
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Centre for Infection Research (HZI), Würzburg, Germany
- Medical faculty, University of Würzburg, Würzburg, Germany
| | - Tanja Woyke
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ying Zhang
- Department of Cell and Molecular Biology, College of the Environment and Life Sciences, University of Rhode Island, Kingston, RI, USA
| | - Alexander J Probst
- Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany.
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany.
- Centre of Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, Germany.
- Centre of Medical Biotechnology (ZMB), University of Duisburg-Essen, Essen, Germany.
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4
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Abstract
Methanogenic archaea are the only organisms that produce CH4 as part of their energy-generating metabolism. They are ubiquitous in oxidant-depleted, anoxic environments such as aquatic sediments, anaerobic digesters, inundated agricultural fields, the rumen of cattle, and the hindgut of termites, where they catalyze the terminal reactions in the degradation of organic matter. Methanogenesis is the only metabolism that is restricted to members of the domain Archaea. Here, we discuss the importance of model organisms in the history of methanogen research, including their role in the discovery of the archaea and in the biochemical and genetic characterization of methanogenesis. We also discuss outstanding questions in the field and newly emerging model systems that will expand our understanding of this uniquely archaeal metabolism.
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Affiliation(s)
- Kyle C. Costa
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, USA
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5
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Bueno de Mesquita CP, Wu D, Tringe SG. Methyl-Based Methanogenesis: an Ecological and Genomic Review. Microbiol Mol Biol Rev 2023; 87:e0002422. [PMID: 36692297 PMCID: PMC10029344 DOI: 10.1128/mmbr.00024-22] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Methyl-based methanogenesis is one of three broad categories of archaeal anaerobic methanogenesis, including both the methyl dismutation (methylotrophic) pathway and the methyl-reducing (also known as hydrogen-dependent methylotrophic) pathway. Methyl-based methanogenesis is increasingly recognized as an important source of methane in a variety of environments. Here, we provide an overview of methyl-based methanogenesis research, including the conditions under which methyl-based methanogenesis can be a dominant source of methane emissions, experimental methods for distinguishing different pathways of methane production, molecular details of the biochemical pathways involved, and the genes and organisms involved in these processes. We also identify the current gaps in knowledge and present a genomic and metagenomic survey of methyl-based methanogenesis genes, highlighting the diversity of methyl-based methanogens at multiple taxonomic levels and the widespread distribution of known methyl-based methanogenesis genes and families across different environments.
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Affiliation(s)
| | - Dongying Wu
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Susannah G. Tringe
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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6
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V. K P, Sinha S. A systems level approach to study metabolic networks in prokaryotes with the aromatic amino acid biosynthesis pathway. Front Genet 2023; 13:1084727. [PMID: 36726720 PMCID: PMC9885046 DOI: 10.3389/fgene.2022.1084727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 12/30/2022] [Indexed: 01/18/2023] Open
Abstract
Metabolism of an organism underlies its phenotype, which depends on many factors, such as the genetic makeup, habitat, and stresses to which it is exposed. This is particularly important for the prokaryotes, which undergo significant vertical and horizontal gene transfers. In this study we have used the energy-intensive Aromatic Amino Acid (Tryptophan, Tyrosine and Phenylalanine, TTP) biosynthesis pathway, in a large number of prokaryotes, as a model system to query the different levels of organization of metabolism in the whole intracellular biochemical network, and to understand how perturbations, such as mutations, affects the metabolic flux through the pathway - in isolation and in the context of other pathways connected to it. Using an agglomerative approach involving complex network analysis and Flux Balance Analyses (FBA), of the Tryptophan, Tyrosine and Phenylalanine and other pathways connected to it, we identify several novel results. Using the reaction network analysis and Flux Balance Analyses of the Tryptophan, Tyrosine and Phenylalanine and the genome-scale reconstructed metabolic pathways, many common hubs between the connected networks and the whole genome network are identified. The results show that the connected pathway network can act as a proxy for the whole genome network in Prokaryotes. This systems level analysis also points towards designing functional smaller synthetic pathways based on the reaction network and Flux Balance Analyses analysis.
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Affiliation(s)
- Priya V. K
- National Institute of Technology Calicut, Kattangal, Kerala, India,*Correspondence: Priya V. K, ; Somdatta Sinha,
| | - Somdatta Sinha
- Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, India,*Correspondence: Priya V. K, ; Somdatta Sinha,
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7
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Carr S, Buan NR. Insights into the biotechnology potential of Methanosarcina. Front Microbiol 2022; 13:1034674. [PMID: 36590411 PMCID: PMC9797515 DOI: 10.3389/fmicb.2022.1034674] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/28/2022] [Indexed: 12/23/2022] Open
Abstract
Methanogens are anaerobic archaea which conserve energy by producing methane. Found in nearly every anaerobic environment on earth, methanogens serve important roles in ecology as key organisms of the global carbon cycle, and in industry as a source of renewable biofuels. Environmentally, methanogenic archaea play an essential role in the reintroducing unavailable carbon to the carbon cycle by anaerobically converting low-energy, terminal metabolic degradation products such as one and two-carbon molecules into methane which then returns to the aerobic portion of the carbon cycle. In industry, methanogens are commonly used as an inexpensive source of renewable biofuels as well as serving as a vital component in the treatment of wastewater though this is only the tip of the iceberg with respect to their metabolic potential. In this review we will discuss how the efficient central metabolism of methanoarchaea could be harnessed for future biotechnology applications.
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8
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Jin Q, Wu Q, Shapiro BM, McKernan SE. Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling. Microbiol Spectr 2022; 10:e0225921. [PMID: 35238612 PMCID: PMC9045329 DOI: 10.1128/spectrum.02259-21] [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: 01/10/2022] [Accepted: 02/06/2022] [Indexed: 11/20/2022] Open
Abstract
The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen's metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth.
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Affiliation(s)
- Qusheng Jin
- Geobiology Group, University of Oregon, Eugene, Oregon, USA
| | - Qiong Wu
- Geobiology Group, University of Oregon, Eugene, Oregon, USA
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9
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He B, Cai C, McCubbin T, Muriel JC, Sonnenschein N, Hu S, Yuan Z, Marcellin E. A Genome-Scale Metabolic Model of Methanoperedens nitroreducens: Assessing Bioenergetics and Thermodynamic Feasibility. Metabolites 2022; 12:metabo12040314. [PMID: 35448501 PMCID: PMC9024614 DOI: 10.3390/metabo12040314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/18/2022] [Accepted: 03/26/2022] [Indexed: 11/16/2022] Open
Abstract
Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle. Candidatus ‘Methanoperedens nitroreducens’ (M. nitroreducens) is a recently discovered methanotrophic archaeon capable of performing AOM via a reverse methanogenesis pathway utilizing nitrate as the terminal electron acceptor. Recently, reverse methanogenic pathways and energy metabolism among anaerobic methane-oxidizing archaea (ANME) have gained significant interest. However, the energetics and the mechanism for electron transport in nitrate-dependent AOM performed by M. nitroreducens is unclear. This paper presents a genome-scale metabolic model of M. nitroreducens, iMN22HE, which contains 813 reactions and 684 metabolites. The model describes its cellular metabolism and can quantitatively predict its growth phenotypes. The essentiality of the cytoplasmic heterodisulfide reductase HdrABC in the reverse methanogenesis pathway is examined by modeling the electron transfer direction and the specific energy-coupling mechanism. Furthermore, based on better understanding electron transport by modeling, a new energy transfer mechanism is suggested. The new mechanism involves reactions capable of driving the endergonic reactions in nitrate-dependent AOM, including the step reactions in reverse canonical methanogenesis and the novel electron-confurcating reaction HdrABC. The genome metabolic model not only provides an in silico tool for understanding the fundamental metabolism of ANME but also helps to better understand the reverse methanogenesis energetics and its thermodynamic feasibility.
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Affiliation(s)
- Bingqing He
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia; (B.H.); (T.M.)
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
| | - Chen Cai
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
- CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China
| | - Tim McCubbin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia; (B.H.); (T.M.)
| | - Jorge Carrasco Muriel
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kongens Lyngby, Denmark; (J.C.M.); (N.S.)
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kongens Lyngby, Denmark; (J.C.M.); (N.S.)
| | - Shihu Hu
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
| | - Zhiguo Yuan
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia; (B.H.); (T.M.)
- Correspondence:
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10
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Passi A, Tibocha-Bonilla JD, Kumar M, Tec-Campos D, Zengler K, Zuniga C. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites 2021; 12:14. [PMID: 35050136 PMCID: PMC8778254 DOI: 10.3390/metabo12010014] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022] Open
Abstract
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.
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Affiliation(s)
- Anurag Passi
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA;
| | - Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Diego Tec-Campos
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Facultad de Ingeniería Química, Campus de Ciencias Exactas e Ingenierías, Universidad Autónoma de Yucatán, Merida 97203, Yucatan, Mexico
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0403, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
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Methanothermobacter thermautotrophicus strain ΔH as a potential microorganism for bioconversion of CO2 to methane. J CO2 UTIL 2020. [DOI: 10.1016/j.jcou.2020.101210] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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12
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M B, P C. Comparative analysis of differential proteome-wide protein-protein interaction network of Methanobrevibacter ruminantium M1. Biochem Biophys Rep 2019; 20:100698. [PMID: 31763465 PMCID: PMC6859225 DOI: 10.1016/j.bbrep.2019.100698] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/12/2019] [Accepted: 10/14/2019] [Indexed: 11/22/2022] Open
Abstract
A proteome-wide protein-protein interaction (PPI) network of Methanobrevibacter ruminantium M1 (MRU), a predominant rumen methanogen, was constructed from its metabolic genes using a gene neighborhood algorithm and then compared with closely related rumen methanogens Using proteome-wide PPI approach, we constructed network encompassed 2194 edges and 637 nodes interacting with 634 genes. Network quality and robustness of functional modules were assessed with gene ontology terms. A structure-function-metabolism mapping for each protein has been carried out with efforts to extract experimental PPI concomitant information from the literature. The results of our study revealed that some topological properties of its network were robust for sharing homologous protein interactions across heterotrophic and hydrogenotrophic methanogens. MRU proteome has shown to establish many PPI sub-networks for associated metabolic subsystems required to survive in the rumen environment. MRU genome found to share interacting proteins from its PPI network involved in specific metabolic subsystems distinct to heterotrophic and hydrogenotrophic methanogens. Across these proteomes, the interacting proteins from differential PPI networks were shared in common for the biosynthesis of amino acids, nucleosides, and nucleotides and energy metabolism in which more fractions of protein pairs shared with Methanosarcina acetivorans. Our comparative study expedites our knowledge to understand a complex proteome network associated with typical metabolic subsystems of MRU and to improve its genome-scale reconstruction in the future.
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Affiliation(s)
| | - Chellapandi P
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620 024, Tamil Nadu, India
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13
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Weinrich S, Koch S, Bonk F, Popp D, Benndorf D, Klamt S, Centler F. Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity. Front Microbiol 2019; 10:1095. [PMID: 31156601 PMCID: PMC6533897 DOI: 10.3389/fmicb.2019.01095] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 04/30/2019] [Indexed: 01/23/2023] Open
Abstract
The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques.
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Affiliation(s)
- Sören Weinrich
- Biochemical Conversion Department, Deutsches Biomasseforschungszentrum gGmbH, Leipzig, Germany
| | - Sabine Koch
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Fabian Bonk
- Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Denny Popp
- Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Dirk Benndorf
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.,Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Florian Centler
- Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany
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Bonk F, Popp D, Weinrich S, Sträuber H, Becker D, Kleinsteuber S, Harms H, Centler F. Determination of Microbial Maintenance in Acetogenesis and Methanogenesis by Experimental and Modeling Techniques. Front Microbiol 2019; 10:166. [PMID: 30800108 PMCID: PMC6375858 DOI: 10.3389/fmicb.2019.00166] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 01/22/2019] [Indexed: 11/21/2022] Open
Abstract
For biogas-producing continuous stirred tank reactors, an increase in dilution rate increases the methane production rate as long as substrate input can be converted fully. However, higher dilution rates necessitate higher specific microbial growth rates, which are assumed to have a strong impact on the apparent microbial biomass yield due to cellular maintenance. To test this, we operated two reactors at 37°C in parallel at dilution rates of 0.18 and 0.07 days-1 (hydraulic retention times of 5.5 and 14 days, doubling times of 3.9 and 9.9 days in steady state) with identical inoculum and a mixture of volatile fatty acids as sole carbon sources. We evaluated the performance of the Anaerobic Digestion Model No. 1 (ADM1), a thermodynamic black box approach (TBA), and dynamic flux balance analysis (dFBA), to describe the experimental observations. All models overestimated the impact of dilution rate on the apparent microbial biomass yield when using default parameter values. Based on our analysis, a maintenance coefficient value below 0.2 kJ per carbon mole of microbial biomass per hour should be used for the TBA, corresponding to 0.12 mmol ATP per gram dry weight per hour for dFBA, which strongly deviates from the value of 9.8 kJ Cmol h-1 that has been suggested to apply to all anaerobic microorganisms at 37°C. We hypothesized that a decrease in dilution rate might select taxa with minimized maintenance expenditure. However, no major differences in the dominating taxa between the reactors were observed based on amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism analysis of mcrA genes. Surprisingly, Methanosaeta dominated over Methanosarcina even at a dilution rate of 0.18 days-1, which contradicts previous model expectations. Furthermore, only 23-49% of the bacterial reads could be assigned to known syntrophic fatty acid oxidizers, indicating that unknown members of this functional group remain to be discovered. In conclusion, microbial maintenance was found to be much lower for acetogenesis and methanogenesis than previously assumed, likely due to the exceptionally low growth rates in anaerobic digestion. This finding might also be relevant for other microbial systems operating at similarly low growth rates.
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Affiliation(s)
- Fabian Bonk
- Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Denny Popp
- Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Sören Weinrich
- Biochemical Conversion Department, DBFZ-Deutsches Biomasseforschungszentrum gGmbH, Leipzig, Germany
| | - Heike Sträuber
- Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Daniela Becker
- Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Sabine Kleinsteuber
- Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Hauke Harms
- Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Florian Centler
- Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
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15
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Holmes DE, Rotaru AE, Ueki T, Shrestha PM, Ferry JG, Lovley DR. Electron and Proton Flux for Carbon Dioxide Reduction in Methanosarcina barkeri During Direct Interspecies Electron Transfer. Front Microbiol 2018; 9:3109. [PMID: 30631315 PMCID: PMC6315138 DOI: 10.3389/fmicb.2018.03109] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 11/30/2018] [Indexed: 11/13/2022] Open
Abstract
Direct interspecies electron transfer (DIET) is important in diverse methanogenic environments, but how methanogens participate in DIET is poorly understood. Therefore, the transcriptome of Methanosarcina barkeri grown via DIET in co-culture with Geobacter metallireducens was compared with its transcriptome when grown via H2 interspecies transfer (HIT) with Pelobacter carbinolicus. Notably, transcripts for the F420H2 dehydrogenase, Fpo, and the heterodisulfide reductase, HdrABC, were more abundant during growth on DIET. A model for CO2 reduction was developed from these results in which electrons delivered to methanophenazine in the cell membrane are transferred to Fpo. The external proton gradient necessary to drive the otherwise thermodynamically unfavorable reverse electron transport for Fpo-catalyzed F420 reduction is derived from protons released from G. metallireducens metabolism. Reduced F420 is a direct electron donor in the carbon dioxide reduction pathway and also serves as the electron donor for the proposed HdrABC-catalyzed electron bifurcation reaction in which reduced ferredoxin (also required for carbon dioxide reduction) is generated with simultaneous reduction of CoM-S-S-CoB. Expression of genes for putative redox-active proteins predicted to be localized on the outer cell surface was higher during growth on DIET, but further analysis will be required to identify the electron transfer route to methanophenazine. The results indicate that the pathways for electron and proton flux for CO2 reduction during DIET are substantially different than for HIT and suggest that gene expression patterns may also be useful for determining whether Methanosarcina are directly accepting electrons from other extracellular electron donors, such as corroding metals or electrodes.
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Affiliation(s)
- Dawn E. Holmes
- Department of Microbiology, University of Massachusetts, Amherst, MA, United States
- Department of Physical and Biological Sciences, Western New England University, Springfield, MA, United States
| | - Amelia-Elena Rotaru
- Department of Microbiology, University of Massachusetts, Amherst, MA, United States
- Department of Biology, University of Southern Denmark, Odense, Denmark
| | - Toshiyuki Ueki
- Department of Microbiology, University of Massachusetts, Amherst, MA, United States
| | - Pravin M. Shrestha
- Department of Microbiology, University of Massachusetts, Amherst, MA, United States
- Assembly Biosciences, San Francisco, CA, United States
| | - James G. Ferry
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, United States
| | - Derek R. Lovley
- Department of Microbiology, University of Massachusetts, Amherst, MA, United States
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16
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Chellapandi P, Bharathi M, Sangavai C, Prathiviraj R. Methanobacterium formicicum as a target rumen methanogen for the development of new methane mitigation interventions: A review. Vet Anim Sci 2018; 6:86-94. [PMID: 32734058 PMCID: PMC7386643 DOI: 10.1016/j.vas.2018.09.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 08/29/2018] [Accepted: 09/12/2018] [Indexed: 12/18/2022] Open
Abstract
Methanobacterium formicicum (Methanobacteriaceae family) is an endosymbiotic methanogenic Archaean found in the digestive tracts of ruminants and elsewhere. It has been significantly implicated in global CH4 emission during enteric fermentation processes. In this review, we discuss current genomic and metabolic aspects of this microorganism for the purpose of the discovery of novel veterinary therapeutics. This microorganism encompasses a typical H2 scavenging system, which facilitates a metabolic symbiosis across the H2 producing cellulolytic bacteria and fumarate reducing bacteria. To date, five genome-scale metabolic models (iAF692, iMG746, iMB745, iVS941 and iMM518) have been developed. These metabolic reconstructions revealed the cellular and metabolic behaviors of methanogenic archaea. The characteristics of its symbiotic behavior and metabolic crosstalk with competitive rumen anaerobes support understanding of the physiological function and metabolic fate of shared metabolites in the rumen ecosystem. Thus, systems biological characterization of this microorganism may provide a new insight to realize its metabolic significance for the development of a healthy microbiota in ruminants. An in-depth knowledge of this microorganism may allow us to ensure a long term sustainability of ruminant-based agriculture.
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Affiliation(s)
- P Chellapandi
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
| | - M Bharathi
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
| | - C Sangavai
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
| | - R Prathiviraj
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
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17
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Li F, Xie W, Yuan Q, Luo H, Li P, Chen T, Zhao X, Wang Z, Ma H. Genome-scale metabolic model analysis indicates low energy production efficiency in marine ammonia-oxidizing archaea. AMB Express 2018; 8:106. [PMID: 29946801 PMCID: PMC6038301 DOI: 10.1186/s13568-018-0635-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 06/18/2018] [Indexed: 12/02/2022] Open
Abstract
Marine ammonia-oxidizing archaea (AOA) play an important role in the global nitrogen cycle by obtaining energy for biomass production from CO2 via oxidation of ammonium. The isolation of Candidatus “Nitrosopumilus maritimus” strain SCM1, which represents the globally distributed AOA in the ocean, provided an opportunity for uncovering the contributions of those AOA to carbon and nitrogen cycles in ocean. Although several ammonia oxidation pathways have been proposed for SCM1, little is known about its ATP production efficiency. Here, based on the published genome of Nitrosopumilus maritimus SCM1, a genome-scale metabolic model named NmrFL413 was reconstructed. Based on the model NmrFL413, the estimated ATP/NH4+ yield (0.149–0.276 ATP/NH4+) is tenfold lower than the calculated theoretical yield of the proposed ammonia oxidation pathways in marine AOA (1.5–1.75 ATP/NH4+), indicating a low energy production efficiency of SCM1. Our model also suggested the minor contribution of marine AOA to carbon cycle comparing with their significant contribution to nitrogen cycle in the ocean.
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18
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Zheng S, Wang B, Liu F, Wang O. Magnetite production and transformation in the methanogenic consortia from coastal riverine sediments. J Microbiol 2017; 55:862-870. [DOI: 10.1007/s12275-017-7104-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 09/21/2017] [Accepted: 09/22/2017] [Indexed: 10/18/2022]
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19
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Different substrate regimes determine transcriptional profiles and gene co-expression in Methanosarcina barkeri (DSM 800). Appl Microbiol Biotechnol 2017; 101:7303-7316. [DOI: 10.1007/s00253-017-8457-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 07/26/2017] [Accepted: 07/30/2017] [Indexed: 01/15/2023]
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20
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BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput Biol 2017; 13:e1005544. [PMID: 28531184 PMCID: PMC5460873 DOI: 10.1371/journal.pcbi.1005544] [Citation(s) in RCA: 152] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 06/06/2017] [Accepted: 04/27/2017] [Indexed: 12/22/2022] Open
Abstract
Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena. In nature, organisms are typically found in near proximity to each other, forming symbiotic relationships. Particularly bacteria are often part of highly organized communities such as biofilms. In this study, we integrate the detailed knowledge about the metabolic capabilities of individual organisms into an individual-based modeling approach for simulating the dynamics of local interactions. We provide a fast and flexible framework, in which established computational models for individual organisms can be simulated in communities. Nutrients can diffuse in an area where cells move, divide, and die. The resulting spatial as well as temporal dynamics and metabolic interactions can be analyzed as well as visualized and subsequently compared to experimental findings. We demonstrate how our approach can be used to gain novel insights on dynamics in single species biofilm formation and multi-species intestinal microbial communities.
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21
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Phylogenomic proximity and metabolic discrepancy of Methanosarcina mazei Go1 across methanosarcinal genomes. Biosystems 2017; 155:20-28. [DOI: 10.1016/j.biosystems.2017.03.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 03/15/2017] [Accepted: 03/20/2017] [Indexed: 02/04/2023]
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22
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Ye C, Xu N, Dong C, Ye Y, Zou X, Chen X, Guo F, Liu L. IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models. Sci Rep 2017; 7:727. [PMID: 28389638 PMCID: PMC5429687 DOI: 10.1038/s41598-017-00820-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 03/16/2017] [Indexed: 11/12/2022] Open
Abstract
Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.
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Affiliation(s)
- Chao Ye
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Nan Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Chuan Dong
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, No. 4, 2nd Section, North Jianshe Road, Chengdu, Sichuan, 610054, China
| | - Yuannong Ye
- School of Biology and Engineering, Guizhou Medical University, Dongqing Road, Huaxi District, Guiyang, Guizhou, 550025, China
- School of Big Health, Guizhou Medical University, Dongqing Road, Huaxi District, Guiyang, Guizhou, 550025, China
| | - Xuan Zou
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Xiulai Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Fengbiao Guo
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, No. 4, 2nd Section, North Jianshe Road, Chengdu, Sichuan, 610054, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
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Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function. ARCHAEA-AN INTERNATIONAL MICROBIOLOGICAL JOURNAL 2017; 2017:9763848. [PMID: 28133437 PMCID: PMC5241448 DOI: 10.1155/2017/9763848] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 10/17/2016] [Accepted: 11/01/2016] [Indexed: 02/07/2023]
Abstract
Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.
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Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis. J Bacteriol 2016; 198:3379-3390. [PMID: 27736793 PMCID: PMC5116941 DOI: 10.1128/jb.00571-16] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 09/22/2016] [Indexed: 02/03/2023] Open
Abstract
Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process. IMPORTANCE Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.
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25
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PSAMM: A Portable System for the Analysis of Metabolic Models. PLoS Comput Biol 2016; 12:e1004732. [PMID: 26828591 PMCID: PMC4734835 DOI: 10.1371/journal.pcbi.1004732] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 01/05/2016] [Indexed: 11/19/2022] Open
Abstract
The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies. The broad application of genome-scale metabolic modeling has made it a useful technique for tackling fundamental questions in biological research and engineering. Today over 100 models have been constructed for organisms that carry out a diverse array of metabolic activities spanning all three kingdoms of life. These models, however, have been curated independently following different conventions. The maintenance of model consistency has been challenging due to the lack of consensus in model representation and the absence of integrated modeling software for associating mathematical simulations with the annotation and biological interpretation of metabolic models. To solve this problem, we developed a new software package, PSAMM, and a new model format that incorporates heterogeneous, model-specific annotation information into modular representations of model definitions and simulation settings. PSAMM provides significant advances in standardizing the workflow of model annotation and consistency checking. Compared to existing tools, PSAMM supports more flexible configurations and is more efficient in running constraint-based simulations. All functions of PSAMM are freely available for academic users and can be downloaded from a public Git repository (https://zhanglab.github.io/psamm/) under the GNU General Public License.
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Lambie SC, Kelly WJ, Leahy SC, Li D, Reilly K, McAllister TA, Valle ER, Attwood GT, Altermann E. The complete genome sequence of the rumen methanogen Methanosarcina barkeri CM1. Stand Genomic Sci 2015; 10:57. [PMID: 26413197 PMCID: PMC4582637 DOI: 10.1186/s40793-015-0038-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 07/09/2015] [Indexed: 01/05/2023] Open
Abstract
Methanosarcina species are the most metabolically versatile of the methanogenic Archaea and can obtain energy for growth by producing methane via the hydrogenotrophic, acetoclastic or methylotrophic pathways. Methanosarcina barkeri CM1 was isolated from the rumen of a New Zealand Friesian cow grazing a ryegrass/clover pasture, and its genome has been sequenced to provide information on the phylogenetic diversity of rumen methanogens with a view to developing technologies for methane mitigation. The 4.5 Mb chromosome has an average G + C content of 39 %, and encodes 3523 protein-coding genes, but has no plasmid or prophage sequences. The gene content is very similar to that of M. barkeri Fusaro which was isolated from freshwater sediment. CM1 has a full complement of genes for all three methanogenesis pathways, but its genome shows many differences from those of other sequenced rumen methanogens. Consequently strategies to mitigate ruminant methane need to include information on the different methanogens that occur in the rumen.
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Affiliation(s)
- Suzanne C Lambie
- Rumen Microbiology, Animal Nutrition and Health, AgResearch Limited, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - William J Kelly
- Rumen Microbiology, Animal Nutrition and Health, AgResearch Limited, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - Sinead C Leahy
- Rumen Microbiology, Animal Nutrition and Health, AgResearch Limited, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand ; New Zealand Agricultural Greenhouse Gas Research Centre, Grasslands Research Centre, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - Dong Li
- Rumen Microbiology, Animal Nutrition and Health, AgResearch Limited, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - Kerri Reilly
- Rumen Microbiology, Animal Nutrition and Health, AgResearch Limited, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - Tim A McAllister
- Agriculture and Agri-Food Canada, Lethbridge Research Centre, Lethbridge, Alberta T1J 4B1 Canada
| | - Edith R Valle
- Agriculture and Agri-Food Canada, Lethbridge Research Centre, Lethbridge, Alberta T1J 4B1 Canada
| | - Graeme T Attwood
- Rumen Microbiology, Animal Nutrition and Health, AgResearch Limited, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand ; New Zealand Agricultural Greenhouse Gas Research Centre, Grasslands Research Centre, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - Eric Altermann
- Rumen Microbiology, Animal Nutrition and Health, AgResearch Limited, Tennent Drive, Private Bag 11008, Palmerston North, 4442 New Zealand ; Riddet Institute, Massey University, Palmerston North, 4442 New Zealand
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Abstract
Metabolic processes are altered in cancer cells, which obtain advantages from this metabolic reprogramming in terms of energy production and synthesis of biomolecules that sustain their uncontrolled proliferation. Due to the conceptual progresses in the last decade, metabolic reprogramming was recently included as one of the new hallmarks of cancer. The advent of high-throughput technologies to amass an abundance of omic data, together with the development of new computational methods that allow the integration and analysis of omic data by using genome-scale reconstructions of human metabolism, have increased and accelerated the discovery and development of anticancer drugs and tumor-specific metabolic biomarkers. Here we review and discuss the latest advances in the context of metabolic reprogramming and the future in cancer research.
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Genome-scale modeling for metabolic engineering. J Ind Microbiol Biotechnol 2015; 42:327-38. [PMID: 25578304 DOI: 10.1007/s10295-014-1576-3] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 12/20/2014] [Indexed: 01/04/2023]
Abstract
We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.
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Senger RS, Yen JY, Fong SS. A review of genome-scale metabolic flux modeling of anaerobiosis in biotechnology. Curr Opin Chem Eng 2014. [DOI: 10.1016/j.coche.2014.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Costa KC, Leigh JA. Metabolic versatility in methanogens. Curr Opin Biotechnol 2014; 29:70-5. [DOI: 10.1016/j.copbio.2014.02.012] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2013] [Revised: 02/14/2014] [Accepted: 02/18/2014] [Indexed: 10/25/2022]
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Kim M, Sang Yi J, Kim J, Kim JN, Kim MW, Kim BG. Reconstruction of a high-quality metabolic model enables the identification of gene overexpression targets for enhanced antibiotic production inStreptomyces coelicolorA3(2). Biotechnol J 2014; 9:1185-94. [DOI: 10.1002/biot.201300539] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Revised: 02/13/2014] [Accepted: 03/11/2014] [Indexed: 12/22/2022]
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Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction. BMC SYSTEMS BIOLOGY 2014; 8:41. [PMID: 24708835 PMCID: PMC4108055 DOI: 10.1186/1752-0509-8-41] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2013] [Accepted: 03/21/2014] [Indexed: 12/20/2022]
Abstract
Background The gut microbiota plays an important role in human health and disease by acting as a metabolic organ. Metagenomic sequencing has shown how dysbiosis in the gut microbiota is associated with human metabolic diseases such as obesity and diabetes. Modeling may assist to gain insight into the metabolic implication of an altered microbiota. Fast and accurate reconstruction of metabolic models for members of the gut microbiota, as well as methods to simulate a community of microorganisms, are therefore needed. The Integrated Microbial Genomes (IMG) database contains functional annotation for nearly 4,650 bacterial genomes. This tremendous new genomic information adds new opportunities for systems biology to reconstruct accurate genome scale metabolic models (GEMs). Results Here we assembled a reaction data set containing 2,340 reactions obtained from existing genome-scale metabolic models, where each reaction is assigned with KEGG Orthology. The reaction data set was then used to reconstruct two genome scale metabolic models for gut microorganisms available in the IMG database Bifidobacterium adolescentis L2-32, which produces acetate during fermentation, and Faecalibacterium prausnitzii A2-165, which consumes acetate and produces butyrate. F. prausnitzii is less abundant in patients with Crohn’s disease and has been suggested to play an anti-inflammatory role in the gut ecosystem. The B. adolescentis model, iBif452, comprises 699 reactions and 611 unique metabolites. The F. prausnitzii model, iFap484, comprises 713 reactions and 621 unique metabolites. Each model was validated with in vivo data. We used OptCom and Flux Balance Analysis to simulate how both organisms interact. Conclusions The consortium of iBif452 and iFap484 was applied to predict F. prausnitzii’s demand for acetate and production of butyrate which plays an essential role in colonic homeostasis and cancer prevention. The assembled reaction set is a useful tool to generate bacterial draft models from KEGG Orthology.
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Mattanovich D, Hatzimanikatis V. Editorial: metabolic modeling in biotechnology and medical research. Biotechnol J 2014; 8:962-3. [PMID: 24031032 DOI: 10.1002/biot.201300378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Metabolic Modeling and Simulation: This special issue of Biotechnology Journal is edited by Diethard Mattanovich and Vassily Hatzimanikatis and covers the state-of-the-art in metabolic modeling, including the major themes of methods in metabolic modeling, modeling of human and microbial metabolism, and modeling of bioprocesses.
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Affiliation(s)
- Diethard Mattanovich
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria.
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