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Abstract
Abstract
Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.
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252
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Abstract
Streptococcus mutans is a Gram-positive bacterium that thrives under acidic conditions and is a primary cause of tooth decay (dental caries). To better understand the metabolism of S. mutans on a systematic level, we manually constructed a genome-scale metabolic model of the S. mutans type strain UA159. The model, called iSMU, contains 675 reactions involving 429 metabolites and the products of 493 genes. We validated iSMU by comparing simulations with growth experiments in defined medium. The model simulations matched experimental results for 17 of 18 carbon source utilization assays and 47 of 49 nutrient depletion assays. We also simulated the effects of single gene deletions. The model's predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental data sets. Our manually curated model is more accurate than S. mutans models generated from automated reconstruction pipelines and more complete than other manually curated models. We used iSMU to generate hypotheses about the S. mutans metabolic network. Subsequent genetic experiments confirmed that (i) S. mutans catabolizes sorbitol via a sorbitol-6-phosphate 2-dehydrogenase (SMU_308) and (ii) the Leloir pathway is required for growth on complex carbohydrates such as raffinose. We believe the iSMU model is an important resource for understanding the metabolism of S. mutans and guiding future experiments.IMPORTANCE Tooth decay is the most prevalent chronic disease in the United States. Decay is caused by the bacterium Streptococcus mutans, an oral pathogen that ferments sugars into tooth-destroying lactic acid. We constructed a complete metabolic model of S. mutans to systematically investigate how the bacterium grows. The model provides a valuable resource for understanding and targeting S. mutans' ability to outcompete other species in the oral microbiome.
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253
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Gale GAR, Schiavon Osorio AA, Mills LA, Wang B, Lea-Smith DJ, McCormick AJ. Emerging Species and Genome Editing Tools: Future Prospects in Cyanobacterial Synthetic Biology. Microorganisms 2019; 7:E409. [PMID: 31569579 PMCID: PMC6843473 DOI: 10.3390/microorganisms7100409] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 09/22/2019] [Accepted: 09/24/2019] [Indexed: 12/19/2022] Open
Abstract
Recent advances in synthetic biology and an emerging algal biotechnology market have spurred a prolific increase in the availability of molecular tools for cyanobacterial research. Nevertheless, work to date has focused primarily on only a small subset of model species, which arguably limits fundamental discovery and applied research towards wider commercialisation. Here, we review the requirements for uptake of new strains, including several recently characterised fast-growing species and promising non-model species. Furthermore, we discuss the potential applications of new techniques available for transformation, genetic engineering and regulation, including an up-to-date appraisal of current Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated protein (CRISPR/Cas) and CRISPR interference (CRISPRi) research in cyanobacteria. We also provide an overview of several exciting molecular tools that could be ported to cyanobacteria for more advanced metabolic engineering approaches (e.g., genetic circuit design). Lastly, we introduce a forthcoming mutant library for the model species Synechocystis sp. PCC 6803 that promises to provide a further powerful resource for the cyanobacterial research community.
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Affiliation(s)
- Grant A R Gale
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FF, UK.
| | - Alejandra A Schiavon Osorio
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
| | - Lauren A Mills
- School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
| | - Baojun Wang
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FF, UK.
| | - David J Lea-Smith
- School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
| | - Alistair J McCormick
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
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254
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Abstract
Cells require energy for growth and maintenance and have evolved to have multiple pathways to produce energy in response to varying conditions. A basic question in this context is how cells organize energy metabolism, which is, however, challenging to elucidate due to its complexity, i.e., the energy-producing pathways overlap with each other and even intertwine with biomass formation pathways. Here, we propose a modeling concept that decomposes energy metabolism into biomass formation and ATP-producing pathways. The latter can be further decomposed into a high-yield and a low-yield pathway. This enables independent estimation of protein efficiency for each pathway. With this concept, we modeled energy metabolism for Escherichia coli and Saccharomyces cerevisiae and found that the high-yield pathway shows lower protein efficiency than the low-yield pathway. Taken together with a fixed protein constraint, we predict overflow metabolism in E. coli and the Crabtree effect in S. cerevisiae, meaning that energy metabolism is sufficient to explain the metabolic switches. The static protein constraint is supported by the findings that protein mass of energy metabolism is conserved across conditions based on absolute proteomics data. This also suggests that enzymes may have decreased saturation or activity at low glucose uptake rates. Finally, our analyses point out three ways to improve growth, i.e., increasing protein allocation to energy metabolism, decreasing ATP demand, or increasing activity for key enzymes.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden;
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
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255
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Abstract
Lipopolysaccharides are a major component of the outer membrane in Gram-negative bacteria. They are composed of a conserved lipid structure that is embedded in the outer leaflet of the outer membrane and a polysaccharide known as the O-antigen. O-antigens are highly variable in structure across strains of a species and are crucial to a bacterium’s interactions with its environment. They constitute the first line of defense against both the immune system and bacteriophage infections and have been shown to mediate antimicrobial resistance. The significance of our research is in identifying the metabolic and genetic differences within and across O-antigen groups in Salmonella strains. Our effort constitutes a first step toward characterizing the O-antigen metabolic network across Gram-negative organisms and a comprehensive overview of genetic variations in Salmonella. O-antigens are glycopolymers in lipopolysaccharides expressed on the cell surface of Gram-negative bacteria. Variability in the O-antigen structure constitutes the basis for the establishment of the serotyping schema. We pursued a two-pronged approach to define the basis for O-antigen structural diversity. First, we developed a bottom-up systems biology approach to O-antigen metabolism by building a reconstruction of Salmonella O-antigen biosynthesis and used it to (i) update 410 existing Salmonella strain-specific metabolic models, (ii) predict a strain’s serogroup and its O-antigen glycan synthesis capability (yielding 98% agreement with experimental data), and (iii) extend our workflow to more than 1,400 Gram-negative strains. Second, we used a top-down pangenome analysis to elucidate the genetic basis for intraserogroup O-antigen structural variations. We assembled a database of O-antigen gene islands from over 11,000 sequenced Salmonella strains, revealing (i) that gene duplication, pseudogene formation, gene deletion, and bacteriophage insertion elements occur ubiquitously across serogroups; (ii) novel serotypes in the group O:4 B2 variant, as well as an additional genotype variant for group O:4, and (iii) two novel O-antigen gene islands in understudied subspecies. We thus comprehensively defined the genetic basis for O-antigen diversity.
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256
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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: 154] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [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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257
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Mendoza SN, Olivier BG, Molenaar D, Teusink B. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol 2019; 20:158. [PMID: 31391098 PMCID: PMC6685185 DOI: 10.1186/s13059-019-1769-1] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 07/22/2019] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research. RESULTS In this work, we perform a systematic assessment of current genome-scale reconstruction software platforms. To meet our goal, we first define a list of features for assessing software quality related to genome-scale reconstruction. Subsequently, we use the feature list to evaluate the performance of each tool. To assess the similarity of the draft reconstructions to high-quality models, we compare each tool's output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We additionally compare draft reconstructions with a model of Pseudomonas putida to further confirm our findings. We show that none of the tools outperforms the others in all the defined features. CONCLUSIONS Model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. They can use this benchmark study as a guide to select the best tool for their research. Finally, developers can also benefit from this evaluation by getting feedback to improve their software.
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Affiliation(s)
- Sebastián N. Mendoza
- Systems Bioinformatics, AIMMS, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Brett G. Olivier
- Systems Bioinformatics, AIMMS, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- BioQUANT/COS, Heidelberg University, Heidelberg, Germany
| | - Douwe Molenaar
- Systems Bioinformatics, AIMMS, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bas Teusink
- Systems Bioinformatics, AIMMS, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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258
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Systems Metabolic Engineering Strategies: Integrating Systems and Synthetic Biology with Metabolic Engineering. Trends Biotechnol 2019; 37:817-837. [DOI: 10.1016/j.tibtech.2019.01.003] [Citation(s) in RCA: 226] [Impact Index Per Article: 45.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 01/07/2019] [Accepted: 01/10/2019] [Indexed: 12/12/2022]
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259
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Mih N, Brunk E, Chen K, Catoiu E, Sastry A, Kavvas E, Monk JM, Zhang Z, Palsson BO. ssbio: a Python framework for structural systems biology. Bioinformatics 2019; 34:2155-2157. [PMID: 29444205 DOI: 10.1093/bioinformatics/bty077] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 02/09/2018] [Indexed: 11/13/2022] Open
Abstract
Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nathan Mih
- Department of Bioengineering, Bioinformatics and Systems Biology Graduate Program.,Department of Bioengineering, University of California, San Diego, CA, USA
| | - Elizabeth Brunk
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Ke Chen
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Edward Catoiu
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Anand Sastry
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Erol Kavvas
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Zhen Zhang
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, CA, USA
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260
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Cellular responses to reactive oxygen species are predicted from molecular mechanisms. Proc Natl Acad Sci U S A 2019; 116:14368-14373. [PMID: 31270234 DOI: 10.1073/pnas.1905039116] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.
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261
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Expanded synthetic small regulatory RNA expression platforms for rapid and multiplex gene expression knockdown. Metab Eng 2019; 54:180-190. [DOI: 10.1016/j.ymben.2019.04.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/11/2019] [Accepted: 04/13/2019] [Indexed: 12/28/2022]
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262
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Amin SA, Chavez E, Porokhin V, Nair NU, Hassoun S. Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data. Microb Cell Fact 2019; 18:109. [PMID: 31196115 PMCID: PMC6567437 DOI: 10.1186/s12934-019-1156-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 06/05/2019] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Metabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous-i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity. RESULTS Our workflow utilizes PROXIMAL-a tool that uses reactant-product transformation patterns from the KEGG database-to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in Escherichia coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB). CONCLUSIONS We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.
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Affiliation(s)
- Sara A. Amin
- Department of Computer Science, Tufts University, Medford, MA USA
| | - Elizabeth Chavez
- Department of Biology, University of North Carolina, Chapel Hill, NC USA
| | | | - Nikhil U. Nair
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA USA
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA USA
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA USA
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263
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Abstract
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
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Affiliation(s)
- Changdai Gu
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Won Jun Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Systems Biology and Medicine Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
| | - Sang Yup Lee
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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264
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Cho JS, Gu C, Han TH, Ryu JY, Lee SY. Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.02.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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265
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Enhanced production of poly‑3‑hydroxybutyrate (PHB) by expression of response regulator DR1558 in recombinant Escherichia coli. Int J Biol Macromol 2019; 131:29-35. [DOI: 10.1016/j.ijbiomac.2019.03.044] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 03/04/2019] [Accepted: 03/06/2019] [Indexed: 01/14/2023]
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266
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Yang JH, Wright SN, Hamblin M, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action. Cell 2019; 177:1649-1661.e9. [PMID: 31080069 PMCID: PMC6545570 DOI: 10.1016/j.cell.2019.04.016] [Citation(s) in RCA: 188] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 03/19/2019] [Accepted: 04/08/2019] [Indexed: 12/13/2022]
Abstract
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.
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Affiliation(s)
- Jason H Yang
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sarah N Wright
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Meagan Hamblin
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Miguel A Alcantar
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lars Schrübbers
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Allison J Lopatkin
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Sangeeta Satish
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Amir Nili
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Graham C Walker
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James J Collins
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
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267
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Mori M, Marinari E, De Martino A. A yield-cost tradeoff governs Escherichia coli's decision between fermentation and respiration in carbon-limited growth. NPJ Syst Biol Appl 2019; 5:16. [PMID: 31069113 PMCID: PMC6494807 DOI: 10.1038/s41540-019-0093-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Accepted: 04/12/2019] [Indexed: 12/21/2022] Open
Abstract
Living cells react to changes in growth conditions by re-shaping their proteome. This accounts for different stress-response strategies, both specific (i.e., aimed at increasing the availability of stress-mitigating proteins) and systemic (such as large-scale changes in the use of metabolic pathways aimed at a more efficient exploitation of resources). Proteome re-allocation can, however, imply significant biosynthetic costs. Whether and how such costs impact the growth performance are largely open problems. Focusing on carbon-limited E. coli growth, we integrate genome-scale modeling and proteomic data to address these questions at quantitative level. After deriving a simple formula linking growth rate, carbon intake, and biosynthetic costs, we show that optimal growth results from the tradeoff between yield maximization and protein burden minimization. Empirical data confirm that E. coli growth is indeed close to Pareto-optimal over a broad range of growth rates. Moreover, we establish that, while most of the intaken carbon is diverted into biomass precursors, the efficiency of ATP synthesis is the key driver of the yield-cost tradeoff. These findings provide a quantitative perspective on carbon overflow, the origin of growth laws and the multidimensional optimality of E. coli metabolism.
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Affiliation(s)
- Matteo Mori
- Department of Physics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Enzo Marinari
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 2, Rome, 00185 Italy
- INFN, Sezione di Roma 1, Piazzale Aldo Moro 2, Rome, 00185 Italy
| | - Andrea De Martino
- Soft & Living Matter Lab, Institute of Nanotechnology (CNR-NANOTEC), c/o Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 2, Rome, 00185 Italy
- Italian Institute for Genomic Medicine, via Nizza 52, Turin, 10126 Italy
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268
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Grimbs A, Klosik DF, Bornholdt S, Hütt MT. A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli. PLoS Comput Biol 2019; 15:e1006962. [PMID: 31050661 PMCID: PMC6519848 DOI: 10.1371/journal.pcbi.1006962] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 05/15/2019] [Accepted: 03/18/2019] [Indexed: 11/19/2022] Open
Abstract
Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. Since the pioneering work of Covert and co-workers as well as Shlomi and co-workers it is debated, how regulation and metabolism synergistically characterize a coherent cellular state. The first approaches started from metabolic models, which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes, from metabolic processes via protein modification to a variety of regulatory events. Besides transcriptional regulation, we also take into account regulation of translation, enzyme activities and reactions. Our network model provides novel topological characterizations of system components based on their positions in the network. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks) instead of two. This new three-domain representation reveals the structural centrality of components with known high functional relevance. This integrated network can serve as a platform for understanding coherent cellular states as active subnetworks and to elucidate crossover effects between metabolism and gene regulation.
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Affiliation(s)
- Anne Grimbs
- Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany
| | - David F. Klosik
- Institute for Theoretical Physics, University of Bremen, Bremen, Germany
| | - Stefan Bornholdt
- Institute for Theoretical Physics, University of Bremen, Bremen, Germany
| | - Marc-Thorsten Hütt
- Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany
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269
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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput Biol 2019; 15:e1006971. [PMID: 31009451 PMCID: PMC6497307 DOI: 10.1371/journal.pcbi.1006971] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 05/02/2019] [Accepted: 03/21/2019] [Indexed: 12/12/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
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270
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Blazier AS, Papin JA. Reconciling high-throughput gene essentiality data with metabolic network reconstructions. PLoS Comput Biol 2019; 15:e1006507. [PMID: 30973869 PMCID: PMC6478342 DOI: 10.1371/journal.pcbi.1006507] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 04/23/2019] [Accepted: 03/06/2019] [Indexed: 11/30/2022] Open
Abstract
The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.
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Affiliation(s)
- Anna S. Blazier
- Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Medicine, Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
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271
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Luo H, Hansen ASL, Yang L, Schneider K, Kristensen M, Christensen U, Christensen HB, Du B, Özdemir E, Feist AM, Keasling JD, Jensen MK, Herrgård MJ, Palsson BO. Coupling S-adenosylmethionine-dependent methylation to growth: Design and uses. PLoS Biol 2019; 17:e2007050. [PMID: 30856169 PMCID: PMC6411097 DOI: 10.1371/journal.pbio.2007050] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 02/15/2019] [Indexed: 11/29/2022] Open
Abstract
We present a selection design that couples S-adenosylmethionine–dependent methylation to growth. We demonstrate its use in improving the enzyme activities of not only N-type and O-type methyltransferases by 2-fold but also an acetyltransferase of another enzyme category when linked to a methylation pathway in Escherichia coli using adaptive laboratory evolution. We also demonstrate its application for drug discovery using a catechol O-methyltransferase and its inhibitors entacapone and tolcapone. Implementation of this design in Saccharomyces cerevisiae is also demonstrated. Many important biological processes require methylation, e.g., DNA methylation and synthesis of flavoring compounds, neurotransmitters, and antibiotics. Most methylation reactions in cells are catalyzed by S-adenosylmethionine (SAM)–dependent methyltransferases (Mtases) using SAM as a methyl donor. Thus, SAM-dependent Mtases have become an important enzyme category of biotechnological interests and as healthcare targets. However, functional implementation and engineering of SAM-dependent Mtases remains difficult and is neither cost effective nor high throughput. Here, we are able to address these challenges by establishing a synthetic biology approach, which links Mtase activity to cell growth such that higher Mtase activity ultimately leads to faster cell growth. We show that better-performing variants of the examined Mtases can be readily obtained by growth selection after repetitive cell passages. We also demonstrate the usefulness of our approach for discovery of Mtase-specific drug candidates. We further show our approach is not only applicable in bacteria, exemplified by Escherichia coli, but also in eurkaryotic organisms such as budding yeast Saccharomyces cerevisiae.
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Affiliation(s)
- Hao Luo
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
- * E-mail:
| | - Anne Sofie L. Hansen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lei Yang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Konstantin Schneider
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Mette Kristensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ulla Christensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Hanne B. Christensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Bin Du
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Emre Özdemir
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Adam M. Feist
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Jay D. Keasling
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
- Joint BioEnergy Institute, Emeryville, California, United States of America
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes of Advanced Technologies, Shenzhen, China
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Department of Chemical and Biomolecular Engineering and Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Michael K. Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Markus J. Herrgård
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Bernhard O. Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
- 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
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272
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Lloyd CJ, King ZA, Sandberg TE, Hefner Y, Olson CA, Phaneuf PV, O’Brien EJ, Sanders JG, Salido RA, Sanders K, Brennan C, Humphrey G, Knight R, Feist AM. The genetic basis for adaptation of model-designed syntrophic co-cultures. PLoS Comput Biol 2019; 15:e1006213. [PMID: 30822347 PMCID: PMC6415869 DOI: 10.1371/journal.pcbi.1006213] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 03/13/2019] [Accepted: 02/07/2019] [Indexed: 11/18/2022] Open
Abstract
Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains—with diverse metabolic deficiencies—were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities. Many basic characteristics underlying the establishment of cooperative growth in bacterial communities have not been studied in detail. The presented work sought to understand the adaptation of syntrophic communities by first employing a new computational method to generate a comprehensive catalog of E. coli auxotrophic mutants. Many of the knockouts in the catalog had the predicted effect of disabling a major biosynthetic process. As a result, these strains were predicted to be capable of growing when supplemented with many different individual metabolites (i.e., a non-specific auxotroph), but the strains would require a high amount of metabolic cooperation to grow in community. Three such non-specific auxotroph mutants from this catalog were co-cultured with a proven auxotrophic partner in vivo and evolved via adaptive laboratory evolution. In order to successfully grow, each strain in co-culture had to evolve under a pressure to grow cooperatively in its new niche. The non-specific auxotrophs further had to adapt to significant homeostatic changes in cell’s metabolic state caused by knockouts in metabolic genes. The genomes of the successfully growing communities were sequenced, thus providing unique insights into the genetic changes accompanying the formation and optimization of the viable communities. A computational model was further developed to predict how finite protein availability, a fundamental constraint on cell metabolism, could impact the composition of the community (i.e., the relative abundances of each community member).
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Affiliation(s)
- Colton J. Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, United States of America
| | - Zachary A. King
- Department of Bioengineering, University of California, San Diego, La Jolla, United States of America
| | - Troy E. Sandberg
- Department of Bioengineering, University of California, San Diego, La Jolla, United States of America
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, United States of America
| | - Connor A. Olson
- Department of Bioengineering, University of California, San Diego, La Jolla, United States of America
| | - Patrick V. Phaneuf
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, United States of America
| | - Edward J. O’Brien
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, United States of America
| | - Jon G. Sanders
- Department of Pediatrics, University of California, San Diego, La Jolla, United States of America
- Cornell Institute of Host-Microbe Interactions and Disease, Cornell University, Ithaca, United States of America
| | - Rodolfo A. Salido
- Department of Pediatrics, University of California, San Diego, La Jolla, United States of America
| | - Karenina Sanders
- Department of Pediatrics, University of California, San Diego, La Jolla, United States of America
| | - Caitriona Brennan
- Department of Pediatrics, University of California, San Diego, La Jolla, United States of America
| | - Gregory Humphrey
- Department of Pediatrics, University of California, San Diego, La Jolla, United States of America
| | - Rob Knight
- Department of Bioengineering, University of California, San Diego, La Jolla, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, United States of America
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, United States of America
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, United States of America
| | - Adam M. Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark
- * E-mail:
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273
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Oyetunde T, Liu D, Martin HG, Tang YJ. Machine learning framework for assessment of microbial factory performance. PLoS One 2019; 14:e0210558. [PMID: 30645629 PMCID: PMC6333410 DOI: 10.1371/journal.pone.0210558] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 12/27/2018] [Indexed: 01/01/2023] Open
Abstract
Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).
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Affiliation(s)
- Tolutola Oyetunde
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, Missouri, United States of America
| | - Di Liu
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, Missouri, United States of America
| | - Hector Garcia Martin
- DOE Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
| | - Yinjie J. Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, Missouri, United States of America
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274
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Sánchez BJ, Li F, Kerkhoven EJ, Nielsen J. SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework. BMC SYSTEMS BIOLOGY 2019; 13:4. [PMID: 30634957 PMCID: PMC6330394 DOI: 10.1186/s12918-018-0673-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 12/19/2018] [Indexed: 11/18/2022]
Abstract
BACKGROUND A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. RESULTS SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can represent accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. CONCLUSIONS The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr .
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Affiliation(s)
- Benjamín J. Sánchez
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Eduard J. Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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275
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Seif Y, Monk JM, Mih N, Tsunemoto H, Poudel S, Zuniga C, Broddrick J, Zengler K, Palsson BO. A computational knowledge-base elucidates the response of Staphylococcus aureus to different media types. PLoS Comput Biol 2019; 15:e1006644. [PMID: 30625152 PMCID: PMC6326480 DOI: 10.1371/journal.pcbi.1006644] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 11/14/2018] [Indexed: 12/15/2022] Open
Abstract
S. aureus is classified as a serious threat pathogen and is a priority that guides the discovery and development of new antibiotics. Despite growing knowledge of S. aureus metabolic capabilities, our understanding of its systems-level responses to different media types remains incomplete. Here, we develop a manually reconstructed genome-scale model (GEM-PRO) of metabolism with 3D protein structures for S. aureus USA300 str. JE2 containing 854 genes, 1,440 reactions, 1,327 metabolites and 673 3-dimensional protein structures. Computations were in 85% agreement with gene essentiality data from random barcode transposon site sequencing (RB-TnSeq) and 68% agreement with experimental physiological data. Comparisons of computational predictions with experimental observations highlight: 1) cases of non-essential biomass precursors; 2) metabolic genes subject to transcriptional regulation involved in Staphyloxanthin biosynthesis; 3) the essentiality of purine and amino acid biosynthesis in synthetic physiological media; and 4) a switch to aerobic fermentation upon exposure to extracellular glucose elucidated as a result of integrating time-course of quantitative exo-metabolomics data. An up-to-date GEM-PRO thus serves as a knowledge-based platform to elucidate S. aureus’ metabolic response to its environment. Environmental perturbations (e.g., antibiotic stress, nutrient starvation, oxidative stress) induce systems-level perturbations of bacterial cells that vary depending on the growth environment. The generation of omics data is aimed at capturing a complete view of the organism’s response under different conditions. Genome-scale models (GEMs) of metabolism represent a knowledge-based platform for the contextualization and integration of multi-omic measurements and can serve to offer valuable insights of system-level responses. This work provides the most up to date reconstruction effort integrating recent advances in the knowledge of S. aureus molecular biology with previous annotations resulting in the first quantitatively and qualitatively validated S. aureus GEM. GEM guided predictions obtained from model analysis provided insights into the effects of medium composition on metabolic flux distribution and gene essentiality. The model can also serve as a platform to guide network reconstructions for other Staphylococci as well as direct hypothesis generation following the integration of omics data sets, including transcriptomics, proteomics, metabolomics, and multi-strain genomic data.
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Affiliation(s)
- Yara Seif
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Jonathan M. Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Nathan Mih
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Hannah Tsunemoto
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, United States of America
| | - Saugat Poudel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Cristal Zuniga
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Jared Broddrick
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Karsten Zengler
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States of America
- * E-mail:
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276
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Sander T, Farke N, Diehl C, Kuntz M, Glatter T, Link H. Allosteric Feedback Inhibition Enables Robust Amino Acid Biosynthesis in E. coli by Enforcing Enzyme Overabundance. Cell Syst 2019; 8:66-75.e8. [PMID: 30638812 PMCID: PMC6345581 DOI: 10.1016/j.cels.2018.12.005] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 10/08/2018] [Accepted: 12/10/2018] [Indexed: 02/06/2023]
Abstract
Microbes must ensure robust amino acid metabolism in the face of external and internal perturbations. This robustness is thought to emerge from regulatory interactions in metabolic and genetic networks. Here, we explored the consequences of removing allosteric feedback inhibition in seven amino acid biosynthesis pathways in Escherichia coli (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline). Proteome data revealed that enzyme levels decreased in five of the seven dysregulated pathways. Despite that, flux through the dysregulated pathways was not limited, indicating that enzyme levels are higher than absolutely needed in wild-type cells. We showed that such enzyme overabundance renders the arginine, histidine, and tryptophan pathways robust against perturbations of gene expression, using a metabolic model and CRISPR interference experiments. The results suggested a sensitive interaction between allosteric feedback inhibition and enzyme-level regulation that ensures robust yet efficient biosynthesis of histidine, arginine, and tryptophan in E. coli. Amino acid biosynthesis enzymes do not normally operate at maximum capacity Allosteric feedback inhibition ensures that enzymes are overabundant Enzyme overabundance provides robustness against decreases in gene expression
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Affiliation(s)
- Timur Sander
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Niklas Farke
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Christoph Diehl
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Michelle Kuntz
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Timo Glatter
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany.
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277
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Dunphy LJ, Yen P, Papin JA. Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa. Cell Syst 2019; 8:3-14.e3. [PMID: 30611675 DOI: 10.1016/j.cels.2018.12.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 10/08/2018] [Accepted: 12/04/2018] [Indexed: 12/13/2022]
Abstract
Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To study this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. Our data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. A genome-scale metabolic network reconstruction of P. aeruginosa was paired with whole-genome sequencing data to predict genes contributing to observed changes in metabolism. We experimentally validated computational predictions to identify mutations in resistant P. aeruginosa affecting loss of catabolic function. Finally, we found a shared metabolic phenotype between lab-evolved P. aeruginosa and clinical isolates with similar mutational landscapes. Our results build upon previous knowledge of antibiotic-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens.
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Affiliation(s)
- Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Phillip Yen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
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278
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Multiobjective strain design: A framework for modular cell engineering. Metab Eng 2019; 51:110-120. [DOI: 10.1016/j.ymben.2018.09.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 09/05/2018] [Accepted: 09/05/2018] [Indexed: 01/28/2023]
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279
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Metabolic engineering of Corynebacterium glutamicum for the production of glutaric acid, a C5 dicarboxylic acid platform chemical. Metab Eng 2019; 51:99-109. [DOI: 10.1016/j.ymben.2018.08.007] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 08/08/2018] [Accepted: 08/17/2018] [Indexed: 01/24/2023]
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280
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Beuter D, Gomes-Filho JV, Randau L, Díaz-Pascual F, Drescher K, Link H. Selective Enrichment of Slow-Growing Bacteria in a Metabolism-Wide CRISPRi Library with a TIMER Protein. ACS Synth Biol 2018; 7:2775-2782. [PMID: 30424596 DOI: 10.1021/acssynbio.8b00379] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Construction of pooled genetic variant libraries has become very fast and versatile. The current limitation of this technique is to select cells with a desired phenotype from very large libraries. Especially cells with poor fitness and slow growth are difficult to select because they are rapidly outcompeted by fitter cells. Here, we demonstrate selective and high-throughput enrichment of slow-growing strains using a fluorescent TIMER protein and flow cytometry. As a proof of principle, we created a metabolism-wide CRISPR interference library for Escherichia coli and enriched targets that interfere with amino acid metabolism. After enrichment of slow-growing cells, the CRISPRi library consisted almost entirely of targets that block amino acid biosynthesis. These results provide general guidelines for how to enrich slow-growing strains from a large pool of genetic variants, with applications in genetic screens, metabolic engineering, and synthetic biology.
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Affiliation(s)
- Dominik Beuter
- Max Planck Institute for Terrestrial Microbiology, 35043 Marburg, Germany
| | | | - Lennart Randau
- Max Planck Institute for Terrestrial Microbiology, 35043 Marburg, Germany
| | | | - Knut Drescher
- Max Planck Institute for Terrestrial Microbiology, 35043 Marburg, Germany
- Department of Physics, Philipps-Universität Marburg, 35032 Marburg, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, 35043 Marburg, Germany
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281
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Guzmán GI, Olson CA, Hefner Y, Phaneuf PV, Catoiu E, Crepaldi LB, Micas LG, Palsson BO, Feist AM. Reframing gene essentiality in terms of adaptive flexibility. BMC SYSTEMS BIOLOGY 2018; 12:143. [PMID: 30558585 PMCID: PMC6296033 DOI: 10.1186/s12918-018-0653-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Accepted: 11/13/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality. RESULTS In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing and transcriptome analysis. Of the twenty false positive strains available and confirmed from the Keio single gene knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. The lag phase observed for these strains ranged from less than one day to more than 7 days. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations, modeling predictions of alternate pathways and isozymes, and transcriptome analysis of KO strains suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed. CONCLUSIONS Longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events.
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Affiliation(s)
- Gabriela I Guzmán
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA
| | - Connor A Olson
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA
| | - Patrick V Phaneuf
- Department of Bioinformatics and Systems Biology, University of California, San Diego, 92093, La Jolla, CA, USA
| | - Edward Catoiu
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA
| | - Lais B Crepaldi
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA.,Department of Chemical Engineering, University of Ribeirão Preto, São Paulo, Brazil
| | - Lucas Goldschmidt Micas
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA.,Department of Chemical and Petroleum Engineering, Fluminense Federal University, Niterói, Rio de Janeiro, Brazil
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.,Department of Pediatrics, University of California, San Diego, La Jolla, 92093, CA, USA
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA. .,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
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282
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Heckmann D, Lloyd CJ, Mih N, Ha Y, Zielinski DC, Haiman ZB, Desouki AA, Lercher MJ, Palsson BO. Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat Commun 2018; 9:5252. [PMID: 30531987 PMCID: PMC6286351 DOI: 10.1038/s41467-018-07652-6] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 11/15/2018] [Indexed: 11/09/2022] Open
Abstract
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.
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Affiliation(s)
- David Heckmann
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA.
| | - Colton J Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Nathan Mih
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Yuanchi Ha
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Daniel C Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Zachary B Haiman
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Abdelmoneim Amer Desouki
- Institute for Computer Science and Department of Biology, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark.
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283
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Estimating Metabolic Equilibrium Constants: Progress and Future Challenges. Trends Biochem Sci 2018; 43:960-969. [DOI: 10.1016/j.tibs.2018.09.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 09/17/2018] [Accepted: 09/19/2018] [Indexed: 01/03/2023]
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284
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Niu W, Kramer L, Mueller J, Liu K, Guo J. Metabolic engineering of Escherichia coli for the de novo stereospecific biosynthesis of 1,2-propanediol through lactic acid. Metab Eng Commun 2018; 8:e00082. [PMID: 30591904 PMCID: PMC6304458 DOI: 10.1016/j.mec.2018.e00082] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 11/19/2018] [Accepted: 11/19/2018] [Indexed: 11/25/2022] Open
Abstract
1,2-propanediol (1,2-PDO) is an industrial chemical with a broad range of applications, such as the production of alkyd and unsaturated polyester resins. It is currently produced as a racemic mixture from nonrenewable petroleum-based feedstocks. We have reported a novel artificial pathway for the biosynthesis of 1,2-PDO via lactic acid isomers as the intermediates. The pathway circumvents the cytotoxicity issue caused by methylglyoxal intermediate in the naturally existing pathway. Successful E. coli bioconversion of lactic acid to 1,2-PDO was shown in previous report. Here, we demonstrated the engineering of E. coli host strains for the de novo biosynthesis of 1,2-PDO through this pathway. Under fermenter-controlled conditions, the R-1,2-PDO was produced at 17.3 g/L with a molar yield of 42.2% from glucose, while the S-isomer was produced at 9.3 g/L with a molar yield of 23.2%. The optical purities of the two isomers were 97.5% ee (R) and 99.3% ee (S), respectively. To the best of our knowledge, these are the highest titers of 1,2-PDO biosynthesized by either natural producer or engineered microbial strains that are published in peer-reviewed journals. 1,2-Propanediol is a commodity chemical in the productions of alkyd and high-performance, unsaturated polyesters. E. coli strains were engineered for biosyntheses of 1,2-propanediol from glucose via the reduction of lactic acids. The biosynthesis is stereospecific, which allowed the production of 1,2-propanediol stereoisomers with high optical purity. The highest reported titers of 17.3 g/L and 9.3 g/L were achieved for R-1,2-PDO and S-1,2-PDO, respectively.
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Affiliation(s)
- Wei Niu
- Department of Chemical&Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Levi Kramer
- Department of Chemical&Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Joshua Mueller
- Department of Chemical&Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Kun Liu
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Jiantao Guo
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
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285
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Karp PD, Ong WK, Paley S, Billington R, Caspi R, Fulcher C, Kothari A, Krummenacker M, Latendresse M, Midford PE, Subhraveti P, Gama-Castro S, Muñiz-Rascado L, Bonavides-Martinez C, Santos-Zavaleta A, Mackie A, Collado-Vides J, Keseler IM, Paulsen I. The EcoCyc Database. EcoSal Plus 2018; 8:10.1128/ecosalplus.ESP-0006-2018. [PMID: 30406744 PMCID: PMC6504970 DOI: 10.1128/ecosalplus.esp-0006-2018] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Indexed: 01/28/2023]
Abstract
EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.
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Affiliation(s)
- Peter D Karp
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Wai Kit Ong
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Suzanne Paley
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | | | - Ron Caspi
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Carol Fulcher
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Anamika Kothari
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | | | - Mario Latendresse
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Peter E Midford
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | | | - Socorro Gama-Castro
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Luis Muñiz-Rascado
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - César Bonavides-Martinez
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Alberto Santos-Zavaleta
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Amanda Mackie
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Ingrid M Keseler
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Ian Paulsen
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
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286
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Fang X, Monk JM, Nurk S, Akseshina M, Zhu Q, Gemmell C, Gianetto-Hill C, Leung N, Szubin R, Sanders J, Beck PL, Li W, Sandborn WJ, Gray-Owen SD, Knight R, Allen-Vercoe E, Palsson BO, Smarr L. Metagenomics-Based, Strain-Level Analysis of Escherichia coli From a Time-Series of Microbiome Samples From a Crohn's Disease Patient. Front Microbiol 2018; 9:2559. [PMID: 30425690 PMCID: PMC6218438 DOI: 10.3389/fmicb.2018.02559] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 10/08/2018] [Indexed: 12/12/2022] Open
Abstract
Dysbiosis of the gut microbiome, including elevated abundance of putative leading bacterial triggers such as E. coli in inflammatory bowel disease (IBD) patients, is of great interest. To date, most E. coli studies in IBD patients are focused on clinical isolates, overlooking their relative abundances and turnover over time. Metagenomics-based studies, on the other hand, are less focused on strain-level investigations. Here, using recently developed bioinformatic tools, we analyzed the abundance and properties of specific E. coli strains in a Crohns disease (CD) patient longitudinally, while also considering the composition of the entire community over time. In this report, we conducted a pilot study on metagenomic-based, strain-level analysis of a time-series of E. coli strains in a left-sided CD patient, who exhibited sustained levels of E. coli greater than 100X healthy controls. We: (1) mapped out the composition of the gut microbiome over time, particularly the presence of E. coli strains, and found that the abundance and dominance of specific E. coli strains in the community varied over time; (2) performed strain-level de novo assemblies of seven dominant E. coli strains, and illustrated disparity between these strains in both phylogenetic origin and genomic content; (3) observed that strain ST1 (recovered during peak inflammation) is highly similar to known pathogenic AIEC strains NC101 and LF82 in both virulence factors and metabolic functions, while other strains (ST2-ST7) that were collected during more stable states displayed diverse characteristics; (4) isolated, sequenced, experimentally characterized ST1, and confirmed the accuracy of the de novo assembly; and (5) assessed growth capability of ST1 with a newly reconstructed genome-scale metabolic model of the strain, and showed its potential to use substrates found abundantly in the human gut to outcompete other microbes. In conclusion, inflammation status (assessed by the blood C-reactive protein and stool calprotectin) is likely correlated with the abundance of a subgroup of E. coli strains with specific traits. Therefore, strain-level time-series analysis of dominant E. coli strains in a CD patient is highly informative, and motivates a study of a larger cohort of IBD patients.
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Affiliation(s)
- Xin Fang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Sergey Nurk
- Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Margarita Akseshina
- St. Petersburg Academic University, Russian Academy of Sciences, St. Petersburg, Russia
| | - Qiyun Zhu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States
| | - Christopher Gemmell
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada
| | - Connor Gianetto-Hill
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada
| | - Nelly Leung
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Jon Sanders
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Paul L Beck
- Division of Gastroenterology, University of Calgary, Calgary, AB, Canada
| | - Weizhong Li
- Human Longevity Inc., San Diego, CA, United States.,J. Craig Venter Institute, La Jolla, CA, United States
| | - William J Sandborn
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States.,Inflammatory Bowel Disease Center, University of California, San Diego, La Jolla, CA, United States
| | - Scott D Gray-Owen
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States.,Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, United States.,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, United States
| | - Emma Allen-Vercoe
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States.,Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, United States.,The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Larry Smarr
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, United States.,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, United States.,California Institute for Telecommunications and Information Technology, University of California, San Diego, La Jolla, CA, United States
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287
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Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice. Proc Natl Acad Sci U S A 2018; 115:11339-11344. [PMID: 30309961 DOI: 10.1073/pnas.1805367115] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The structure of the metabolic network contains myriad organism-specific variations across the tree of life, but the selection basis for pathway choices in different organisms is not well understood. Here, we examined the metabolic capabilities with respect to cofactor use and pathway thermodynamics of all sequenced organisms in the Kyoto Encyclopedia of Genes and Genomes Database. We found that (i) many biomass precursors have alternate synthesis routes that vary substantially in thermodynamic favorability and energy cost, creating tradeoffs that may be subject to selection pressure; (ii) alternative pathways in amino acid synthesis are characteristically distinguished by the use of biosynthetically unnecessary acyl-CoA cleavage; (iii) distinct choices preferring thermodynamic-favorable or cofactor-use-efficient pathways exist widely among organisms; (iv) cofactor-use-efficient pathways tend to have a greater yield advantage under anaerobic conditions specifically; and (v) lysine biosynthesis in particular exhibits temperature-dependent thermodynamics and corresponding differential pathway choice by thermophiles. These findings present a view on the evolution of metabolic network structure that highlights a key role of pathway thermodynamics and cofactor use in determining organism pathway choices.
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288
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Yang L, Yurkovich JT, King ZA, Palsson BO. Modeling the multi-scale mechanisms of macromolecular resource allocation. Curr Opin Microbiol 2018; 45:8-15. [PMID: 29367175 PMCID: PMC6419967 DOI: 10.1016/j.mib.2018.01.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/16/2022]
Abstract
As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.
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Affiliation(s)
- Laurence Yang
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA.
| | - James T Yurkovich
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Zachary A King
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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289
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Rowe E, Palsson BO, King ZA. Escher-FBA: a web application for interactive flux balance analysis. BMC SYSTEMS BIOLOGY 2018; 12:84. [PMID: 30257674 PMCID: PMC6158907 DOI: 10.1186/s12918-018-0607-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 09/12/2018] [Indexed: 11/23/2022]
Abstract
Background Flux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works. Results To meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization. Escher-FBA allows users to set flux bounds, knock out reactions, change objective functions, upload metabolic models, and generate high-quality figures without downloading software or writing code. We provide detailed instructions on how to use Escher-FBA to replicate several FBA simulations that generate real scientific hypotheses. Conclusions We designed Escher-FBA to be as intuitive as possible so that users can quickly and easily understand the core concepts of FBA. The web application can be accessed at https://sbrg.github.io/escher-fba. Electronic supplementary material The online version of this article (10.1186/s12918-018-0607-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Elliot Rowe
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, USA.,Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kongens, Lyngby, Denmark
| | - Zachary A King
- Department of Bioengineering, University of California, San Diego, La Jolla, USA.
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290
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Kim H, Kim S, Yoon SH. Metabolic network reconstruction and phenome analysis of the industrial microbe, Escherichia coli BL21(DE3). PLoS One 2018; 13:e0204375. [PMID: 30240424 PMCID: PMC6150544 DOI: 10.1371/journal.pone.0204375] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 09/06/2018] [Indexed: 01/25/2023] Open
Abstract
Escherichia coli BL21(DE3) is an industrial model microbe for the mass-production of bioproducts such as biofuels, biorefineries, and recombinant proteins. However, despite its important role in scientific research and biotechnological applications, a high-quality metabolic network model for metabolic engineering is yet to be developed. Here, we present the comprehensive metabolic network model of E. coli BL21(DE3), named iHK1487, based on the latest genome reannotation and phenome analysis. The metabolic model consists of 1,164 unique metabolites, 2,701 metabolic reactions, and 1,487 genes. The model was validated and improved by comparing the simulation results with phenome data from phenotype microarray tests. Previous transcriptome profile data was incorporated during model reconstruction, and flux prediction was simulated using the model. iHK1487 was simulated to explore the metabolic features of BL21(DE3) such as broad spectrum amino acid utilization and enhanced flux through the upper glycolytic pathway and TCA cycle. iHK1487 will contribute to systematic understanding of cellular physiology and metabolism of E. coli BL21(DE3) and highlight its biotechnological applications.
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Affiliation(s)
- Hanseol Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea
| | - Sinyeon Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea
| | - Sung Ho Yoon
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea
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291
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Mohite OS, Weber T, Kim HU, Lee SY. Genome-Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production. Biotechnol J 2018; 14:e1800377. [DOI: 10.1002/biot.201800377] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 08/11/2018] [Indexed: 12/13/2022]
Affiliation(s)
- Omkar S. Mohite
- The Novo Nordisk Foundation Center for Biosustainability; Technical University of Denmark; 2800 kongens Lyngby Denmark
| | - Tilmann Weber
- The Novo Nordisk Foundation Center for Biosustainability; Technical University of Denmark; 2800 kongens Lyngby Denmark
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Korea Advanced Institute of Science and Technology (KAIST); 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Sang Yup Lee
- The Novo Nordisk Foundation Center for Biosustainability; Technical University of Denmark; 2800 kongens Lyngby Denmark
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Korea Advanced Institute of Science and Technology (KAIST); 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
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292
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Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 2018; 46:7542-7553. [PMID: 30192979 PMCID: PMC6125623 DOI: 10.1093/nar/gky537] [Citation(s) in RCA: 311] [Impact Index Per Article: 51.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 05/17/2018] [Accepted: 05/29/2018] [Indexed: 12/26/2022] Open
Abstract
Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.
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Affiliation(s)
- Daniel Machado
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Sergej Andrejev
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Melanie Tramontano
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
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293
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Bauer E, Thiele I. From metagenomic data to personalized in silico microbiotas: predicting dietary supplements for Crohn's disease. NPJ Syst Biol Appl 2018; 4:27. [PMID: 30083388 PMCID: PMC6068170 DOI: 10.1038/s41540-018-0063-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 05/17/2018] [Accepted: 05/30/2018] [Indexed: 12/13/2022] Open
Abstract
Crohn's disease (CD) is associated with an ecological imbalance of the intestinal microbiota, consisting of hundreds of species. The underlying complexity as well as individual differences between patients contributes to the difficulty to define a standardized treatment. Computational modeling can systematically investigate metabolic interactions between gut microbes to unravel mechanistic insights. In this study, we integrated metagenomic data of CD patients and healthy controls with genome-scale metabolic models into personalized in silico microbiotas. We predicted short chain fatty acid (SFCA) levels for patients and controls, which were overall congruent with experimental findings. As an emergent property, low concentrations of SCFA were predicted for CD patients and the SCFA signatures were unique to each patient. Consequently, we suggest personalized dietary treatments that could improve each patient's SCFA levels. The underlying modeling approach could aid clinical practice to find dietary treatment and guide recovery by rationally proposing food aliments.
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Affiliation(s)
- Eugen Bauer
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg, L-4362 Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg, L-4362 Luxembourg
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294
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Amara A, Takano E, Breitling R. Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism. BMC Genomics 2018; 19:519. [PMID: 29973148 PMCID: PMC6040156 DOI: 10.1186/s12864-018-4905-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 06/28/2018] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose. RESULTS Here, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics. CONCLUSIONS The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.
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Affiliation(s)
- Adam Amara
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, School of Chemistry, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
| | - Eriko Takano
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, School of Chemistry, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, School of Chemistry, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
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295
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Lloyd CJ, Ebrahim A, Yang L, King ZA, Catoiu E, O’Brien EJ, Liu JK, Palsson BO. COBRAme: A computational framework for genome-scale models of metabolism and gene expression. PLoS Comput Biol 2018; 14:e1006302. [PMID: 29975681 PMCID: PMC6049947 DOI: 10.1371/journal.pcbi.1006302] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 07/17/2018] [Accepted: 06/13/2018] [Indexed: 12/29/2022] Open
Abstract
Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in iJL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.
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Affiliation(s)
- Colton J. Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Ali Ebrahim
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Laurence Yang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Zachary A. King
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Edward Catoiu
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Edward J. O’Brien
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, United States of America
| | - Joanne K. Liu
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States of America
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296
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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] [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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297
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Shepelin D, Hansen ASL, Lennen R, Luo H, Herrgård MJ. Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes. Genes (Basel) 2018; 9:E249. [PMID: 29751691 PMCID: PMC5977189 DOI: 10.3390/genes9050249] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 05/02/2018] [Accepted: 05/02/2018] [Indexed: 01/10/2023] Open
Abstract
Microbial cell factories have proven to be an economical means of production for many bulk, specialty, and fine chemical products. However, we still lack both a holistic understanding of organism physiology and the ability to predictively tune enzyme activities in vivo, thus slowing down rational engineering of industrially relevant strains. An alternative concept to rational engineering is to use evolution as the driving force to select for desired changes, an approach often described as evolutionary engineering. In evolutionary engineering, in vivo selections for a desired phenotype are combined with either generation of spontaneous mutations or some form of targeted or random mutagenesis. Evolutionary engineering has been used to successfully engineer easily selectable phenotypes, such as utilization of a suboptimal nutrient source or tolerance to inhibitory substrates or products. In this review, we focus primarily on a more challenging problem-the use of evolutionary engineering for improving the production of chemicals in microbes directly. We describe recent developments in evolutionary engineering strategies, in general, and discuss, in detail, case studies where production of a chemical has been successfully achieved through evolutionary engineering by coupling production to cellular growth.
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Affiliation(s)
- Denis Shepelin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Anne Sofie Lærke Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Rebecca Lennen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Hao Luo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
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298
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McCloskey D, Xu J, Schrübbers L, Christensen HB, Herrgård MJ. RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli. Metab Eng 2018; 47:383-392. [PMID: 29702276 DOI: 10.1016/j.ymben.2018.04.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 03/27/2018] [Accepted: 04/12/2018] [Indexed: 11/20/2022]
Abstract
Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.
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Affiliation(s)
- Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Julia Xu
- Department of Bioengineering, University of California - San Diego, La Jolla, CA 92093, USA
| | - Lars Schrübbers
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Hanne B Christensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Markus J Herrgård
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark.
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299
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Stalidzans E, Seiman A, Peebo K, Komasilovs V, Pentjuss A. Model-based metabolism design: constraints for kinetic and stoichiometric models. Biochem Soc Trans 2018; 46:261-267. [PMID: 29472367 PMCID: PMC5906704 DOI: 10.1042/bst20170263] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 12/19/2017] [Accepted: 01/01/2018] [Indexed: 02/06/2023]
Abstract
The implementation of model-based designs in metabolic engineering and synthetic biology may fail. One of the reasons for this failure is that only a part of the real-world complexity is included in models. Still, some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. Some constraints (mass balance, energy balance, and steady-state assumption) serve as a basis for many modelling approaches. There are others (total enzyme activity constraint and homeostatic constraint) proposed decades ago, but which are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, and resource balance. Constraints for kinetic and stoichiometric models are grouped according to their applicability preconditions in (1) general constraints, (2) organism-level constraints, and (3) experiment-level constraints. General constraints are universal and are applicable for any system. Organism-level constraints are applicable for biological systems and usually are organism-specific, but these constraints can be applied without information about experimental conditions. To apply experimental-level constraints, peculiarities of the organism and the experimental set-up have to be taken into account to calculate the values of constraints. The limitations of applicability of particular constraints for kinetic and stoichiometric models are addressed.
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Affiliation(s)
- Egils Stalidzans
- Biosystems Group, Latvia University of Agriculture, Liela Iela 2, LV 3001 Jelgava, Latvia
| | - Andrus Seiman
- Center of Food and Fermentation Technologies, Akadeemia tee 15A, 12618 Tallinn, Estonia
| | - Karl Peebo
- Center of Food and Fermentation Technologies, Akadeemia tee 15A, 12618 Tallinn, Estonia
| | - Vitalijs Komasilovs
- Biosystems Group, Latvia University of Agriculture, Liela Iela 2, LV 3001 Jelgava, Latvia
| | - Agris Pentjuss
- Biosystems Group, Latvia University of Agriculture, Liela Iela 2, LV 3001 Jelgava, Latvia
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300
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Meyer F, Keller P, Hartl J, Gröninger OG, Kiefer P, Vorholt JA. Methanol-essential growth of Escherichia coli. Nat Commun 2018; 9:1508. [PMID: 29666370 PMCID: PMC5904121 DOI: 10.1038/s41467-018-03937-y] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 03/22/2018] [Indexed: 12/22/2022] Open
Abstract
Methanol represents an attractive substrate for biotechnological applications. Utilization of reduced one-carbon compounds for growth is currently limited to methylotrophic organisms, and engineering synthetic methylotrophy remains a major challenge. Here we apply an in silico-guided multiple knockout approach to engineer a methanol-essential Escherichia coli strain, which contains the ribulose monophosphate cycle for methanol assimilation. Methanol conversion to biomass was stoichiometrically coupled to the metabolization of gluconate and the designed strain was subjected to laboratory evolution experiments. Evolved strains incorporate up to 24% methanol into core metabolites under a co-consumption regime and utilize methanol at rates comparable to natural methylotrophs. Genome sequencing reveals mutations in genes coding for glutathione-dependent formaldehyde oxidation (frmA), NAD(H) homeostasis/biosynthesis (nadR), phosphopentomutase (deoB), and gluconate metabolism (gntR). This study demonstrates a successful metabolic re-routing linked to a heterologous pathway to achieve methanol-dependent growth and represents a crucial step in generating a fully synthetic methylotrophic organism. Engineering synthetic methylotrophy remains challenging. Here, the authors engineer a methanol-essential E. coli by an in silico-guided multiple knockout approach and show a laboratory evolved strain can incorporate up to 24% methanol into core metabolites during growth.
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Affiliation(s)
- Fabian Meyer
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, 8093, Switzerland
| | - Philipp Keller
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, 8093, Switzerland
| | - Johannes Hartl
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, 8093, Switzerland
| | - Olivier G Gröninger
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, 8093, Switzerland
| | - Patrick Kiefer
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, 8093, Switzerland
| | - Julia A Vorholt
- Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, 8093, Switzerland.
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