1
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Zielinski DC, Matos MR, de Bree JE, Glass K, Sonnenschein N, Palsson BO. Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data. Metab Eng Commun 2024; 18:e00234. [PMID: 38711578 PMCID: PMC11070925 DOI: 10.1016/j.mec.2024.e00234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of 'bottom up' parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard 'macroscopic' kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and 'microscopic' rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.
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Affiliation(s)
- Daniel C. Zielinski
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Marta R.A. Matos
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - James E. de Bree
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Kevin Glass
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA
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2
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Petersen SD, Levassor L, Pedersen CM, Madsen J, Hansen LG, Zhang J, Haidar AK, Frandsen RJN, Keasling JD, Weber T, Sonnenschein N, K. Jensen M. teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. PLoS Comput Biol 2024; 20:e1011929. [PMID: 38457467 PMCID: PMC10954146 DOI: 10.1371/journal.pcbi.1011929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/20/2024] [Accepted: 02/17/2024] [Indexed: 03/10/2024] Open
Abstract
Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.
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Affiliation(s)
- Søren D. Petersen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lucas Levassor
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Christine M. Pedersen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jan Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lea G. Hansen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jie Zhang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Ahmad K. Haidar
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Rasmus J. N. Frandsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jay D. Keasling
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
- Joint BioEnergy Institute, Emeryville, California, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of California, Berkeley, California, United States of America
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes of Advanced Technologies, Shenzhen, China
| | - Tilmann Weber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Michael K. Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
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3
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Gorter de Vries PJ, Mol V, Sonnenschein N, Jensen TØ, Nielsen AT. Probing efficient microbial CO 2 utilisation through metabolic and process modelling. Microb Biotechnol 2024; 17:e14414. [PMID: 38380934 PMCID: PMC10880515 DOI: 10.1111/1751-7915.14414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/29/2023] [Accepted: 01/10/2024] [Indexed: 02/22/2024] Open
Abstract
Acetogenic gas fermentation is increasingly studied as a promising technology to upcycle carbon-rich waste gasses. Currently the product range is limited, and production yields, rates and titres for a number of interesting products do not allow for economically viable processes. By pairing process modelling and host-agnostic metabolic modelling, we compare fermentation conditions and various products to optimise the processes. The models were then used in a simulation of an industrial-scale bubble column reactor. We find that increased temperatures favour gas transfer rates, particularly for the valuable and limiting H2 , while furthermore predicting an optimal feed composition of 9:1 mol H2 to mol CO2 . Metabolically, the increased non-growth associated maintenance requirements of thermophiles favours the formation of catabolic products. To assess the expansion of the product portfolio beyond acetate, both a product volatility analysis and a metabolic pathway model were implemented. In-situ recovery of volatile products is shown to be within range for acetone but challenging due to the extensive evaporation of water, while the direct production of more valuable compounds by acetogens is metabolically unfavourable compared to acetate and ethanol. We discuss alternative approaches to overcome these challenges to utilise acetogenic CO2 fixation to produce a wider range of carbon negative chemicals.
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Affiliation(s)
- Philip J. Gorter de Vries
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
| | - Viviënne Mol
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
| | - Nikolaus Sonnenschein
- Department of Biotechnology and BiomedicineTechnical University of DenmarkKongens LyngbyDenmark
| | - Torbjørn Ølshøj Jensen
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
- AgainSøborgDenmark
| | - Alex Toftgaard Nielsen
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
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Carrasco Muriel J, Long C, Sonnenschein N. Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO. Microbiol Spectr 2023; 11:e0170523. [PMID: 37931133 PMCID: PMC10783817 DOI: 10.1128/spectrum.01705-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/11/2023] [Indexed: 11/08/2023] Open
Abstract
IMPORTANCE The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.
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Affiliation(s)
- Jorge Carrasco Muriel
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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Cachera P, Olsson H, Coumou H, Jensen ML, Sánchez B, Strucko T, van den Broek M, Daran JM, Jensen M, Sonnenschein N, Lisby M, Mortensen U. CRI-SPA: a high-throughput method for systematic genetic editing of yeast libraries. Nucleic Acids Res 2023; 51:e91. [PMID: 37572348 PMCID: PMC10516668 DOI: 10.1093/nar/gkad656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 07/07/2023] [Accepted: 08/10/2023] [Indexed: 08/14/2023] Open
Abstract
Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes.
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Affiliation(s)
- Paul Cachera
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark
| | - Helén Olsson
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark
| | - Hilde Coumou
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Denmark
| | - Mads L Jensen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Denmark
| | - Benjamín J Sánchez
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Denmark
| | - Tomas Strucko
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Denmark
| | - Marcel van den Broek
- Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | - Jean-Marc Daran
- Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | - Michael K Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Denmark
| | - Michael Lisby
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Uffe H Mortensen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Denmark
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6
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Bujdoš D, Popelářová B, Volke DC, Nikel PI, Sonnenschein N, Dvořák P. Engineering of Pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model. Metab Eng 2023; 75:29-46. [PMID: 36343876 DOI: 10.1016/j.ymben.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/11/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022]
Abstract
Pseudomonas putida KT2440 is an attractive bacterial host for biotechnological production of valuable chemicals from renewable lignocellulosic feedstocks as it can valorize lignin-derived aromatics or glucose obtainable from cellulose. P. putida EM42, a genome-reduced variant of strain KT2440 endowed with advantageous physiological properties, was recently engineered for growth on cellobiose, a major cellooligosaccharide product of enzymatic cellulose hydrolysis. Co-utilization of cellobiose and glucose was achieved in a mutant lacking periplasmic glucose dehydrogenase Gcd (PP_1444). However, the cause of the co-utilization phenotype remained to be understood and the Δgcd strain had a significant growth defect. In this study, we investigated the basis of the simultaneous uptake of the two sugars and accelerated the growth of P. putida EM42 Δgcd mutant for the bioproduction of valuable compounds from glucose and cellobiose. We show that the gcd deletion lifted the inhibition of the exogenous β-glucosidase BglC from Thermobifida fusca exerted by the intermediates of the periplasmic glucose oxidation pathway. The additional deletion of hexR gene, which encodes a repressor of the upper glycolysis genes, failed to restore rapid growth on glucose. The reduced growth rate of the Δgcd mutant was partially compensated by the implantation of heterologous glucose and cellobiose transporters (Glf from Zymomonas mobilis and LacY from Escherichia coli, respectively). Remarkably, this intervention resulted in the accumulation of pyruvate in aerobic P. putida cultures. We demonstrated that the excess of this key metabolic intermediate can be redirected to the enhanced biosynthesis of ethanol and lactate. The pyruvate overproduction phenotype was then unveiled by an upgraded genome-scale metabolic model constrained with proteomic and kinetic data. The model pointed to the saturation of glucose catabolism enzymes due to unregulated substrate uptake and it predicted improved bioproduction of pyruvate-derived chemicals by the engineered strain. This work sheds light on the co-metabolism of cellulosic sugars in an attractive biotechnological host and introduces a novel strategy for pyruvate overproduction in bacterial cultures under aerobic conditions.
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Affiliation(s)
- Dalimil Bujdoš
- Department of Experimental Biology (Section of Microbiology, Microbial Bioengineering Laboratory), Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic
| | - Barbora Popelářová
- Department of Experimental Biology (Section of Microbiology, Microbial Bioengineering Laboratory), Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic
| | - Daniel C Volke
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kgs, Lyngby, Denmark
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kgs, Lyngby, Denmark
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kgs, Lyngby, Denmark
| | - Pavel Dvořák
- Department of Experimental Biology (Section of Microbiology, Microbial Bioengineering Laboratory), Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic.
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7
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Nowak N, Sonnenschein N, Hansen T, Ritter D, Blümlein K, Escher S, Schwarz K. P17-10 Design and application of a physiologically based kinetic (PBK) model for uptake of airborne particulates. Toxicol Lett 2022. [DOI: 10.1016/j.toxlet.2022.07.620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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8
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Domenzain I, Sánchez B, Anton M, Kerkhoven EJ, Millán-Oropeza A, Henry C, Siewers V, Morrissey JP, Sonnenschein N, Nielsen J. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat Commun 2022; 13:3766. [PMID: 35773252 PMCID: PMC9246944 DOI: 10.1038/s41467-022-31421-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/16/2022] [Indexed: 01/08/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
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Affiliation(s)
- Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Benjamín Sánchez
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Mihail Anton
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-412 58, Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Aarón Millán-Oropeza
- Plateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Céline Henry
- Plateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - John P Morrissey
- School of Microbiology, Environmental Research Institute and APC Microbiome Ireland, University College Cork, T12 K8AF, Cork, Ireland
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
- BioInnovation Institute, Ole Maaløes Vej 3, 2200, Copenhagen, Denmark.
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9
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He B, Cai C, McCubbin T, Muriel JC, Sonnenschein N, Hu S, Yuan Z, Marcellin E. A Genome-Scale Metabolic Model of Methanoperedens nitroreducens: Assessing Bioenergetics and Thermodynamic Feasibility. Metabolites 2022; 12:metabo12040314. [PMID: 35448501 PMCID: PMC9024614 DOI: 10.3390/metabo12040314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/18/2022] [Accepted: 03/26/2022] [Indexed: 11/16/2022] Open
Abstract
Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle. Candidatus ‘Methanoperedens nitroreducens’ (M. nitroreducens) is a recently discovered methanotrophic archaeon capable of performing AOM via a reverse methanogenesis pathway utilizing nitrate as the terminal electron acceptor. Recently, reverse methanogenic pathways and energy metabolism among anaerobic methane-oxidizing archaea (ANME) have gained significant interest. However, the energetics and the mechanism for electron transport in nitrate-dependent AOM performed by M. nitroreducens is unclear. This paper presents a genome-scale metabolic model of M. nitroreducens, iMN22HE, which contains 813 reactions and 684 metabolites. The model describes its cellular metabolism and can quantitatively predict its growth phenotypes. The essentiality of the cytoplasmic heterodisulfide reductase HdrABC in the reverse methanogenesis pathway is examined by modeling the electron transfer direction and the specific energy-coupling mechanism. Furthermore, based on better understanding electron transport by modeling, a new energy transfer mechanism is suggested. The new mechanism involves reactions capable of driving the endergonic reactions in nitrate-dependent AOM, including the step reactions in reverse canonical methanogenesis and the novel electron-confurcating reaction HdrABC. The genome metabolic model not only provides an in silico tool for understanding the fundamental metabolism of ANME but also helps to better understand the reverse methanogenesis energetics and its thermodynamic feasibility.
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Affiliation(s)
- Bingqing He
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia; (B.H.); (T.M.)
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
| | - Chen Cai
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
- CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China
| | - Tim McCubbin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia; (B.H.); (T.M.)
| | - Jorge Carrasco Muriel
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kongens Lyngby, Denmark; (J.C.M.); (N.S.)
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kongens Lyngby, Denmark; (J.C.M.); (N.S.)
| | - Shihu Hu
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
| | - Zhiguo Yuan
- Australian Centre for Water and Environmental Biotechnology (ACWEB, Formerly AWMC), The University of Queensland, Brisbane, QLD 4072, Australia; (C.C.); (S.H.); (Z.Y.)
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia; (B.H.); (T.M.)
- Correspondence:
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10
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Elsemman IE, Rodriguez Prado A, Grigaitis P, Garcia Albornoz M, Harman V, Holman SW, van Heerden J, Bruggeman FJ, Bisschops MMM, Sonnenschein N, Hubbard S, Beynon R, Daran-Lapujade P, Nielsen J, Teusink B. Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies. Nat Commun 2022; 13:801. [PMID: 35145105 PMCID: PMC8831649 DOI: 10.1038/s41467-022-28467-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 01/27/2022] [Indexed: 01/20/2023] Open
Abstract
When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells. Metabolically active organelles compete for cytosolic space and resources during metabolism rewiring. Here, the authors develop a computational model of yeast metabolism and resource allocation to predict condition- and compartment-specific proteome constraints that govern metabolic strategies.
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Affiliation(s)
- Ibrahim E Elsemman
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark.,Department of Information Systems, Faculty of Computers and Information, Assiut University, Assiut, Egypt
| | - Angelica Rodriguez Prado
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Industrial Microbiology, Technical University Delft, Delft, The Netherlands
| | - Pranas Grigaitis
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Victoria Harman
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Stephen W Holman
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Johan van Heerden
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank J Bruggeman
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark M M Bisschops
- Department of Industrial Microbiology, Technical University Delft, Delft, The Netherlands
| | - Nikolaus Sonnenschein
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark
| | - Simon Hubbard
- Division of Evolution & Genomic Sciences, University of Manchester, Manchester, UK
| | - Rob Beynon
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Pascale Daran-Lapujade
- Department of Industrial Microbiology, Technical University Delft, Delft, The Netherlands
| | - Jens Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark. .,Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden.
| | - Bas Teusink
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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11
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Mol V, Bennett M, Sánchez BJ, Lisowska BK, Herrgård MJ, Nielsen AT, Leak DJ, Sonnenschein N. Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism. Metab Eng 2021; 65:123-134. [PMID: 33753231 DOI: 10.1016/j.ymben.2021.03.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 03/01/2021] [Indexed: 12/27/2022]
Abstract
Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.
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Affiliation(s)
- Viviënne Mol
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Martyn Bennett
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Benjamín J Sánchez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Beata K Lisowska
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; BioInnovation Institute, Copenhagen N, Denmark
| | - Alex Toftgaard Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - David J Leak
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom.
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
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12
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Wright NR, Rønnest NP, Sonnenschein N. Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats. Front Bioeng Biotechnol 2020; 8:579841. [PMID: 33392163 PMCID: PMC7775484 DOI: 10.3389/fbioe.2020.579841] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 11/30/2020] [Indexed: 11/13/2022] Open
Abstract
There is a growing interest in continuous manufacturing within the bioprocessing community. In this context, the chemostat process is an important unit operation. The current application of chemostat processes in industry is limited although many high yielding processes are reported in literature. In order to reach the full potential of the chemostat in continuous manufacture, the output should be constant. However, adaptation is often observed resulting in changed productivities over time. The observed adaptation can be coupled to the selective pressure of the nutrient-limited environment in the chemostat. We argue that population heterogeneity should be taken into account when studying adaptation in the chemostat. We propose to investigate adaptation at the single-cell level and discuss the potential of different single-cell technologies, which could be used to increase the understanding of the phenomena. Currently, none of the discussed single-cell technologies fulfill all our criteria but in combination they may reveal important information, which can be used to understand and potentially control the adaptation.
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Affiliation(s)
- Naia Risager Wright
- Novo Nordisk A/S, Bagsvaerd, Denmark
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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13
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Lu H, Li F, Sánchez BJ, Zhu Z, Li G, Domenzain I, Marcišauskas S, Anton PM, Lappa D, Lieven C, Beber ME, Sonnenschein N, Kerkhoven EJ, Nielsen J. Author Correction: A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun 2020; 11:5443. [PMID: 33093448 PMCID: PMC7581740 DOI: 10.1038/s41467-020-19358-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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14
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Li S, Jendresen CB, Landberg J, Pedersen LE, Sonnenschein N, Jensen SI, Nielsen AT. Genome-Wide CRISPRi-Based Identification of Targets for Decoupling Growth from Production. ACS Synth Biol 2020; 9:1030-1040. [PMID: 32268068 DOI: 10.1021/acssynbio.9b00143] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Growth decoupling can be used to optimize microbial production of biobased compounds by inhibiting excess biomass formation and redirect carbon flux from growth to product formation. However, identifying suitable genetic targets through rational design is challenging. Here, we conduct a genome-wide CRISPRi screen to discover growth switches suitable for decoupling growth and production. Using an sgRNA library covering 12 238 loci in the Escherichia coli genome, we screen for targets that inhibit growth while allowing for continued protein production. In total, we identify 1332 sgRNAs that simultaneously decrease growth and maintain or increase accumulation of GFP. The top target sibB/ibsB shows more than 5-fold increase in GFP accumulation and 45% decrease in biomass formation. Overall, our genome-wide CRISPRi screen provides key targets for growth decoupling, and the approach can be applied to improve biobased production in other microorganisms.
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Affiliation(s)
- Songyuan Li
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kongens Lyngby, Denmark
| | - Christian Bille Jendresen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kongens Lyngby, Denmark
- CysBio ApS, Agern Allé 1, 2970 Hørsholm, Denmark
| | - Jenny Landberg
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kongens Lyngby, Denmark
| | - Lasse Ebdrup Pedersen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kongens Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kongens Lyngby, Denmark
| | - Sheila Ingemann Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kongens Lyngby, Denmark
| | - Alex Toftgaard Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, 2800 Kongens Lyngby, Denmark
- CysBio ApS, Agern Allé 1, 2970 Hørsholm, Denmark
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15
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Wright NR, Wulff T, Palmqvist EA, Jørgensen TR, Workman CT, Sonnenschein N, Rønnest NP, Herrgård MJ. Fluctuations in glucose availability prevent global proteome changes and physiological transition during prolonged chemostat cultivations of
Saccharomyces cerevisiae. Biotechnol Bioeng 2020; 117:2074-2088. [DOI: 10.1002/bit.27353] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 04/09/2020] [Indexed: 11/06/2022]
Affiliation(s)
- Naia R. Wright
- Novo Nordisk A/S Bagsværd Denmark
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of Denmark Lyngby Denmark
- Department of Biotechnology and BiomedicineTechnical University of Denmark Lyngby Denmark
| | - Tune Wulff
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of Denmark Lyngby Denmark
| | | | - Thomas R. Jørgensen
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of Denmark Lyngby Denmark
| | - Christopher T. Workman
- Department of Biotechnology and BiomedicineTechnical University of Denmark Lyngby Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of Denmark Lyngby Denmark
- Department of Biotechnology and BiomedicineTechnical University of Denmark Lyngby Denmark
| | | | - Markus J. Herrgård
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of Denmark Lyngby Denmark
- BioInnovation Institute København N Denmark
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16
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Cardoso JGR, Zeidan AA, Jensen K, Sonnenschein N, Neves AR, Herrgård MJ. MARSI: metabolite analogues for rational strain improvement. Bioinformatics 2019; 34:2319-2321. [PMID: 29949953 PMCID: PMC6022549 DOI: 10.1093/bioinformatics/bty108] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 02/22/2018] [Indexed: 01/06/2023] Open
Abstract
Summary Metabolite analogues (MAs) mimic the structure of native metabolites, can competitively inhibit their utilization in enzymatic reactions, and are commonly used as selection tools for isolating desirable mutants of industrial microorganisms. Genome-scale metabolic models representing all biochemical reactions in an organism can be used to predict effects of MAs on cellular phenotypes. Here, we present the metabolite analogues for rational strain improvement (MARSI) framework. MARSI provides a rational approach to strain improvement by searching for metabolites as targets instead of genes or reactions. The designs found by MARSI can be implemented by supplying MAs in the culture media, enabling metabolic rewiring without the use of recombinant DNA technologies that cannot always be used due to regulations. To facilitate experimental implementation, MARSI provides tools to identify candidate MAs to a target metabolite from a database of known drugs and analogues. Availability and implementation The code is freely available at https://github.com/biosustain/marsi under the Apache License V2. MARSI is implemented in Python. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- João G R Cardoso
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Kristian Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
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17
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Lu H, Li F, Sánchez BJ, Zhu Z, Li G, Domenzain I, Marcišauskas S, Anton PM, Lappa D, Lieven C, Beber ME, Sonnenschein N, Kerkhoven EJ, Nielsen J. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun 2019; 10:3586. [PMID: 31395883 PMCID: PMC6687777 DOI: 10.1038/s41467-019-11581-3] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 07/17/2019] [Indexed: 01/06/2023] Open
Abstract
Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae--an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.
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Affiliation(s)
- Hongzhong Lu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Benjamín J Sánchez
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Zhengming Zhu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
- School of Biotechnology, Jiangnan University, 1800 Lihu Road, 214122, Wuxi, Jiangsu, China
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Simonas Marcišauskas
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Petre Mihail Anton
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Dimitra Lappa
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Christian Lieven
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Moritz Emanuel Beber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark.
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
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18
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Jensen K, Broeken V, Hansen ASL, Sonnenschein N, Herrgård MJ. OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs. Metab Eng Commun 2019; 8:e00087. [PMID: 30956947 PMCID: PMC6431744 DOI: 10.1016/j.mec.2019.e00087] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 02/20/2019] [Accepted: 02/26/2019] [Indexed: 01/01/2023] Open
Abstract
Biological production of chemicals is an attractive alternative to petrochemical-based production, due to advantages in environmental impact and the spectrum of feasible targets. However, engineering microbial strains to overproduce a compound of interest can be a long, costly and painstaking process. If production can be coupled to cell growth it is possible to use adaptive laboratory evolution to increase the production rate. Strategies for coupling production to growth, however, are often not trivial to find. Here we present OptCouple, a constraint-based modeling algorithm to simultaneously identify combinations of gene knockouts, insertions and medium supplements that lead to growth-coupled production of a target compound. We validated the algorithm by showing that it can find novel strategies that are growth-coupled in silico for a compound that has not been coupled to growth previously, as well as reproduce known growth-coupled strain designs for two different target compounds. Furthermore, we used OptCouple to construct an alternative design with potential for higher production. We provide an efficient and easy-to-use implementation of the OptCouple algorithm in the cameo Python package for computational strain design.
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Affiliation(s)
- Kristian Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Valentijn Broeken
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Anne Sofie Lærke Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Markus J. Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
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19
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D'Arrigo I, Cardoso JGR, Rennig M, Sonnenschein N, Herrgård MJ, Long KS. Analysis of Pseudomonas putida growth on non-trivial carbon sources using transcriptomics and genome-scale modelling. Environ Microbiol Rep 2019; 11:87-97. [PMID: 30298597 DOI: 10.1111/1758-2229.12704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 10/02/2018] [Accepted: 10/03/2018] [Indexed: 06/08/2023]
Abstract
Pseudomonas putida is characterized by a versatile metabolism and stress tolerance traits that allow the bacterium to cope with different environmental conditions. In this work, the mechanisms that allow P. putida KT2440 to grow in the presence of four sole carbon sources (glucose, citrate, ferulic acid, serine) were investigated by RNA sequencing (RNA-seq) and genome-scale metabolic modelling. Transcriptomic data identified uptake systems for the four carbon sources, and candidates were subjected to preliminary experimental characterization by mutant strain growth to test their involvement in substrate assimilation. The OpdH and BenF-like porins were involved in citrate and ferulic acid uptake respectively. The citrate transporter (encoded by PP_0147) and the TctABC system were important for supporting cell growth in citrate; PcaT and VanK were associated with ferulic acid uptake; and the ABC transporter AapJPQM was involved in serine transport. A genome-scale metabolic model of P. putida KT2440 was used to integrate and analyze the transcriptomic data, identifying and confirming the active catabolic pathways for each carbon source. This study reveals novel information about transporters that are essential for understanding bacterial adaptation to different environments.
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Affiliation(s)
- Isotta D'Arrigo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, DK-2800, Kongens Lyngby, Denmark
| | - João G R Cardoso
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, DK-2800, Kongens Lyngby, Denmark
| | - Maja Rennig
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, DK-2800, Kongens Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, DK-2800, Kongens Lyngby, Denmark
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, DK-2800, Kongens Lyngby, Denmark
| | - Katherine S Long
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, DK-2800, Kongens Lyngby, Denmark
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20
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Jensen PR, Matos MRA, Sonnenschein N, Meier S. Combined In-Cell NMR and Simulation Approach to Probe Redox-Dependent Pathway Control. Anal Chem 2019; 91:5395-5402. [PMID: 30896922 DOI: 10.1021/acs.analchem.9b00660] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Dynamic response of intracellular reaction cascades to changing environments is a hallmark of living systems. As metabolism is complex, mechanistic models have gained popularity for describing the dynamic response of cellular metabolism and for identifying target genes for engineering. At the same time, the detailed tracking of transient metabolism in living cells on the subminute time scale has become amenable using dynamic nuclear polarization-enhanced 13C NMR. Here, we suggest an approach combining in-cell NMR spectroscopy with perturbation experiments and modeling to obtain evidence that the bottlenecks of yeast glycolysis depend on intracellular redox state. In pre-steady-state glycolysis, pathway bottlenecks shift from downstream to upstream reactions within a few seconds, consistent with a rapid decline in the NAD+/NADH ratio. Simulations using mechanistic models reproduce the experimentally observed response and help identify unforeseen biochemical events. Remaining inaccuracies in the computational models can be identified experimentally. The combined use of rapid injection NMR spectroscopy and in silico simulations provides a promising method for characterizing cellular reactions with increasing mechanistic detail.
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Affiliation(s)
- Pernille R Jensen
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
| | - Marta R A Matos
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
| | - Nikolaus Sonnenschein
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
| | - Sebastian Meier
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
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21
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Abstract
Synthetic biology holds great promise to deliver transformative technologies to the world in the coming years. However, several challenges still remain to be addressed before it can deliver on its promises. One of the most important issues to address is the lack of reproducibility within research of the life sciences. This problem is beginning to be recognised by the community and solutions are being developed to tackle the problem. The recent emergence of automated facilities that are open for use by researchers (such as biofoundries and cloud labs) may be one of the ways that synthetic biologists can improve the quality and reproducibility of their work. In this perspective article, we outline these and some of the other technologies that are currently being developed which we believe may help to transform how synthetic biologists approach their research activities.
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Affiliation(s)
- Mathew M Jessop-Fabre
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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22
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Massaiu I, Pasotti L, Sonnenschein N, Rama E, Cavaletti M, Magni P, Calvio C, Herrgård MJ. Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains. Microb Cell Fact 2019; 18:3. [PMID: 30626384 PMCID: PMC6325765 DOI: 10.1186/s12934-018-1052-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 12/29/2018] [Indexed: 12/15/2022] Open
Abstract
Background Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design. Results Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain. Conclusions This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis.
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Affiliation(s)
- Ilaria Massaiu
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Dep. Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100, Pavia, Italy.,Centre for Health Technologies, University of Pavia, Via Ferrata 5, 27100, Pavia, Italy
| | - Lorenzo Pasotti
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Dep. Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100, Pavia, Italy.,Centre for Health Technologies, University of Pavia, Via Ferrata 5, 27100, Pavia, Italy
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Erlinda Rama
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via Ferrata 9, 27100, Pavia, Italy
| | - Matteo Cavaletti
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via Ferrata 9, 27100, Pavia, Italy
| | - Paolo Magni
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Dep. Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100, Pavia, Italy.,Centre for Health Technologies, University of Pavia, Via Ferrata 5, 27100, Pavia, Italy
| | - Cinzia Calvio
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via Ferrata 9, 27100, Pavia, Italy
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
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23
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Lieven C, Petersen LAH, Jørgensen SB, Gernaey KV, Herrgard MJ, Sonnenschein N. A Genome-Scale Metabolic Model for Methylococcus capsulatus (Bath) Suggests Reduced Efficiency Electron Transfer to the Particulate Methane Monooxygenase. Front Microbiol 2018; 9:2947. [PMID: 30564208 PMCID: PMC6288188 DOI: 10.3389/fmicb.2018.02947] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 11/16/2018] [Indexed: 12/21/2022] Open
Abstract
Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers. Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature. Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus.
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Affiliation(s)
- Christian Lieven
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Leander A H Petersen
- Unibio A/S, Kongens Lyngby, Denmark.,Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sten Bay Jørgensen
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Markus J Herrgard
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
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24
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Lieven C, Herrgård MJ, Sonnenschein N. Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology. Biotechnol J 2018; 13:e1800011. [PMID: 29917330 DOI: 10.1002/biot.201800011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 05/31/2018] [Indexed: 11/08/2022]
Abstract
Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.
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Affiliation(s)
- Christian Lieven
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Markus J Herrgård
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Nikolaus Sonnenschein
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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25
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Cardoso JGR, Jensen K, Lieven C, Lærke Hansen AS, Galkina S, Beber M, Özdemir E, Herrgård MJ, Redestig H, Sonnenschein N. Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories. ACS Synth Biol 2018; 7:1163-1166. [PMID: 29558112 DOI: 10.1021/acssynbio.7b00423] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .
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Affiliation(s)
- João G. R. Cardoso
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Kristian Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Christian Lieven
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Anne Sofie Lærke Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Svetlana Galkina
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Moritz Beber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Emre Özdemir
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Markus J. Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Henning Redestig
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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26
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Hansen ASL, Lennen RM, Sonnenschein N, Herrgård MJ. Systems biology solutions for biochemical production challenges. Curr Opin Biotechnol 2017; 45:85-91. [PMID: 28319856 DOI: 10.1016/j.copbio.2016.11.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Revised: 11/20/2016] [Accepted: 11/23/2016] [Indexed: 11/28/2022]
Abstract
There is an urgent need to significantly accelerate the development of microbial cell factories to produce fuels and chemicals from renewable feedstocks in order to facilitate the transition to a biobased society. Methods commonly used within the field of systems biology including omics characterization, genome-scale metabolic modeling, and adaptive laboratory evolution can be readily deployed in metabolic engineering projects. However, high performance strains usually carry tens of genetic modifications and need to operate in challenging environmental conditions. This additional complexity compared to basic science research requires pushing systems biology strategies to their limits and often spurs innovative developments that benefit fields outside metabolic engineering. Here we survey recent advanced applications of systems biology methods in engineering microbial production strains for biofuels and -chemicals.
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Affiliation(s)
- Anne Sofie Lærke Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark
| | - Rebecca M Lennen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark.
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27
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Abstract
Genetically encoded biosensors have emerged as powerful tools for timely and precise in vivo evaluation of cellular metabolism. In particular, biosensors that can couple intercellular cues with downstream signaling responses are currently attracting major attention within health science and biotechnology. Still, there is a need for bioprospecting and engineering of more biosensors to enable real-time monitoring of specific cellular states and controlling downstream actuation. In this study, we report the engineering and application of a transcription factor-based NADPH/NADP+ redox biosensor in the budding yeast Saccharomyces cerevisiae. Using the biosensor, we are able to monitor the cause of oxidative stress by chemical induction, and changes in NADPH/NADP+ ratios caused by genetic manipulations. Because of the regulatory potential of the biosensor, we also show that the biosensor can actuate upon NADPH deficiency by activation of NADPH regeneration. Finally, we couple the biosensor with an expression of dosage-sensitive genes (DSGs) and thereby create a novel tunable sensor-selector useful for synthetic selection of cells with higher NADPH/NADP+ ratios from mixed cell populations. We show that the combination of exploitation and rational engineering of native signaling components is applicable for diagnosis, regulation, and selection of cellular redox states.
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Affiliation(s)
- Jie Zhang
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Nikolaus Sonnenschein
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Thomas P. B. Pihl
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Kasper R. Pedersen
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Michael K. Jensen
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Jay D. Keasling
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
- Joint BioEnergy Institute, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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28
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Machado D, Zhuang KH, Sonnenschein N, Herrgård MJ. Editorial: Current Challenges in Modeling Cellular Metabolism. Front Bioeng Biotechnol 2015; 3:193. [PMID: 26636080 PMCID: PMC4659907 DOI: 10.3389/fbioe.2015.00193] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 11/09/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Daniel Machado
- Centre of Biological Engineering, University of Minho , Braga , Portugal
| | - Kai H Zhuang
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
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29
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Ebrahim A, Almaas E, Bauer E, Bordbar A, Burgard AP, Chang RL, Dräger A, Famili I, Feist AM, Fleming RM, Fong SS, Hatzimanikatis V, Herrgård MJ, Holder A, Hucka M, Hyduke D, Jamshidi N, Lee SY, Le Novère N, Lerman JA, Lewis NE, Ma D, Mahadevan R, Maranas C, Nagarajan H, Navid A, Nielsen J, Nielsen LK, Nogales J, Noronha A, Pal C, Palsson BO, Papin JA, Patil KR, Price ND, Reed JL, Saunders M, Senger RS, Sonnenschein N, Sun Y, Thiele I. Do genome-scale models need exact solvers or clearer standards? Mol Syst Biol 2015; 11:831. [PMID: 26467284 PMCID: PMC4631202 DOI: 10.15252/msb.20156157] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Ali Ebrahim
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Eivind Almaas
- Department of Biotechnology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Eugen Bauer
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | | | | | - Roger L Chang
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Andreas Dräger
- Department of Bioengineering, University of California, San Diego, CA, USA Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | | | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Ronan Mt Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | - Stephen S Fong
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Allen Holder
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Michael Hucka
- Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, CA, USA
| | - Daniel Hyduke
- Department of Biological Engineering, Utah State University, Logan, UT, USA
| | - Neema Jamshidi
- Department of Radiology, University of California, Los Angeles, CA, USA Institute of Engineering in Medicine, University of California, San Diego, CA, USA
| | - Sang Yup Lee
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | | | - Joshua A Lerman
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, CA, USA
| | - Ding Ma
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Costas Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | | | - Ali Navid
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Jens Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Lars K Nielsen
- Australian Institute for Bioengineering & Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland, Australia
| | - Juan Nogales
- Department of Environmental Biology, Centro de Investigaciones Biológicas (CSIC), Madrid, Spain
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | - Csaba Pal
- Synthetic and Systems Biology Unit, Biological Research Center, Szeged, Hungary
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Kiran R Patil
- European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Jennifer L Reed
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Michael Saunders
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Ryan S Senger
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Yuekai Sun
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
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30
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King ZA, Dräger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO. Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways. PLoS Comput Biol 2015; 11:e1004321. [PMID: 26313928 PMCID: PMC4552468 DOI: 10.1371/journal.pcbi.1004321] [Citation(s) in RCA: 215] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Accepted: 05/05/2015] [Indexed: 01/19/2023] Open
Abstract
Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools. We are now in the age of big data. More than ever before, biological discoveries require powerful and flexible tools for managing large datasets, including both visual and statistical tools. Pathway-based visualization is particularly powerful since it enables one to analyze complex datasets within the context of actual biological processes and to elucidate how each change in a cell effects related processes. To facilitate such approaches, we present Escher, a web application that can be used to rapidly build pathway maps. On Escher maps, diverse datasets related to genes, reactions, and metabolites can be quickly contextualized within metabolism and, increasingly, beyond metabolism. Escher is available now for free use (under the MIT license) at https://escher.github.io.
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Affiliation(s)
- Zachary A. King
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Andreas Dräger
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Ali Ebrahim
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Nikolaus Sonnenschein
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Nathan E. Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- * E-mail:
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31
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Cardoso JGR, Andersen MR, Herrgård MJ, Sonnenschein N. Analysis of genetic variation and potential applications in genome-scale metabolic modeling. Front Bioeng Biotechnol 2015; 3:13. [PMID: 25763369 PMCID: PMC4329917 DOI: 10.3389/fbioe.2015.00013] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 01/22/2015] [Indexed: 11/13/2022] Open
Abstract
Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.
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Affiliation(s)
- João G. R. Cardoso
- The Novo Nordisk Foundation Center of Biosustainability, Technical University of Denmark, Hørsholm, Denmark
| | | | - Markus J. Herrgård
- The Novo Nordisk Foundation Center of Biosustainability, Technical University of Denmark, Hørsholm, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center of Biosustainability, Technical University of Denmark, Hørsholm, Denmark
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32
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Kildegaard KR, Hallström BM, Blicher TH, Sonnenschein N, Jensen NB, Sherstyk S, Harrison SJ, Maury J, Herrgård MJ, Juncker AS, Forster J, Nielsen J, Borodina I. Evolution reveals a glutathione-dependent mechanism of 3-hydroxypropionic acid tolerance. Metab Eng 2014; 26:57-66. [PMID: 25263954 DOI: 10.1016/j.ymben.2014.09.004] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2014] [Revised: 08/15/2014] [Accepted: 09/15/2014] [Indexed: 12/19/2022]
Abstract
Biologically produced 3-hydroxypropionic acid (3 HP) is a potential source for sustainable acrylates and can also find direct use as monomer in the production of biodegradable polymers. For industrial-scale production there is a need for robust cell factories tolerant to high concentration of 3 HP, preferably at low pH. Through adaptive laboratory evolution we selected S. cerevisiae strains with improved tolerance to 3 HP at pH 3.5. Genome sequencing followed by functional analysis identified the causal mutation in SFA1 gene encoding S-(hydroxymethyl)glutathione dehydrogenase. Based on our findings, we propose that 3 HP toxicity is mediated by 3-hydroxypropionic aldehyde (reuterin) and that glutathione-dependent reactions are used for reuterin detoxification. The identified molecular response to 3 HP and reuterin may well be a general mechanism for handling resistance to organic acid and aldehydes by living cells.
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Affiliation(s)
- Kanchana R Kildegaard
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Björn M Hallström
- Science for Life Laboratory, KTH Royal Institution of Technology, Box 1031, SE-171 21 Solna, Sweden
| | - Thomas H Blicher
- The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3b, DK-2200 Copenhagen , Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Niels B Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Svetlana Sherstyk
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Scott J Harrison
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Jérôme Maury
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Agnieszka S Juncker
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Jochen Forster
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark
| | - Jens Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark; Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96 Göteborg, Sweden
| | - Irina Borodina
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, DK-2970 Hørsholm, Denmark.
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33
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Beber ME, Fretter C, Jain S, Sonnenschein N, Müller-Hannemann M, Hütt MT. Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks. J R Soc Interface 2012; 9:3426-35. [PMID: 22896565 DOI: 10.1098/rsif.2012.0490] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.
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Sonnenschein N, Golib Dzib JF, Lesne A, Eilebrecht S, Boulkroun S, Zennaro MC, Benecke A, Hütt MT. A network perspective on metabolic inconsistency. BMC Syst Biol 2012; 6:41. [PMID: 22583819 PMCID: PMC3579709 DOI: 10.1186/1752-0509-6-41] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2011] [Accepted: 04/14/2012] [Indexed: 11/10/2022]
Abstract
BACKGROUND Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation. RESULTS Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns. CONCLUSIONS The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical function. Finally, we demonstrate how physiologically relevant insights into the structure and dynamics of metabolic networks can be obtained using this novel approach.
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Affiliation(s)
- Nikolaus Sonnenschein
- School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany.
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Abstract
Background The 3D structure of the chromosome of the model organism Escherichia coli is one key component of its gene regulatory machinery. This type of regulation mediated by topological transitions of the chromosomal DNA can be thought of as an analog control, complementing the digital control, i.e. the network of regulation mediated by dedicated transcription factors. It is known that alterations in the superhelical density of chromosomal DNA lead to a rich pattern of differential expressed genes. Using a network approach, we analyze these expression changes for wild type E. coli and mutants lacking nucleoid associated proteins (NAPs) from a metabolic and transcriptional regulatory network perspective. Results We find a significantly higher correspondence between gene expression and metabolism for the wild type expression changes compared to mutants in NAPs, indicating that supercoiling induces meaningful metabolic adjustments. As soon as the underlying regulatory machinery is impeded (as for the NAP mutants), this coherence between expression changes and the metabolic network is substantially reduced. This effect is even more pronounced, when we compute a wild type metabolic flux distribution using flux balance analysis and restrict our analysis to active reactions. Furthermore, we are able to show that the regulatory control exhibited by DNA supercoiling is not mediated by the transcriptional regulatory network (TRN), as the consistency of the expression changes with the TRN logic of activation and suppression is strongly reduced in the wild type in comparison to the mutants. Conclusions So far, the rich patterns of gene expression changes induced by alterations of the superhelical density of chromosomal DNA have been difficult to interpret. Here we characterize the effective networks formed by supercoiling-induced gene expression changes mapped onto reconstructions of E. coli's metabolic and transcriptional regulatory network. Our results show that DNA supercoiling coordinates gene expression with metabolism. Furthermore, this control is acting directly because we can exclude the potential role of the TRN as a mediator.
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Sonnenschein N, Hütt MT, Stoyan H, Stoyan D. Ranges of control in the transcriptional regulation of Escherichia coli. BMC Syst Biol 2009; 3:119. [PMID: 20034377 PMCID: PMC2804738 DOI: 10.1186/1752-0509-3-119] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2009] [Accepted: 12/24/2009] [Indexed: 11/10/2022]
Abstract
BACKGROUND The positioning of genes in the genome is an important evolutionary degree of freedom for organizing gene regulation. Statistical properties of these distributions have been studied particularly in relation to the transcriptional regulatory network. The systematics of gene-gene distances then become important sources of information on the control, which different biological mechanisms exert on gene expression. RESULTS Here we study a set of categories, which has to our knowledge not been analyzed before. We distinguish between genes that do not participate in the transcriptional regulatory network (i.e. that are according to current knowledge not producing transcription factors and do not possess binding sites for transcription factors in their regulatory region), and genes that via transcription factors either are regulated by or regulate other genes. We find that the two types of genes ("isolated" and "regulatory" genes) show a clear statistical repulsion and have different ranges of correlations. In particular we find that isolated genes have a preference for shorter intergenic distances. CONCLUSIONS These findings support previous evidence from gene expression patterns for two distinct logical types of control, namely digital control (i.e. network-based control mediated by dedicated transcription factors) and analog control (i.e. control based on genome structure and mediated by neighborhood on the genome).
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Affiliation(s)
- Nikolaus Sonnenschein
- School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany.
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