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Zhang L, Ye JW, Li G, Park H, Luo H, Lin Y, Li S, Yang W, Guan Y, Wu F, Huang W, Wu Q, Scrutton NS, Nielsen J, Chen GQ. A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM. Metab Eng 2024; 84:95-108. [PMID: 38901556 DOI: 10.1016/j.ymben.2024.06.004] [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: 01/01/2024] [Revised: 05/02/2024] [Accepted: 06/06/2024] [Indexed: 06/22/2024]
Abstract
Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.
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Affiliation(s)
- Lizhan Zhang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Jian-Wen Ye
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden
| | - Helen Park
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Hao Luo
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden
| | - Yina Lin
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Shaowei Li
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Weinan Yang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yuying Guan
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Fuqing Wu
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Wuzhe Huang
- PhaBuilder Biotechnol Co. Ltd., PhaBuilder Biotech Co. Ltd., Shunyi District, Zhaoquan Ying, Beijing, 101309, China
| | - Qiong Wu
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Nigel S Scrutton
- Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester, M1 7DN, UK
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
| | - Guo-Qiang Chen
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China; MOE Key Laboratory for Industrial Biocatalysts, Dept Chemical Engineering, Tsinghua University, Beijing, 100084, China; Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China.
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2
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Camargo FDG, Santamaria-Torres M, Cala MP, Guevara-Suarez M, Restrepo SR, Sánchez-Camargo A, Fernández-Niño M, Corujo M, Gallo Molina AC, Cifuentes J, Serna JA, Cruz JC, Muñoz-Camargo C, Gonzalez Barrios AF. Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts. Metabolites 2023; 13:788. [PMID: 37512495 PMCID: PMC10385671 DOI: 10.3390/metabo13070788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/30/2023] Open
Abstract
Over the past decades, Colombia has suffered complex social problems related to illicit crops, including forced displacement, violence, and environmental damage, among other consequences for vulnerable populations. Considerable effort has been made in the regulation of illicit crops, predominantly Cannabis sativa, leading to advances such as the legalization of medical cannabis and its derivatives, the improvement of crops, and leaving an open window to the development of scientific knowledge to explore alternative uses. It is estimated that C. sativa can produce approximately 750 specialized secondary metabolites. Some of the most relevant due to their anticancer properties, besides cannabinoids, are monoterpenes, sesquiterpenoids, triterpenoids, essential oils, flavonoids, and phenolic compounds. However, despite the increase in scientific research on the subject, it is necessary to study the primary and secondary metabolism of the plant and to identify key pathways that explore its great metabolic potential. For this purpose, a genome-scale metabolic reconstruction of C. sativa is described and contextualized using LC-QTOF-MS metabolic data obtained from the leaf extract from plants grown in the region of Pesca-Boyaca, Colombia under greenhouse conditions at the Clever Leaves facility. A compartmentalized model with 2101 reactions and 1314 metabolites highlights pathways associated with fatty acid biosynthesis, steroids, and amino acids, along with the metabolism of purine, pyrimidine, glucose, starch, and sucrose. Key metabolites were identified through metabolomic data, such as neurine, cannabisativine, cannflavin A, palmitoleic acid, cannabinoids, geranylhydroquinone, and steroids. They were analyzed and integrated into the reconstruction, and their potential applications are discussed. Cytotoxicity assays revealed high anticancer activity against gastric adenocarcinoma (AGS), melanoma cells (A375), and lung carcinoma cells (A549), combined with negligible impact against healthy human skin cells.
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Affiliation(s)
- Fidias D González Camargo
- Group of Product and Process Design, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá 111711, Colombia
- Applied Genomics Research Group Vice-Presidency for Research and Creation, Universidad de los Andes, Bogotá 111711, Colombia
| | - Mary Santamaria-Torres
- Metabolomics Core Facility-MetCore Vice-Presidency for Research and Creation, Universidad de los Andes, Bogotá 111711, Colombia
| | - Mónica P Cala
- Metabolomics Core Facility-MetCore Vice-Presidency for Research and Creation, Universidad de los Andes, Bogotá 111711, Colombia
| | - Marcela Guevara-Suarez
- Applied Genomics Research Group Vice-Presidency for Research and Creation, Universidad de los Andes, Bogotá 111711, Colombia
| | - Silvia Restrepo Restrepo
- Laboratory of Mycology and Phytopathology (LAMFU), Department of Biological Sciences and Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá 111711, Colombia
| | - Andrea Sánchez-Camargo
- Group of Product and Process Design, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá 111711, Colombia
| | - Miguel Fernández-Niño
- Leibniz-Institute of Plant Biochemistry, Department of Bioorganic Chemistry, Weinberg 3, 06110 Halle, Germany
| | - María Corujo
- Ecomedics S.A.S., Commercially Known as Clever Leaves, Calle 95 # 11A-94, Bogota 110221, Colombia
| | - Ada Carolina Gallo Molina
- Chemical and Biochemical Processes Group, Department of Chemical and Environmental Engineering, National University of Colombia, Bogotá 11001, Colombia
| | - Javier Cifuentes
- Research Group on Nanobiomaterials, Cell Engineering and Bioprinting (GINIB), Department of Biomedical Engineering, Universidad de los Andes, Bogotá 111711, Colombia
| | - Julian A Serna
- Research Group on Nanobiomaterials, Cell Engineering and Bioprinting (GINIB), Department of Biomedical Engineering, Universidad de los Andes, Bogotá 111711, Colombia
| | - Juan C Cruz
- Research Group on Nanobiomaterials, Cell Engineering and Bioprinting (GINIB), Department of Biomedical Engineering, Universidad de los Andes, Bogotá 111711, Colombia
| | - Carolina Muñoz-Camargo
- Research Group on Nanobiomaterials, Cell Engineering and Bioprinting (GINIB), Department of Biomedical Engineering, Universidad de los Andes, Bogotá 111711, Colombia
| | - Andrés F Gonzalez Barrios
- Group of Product and Process Design, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá 111711, Colombia
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Abdullah-Zawawi MR, Govender N, Harun S, Muhammad NAN, Zainal Z, Mohamed-Hussein ZA. Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom. PLANTS (BASEL, SWITZERLAND) 2022; 11:2614. [PMID: 36235479 PMCID: PMC9573505 DOI: 10.3390/plants11192614] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.
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Affiliation(s)
- Muhammad-Redha Abdullah-Zawawi
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nisha Govender
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Sarahani Harun
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zamri Zainal
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
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4
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Sahu A, Blätke MA, Szymański JJ, Töpfer N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput Struct Biotechnol J 2021; 19:4626-4640. [PMID: 34471504 PMCID: PMC8382995 DOI: 10.1016/j.csbj.2021.08.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Affiliation(s)
- Ankur Sahu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Jędrzej Jakub Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Nadine Töpfer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
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5
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Škrlj B, Novak MP, Brader G, Anžič B, Ramšak Ž, Gruden K, Kralj J, Kladnik A, Lavrač N, Roitsch T, Dermastia M. New Cross-Talks between Pathways Involved in Grapevine Infection with ' Candidatus Phytoplasma solani' Revealed by Temporal Network Modelling. PLANTS (BASEL, SWITZERLAND) 2021; 10:646. [PMID: 33805409 PMCID: PMC8065506 DOI: 10.3390/plants10040646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/18/2021] [Accepted: 03/24/2021] [Indexed: 12/17/2022]
Abstract
Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with 'Candidatus Phytoplasma solani', and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with 'Ca. P. solani'. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to 'Ca. P. solani' to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.
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Affiliation(s)
- Blaž Škrlj
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Maruša Pompe Novak
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
- School of Viticulture and Enology, University of Nova Gorica, 5271 Vipava, Slovenia
| | - Günter Brader
- Austrian Institute of Technology, Bioresources Unit, 3430 Tulln, Austria;
| | - Barbara Anžič
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Živa Ramšak
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Kristina Gruden
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Jan Kralj
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Aleš Kladnik
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Nada Lavrač
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, 2630 Taastrup, Denmark;
| | - Marina Dermastia
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
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6
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Environment-coupled models of leaf metabolism. Biochem Soc Trans 2021; 49:119-129. [PMID: 33492365 PMCID: PMC7925006 DOI: 10.1042/bst20200059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/30/2020] [Accepted: 12/17/2020] [Indexed: 12/15/2022]
Abstract
The plant leaf is the main site of photosynthesis. This process converts light energy and inorganic nutrients into chemical energy and organic building blocks for the biosynthesis and maintenance of cellular components and to support the growth of the rest of the plant. The leaf is also the site of gas–water exchange and due to its large surface, it is particularly vulnerable to pathogen attacks. Therefore, the leaf's performance and metabolic modes are inherently determined by its interaction with the environment. Mathematical models of plant metabolism have been successfully applied to study various aspects of photosynthesis, carbon and nitrogen assimilation and metabolism, aided suggesting metabolic intervention strategies for optimized leaf performance, and gave us insights into evolutionary drivers of plant metabolism in various environments. With the increasing pressure to improve agricultural performance in current and future climates, these models have become important tools to improve our understanding of plant–environment interactions and to propel plant breeders efforts. This overview article reviews applications of large-scale metabolic models of leaf metabolism to study plant–environment interactions by means of flux-balance analysis. The presented studies are organized in two ways — by the way the environment interactions are modelled — via external constraints or data-integration and by the studied environmental interactions — abiotic or biotic.
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7
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Fang X, Lloyd CJ, Palsson BO. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat Rev Microbiol 2020; 18:731-743. [PMID: 32958892 PMCID: PMC7981288 DOI: 10.1038/s41579-020-00440-4] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.
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Affiliation(s)
- Xin Fang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Colton J Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
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8
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Walker LP, Buhler D. Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology. Ind Biotechnol (New Rochelle N Y) 2020. [DOI: 10.1089/ind.2020.29222.lpw] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Affiliation(s)
- Larry P. Walker
- Biosystems and Agricultural Engineering Department, Michigan State University, East Lansing, Michigan, USA
- Somaiya Vidyavihar University, Mumbai, India
- Biological and Environmental Engineering Department, Cornell University, Ithaca, New York, USA
| | - Douglas Buhler
- Michigan State University AgBioResearch and Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
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9
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Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, Goh HH, Aizat WM. Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology. FRONTIERS IN PLANT SCIENCE 2020; 11:944. [PMID: 32754171 PMCID: PMC7371031 DOI: 10.3389/fpls.2020.00944] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/10/2020] [Indexed: 05/03/2023]
Abstract
Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.
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Affiliation(s)
- Ili Nadhirah Jamil
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Juwairiah Remali
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Kamalrul Azlan Azizan
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Masanori Arita
- Bioinformation & DDBJ Center, National Institute of Genetics (NIG), Mishima, Japan
- Metabolome Informatics Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Hoe-Han Goh
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Wan Mohd Aizat
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
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10
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Srinivasan A, S V, Raman K, Srivastava S. Rational metabolic engineering for enhanced alpha-tocopherol production in Helianthus annuus cell culture. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.107256] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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11
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Fürtauer L, Weiszmann J, Weckwerth W, Nägele T. Dynamics of Plant Metabolism during Cold Acclimation. Int J Mol Sci 2019; 20:E5411. [PMID: 31671650 PMCID: PMC6862541 DOI: 10.3390/ijms20215411] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 10/28/2019] [Accepted: 10/29/2019] [Indexed: 12/26/2022] Open
Abstract
Plants have evolved strategies to tightly regulate metabolism during acclimation to a changing environment. Low temperature significantly constrains distribution, growth and yield of many temperate plant species. Exposing plants to low but non-freezing temperature induces a multigenic processes termed cold acclimation, which eventually results in an increased freezing tolerance. Cold acclimation comprises reprogramming of the transcriptome, proteome and metabolome and affects communication and signaling between subcellular organelles. Carbohydrates play a central role in this metabolic reprogramming. This review summarizes current knowledge about the role of carbohydrate metabolism in plant cold acclimation with a focus on subcellular metabolic reprogramming, its thermodynamic constraints under low temperature and mathematical modelling of metabolism.
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Affiliation(s)
- Lisa Fürtauer
- Plant Evolutionary Cell Biology, Department Biology I, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Bavaria, Germany.
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna 1090, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna 1090, Austria.
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna 1090, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna 1090, Austria.
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Department Biology I, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Bavaria, Germany.
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12
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Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019; 15:e1007084. [PMID: 31295267 PMCID: PMC6622478 DOI: 10.1371/journal.pcbi.1007084] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
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Affiliation(s)
- Guido Zampieri
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
- Healthcare Innovation Centre, Teesside University, Middlesbrough, United Kingdom
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13
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Abstract
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
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Affiliation(s)
- Changdai Gu
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Won Jun Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Systems Biology and Medicine Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
| | - Sang Yup Lee
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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14
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Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, Gruden K, Stitt M, Bolger ME, Usadel B. MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. MOLECULAR PLANT 2019; 12:879-892. [PMID: 30639314 DOI: 10.1016/j.molp.2019.01.003] [Citation(s) in RCA: 245] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 12/14/2018] [Accepted: 01/01/2019] [Indexed: 05/18/2023]
Abstract
Genome sequences from over 200 plant species have already been published, with this number expected to increase rapidly due to advances in sequencing technologies. Once a new genome has been assembled and the genes identified, the functional annotation of their putative translational products, proteins, using ontologies is of key importance as it places the sequencing data in a biological context. Furthermore, to keep pace with rapid production of genome sequences, this functional annotation process must be fully automated. Here we present a redesigned and significantly enhanced MapMan4 framework, together with a revised version of the associated online Mercator annotation tool. Compared with the original MapMan, the new ontology has been expanded almost threefold and enforces stricter assignment rules. This framework was then incorporated into Mercator4, which has been upgraded to reflect current knowledge across the land plant group, providing protein annotations for all embryophytes with a comparably high quality. The annotation process has been optimized to allow a plant genome to be annotated in a matter of minutes. The output results continue to be compatible with the established MapMan desktop application.
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Affiliation(s)
- Rainer Schwacke
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Gabriel Y Ponce-Soto
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Kirsten Krause
- Department of Arctic and Marine Biology, The Arctic University of Norway, Biology Building, 9037 Tromsø, Norway
| | - Anthony M Bolger
- Institute for Botany and Molecular Genetics, BioEconomy Science Center, Worringer Weg, RWTH Aachen University, 52074 Aachen, Germany
| | - Borjana Arsova
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Asis Hallab
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Kristina Gruden
- National Institute of Biology, Department of Biotechnology and Systems Biology, Večna Pot 111, 1000 Ljubljana, Slovenia
| | - Mark Stitt
- Max Planck Institute for Molecular Plant Physiology, Department of Systems Regulation, 14476 Potsdam-Golm, Germany
| | - Marie E Bolger
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany.
| | - Björn Usadel
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany; Institute for Botany and Molecular Genetics, BioEconomy Science Center, Worringer Weg, RWTH Aachen University, 52074 Aachen, Germany
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15
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Gomes de Oliveira Dal'Molin C, Quek LE, Saa PA, Palfreyman R, Nielsen LK. From reconstruction to C 4 metabolic engineering: A case study for overproduction of polyhydroxybutyrate in bioenergy grasses. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2018; 273:50-60. [PMID: 29907309 DOI: 10.1016/j.plantsci.2018.03.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 02/27/2018] [Accepted: 03/01/2018] [Indexed: 06/08/2023]
Abstract
The compartmentalization of C4 plants increases photosynthetic efficiency, while constraining how material and energy must flow in leaf tissues. To capture this metabolic phenomenon, a generic plant metabolic reconstruction was replicated into four connected spatiotemporal compartments, namely bundle sheath (B) and mesophyll (M) across the day and night cycle. The C4 leaf model was used to explore how amenable polyhydroxybutyrate (PHB) production is with these four compartments working cooperatively. A strategic pattern of metabolite conversion and exchange emerged from a systems-level network that has very few constraints imposed; mainly the sequential two-step carbon capture in mesophyll, then bundle sheath and photosynthesis during the day only. The building of starch reserves during the day and their mobilization during the night connects day and night metabolism. Flux simulations revealed that PHB production did not require rerouting of metabolic pathways beyond what is already utilised for growth. PHB yield was sensitive to photoassimilation capacity, availability of carbon reserves, ATP maintenance, relative photosynthetic activity of B and M, and type of metabolites exchanged in the plasmodesmata, but not sensitive towards compartmentalization. Hence, the compartmentalization issues currently encountered are likely to be kinetic or thermodynamic limitations rather than stoichiometric.
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Affiliation(s)
- Cristiana Gomes de Oliveira Dal'Molin
- Australian Institute for Bioengineering and Nanotechnology, School of Chemical Engineering, University of Queensland, Brisbane, Queensland 4072, Australia.
| | - Lake-Ee Quek
- School of Mathematics and Statistics, The University of Sydney, New South Wales 2006, Australia
| | - Pedro A Saa
- Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile, Santiago, Casilla 306, Correo 22, Chile; Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile
| | - Robin Palfreyman
- Australian Institute for Bioengineering and Nanotechnology, School of Chemical Engineering, University of Queensland, Brisbane, Queensland 4072, Australia
| | - Lars Keld Nielsen
- Australian Institute for Bioengineering and Nanotechnology, School of Chemical Engineering, University of Queensland, Brisbane, Queensland 4072, Australia; Novo Nordisk Foundation Center for Biosustainability, The Technical University of Denmark, Lyngby, DK-2800, Denmark
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16
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Zager JJ, Lange BM. Assessing Flux Distribution Associated with Metabolic Specialization of Glandular Trichomes. TRENDS IN PLANT SCIENCE 2018; 23:638-647. [PMID: 29735428 DOI: 10.1016/j.tplants.2018.04.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 03/27/2018] [Accepted: 04/07/2018] [Indexed: 05/22/2023]
Abstract
Many aromatic plants accumulate mixtures of secondary (or specialized) metabolites in anatomical structures called glandular trichomes (GTs). Different GT types may also synthesize different mixtures of secreted metabolites, and this contributes to the enormous chemical diversity reported to occur across species. Over the past two decades, significant progress has been made in characterizing the genes and enzymes that are responsible for the unique metabolic capabilities of GTs in different lineages of flowering plants. Less is known about the processes that regulate flux distribution through precursor pathways toward metabolic end-products. We discuss here the results from a meta-analysis of genome-scale models that were developed to capture the unique metabolic capabilities of different GT types.
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Affiliation(s)
- Jordan J Zager
- Institute of Biological Chemistry and M.J. Murdock Metabolomics Laboratory, Washington State University, Pullman, WA 99164, USA
| | - B Markus Lange
- Institute of Biological Chemistry and M.J. Murdock Metabolomics Laboratory, Washington State University, Pullman, WA 99164, USA.
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17
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Shaw R, Cheung CYM. A Dynamic Multi-Tissue Flux Balance Model Captures Carbon and Nitrogen Metabolism and Optimal Resource Partitioning During Arabidopsis Growth. FRONTIERS IN PLANT SCIENCE 2018; 9:884. [PMID: 29997643 PMCID: PMC6028781 DOI: 10.3389/fpls.2018.00884] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 06/06/2018] [Indexed: 05/19/2023]
Abstract
Plant metabolism is highly adapted in response to its surrounding for acquiring limiting resources. In this study, a dynamic flux balance modeling framework with a multi-tissue (leaf and root) diel genome-scale metabolic model of Arabidopsis thaliana was developed and applied to investigate the reprogramming of plant metabolism through multiple growth stages under different nutrient availability. The framework allowed the modeling of optimal partitioning of resources and biomass in leaf and root over diel phases. A qualitative flux map of carbon and nitrogen metabolism was identified which was consistent across growth phases under both nitrogen rich and limiting conditions. Results from the model simulations suggested distinct metabolic roles in nitrogen metabolism played by enzymes with different cofactor specificities. Moreover, the dynamic model was used to predict the effect of physiological or environmental perturbation on the growth of Arabidopsis leaves and roots.
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18
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Sonnewald U, Fernie AR. Next-generation strategies for understanding and influencing source-sink relations in crop plants. CURRENT OPINION IN PLANT BIOLOGY 2018; 43:63-70. [PMID: 29428477 DOI: 10.1016/j.pbi.2018.01.004] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 12/21/2017] [Accepted: 01/10/2018] [Indexed: 05/03/2023]
Abstract
Whether plants are source or sink limited, that is, whether carbon assimilation or rather assimilate usage is ultimately responsible for crop yield, has been the subject of intense debate over several decades. Here we provide a short review of this debate before focusing on the use of transgenic intervention as a means to influence yield by modifying either source or sink function (or both). Given the relatively low success rates of strategies targeting single genes we highlight the success of multi-target transformations. The emergence of whole plant models and the potential impact that these will have in aiding yield improvement strategies are then discussed. We end by providing our perspective for next generation strategies for improving crop plants by means of manipulating their source-sink relations.
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Affiliation(s)
- Uwe Sonnewald
- Division of Biochemistry, Department of Biology, University of Erlangen-Nürnberg, Staudtstr. 5, 91058 Erlangen, Germany.
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
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19
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Faraji M, Fonseca LL, Escamilla-Treviño L, Barros-Rios J, Engle N, Yang ZK, Tschaplinski TJ, Dixon RA, Voit EO. Mathematical models of lignin biosynthesis. BIOTECHNOLOGY FOR BIOFUELS 2018; 11:34. [PMID: 29449882 PMCID: PMC5806469 DOI: 10.1186/s13068-018-1028-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 01/20/2018] [Indexed: 05/26/2023]
Abstract
BACKGROUND Lignin is a natural polymer that is interwoven with cellulose and hemicellulose within plant cell walls. Due to this molecular arrangement, lignin is a major contributor to the recalcitrance of plant materials with respect to the extraction of sugars and their fermentation into ethanol, butanol, and other potential bioenergy crops. The lignin biosynthetic pathway is similar, but not identical in different plant species. It is in each case comprised of a moderate number of enzymatic steps, but its responses to manipulations, such as gene knock-downs, are complicated by the fact that several of the key enzymes are involved in several reaction steps. This feature poses a challenge to bioenergy production, as it renders it difficult to select the most promising combinations of genetic manipulations for the optimization of lignin composition and amount. RESULTS Here, we present several computational models than can aid in the analysis of data characterizing lignin biosynthesis. While minimizing technical details, we focus on the questions of what types of data are particularly useful for modeling and what genuine benefits the biofuel researcher may gain from the resulting models. We demonstrate our analysis with mathematical models for black cottonwood (Populus trichocarpa), alfalfa (Medicago truncatula), switchgrass (Panicum virgatum) and the grass Brachypodium distachyon. CONCLUSIONS Despite commonality in pathway structure, different plant species show different regulatory features and distinct spatial and topological characteristics. The putative lignin biosynthes pathway is not able to explain the plant specific laboratory data, and the necessity of plant specific modeling should be heeded.
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Affiliation(s)
- Mojdeh Faraji
- The Wallace H. Coulter, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313, Ferst Drive, Atlanta, GA 30332 USA
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
| | - Luis L. Fonseca
- The Wallace H. Coulter, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313, Ferst Drive, Atlanta, GA 30332 USA
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
| | - Luis Escamilla-Treviño
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
- Department of Biological Sciences, University of North Texas, 1155 Union Circle #305220, Denton, TX 76203-5017 USA
| | - Jaime Barros-Rios
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
- Department of Biological Sciences, University of North Texas, 1155 Union Circle #305220, Denton, TX 76203-5017 USA
| | - Nancy Engle
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
- Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831 USA
| | - Zamin K. Yang
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
- Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831 USA
| | - Timothy J. Tschaplinski
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
- Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831 USA
| | - Richard A. Dixon
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
- Department of Biological Sciences, University of North Texas, 1155 Union Circle #305220, Denton, TX 76203-5017 USA
| | - Eberhard O. Voit
- The Wallace H. Coulter, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313, Ferst Drive, Atlanta, GA 30332 USA
- BioEnergy Sciences Center (BESC), Oak Ridge National Lab, Oak Ridge, TN USA
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20
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Gomes de Oliveira Dal'Molin C, Nielsen LK. Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses. Curr Opin Biotechnol 2017; 49:42-48. [PMID: 28806583 DOI: 10.1016/j.copbio.2017.07.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 07/17/2017] [Accepted: 07/17/2017] [Indexed: 10/25/2022]
Abstract
In this review, we present the latest developments in plant systems biology with particular emphasis on plant genome-scale reconstructions and multi-omics analyses. Understanding multicellular metabolism is far from trivial and 'omics' data are difficult to interpret in the absence of a systems framework. 'Omics' data appropriately integrated with genome-scale reconstructions and modelling facilitates our understanding of how individual components interact and influence overall cell, tissue or organisms function. Here we present examples of how plant metabolic reconstructions and modelling are used as a systems-based framework for improving our understanding of the plant metabolic processes in single cells and multiple tissues.
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Affiliation(s)
| | - Lars Keld Nielsen
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland 4072, Australia.
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21
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22
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Sweetlove LJ, Nielsen J, Fernie AR. Engineering central metabolism - a grand challenge for plant biologists. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 90:749-763. [PMID: 28004455 DOI: 10.1111/tpj.13464] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Revised: 12/14/2016] [Accepted: 12/15/2016] [Indexed: 06/06/2023]
Abstract
The goal of increasing crop productivity and nutrient-use efficiency is being addressed by a number of ambitious research projects seeking to re-engineer photosynthetic biochemistry. Many of these projects will require the engineering of substantial changes in fluxes of central metabolism. However, as has been amply demonstrated in simpler systems such as microbes, central metabolism is extremely difficult to rationally engineer. This is because of multiple layers of regulation that operate to maintain metabolic steady state and because of the highly connected nature of central metabolism. In this review we discuss new approaches for metabolic engineering that have the potential to address these problems and dramatically improve the success with which we can rationally engineer central metabolism in plants. In particular, we advocate the adoption of an iterative 'design-build-test-learn' cycle using fast-to-transform model plants as test beds. This approach can be realised by coupling new molecular tools to incorporate multiple transgenes in nuclear and plastid genomes with computational modelling to design the engineering strategy and to understand the metabolic phenotype of the engineered organism. We also envisage that mutagenesis could be used to fine-tune the balance between the endogenous metabolic network and the introduced enzymes. Finally, we emphasise the importance of considering the plant as a whole system and not isolated organs: the greatest increase in crop productivity will be achieved if both source and sink metabolism are engineered.
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Affiliation(s)
- Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41128, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark
- Science for Life Laboratory, Royal Institute of Technology, SE17121, Stockholm, Sweden
| | - Alisdair R Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany
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23
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Nägele T, Fürtauer L, Nagler M, Weiszmann J, Weckwerth W. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information. Front Mol Biosci 2016; 3:6. [PMID: 27014700 PMCID: PMC4779852 DOI: 10.3389/fmolb.2016.00006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 02/19/2016] [Indexed: 12/01/2022] Open
Abstract
The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian, or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels, and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome, and metabolome has become a common part of many systems biological studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e., first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e., second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities, and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®.
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Affiliation(s)
- Thomas Nägele
- Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria; Vienna Metabolomics Center, University of ViennaVienna, Austria
| | - Lisa Fürtauer
- Department of Ecogenomics and Systems Biology, University of Vienna Vienna, Austria
| | - Matthias Nagler
- Department of Ecogenomics and Systems Biology, University of Vienna Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, University of Vienna Vienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria; Vienna Metabolomics Center, University of ViennaVienna, Austria
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24
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Dersch LM, Beckers V, Wittmann C. Green pathways: Metabolic network analysis of plant systems. Metab Eng 2016; 34:1-24. [DOI: 10.1016/j.ymben.2015.12.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 11/30/2015] [Accepted: 12/01/2015] [Indexed: 12/18/2022]
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25
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Yuan H, Cheung CYM, Hilbers PAJ, van Riel NAW. Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions. FRONTIERS IN PLANT SCIENCE 2016; 7:537. [PMID: 27200014 PMCID: PMC4845513 DOI: 10.3389/fpls.2016.00537] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 04/05/2016] [Indexed: 05/22/2023]
Abstract
The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.
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Affiliation(s)
- Huili Yuan
- Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
| | | | - Peter A. J. Hilbers
- Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of TechnologyEindhoven, Netherlands
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of TechnologyEindhoven, Netherlands
- Natal A. W. van Riel
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26
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Chaudhary N, Tøndel K, Bhatnagar R, Martins dos Santos VAP, Puchałka J. Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling. MOLECULAR BIOSYSTEMS 2016; 12:994-1005. [DOI: 10.1039/c5mb00457h] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Sampling of the optimal flux space using modified LHS gives a more uniform coverage than Monte-Carlo Sampling. Analysis of the flux data shows that majority of variation in the flux distribution pattern within the space arises due to the presence of few alternate pathways.
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Affiliation(s)
- Neha Chaudhary
- Laboratory of Genetic Engineering and Molecular Biology
- School of Biotechnology
- Jawaharlal Nehru University
- New Delhi -110067
- India
| | - Kristin Tøndel
- Department of Mathematical Sciences and Technology
- Norwegian University of Life Sciences
- 1432 Ås
- Norway
| | - Rakesh Bhatnagar
- Laboratory of Genetic Engineering and Molecular Biology
- School of Biotechnology
- Jawaharlal Nehru University
- New Delhi -110067
- India
| | - Vítor A. P. Martins dos Santos
- Chair of Systems and Synthetic Biology
- Wageningen University Dreijenplein 10
- 6703 HB Wageningen
- The Netherlands
- Synthetic and Systems Biology Research Group
| | - Jacek Puchałka
- Synthetic and Systems Biology Research Group
- Helmholtz Centre for Infection Research
- Braunschweig
- Germany
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27
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de Oliveira Dal'Molin CG, Orellana C, Gebbie L, Steen J, Hodson MP, Chrysanthopoulos P, Plan MR, McQualter R, Palfreyman RW, Nielsen LK. Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses. FRONTIERS IN PLANT SCIENCE 2016; 7:1138. [PMID: 27559337 PMCID: PMC4978736 DOI: 10.3389/fpls.2016.01138] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 07/18/2016] [Indexed: 05/19/2023]
Abstract
The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated that this systems approach is powerful enough to complement the functional metabolic annotation of bioenergy grasses.
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Affiliation(s)
- Cristiana G. de Oliveira Dal'Molin
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
- *Correspondence: Cristiana G. de Oliveira Dal'Molin
| | - Camila Orellana
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Leigh Gebbie
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Jennifer Steen
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Mark P. Hodson
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
- Metabolomics Australia, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Panagiotis Chrysanthopoulos
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
- Metabolomics Australia, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Manuel R. Plan
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
- Metabolomics Australia, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Richard McQualter
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Robin W. Palfreyman
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
| | - Lars K. Nielsen
- Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandBrisbane, QLD, Australia
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In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories. Microbiol Mol Biol Rev 2015; 80:45-67. [PMID: 26609052 DOI: 10.1128/mmbr.00014-15] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.
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Cheung CYM, Ratcliffe RG, Sweetlove LJ. A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf. PLANT PHYSIOLOGY 2015; 169:1671-82. [PMID: 26265776 PMCID: PMC4634065 DOI: 10.1104/pp.15.00880] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 08/04/2015] [Indexed: 05/02/2023]
Abstract
Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing flux-weighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the Calvin-Benson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the light-dependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture.
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Affiliation(s)
- C Y Maurice Cheung
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - R George Ratcliffe
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
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Flux Control in a Defense Pathway in Arabidopsis thaliana Is Robust to Environmental Perturbations and Controls Variation in Adaptive Traits. G3-GENES GENOMES GENETICS 2015; 5:2421-7. [PMID: 26362766 PMCID: PMC4632061 DOI: 10.1534/g3.115.021816] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The connections leading from genotype to fitness are not well understood, yet they are crucial for a diverse set of disciplines. Uncovering the general properties of biochemical pathways that influence ecologically important traits is an effective way to understand these connections. Enzyme flux control (or, control over pathway output) is one such pathway property. The flux-controlling enzyme in the antiherbivory aliphatic glucosinolate pathway of Arabidopsis thaliana has majority flux control under benign greenhouse conditions and has evidence of nonneutral evolution. However, it is unknown how patterns of flux control may change in different environments, or if insect herbivores respond to differences in pathway flux. We test this, first through genetic manipulation of the loci that code for the aliphatic glucosinolate pathway enzymes under a variety of environments (reduced water, reduced soil nutrients, leaf wounding and methyl jasmonate treatments), and find that flux control is consistently in the first enzyme of the pathway. We also find that a generalist herbivore, Trichoplusia ni, modifies its feeding behavior depending on the flux through the glucosinolate pathway. The influence over herbivore behavior combined with the consistency of flux control suggests that genes controlling flux might be repeatedly targeted by natural selection in diverse environments and species.
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31
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Yizhak K, Chaneton B, Gottlieb E, Ruppin E. Modeling cancer metabolism on a genome scale. Mol Syst Biol 2015; 11:817. [PMID: 26130389 PMCID: PMC4501850 DOI: 10.15252/msb.20145307] [Citation(s) in RCA: 136] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Revised: 04/04/2015] [Accepted: 05/26/2015] [Indexed: 12/16/2022] Open
Abstract
Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field.
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Affiliation(s)
- Keren Yizhak
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | | | | | - Eytan Ruppin
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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32
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Fluxes through plant metabolic networks: measurements, predictions, insights and challenges. Biochem J 2015; 465:27-38. [PMID: 25631681 DOI: 10.1042/bj20140984] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.
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Metabolic engineering of higher plants and algae for isoprenoid production. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2015; 148:161-99. [PMID: 25636485 DOI: 10.1007/10_2014_290] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Isoprenoids are a class of compounds derived from the five carbon precursors, dimethylallyl diphosphate, and isopentenyl diphosphate. These molecules present incredible natural chemical diversity, which can be valuable for humans in many aspects such as cosmetics, agriculture, and medicine. However, many terpenoids are only produced in small quantities by their natural hosts and can be difficult to generate synthetically. Therefore, much interest and effort has been directed toward capturing the genetic blueprint for their biochemistry and engineering it into alternative hosts such as plants and algae. These autotrophic organisms are attractive when compared to traditional microbial platforms because of their ability to utilize atmospheric CO2 as a carbon substrate instead of supplied carbon sources like glucose. This chapter will summarize important techniques and strategies for engineering the accumulation of isoprenoid metabolites into higher plants and algae by choosing the correct host, avoiding endogenous regulatory mechanisms, and optimizing potential flux into the target compound. Future endeavors will build on these efforts by fine-tuning product accumulation levels via the vast amount of available "-omic" data and devising metabolic engineering schemes that integrate this into a whole-organism approach. With the development of high-throughput transformation protocols and synthetic biology molecular tools, we have only begun to harness the power and utility of plant and algae metabolic engineering.
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34
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Gomes de Oliveira Dal'Molin C, Quek LE, Saa PA, Nielsen LK. A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. FRONTIERS IN PLANT SCIENCE 2015; 6:4. [PMID: 25657653 PMCID: PMC4302846 DOI: 10.3389/fpls.2015.00004] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 01/05/2015] [Indexed: 05/18/2023]
Abstract
Genome scale metabolic modeling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM (a genome-scale reconstruction of Arabidopsis metabolism) in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night (diurnal) cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the "division-of-labor" between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO(-) 3 or NH(+) 4). The "division-of-labor" between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step toward autonomous modeling of whole plant metabolism.
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Affiliation(s)
- Cristiana Gomes de Oliveira Dal'Molin
- *Correspondence: Cristiana Gomes de Oliveira Dal'Molin, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Corner College and Cooper Roads (Bldg. 75), Brisbane, Qld 4072, Australia e-mail:
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35
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Prigent S, Collet G, Dittami SM, Delage L, Ethis de Corny F, Dameron O, Eveillard D, Thiele S, Cambefort J, Boyen C, Siegel A, Tonon T. The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2014; 80:367-81. [PMID: 25065645 DOI: 10.1111/tpj.12627] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Revised: 07/04/2014] [Accepted: 07/21/2014] [Indexed: 06/03/2023]
Abstract
Brown algae (stramenopiles) are key players in intertidal ecosystems, and represent a source of biomass with several industrial applications. Ectocarpus siliculosus is a model to study the biology of these organisms. Its genome has been sequenced and a number of post-genomic tools have been implemented. Based on this knowledge, we report the reconstruction and analysis of a genome-scale metabolic network for E. siliculosus, EctoGEM (http://ectogem.irisa.fr). This atlas of metabolic pathways consists of 1866 reactions and 2020 metabolites, and its construction was performed by means of an integrative computational approach for identifying metabolic pathways, gap filling and manual refinement. The capability of the network to produce biomass was validated by flux balance analysis. EctoGEM enabled the reannotation of 56 genes within the E. siliculosus genome, and shed light on the evolution of metabolic processes. For example, E. siliculosus has the potential to produce phenylalanine and tyrosine from prephenate and arogenate, but does not possess a phenylalanine hydroxylase, as is found in other stramenopiles. It also possesses the complete eukaryote molybdenum co-factor biosynthesis pathway, as well as a second molybdopterin synthase that was most likely acquired via horizontal gene transfer from cyanobacteria by a common ancestor of stramenopiles. EctoGEM represents an evolving community resource to gain deeper understanding of the biology of brown algae and the diversification of physiological processes. The integrative computational method applied for its reconstruction will be valuable to set up similar approaches for other organisms distant from biological benchmark models.
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Affiliation(s)
- Sylvain Prigent
- Université de Rennes 1, IRISA UMR 6074, Campus de Beaulieu, 35042, Rennes, France; CNRS, IRISA UMR 6074, Campus de Beaulieu, 35042, Rennes, France; Centre Rennes-Bretagne-Atlantique, Projet Dyliss, INRIA, Campus de Beaulieu, 35042, Rennes Cedex, France
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36
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Simons M, Saha R, Guillard L, Clément G, Armengaud P, Cañas R, Maranas CD, Lea PJ, Hirel B. Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling. JOURNAL OF EXPERIMENTAL BOTANY 2014; 65:5657-71. [PMID: 24863438 DOI: 10.1093/jxb/eru227] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.
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Affiliation(s)
- Margaret Simons
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rajib Saha
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Lenaïg Guillard
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Gilles Clément
- Plateau Technique Spécifique de Chimie du Végétal, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Route de St Cyr, F-78026 Versailles Cedex, France
| | - Patrick Armengaud
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Rafael Cañas
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Peter J Lea
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
| | - Bertrand Hirel
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
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37
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Vickers CE, Bongers M, Liu Q, Delatte T, Bouwmeester H. Metabolic engineering of volatile isoprenoids in plants and microbes. PLANT, CELL & ENVIRONMENT 2014; 37:1753-75. [PMID: 24588680 DOI: 10.1111/pce.12316] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2014] [Revised: 02/18/2014] [Accepted: 02/18/2014] [Indexed: 05/09/2023]
Abstract
The chemical properties and diversity of volatile isoprenoids lends them to a broad variety of biological roles. It also lends them to a host of biotechnological applications, both by taking advantage of their natural functions and by using them as industrial chemicals/chemical feedstocks. Natural functions include roles as insect attractants and repellents, abiotic stress protectants in pathogen defense, etc. Industrial applications include use as pharmaceuticals, flavours, fragrances, fuels, fuel additives, etc. Here we will examine the ways in which researchers have so far found to exploit volatile isoprenoids using biotechnology. Production and/or modification of volatiles using metabolic engineering in both plants and microorganisms are reviewed, including engineering through both mevalonate and methylerythritol diphosphate pathways. Recent advances are illustrated using several case studies (herbivores and bodyguards, isoprene, and monoterpene production in microbes). Systems and synthetic biology tools with particular utility for metabolic engineering are also reviewed. Finally, we discuss the practical realities of various applications in modern biotechnology, explore possible future applications, and examine the challenges of moving these technologies forward so that they can deliver tangible benefits. While this review focuses on volatile isoprenoids, many of the engineering approaches described here are also applicable to non-isoprenoid volatiles and to non-volatile isoprenoids.
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Affiliation(s)
- Claudia E Vickers
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland, 4072, Australia
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Wilson SA, Cummings EM, Roberts SC. Multi-scale engineering of plant cell cultures for promotion of specialized metabolism. Curr Opin Biotechnol 2014; 29:163-70. [PMID: 25063984 DOI: 10.1016/j.copbio.2014.07.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 07/07/2014] [Accepted: 07/08/2014] [Indexed: 12/19/2022]
Abstract
To establish plant culture systems for product synthesis, a multi-scale engineering approach is necessary. At the intracellular level, the influx of 'omics' data has necessitated development of new methods to properly annotate and establish useful metabolic models that can be applied to elucidate unknown steps in specialized metabolite biosynthesis, define effective metabolic engineering strategies and increase enzyme diversity available for synthetic biology platforms. On an intercellular level, the presence of aggregates in culture leads to distinct metabolic sub-populations. Recent advances in flow cytometric analyses and mass spectrometry imaging allow for resolution of metabolites on the single cell level, providing an increased understanding of culture heterogeneity. Finally, extracellular engineering can be used to enhance culture performance through media manipulation, co-culture with bacteria, the use of exogenous elicitors or modulation of shear stress.
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Affiliation(s)
- Sarah A Wilson
- Department of Chemical Engineering, University of Massachusetts Amherst, 159 Goessmann Laboratory, 686 North Pleasant Street, Amherst, MA 01003, United States
| | - Elizabeth M Cummings
- Department of Chemical Engineering, University of Massachusetts Amherst, 159 Goessmann Laboratory, 686 North Pleasant Street, Amherst, MA 01003, United States
| | - Susan C Roberts
- Department of Chemical Engineering, University of Massachusetts Amherst, 159 Goessmann Laboratory, 686 North Pleasant Street, Amherst, MA 01003, United States.
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39
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Sweetlove LJ, Obata T, Fernie AR. Systems analysis of metabolic phenotypes: what have we learnt? TRENDS IN PLANT SCIENCE 2014; 19:222-30. [PMID: 24139444 DOI: 10.1016/j.tplants.2013.09.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Revised: 09/12/2013] [Accepted: 09/18/2013] [Indexed: 05/26/2023]
Abstract
Flux is one of the most informative measures of metabolic behavior. Its estimation requires integration of experimental and modeling approaches and, thus, is at the heart of metabolic systems biology. In this review, we argue that flux analysis and modeling of a range of plant systems points to the importance of the supply of metabolic inputs and demand for metabolic end-products as key drivers of metabolic behavior. This has implications for metabolic engineering, and the use of in silico models will be important to help design more effective engineering strategies. We also consider the importance of cell type-specific metabolism and the challenges of characterizing metabolism at this resolution. A combination of new measurement technologies and modeling approaches is bringing us closer to integrating metabolic behavior with whole-plant physiology and growth.
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Affiliation(s)
- Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK.
| | - Toshihiro Obata
- Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany.
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Junker BH. Flux analysis in plant metabolic networks: increasing throughput and coverage. Curr Opin Biotechnol 2014; 26:183-8. [PMID: 24561560 DOI: 10.1016/j.copbio.2014.01.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Revised: 01/27/2014] [Accepted: 01/27/2014] [Indexed: 12/17/2022]
Abstract
Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic fluxes can be predicted by Flux Balance Analysis or determined experimentally by (13)C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of flux measurements. This review summarizes advances to increase coverage and throughput of metabolic flux analysis in plants.
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Affiliation(s)
- Björn H Junker
- Institute of Pharmacy, Martin-Luther-University, Hoher Weg 8, 06120 Halle, Germany.
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41
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Ernst M, Silva DB, Silva RR, Vêncio RZN, Lopes NP. Mass spectrometry in plant metabolomics strategies: from analytical platforms to data acquisition and processing. Nat Prod Rep 2014; 31:784-806. [DOI: 10.1039/c3np70086k] [Citation(s) in RCA: 129] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Dal'molin CGO, Quek LE, Palfreyman RW, Nielsen LK. Plant genome-scale modeling and implementation. Methods Mol Biol 2014; 1090:317-32. [PMID: 24222424 DOI: 10.1007/978-1-62703-688-7_19] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Considerable progress has been made in plant genome-scale metabolic reconstruction and modeling in recent years. Such reconstructions made it possible to explore metabolic phenotypes through appropriate model formulation and optimization methods. As a result, plant genome-scale modeling has increasingly attracted interest from the plant research community. In this chapter, the first generation of plant genome-scale metabolic reconstructions is presented, along with the important concepts behind model and constraint formulation. A brief protocol describing the use of constraint-based reconstruction and analysis (COBRA) Toolbox in flux simulation and model modification is provided. This is followed by a presentation of metabolic constraints required to generate fluxes in AraGEM using COBRA that describe photosynthesis, photorespiration, and respiration, respectively. Overall, plant genome-scale modeling is a powerful approach that is accessible and readily adopted.
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Affiliation(s)
- Cristiana G O Dal'molin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, Australia
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Abstract
Stoichiometric models describe cellular biochemistry with systems of linear equations. The models which are fundamentally based on the steady-state assumption are comparatively easy to construct and can be applied to networks up to genome scale. Fluxes are inherent variables in stoichiometric models and linear optimization can be used to identify intracellular flux distributions. Great caution, however, has to be paid to the selection of the specific objective function which inevitably implies the existence of a specific global cellular rationale. On the other hand, stoichiometric models provide an analytical platform for contextualization of experimental data. Equally important, the stoichiometric models can be used for structural analyses of metabolic networks as such supporting for example rational model-driven strategies in metabolic engineering.
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Affiliation(s)
- Lars Kuepfer
- Applied Microbiology, RWTH Aachen University, Worringer Weg 1, Room 42A/114, Aachen, 52074, Germany,
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Hay JO, Shi H, Heinzel N, Hebbelmann I, Rolletschek H, Schwender J. Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis. FRONTIERS IN PLANT SCIENCE 2014; 5:724. [PMID: 25566296 PMCID: PMC4271587 DOI: 10.3389/fpls.2014.00724] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 12/01/2014] [Indexed: 05/19/2023]
Abstract
The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for (13)C-Metabolic Flux Analysis ((13)C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from (13)C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.
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Affiliation(s)
- Jordan O. Hay
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Hai Shi
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Nicolas Heinzel
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und KulturpflanzenforschungGatersleben, Germany
| | - Inga Hebbelmann
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Hardy Rolletschek
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und KulturpflanzenforschungGatersleben, Germany
| | - Jorg Schwender
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
- *Correspondence: Jorg Schwender, Brookhaven National Laboratory, Biological, Environment and Climate Sciences Department, Building 463, Upton, NY 11973, USA e-mail:
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Fukushima A, Kanaya S, Nishida K. Integrated network analysis and effective tools in plant systems biology. FRONTIERS IN PLANT SCIENCE 2014; 5:598. [PMID: 25408696 PMCID: PMC4219401 DOI: 10.3389/fpls.2014.00598] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 10/14/2014] [Indexed: 05/18/2023]
Abstract
One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.
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Affiliation(s)
- Atsushi Fukushima
- RIKEN Center for Sustainable Resource ScienceTsurumi, Yokohama, Japan
- Japan Science and Technology Agency, National Bioscience Database CenterTokyo, Japan
- *Correspondence: Atsushi Fukushima, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehirocho, Tsurumi, Yokohama 230-0045, Japan e-mail:
| | - Shigehiko Kanaya
- Graduate School of Information Science, Nara Institute of Science and TechnologyNara, Japan
| | - Kozo Nishida
- Japan Science and Technology Agency, National Bioscience Database CenterTokyo, Japan
- Laboratory for Biochemical Simulation, RIKEN Quantitative Biology CenterOsaka, Japan
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46
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Robaina Estévez S, Nikoloski Z. Generalized framework for context-specific metabolic model extraction methods. FRONTIERS IN PLANT SCIENCE 2014; 5:491. [PMID: 25285097 PMCID: PMC4168813 DOI: 10.3389/fpls.2014.00491] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 09/03/2014] [Indexed: 05/21/2023]
Abstract
Genome-scale metabolic models (GEMs) are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.
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Affiliation(s)
| | - Zoran Nikoloski
- *Correspondence: Zoran Nikoloski, Systems Biology and Mathematical Modeling Group, Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14424 Potsdam, Germany e-mail:
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Nägele T, Weckwerth W. A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2013; 4:541. [PMID: 24400018 PMCID: PMC3872044 DOI: 10.3389/fpls.2013.00541] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Accepted: 12/12/2013] [Indexed: 05/23/2023]
Abstract
During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation. In this way, different strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.
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Affiliation(s)
- Thomas Nägele
- *Correspondence: Thomas Nägele, Department Ecogenomics and Systems Biology, University of Vienna, Althanstr. 14, 1090 Vienna, Austria e-mail:
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48
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Sweetlove LJ, Williams TCR, Cheung CYM, Ratcliffe RG. Modelling metabolic CO₂ evolution--a fresh perspective on respiration. PLANT, CELL & ENVIRONMENT 2013; 36:1631-1640. [PMID: 23531106 DOI: 10.1111/pce.12105] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Revised: 03/06/2013] [Accepted: 03/19/2013] [Indexed: 05/28/2023]
Abstract
Respiration is a major contributor to net exchange of CO₂ between plants and the atmosphere and thus an important aspect of the vegetation component of global climate change models. However, a mechanistic model of respiration is lacking, and so here we explore the potential for flux balance analysis (FBA) to predict cellular CO₂ evolution rates. Metabolic flux analysis reveals that respiration is not always the dominant source of CO₂, and that metabolic processes such as the oxidative pentose phosphate pathway (OPPP) and lipid synthesis can be quantitatively important. Moreover, there is considerable variation in the metabolic origin of evolved CO₂ between tissues, species and conditions. Comparison of FBA-predicted CO₂ evolution profiles with those determined from flux measurements reveals that FBA is able to predict the metabolic origin of evolved CO₂ in different tissues/species and under different conditions. However, FBA is poor at predicting flux through certain metabolic processes such as the OPPP and we identify the way in which maintenance costs are accounted for as a major area of improvement for future FBA studies. We conclude that FBA, in its standard form, can be used to predict CO₂ evolution in a range of plant tissues and in response to environment.
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Affiliation(s)
- Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK.
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49
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Cheung CYM, Williams TCR, Poolman MG, Fell DA, Ratcliffe RG, Sweetlove LJ. A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2013; 75:1050-61. [PMID: 23738527 DOI: 10.1111/tpj.12252] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2013] [Revised: 05/23/2013] [Accepted: 05/30/2013] [Indexed: 05/24/2023]
Abstract
Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.
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Affiliation(s)
- C Y Maurice Cheung
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
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50
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Koskimaki JE, Blazier AS, Clarens AF, Papin JA. Computational Models of Algae Metabolism for Industrial Applications. Ind Biotechnol (New Rochelle N Y) 2013. [DOI: 10.1089/ind.2013.0012] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
- Jacob E. Koskimaki
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA
| | - Anna S. Blazier
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA
| | - Andres F. Clarens
- Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA
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