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.     

Metabolomics – an overview. From basic principles to potential biomarkers (part 2)

The metabolome, which represents the complete set of molecules (metabolites) of a biological sample (cell, tissue, organ, organism), is the final downstream product of the metabolic cell process that involves the genome and exogenous sources. The metabolome is characterized by a large number of small molecules with a huge diversity of chemical structures and abundances. Exploring the metabolome requires complementary analytical platforms to reach its extensive coverage. The metabolome is continually evolving, reflecting the continuous flux of metabolic and signaling pathways. Metabolomic research aims to study the biochemical processes by detecting and quantifying metabolites to obtain a metabolic picture able to give a functional readout of the physiological state. Recent advances in mass spectrometry (one of the mostly used technologies for metabolomics studies) have given the opportunity to determine the spatial distribution of metabolites in tissues. In a two-part article, we describe the usual metabolomics technologies, workflows and strategies leading to the implementation of new clinical biomarkers. In this second part, we first develop the steps of a metabolomic analysis from sample collection to biomarker validation. Then with two examples, autism spectrum disorders and Alzheimer's disease, we illustrate the contributions of metabolomics to clinical practice. Finally, we discuss the complementarity of in vivo (positron emission tomography) and in vitro (metabolomics) molecular explorations for biomarker research.     

Two birds with one stone: Doing metabolomics with your proteomics kit

Proteomic research facilities and laboratories are facing increasing demands for the integration of biological data from multiple ‘‐OMICS’ approaches. The aim to fully understand biological processes requires the integrated study of genomes, proteomes and metabolomes. While genomic and proteomic workflows are different, the study of the metabolome overlaps significantly with the latter, both in instrumentation and methodology. However, chemical diversity complicates an easy and direct access to the metabolome by mass spectrometry (MS). The present review provides an introduction into metabolomics workflows from the viewpoint of proteomic researchers. We compare the physicochemical properties of proteins and peptides with metabolites/small molecules to establish principle differences between these analyte classes based on human data. We highlight the implications this may have on sample preparation, separation, ionisation, detection and data analysis. We argue that a typical proteomic workflow (nLC‐MS) can be exploited for the detection of a number of aliphatic and aromatic metabolites, including fatty acids, lipids, prostaglandins, di/tripeptides, steroids and vitamins, thereby providing a straightforward entry point for metabolomics‐based studies. Limitations and requirements are discussed as well as extensions to the LC‐MS workflow to expand the range of detectable molecular classes without investing in dedicated instrumentation such as GC‐MS, CE‐MS or NMR.     

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.     

Tissue culture and metabolome investigation of a wild endangered medicinal plant using high definition mass spectrometry

An increasing effort is dedicated to investigate the potential of native plants used in traditional medicine as a source of bioactive compounds for numerous industries. The bioprospection of the metabolome of medicinal and/or endangered plants has two important merits: confirming or revealing the biotechnological potential of that species, and assisting in its conservation. In addition, biotechnological techniques, such as tissue culture, are key strategies in conservation and multiplication of medicinal plants. This is the first in vitro development and non-targeted metabolome study by UPLC–QTOF–MS^E of extracts from C. menthoides, an endangered medicinal plant. In vitro development investigation with a wide range of plant growth regulators resulted in maximum survival rate (81%) and the highest growth rate (1.74 cm?±?0.36) for plantlets cultured on Murashige and Skoog medium, supplemented with 1 µM gibberellic acid. Maximum rooting occurred on medium supplemented with 4.4 µM 6-benzyladenine, which also resulted in more leaves per plantlet (10.16?±?1.7). We developed a protocol that can be used for the clonal propagation and ex situ conservation of this species. In terms of metabolome analysis, a total of 107 metabolites from several classes were detected and identified in its hydrophilic extract (HE), including organic acids and derivatives, glucosinolates, terpenes, phenolic compounds as well as other polar metabolites. The metabolites in HE with the greatest signal intensity included the isoquinoline alkaloid magnoflorine; the coumaric acid rosmarinic acid; the steroid-cardanolide convallatoxin; two anthraquinones including the poorly investigated ventinone A. Several molecules identified here carry potential pharmacological benefits such as anti-inflammatory and anticancer applications.     

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.     

A bioinformatician's view of the metabolome

The study of a collection of metabolites as a whole (metabolome), as opposed to isolated small molecules, is a fast-growing field promising to take us one step further towards understanding cell biology, and relating the genetic capabilities of an organism to its observed phenotype. The new sciences of metabolomics and metabonomics can exploit a variety of existing experimental and computational methods, but they also require new technology that can deal with both the amount and the diversity of the data relating to the rich world of metabolites. More specifically, the collaboration between bioinformaticians and chemoinformaticians promises to advance our view of cognate molecules, by shedding light on their atomic structure and properties. Modelling of the interactions of metabolites with other entities in the cell, and eventually complete modelling of reaction pathways will be essential for analysis of the experimental data, and prediction of an organism's response to environmental challenges.     

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.     

Downy mildew resistance is genetically mediated by prophylactic production of phenylpropanoids in hop

Downy mildew in hop (Humulus lupulus L.) is caused by Pseudoperonospora humuli and generates significant losses in quality and yield. To identify the biochemical processes that confer natural downy mildew resistance (DMR), a metabolome‐ and genome‐wide association study was performed. Inoculation of a high density genotyped F1 hop population (n = 192) with the obligate biotrophic oomycete P. humuli led to variation in both the levels of thousands of specialized metabolites and DMR. We observed that metabolites of almost all major phytochemical classes were induced 48 hr after inoculation. But only a small number of metabolites were found to be correlated with DMR and these were enriched with phenylpropanoids. These metabolites were also correlated with DMR when measured from the non‐infected control set. A genome‐wide association study revealed co‐localization of the major DMR loci and the phenylpropanoid pathway markers indicating that the major contribution to resistance is mediated by these metabolites in a heritable manner. The application of three putative prophylactic phenylpropanoids led to a reduced degree of leaf infection in susceptible genotypes, confirming their protective activity either directly or as precursors of active compounds.     

High‐throughput metabolomics predicts drug–target relationships for eukaryotic proteins

Chemical probes are important tools for understanding biological systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high‐throughput metabolomics and overexpression to predict drug–target interactions. The metabolome profiles of yeast treated with 1,280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug–target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug–target relationships for eukaryotic proteins using high‐throughput metabolomics.     

Changes in metabolite profiles in Norway spruce shoot tips during short-day induced winter bud development and long-day induced bud flush

The seasonal change in photoperiod is a primary environmental signal influencing tree growth. Long days (LD) sustain growth, whereas short days (SD) induce winter bud formation. In this respect, metabolomic responses have been studied to a limited extent only in conifers. Here we identified changes in metabolite profile in the conifer Norway spruce after transition to SD and following re-transfer to LDs inducing bud flush. After 1 week in SD initial changes in metabolite profile was visible but for the majority of compounds magnitudes of changes were small. However, the ascorbate content was strongly reduced and there were often temporary increases in several energy metabolism-related compounds, secondary metabolites, nucleosides, amino acids and lipids. After 8 weeks in SD substantial changes were observed; proper winter buds had high pools of ABA, antioxidants, flavonoids, terpenoids, phenylpropanoids, sugars, amino acids and lipids related to stress tolerance and hardening, and low levels of nucleosides and metabolites in energy metabolism. One week after re-transfer to LD the metabolome was generally relatively similar to under long-term SD, except e.g. increased urate and strongly decreased ABA and oxidized glutathione. Two weeks later, bud flush had occurred, and the metabolite profile resembled the situation before transfer to SD. This study thus revealed comprehensive modulation of the metabolome in Norway spruce in response to a day length shift, indicating substantially increased stress resistance under SD-induced bud set, and reversal upon bud flush in LD.     

Metabolic fingerprinting and profiling of Arabidopsis thaliana leaf and its cultured cells T87 by GC/MS

Cell suspension cultures are now recognized as important model materials for plant bioscience and biotechnology. Very few studies of metabolic comparisons between cell cultures and original plants have been reported, even though the biological identity of cultured cells with the normally grown plant is of great importance. In this study, a comparison of the metabolome for primary metabolites extracted from the leaves of Arabidopsis thaliana and cultured cells from an Arabidopsis suspension culture (cell line T87) was performed. The results suggest that although cell suspension cultures and Arabidopsis leaves showed similarities in the common primary metabolite profile, nonetheless, moderate differences in quantitative profile were revealed.     

Metabolic Profiling in Banana Pseudo-Stem Reveals a Diverse Set of Bioactive Compounds with Potential Nutritional and Industrial Applications

Banana (Musa spp.) is an ancient and popular fruit plant with highly nutritious fruit. The pseudo-stem of banana represents on average 75% of the total dry mass but its valorization as a nutritional and industrial by-product is limited. Recent advances in metabolomics have paved the way to understand and evaluate the presence of diverse sets of metabolites in different plant parts. This study aimed at exploring the diversity of primary and secondary metabolites in the banana pseudo-stem. Hereby, we identified and quantified 373 metabolites from a diverse range of classes including, alkaloids, flavonoids, lipids, phenolic acids, amino acids and its derivatives, nucleotide and its derivatives, organic acids, lignans and coumarins, tannins, and terpene using the widely-targeted metabolomics approach. Banana pseudo-stem is enriched in metabolites for utilization in the food industry (L-lysine and L-tryptophan, L-glutamic acid, Phenylalanine, Palmitoleic acid, α-Linolenic acid, and Lauric acid, and Adenine) and pharmaceutical industry (Guanosine and Cimidahurinine, Bergapten, Coumarins, Procyanidin A2, Procyanidin B1, Procyanidin B3, Procyanidin B2, and Procyanidin B4, Asiatic acid). The metabolome of banana pseudo-stem with integration across multiomics data may provide the opportunity to exploit the rich metabolome of banana pseudo-stem for industrial and nutritional applications.     

Metabolomic analysis reveals that the accumulation of specific secondary metabolites in <Emphasis Type="Italic">Echinacea angustifolia</Emphasis> cells cultured in vitro can be controlled by light

Echinacea angustifolia cell suspension cultures are usually grown and maintained in the dark, but we also exposed cells to light for one culture cycle (14 days) and then compared the metabolomes of dark-grown and illuminated cells by liquid chromatography–mass spectrometry. Among 256 signals, we putatively identified 159 molecules corresponding to 56 different metabolites plus their fragments, adducts and isotopologs. The E. angustifolia metabolome consisted mainly of caffeic acid derivatives, comprising (a) caffeic acid conjugated with tartaric, quinic and hexaric acids; and (b) caffeic acid conjugated with hydroxytyrosol glycosides (e.g., echinacoside, verbascoside and related molecules). Many of these metabolites have not been previously described in E. angustifolia, which currently lacks detailed metabolic profiles. Exposure to light significantly increased the levels of certain caffeic acid derivatives (particularly caffeoylquinic acids and hydroxytyrosol derivatives lacking rhamnose residues) and reduced the level of hydroxytyrosol derivatives with rhamnose residues, revealing that light specifically inhibits the rhamnosylation of caffeoyl phenylethanoid glycosides. These results are significant because they suggest that the metabolic profile of cell cultures can be manipulated by controlling simple environmental variables such as illumination to modulate the levels of potentially therapeutic compounds.     

Disease-related changes in the cerebrospinal fluid metabolome in amyotrophic lateral sclerosis detected by GC/TOFMS

Background/Aim

The changes in the cerebrospinal fluid (CSF) metabolome associated with the fatal neurodegenerative disease amyotrophic lateral sclerosis (ALS) are poorly understood and earlier smaller studies have shown conflicting results. The metabolomic methodology is suitable for screening large cohorts of samples. Global metabolomics can be used for detecting changes of metabolite concentrations in samples of fluids such as CSF.

Methodology

Using gas chromatography coupled to mass spectrometry (GC/TOFMS) and multivariate statistical modeling, we simultaneously studied the metabolome signature of ∼120 small metabolites in the CSF of patients with ALS, stratified according to hereditary disposition and clinical subtypes of ALS in relation to controls.

Principal Findings

The study is the first to report data validated over two sub-sets of ALS vs. control patients for a large set of metabolites analyzed by GC/TOFMS. We find that patients with sporadic amyotrophic lateral sclerosis (SALS) have a heterogeneous metabolite signature in the cerebrospinal fluid, in some patients being almost identical to controls. However, familial amyotrophic lateral sclerosis (FALS) without superoxide dismutase-1 gene (SOD1) mutation is less heterogeneous than SALS. The metabolome of the cerebrospinal fluid of 17 ALS patients with a SOD1 gene mutation was found to form a separate homogeneous group. Analysis of metabolites revealed that glutamate and glutamine were reduced, in particular in patients with a familial predisposition. There are significant differences in the metabolite profile and composition among patients with FALS, SALS and patients carrying a mutation in the SOD1 gene suggesting that the neurodegenerative process in different subtypes of ALS may be partially dissimilar.

Conclusions/Significance

Patients with a genetic predisposition to amyotrophic lateral sclerosis have a more distinct and homogeneous signature than patients with a sporadic disease.     

Use of 1H nuclear magnetic resonance to measure intracellular metabolite levels during growth and asexual sporulation in Neurospora crassa

Conidiation is an asexual sporulation pathway that is a response to adverse conditions and is the main mode of dispersal utilized by filamentous fungal pathogens for reestablishment in a more favorable environment. Heterotrimeric G proteins (consisting of α, β, and γ subunits) have been shown to regulate conidiation in diverse fungi. Previous work has demonstrated that all three of the Gα subunits in the filamentous fungus Neurospora crassa affect the accumulation of mass on poor carbon sources and that loss of gna-3 leads to the most dramatic effects on conidiation. In this study, we used (1)H nuclear magnetic resonance (NMR) to profile the metabolome of N. crassa in extracts isolated from vegetative hyphae and conidia from cultures grown under conditions of high or low sucrose. We compared wild-type and Δgna-3 strains to determine whether lack of gna-3 causes a significant difference in the global metabolite profile. The results demonstrate that the global metabolome of wild-type hyphae is influenced by carbon availability. The metabolome of the Δgna-3 strain cultured on both high and low sucrose is similar to that of the wild type grown on high sucrose, suggesting an overall defect in nutrient sensing in the mutant. However, analysis of individual metabolites revealed differences in wild-type and Δgna-3 strains cultured under conditions of low and high sucrose.     

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large‐scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back‐crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m‐trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m‐trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin‐6,8‐di‐C‐α‐l‐ arabinoside are presented as an example of a critical mQTL identified by the analysis.     

Induced Pluripotent Stem Cells Show Metabolomic Differences to Embryonic Stem Cells in Polyunsaturated Phosphatidylcholines and Primary Metabolism

Induced pluripotent stem cells are different from embryonic stem cells as shown by epigenetic and genomics analyses. Depending on cell types and culture conditions, such genetic alterations can lead to different metabolic phenotypes which may impact replication rates, membrane properties and cell differentiation. We here applied a comprehensive metabolomics strategy incorporating nanoelectrospray ion trap mass spectrometry (MS), gas chromatography-time of flight MS, and hydrophilic interaction- and reversed phase-liquid chromatography-quadrupole time-of-flight MS to examine the metabolome of induced pluripotent stem cells (iPSCs) compared to parental fibroblasts as well as to reference embryonic stem cells (ESCs). With over 250 identified metabolites and a range of structurally unknown compounds, quantitative and statistical metabolome data were mapped onto a metabolite networks describing the metabolic state of iPSCs relative to other cell types. Overall iPSCs exhibited a striking shift metabolically away from parental fibroblasts and toward ESCs, suggestive of near complete metabolic reprogramming. Differences between pluripotent cell types were not observed in carbohydrate or hydroxyl acid metabolism, pentose phosphate pathway metabolites, or free fatty acids. However, significant differences between iPSCs and ESCs were evident in phosphatidylcholine and phosphatidylethanolamine lipid structures, essential and non-essential amino acids, and metabolites involved in polyamine biosynthesis. Together our findings demonstrate that during cellular reprogramming, the metabolome of fibroblasts is also reprogrammed to take on an ESC-like profile, but there are select unique differences apparent in iPSCs. The identified metabolomics signatures of iPSCs and ESCs may have important implications for functional regulation of maintenance and induction of pluripotency.     

Development of biomarkers based on diet-dependent metabolic serotypes: concerns and approaches for cohort and gender issues in serum metabolome studies

Mathematical models that reflect the effects of dietary restriction (DR) on the sera metabolome may have utility in understanding the mechanisms of DR and in applying this knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility of identifying biomarkers through metabolome analysis and the validity of our approach in independent cohorts of 6-month-old male and female ad libitum fed or DR rats. Cross-cohort studies showed that cohort-specific effects distorted the dataset. The present study extends these observations across the entire sample set, thereby validating our markers independently of specific cohorts. Metabolites originally identified in males were examined in females and vice-versa. DR's effect on the metabolome is partially gender-specific and is modulated by environmental factors. DR reduces inter-gender differences in the metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples and 39/93 metabolites in the male samples were significantly altered (using our previous cut-off criteria of p < or = 0.2) by DR. The metabolites modulated by DR present a wide spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype is broadly representative of the metabolome and that DR has broad effects on the metabolome. These studies, coupled with those in the preceding and following reports, also highlight the utility for consideration of the metabolome as a network of metabolites using appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed herein suggest potential cautions, and potential approaches, for identification of multivariate biomarker profiles that reflect changes in physiological status, such as a metabolism that predisposes to increased risk of neoplasia.