The light-hyperresponsive high pigment-2dg mutation of tomato: alterations in the fruit metabolome

Overall metabolic modifications between fruit of light-hyperresponsive high-pigment (hp) tomato (Lycopersicon esculentum) mutant plants and isogenic nonmutant (wt) control plants were compared. Targeted metabolite analyses, as well as large-scale nontargeted mass spectrometry (MS)-based metabolite profiling, were used to phenotype the differences in fruit metabolite composition. Targeted high-performance liquid chromatography with photodiode array detection (HPLC-PDA) metabolite analyses showed higher levels of isoprenoids and phenolic compounds in hp-2dg fruit. Nontargeted GC-MS profiling of red fruits produced 25 volatile compounds that showed a 1.5-fold difference between the genotypes. Analyses of red fruits using HPLC coupled to high-resolution quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) in both ESI-positive and ESI-negative mode generated, respectively, 6168 and 5401 mass signals, of which 142 and 303 showed a twofold difference between the genotypes. hp-2dg fruits are characterized by overproduction of many metabolites, several of which are known for their antioxidant or photoprotective activities. These metabolites may now be more closely implicated as resources recruited by plants to respond to and manage light stress. The similarity in metabolic alterations in fruits of hp-1 and hp-2 mutant plants helps us to understand how hp mutations affect cellular processes.     

GC-EI-TOF-MS analysis of in vivo carbon-partitioning into soluble metabolite pools of higher plants by monitoring isotope dilution after 13CO2 labelling

The established GC-EI-TOF-MS method for the profiling of soluble polar metabolites from plant tissue was employed for the kinetic metabolic phenotyping of higher plants. Approximately 100 typical GC-EI-MS mass fragments of trimethylsilylated and methoxyaminated metabolite derivatives were structurally interpreted for mass isotopomer analysis, thus enabling the kinetic study of identified metabolites as well as the so-called functional group monitoring of yet non-identified metabolites. The monitoring of isotope dilution after (13)CO(2) labelling was optimized using Arabidopsis thaliana Col-0 or Oryza sativa IR57111 plants, which were maximally labelled with (13)C. Carbon isotope dilution was evaluated for short (2h) and long-term (3 days) kinetic measurements of metabolite pools in root and shoots. Both approaches were shown to enable the characterization of metabolite specific partitioning processes and kinetics. Simplifying data reduction schemes comprising calculation of (13)C-enrichment from mass isotopomer distributions and of initial (13)C-dilution rates were employed. Metabolites exhibited a highly diverse range of metabolite and organ specific half-life of (13)C-label in their respective pools ((13)C-half-life). This observation implied the setting of metabolite specific periods for optimal kinetic monitoring. A current experimental design for the kinetic metabolic phenotyping of higher plants is proposed.     

Optimization of Metabolite Profiling for Black Medick (<Emphasis Type="Italic">Medicago lupulina</Emphasis>) and Peas (<Emphasis Type="Italic">Pisum sativum</Emphasis>)

Metabolic profiling is a key approach in current basic and applied research in biology. Comparative analysis of different metabolite extraction methods for pea (P. sativum) and black medick (M. lupulina) made it possible to find the optimal conditions for metabolite extraction and subsequent detection by gas chromatography coupled with mass spectrometry. The optimized method was shown to be reliable for assessment of the organ and species metabolic profiles for roots and leaves in pea and black medick plants.     

Unintended effects in genetically modified crops: revealed by metabolomics?

In Europe the commercialization of food derived from genetically modified plants has been slow because of the complex regulatory process and the concerns of consumers. Risk assessment is focused on potential adverse effects on humans and the environment, which could result from unintended effects of genetic modifications: unintended effects are connected to changes in metabolite levels in the plants. One of the major challenges is how to analyze the overall metabolite composition of GM plants in comparison to conventional cultivars, and one possible solution is offered by metabolomics. The ultimate aim of metabolomics is the identification and quantification of all small molecules in an organism; however, a single method enabling complete metabolome analysis does not exist. Given a comprehensive extraction method, a hierarchical strategy--starting with global fingerprinting and followed by complementary profiling attempts--is the most logical and economic approach to detect unintended effects in GM crops.     

Metabolite profiling: from diagnostics to systems biology

The concept of metabolite profiling has been around for several decades, but only recent technical innovations have allowed metabolite profiling to be carried out on a large scale - with respect to both the number of metabolites measured and the number of experiments carried out. As a result, the power of metabolite profiling as a technology platform for diagnostics, and the research areas of gene-function analysis and systems biology, is now beginning to be fully realized.     

Emerging bioinformatics for the metabolome

Metabolic profiling applied to functional genomics (metabolomics) is in an early stage of development. Here, the technologies used for metabolite profiling are briefly covered, illustrated by a few pioneering studies. Issues related to bioinformatics, namely data analysis, visualisation and archival, are the main focus of this review. Arguably there is already a need for databases containing metabolite profiles specific for a single organism, and a generic repository containing all metabolite profiling results, regardless of species. Data analyses and visualisations that combine the biological context with chemistry details are suggested as being the most promising.     

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims toanalyze all metabolites in a biological sample comprehensively.The detailed metabolite profiling of thousands of plant sampleshas great potential for directly elucidating plant metabolicprocesses. However, both a comprehensive analysis and a highthroughput are difficult to achieve at the same time due tothe wide diversity of metabolites in plants. Here, we have establisheda novel and practical metabolomics methodology for quantifyinghundreds of targeted metabolites in a high-throughput manner.Multiple reaction monitoring (MRM) using tandem quadrupole massspectrometry (TQMS), which monitors both the specific precursorions and product ions of each metabolite, is a standard techniquein targeted metabolomics, as it enables high sensitivity, reproducibilityand a broad dynamic range. In this study, we optimized the MRMconditions for specific compounds by performing automated flowinjection analyses with TQMS. Based on a total of 61,920 spectrafor 860 authentic compounds, the MRM conditions of 497 compoundswere successfully optimized. These were applied to high-throughputautomated analysis of biological samples using TQMS coupledwith ultra performance liquid chromatography (UPLC). By thisanalysis, approximately 100 metabolites were quantified in eachof 14 plant accessions from Brassicaceae, Gramineae and Fabaceae.A hierarchical cluster analysis based on the metabolite accumulationpatterns clearly showed differences among the plant families,and family-specific metabolites could be predicted using a batch-learningself-organizing map analysis. Thus, the automated widely targetedmetabolomics approach established here should pave the way forlarge-scale metabolite profiling and comparative metabolomics.     

Progress in medicinal plant <Emphasis Type="Italic">Rehmannia glutinosa</Emphasis>: Metabolite profiling,tissue culture,growth and its regulation,and functional genomics

As an important medicinal plant, Rehmannia glutinosa Libosch. is widely spread in East Asian countries, and its root, possessing multiple pharmacological values, is used as traditional Chinese medicine in clinics. Recently, much progress in R. glutinosa has been made. Tissue culture and micropropagation have been applied to generate virus-free germs or homogeneous plants. In vitro culture and generation of transgenic R. glutinosa plants has been recently setup, which is helpful to develop more genetically-modified germplasms. Multiple environmental factors (e.g. CO₂ concentration, humidity, transpiration, drought, and viral diseases) play an important role in growth and development of R. glutinosa plants. Gene cloning, genetic transformation and metabolite profiling are becoming attractive. The review aims to summarize advances on metabolomics, tissue culture and regeneration, growth and its regulation, and functional genomics of R. glutinosa, which is in favor of the cultivation, processing, in addition to the study of metabolic engineering and metabolite profiling in R. glutinosa.     

Transgenic tobacco plants having a higher level of methionine are more sensitive to oxidative stress

Methionine is an essential amino acid the low level of which limits the nutritional quality of plants. We formerly produced transgenic tobacco (Nicotiana tabacum) plants overexpressing CYSTATHIONE γ‐SYNTHASE (CGS) (FA plants), methionine's main regulatory enzyme. These plants accumulate significantly higher levels of methionine compared with wild‐type (WT) plants. The aim of this study was to gain more knowledge about the effect of higher methionine content on the metabolic profile of vegetative tissue and on the morphological and physiological phenotypes. FA plants exhibit slightly reduced growth, and metabolic profiling analysis shows that they have higher contents of stress‐related metabolites. Despite this, FA plants were more sensitive to short‐ and long‐term oxidative stresses. In addition, compared with WT plants and transgenic plants expressing an empty vector, the primary metabolic profile of FA was altered less during oxidative stress. Based on morphological and metabolic phenotypes, we strongly proposed that FA plants having higher levels of methionine suffer from stress under non‐stress conditions. This might be one of the reasons for their lesser ability to cope with oxidative stress when it appeared. The observation that their metabolic profiling is much less responsive to stress compared with control plants indicates that the delta changes in metabolite contents between non‐stress and stress conditions is important for enabling the plants to cope with stress conditions.     

Metabolic profiling of transgenic tomato plants overexpressing hexokinase reveals that the influence of hexose phosphorylation diminishes during fruit development

We have conducted a comprehensive metabolic profiling on tomato (Lycopersicon esculentum) leaf and developing fruit tissue using a recently established gas chromatography-mass spectrometry profiling protocol alongside conventional spectrophotometric and liquid chromatographic methodologies. Applying a combination of these techniques, we were able to identify in excess of 70 small-M(r) metabolites and to catalogue the metabolite composition of developing tomato fruit. In addition to comparing differences in metabolite content between source and sink tissues of the tomato plant and after the change in metabolite pool sizes through fruit development, we have assessed the influence of hexose phosphorylation through fruit development by analyzing transgenic plants constitutively overexpressing Arabidopsis hexokinase AtHXK1. Analysis of the total hexokinase activity in developing fruits revealed that both wild-type and transgenic fruits exhibit decreasing hexokinase activity with development but that the relative activity of the transgenic lines with respect to wild type increases with development. Conversely, both point-by-point and principal component analyses suggest that the metabolic phenotype of these lines becomes less distinct from wild type during development. In summary, the data presented in this paper demonstrate that the influence of hexose phosphorylation diminishes during fruit development and highlights the importance of greater temporal resolution of metabolism.     

The patterns of population differentiation in a <Emphasis Type="Italic">Brassica rapa</Emphasis> core collection

With the recent advances in high throughput profiling techniques the amount of genetic and phenotypic data available has increased dramatically. Although many genetic diversity studies combine morphological and genetic data, metabolite profiling has yet to be integrated into these studies. For our study we selected 168 accessions representing the different morphotypes and geographic origins of Brassica rapa. Metabolite profiling was performed on all plants of this collection in the youngest expanded leaves, 5 weeks after transplanting and the same material was used for molecular marker profiling. During the same season a year later, 26 morphological characteristics were measured on plants that had been vernalized in the seedling stage. The number of groups and composition following a hierarchical clustering with molecular markers was highly correlated to the groups based on morphological traits (r = 0.420) and metabolic profiles (r = 0.476). To reveal the admixture levels in B. rapa, comparison with the results of the programme STRUCTURE was needed to obtain information on population substructure. To analyze 5546 metabolite (LC–MS) signals the groups identified with STRUCTURE were used for random forests classification. When comparing the random forests and STRUCTURE membership probabilities 86% of the accessions were allocated into the same subgroup. Our findings indicate that if extensive phenotypic data (metabolites) are available, classification based on this type of data is very comparable to genetic classification. These multivariate types of data and methodological approaches are valuable for the selection of accessions to study the genetics of selected traits and for genetic improvement programs, and additionally provide information on the evolution of the different morphotypes in B. rapa.     

Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts

The biochemical mode-of-action (MOA) for herbicides and other bioactive compounds can be rapidly and simultaneously classified by automated pattern recognition of the metabonome that is embodied in the 1H NMR spectrum of a crude plant extract. The ca. 300 herbicides that are used in agriculture today affect less than 30 different biochemical pathways. In this report, 19 of the most interesting MOAs were automatically classified. Corn (Zea mays) plants were treated with various herbicides such as imazethapyr, glyphosate, sethoxydim, and diuron, which represent various biochemical modes-of-action such as inhibition of specific enzymes (acetohydroxy acid synthase [AHAS], protoporphyrin IX oxidase [PROTOX], 5-enolpyruvylshikimate-3-phosphate synthase [EPSPS], acetyl CoA carboxylase [ACC-ase], etc.), or protein complexes (photosystems I and II), or major biological process such as oxidative phosphorylation, auxin transport, microtubule growth, and mitosis. Crude isolates from the treated plants were subjected to 1H NMR spectroscopy, and the spectra were classified by artificial neural network analysis to discriminate the herbicide modes-of-action. We demonstrate the use and refinement of the method, and present cross-validated assignments for the metabolite NMR profiles of over 400 plant isolates. The MOA screen also recognizes when a new mode-of-action is present, which is considered extremely important for the herbicide discovery process, and can be used to study deviations in the metabolism of compounds from a chemical synthesis program. The combination of NMR metabolite profiling and neural network classification is expected to be similarly relevant to other metabonomic profiling applications, such as in drug discovery.     

Metabolomic profiling reveals suppression of oxylipin biosynthesis during the early stages of legume-rhizobia symbiosis

The establishment of symbiosis between leguminous plants and rhizobial bacteria requires rapid metabolic changes in both partners. We utilized untargeted quantitative mass spectrometry to perform metabolomic profiling of small molecules in extracts of the model legume Medicago truncatula treated with rhizobial Nod factors. One metabolite closely resembling the 9(R)-HODE class of oxylipins reproducibly showed a decrease in concentration within the first hour of in planta nod factor treatment. Oxylipins are precursors of the jasmonic acid biosynthetic pathway and we showed that both this metabolite and jasmonic acid inhibit Nod factor signaling. Since, oxylipins have been implicated as antimicrobial compounds produced by plants, these observations suggest that the oxylipin pathway may play multiple roles in facilitating Nod factor signaling during the early stages of symbiosis.     

Plant metabolomics: large-scale phytochemistry in the functional genomics era

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.     

An improved extraction method enables the comprehensive analysis of lipids,proteins, metabolites and phytohormones from a single sample of leaf tissue under water‐deficit stress

Phytohormones play essential roles in the regulation of growth and development in plants. Plant hormone profiling is therefore essential to understand developmental processes and the adaptation of plants to biotic and/or abiotic stresses. Interestingly, commonly used hormone extraction and profiling methods do not adequately resolve other molecular entities, such as polar metabolites, lipids, starch and proteins, which would be required to comprehensively describe the continuing biological processes at a systematic level. In this article we introduce an updated version of a previously published liquid:liquid metabolite extraction protocol, which not only allows for the profiling of primary and secondary metabolites, lipids, starch and proteins, but also enables the quantitative analysis of the major plant hormone classes, including abscisic acid, auxins, cytokinins, jasmonates and salicylates, from a single sample aliquot. The optimization of the method, which uses the introduction of acidified water, enabling the complete purification of major plant hormones into the organic (methyl‐tert‐butyl‐ether) phase, eliminated the need for solid‐phase extraction for sample clean‐up, and therefore reduces both sampling time and cost. As a proof‐of‐concept analysis, Arabidopsis thaliana plants were subjected to water‐deficit stress, which were then profiled for hormonal, metabolic, lipidomic and proteomic changes. Surprisingly, we determined not only previously described molecular changes but also significant changes regarding the breakdown of specific galactolipids, followed by the substantial accumulation of unsaturated fatty‐acid derivatives and diverse jasmonates in the course of adaptation to water‐deficit stress.