A structure-based anatomy of the E.coli metabolome

The Escherichia coli metabolome has been characterised using the two-dimensional structures of 745 metabolites, obtained from the EcoCyc and KEGG databases. Physicochemical properties of the metabolome have been calculated to provide an overview of this set of cognate ligands. A library of fragments commonly found among these molecules has been employed to reveal the main constituents of metabolites, and to assist a broad classification of the metabolome into biochemically relevant classes. Fragment-based fingerprints reveal the metabolome as a continuum in the two-dimensional structural space, where clusters of molecules sharing similar scaffolds can be identified, but are generally overlapping. Nucleotide, carbohydrate and amino acid-like molecules are the most prominent, but at high levels of similarity, a more detailed classification is possible. Classification schemes for the metabolome are a promising tool for understanding the chemical diversity of the metabolome. When used in conjunction with existing classifications of the proteome, they can help to elucidate the binding preferences and promiscuity of proteins and their cognate substrates.     

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Metabolomics and metabolite profiling — can we achieve the goal?

Deciphering of the plant metabolome is one of the most difficult analytical tasks in functional genomic research. Studies directed at the gene or protein expression are well established, sequencing analyses of these kinds of biopolymers on genome or proteome level are possible. This is not the case for metabolites, where identification in single sample of many chemical entities of different elemental composition and structures and various physicochemical properties is necessary. Different instrumental methods are applied for identification of metabolites but none of them allows obtaining unambiguous structural information about more than 500 compounds in single mixture (metabolite profiling). This is a much smaller number of metabolites than is predicted for single plant metabolome. However, instrumental approaches were proposed (metabolite fingerprinting) in which biochemical phenotype of an organism may be estimated, but identification of individual compounds is not possible.     

Metabolomics reveals organ-specific metabolic rearrangements during early tomato seedling development

Tomato seedlings (Solanum lycopersicum cv. MoneyMaker), grown under strictly controlled conditions, have been used to study alterations occurring in secondary metabolite biosynthetic pathways following developmental and environmental perturbations. Robustness and reproducibility of the system were confirmed using detailed statistical analyses of the metabolome. LCMS profiling was applied using whole germinated seeds as well as cotyledons, hypocotyls and roots from 3 to 9 days old seedlings to generate relative levels of 433 metabolites, of which 62 were annotated. Initial focus was given to the polyphenol pathway and several additional mass signals have been putatively annotated using high mass resolution fragmentation. Clear organ and developmental stage—specific differences were observed. Seeds accumulated saponin-like compounds; roots accumulated mainly alkaloids; cotyledons contained mainly glycosylated flavonols and; hypocotyls contained mainly anthocyanins. For each organ, the developmental changes in metabolite profiles were described by using linear mixed models. Across three independent experiments, 85 % of the metabolites showed similar developmental trends. This tomato seedling system has given us valuable additional insights into the complexity of seedling secondary metabolism. How metabolic profiles reflect an interplay between depletion of stored molecules and de novo synthesis and how the overall picture for this important crop plant contrasts to e.g. Arabidopsis are emphasised.     

The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text

The exponential growth of the biomedical literature is making the need for efficient, accurate text-mining tools increasingly clear. The identification of named biological entities in text is a central and difficult task. We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition, which we here use to identify names of species and other taxa in text. The tool, SPECIES, is more than an order of magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. The SPECIES software is open source and can be downloaded from http://species.jensenlab.org along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at http://organisms.jensenlab.org.     

Metabolomics analysis and metabolite‐agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines

Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high‐density genetic map, we conducted a comprehensive metabolome study via widely targeted LC‐MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty‐four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite‐agronomic traits with the co‐localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co‐localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.     

mzGroupAnalyzer-Predicting Pathways and Novel Chemical Structures from Untargeted High-Throughput Metabolomics Data

The metabolome is a highly dynamic entity and the final readout of the genotype x environment x phenotype (GxExP) relationship of an organism. Monitoring metabolite dynamics over time thus theoretically encrypts the whole range of possible chemical and biochemical transformations of small molecules involved in metabolism. The bottleneck is, however, the sheer number of unidentified structures in these samples. This represents the next challenge for metabolomics technology and is comparable with genome sequencing 30 years ago. At the same time it is impossible to handle the amount of data involved in a metabolomics analysis manually. Algorithms are therefore imperative to allow for automated m/z feature extraction and subsequent structure or pathway assignment. Here we provide an automated pathway inference strategy comprising measurements of metabolome time series using LC- MS with high resolution and high mass accuracy. An algorithm was developed, called mzGroupAnalyzer, to automatically explore the metabolome for the detection of metabolite transformations caused by biochemical or chemical modifications. Pathways are extracted directly from the data and putative novel structures can be identified. The detected m/z features can be mapped on a van Krevelen diagram according to their H/C and O/C ratios for pattern recognition and to visualize oxidative processes and biochemical transformations. This method was applied to Arabidopsis thaliana treated simultaneously with cold and high light. Due to a protective antioxidant response the plants turn from green to purple color via the accumulation of flavonoid structures. The detection of potential biochemical pathways resulted in 15 putatively new compounds involved in the flavonoid-pathway. These compounds were further validated by product ion spectra from the same data. The mzGroupAnalyzer is implemented in the graphical user interface (GUI) of the metabolomics toolbox COVAIN (Sun & Weckwerth, 2012, Metabolomics 8: 81–93). The strategy can be extended to any biological system.     

Microbial metabolomics: toward a platform with full metabolome coverage

Achieving metabolome data with satisfactory coverage is a formidable challenge in metabolomics because metabolites are a chemically highly diverse group of compounds. Here we present a strategy for the development of an advanced analytical platform that allows the comprehensive analysis of microbial metabolomes. Our approach started with in silico metabolome information from three microorganisms-Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae-and resulted in a list of 905 different metabolites. Subsequently, these metabolites were classified based on their physicochemical properties, followed by the development of complementary gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry methods, each of which analyzes different metabolite classes. This metabolomics platform, consisting of six different analytical methods, was applied for the analysis of the metabolites for which commercial standards could be purchased (399 compounds). Of these 399 metabolites, 380 could be analyzed with the platform. To demonstrate the potential of this metabolomics platform, we report on its application to the analysis of the metabolome composition of mid-logarithmic E. coli cells grown on a mineral salts medium using glucose as the carbon source. Of the 431 peaks detected, 235 (=176 unique metabolites) could be identified. These include 61 metabolites that were not previously identified or annotated in existing E. coli databases.     

Metabolomic profiling and genomic analysis of wheat aneuploid lines to identify genes controlling biochemical pathways in mature grain

Metabolomics is becoming an increasingly important tool in plant genomics to decipher the function of genes controlling biochemical pathways responsible for trait variation. Although theoretical models can integrate genes and metabolites for trait variation, biological networks require validation using appropriate experimental genetic systems. In this study, we applied an untargeted metabolite analysis to mature grain of wheat homoeologous group 3 ditelosomic lines, selected compounds that showed significant variation between wheat lines Chinese Spring and at least one ditelosomic line, tracked the genes encoding enzymes of their biochemical pathway using the wheat genome survey sequence and determined the genetic components underlying metabolite variation. A total of 412 analytes were resolved in the wheat grain metabolome, and principal component analysis indicated significant differences in metabolite profiles between Chinese Spring and each ditelosomic lines. The grain metabolome identified 55 compounds positively matched against a mass spectral library where the majority showed significant differences between Chinese Spring and at least one ditelosomic line. Trehalose and branched‐chain amino acids were selected for detailed investigation, and it was expected that if genes encoding enzymes directly related to their biochemical pathways were located on homoeologous group 3 chromosomes, then corresponding ditelosomic lines would have a significant reduction in metabolites compared with Chinese Spring. Although a proportion showed a reduction, some lines showed significant increases in metabolites, indicating that genes directly and indirectly involved in biosynthetic pathways likely regulate the metabolome. Therefore, this study demonstrated that wheat aneuploid lines are suitable experimental genetic system to validate metabolomics–genomics networks.     

Stress associated protein 1 regulates the development of glandular trichomes in Artemisia annua

Undifferentiated plant cells in culture represent a renewable system conducive to understanding biological processes and a valuable alternative for secondary metabolite production. Additionally, manipulation of these systems by plant growth regulators (PGRs) may result in redifferentiation/organogenesis and hence changes in metabolic profiles. The aim of the study was to investigate the effects of combining auxin (2,4-dichlorophenoxyacetic acid) and cytokinin (kinetin) at concentrations of 2, 4, 6 and 9 µM on undifferentiated Moringa oleifera callus cells, at a metabolome level. Results indicated that the callus became habituated, i.e. developed the ability to grow without added stimulatory PGRs, and no organogenesis was observed on any of the different PGR combinations under investigation. Methanolic extracts were screened for total phenolic content (TPC) and anti-oxidant activity, and further analysed using liquid chromatography coupled to mass spectrometry combined with multivariate data analysis to facilitate analysis of the metabolite profiles. While the anti-oxidant capacity of extracts from the various treatments exhibited little variation, the TPC differed significantly. Despite the observed habituation phenomenon, the calli retained responsiveness towards external PGRs and each of the 25 conditions generated a unique metabolome as found by principal component analysis. This was also reflected by a number of phytochemicals that were annotated as biomarkers from PGR-treated calli. These findings demonstrate the differential influence of 2,4-D and kinetin on M. oleifera callus for the production of secondary metabolites.

     

Chemoselective probes for metabolite enrichment and profiling

Chemical probes that target classes of proteins based on shared functional properties have emerged as powerful tools for proteomics. The metabolome rivals, if not surpasses, the proteome in terms of size and complexity, suggesting that efforts to profile metabolites would also benefit from targeted technologies. Here we apply the principle of chemoselective probes to the metabolome, creating a general strategy to tag, enrich and profile large classes of small molecules from biological systems. Key to success was incorporation of a protease-cleavage step to release captured metabolites in a format compatible with liquid chromatography-mass spectrometry (LC-MS) analysis. This technology, termed metabolite enrichment by tagging and proteolytic release (METPR), is applicable to small molecules of any physicochemical class, including polar, labile and low-mass (<100 Da) compounds. We applied METPR to profile changes in the thiol metabolome of human cancer cells treated with the antioxidant N-acetyl-L-cysteine.     

Metabolic source isotopic pair labeling and genome-wide association are complementary tools for the identification of metabolite–gene associations in plants

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography– mass spectrometry (LC–MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. We identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway’s products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.

An isotopic labeling and LC–MS pipeline to identify metabolites produced from Phe and its integration with genome-wide association identifies genes associated with the phenylpropanoid pathway.     

The effects of age and dietary restriction on the tissue‐specific metabolome of Drosophila

Dietary restriction (DR) is a robust intervention that extends lifespan and slows the onset of age‐related diseases in diverse organisms. While significant progress has been made in attempts to uncover the genetic mechanisms of DR, there are few studies on the effects of DR on the metabolome. In recent years, metabolomic profiling has emerged as a powerful technology to understand the molecular causes and consequences of natural aging and disease‐associated phenotypes. Here, we use high‐resolution mass spectroscopy and novel computational approaches to examine changes in the metabolome from the head, thorax, abdomen, and whole body at multiple ages in Drosophila fed either a nutrient‐rich ad libitum (AL) or nutrient‐restricted (DR) diet. Multivariate analysis clearly separates the metabolome by diet in different tissues and different ages. DR significantly altered the metabolome and, in particular, slowed age‐related changes in the metabolome. Interestingly, we observed interacting metabolites whose correlation coefficients, but not mean levels, differed significantly between AL and DR. The number and magnitude of positively correlated metabolites was greater under a DR diet. Furthermore, there was a decrease in positive metabolite correlations as flies aged on an AL diet. Conversely, DR enhanced these correlations with age. Metabolic set enrichment analysis identified several known (e.g., amino acid and NAD metabolism) and novel metabolic pathways that may affect how DR effects aging. Our results suggest that network structure of metabolites is altered upon DR and may play an important role in preventing the decline of homeostasis with age.     

Analysis of metabolite profile data using batch-learning self-organizing maps

Novel tools are needed for efficient analysis and visualization of the massive data sets associated with metabolomics. Here, we describe a batch-learning self-organizing map (BL-SOM) for metabolome informatics that makes the learning process and resulting map independent of the order of data input. This approach was successfully used in analyzing and organizing the metabolome data forArabidopsis thaliana cells cultured under salt stress. Our 6 × 4 matrix presented patterns of metabolite levels at different time periods. A negative correlation was found between the levels of amino acids and metabolites related to glycolysis metabolism in response to this stress. Therefore, BL-SOM could be an excellent tool for clustering and visualizing high dimensional, complex metabolome data in a single map.     

Changes in the uterine metabolome of the cow during the first 7 days after estrus

The uterine microenvironment during the first 7 days after ovulation accommodates and facilitates sperm transit to the oviduct and constitutes the sole source of nutrients required for the development of preimplantation embryos. Knowledge of the composition of uterine fluid is largely incomplete. Using untargeted mass spectrometry, we characterized the uterine metabolome during the first 7 days of the estrous cycle. Bovine uteri were collected on Days 0 (N = 4), 3 ( N = 4), 5 ( N = 3), and 7 ( N = 4) relative to ovulation and flushed with Dulbecco’s phosphate‐buffered saline. A total of 1,993 molecular features were detected of which 184 peaks with putative identification represent 147 unique metabolites, including amino acids, benzoic acids, lipid molecules, carbohydrates, purines, pyrimidines, vitamins, and other intermediate and secondary metabolites. Results revealed changes in the uterine metabolome as the cow transitions from ovulation to Day 7 of the estrous cycle. The majority of metabolites that changed with day reached maximum intensity on either Day 5 or 7 relative to ovulation. Moreover, several metabolites found in the uterine fluid have signaling capabilities and some have been shown to affect preimplantation embryonic development. In conclusion, the metabolome of the bovine uterus changes during early stages of the estrous cycle and is likely to participate in the regulation of preimplantation embryonic development. Data reported here will serve as the basis for future studies aiming to evaluate maternal regulation of preimplantation embryonic development and optimal conditions for the culture of embryos.     

Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards

A novel method was developed for the quantitative analysis of the microbial metabolome using a mixture of fully uniformly (U) (13)C-labeled metabolites as internal standard (IS) in the metabolite extraction procedure the subsequent liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis. This mixture of fully U (13)C-labeled metabolites was extracted from biomass of Saccharomyces cerevisiae cultivated in a fed-batch fermentation on fully U (13)C-labeled substrates. The obtained labeled cell extract contained, in principle, the whole yeast metabolome, allowing the quantification of any intracellular metabolite of interest in S. cerevisiae. We have applied the labeled cell extract as IS in the analysis of glycolytic and tricarboxylic acid (TCA) cycle intermediates in S. cerevisiae sampled in both steady-state and transient conditions following a glucose pulse. The use of labeled IS effectively reduced errors due to variations occurring in the analysis and sample processing. As a result, the linearity of calibration lines and the precision of measurements were significantly improved. Coextraction of the labeled cell extract with the samples also eliminates the need to perform elaborate recovery checks for each metabolite to be analyzed. In conclusion, the method presented leads to less workload, more robustness, and a higher precision in metabolome analysis.     

Metabolic profiling of transgenic wheat over-expressing the high-molecular-weight Dx5 glutenin subunit

The primary aim of this work was to evaluate potential changes in the metabolic network of transgenic wheat grain over-expressing the high-molecular-weight (HMW) glutenin Dx5-subunit gene. GC–MS and multivariate analyses were used to compare the metabolite profiles of developing caryopses of two independently transformed lines over-expressing Dx5 and another two independently transformed lines expressing only the selectable-marker gene (controls). Developing grain at 7, 14 and 21 Days Post-Anthesis (DPA) was studied to observe differences in metabolically active tissues. There was no distinction between the Dx5 transformants and the controls by principal component analysis (PCA) suggesting that their metabolite compositions were similar. Most changes in metabolite levels and starch occurred at 14 DPA but tapered off by 21 DPA. Only 3 metabolites, guanine, 4-hydroxycinnamic acid and Unknown 071306a, were altered due to Dx5 expression after correction for false discovery rates (P < 0.0005). However, discriminant function analysis (DFA) and correlative analyses of the metabolites showed that Dx5-J, which had the highest level of Dx5 protein in ripe caryopses, could be distinguished from the other genotypes. The second aim of this work was to determine the influence of gene transformation on the metabolome. Cross-comparison of the transformed controls to each other, and to the Dx5 genotypes showed that approximately 50% of the metabolic changes in the Dx5 genotypes were potentially due to variations arising from gene transformation and not from the expression of the Dx5-gene per se. This study therefore suggests the extent to which plant transformation by biolistics can potentially influence phenotype. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Negative mode nanostructure-initiator mass spectrometry for detection of phosphorylated metabolites

The chemical complexity of the metabolome requires the development of new detection methods to enlarge the range of compounds detectable in a biological sample. Recently, a novel matrix-free laser desorption/ionization method called nanostructure-initiator mass spectrometry (NIMS) [Northen et al., Nature 449(7165):1033–1036, 2007] was reported. Here we investigate NIMS in negative ion mode for the detection of endogenous metabolites, namely small phosphorylated molecules. 3-Aminopropyldimethylethoxysilane was found to be suitable as initiator for the analytes studied and a limit of detection in the tens of femtomoles was reached. The detection of different endogenous cell metabolites in a yeast cell extract is demonstrated.