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Nguyen V, Li Y, Lu T. Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae. ACS Synth Biol 2024; 13:1442-1453. [PMID: 38657170 PMCID: PMC11103795 DOI: 10.1021/acssynbio.3c00542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.
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Affiliation(s)
- Viviana Nguyen
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Yifei Li
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Ting Lu
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Carl R Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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2
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Ye A, Shen JN, Li Y, Lian X, Ma BG, Guo FB. Reconstruction of the genome-scale metabolic network model of Sinorhizobium fredii CCBAU45436 for free-living and symbiotic states. Front Bioeng Biotechnol 2024; 12:1377334. [PMID: 38590605 PMCID: PMC10999553 DOI: 10.3389/fbioe.2024.1377334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 03/11/2024] [Indexed: 04/10/2024] Open
Abstract
Sinorhizobium fredii CCBAU45436 is an excellent rhizobium that plays an important role in agricultural production. However, there still needs more comprehensive understanding of the metabolic system of S. fredii CCBAU45436, which hinders its application in agriculture. Therefore, based on the first-generation metabolic model iCC541 we developed a new genome-scale metabolic model iAQY970, which contains 970 genes, 1,052 reactions, 942 metabolites and is scored 89% in the MEMOTE test. Cell growth phenotype predicted by iAQY970 is 81.7% consistent with the experimental data. The results of mapping the proteome data under free-living and symbiosis conditions to the model showed that the biomass production rate in the logarithmic phase was faster than that in the stable phase, and the nitrogen fixation efficiency of rhizobia parasitized in cultivated soybean was higher than that in wild-type soybean, which was consistent with the actual situation. In the symbiotic condition, there are 184 genes that would affect growth, of which 94 are essential; In the free-living condition, there are 143 genes that influence growth, of which 78 are essential. Among them, 86 of the 94 essential genes in the symbiotic condition were consistent with the prediction of iCC541, and 44 essential genes were confirmed by literature information; meanwhile, 30 genes were identified by DEG and 33 genes were identified by Geptop. In addition, we extracted four key nitrogen fixation modules from the model and predicted that sulfite reductase (EC 1.8.7.1) and nitrogenase (EC 1.18.6.1) as the target enzymes to enhance nitrogen fixation by MOMA, which provided a potential focus for strain optimization. Through the comprehensive metabolic model, we can better understand the metabolic capabilities of S. fredii CCBAU45436 and make full use of it in the future.
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Affiliation(s)
- Anqiang Ye
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University, Wuhan, China
| | - Jian-Ning Shen
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Yong Li
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Xiang Lian
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
| | - Feng-Biao Guo
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University, Wuhan, China
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3
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Hasibi R, Michoel T, Oyarzún DA. Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality. NPJ Syst Biol Appl 2024; 10:24. [PMID: 38448436 PMCID: PMC10917767 DOI: 10.1038/s41540-024-00348-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.
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Affiliation(s)
- Ramin Hasibi
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
- School of Informatics, University of Edinburgh, Edinburgh, UK.
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4
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Kerruish DWM, Cormican P, Kenny EM, Kearns J, Colgan E, Boulton CA, Stelma SNE. The origins of the Guinness stout yeast. Commun Biol 2024; 7:68. [PMID: 38216745 PMCID: PMC10786833 DOI: 10.1038/s42003-023-05587-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/14/2023] [Indexed: 01/14/2024] Open
Abstract
Beer is made via the fermentation of an aqueous extract predominantly composed of malted barley flavoured with hops. The transforming microorganism is typically a single strain of Saccharomyces cerevisiae, and for the majority of major beer brands the yeast strain is a unique component. The present yeast used to make Guinness stout brewed in Dublin, Ireland, can be traced back to 1903, but its origins are unknown. To that end, we used Illumina and Nanopore sequencing to generate whole-genome sequencing data for a total of 22 S. cerevisiae yeast strains: 16 from the Guinness collection and 6 other historical Irish brewing. The origins of the Guinness yeast were determined with a SNP-based analysis, demonstrating that the Guinness strains occupy a distinct group separate from other historical Irish brewing yeasts. Assessment of chromosome number, copy number variation and phenotypic evaluation of key brewing attributes established Guinness yeast-specific SNPs but no specific chromosomal amplifications. Our analysis also demonstrated the effects of yeast storage on phylogeny. Altogether, our results suggest that the Guinness yeast used today is related to the first deposited Guinness yeast; the 1903 Watling Laboratory Guinness yeast.
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Affiliation(s)
| | | | | | - Jessica Kearns
- Diageo Ireland, St James's Gate, The Liberties, Dublin, Ireland
| | - Eibhlin Colgan
- Diageo Ireland, St James's Gate, The Liberties, Dublin, Ireland
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5
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Sun ML, Gao X, Lin L, Yang J, Ledesma-Amaro R, Ji XJ. Building Yarrowia lipolytica Cell Factories for Advanced Biomanufacturing: Challenges and Solutions. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:94-107. [PMID: 38126236 DOI: 10.1021/acs.jafc.3c07889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Microbial cell factories have shown great potential for industrial production with the benefit of being environmentally friendly and sustainable. Yarrowia lipolytica is a promising and superior non-model host for biomanufacturing due to its cumulated advantages compared to model microorganisms, such as high fluxes of metabolic precursors (acetyl-CoA and malonyl-CoA) and its naturally hydrophobic microenvironment. However, although diverse compounds have been synthesized in Y. lipolytica cell factories, most of the relevant studies have not reached the level of industrialization and commercialization due to a number of remaining challenges, including unbalanced metabolic flux, conflict between cell growth and product synthesis, and cytotoxic effects. Here, various metabolic engineering strategies for solving the challenges are summarized, which is developing fast and extremely conducive to rational design and reconstruction of robust Y. lipolytica cell factories for advanced biomanufacturing. Finally, future engineering efforts for enhancing the production efficiency of this platform strain are highlighted.
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Affiliation(s)
- Mei-Li Sun
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Xiaoxia Gao
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Lu Lin
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Jing Yang
- 2011 College, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, United Kingdom
| | - Xiao-Jun Ji
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China
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6
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Szatkowska R, Furmanek E, Kierzek AM, Ludwig C, Adamczyk M. Mitochondrial Metabolism in the Spotlight: Maintaining Balanced RNAP III Activity Ensures Cellular Homeostasis. Int J Mol Sci 2023; 24:14763. [PMID: 37834211 PMCID: PMC10572830 DOI: 10.3390/ijms241914763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/21/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023] Open
Abstract
RNA polymerase III (RNAP III) holoenzyme activity and the processing of its products have been linked to several metabolic dysfunctions in lower and higher eukaryotes. Alterations in the activity of RNAP III-driven synthesis of non-coding RNA cause extensive changes in glucose metabolism. Increased RNAP III activity in the S. cerevisiae maf1Δ strain is lethal when grown on a non-fermentable carbon source. This lethal phenotype is suppressed by reducing tRNA synthesis. Neither the cause of the lack of growth nor the underlying molecular mechanism have been deciphered, and this area has been awaiting scientific explanation for a decade. Our previous proteomics data suggested mitochondrial dysfunction in the strain. Using model mutant strains maf1Δ (with increased tRNA abundance) and rpc128-1007 (with reduced tRNA abundance), we collected data showing major changes in the TCA cycle metabolism of the mutants that explain the phenotypic observations. Based on 13C flux data and analysis of TCA enzyme activities, the present study identifies the flux constraints in the mitochondrial metabolic network. The lack of growth is associated with a decrease in TCA cycle activity and downregulation of the flux towards glutamate, aspartate and phosphoenolpyruvate (PEP), the metabolic intermediate feeding the gluconeogenic pathway. rpc128-1007, the strain that is unable to increase tRNA synthesis due to a mutation in the C128 subunit, has increased TCA cycle activity under non-fermentable conditions. To summarize, cells with non-optimal activity of RNAP III undergo substantial adaptation to a new metabolic state, which makes them vulnerable under specific growth conditions. Our results strongly suggest that balanced, non-coding RNA synthesis that is coupled to glucose signaling is a fundamental requirement to sustain a cell's intracellular homeostasis and flexibility under changing growth conditions. The presented results provide insight into the possible role of RNAP III in the mitochondrial metabolism of other cell types.
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Affiliation(s)
- Roza Szatkowska
- Laboratory of Systems and Synthetic Biology, Chair of Drugs and Cosmetics Biotechnology, Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664 Warsaw, Poland; (R.S.)
| | - Emil Furmanek
- Laboratory of Systems and Synthetic Biology, Chair of Drugs and Cosmetics Biotechnology, Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664 Warsaw, Poland; (R.S.)
| | - Andrzej M. Kierzek
- Certara UK Limited, Sheffield S1 2BJ, UK;
- School of Biosciences and Medicine, University of Surrey, Guildford GU2 7XH, UK
| | - Christian Ludwig
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, UK;
| | - Malgorzata Adamczyk
- Laboratory of Systems and Synthetic Biology, Chair of Drugs and Cosmetics Biotechnology, Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664 Warsaw, Poland; (R.S.)
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7
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Ge J, Li D, Ding J, Xiao X, Liang Y. Microbial coexistence in the rhizosphere and the promotion of plant stress resistance: A review. ENVIRONMENTAL RESEARCH 2023; 222:115298. [PMID: 36642122 DOI: 10.1016/j.envres.2023.115298] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Plants can recruit soil microorganisms into the rhizosphere when experiencing various environmental stresses, including biotic (e.g., insect pests) and abiotic (e.g., heavy metal pollution, droughts, floods, and salinity) stresses. However, species coexistence in plant resistance has not received sufficient attention. Current research on microbial coexistence is only at the community scale, and there is a limited understanding of the interaction patterns between species, especially microbe‒microbe interactions. The relevant interaction patterns are limited to a few model strains. The coexisting microbial communities form a stable system involving complex nutritional competition, metabolic exchange, and even interdependent interactions. This pattern of coexistence can ultimately enhance plant stress tolerance. Hence, a systematic understanding of the coexistence pattern of rhizosphere microorganisms under stress is essential for the precise development and utilization of synthetic microbial communities and the achievement of efficient ecological control. Here, we integrated current analytical methods and introduced several new experimental methods to elucidate rhizosphere microbial coexistence patterns. Some advancements (e.g., network analysis, coculture experiments, and synthetic communities) that can be applied to plant stress resistance are also updated. This review aims to summarize the key role and potential application prospects of microbial coexistence in the resistance of plants to environmental stresses. Our suggestions, enhancing plant resistance with coexisting microbes, would allow us to gain further knowledge on plant-microbial and microbial-microbial functions, and facilitate translation to more effective measures.
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Affiliation(s)
- Jiaqi Ge
- School of Environmental Science and Engineering, Changzhou University, Changzhou, 213164, China; State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Dong Li
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Jixian Ding
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Xian Xiao
- School of Environmental Science and Engineering, Changzhou University, Changzhou, 213164, China.
| | - Yuting Liang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
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8
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Amaradio MN, Ojha V, Jansen G, Gulisano M, Costanza J, Nicosia G. Pareto Optimal Metabolic Engineering for the Growth-coupled Overproduction of Sustainable Chemicals. Biotechnol Bioeng 2022; 119:1890-1902. [PMID: 35419827 PMCID: PMC9321710 DOI: 10.1002/bit.28103] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 11/16/2022]
Abstract
Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta‐carotene (β‐carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of β‐carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution space to highlight near‐optimal strains on Pareto Fronts, balancing the above‐cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this study produced robust results: seven significant strains of Y. lipolytica and seven strains of S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. This study offers solutions for developing an efficient in vitro mutagenesis for microorganisms and demonstrates the efficiency of multiobjective optimization in automatizing metabolic engineering processes.
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Affiliation(s)
- Matteo N Amaradio
- Department of Biomedical & Biotechnological Sciences, University of Catania, Catania, Italy
| | - Varun Ojha
- Department of Computer Science, University of Reading, Reading, United Kingdom
| | - Giorgio Jansen
- Department of Biomedical & Biotechnological Sciences, University of Catania, Catania, Italy.,Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Massimo Gulisano
- Department of Drug Science, University of Catania, Catania, Italy
| | - Jole Costanza
- National Institute of Molecular Genetics, Milan, Italy
| | - Giuseppe Nicosia
- Department of Biomedical & Biotechnological Sciences, University of Catania, Catania, Italy.,Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
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9
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Chen Y, Li F, Nielsen J. Genome-scale modeling of yeast metabolism: retrospectives and perspectives. FEMS Yeast Res 2022; 22:foac003. [PMID: 35094064 PMCID: PMC8862083 DOI: 10.1093/femsyr/foac003] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 11/30/2022] Open
Abstract
Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Feiran Li
- 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
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
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10
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Rhizospheric microbiome: Bio-based emerging strategies for sustainable agriculture development and future perspectives. Microbiol Res 2021; 254:126901. [PMID: 34700186 DOI: 10.1016/j.micres.2021.126901] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 10/16/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
In the light of intensification of cropping practices and changing climatic conditions, nourishing a growing global population requires optimizing environmental sustainability and reducing ecosystem impacts of food production. The use of microbiological systems to ameliorate the agricultural production in a sustainable and eco-friendly way is widespread accepted as a future key-technology. However, the multitude of interaction possibilities between the numerous beneficial microbes and plants in their habitat calls for systematic analysis and management of the rhizospheric microbiome. This review exploits present and future strategies for rhizospheric microbiome management with the aim to generate a comprehensive understanding of the known tools and techniques. Significant information on the structure and dynamics of rhizospheric microbiota of isolated microbial communities is now available. These microbial communities have beneficial effects including increased plant growth, essential nutrient acquisition, pathogens tolerance, and increased abiotic as well as biotic stress tolerance such as drought, temperature, salinity and antagonistic activities against the phyto-pathogens. A better and comprehensive understanding of the various effects and microbial interactions can be gained by application of molecular approaches as extraction of DNA/RNA and other biochemical markers to analyze microbial soil diversity. Novel techniques like interactome network analysis and split-ubiquitin system framework will enable to gain more insight into communication and interactions between the proteins from microbes and plants. The aim of the analysis tasks leads to the novel approach of Rhizosphere microbiome engineering. The capability of forming the rhizospheric microbiome in a defined way will allow combining several microbes (e.g. bacteria and fungi) for a given environment (soil type and climatic zone) in order to exert beneficial influences on specific plants. This integration will require a large-scale effort among academic researchers, industry researchers and farmers to understand and manage interactions of plant-microbiomes within modern farming systems, and is clearly a multi-domain approach and can be mastered only jointly by microbiology, mathematics and information technology. These innovations will open up a new avenue for designing and implementing intensive farming microbiome management approaches to maximize resource productivity and stress tolerance of agro-ecosystems, which in return will create value to the increasing worldwide population, for both food production and consumption.
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11
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Lu H, Li F, Yuan L, Domenzain I, Yu R, Wang H, Li G, Chen Y, Ji B, Kerkhoven EJ, Nielsen J. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Mol Syst Biol 2021; 17:e10427. [PMID: 34676984 PMCID: PMC8532513 DOI: 10.15252/msb.202110427] [Citation(s) in RCA: 7] [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: 05/01/2021] [Revised: 10/02/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.
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Affiliation(s)
- Hongzhong Lu
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Feiran Li
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Le Yuan
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Iván Domenzain
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Rosemary Yu
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Hao Wang
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- National Bioinformatics Infrastructure SwedenScience for Life LaboratoryChalmers University of TechnologyGothenburgSweden
| | - Gang Li
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Yu Chen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Boyang Ji
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
| | - Eduard J Kerkhoven
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Jens Nielsen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
- BioInnovation InstituteCopenhagen NDenmark
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12
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Alonso-Lavin AJ, Bajić D, Poyatos JF. Tolerance to NADH/NAD + imbalance anticipates aging and anti-aging interventions. iScience 2021; 24:102697. [PMID: 34195572 PMCID: PMC8239738 DOI: 10.1016/j.isci.2021.102697] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/26/2021] [Accepted: 06/04/2021] [Indexed: 12/31/2022] Open
Abstract
Redox couples coordinate cellular function, but the consequences of their imbalances are unclear. This is somewhat associated with the limitations of their experimental quantification. Here we circumvent these difficulties by presenting an approach that characterizes fitness-based tolerance profiles to redox couple imbalances using an in silico representation of metabolism. Focusing on the NADH/NAD+ redox couple in yeast, we demonstrate that reductive disequilibria generate metabolic syndromes comparable to those observed in cancer cells. The tolerance of yeast mutants to redox disequilibrium can also explain 30% of the variability in their experimentally measured chronological lifespan. Moreover, by predicting the significance of some metabolites to help stand imbalances, we correctly identify nutrients underlying mechanisms of pathology, lifespan-protecting molecules, or caloric restriction mimetics. Tolerance to redox imbalances becomes, in this way, a sound framework to recognize properties of the aging phenotype while providing a consistent biological rationale to assess anti-aging interventions. We simulate how imbalances in NADH/NAD+ ratio modify cellular metabolic behavior This reveals a mechanism to understand metabolic alterations at low growth rates Tolerance to imbalance explains experimentally measured lifespan in yeast We predict lifespan-protecting metabolites in yeast, animal, and human models
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Affiliation(s)
- Alvar J. Alonso-Lavin
- Logic of Genomic Systems Laboratory (CNB-CSIC), Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain
| | - Djordje Bajić
- Logic of Genomic Systems Laboratory (CNB-CSIC), Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Juan F. Poyatos
- Logic of Genomic Systems Laboratory (CNB-CSIC), Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, USA
- Corresponding author
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13
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Domenzain I, Li F, Kerkhoven EJ, Siewers V. Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species. FEMS Yeast Res 2021; 21:foab002. [PMID: 33428734 PMCID: PMC7943257 DOI: 10.1093/femsyr/foab002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/08/2021] [Indexed: 12/18/2022] Open
Abstract
Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public.
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Affiliation(s)
- Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
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14
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Cheng Y, Schlosser P, Hertel J, Sekula P, Oefner PJ, Spiekerkoetter U, Mielke J, Freitag DF, Schmidts M, Kronenberg F, Eckardt KU, Thiele I, Li Y, Köttgen A. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nat Commun 2021; 12:964. [PMID: 33574263 PMCID: PMC7878905 DOI: 10.1038/s41467-020-20877-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.
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Affiliation(s)
- Yurong Cheng
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland
- University of Greifswald, University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, Germany
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Peter J Oefner
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Ute Spiekerkoetter
- Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | - Johanna Mielke
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Daniel F Freitag
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Miriam Schmidts
- Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Erlangen, Germany
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland
- Division of Microbiology, National University of Ireland, Galway, University Road, Galway, Ireland
- APC Microbiome Ireland, Galway, Ireland
| | - Yong Li
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
- CIBSS - Centre for Integrative Biological Signalling Studies, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
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15
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Patra P, Das M, Kundu P, Ghosh A. Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts. Biotechnol Adv 2021; 47:107695. [PMID: 33465474 DOI: 10.1016/j.biotechadv.2021.107695] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 12/14/2020] [Accepted: 01/09/2021] [Indexed: 12/14/2022]
Abstract
Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using 13C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.
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Affiliation(s)
- Pradipta Patra
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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16
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Choi K, Karr JR, Sauro HM. Status and Challenges of Reproducibility in Computational Systems and Synthetic Biology. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11525-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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17
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Zha WL, Zi JC. Advances in biotechnological production of santalenes and santalols. CHINESE HERBAL MEDICINES 2021; 13:90-97. [PMID: 36117763 PMCID: PMC9476758 DOI: 10.1016/j.chmed.2020.11.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/17/2020] [Accepted: 07/27/2020] [Indexed: 11/30/2022] Open
Abstract
Sandalwood essential oil has been widely used not only as natural medicines but also in perfumery and food industries, with sesquiterpenoids as its major components including (Z)- α-santalol and (Z)-β-santalol and so on. The mature heartwoods of Santalum album, Santalum austrocaledonicum and Santalum spicatum are the major plant resources for extracting sandalwood essential oil, which have been overexploited. Synthetic biology approaches have been successfully applied to produce natural products on large scale. In this review, we summarize biosynthetic enzymes of santalenes and santalols, including various santalene synthases (STSs) and cytochrome P450 monooxygenases (CYPs), and then highlight the advances of biotechnological production of santalenes and santalols in heterologous hosts, especially metabolic engineering strategies for constructing santalene- and santalol-producing Saccharomyces cerevisiae.
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Affiliation(s)
- Wen-long Zha
- Biotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, China
- College of Pharmacy, Jinan University, Guangzhou 510632, China
| | - Jia-chen Zi
- Biotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, China
- College of Pharmacy, Jinan University, Guangzhou 510632, China
- Corresponding author.
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18
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Liu S, Wang SX, Liu W, Wang C, Zhang FZ, Ye YN, Wu CS, Zheng WX, Rao N, Guo FB. CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:6031000. [PMID: 33306800 PMCID: PMC7731928 DOI: 10.1093/database/baaa112] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/12/2020] [Accepted: 12/02/2020] [Indexed: 02/06/2023]
Abstract
Essential genes are key elements for organisms to maintain their living. Building databases that store essential genes in the form of homologous clusters, rather than storing them as a singleton, can provide more enlightening information such as the general essentiality of homologous genes in multiple organisms. In 2013, the first database to store prokaryotic essential genes in clusters, CEG (Clusters of Essential Genes), was constructed. Afterward, the amount of available data for essential genes increased by a factor >3 since the last revision. Herein, we updated CEG to version 2, including more prokaryotic essential genes (from 16 gene datasets to 29 gene datasets) and newly added eukaryotic essential genes (nine species), specifically the human essential genes of 12 cancer cell lines. For prokaryotes, information associated with drug targets, such as protein structure, ligand–protein interaction, virulence factor and matched drugs, is also provided. Finally, we provided the service of essential gene prediction for both prokaryotes and eukaryotes. We hope our updated database will benefit more researchers in drug targets and evolutionary genomics. Database URL:http://cefg.uestc.cn/ceg
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Affiliation(s)
- Shuo Liu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
| | - Shu-Xuan Wang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Liu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Chen Wang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fa-Zhan Zhang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan-Nong Ye
- Bioinformatics and BioMedical Bigdata Mining Laboratory, Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China
| | - Candy-S Wu
- Thomas Worthington High School, 300 West Granville Road, Worthington, OH 43085, USA
| | - Wen-Xin Zheng
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Nini Rao
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Feng-Biao Guo
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
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19
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Curation and Analysis of a Saccharomyces cerevisiae Genome-Scale Metabolic Model for Predicting Production of Sensory Impact Molecules under Enological Conditions. Processes (Basel) 2020. [DOI: 10.3390/pr8091195] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
One approach for elucidating strain-to-strain metabolic differences is the use of genome-scale metabolic models (GSMMs). To date GSMMs have not focused on the industrially important area of flavor production and, as such; do not cover all the pathways relevant to flavor formation in yeast. Moreover, current models for Saccharomyces cerevisiae generally focus on carbon-limited and/or aerobic systems, which is not pertinent to enological conditions. Here, we curate a GSMM (iWS902) to expand on the existing Ehrlich pathway and ester formation pathways central to aroma formation in industrial winemaking, in addition to the existing sulfur metabolism and medium-chain fatty acid (MCFA) pathways that also contribute to production of sensory impact molecules. After validating the model using experimental data, we predict key differences in metabolism for a strain (EC 1118) in two distinct growth conditions, including differences for aroma impact molecules such as acetic acid, tryptophol, and hydrogen sulfide. Additionally, we propose novel targets for metabolic engineering for aroma profile modifications employing flux variability analysis with the expanded GSMM. The model provides mechanistic insights into the key metabolic pathways underlying aroma formation during alcoholic fermentation and provides a potential framework to contribute to new strategies to optimize the aroma of wines.
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20
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Poyatos JF. Genetic buffering and potentiation in metabolism. PLoS Comput Biol 2020; 16:e1008185. [PMID: 32925942 PMCID: PMC7514045 DOI: 10.1371/journal.pcbi.1008185] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 09/24/2020] [Accepted: 07/23/2020] [Indexed: 01/12/2023] Open
Abstract
Cells adjust their metabolism in response to mutations, but how this reprogramming depends on the genetic context is not well known. Specifically, the absence of individual enzymes can affect reprogramming, and thus the impact of mutations in cell growth. Here, we examine this issue with an in silico model of Saccharomyces cerevisiae's metabolism. By quantifying the variability in the growth rate of 10000 different mutant metabolisms that accumulated changes in their reaction fluxes, in the presence, or absence, of a specific enzyme, we distinguish a subset of modifier genes serving as buffers or potentiators of variability. We notice that the most potent modifiers refer to the glycolysis pathway and that, more broadly, they show strong pleiotropy and epistasis. Moreover, the evidence that this subset depends on the specific growing condition strengthens its systemic underpinning, a feature only observed before in a toy model of a gene-regulatory network. Some of these enzymes also modulate the effect that biochemical noise and environmental fluctuations produce in growth. Thus, the reorganization of metabolism induced by mutations has not only direct physiological implications but also transforms the influence that other mutations have on growth. This is a general result with implications in the development of cancer therapies based on metabolic inhibitors.
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Affiliation(s)
- Juan F. Poyatos
- Logic of Genomic Systems Lab (CNB-CSIC), Madrid, Spain
- Center for Genomics and Systems Biology (NYU), New York, United States of America
- * E-mail:
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21
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Daletos G, Katsimpouras C, Stephanopoulos G. Novel Strategies and Platforms for Industrial Isoprenoid Engineering. Trends Biotechnol 2020; 38:811-822. [DOI: 10.1016/j.tibtech.2020.03.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 12/13/2022]
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22
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Stanway RR, Bushell E, Chiappino-Pepe A, Roques M, Sanderson T, Franke-Fayard B, Caldelari R, Golomingi M, Nyonda M, Pandey V, Schwach F, Chevalley S, Ramesar J, Metcalf T, Herd C, Burda PC, Rayner JC, Soldati-Favre D, Janse CJ, Hatzimanikatis V, Billker O, Heussler VT. Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage. Cell 2020; 179:1112-1128.e26. [PMID: 31730853 PMCID: PMC6904910 DOI: 10.1016/j.cell.2019.10.030] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 07/23/2019] [Accepted: 10/23/2019] [Indexed: 12/11/2022]
Abstract
Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development. 1,342 barcoded P. berghei knockout (KO) mutants analyzed for stage-specific phenotypes Life-stage-specific metabolic models reveal reprogramming of cellular function High agreement between blood/liver stage metabolic models and genetic screening data Essential metabolic pathways for parasite development and mechanistic origin revealed
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Affiliation(s)
- Rebecca R Stanway
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland
| | - Ellen Bushell
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Molecular Infection Medicine Sweden (MIMS), Department of Molecular Biology, Umeå University, Umeå 901 87, Sweden
| | - Anush Chiappino-Pepe
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne 1015, Switzerland
| | - Magali Roques
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland
| | - Theo Sanderson
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Blandine Franke-Fayard
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Reto Caldelari
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland
| | | | - Mary Nyonda
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Vikash Pandey
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne 1015, Switzerland; Molecular Infection Medicine Sweden (MIMS), Department of Molecular Biology, Umeå University, Umeå 901 87, Sweden
| | - Frank Schwach
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Séverine Chevalley
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Jai Ramesar
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Tom Metcalf
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Colin Herd
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Paul-Christian Burda
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland; Bernhard Nocht Institute for Tropical Medicine, Hamburg 20359, Germany
| | - Julian C Rayner
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2, 0XY, UK
| | - Dominique Soldati-Favre
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Chris J Janse
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne 1015, Switzerland
| | - Oliver Billker
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Molecular Infection Medicine Sweden (MIMS), Department of Molecular Biology, Umeå University, Umeå 901 87, Sweden.
| | - Volker T Heussler
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland.
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23
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Libkind D, Peris D, Cubillos FA, Steenwyk JL, Opulente DA, Langdon QK, Rokas A, Hittinger CT. Into the wild: new yeast genomes from natural environments and new tools for their analysis. FEMS Yeast Res 2020; 20:foaa008. [PMID: 32009143 PMCID: PMC7067299 DOI: 10.1093/femsyr/foaa008] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 01/31/2020] [Indexed: 12/16/2022] Open
Abstract
Genomic studies of yeasts from the wild have increased considerably in the past few years. This revolution has been fueled by advances in high-throughput sequencing technologies and a better understanding of yeast ecology and phylogeography, especially for biotechnologically important species. The present review aims to first introduce new bioinformatic tools available for the generation and analysis of yeast genomes. We also assess the accumulated genomic data of wild isolates of industrially relevant species, such as Saccharomyces spp., which provide unique opportunities to further investigate the domestication processes associated with the fermentation industry and opportunistic pathogenesis. The availability of genome sequences of other less conventional yeasts obtained from the wild has also increased substantially, including representatives of the phyla Ascomycota (e.g. Hanseniaspora) and Basidiomycota (e.g. Phaffia). Here, we review salient examples of both fundamental and applied research that demonstrate the importance of continuing to sequence and analyze genomes of wild yeasts.
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Affiliation(s)
- D Libkind
- Centro de Referencia en Levaduras y Tecnología Cervecera (CRELTEC), Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales (IPATEC) – CONICET/Universidad Nacional del Comahue, Quintral 1250 (8400), Bariloche., Argentina
| | - D Peris
- Department of Food Biotechnology, Institute of Agrochemistry and Food Technology-CSIC, Calle Catedrático Dr. D. Agustin Escardino Benlloch n°7, 46980 Paterna, Valencia, Spain
| | - F A Cubillos
- Millennium Institute for Integrative Biology (iBio). General del Canto 51 (7500574), Santiago
- Universidad de Santiago de Chile, Facultad de Química y Biología, Departamento de Biología. Alameda 3363 (9170002). Estación Central. Santiago, Chile
| | - J L Steenwyk
- Department of Biological Sciences, VU Station B#35-1634, Vanderbilt University, Nashville, TN 37235, USA
| | - D A Opulente
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, 1552 University Avenue, Madison, WI 53726-4084, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Avenue, Madison, I 53726-4084, Madison, WI, USA
| | - Q K Langdon
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, 1552 University Avenue, Madison, WI 53726-4084, USA
| | - A Rokas
- Department of Biological Sciences, VU Station B#35-1634, Vanderbilt University, Nashville, TN 37235, USA
| | - C T Hittinger
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, 1552 University Avenue, Madison, WI 53726-4084, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Avenue, Madison, I 53726-4084, Madison, WI, USA
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Comparison and Analysis of Published Genome-scale Metabolic Models of Yarrowia lipolytica. BIOTECHNOL BIOPROC E 2020. [DOI: 10.1007/s12257-019-0208-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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An T, Zha W, Zi J. Biotechnological production of betulinic acid and derivatives and their applications. Appl Microbiol Biotechnol 2020; 104:3339-3348. [DOI: 10.1007/s00253-020-10495-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/14/2020] [Accepted: 02/20/2020] [Indexed: 11/25/2022]
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Tsouka S, Hatzimanikatis V. redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast. FEMS Yeast Res 2020; 20:5739921. [DOI: 10.1093/femsyr/foaa006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
ABSTRACTOver the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.
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Affiliation(s)
- S Tsouka
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - V Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Yang J, Liang J, Shao L, Liu L, Gao K, Zhang JL, Sun Z, Xu W, Lin P, Yu R, Zi J. Green production of silybin and isosilybin by merging metabolic engineering approaches and enzymatic catalysis. Metab Eng 2020; 59:44-52. [PMID: 32004707 DOI: 10.1016/j.ymben.2020.01.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 01/23/2020] [Accepted: 01/23/2020] [Indexed: 01/30/2023]
Abstract
Silymarin extracted from milk thistle seeds, is used for treating hepatic diseases. Silybin and isosilybin are its main components, and synthesized from coupling of taxifolin and coniferyl alcohol. Here, the biosynthetic pathways of taxifolin and coniferyl alcohol were reconstructed in Saccharomyces cerevisiae for the first time. To alleviate substantial burden caused by a great deal of genetic manipulation, expression of the enzymes (e.g. ZWF1, TYR1 and ARO8) playing multiple roles in the relevant biosynthetic pathways was selectively optimized. The strain YT1035 overexpressing seven heterologous enzymes and five native enzymes and the strain YC1053 overexpressing seven heterologous enzymes and four native enzymes, respectively produce 336.8 mg/L taxifolin and 201.1 mg/L coniferyl alcohol. Silybin and isosilybin are synthesized from taxifolin and coniferyl alcohol under catalysis of APX1t (the truncated milk thistle peroxidase), with a yield of 62.5%. This study demonstrates an approach for producing silybin and isosilybin from glucose for the first time.
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Affiliation(s)
- Jiazeng Yang
- Biotechnological Institute of Chinese Materia Medic, Jinan University, Guangzhou, 510632, China
| | - Jincai Liang
- Biotechnological Institute of Chinese Materia Medic, Jinan University, Guangzhou, 510632, China
| | - Lei Shao
- Microbial Pharmacology Laboratory, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Lihong Liu
- Biotechnological Institute of Chinese Materia Medic, Jinan University, Guangzhou, 510632, China
| | - Ke Gao
- Biotechnological Institute of Chinese Materia Medic, Jinan University, Guangzhou, 510632, China
| | - Jun-Liang Zhang
- State Key Laboratory of New Drug and Pharmaceutical Process, Shanghai Institute of Pharmaceutical Industry, Shanghai, 200040, China
| | - Zhenjiao Sun
- Guangdong Qingyunshan Pharmaceutical Co., Ltd., Shaoguan, 512600, China
| | - Wendong Xu
- National Engineering Research Center for Modernization of Extraction and Separation Process of TCM/Guangzhou Hanfang Pharmaceutical Co., Ltd., Guangzhou, 510240, China
| | - Pengcheng Lin
- College of Pharmacy, Qinghai Nationalities University, Xining, 810007, China
| | - Rongmin Yu
- Biotechnological Institute of Chinese Materia Medic, Jinan University, Guangzhou, 510632, China
| | - Jiachen Zi
- Biotechnological Institute of Chinese Materia Medic, Jinan University, Guangzhou, 510632, China.
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Krishnan A, Kloehn J, Lunghi M, Chiappino-Pepe A, Waldman BS, Nicolas D, Varesio E, Hehl A, Lourido S, Hatzimanikatis V, Soldati-Favre D. Functional and Computational Genomics Reveal Unprecedented Flexibility in Stage-Specific Toxoplasma Metabolism. Cell Host Microbe 2020; 27:290-306.e11. [PMID: 31991093 DOI: 10.1016/j.chom.2020.01.002] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/02/2019] [Accepted: 01/03/2020] [Indexed: 12/31/2022]
Abstract
To survive and proliferate in diverse host environments with varying nutrient availability, the obligate intracellular parasite Toxoplasma gondii reprograms its metabolism. We have generated and curated a genome-scale metabolic model (iTgo) for the fast-replicating tachyzoite stage, harmonized with experimentally observed phenotypes. To validate the importance of four metabolic pathways predicted by the model, we have performed in-depth in vitro and in vivo phenotyping of mutant parasites including targeted metabolomics and CRISPR-Cas9 fitness screening of all known metabolic genes. This led to unexpected insights into the remarkable flexibility of the parasite, addressing the dependency on biosynthesis or salvage of fatty acids (FAs), purine nucleotides (AMP and GMP), a vitamin (pyridoxal-5P), and a cofactor (heme) in both the acute and latent stages of infection. Taken together, our experimentally validated metabolic network leads to a deeper understanding of the parasite's biology, opening avenues for the development of therapeutic intervention against apicomplexans.
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Affiliation(s)
- Aarti Krishnan
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Joachim Kloehn
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Matteo Lunghi
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Anush Chiappino-Pepe
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | | | - Damien Nicolas
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Emmanuel Varesio
- School of Pharmaceutical Sciences Geneva-Lausanne (EPGL), Geneva 1211, Switzerland; Mass Spectrometry Core Facility (MZ 2.0), University of Geneva, Geneva 1211, Switzerland
| | - Adrian Hehl
- Institute of Parasitology, University of Zürich, Zürich 8057, Switzerland
| | - Sebastian Lourido
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Dominique Soldati-Favre
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland.
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Payen C, Thompson D. The renaissance of yeasts as microbial factories in the modern age of biomanufacturing. Yeast 2019; 36:685-700. [DOI: 10.1002/yea.3439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 07/09/2019] [Accepted: 08/04/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Celia Payen
- DuPont Nutrition and Biosciences Wilmington Delaware
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Abstract
On January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether (PPH) were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents. Crude MCHM is an industrial chemical used as flotation reagent to clean coal. At the time of the spill, MCHM's toxicological data were limited, an issue that has been addressed by different studies focused on understanding the immediate and long-term effects of MCHM on human health and the environment. Using S. cerevisiae as a model organism we study the effect of acute exposure to crude MCHM on metabolism. Yeasts were treated with MCHM 550 ppm in YPD for 30 minutes. Polar and lipid metabolites were extracted from cells by a chloroform-methanol-water mixture. The extracts were then analyzed by direct injection ESI-MS and by GC-MS. The metabolomics analysis was complemented with flux balance analysis simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control. We integrated the effect of MCHM on yeast gene expression from RNA-Seq data within these GSMNM. A total of 215 and 73 metabolites were identified by the ESI-MS and GC-MS procedures, respectively. From these 26 and 23 relevant metabolites were selected from ESI-MS and GC-MS respectively, for 49 unique compounds. MCHM induced amino acid accumulation, via its effects on amino acid metabolism, as well as a potential impairment of ribosome biogenesis. MCHM affects phospholipid biosynthesis, with a potential impact on the biophysical properties of yeast cellular membranes. The FBA simulations were able to reproduce the deleterious effect of MCHM on cellular growth and suggest that the effect of MCHM on ubiquinol:ferricytochrome c reductase reaction, caused by the under-expression of CYT1 gene, could be the driven force behind the observed effect on yeast metabolism and growth.
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Affiliation(s)
- Amaury Pupo
- Department of Biology, West Virginia University, Morgantown, West Virginia, United States of America
| | - Kang Mo Ku
- Division of Plant and Soil Sciences, West Virginia University, Morgantown, West Virginia, United States of America
- Department of Horticulture, College of Agriculture and Life Sciences, Chonnam National University, Gwangju, Republic of Korea
| | - Jennifer E. G. Gallagher
- Department of Biology, West Virginia University, Morgantown, West Virginia, United States of America
- * E-mail:
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Mesquita TJB, Sargo CR, Fuzer JR, Paredes SAH, Giordano RDC, Horta ACL, Zangirolami TC. Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations. Microb Cell Fact 2019; 18:150. [PMID: 31484570 PMCID: PMC6724378 DOI: 10.1186/s12934-019-1198-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 08/25/2019] [Indexed: 01/24/2023] Open
Abstract
Background Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation. Results The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae, defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L−1 h−1), with a final ethanol concentration of 87 g L−1 and yield of 0.46 gethanol gsubstrate−1. The other fermentation strategies showed lower yields (close to 0.40 gethanol gsubstrate−1) and ethanol productivity around 4.0 g L−1 h−1. Conclusion The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.
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Affiliation(s)
- Thiago José Barbosa Mesquita
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), Rodovia Washington Luís, Km 235, São Carlos, SP, 13565-905, Brazil
| | - Cíntia Regina Sargo
- Graduate Program of Chemical Engineering-Institute of Chemistry, Federal University of Goiás (PPGEQ/IQ-UFG), Avenida Esperança, Campus Samambaia, Goiânia, GO, 74690-900, Brazil
| | - José Roberto Fuzer
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), Rodovia Washington Luís, Km 235, São Carlos, SP, 13565-905, Brazil
| | - Sheyla Alexandra Hidalgo Paredes
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), Rodovia Washington Luís, Km 235, São Carlos, SP, 13565-905, Brazil
| | - Roberto de Campos Giordano
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), Rodovia Washington Luís, Km 235, São Carlos, SP, 13565-905, Brazil
| | - Antonio Carlos Luperni Horta
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), Rodovia Washington Luís, Km 235, São Carlos, SP, 13565-905, Brazil
| | - Teresa Cristina Zangirolami
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), Rodovia Washington Luís, Km 235, São Carlos, SP, 13565-905, Brazil.
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32
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Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast. Proc Natl Acad Sci U S A 2019; 116:18142-18147. [PMID: 31420515 PMCID: PMC6731661 DOI: 10.1073/pnas.1900548116] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Systems biology involves the development of large computational models of biological systems. The radical improvement of systems biology models will necessarily involve the automation of model improvement cycles. We present here a general approach to automating systems biology model improvement. Humans are eukaryotic organisms, and the yeast Saccharomyces cerevisiae is widely used in biology as a “model” for eukaryotic cells. The yeast diauxic shift is the most studied cellular transformation. We combined multiple software tools with integrated laboratory robotics to execute three semiautomated cycles of diauxic shift model improvement. All the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for execution. The resulting improved model is relevant to understanding cancer, the immune system, and aging. One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast (Saccharomyces cerevisiae) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.
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Alzoubi D, Desouki AA, Lercher MJ. Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast. Sci Rep 2019; 9:11837. [PMID: 31413270 PMCID: PMC6694147 DOI: 10.1038/s41598-019-47935-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 07/08/2019] [Indexed: 12/15/2022] Open
Abstract
Computational predictions of double gene knockout effects by flux balance analysis (FBA) have been used to characterized genome-wide patterns of epistasis in microorganisms. However, it is unclear how in silico predictions are related to in vivo epistasis, as FBA predicted only a minority of experimentally observed genetic interactions between non-essential metabolic genes in yeast. Here, we perform a detailed comparison of yeast experimental epistasis data to predictions generated with different constraint-based metabolic modeling algorithms. The tested methods comprise standard FBA; a variant of MOMA, which was specifically designed to predict fitness effects of non-essential gene knockouts; and two alternative implementations of FBA with macro-molecular crowding, which account approximately for enzyme kinetics. The number of interactions uniquely predicted by one method is typically larger than its overlap with any alternative method. Only 20% of negative and 10% of positive interactions jointly predicted by all methods are confirmed by the experimental data; almost all unique predictions appear to be false. More than two thirds of epistatic interactions are undetectable by any of the tested methods. The low prediction accuracies indicate that the physiology of yeast double metabolic gene knockouts is dominated by processes not captured by current constraint-based analysis methods.
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Affiliation(s)
- Deya Alzoubi
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany
| | - Abdelmoneim Amer Desouki
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany.
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34
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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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Rodriguez PA, Rothballer M, Chowdhury SP, Nussbaumer T, Gutjahr C, Falter-Braun P. Systems Biology of Plant-Microbiome Interactions. MOLECULAR PLANT 2019; 12:804-821. [PMID: 31128275 DOI: 10.1016/j.molp.2019.05.006] [Citation(s) in RCA: 200] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/07/2019] [Accepted: 05/15/2019] [Indexed: 05/02/2023]
Abstract
In natural environments, plants are exposed to diverse microbiota that they interact with in complex ways. While plant-pathogen interactions have been intensely studied to understand defense mechanisms in plants, many microbes and microbial communities can have substantial beneficial effects on their plant host. Such beneficial effects include improved acquisition of nutrients, accelerated growth, resilience against pathogens, and improved resistance against abiotic stress conditions such as heat, drought, and salinity. However, the beneficial effects of bacterial strains or consortia on their host are often cultivar and species specific, posing an obstacle to their general application. Remarkably, many of the signals that trigger plant immune responses are molecularly highly similar and often identical in pathogenic and beneficial microbes. Thus, it is unclear what determines the outcome of a particular microbe-host interaction and which factors enable plants to distinguish beneficials from pathogens. To unravel the complex network of genetic, microbial, and metabolic interactions, including the signaling events mediating microbe-host interactions, comprehensive quantitative systems biology approaches will be needed.
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Affiliation(s)
- Patricia A Rodriguez
- Institute of Network Biology (INET), Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Michael Rothballer
- Institute of Network Biology (INET), Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Soumitra Paul Chowdhury
- Institute of Network Biology (INET), Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Thomas Nussbaumer
- Institute of Network Biology (INET), Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany; Institute of Environmental Medicine (IEM), UNIKA-T, Technical University of Munich, Augsburg, Germany
| | - Caroline Gutjahr
- Plant Genetics, TUM School of Life Science Weihenstephan, Technical University of Munich (TUM), Freising, Germany
| | - Pascal Falter-Braun
- Institute of Network Biology (INET), Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany; Microbe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany.
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36
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Metabolism and Development during Conidial Germination in Response to a Carbon-Nitrogen-Rich Synthetic or a Natural Source of Nutrition in Neurospora crassa. mBio 2019; 10:mBio.00192-19. [PMID: 30914504 PMCID: PMC6437048 DOI: 10.1128/mbio.00192-19] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Fungal spores germinate and undergo vegetative growth, leading to either asexual or sexual reproductive dispersal. Previous research has indicated that among developmental regulatory genes, expression is conserved across nutritional environments, whereas pathways for carbon and nitrogen metabolism appear highly responsive-perhaps to accommodate differential nutritive processing. To comprehensively investigate conidial germination and the adaptive life history decision-making underlying these two modes of reproduction, we profiled transcription of Neurospora crassa germinating on two media: synthetic Bird medium, designed to promote asexual reproduction; and a natural maple sap medium, on which both asexual reproduction and sexual reproduction manifest. A later start to germination but faster development was observed on synthetic medium. Metabolic genes exhibited altered expression in response to nutrients-at least 34% of the genes in the genome were significantly downregulated during the first two stages of conidial germination on synthetic medium. Knockouts of genes exhibiting differential expression across development altered germination and growth rates, as well as in one case causing abnormal germination. A consensus Bayesian network of these genes indicated especially tight integration of environmental sensing, asexual and sexual development, and nitrogen metabolism on a natural medium, suggesting that in natural environments, a more dynamic and tentative balance of asexual and sexual development may be typical of N. crassa colonies.IMPORTANCE One of the most remarkable successes of life is its ability to flourish in response to temporally and spatially varying environments. Fungi occupy diverse ecosystems, and their sensitivity to these environmental changes often drives major fungal life history decisions, including the major switch from vegetative growth to asexual or sexual reproduction. Spore germination comprises the first and simplest stage of vegetative growth. We examined the dependence of this early life history on the nutritional environment using genome-wide transcriptomics. We demonstrated that for developmental regulatory genes, expression was generally conserved across nutritional environments, whereas metabolic gene expression was highly labile. The level of activation of developmental genes did depend on current nutrient conditions, as did the modularity of metabolic and developmental response network interactions. This knowledge is critical to the development of future technologies that could manipulate fungal growth for medical, agricultural, or industrial purposes.
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Shen F, Sun R, Yao J, Li J, Liu Q, Price ND, Liu C, Wang Z. OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling. PLoS Comput Biol 2019; 15:e1006835. [PMID: 30849073 PMCID: PMC6426274 DOI: 10.1371/journal.pcbi.1006835] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 03/20/2019] [Accepted: 02/01/2019] [Indexed: 02/07/2023] Open
Abstract
The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.
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Affiliation(s)
- Fangzhou Shen
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Renliang Sun
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Yao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Li
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Qian Liu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Chenguang Liu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhuo Wang
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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Caudy AA, Hanchard JA, Hsieh A, Shaan S, Rosebrock AP. Functional genetic discovery of enzymes using full-scan mass spectrometry metabolomics. Biochem Cell Biol 2019; 97:73-84. [DOI: 10.1139/bcb-2018-0058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Our understanding of metabolic networks is incomplete, and new enzymatic activities await discovery in well-studied organisms. Mass spectrometric measurement of cellular metabolites reveals compounds inside cells that are unexplained by current maps of metabolic reactions, and existing computational models are unable to account for all activities observed within cells. Additional large-scale genetic and biochemical approaches are required to elucidate metabolic gene function. We have used full-scan mass spectrometry metabolomics of polar small molecules to examine deletion mutants of candidate enzymes in the model yeast Saccharomyces cerevisiae. We report the identification of 25 genes whose deletion results in focal metabolic changes consistent with loss of enzymatic activity and describe the informatic approaches used to enrich for candidate enzymes from uncharacterized open reading frames. Triumphs and pitfalls of metabolic phenotyping screens are discussed, including estimates of the frequency of uncharacterized eukaryotic genes that affect metabolism and key issues to consider when searching for new enzymatic functions in other organisms.
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Affiliation(s)
- Amy A. Caudy
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Julia A. Hanchard
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Alan Hsieh
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Saravannan Shaan
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Adam P. Rosebrock
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
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Dikicioglu D, Oliver SG. Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements. Biotechnol Bioeng 2019; 116:610-621. [PMID: 30578666 PMCID: PMC6492170 DOI: 10.1002/bit.26905] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 11/21/2018] [Accepted: 12/20/2018] [Indexed: 12/15/2022]
Abstract
Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor‐utilising enzymes and transporters through constraint‐based modelling. The first eukaryotic genome‐scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron‐sulphur (Fe‐S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron‐recruiting enzyme at a rate of 3.02 × 10
−11 mmol·(g biomass)
−1·h
−1.
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Affiliation(s)
- Duygu Dikicioglu
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.,Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Stephen G Oliver
- Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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Castillo S, Patil KR, Jouhten P. Yeast Genome-Scale Metabolic Models for Simulating Genotype-Phenotype Relations. PROGRESS IN MOLECULAR AND SUBCELLULAR BIOLOGY 2019; 58:111-133. [PMID: 30911891 DOI: 10.1007/978-3-030-13035-0_5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Understanding genotype-phenotype dependency is a universal aim for all life sciences. While the complete genotype-phenotype relations remain challenging to resolve, metabolic phenotypes are moving within the reach through genome-scale metabolic model simulations. Genome-scale metabolic models are available for commonly investigated yeasts, such as model eukaryote and domesticated fermentation species Saccharomyces cerevisiae, and automatic reconstruction methods facilitate obtaining models for any sequenced species. The models allow for investigating genotype-phenotype relations through simulations simultaneously considering the effects of nutrient availability, and redox and energy homeostasis in cells. Genome-scale models also offer frameworks for omics data integration to help to uncover how the translation of genotypes to the apparent phenotypes is regulated at different levels. In this chapter, we provide an overview of the yeast genome-scale metabolic models and the simulation approaches for using these models to interrogate genotype-phenotype relations. We review the methodological approaches according to the underlying biological reasoning in order to inspire formulating novel questions and applications that the genome-scale metabolic models could contribute to. Finally, we discuss current challenges and opportunities in the genome-scale metabolic model simulations.
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Affiliation(s)
- Sandra Castillo
- VTT Technical Research Centre of Finland Ltd., Tietotie 2, 02044, Espoo, Finland
| | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Paula Jouhten
- VTT Technical Research Centre of Finland Ltd., Tietotie 2, 02044, Espoo, Finland.
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Chen Y, Li G, Nielsen J. Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges. Methods Mol Biol 2019; 2049:329-345. [PMID: 31602620 DOI: 10.1007/978-1-4939-9736-7_19] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Gang 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
| | - 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, Kongens Lyngby, Denmark.
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Abstract
Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth-rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this work, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate measurements. Using a machine-learning technique called k-nearest-neighbors regression, we build a model which predicts growth rate from gene expression. By exploiting the correlated nature of gene expression and sparsifying the model, we capture 81% of the variance in growth rate of the E. coli dataset, while reducing the number of features from >4,000 to 9. In S. cerevisiae, we account for 89% of the variance in growth rate, while reducing from >5,500 dimensions to 18. Such a model provides a basis for selecting successful strategies from among the combinatorial number of experimental possibilities when attempting to optimize complex phenotypic traits like growth rate.
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Mancini A, Eyassu F, Conway M, Occhipinti A, Liò P, Angione C, Pucciarelli S. CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design. BMC Bioinformatics 2018; 19:442. [PMID: 30497359 PMCID: PMC6266953 DOI: 10.1186/s12859-018-2422-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates. Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes. RESULTS After generating the model, we performed an in-silico simulation with the presence and absence of glucose. The lack of this nutrient caused a 32.1% reduction rate in biomass synthesis. Despite the glucose starvation, the growth did not stop due to the use of alternative carbon sources such as amino acids. CONCLUSIONS The future models obtained from CiliateGEM may represent a new approach to describe the metabolism of ciliates. This tool will be a useful resource for the ciliate research community in order to extend these species as model organisms in different research fields. An improved understanding of ciliate metabolism could be relevant to elucidate the basis of biological phenomena like genotype-phenotype relationships, population genetics, and cilia-related disease mechanisms.
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Affiliation(s)
- Alessio Mancini
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Filmon Eyassu
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Maxwell Conway
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | | | - Pietro Liò
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Sandra Pucciarelli
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
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Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches. FERMENTATION-BASEL 2018. [DOI: 10.3390/fermentation4040092] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.
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Lian J, Mishra S, Zhao H. Recent advances in metabolic engineering of Saccharomyces cerevisiae: New tools and their applications. Metab Eng 2018; 50:85-108. [DOI: 10.1016/j.ymben.2018.04.011] [Citation(s) in RCA: 140] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 04/09/2018] [Accepted: 04/13/2018] [Indexed: 10/17/2022]
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Sertbas M, Ulgen KO. Unlocking Human Brain Metabolism by Genome-Scale and Multiomics Metabolic Models: Relevance for Neurology Research, Health, and Disease. ACTA ACUST UNITED AC 2018; 22:455-467. [DOI: 10.1089/omi.2018.0088] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Mustafa Sertbas
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
| | - Kutlu O. Ulgen
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
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47
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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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Labhsetwar P, Melo MCR, Cole JA, Luthey-Schulten Z. Population FBA predicts metabolic phenotypes in yeast. PLoS Comput Biol 2017; 13:e1005728. [PMID: 28886026 PMCID: PMC5626512 DOI: 10.1371/journal.pcbi.1005728] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 10/03/2017] [Accepted: 08/21/2017] [Indexed: 01/21/2023] Open
Abstract
Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells are predicted to perform significant amount of respiration, use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to recover the observed growth rate distribution in SD medium. No two living cells are exactly the same. Even cells from a clonal population with identical genomes living in the same environment will express proteins in different numbers simply due to the random nature of the chemistry involved in gene expression. The consequences of this stochastic gene expression are complex and not well understood, especially at the level of large reaction networks like metabolism. Here we investigate how variability in the copy numbers of metabolic enzymes affects how individual cells extract nourishment from their environment and grow. We model 100,000 independent yeast cells, each with their own set of enzyme copy numbers sampled from experimental distributions, and use flux balance analysis (FBA) to compute the optimal way that each cell can use its metabolic pathways—an approach we dubbed Population FBA. We find that enzyme variability gives rise to a wide distribution of growth rates, and several metabolic phenotypes—subpopulations relying on diverse metabolic pathways. Most importantly, we compare the predicted fluxes through the different pathways to experimental values; we find that Population FBA is able to correctly predict Crabtree effect, while traditional FBA, which lacks the proteomics constraints our method imposes, differs both qualitatively and quantitatively from experiment.
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Affiliation(s)
- Piyush Labhsetwar
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Marcelo C. R. Melo
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - John A. Cole
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Zaida Luthey-Schulten
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- * E-mail:
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Extracellular Microbial Metabolomics: The State of the Art. Metabolites 2017; 7:metabo7030043. [PMID: 28829385 PMCID: PMC5618328 DOI: 10.3390/metabo7030043] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 08/21/2017] [Accepted: 08/22/2017] [Indexed: 02/04/2023] Open
Abstract
Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than the extraction and analysis of intracellular metabolites as there is no need for cell rupture. Many analytical methods are already available and have been used for the analysis of extracellular metabolites from microorganisms over the last two decades. Here, we review the applications and benefits of extracellular metabolite analysis. We also discuss different sample preparation protocols available in the literature for both types (e.g., metabolites in solution and in gas) of extracellular microbial metabolites. Lastly, we evaluate the authenticity of using extracellular metabolomics data in the metabolic modelling of different industrially important microorganisms.
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50
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Lopes H, Rocha I. Genome-scale modeling of yeast: chronology, applications and critical perspectives. FEMS Yeast Res 2017; 17:3950252. [PMID: 28899034 PMCID: PMC5812505 DOI: 10.1093/femsyr/fox050] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/07/2017] [Indexed: 01/21/2023] Open
Abstract
Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.
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Affiliation(s)
- Helder Lopes
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
| | - Isabel Rocha
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
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