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Benito-Vaquerizo S, Nouse N, Schaap PJ, Hugenholtz J, Brul S, López-Contreras AM, Martins dos Santos VAP, Suarez-Diez M. Model-driven approach for the production of butyrate from CO 2/H 2 by a novel co-culture of C. autoethanogenum and C. beijerinckii. Front Microbiol 2022; 13:1064013. [PMID: 36620068 PMCID: PMC9815533 DOI: 10.3389/fmicb.2022.1064013] [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: 10/07/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
One-carbon (C1) compounds are promising feedstocks for the sustainable production of commodity chemicals. CO2 is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted into valuable products while reducing its atmospheric levels. Acetogens are microorganisms that can grow on CO2/H2 gas mixtures and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with other microbial species that can further process such products, can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone-butanol-ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of an acetogen and a solventogen in a consortium provides a potential platform to produce valuable chemicals from CO2. In this study, metabolic modeling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO2/H2 mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in batch serum bottles and subsequently in pH-controlled bioreactors. Community modeling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO2/H2 producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modeling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.
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Affiliation(s)
- Sara Benito-Vaquerizo
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
| | - Niels Nouse
- Molecular Biology and Microbial Food Safety, University of Amsterdam, Amsterdam, Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands,UNLOCK Large Scale Infrastructure for Microbial Communities, Wageningen University and Research and Delft University of Technology, Wageningen, Netherlands
| | - Jeroen Hugenholtz
- Molecular Biology and Microbial Food Safety, University of Amsterdam, Amsterdam, Netherlands
| | - Stanley Brul
- Molecular Biology and Microbial Food Safety, University of Amsterdam, Amsterdam, Netherlands
| | - Ana M. López-Contreras
- Wageningen Food and Biobased Research, Wageningen University and Research, Wageningen, Netherlands
| | | | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands,*Correspondence: Maria Suarez-Diez ✉
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2
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Feng J, Guo X, Cai F, Fu H, Wang J. Model-based driving mechanism analysis for butyric acid production in Clostridium tyrobutyricum. BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2022; 15:71. [PMID: 35752796 PMCID: PMC9233315 DOI: 10.1186/s13068-022-02169-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/13/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Butyric acid, an essential C4 platform chemical, is widely used in food, pharmaceutical, and animal feed industries. Clostridium tyrobutyricum is the most promising microorganism for industrial bio-butyrate production. However, the metabolic driving mechanism for butyrate synthesis was still not profoundly studied.
Results
This study reports a first-generation genome-scale model (GEM) for C. tyrobutyricum, which provides a comprehensive and systematic analysis for the butyrate synthesis driving mechanisms. Based on the analysis in silico, an energy conversion system, which couples the proton efflux with butyryl-CoA transformation by two redox loops of ferredoxin, could be the main driving force for butyrate synthesis. For verifying the driving mechanism, a hydrogenase (HydA) expression was perturbed by inducible regulation and knockout. The results showed that HydA deficiency significantly improved the intracellular NADH/NAD+ rate, decreased acetate accumulation (63.6% in serum bottle and 58.1% in bioreactor), and improved the yield of butyrate (26.3% in serum bottle and 34.5% in bioreactor). It was in line with the expectation based on the energy conversion coupling driving mechanism.
Conclusions
This work show that the first-generation GEM and coupling metabolic analysis effectively promoted in-depth understanding of the metabolic driving mechanism in C. tyrobutyricum and provided a new insight for tuning metabolic flux direction in Clostridium chassis cells.
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3
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Hartmann FSF, Udugama IA, Seibold GM, Sugiyama H, Gernaey KV. Digital models in biotechnology: Towards multi-scale integration and implementation. Biotechnol Adv 2022; 60:108015. [PMID: 35781047 DOI: 10.1016/j.biotechadv.2022.108015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/03/2022] [Accepted: 06/27/2022] [Indexed: 12/28/2022]
Abstract
Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.
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Affiliation(s)
- Fabian S F Hartmann
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Isuru A Udugama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan; Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
| | - Gerd M Seibold
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
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4
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Benito-Vaquerizo S, Parera Olm I, de Vroet T, Schaap PJ, Sousa DZ, Martins Dos Santos VAP, Suarez-Diez M. Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum. Microb Cell Fact 2022; 21:116. [PMID: 35710409 PMCID: PMC9205015 DOI: 10.1186/s12934-022-01841-1] [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: 04/12/2022] [Accepted: 05/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Microbial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO2 to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate. RESULTS Our work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP+ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium. CONCLUSIONS The realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.
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Affiliation(s)
- Sara Benito-Vaquerizo
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, Wageningen, 6708WE, The Netherlands
| | - Ivette Parera Olm
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, Wageningen, 6708WE, The Netherlands
| | - Thijs de Vroet
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, Wageningen, 6708WE, The Netherlands
| | - Peter J Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, Wageningen, 6708WE, The Netherlands
| | - Diana Z Sousa
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, Wageningen, 6708WE, The Netherlands.,Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Vening Meinesz building C, Princetonlaan 6, Utrecht, 3584 CB, The Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, Wageningen, 6708WE, The Netherlands.,Bioprocess Engineering, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, Wageningen, 6708WE, The Netherlands.
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5
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Patakova P, Branska B, Vasylkivska M, Jureckova K, Musilova J, Provaznik I, Sedlar K. Transcriptomic studies of solventogenic clostridia, Clostridium acetobutylicum and Clostridium beijerinckii. Biotechnol Adv 2021; 58:107889. [PMID: 34929313 DOI: 10.1016/j.biotechadv.2021.107889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/10/2021] [Accepted: 12/14/2021] [Indexed: 12/13/2022]
Abstract
Solventogenic clostridia are not a strictly defined group within the genus Clostridium but its representatives share some common features, i.e. they are anaerobic, non-pathogenic, non-toxinogenic and endospore forming bacteria. Their main metabolite is typically 1-butanol but depending on species and culture conditions, they can form other metabolites such as acetone, isopropanol, ethanol, butyric, lactic and acetic acids, and hydrogen. Although these organisms were previously used for the industrial production of solvents, they later fell into disuse, being replaced by more efficient chemical production. A return to a more biological production of solvents therefore requires a thorough understanding of clostridial metabolism. Transcriptome analysis, which reflects the involvement of individual genes in all cellular processes within a population, at any given (sampling) moment, is a valuable tool for gaining a deeper insight into clostridial life. In this review, we describe techniques to study transcription, summarize the evolution of these techniques and compare methods for data processing and visualization of solventogenic clostridia, particularly the species Clostridium acetobutylicum and Clostridium beijerinckii. Individual approaches for evaluating transcriptomic data are compared and their contributions to advancements in the field are assessed. Moreover, utilization of transcriptomic data for reconstruction of computational clostridial metabolic models is considered and particular models are described. Transcriptional changes in glucose transport, central carbon metabolism, the sporulation cycle, butanol and butyrate stress responses, the influence of lignocellulose-derived inhibitors on growth and solvent production, and other respective topics, are addressed and common trends are highlighted.
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Affiliation(s)
- Petra Patakova
- University of Chemistry and Technology Prague, Technicka 5, 16628 Prague 6, Czech Republic.
| | - Barbora Branska
- University of Chemistry and Technology Prague, Technicka 5, 16628 Prague 6, Czech Republic
| | - Maryna Vasylkivska
- University of Chemistry and Technology Prague, Technicka 5, 16628 Prague 6, Czech Republic
| | | | - Jana Musilova
- Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Ivo Provaznik
- Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Karel Sedlar
- Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
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6
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Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber M, Best AA, DeJongh M, Kimbrel JA, D’haeseleer P, McCorkle SR, Bolton JR, Pearson E, Canon S, Wood-Charlson EM, Cottingham RW, Arkin AP, Henry CS. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 2021; 49:D575-D588. [PMID: 32986834 PMCID: PMC7778927 DOI: 10.1093/nar/gkaa746] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/25/2020] [Accepted: 09/24/2020] [Indexed: 12/31/2022] Open
Abstract
For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.
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Affiliation(s)
- Samuel M D Seaver
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Filipe Liu
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Qizhi Zhang
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - James Jeffryes
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - José P Faria
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Janaka N Edirisinghe
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Michael Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas Chia
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Elad Noor
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Aaron A Best
- Department of Biology, Hope College, Holland, MI 49423, USA
| | - Matthew DeJongh
- Department of Computer Science, Hope College, Holland, MI 49423, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Patrik D’haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sean R McCorkle
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jay R Bolton
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Erik Pearson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Shane Canon
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Elisha M Wood-Charlson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert W Cottingham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Adam P Arkin
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
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7
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Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber ME, Best AA, DeJongh M, Kimbrel JA, D'haeseleer P, McCorkle SR, Bolton JR, Pearson E, Canon S, Wood-Charlson EM, Cottingham RW, Arkin AP, Henry CS. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 2021; 49:D1555. [PMID: 33179751 DOI: 10.1101/2020.03.31.018663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023] Open
Abstract
ABSTRACTFor over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.
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Affiliation(s)
- Samuel M D Seaver
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Filipe Liu
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Qizhi Zhang
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - James Jeffryes
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - José P Faria
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Janaka N Edirisinghe
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Michael Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas Chia
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Elad Noor
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Aaron A Best
- Department of Biology, Hope College, Holland, MI 49423, USA
| | - Matthew DeJongh
- Department of Computer Science, Hope College, Holland, MI 49423, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Patrik D'haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sean R McCorkle
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jay R Bolton
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Erik Pearson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Shane Canon
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Elisha M Wood-Charlson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert W Cottingham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Adam P Arkin
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
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8
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Vees CA, Neuendorf CS, Pflügl S. Towards continuous industrial bioprocessing with solventogenic and acetogenic clostridia: challenges, progress and perspectives. J Ind Microbiol Biotechnol 2020; 47:753-787. [PMID: 32894379 PMCID: PMC7658081 DOI: 10.1007/s10295-020-02296-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 07/20/2020] [Indexed: 12/11/2022]
Abstract
The sustainable production of solvents from above ground carbon is highly desired. Several clostridia naturally produce solvents and use a variety of renewable and waste-derived substrates such as lignocellulosic biomass and gas mixtures containing H2/CO2 or CO. To enable economically viable production of solvents and biofuels such as ethanol and butanol, the high productivity of continuous bioprocesses is needed. While the first industrial-scale gas fermentation facility operates continuously, the acetone-butanol-ethanol (ABE) fermentation is traditionally operated in batch mode. This review highlights the benefits of continuous bioprocessing for solvent production and underlines the progress made towards its establishment. Based on metabolic capabilities of solvent producing clostridia, we discuss recent advances in systems-level understanding and genome engineering. On the process side, we focus on innovative fermentation methods and integrated product recovery to overcome the limitations of the classical one-stage chemostat and give an overview of the current industrial bioproduction of solvents.
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Affiliation(s)
- Charlotte Anne Vees
- Institute of Chemical, Environmental and Bioscience Engineering, Research Area Biochemical Engineering, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
| | - Christian Simon Neuendorf
- Institute of Chemical, Environmental and Bioscience Engineering, Research Area Biochemical Engineering, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
| | - Stefan Pflügl
- Institute of Chemical, Environmental and Bioscience Engineering, Research Area Biochemical Engineering, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
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9
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Garcia S, Thompson RA, Giannone RJ, Dash S, Maranas CD, Trinh CT. Development of a Genome-Scale Metabolic Model of Clostridium thermocellum and Its Applications for Integration of Multi-Omics Datasets and Computational Strain Design. Front Bioeng Biotechnol 2020; 8:772. [PMID: 32974289 PMCID: PMC7471609 DOI: 10.3389/fbioe.2020.00772] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 06/18/2020] [Indexed: 01/29/2023] Open
Abstract
Solving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as biocatalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also present novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.
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Affiliation(s)
- Sergio Garcia
- Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, TN, United States.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - R Adam Thompson
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States.,Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States.,Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Richard J Giannone
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States.,Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Satyakam Dash
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States.,Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Costas D Maranas
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States.,Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Cong T Trinh
- Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, TN, United States.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States.,Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States.,Oak Ridge National Laboratory, Oak Ridge, TN, United States
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10
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Karimian E, Motamedian E. ACBM: An Integrated Agent and Constraint Based Modeling Framework for Simulation of Microbial Communities. Sci Rep 2020; 10:8695. [PMID: 32457521 PMCID: PMC7250870 DOI: 10.1038/s41598-020-65659-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 05/05/2020] [Indexed: 12/25/2022] Open
Abstract
The development of new methods capable of more realistic modeling of microbial communities necessitates that their results be quantitatively comparable with experimental findings. In this research, a new integrated agent and constraint based modeling framework abbreviated ACBM has been proposed that integrates agent-based and constraint-based modeling approaches. ACBM models the cell population in three-dimensional space to predict spatial and temporal dynamics and metabolic interactions. When used to simulate the batch growth of C. beijerinckii and two-species communities of F. prausnitzii and B. adolescent., ACBM improved on predictions made by two previous models. Furthermore, when transcriptomic data were integrated with a metabolic model of E. coli to consider intracellular constraints in the metabolism, ACBM accurately predicted growth rate, half-rate constant, and concentration of biomass, glucose, and acidic products over time. The results also show that the framework was able to predict the metabolism changes in the early stationary compared to the log phase. Finally, ACBM was implemented to estimate starved cells under heterogeneous feeding and it was concluded that a percentage of cells are always subject to starvation in a bioreactor with high volume.
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Affiliation(s)
- Emadoddin Karimian
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, P.O. Box, 14115-143, Tehran, Iran
| | - Ehsan Motamedian
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, P.O. Box, 14115-143, Tehran, Iran.
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11
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Lee NR, Lee CH, Lee DY, Park JB. Genome-Scale Metabolic Network Reconstruction and In Silico Analysis of Hexanoic acid Producing Megasphaera elsdenii. Microorganisms 2020; 8:microorganisms8040539. [PMID: 32283671 PMCID: PMC7232489 DOI: 10.3390/microorganisms8040539] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/01/2020] [Accepted: 04/05/2020] [Indexed: 12/18/2022] Open
Abstract
Hexanoic acid and its derivatives have been recently recognized as value-added materials and can be synthesized by several microbes. Of them, Megasphaera elsdenii has been considered as an interesting hexanoic acid producer because of its capability to utilize a variety of carbons sources. However, the cellular metabolism and physiology of M. elsdenii still remain uncharacterized. Therefore, in order to better understand hexanoic acid synthetic metabolism in M. elsdenii, we newly reconstructed its genome-scale metabolic model, iME375, which accounts for 375 genes, 521 reactions, and 443 metabolites. A constraint-based analysis was then employed to evaluate cell growth under various conditions. Subsequently, a flux ratio analysis was conducted to understand the mechanism of bifurcated hexanoic acid synthetic pathways, including the typical fatty acid synthetic pathway via acetyl-CoA and the TCA cycle in a counterclockwise direction through succinate. The resultant metabolic states showed that the highest hexanoic acid production could be achieved when the balanced fractional contribution via acetyl-CoA and succinate in reductive TCA cycle was formed in various cell growth rates. The highest hexanoic acid production was maintained in the most perturbed flux ratio, as phosphoenolpyruvate carboxykinase (pck) enables the bifurcated pathway to form consistent fluxes. Finally, organic acid consuming simulations suggested that succinate can increase both biomass formation and hexanoic acid production.
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Affiliation(s)
- Na-Rae Lee
- Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Korea; (N.-R.L.); (C.H.L.)
- Department of Food Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Choong Hwan Lee
- Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Korea; (N.-R.L.); (C.H.L.)
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea
- Correspondence: (D.-Y.L.); (J.-B.P.); Tel.: +82-31-290-7253 (D.-Y.L.); +82-2-3277-4509 (J.-B.P.)
| | - Jin-Byung Park
- Department of Food Science and Engineering, Ewha Womans University, Seoul 03760, Korea
- Correspondence: (D.-Y.L.); (J.-B.P.); Tel.: +82-31-290-7253 (D.-Y.L.); +82-2-3277-4509 (J.-B.P.)
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12
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Ferrous-Iron-Activated Transcriptional Factor AdhR Regulates Redox Homeostasis in Clostridium beijerinckii. Appl Environ Microbiol 2020; 86:AEM.02782-19. [PMID: 32005735 DOI: 10.1128/aem.02782-19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 01/27/2020] [Indexed: 11/20/2022] Open
Abstract
The AdhR regulatory protein is an activator of σ54-dependent transcription of adhA1 and adhA2 genes, which are required for alcohol synthesis in Clostridium beijerinckii Here, we identified the signal perceived by AdhR and determined the regulatory mechanism of AdhR activity. By assaying the activity of AdhR in N-terminally truncated forms, a negative control mechanism of AdhR activity was identified in which the central AAA+ domain is subject to repression by the N-terminal GAF and PAS domains. Binding of Fe2+ to the GAF domain was found to relieve intramolecular repression and stimulate the ATPase activity of AdhR, allowing the AdhR to activate transcription. This control mechanism enables AdhR to regulate transcription of adhA1 and adhA2 in response to cellular redox status. The mutants deficient in AdhR or σ54 showed large shifts in intracellular redox state indicated by the NADH/NAD+ ratio under conditions of increased electron availability or oxidative stress. We demonstrated that the Fe2+-activated transcriptional regulator AdhR and σ54 control alcohol synthesis to maintain redox homeostasis in clostridial cells. Expression of N-terminally truncated forms of AdhR resulted in improved solvent production by C. beijerinckii IMPORTANCE Solventogenic clostridia are anaerobic bacteria that can produce butanol, ethanol, and acetone, which can be used as biofuels or building block chemicals. Here, we show that AdhR, a σ54-dependent transcriptional activator, senses the intracellular redox status and controls alcohol synthesis in Clostridium beijerinckii AdhR provides a new example of a GAF domain coordinating a mononuclear non-heme iron to sense and transduce the redox signal. Our study reveals a previously unrecognized functional role of σ54 in control of cellular redox balance and provides new insights into redox signaling and regulation in clostridia. Our results reveal AdhR as a novel engineering target for improving solvent production by C. beijerinckii and other solventogenic clostridia.
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13
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Ou J, Bao T, Ernst P, Si Y, Prabhu SD, Wu H, Zhang J(J, Zhou L, Yang ST, Liu X(M. Intracellular metabolism analysis of Clostridium cellulovorans via modeling integrating proteomics, metabolomics and fermentation. Process Biochem 2020. [DOI: 10.1016/j.procbio.2019.10.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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14
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Lillington SP, Leggieri PA, Heom KA, O'Malley MA. Nature's recyclers: anaerobic microbial communities drive crude biomass deconstruction. Curr Opin Biotechnol 2019; 62:38-47. [PMID: 31593910 DOI: 10.1016/j.copbio.2019.08.015] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 08/25/2019] [Accepted: 08/29/2019] [Indexed: 12/13/2022]
Abstract
Microbial communities within anaerobic ecosystems have evolved to degrade and recycle carbon throughout the earth. A number of strains have been isolated from anaerobic microbial communities, which are rich in carbohydrate active enzymes (CAZymes) to liberate fermentable sugars from crude plant biomass (lignocellulose). However, natural anaerobic communities host a wealth of microbial diversity that has yet to be harnessed for biotechnological applications to hydrolyze crude biomass into sugars and value-added products. This review highlights recent advances in 'omics' techniques to sequence anaerobic microbial genomes, decipher microbial membership, and characterize CAZyme diversity in anaerobic microbiomes. With a focus on the herbivore rumen, we further discuss methods to discover new CAZymes, including those found within multi-enzyme fungal cellulosomes. Emerging techniques to characterize the interwoven metabolism and spatial interactions between anaerobes are also reviewed, which will prove critical to developing a predictive understanding of anaerobic communities to guide in microbiome engineering.
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Affiliation(s)
- Stephen P Lillington
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States
| | - Patrick A Leggieri
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States
| | - Kellie A Heom
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States
| | - Michelle A O'Malley
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States.
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15
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Norman RO, Millat T, Schatschneider S, Henstra AM, Breitkopf R, Pander B, Annan FJ, Piatek P, Hartman HB, Poolman MG, Fell DA, Winzer K, Minton NP, Hodgman C. Genome‐scale model of
C. autoethanogenum
reveals optimal bioprocess conditions for high‐value chemical production from carbon monoxide. ENGINEERING BIOLOGY 2019. [DOI: 10.1049/enb.2018.5003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Affiliation(s)
- Rupert O.J. Norman
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
- School of BiosciencesUniversity of NottinghamSutton Bonington Campus, Sutton BoningtonLeicestershireLE12 5RDUK
| | - Thomas Millat
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Sarah Schatschneider
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
- Evonik Nutrition and Care GmbHKantstr. 233798Halle‐KinsbeckGermany
| | - Anne M. Henstra
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Ronja Breitkopf
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Bart Pander
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Florence J. Annan
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Pawel Piatek
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Hassan B. Hartman
- Department of Biology and Medical SciencesOxford Brookes UniversityOxfordOX3 0BPUK
- Public Health England61 Colindale AvenueLondonNW9 5EQUK
| | - Mark G. Poolman
- Department of Biology and Medical SciencesOxford Brookes UniversityOxfordOX3 0BPUK
| | - David A. Fell
- Department of Biology and Medical SciencesOxford Brookes UniversityOxfordOX3 0BPUK
| | - Klaus Winzer
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Nigel P. Minton
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Charlie Hodgman
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
- School of BiosciencesUniversity of NottinghamSutton Bonington Campus, Sutton BoningtonLeicestershireLE12 5RDUK
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16
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Serrano-Bermúdez LM, González Barrios AF, Montoya D. Clostridium butyricum population balance model: Predicting dynamic metabolic flux distributions using an objective function related to extracellular glycerol content. PLoS One 2018; 13:e0209447. [PMID: 30571717 PMCID: PMC6301710 DOI: 10.1371/journal.pone.0209447] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 12/05/2018] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Extensive experimentation has been conducted to increment 1,3-propanediol (PDO) production using Clostridium butyricum cultures in glycerol, but computational predictions are limited. Previously, we reconstructed the genome-scale metabolic (GSM) model iCbu641, the first such model of a PDO-producing Clostridium strain, which was validated at steady state using flux balance analysis (FBA). However, the prediction ability of FBA is limited for batch and fed-batch cultures, which are the most often employed industrial processes. RESULTS We used the iCbu641 GSM model to develop a dynamic flux balance analysis (DFBA) approach to predict the PDO production of the Colombian strain Clostridium sp IBUN 158B. First, we compared the predictions of the dynamic optimization approach (DOA), static optimization approach (SOA), and direct approach (DA). We found no differences between approaches, but the DOA simulation duration was nearly 5000 times that of the SOA and DA simulations. Experimental results at glycerol limitation and glycerol excess allowed for validating dynamic predictions of growth, glycerol consumption, and PDO formation. These results indicated a 4.4% error in PDO prediction and therefore validated the previously proposed objective functions. We performed two global sensitivity analyses, finding that the kinetic input parameters of glycerol uptake flux had the most significant effect on PDO predictions. The other input parameters evaluated during global sensitivity analysis were biomass composition (precursors and macromolecules), death constants, and the kinetic parameters of acetic acid secretion flux. These last input parameters, all obtained from other Clostridium butyricum cultures, were used to develop a population balance model (PBM). Finally, we simulated fed-batch cultures, predicting a final PDO production near to 66 g/L, almost three times the PDO predicted in the best batch culture. CONCLUSIONS We developed and validated a dynamic approach to predict PDO production using the iCbu641 GSM model and the previously proposed objective functions. This validated approach was used to propose a population model and then an increment in predictions of PDO production through fed-batch cultures. Therefore, this dynamic model could predict different scenarios, including its integration into downstream processes to predict technical-economic feasibilities and reducing the time and costs associated with experimentation.
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Affiliation(s)
- Luis Miguel Serrano-Bermúdez
- Bioprocesses and Bioprospecting Group, Universidad Nacional de Colombia, Ciudad Universitaria, Carrera, Bogotá D.C., Colombia
- Grupo Cundinamarca Agroambiental, Departamento de Ingeniería Ambiental, Universidad de Cundinamarca, Facatativá, Colombia
| | - Andrés Fernando González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), Departamento de Ingeniería Química, Universidad de los Andes, Bogotá D.C., Colombia
| | - Dolly Montoya
- Bioprocesses and Bioprospecting Group, Universidad Nacional de Colombia, Ciudad Universitaria, Carrera, Bogotá D.C., Colombia
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17
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Griesemer M, Kimbrel JA, Zhou CE, Navid A, D'haeseleer P. Combining multiple functional annotation tools increases coverage of metabolic annotation. BMC Genomics 2018; 19:948. [PMID: 30567498 PMCID: PMC6299973 DOI: 10.1186/s12864-018-5221-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 11/05/2018] [Indexed: 12/15/2022] Open
Abstract
Background Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30–50% of genes without functional annotation, severely limiting our knowledge of the “parts lists” that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes. Results Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3–8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli. Conclusions Metabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and non-core pathways. Electronic supplementary material The online version of this article (10.1186/s12864-018-5221-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marc Griesemer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Carol E Zhou
- Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Ali Navid
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Patrik D'haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA. .,Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA.
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18
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Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes. PLoS Comput Biol 2018; 14:e1006556. [PMID: 30444863 PMCID: PMC6283598 DOI: 10.1371/journal.pcbi.1006556] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 12/06/2018] [Accepted: 10/09/2018] [Indexed: 01/13/2023] Open
Abstract
Essential metabolic reactions are shaping constituents of metabolic networks, enabling viable and distinct phenotypes across diverse life forms. Here we analyse and compare modelling predictions of essential metabolic functions with experimental data and thereby identify core metabolic pathways in prokaryotes. Simulations of 15 manually curated genome-scale metabolic models were integrated with 36 large-scale gene essentiality datasets encompassing a wide variety of species of bacteria and archaea. Conservation of metabolic genes was estimated by analysing 79 representative genomes from all the branches of the prokaryotic tree of life. We find that essentiality patterns reflect phylogenetic relations both for modelling and experimental data, which correlate highly at the pathway level. Genes that are essential for several species tend to be highly conserved as opposed to non-essential genes which may be conserved or not. The tRNA-charging module is highlighted as ancestral and with high centrality in the networks, followed closely by cofactor metabolism, pointing to an early information processing system supplied by organic cofactors. The results, which point to model improvements and also indicate faults in the experimental data, should be relevant to the study of centrality in metabolic networks and ancient metabolism but also to metabolic engineering with prokaryotes. If we tried to list every known chemical reaction within an organism–human, plant or even bacteria–we would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular species–prokaryotes–to understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design.
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19
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Cashew apple bagasse as new feedstock for the hydrogen production using dark fermentation process. J Biotechnol 2018; 286:71-78. [DOI: 10.1016/j.jbiotec.2018.09.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 08/22/2018] [Accepted: 09/07/2018] [Indexed: 11/19/2022]
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20
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Patakova P, Kolek J, Sedlar K, Koscova P, Branska B, Kupkova K, Paulova L, Provaznik I. Comparative analysis of high butanol tolerance and production in clostridia. Biotechnol Adv 2018; 36:721-738. [DOI: 10.1016/j.biotechadv.2017.12.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 12/05/2017] [Accepted: 12/12/2017] [Indexed: 12/24/2022]
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21
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Xu N, Ye C, Liu L. Genome-scale biological models for industrial microbial systems. Appl Microbiol Biotechnol 2018; 102:3439-3451. [PMID: 29497793 DOI: 10.1007/s00253-018-8803-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 01/08/2023]
Abstract
The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.
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Affiliation(s)
- Nan Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, Jiangsu, 225009, China.,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China
| | - Chao Ye
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China.
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22
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Kaushal M, Chary KVN, Ahlawat S, Palabhanvi B, Goswami G, Das D. Understanding regulation in substrate dependent modulation of growth and production of alcohols in Clostridium sporogenes NCIM 2918 through metabolic network reconstruction and flux balance analysis. BIORESOURCE TECHNOLOGY 2018; 249:767-776. [PMID: 29136931 DOI: 10.1016/j.biortech.2017.10.080] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 10/18/2017] [Accepted: 10/20/2017] [Indexed: 06/07/2023]
Abstract
Flux Balance Analysis was performed for Clostridium sporogenes NCIM 2918 grown on sole glucose and glycerol or glucose-glycerol combinations at varied concentrations. During acidogenesis, glucose and glucose-glycerol combinations favored improved growth and butyric acid production. Glycerol fermentation was however marked by reduced growth and predominant ethanol synthesis. Further, with increase of glycerol fraction in glucose-glycerol blend, flux towards ethanol synthesis linearly increased with simultaneous decrease in butanol flux. Elevated ATP demand due to improved growth was satisfied by upregulated carbon flux towards butyric acid synthesis during both glucose and dual substrate fermentations. Possible repression of pyruvate carboxylase by glycerol resulting in downturn of carbon uptake flux towards TCA cycle through anaplerotic reaction may be responsible for reduced growth in glycerol fermentation. Ammonium acetate mediated induction of acetic acid utilization, during acidogenesis, led to excess acetyl-CoA generation and its subsequent metabolism to lesser reduced products, butyric acid or ethanol.
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Affiliation(s)
- Mehak Kaushal
- Department of Biosciences & Bioengineering, Indian Institute of Technology, Guwahati, Assam 781039, India; DBT-PAN IIT Centre for Bioenergy, Indian Institute of Technology, Guwahati, Assam 781039, India
| | - K Venkata Narayana Chary
- Department of Biosciences & Bioengineering, Indian Institute of Technology, Guwahati, Assam 781039, India; DBT-PAN IIT Centre for Bioenergy, Indian Institute of Technology, Guwahati, Assam 781039, India
| | - Saumya Ahlawat
- Department of Biosciences & Bioengineering, Indian Institute of Technology, Guwahati, Assam 781039, India; DBT-PAN IIT Centre for Bioenergy, Indian Institute of Technology, Guwahati, Assam 781039, India
| | - Basavaraj Palabhanvi
- Department of Biosciences & Bioengineering, Indian Institute of Technology, Guwahati, Assam 781039, India; DBT-PAN IIT Centre for Bioenergy, Indian Institute of Technology, Guwahati, Assam 781039, India
| | - Gargi Goswami
- Department of Biosciences & Bioengineering, Indian Institute of Technology, Guwahati, Assam 781039, India; DBT-PAN IIT Centre for Bioenergy, Indian Institute of Technology, Guwahati, Assam 781039, India
| | - Debasish Das
- Department of Biosciences & Bioengineering, Indian Institute of Technology, Guwahati, Assam 781039, India; DBT-PAN IIT Centre for Bioenergy, Indian Institute of Technology, Guwahati, Assam 781039, India.
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23
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Palomo-Briones R, Trably E, López-Lozano NE, Celis LB, Méndez-Acosta HO, Bernet N, Razo-Flores E. Hydrogen metabolic patterns driven by Clostridium-Streptococcus community shifts in a continuous stirred tank reactor. Appl Microbiol Biotechnol 2018; 102:2465-2475. [DOI: 10.1007/s00253-018-8737-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/20/2017] [Accepted: 12/22/2017] [Indexed: 01/08/2023]
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24
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Kelly W, Veigne S, Li X, Subramanian SS, Huang Z, Schaefer E. Optimizing performance of semi-continuous cell culture in an ambr15™ microbioreactor using dynamic flux balance modeling. Biotechnol Prog 2017; 34:420-431. [DOI: 10.1002/btpr.2585] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 11/05/2017] [Indexed: 12/29/2022]
Affiliation(s)
- William Kelly
- Villanova University, Chemical Engineering; Villanova PA 19085
| | - Sorelle Veigne
- Janssen R&D, cAPI Large Molecule Pharmaceutical, Development and Manufacturing Sciences; Malvern PA
| | - Xianhua Li
- Villanova University, Chemical Engineering; Villanova PA 19085
| | | | - Zuyi Huang
- Villanova University, Chemical Engineering; Villanova PA 19085
| | - Eugene Schaefer
- Janssen R&D, cAPI Large Molecule Pharmaceutical, Development and Manufacturing Sciences; Malvern PA
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Sangavai C, Chellapandi P. Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering. ACTA ACUST UNITED AC 2017; 16:32-43. [PMID: 29167757 PMCID: PMC5686429 DOI: 10.1016/j.btre.2017.11.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 10/17/2017] [Accepted: 11/03/2017] [Indexed: 01/01/2023]
Abstract
Model-driven systems engineering has been more fascinating process for microbial biofuel production. Clostridium sticklandii is a potential strain for the solventogenesis and acidogenesis. The present review provides an insight for the protein catabolism-directed biofuel production.
Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n-butanol, n-butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways.
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Affiliation(s)
- C Sangavai
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620 024, Tamil Nadu, India
| | - P Chellapandi
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620 024, Tamil Nadu, India
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Jamialahmadi O, Motamedian E, Hashemi-Najafabadi S. BiKEGG: a COBRA toolbox extension for bridging the BiGG and KEGG databases. MOLECULAR BIOSYSTEMS 2017; 12:3459-3466. [PMID: 27714042 DOI: 10.1039/c6mb00532b] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Development of an interface tool between the Biochemical, Genetic and Genomic (BiGG) and KEGG databases is necessary for simultaneous access to the features of both databases. For this purpose, we present the BiKEGG toolbox, an open source COBRA toolbox extension providing a set of functions to infer the reaction correspondences between the KEGG reaction identifiers and those in the BiGG knowledgebase using a combination of manual verification and computational methods. Inferred reaction correspondences using this approach are supported by evidence from the literature, which provides a higher number of reconciled reactions between these two databases compared to the MetaNetX and MetRxn databases. This set of equivalent reactions is then used to automatically superimpose the predicted fluxes using COBRA methods on classical KEGG pathway maps or to create a customized metabolic map based on the KEGG global metabolic pathway, and to find the corresponding reactions in BiGG based on the genome annotation of an organism in the KEGG database. Customized metabolic maps can be created for a set of pathways of interest, for the whole KEGG global map or exclusively for all pathways for which there exists at least one flux carrying reaction. This flexibility in visualization enables BiKEGG to indicate reaction directionality as well as to visualize the reaction fluxes for different static or dynamic conditions in an animated manner. BiKEGG allows the user to export (1) the output visualized metabolic maps to various standard image formats or save them as a video or animated GIF file, and (2) the equivalent reactions for an organism as an Excel spreadsheet.
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Affiliation(s)
- Oveis Jamialahmadi
- Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, P.O. Box 14115-114, Tehran, Iran.
| | - Ehsan Motamedian
- Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, P.O. Box 14115-114, Tehran, Iran.
| | - Sameereh Hashemi-Najafabadi
- Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, P.O. Box 14115-114, Tehran, Iran.
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Serrano-Bermúdez LM, González Barrios AF, Maranas CD, Montoya D. Clostridium butyricum maximizes growth while minimizing enzyme usage and ATP production: metabolic flux distribution of a strain cultured in glycerol. BMC SYSTEMS BIOLOGY 2017; 11:58. [PMID: 28571567 PMCID: PMC5455137 DOI: 10.1186/s12918-017-0434-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 05/16/2017] [Indexed: 01/15/2023]
Abstract
BACKGROUND The increase in glycerol obtained as a byproduct of biodiesel has encouraged the production of new industrial products, such as 1,3-propanediol (PDO), using biotechnological transformation via bacteria like Clostridium butyricum. However, despite the increasing role of Clostridium butyricum as a bio-production platform, its metabolism remains poorly modeled. RESULTS We reconstructed iCbu641, the first genome-scale metabolic (GSM) model of a PDO producer Clostridium strain, which included 641 genes, 365 enzymes, 891 reactions, and 701 metabolites. We found an enzyme expression prediction of nearly 84% after comparison of proteomic data with flux distribution estimation using flux balance analysis (FBA). The remaining 16% corresponded to enzymes directionally coupled to growth, according to flux coupling findings (FCF). The fermentation data validation also revealed different phenotype states that depended on culture media conditions; for example, Clostridium maximizes its biomass yield per enzyme usage under glycerol limitation. By contrast, under glycerol excess conditions, Clostridium grows sub-optimally, maximizing biomass yield while minimizing both enzyme usage and ATP production. We further evaluated perturbations in the GSM model through enzyme deletions and variations in biomass composition. The GSM predictions showed no significant increase in PDO production, suggesting a robustness to perturbations in the GSM model. We used the experimental results to predict that co-fermentation was a better alternative than iCbu641 perturbations for improving PDO yields. CONCLUSIONS The agreement between the predicted and experimental values allows the use of the GSM model constructed for the PDO-producing Clostridium strain to propose new scenarios for PDO production, such as dynamic simulations, thereby reducing the time and costs associated with experimentation.
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Affiliation(s)
- Luis Miguel Serrano-Bermúdez
- Bioprocesses and Bioprospecting Group, Universidad Nacional de Colombia. Ciudad Universitaria, Carrera 30 No. 45-03, Bogotá, D.C Colombia
| | - Andrés Fernando González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), Departamento de Ingeniería Química, Universidad de los Andes, Carrera 1 N.° 18A – 12, Bogotá, Colombia
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802 USA
| | - Dolly Montoya
- Bioprocesses and Bioprospecting Group, Universidad Nacional de Colombia. Ciudad Universitaria, Carrera 30 No. 45-03, Bogotá, D.C Colombia
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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: 163] [Impact Index Per Article: 23.3] [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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Dash S, Khodayari A, Zhou J, Holwerda EK, Olson DG, Lynd LR, Maranas CD. Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations. BIOTECHNOLOGY FOR BIOFUELS 2017; 10:108. [PMID: 28469704 PMCID: PMC5414155 DOI: 10.1186/s13068-017-0792-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2016] [Accepted: 04/18/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes. RESULTS In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances, and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis-Menten kinetic parameters. CONCLUSIONS The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k-ctherm118 and suggest additional experiments to improve kinetic model prediction fidelity. Overall, the study quantitatively assesses the advantages of EM-based kinetic modeling towards improved prediction of C. thermocellum metabolism and develops a predictive kinetic model which can be used to design biofuel-overproducing strains.
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Affiliation(s)
- Satyakam Dash
- Department of Chemical Engineering, The Pennsylvania State University, 126 Land and Water Research Building, University Park, PA 16802 USA
| | - Ali Khodayari
- Department of Chemical Engineering, The Pennsylvania State University, 126 Land and Water Research Building, University Park, PA 16802 USA
| | - Jilai Zhou
- Thayer School of Engineering at Dartmouth College, Hanover, NH USA
| | | | - Daniel G. Olson
- Thayer School of Engineering at Dartmouth College, Hanover, NH USA
| | - Lee R. Lynd
- Thayer School of Engineering at Dartmouth College, Hanover, NH USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, 126 Land and Water Research Building, University Park, PA 16802 USA
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Valgepea K, Loi KQ, Behrendorff JB, Lemgruber RDSP, Plan M, Hodson MP, Köpke M, Nielsen LK, Marcellin E. Arginine deiminase pathway provides ATP and boosts growth of the gas-fermenting acetogen Clostridium autoethanogenum. Metab Eng 2017; 41:202-211. [PMID: 28442386 DOI: 10.1016/j.ymben.2017.04.007] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 03/10/2017] [Accepted: 04/20/2017] [Indexed: 12/20/2022]
Abstract
Acetogens are attractive organisms for the production of chemicals and fuels from inexpensive and non-food feedstocks such as syngas (CO, CO2 and H2). Expanding their product spectrum beyond native compounds is dictated by energetics, particularly ATP availability. Acetogens have evolved sophisticated strategies to conserve energy from reduction potential differences between major redox couples, however, this coupling is sensitive to small changes in thermodynamic equilibria. To accelerate the development of strains for energy-intensive products from gases, we used a genome-scale metabolic model (GEM) to explore alternative ATP-generating pathways in the gas-fermenting acetogen Clostridium autoethanogenum. Shadow price analysis revealed a preference of C. autoethanogenum for nine amino acids. This prediction was experimentally confirmed under heterotrophic conditions. Subsequent in silico simulations identified arginine (ARG) as a key enhancer for growth. Predictions were experimentally validated, and faster growth was measured in media containing ARG (tD~4h) compared to growth on yeast extract (tD~9h). The growth-boosting effect of ARG was confirmed during autotrophic growth. Metabolic modelling and experiments showed that acetate production is nearly abolished and fast growth is realised by a three-fold increase in ATP production through the arginine deiminase (ADI) pathway. The involvement of the ADI pathway was confirmed by metabolomics and RNA-sequencing which revealed a ~500-fold up-regulation of the ADI pathway with an unexpected down-regulation of the Wood-Ljungdahl pathway. The data presented here offer a potential route for supplying cells with ATP, while demonstrating the usefulness of metabolic modelling for the discovery of native pathways for stimulating growth or enhancing energy availability.
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Affiliation(s)
- Kaspar Valgepea
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia
| | - Kim Q Loi
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia
| | | | - Renato de S P Lemgruber
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia
| | - Manuel Plan
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia; Metabolomics Australia, AIBN, The University of Queensland, Brisbane, Australia
| | - Mark P Hodson
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia; Metabolomics Australia, AIBN, The University of Queensland, Brisbane, Australia; School of Pharmacy, The University of Queensland, Brisbane, Australia
| | | | - Lars K Nielsen
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia.
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Mathematical modelling of clostridial acetone-butanol-ethanol fermentation. Appl Microbiol Biotechnol 2017; 101:2251-2271. [PMID: 28210797 PMCID: PMC5320022 DOI: 10.1007/s00253-017-8137-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 01/14/2017] [Accepted: 01/16/2017] [Indexed: 12/24/2022]
Abstract
Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists.
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Lee SH, Kim S, Kim JY, Cheong NY, Kim KH. Enhanced butanol fermentation using metabolically engineered Clostridium acetobutylicum with ex situ recovery of butanol. BIORESOURCE TECHNOLOGY 2016; 218:909-917. [PMID: 27441828 DOI: 10.1016/j.biortech.2016.07.060] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Revised: 07/12/2016] [Accepted: 07/13/2016] [Indexed: 06/06/2023]
Abstract
In this study, metabolic target reactions for strain engineering were searched via intracellular coenzyme A (CoA) metabolite analysis. The metabolic reactions catalyzed by thiolase (AtoB) and aldehyde-alcohol dehydrogenase (AdhE1) were considered potential rate-limiting steps. In addition, CoA transferase (CtfAB) was highlighted as being important for the assimilation of organic acids, in order to achieve high butanol production. Based on this quantitative analysis, the BEKW_E1AB-atoB strain was constructed by overexpressing the thl (atoB), adhE1, and ctfAB genes in Clostridium acetobutylicum strain BEKW, which has the phosphotransacetylase (pta) and butyrate kinase (buk) genes knocked out. After 100h of continuous fermentation coupled with adsorptive ex situ butanol recovery, the concentrations found after considering desorption, yield, and productivity for the BEKW_E1AB-atoB strain were 55.7g/L, 0.38g/g, and 2.64g/L/h, respectively. The level of butanol production achieved (2.64g/L/h) represents the highest reported value obtained after adsorptive, long-term fermentation.
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Affiliation(s)
- Sang-Hyun Lee
- Department of Biotechnology, Graduate School, Korea University, Seoul 02841, South Korea
| | - Sooah Kim
- Department of Biotechnology, Graduate School, Korea University, Seoul 02841, South Korea
| | - Jung Yeon Kim
- Department of Biotechnology, Graduate School, Korea University, Seoul 02841, South Korea
| | - Nam Yong Cheong
- Environmental Analysis Division, Korea Apparel Testing & Research Institute, Anyang 14088, South Korea
| | - Kyoung Heon Kim
- Department of Biotechnology, Graduate School, Korea University, Seoul 02841, South Korea.
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Thompson RA, Dahal S, Garcia S, Nookaew I, Trinh CT. Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome. BIOTECHNOLOGY FOR BIOFUELS 2016; 9:194. [PMID: 27602057 PMCID: PMC5012057 DOI: 10.1186/s13068-016-0607-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 08/26/2016] [Indexed: 05/06/2023]
Abstract
BACKGROUND Clostridium thermocellum is a gram-positive thermophile that can directly convert lignocellulosic material into biofuels. The metabolism of C. thermocellum contains many branches and redundancies which limit biofuel production, and typical genetic techniques are time-consuming. Further, the genome sequence of a genetically tractable strain C. thermocellum DSM 1313 has been recently sequenced and annotated. Therefore, developing a comprehensive, predictive, genome-scale metabolic model of DSM 1313 is desired for elucidating its complex phenotypes and facilitating model-guided metabolic engineering. RESULTS We constructed a genome-scale metabolic model iAT601 for DSM 1313 using the KEGG database as a scaffold and an extensive literature review and bioinformatic analysis for model refinement. Next, we used several sets of experimental data to train the model, e.g., estimation of the ATP requirement for growth-associated maintenance (13.5 mmol ATP/g DCW/h) and cellulosome synthesis (57 mmol ATP/g cellulosome/h). Using our tuned model, we investigated the effect of cellodextrin lengths on cell yields, and could predict in silico experimentally observed differences in cell yield based on which cellodextrin species is assimilated. We further employed our tuned model to analyze the experimentally observed differences in fermentation profiles (i.e., the ethanol to acetate ratio) between cellobiose- and cellulose-grown cultures and infer regulatory mechanisms to explain the phenotypic differences. Finally, we used the model to design over 250 genetic modification strategies with the potential to optimize ethanol production, 6155 for hydrogen production, and 28 for isobutanol production. CONCLUSIONS Our developed genome-scale model iAT601 is capable of accurately predicting complex cellular phenotypes under a variety of conditions and serves as a high-quality platform for model-guided strain design and metabolic engineering to produce industrial biofuels and chemicals of interest.
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Affiliation(s)
- R. Adam Thompson
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996 USA
- Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Sanjeev Dahal
- BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Comparative Genomics Group, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Sergio Garcia
- BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Department of Chemical and Biomolecular Engineering, University of Tennessee, 1512 Middle Dr., DO#432, Knoxville, TN 37996 USA
| | - Intawat Nookaew
- BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Comparative Genomics Group, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205 USA
| | - Cong T. Trinh
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996 USA
- Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Department of Chemical and Biomolecular Engineering, University of Tennessee, 1512 Middle Dr., DO#432, Knoxville, TN 37996 USA
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Liao C, Seo SO, Lu T. System-level modeling of acetone-butanol-ethanol fermentation. FEMS Microbiol Lett 2016; 363:fnw074. [PMID: 27020410 DOI: 10.1093/femsle/fnw074] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2016] [Indexed: 11/12/2022] Open
Abstract
Acetone-butanol-ethanol (ABE) fermentation is a metabolic process of clostridia that produces bio-based solvents including butanol. It is enabled by an underlying metabolic reaction network and modulated by cellular gene regulation and environmental cues. Mathematical modeling has served as a valuable strategy to facilitate the understanding, characterization and optimization of this process. In this review, we highlight recent advances in system-level, quantitative modeling of ABE fermentation. We begin with an overview of integrative processes underlying the fermentation. Next we survey modeling efforts including early simple models, models with a systematic metabolic description, and those incorporating metabolism through simple gene regulation. Particular focus is given to a recent system-level model that integrates the metabolic reactions, gene regulation and environmental cues. We conclude by discussing the remaining challenges and future directions towards predictive understanding of ABE fermentation.
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Affiliation(s)
- Chen Liao
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Seung-Oh Seo
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Ting Lu
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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35
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Characterizing the Phenotypic Responses of Escherichia coli to Multiple 4-Carbon Alcohols with Raman Spectroscopy. FERMENTATION-BASEL 2016. [DOI: 10.3390/fermentation2010003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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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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Rebalancing Redox to Improve Biobutanol Production by Clostridium tyrobutyricum. Bioengineering (Basel) 2015; 3:bioengineering3010002. [PMID: 28952564 PMCID: PMC5597160 DOI: 10.3390/bioengineering3010002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Revised: 11/24/2015] [Accepted: 12/18/2015] [Indexed: 12/26/2022] Open
Abstract
Biobutanol is a sustainable green biofuel that can substitute for gasoline. Carbon flux has been redistributed in Clostridium tyrobutyricum via metabolic cell engineering to produce biobutanol. However, the lack of reducing power hampered the further improvement of butanol production. The objective of this study was to improve butanol production by rebalancing redox. Firstly, a metabolically-engineered mutant CTC-fdh-adhE2 was constructed by introducing heterologous formate dehydrogenase (fdh) and bifunctional aldehyde/alcohol dehydrogenase (adhE2) simultaneously into wild-type C. tyrobutyricum. The mutant evaluation indicated that the fdh-catalyzed NADH-producing pathway improved butanol titer by 2.15-fold in the serum bottle and 2.72-fold in the bioreactor. Secondly, the medium supplements that could shift metabolic flux to improve the production of butyrate or butanol were identified, including vanadate, acetamide, sodium formate, vitamin B12 and methyl viologen hydrate. Finally, the free-cell fermentation produced 12.34 g/L of butanol from glucose using the mutant CTC-fdh-adhE2, which was 3.88-fold higher than that produced by the control mutant CTC-adhE2. This study demonstrated that the redox engineering in C. tyrobutyricum could greatly increase butanol production.
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Gallardo R, Acevedo A, Quintero J, Paredes I, Conejeros R, Aroca G. In silico analysis of Clostridium acetobutylicum ATCC 824 metabolic response to an external electron supply. Bioprocess Biosyst Eng 2015; 39:295-305. [PMID: 26650720 DOI: 10.1007/s00449-015-1513-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 11/21/2015] [Indexed: 11/26/2022]
Abstract
The biological production of butanol has become an important research field and thanks to genome sequencing and annotation; genome-scale metabolic reconstructions have been developed for several Clostridium species. This work makes use of the iCAC490 model of Clostridium acetobutylicum ATCC 824 to analyze its metabolic capabilities and response to an external electron supply through a constraint-based approach using the Constraint-Based Reconstruction Analysis Toolbox. Several analyses were conducted, which included sensitivity, production envelope, and phenotypic phase planes. The model showed that the use of an external electron supply, which acts as co-reducing agent along with glucose-derived reducing power (electrofermentation), results in an increase in the butanol-specific productivity. However, a proportional increase in the butyrate uptake flux is required. Besides, the uptake of external butyrate leads to the coupling of butanol production and growth, which coincides with results reported in literature. Phenotypic phase planes showed that the reducing capacity becomes more limiting for growth at high butyrate uptake fluxes. An electron uptake flux allows the metabolism to reach the growth optimality line. Although the maximum butanol flux does not coincide with the growth optimality line, a butyrate uptake combined with an electron uptake flux would result in an increased butanol volumetric productivity, being a potential strategy to optimize the production of butanol by C. acetobutylicum ATCC 824.
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Affiliation(s)
- Roberto Gallardo
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Av. Brasil, 2085, Valparaíso, Chile
| | - Alejandro Acevedo
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Av. Brasil, 2085, Valparaíso, Chile
| | - Julián Quintero
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Av. Brasil, 2085, Valparaíso, Chile
| | - Ivan Paredes
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Av. Brasil, 2085, Valparaíso, Chile
| | - Raúl Conejeros
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Av. Brasil, 2085, Valparaíso, Chile
| | - Germán Aroca
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Av. Brasil, 2085, Valparaíso, Chile.
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39
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Fermentation and genomic analysis of acetone-uncoupled butanol production by Clostridium tetanomorphum. Appl Microbiol Biotechnol 2015; 100:1523-1529. [DOI: 10.1007/s00253-015-7121-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 10/21/2015] [Accepted: 10/24/2015] [Indexed: 11/27/2022]
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40
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Liu R, Bassalo MC, Zeitoun RI, Gill RT. Genome scale engineering techniques for metabolic engineering. Metab Eng 2015; 32:143-154. [PMID: 26453944 DOI: 10.1016/j.ymben.2015.09.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 08/15/2015] [Accepted: 09/02/2015] [Indexed: 12/18/2022]
Abstract
Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.
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Affiliation(s)
- Rongming Liu
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Marcelo C Bassalo
- Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ramsey I Zeitoun
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ryan T Gill
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
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41
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Insights into copper effect on Proteus hauseri through proteomic and metabolic analyses. J Biosci Bioeng 2015; 121:178-85. [PMID: 26194304 DOI: 10.1016/j.jbiosc.2015.06.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 06/04/2015] [Accepted: 06/18/2015] [Indexed: 11/23/2022]
Abstract
This is the first-attempt to use liquid chromatography coupled with tandem mass (LC-MS-MS) in deciphering the effects of copper ion on Proteus hauseri. Total 941 proteins in copper-addition (+Cu) group and 898 proteins in non-copper-addition (-Cu) group were found, which containing 221 and 178 differential proteins in +Cu and -Cu group, respectively. Differential proteins in both groups were defined into 14 groups by their functional classification which transport/membrane function proteins were the major different part between the two groups, which took 19.5% and 7.7%, respectively. The result of BioCyc and KEGG analyses on metabolic pathway indicated that copper could interrupted the pathway of chemotaxis CheY and inhibited the swarming of P. hauseri, which provided a potential in controlling the pathogenicity of this strain.
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42
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Kim B, Kim WJ, Kim DI, Lee SY. Applications of genome-scale metabolic network model in metabolic engineering. ACTA ACUST UNITED AC 2015; 42:339-48. [DOI: 10.1007/s10295-014-1554-9] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 11/19/2014] [Indexed: 12/11/2022]
Abstract
Abstract
Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.
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Affiliation(s)
- Byoungjin Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Won Jun Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Dong In Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Sang Yup Lee
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
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Monk J, Nogales J, Palsson BO. Optimizing genome-scale network reconstructions. Nat Biotechnol 2015; 32:447-52. [PMID: 24811519 DOI: 10.1038/nbt.2870] [Citation(s) in RCA: 138] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Jonathan Monk
- 1] Department of Bioengineering, University of California, San Diego, La Jolla, California, USA. [2]
| | - Juan Nogales
- 1] Department of Bioengineering, University of California, San Diego, La Jolla, California, USA. [2] Department of Environmental Biology, Centro de Investigaciones Biológicas, CSIC, Madrid, Spain. [3]
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
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44
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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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45
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Basler G. Computational prediction of essential metabolic genes using constraint-based approaches. Methods Mol Biol 2015; 1279:183-204. [PMID: 25636620 DOI: 10.1007/978-1-4939-2398-4_12] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In this chapter, we describe the application of constraint-based modeling to predict the impact of gene deletions on a metabolic phenotype. The metabolic reactions taking place inside cells form large networks, which have been reconstructed at a genome-scale for several organisms at increasing levels of detail. By integrating mathematical modeling techniques with biochemical principles, constraint-based approaches enable predictions of metabolite fluxes and growth under specific environmental conditions or for genetically modified microorganisms. Similar to the experimental knockout of a gene, predicting the essentiality of a metabolic gene for a phenotype further allows to generate hypotheses on its biological function and design of genetic engineering strategies for biotechnological applications. Here, we summarize the principles of constraint-based approaches and provide a detailed description of the procedure to predict the essentiality of metabolic genes with respect to a specific metabolic function. We exemplify the approach by predicting the essentiality of reactions in the citric acid cycle for the production of glucose from fatty acids.
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Affiliation(s)
- Georg Basler
- Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Profesor Albareda 1, 18008, Granada, Spain,
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46
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Daniell J, Nagaraju S, Burton F, Köpke M, Simpson SD. Low-Carbon Fuel and Chemical Production by Anaerobic Gas Fermentation. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2015; 156:293-321. [PMID: 26957126 DOI: 10.1007/10_2015_5005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
World energy demand is expected to increase by up to 40% by 2035. Over this period, the global population is also expected to increase by a billion people. A challenge facing the global community is not only to increase the supply of fuel, but also to minimize fossil carbon emissions to safeguard the environment, at the same time as ensuring that food production and supply is not detrimentally impacted. Gas fermentation is a rapidly maturing technology which allows low carbon fuel and commodity chemical synthesis. Unlike traditional biofuel technologies, gas fermentation avoids the use of sugars, relying instead on gas streams rich in carbon monoxide and/or hydrogen and carbon dioxide as sources of carbon and energy for product synthesis by specialized bacteria collectively known as acetogens. Thus, gas fermentation enables access to a diverse array of novel, large volume, and globally available feedstocks including industrial waste gases and syngas produced, for example, via the gasification of municipal waste and biomass. Through the efforts of academic labs and early stage ventures, process scale-up challenges have been surmounted through the development of specialized bioreactors. Furthermore, tools for the genetic improvement of the acetogenic bacteria have been reported, paving the way for the production of a spectrum of ever-more valuable products via this process. As a result of these developments, interest in gas fermentation among both researchers and legislators has grown significantly in the past 5 years to the point that this approach is now considered amongst the mainstream of emerging technology solutions for near-term low-carbon fuel and chemical synthesis.
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Affiliation(s)
- James Daniell
- LanzaTech Inc., 8045 Lamon Ave, Suite 400, Skokie, IL, 60077, USA.,School of Biological Sciences, University of Auckland, Auckland, New Zealand
| | - Shilpa Nagaraju
- LanzaTech Inc., 8045 Lamon Ave, Suite 400, Skokie, IL, 60077, USA
| | - Freya Burton
- LanzaTech Inc., 8045 Lamon Ave, Suite 400, Skokie, IL, 60077, USA
| | - Michael Köpke
- LanzaTech Inc., 8045 Lamon Ave, Suite 400, Skokie, IL, 60077, USA
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47
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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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48
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Benedict MN, Mundy MB, Henry CS, Chia N, Price ND. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models. PLoS Comput Biol 2014; 10:e1003882. [PMID: 25329157 PMCID: PMC4199484 DOI: 10.1371/journal.pcbi.1003882] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Accepted: 08/25/2014] [Indexed: 12/27/2022] Open
Abstract
Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.
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Affiliation(s)
- Matthew N. Benedict
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Michael B. Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Christopher S. Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois, United States of America
| | - Nicholas Chia
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Surgery, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Physiology and Bioengineering, Mayo Clinic, Rochester, Minnesota, United States of America
- * E-mail: (NC); (NDP)
| | - Nathan D. Price
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
- * E-mail: (NC); (NDP)
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49
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Utilization of economical substrate-derived carbohydrates by solventogenic clostridia: pathway dissection, regulation and engineering. Curr Opin Biotechnol 2014; 29:124-31. [DOI: 10.1016/j.copbio.2014.04.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2013] [Revised: 03/21/2014] [Accepted: 04/02/2014] [Indexed: 01/15/2023]
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50
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Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum-evidence from a mathematical model. Appl Microbiol Biotechnol 2014; 98:9059-72. [PMID: 25149445 DOI: 10.1007/s00253-014-5987-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Revised: 07/23/2014] [Accepted: 07/24/2014] [Indexed: 01/07/2023]
Abstract
The hetero-dimeric CoA-transferase CtfA/B is believed to be crucial for the metabolic transition from acidogenesis to solventogenesis in Clostridium acetobutylicum as part of the industrial-relevant acetone-butanol-ethanol (ABE) fermentation. Here, the enzyme is assumed to mediate re-assimilation of acetate and butyrate during a pH-induced metabolic shift and to faciliate the first step of acetone formation from acetoacetyl-CoA. However, recent investigations using phosphate-limited continuous cultures have questioned this common dogma. To address the emerging experimental discrepancies, we investigated the mutant strain Cac-ctfA398s::CT using chemostat cultures. As a consequence of this mutation, the cells are unable to express functional ctfA and are thus lacking CoA-transferase activity. A mathematical model of the pH-induced metabolic shift, which was recently developed for the wild type, is used to analyse the observed behaviour of the mutant strain with a focus on re-assimilation activities for the two produced acids. Our theoretical analysis reveals that the ctfA mutant still re-assimilates butyrate, but not acetate. Based upon this finding, we conclude that C. acetobutylicum possesses a CoA-tranferase-independent butyrate uptake mechanism that is activated by decreasing pH levels. Furthermore, we observe that butanol formation is not inhibited under our experimental conditions, as suggested by previous batch culture experiments. In concordance with recent batch experiments, acetone formation is abolished in chemostat cultures using the ctfa mutant.
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