Precision mapping of the metabolome

Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

The molecular map of diverse biological molecules linked with structure, function, signaling, and regulation within a cell can be elucidated using an analytically demanding omic approach. The latest trend of using "metabolomics" technologies has explained the natural phenomenon of opening a new avenue to understand and enhance bioactive compounds' production. Examination of sequenced plant genomes has revealed that a considerable portion of these encodes genes of secondary metabolism. In addition to genetic and molecular tools developed in the current era, the ever-increasing knowledge about plant metabolism's biochemistry has initiated an approach for wisely designed, more productive genetic engineering of plant secondary metabolism for improved defense systems and enhanced biosynthesis of beneficial metabolites. Secondary plant metabolites are natural products synthesized by plants that are not directly involved with their average growth and development but play a vital role in plant defense mechanisms. Plant secondary metabolites are classified into four major classes: terpenoids, phenolic compounds, alkaloids, and sulfur-containing compounds. More than 200,000 secondary metabolites are synthesized by plants having a unique and complex structure. Secondary plant metabolites are well characterized and quantified by omics approaches and therefore used by humans in different sectors such as agriculture, pharmaceuticals, chemical industries, and biofuel. The aim is to establish metabolomics as a comprehensive and dynamic model of diverse biological molecules for biomarkers and drug discovery. In this chapter, we aim to illustrate the role of metabolomic technology, precisely liquid chromatography-mass spectrometry, capillary electrophoresis mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy, specifically as a research tool in the production and identification of novel bioactive compounds for drug discovery and to obtain a unified insight of secondary metabolism in plants.

A History of Molecular Level Analysis of Natural Organic Matter by FTICR Mass Spectrometry and The Paradigm Shift in Organic Geochemistry

Natural organic matter (NOM) is a complex mixture of biogenic molecules resulting from the deposition and transformation of plant and animal matter. It has long been recognized that NOM plays an important role in many geological, geochemical, and environmental processes. Of particular concern is the fate of NOM in response to a warming climate in environments that have historically sequestered carbon (e.g., peatlands and swamps) but may transition to net carbon emitters. In this review, we will highlight developments in the application of high-field Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) in identifying the individual components of complex NOM mixtures, focusing primarily on the fraction that is dissolved in natural waters (dissolved organic matter or DOM). We will first provide some historical perspective on developments in FTICR technology that made molecular-level characterizations of DOM possible. A variety of applications of the technique will then be described, followed by our view of the future of high-field FTICR MS in carbon cycling research, including a particularly exciting metabolomic approach.

Microbial Communities Influence Soil Dissolved Organic Carbon Concentration by Altering Metabolite Composition

Rapid microbial growth in the early phase of plant litter decomposition is viewed as an important component of soil organic matter (SOM) formation. However, the microbial taxa and chemical substrates that correlate with carbon storage are not well resolved. The complexity of microbial communities and diverse substrate chemistries that occur in natural soils make it difficult to identify links between community membership and decomposition processes in the soil environment. To identify potential relationships between microbes, soil organic matter, and their impact on carbon storage, we used sand microcosms to control for external environmental factors such as changes in temperature and moisture as well as the variability in available carbon that exist in soil cores. Using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) on microcosm samples from early phase litter decomposition, we found that protein- and tannin-like compounds exhibited the strongest correlation to dissolved organic carbon (DOC) concentration. Proteins correlated positively with DOC concentration, while tannins correlated negatively with DOC. Through random forest, neural network, and indicator species analyses, we identified 42 bacterial and 9 fungal taxa associated with DOC concentration. The majority of bacterial taxa (26 out of 42 taxa) belonged to the phylum Proteobacteria while all fungal taxa belonged to the phylum Ascomycota. Additionally, we identified significant connections between microorganisms and protein-like compounds and found that most taxa (12/14) correlated negatively with proteins indicating that microbial consumption of proteins is likely a significant driver of DOC concentration. This research links DOC concentration with microbial production and/or decomposition of specific metabolites to improve our understanding of microbial metabolism and carbon persistence.

Understanding the fundamentals of microbial remediation with emphasize on metabolomics

The post-genomic tool metabolomics is a great advancement in science and technology which acquires novel strategies and pathways to analyze various biological compounds. Metabolomics aids in retrieving the qualitative and quantitative data from the various biological system. The current review is focused on the application of metabolomics in bioremediation and helps to focus on the xenobiotic compounds which are discharged into the environment and have long term impact. The microbial based biodegradation can be effectively used along with the combination of metabolomic approach for a better understanding of the breakdown of certain recalcitrant. Additionally, this review also discusses the candidate gene approach which helps to comprehend the functional analysis of microbial genes in response to different contaminants. Therefore, this review intends to discuss the metabolomics in bioremediation by studying the complete set of metabolites involved during the process of degradation and their interaction with the environment.

Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships

Untargeted metabolomics analysis captures chemical reactions among small molecules. Common mass spectrometry-based metabolomics workflows first identify the small molecules significantly associated with the outcome of interest, then begin exploring their biochemical relationships to understand biological fate or impact. We suggest an alternative by which general chemical relationships including abiotic reactions can be directly retrieved through untargeted high-resolution paired mass distance (PMD) analysis without a priori knowledge of the identities of participating compounds. PMDs calculated from the mass spectrometry data are linked to chemical reactions obtained via data mining of small molecule and reaction databases, i.e. 'PMD-based reactomics'. We demonstrate applications of PMD-based reactomics including PMD network analysis, source appointment of unknown compounds, and biomarker reaction discovery as complements to compound discovery analyses used in traditional untargeted workflows. An R implementation of reactomics analysis and the reaction/PMD databases is available as the pmd package.

Integrated Probabilistic Annotation: A Bayesian-Based Annotation Method for Metabolomic Profiles Integrating Biochemical Connections, Isotope Patterns, and Adduct Relationships

In a typical untargeted metabolomics experiment, the huge amount of complex data generated by mass spectrometry necessitates automated tools for the extraction of useful biological information. Each metabolite generates numerous mass spectrometry features. The association of these experimental features to the underlying metabolites still represents one of the major bottlenecks in metabolomics data processing. While certain identification (e.g., by comparison to authentic standards) is always desirable, it is usually achievable only for a limited number of compounds, and scientists often deal with a significant amount of putatively annotated metabolites. The confidence in a specific annotation is usually assessed by considering different sources of information (e.g., isotope patterns, adduct formation, chromatographic retention times, and fragmentation patterns). IPA (integrated probabilistic annotation) offers a rigorous and reproducible method to automatically annotate metabolite profiles and evaluate the resulting confidence of the putative annotations. It is able to provide a rigorous measure of our confidence in any putative annotation and is also able to update and refine our beliefs (i.e., background prior knowledge) by incorporating different sources of information in the annotation process, such as isotope patterns, adduct formation and biochemical relations. The IPA package is freely available on GitHub ( https://github.com/francescodc87/IPA ), together with the related extensive documentation.

Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS

Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.

Metabolomic investigations in cerebrospinal fluid of Parkinson's disease

The underlying mechanisms of Parkinson´s disease are not completely revealed. Especially, early diagnostic biomarkers are lacking. To characterize early pathophysiological events, research is focusing on metabolomics. In this case-control study we investigated the metabolic profile of 31 Parkinson´s disease-patients in comparison to 95 neurologically healthy controls. The investigation of metabolites in CSF was performed by a 12 Tesla SolariX Fourier transform-ion cyclotron resonance-mass spectrometer (FT-ICR-MS). Multivariate statistical analysis sorted the most important biomarkers in relation to their ability to differentiate Parkinson versus control. The affected metabolites, their connection and their conversion pathways are described by means of network analysis. The metabolic profiling by FT-ICR-MS in CSF yielded in a good group separation, giving insights into the disease mechanisms. A total number of 243 metabolites showed an affected intensity in Parkinson´s disease, whereas 15 of these metabolites seem to be the main biological contributors. The network analysis showed a connection to the tricarboxylic cycle (TCA cycle) and therefore to mitochondrial dysfunction and increased oxidative stress within mitochondria. The metabolomic analysis of CSF in Parkinson´s disease showed an association to pathways which are involved in lipid/ fatty acid metabolism, energy metabolism, glutathione metabolism and mitochondrial dysfunction.

Metabotype variation in a field population of tansy plants influences aphid host selection

It is well known that plant volatiles influence herbivores in their selection of a host plant; however, less is known about how the nonvolatile metabolome affects herbivore host selection. Metabolic diversity between intraspecific plants can be characterized using non-targeted mass spectrometry that gives us a snapshot overview of all metabolic processes occurring within a plant at a particular time. Here, we show that non-targeted metabolomics can be used to reveal links between intraspecific chemical diversity and ecological processes in tansy (Tanacetum vulgare). First, we show that tansy plants can be categorized into five subgroups based up on their metabolic profiles, and that these "metabotypes" influenced natural aphid colonization in the field. Second, this grouping was not due to induced metabolomic changes within the plant due to aphid feeding but rather resulted from constitutive differences in chemical diversity between plants. These findings highlight the importance of intraspecific chemical diversity within one plant population and provide the first report of a non-targeted metabolomic field study in chemical ecology.

pH plays a role in the mode of action of trimethoprim on Escherichia coli

Metabolomics-based approaches were applied to understand interactions of trimethoprim with Escherichia coli K-12 at sub-minimum inhibitory concentrations (MIC≈0.2, 0.03 and 0.003 mg L-1). Trimethoprim inhibits dihydrofolate reductase and thereby is an indirect inhibitor of nucleic acid synthesis. Due to the basicity of trimethoprim, two pH levels (5 and 7) were selected which mimicked healthy urine pH. This also allowed investigation of the effect on bacterial metabolism when trimethoprim exists in different ionization states. UHPLC-MS was employed to detect trimethoprim molecules inside the bacterial cell and this showed that at pH 7 more of the drug was recovered compared to pH 5; this correlated with classical growth curve measurements. FT-IR spectroscopy was used to establish recovery of reproducible phenotypes under all 8 conditions (3 drug levels and control in 2 pH levels) and GC-MS was used to generate global metabolic profiles. In addition to finding direct mode-of-action effects where nucleotides were decreased at pH 7 with increasing trimethoprim levels, off-target pH-related effects were observed for many amino acids. Additionally, stress-related effects were observed where the osmoprotectant trehalose was higher at increased antibiotic levels at pH 7. This correlated with glucose and fructose consumption and increase in pyruvate-related products as well as lactate and alanine. Alanine is a known regulator of sugar metabolism and this increase may be to enhance sugar consumption and thus trehalose production. These results provide a wider view of the action of trimethoprim. Metabolomics indicated alternative metabolism areas to be investigated to further understand the off-target effects of trimethoprim.

Metabolomics-assisted biotechnological interventions for developing plant-based functional foods and nutraceuticals

Today, the dramatic changes in types of food consumed have led to an increased burden of chronic diseases. Therefore, the emphasis of food research is not only to ensure quality food that can supply adequate nutrients to prevent nutrition related diseases, but also to ensure overall physical and mental-health. This has led to the concept of functional foods and nutraceuticals (FFNs), which can be ideally produced and delivered through plants. Metabolomics can help in getting the most relevant functional information, and thus has been considered the greatest -OMICS technology to date. However, metabolomics has not been exploited to the best potential in plant sciences. The technology can be leveraged to identify the health promoting compounds and metabolites that can be used for the development of FFNs. This article reviews (i) plant-based FFNs-related metabolites and their health benefits; (ii) use of different analytic platforms for targeted and non-targeted metabolite profiling along with experimental considerations; (iii) exploitation of metabolomics to develop FFNs in plants using various biotechnological tools; and (iv) potential use of metabolomics in plant breeding. We have also provided some insights into integration of metabolomics with latest genome editing tools for metabolic pathway regulation in plants.

Characterization of poplar metabotypes via mass difference enrichment analysis

Instrumentation technology for metabolomics has advanced drastically in recent years in terms of sensitivity and specificity. Despite these technical advances, data analytical strategies are still in their infancy in comparison with other 'omics'. Plants are known to possess an immense diversity of secondary metabolites. Typically, more than 70% of metabolomics data are not amenable to systems biological interpretation because of poor database coverage. Here, we propose a new general strategy for mass-spectrometry-based metabolomics that incorporates all exact mass features with known sum formulas into the evaluation and interpretation of metabolomics studies. We extend the use of mass differences, commonly used for feature annotation, by redefining them as variables that reflect the remaining 'omic' domains. The strategy uses exact mass difference network analyses exemplified for the metabolomic description of two grey poplar (Populus × canescens) genotypes that differ in their capability to emit isoprene. This strategy established a direct connection between the metabotype and the non-isoprene-emitting phenotype, as mass differences pertaining to prenylation reactions were over-represented in non-isoprene-emitting poplars. Not only was the analysis of mass differences able to grasp the known chemical biology of poplar, but it also improved the interpretability of yet unknown biochemical relationships.

Combination of electrospray ionization, atmospheric pressure photoionization and laser desorption ionization Fourier transform ion cyclotronic resonance mass spectrometry for the investigation of complex mixtures - Application to the petroleomic analysis of bio-oils

The comprehensive description of complex mixtures such as bio-oils is required to understand and improve the different processes involved during biological, environmental or industrial operation. In this context, we have to consider how different ionization sources can improve a non-targeted approach. Thus, the Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has been coupled to electrospray ionization (ESI), laser desorption ionization (LDI) and atmospheric pressure photoionization (APPI) to characterize an oak pyrolysis bio-oil. Close to 90% of the all 4500 compound formulae has been attributed to C_xH_yO_z with similar oxygen class compound distribution. Nevertheless, their relative abundance in respect with their double bound equivalent (DBE) value has evidenced significant differences depending on the ion source used. ESI has allowed compounds with low DBE but more oxygen atoms to be ionized. APPI has demonstrated the efficient ionization of less polar compounds (high DBE values and less oxygen atoms). The LDI behavior of bio-oils has been considered intermediate in terms of DBE and oxygen amounts but it has also been demonstrated that a significant part of the features are specifically detected by this ionization method. Thus, the complementarity of three different ionization sources has been successfully demonstrated for the exhaustive characterization by petroleomic approach of a complex mixture.

Nanoparticle exposure reactivates latent herpesvirus and restores a signature of acute infection

Background

Inhalation of environmental (nano) particles (NP) as well as persistent herpesvirus-infection are potentially associated with chronic lung disease and as both are omnipresent in human society a coincidence of these two factors is highly likely. We hypothesized that NP-exposure of persistently herpesvirus-infected cells as a second hit might disrupt immune control of viral latency, provoke reactivation of latent virus and eventually lead to an inflammatory response and tissue damage.

Results

To test this hypothesis, we applied different NP to cells or mice latently infected with murine gammaherpesvirus 68 (MHV-68) which provides a small animal model for the study of gammaherpesvirus-pathogenesis in vitro and in vivo. In vitro, NP-exposure induced expression of the typically lytic viral gene ORF50 and production of lytic virus. In vivo, lytic viral proteins in the lung increased after intratracheal instillation with NP and elevated expression of the viral gene ORF50 could be detected in cells from bronchoalveolar lavage. Gene expression and metabolome analysis of whole lung tissue revealed patterns with striking similarities to acute infection. Likewise, NP-exposure of human cells latently infected with Epstein-Barr-Virus also induced virus production.

Conclusions

Our results indicate that NP-exposure of persistently herpesvirus-infected cells – murine or human – restores molecular signatures found in acute virus infection, boosts production of lytic viral proteins, and induces an inflammatory response in the lung – a combination which might finally result in tissue damage and pathological alterations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12989-016-0181-1) contains supplementary material, which is available to authorized users.

Probing the metabolic network in bloodstream-form Trypanosoma brucei using untargeted metabolomics with stable isotope labelled glucose

Metabolomics coupled with heavy-atom isotope-labelled glucose has been used to probe the metabolic pathways active in cultured bloodstream form trypomastigotes of Trypanosoma brucei, a parasite responsible for human African trypanosomiasis. Glucose enters many branches of metabolism beyond glycolysis, which has been widely held to be the sole route of glucose metabolism. Whilst pyruvate is the major end-product of glucose catabolism, its transamination product, alanine, is also produced in significant quantities. The oxidative branch of the pentose phosphate pathway is operative, although the non-oxidative branch is not. Ribose 5-phosphate generated through this pathway distributes widely into nucleotide synthesis and other branches of metabolism. Acetate, derived from glucose, is found associated with a range of acetylated amino acids and, to a lesser extent, fatty acids; while labelled glycerol is found in many glycerophospholipids. Glucose also enters inositol and several sugar nucleotides that serve as precursors to macromolecule biosynthesis. Although a Krebs cycle is not operative, malate, fumarate and succinate, primarily labelled in three carbons, were present, indicating an origin from phosphoenolpyruvate via oxaloacetate. Interestingly, the enzyme responsible for conversion of phosphoenolpyruvate to oxaloacetate, phosphoenolpyruvate carboxykinase, was shown to be essential to the bloodstream form trypanosomes, as demonstrated by the lethal phenotype induced by RNAi-mediated downregulation of its expression. In addition, glucose derivatives enter pyrimidine biosynthesis via oxaloacetate as a precursor to aspartate and orotate.

In this work we have followed the distribution of carbon derived from glucose in bloodstream form trypanosomes, the causative agent of African trypanosomiasis, revealing it to enter a diverse range of metabolites. The work involved using ¹³C-labelled glucose and following the fate of the labelled carbon with an LC-MS based metabolomics platform. Beyond glycolysis and the oxidative branch of the pentose phosphate pathway the label entered lipid biosynthesis both through glycerol 3-phosphate and also acetate. Glucose derived carbon also entered nucleotide synthesis through ribose and pyrimidine synthesis through oxaloacetate-derived aspartate. Appreciable quantities of the carboxylic acids succinate and malate were identified, although labelling patterns indicate they are not TCA cycle derived. Amino sugars and sugar nucleotides were also labelled as was inositol used in protein modification but not in inositol phospholipid headgroup production. We confirm active and essential oxaloacetate production in bloodstream form trypanosomes and show that phosphoenolpyruvate carboxykinase is essential to these parasites using RNA interference. The amount of glucose entering these metabolites is minor compared to the quantity that enters pyruvate excreted from the cell, but the observation that enzymes contributing to the metabolism of glucose beyond glycolysis can be essential offers potential new targets for chemotherapy against trypanosomiasis.

Identification of low-dose responsive metabolites in X-irradiated human B lymphoblastoid cells and fibroblasts

Ionizing radiation (IR) induces cellular stress responses, such as signal transduction, gene expression, protein modification, and metabolite change that affect cellular behavior. We analyzed X-irradiated human Epstein-Barr virus-transformed B lymphoblastoid cells and normal fibroblasts to search for metabolites that would be suitable IR-responsive markers by Liquid Chromotography-Mass spectrometry (LC-MS). Mass spectra, as analyzed with principal component analysis, showed that the proportion of peaks with IR-induced change was relatively small compared with the influence of culture time. Dozens of peaks that had either been upregulated or downregulated by IR were extracted as candidate IR markers. The IR-changed peaks were identified by comparing mock-treated groups to 100 mGy-irradiated groups that had recovered after 10 h, and the results indicated that the metabolites involved in nucleoside synthesis increased and that some acylcarnitine levels decreased in B lymphoblastoids. Some peaks changed by as much as 20 mGy, indicating the presence of an IR-sensitive signal transduction/metabolism control mechanism in these cells. On the other hand, we could not find common IR-changed peaks in fibroblasts of different origin. These data suggest that cell phenotype-specific pathways exist, even in low-dose responses, and could determine cell behavior.

Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks

The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics.

Metabolic engineering and profiling of rice with increased lysine

Lysine (Lys) is the first limiting essential amino acid in rice, a stable food for half of the world population. Efforts, including genetic engineering, have not achieved a desirable level of Lys in rice. Here, we genetically engineered rice to increase Lys levels by expressing bacterial lysine feedback-insensitive aspartate kinase (AK) and dihydrodipicolinate synthase (DHPS) to enhance Lys biosynthesis; through RNA interference of rice lysine ketoglutaric acid reductase/saccharopine dehydropine dehydrogenase (LKR/SDH) to down-regulate its catabolism; and by combined expression of AK and DHPS and interference of LKR/SDH to achieve both metabolic effects. In these transgenic plants, free Lys levels increased up to ~12-fold in leaves and ~60-fold in seeds, substantially greater than the 2.5-fold increase in transgenic rice seeds reported by the only previous related study. To better understand the metabolic regulation of Lys accumulation in rice, metabolomic methods were employed to analyse the changes in metabolites of the Lys biosynthesis and catabolism pathways in leaves and seeds at different stages. Free Lys accumulation was mainly regulated by its biosynthesis in leaves and to a greater extent by catabolism in seeds. The transgenic plants did not show observable changes in plant growth and seed germination nor large changes in levels of asparagine (Asn) and glutamine (Gln) in leaves, which are the major amino acids transported into seeds. Although Lys was highly accumulated in leaves of certain transgenic lines, a corresponding higher Lys accumulation was not observed in seeds, suggesting that free Lys transport from leaves into seeds did not occur.

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2-4 h to complete per feature, depending on the compound.

Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation

The combination of high-resolution LC–MS-based untargeted metabolomics with stable isotope tracing provides a global overview of the cellular fate of precursor metabolites. This methodology enables detection of putative metabolites from biological samples and simultaneous quantification of the pattern and extent of isotope labeling. Labeling of Trypanosoma brucei cell cultures with 50% uniformly ¹³C-labeled glucose demonstrated incorporation of glucose-derived carbon into 187 of 588 putatively identified metabolites in diverse pathways including carbohydrate, nucleotide, lipid, and amino acid metabolism. Labeling patterns confirmed the metabolic pathways responsible for the biosynthesis of many detected metabolites, and labeling was detected in unexpected metabolites, including two higher sugar phosphates annotated as octulose phosphate and nonulose phosphate. This untargeted approach to stable isotope tracing facilitates the biochemical analysis of known pathways and yields rapid identification of previously unexplored areas of metabolism.

Exploring the metabolic state of microorganisms using metabolomics

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

Fourier Transform Ion Cyclotron Resonance mass spectrometry for plant metabolite profiling and metabolite identification

Mass spectrometry (MS) is usually the technique of choice for metabolomic studies where the volume of sample material is too limited for applications employing nuclear magnetic resonance (NMR) spectroscopy. With the advent of ultra-high accuracy mass spectrometers such as the Orbitrap (resolution ∼ 10(5)) and the Fourier Transform Ion Cyclotron Resonance (FT-ICR) analysers (resolution potentially in excess of 10(6)) there is the opportunity to generate an accurate mass fingerprint (often referred to as a profile since the variables are considered as effectively discrete) of an infused sample extract. In such data representations mass "peaks" are detected in the raw data and the centroid mass intensity calculated. The resolving power and sensitivity of these ultra-high accuracy mass analysers is such that metabolite signals from molecules containing naturally abundant elemental isotopes (e.g. (13)C, (41)K, (15)N, (17)O, (34)S, and (37)Cl) are visible in the data. Such is the instruments precision that it allows for the calculation of highly accurate elemental compositions for the unknown signals, thus aiding greatly in the selection of potential metabolite candidates for the annotation of unknowns prior to their confirmation by comparisons to analytical standards. The application of FT-ICR-MS to plant metabolomics has thus far been limited to a few studies and clear step-by-step methodologies are as yet unavailable. This chapter presents a rigorous method for the extraction and FT-ICR-MS analysis of plant leaf tissues as well as downstream data processing.

Semi-targeted analysis of metabolites using capillary-flow ion chromatography coupled to high-resolution mass spectrometry

This work describes a novel application of capillary-flow ion chromatography mass spectrometry for metabolomic analysis, and comparison of the technique to octadecyl silica and hydrophilic interaction chromatography (HILIC)-based mass spectrometry. While liquid chromatography/mass spectrometry (LC/MS) is rapidly becoming the standard technique for metabolomic analysis, metabolomic samples are extremely heterogeneous, leading to a requirement for multiple methods of analysis and separation techniques to perform a 'global' metabolomic analysis. While C18 is suitable for hydrophobic metabolites and has been used extensively in pharmaceutical drug metabolism studies, HILIC is, in general, efficient at separating polar metabolites. Phosphorylated species and organic acids are challenging to analyse and effectively quantitate on both systems. There is therefore a requirement for an MS-compatible analytical technique that can separate negatively charged compounds, such as ion-exchange chromatography. Evaluation of capillary flow ion chromatography with electrolytic suppression was performed on a library of metabolite standards and was shown to effectively separate organic acids and sugar di- and tri-phosphates. Limits of detection for these compounds range from 0.01 to 100 pmol on-column. Application of capillary ion chromatography to a comparative analysis of energy metabolism in procyclic forms of the parasitic protozoan Trypanosoma brucei where cells were grown on glucose or proline as a carbon source was demonstrated to be more effective than HILIC for detection of the organic acids that comprise glucose central metabolism and the tricarboxylic acid (TCA) cycle.

AMDORAP: non-targeted metabolic profiling based on high-resolution LC-MS

Background

Liquid chromatography-mass spectrometry (LC-MS) utilizing the high-resolution power of an orbitrap is an important analytical technique for both metabolomics and proteomics. Most important feature of the orbitrap is excellent mass accuracy. Thus, it is necessary to convert raw data to accurate and reliable m/z values for metabolic fingerprinting by high-resolution LC-MS.

Results

In the present study, we developed a novel, easy-to-use and straightforward m/z detection method, AMDORAP. For assessing the performance, we used real biological samples, Bacillus subtilis strains 168 and MGB874, in the positive mode by LC-orbitrap. For 14 identified compounds by measuring the authentic compounds, we compared obtained m/z values with other LC-MS processing tools. The errors by AMDORAP were distributed within ±3 ppm and showed the best performance in m/z value accuracy.

Conclusions

Our method can detect m/z values of biological samples much more accurately than other LC-MS analysis tools. AMDORAP allows us to address the relationships between biological effects and cellular metabolites based on accurate m/z values. Obtaining the accurate m/z values from raw data should be indispensable as a starting point for comparative LC-orbitrap analysis. AMDORAP is freely available under an open-source license at http://amdorap.sourceforge.net/.

Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds

MOTIVATION

Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning.

RESULTS

We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment.

AVAILABILITY

The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Leishmaniasis is a debilitating disease caused by the parasite Leishmania. There is extensive clinical polymorphism, including variable responsiveness to treatment. We study Leishmania donovani parasites isolated from visceral leishmaniasis patients in Nepal that responded differently to antimonial treatment due to differing intrinsic drug sensitivity of the parasites. Here, we present a proof-of-principle study in which we applied a metabolomics pipeline specifically developed for L. donovani to characterize the global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates. Clones of drug-sensitive and drug-resistant parasite isolates from clinical samples were cultured in vitro and harvested for metabolomics analysis. The relative abundance of 340 metabolites was determined by ZIC-HILIC chromatography coupled to LTQ-Orbitrap mass spectrometry. Our measurements cover approximately 20% of the predicted core metabolome of Leishmania and additionally detected a large number of lipids. Drug-sensitive and drug-resistant parasites showed distinct metabolic profiles, and unsupervised clustering and principal component analysis clearly distinguished the two phenotypes. For 100 metabolites, the detected intensity differed more than three-fold between the 2 phenotypes. Many of these were in specific areas of lipid metabolism, suggesting that the membrane composition of the drug-resistant parasites is extensively modified. Untargeted metabolomics has been applied on clinical Leishmania isolates to uncover major metabolic differences between drug-sensitive and drug-resistant isolates. The identified major differences provide novel insights into the mechanisms involved in resistance to antimonial drugs, and facilitate investigations using targeted approaches to unravel the key changes mediating drug resistance.

Fourier transform ion cyclotron resonance mass spectrometer with coaxial multi-electrode cell ('O-trap'): first experimental demonstration

The conceptual design of the O-trap Fourier transform ion cyclotron resonance (FT-ICR) cell addresses the speed of analysis issue in FT-ICR mass spectrometry. The concept of the O-trap includes separating the functions of ion excitation and detection between two different FT-ICR cell compartments. The detection compartment of the O-trap implements additional internal coaxial electrodes around which ions with excited cyclotron motion revolve. The expected benefits are higher resolving power and the lesser effect of the space charge. In this work we present the first experimental demonstration of the O-trap cell and its features, including the high ion transfer efficiency between two distinct compartments of an ICR cell after excitation of the coherent cyclotron motion. We demonstrate that utilization of the multiple-electrode detection in the O-trap provides mass resolving power enhancement (achieved over a certain time) equal to the order of the frequency multiplication. In an O-trap installed in a 5 T desk-top cryogen-free superconducting magnet, the resolving power of R = 80,000 was achieved for bradykinin [M + 2H](2+) (m/z 531; equivalent to 100,000 when recalculated for m/z 400) in 0.2 s analysis time (transient length), and R = 300,000 at m/z 531 for a 1 s transient. In both cases, detection on the third multiple of the cyclotron frequency was implemented. In terms of the acquisition speed at fixed resolving power, such performance is equivalent to conventional FT-ICR detection using a 15 T magnet.

Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining

Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

Graph methods for the investigation of metabolic networks in parasitology

Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

The evolution of metabolic profiling in parasitology

The uses of metabolic profiling technologies such as mass spectrometry and nuclear magnetic resonance spectroscopy in parasitology have been multi-faceted. Traditional uses of spectroscopic platforms focused on determining the chemical composition of drugs or natural products used for treatment of parasitic infection. A natural progression of the use of these tools led to the generation of chemical profiles of the parasite in in vitro systems, monitoring the response of the parasite to chemotherapeutics, profiling metabolic consequences in the host organism and to deriving host-parasite interactions. With the dawn of the post-genomic era the paradigm in many research areas shifted towards Systems Biology and the integration of biomolecular interactions at the level of the gene, protein and metabolite. Although these technologies have yet to deliver their full potential, metabolic profiling has a key role to play in defining diagnostic or even prognostic metabolic signatures of parasitic infection and in deciphering the molecular mechanisms underpinning the development of parasite-induced pathologies. The strengths and weaknesses of the various spectroscopic technologies and analytical strategies are summarized here with respect to achieving these goals.

Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer

We present a liquid chromatography-mass spectrometry (LC-MS) method that capitalizes on the mass-resolving power of the orbitrap to enable sensitive and specific measurement of known and unanticipated metabolites in parallel, with a focus on water-soluble species involved in core metabolism. The reversed phase LC method, with a cycle time 25 min, involves a water-methanol gradient on a C18 column with tributylamine as the ion pairing agent. The MS portion involves full scans from 85 to 1000 m/z at 1 Hz and 100,000 resolution in negative ion mode on a stand alone orbitrap ("Exactive"). The median limit of detection, across 80 metabolite standards, was 5 ng/mL with the linear range typically >or=100-fold. For both standards and a cellular extract from Saccharomyces cerevisiae (Baker's yeast), the median inter-run relative standard deviation in peak intensity was 8%. In yeast exact, we detected 137 known compounds, whose (13)C-labeling patterns could also be tracked to probe metabolic flux. In yeast engineered to lack a gene of unknown function (YKL215C), we observed accumulation of an ion of m/z 128.0351, which we subsequently confirmed to be oxoproline, resulting in annotation of YKL215C as an oxoprolinase. These examples demonstrate the suitability of the present method for quantitative metabolomics, fluxomics, and discovery metabolite profiling.

Mass spectrometry-based metabolomics of yeast

Driven by the advent of metabolomics, recent years have seen renewed interest in the investigation of yeast metabolism. Here we provide a practical guide to metabolomic analysis of yeast using liquid chromatography-mass spectrometry (LC-MS). We begin with background on LC-MS and its utility in studying yeast metabolism. We then describe key issues involved at each step of a typical yeast metabolomics experiment: in experimental design, cell culture, metabolite extraction, LC-MS, and data processing and analysis. Throughout, we highlight interdependencies between the steps that are relevant to developing an integrated workflow which effectively leverages LC-MS to reveal yeast biology.

Metabolomic characterization of the salt stress response in Streptomyces coelicolor

The humicolous actinomycete Streptomyces coelicolor routinely adapts to a wide variety of habitats and rapidly changing environments. Upon salt stress, the organism is also known to increase the levels of various compatible solutes. Here we report the results of the first high-resolution metabolomics time series analysis of various strains of S. coelicolor exposed to salt stress: the wild type, mutants with progressive knockouts of the ectoine biosynthesis pathway, and two stress regulator mutants (with disruptions of the sigB and osaB genes). Samples were taken from cultures at 0, 4, 8, and 24 h after salt stress treatment and analyzed by liquid chromatography-mass spectrometry with an LTQ Orbitrap XL mass spectrometer. The results suggest that a large fraction of amino acids is upregulated in response to the salt stress, as are proline/glycine-containing di- and tripeptides. Additionally we found that 5'-methylthioadenosine, a known inhibitor of polyamine biosynthesis, is downregulated upon salt stress. Strikingly, no major differences between the wild-type cultures and the two stress regulator mutants were found, indicating a considerable robustness of the metabolomic response to salt stress, compared to the more volatile changes in transcript abundance reported earlier.

Metabolomic systems biology of trypanosomes

Metabolomics analysis, which aims at the systematic identification and quantification of all metabolites in biological systems, is emerging as a powerful new tool to identify biomarkers of disease, report on cellular responses to environmental perturbation, and to identify the targets of drugs. Here we discuss recent developments in metabolomic analysis, from the perspective of trypanosome research, highlighting remaining challenges and the most promising areas for future research.