Predictive modeling of biomass component tradeoffs in Brassica napus developing oilseeds based on in silico manipulation of storage metabolism

Comparative transcriptomic analysis provides insights into the genetic networks regulating oil differential production in oil crops

BACKGROUND

Plants differ more than threefold in seed oil contents (SOCs). Soybean (Glycine max), cotton (Gossypium hirsutum), rapeseed (Brassica napus), and sesame (Sesamum indicum) are four important oil crops with markedly different SOCs and fatty acid compositions.

RESULTS

Compared to grain crops like maize and rice, expanded acyl-lipid metabolism genes and relatively higher expression levels of genes involved in seed oil synthesis (SOS) in the oil crops contributed to the oil accumulation in seeds. Here, we conducted comparative transcriptomics on oil crops with two different SOC materials. In common, DIHYDROLIPOAMIDE DEHYDROGENASE, STEAROYL-ACYL CARRIER PROTEIN DESATURASE, PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE, and oil-body protein genes were both differentially expressed between the high- and low-oil materials of each crop. By comparing functional components of SOS networks, we found that the strong correlations between genes in "glycolysis/gluconeogenesis" and "fatty acid synthesis" were conserved in both grain and oil crops, with PYRUVATE KINASE being the common factor affecting starch and lipid accumulation. Network alignment also found a conserved clique among oil crops affecting seed oil accumulation, which has been validated in Arabidopsis. Differently, secondary and protein metabolism affected oil synthesis to different degrees in different crops, and high SOC was due to less competition of the same precursors. The comparison of Arabidopsis mutants and wild type showed that CINNAMYL ALCOHOL DEHYDROGENASE 9, the conserved regulator we identified, was a factor resulting in different relative contents of lignins to oil in seeds. The interconnection of lipids and proteins was common but in different ways among crops, which partly led to differential oil production.

CONCLUSIONS

This study goes beyond the observations made in studies of individual species to provide new insights into which genes and networks may be fundamental to seed oil accumulation from a multispecies perspective.

Tandem Mass Tag-Based Quantitative Proteomics Reveals Implication of a Late Embryogenesis Abundant Protein (BnLEA57) in Seed Oil Accumulation in Brassica napus L

Enhancing oil content is one of the major goals in Brassica napus breeding; however, genetic regulation of seed oil content in plants is complex and not fully elucidated. In this study, we report proteins that were differentially accumulated in immature seeds of 35 days after anthesis between two recombinant inbred lines with contrasting seed oil content, high oil content line (HOCL) and low oil content line (LOCL) using a multiplex isobaric tandem mass tags (TMT)-based quantitative proteomic approach. Over 4,600 proteins were quantified in seeds of the two lines, and 342 proteins showed differential accumulation between seeds of HOCL and LOCL. Gene Ontology enrichment analysis revealed that the differentially accumulated proteins were enriched in proteins involved in lipid biosynthesis and metabolism, photosynthesis, and nutrient reservoir activity. Western blot confirmed the increased abundance of a late embryogenesis abundant protein (BnLEA57) in HOCL seeds compared with LOCL seeds, and overexpression of either BnLEA57 gene or its homology BnLEA55 in transgenic Arabidopsis thaliana enhanced oil content in Arabidopsis seeds. Our work provides new insights into the molecular regulatory mechanism of seed oil content in B. napus.

Elucidation of Triacylglycerol Overproduction in the C4 Bioenergy Crop Sorghum bicolor by Constraint-Based Analysis

Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C4 biomass crop Sorghum bicolor (iTJC1414) along with a minimal model for photosynthetic CO2 assimilation, sucrose and TAG biosynthesis in C3 plants. Extending iTJC1414 to a four-cell diel model we simulate C4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

Plant cell cultures as heterologous bio-factories for secondary metabolite production

Synthetic biology has been developing rapidly in the last decade and is attracting increasing attention from many plant biologists. The production of high-value plant-specific secondary metabolites is, however, limited mostly to microbes. This is potentially problematic because of incorrect post-translational modification of proteins and differences in protein micro-compartmentalization, substrate availability, chaperone availability, product toxicity, and cytochrome p450 reductase enzymes. Unlike other heterologous systems, plant cells may be a promising alternative for the production of high-value metabolites. Several commercial plant suspension cell cultures from different plant species have been used successfully to produce valuable metabolites in a safe, low cost, and environmentally friendly manner. However, few metabolites are currently being biosynthesized using plant platforms, with the exception of the natural pigment anthocyanin. Both Arabidopsis thaliana and Nicotiana tabacum cell cultures can be developed by multiple gene transformations and CRISPR-Cas9 genome editing. Given that the introduction of heterologous biosynthetic pathways into Arabidopsis and N. tabacum is not widely used, the biosynthesis of foreign metabolites is currently limited; however, therein lies great potential. Here, we discuss the exemplary use of plant cell cultures and prospects for using A. thaliana and N. tabacum cell cultures to produce valuable plant-specific metabolites.

Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava

The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.

Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways

Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom-up metabolic reconstruction of J. curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint-based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J. curcas.

Cellular Plasticity in Response to Suppression of Storage Proteins in the Brassica napus Embryo

The tradeoff between protein and oil storage in oilseed crops has been tested here in oilseed rape (Brassica napus) by analyzing the effect of suppressing key genes encoding protein storage products (napin and cruciferin). The phenotypic outcomes were assessed using NMR and mass spectrometry imaging, microscopy, transcriptomics, proteomics, metabolomics, lipidomics, immunological assays, and flux balance analysis. Surprisingly, the profile of storage products was only moderately changed in RNA interference transgenics. However, embryonic cells had undergone remarkable architectural rearrangements. The suppression of storage proteins led to the elaboration of membrane stacks enriched with oleosin (sixfold higher protein abundance) and novel endoplasmic reticulum morphology. Protein rebalancing and amino acid metabolism were focal points of the metabolic adjustments to maintain embryonic carbon/nitrogen homeostasis. Flux balance analysis indicated a rather minor additional demand for cofactors (ATP and NADPH). Thus, cellular plasticity in seeds protects against perturbations to its storage capabilities and, hence, contributes materially to homeostasis. This study provides mechanistic insights into the intriguing link between lipid and protein storage, which have implications for biotechnological strategies directed at improving oilseed crops.

Flux balance analysis of metabolism during growth by osmotic cell expansion and its application to tomato fruits

Cell expansion is a significant contributor to organ growth and is driven by the accumulation of osmolytes to increase cell turgor pressure. Metabolic modelling has the potential to provide insights into the processes that underpin osmolyte synthesis and transport, but the main computational approach for predicting metabolic network fluxes, flux balance analysis, often uses biomass composition as the main output constraint and ignores potential changes in cell volume. Here we present growth-by-osmotic-expansion flux balance analysis (GrOE-FBA), a framework that accounts for both the metabolic and ionic contributions to the osmotica that drive cell expansion, as well as the synthesis of protein, cell wall and cell membrane components required for cell enlargement. Using GrOE-FBA, the metabolic fluxes in dividing and expanding cells were analysed, and the energetic costs for metabolite biosynthesis and accumulation in the two scenarios were found to be surprisingly similar. The expansion phase of tomato fruit growth was also modelled using a multiphase single-optimization GrOE-FBA model and this approach gave accurate predictions of the major metabolite levels throughout fruit development, as well as revealing a role for transitory starch accumulation in ensuring optimal fruit development.

13C-Metabolic Flux Analysis in Developing Flax (Linum usitatissinum L.) Embryos to Understand Storage Lipid Biosynthesis

Flax (Linum usitatissinum L.) oil is an important source of α-linolenic (C18:3 ω-3). This polyunsaturated fatty acid is well known for its nutritional role in human and animal diets. Understanding storage lipid biosynthesis in developing flax embryos can lead to an increase in seed yield via marker-assisted selection. While a tremendous amount of work has been done on different plant species to highlight their metabolism during embryo development, a comprehensive analysis of metabolic flux in flax is still lacking. In this context, we have utilized in vitro cultured developing embryos of flax and determined net fluxes by performing three complementary parallel labeling experiments with 13C-labeled glucose and glutamine. Metabolic fluxes were estimated by computer-aided modeling of the central metabolic network including 11 cofactors of 118 reactions of the central metabolism and 12 pseudo-fluxes. A focus on lipid storage biosynthesis and the associated pathways was done in comparison with rapeseed, arabidopsis, maize and sunflower embryos. In our hands, glucose was determined to be the main source of carbon in flax embryos, leading to the conversion of phosphoenolpyruvate to pyruvate. The oxidative pentose phosphate pathway (OPPP) was identified as the producer of NADPH for fatty acid biosynthesis. Overall, the use of 13C-metabolic flux analysis provided new insights into the flax embryo metabolic processes involved in storage lipid biosynthesis. The elucidation of the metabolic network of this important crop plant reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

Extended Utilization of Constraint-Based Metabolic Model in a Long-Growing Crop

The constraint-based rMeCBM-KU50 model of cassava storage root growth was analyzed to evaluate its sensitivity, with respect to reaction flux distribution and storage root growth rate, to changes in model inputted data and constraints, including sucrose uptake rate-related data—photosynthetic rate, total leaf area, total photosynthetic rate, storage root dry weight, and biomass function-related data. These mainly varied within ±90% of the model default values, although exceptions were made for the carbohydrate (−90% to 8%) and starch (−90% to 9%) contents. The results indicated that the predicted storage root growth rate was highly affected by specific sucrose uptake rates through the total photosynthetic rate and storage root dry weight variations; whereas the carbon flux distribution, direction and partitioning inclusive, was more sensitive to the variation in biomass content, particularly the carbohydrate content. This study showed that the specific sucrose uptake rate based on the total photosynthetic rate, storage root dry weight, and carbohydrate content were critical to the constraint-based metabolic modeling and deepened our understanding of the input–output relationship—specifically regarding the rMeCBM-KU50 model—providing a valuable platform for the modeling of plant metabolic systems, especially long-growing crops.

Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through Flux Balance Analysis

Analysis of metabolic flux was used for system level assessment of carbon partitioning in Kasetsart 50 (KU50) and Hanatee (HN) cassava cultivars to understand the metabolic routes for their distinct phenotypes. First, the constraint-based metabolic model of cassava storage roots, rMeCBM, was developed based on the carbon assimilation pathway of cassava. Following the subcellular compartmentalization and curation to ensure full network connectivity and reflect the complexity of eukaryotic cells, cultivar specific data on sucrose uptake and biomass synthesis were input, and rMeCBM model was used to simulate storage root growth in KU50 and HN. Results showed that rMeCBM-KU50 and rMeCBM-HN models well imitated the storage root growth. The flux-sum analysis revealed that both cultivars utilized different metabolic precursors to produce energy in plastid. More carbon flux was invested in the syntheses of carbohydrates and amino acids in KU50 than in HN. Also, KU50 utilized less flux for respiration and less energy to synthesize one gram of dry storage root. These results may disclose metabolic potential of KU50 underlying its higher storage root and starch yield over HN. Moreover, sensitivity analysis indicated the robustness of rMeCBM model. The knowledge gained might be useful for identifying engineering targets for cassava yield improvement.

Fluxomics links cellular functional analyses to whole-plant phenotyping

Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.

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.

An Integrated Bioinformatics Analysis Reveals Divergent Evolutionary Pattern of Oil Biosynthesis in High- and Low-Oil Plants

Seed oils provide a renewable source of food, biofuel and industrial raw materials that is important for humans. Although many genes and pathways for acyl-lipid metabolism have been identified, little is known about whether there is a specific mechanism for high-oil content in high-oil plants. Based on the distinct differences in seed oil content between four high-oil dicots (20~50%) and three low-oil grasses (<3%), comparative genome, transcriptome and differential expression analyses were used to investigate this mechanism. Among 4,051 dicot-specific soybean genes identified from 252,443 genes in the seven species, 54 genes were shown to directly participate in acyl-lipid metabolism, and 93 genes were found to be associated with acyl-lipid metabolism. Among the 93 dicot-specific genes, 42 and 27 genes, including CBM20-like SBDs and GPT2, participate in carbohydrate degradation and transport, respectively. 40 genes highly up-regulated during seed oil rapid accumulation period are mainly involved in initial fatty acid synthesis, triacylglyceride assembly and oil-body formation, for example, ACCase, PP, DGAT1, PDAT1, OLEs and STEROs, which were also found to be differentially expressed between high- and low-oil soybean accessions. Phylogenetic analysis revealed distinct differences of oleosin in patterns of gene duplication and loss between high-oil dicots and low-oil grasses. In addition, seed-specific GmGRF5, ABI5 and GmTZF4 were predicted to be candidate regulators in seed oil accumulation. This study facilitates future research on lipid biosynthesis and potential genetic improvement of seed oil content.

Triacylglycerol Metabolism, Function, and Accumulation in Plant Vegetative Tissues

Oils in the form of triacylglycerols are the most abundant energy-dense storage compounds in eukaryotes, and their metabolism plays a key role in cellular energy balance, lipid homeostasis, growth, and maintenance. Plants accumulate oils primarily in seeds and fruits. Plant oils are used for food and feed and, increasingly, as feedstocks for biodiesel and industrial chemicals. Although plant vegetative tissues do not accumulate significant levels of triacylglycerols, they possess a high capacity for their synthesis, storage, and metabolism. The development of plants that accumulate oil in vegetative tissues presents an opportunity for expanded production of triacylglycerols as a renewable and sustainable bioenergy source. Here, we review recent progress in the understanding of triacylglycerol synthesis, turnover, storage, and function in leaves and discuss emerging genetic engineering strategies targeted at enhancing triacylglycerol accumulation in biomass crops. Such plants could potentially be modified to produce oleochemical feedstocks or nutraceuticals.

Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions

The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.

Modeling Rice Metabolism: From Elucidating Environmental Effects on Cellular Phenotype to Guiding Crop Improvement

Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.

Mathematical models of plant metabolism

Among various modeling approaches in plant metabolic research, applications of Constraint-Based modeling are fast increasing in recent years, apparently driven by current advances in genomics and genome sequencing. Constraint-Based modeling, the functional analysis of metabolic networks at the whole cell or genome scale, is more difficult to apply to plants than to microbes. Here we discuss recent developments in Constraint-Based modeling in plants with focus on issues of model reconstruction and flux prediction. Another topic is the emerging application of integration of Constraint-Based modeling with omics data to increase predictive power. Furthermore, advances in experimental measurements of cellular fluxes by (13)C-Metabolic Flux Analysis are highlighted, including instationary (13)C-MFA used to probe autotrophic metabolism in photosynthetic tissue in the light.

Inference and Prediction of Metabolic Network Fluxes

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.

Oil biosynthesis in a basal angiosperm: transcriptome analysis of Persea Americana mesocarp

BACKGROUND

The mechanism by which plants synthesize and store high amounts of triacylglycerols (TAG) in tissues other than seeds is not well understood. The comprehension of controls for carbon partitioning and oil accumulation in nonseed tissues is essential to generate oil-rich biomass in perennial bioenergy crops. Persea americana (avocado), a basal angiosperm with unique features that are ancestral to most flowering plants, stores ~ 70 % TAG per dry weight in its mesocarp, a nonseed tissue. Transcriptome analyses of select pathways, from generation of pyruvate and leading up to TAG accumulation, in mesocarp tissues of avocado was conducted and compared with that of oil-rich monocot (oil palm) and dicot (rapeseed and castor) tissues to identify tissue- and species-specific regulation and biosynthesis of TAG in plants.

RESULTS

RNA-Seq analyses of select lipid metabolic pathways of avocado mesocarp revealed patterns similar to that of other oil-rich species. However, only some predominant orthologs of the fatty acid biosynthetic pathway genes in this basal angiosperm were similar to those of monocots and dicots. The accumulation of TAG, rich in oleic acid, was associated with higher transcript levels for a putative stearoyl-ACP desaturase and endoplasmic reticulum (ER)-associated acyl-CoA synthetases, during fruit development. Gene expression levels for enzymes involved in terminal steps to TAG biosynthesis in the ER further indicated that both acyl-CoA-dependent and -independent mechanisms might play a role in TAG assembly, depending on the developmental stage of the fruit. Furthermore, in addition to the expression of an ortholog of WRINKLED1 (WRI1), a regulator of fatty acid biosynthesis, high transcript levels for WRI2-like and WRI3-like suggest a role for additional transcription factors in nonseed oil accumulation. Plastid pyruvate necessary for fatty acid synthesis is likely driven by the upregulation of genes involved in glycolysis and transport of its intermediates. Together, a comparative transcriptome analyses for storage oil biosynthesis in diverse plants and tissues suggested that several distinct and conserved features in this basal angiosperm species might contribute towards its rich TAG content.

CONCLUSIONS

Our work represents a comprehensive transcriptome resource for a basal angiosperm species and provides insight into their lipid metabolism in mesocarp tissues. Furthermore, comparison of the transcriptome of oil-rich mesocarp of avocado, with oil-rich seed and nonseed tissues of monocot and dicot species, revealed lipid gene orthologs that are highly conserved during evolution. The orthologs that are distinctively expressed in oil-rich mesocarp tissues of this basal angiosperm, such as WRI2, ER-associated acyl-CoA synthetases, and lipid-droplet associated proteins were also identified. This study provides a foundation for future investigations to increase oil-content and has implications for metabolic engineering to enhance storage oil content in nonseed tissues of diverse species.

Oil biosynthesis in a basal angiosperm: transcriptome analysis of Persea Americana mesocarp

BACKGROUND

The mechanism by which plants synthesize and store high amounts of triacylglycerols (TAG) in tissues other than seeds is not well understood. The comprehension of controls for carbon partitioning and oil accumulation in nonseed tissues is essential to generate oil-rich biomass in perennial bioenergy crops. Persea americana (avocado), a basal angiosperm with unique features that are ancestral to most flowering plants, stores ~ 70 % TAG per dry weight in its mesocarp, a nonseed tissue. Transcriptome analyses of select pathways, from generation of pyruvate and leading up to TAG accumulation, in mesocarp tissues of avocado was conducted and compared with that of oil-rich monocot (oil palm) and dicot (rapeseed and castor) tissues to identify tissue- and species-specific regulation and biosynthesis of TAG in plants.

RESULTS

RNA-Seq analyses of select lipid metabolic pathways of avocado mesocarp revealed patterns similar to that of other oil-rich species. However, only some predominant orthologs of the fatty acid biosynthetic pathway genes in this basal angiosperm were similar to those of monocots and dicots. The accumulation of TAG, rich in oleic acid, was associated with higher transcript levels for a putative stearoyl-ACP desaturase and endoplasmic reticulum (ER)-associated acyl-CoA synthetases, during fruit development. Gene expression levels for enzymes involved in terminal steps to TAG biosynthesis in the ER further indicated that both acyl-CoA-dependent and -independent mechanisms might play a role in TAG assembly, depending on the developmental stage of the fruit. Furthermore, in addition to the expression of an ortholog of WRINKLED1 (WRI1), a regulator of fatty acid biosynthesis, high transcript levels for WRI2-like and WRI3-like suggest a role for additional transcription factors in nonseed oil accumulation. Plastid pyruvate necessary for fatty acid synthesis is likely driven by the upregulation of genes involved in glycolysis and transport of its intermediates. Together, a comparative transcriptome analyses for storage oil biosynthesis in diverse plants and tissues suggested that several distinct and conserved features in this basal angiosperm species might contribute towards its rich TAG content.

CONCLUSIONS

Our work represents a comprehensive transcriptome resource for a basal angiosperm species and provides insight into their lipid metabolism in mesocarp tissues. Furthermore, comparison of the transcriptome of oil-rich mesocarp of avocado, with oil-rich seed and nonseed tissues of monocot and dicot species, revealed lipid gene orthologs that are highly conserved during evolution. The orthologs that are distinctively expressed in oil-rich mesocarp tissues of this basal angiosperm, such as WRI2, ER-associated acyl-CoA synthetases, and lipid-droplet associated proteins were also identified. This study provides a foundation for future investigations to increase oil-content and has implications for metabolic engineering to enhance storage oil content in nonseed tissues of diverse species.

Quantitative Multilevel Analysis of Central Metabolism in Developing Oilseeds of Oilseed Rape during in Vitro Culture

Seeds provide the basis for many food, feed, and fuel products. Continued increases in seed yield, composition, and quality require an improved understanding of how the developing seed converts carbon and nitrogen supplies into storage. Current knowledge of this process is often based on the premise that transcriptional regulation directly translates via enzyme concentration into flux. In an attempt to highlight metabolic control, we explore genotypic differences in carbon partitioning for in vitro cultured developing embryos of oilseed rape (Brassica napus). We determined biomass composition as well as 79 net fluxes, the levels of 77 metabolites, and 26 enzyme activities with specific focus on central metabolism in nine selected germplasm accessions. Overall, we observed a tradeoff between the biomass component fractions of lipid and starch. With increasing lipid content over the spectrum of genotypes, plastidic fatty acid synthesis and glycolytic flux increased concomitantly, while glycolytic intermediates decreased. The lipid/starch tradeoff was not reflected at the proteome level, pointing to the significance of (posttranslational) metabolic control. Enzyme activity/flux and metabolite/flux correlations suggest that plastidic pyruvate kinase exerts flux control and that the lipid/starch tradeoff is most likely mediated by allosteric feedback regulation of phosphofructokinase and ADP-glucose pyrophosphorylase. Quantitative data were also used to calculate in vivo mass action ratios, reaction equilibria, and metabolite turnover times. Compounds like cyclic 3',5'-AMP and sucrose-6-phosphate were identified to potentially be involved in so far unknown mechanisms of metabolic control. This study provides a rich source of quantitative data for those studying central metabolism.

Production of high levels of poly-3-hydroxybutyrate in plastids of Camelina sativa seeds

Poly-3-hydroxybutyrate (PHB) production in plastids of Camelina sativa seeds was investigated by comparing levels of polymer produced upon transformation of plants with five different binary vectors containing combinations of five seed-specific promoters for expression of transgenes. Genes encoding PHB biosynthetic enzymes were modified at the N-terminus to encode a plastid targeting signal. PHB levels of up to 15% of the mature seed weight were measured in single sacrificed T1 seeds with a genetic construct containing the oleosin and glycinin promoters. A more detailed analysis of the PHB production potential of two of the best performing binary vectors in a Camelina line bred for larger seed size yielded lines containing up to 15% polymer in mature T2 seeds. Transmission electron microscopy showed the presence of distinct granules of PHB in the seeds. PHB production had varying effects on germination, emergence and survival of seedlings. Once true leaves formed, plants grew normally and were able to set seeds. PHB synthesis lowered the total oil but not the protein content of engineered seeds. A change in the oil fatty acid profile was also observed. High molecular weight polymer was produced with weight-averaged molecular weights varying between 600 000 and 1 500 000, depending on the line. Select lines were advanced to later generations yielding a line with 13.7% PHB in T4 seeds. The levels of polymer produced in this study are the highest reported to date in a seed and are an important step forward for commercializing an oilseed-based platform for PHB production.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

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.

Multigene Engineering of Triacylglycerol Metabolism Boosts Seed Oil Content in Arabidopsis

Increasing the yield of oilseed crops is an important objective for biotechnologists. A number of individual genes involved in triacylglycerol metabolism have previously been reported to enhance the oil content of seeds when their expression is altered. However, it has yet to be established whether specific combinations of these genes can be used to achieve an additive effect and whether this leads to enhanced yield. Using Arabidopsis (Arabidopsis thaliana) as an experimental system, we show that seed-specific overexpression of WRINKLED1 (a transcriptional regulator of glycolysis and fatty acid synthesis) and DIACYLGLYCEROL ACYLTRANSFERASE1 (a triacylglycerol biosynthetic enzyme) combined with suppression of the triacylglycerol lipase SUGAR-DEPENDENT1 results in a higher percentage seed oil content and greater seed mass than manipulation of each gene individually. Analysis of total seed yield per plant suggests that, despite a reduction in seed number, the total yield of oil is also increased.

Analysis of flavonoids and hydroxycinnamic acid derivatives in rapeseeds (Brassica napus L. var. napus) by HPLC-PDA--ESI(--)-MS(n)/HRMS

A comprehensive description of flavonoids and hydroxycinnamic acid derivatives in Brassica napus L. var. napus seeds is important to improve rapeseed quality. HPLC-PDA-ESI(-)-MS(n)/HRMS has been broadly applied to study phenolic compounds in plants. In the present study, crude phenolic compounds extracted from rapeseed were subjected to column chromatography, alkaline hydrolysis, and HPLC-PDA-ESI(-)-MS(n)/HRMS analysis. A total of 91 flavonoids and hydroxycinnamic acid derivatives were detected, including 39 kaempferol derivatives, 11 isorhamnetin derivatives, 5 quercetin derivatives, 6 flavanols and their oligomers, and 30 hydroxycinnamic acid derivatives. A total of 78 of these compounds were tentatively identified; of these, 55 were reported for the first time in B. napus L. var. napus and 24 were detected for the first time in the genus Brassica. This research enriches our knowledge of the phenolic composition of rapeseed and provides a reliable guide for the selection of rapeseed with valuable breeding potential.

Systems analysis of metabolic phenotypes: what have we learnt?

Flux is one of the most informative measures of metabolic behavior. Its estimation requires integration of experimental and modeling approaches and, thus, is at the heart of metabolic systems biology. In this review, we argue that flux analysis and modeling of a range of plant systems points to the importance of the supply of metabolic inputs and demand for metabolic end-products as key drivers of metabolic behavior. This has implications for metabolic engineering, and the use of in silico models will be important to help design more effective engineering strategies. We also consider the importance of cell type-specific metabolism and the challenges of characterizing metabolism at this resolution. A combination of new measurement technologies and modeling approaches is bringing us closer to integrating metabolic behavior with whole-plant physiology and growth.

Transcript abundance on its own cannot be used to infer fluxes in central metabolism

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. This limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.

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.

Flux balance analysis as an alternative method to estimate fluxes without labeling

The analysis of plant metabolic networks essentially contributes to the understanding of the efficiency of plant systems in terms of their biotechnological usage. Metabolic fluxes are determined by biochemical parameters such as metabolite concentrations as well as enzyme properties and activities, which in turn are the result of various regulatory events at various levels between control of transcription and posttranslational regulation of enzyme protein activity. Thus, knowledge about metabolic fluxes on a large scale provides an integrated view on the functional state of a metabolically active cell, organ, or system. In this chapter, we introduce flux balance analysis as a constraint-based method for the prediction of optimal metabolic fluxes in a given metabolic network. Furthermore, we provide a step-by-step protocol for metabolic network reconstruction and constraint-based analysis using the COBRA Toolbox.

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.

Commentary: why don't plant leaves get fat?

Recent pressures to obtain energy from plant biomass have encouraged new metabolic engineering strategies that focus on accumulating lipids in vegetative tissues at the expense of lignin, cellulose and/or carbohydrates. There are at least three important factors that support this rationale. (i) Lipids are more reduced than carbohydrates and so they have more energy per unit of mass. (ii) Lipids are hydrophobic and thus take up less volume than hydrated carbohydrates on a mass basis for storage in tissues. (iii) Lipids are more easily extracted and converted into useable biofuels than cellulosic-derived fuels, which require extensive fractionation, degradation of lignocellulose and fermentation of plant tissues. However, while vegetative organs such as leaves are the majority of harvestable biomass and would be ideal for accumulation of lipids, they have evolved as "source" tissues that are highly specialized for carbohydrate synthesis and export and do not have a propensity to accumulate lipid. Metabolism in leaves is directed mostly toward the synthesis and export of sucrose, and engineering strategies have been devised to divert the flow of photosynthetic carbon from sucrose, starch, lignocellulose, etc. toward the accumulation of triacylglycerols in non-seed, vegetative tissues for bioenergy applications.

Phenolic composition analysis and gene expression in developing seeds of yellow- and black-seeded Brassica napus

Breeders have focused on yellow-seeded Brassica napus (rapeseed) for its better quality compared with the black-seeded variety. Moreover, flavonoids have been associated with this kind of rapeseed. In this study, we applied lipid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS(n)) to compare flavonoids in developing seeds of natural black-seeded B. napus and yellow-seeded introgression lines selected from progenies of B. napus-Sinapis alba somatic hybrids. Aside from the most abundant phenolic compounds (sinapine and sinapic acid) and 1, 2-disinapoylglucose, 16 different flavonoids were identified and quantified, including (-)-epicatechin, five monocharged oligomers of (-)-epicatechin ([DP 2](-), [DP 3](-), [DP 4] [DP 2](-) B2 and [DP 2](-) B5), quercetin, kaempferol, isorhamnetin-dihexoside, kaempferol-sinapoyl-trihexoside, isorhamnetin-sinapoyl-trihexoside, isorhamnetin-hexoside-sulfate, and isorhamnetin-3-O-glucoside. Most of the flavonoids accumulated with seed development, whereas some rapidly decreased during maturation. The content of these flavonoids were lower in the yellow-seeded materials than in the black seeds. In addition, variations of insoluble procyanidin oligomers and soluble phenolic acids were observed among both rapeseed varieties. Transcriptome changes of genes participating in the flavonoid pathway were discovered by quantitative reverse transcription polymerase chain reaction analysis. Consistent with flavonoid changes identified by high performance liquid chromatography analysis, the expression of most genes in the flavonoid biosynthetic pathway was also downregulated.