Separation and mass spectrometry in microbial metabolomics

Development of a rapid and highly accurate method for 13C tracer-based metabolomics and its application on a hydrogenotrophic methanogen

Microfluidic capillary electrophoresis-mass spectrometry (CE-MS) is a rapid and highly accurate method to determine isotopomer patterns in isotopically labeled compounds. Here, we developed a novel method for tracer-based metabolomics using CE-MS for underivatized proteinogenic amino acids. The method consisting of a ZipChip CE system and a high-resolution Orbitrap Fusion Tribrid mass spectrometer allows us to obtain highly accurate data from 1 μl of 100 nmol/l amino acids comparable to a mere 1 [Formula: see text] 104-105 prokaryotic cells. To validate the capability of the CE-MS method, we analyzed 16 protein-derived amino acids from a methanogenic archaeon Methanothermobacter thermautotrophicus as a model organism, and the mass spectra showed sharp peaks with low mass errors and background noise. Tracer-based metabolome analysis was then performed to identify the central carbon metabolism in M. thermautotrophicus using 13C-labeled substrates. The mass isotopomer distributions of serine, aspartate, and glutamate revealed the occurrence of both the Wood-Ljungdahl pathway and an incomplete reductive tricarboxylic acid cycle for carbon fixation. In addition, biosynthesis pathways of 15 amino acids were constructed based on the mass isotopomer distributions of the detected protein-derived amino acids, genomic information, and public databases. Among them, the presence of alternative enzymes of alanine dehydrogenase, ornithine cyclodeaminase, and homoserine kinase was suggested in the biosynthesis pathways of alanine, proline, and threonine, respectively. To our knowledge, the novel 13C tracer-based metabolomics using CE-MS can be considered the most efficient method to identify central carbon metabolism and amino acid biosynthesis pathways and is applicable to any kind of isolated microbe.

Different Wines from Different Yeasts? "Saccharomyces cerevisiae Intraspecies Differentiation by Metabolomic Signature and Sensory Patterns in Wine"

Alcoholic fermentation is known to be a key stage in the winemaking process that directly impacts the composition and quality of the final product. Twelve wines were obtained from fermentations of Chardonnay must made with twelve different commercial wine yeast strains of Saccharomyces cerevisiae. In our study, FT-ICR-MS, GC-MS, and sensory analysis were combined with multivariate analysis. Ultra-high-resolution mass spectrometry (uHRMS) was able to highlight hundreds of metabolites specific to each strain from the same species, although they are characterized by the same technological performances. Furthermore, the significant involvement of nitrogen metabolism in this differentiation was considered. The modulation of primary metabolism was also noted at the volatilome and sensory levels. Sensory analysis allowed us to classify wines into three groups based on descriptors associated with white wine. Thirty-five of the volatile compounds analyzed, including esters, medium-chain fatty acids, superior alcohols, and terpenes discriminate and give details about differences between wines. Therefore, phenotypic differences within the same species revealed metabolic differences that resulted in the diversity of the volatile fraction that participates in the palette of the sensory pattern. This original combination of metabolomics with the volatilome and sensory approaches provides an integrative vision of the characteristics of a given strain. Metabolomics shine the new light on intraspecific discrimination in the Saccharomyces cerevisiae species.

Microbial Metabolomics: From Methods to Translational Applications

Most microbe-associated infectious diseases severely affect human health. However, clinical diagnosis of pathogenic diseases remains challenging due to the lack of specific and highly reliable methods. To better understand the diagnosis, pathogenesis, and treatment of these diseases, systems biology-driven metabolomics goes beyond the annotated phenotype and better targets the functions than conventional approaches. As a novel strategy for analysis of metabolomes in microbes, microbial metabolomics has been recently used to study many diseases, such as obesity, urinary tract infection (UTI), and hepatitis C. In this chapter, we attempt to introduce various microbial metabolomics methods to better interpret the microbial metabolism underlying a diversity of infectious diseases and inspire scientists to pay more attention to microbial metabolomics, enabling broadly and efficiently its translational applications to infectious diseases, from molecular diagnosis to therapeutic discovery.

Biolayer interferometry provides a robust method for detecting DNA binding small molecules in microbial extracts

DNA replication is an exceptional point of therapeutic intervention for many cancer types and several small molecules targeting DNA have been developed into clinically used antitumor agents. Many of these molecules are naturally occurring metabolites from plants and microorganisms, such as the widely used chemotherapeutic doxorubicin. While natural product sources contain a vast number of DNA binding small molecules, isolating and identifying these molecules is challenging. Typical screening campaigns utilize time-consuming bioactivity-guided fractionation approaches, which use sequential rounds of cell-based assays to guide the isolation of active compounds. In this study, we explore the use of biolayer interferometry (BLI) as a tool for rapidly screening natural product sources for DNA targeting small molecules. We first verified that BLI robustly detected DNA binding using designed GC- and AT-rich DNA oligonucleotides with known DNA intercalating, groove binding, and covalent binding agents including actinomycin D (1), doxorubicin (2), ethidium bromide (3), propidium iodide (4), Hoechst 33342 (5), and netropsin (6). Although binding varied with the properties of the oligonucleotides, measured binding affinities agreed with previously reported values. We next utilized BLI to screen over 100 bacterial extracts from our microbial library for DNA binding activity and found three highly active extracts. Binding-guided isolation was used to isolate the active principle component from each extract, which were identified as echinomycin (8), actinomycin V (9), and chartreusin (10). This biosensor-based DNA binding screen is a novel, low-cost, easy to use, and sensitive approach for medium-throughput screening of complex chemical libraries. Graphical abstract.

Effect of ozone stress on the intracellular metabolites from Cobetia marina

A GCxGC-MS system was employed with a non-polar × mid-polar column set for the metabolic non-target analysis of Cobetia marina, the model bacteria for marine biofouling. C. marina was treated with ozone to investigate the intracellular metabolic state change under oxidative stress. A minimal inhibitory concentration test was involved to guarantee that the applied ozone dosages were not lethal for the cells. In this study, non-target analyses were performed to identify the metabolites according to the NIST database. As a result, over 170 signals were detected under normal living conditions including 35 potential metabolites. By the comparison of ozone-treated and non-treated samples, five compounds were selected to describe observed trends of signals in the contour plots. Oleic acid exhibited a slight growth by increasing ozone dosage. In contrast, other metabolites such as the amino acid L-proline showed less abundance after ozone treatment, which was more evident once ozone dosage was raised. Thus, this work could provide a hint for searching for up/downregulating factors in such environmental stress conditions for C. marina. Graphical abstract.

Characterization and analysis of estrogenic cyclic phenone metabolites produced in vitro by rainbow trout liver slices using GC-MS, LC-MS and LC-TOF-MS

Cyclic phenones are chemicals of interest to the USEPA and international organizations due to their potential for endocrine disruption to aquatic and terrestrial species. The metabolic conversion of cyclic phenones by liver hepatocytes and the structure of main metabolites yielded have not been assessed in fish species. As part of a larger project, in this study we investigated the structure of metabolites produced in vitro by rainbow trout (rt) liver slices after exposure to the model cyclic phenones benzophenone (DPK), cyclobutyl phenyl ketone (CBP) and cyclohexyl phenyl ketone (CPK). While only one distinct metabolite was detected for DPK and CBP (benzhydrol and CBPOH, respectively), CPK yielded nine positional isomers (M1-M9) as products. In absence of standards, improved inference of CPK metabolites tentative structures was achieved by combining GC-MS with and without derivatization, LC with tandem MS, LC with high resolution time of flight (TOF) MS and LC fractionation data with CPK phase II conjugative metabolism information. Data supported that CPK is metabolized by phase I oxidation of the cyclohexyl ring and not the phenyl group as predicted by metabolism simulators. CPK metabolites M1 and M2 (MW 186), were proposed to be cyclohexenyl-derivatives. Also, M6-M9 were proposed to be hydroxylated metabolites (MW 204), with the potential for undergoing phase II conjugative metabolism to glucuronides and sulfates. Finally, M3, M4 and M5 were proposed as cyclohexanone-derivatives of CPK (MW 202), resulting from the limited redox-interconversion of their hydroxylated pairs M8, M6 and M7, respectively. Assessment of metabolite role in biological responses associated with endocrine disruption will advance the development of methods for species extrapolation and the understanding of differential sensitivity of species to chemical exposure.

Advances in Chemical and Biological Methods to Identify Microorganisms-From Past to Present

Fast detection and identification of microorganisms is a challenging and significant feature from industry to medicine. Standard approaches are known to be very time-consuming and labor-intensive (e.g., culture media and biochemical tests). Conversely, screening techniques demand a quick and low-cost grouping of bacterial/fungal isolates and current analysis call for broad reports of microorganisms, involving the application of molecular techniques (e.g., 16S ribosomal RNA gene sequencing based on polymerase chain reaction). The goal of this review is to present the past and the present methods of detection and identification of microorganisms, and to discuss their advantages and their limitations.

Integrating transcriptomics and metabolomics for the analysis of the aroma profiles of Saccharomyces cerevisiae strains from diverse origins

Background

During must fermentation thousands of volatile aroma compounds are formed, with higher alcohols, acetate esters and ethyl esters being the main aromatic compounds contributing to floral and fruity aromas. The action of yeast, in particular Saccharomyces cerevisiae, on the must components will build the architecture of the wine flavour and its fermentation bouquet. The objective of the present work was to better understand the molecular and metabolic bases of aroma production during a fermentation process. For such, comparative transcriptomic and metabolic analysis was performed at two time points (5 and 50 g/L of CO₂ released) in fermentations conducted by four yeast strains from different origins and/or technological applications (cachaça, sake, wine, and laboratory), and multivariate factorial analyses were used to rationally identify new targets for improving aroma production.

Results

Results showed that strains from cachaça, sake and wine produced higher amounts of acetate esters, ethyl esters, acids and higher alcohols, in comparison with the laboratory strain. At fermentation time T1 (5 g/L CO₂ released), comparative transcriptomics of the three S. cerevisiae strains from different fermentative environments in comparison with the laboratory yeast S288c, showed an increased expression of genes related with tetracyclic and pentacyclic triterpenes metabolism, involved in sterol synthesis. Sake strain also showed upregulation of genes ADH7 and AAD6, involved in the formation of higher alcohols in the Ehrlich pathway. For fermentation time point T2 (50 g/L CO₂ released), again sake strain, but also VL1 strain, showed an increased expression of genes involved in formation of higher alcohols in the Ehrlich pathway, namely ADH7, ADH6 and AAD6, which is in accordance with the higher levels of methionol, isobutanol, isoamyl alcohol and phenylethanol observed.

Conclusions

Our approach revealed successful to integrate data from several technologies (HPLC, GC-MS, microarrays) and using different data analysis methods (PCA, MFA). The results obtained increased our knowledge on the production of wine aroma and flavour, identifying new gene in association to the formation of flavour active compounds, mainly in the production of fatty acids, and ethyl and acetate esters.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3816-1) contains supplementary material, which is available to authorized users.

Wine microbiome: A dynamic world of microbial interactions

Most fermented products are generated by a mixture of microbes. These microbial consortia perform various biological activities responsible for the nutritional, hygienic, and aromatic qualities of the product. Wine is no exception. Substantial yeast and bacterial biodiversity is observed on grapes, and in both must and wine. The diverse microorganisms present interact throughout the winemaking process. The interactions modulate the hygienic and sensorial properties of the wine. Many studies have been conducted to elucidate the nature of these interactions, with the aim of establishing better control of the two fermentations occurring during wine processing. However, wine is a very complex medium making such studies difficult. In this review, we present the current state of research on microbial interactions in wines. We consider the different kinds of interactions between different microorganisms together with the consequences of these interactions. We underline the major challenges to obtaining a better understanding of how microbes interact. Finally, strategies and methodologies that may help unravel microbe interactions in wine are suggested.

Sample preparation procedures utilized in microbial metabolomics: An overview

Bacteria are remarkably diverse in terms of their size, structure and biochemical properties. Due to this fact, it is hard to develop a universal method for handling bacteria cultures during metabolomic analysis. The choice of suitable processing methods constitutes a key element in any analysis, because only appropriate selection of procedures may provide accurate results, leading to reliable conclusions. Because of that, every analytical experiment concerning bacteria requires individually and very carefully planned research methodology. Although every study varies in terms of sample preparation, there are few general steps to follow while planning experiment, like sampling, separation of cells from growth medium, stopping their metabolism and extraction. As a result of extraction, all intracellular metabolites should be washed out from cell environment. What is more, extraction method utilized cannot cause any chemical decomposition or degradation of the metabolome. Furthermore, chosen extraction method should correlate with analytical technique, so it will not disturb or prolong following sample preparation steps. For those reasons, we observe a need to summarize sample preparation procedures currently utilized in microbial metabolomic studies. In the presented overview, papers concerning analysis of extra- and intracellular metabolites, published over the last decade, have been discussed. Presented work gives some basic guidelines that might be useful while planning experiments in microbial metabolomics.

Unraveling the Functions of the Macroalgal Microbiome

Macroalgae are a diverse group of photosynthetic eukaryotic lower organisms and offer indispensable ecosystem services toward sustainable productivity of rocky coastal areas. The earlier studies have mainly focused on elucidation of the roles of the epiphytic bacterial communities in the ecophysiology of the host macroalga. However, mutualistic interactions have become topic of current interest. It is evident from recent studies that a fraction of epiphytic bacterial communities can be categorized as “core microbial species”, suggesting an obligate association. Epiphytic bacterial communities have also been reported to protect macroalgal surfaces from biofouling microorganisms through production of biologically active metabolites. Because of their intrinsic roles in the host life cycle, the host in turn may provide necessary organic nutrients in order to woo pelagic microbial communities to settle on the host surfaces. However, the precise composition of microbiomes and their functional partnership with hosts are hardly understood. In contrast, the microbial studies associated with human skin and gut and plants have significantly advanced our knowledge on microbiome and their functional interactions with the host. This has led to manipulation of the microbial flora of the human gut and of agricultural plants for improving health and performance. Therefore, it is highly imperative to investigate the functional microbiome that is closely involved in the life cycles of the host macroalgae using high-throughput techniques (metagenomics and metatranscriptomics). The findings from such investigations would help in promoting health and productivity in macroalgal species through regulation of functionally active microbiome.

Capillary Electrophoresis in Metabolomics

Metabolomics is an analytical toolbox to describe (all) low-molecular-weight compounds in a biological system, as cells, tissues, urine, and feces, as well as in serum and plasma. To analyze such complex biological samples, high requirements on the analytical technique are needed due to the high variation in compound physico-chemistry (cholesterol derivatives, amino acids, fatty acids as SCFA, MCFA, or LCFA, or pathway-related metabolites belonging to each individual organism) and concentration dynamic range. All main separation techniques (LC-MS, GC-MS) are applied in routine to metabolomics hyphenated or not to mass spectrometry, and capillary electrophoresis is a powerful high-resolving technique but still underused in this field of complex samples. Metabolomics can be performed in the non-targeted way to gain an overview on metabolite profiles in biological samples. Targeted metabolomics is applied to analyze quantitatively pre-selected metabolites. This chapter reviews the use of capillary electrophoresis in the field of metabolomics and exemplifies solutions in metabolite profiling and analysis in urine and plasma.

Towards a Universal Approach Based on Omics Technologies for the Quality Control of Food

In the last decades, food science has greatly developed, turning from the consideration of food as mere source of energy to a growing awareness on its importance for health and particularly in reducing the risk of diseases. Such vision led to an increasing attention towards the origin and quality of raw materials as well as their derived food products. The continuous advance in molecular biology allowed setting up efficient and universal omics tools to unequivocally identify the origin of food items and their traceability. In this review, we considered the application of a genomics approach known as DNA barcoding in characterizing the composition of foodstuffs and its traceability along the food supply chain. Moreover, metabolomics analytical strategies based on Nuclear Magnetic Resonance (NMR) and Mass Spectroscopy (MS) were discussed as they also work well in evaluating food quality. The combination of both approaches allows us to define a sort of molecular labelling of food that is easily understandable by the operators involved in the food sector: producers, distributors, and consumers. Current technologies based on digital information systems such as web platforms and smartphone apps can facilitate the adoption of such molecular labelling.

Omics on bioleaching: current and future impacts

Bioleaching corresponds to the microbial-catalyzed process of conversion of insoluble metals into soluble forms. As an applied biotechnology globally used, it represents an extremely interesting field of research where omics techniques can be applied in terms of knowledge development, but moreover in terms of process design, control, and optimization. In this mini-review, the current state of genomics, proteomics, and metabolomics of bioleaching and the major impacts of these analytical methods at industrial scale are highlighted. In summary, genomics has been essential in the determination of the biodiversity of leaching processes and for development of conceptual and functional metabolic models. Proteomic impacts are mostly related to microbe-mineral interaction analysis, including copper resistance and biofilm formation. Early steps of metabolomics in the field of bioleaching have shown a significant potential for the use of metabolites as industrial biomarkers. Development directions are given in order to enhance the future impacts of the omics in biohydrometallurgy.

Complementing reversed-phase selectivity with porous graphitized carbon to increase the metabolome coverage in an on-line two-dimensional LC-MS setup for metabolomics

Efficient and robust separation methods are indispensable in modern LC-MS based metabolomics, where high-resolution mass spectrometers are challenged by isomeric and isobaric metabolites. The optimization of chromatographic separation hence remains an invaluable tool in the comprehensive analysis of the chemically diverse intracellular metabolome. While it is widely accepted that a single method with comprehensive metabolome coverage does not exist, the potential of combining different chromatographic selectivities in two-dimensional liquid chromatography is underestimated in the field. Here, we introduce a novel separation system combining reversed-phase and porous graphitized carbon liquid chromatography in a heart-cut on-line two-dimensional setup for mass spectrometry. The proposed experimental setup can be readily implemented using standard HPLC equipment with only one additional HPLC pump and a two-position six-port valve. The method proved to be robust with excellent retention time stability (average 0.4%) even in the presence of biological matrix. Testing the presented approach on a test mixture of 82 relevant intracellular metabolites, the number of metabolites that are retained could be doubled as compared to reversed-phase liquid chromatography alone. The presented work further demonstrates how the distinct selectivity of porous graphitized carbon complements reversed-phase liquid chromatography and extends the metabolome coverage of conventional LC-MS based methods in metabolomics to biologically important, but analytically challenging compound groups such as sugar phosphates. Both metabolic profiling and metabolic fingerprinting benefit from this method's increased separation capabilities that enhance sample throughput and the biological information content of LC-MS data. An inter-platform comparison with GC- and LC-tandem MS analyses confirmed the validity of the presented two-dimensional approach in the analysis of yeast cell extracts from P. pastoris.

Avoiding misannotation of in-source fragmentation products as cellular metabolites in liquid chromatography-mass spectrometry-based metabolomics

Liquid chromatography-mass spectrometry (LC-MS) technology allows for rapid quantitation of cellular metabolites, with metabolites identified by mass spectrometry and chromatographic retention time. Recently, with the development of rapid scanning high-resolution high accuracy mass spectrometers and the desire for high throughput screening, minimal or no chromatographic separation has become increasingly popular. When analyzing complex cellular extracts, however, the lack of chromatographic separation could potentially result in misannotation of structurally related metabolites. Here, we show that, even using electrospray ionization, a soft ionization method, in-source fragmentation generates unwanted byproducts of identical mass to common metabolites. For example, nucleotide-triphosphates generate nucleotide-diphosphates, and hexose-phosphates generate triose-phosphates. We evaluated yeast intracellular metabolite extracts and found more than 20 cases of in-source fragments that mimic common metabolites. Accordingly, chromatographic separation is required for accurate quantitation of many common cellular metabolites.

Chemometric evaluation of Saccharomyces cerevisiae metabolic profiles using LC-MS

A new liquid chromatography mass spectrometry (LC-MS) metabolomics strategy coupled to chemometric evaluation, including variable and biomarker selection, has been assessed as a tool to discriminate between control and stressed Saccharomyces cerevisiae yeast samples. Metabolic changes occurring during yeast culture at different temperatures (30 and 42 °C) were analysed and the complex data generated in profiling experiments were evaluated by different chemometric multivariate approaches. Multivariate curve resolution alternating least squares (MCR-ALS) was applied to full spectral scan LC-MS preprocessed data multisets arranged in augmented column-wise data matrices. The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks. The investigation of possible relationships between MCR-ALS resolved chromatographic peak areas and culture temperature was then investigated by partial least squares discriminant analysis (PLS-DA). Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA-Variable Importance in Projection scores. A metabolite identification workflow was developed utilizing MCR-ALS resolved pure MS spectra and high-resolution accurate mass measurements to confirm assigned structures based on entries in metabolite databases. A total of 65 metabolites were identified. A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies.

Perturbation Experiments: Approaches for Metabolic Pathway Analysis in Bioreactors

In the last decades, targeted metabolic engineering of microbial cells has become one of the major tools in bioprocess design and optimization. For successful application, a detailed knowledge is necessary about the relevant metabolic pathways and their regulation inside the cells. Since in vitro experiments cannot display process conditions and behavior properly, process data about the cells' metabolic state have to be collected in vivo. For this purpose, special techniques and methods are necessary. Therefore, most techniques enabling in vivo characterization of metabolic pathways rely on perturbation experiments, which can be divided into dynamic and steady-state approaches. To avoid any process disturbance, approaches which enable perturbation of cell metabolism in parallel to the continuing production process are reasonable. Furthermore, the fast dynamics of microbial production processes amplifies the need of parallelized data generation. These points motivate the development of a parallelized approach for multiple metabolic perturbation experiments outside the operating production reactor. An appropriate approach for in vivo characterization of metabolic pathways is presented and applied exemplarily to a microbial L-phenylalanine production process on a 15 L-scale.

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.

Dynamic metabolic profiling of cyanobacterial glycogen biosynthesis under conditions of nitrate depletion

Cyanobacteria represent a globally important biomass because they are responsible for a substantial proportion of primary production in the hydrosphere. Arthrospira platensis is a fast-growing halophilic cyanobacterium capable of accumulating glycogen and has the potential to serve as a feedstock in the fermentative production of third-generation biofuels. Accordingly, enhancing cyanobacterial glycogen production is a promising biofuel production strategy. However, the regulatory mechanism of glycogen metabolism in cyanobacteria is poorly understood. The aim of the present study was to determine the metabolic flux of glycogen biosynthesis using a dynamic metabolomic approach. Time-course profiling of widely targeted cyanobacterial metabolic intermediates demonstrated a global metabolic reprogramming that involves transient increases in the levels of some amino acids during the glycogen production phase induced by nitrate depletion. Also, in vivo labelling with NaH(13)CO3 enabled direct measurement of metabolic intermediate turnover in A. platensis, revealing that under conditions of nitrate depletion glycogen is biosynthesized with carbon derived from amino acids released from proteins via gluconeogenesis. This dynamic metabolic profiling approach provided conclusive evidence of temporal alterations in the metabolic profile in cyanobacterial cells.

Metabolites associated with adaptation of microorganisms to an acidophilic, metal-rich environment identified by stable-isotope-enabled metabolomics

Microorganisms grow under a remarkable range of extreme conditions. Environmental transcriptomic and proteomic studies have highlighted metabolic pathways active in extremophilic communities. However, metabolites directly linked to their physiology are less well defined because metabolomics methods lag behind other omics technologies due to a wide range of experimental complexities often associated with the environmental matrix. We identified key metabolites associated with acidophilic and metal-tolerant microorganisms using stable isotope labeling coupled with untargeted, high-resolution mass spectrometry. We observed >3,500 metabolic features in biofilms growing in pH ~0.9 acid mine drainage solutions containing millimolar concentrations of iron, sulfate, zinc, copper, and arsenic. Stable isotope labeling improved chemical formula prediction by >50% for larger metabolites (>250 atomic mass units), many of which were unrepresented in metabolic databases and may represent novel compounds. Taurine and hydroxyectoine were identified and likely provide protection from osmotic stress in the biofilms. Community genomic, transcriptomic, and proteomic data implicate fungi in taurine metabolism. Leptospirillum group II bacteria decrease production of ectoine and hydroxyectoine as biofilms mature, suggesting that biofilm structure provides some resistance to high metal and proton concentrations. The combination of taurine, ectoine, and hydroxyectoine may also constitute a sulfur, nitrogen, and carbon currency in the communities.

IMPORTANCE

Microbial communities are central to many critical global processes and yet remain enigmatic largely due to their complex and distributed metabolic interactions. Metabolomics has the possibility of providing mechanistic insights into the function and ecology of microbial communities. However, our limited knowledge of microbial metabolites, the difficulty of identifying metabolites from complex samples, and the inability to link metabolites directly to community members have proven to be major limitations in developing advances in systems interactions. Here, we show that combining stable-isotope-enabled metabolomics with genomics, transcriptomics, and proteomics can illuminate the ecology of microorganisms at the community scale.

Metabolic footprinting of mutant libraries to map metabolite utilization to genotype

The discrepancy between the pace of sequencing and functional characterization of genomes is a major challenge in understanding complex microbial metabolic processes and metabolic interactions in the environment. Here, we identified and validated genes related to the utilization of specific metabolites in bacteria by profiling metabolite utilization in libraries of mutant strains. Untargeted mass spectrometry based metabolomics was used to identify metabolites utilized by Escherichia coli and Shewanella oneidensis MR-1. Targeted high-throughput metabolite profiling of spent media of 8042 individual mutant strains was performed to link utilization to specific genes. Using this approach we identified genes of known function as well as novel transport proteins and enzymes required for the utilization of tested metabolites. Specific examples include two subunits of a predicted ABC transporter encoded by the genes SO1043 and SO1044 required for the utilization of citrulline and a predicted histidase encoded by the gene SO3057 required for the utilization of ergothioneine by S. oneidensis. In vitro assays with purified proteins showed substrate specificity of SO3057 toward ergothioneine and histidine betaine in contrast to substrate specificity of a paralogous histidase SO0098 toward histidine. This generally applicable, high-throughput workflow has the potential both to discover novel metabolic capabilities of microorganisms and to identify the corresponding genes.

Retrosynthetic design of heterologous pathways

Tools from metabolic engineering and synthetic biology are synergistically used in order to develop high-performance cell factories. However, the number of successful applications has been limited due to the complexity of exploring efficiently the metabolic space for the discovery of candidate heterologous pathways. To address this challenge, retrosynthetic biology provides an integrated framework to formalize and rationalize the problem of importing biosynthetic pathways into a chassis organism using methods at the interface from bottom-up and top-down strategies. Here, we describe step by step the process of implementing a retrosynthetic framework for the design of heterologous biosynthetic pathways in a chassis organism. The method consists of the following steps: choosing the chassis and the target, selection of an in silico model for the chassis, definition of the metabolic space, pathway enumeration, gene selection, estimation of yields, toxicity prediction of pathway metabolites, definition of an objective function to select the best pathway candidates, and pathway implementation and verification.

Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for (13)C-metabolic flux analysis ((13)C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of (13)C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for (13)C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.

Microbial metabolomics

Microbial metabolomics constitutes an integrated component of systems biology. By studying the complete set of metabolites within a microorganism and monitoring the global outcome of interactions between its development processes and the environment, metabolomics can potentially provide a more accurate snap shot of the actual physiological state of the cell. Recent advancement of technologies and post-genomic developments enable the study and analysis of metabolome. This unique contribution resulted in many scientific disciplines incorporating metabolomics as one of their “omics” platforms. This review focuses on metabolomics in microorganisms and utilizes selected topics to illustrate its impact on the understanding of systems microbiology.

Utilization of a deuterated derivatization agent to synthesize internal standards for gas chromatography-tandem mass spectrometry quantification of silylated metabolites

GC-MS analysis of silylated metabolites is a sensitive method that covers important metabolite groups such as sugars, amino acids and non-amino organic acids, and it has become one of the most important analytical methods for exploring the metabolome. Absolute quantitative GC-MS analysis of silylated metabolites poses a challenge as different metabolites have different derivatization kinetics and as their silyl-derivates have varying stability. This report describes the development of a targeted GC-MS/MS method for quantification of metabolites. Internal standards for each individual metabolite were obtained by derivatization of a mixture of standards with deuterated N-methyl-N-trimethylsilyltrifluoroacetamide (d9-MSTFA), and spiking this solution into MSTFA derivatized samples prior to GC-MS/MS analysis. The derivatization and spiking protocol needed optimization to ensure that the behaviour of labelled compound responses in the spiked sample correctly reflected the behaviour of unlabelled compound responses. Using labelled and unlabelled MSTFA in this way enabled normalization of metabolite responses by the response of their deuterated counterpart (i.e. individual correction). Such individual correction of metabolite responses reproducibly resulted in significantly higher precision than traditional data correction strategies when tested on samples both with and without serum and urine matrices. The developed method is thus a valuable contribution to the field of absolute quantitative metabolomics.

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or "awakening" of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology.

Metabolic pathway determination and flux analysis in nonmodel microorganisms through 13C-isotope labeling

C-isotope labeling is a commonly used technique for determining and quantifying pathways in microorganisms under various growth conditions. The experimental protocol consists of feeding the cell with a composition-defined substrate and measuring isotopic labeling patterns in the synthesized metabolites (often the amino acids). Not only can the labeling information be cross-referenced with genomic information to identify the novel pathways, but it can also be used to decipher absolute carbon fluxes through the metabolic network of interest. This technique can be widely used for functional characterization of nonmodel microbial species, and thus we provide a (13)C-pathway and flux analysis protocol. The five key procedures are: (1) growing cells using labeled substrates, (2) measuring extracellular metabolite and biomass component, (3) analyzing isotopic labeling patterns in amino acids and central metabolites using gas chromatography-mass spectrometry, (4) tracing (13)C carbon transitions in metabolites and discovering new pathways, and (5) estimating flux distributions based on isotopomer constraints. This protocol provides complementary information to the recently published protocol for (13)C-based metabolic flux analysis of the model species Escherichia coli (Nat Protoc 4:878-892, 2009).

Foodomics: MS-based strategies in modern food science and nutrition

Modern research in food science and nutrition is moving from classical methodologies to advanced analytical strategies in which MS-based techniques play a crucial role. In this context, Foodomics has been recently defined as a new discipline that studies food and nutrition domains through the application of advanced omics technologies in which MS techniques are considered indispensable. Applications of Foodomics include the genomic, transcriptomic, proteomic, and/or metabolomic study of foods for compound profiling, authenticity, and/or biomarker-detection related to food quality or safety; the development of new transgenic foods, food contaminants, and whole toxicity studies; new investigations on food bioactivity, food effects on human health, etc. This review work does not intend to provide an exhaustive revision of the many works published so far on food analysis using MS techniques. The aim of the present work is to provide an overview of the different MS-based strategies that have been (or can be) applied in the new field of Foodomics, discussing their advantages and drawbacks. Besides, some ideas about the foreseen development and applications of MS-techniques in this new discipline are also provided.

Is metabolomics reachable? Different purification strategies of human colon cancer cells provide different CE-MS metabolite profiles

In this work, four different metabolite purification approaches are investigated prior to metabolomics of human HT29 colon cancer cells. Namely, methanol deproteinization, ultrafiltration and two SPE methods using C18 and polymer-based cartridges were studied. The extracts were characterized via a metabolomic approach based on the application of CE TOF MS (CE-MS). CE-MS analysis time was less than 20 min per sample and allowed the simultaneous and reproducible analysis of more than 80 metabolites in a single run with a minimum consumption of sample and reagents. Metabolome analysis revealed in some cases important differences among the studied metabolite purification procedures. No significant differences were observed in the metabolite profile using C18 and polymer-based cartridges, or between ultrafiltration and methanol deproteinization. However, important differences were observed in the metabolomic profiles obtained from SPE and methanol deproteinization samples. These results demonstrate the crucial role of the metabolite purification strategy in metabolomics since it can bias (and in some cases mislead) the conclusions achieved by the metabolomic study.

Mass spectrometry-based metabolomics, analysis of metabolite-protein interactions, and imaging

Our understanding of biology has been greatly improved through recent developments in mass spectrometry, which is providing detailed information on protein and metabolite composition as well as protein-metabolite interactions. The high sensitivity and resolution of mass spectrometry achieved with liquid or gas chromatography allows for detection and quantification of hundreds to thousands of molecules in a single measurement. Where homogenization-based sample preparation and extraction methods result in a loss of spatial information, mass spectrometry imaging technologies provide the in situ distribution profiles of metabolites and proteins within tissues. Mass spectrometry-based analysis of metabolite abundance, protein-metabolite interactions, and spatial distribution of compounds facilitates the high-throughput screening of biochemical reactions, the reconstruction of metabolic networks, biomarker discovery, determination of tissue compositions, and functional annotation of both proteins and metabolites.

Metabolic pathway engineering based on metabolomics confers acetic and formic acid tolerance to a recombinant xylose-fermenting strain of Saccharomyces cerevisiae

Background

The development of novel yeast strains with increased tolerance toward inhibitors in lignocellulosic hydrolysates is highly desirable for the production of bio-ethanol. Weak organic acids such as acetic and formic acids are necessarily released during the pretreatment (i.e. solubilization and hydrolysis) of lignocelluloses, which negatively affect microbial growth and ethanol production. However, since the mode of toxicity is complicated, genetic engineering strategies addressing yeast tolerance to weak organic acids have been rare. Thus, enhanced basic research is expected to identify target genes for improved weak acid tolerance.

Results

In this study, the effect of acetic acid on xylose fermentation was analyzed by examining metabolite profiles in a recombinant xylose-fermenting strain of Saccharomyces cerevisiae. Metabolome analysis revealed that metabolites involved in the non-oxidative pentose phosphate pathway (PPP) [e.g. sedoheptulose-7-phosphate, ribulose-5-phosphate, ribose-5-phosphate and erythrose-4-phosphate] were significantly accumulated by the addition of acetate, indicating the possibility that acetic acid slows down the flux of the pathway. Accordingly, a gene encoding a PPP-related enzyme, transaldolase or transketolase, was overexpressed in the xylose-fermenting yeast, which successfully conferred increased ethanol productivity in the presence of acetic and formic acid.

Conclusions

Our metabolomic approach revealed one of the molecular events underlying the response to acetic acid and focuses attention on the non-oxidative PPP as a target for metabolic engineering. An important challenge for metabolic engineering is identification of gene targets that have material importance. This study has demonstrated that metabolomics is a powerful tool to develop rational strategies to confer tolerance to stress through genetic engineering.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.

Metabolomics and malaria biology

Metabolomics has ushered in a novel and multi-disciplinary realm in biological research. It has provided researchers with a platform to combine powerful biochemical, statistical, computational, and bioinformatics techniques to delve into the mysteries of biology and disease. The application of metabolomics to study malaria parasites represents a major advance in our approach towards gaining a more comprehensive perspective on parasite biology and disease etiology. This review attempts to highlight some of the important aspects of the field of metabolomics, and its ongoing and potential future applications to malaria research.

Dealing with the unknown: metabolomics and metabolite atlases

Metabolomics is the comprehensive profiling of the small molecule composition of a biological sample. Since metabolites are often the indirect products of gene expression, this approach is being used to provide new insights into a variety of biological systems (clinical, bioenergy, etc.). A grand challenge for metabolomics is the complexity of the data, which often include many experimental artifacts. This is compounded by the tremendous chemical diversity of metabolites. Identification of each uncharacterized metabolite is in many ways its own puzzle (compared with proteomics, which is based on predictable fragmentation patterns of polypeptides). Therefore, effective data reduction/prioritization strategies are critical for this rapidly developing field. Here we review liquid chromatography electrospray ionization mass spectrometry (LC/MS)-based metabolomics, methods for feature finding/prioritization, approaches for identifying unknown metabolites, and construction of method specific 'Metabolite Atlases'.

Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity

Substrate metabolite concentrations are inversely related to the in vivo capacity of their converting enzymes.

Local metabolite responses represent a passive mechanism to achieve metabolic homeostasis upon perturbations in enzyme capacity.

Enzyme capacity and metabolite concentration control the metabolic reaction rate.

Physiological behavior emerges from complex dynamic interactions between transcripts, enzymes, and metabolites, the constituents of metabolism, and its regulatory network (Sauer, 2006). Although large data sets can be generated on all these variables, data integration, in particular across different omics levels, is becoming the key challenge (Stitt and Fernie, 2003; Sauer et al, 2007). In this study, we identify a general relationship between substrates of an enzymatic reaction and enzymatic capacity in central carbon metabolism that allows the prediction of changes in metabolite concentration based on changes in enzyme capacity and vise versa. To elucidate whether general relationships exist between metabolite concentrations and enzyme capacities (i.e. the outcome of enzyme abundance combined with activity), we propose three hypothetical and alternative governing principles. The first hypothesis postulates a minimization of metabolite concentration at a given flux. In this case, no correlation between alterations in metabolite concentrations and enzyme capacities is expected. The second hypothesis postulates a tradeoff between metabolite concentration and enzyme capacity. In this case, a negative correlation between differences in concentrations of substrate metabolites and differences in enzyme capacity is expected. The third hypothesis postulates a minimization of enzyme capacity at a given flux. In this case, we expect a positive correlation between differences in concentrations of product metabolites and differences in enzyme capacity. As hypotheses I–III imply different relationships between enzyme capacities and metabolite concentrations, identification of the prevailing situation in microbial metabolism requires quantitative in vivo metabolite concentration and enzyme capacity data upon moderate changes in enzyme capacity. As a first test, we chose wild type Saccharomyces cerevisiae and an otherwise isogenic mutant with a complete deletion of the transcription factor Gcr2p, an activator of glycolysis (Chambers et al, 1995). This mutant exhibits altered transcript abundances, enzyme activities, and metabolite concentrations within closely connected reactions in glycolysis and in the tricarboxylic acid cycle (Uemura and Fraenkel, 1990, 1999; Sasaki and Uemura, 2005). To quantify the relationship between metabolite concentrations and enzyme capacities, we determined transcript, enzyme, and metabolite abundances in wild type and GCR2 mutant in batch culture on glucose minimal medium. Transcript and enzyme abundances are used as surrogates for enzyme capacities. The most significant correlation was observed for fold-changes in substrate metabolite concentrations with fold-changes in enzyme abundance. Not unexpectedly, enzyme abundances were a significantly better approximation for enzyme capacities than transcript abundances. A further improved correlation was achieved by considering all diverging enzymes that react upon a given substrate metabolite simultaneously rather than considering them as a separate reaction (Figure 4). The high correlation between substrate metabolite and enzyme fold-changes suggests a tradeoff between enzyme capacity and metabolite concentrations in central metabolism. To test the general validity for central carbon metabolism of the above-identified tradeoff between reaction substrate metabolite concentrations and enzyme abundances, we performed four independent validations: a statistical, a literature based, and two experimental ones. Statistically, we verified that the correlation between substrate metabolites and enzymes could not have been found by chance. On the basis of the literature data, we performed the above correlation analysis with literature data. All available data followed the proposed correlation, thus providing further evidence for the general validity of this relationship. As a more serious challenge of the identified correlation, we designed an experiment where the absolute flux alterations are large and additionally the flux directions are altered. We expected the substrate metabolites to occur at higher concentrations in the mutant than in the wild type. This expectation was fulfilled by the experimental data in all cases, thereby further corroborating the negative correlation between enzyme capacity and metabolite concentrations. So far, our experimental evidence was based on perturbing multiple enzyme abundances through a transcription factor mutant. To ensure that our findings are also valid for single-reaction perturbations, we modulated individual abundances of the four glycolytic enzymes Pgi1p, Tpi1p, Eno2p, and Cdc19p using strains whose endogenous genomic promotor was replaced by a Tet-controlled promotor (Mnaimneh et al, 2004) (Figure 7). Thus, we determined intracellular metabolites concentrations during exponential growth in the strains with modulated enzyme abundance. Our above-identified correlation predicts metabolite concentrations to increase only for the substrate of the such perturbed reaction and all other metabolite concentrations to remain constant. This prediction was verified. We demonstrate here that global or local alterations in enzyme abundance correlate negatively with enzyme reaction substrate concentration at least in central carbon metabolism. This implies a tradeoff between enzyme and metabolite efficiency in metabolic networks. These findings can be interpreted as a passive network mechanism to maintain close-to-wild-type homeostasis of central carbon metabolism upon perturbations that alter the enzyme capacity. The alterations are compensated by converse changes in reaction substrate concentrations, thereby minimizing the difference in metabolic flux that is caused by the alteration.

What is the relationship between enzymes and metabolites, the two major constituents of metabolic networks? We propose three alternative relationships between enzyme capacity and metabolite concentration alterations based on a Michaelis–Menten kinetic; that is enzyme capacities, metabolite concentrations, or both could limit the metabolic reaction rates. These relationships imply different correlations between changes in enzyme capacity and metabolite concentration, which we tested by quantifying metabolite, transcript, and enzyme abundances upon local (single-enzyme modulation) and global (GCR2 transcription factor mutant) perturbations in Saccharomyces cerevisiae. Our results reveal an inverse relationship between fold-changes in substrate metabolites and their catalyzing enzymes. These data provide evidence for the hypothesis that reaction rates are jointly limited by enzyme capacity and metabolite concentration. Hence, alteration in one network constituent can be efficiently buffered by converse alterations in the other constituent, implying a passive mechanism to maintain metabolic homeostasis upon perturbations in enzyme capacity.

Production of biofuels and biochemicals: in need of an ORACLE

The engineering of cells for the production of fuels and chemicals involves simultaneous optimization of multiple objectives, such as specific productivity, extended substrate range and improved tolerance - all under a great degree of uncertainty. The achievement of these objectives under physiological and process constraints will be impossible without the use of mathematical modeling. However, the limited information and the uncertainty in the available information require new methods for modeling and simulation that will characterize the uncertainty and will quantify, in a statistical sense, the expectations of success of alternative metabolic engineering strategies. We discuss these considerations toward developing a framework for the Optimization and Risk Analysis of Complex Living Entities (ORACLE) - a computational method that integrates available information into a mathematical structure to calculate control coefficients.