Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems

Metabolic profiling using gas chromatography-mass spectrometry technologies is a technique whose potential in the field of functional genomics is largely untapped. To demonstrate the general usefulness of this technique, we applied to diverse plant genotypes a recently developed profiling protocol that allows detection of a wide range of hydrophilic metabolites within a single chromatographic run. For this purpose, we chose four independent potato genotypes characterized by modifications in sucrose metabolism. Using data-mining tools, including hierarchical cluster analysis and principle component analysis, we were able to assign clusters to the individual plant systems and to determine relative distances between these clusters. Extraction analysis allowed identification of the most important components of these clusters. Furthermore, correlation analysis revealed close linkages between a broad spectrum of metabolites. In a second, complementary approach, we subjected wild-type potato tissue to environmental manipulations. The metabolic profiles from these experiments were compared with the data sets obtained for the transgenic systems, thus illustrating the potential of metabolic profiling in assessing how a genetic modification can be phenocopied by environmental conditions. In summary, these data demonstrate the use of metabolic profiling in conjunction with data-mining tools as a technique for the comprehensive characterization of a plant genotype.     

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.     

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.     

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

Plant metabolomics reveals conserved and divergent metabolic responses to salinity

New metabolic profiling technologies provide data on a wider range of metabolites than traditional targeted approaches. Metabolomic technologies currently facilitate acquisition of multivariate metabolic data using diverse, mostly hyphenated, chromatographic detection systems, such as GC-MS or liquid chromatography coupled to mass spectrometry, Fourier-transformed infrared spectroscopy or NMR-based methods. Analysis of the resulting data can be performed through a combination of non-supervised and supervised statistical methods, such as independent component analysis and analysis of variance, respectively. These methods reduce the complex data sets to information, which is relevant for the discovery of metabolic markers or for hypothesis-driven, pathway-based analysis. Plant responses to salinity involve changes in the activity of genes and proteins, which invariably lead to changes in plant metabolism. Here, we highlight a selection of recent publications in the salt stress field, and use gas chromatography time-of-flight mass spectrometry profiles of polar fractions from the plant models, Arabidopsis thaliana, Lotus japonicus and Oryza sativa to demonstrate the power of metabolite profiling. We present evidence for conserved and divergent metabolic responses among these three species and conclude that a change in the balance between amino acids and organic acids may be a conserved metabolic response of plants to salt stress.     

NMR and plant metabolism

Recent advances in NMR methodology offer a way to acquire a comprehensive profile of a wide range of metabolites from various plant tissues or cells. NMR is a powerful approach for plant metabolite profiling and provides a capacity for the dynamic exploration of plant metabolism that is virtually unmatched by any other analytical technique.     

Assignment of MS-based metabolomic datasets via compound interaction pair mapping

Assignment of physical meaning to mass spectrometry (MS) data peaks is an important scientific challenge for metabolomics investigators. Improvements in instrumental mass accuracy reduce the number of spurious database matches, however, this alone is insufficient for accurate, unique high-throughput assignment. We present a method for clustering MS instrumental artifacts and a stochastic local search algorithm for the automated assignment of large, complex MS-based metabolomic datasets. Artifact peaks and their associated source peaks are grouped into “instrumental clusters.” Instrumental clusters, peaks grouped together by shared peak shape in the temporal domain, serve as a guide for the number of assignments necessary to completely explain a given dataset. We refine mass only assignments through the intersection of peak correlation pairs with a database of biochemically relevant interaction pairs. Further refinement is achieved through a stochastic local search optimization algorithm that selects individual assignments for each instrumental cluster. The algorithm works by choosing the peak assignment that maximally explains the connectivity of a given cluster. We demonstrate that this methodology provides a significant advantage over standard methods for the assignment of metabolites in a UPLC-MS diabetes dataset. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Comparison of 1D and 2D NMR spectroscopy for metabolic profiling

High-resolution, liquid state nuclear magnetic resonance (NMR) spectroscopy is a popular platform for metabolic profiling because the technique is nondestructive, quantitative, reproducible, and the spectra contain a wealth of biochemical information. Because of the large dynamic range of metabolite concentrations in biofluids, statistical analyses of one-dimensional (1D) proton NMR data tend to be biased toward selecting changes in more abundant metabolites. Although two-dimensional (2D) proton-proton experiments can alleviate spectral crowding, they have been mainly used for structural determination. In this study, 2D total correlation spectroscopy NMR was used to compare the global metabolic profiles of urine obtained from wild-type and Abcc6-knockout mice. The 2D data were compared to an improved 1D experiment in which signal contributions from macromolecules and the urea peak have been spectroscopically removed for more accurate quantitation of low-abundance metabolites. Although statistical models from both 1D and 2D data could differentiate samples acquired from the two groups of mice, only the 2D spectra allowed the characterization of statistically relevant changes in the low-abundance metabolites. While acquisition of the 2D data require more time, the data obtained resulted in a more meaningful and comprehensive metabolic profile, aided in metabolite identifications, and minimized ambiguities in peak assignments.     

Quo vadis plant hormone analysis?

Plant hormones act as chemical messengers in the regulation of myriads of physiological processes that occur in plants. To date, nine groups of plant hormones have been identified and more will probably be discovered. Furthermore, members of each group may participate in the regulation of physiological responses in planta both alone and in concert with members of either the same group or other groups. The ideal way to study biochemical processes involving these signalling molecules is ‘hormone profiling’, i.e. quantification of not only the hormones themselves, but also their biosynthetic precursors and metabolites in plant tissues. However, this is highly challenging since trace amounts of all of these substances are present in highly complex plant matrices. Here, we review advances, current trends and future perspectives in the analysis of all currently known plant hormones and the associated problems of extracting them from plant tissues and separating them from the numerous potentially interfering compounds.     

Semi‐polar root exudates in natural grassland communities

In the rhizosphere, plants are exposed to a multitude of different biotic and abiotic factors, to which they respond by exuding a wide range of secondary root metabolites. So far, it has been unknown to which degree root exudate composition is species‐specific and is affected by land use, the local impact and local neighborhood under field conditions. In this study, root exudates of 10 common grassland species were analyzed, each five of forbs and grasses, in the German Biodiversity Exploratories using a combined phytometer and untargeted liquid chromatography‐mass spectrometry (LC‐MS) approach. Redundancy analysis and hierarchical clustering revealed a large set of semi‐polar metabolites common to all species in addition to species‐specific metabolites. Chemical richness and exudate composition revealed that forbs, such as Plantago lanceolata and Galium species, exuded more species‐specific metabolites than grasses. Grasses instead were primarily affected by environmental conditions. In both forbs and grasses, plant functional traits had only a minor impact on plant root exudation patterns. Overall, our results demonstrate the feasibility of obtaining and untargeted profiling of semi‐polar metabolites under field condition and allow a deeper view in the exudation of plants in a natural grassland community.     

Metabolomics-assisted refinement of the pathways of steroidal glycoalkaloid biosynthesis in the tomato clade

Steroidal glycoalkaloids (SGAs) are nitrogen-con-taining secondary metabolites of the Solanum species, which are known to have large chemical and bioactive diversity in nature. While recent effort and ...     

Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

Integrating genomics and metabolomics for engineering plant metabolic pathways

Plant metabolites are characterized by an enormous chemical diversity, every plant having its own complex set of metabolites. This variety poses analytical challenges, both for profiling multiple metabolites in parallel and for the quantitative analysis of selected metabolites. We are only just starting to understand the roles of these metabolites, many of them being involved in adaptations to specific ecological niches and some finding beneficial use (e.g. as pharmaceuticals). Spectacular advances in plant metabolomics offer new possibilities, together with the aid of systems biology, to explore the extraordinary complexity of the plant biochemical capacity. State-of-the art genomics tools can be combined with metabolic profiling to identify key genes that could be engineered for the production of improved crop plants.     

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.     

P450s in plant-insect interactions

Cytochrome P450 monooxygenases (P450s) are integral in defining the relationships between plants and insects. Secondary metabolites produced in plants for protection against insects and other organisms are synthesized via pathways that include P450s in many different families and subfamilies. Survival of insects in the presence of toxic secondary metabolites depends on their metabolism by more limited groups of P450s. Examples of functionally characterized plant and insect P450s known to be involved in these interactions are discussed in terms of their diversities, reactivities and regulators. These and future examples, which will be uncovered as the fields of plant biology and entomology converge on this interesting area, provide much insight into the array of plant metabolites that are mainline defenses against insects, the range of insect monooxygenases that inactivate these compounds and the evolutionary processes occurring as these organisms wage daily battles with one another. Molecular perspectives on these interactions will provide the scientific community with information critical for genetic manipulation of these organisms aimed at enhancing plant resistance to insects and eliminating insect resistance to natural plant toxins and synthetic insecticides.     

基于核磁共振波谱的盐芥盐胁迫代谢组学分析

以甲醇/水(1∶1)作为溶剂,利用高分辨核磁共振氢谱分析了盐生模式植物盐芥(Thellungiella salsuginea)代谢组对盐胁迫的响应。根据1H核磁共振(NMR)波谱,在盐芥莲座叶中准确鉴定出23种代谢产物,包括11种氨基酸、4种糖类、6种有机酸和2种其他代谢产物。主成分分析表明,150、300 mmol/L NaCl处理盐芥的代谢组与对照均有显著差异(P<0.05),两种浓度的NaCl处理对盐芥代谢组的影响也不相同。盐胁迫处理以后,盐芥23种代谢产物含量均发生显著变化,除天冬氨酸、延胡索酸受盐胁迫诱导含量下降以外,其余代谢物含量均不同程度升高。这些代谢物主要参与了糖类代谢途径、氨基酸合成途径、三羧酸循环和甜菜碱合成途径,这些代谢途径在盐芥响应盐胁迫过程中有重要作用。