In silico metabolic network analysis of Arabidopsis leaves

Beckers, Véronique; Dersch, Lisa Maria; Lotz, Katrin; Melzer, Guido; Bläsing, Oliver E.; Fuchs, Regine; Ehrhardt, Thomas; Wittmann, Christoph

doi:10.1186/s12918-016-0347-3

Cited by 14 publications

(23 citation statements)

References 112 publications

(132 reference statements)

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“…For Arabidopsis , we adopted the previously published and extensively used leaf‐specific metabolic model AraGEM (Gomes de Oliveira Dal’Molin et al , 2015; de Oliveira Dal’Molin et al , 2010) and altered 50 reaction directions so as to remove thermodynamically infeasible cycles (Table S4). AraGEM captured more metabolites present in the metabolomics dataset than the more recently published model (Beckers et al , 2016) thereby enabling us to better constrain the feasible solution space using reaction rate expressions derived from mass‐action kinetics. The Arabidopsis GSM constructed by (Mintz‐Oron et al , 2012) had a greater number of reactions participating in thermodynamically infeasible cycles.…”

Section: Genome‐scale Metabolic Model Reconstructionmentioning

confidence: 99%

SNPeffect: identifying functional roles of SNPs using metabolic networks

Sarkar

Maranas

2020

The Plant Journal

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Summary Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome‐wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single‐nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype‐to‐phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme‐coding genes. To this end, we also constructed a genome‐scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth‐determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

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Section: Genome‐scale Metabolic Model Reconstructionmentioning

confidence: 99%

SNPeffect: identifying functional roles of SNPs using metabolic networks

Sarkar

Maranas

2020

The Plant Journal

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“…First, as outlined in Supplementary Figure 1, the network was reconstructed based on biochemical reactions derived from the genomebased pathway of carbon metabolism in cassava, MeRecon (Siriwat, 2012). Next, the 129 biochemical reactions (Figure 1) common in the rice (Lakshmanan et al, 2015;Lakshmanan et al, 2013) and Arabidopsis leaf models (Beckers et al, 2016;de Oliveira Dal'Molin et al, 2010a;Mintz-Oron et al, 2012;Saha, et al, 2011) were added, to reflect the metabolism in photosynthetic tissues. Additionally, 47 reactions overlapping two of the leaf models, at least, were added to fully connect the common reactions to MeRecon.…”

Section: A Qualitative Model Of Carbon Metabolism In Cassava Leavesmentioning

confidence: 99%

“…To date, over 50 genome-scale metabolic models have been constructed in a broad range of organisms, from simple prokaryotes to complex eukaryotes (Kim et al, 2012). However, due to the following difficulties: (i) lack of complete genome information, (ii) insufficient knowledge about the metabolic network and missing components, (iii) duplication of metabolic pathways and transport of metabolites across compartments, (iv) metabolic differences at cell and tissue levels, and varied metabolic response to environmental change, only a few exist for plants, such as Arabidopsis (Beckers et al, 2016;de Oliveira Dal'Molin et al, 2010a;Mintz-Oron et al, 2012), maize (Zea mays) (de Oliveira Dal'Molin et al, 2010b;Saha et al, 2011), and rice (Oryza sativa) (Lakshmanan et al, 2015;Lakshmanan et al, 2013). Metabolic modeling has been employed to study metabolic adaptation to abiotic stress (Lakshmanan et al, 2016;Lakshmanan et al, 2013) and yield improvement in plants, e.g.…”

Section: Introductionmentioning

confidence: 99%

Development of a compartmentalized model for insight into the structured metabolic pathway of carbon metabolism in cassava leaves

2019

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In silico metabolic modeling has enabled systematic study of complicated metabolic processes underlying phenotypes of organisms. Modeling of plant metabolism is often hampered by the network complexity and lack of adequate knowledge. The existing metabolic networks of cassava only cover broad metabolism and are not compartmentalized to truly represent metabolism in photosynthetic tissues. To address the aforementioned limitations and develop a robust metabolic network, physiological and genomic data derived from cassava and leaf models of Arabidopsis and rice were to extend the scope of the existing model. The proposed compartmentalized network of metabolism in photosynthetic tissues of cassava, ph-MeRecon (photosynthetic-Manihot esculenta Metabolic Pathway Reconstruction) was developed based on the information resulting of the comparative study of multiple model plants and cassava genome. The ph-MeRecon covers primary carbon metabolism and comprises 461 metabolites, 550 reactions, and 1,037 metabolic genes. Enzymatic genes on the network were validated using RNA-expression data, and the reactions and pathways were compartmentalized into cytoplasm, chloroplast, mitochondria, and peroxisome. To ensure network connectivity, metabolic gaps were filled using gap reactions obtained from literature and metabolic pathway omnibus. In addition, information on plant physiology, including photosynthetic light-dependent reactions, carboxylase and oxygenase activity of RuBisCO enzyme, and phosphoenolpyruvate carboxylase enzyme activity was incorporated into ph-MeRecon to mimic cellular metabolism in cassava leaves. Thus, ph-MeRecon offers a multi-level platform for system analysis of cellular mechanisms underlying phenotypes of interest in cassava. The ph-MeRecon metabolic model is available at http://bml.sbi.kmutt.ac.th/ph-MeRecon/.

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“…Metabolic Control Analysis (MCA) (Kacser and Burns, 1973;Moreno-Sánchez et al, 2008), which is used to quantify the amount of control a specific step exerts on a pathway, has shown that the control of oil accumulation in seeds occurs both through FA synthesis (e.g., the FA synthase complex) and through triacylglyceride (TAG) assembly (Ramli et al, 2002). Combined network analysis for prediction of metabolic pathways based on metabolomics data, in silico analysis and machine learning was recently conducted in tomato, displaying the potential of artificial intelligence in model simulation of metabolic networks (Beckers et al, 2016;de Luis Balaguer and Sozzani, 2017;Toubiana et al, 2019). However, these modeling efforts are either limited to steady-state conditions or conducted in the context of a single pathway.…”

Section: Introductionmentioning

confidence: 99%

Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes

et al. 2021

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Fatty acids in crop seeds are a major source for both vegetable oils and industrial applications. Genetic improvement of fatty acid composition and oil content is critical to meet the current and future demands of plant-based renewable seed oils. Addressing this challenge can be approached by network modeling to capture key contributors of seed metabolism and to identify underpinning genetic targets for engineering the traits associated with seed oil composition and content. Here, we present a dynamic model, using an Ordinary Differential Equations model and integrated time-course gene expression data, to describe metabolic networks during Arabidopsis thaliana seed development. Through in silico perturbation of genes, targets were predicted in seed oil traits. Validation and supporting evidence were obtained for several of these predictions using published reports in the scientific literature. Furthermore, we investigated two predicted targets using omics datasets for both gene expression and metabolites from the seed embryo, and demonstrated the applicability of this network-based model. This work highlights that integration of dynamic gene expression atlases generates informative models which can be explored to dissect metabolic pathways and lead to the identification of causal genes associated with seed oil traits.

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In silico metabolic network analysis of Arabidopsis leaves

Cited by 14 publications

References 112 publications

SNPeffect: identifying functional roles of SNPs using metabolic networks

SNPeffect: identifying functional roles of SNPs using metabolic networks

Development of a compartmentalized model for insight into the structured metabolic pathway of carbon metabolism in cassava leaves

Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes

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