1
|
Huß S, Judd RS, Koper K, Maeda HA, Nikoloski Z. An automated workflow that generates atom mappings for large-scale metabolic models and its application to Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1486-1500. [PMID: 35819300 DOI: 10.1111/tpj.15903] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large-scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large-scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana. We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating 15 N isotope enrichment and identifying nitrogen (N)-containing metabolites which show enrichment patterns that are informative for flux estimation in future 15 N-MFA studies of A. thaliana. The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph-theoretic structural analyses with large-scale metabolic networks.
Collapse
Affiliation(s)
- Sebastian Huß
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24- 25, 14476, Potsdam, Germany
| | - Rika Siedah Judd
- Department of Botany, University of Wisconsin-Madison, 430, Lincoln, Dr. Madison, Wisconsin, 53706, USA
| | - Kaan Koper
- Department of Botany, University of Wisconsin-Madison, 430, Lincoln, Dr. Madison, Wisconsin, 53706, USA
| | - Hiroshi A Maeda
- Department of Botany, University of Wisconsin-Madison, 430, Lincoln, Dr. Madison, Wisconsin, 53706, USA
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24- 25, 14476, Potsdam, Germany
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Characterization of effects of genetic variants via genome-scale metabolic modelling. Cell Mol Life Sci 2021; 78:5123-5138. [PMID: 33950314 PMCID: PMC8254712 DOI: 10.1007/s00018-021-03844-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 04/23/2021] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.
Collapse
|
4
|
Selinski J, Scheibe R. Central Metabolism in Mammals and Plants as a Hub for Controlling Cell Fate. Antioxid Redox Signal 2021; 34:1025-1047. [PMID: 32620064 PMCID: PMC8060724 DOI: 10.1089/ars.2020.8121] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/15/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023]
Abstract
Significance: The importance of oxidoreductases in energy metabolism together with the occurrence of enzymes of central metabolism in the nucleus gave rise to the active research field aiming to understand moonlighting enzymes that undergo post-translational modifications (PTMs) before carrying out new tasks. Recent Advances: Cytosolic enzymes were shown to induce gene transcription after PTM and concomitant translocation to the nucleus. Changed properties of the oxidized forms of cytosolic glyceraldehyde 3-phosphate dehydrogenase, and also malate dehydrogenases and others, are the basis for a hypothesis suggesting moonlighting functions that directly link energy metabolism to adaptive responses required for maintenance of redox-homeostasis in all eukaryotes. Critical Issues: Small molecules, such as metabolic intermediates, coenzymes, or reduced glutathione, were shown to fine-tune the redox switches, interlinking redox state, metabolism, and induction of new functions via nuclear gene expression. The cytosol with its metabolic enzymes connecting energy fluxes between the various cell compartments can be seen as a hub for redox signaling, integrating the different signals for graded and directed responses in stressful situations. Future Directions: Enzymes of central metabolism were shown to interact with p53 or the assumed plant homologue suppressor of gamma response 1 (SOG1), an NAM, ATAF, and CUC transcription factor involved in the stress response upon ultraviolet exposure. Metabolic enzymes serve as sensors for imbalances, their inhibition leading to changed energy metabolism, and the adoption of transcriptional coactivator activities. Depending on the intensity of the impact, rerouting of energy metabolism, proliferation, DNA repair, cell cycle arrest, immune responses, or cell death will be induced. Antioxid. Redox Signal. 34, 1025-1047.
Collapse
Affiliation(s)
- Jennifer Selinski
- Department of Biochemistry and Physiology of Plants, Faculty of Biology, Bielefeld University, Bielefeld, Germany
| | - Renate Scheibe
- Department of Plant Physiology, Faculty of Biology/Chemistry, Osnabrueck University, Osnabrueck, Germany
| |
Collapse
|
5
|
Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
Collapse
Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
6
|
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.
Collapse
|
7
|
Daloso DDM, Williams TCR. Current Challenges in Plant Systems Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1346:155-170. [DOI: 10.1007/978-3-030-80352-0_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
8
|
Shameer S, Ratcliffe RG, Sweetlove LJ. Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light. PLANT PHYSIOLOGY 2019; 180:1947-1961. [PMID: 31213510 PMCID: PMC6670072 DOI: 10.1104/pp.19.00624] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 06/04/2019] [Indexed: 05/04/2023]
Abstract
Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is debate about the relative importance of exporting reducing equivalents from mitochondria for the peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative phosphorylation. Here, we address this and explore energetic coupling between organelles in the light using a diel flux balance analysis model. The model included more than 600 reactions of central metabolism with full stoichiometric accounting of energy production and consumption. Different scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were considered, and the model was constrained by the nonlinear relationship between light and CO2 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. However, this requires unrealistic high light use efficiency and, in practice, the availability of chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as nonphotochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these limitations, substantial mitochondrial ATP synthesis is required to fulfill cytosolic ATP requirements, with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. Hence, the chloroplast malate valve and triose phosphate-3-phosphoglycerate shuttle are predicted to have important metabolic roles, in addition to their more commonly discussed contribution to the avoidance of photooxidative stress.
Collapse
Affiliation(s)
- Sanu Shameer
- 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
| |
Collapse
|
9
|
Moreira TB, Shaw R, Luo X, Ganguly O, Kim HS, Coelho LGF, Cheung CYM, Rhys Williams TC. A Genome-Scale Metabolic Model of Soybean ( Glycine max) Highlights Metabolic Fluxes in Seedlings. PLANT PHYSIOLOGY 2019; 180:1912-1929. [PMID: 31171578 PMCID: PMC6670085 DOI: 10.1104/pp.19.00122] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 05/25/2019] [Indexed: 05/12/2023]
Abstract
Until they become photoautotrophic juvenile plants, seedlings depend upon the reserves stored in seed tissues. These reserves must be mobilized and metabolized, and their breakdown products must be distributed to the different organs of the growing seedling. Here, we investigated the mobilization of soybean (Glycine max) seed reserves during seedling growth by initially constructing a genome-scale stoichiometric model for this important crop plant and then adapting the model to reflect metabolism in the cotyledons and hypocotyl/root axis (HRA). A detailed analysis of seedling growth and alterations in biomass composition was performed over 4 d of postgerminative growth and used to constrain the stoichiometric model. Flux balance analysis revealed marked differences in metabolism between the two organs, together with shifts in primary metabolism occurring during different periods postgermination. In particular, from 48 h onward, cotyledons were characterized by the oxidation of fatty acids to supply carbon for the tricarboxylic acid cycle as well as production of sucrose and glutamate for export to the HRA, while the HRA was characterized by the use of a range of imported amino acids in protein synthesis and catabolic processes. Overall, the use of flux balance modeling provided new insight into well-characterized metabolic processes in an important crop plant due to their analysis within the context of a metabolic network and reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.
Collapse
Affiliation(s)
- Thiago Batista Moreira
- Departament of Botany, University of Brasília, Campus Darcy Ribeiro, Asa Norte, Brasília, Brazil, 70910-900
| | - Rahul Shaw
- Division of Science, Yale-National University of Singapore College, Singapore, 138527
| | - Xinyu Luo
- Division of Science, Yale-National University of Singapore College, Singapore, 138527
| | - Oishik Ganguly
- Division of Science, Yale-National University of Singapore College, Singapore, 138527
| | - Hyung-Seok Kim
- Division of Science, Yale-National University of Singapore College, Singapore, 138527
| | | | | | | |
Collapse
|
10
|
Ajjolli Nagaraja A, Fontaine N, Delsaut M, Charton P, Damour C, Offmann B, Grondin-Perez B, Cadet F. Flux prediction using artificial neural network (ANN) for the upper part of glycolysis. PLoS One 2019; 14:e0216178. [PMID: 31067238 PMCID: PMC6505829 DOI: 10.1371/journal.pone.0216178] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 04/15/2019] [Indexed: 01/08/2023] Open
Abstract
The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments. The artificial neural network (ANN) method has been successively applied in different fields of science to perform complex functions. In this study, ANN models were trained to predict the flux for the upper part of glycolysis as inferred by NADH consumption, using four enzyme concentrations i.e., phosphoglucoisomerase, phosphofructokinase, fructose-bisphosphate-aldolase, triose-phosphate-isomerase. Out of three ANN algorithms, the neuralnet package with two activation functions, “logistic” and “tanh” were implemented. The prediction of the flux was very efficient: RMSE and R2 were 0.847, 0.93 and 0.804, 0.94 respectively for logistic and tanh functions using a cross validation procedure. This study showed that a systemic approach such as ANN could be used for accurate prediction of the flux through the metabolic pathway. This could help to save a lot of time and costs, particularly from an industrial perspective. The R-code is available at: https://github.com/DSIMB/ANN-Glycolysis-Flux-Prediction.
Collapse
Affiliation(s)
- Anamya Ajjolli Nagaraja
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | | | - Mathieu Delsaut
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | - Philippe Charton
- DSIMB, INSERM, UMR S-1134, Laboratory of ExcellenceLABEX GR, Faculty of Sciences and Technology, University of La Reunion & University Paris Diderot, Paris, France
| | - Cedric Damour
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | - Bernard Offmann
- Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UMR 6286 CNRS, UFR Sciences et Techniques, chemin de la Houssinière, France
| | - Brigitte Grondin-Perez
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | - Frederic Cadet
- DSIMB, INSERM, UMR S-1134, Laboratory of ExcellenceLABEX GR, Faculty of Sciences and Technology, University of La Reunion & University Paris Diderot, Paris, France
- * E-mail:
| |
Collapse
|
11
|
Küken A, Nikoloski Z. Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways. PLANT PHYSIOLOGY 2019; 179:894-906. [PMID: 30647083 PMCID: PMC6393797 DOI: 10.1104/pp.18.01273] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 01/09/2019] [Indexed: 05/05/2023]
Abstract
Successfully designed and implemented plant-specific synthetic metabolic pathways hold promise to increase crop yield and nutritional value. Advances in synthetic biology have already demonstrated the capacity to design artificial biological pathways whose behavior can be predicted and controlled in microbial systems. However, the transfer of these advances to model plants and crops faces the lack of characterization of plant cellular pathways and increased complexity due to compartmentalization and multicellularity. Modern computational developments provide the means to test the feasibility of plant synthetic metabolic pathways despite gaps in the accumulated knowledge of plant metabolism. Here, we provide a succinct systematic review of optimization-based and retrobiosynthesis approaches that can be used to design and in silico test synthetic metabolic pathways in large-scale plant context-specific metabolic models. In addition, by surveying the existing case studies, we highlight the challenges that these approaches face when applied to plants. Emphasis is placed on understanding the effect that metabolic designs can have on native metabolism, particularly with respect to metabolite concentrations and thermodynamics of biochemical reactions. In addition, we discuss the computational developments that may help to transform the identified challenges into opportunities for plant synthetic biology.
Collapse
Affiliation(s)
- Anika Küken
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| |
Collapse
|
12
|
Selinski J, Scheibe R. Malate valves: old shuttles with new perspectives. PLANT BIOLOGY (STUTTGART, GERMANY) 2019; 21 Suppl 1:21-30. [PMID: 29933514 PMCID: PMC6586076 DOI: 10.1111/plb.12869] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 06/18/2018] [Indexed: 05/18/2023]
Abstract
Malate valves act as powerful systems for balancing the ATP/NAD(P)H ratio required in various subcellular compartments in plant cells. As components of malate valves, isoforms of malate dehydrogenases (MDHs) and dicarboxylate translocators catalyse the reversible interconversion of malate and oxaloacetate and their transport. Depending on the co-enzyme specificity of the MDH isoforms, either NADH or NADPH can be transported indirectly. Arabidopsis thaliana possesses nine genes encoding MDH isoenzymes. Activities of NAD-dependent MDHs have been detected in mitochondria, peroxisomes, cytosol and plastids. In addition, chloroplasts possess a NADP-dependent MDH isoform. The NADP-MDH as part of the 'light malate valve' plays an important role as a poising mechanism to adjust the ATP/NADPH ratio in the stroma. Its activity is strictly regulated by post-translational redox-modification mediated via the ferredoxin-thioredoxin system and fine control via the NADP+ /NADP(H) ratio, thereby maintaining redox homeostasis under changing conditions. In contrast, the plastid NAD-MDH ('dark malate valve') is constitutively active and its lack leads to failure in early embryo development. While redox regulation of the main cytosolic MDH isoform has been shown, knowledge about regulation of the other two cytosolic MDHs as well as NAD-MDH isoforms from peroxisomes and mitochondria is still lacking. Knockout mutants lacking the isoforms from chloroplasts, mitochondria and peroxisomes have been characterised, but not much is known about cytosolic NAD-MDH isoforms and their role in planta. This review updates the current knowledge on MDH isoforms and the shuttle systems for intercompartmental dicarboxylate exchange, focusing on the various metabolic functions of these valves.
Collapse
Affiliation(s)
- J. Selinski
- Department of Animal, Plant, and Soil ScienceAustralian Research Council Centre of Excellence in Plant Energy BiologySchool of Life ScienceLa Trobe University BundooraBundooraAustralia
| | - R. Scheibe
- Division of Plant PhysiologyDepartment of Biology/ChemistryUniversity of OsnabrueckOsnabrueckGermany
| |
Collapse
|
13
|
Advances in metabolic flux analysis toward genome-scale profiling of higher organisms. Biosci Rep 2018; 38:BSR20170224. [PMID: 30341247 PMCID: PMC6250807 DOI: 10.1042/bsr20170224] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 10/06/2018] [Accepted: 10/14/2018] [Indexed: 11/25/2022] Open
Abstract
Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.
Collapse
|
14
|
Bolger ME, Arsova B, Usadel B. Plant genome and transcriptome annotations: from misconceptions to simple solutions. Brief Bioinform 2018; 19:437-449. [PMID: 28062412 PMCID: PMC5952960 DOI: 10.1093/bib/bbw135] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 11/29/2016] [Indexed: 12/14/2022] Open
Abstract
Next-generation sequencing has triggered an explosion of available genomic and transcriptomic resources in the plant sciences. Although genome and transcriptome sequencing has become orders of magnitudes cheaper and more efficient, often the functional annotation process is lagging behind. This might be hampered by the lack of a comprehensive enumeration of simple-to-use tools available to the plant researcher. In this comprehensive review, we present (i) typical ontologies to be used in the plant sciences, (ii) useful databases and resources used for functional annotation, (iii) what to expect from an annotated plant genome, (iv) an automated annotation pipeline and (v) a recipe and reference chart outlining typical steps used to annotate plant genomes/transcriptomes using publicly available resources.
Collapse
Affiliation(s)
- Marie E Bolger
- Forschungszentrum Jülich, Wilhelm Johnen Str, Jülich, Germany
| | - Borjana Arsova
- Forschungszentrum Jülich, Wilhelm Johnen Str, Jülich, Germany
- FRS-FNRS Chargé de Recherches, Functional Genomics and Plant Molecular Imaging Center for Protein Engineering (CIP), Dpt of Life Sciences, University of Liège, Quartier de la Vallée, 1, Chemin de la Vallée, 4 - Bât B22, 4000 LIEGE, Belgium
| | - Björn Usadel
- Forschungszentrum Jülich, Wilhelm Johnen Str, Jülich, Germany
- RWTH Aachen University, Institute for Biology I Botany, BioSC, Worringer Weg 3, Aachen, Germany
| |
Collapse
|
15
|
Dahal K, Vanlerberghe GC. Improved chloroplast energy balance during water deficit enhances plant growth: more crop per drop. JOURNAL OF EXPERIMENTAL BOTANY 2018; 69:1183-1197. [PMID: 29281082 PMCID: PMC6018952 DOI: 10.1093/jxb/erx474] [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: 11/23/2017] [Accepted: 12/07/2017] [Indexed: 05/02/2023]
Abstract
The non-energy-conserving alternative oxidase (AOX) respiration of plant mitochondria is known to interact with chloroplast photosynthesis. This may have consequences for growth, particularly under sub-optimal conditions when energy imbalances can impede photosynthesis. This hypothesis was tested by comparing the metabolism and growth of wild-type Nicotiana tabacum with that of AOX knockdown and overexpression lines during a prolonged steady-state mild to moderate water deficit. Under moderate water deficit, the AOX amount was an important determinant of the rate of both mitochondrial respiration in the light and net photosynthetic CO2 assimilation (A) at the growth irradiance. In particular, AOX respiration was necessary to maintain optimal proton and electron fluxes at the chloroplast thylakoid membrane, which in turn prevented a water-deficit-induced biochemical limitation of photosynthesis. As a result of differences in A, AOX overexpressors gained more biomass and knockdowns gained less biomass than wild-type during moderate water deficit. Biomass partitioning also differed, with the overexpressors having a higher percentage, and the knockdowns having a lower percentage, of total above-ground biomass in reproductive tissue than wild-type. The results establish that improving chloroplast energy balance by using a non-energy-conserving respiratory electron sink can increase photosynthesis and growth during prolonged water deficit.
Collapse
Affiliation(s)
- Keshav Dahal
- Department of Biological Sciences and Department of Cell and Systems Biology, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - Greg C Vanlerberghe
- Department of Biological Sciences and Department of Cell and Systems Biology, University of Toronto Scarborough, Toronto, Ontario, Canada
| |
Collapse
|
16
|
Lima VF, Medeiros DB, Dos Anjos L, Gago J, Fernie AR, Daloso DM. Toward multifaceted roles of sucrose in the regulation of stomatal movement. PLANT SIGNALING & BEHAVIOR 2018; 13:e1494468. [PMID: 30067434 PMCID: PMC6149408 DOI: 10.1080/15592324.2018.1494468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Plant atmospheric CO2 fixation depends on the aperture of stomatal pores at the leaf epidermis. Stomatal aperture or closure is regulated by changes in the metabolism of the two surrounding guard cells, which respond directly to environmental and internal cues such as mesophyll-derived metabolites. Sucrose has been shown to play a dual role during stomatal movements. The sucrose produced in the mesophyll cells can be transported to the vicinity of the guard cells via the transpiration stream, inducing closure in periods of high photosynthetic rate. By contrast, sucrose breakdown within guard cells sustains glycolysis and glutamine biosynthesis during light-induced stomatal opening. Here, we provide an update regarding the role of sucrose in the regulation of stomatal movement highlighting recent findings from metabolic and systems biology studies. We further explore how sucrose-mediated mechanisms of stomatal movement regulation could be useful to understand evolution of stomatal physiology among different plant groups.
Collapse
Affiliation(s)
- V. F. Lima
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Ceará, Fortaleza-CE, Brasil
- CONTACT Danilo M. Daloso Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Ceará, Fortaleza-CE, Brasil
| | - D. B. Medeiros
- Central metabolism group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm Germany
| | - L. Dos Anjos
- Departamento de Biologia, Universidade Federal de Lavras, Lavras-MG, Brasil
| | - J. Gago
- Research Group on Plant Biology under Mediterranean Conditions. Departament de Biologia, Universitat de les Illes Balears)/Instituto de investigaciones Agroambientales y de la Economía del Agua (INAGEA), Illes Balears, Spain
| | - A. R. Fernie
- Central metabolism group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm Germany
| | - D. M. Daloso
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Ceará, Fortaleza-CE, Brasil
| |
Collapse
|
17
|
Abstract
Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.
Collapse
|
18
|
Voit EO. The best models of metabolism. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2017; 9:10.1002/wsbm.1391. [PMID: 28544810 PMCID: PMC5643013 DOI: 10.1002/wsbm.1391] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 03/31/2017] [Accepted: 04/01/2017] [Indexed: 12/25/2022]
Abstract
Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.
Collapse
Affiliation(s)
- Eberhard O Voit
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| |
Collapse
|
19
|
Robaina-Estévez S, Daloso DM, Zhang Y, Fernie AR, Nikoloski Z. Resolving the central metabolism of Arabidopsis guard cells. Sci Rep 2017; 7:8307. [PMID: 28814793 PMCID: PMC5559522 DOI: 10.1038/s41598-017-07132-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 06/23/2017] [Indexed: 12/22/2022] Open
Abstract
Photosynthesis and water use efficiency, key factors affecting plant growth, are directly controlled by microscopic and adjustable pores in the leaf-the stomata. The size of the pores is modulated by the guard cells, which rely on molecular mechanisms to sense and respond to environmental changes. It has been shown that the physiology of mesophyll and guard cells differs substantially. However, the implications of these differences to metabolism at a genome-scale level remain unclear. Here, we used constraint-based modeling to predict the differences in metabolic fluxes between the mesophyll and guard cells of Arabidopsis thaliana by exploring the space of fluxes that are most concordant to cell-type-specific transcript profiles. An independent 13C-labeling experiment using isolated mesophyll and guard cells was conducted and provided support for our predictions about the role of the Calvin-Benson cycle in sucrose synthesis in guard cells. The combination of in silico with in vivo analyses indicated that guard cells have higher anaplerotic CO2 fixation via phosphoenolpyruvate carboxylase, which was demonstrated to be an important source of malate. Beyond highlighting the metabolic differences between mesophyll and guard cells, our findings can be used in future integrated modeling of multi-cellular plant systems and their engineering towards improved growth.
Collapse
Affiliation(s)
- Semidán Robaina-Estévez
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Golm, Germany
| | - Danilo M Daloso
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Golm, Germany
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Ceará, Fortaleza, CE, Brazil
| | - Youjun Zhang
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Golm, Germany
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Golm, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Golm, Germany.
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Golm, Germany.
| |
Collapse
|
20
|
Robaina-Estévez S, Nikoloski Z. Metabolic Network Constrains Gene Regulation of C4 Photosynthesis: The Case of Maize. PLANT & CELL PHYSIOLOGY 2016; 57:933-43. [PMID: 26903529 PMCID: PMC4867049 DOI: 10.1093/pcp/pcw034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Accepted: 02/09/2016] [Indexed: 05/21/2023]
Abstract
Engineering C3 plants to increase their efficiency of carbon fixation as well as of nitrogen and water use simultaneously may be facilitated by understanding the mechanisms that underpin the C4 syndrome. Existing experimental studies have indicated that the emergence of the C4 syndrome requires co-ordination between several levels of cellular organization, from gene regulation to metabolism, across two co-operating cell systems-mesophyll and bundle sheath cells. Yet, determining the extent to which the structure of the C4 plant metabolic network may constrain gene expression remains unclear, although it will provide an important consideration in engineering C4 photosynthesis in C3 plants. Here, we utilize flux coupling analysis with the second-generation maize metabolic models to investigate the correspondence between metabolic network structure and transcriptomic phenotypes along the maize leaf gradient. The examined scenarios with publically available data from independent experiments indicate that the transcriptomic programs of the two cell types are co-ordinated, quantitatively and qualitatively, due to the presence of coupled metabolic reactions in specific metabolic pathways. Taken together, our study demonstrates that precise quantitative coupling will have to be achieved in order to ensure a successfully engineered transition from C3 to C4 crops.
Collapse
Affiliation(s)
- Semidán Robaina-Estévez
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, D-14476 Potsdam-Golm, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, D-14476 Potsdam-Golm, Germany
| |
Collapse
|
21
|
Basler G, Küken A, Fernie AR, Nikoloski Z. Photorespiratory Bypasses Lead to Increased Growth in Arabidopsis thaliana: Are Predictions Consistent with Experimental Evidence? Front Bioeng Biotechnol 2016; 4:31. [PMID: 27092301 PMCID: PMC4823303 DOI: 10.3389/fbioe.2016.00031] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 03/24/2016] [Indexed: 11/13/2022] Open
Abstract
Arguably, the biggest challenge of modern plant systems biology lies in predicting the performance of plant species, and crops in particular, upon different intracellular and external perturbations. Recently, an increased growth of Arabidopsis thaliana plants was achieved by introducing two different photorespiratory bypasses via metabolic engineering. Here, we investigate the extent to which these findings match the predictions from constraint-based modeling. To determine the effect of the employed metabolic network model on the predictions, we perform a comparative analysis involving three state-of-the-art metabolic reconstructions of A. thaliana. In addition, we investigate three scenarios with respect to experimental findings on the ratios of the carboxylation and oxygenation reactions of Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). We demonstrate that the condition-dependent growth phenotypes of one of the engineered bypasses can be qualitatively reproduced by each reconstruction, particularly upon considering the additional constraints with respect to the ratio of fluxes for the RuBisCO reactions. Moreover, our results lend support for the hypothesis of a reduced photorespiration in the engineered plants, and indicate that specific changes in CO2 exchange as well as in the proxies for co-factor turnover are associated with the predicted growth increase in the engineered plants. We discuss our findings with respect to the structure of the used models, the modeling approaches taken, and the available experimental evidence. Our study sets the ground for investigating other strategies for increase of plant biomass by insertion of synthetic reactions.
Collapse
Affiliation(s)
- Georg Basler
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA; Department of Environmental Protection, Estación Experimental del Zaidín CSIC, Granada, Spain
| | - Anika Küken
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm , Germany
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm , Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm , Germany
| |
Collapse
|
22
|
Fernie AR, Pichersky E. Focus Issue on Metabolism: Metabolites, Metabolites Everywhere. PLANT PHYSIOLOGY 2015; 169:1421-3. [PMID: 26531677 PMCID: PMC4634105 DOI: 10.1104/pp.15.01499] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Affiliation(s)
- Alisdair R Fernie
- Max-Planck-Institute of MolecularPlant Physiology14476 Potsdam-GolmGermany
| | - Eran Pichersky
- Department of Molecular, Cellular, andDevelopmental BiologyUniversity of MichiganAnn Arbor, MI 48109
| |
Collapse
|