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In silico metabolic network analysis of Arabidopsis leaves

BACKGROUND: During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of de...

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Autores principales: Beckers, Veronique, Dersch, Lisa Maria, Lotz, Katrin, Melzer, Guido, Bläsing, Oliver Ernst, Fuchs, Regine, Ehrhardt, Thomas, Wittmann, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086045/
https://www.ncbi.nlm.nih.gov/pubmed/27793154
http://dx.doi.org/10.1186/s12918-016-0347-3
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author Beckers, Veronique
Dersch, Lisa Maria
Lotz, Katrin
Melzer, Guido
Bläsing, Oliver Ernst
Fuchs, Regine
Ehrhardt, Thomas
Wittmann, Christoph
author_facet Beckers, Veronique
Dersch, Lisa Maria
Lotz, Katrin
Melzer, Guido
Bläsing, Oliver Ernst
Fuchs, Regine
Ehrhardt, Thomas
Wittmann, Christoph
author_sort Beckers, Veronique
collection PubMed
description BACKGROUND: During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants. RESULTS: The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response. CONCLUSIONS: The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-50860452016-11-02 In silico metabolic network analysis of Arabidopsis leaves Beckers, Veronique Dersch, Lisa Maria Lotz, Katrin Melzer, Guido Bläsing, Oliver Ernst Fuchs, Regine Ehrhardt, Thomas Wittmann, Christoph BMC Syst Biol Research Article BACKGROUND: During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants. RESULTS: The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response. CONCLUSIONS: The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-29 /pmc/articles/PMC5086045/ /pubmed/27793154 http://dx.doi.org/10.1186/s12918-016-0347-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Beckers, Veronique
Dersch, Lisa Maria
Lotz, Katrin
Melzer, Guido
Bläsing, Oliver Ernst
Fuchs, Regine
Ehrhardt, Thomas
Wittmann, Christoph
In silico metabolic network analysis of Arabidopsis leaves
title In silico metabolic network analysis of Arabidopsis leaves
title_full In silico metabolic network analysis of Arabidopsis leaves
title_fullStr In silico metabolic network analysis of Arabidopsis leaves
title_full_unstemmed In silico metabolic network analysis of Arabidopsis leaves
title_short In silico metabolic network analysis of Arabidopsis leaves
title_sort in silico metabolic network analysis of arabidopsis leaves
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086045/
https://www.ncbi.nlm.nih.gov/pubmed/27793154
http://dx.doi.org/10.1186/s12918-016-0347-3
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