An in silico compartmentalized metabolic model of <i>Brassica napus</i> enables the systemic study of regulatory aspects of plant central metabolism

Pilalis, Eleftherios; Chatziioannou, Aristotelis; Thomasset, Brigitte; Kolisis, Fragiskos N.

doi:10.1002/bit.23107

Cited by 56 publications

(38 citation statements)

References 34 publications

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“…Most of these models were generated to represent reactions in a major organ, and only a few accurately reflect metabolism specific to a certain tissue (de Oliveira Dal'Molin et al, 2010b;Hay and Schwender, 2011;Pilalis et al, 2011;Mintz-Oron et al, 2012). To our knowledge, the menpiGT_2015 model is the first attempt to gather data from various sources (literature, transcriptomics, metabolomics, and biochemical assays) and capture metabolism in nonphotosynthetic GTs.…”

Section: Fermentation Is Active In Peppermint Gtsmentioning

confidence: 99%

Bioenergetics of Monoterpenoid Essential Oil Biosynthesis in Nonphotosynthetic Glandular Trichomes

et al. 2017

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ORCID IDs: 0000-0001-8261-9015 (S.R.J.); 0000-0001-7934-7987 (N.S.); 0000-0001-6565-9584 (B.M.L.).The commercially important essential oils of peppermint (Mentha 3 piperita) and its relatives in the mint family (Lamiaceae) are accumulated in specialized anatomical structures called glandular trichomes (GTs). A genome-scale stoichiometric model of secretory phase metabolism in peppermint GTs was constructed based on current biochemical and physiological knowledge. Fluxes through the network were predicted based on metabolomic and transcriptomic data. Using simulated reaction deletions, this model predicted that two processes, the regeneration of ATP and ferredoxin (in its reduced form), exert substantial control over flux toward monoterpenes. Follow-up biochemical assays with isolated GTs indicated that oxidative phosphorylation and ethanolic fermentation were active and that cooperation to provide ATP depended on the concentration of the carbon source. We also report that GTs with high flux toward monoterpenes express, at very high levels, genes coding for a unique pair of ferredoxin and ferredoxin-NADP + reductase isoforms. This study provides, to our knowledge, the first evidence of how bioenergetic processes determine flux through monoterpene biosynthesis in GTs.

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Section: Fermentation Is Active In Peppermint Gtsmentioning

confidence: 99%

Bioenergetics of Monoterpenoid Essential Oil Biosynthesis in Nonphotosynthetic Glandular Trichomes

et al. 2017

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“…The number of GSMs for plants has increased rapidly, with models available for Arabidopsis thaliana (Poolman et al, 2009;de Oliveira Dal'Molin et al, 2010a), barley (Hordeum vulgare) seed (GrafahrendBelau et al, 2009), maize (de Oliveira Dal'Molin et al, 2010bSaha et al, 2011), sorghum (Sorghum bicolor; de Oliveira Dal'Molin et al, 2010b), sugarcane (Saccharum officinarum; de Oliveira Dal'Molin et al, 2010b), rapeseed (Brassica napus; Pilalis et al, 2011), and rice (Oryza sativa; Poolman et al, 2013). These models rely on annotation information to assemble comprehensive compilations of all reactions and metabolites known to occur within the organism.…”

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confidence: 99%

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

et al. 2014

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Maize (Zea mays) is an important C 4 plant due to its widespread use as a cereal and energy crop. A second-generation genomescale metabolic model for the maize leaf was created to capture C 4 carbon fixation and investigate nitrogen (N) assimilation by modeling the interactions between the bundle sheath and mesophyll cells. The model contains gene-protein-reaction relationships, elemental and charge-balanced reactions, and incorporates experimental evidence pertaining to the biomass composition, compartmentalization, and flux constraints. Condition-specific biomass descriptions were introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lignocellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of information from the MetaCrop and MaizeCyc databases, this updated model spans 5,824 genes, 8,525 reactions, and 9,153 metabolites, an increase of approximately 4 times the size of the earlier iRS1563 model. Transcriptomic and proteomic data have also been used to introduce regulatory constraints in the model to simulate an N-limited condition and mutants deficient in glutamine synthetase, gln1-3 and gln1-4. Model-predicted results achieved 90% accuracy when comparing the wild type grown under an N-complete condition with the wild type grown under an N-deficient condition.

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“…In this regard, metabolic network models for several plants, such as Arabidopsis (Arabidopsis thaliana; Poolman et al, 2009;de Oliveira Dal'Molin et al, 2010a;Saha et al, 2011;Chung et al, 2012;Mintz-Oron et al, 2012), barley (Hordeum vulgare;Grafahrend-Belau et al, 2009), rapeseed (Brassica napus;Hay and Schwender, 2011;Pilalis et al, 2011), maize (Zea mays; Saha et al, 2011), and a general C 4 plant model (de Oliveira Dal'Molin et al, 2010b) have already been developed, and some of them are even in genome scale. Nevertheless, to the best of our knowledge, the metabolic model of rice is not available to date.…”

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confidence: 99%

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis

et al. 2013

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Rice (Oryza sativa) is one of the major food crops in world agriculture, especially in Asia. However, the possibility of subsequent occurrence of flood and drought is a major constraint to its production. Thus, the unique behavior of rice toward flooding and drought stresses has required special attention to understand its metabolic adaptations. However, despite several decades of research investigations, the cellular metabolism of rice remains largely unclear. In this study, in order to elucidate the physiological characteristics in response to such abiotic stresses, we reconstructed what is to our knowledge the first metabolic/regulatory network model of rice, representing two tissue types: germinating seeds and photorespiring leaves. The phenotypic behavior and metabolic states simulated by the model are highly consistent with our suspension culture experiments as well as previous reports. The in silico simulation results of seed-derived rice cells indicated (1) the characteristic metabolic utilization of glycolysis and ethanolic fermentation based on oxygen availability and (2) the efficient sucrose breakdown through sucrose synthase instead of invertase. Similarly, flux analysis on photorespiring leaf cells elucidated the crucial role of plastid-cytosol and mitochondrion-cytosol malate transporters in recycling the ammonia liberated during photorespiration and in exporting the excess redox cofactors, respectively. The model simulations also unraveled the essential role of mitochondrial respiration during drought stress. In the future, the combination of experimental and in silico analyses can serve as a promising approach to understand the complex metabolism of rice and potentially help in identifying engineering targets for improving its productivity as well as enabling stress tolerance.

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An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism

Cited by 56 publications

References 34 publications

Bioenergetics of Monoterpenoid Essential Oil Biosynthesis in Nonphotosynthetic Glandular Trichomes

Bioenergetics of Monoterpenoid Essential Oil Biosynthesis in Nonphotosynthetic Glandular Trichomes

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis

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