Differential metabolic networks unravel the effects of silent plant phenotypes

Sunlight Modulates Fruit Metabolic Profile and Shapes the Spatial Pattern of Compound Accumulation within the Grape Cluster

Vineyards are characterized by their large spatial variability of solar irradiance (SI) and temperature, known to effectively modulate grape metabolism. To explore the role of sunlight in shaping fruit composition and cluster uniformity, we studied the spatial pattern of incoming irradiance, fruit temperature and metabolic profile within individual grape clusters under three levels of sunlight exposure. The experiment was conducted in a vineyard of Cabernet Sauvignon cv. located in the Negev Highlands, Israel, where excess SI and midday temperatures are known to degrade grape quality. Filtering SI lowered the surface temperature of exposed fruits and increased the uniformity of irradiance and temperature in the cluster zone. SI affected the overall levels and patterns of accumulation of sugars, organic acids, amino acids and phenylpropanoids, across the grape cluster. Increased exposure to sunlight was associated with lower accumulation levels of malate, aspartate, and maleate but with higher levels of valine, leucine, and serine, in addition to the stress-related proline and GABA. Flavan-3-ols metabolites showed a negative response to SI, whereas flavonols were highly induced. The overall levels of anthocyanins decreased with increased sunlight exposure; however, a hierarchical cluster analysis revealed that the members of this family were grouped into three distinct accumulation patterns, with malvidin anthocyanins and cyanidin-glucoside showing contrasting trends. The flavonol-glucosides, quercetin and kaempferol, exhibited a logarithmic response to SI, leading to improved cluster uniformity under high-light conditions. Comparing the within-cluster variability of metabolite accumulation highlighted the stability of sugars, flavan-3-ols, and cinnamic acid metabolites to SI, in contrast to the plasticity of flavonols. A correlation-based network analysis revealed that extended exposure to SI modified metabolic coordination, increasing the number of negative correlations between metabolites in both pulp and skin. This integrated study of micrometeorology and metabolomics provided insights into the grape-cluster pattern of accumulation of 70 primary and secondary metabolites as a function of spatial variations in SI. Studying compound-specific responses against an extended gradient of quantified conditions improved our knowledge regarding the modulation of berry metabolism by SI, with the aim of using sunlight regulation to accurately modulate fruit composition in warm and arid/semi-arid regions.

Phosphorylation of rice sucrose synthase isoforms promotes the activity of sucrose degradation

Sucrose is utilized as an initial material for production of the storage substances. Sucrose synthase reversibly catalyzes reactions of the sucrose degradation and its synthesis between sucrose with UDP and UDP-glucose with fructose. They also had the activity of the reactions for sucrose degradation of sucrose with ADP, and sucrose synthesis from ADP-glucose and fructose. Rice has three representative isoforms of sucrose synthase, Rsus1, Rsus2, and Rsus3, in which Rsus1 and Rsus3 are highly expressed in developing seeds. These three isoforms were phosphorylated by SPK, a calcium-dependent protein kinase. By phosphorylation, they showed increase of their reactivity for sucrose degradation on both reactions using UDP and ADP. In contrast, the synthetic activity of these isoforms was not altered by phosphorylation in any cases of the reactions with UDP-glucose and ADP-glucose. These results indicated that phosphorylation of sucrose synthase isoforms selectively led to enhance the reactivity for sucrose degradation.

Network-Guided GWAS Improves Identification of Genes Affecting Free Amino Acids

Amino acids are essential for proper growth and development in plants. Amino acids serve as building blocks for proteins but also are important for responses to stress and the biosynthesis of numerous essential compounds. In seed, the pool of free amino acids (FAAs) also contributes to alternative energy, desiccation, and seed vigor; thus, manipulating FAA levels can significantly impact a seed's nutritional qualities. While genome-wide association studies (GWAS) on branched-chain amino acids have identified some regulatory genes controlling seed FAAs, the genetic regulation of FAA levels, composition, and homeostasis in seeds remains mostly unresolved. Hence, we performed GWAS on 18 FAAs from a 313-ecotype Arabidopsis (Arabidopsis thaliana) association panel. Specifically, GWAS was performed on 98 traits derived from known amino acid metabolic pathways (approach 1) and then on 92 traits generated from an unbiased correlation-based metabolic network analysis (approach 2), and the results were compared. The latter approach facilitated the discovery of additional novel metabolic interactions and single-nucleotide polymorphism-trait associations not identified by the former approach. The most prominent network-guided GWAS signal was for a histidine (His)-related trait in a region containing two genes: a cationic amino acid transporter (CAT4) and a polynucleotide phosphorylase resistant to inhibition with fosmidomycin. A reverse genetics approach confirmed CAT4 to be responsible for the natural variation of His-related traits across the association panel. Given that His is a semiessential amino acid and a potent metal chelator, CAT4 orthologs could be considered as candidate genes for seed quality biofortification in crop plants.

System-Level and Granger Network Analysis of Integrated Proteomic and Metabolomic Dynamics Identifies Key Points of Grape Berry Development at the Interface of Primary and Secondary Metabolism

Grapevine is a fruit crop with worldwide economic importance. The grape berry undergoes complex biochemical changes from fruit set until ripening. This ripening process and production processes define the wine quality. Thus, a thorough understanding of berry ripening is crucial for the prediction of wine quality. For a systemic analysis of grape berry development we applied mass spectrometry based platforms to analyse the metabolome and proteome of Early Campbell at 12 stages covering major developmental phases. Primary metabolites involved in central carbon metabolism, such as sugars, organic acids and amino acids together with various bioactive secondary metabolites like flavonols, flavan-3-ols and anthocyanins were annotated and quantified. At the same time, the proteomic analysis revealed the protein dynamics of the developing grape berries. Multivariate statistical analysis of the integrated metabolomic and proteomic dataset revealed the growth trajectory and corresponding metabolites and proteins contributing most to the specific developmental process. K-means clustering analysis revealed 12 highly specific clusters of co-regulated metabolites and proteins. Granger causality network analysis allowed for the identification of time-shift correlations between metabolite-metabolite, protein- protein and protein-metabolite pairs which is especially interesting for the understanding of developmental processes. The integration of metabolite and protein dynamics with their corresponding biochemical pathways revealed an energy-linked metabolism before veraison with high abundances of amino acids and accumulation of organic acids, followed by protein and secondary metabolite synthesis. Anthocyanins were strongly accumulated after veraison whereas other flavonoids were in higher abundance at early developmental stages and decreased during the grape berry developmental processes. A comparison of the anthocyanin profile of Early Campbell to other cultivars revealed similarities to Concord grape and indicates the strong effect of genetic background on metabolic partitioning in primary and secondary metabolism.

Metabolome Dynamics of Smutted Sugarcane Reveals Mechanisms Involved in Disease Progression and Whip Emission

Sugarcane smut disease, caused by the biotrophic fungus Sporisorium scitamineum, is characterized by the development of a whip-like structure from the plant meristem. The disease causes negative effects on sucrose accumulation, fiber content and juice quality. The aim of this study was to exam whether the transcriptomic changes already described during the infection of sugarcane by S. scitamineum result in changes at the metabolomic level. To address this question, an analysis was conducted during the initial stage of the interaction and through disease progression in a susceptible sugarcane genotype. GC-TOF-MS allowed the identification of 73 primary metabolites. A set of these compounds was quantitatively altered at each analyzed point as compared with healthy plants. The results revealed that energetic pathways and amino acid pools were affected throughout the interaction. Raffinose levels increased shortly after infection but decreased remarkably after whip emission. Changes related to cell wall biosynthesis were characteristic of disease progression and suggested a loosening of its structure to allow whip growth. Lignin biosynthesis related to whip formation may rely on Tyr metabolism through the overexpression of a bifunctional PTAL. The altered levels of Met residues along with overexpression of SAM synthetase and ACC synthase genes suggested a role for ethylene in whip emission. Moreover, unique secondary metabolites antifungal-related were identified using LC-ESI-MS approach, which may have potential biomarker applications. Lastly, a putative toxin was the most important fungal metabolite identified whose role during infection remains to be established.

Urine metabolomic analysis to detect metabolites associated with the development of contrast induced nephropathy

Objective

Contrast induced nephropathy (CIN) is a result of injury to the proximal tubules. The incidence of CIN is around 11% for imaging done in the acute care setting. We aim to analyze the metabolic patterns in the urine, before and after dosing with intravenous contrast for computed tomography (CT) imaging of the chest, to determine if metabolomic changes exist in patients who develop CIN.

Methods

A convenience sample of high risk patients undergoing a chest CT with intravenous contrast were eligible for enrollment. Urine samples were collected prior to imaging and 4 to 6 hours post imaging. Samples underwent gas chromatography/mass spectrometry profiling. Peak metabolite values were measured and data was log transformed. Significance analysis of microarrays and partial least squares was used to determine the most significant metabolites prior to CT imaging and within subject. Analysis of variance was used to rank metabolites associated with temporal change and CIN. CIN was defined as an increase in serum creatinine level of ≥ 0.5 mg/dL or ≥ 25% above baseline within 48 hours after contrast administration.

Results

We sampled paired urine samples from 63 subjects. The incidence of CIN was 6/63 (9.5%). Patients without CIN had elevated urinary citric acid and taurine concentrations in the pre-CT urine. Xylulose increased in the post CT sample in patients who developed CIN.

Conclusion

Differences in metabolomics patterns in patients who do and do not develop CIN exist. Metabolites may be potential early identifiers of CIN and identify patients at high-risk for developing this condition prior to imaging.

The Aspergillus fumigatus SchASCH9 kinase modulates SakAHOG1 MAP kinase activity and it is essential for virulence

The serine-threonine kinase TOR, the Target of Rapamycin, is an important regulator of nutrient, energy and stress signaling in eukaryotes. Sch9, a Ser/Thr kinase of AGC family (the cAMP-dependent PKA, cGMP- dependent protein kinase G and phospholipid-dependent protein kinase C family), is a substrate of TOR. Here, we characterized the fungal opportunistic pathogen Aspergillus fumigatus Sch9 homologue (SchA). The schA null mutant was sensitive to rapamycin, high concentrations of calcium, hyperosmotic stress and SchA was involved in iron metabolism. The ΔschA null mutant showed increased phosphorylation of SakA, the A. fumigatus Hog1 homologue. The schA null mutant has increased and decreased trehalose and glycerol accumulation, respectively, suggesting SchA performs different roles for glycerol and trehalose accumulation during osmotic stress. The schA was transcriptionally regulated by osmotic stress and this response was dependent on SakA and MpkC. The double ΔschA ΔsakA and ΔschA ΔmpkC mutants were more sensitive to osmotic stress than the corresponding parental strains. Transcriptomics and proteomics identified direct and indirect targets of SchA post-exposure to hyperosmotic stress. Finally, ΔschA was avirulent in a low dose murine infection model. Our results suggest there is a complex network of interactions amongst the A. fumigatus TOR, SakA and SchA pathways.

Gene expression prediction using low-rank matrix completion

BACKGROUND

An exponential growth of high-throughput biological information and data has occurred in the past decade, supported by technologies, such as microarrays and RNA-Seq. Most data generated using such methods are used to encode large amounts of rich information, and determine diagnostic and prognostic biomarkers. Although data storage costs have reduced, process of capturing data using aforementioned technologies is still expensive. Moreover, the time required for the assay, from sample preparation to raw value measurement is excessive (in the order of days). There is an opportunity to reduce both the cost and time for generating such expression datasets.

RESULTS

We propose a framework in which complete gene expression values can be reliably predicted in-silico from partial measurements. This is achieved by modelling expression data as a low-rank matrix and then applying recently discovered techniques of matrix completion by using nonlinear convex optimisation. We evaluated prediction of gene expression data based on 133 studies, sourced from a combined total of 10,921 samples. It is shown that such datasets can be constructed with a low relative error even at high missing value rates (>50 %), and that such predicted datasets can be reliably used as surrogates for further analysis.

CONCLUSION

This method has potentially far-reaching applications including how bio-medical data is sourced and generated, and transcriptomic prediction by optimisation. We show that gene expression data can be computationally constructed, thereby potentially reducing the costs of gene expression profiling. In conclusion, this method shows great promise of opening new avenues in research on low-rank matrix completion in biological sciences.

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

Metabolic Profiling with Gas Chromatography-Mass Spectrometry and Capillary Electrophoresis-Mass Spectrometry Reveals the Carbon-Nitrogen Status of Tobacco Leaves Across Different Planting Areas

The interaction between carbon (C) and nitrogen (N) metabolism can reflect plant growth status and environmental factors. Little is known regarding the connections between C-N metabolism and growing regions under field conditions. To comprehensively investigate the relationship in mature tobacco leaves, we established metabolomics approaches based on gas chromatography-mass spectrometry (GC-MS) and capillary electrophoresis-time-of-flight-mass spectrometry (CE-TOF-MS). Approximately 240 polar metabolites were determined. Multivariate statistical analysis revealed that the growing region greatly influenced the metabolic profiles of tobacco leaves. A metabolic correlation network and related pathway maps were used to reveal the global overview of the alteration of C-N metabolism across three typical regions. In Yunnan, sugars and tricarboxylic acid (TCA) cycle intermediates were closely correlated with amino acid pools. Henan tobacco leaves showed positive correlation between the pentose phosphate pathway (PPP) intermediates and C-rich secondary metabolism. In Guizhou, the proline and asparagine had significant links with TCA cycle intermediates and urea cycle, and antioxidant accumulation was observed in response to drought. These results demonstrate that combined analytical approaches have great potential to detect polar metabolites and provide information on C-N metabolism related to planting regional characteristics.

Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement

The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several studies on proteomics and metabolomics in model and crop legumes have been discussed. Additionally, applications of advanced proteomics and metabolomics approaches have also been included in this review for future applications in legume research. The integration of these "omics" approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs.

Challenges of Inversely Estimating Jacobian from Metabolomics Data

Inferring dynamics of metabolic networks directly from metabolomics data provides a promising way to elucidate the underlying mechanisms of biological systems, as reported in our previous studies (Weckwerth, 2011; Sun and Weckwerth, 2012; Nägele et al., 2014) by a differential Jacobian approach. The Jacobian is solved from an overdetermined system of equations as JC + CJ^T = −2D, called Lyapunov Equation in its generic form,¹ where J is the Jacobian, C is the covariance matrix of metabolomics data, and D is the fluctuation matrix. Lyapunov Equation can be further simplified as the linear form Ax = b. Frequently, this linear equation system is ill-conditioned, i.e., a small variation in the right side b results in a big change in the solution x, thus making the solution unstable and error-prone. At the same time, inaccurate estimation of covariance matrix and uncertainties in the fluctuation matrix bring biases to the solution x. Here, we first reviewed common approaches to circumvent the ill-conditioned problems, including total least squares, Tikhonov regularization, and truncated singular value decomposition. Then, we benchmarked these methods on several in silico kinetic models with small to large perturbations on the covariance and fluctuation matrices. The results identified that the accuracy of the reverse Jacobian is mainly dependent on the condition number of A, the perturbation amplitude of C, and the stiffness of the kinetic models. Our research contributes a systematical comparison of methods to inversely solve Jacobian from metabolomics data.

Urinary metabolomic analysis to detect changes after intravenous, non-ionic, low osmolar iodinated radiocontrast for computerized tomographic imaging

Introduction

Contrast-induced nephropathy is a result of injury to the proximal tubules caused by oxidative stress and ischemia. Metabolomics is a novel technique that has been used to identify renal damage from drug toxicities. The objective of this study is to analyze the metabolic changes in the urine after dosing with intravenous (IV) contrast for computed tomograph (CT) of the chest

Methods

A convenience sample of patients undergoing a chest CT with IV contrast who had at least one of the following: age ≥50 years, diabetes, heart failure, chronic kidney disease, coronary artery disease, or diastolic blood pressure >90 mmHg -- were eligible for enrollment. Urine samples were collected prior to imaging and 4–6 hours post imaging. Samples underwent gas chromography/mass spectrometry profiling. We measured peak metabolite values and log transformed data. Paired T tests were calculated. We used significance analysis of microarrays (SAM) to determine the most significant metabolites.

Results

The cohort comprised 14 patients with matched samples; 9/14 (64.3) were males, and the median age was 61 years (IQR 50–68). A total of 158 metabolites were identified. Using SAM we identified 9 metabolites that were identified as significant using a delta of 1.6.

Conclusion

Changes in urinary metabolites are present soon after contrast administration. This change in urinary metabolites may be potential early identifiers of contrast-induced nephropathy and could identify patients at high-risk for developing this condition.

Phosphorus and iron deficiencies induce a metabolic reprogramming and affect the exudation traits of the woody plant Fragaria×ananassa

Strawberries are a very popular fruit among berries, for both their commercial and economic importance, but especially for their beneficial effects for human health. However, their bioactive compound content is strictly related to the nutritional status of the plant and might be affected if nutritional disorders (e.g. Fe or P shortage) occur. To overcome nutrient shortages, plants evolved different mechanisms, which often involve the release of root exudates. The biochemical and molecular mechanisms underlying root exudation and its regulation are as yet still poorly known, in particular in woody crop species. The aim of this work was therefore to characterize the pattern of root exudation of strawberry plants grown in either P or Fe deficiency, by investigating metabolomic changes of root tissues and the expression of genes putatively involved in exudate extrusion. Although P and Fe deficiencies differentially affected the total metabolism, some metabolites (e.g. raffinose and galactose) accumulated in roots similarly under both conditions. Moreover, P deficiency specifically affected the content of galactaric acid, malic acid, lysine, proline, and sorbitol-6-phosphate, whereas Fe deficiency specifically affected the content of sucrose, dehydroascorbic acid, galactonate, and ferulic acid. At the same time, the citrate content did not change in roots under both nutrient deficiencies with respect to the control. However, a strong release of citrate was observed, and it increased significantly with time, being +250% and +300% higher in Fe- and P-deficient plants, respectively, compared with the control. Moreover, concomitantly, a significant acidification of the growth medium was observed in both treatments. Gene expression analyses highlighted for the first time that at least two members of the multidrug and toxic compound extrusion (MATE) transporter family and one member of the plasma membrane H(+)-ATPase family are involved in the response to both P and Fe starvation in strawberry plants.

Metabolite profiling and transcript analysis reveal specificities in the response of a berry derived cell culture to abiotic stresses

As climate changes, there is a need to understand the expected effects on viticulture. In nature, stresses exist in a combined manner, hampering the elucidation of the effect of individual cues on grape berry metabolism. Cell suspension culture originated from pea-size Gamy Red grape berry was used to harness metabolic response to high light (HL; 2500 μmol m(-2)s(-1)), high temperature (HT; 40°C) and their combination in comparison to 25°C and 100 μmol m(-2)s(-1) under controlled condition. When LC-MS and GC-MS based metabolite profiling was implemented and integrated with targeted RT-qPCR transcript analysis specific responses were observed to the different cues. HL enhanced polyphenol metabolism while HT and its combination with HL induced amino acid and organic acid metabolism with additional effect on polyphenols. The trend of increment in TCA cycle genes like ATCs, ACo1, and IDH in the combined treatment might support the observed increment in organic acids, GABA shunt, and their derivatives. The apparent phenylalanine reduction with polyphenol increment under HL suggests enhanced fueling of the precursor toward the downstream phenylpropanoid pathway. In the polyphenol metabolism, a differential pattern of expression of flavonoid 3',5' hydroxylase and flavonoid 3' hydroxylase was observed under high light (HL) and combined cues which were accompanied by characteristic metabolite profiles. HT decreased glycosylated cyanidin and peonidin forms while the combined cues increased acetylated and coumarylated peonidin forms. Transcription factors regulating anthocyanin metabolism and their methylation, MYB, OMT, UFGT, and DFR, were expressed differentially among the treatments, overall in agreement with the metabolite profiles. Taken together these data provide insights into the coordination of central and secondary metabolism in relation to multiple abiotic stresses.

Warming differentially influences the effects of drought on stoichiometry and metabolomics in shoots and roots

Plants in natural environments are increasingly being subjected to a combination of abiotic stresses, such as drought and warming, in many regions. The effects of each stress and the combination of stresses on the functioning of shoots and roots have been studied extensively, but little is known about the simultaneous metabolome responses of the different organs of the plant to different stresses acting at once. We studied the shift in metabolism and elemental composition of shoots and roots of two perennial grasses, Holcus lanatus and Alopecurus pratensis, in response to simultaneous drought and warming. These species responded differently to individual and simultaneous stresses. These responses were even opposite in roots and shoots. In plants exposed to simultaneous drought and warming, terpenes, catechin and indole acetic acid accumulated in shoots, whereas amino acids, quinic acid, nitrogenous bases, the osmoprotectants choline and glycine betaine, and elements involved in growth (nitrogen, phosphorus and potassium) accumulated in roots. Under drought, warming further increased the allocation of primary metabolic activity to roots and changed the composition of secondary metabolites in shoots. These results highlight the plasticity of plant metabolomes and stoichiometry, and the different complementary responses of shoots and roots to complex environmental conditions.

Defining the metabolome: size, flux, and regulation

Renewed interest in metabolic research over the last two decades has inspired an explosion of technological developments for studying metabolism. At the forefront of methodological innovation is an approach referred to as "untargeted" or "discovery" metabolomics. The experimental objective of this technique is to comprehensively measure the entire metabolome, which constitutes a largely undefined set of molecules. Given its potential comprehensive coverage, untargeted metabolomics is often the first choice of experiments for investigators pursuing a metabolic research question. It is important to recognize, however, that untargeted metabolomics may not always be the optimal experimental approach. Conventionally, untargeted metabolomics only provides information about relative differences in metabolite pool sizes. Therefore, depending on the specific scientific question at hand, a complementary approach involving stable isotopes (such as metabolic flux analysis) may be better suited to provide biological insights. Unlike untargeted metabolomics, stable-isotope methods can provide information about differences in reaction rates.

Transcriptome sequencing of two phenotypic mosaic Eucalyptus trees reveals large scale transcriptome re-modelling

Phenotypic mosaic trees offer an ideal system for studying differential gene expression. We have investigated two mosaic eucalypt trees from two closely related species (Eucalyptus melliodora and E. sideroxylon), which each support two types of leaves: one part of the canopy is resistant to insect herbivory and the remaining leaves are susceptible. Driving this ecological distinction are differences in plant secondary metabolites. We used these phenotypic mosaics to investigate genome wide patterns of foliar gene expression with the aim of identifying patterns of differential gene expression and the somatic mutation(s) that lead to this phenotypic mosaicism. We sequenced the mRNA pool from leaves of the resistant and susceptible ecotypes from both mosaic eucalypts using the Illumina HiSeq 2000 platform. We found large differences in pathway regulation and gene expression between the ecotypes of each mosaic. The expression of the genes in the MVA and MEP pathways is reflected by variation in leaf chemistry, however this is not the case for the terpene synthases. Apart from the terpene biosynthetic pathway, there are several other metabolic pathways that are differentially regulated between the two ecotypes, suggesting there is much more phenotypic diversity than has been described. Despite the close relationship between the two species, they show large differences in the global patterns of gene and pathway regulation.

Unlocking proteomic heterogeneity in complex diseases through visual analytics

Despite years of preclinical development, biological interventions designed to treat complex diseases such as asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks has been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine.

Metabolic network discovery by top-down and bottom-up approaches and paths for reconciliation

The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.

Evidence supporting distinct functions of three cytosolic glutamine synthetases and two NADH-glutamate synthases in rice

The functions of the three isoenzymes of cytosolic glutamine synthetase (GS1;1, GS1;2, and GS1;3) and two NADH-glutamate synthases (NADH-GOGAT1 and NADH-GOGAT2) in rice (Oryza sativa L.) were characterized using a reverse genetics approach and spatial expression of the corresponding genes. OsGS1;2 and OsNADH-GOGAT1 were mainly expressed in surface cells of rice roots in an NH4 (+)-dependent manner. Disruption of either gene by the insertion of endogenous retrotransposon Tos17 caused reduction in active tiller number and hence panicle number at harvest. Re-introduction of OsGS1;2 cDNA under the control of its own promoter into the knockout mutants successfully restored panicle number to wild-type levels. These results indicate that GS1;2 and NADH-GOGAT1 are important in the primary assimilation of NH4 (+) taken up by rice roots. OsGS1;1 and OsNADH-GOGAT2 were mainly expressed in vascular tissues of mature leaf blades. OsGS1;1 mutants showed severe reduction in growth rate and grain filling, whereas OsNADH-GOGAT2 mutants had marked reduction in spikelet number per panicle. Complementation of phenotypes seen in the OsGS1;1 mutant was successfully observed when OsGS1;1 was re-introduced. Thus, these two enzymes could be important in remobilization of nitrogen during natural senescence. Metabolite profiling data showed a crucial role of GS1;1 in coordinating metabolic balance in rice. Expression of OsGS1:3 was spikelet-specific, indicating that it is probably important in grain ripening and/or germination. Thus, these isoenzymes seem to possess distinct and non-overlapping functions and none was able to compensate for the individual function of another.

Integrated analysis of transcriptome and metabolome of Arabidopsis albino or pale green mutants with disrupted nuclear-encoded chloroplast proteins

We used four mutants having albino or pale green phenotypes with disrupted nuclear-encoded chloroplast proteins to analyze the regulatory system of metabolites in chloroplast. We performed an integrated analyses of transcriptomes and metabolomes of the four mutants. Transcriptome analysis was carried out using the Agilent Arabidopsis 2 Oligo Microarray, and metabolome analysis with two mass spectrometers; a direct-infusion Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR/MS) and a gas chromatograph-time of flight mass spectrometer. Among approximately 200 known metabolites detected by the FT-ICR/MS, 71 metabolites showed significant changes in the mutants when compared with controls (Ds donor plants). Significant accumulation of several amino acids (glutamine, glutamate and asparagine) was observed in the albino and pale green mutants. Transcriptome analysis revealed altered expressions of genes in several metabolic pathways. For example, genes involved in the tricarboxylic acid cycle, the oxidative pentose phosphate pathway, and the de novo purine nucleotide biosynthetic pathway were up-regulated. These results suggest that nitrogen assimilation is constitutively promoted in the albino and pale green mutants. The accumulation of ammonium ions in the albino and pale green mutants was consistently higher than in Ds donor lines. Furthermore, genes related to pyridoxin accumulation and the de novo purine nucleotide biosynthetic pathway were up-regulated, which may have occurred as a result of the accumulation of glutamine in the albino and pale green mutants. The difference in metabolic profiles seems to be correlated with the disruption of chloroplast internal membrane structures in the mutants. In albino mutants, the alteration of metabolites accumulation and genes expression is stronger than pale green mutants.

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/.

Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN

Data processing for 1D NMR spectra is a key bottleneck for metabolomic and other complex-mixture studies, particularly where quantitative data on individual metabolites are required. We present a protocol for automated metabolite deconvolution and quantification from complex NMR spectra by using the Bayesian automated metabolite analyzer for NMR (BATMAN) R package. BATMAN models resonances on the basis of a user-controllable set of templates, each of which specifies the chemical shifts, J-couplings and relative peak intensities for a single metabolite. Peaks are allowed to shift position slightly between spectra, and peak widths are allowed to vary by user-specified amounts. NMR signals not captured by the templates are modeled non-parametrically by using wavelets. The protocol covers setting up user template libraries, optimizing algorithmic input parameters, improving prior information on peak positions, quality control and evaluation of outputs. The outputs include relative concentration estimates for named metabolites together with associated Bayesian uncertainty estimates, as well as the fit of the remainder of the spectrum using wavelets. Graphical diagnostics allow the user to examine the quality of the fit for multiple spectra simultaneously. This approach offers a workflow to analyze large numbers of spectra and is expected to be useful in a wide range of metabolomics studies.

Multi-platform metabolomic analyses of ergosterol-induced dynamic changes in Nicotiana tabacum cells

Metabolomics is providing new dimensions into understanding the intracellular adaptive responses in plants to external stimuli. In this study, a multi-technology-metabolomic approach was used to investigate the effect of the fungal sterol, ergosterol, on the metabolome of cultured tobacco cells. Cell suspensions were treated with different concentrations (0-1000 nM) of ergosterol and incubated for different time periods (0-24 h). Intracellular metabolites were extracted with two methods: a selective dispersive liquid-liquid micro-extraction and a general methanol extraction. Chromatographic techniques (GC-FID, GC-MS, GC × GC-TOF-MS, UHPLC-MS) and (1)H NMR spectroscopy were used for quantitative and qualitative analyses. Multivariate data analyses (PCA and OPLS-DA models) were used to extract interpretable information from the multidimensional data generated from the analytical techniques. The results showed that ergosterol triggered differential changes in the metabolome of the cells, leading to variation in the biosynthesis of secondary metabolites. PCA scores plots revealed dose- and time-dependent metabolic variations, with optimal treatment conditions being found to be 300 nM ergosterol and an 18 h incubation period. The observed ergosterol-induced metabolic changes were correlated with changes in defence-related metabolites. The 'defensome' involved increases in terpenoid metabolites with five antimicrobial compounds (the bicyclic sesquiterpenoid phytoalexins: phytuberin, solavetivone, capsidiol, lubimin and rishitin) and other metabolites (abscisic acid and phytosterols) putatively identified. In addition, various phenylpropanoid precursors, cinnamic acid derivatives and - conjugates, coumarins and lignin monomers were annotated. These annotated metabolites revealed a dynamic reprogramming of metabolic networks that are functionally correlated, with a high complexity in their regulation.

The AT-hook motif-encoding gene METABOLIC NETWORK MODULATOR 1 underlies natural variation in Arabidopsis primary metabolism

Regulation of primary metabolism is a central mechanism by which plants coordinate their various responses to biotic and abiotic challenge. To identify genes responsible for natural variation in primary metabolism, we focused on cloning a locus from Arabidopsis thaliana that influences the level of TCA cycle metabolites in planta. We found that the Met.V.67 locus was controlled by natural variation in METABOLIC NETWORK MODULATOR 1 (MNM1), which encoded an AT-hook motif-containing protein that was unique to the Brassicales lineage. MNM1 had wide ranging effects on plant metabolism and displayed a tissue expression pattern that was suggestive of a function in sink tissues. Natural variation within MNM1 had differential effects during a diurnal time course, and this temporal dependency was supported by analysis of T-DNA insertion and over-expression lines for MNM1. Thus, the cloning of a natural variation locus specifically associated with primary metabolism allowed us to identify MNM1 as a lineage-specific modulator of primary metabolism, suggesting that the regulation of primary metabolism can change during evolution.

Morphological, cytological and metabolic consequences of autopolyploidization in Hylocereus (Cactaceae) species

BACKGROUND

Genome doubling may have multi-level effects on the morphology, viability and physiology of polyploids compared to diploids. We studied the changes associated with autopolyploidization in two systems of somatic newly induced polyploids, diploid-autotetraploid and triploid-autohexaploid, belonging to the genus Hylocereus (Cactaceae). Stomata, fruits, seeds, embryos, and pollen were studied. Fruit pulp and seeds were subjected to metabolite profiling using established gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography (UPLC) Q-TOF-MS/MS (time of flight)-protocols.

RESULTS

Autopolyploid lines produced lower numbers of tetrads, larger pollen grains with lower viability, larger stomata with lower density, and smaller fruits with lower seed numbers and decreased seed viability. The abundance of sugars was lower in the fruits and seeds of the two duplicated lines than in their donor lines, accompanied by increased contents of amino acids, tricarboxylic acid (TCA) cycle intermediates, organic acids and flavonoids. Betacyanins, the major fruit pigments in diploid and triploid donors, decreased following genome doubling. Both autopolyploid Hylocereus lines thus exhibited unfavorable changes, with the outcome being more dramatic in the autohexaploid than in the autotetraploid line.

CONCLUSION

Induced autotetraploid and autohexaploid lines exhibited morphological and cytological characteristics that differed from those of their donor plants and that were accompanied by significant metabolic alterations. It is suggested that a developmental arrest occurs in the fruits of the autohexaploid line, since their pericarp shows a greater abundance of acids and of reduced sugars. We conclude that genome doubling does not necessarily confer a fitness advantage and that the extent of alterations induced by autopolyploidization depends on the genetic background of the donor genotype.

The rare sugar D-allose acts as a triggering molecule of rice defence via ROS generation

Only D-allose, among various rare monosaccharides tested, induced resistance to Xanthomonas oryzae pv. oryzae in susceptible rice leaves with defence responses: reactive oxygen species, lesion mimic formation, and PR-protein gene expression. These responses were suppressed by ascorbic acid or diphenylene iodonium. Transgenic rice plants overexpressing OsrbohC, encoding NADPH oxidase, were enhanced in sensitivity to D-allose. D-Allose-mediated defence responses were suppressed by the presence of a hexokinase inhibitor. 6-Deoxy-D-allose, a structural derivative of D-allose unable to be phosphorylated, did not confer resistance. Transgenic rice plants expressing Escherichia coli AlsK encoding D-allose kinase to increase D-allose 6-phosphate synthesis were more sensitive to D-allose, but E. coli AlsI encoding D-allose 6-phosphate isomerase expression to decrease D-allose 6-phosphate reduced sensitivity. A D-glucose 6-phosphate dehydrogenase-defective mutant was also less sensitive, and OsG6PDH1 complementation restored full sensitivity. These results reveal that a monosaccharide, D-allose, induces rice resistance to X. oryzae pv. oryzae by activating NADPH oxidase through the activity of D-glucose 6-phosphate dehydrogenase, initiated by hexokinase-mediated conversion of D-allose to D-allose 6-phosphate, and treatment with D-allose might prove to be useful for reducing disease development in rice.

Investigation of the relationship between the metabolic profile of tobacco leaves in different planting regions and climate factors using a pseudotargeted method based on gas chromatography/mass spectrometry

An improved pseudotargeted method using gas chromatography/mass spectrometry (GC/MS) was developed to investigate the metabolic profile of tobacco leaves from three planting regions (Yunnan, Guizhou, and Henan provinces). The analytical characteristics of the method with regard to reproducibility, precision, linearity, and stability were satisfactory for metabolic profiling study. Partial least-squares-discriminant analysis and hierarchical cluster analysis demonstrated that the metabolic profiles of tobacco from the Yunnan and Guizhou regions were different from that from the Henan province. The amino acid (e.g., phenylalanine, leucine, and tyrosine) and carbohydrate (e.g., fructose, trehalose, and sucrose) contents were the highest in Henan tobacco. The highest contents of organic acids (e.g., isocitrate, citrate, and fumarate) of the TCA cycle and antioxidants (e.g., quinate, chlorogenic acid, and ascorbate) were found in Guizhou tobacco. The correlation coefficients between metabolite content and climate factors (rainfall, sunshine, and temperature) demonstrated that drought facilitated the accumulation of sugars and amino acids. The content of TCA cycle intermediates could be influenced by multiple climate factors. This study demonstrates that the pseudotargeted method with GC/MS is suitable for the investigation of the metabolic profiling of tobacco leaves and the assessment of differential metabolite levels related to the growing regions.

Modulation of the metabiome by rifaximin in patients with cirrhosis and minimal hepatic encephalopathy

Hepatic encephalopathy (HE) represents a dysfunctional gut-liver-brain axis in cirrhosis which can negatively impact outcomes. This altered gut-brain relationship has been treated using gut-selective antibiotics such as rifaximin, that improve cognitive function in HE, especially its subclinical form, minimal HE (MHE). However, the precise mechanism of the action of rifaximin in MHE is unclear. We hypothesized that modulation of gut microbiota and their end-products by rifaximin would affect the gut-brain axis and improve cognitive performance in cirrhosis. Aim To perform a systems biology analysis of the microbiome, metabolome and cognitive change after rifaximin in MHE.

METHODS

Twenty cirrhotics with MHE underwent cognitive testing, endotoxin analysis, urine/serum metabolomics (GC and LC-MS) and fecal microbiome assessment (multi-tagged pyrosequencing) at baseline and 8 weeks post-rifaximin 550 mg BID. Changes in cognition, endotoxin, serum/urine metabolites (and microbiome were analyzed using recommended systems biology techniques. Specifically, correlation networks between microbiota and metabolome were analyzed before and after rifaximin.

RESULTS

There was a significant improvement in cognition(six of seven tests improved, p<0.01) and endotoxemia (0.55 to 0.48 Eu/ml, p = 0.02) after rifaximin. There was a significant increase in serum saturated (myristic, caprylic, palmitic, palmitoleic, oleic and eicosanoic) and unsaturated (linoleic, linolenic, gamma-linolenic and arachnidonic) fatty acids post-rifaximin. No significant microbial change apart from a modest decrease in Veillonellaceae and increase in Eubacteriaceae was observed. Rifaximin resulted in a significant reduction in network connectivity and clustering on the correlation networks. The networks centered on Enterobacteriaceae, Porphyromonadaceae and Bacteroidaceae indicated a shift from pathogenic to beneficial metabolite linkages and better cognition while those centered on autochthonous taxa remained similar.

CONCLUSIONS

Rifaximin is associated with improved cognitive function and endotoxemia in MHE, which is accompanied by alteration of gut bacterial linkages with metabolites without significant change in microbial abundance.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01069133.

Current challenges and future potential of tomato breeding using omics approaches

As tomatoes are one of the most important vegetables in the world, improvements in the quality and yield of tomato are strongly required. For this purpose, omics approaches such as metabolomics and transcriptomics are used not only for basic research to understand relationships between important traits and metabolism but also for the development of next generation breeding strategies of tomato plants, because an increase in the knowledge improves the taste and quality, stress resistance and/or potentially health-beneficial metabolites and is connected to improvements in the biochemical composition of tomatoes. Such omics data can be applied to network analyses to potentially reveal unknown cellular regulatory networks in tomato plants. The high-quality tomato genome that was sequenced in 2012 will likely accelerate the application of omics strategies, including next generation sequencing for tomato breeding. In this review, we highlight the current studies of omics network analyses of tomatoes and other plant species, in particular, a gene coexpression network. Key applications of omics approaches are also presented as case examples to improve economically important traits for tomato breeding.

Metabolomics as a tool to investigate abiotic stress tolerance in plants

Metabolites reflect the integration of gene expression, protein interaction and other different regulatory processes and are therefore closer to the phenotype than mRNA transcripts or proteins alone. Amongst all –omics technologies, metabolomics is the most transversal and can be applied to different organisms with little or no modifications. It has been successfully applied to the study of molecular phenotypes of plants in response to abiotic stress in order to find particular patterns associated to stress tolerance. These studies have highlighted the essential involvement of primary metabolites: sugars, amino acids and Krebs cycle intermediates as direct markers of photosynthetic dysfunction as well as effectors of osmotic readjustment. On the contrary, secondary metabolites are more specific of genera and species and respond to particular stress conditions as antioxidants, Reactive Oxygen Species (ROS) scavengers, coenzymes, UV and excess radiation screen and also as regulatory molecules. In addition, the induction of secondary metabolites by several abiotic stress conditions could also be an effective mechanism of cross-protection against biotic threats, providing a link between abiotic and biotic stress responses. Moreover, the presence/absence and relative accumulation of certain metabolites along with gene expression data provides accurate markers (mQTL or MWAS) for tolerant crop selection in breeding programs.

Metabolite profiling of mizuna ( Brassica rapa L. var. Nipponsinica) to evaluate the effects of organic matter amendments

Organic matter amendment is an essential agricultural protocol to improve soil function and carbon sequestration. However, the effect of organic matter amendments on crop quality has not been well-defined. This study applied gas chromatography-mass spectrometry to investigate the metabolite profiling of mizuna ( Brassica rapa L. var. Nipponsinica) with different organic matter amendments with respect to quality and quantity. Principal component analysis showed that 33.4, 15.6, and 6.6% of the total variance was attributable to the plant N concentration, fast-release organic fertilizer (fish cake), chicken droppings), and rapeseed cake), and manure application (fresh and dried), respectively. The peak areas of 18 and 15 compounds were significantly altered under organic fertilizer and manure amendment, respectively, compared with pure chemical fertilizer amendment. The compounds altered with manure amendment were similar to those reported in previous studies using other species. This study is the first to show clear metabolic alterations in plants through the amendment of fast-release organic fertilizer. Mizuna is a unique plant species that responds to both organic fertilizer and manure. These observations are useful to clarify the effect of organic matter amendment and quality control in farming systems using organic matter.

Current metabolomics: practical applications

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.

ADEMA: an algorithm to determine expected metabolite level alterations using mutual information

Metabolomics is a relatively new “omics” platform, which analyzes a discrete set of metabolites detected in bio-fluids or tissue samples of organisms. It has been used in a diverse array of studies to detect biomarkers and to determine activity rates for pathways based on changes due to disease or drugs. Recent improvements in analytical methodology and large sample throughput allow for creation of large datasets of metabolites that reflect changes in metabolic dynamics due to disease or a perturbation in the metabolic network. However, current methods of comprehensive analyses of large metabolic datasets (metabolomics) are limited, unlike other “omics” approaches where complex techniques for analyzing coexpression/coregulation of multiple variables are applied. This paper discusses the shortcomings of current metabolomics data analysis techniques, and proposes a new multivariate technique (ADEMA) based on mutual information to identify expected metabolite level changes with respect to a specific condition. We show that ADEMA better predicts De Novo Lipogenesis pathway metabolite level changes in samples with Cystic Fibrosis (CF) than prediction based on the significance of individual metabolite level changes. We also applied ADEMA's classification scheme on three different cohorts of CF and wildtype mice. ADEMA was able to predict whether an unknown mouse has a CF or a wildtype genotype with 1.0, 0.84, and 0.9 accuracy for each respective dataset. ADEMA results had up to 31% higher accuracy as compared to other classification algorithms. In conclusion, ADEMA advances the state-of-the-art in metabolomics analysis, by providing accurate and interpretable classification results.

Metabolomics is an experimental approach that analyzes differences in metabolite levels detected in experimental samples. It has been used in the literature to understand the changes in metabolism with respect to diseases or drugs. Unlike transcriptomics or proteomics, which analyze gene and protein expression levels respectively, the techniques that consider co-regulation of multiple metabolites are quite limited. In this paper, we propose a novel technique, called ADEMA, which computes the expected level changes for each metabolite with respect to a given condition. ADEMA considers multiple metabolites at the same time and is mutual information (MI)-based. We show that ADEMA predicts metabolite level changes for young mice with Cystic Fibrosis (CF) better than significance testing that considers one metabolite at a time. Using three different datasets that contain CF and wild-type (WT) mice, we show that ADEMA can classify an individual as being CF or WT based on the metabolic profiles (with 1.0, 0.84, and 0.9 accuracy, respectively). Compared to other well-known classification algorithms, ADEMA's accuracy is higher by up to 31%.

Recent progress in the development of metabolome databases for plant systems biology

Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology.

Concerted suppression of all starch branching enzyme genes in barley produces amylose-only starch granules

BACKGROUND

Starch is stored in higher plants as granules composed of semi-crystalline amylopectin and amorphous amylose. Starch granules provide energy for the plant during dark periods and for germination of seeds and tubers. Dietary starch is also a highly glycemic carbohydrate being degraded to glucose and rapidly absorbed in the small intestine. But a portion of dietary starch, termed "resistant starch" (RS) escapes digestion and reaches the large intestine, where it is fermented by colonic bacteria producing short chain fatty acids (SCFA) which are linked to several health benefits. The RS is preferentially derived from amylose, which can be increased by suppressing amylopectin synthesis by silencing of starch branching enzymes (SBEs). However all the previous works attempting the production of high RS crops resulted in only partly increased amylose-content and/or significant yield loss.

RESULTS

In this study we invented a new method for silencing of multiple genes. Using a chimeric RNAi hairpin we simultaneously suppressed all genes coding for starch branching enzymes (SBE I, SBE IIa, SBE IIb) in barley (Hordeum vulgare L.), resulting in production of amylose-only starch granules in the endosperm. This trait was segregating 3:1. Amylose-only starch granules were irregularly shaped and showed peculiar thermal properties and crystallinity. Transgenic lines retained high-yield possibly due to a pleiotropic upregualtion of other starch biosynthetic genes compensating the SBEs loss. For gelatinized starch, a very high content of RS (65 %) was observed, which is 2.2-fold higher than control (29%). The amylose-only grains germinated with same frequency as control grains. However, initial growth was delayed in young plants.

CONCLUSIONS

This is the first time that pure amylose has been generated with high yield in a living organism. This was achieved by a new method of simultaneous suppression of the entire complement of genes encoding starch branching enzymes. We demonstrate that amylopectin is not essential for starch granule crystallinity and integrity. However the slower initial growth of shoots from amylose-only grains may be due to an important physiological role played by amylopectin ordered crystallinity for rapid starch remobilization explaining the broad conservation in the plant kingdom of the amylopectin structure.

Arabidopsis scaffold protein RACK1A modulates rare sugar D-allose regulated gibberellin signaling

As energy sources and structural components, sugars are the central regulators of plant growth and development. In addition to the abundant natural sugars in plants, more than 50 different kinds of rare sugars exist in nature, several of which show distinct roles in plant growth and development. Recently, one of the rare sugars, D-allose, an epimer of D-glucose at C3, is found to suppress plant hormone gibberellin (GA) signaling in rice. Scaffold protein RACK1A in the model plant Arabidopsis is implicated in the GA pathway as rack1a knockout mutants show insensitivity to GA in GA-induced seed germination. Using genetic knockout lines and a reporter gene, the functional role of RACK1A in the D-allose pathway was investigated. It was found that the rack1a knockout seeds showed hypersensitivity to D-allose-induced inhibition of seed germination, implicating a role for RACK1A in the D-allose mediated suppression of seed germination. On the other hand, a functional RACK1A in the background of the double knockout mutations in the other two RACK1 isoforms, rack1b/rack1c, showed significant resistance to the D-allose induced inhibition of seed germination. The collective results implicate the RACK1A in the D-allose mediated seed germination inhibition pathway. Elucidation of the rare sugar signaling mechanism will help to advance understanding of this less studied but important cellular signaling pathway.

Genetic analysis of metabolome-phenotype interactions: from model to crop species

The past decade has seen increased interest from the scientific community, and particularly plant biologists, in integrating metabolic approaches into research aimed at unraveling phenotypic diversity and its underlying genetic variation. Advances in plant metabolomics have enabled large-scale analyses that have identified qualitative and quantitative variation in the metabolic content of various species, and this variation has been linked to genetic factors through genetic-mapping approaches, providing a glimpse of the genetic architecture of the plant metabolome. Parallel analyses of morphological phenotypes and physiological performance characteristics have further enhanced our understanding of the complex molecular mechanisms regulating these quantitative traits. This review aims to illustrate the advantages of including assessments of phenotypic and metabolic diversity in investigations of the genetic basis of complex traits, and the value of this approach in studying agriculturally important crops. We highlight the ground-breaking work on model species and discuss recent achievements in important crop species.

Virus-induced multiple gene silencing to study redundant metabolic pathways in plants: silencing the starch degradation pathway in Nicotiana benthamiana

Virus-induced gene silencing (VIGS) is a rapid technique that allows for specific and reproducible post-transcriptional degradation of targeted mRNA. The method has been proven efficient for suppression of expression of many single enzymes. The metabolic networks of plants, however, often contain isoenzymes and gene families that are able to compensate for a mutation and mask the development of a silencing phenotype. Here, we show the application of multiple gene VIGS repression for the study of these redundant biological pathways. Several genes in the starch degradation pathway [disproportionating enzyme 1; (DPE1), disproportionating enzyme 2 (DPE2), and GWD] were silenced. The functionally distinct DPE enzymes are present in alternate routes for sugar export to the cytoplasm and result in an increase in starch production when silenced individually. Simultaneous silencing of DPE1 and DPE2 in Nicotiana benthamiana resulted in a near complete suppression in starch and accumulation of malto-oligosaccharides.