Experimental flux measurements on a network scale

Mechanisms of metabolic adaptation in the duckweed Lemna gibba: an integrated metabolic, transcriptomic and flux analysis

BACKGROUND

Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba cultures to different nutritional conditions.

RESULTS

To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts.

CONCLUSIONS

Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

Plant lipidomics at the crossroads: From technology to biology driven science

The identification and quantification of lipids from plant tissues have become commonplace and many researchers now incorporate lipidomics approaches into their experimental studies. Plant lipidomics research continues to involve technological developments such as those in mass spectrometry imaging, but in large part, lipidomics approaches have matured to the point of being accessible to the novice. Here we review some important considerations for those planning to apply plant lipidomics to their biological questions, and offer suggestions for appropriate tools and practices. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Omics data input for metabolic modeling

Recent advancements in high-throughput large-scale analytical methods to sequence genomes of organisms, and to quantify gene expression, proteins, lipids and metabolites have changed the paradigm of metabolic modeling. The cost of data generation and analysis has decreased significantly, which has allowed exponential increase in the amount of omics data being generated for an organism in a very short time. Compared to progress made in microbial metabolic modeling, plant metabolic modeling still remains limited due to its complex genomes and compartmentalization of metabolic reactions. Herein, we review and discuss different omics-datasets with potential application in the functional genomics. In particular, this review focuses on the application of omics-datasets towards construction and reconstruction of plant metabolic models.

The Peanut (Arachis hypogaea L.) Gene AhLPAT2 Increases the Lipid Content of Transgenic Arabidopsis Seeds

Lysophosphatidic acid acyltransferase (LPAT), which converts lysophosphatidic acid (LPA) to phosphatidic acid (PA), catalyzes the addition of fatty acyl moieties to the sn-2 position of the LPA glycerol backbone in triacylglycerol (TAG) biosynthesis. We recently reported the cloning and temporal-spatial expression of a peanut (Arachis hypogaea) AhLPAT2gene, showing that an increase in AhLPAT2 transcript levels was closely correlated with an increase in seed oil levels. However, the function of the enzyme encoded by the AhLPAT2 gene remains unclear. Here, we report that AhLPAT2 transcript levels were consistently higher in the seeds of a high-oil cultivar than in those of a low-oil cultivar across different seed developmental stages. Seed-specific overexpression of AhLPAT2 in Arabidopsis results in a higher percentage of oil in the seeds and greater-than-average seed weight in the transgenic plants compared with the wild-type plants, leading to a significant increase in total oil yield per plant. The total fatty acid (FA) content and the proportion of unsaturated FAs also increased. In the developing siliques of AhLPAT2-overexpressing plants, the expression levels of genes encoding crucial enzymes involved in de novo FA synthesis, acetyl-CoA subunit (AtBCCP2) and acyl carrier protein 1 (AtACP1) were elevated. AhLPAT2 overexpression also promoted the expression of several key genes related to TAG assembly, sucrose metabolism, and glycolysis. These results demonstrate that the expression of AhLPAT2 plays an important role in glycerolipid production in peanuts.

Flux variability analysis: application to developing oilseed rape embryos using toolboxes for constraint-based modeling

Flux variability analysis enables comprehensive exploration of alternate optimal routes in a metabolic network. This method is especially useful with models such as bna572 for the developing oilseed rape embryo which is highly compartmentalized. Here, we describe a protocol for carrying out flux variability analysis on reactions and network projections of bna572 using well-established software (CellNetAnalyzer and COBRA) for constraint-based analysis of stoichiometric network reconstructions.

Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis

The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for (13)C-Metabolic Flux Analysis ((13)C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from (13)C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.

Analysis of metabolic flux using dynamic labelling and metabolic modelling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

Seed-development programs: a systems biology-based comparison between dicots and monocots

Seeds develop differently in dicots and monocots, especially with respect to the major storage organs. High-resolution transcriptome data have provided the first insights into the molecular networks and pathway interactions that function during the development of individual seed compartments. Here, we review mainly recent data obtained by systems biology-based approaches, which have allowed researchers to construct and model complex metabolic networks and fluxes and identify key limiting steps in seed development. Comparative coexpression network analyses define evolutionarily conservative (FUS3/ABI3/LEC1) and divergent (LEC2) networks in dicots and monocots. Finally, we discuss the determination of seed size--an important yield-related characteristic--as mediated by a number of processes (maternal and epigenetic factors, fine-tuned regulation of cell death in distinct seed compartments, and endosperm growth) and underlying genes defined through mutant analyses. Altogether, systems approaches can make important contributions toward a more complete and holistic knowledge of seed biology and thus support strategies for knowledge-based molecular breeding.

Oil and protein accumulation in developing seeds is influenced by the expression of a cytosolic pyrophosphatase in Arabidopsis

This study describes a dominant low-seed-oil mutant (lo15571) of Arabidopsis (Arabidopsis thaliana) generated by enhancer tagging. Compositional analysis of developing siliques and mature seeds indicated reduced conversion of photoassimilates to oil. Immunoblot analysis revealed increased levels of At1g01050 protein in developing siliques of lo15571. At1g01050 encodes a soluble, cytosolic pyrophosphatase and is one of five closely related genes that share predicted cytosolic localization and at least 70% amino acid sequence identity. Expression of At1g01050 using a seed-preferred promoter recreated most features of the lo15571 seed phenotype, including low seed oil content and increased levels of transient starch and soluble sugars in developing siliques. Seed-preferred RNA interference-mediated silencing of At1g01050 and At3g53620, a second cytosolic pyrophosphatase gene that shows expression during seed filling, led to a heritable oil increase of 1% to 4%, mostly at the expense of seed storage protein. These results are consistent with a scenario in which the rate of mobilization of sucrose, for precursor supply of seed storage lipid biosynthesis by cytosolic glycolysis, is strongly influenced by the expression of endogenous pyrophosphatase enzymes. This emphasizes the central role of pyrophosphate-dependent reactions supporting cytosolic glycolysis during seed maturation when ATP supply is low, presumably due to hypoxic conditions. This route is the major route providing precursors for seed oil biosynthesis. ATP-dependent reactions at the entry point of glycolysis in the cytosol or plastid cannot fully compensate for the loss of oil content observed in transgenic events with increased expression of cytosolic pyrophosphatase enzyme in the cytosol. These findings shed new light on the dynamic properties of cytosolic pyrophosphate pools in developing seed and their influence on carbon partitioning during seed filling. Finally, our work uniquely demonstrates that genes encoding cytosolic pyrophosphatase enzymes provide novel targets to improve seed composition for plant biotechnology applications.

GabiPD - The GABI Primary Database integrates plant proteomic data with gene-centric information

GabiPD is an integrative plant "omics" database that has been established as part of the German initiative for Genome Analysis of the Plant Biological System (GABI). Data from different "omics" disciplines are integrated and interactively visualized. Proteomics is represented by data and tools aiding studies on the identification of post-translational modification and function of proteins. Annotated 2D electrophoresis-gel images are offered to inspect protein sets expressed in different tissues of Arabidopsis thaliana and Brassica napus. From a given protein spot, a link will direct the user to the related GreenCard Gene entry where detailed gene-centric information will support the functional annotation. Beside MapMan- and GO-classification, information on conserved protein domains and on orthologs is integrated in this GreenCard service. Moreover, all other GabiPD data related to the gene, including transcriptomic data, as well as gene-specific links to external resources are provided. Researches interested in plant protein phosphorylation will find information on potential MAP kinase substrates identified in different protein microarray studies integrated in GabiPD's Phosphoproteomics page. These data can be easily compared to experimentally identified or predicted phosphorylation sites in PhosPhAt via the related Gene GreenCard. This will allow the selection of interesting candidates for further experimental validation of their phosphorylation.