AtMetExpress development: a phytochemical atlas of Arabidopsis development

Qualitative analysis of algal secretions with multiple mass spectrometric platforms

Lipid secretions from algae pose a great opportunity for engineering biofueler feedstocks. The lipid exudates could be interesting from a process engineering perspective because lipids could be collected directly from the medium without harvesting and disrupting cells. We here report on the extracellular secretions of algal metabolites from the strain UTEX 2341 (Chlorella minutissima) into the culture medium. No detailed analysis of these lipid secretions has been performed to date. Using multiple mass spectrometric platforms, we observed around 1000 compounds and were able to annotate 50 lipids by means of liquid chromatography coupled to accurate mass quadrupole time-of-flight mass spectrometry (LC-QTOF), direct infusion with positive and negative electrospray ion trap mass spectrometry and gas chromatography coupled to mass spectrometry (GC-MS). These compounds were annotated by tandem mass spectral (MS/MS) database matching and retention time range filtering. We observed a series of triacylglycerols (TG), sulfoquinovosyldiacylglycerols (SQDG), phosphatidylinositols and phosphatidylglycerols, as well as betaine lipids diacylglyceryl-N,N,N-trimethylhomoserines (DGTS).

Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance

Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there are limitations to these approaches for example because of the multi-dimensionality of the data that makes interpretation of the data obtained from untargeted analysis almost impossible for an average human being. These limitations make the biological information that is of prime importance in untargeted studies be partially exploited. Even in the case of full exploitation, current methods for relationship elucidation focus mainly on between groups variation and differences. Therefore, a measure that is capable of exploiting both between- and within-group biological variation would be of great value. Here, we examined the natural variation in the metabolome of nine Arabidopsis thaliana accessions grown under various environmental conditions and established a measure for the metabolic distance between accessions and across environments. This data analysis approach shows that there is just a minor correlation between genetic and metabolic diversity of the nine accessions. On the other hand, it delivers so far in Arabidopsis unexplored chemical information and is shown to be biologically relevant for resistance studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0375-3) contains supplementary material, which is available to authorized users.

Multi-dimensional regulation of metabolic networks shaping plant development and performance

The metabolome is an integral part of a plant's life cycle and determines for a large part its external phenotype. It is the final, internal product of chemical interactions, obtained through developmental, genetic, and environmental inputs, and as such, it defines the state of a plant in terms of development and performance. Understanding its regulation will provide knowledge and new insights into the biochemical pathways and genetic interactions that shape the plant and its surroundings. In this review, we will focus on four dimensions that contribute to the huge diversity of metabolomes and we will illustrate how this diversity shapes the plant in terms of development and performance: (i) temporal regulation: the metabolome is extremely dynamic and temporal changes in the environment can have an immense impact on its composition; (ii) spatial regulation: metabolites can be very specific, in both quantitative and qualitative terms, to specialized organs, tissues, and cell types; (iii) environmental regulation: the metabolic profile of plants is highly dependent on environmental signals, such as light, temperature, and nutrients, and very susceptible to biotic and abiotic stresses; and (iv) genetic regulation: the biosynthesis, structure, and accumulation of metabolites have a genetic origin, and there is quantitative and qualitative variation for metabolomes within a species. We will address the contribution of these dimensions to the wide diversity of metabolomes and highlight how the multi-dimensional regulation of metabolism defines the plant's phenotype.

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large-scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back-crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m-trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m-trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin-6,8-di-C-α-l-arabinoside are presented as an example of a critical mQTL identified by the analysis.

High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions

The main goal of metabolomics is the comprehensive qualitative and quantitative analysis of the time- and space-resolved distribution of all metabolites present in a given biological system. Because metabolite structures, in contrast to transcript and protein sequences, are not directly deducible from the genomic DNA sequence, the massive increase in genomic information is only indirectly of use to metabolomics, leaving compound annotation as a key problem to be solved by the available analytical techniques. Furthermore, as metabolites vary widely in both concentration and chemical behavior, there is no single analytical procedure allowing the unbiased and comprehensive structural elucidation and determination of all metabolites present in a given biological system. In this review the different approaches for targeted and non-targeted metabolomics analysis will be described with special emphasis on mass spectrometry-based techniques. Particular attention is given to approaches which can be employed for the annotation of unknown compounds. In the second part, the different experimental approaches aimed at tissue-specific or subcellular analysis of metabolites are discussed including a range of non-mass spectrometry based technologies.

Metabolomic analysis reveals that the accumulation of specific secondary metabolites in Echinacea angustifolia cells cultured in vitro can be controlled by light

Echinacea angustifolia cell suspension cultures are usually grown and maintained in the dark, but we also exposed cells to light for one culture cycle (14 days) and then compared the metabolomes of dark-grown and illuminated cells by liquid chromatography-mass spectrometry. Among 256 signals, we putatively identified 159 molecules corresponding to 56 different metabolites plus their fragments, adducts and isotopologs. The E. angustifolia metabolome consisted mainly of caffeic acid derivatives, comprising (a) caffeic acid conjugated with tartaric, quinic and hexaric acids; and (b) caffeic acid conjugated with hydroxytyrosol glycosides (e.g., echinacoside, verbascoside and related molecules). Many of these metabolites have not been previously described in E. angustifolia, which currently lacks detailed metabolic profiles. Exposure to light significantly increased the levels of certain caffeic acid derivatives (particularly caffeoylquinic acids and hydroxytyrosol derivatives lacking rhamnose residues) and reduced the level of hydroxytyrosol derivatives with rhamnose residues, revealing that light specifically inhibits the rhamnosylation of caffeoyl phenylethanoid glycosides. These results are significant because they suggest that the metabolic profile of cell cultures can be manipulated by controlling simple environmental variables such as illumination to modulate the levels of potentially therapeutic compounds.

Plantmetabolomics.org: mass spectrometry-based Arabidopsis metabolomics--database and tools update

The PlantMetabolomics (PM) database (http://www.plantmetabolomics.org) contains comprehensive targeted and untargeted mass spectrum metabolomics data for Arabidopsis mutants across a variety of metabolomics platforms. The database allows users to generate hypotheses about the changes in metabolism for mutants with genes of unknown function. Version 2.0 of PlantMetabolomics.org currently contains data for 140 mutant lines along with the morphological data. A web-based data analysis wizard allows researchers to select preprocessing and data-mining procedures to discover differences between mutants. This community resource enables researchers to formulate models of the metabolic network of Arabidopsis and enhances the research community's ability to formulate testable hypotheses concerning gene functions. PM features new web-based tools for data-mining analysis, visualization tools and enhanced cross links to other databases. The database is publicly available. PM aims to provide a hypothesis building platform for the researchers interested in any of the mutant lines or metabolites.

MYB8 controls inducible phenolamide levels by activating three novel hydroxycinnamoyl-coenzyme A:polyamine transferases in Nicotiana attenuata

A large number of plants accumulate N-acylated polyamines (phenolamides [PAs]) in response to biotic and/or abiotic stress conditions. In the native tobacco (Nicotiana attenuata), the accumulation of two major PAs, caffeoylputrescine and dicaffeoylspermidine (DCS), after herbivore attack is known to be controlled by a key transcription factor, MYB8. Using a broadly targeted metabolomics approach, we show that a much larger spectrum of PAs composed of hydroxycinnamic acids and two polyamines, putrescine and spermidine, is regulated by this transcription factor. We cloned several novel MYB8-regulated genes, annotated as putative acyltransferases, and analyzed their function. One of the novel acyltransferases (AT1) is shown to encode a hydroxycinnamoyl-coenzyme A:putrescine acyltransferase responsible for caffeoylputrescine biosynthesis in tobacco. Another gene (acyltransferase DH29), specific for spermidine conjugation, mediates the initial acylation step in DCS formation. Although this enzyme was not able to perform the second acylation toward DCS biosynthesis, another acyltransferase gene, CV86, proposed to act on monoacylated spermidines, was isolated and partially characterized. The activation of MYB8 in response to herbivore attack and associated signals required the activity of LIPOXYGENASE3, a gene involved in jasmonic acid (JA) biosynthesis in N. attenuata. These new results allow us to reconstruct a complete branch in JA signaling that defends N. attenuata plants against herbivores: JA via MYB8's transcriptional control of AT1 and DH29 genes controls the entire branch of PA biosynthesis, which allows N. attenuata to mount a chemically diverse (and likely efficient) defense shield against herbivores.

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants.

Advances in omics and bioinformatics tools for systems analyses of plant functions

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.

Advances in omics and bioinformatics tools for systems analyses of plant functions

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.

Retrogenes in rice (Oryza sativa L. ssp. japonica) exhibit correlated expression with their source genes

Gene duplication occurs by either DNA- or RNA-based processes; the latter duplicates single genes via retroposition of messenger RNA. The expression of a retroposed gene copy (retrocopy) is expected to be uncorrelated with its source gene because upstream promoter regions are usually not part of the retroposition process. In contrast, DNA-based duplication often encompasses both the coding and the intergenic (promoter) regions; hence, expression is often correlated, at least initially, between DNA-based duplicates. In this study, we identified 150 retrocopies in rice (Oryza sativa L. ssp japonica), most of which represent ancient retroposition events. We measured their expression from high-throughput RNA sequencing (RNAseq) data generated from seven tissues. At least 66% of the retrocopies were expressed but at lower levels than their source genes. However, the tissue specificity of retrogenes was similar to their source genes, and expression between retrocopies and source genes was correlated across tissues. The level of correlation was similar between RNA- and DNA-based duplicates, and they decreased over time at statistically indistinguishable rates. We extended these observations to previously identified retrocopies in Arabidopsis thaliana, suggesting they may be general features of the process of retention of plant retrogenes.

High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions

The time-resolved response of Arabidopsis thaliana towards changing light and/or temperature at the transcriptome and metabolome level is presented. Plants grown at 21°C with a light intensity of 150 μE m⁻² sec⁻¹ were either kept at this condition or transferred into seven different environments (4°C, darkness; 21°C, darkness; 32°C, darkness; 4°C, 85 μE m⁻² sec⁻¹; 21 °C, 75 μE m⁻² sec⁻¹; 21°C, 300 μE m⁻² sec⁻¹ ; 32°C, 150 μE m⁻² sec⁻¹). Samples were taken before (0 min) and at 22 time points after transfer resulting in (8×) 22 time points covering both a linear and a logarithmic time series totaling 177 states. Hierarchical cluster analysis shows that individual conditions (defined by temperature and light) diverge into distinct trajectories at condition-dependent times and that the metabolome follows different kinetics from the transcriptome. The metabolic responses are initially relatively faster when compared with the transcriptional responses. Gene Ontology over-representation analysis identifies a common response for all changed conditions at the transcriptome level during the early response phase (5-60 min). Metabolic networks reconstructed via metabolite-metabolite correlations reveal extensive environment-specific rewiring. Detailed analysis identifies conditional connections between amino acids and intermediates of the tricarboxylic acid cycle. Parallel analysis of transcriptional changes strongly support a model where in the absence of photosynthesis at normal/high temperatures protein degradation occurs rapidly and subsequent amino acid catabolism serves as the main cellular energy supply. These results thus demonstrate the engagement of the electron transfer flavoprotein system under short-term environmental perturbations.

Mass spectra-based framework for automated structural elucidation of metabolome data to explore phytochemical diversity

A novel framework for automated elucidation of metabolite structures in liquid chromatography-mass spectrometer metabolome data was constructed by integrating databases. High-resolution tandem mass spectra data automatically acquired from each metabolite signal were used for database searches. Three distinct databases, KNApSAcK, ReSpect, and the PRIMe standard compound database, were employed for the structural elucidation. The outputs were retrieved using the CAS metabolite identifier for identification and putative annotation. A simple metabolite ontology system was also introduced to attain putative characterization of the metabolite signals. The automated method was applied for the metabolome data sets obtained from the rosette leaves of 20 Arabidopsis accessions. Phenotypic variations in novel Arabidopsis metabolites among these accessions could be investigated using this method.

From models to crop species: caveats and solutions for translational metabolomics

Although plant metabolomics is largely carried out on Arabidopsis it is essentially genome-independent, and thus potentially applicable to a wide range of species. However, transfer between species, or even between different tissues of the same species, is not facile. This is because the reliability of protocols for harvesting, handling and analysis depends on the biological features and chemical composition of the plant tissue. In parallel with the diversification of model species it is important to establish good handling and analytic practice, in order to augment computational comparisons between tissues and species. Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is one of the powerful approaches for metabolite profiling. By using a combination of different extraction methods, separation columns, and ion detection, a very wide range of metabolites can be analyzed. However, its application requires careful attention to exclude potential pitfalls, including artifactual changes in metabolite levels during sample preparation under variations of light or temperature and analytic errors due to ion suppression. Here we provide case studies with two different LC-MS-based metabolomics platforms and four species (Arabidopsis thaliana, Chlamydomonas reinhardtii, Solanum lycopersicum, and Oryza sativa) that illustrate how such dangers can be detected and circumvented.

Triterpenoid biosynthesis and engineering in plants

Triterpenoid saponins are a diverse group of natural products in plants and are considered defensive compounds against pathogenic microbes and herbivores. Because of their various beneficial properties for humans, saponins are used in wide-ranging applications in addition to medicinally. Saponin biosynthesis involves three key enzymes: oxidosqualene cyclases, which construct the basic triterpenoid skeletons; cytochrome P450 monooxygenases, which mediate oxidations; and uridine diphosphate-dependent glycosyltransferases, which catalyze glycosylations. The discovery of genes committed to saponin biosynthesis is important for the stable supply and biotechnological application of these compounds. Here, we review the identified genes involved in triterpenoid biosynthesis, summarize the recent advances in the biotechnological production of useful plant terpenoids, and discuss the bioengineering of plant triterpenoids.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.

Functional genomics of soybean for improvement of productivity in adverse conditions

Global soybean production is frequently impacted by various stresses, including both abiotic and biotic stresses. To develop soybean plants with enhanced tolerance to different stressors, functional genomics of soybean and a comprehensive understanding of available biotechnological resources and approaches are essential. In this review, we will discuss recent advances in soybean functional genomics which provide unprecedented opportunities to understand global patterns of gene expression, gene regulatory networks, various physiological, biochemical, and metabolic pathways as well as their association with the development of specific phenotypes. Soybean functional genomics, therefore, will ultimately enable us to develop new soybean varieties with improved productivity under adverse conditions by genetic engineering.

Computational mass spectrometry for metabolomics: identification of metabolites and small molecules

The identification of compounds from mass spectrometry (MS) data is still seen as a major bottleneck in the interpretation of MS data. This is particularly the case for the identification of small compounds such as metabolites, where until recently little progress has been made. Here we review the available approaches to annotation and identification of chemical compounds based on electrospray ionization (ESI-MS) data. The methods are not limited to metabolomics applications, but are applicable to any small compounds amenable to MS analysis. Starting with the definition of identification, we focus on the analysis of tandem mass and MS(n) spectra, which can provide a wealth of structural information. Searching in libraries of reference spectra provides the most reliable source of identification, especially if measured on comparable instruments. We review several choices for the distance functions. The identification without reference spectra is even more challenging, because it requires approaches to interpret tandem mass spectra with regard to the molecular structure. Both commercial and free tools are capable of mining general-purpose compound libraries, and identifying candidate compounds. The holy grail of computational mass spectrometry is the de novo deduction of structure hypotheses for compounds, where method development has only started thus far. In a case study, we apply several of the available methods to the three compounds, kaempferol, reserpine, and verapamil, and investigate whether this results in reliable identifications.

Potential of Arabidopsis systems biology to advance the biofuel field

Plant biomass is a renewable and potentially sustainable resource for the production of liquid biofuels and a multitude of bio-based materials. To tailor plants for biofuel production, a powerful gene discovery program targeted to cell wall recalcitrance genes is needed. In parallel, a system is required that reveals the pleiotropic effects of gene modifications and that delivers the fundamental knowledge necessary for successful gene stacking. In our opinion, these objectives can be pioneered through a systems biology approach in Arabidopsis. We develop our ideas with a focus on the lignin biosynthetic pathway, because lignin is among the most important factors determining cell wall recalcitrance.

Toward genome-wide metabolotyping and elucidation of metabolic system: metabolic profiling of large-scale bioresources

An improvement in plant production is increasingly important for a sustainable human society. For this purpose, understanding the mechanism of plant production, that is, the plant metabolic system, is an immediate necessity. After the sequencing of the Arabidopsis genome, it has become possible to obtain a bird's eye view of its metabolism by means of omics such as transcriptomics and proteomics. Availability of thousands of transcriptome data points in the public domain has resulted in great advances in the methodology of functional genomics. Metabolome data can be a "gold mine" of biological findings. However, as the total throughput of metabolomics is far lower than that of transcriptomics due to technical difficulties, there is currently no publicly available large-scale metabolome dataset that is comparable in size to the transcriptome dataset. Recently, we established a novel methodology, termed widely targeted metabolomics, which can generate thousands of metabolome data points in a high-throughput manner. We previously conducted a targeted metabolite analysis of large-scale Arabidopsis bioresources, namely transposon-tagged mutants and accessions, to make a smaller dataset of metabolite accumulation. In this paper, we release approximately 3,000 metabolic profiles obtained by targeted analysis for 36 metabolites and discuss the possible regulation of amino acid accumulation.

A polyhedral approach for understanding flavonoid biosynthesis in Arabidopsis

The sequencing of the entire genome of model plants has made it possible to use these plants for producing metabolites that are beneficial to humans. Plants produce a large number of metabolites, which are potentially valuable sources of novel pharmaceutically active agents that benefit human health. The industrial use of these beneficial metabolites from plants, known as secondary metabolites or specialized metabolites, necessitates the systematic understanding of the biosynthetic mechanisms from the genetic to the metabolic level. Here, we review the recent developments on flavonoid biosynthesis in Arabidopsis thaliana (Arabidopsis) as a model plant. These developments have been made in the fields of metabolomics, transcriptomics, natural products chemistry, genetics and biochemistry. We focused on natural products chemistry in this polyhedral approach, which proved to be indispensable for elucidating the secondary metabolism in not only Arabidopsis but also other model-like and crop plants.

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.