Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield.

A combined metabolomic, biochemical, fluxomic, and metabolic modeling approach was developed using 19 genetically distant maize (Zea mays) lines from Europe and America. Considerable differences were detected between the lines when leaf metabolic profiles and activities of the main enzymes involved in primary metabolism were compared. During grain filling, the leaf metabolic composition appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lines. During the same period, there was a significant correlation between the genetic distance of the lines and the activities of enzymes involved in carbon metabolism, notably glycolysis. Although large differences were observed in terms of leaf metabolic fluxes, these variations were not tightly linked to the genome structure of the lines. Both correlation studies and metabolic network analyses allowed the description of a maize ideotype with a high grain yield potential. Such an ideotype is characterized by low accumulation of soluble amino acids and carbohydrates in the leaves and high activity of enzymes involved in the C4 photosynthetic pathway and in the biosynthesis of amino acids derived from glutamate. Chlorogenates appear to be important markers that can be used to select for maize lines that produce larger kernels.

SteadyCom: Predicting microbial abundances while ensuring community stability.

Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability. In the absence of this constraint, the faster growing organism will ultimately displace all other microbes in the community. This is particularly important for predicting steady-state microbiota composition as it imposes significant restrictions on the allowable community membership, composition and phenotypes. In this study, we introduce the SteadyCom optimization framework for predicting metabolic flux distributions consistent with the steady-state requirement. SteadyCom can be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterations is independent of the number of organisms. A significant advantage of SteadyCom is compatibility with flux variability analysis. SteadyCom is first demonstrated for a community of four E. coli double auxotrophic mutants and is then applied to a gut microbiota model consisting of nine species, with representatives from the phyla Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. In contrast to the direct use of FBA, SteadyCom is able to predict the change in species abundance in response to changes in diets with minimal additional imposed constraints on the model. By randomizing the uptake rates of microbes, an abundance profile with a good agreement to experimental gut microbiota is inferred. SteadyCom provides an important step towards the cross-cutting task of predicting the composition of a microbial community in a given environment.

Assessing the metabolic impact of nitrogen availability using a compartmentalized maize leaf genome-scale model.

Maize (Zea mays) is an important C4 plant due to its widespread use as a cereal and energy crop. A second-generation genome-scale metabolic model for the maize leaf was created to capture C4 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.

Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling.

In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.

Farnesoid X Receptor Signaling Shapes the Gut Microbiota and Controls Hepatic Lipid Metabolism.

The gut microbiota modulates obesity and associated metabolic phenotypes in part through intestinal farnesoid X receptor (FXR) signaling. Glycine-ß-muricholic acid (Gly-MCA), an intestinal FXR antagonist, has been reported to prevent or reverse high-fat diet (HFD)-induced and genetic obesity, insulin resistance, and fatty liver; however, the mechanism by which these phenotypes are improved is not fully understood. The current study investigated the influence of FXR activity on the gut microbiota community structure and function and its impact on hepatic lipid metabolism. Predictions about the metabolic contribution of the gut microbiota to the host were made using 16S rRNA-based PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states), then validated using 1H nuclear magnetic resonance-based metabolomics, and results were summarized by using genome-scale metabolic models. Oral Gly-MCA administration altered the gut microbial community structure, notably reducing the ratio of Firmicutes to Bacteroidetes and its PICRUSt-predicted metabolic function, including reduced production of short-chain fatty acids (substrates for hepatic gluconeogenesis and de novo lipogenesis) in the ceca of HFD-fed mice. Metabolic improvement was intestinal FXR dependent, as revealed by the lack of changes in HFD-fed intestine-specific Fxr-null (FxrΔIE) mice treated with Gly-MCA. Integrative analyses based on genome-scale metabolic models demonstrated an important link between Lactobacillus and Clostridia bile salt hydrolase activity and bacterial fermentation. Hepatic metabolite levels after Gly-MCA treatment correlated with altered levels of gut bacterial species. In conclusion, modulation of the gut microbiota by inhibition of intestinal FXR signaling alters host liver lipid metabolism and improves obesity-related metabolic dysfunction. IMPORTANCE The farnesoid X receptor (FXR) plays an important role in mediating the dialog between the host and gut microbiota, particularly through modulation of enterohepatic circulation of bile acids. Mounting evidence suggests that genetic ablation of Fxr in the gut or gut-restricted chemical antagonism of the FXR promotes beneficial health effects, including the prevention of nonalcoholic fatty liver disease in rodent models. However, questions remain unanswered, including whether modulation of FXR activity plays a role in shaping the gut microbiota community structure and function and what metabolic pathways of the gut microbiota contribute in an FXR-dependent manner to the host phenotype. In this report, new insights are gained into the metabolic contribution of the gut microbiota to the metabolic phenotypes, including establishing a link between FXR antagonism, bacterial bile salt hydrolase activity, and fermentation. Multiple approaches, including unique mouse models as well as metabolomics and genome-scale metabolic models, were employed to confirm these results.

Genome-scale models of plant metabolism.

A genome-scale model (GSM) is an in silico metabolic model comprising hundreds or thousands of chemical reactions that constitute the metabolic inventory of a cell, tissue, or organism. A complete, accurate GSM, in conjunction with a simulation technique such as flux balance analysis (FBA), can be used to comprehensively predict cellular metabolic flux distributions for a given genotype and given environmental conditions. Apart from enabling a user to quantitatively visualize carbon flow through metabolic pathways, these flux predictions also facilitate the hypothesis of new network properties. By simulating the impacts of environmental stresses or genetic interventions on metabolism, GSMs can aid the formulation of nontrivial metabolic engineering strategies. GSMs for plants and other eukaryotes are significantly more complicated than those for prokaryotes due to their extensive compartmentalization and size. The reconstruction of a GSM involves creating an initial model, curating the model, and then rendering the model ready for FBA. Model reconstruction involves obtaining organism-specific reactions from the annotated genome sequence or organism-specific databases. Model curation involves determining metabolite protonation status or charge, ensuring that reactions are stoichiometrically balanced, assigning reactions to appropriate subcellular compartments, deleting generic reactions or creating specific versions of them, linking dead-end metabolites, and filling of pathway gaps to complete the model. Subsequently, the model requires the addition of transport, exchange, and biomass synthesis reactions to make it FBA-ready. This cycle of editing, refining, and curation has to be performed iteratively to obtain an accurate model. This chapter outlines the reconstruction and curation of GSMs with a focus on models of plant metabolism.