Metabolite profiling: from diagnostics to systems biology

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

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.     

Analytical aspects of plant metabolite profiling platforms: current standings and future aims

Over the past years, metabolic profiling has been established as a comprehensive systems biology tool. Mass spectrometry or NMR spectroscopy-based technology platforms combined with unsupervised or supervised multivariate statistical methodologies allow a deep insight into the complex metabolite patterns of plant-derived samples. Within this review, we provide a thorough introduction to the analytical hard- and software requirements of metabolic profiling platforms. Methodological limitations are addressed, and the metabolic profiling workflow is exemplified by summarizing recent applications ranging from model systems to more applied topics.     

Metabolite fingerprinting and profiling in plants using NMR

Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. In one approach, metabolite fingerprinting, multivariate analysis of unassigned 1H NMR spectra is used to compare the overall metabolic composition of wild-type, mutant, and transgenic plant material, and to assess the impact of stress conditions on the plant metabolome. Metabolite fingerprinting by NMR is a fast, convenient, and effective tool for discriminating between groups of related samples and it identifies the most important regions of the spectrum for further analysis. In a second approach, metabolite profiling, the 1H NMR spectra of tissue extracts are assigned, a process that typically identifies 20-40 metabolites in an unfractionated extract. These profiles may also be used to compare groups of samples, and significant differences in metabolite concentrations provide the basis for hypotheses on the underlying causes for the observed segregation of the groups. Both approaches generate a metabolic phenotype for a plant, based on a system-wide but incomplete analysis of the plant metabolome. However, a review of the literature suggests that the emphasis so far has been on the accumulation of analytical data and sample classification, and that the potential of 1H NMR spectroscopy as a tool for probing the operation of metabolic networks, or as a functional genomics tool for identifying gene function, is largely untapped.     

Metabolite profiling of plant carotenoids using the matrix-assisted laser desorption ionization time-of-flight mass spectrometry

Although modern MS has facilitated the advent of metabolomics, some natural products such as carotenoids are not readily compatible to detection by MS. In the present article, we describe how matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI/TOF-MS) can be utilized to acquire mass spectra of carotenoids effectively. The procedure is sensitive (pmole range), reduces 'spot to spot' variation and provides high mass accuracy, thus aiding identification. The technique has been applied in vivo to the analysis of carotenoids in isolated plant cells and in vitro to three applications: (i) to show compatibility with purification methods such as LC, TLC and HPLC; (ii) for the rapid identification and quantification (by isotope dilution) of carotenoids present in crude extracts from plant tissues and whole cells; (iii) simultaneous semi-quantitative determination of carotenoids metabolites (m/z values) in crude plant extracts. Multivariate analysis of the recorded m/z values shows the effectiveness of the procedure in distinguishing genotypes from each other. In addition, the utility of the technique has been demonstrated on two mutant tomato populations, to determine alterations in carotenoid content, and a comparison made with traditional HPLC-photodiode array analysis. These data show that MALDI/TOF-MS can be used to rapidly profile, identify and quantify plant carotenoids reproducibly, as well as detecting other metabolites (m/z) in complex biological systems.     

Metabolite profiling of Alzheimer's disease cerebrospinal fluid

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive loss of cognitive functions. Today the diagnosis of AD relies on clinical evaluations and is only late in the disease. Biomarkers for early detection of the underlying neuropathological changes are still lacking and the biochemical pathways leading to the disease are still not completely understood. The aim of this study was to identify the metabolic changes resulting from the disease phenotype by a thorough and systematic metabolite profiling approach. For this purpose CSF samples from 79 AD patients and 51 healthy controls were analyzed by gas and liquid chromatography-tandem mass spectrometry (GC-MS and LC-MS/MS) in conjunction with univariate and multivariate statistical analyses. In total 343 different analytes have been identified. Significant changes in the metabolite profile of AD patients compared to healthy controls have been identified. Increased cortisol levels seemed to be related to the progression of AD and have been detected in more severe forms of AD. Increased cysteine associated with decreased uridine was the best paired combination to identify light AD (MMSE>22) with specificity and sensitivity above 75%. In this group of patients, sensitivity and specificity above 80% were obtained for several combinations of three to five metabolites, including cortisol and various amino acids, in addition to cysteine and uridine.     

Metabolite profiling of Panax notoginseng using UPLC-ESI-MS

The metabolite profiling of different parts of Panax notoginseng was carried out using rapid ultra-performance liquid chromatography-electrospray ionization mass spectrometry (UPLC-ESI-MS) and multivariate statistical analysis. Principal component analysis (PCA) of the UPLC-ESI-MS data showed a clear separation of compositions among the flower buds, roots and rhizomes of P. notoginseng. The saponins accounting for such variations were identified through the corresponding loadings weights and were further verified by accurate mass, tandem mass and retention times of available standard saponins using UPLC quadrupole time-of-flight mass spectrometer (UPLC-QtofMS). Finally, the influential factors of different metabolic phenotypes of P. notoginseng was elucidated. The currently proposed UPLC-ESI-MS/MS analytical method coupled with multivariate statistical analysis can be further utilized to evaluate chemical components obtained from different parts of the plant and/or the plant of different geographical locations, thereby classifying the medicinal plant resources and potentially elucidating the mechanism of inherent phytochemical diversity.     

Metabolite profiling can assist variability analysis in Trichoderma species

Variability in 41 isolates of Trichoderma belonging to 21 species was observed in the phenolic acid profile of their culture filtrates. The phenolic acid profiles were observed to be very stable in the culture filtrate of Trichoderma species. The similarity in phenolic acid profile was recorded and based on it the species were grouped into three distinct groups, viz. highly similar, moderately similar and least similar. Of the 21 species, seven species showed highly similar trend, whereas two and four species showed moderate and least similarity in their phenolic acid profiles, respectively. Looking into the stability of phenolic acid profile in the culture filtrate of the Trichoderma species the present tool may help in diversity analysis in Trichoderma species originating from different geographical areas.     

Metabolite profiling of maize grain: differentiation due to genetics and environment

A comparative metabolite profiling approach based on gas chromatography-mass spectrometry (GC/MS) was applied to investigate the impact of genetic background, growing location and season on the chemical composition of maize grain. The metabolite profiling protocol involved sub-fractionation of the metabolites and allowed the assessment of about 300 distinct analytes from different chemical classes (polar to lipophilic), of which 167 could be identified. A comparison, over three consecutive growing seasons, of the metabolite profiles of four maize cultivars which differed in their maturity classification, was carried out using principal component analysis (PCA). This revealed a strong separation of one cultivar in the first growing season, which could be explained by the immaturity of the kernels of this cultivar compared with others in the field trial. Further evaluations by pair-wise comparison using Student’s t-test and analysis of variance (ANOVA) showed that the growing season was the most prominent impact factor driving variation of the metabolite pool. An increased understanding of metabolic variation was achieved by analysis of a second sample set comprising one cultivar grown for 3 years at four locations. The applied GC/MS-based metabolite profiling demonstrated the natural variation in maize grain metabolite pools resulting from the interplay of environment, season, and genotype.     

Gene expression profiling in osteoblast biology: bioinformatic tools

This review focuses on using microarray data on a clonal osteoblast cell model to demonstrate how various current and future bioinformatic tools can be used to understand, at a more global or comprehensible level, how cells grow and differentiate. In this example, BMP2 was used to stimulate growth and differentiation of osteoblast to a mineralized matrix. A discussion is included on various methods for clustering gene expression data, statistical evaluation of data, and various new tools that can be used to derive deeper insight into a particular biological problem. How these tools can be obtained is also discussed. New tools for the biologists to compare their datasets with others, as well as examples of future bioinformatic tools that can be used for developing gene networks and pathways for a given set of data are included and discussed.     

Software for systems biology: from tools to integrated platforms

Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.     

Metabolite profiling of CHO cells: Molecular reflections of bioprocessing effectiveness

Whilst development of medium and feeds has provided major advances in recombinant protein production in CHO cells, the fundamental understanding is limited. We have applied metabolite profiling with established robust (GC‐MS) analytics to define the molecular loci by which two yield‐enhancing feeds improve recombinant antibody yields from a model GS‐CHO cell line. With data across core metabolic pathways, that report on metabolism within several cellular compartments, these data identify key metabolites and events associated with increased cell survival and specific productivity of cells. Of particular importance, increased process efficiency was linked to the functional activity of the mitochondria, with the amount and time course of use/production of intermediates of the citric acid cycle, for uses such as lipid biosynthesis, precursor generation and energy production, providing direct indicators of cellular status with respect to productivity. The data provide clear association between specific cellular metabolic indicators and cell process efficiency, extending from prior indications of the relevance of lactate metabolic balance to other redox sinks (glycerol, sorbitol and threitol). The information, and its interpretation, identifies targets for engineering cell culture efficiency, either from genetic or environmental perspectives, and greater understanding of the significance of specific medium components towards overall CHO cell bioprocessing.     

Metabolite profiling of Chlamydomonas reinhardtii under nutrient deprivation

A metabolite profiling technique for Chlamydomonas reinhardtii cells for multiparallel analysis of low-molecular weight polar compounds was developed. The experimental protocol was optimized to quickly inactivate enzymatic activity, achieve maximum extraction capacity, and process large sample quantities. As a result of the rapid sampling, extraction, and analysis by gas chromatography coupled to time-of-flight mass spectrometry, more than 800 analytes from a single sample could be measured, of which more than 100 could be identified. Analyte responses could be determined mostly with ses less than 10%. Wild-type cells of C. reinhardtii strain CC-125 subjected to nitrogen-, phosphorus-, sulfur-, or iron-depleted growth conditions develop highly distinctive metabolite profiles. Individual metabolites undergo marked changes in their steady-state levels. Compared to control conditions, sulfur-depleted cells accumulated 4-hydroxyproline more than 50-fold, whereas the amount of 2-ketovaline was reduced to 2% of control levels. The contribution of each compound to the differences observed in the metabolic phenotypes is summarized in a quantitatively rigorous way by principal component analysis, which clearly discriminates the cells from different growth regimes and indicates that phosphorus-depleted conditions induce a deficiency syndrome quite different from the response to nitrogen, sulfur, or iron starvation.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Metabolite profiling of mycorrhizal roots of Medicago truncatula

Metabolite profiling of soluble primary and secondary metabolites, as well as cell wall-bound phenolic compounds from roots of barrel medic (Medicago truncatula) was carried out by GC-MS, HPLC and LC-MS. These analyses revealed a number of metabolic characteristics over 56 days of symbiotic interaction with the arbuscular mycorrhizal (AM) fungus Glomus intraradices, when compared to the controls, i.e. nonmycorrhizal roots supplied with low and high amounts of phosphate. During the most active stages of overall root mycorrhization, elevated levels of certain amino acids (Glu, Asp, Asn) were observed accompanied by increases in amounts of some fatty acids (palmitic and oleic acids), indicating a mycorrhiza-specific activation of plastidial metabolism. In addition, some accumulating fungus-specific fatty acids (palmitvaccenic and vaccenic acids) were assigned that may be used as markers of fungal root colonization. Stimulation of the biosynthesis of some constitutive isoflavonoids (daidzein, ononin and malonylononin) occurred, however, only at late stages of root mycorrhization. Increase of the levels of saponins correlated AM-independently with plant growth. Only in AM roots was the accumulation of apocarotenoids (cyclohexenone and mycorradicin derivatives) observed. The structures of the unknown cyclohexenone derivatives were identified by spectroscopic methods as glucosides of blumenol C and 13-hydroxyblumenol C and their corresponding malonyl conjugates. During mycorrhization, the levels of typical cell wall-bound phenolics (e.g. 4-hydroxybenzaldehyde, vanillin, ferulic acid) did not change; however, high amounts of cell wall-bound tyrosol were exclusively detected in AM roots. Principal component analyses of nonpolar primary and secondary metabolites clearly separated AM roots from those of the controls, which was confirmed by an hierarchical cluster analysis. Circular networks of primary nonpolar metabolites showed stronger and more frequent correlations between metabolites in the mycorrhizal roots. The same trend, but to a lesser extent, was observed in nonmycorrhizal roots supplied with high amounts of phosphate. These results indicate a tighter control of primary metabolism in AM roots compared to control plants. Network correlation analyses revealed distinct clusters of amino acids and sugars/aliphatic acids with strong metabolic correlations among one another in all plants analyzed; however, mycorrhizal symbiosis reduced the cluster separation and enlarged the sugar cluster size. The amino acid clusters represent groups of metabolites with strong correlations among one another (cliques) that are differently composed in mycorrhizal and nonmycorrhizal roots. In conclusion, the present work shows for the first time that there are clear differences in development- and symbiosis-dependent primary and secondary metabolism of M. truncatula roots.     

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.     

Three microarray platforms: an analysis of their concordance in profiling gene expression

Background

Bacillus cereus, Bacillus anthracis and Bacillus thuringiensis belong to the genetically close-knit Bacillus cereus sensu lato group, a family of rod-shaped Gram-positive bacteria. pAW63 is the first conjugative plasmid from the B. cereus group to be completely sequenced.

Results

The 71,777 bp nucleotide sequence of pAW63 reveals a modular structure, including a 42 kb tra region encoding homologs of the Type IV secretion systems components VirB11, VirB4 and VirD4, as well as homologs of Gram-positive conjugation genes from Enterococcus, Lactococcus, Listeria, Streptococcus and Staphylococcus species. It also firmly establishes the existence of a common backbone between pAW63, pXO2 from Bacillus anthracis and pBT9727 from the pathogenic Bacillus thuringiensis serovar konkukian strain 97-27. The alignment of these three plasmids highlights the presence of well conserved segments, in contrast to distinct regions of high sequence plastiCity. The study of their specific differences has provided a three-point reference framework that can be exploited to formulate solid hypotheses concerning the functionalities and the molecular evolution of these three closely related plasmids. This has provided insight into the chronology of their divergence, and led to the discovery of two Type II introns on pAW63, matching copies of the mobile element IS231L in different loci of pXO2 and pBT9727, and the identification on pXO2 of a 37 kb pathogeniCity island (PAI) containing the anthrax capsule genes.

Conclusion

The complete sequence determination of pAW63 has led to a functional map of the plasmid yielding insights into its conjugative apparatus, which includes T4SS-like components, as well as its resemblance to other large plasmids of Gram-positive bacteria. Of particular interest is the extensive homology shared between pAW63 and pXO2, the second virulence plasmid of B. anthracis, as well as pBT9727 from the pathogenic strain B. thuringiensis serovar konkukian strain 97-27.     

Kinome profiling of sugar signaling in plants using multiple platforms

Although the primary sequence of kinases shows substantial divergence between unrelated eukaryotes, variation in the motifs that are actually phosphorylated by eukaryotic kinases is much smaller. Hence arrays developed for kinome profiling of mammalian cells are useful for kinome profiling of plant tissues as well, facilitating the study of plant signal transduction. We recently employed the Pepscan kinomics chip to reveal the small GTPases in plant sucrose signaling. Here we show that employing a different peptide library (the Pepscan kinase chip) largely similar results are obtained, confirming these earlier data, but such a different library also contributes new insights into the molecular details mediating plant cell responses to a sugar stimulus. Thus when studying plant signal transduction employing peptide arrays, using multiple platforms both increases the confidence of results and provides additional information.Key words: sucrose, arabidopsis, kinome profiling, kinase, MAPK, SnRK, casein kinase, tyrosine phosphorylation, CDC2, AGC kinaseIn our recently published paper1 we analyze the changes in the plant kinome after sucrose feeding compared to control sorbitol feeding of Arabidopsis. We employed kinomics chips (Pepscan Presto, The Netherlands) containing 960 different kinase consensus peptides selected for their importance in mammalian signal transduction. In addition we used kinase chips (Pepscan Presto, The Netherlands), containing 1,152 peptides covering the majority of peptides available through Phosphobase (version 2.0). These chips contain peptides derived from phosphorylation events described in many kingdoms and are taken from animals, plants, fungi and even bacteria.^2,3 Full details as to the peptides spotted can be found at www. pepscanpresto.com. Results of the cluster analysis of three independent biological replicas of kinome profiling after treatment with water, sorbitol, sucrose and glucose are depicted in Figure 3A of our recent paper.¹ A further analysis of this set of experiments was not given, due to a lower than expected biological reproducibility. In contrast to the >0.8 correlation observed for the set described in our recent paper, correlations of below 0.5 were seen for the experimental set analyzed on the kinase1 chips. However, we feel that some observation made based on these kinome profiles contain valuable information, if only to suggest follow-up experiments.Comparison of the kinome profiles of Arabidopsis treated with sucrose or sorbitol for 1 h revealed a set of 93 differentially phosphorylated consensus peptides, with the majority of 59 peptides showing reduced phosphorylation after sucrose treatment (