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

A novel thiolase-reductase gene fusion promotes the production of polyhydroxybutyrate in Arabidopsis

The production of polyhydroxybutyrate (PHB) involves a multigene pathway consisting of thiolase, reductase and synthase genes. In order to simplify this pathway for plant-based expression, a library of thiolase and reductase gene fusions was generated by randomly ligating a short core linker DNA sequence to create in-frame fusions between the thiolase and reductase genes. The resulting fusion constructs were screened for PHB formation in Escherichia coli. This screen identified a polymer-producing candidate in which the thiolase and reductase genes were fused via a 26-amino-acid linker. This gene fusion, designated phaA-phaB, represents an active gene fusion of two homotetrameric enzymes. Expression of phaA-phaB in E. coli and Arabidopsis yielded a fusion protein observed to be the expected size by Western blotting techniques. The fusion protein exhibited thiolase and reductase enzyme activities in crude extracts of recombinant E. coli that were three-fold and nine-fold less than those of the individually expressed thiolase and reductase enzymes, respectively. When targeted to the plastid, and coexpressed with a plastid-targeted polyhydroxyalkanoate (PHA) synthase, the fusion protein enabled PHB formation in Arabidopsis, yielding roughly half the PHB formed in plants expressing individual thiolase, reductase and synthase enzymes. This work represents a first step towards simplifying the expression of the PHB biosynthetic pathway in plants.

KaPPA-view: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps

The application of DNA array technology and chromatographic separation techniques coupled with mass spectrometry to transcriptomic and metabolomic analyses in plants has resulted in the generation of considerable quantitative data related to transcription and metabolism. The integration of "omic" data is one of the major concerns associated with research into identifying gene function. Thus, we developed a Web-based tool, KaPPA-View, for representing quantitative data for individual transcripts and/or metabolites on plant metabolic pathway maps. We prepared a set of comprehensive metabolic pathway maps for Arabidopsis (Arabidopsis thaliana) and depicted these graphically in Scalable Vector Graphics format. Individual transcripts assigned to a reaction are represented symbolically together with the symbols of the reaction and metabolites on metabolic pathway maps. Using quantitative values for transcripts and/or metabolites submitted by the user as Comma Separated Value-formatted text through the Internet, the KaPPA-View server inserts colored symbols corresponding to a defined metabolic process at that site on the maps and returns them to the user's browser. The server also provides information on transcripts and metabolites in pop-up windows. To demonstrate the process, we describe the dataset obtained for transgenic plants that overexpress the PAP1 gene encoding a MYB transcription factor on metabolic pathway maps. The presentation of data in this manner is useful for viewing metabolic data in a way that facilitates the discussion of gene function.

Dihydrocaffeoyl polyamines (kukoamine and allies) in potato (Solanum tuberosum) tubers detected during metabolite profiling

Four related phenolic amides previously undescribed from the species were revealed during metabolic profiling of potato (Solanum tuberosum) tubers. N(1),N(12)-Bis(dihydrocaffeoyl)spermine (kukoamine A) and N(1),N(8)-bis(dihydrocaffeoyl)spermidine were positively identified by comparison with authentic standards, while the structures N(1),N(4),N(12)-tris(dihydrocaffeoyl)spermine and N(1),N(4),N(8)-tris(dihydrocaffeoyl)spermidine are proposed for the other two metabolites. Each amide was present at several tens of micrograms per gram of dry matter. Several of these compounds were subsequently detected in other solanaceous species, such as tomato (Lycopersicon esculentum) and Nicotiana sylvestris. They appeared not to be present in Arabidopsis thaliana or Beta vulgaris. Bis(dihydrocaffeoyl)spermine isomers have previously been identified in only a single plant, the Chinese medicinal species Lycium chinense (Solanaceae), where they may account for some of the described biological activity. The other compounds have not until now been reported in vivo, though some of the equivalent hydroxycinnamoyl derivatives are known. The surprising discovery of kukoamine and allies in a range of solanaceous species including potato, a common food crop that has a long history of scientific investigation, provides exemplary evidence for the potential of the nontargeted techniques of metabolomics in studying plant metabolites.

Metabolomics, genomics, proteomics, and the identification of enzymes and their substrates and products

A large proportion of the genes in any plant genome encode enzymes of primary and specialized (secondary) metabolism. Not all plant primary metabolites, those that are found in all or most species, have been identified. Moreover, only a small portion of the estimated hundreds of thousand specialized metabolites, those found only in restricted lineages, have been studied in any species. The correlative analysis of extensive metabolic profiling and gene expression profiling has proven a powerful approach for the identification of candidate genes and enzymes, particularly those in secondary metabolism. The final characterization of substrates, enzymatic activities, and products requires biochemical analysis, which has been most successful when candidate proteins have homology to other enzymes of known function. The challenges are to identify new types of enzymes and to develop biochemical techniques that are suitable for large-scale analysis.

Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period

BACKGROUND

This study analyzes metabolomic data from a rice tillering (branching) developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype. Changes in metabolism, and in metabolite profile, occur as a part of, and in response to, developmental events. These changes are influenced by the developmental program, as well as external factors impinging on it. Many samples are needed, however, to characterize quantitative aspects of developmental variation. A biomarker metabolite set could benefit screening of quantitative plant developmental variation by providing some of the advantages of both comprehensive metabolomic studies and focused studies of particular metabolites or pathways.

RESULTS

An appropriate set of biomarker metabolites to represent the plant developmental period including the initiation and early growth of rice tillering (branching) was obtained by: (1) determining principal components of the comprehensive metabolomic profile, then (2) identifying clusters of metabolites representing variation in loading on the first three principal components, and finally (3) selecting individual metabolites from these clusters that were known to be common among diverse organisms. The resultant set of 21 biomarker metabolites was reliable (P = 0.001) in capturing 83% of the metabolite variation in development. Furthermore, a subset of the biomarker metabolites was successful (P = 0.05) in correctly predicting metabolite change in response to environment as determined in another rice metabolomics study.

CONCLUSION

The ability to define a set of biomarker metabolites that reliably captures the metabolite variance of a plant developmental event was established. The biomarker metabolites are all commonly present in diverse organisms, so studies of their quantitative relationships can provide comparative information concerning metabolite profiles in relation to change in plant development, environment, or genotype.

A survey of the methods for the characterization of microbial consortia and communities

A survey of the available literature on methods most frequently used for the identification and characterization of microbial strains, communities, or consortia is presented. The advantages and disadvantages of the various methodologies were examined from several perspectives including technical, economic (time and cost), and regulatory. The methods fall into 3 broad categories: molecular biological, biochemical, and microbiological. Molecular biological methods comprise a broad range of techniques that are based on the analysis and differentiation of microbial DNA. This class of methods possesses several distinct advantages. Unlike most other commonly used methods, which require the production of secondary materials via the manipulation of microbial growth, molecular biological methods recover and test their source materials (DNA) directly from the microbial cells themselves, without the requirement for culturing. This eliminates both the time required for growth and the biases associated with cultured growth, which is unavoidably and artificially selective. The recovered nucleic acid can be cloned and sequenced directly or subpopulations can be specifically amplified using polymerase chain reaction (PCR), and subsequently cloned and sequenced. PCR technology, used extensively in forensic science, provides researchers with the unique ability to detect nucleic acids (DNA and RNA) in minute amounts, by amplifying a single target molecule by more than a million-fold. Molecular methods are highly sensitive and allow for a high degree of specificity, which, coupled with the ability to separate similar but distinct DNA molecules, means that a great deal of information can be gleaned from even very complex microbial communities. Biochemical methods are composed of a more varied set of methodologies. These techniques share a reliance on gas chromatography and mass spectrometry to separate and precisely identify a range of biomolecules, or else investigate biochemical properties of key cellular biomolecules. Like the molecular biological methods, some biochemical methods such as lipid analyses are also independent of cultured growth. However, many of these techniques are only capable of producing a profile that is characteristic of the microbial community as a whole, providing no information about individual members of the community. A subset of these methodologies are used to derive taxonomic information from a community sample; these rely on the identification of key subspecies of biomolecules that differ slightly but characteristically between species, genera, and higher biological groupings. However, when the consortium is already growing in chemically defined media (as is often the case with commercial products), the rapidity and relatively low costs of these procedures can mitigate concerns related to culturing biases. Microbiological methods are the most varied and the least useful for characterizing microbial consortia. These methods rely on traditional tools (cell counting, selective growth, and microscopic examination) to provide more general characteristics of the community as a whole, or else to narrow down and identify only a small subset of the members of that community. As with many of the biochemical methods, some of the microbiological methods can fairly rapidly and inexpensively create a community profile, which can be used to compare 2 or more entire consortia. However, for taxonomic identification of individual members, microbiological methods are useful only to screen for the presence of a few key predetermined species, whose preferred growth conditions and morphological characteristics are well defined and reproducible.Key words: microbial communities, microbial consortia, characterization methods, taxonomic identification.

What it takes to get a herbicide's mode of action. Physionomics, a classical approach in a new complexion

Discovering new herbicides with novel modes of action is a priority assignment in plant protection research. However, for active compounds identified in greenhouse screens, the crucial point is to tread the most efficient path in determining a herbicide's target site, regarding chance of success, time and research costs. Today, in the literature, molecular (functional genomics, transcriptomics), biochemical (proteomics) and analytical (metabolomics) approaches are particularly discussed. So far, less attention has been focused on the comprehensive physiological profiling of the complex plant system as a procedure which enables new herbicides, with an unknown target site for their mode of action, to be screened rapidly. Here, the concept of an array of 'functional' bioassays is presented which has ultimately been developed from the classical tool of mode of action diagnosis by symptoms. These bioassays are designed to differentiate between the distinct responses of the multiple organization units (plant, tissue, meristematic cell, organelle), developmental stages, types of metabolism (phototrophic, heterotrophic) and physiological processes in the plant organism. The response pattern to a herbicide can be viewed as the end result of changes induced in the molecular and biochemical process chain and should be diagnostic of its physiological mode of action. The results can be interpreted directly or a fingerprint database for all known modes of action to be screened for analogy. The term 'physionomics' is proposed for this comprehensive physiological profiling of the plant system, following the parallel terminology of the molecular and biochemical 'omics' technologies. Physionomics procedures provide a first clue to the mode of action of a new herbicide that can direct more time-consuming and costly molecular, biochemical, histochemical or analytical studies to identify a target site more efficiently.

A proposed framework for the description of plant metabolomics experiments and their results

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.

Profiling C6-C3 and C6-C1 phenolic metabolites in Cocos nucifera

This paper reports the detection and identification of phenolic metabolites (C6-C3 and C6-C1 compounds) in Cocos nucifera. An HPLC/UV system was used to analyze the soluble and wall-associated phenolics in mesocarp and leaf tissues of C. nucifera. Alkaline hydrolysis of the cell wall material of the mesocarpic and leaf tissues yielded 4-hydroxybenzoic acid as the major phenolic compound. Other phenolic acids identified were ferulic acid, 4-coumaric acid, 4-hydroxybenzaldehyde and vanillic acid. No significant qualitative differences in composition were observed between leaf and mesocarp, but there were quantitative variations in the metabolite levels.

Lotus japonicus metabolic profiling. Development of gas chromatography-mass spectrometry resources for the study of plant-microbe interactions

Symbiotic nitrogen fixation (SNF) in legume root nodules requires differentiation and integration of both plant and bacterial metabolism. Classical approaches of biochemistry, molecular biology, and genetics have revealed many aspects of primary metabolism in legume nodules that underpin SNF. Functional genomics approaches, especially transcriptomics and proteomics, are beginning to provide a more holistic picture of the metabolic potential of nodules in model legumes like Medicago truncatula and Lotus japonicus. To extend these approaches, we have established protocols for nonbiased measurement and analysis of hundreds of metabolites from L. japonicus, using gas chromatography coupled with mass spectrometry. Following creation of mass spectral tag libraries, which represent both known and unknown metabolites, we measured and compared relative metabolite levels in nodules, roots, leaves, and flowers of symbiotic plants. Principal component analysis of the data revealed distinct metabolic phenotypes for the different organs and led to the identification of marker metabolites for each. Metabolites that were enriched in nodules included: octadecanoic acid, asparagine, glutamate, homoserine, cysteine, putrescine, mannitol, threonic acid, gluconic acid, glyceric acid-3-P, and glycerol-3-P. Hierarchical cluster analysis enabled discrimination of 10 groups of metabolites, based on distribution patterns in diverse Lotus organs. The resources and tools described here, together with ongoing efforts in the areas of genome sequencing, and transcriptome and proteome analysis of L. japonicus and Mesorhizobium loti, should lead to a better understanding of nodule metabolism that underpins SNF.

Measuring the metabolome: current analytical technologies

The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.

Methyl jasmonate and yeast elicitor induce differential transcriptional and metabolic re-programming in cell suspension cultures of the model legume Medicago truncatula

Exposure of cell suspension cultures of Medicago truncatula Gaerth. to methyl jasmonate (MeJA) resulted in up to 50-fold induction of transcripts encoding the key triterpene biosynthetic enzyme beta-amyrin synthase (betaAS; EC 5.4.99.-). Transcripts reached maximum levels at 24 h post-elicitation with 0.5 mM MeJA. The entry point enzymes into the phenylpropanoid and flavonoid pathways, L: -phenylalanine ammonia-lyase (PAL; EC 4.3.1.5) and chalcone synthase (CHS; EC 2.3.1.74), respectively, were not induced by MeJA. In contrast, exposure of cells to yeast elicitor (YE) resulted in up to 45- and 14-fold induction of PAL and CHS transcripts, respectively, at only 2 h post-elicitation. betaAS transcripts were weakly induced at 12 h after exposure to YE. Over 30 different triterpene saponins were identified in the cultures, many of which were strongly induced by MeJA, but not by YE. In contrast, cinnamic acids, benzoic acids and isoflavone-derived compounds accumulated following exposure of cultures to YE, but few changes in phenylpropanoid levels were observed in response to MeJA. DNA microarray analysis confirmed the strong differential transcriptional re-programming of the cell cultures for multiple genes in the phenylpropanoid and triterpene pathways in response to MeJA and YE, and indicated different responses of individual members of gene families. This work establishes Medicago cell cultures as an excellent model for future genomics approaches to understand the regulation of legume secondary metabolism.

Metabolic Profiling of Tryptophan-overproducing Rice Calli that Express a Feedback-insensitive α Subunit of Anthranilate Synthase

The profile of aromatic metabolites in calli was compared between wild-type rice (Oryza sativa cv. Nipponbare) and tryptophan-overproducing transgenic rice lines that express a gene (OASA1D) for a feedback-insensitive alpha subunit of anthranilate synthase. Metabolic profiling by high-performance liquid chromatography coupled with photodiode array detection of ultraviolet absorbance revealed a total of 71 peaks in both wild-type and transgenic calli. Only a limited effect on the pattern of major aromatic compounds was observed in tryptophan-accumulating transgenic rice lines, with the exception of an approximately 80-fold increase in the amount of tryptophan. Expression of OASA1D induced relatively small changes in several minor metabolites. One of the minor metabolites whose abundance was increased by OASA1D expression was purified and identified as a previously unknown indole-alkaloid glucoside. The levels of free and conjugated forms of indole-3-acetic acid (IAA), a plant hormone derived from the tryptophan biosynthetic pathway, were determined separately by liquid chromatography and tandem mass spectrometry (LC-MS/MS). The amounts of both free IAA and its conjugates were increased in the transgenic calli, suggesting that the activity of anthranilate synthase or the concentration of tryptophan (or both) is an important determinant of IAA biosynthesis.

Liquid Chromatography−Mass Spectrometry and 15N Metabolic Labeling for Quantitative Metabolic Profiling

Metabolomics, i.e., the global analysis of cellular metabolites, is becoming a powerful tool for gaining insights into biological functions in the postgenomic context. However, absolute quantitation of endogenous metabolites in biological media remains an issue, and available technologies for the analysis of metabolome still lack robustness and accuracy. We describe here a new method based on liquid chromatography-mass spectrometry and (15)N uniform metabolic labeling of Saccharomyces cerevisiae for accurate and absolute quantitation of nitrogen-containing cell metabolites in metabolic profiling experiments. As a proof of concept study, eight sulfur metabolites involved in the glutathione biosynthesis pathway (i.e., cysteine, homocysteine, methionine, gamma-glutamylcysteine, cystathionine, reduced and oxidized forms of glutathione, and S-adenosylhomocysteine) were simultaneously quantified. The analytical method has been validated by studies of stability, selectivity, precision, and linearity and by the determination of the limits of detection and quantification. It was then applied to the analysis of extracts from cadmium-treated yeasts. In these conditions, the intracellular concentrations of most of the metabolites involved in the glutathione biosynthesis pathway were increased when compared to control extracts. These data correlate with previous proteomic results and also underline the importance of glutathione in cadmium detoxication.

Revealing metabolic phenotypes in plants: inputs from NMR analysis

Assessing the performance of the plant metabolic network, with its varied biosynthetic capacity and its characteristic subcellular compartmentation, remains a considerable challenge. The complexity of the network is such that it is not yet possible to build large-scale predictive models of the fluxes it supports, whether on the basis of genomic and gene expression analysis or on the basis of more traditional measurements of metabolites and their interconversions. This limits the agronomic and biotechnological exploitation of plant metabolism, and it undermines the important objective of establishing a rational metabolic engineering strategy. Metabolic analysis is central to removing this obstacle and currently there is particular interest in harnessing high-throughput and/or large-scale analyses to the task of defining metabolic phenotypes. Nuclear magnetic resonance (NMR) spectroscopy contributes to this objective by providing a versatile suite of analytical techniques for the detection of metabolites and the fluxes between them. The principles that underpin the analysis of plant metabolism by NMR are described, including a discussion of the measurement options for the detection of metabolites in vivo and in vitro, and a description of the stable isotope labelling experiments that provide the basis for metabolic flux analysis. Despite a relatively low sensitivity, NMR is suitable for high-throughput system-wide analyses of the metabolome, providing methods for both metabolite fingerprinting and metabolite profiling, and in these areas NMR can contribute to the definition of plant metabolic phenotypes that are based on metabolic composition. NMR can also be used to investigate the operation of plant metabolic networks. Labelling experiments provide information on the operation of specific pathways within the network, and the quantitative analysis of steady-state labelling experiments leads to the definition of large-scale flux maps for heterotrophic carbon metabolism. These maps define multiple unidirectional fluxes between branch-points in the metabolic network, highlighting the existence of substrate cycles and discriminating in favourable cases between fluxes in the cytosol and plastid. Flux maps can be used to define a functionally relevant metabolic phenotype and the extensive application of such maps in microbial systems suggests that they could have important applications in characterising the genotypes produced by plant genetic engineering.

In-Situ probing of the biotic-abiotic boundary of plants by laser desorption/ionization time-of-flight mass spectrometry

Laser desorption/ionization time-of-flight (LDI-TOF) mass spectrometry was applied for the direct analysis of cuticular waxes on intact plant tissues. Cuticular wax compounds were ionized by laser desorption in the presence of colloidal silver. Silver-adduct ions were detected on samples from Arabidopsis thaliana and from maize. Good spot-to-spot reproducibility indicated homogeneous coverage of the sample by the fine colloidal material. The results were consistent with GC-MS analyses of cuticular extracts, thus confirming the feasibility of direct analysis based on this protocol. Molecular masses of the adduct ions correspond well with the known composition of cuticular waxes. Moreover, LDI-TOF gave good estimates of the relative local abundances of a given compound. However, bias was found in cases where compounds with different ionization efficiencies were analyzed.

Metabolomics data analysis, visualization, and integration

Metabolomics is the large-scale analysis of metabolites and as such requires bioinformatics tools for data analysis, visualization, and integration. This chapter describes the basic composition of chromatographically coupled mass spectrometry (MS) data sets used in metabolomics and describes in detail the steps necessary for extracting large-scale qualitative and quantitative information. This process involves noise filtering, peak picking and deconvolution, peak identification, peak alignment, and the creation of a final data matrix for statistical processing. Multivariate tools for comparative analysis are presented and illustrated using data for Medicago truncatula. Additional tools for visualizing and integrating metabolomics data within a biological context are discussed. Two tables are provided listing current metabolomics data processing and visualization software. Because metabolomics is rapidly maturing, a final section is presented concerning the need for data standardization and current efforts.

Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling

Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.

NMR analysis of plant nitrogen metabolism

The analysis of primary and secondary nitrogen metabolism in plants by nuclear magnetic resonance (NMR) spectroscopy is comprehensively reviewed. NMR is a versatile analytical tool, and the combined use of (1)H, (2)H, (13)C, (14)N and (15)N NMR allows detailed investigation of the acquisition, assimilation and metabolism of nitrogen. The analysis of tissue extracts can be complemented by the in vivo NMR analysis of functioning tissues and cell suspensions, and by the application of solid state NMR techniques. Moreover stable isotope labelling with (2)H-, (13)C- and (15)N-labelled precursors provides direct insight into specific pathways, with the option of both time-course and steady state analysis increasing the potential value of the approach. The scope of the NMR method, and its contribution to studies of plant nitrogen metabolism, are illustrated with a wide range of examples. These include studies of the GS/GOGAT pathway of ammonium assimilation, investigations of the metabolism of glutamate, glycine and other amino acids, and applications to tropane alkaloid metabolism. The continuing development of the NMR technique, together with potential applications in the emerging fields of metabolomics and metabolic flux analysis, leads to the conclusion that NMR will play an increasingly valuable role in the analysis of plant nitrogen metabolism.

Metabolomics in the context of systems biology: bridging traditional Chinese medicine and molecular pharmacology

The introduction of the concept of systems biology, enabling the study of living systems from a holistic perspective based on the profiling of a multitude of biochemical components, opens up a unique and novel opportunity to reinvestigate natural products. In the study of their bioactivity, the necessary reductionistic approach on single active components has been successful in the discovery of new medicines, but at the same time the synergetic effects of components were lost. Systems biology, and especially metabolomics, is the ultimate phenotyping. It opens up the possibility of studying the effect of complex mixtures, such as those used in Traditional Chinese Medicine, in complex biological systems; abridging it with molecular pharmacology. This approach is considered to have the potential to revolutionize natural product research and to advance the development of scientific based herbal medicine.

A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS

In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.

Metabolic profiling of the sink-to-source transition in developing leaves of quaking aspen

Profiles of small polar metabolites from aspen (Populus tremuloides Michx.) leaves spanning the sink-to-source transition zone were compared. Approximately 25% of 250 to 300 routinely resolved peaks were identified, with carbohydrates, organic acids, and amino acids being most abundant. Two-thirds of identified metabolites exhibited greater than 4-fold changes in abundance during leaf ontogeny. In the context of photosynthetic and respiratory measurements, profile data yielded information consistent with expected developmental trends in carbon-heterotrophic and carbon-autotrophic metabolism. Suc concentration increased throughout leaf expansion, while hexose sugar concentrations peaked at mid-expansion and decreased sharply thereafter. Amino acid contents generally decreased during leaf expansion, but an early increase in Phe and a later one in Gly and Ser reflected growing commitments to secondary metabolism and photorespiration, respectively. The assimilation of nitrate and utilization of stored Asn appeared to be marked by sequential changes in malate concentration and Asn transaminase activity. Principal component and hierarchical clustering analysis facilitated the grouping of cell wall maturation (pectins, hemicelluloses, and oxalate) and membrane biogenesis markers in relation to developmental changes in carbon and nitrogen assimilation. Metabolite profiling will facilitate investigation of nitrogen use and cellular development in Populus sp. varying widely in their growth and pattern of carbon allocation during sink-to-source development and in response to stress.

Quantitative metabolic profiling by 1-dimensional 1H-NMR analyses: application to plant genetics and functional genomics

Metabolic profiling by 1-dimensional (1-D) ¹H-nuclear magnetic resonance (NMR) was tested for absolute quantification of soluble sugars, organic acids, amino acids and some secondary metabolites in fruit, roots and leaves. The metabolite responsible for each peak of the ¹H-NMR spectra was identified from spectra of pure compounds. Peak identity was confirmed by the addition of a small amount of commercially-available pure substance. ¹H-NMR spectra acquisition was automated. ¹H-NMR absolute quantification was performed with a synthesised electronic reference signal and validated by comparison with enzymatic or HPLC analyses; the correlation coefficients between ¹H-NMR data and enzymatic or HPLC data were highly significant. Depending on the species and tissues, 14-17 metabolites could be quantified with 15-25 min acquisition time. The detection limit was approximately 1-9 µg in the NMR tube, depending on the compound. Quantitative data were used for (1) a genetic study of strawberry fruit quality, (2) a functional study of tomato transformants overexpressing hexokinase and (3) a study of Arabidopsis phosphoenolpyruvate carboxylase transformants with several lines showing decreased activity of the enzyme. Biochemical phenotyping of the fruits of a strawberry offspring allowed the detection of quantitative trait loci (QTL) controlling fruit quality. Comparison of the roots of wild types and hexokinase tomato transformants using principal component analysis of metabolic profiles revealed that environmental factors, i.e. culture conditions, can significantly modify the metabolic status of plants and thus hide or emphasise the expression of a given genetic background. The decrease in phosphoenolpyruvate carboxylase activity (up to 75%) in Arabidopsis transformants impacted on the metabolic profiles without compromising plant growth, thus supporting the idea that the enzyme has a low influence on the carbon flux through the anaplerotic pathway.

The use of vapor phase extraction in metabolic profiling of phytohormones and other metabolites

Through complex networks of signaling interactions, phytohormones regulate growth, development, reproduction and responses to biotic and abiotic stress. Comprehensive metabolomic approaches, seeking to quantify changes in vast numbers of plant metabolites, may ultimately clarify these complex signaling interactions and consequently explain pleiotropic effects on plant metabolism. Synergistic and antagonistic phytohormone signaling interactions, referred to as crosstalk, are often considered at the level of transduction without proper consideration of synthesis or accumulation of phytohormones because of the limitation and difficulty in quantifying numerous signals. Significant progress has recently been made in the expansion of metabolic profiling and analysis of multiple phytohormones [Birkemeyer et al. (J. Chromatogr. A, 2003, 993, 89); Chiwocha et al. (Plant J., 2003, 35, 405); Müller et al. (Planta, 2002, 216, 44); Schmelz et al. (Proc. Natl Acad. Sci. USA, 2003, 100, 10552)]. We recently presented a novel metabolic profiling approach to the analysis of acidic phytohormones and other metabolites based on a simplistic preparation scheme and analysis by chemical ionization-gas chromatography/mass spectrometry. We now provide a detailed description of this vapor phase extraction technique and use pathogen infection of Arabidopsis with Pseudomonas syringae DC3000 to illustrate metabolic changes in salicylic acid, cinnamic acid, jasmonic acid, indole-3-acetic acid, abscisic acid, unsaturated C(18) fatty acids, 12-oxo-phytodienoic acid, and phytotoxin coronatine. Directions for further method expansion are provided and include issues of recovery, derivatization, range of accessible analytes, optimization, reproducibility and future directions.

Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry

The usual aim in metabolomic studies is to quantify the entire metabolome of each of a series of biological samples. To do this for complex biological matrices, e.g., plant tissues, efficient and reproducible extraction protocols must be developed. However, derivatization protocols must also be developed if GC/MS (one of the mostly widely used analytical methods for metabolomics) is involved. The aim of this study was to investigate how different chemical and physical factors (extraction solvent, derivatization reagents, and temperature) affect the extraction and derivatization of the metabolome from leaves of the plant Arabidopsis thaliana. Using design of experiment procedures, variation was systematically introduced, and the effects of this variation were analyzed using regression models. The results show that this approach allows a reliable protocol for metabolomic analysis of Arabidopsis to be determined with a relatively limited number of experiments. Following two different investigations an extraction and derivatization protocol was chosen. Further, the reproducibility of the analysis of 66 endogenous compounds was investigated, and it was shown that both hydrophilic and lipophilic compounds were detected with high reproducibility.

Stable isotope labeling of Arabidopsis thaliana for an NMR-based metabolomics approach

Nuclear magnetic resonance (NMR) will become a key technology in plant metabolomics with the use of stable isotope labeling and advanced hetero-nuclear NMR methodologies. To demonstrate the power of this approach, we performed multi-dimensional hetero-nuclear NMR analysis of metabolic movement of carbon and nitrogen nuclei in Arabidopsis thaliana. First, distinct ethanol-stress response was investigated using (13)C-labeled wild type and an ethanol-hypersensitive mutant plants. Furthermore, we followed nitrogen fluxes in (15)N-labeled seeds during the initiation of germination in vivo. The future role of stable isotope-labeling combined with advanced hetero-nuclear NMR in plant metabolomics is discussed.

Metabolic discrimination of Catharanthus roseus leaves infected by phytoplasma using 1H-NMR spectroscopy and multivariate data analysis

A comprehensive metabolomic profiling of Catharanthus roseus L. G. Don infected by 10 types of phytoplasmas was carried out using one-dimensional and two-dimensional NMR spectroscopy followed by principal component analysis (PCA), an unsupervised clustering method requiring no knowledge of the data set and used to reduce the dimensionality of multivariate data while preserving most of the variance within it. With a combination of these techniques, we were able to identify those metabolites that were present in different levels in phytoplasma-infected C. roseus leaves than in healthy ones. The infection by phytoplasma in C. roseus leaves causes an increase of metabolites related to the biosynthetic pathways of phenylpropanoids or terpenoid indole alkaloids: chlorogenic acid, loganic acid, secologanin, and vindoline. Furthermore, higher abundance of Glc, Glu, polyphenols, succinic acid, and Suc were detected in the phytoplasma-infected leaves. The PCA of the (1)H-NMR signals of healthy and phytoplasma-infected C. roseus leaves shows that these metabolites are major discriminating factors to characterize the phytoplasma-infected C. roseus leaves from healthy ones. Based on the NMR and PCA analysis, it might be suggested that the biosynthetic pathway of terpenoid indole alkaloids, together with that of phenylpropanoids, is stimulated by the infection of phytoplasma.

Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana

Plant metabolism is a complex set of processes that produce a wide diversity of foods, woods, and medicines. With the genome sequences of Arabidopsis and rice in hands, postgenomics studies integrating all "omics" sciences can depict precise pictures of a whole-cellular process. Here, we present, to our knowledge, the first report of investigation for gene-to-metabolite networks regulating sulfur and nitrogen nutrition and secondary metabolism in Arabidopsis, with integration of metabolomics and transcriptomics. Transcriptome and metabolome analyses were carried out, respectively, with DNA macroarray and several chemical analytical methods, including ultra high-resolution Fourier transform-ion cyclotron MS. Mathematical analyses, including principal component analysis and batch-learning self-organizing map analysis of transcriptome and metabolome data suggested the presence of general responses to sulfur and nitrogen deficiencies. In addition, specific responses to either sulfur or nitrogen deficiency were observed in several metabolic pathways: in particular, the genes and metabolites involved in glucosinolate metabolism were shown to be coordinately modulated. Understanding such gene-to-metabolite networks in primary and secondary metabolism through integration of transcriptomics and metabolomics can lead to identification of gene function and subsequent improvement of production of useful compounds in plants.

Metabolomic differentiation of Cannabis sativa cultivars using 1H NMR spectroscopy and principal component analysis

The metabolomic analysis of 12 Cannabis sativa cultivars was carried out by 1H NMR spectroscopy and multivariate analysis techniques. Principal component analysis (PCA) of the 1H NMR spectra showed a clear discrimination between those samples by principal component 1 (PC1) and principal component 3 (PC3) in cannabinoid fraction. The loading plot of PC value obtained from all 1)H NMR signals shows that Delta9-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) are important metabolites to differentiate the cultivars from each other. The discrimination of the cultivars could also be obtained from a water extract containing carbohydrates and amino acids. The level of sucrose, glucose, asparagine, and glutamic acid are found to be major discriminating metabolites of these cultivars. This method allows an efficient differentiation between cannabis cultivars without any prepurification steps.

Multivariate approaches in plant science

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.

Metabolite profiling in plant biology: platforms and destinations

Optimal use of genome sequences and gene-expression resources requires powerful phenotyping platforms, including those for systematic analysis of metabolite composition. The most used technologies for metabolite profiling, including mass spectral, nuclear magnetic resonance and enzyme-based approaches, have various advantages and disadvantages, and problems can arise with reliability and the interpretation of the huge datasets produced. These techniques will be useful for answering important biological questions in the future.

Progress in plant metabolic engineering

Over the past few years, there has been a growing realization that metabolic pathways must be studied in the context of the whole cell rather than at the single pathway level, and that even the simplest modifications can send ripples throughout the entire system. Attention has therefore shifted away from reductionist, single-gene engineering strategies and towards more complex approaches involving the simultaneous overexpression and/or suppression of multiple genes. The use of regulatory factors to control the abundance or activity of several enzymes is also becoming more widespread. In combination with emerging methods to model metabolic pathways, this should facilitate the enhanced production of natural products and the synthesis of novel materials in a predictable and useful manner.

Metabolite profiling as an aid to metabolic engineering in plants

The past decade has seen some impressive successes in the metabolic engineering of biotechnologically important plant pathways. However, plant metabolic engineering currently proceeds more by trial and error than by intelligent system design. A change in philosophy away from studying pathways in isolation and towards studying metabolism as a network is necessary. To support this development, improvements in technologies for metabolic analysis, a wider adoption of metabolite-profiling approaches and significant innovations in data analysis methodologies are required.

Metabolic fingerprinting of wild type and transgenic tobacco plants by 1H NMR and multivariate analysis technique

The metabolomic analysis of wild type and constitutive salicylic acid producing tobacco plants (CSA tobacco, Nicotiana tabacum 'Samsun' NN) plants overexpressing salicylate biosynthetic genes was carried out by 1H NMR spectrometry and multivariate analysis techniques. The principle component analysis (PCA) of the 1H NMR spectra showed a clear discrimination between those samples by PC1 and PC2. The discrimination of non-inoculated, TMV-virus inoculated, and systemic leaves or veins could also be obtained by PCA analysis. Major peaks in 1H NMR spectra contributing to the discrimination were assigned as those of chlorogenic acid, malic acid, and sugars. This method allows an efficient differentiation between wild type and transgenic plants without any pre-purification steps.

Target-based discovery of novel herbicides

In the past 10 years, strategies for the first steps of herbicide discovery have switched from the testing of chemicals for efficacy on whole plants towards the use of in-vitro assays against molecular targets. Many different approaches have been developed to identify bona fide targets for in-vitro screening. Developments in functional genomics and in pharmaceutical research could aid the development of assay systems for the evaluation of chemicals for their suitability as lead structures in herbicide discovery.

Profiling of Arabidopsis secondary metabolites by capillary liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry

Large-scale metabolic profiling is expected to develop into an integral part of functional genomics and systems biology. The metabolome of a cell or an organism is chemically highly complex. Therefore, comprehensive biochemical phenotyping requires a multitude of analytical techniques. Here, we describe a profiling approach that combines separation by capillary liquid chromatography with the high resolution, high sensitivity, and high mass accuracy of quadrupole time-of-flight mass spectrometry. About 2000 different mass signals can be detected in extracts of Arabidopsis roots and leaves. Many of these originate from Arabidopsis secondary metabolites. Detection based on retention times and exact masses is robust and reproducible. The dynamic range is sufficient for the quantification of metabolites. Assessment of the reproducibility of the analysis showed that biological variability exceeds technical variability. Tools were optimized or established for the automatic data deconvolution and data processing. Subtle differences between samples can be detected as tested with the chalcone synthase deficient tt4 mutant. The accuracy of time-of-flight mass analysis allows to calculate elemental compositions and to tentatively identify metabolites. In-source fragmentation and tandem mass spectrometry can be used to gain structural information. This approach has the potential to significantly contribute to establishing the metabolome of Arabidopsis and other model systems. The principles of separation and mass analysis of this technique, together with its sensitivity and resolving power, greatly expand the range of metabolic profiling.

Phytoestrogens

Collectively, plants contain several different families of natural products among which are compounds with weak estrogenic or antiestrogenic activity toward mammals. These compounds, termed phytoestrogens, include certain isoflavonoids, flavonoids, stilbenes, and lignans. The best-studied dietary phytoestrogens are the soy isoflavones and the flaxseed lignans. Their perceived health beneficial properties extend beyond hormone-dependent breast and prostate cancers and osteoporosis to include cognitive function, cardiovascular disease, immunity and inflammation, and reproduction and fertility. In the future, metabolic engineering of plants could generate novel and exquisitely controlled dietary sources with which to better assess the potential health beneficial effects of phytoestrogens.

Screening of Biomarkers in Rat Urine Using LC/Electrospray Ionization-MS and Two-Way Data Analysis

Biofluids, like urine, form very complex matrixes containing a large number of potential biomarkers, that is, changes of endogenous metabolites in response to xenobiotic exposure. This paper describes a fast and sensitive method of screening biomarkers in rat urine. Biomarkers for phospholipidosis, induced by an antidepressant drug, were studied. Urine samples from rats exposed to citalopram were analyzed using solid-phase extraction (SPE) and liquid chromatography mass spectrometry (LC/MS) analysis detecting negative ions. A fast iterative method, called Gentle, was used for the automatic curve resolution, and metabolic fingerprints were obtained. After peak alignment principal component analysis (PCA) was performed for pattern recognition, PCA loadings were studied as a means of discovering potential biomarkers. In this study a number of potential biomarkers of phospholipidosis in rats are discussed. They are reported by their retention time and base peak, as their identification is not within the scope of the study. In addition to the fact that it was possible to differentiate control samples from dosed samples, the data were very easy to interpret, and signals from xenobiotic-related substances were easily removed without affecting the endogenous compounds. The proposed method is a complement or an alternative to NMR for metabolomic applications.