Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering

Alonso, Ana Paula; Val, Dale L.; Shachar‐Hill, Yair

doi:10.1016/j.ymben.2010.10.002

Cited by 94 publications

(92 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The benefits of this energy source are seen not just in leaves but can also be observed in the metabolism of green seeds (Chen and Shachar-Hill, 2012). Specifically, the additional NADPH allows the refixation of metabolic CO 2 by Rubisco (Schwender et al, 2004), resulting in spectacularly high carbon conversion efficiencies for green oilseeds in the light (Allen et al, 2009b) in comparison with nongreen oilseeds (Alonso et al, 2007(Alonso et al, , 2011 or green oilseeds in the dark (Schwender et al, 2006). In photosynthetic tissues, a major challenge is presented by natural variations in light conditions, which lead to variable rates of photosynthetic NADPH and ATP production.…”

Section: Energy Metabolism In Photosynthetic Tissuesmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

View full text Add to dashboard Cite

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

show abstract

Section: Energy Metabolism In Photosynthetic Tissuesmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

View full text Add to dashboard Cite

show abstract

“…These imbalances, as demonstrated by this experimental and modeling approach, can be balanced by local metabolic adjustments. We suggest that the metabolic compartmentation of the starchy endosperm provides a mechanism to ensure metabolic flexibility and eventually contributes to the high carbon conversion efficiency shown by the cereal starchy endosperm (Alonso et al, 2011).…”

Section: Implications Of the Existence Of Metabolic Compartmentation mentioning

confidence: 95%

Combined Noninvasive Imaging and Modeling Approaches Reveal Metabolic Compartmentation in the Barley Endosperm

et al. 2011

View full text Add to dashboard Cite

The starchy endosperm of cereals is a priori taken as a metabolically uniform tissue. By applying a noninvasive assay based on 13 C/ 1 H-magnetic resonance imaging (MRI) to barley (Hordeum vulgare) grains, we uncovered metabolic compartmentation in the endosperm. 13 C-Suc feeding during grain filling showed that the primary site of Ala synthesis was the central region of the endosperm, the part of the caryopsis experiencing the highest level of hypoxia. Region-specific metabolism in the endosperm was characterized by flux balance analysis (FBA) and metabolite profiling. FBA predicts that in the central region of the endosperm, the tricarboxylic acid cycle shifts to a noncyclic mode, accompanied by elevated glycolytic flux and the accumulation of Ala. The metabolic compartmentation within the endosperm is advantageous for the grain's carbon and energy economy, with a prominent role being played by Ala aminotransferase. An investigation of caryopses with a genetically perturbed tissue pattern demonstrated that Ala accumulation is a consequence of oxygen status, rather than being either tissue specific or dependent on the supply of Suc. Hence the 13 C-Ala gradient can be used as an in vivo marker for hypoxia. The combination of MRI and metabolic modeling offers opportunities for the noninvasive analysis of metabolic compartmentation in plants.

show abstract

“…The endosperm biomass fraction attributable to carbohydrates was calculated to be about 90% of dry mass (Ingle et al, 1965;Alonso et al, 2011). Of this carbohydrate fraction, 77.6% is starch, 16.6% is cell walls (Alonso et al, 2011) and the remaining 5.8% is free sucrose, fructose, and glucose (Ingle et al, 1965).…”

Section: Carbohydratesmentioning

confidence: 99%

“…We also adjusted the biomass composition of our embryo and endosperm models to better fit the actual composition data for these tissues by curating data for individual components from a variety of literature sources. To test the accuracy of our models, we explored how well they replicate the flux profiles measured for central carbon metabolism in embryo and endosperm tissues (Alonso et al, 2010(Alonso et al, , 2011. This analysis demonstrates that our models have an improved fit between the fluxes generated in silico and the fluxes measured in vivo.…”

Section: Introductionmentioning

confidence: 97%

Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

Seaver¹,

Bradbury²,

Frelin³

et al. 2016

Crop Breeding

View full text Add to dashboard Cite

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

show abstract

Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering

Cited by 94 publications

References 67 publications

Inference and prediction of metabolic network fluxes

Inference and prediction of metabolic network fluxes

Combined Noninvasive Imaging and Modeling Approaches Reveal Metabolic Compartmentation in the Barley Endosperm

Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

Contact Info

Product

Resources

About