Integration of Genome-Scale Modeling and Transcript Profiling Reveals Metabolic Pathways Underlying Light and Temperature Acclimation in <i>Arabidopsis</i>

Töpfer, Nadine; Caldana, Camila; Grimbs, Sergio; Willmitzer, Lothar; Fernie, Alisdair R.; Nikoloski, Zoran

doi:10.1105/tpc.112.108852

Cited by 43 publications

(44 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1). The inclusion of flux bounds based on transcript and protein levels has proved to be a useful tool to assess and interpret metabolic behavior between conditions (Töpfer et al, 2013). However, the coordination of several regulatory levels between gene transcription and reaction rates may not result in a direct correspondence between gene expression and fluxes, and this can affect the usefulness of these methods (Machado and Herrgård, 2014).…”

Section: Building and Analyzing Context-specific Metabolic Modelsmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

Self Cite

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: Building and Analyzing Context-specific Metabolic Modelsmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among these, the human genome-scale metabolic model (GEM) has been integrated with transcriptome and proteome data to characterize the transcriptional regulatory mechanisms and metabolic phenotypes of various diseases, which could not be deciphered from either of them alone (Zelezniak et al, 2010;Hu et al, 2013;Mardinoglu et al, 2014). In plants, the transcriptome data were successfully integrated with the Arabidopsis GEM to understand its metabolic acclimation under different light and/or temperature conditions (Töpfer et al, 2013(Töpfer et al, , 2014. Similarly, here, we combined the GEM of rice cells with multiple omics data to uncover the heterogeneity in light-mediated transcriptional regulation of cellular metabolism across various colors.…”

mentioning

confidence: 99%

Unraveling the light-specific metabolic and regulatory signatures of rice through combined in silico modeling and multi-omics analysis

et al. 2015

View full text Add to dashboard Cite

Light quality is an important signaling component upon which plants orchestrate various morphological processes, including seed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense various light qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initially profiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) as well as in the dark. Concurrently, we reconstructed a fully compartmentalized genome-scale metabolic model of rice cells, iOS2164, containing 2,164 unique genes, 2,283 reactions, and 1,999 metabolites. We then combined the model with transcriptome profiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice gene expressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were upregulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis of gene expression data with the rice genome-scale metabolic model further uncovered that phytohormones, such as abscisate, ethylene, gibberellin, and jasmonate, are the key biomarkers of light-mediated regulation, and subsequent analysis of the associated genes' promoter regions identified several light-specific transcription factors. Finally, the transcriptional control of rice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data with constraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potential applications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbial and mammalian systems.Light is the primary energy source as well as a key signaling element for plant growth and development. Although both light quantity (fluence) and quality (wavelength) are important for plant life, the latter is a crucial environmental indicator for plants to modulate their growth and morphological processes, such as seed germination, stem elongation, phototropism, circadian rhythms, and flowering induction (Neff et al., 2000). Since the discovery of red light (R)-stimulated seed germination in lettuce (Lactuca sativa;Borthwick et al., 1952), several studies have been focused on investigating the effect of individual light quality on plant growth and development. Earlier works used the classical genetic and molecular approaches, such as the use of light signaling-deficient mutants, measurement of enzyme activities, and enzyme/metabolite levels of certain pathway(s). However, it is required to comprehensively interrogate plant metabolism, because the transitions of light quality are likely to affect the plant physiology by modulating several metabolic effectors

show abstract

“…For example, Schwender and Hay (2012) investigated how a metabolic reconstruction exhibited variation in reaction activity in response to variation in the biosynthetic demands of oil and protein as storage products in the plant embryo and were able to identify the utilization of a pathway within the network of reactions that was not yet characterized in the literature. Similarly, Töpfer et al (2013) explored the means with which a set of pathways in a metabolic reconstruction responded to various conditions of light and temperature, showing, in one case, the preference for methylerythritol 4-phosphate pathway over the mevalonate pathway in isoprenoid biosynthesis, and also generating a new hypothesis for the role of homocysteine-cysteine conversion.…”

Section: Introductionmentioning

confidence: 99%

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

Integration of Genome-Scale Modeling and Transcript Profiling Reveals Metabolic Pathways Underlying Light and Temperature Acclimation in Arabidopsis

Cited by 43 publications

References 84 publications

Inference and prediction of metabolic network fluxes

Inference and prediction of metabolic network fluxes

Unraveling the light-specific metabolic and regulatory signatures of rice through combined in silico modeling and multi-omics analysis

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

Contact Info

Product

Resources

About