Robustness of central carbohydrate metabolism in developing maize kernels

Quantitative proteomic analysis of super soft kernel texture in soft white spring wheat

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar 'Alpowa', a soft white spring wheat, was crossed to a closely related super soft spring wheat line 'BC2SS163' to produce F6 recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.

Genetic basis of kernel starch content decoded in a maize multi-parent population

Starch is the most abundant storage carbohydrate in maize kernels and provides calories for humans and other animals as well as raw materials for various industrial applications. Decoding the genetic basis of natural variation in kernel starch content is needed to manipulate starch quantity and quality via molecular breeding to meet future needs. Here, we identified 50 unique single quantitative trait loci (QTLs) for starch content with 18 novel QTLs via single linkage mapping, joint linkage mapping and a genome-wide association study in a multi-parent population containing six recombinant inbred line populations. Only five QTLs explained over 10% of phenotypic variation in single populations. In addition to a few large-effect and many small-effect additive QTLs, limited pairs of epistatic QTLs also contributed to the genetic basis of the variation in kernel starch content. A regional association study identified five non-starch-pathway genes that were the causal candidate genes underlying the identified QTLs for starch content. The pathway-driven analysis identified ZmTPS9, which encodes a trehalose-6-phosphate synthase in the trehalose pathway, as the causal gene for the QTL qSTA4-2, which was detected by all three statistical analyses. Knockout of ZmTPS9 increased kernel starch content and, in turn, kernel weight in maize, suggesting potential applications for ZmTPS9 in maize starch and yield improvement. These findings extend our knowledge about the genetic basis of starch content in maize kernels and provide valuable information for maize genetic improvement of starch quantity and quality.

Transcriptome profiling provides insights into the molecular mechanisms of maize kernel and silk development

Background

Maize kernel filling, which is closely related to the process of double fertilization and is sensitive to a variety of environmental conditions, is an important component of maize yield determination. Silk is an important tissue of maize ears that can discriminate pollen and conduct pollination. Therefore, investigating the molecular mechanisms of kernel development and silk senescence will provide important information for improving the pollination rate to obtain high maize yields.

Results

In this study, transcript profiles were determined in an elite maize inbred line (KA105) to investigate the molecular mechanisms functioning in self-pollinated and unpollinated maize kernels and silks. A total of 5285 and 3225 differentially expressed transcripts (DETs) were identified between self-pollinated and unpollinated maize in a kernel group and a silk group, respectively. We found that a large number of genes involved in key steps in the biosynthesis of endosperm storage compounds were upregulated after pollination in kernels, and that abnormal development and senescence appeared in unpollinated kernels (KUP). We also identified several genes with functions in the maintenance of silk structure that were highly expressed in silk. Further investigation suggested that the expression of autophagy-related genes and senescence-related genes is prevalent in maize kernels and silks. In addition, pollination significantly altered the expression levels of senescence-related and autophagy-related genes in maize kernels and silks. Notably, we identified some specific genes and transcription factors (TFs) that are highly expressed in single tissues.

Conclusions

Our results provide novel insights into the potential regulatory mechanisms of self-pollinated and unpollinated maize kernels and silks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-00981-4.

Engineering 6-phosphogluconate dehydrogenase improves grain yield in heat-stressed maize

Endosperm starch synthesis is a primary determinant of grain yield and is sensitive to high-temperature stress. The maize chloroplast-localized 6-phosphogluconate dehydrogenase (6PGDH), PGD3, is critical for endosperm starch accumulation. Maize also has two cytosolic isozymes, PGD1 and PGD2, that are not required for kernel development. We found that cytosolic PGD1 and PGD2 isozymes have heat-stable activity, while amyloplast-localized PGD3 activity is labile under heat stress conditions. We targeted heat-stable 6PGDH to endosperm amyloplasts by fusing the Waxy1 chloroplast targeting the peptide coding sequence to the Pgd1 and Pgd2 open reading frames (ORFs). These WPGD1 and WPGD2 fusion proteins import into isolated chloroplasts, demonstrating a functional targeting sequence. Transgenic maize plants expressing WPGD1 and WPGD2 with an endosperm-specific promoter increased 6PGDH activity with enhanced heat stability in vitro. WPGD1 and WPGD2 transgenes complement the pgd3-defective kernel phenotype, indicating the fusion proteins are targeted to the amyloplast. In the field, the WPGD1 and WPGD2 transgenes can mitigate grain yield losses in high-nighttime-temperature conditions by increasing kernel number. These results provide insight into the subcellular distribution of metabolic activities in the endosperm and suggest the amyloplast pentose phosphate pathway is a heat-sensitive step in maize kernel metabolism that contributes to yield loss during heat stress.

Engineering 6-phosphogluconate dehydrogenase improves grain yield in heat-stressed maize

Endosperm starch synthesis is a primary determinant of grain yield and is sensitive to high-temperature stress. The maize chloroplast-localized 6-phosphogluconate dehydrogenase (6PGDH), PGD3, is critical for endosperm starch accumulation. Maize also has two cytosolic isozymes, PGD1 and PGD2, that are not required for kernel development. We found that cytosolic PGD1 and PGD2 isozymes have heat-stable activity, while amyloplast-localized PGD3 activity is labile under heat stress conditions. We targeted heat-stable 6PGDH to endosperm amyloplasts by fusing the Waxy1 chloroplast targeting the peptide coding sequence to the Pgd1 and Pgd2 open reading frames (ORFs). These WPGD1 and WPGD2 fusion proteins import into isolated chloroplasts, demonstrating a functional targeting sequence. Transgenic maize plants expressing WPGD1 and WPGD2 with an endosperm-specific promoter increased 6PGDH activity with enhanced heat stability in vitro. WPGD1 and WPGD2 transgenes complement the pgd3-defective kernel phenotype, indicating the fusion proteins are targeted to the amyloplast. In the field, the WPGD1 and WPGD2 transgenes can mitigate grain yield losses in high-nighttime-temperature conditions by increasing kernel number. These results provide insight into the subcellular distribution of metabolic activities in the endosperm and suggest the amyloplast pentose phosphate pathway is a heat-sensitive step in maize kernel metabolism that contributes to yield loss during heat stress.

A proteomic analysis of grain yield-related traits in wheat

Grain yield, which is mainly contributed by tillering capacity as well as kernel number and weight, is the most important trait to plant breeders and agronomists. Label-free quantitative proteomics was used to analyse yield-contributing organs in wheat. These were leaf sample, tiller initiation, spike initiation, ovary and three successive kernel development stages at 5, 10 and 15 days after anthesis (DAA). We identified 3182 proteins across all samples. The largest number was obtained for spike initiation (1673), while the smallest was kernel sample at 15 DAA (709). Of the 3182 proteins, 296 of them were common to all seven organs. Organ-specific proteins ranged from 148 in ovary to 561 in spike initiation. When relative protein abundances were compared to that of leaf sample, 347 and 519 proteins were identified as differentially abundant in tiller initiation and spike initiation, respectively. When compared with ovary, 81, 35 and 96 proteins were identified as differentially abundant in kernels sampled at 5, 10 and 15 DAA, respectively. Our study indicated that two Argonaute proteins were solely expressed in spike initiation. Of the four expansin proteins detected, three of them were mainly expressed during the first 10 days of kernel development after anthesis. We also detected cell wall invertases and sucrose and starch synthases mainly during the kernel development period. The manipulation of these proteins could lead to increases in tillers, kernels per spike or final grain weight, and is worth exploring in future studies.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

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.

Unveiling the mechanism of melatonin impacts on maize seedling growth: sugar metabolism as a case

Melatonin regulates growth in many plants; however, the mechanism remains unclear. In this study, exogenous melatonin feeding resulted in both promotional (≤10 μm) and inhibitory (≥100 μm) effects on maize seedling growth. Initial analyses suggested positive correlations between the amount of melatonin and sucrose synthesis and hydrolysis-related gene expression, enzyme activities, and sucrose metabolites. However, assays of photosynthetic rate, hexokinase (HxK) activity, expression of photosynthetic marker genes, and HxK-related genes showed opposite effects under 10 μm (positive) and 100 μm (negative) melatonin treatments. Similarly, 10 μm melatonin accelerated starch catabolism at night, whereas 100 μm melatonin significantly decreased this process and led to starch accumulation in photosynthetic tissues. Furthermore, expression analysis of genes related to sucrose phloem loading resulted in a slight upregulation of sucrose transporters (SUT1 and SUT2) when seedlings were induced with 10 μm melatonin, while treatment with 100 μm melatonin resulted in significant downregulation of these sucrose transporter genes (SUT1 and SUT2), as well as tie-dyed2 (Tdy2) and sucrose export defective 1. Taken together, these results suggest that low doses of melatonin benefit maize seedling growth by promoting sugar metabolism, photosynthesis, and sucrose phloem loading. Conversely, high doses of melatonin inhibit seedling growth by inducing the excessive accumulation of sucrose, hexose and starch, suppressing photosynthesis and sucrose phloem loading.

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.

Tracking the metabolic pulse of plant lipid production with isotopic labeling and flux analyses: Past, present and future

Metabolism is comprised of networks of chemical transformations, organized into integrated biochemical pathways that are the basis of cellular operation, and function to sustain life. Metabolism, and thus life, is not static. The rate of metabolites transitioning through biochemical pathways (i.e., flux) determines cellular phenotypes, and is constantly changing in response to genetic or environmental perturbations. Each change evokes a response in metabolic pathway flow, and the quantification of fluxes under varied conditions helps to elucidate major and minor routes, and regulatory aspects of metabolism. To measure fluxes requires experimental methods that assess the movements and transformations of metabolites without creating artifacts. Isotopic labeling fills this role and is a long-standing experimental approach to identify pathways and quantify their metabolic relevance in different tissues or under different conditions. The application of labeling techniques to plant science is however far from reaching it potential. In light of advances in genetics and molecular biology that provide a means to alter metabolism, and given recent improvements in instrumentation, computational tools and available isotopes, the use of isotopic labeling to probe metabolism is becoming more and more powerful. We review the principal analytical methods for isotopic labeling with a focus on seminal studies of pathways and fluxes in lipid metabolism and carbon partitioning through central metabolism. Central carbon metabolic steps are directly linked to lipid production by serving to generate the precursors for fatty acid biosynthesis and lipid assembly. Additionally some of the ideas for labeling techniques that may be most applicable for lipid metabolism in the future were originally developed to investigate other aspects of central metabolism. We conclude by describing recent advances that will play an important future role in quantifying flux and metabolic operation in plant tissues.

Aspergillus flavus infection induces transcriptional and physical changes in developing maize kernels

Maize kernels are susceptible to infection by the opportunistic pathogen Aspergillus flavus. Infection results in reduction of grain quality and contamination of kernels with the highly carcinogenic mycotoxin, aflatoxin. To understanding host response to infection by the fungus, transcription of approximately 9000 maize genes were monitored during the host-pathogen interaction with a custom designed Affymetrix GeneChip® DNA array. More than 4000 maize genes were found differentially expressed at a FDR of 0.05. This included the up regulation of defense related genes and signaling pathways. Transcriptional changes also were observed in primary metabolism genes. Starch biosynthetic genes were down regulated during infection, while genes encoding maize hydrolytic enzymes, presumably involved in the degradation of host reserves, were up regulated. These data indicate that infection of the maize kernel by A. flavus induced metabolic changes in the kernel, including the production of a defense response, as well as a disruption in kernel development.

¹⁴C pulse labeling to estimate external fluxes and turnovers in primary metabolism

Steady state (13)C-MFA is classically used to measure fluxes in complex metabolic networks. However, the modeling of steady state labeling allows the quantification of internal fluxes only and requires the estimation, by other methods, of the external fluxes, corresponding to substrate uptake (carbon input into the network) and to the production rate of compounds that accumulate within plant cells (network output). Additionally, it is not always possible to discriminate between different pathways that lead to the same label distribution. Methods to measure fluxes, based on direct measurements of pool size and on (14)C short-time labeling experiments, are described in this chapter. To illustrate this approach, we focus on the quantification of sucrose and starch turnovers.

Flux analysis in plant metabolic networks: increasing throughput and coverage

Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic fluxes can be predicted by Flux Balance Analysis or determined experimentally by (13)C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of flux measurements. This review summarizes advances to increase coverage and throughput of metabolic flux analysis in plants.

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.

Application of metabolic flux analysis to plants

This volume compiles a series of chapters that cover the major aspects of plant metabolic flux analysis, such as but not limited to labeling of plant material, acquisition of labeling data, mathematical modeling of metabolic network at the cell, tissue, and plant level. A short revue, including methodological points and applications of flux analysis to plants, is presented in this introductory chapter.

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.

Chloroplast-localized 6-phosphogluconate dehydrogenase is critical for maize endosperm starch accumulation

Plants have duplicate versions of the oxidative pentose phosphate pathway (oxPPP) enzymes with a subset localized to the chloroplast. The chloroplast oxPPP provides NADPH and pentose sugars for multiple metabolic pathways. This study identified two loss-of-function alleles of the Zea mays (maize) chloroplast-localized oxPPP enzyme 6-phosphogluconate dehydrogenase (6PGDH). These mutations caused a rough endosperm seed phenotype with reduced embryo oil and endosperm starch. Genetic translocation experiments showed that pgd3 has separate, essential roles in both endosperm and embryo development. Endosperm metabolite profiling experiments indicated that pgd3 shifts redox-related metabolites and increases reducing sugars similar to starch-biosynthetis mutants. Heavy isotope-labelling experiments indicates that carbon flux into starch is altered in pgd3 mutants. Labelling experiments with a loss of cytosolic 6PGDH did not affect flux into starch. These results support the known role for plastid-localized oxPPP in oil synthesis and argue that amyloplast-localized oxPPP reactions are integral to endosperm starch accumulation in maize kernels.

Carbon and nitrogen provisions alter the metabolic flux in developing soybean embryos

Soybean (Glycine max) seeds store significant amounts of their biomass as protein, levels of which reflect the carbon and nitrogen received by the developing embryo. The relationship between carbon and nitrogen supply during filling and seed composition was examined through a series of embryo-culturing experiments. Three distinct ratios of carbon to nitrogen supply were further explored through metabolic flux analysis. Labeling experiments utilizing [U-(13)C5]glutamine, [U-(13)C4]asparagine, and [1,2-(13)C2]glucose were performed to assess embryo metabolism under altered feeding conditions and to create corresponding flux maps. Additionally, [U-(14)C12]sucrose, [U-(14)C6]glucose, [U-(14)C5]glutamine, and [U-(14)C4]asparagine were used to monitor differences in carbon allocation. The analyses revealed that: (1) protein concentration as a percentage of total soybean embryo biomass coincided with the carbon-to-nitrogen ratio; (2) altered nitrogen supply did not dramatically impact relative amino acid or storage protein subunit profiles; and (3) glutamine supply contributed 10% to 23% of the carbon for biomass production, including 9% to 19% of carbon to fatty acid biosynthesis and 32% to 46% of carbon to amino acids. Seed metabolism accommodated different levels of protein biosynthesis while maintaining a consistent rate of dry weight accumulation. Flux through ATP-citrate lyase, combined with malic enzyme activity, contributed significantly to acetyl-coenzyme A production. These fluxes changed with plastidic pyruvate kinase to maintain a supply of pyruvate for amino and fatty acids. The flux maps were independently validated by nitrogen balancing and highlight the robustness of primary metabolism.

Experimental flux measurements on a network scale

Metabolic flux is a fundamental property of living organisms. In recent years, methods for measuring metabolic flux in plants on a network scale have evolved further. One major challenge in studying flux in plants is the complexity of the plant's metabolism. In particular, in the presence of parallel pathways in multiple cellular compartments, the core of plant central metabolism constitutes a complex network. Hence, a common problem with the reliability of the contemporary results of (13)C-Metabolic Flux Analysis in plants is the substantial reduction in complexity that must be included in the simulated networks; this omission partly is due to limitations in computational simulations. Here, I discuss recent emerging strategies that will better address these shortcomings.

Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to ¹³C metabolic flux analysis

Plant oils are an important renewable resource, and seed oil content is a key agronomical trait that is in part controlled by the metabolic processes within developing seeds. A large-scale model of cellular metabolism in developing embryos of Brassica napus (bna572) was used to predict biomass formation and to analyze metabolic steady states by flux variability analysis under different physiological conditions. Predicted flux patterns are highly correlated with results from prior ¹³C metabolic flux analysis of B. napus developing embryos. Minor differences from the experimental results arose because bna572 always selected only one sugar and one nitrogen source from the available alternatives, and failed to predict the use of the oxidative pentose phosphate pathway. Flux variability, indicative of alternative optimal solutions, revealed alternative pathways that can provide pyruvate and NADPH to plastidic fatty acid synthesis. The nutritional values of different medium substrates were compared based on the overall carbon conversion efficiency (CCE) for the biosynthesis of biomass. Although bna572 has a functional nitrogen assimilation pathway via glutamate synthase, the simulations predict an unexpected role of glycine decarboxylase operating in the direction of NH₄⁺ assimilation. Analysis of the light-dependent improvement of carbon economy predicted two metabolic phases. At very low light levels small reductions in CO₂ efflux can be attributed to enzymes of the tricarboxylic acid cycle (oxoglutarate dehydrogenase, isocitrate dehydrogenase) and glycine decarboxylase. At higher light levels relevant to the ¹³C flux studies, ribulose-1,5-bisphosphate carboxylase activity is predicted to account fully for the light-dependent changes in carbon balance.

Nonadditive protein accumulation patterns in Maize (Zea mays L.) hybrids during embryo development

Heterosis describes the superior performance of heterozygous F(1)-hybrid plants compared to their homozygous parental inbred lines. In the present study, heterosis was detected for length, weight, and the time point of seminal root primordia initiation in maize (Zea mays L.) embryos of the reciprocal F(1)-hybrids UH005xUH250 and UH250xUH005. A two-dimensional gel electrophoresis (2-DE) proteome survey of the most abundant proteins of the reciprocal hybrids and their parental inbred lines 25 and 35 days after pollination revealed that 141 of 597 detected proteins (24%) exhibited nonadditive accumulation in at least one hybrid. Approximately 44% of all nonadditively accumulated proteins displayed an expression pattern that was not distinguishable from the low parent value. Electrospray ionization-tandem mass spectrometry (ESI-MS/MS) analyses and subsequent functional classification of the 141 proteins revealed that development, protein metabolism, redox-regulation, glycolysis, and amino acid metabolism were the most prominent functional classes among nonadditively accumulated proteins. In 35-day-old embryos of the hybrid UH250xUH005, a significant up-regulation of enzymes related to glucose metabolism which often exceeded the best parent values was observed. A comparison of nonadditive protein accumulation between rice and maize embryo data sets revealed a significant overlap of nonadditively accumulated proteins suggesting conserved organ- or tissue-specific regulatory mechanisms in monocots related to heterosis.

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

¹⁴C labeling experiments performed with kernel cultures showed that developing maize endosperm is more efficient than other non-photosynthetic tissues such as sunflower and maize embryos at converting maternally supplied substrates into biomass. To characterize the metabolic fluxes in endosperm, maize kernels were labeled to isotopic steady state using ¹³C-labeled glucose. The resultant labeling in free metabolites and biomass was analyzed by NMR and GC-MS. After taking into account the labeling of substrates supplied by the metabolically active cob, the fluxes through central metabolism were quantified by computer-aided modeling. The flux map indicates that 51-69% of the ATP produced is used for biomass synthesis and up to 47% is expended in substrate cycling. These findings point to potential engineering targets for improving yield and increasing oil contents by, respectively, reducing substrate cycling and increasing the commitment of plastidic carbon into fatty acid synthesis at the level of pyruvate kinase.

Sugar levels regulate tryptophan-dependent auxin biosynthesis in developing maize kernels

The maize (Zea mays) Miniature1 (Mn1) locus encodes the cell wall invertase INCW2, which is localized predominantly in the basal endosperm transfer layer of developing kernels and catalyzes the conversion of sucrose into glucose and fructose. Mutations in Mn1 result in pleiotropic changes, including a reduction in kernel mass and a recently reported decrease in indole-3-acetic acid (IAA) levels throughout kernel development. Here, we show that mn1-1 basal kernel regions (pedicels and basal endosperm transfer layer) accumulate higher levels of sucrose and lower levels of glucose and fructose between 8 and 28 d after pollination when compared with the wild type, whereas upper regions of mn1 accumulate similar or increased concentrations of sugars. To determine the cause of the reduction in IAA accumulation, we investigated transcript levels of several potential IAA biosynthetic enzymes. We demonstrate that reduced IAA levels most closely correspond to reduced transcript levels of ZmYUCCA (ZmYUC), a newly identified homolog of the Arabidopsis (Arabidopsis thaliana) gene YUCCA. We further demonstrate that ZmYUC catalyzes the N-hydroxylation of tryptamine and that sugar levels regulate transcript levels of ZmYUC, both in in vitro-cultured kernels and in a promoter-reporter fusion in Arabidopsis. These results indicate that developing seeds may modulate growth by altering auxin biosynthesis in response to sugar concentrations.

Understanding fatty acid synthesis in developing maize embryos using metabolic flux analysis

The efficiency with which developing maize embryos convert substrates into seed storage reserves was determined to be 57-71%, by incubating developing maize embryos with uniformly labeled 14C substrates and measuring their conversion to CO2 and biomass products. To map the pattern of metabolic fluxes underlying this efficiency, maize embryos were labeled to isotopic steady state using a combination of labeled 13C-substrates. Intermediary metabolic fluxes were estimated by computer-aided modeling of the central metabolic network using the labeling data collected by NMR and GC-MS and the biomass composition. The resultant flux map reveals that even though 36% of the entering carbon goes through the oxidative pentose-phosphate pathway, this does not fully meet the NADPH demands for fatty acid synthesis. Metabolic flux analysis and enzyme activities highlight the importance of plastidic NADP-dependent malic enzyme, which provides one-third of the carbon and NADPH required for fatty acid synthesis in developing maize embryos.

Metabolomics for functional genomics, systems biology, and biotechnology

Metabolomics now plays a significant role in fundamental plant biology and applied biotechnology. Plants collectively produce a huge array of chemicals, far more than are produced by most other organisms; hence, metabolomics is of great importance in plant biology. Although substantial improvements have been made in the field of metabolomics, the uniform annotation of metabolite signals in databases and informatics through international standardization efforts remains a challenge, as does the development of new fields such as fluxome analysis and single cell analysis. The principle of transcript and metabolite cooccurrence, particularly transcriptome coexpression network analysis, is a powerful tool for decoding the function of genes in Arabidopsis thaliana. This strategy can now be used for the identification of genes involved in specific pathways in crops and medicinal plants. Metabolomics has gained importance in biotechnology applications, as exemplified by quantitative loci analysis, prediction of food quality, and evaluation of genetically modified crops. Systems biology driven by metabolome data will aid in deciphering the secrets of plant cell systems and their application to biotechnology.

Heterotic patterns of sugar and amino acid components in developing maize kernels

Heterosis is the superior performance of hybrids over their inbred parents. Despite its importance, little is known about the genetic and molecular basis of this phenomenon. Heterosis has been extensively exploited in plant breeding, particularly in maize (Zea mays, L.), and is well documented in the B73 and Mo17 maize inbred lines and their F1 hybrids. In this study, we determined the dry matter, the levels of starch and protein components and a total of 24 low-molecular weight metabolites including sugars, sugar-phosphates, and free amino acids, in developing maize kernels between 8 and 30 days post-pollination (DPP) of the hybrid B73 x Mo17 and its parental lines. The tissue specificity of amino acid and protein content was investigated between 16 and 30 DPP. Key observations include: (1) most of the significant differences in the investigated tissue types occurred between Mo17 and the other two genotypes; (2) heterosis of dry matter and metabolite content was detectable from the early phase of kernel development onwards; (3) the majority of metabolites exhibited an additive pattern. Nearly 10% of the metabolites exhibited nonadditive effects such as overdominance, underdominance, and high-parent and low-parent dominance; (4) The metabolite composition was remarkably dependent on kernel age, and this large developmental effect could possibly mask genotypic differences; (5) the metabolite profiles and the heterotic patterns are specific for endosperm and embryo. Our findings illustrate the power of metabolomics to characterize heterotic maize lines and suggest that the metabolite composition is a potential marker in the context of heterosis research.

Molecular dissection of heterosis manifestation during early maize root development

Heterosis is of paramount agronomic importance and has been successfully exploited in maize hybrid breeding for decades. Nevertheless, the molecular basis of heterosis remains elusive. Heterosis is not only observed in adult traits like yield or plant height, but is already detected during embryo and seedling development. Hence, the maize (Zea mays L.) primary root which is the first organ that emerges after germination is a suitable model to study heterosis manifestation. Various seedling root traits including primary root length and lateral root density display heterosis. Microarray studies suggest organ specific patterns of nonadditive gene expression in maize hybrids. Moreover, such experiments support the notion that global expression trends in maize primary roots are conserved between different hybrids. Furthermore, nonadditive expression patterns of specific genes such as a SUPEROXIDE DISMUTASE 2 might contribute to the early manifestation of heterosis. Proteome profiling experiments of maize hybrid primary roots revealed nonadditive accumulation patterns that were distinct from the corresponding RNA profiles underscoring the importance of posttranscriptional processes such as protein modifications that might be related to heterosis. Finally, analysis of selected metabolites imply that a subtle regulation of particular biochemical pathways such as the phenylpropanoid pathway in hybrids might contribute to the manifestation of heterosis in maize primary roots. In the future, recently developed molecular tools will facilitate the analysis of the molecular principles underlying heterosis in maize roots.

Analysis of metabolic flux phenotypes for two Arabidopsis mutants with severe impairment in seed storage lipid synthesis

Major storage reserves of Arabidopsis (Arabidopsis thaliana) seeds are triacylglycerols (seed oils) and proteins. Seed oil content is severely reduced for the regulatory mutant wrinkled1 (wri1-1; At3g54320) and for a double mutant in two isoforms of plastidic pyruvate kinase (pkpbeta(1)pkpalpha; At5g52920 and At3g22960). Both already biochemically well-characterized mutants were now studied by (13)C metabolic flux analysis of cultured developing embryos based on comparison with their respective genetic wild-type backgrounds. For both mutations, in seeds as well as in cultured embryos, the oil fraction was strongly reduced while the fractions of proteins and free metabolites increased. Flux analysis in cultured embryos revealed changes in nutrient uptakes and fluxes into biomass as well as an increase in tricarboxylic acid cycle activity for both mutations. While in both wild types plastidic pyruvate kinase (PK(p)) provides most of the pyruvate for plastidic fatty acid synthesis, the flux through PK(p) is reduced in pkpbeta(1)pkpalpha by 43% of the wild-type value. In wri1-1, PK(p) flux is even more reduced (by 82%), although the genes PKpbeta(1) and PKpalpha are still expressed. Along a common paradigm of metabolic control theory, it is hypothesized that a large reduction in PK(p) enzyme activity in pkpbeta(1)pkpalpha has less effect on PK(p) flux than multiple smaller reductions in glycolytic enzymes in wri1-1. In addition, only in the wri1-1 mutant is the large reduction in PK(p) flux compensated in part by an increased import of cytosolic pyruvate and by plastidic malic enzyme. No such limited compensatory bypass could be observed in pkpbeta(1)pkpalpha.

A genome-scale metabolic model of Arabidopsis and some of its properties

We describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast.

Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity. This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.

Plant proteomics: concepts, applications, and novel strategies for data interpretation

Proteomics is an essential source of information about biological systems because it generates knowledge about the concentrations, interactions, functions, and catalytic activities of proteins, which are the major structural and functional determinants of cells. In the last few years significant technology development has taken place both at the level of data analysis software and mass spectrometry hardware. Conceptual progress in proteomics has made possible the analysis of entire proteomes at previously unprecedented density and accuracy. New concepts have emerged that comprise quantitative analyses of full proteomes, database-independent protein identification strategies, targeted quantitative proteomics approaches with proteotypic peptides and the systematic analysis of an increasing number of posttranslational modifications at high temporal and spatial resolution. Although plant proteomics is making progress, there are still several analytical challenges that await experimental and conceptual solutions. With this review I will highlight the current status of plant proteomics and put it into the context of the aforementioned conceptual progress in the field, illustrate some of the plant-specific challenges and present my view on the great opportunities for plant systems biology offered by proteomics.

Sugar levels modulate sorbitol dehydrogenase expression in maize

The first step in sucrose use by maize kernels produces fructose, regardless of whether the initial reaction is catalyzed by an invertase or the reversible sucrose synthase. This fructose can enter subsequent metabolism via hexokinase, or in maize kernels, by a sorbitol dehydrogenase that reversibly converts fructose + NADH to sorbitol + NAD. High levels of SDH activity suggest that kernels synthesize considerable amounts of sorbitol, but the molecular mechanism and functional role for this process have remained equivocal. To gain insights on the role of sorbitol synthesis in maize endosperm we cloned and characterized the transcriptional control of the maize sorbitol dehydrogenase (Sdh1) gene. Data indicated that Sdh1 was essentially kernel- and endosperm-specific, with maximal expression at both the mRNA and enzyme activity levels during early kernel development. Expression was elevated in high-sugar mutants (sugary1, shrunken2), also by sugar injections, and was more pronounced when transfected tissues were incubated at low oxygen concentrations. Control of Sdh1 expression in our transient assays was largely dependent on the first intron of Sdh1. We speculate that SDH activity may represent an adaptation to the high-sugar/low-oxygen environment of the endosperm. Under these conditions, the NADH-dependent reduction of fructose to sorbitol would regenerate NAD[+], thus contributing to the maintenance of the redox and energy status of the cell.

Metabolic network fluxes in heterotrophic Arabidopsis cells: stability of the flux distribution under different oxygenation conditions

Steady-state labeling experiments with [1-(13)C]Glc were used to measure multiple metabolic fluxes through the pathways of central metabolism in a heterotrophic cell suspension culture of Arabidopsis (Arabidopsis thaliana). The protocol was based on in silico modeling to establish the optimal labeled precursor, validation of the isotopic and metabolic steady state, extensive nuclear magnetic resonance analysis of the redistribution of label into soluble metabolites, starch, and protein, and a comprehensive set of biomass measurements. Following a simple modification of the cell culture procedure, cells were grown at two oxygen concentrations, and flux maps of central metabolism were constructed on the basis of replicated experiments and rigorous statistical analysis. Increased growth rate at the higher O(2) concentration was associated with an increase in fluxes throughout the network, and this was achieved without any significant change in relative fluxes despite differences in the metabolite profile of organic acids, amino acids, and carbohydrates. The balance between biosynthesis and respiration within the tricarboxylic acid cycle was unchanged, with 38% +/- 5% of carbon entering used for biosynthesis under standard O(2) conditions and 33% +/- 2% under elevated O(2). These results add to the emerging picture of the stability of the central metabolic network and its capacity to respond to physiological perturbations with the minimum of rearrangement. The lack of correlation between the change in metabolite profile, which implied significant disruption of the metabolic network following the alteration in the oxygen supply, and the unchanging flux distribution highlights a potential difficulty in the interpretation of metabolomic data.

The complexities of starch biosynthesis in cereal endosperms

Starch serves not only as an energy source for plants, animals, and humans but also as an environmentally friendly alternative for fossil fuels. Here, we describe recent findings concerning the synthesis of this important molecule in the cereal endosperm. Results from six separate transgenic reports point to the importance of adenosine diphosphate glucose pyrophosphorylase in controlling the amount of starch synthesized. The unexpected cause underlying the contrast in sequence divergence of its two subunits is also described. A major unresolved question concerning the synthesis of starch is the origin of nonrandom or clustered alpha-1,6 branch-points within the major component of starch, amylopectin. Developing evidence that several of the starch biosynthetic enzymes involved in amylopectin synthesis occur in complexes is reviewed. These complexes may provide the specificity for the formation of nonrandom branch-points.

Transcriptional and metabolic adjustments in ADP-glucose pyrophosphorylase-deficient bt2 maize kernels

During the cloning of monogenic recessive mutations responsible for a defective kernel phenotype in a Mutator-induced Zea mays mutant collection, we isolated a new mutant allele in Brittle2 (Bt2), which codes for the small subunit of ADP-glucose pyrophosphorylase (AGPase), a key enzyme in starch synthesis. Reverse transcription-polymerase chain reaction experiments with gene-specific primers confirmed a predominant expression of Bt2 in endosperm, of Agpsemzm in embryo, and of Agpslzm in leaf, but also revealed considerable additional expression in various tissues for all three genes. Bt2a, the classical transcript coding for a cytoplasmic isoform, was almost exclusively expressed in the developing endosperm, whereas Bt2b, an alternative transcript coding for a plastidial isoform, was expressed in almost all tissues tested with a pattern very similar to that of Agpslzm. The phenotypic analysis showed that, at 30 d after pollination (DAP), mutant kernels were plumper than wild-type kernels, that the onset of kernel collapse took place between 31 and 35 DAP, and that the number of starch grains was greatly reduced in the mutant endosperm but not the mutant embryo. A comparative transcriptome analysis of wild-type and bt2-H2328 kernels at middevelopment (35 DAP) with the 18K GeneChip Maize Genome Array led to the conclusion that the lack of Bt2-encoded AGPase triggers large-scale changes on the transcriptional level that concern mainly genes involved in carbohydrate or amino acid metabolic pathways. Principal component analysis of (1)H nuclear magnetic resonance metabolic profiles confirmed the impact of the bt2-H2328 mutation on these pathways and revealed that the bt2-H2328 mutation did not only affect the endosperm, but also the embryo at the metabolic level. These data suggest that, in the bt2-H2328 endosperms, regulatory networks are activated that redirect excess carbon into alternative biosynthetic pathways (amino acid synthesis) or into other tissues (embryo).

Identification of hexose hydrolysis products in metabolic flux analytes: a case study of levulinic acid in plant protein hydrolysate

Biosynthetically directed fractional (13)C labeling, a popular methodology of metabolic flux analysis, involves culture on a mixture of (13)C and (12)C substrates and preparation a 'metabolic flux analyte' (typically protein hydrolysate) from the biomass. Metabolic flux analytes prepared from complex eukaryotes may contain additional compounds than those prepared from microorganisms. We report the presence of such compounds (hexose hydrolysis products) in a plant metabolic flux analyte (acid hydrolyzed protein from soybean embryos). We designed NMR experiments to systematically identify these compounds, and found that they were levulinic acid (LVA; major) and hydroxyacetone (HyA; minor). These acid hydrolysis products of hexoses (glucose and mannose) were generated during acid hydrolysis of glycosylating sugars (glucosamine and mannose) associated with soybean embryo protein. Analysis of LVA by two-dimensional [(13)C, (1)H] NMR and measurement of its J-coupling constants revealed long-range coupling between atoms C3 and C5, which enables LVA to provide more isotopomer information than its precursor hexose. Furthermore, we found that LVA and HyA preserve the isotopomeric composition of the metabolic hexose from which they are derived. An important consequence of these results is that comparison of LVA and HyA isotopomers from two separate metabolic flux analytes (protein hydrolysate and starch hydrolysate) from the same plant tissue can distinguish between parallel glycolysis and pentose phosphate pathways in different subcellular compartments.

Flux quantification in central carbon metabolism of Catharanthus roseus hairy roots by 13C labeling and comprehensive bondomer balancing

Methods for accurate and efficient quantification of metabolic fluxes are desirable in plant metabolic engineering and systems biology. Toward this objective, we introduce the application of "bondomers", a computationally efficient and intuitively appealing alternative to the commonly used isotopomer concept, to flux evaluation in plants, by using Catharanthus roseus hairy roots as a model system. We cultured the hairy roots on (5% w/w U-(13)C, 95% w/w naturally abundant) sucrose, and acquired two-dimensional [(13)C, (1)H] and [(1)H, (1)H] NMR spectra of hydrolyzed aqueous extract from the hairy roots. Analysis of these spectra yielded a data set of 116 bondomers of beta-glucans and proteinogenic amino acids from the hairy roots. Fluxes were evaluated from the bondomer data by using comprehensive bondomer balancing. We identified most fluxes in a three-compartmental model of central carbon metabolism with good precision. We observed parallel pentose phosphate pathways in the cytosol and the plastid with significantly different fluxes. The anaplerotic fluxes between phosphoenolpyruvate and oxaloacetate in the cytosol and between malate and pyruvate in the mitochondrion were relatively high (60.1+/-2.5 mol per 100 mol sucrose uptake, or 22.5+/-0.5 mol per 100 mol mitochondrial pyruvate dehydrogenase flux). The development of a comprehensive flux analysis tool for this plant hairy root system is expected to be valuable in assessing the metabolic impact of genetic or environmental changes, and this methodology can be extended to other plant systems.

Experimental and mathematical approaches to modeling plant metabolic networks

To support their sessile and autotrophic lifestyle higher plants have evolved elaborate networks of metabolic pathways. Dynamic changes in these metabolic networks are among the developmental forces underlying the functional differentiation of organs, tissues and specialized cell types. They are also important in the various interactions of a plant with its environment. Further complexity is added by the extensive compartmentation of the various interconnected metabolic pathways in plants. Thus, although being used widely for assessing the control of metabolic flux in microbes, mathematical modeling approaches that require steady-state approximations are of limited utility for understanding complex plant metabolic networks. However, considerable progress has been made when manageable metabolic subsystems were studied. In this article, we will explain in general terms and using simple examples the concepts underlying stoichiometric modeling (metabolic flux analysis and metabolic pathway analysis) and kinetic approaches to modeling (including metabolic control analysis as a special case). Selected studies demonstrating the prospects of these approaches, or combinations of them, for understanding the control of flux through particular plant pathways are discussed. We argue that iterative cycles of (dry) mathematical modeling and (wet) laboratory testing will become increasingly important for simulating the distribution of flux in plant metabolic networks and deriving rational experimental designs for metabolic engineering efforts.

Compartment-specific labeling information in 13C metabolic flux analysis of plants

Metabolic engineering of plants has great potential for the low cost production of chemical feedstocks and novel compounds, but to take full advantage of this potential a better understanding of plant central carbon metabolism is needed. Flux studies define the cellular phenotype of living systems and can facilitate rational metabolic engineering. However the measurements usually made in these analyses are often not sufficient to reliably determine many fluxes that are distributed between different subcellular compartments of eukaryotic cells. We have begun to address this shortcoming by increasing the number and quality of measurements that provide (13)C labeling information from specific compartments within the plant cell. The analysis of fatty acid groups, cell wall components, protein glycans, and starch, using both gas chromatography/mass spectrometry and nuclear magnetic resonance spectroscopy are presented here. Fatty acid labeling determinations are sometimes highly convoluted. Derivatization to butyl amides reduces the errors in isotopomer resolution and quantification, resulting in better determination of fluxes into seed lipid reserves, including both plastidic and cytosolic reactions. While cell walls can account for a third or more of biomass in many seeds, no quantitative cell wall labeling measurements have been reported for plant flux analysis. Hydrolyzing cell wall and derivatizing sugars to the alditol acetates, provides novel labeling information and thereby can improve identification of flux through upper glycolytic intermediates of the cytosol. These strategies improve the quantification of key carbon fluxes in the compartmentalized flux network of plant cells.

Metabolic flux analysis: recent advances in carbon metabolism in plants

Isotopic tracers are used to both trace metabolic pathways and quantify fluxes through these pathways. The use of different labeling methods recently led to profound changes in our views of plant metabolism. Examples are taken from primary metabolism, with sugar interconversions, carbon partitioning between glycolysis and the pentose phosphate pathway, or metabolite inputs into the tricarboxylic acid (TCA) cycle, as well as from secondary metabolism with the relative contribution of the plastidial and cytosolic pathways to the biosynthesis of terpenoids. While labeling methods are often distinguished according to the instruments used for label detection, emphasis is put here on labeling duration. Short time labeling is adequate to study limited areas of the metabolic network. Long-term labeling, when designed to obtain metabolic and isotopic steady-state, allows to calculate various fluxes in large areas ofcentral metabolism. After longer labeling periods, large amounts of label accumulate in structural or storage compounds: their detailed study through the retrobiosynthetic method gives access to the biosynthetic pathways of otherwise undetectable precursors. This chapter presents the power and limits of the different methods, and illustrates how they can be associated with each other and with other methods of cell biology, to provide the information needed for a rational approach of metabolic engineering.