Metabolic networks of Cucurbita maxima phloem

Metabolomic analysis aims at a comprehensive characterization of biological samples. Yet, biologically meaningful interpretations are often limited by the poor spatial and temporal resolution of the acquired data sets. One way to remedy this is to limit the complexity of the cell types being studied. Cucurbita maxima Duch. vascular exudates provide an excellent material for metabolomics in this regard. Using automated mass spectral deconvolution, over 400 components have been detected in these exudates, but only 90 of them were tentatively identified. Many amino compounds were found in vascular exudates from leaf petioles at concentrations several orders of magnitude higher than in tissue disks from the same leaves, whereas hexoses and sucrose were found in far lower amounts. In order to find the expected impact of assimilation rates on sugar levels, total phloem composition of eight leaves from four plants was followed over 4.5 days. Surprisingly, no diurnal rhythm was found for any of the phloem metabolites that was statistically valid for all eight leaves. Instead, each leaf had its own distinct vascular exudate profile similar to leaves from the same plant, but clearly different from leaves harvested from plants at the same developmental stage. Thirty to forty per cent of all metabolite levels of individual leaves were different from the average of all metabolite profiles. Using metabolic co-regulation analysis, similarities and differences between the exudate profiles were more accurately characterized through network computation, specifically with respect to nitrogen metabolism.

Deciphering metabolic networks

All higher organisms divide major biochemical steps into different cellular compartments and often use tissue-specific division of metabolism for the same purpose. Such spatial resolution is accompanied with temporal changes of metabolite synthesis in response to environmental stimuli or developmental needs. Although analyses of primary and secondary gene products, i.e. transcripts, proteins, and metabolites, regularly do not cope with this spatial and temporal resolution, these gene products are often observed to be highly coregulated forming complex networks. Methods to study such networks are reviewed with respect to data acquisition, network statistics, and biochemical interpretation.

Can we discover novel pathways using metabolomic analysis?

Metabolomic analysis aims at the identification and quantitation of all metabolites in a given biological sample. Current data acquisition and network analysis strategies are classified on the basis of pathway elucidation and characteristics of theoretical networks. The development of metabolomic methods and tools is progressing rapidly, but an understanding of the resulting data is limited owing to a fundamental lack of biochemical and physiological knowledge about network organization in plants.

Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry

The primary goal of metabolomic analysis is the unbiased relative quantification of every metabolite in a biological system. A number of different metabolite-profiling techniques must be combined to make this possible. Here we report the separation and analysis of highly polar compounds in a proof of concept study. Compounds were separated and analyzed using hydrophilic interaction liquid chromatography (HILIC) coupled to electrospray ionization (ESI) mass spectrometry. Two types of HILIC microbore columns (Polyhydroxyethyl A and TSK Gel Amide 80) were compared to normal phase silica HPLC columns. The best separations of standards mixtures and plant samples were achieved using the Amide 80 stationary phase. ESI enabled the detection of both positively and negatively charged metabolites, when coupled to a quadrupole ion trap mass spectrometer using continuous polarity switching. By stepwise mass spectrometric fragmentation of the most intense ions, unknown compounds could be identified and then included into a custom mass spectrometric library. This method was used to detect oligosaccharides, glycosides, amino sugars, amino acids, and sugar nucleotides in phloem exudates from petioles of fully expanded Cucurbita maxima leaves. Quantitative analysis was performed using external standards. The detection limit for stachyose was 0.5 ng per injection (Amide 80). The concentration of stachyose in investigated phloem samples was in the range of 1-7 mM depending on the plant.

Metabolomics--the link between genotypes and phenotypes

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

Metabolomics--the link between genotypes and phenotypes

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

Visualizing plant metabolomic correlation networks using clique-metabolite matrices

MOTIVATION

Today, metabolite levels in biological samples can be determined using multiparallel, fast, and precise metabolomic approaches. Correlations between the levels of various metabolites can be searched to gain information about metabolic links. Such correlations are the net result of direct enzymatic conversions and of indirect cellular regulation over transcriptional or biochemical processes. In order to visualize metabolic networks derived from correlation lists graphically, each metabolite pair may be represented as vertices connected by an edge. However, graph complexity rapidly increases with the number of edges and vertices. To gain structural information from metabolite correlation networks, improvements in clarity are needed.

RESULTS

To achieve this clarity, three algorithms are combined. First, a list of linear metabolite correlations is generated that can be regarded as a set of pairs of edges (or as 2-cliques). Next, a branch-and-bound algorithm was developed to find all maximal cliques by combining submaximal cliques. Due to a clique assignment procedure, the generation of unnecessary submaximal cliques is avoided in order to maintain high efficiency. Differences and similarities to the Bron-Kerbosch algorithm are pointed out. Lastly, metabolite correlation networks are visualized by clique-metabolite matrices that are sorted to minimize the length of lines that connect different cliques and metabolites. Examples of biochemical hypotheses are given that can be built from interpretation of such clique matrices.

AVAILABILITY

The algorithms are implemented in Visual Basic and can be downloaded from our web site along with a test data set (http://www.mpimp-golm.mpg.de/fiehn/projekte/data-mining-e.html).

CONTACT

kose@mpimp-golm.mpg.de

Trehalose metabolism in Arabidopsis: occurrence of trehalose and molecular cloning and characterization of trehalose-6-phosphate synthase homologues

Axenically grown Arabidopsis thaliana plants were analysed for the occurrence of trehalose. Using gas chromatography-mass spectrometry (GC-MS) analysis, trehalose was unambiguously identified in extracts from Arabidopsis inflorescences. In a variety of organisms, the synthesis of trehalose is catalysed by trehalose-6-phosphate synthase (TPS; EC 2.4.1.15) and trehalose-6-phosphate phosphatase (TPP; EC 3.1.3.12). Based on EST (expressed sequence tag) sequences, three full-length Arabidopsis cDNAs whose predicted protein sequences show extensive homologies to known TPS and TPP proteins were amplified by RACE-PCR. The expression of the corresponding genes, AtTPSA, AtTPSB and AtTPSC, and of the previously described TPS gene, AtTPS1, was analysed by quantitative RT-PCR. All of the genes were expressed in the rosette leaves, stems and flowers of Arabidopsis plants and, to a lower extent, in the roots. To study the role of the Arabidopsis genes, the AtTPSA and AtTPSC cDNAs were expressed in Saccharomyces cerevisiae mutants deficient in trehalose synthesis. In contrast to AtTPS1, expression of AtTPSA and AtTPSC in the tps1 mutant lacking TPS activity did not complement trehalose formation after heat shock or growth on glucose. In addition, no TPP function could be identified for AtTPSA and AtTPSC in complementation studies with the S. cerevisiae tps2 mutant lacking TPP activity. The results indicate that while AtTPS1 is involved in the formation of trehalose in Arabidopsis, some of the Arabidopsis genes with homologies to known TPS/TPP genes encode proteins lacking catalytic activity in trehalose synthesis.

Identification and quantification of catecholamines in potato plants (Solanum tuberosum) by GC-MS

Dopamine, norepinephrine, and normetanephrine were identified by GC-MS in potato (Solanum tuberosum L.) plants, the latter was new for plants. The highest amount of catecholamines was found in leaves. A developmental stage dependent variation in potato leaf catecholamines accumulation was also observed with highest level in third leaves. Catecholamine contents decrease during cold storage of tubers to undetectable levels. Mechanical wounding of leaves led to a small increase in the level of catecholamines investigated.

Integrated studies on plant biology using multiparallel techniques

Plant biology, especially the fields of molecular genetics and molecular physiology, is currently undergoing a change in paradigm from 'vertical' analysis of the role(s) of one or a few genes to 'horizontal' holistic approaches, studying the function of many or even all of the genes of an organism simultaneously. This change is leading us beyond genomes to transcriptomes, proteomes and metabalomes, and to an understanding of life at an entirely new level. Profiling strategies are putting this change into effect through the generation of large amounts of data, requiring that current bioinformatic approaches adapt and grow in order to make the most of these data.

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.

Metabolite profiling for plant functional genomics

Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.

Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry

Unknown compounds in polar fractions of Arabidopsis thaliana crude leaf extracts were identified on the basis of calculations of elemental compositions obtained from gas chromatography/low-resolution quadrupole mass spectrometric data. Plant metabolites were methoximated and silylated prior to analysis. All known peaks were used as internal references to construct polynomial recalibration curves of from raw mass spectrometric data. Mass accuracies of 0.005 +/- 0.003 amu and isotope ratio errors of 0.5 +/- 0.3% (A + 1/A), respectively, 0.3 +/- 0.2% (A + 2/A), could be achieved. Both masses and isotope ratios were combined when the elemental compositions of unknown peaks were calculated. After calculation, compound identities were elucidated by searching metabolic databases, interpreting spectra, and, finally, by comparison with reference compounds. Sum formulas of more than 70 peaks were determined throughout single GC/MS chromatograms. Exact masses were confirmed by high-resolution mass spectrometric data. More than 15 uncommon plant metabolites were identified, some of which are novel in Arabidopsis, such as tartronate semialdehyde, citramalic acid, allothreonine, or glycolic amide.

Protein identification with a single accurate mass of a cysteine-containing peptide and constrained database searching

A method for rapid and unambiguous identification of proteins by sequence database searching using the accurate mass of a single peptide and specific sequence constraints is described. Peptide masses were measured using electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry to an accuracy of 1 ppm. The presence of a cysteine residue within a peptide sequence was used as a database searching constraint to reduce the number of potential database hits. Cysteine-containing peptides were detected within a mixture of peptides by incorporating chlorine into a general alkylating reagent specific for cysteine residues. Secondary search constraints included the specificity of the protease used for protein digestion and the molecular mass of the protein estimated by gel electrophoresis. The natural isotopic distribution of chlorine encoded the cysteine-containing peptide with a distinctive isotopic pattern that allowed automatic screening of mass spectra. The method is demonstrated for a peptide standard and unknown proteins from a yeast lysate using all 6118 possible yeast open reading frames as a database. As judged by calculation of codon bias, low-abundance proteins were identified from the yeast lysate using this new method but not by traditional methods such as tandem mass spectrometry via data-dependent acquisition or mass mapping.

Comparative quantification and identification of phosphoproteins using stable isotope labeling and liquid chromatography/mass spectrometry

A new liquid chromatography/mass spectrometry (LC/MS) method is described for relative quantification of phosphoproteins to simultaneously compare the phosphorylation status of proteins under two different conditions. Quantification was achieved by beta-elimination of phosphate from phospho-Ser/Thr followed by Micheal addition of ethanethiol and/or ethane-d(5)-thiol selectively at the vinyl moiety of dehydroalanine and dehydroamino-2-butyric acid. The method was evaluated using the model phosphoprotein alpha(S1)-casein, for which three phosphopeptides were found after tryptic digestion. Reproducibility of the relative quantification of seven independent replicates was found to be 11% SD. The dynamic range covered two orders of magnitude, and quantification was linear for mixtures of 0 to 100% alpha(S1)-casein and dephospho-alpha(S1)-casein (R(2) = 0.986). Additionally, the method allowed protein identification and determination of the phosphorylation sites via MS/MS fragmentation.