Gas chromatographic metabolic profiling: a sensitive tool for functional microbial ecology

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from ('exometabolome') or existing in ('endometabolome') a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 degrees C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.     

The biological interpretation of metabolomic data can be misled by the extraction method used

The field of metabolomics is getting more and more popular and a wide range of different sample preparation procedures are in use by different laboratories. Chemical extraction methods using one or more organic solvents as the extraction agent are the most commonly used approach to extract intracellular metabolites and generate metabolite profiles. Metabolite profiles are the scaffold supporting the biological interpretation in metabolomics. Therefore, we aimed to address the following fundamental question: can we obtain similar metabolomic results and, consequently, reach the same biological interpretation by using different protocols for extraction of intracellular metabolites? We have used four different methods for extraction of intracellular metabolites using four different microbial cell types (Gram negative bacterium, Gram positive bacterium, yeast, and a filamentous fungus). All the quenched samples were pooled together before extraction, and, therefore, they were identical. After extraction and GC?CMS analysis of metabolites, we did not only detect different numbers of compounds depending on the extraction method used and regardless of the cell type tested, but we also obtained distinct metabolite levels for the compounds commonly detected by all methods (P-value?<?0.001). These differences between methods resulted in contradictory biological interpretation regarding the activity of different metabolic pathways. Therefore, our results show that different solvent-based extraction methods can yield significantly different metabolite profiles, which impact substantially in the biological interpretation of metabolomics data. Thus, development of alternative extraction protocols and, most importantly, standardization of sample preparation methods for metabolomics should be seriously pursued by the scientific community.     

1H NMR-based metabolic profiling reveals inherent biological variation in yeast and nematode model systems

The application of metabolomics to human and animal model systems is poised to provide great insight into our understanding of disease etiology and the metabolic changes that are associated with these conditions. However, metabolomic studies have also revealed that there is significant, inherent biological variation in human samples and even in samples from animal model systems where the animals are housed under carefully controlled conditions. This inherent biological variability is an important consideration for all metabolomics analyses. In this study, we examined the biological variation in (1)H NMR-based metabolic profiling of two model systems, the yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans. Using relative standard deviations (RSD) as a measure of variability, our results reveal that both model systems have significant amounts of biological variation. The C. elegans metabolome possesses greater metabolic variance with average RSD values of 29 and 39%, depending on the food source that was used. The S. cerevisiae exometabolome RSD values ranged from 8% to 12% for the four strains examined. We also determined whether biological variation occurs between pairs of phenotypically identical yeast strains. Multivariate statistical analysis allowed us to discriminate between pair members based on their metabolic phenotypes. Our results highlight the variability of the metabolome that exists even for less complex model systems cultured under defined conditions. We also highlight the efficacy of metabolic profiling for defining these subtle metabolic alterations.     

Metabolomics reveals stage-specific metabolic pathways of microbial communities in two-stage anaerobic fermentation of corn-stalk

Analysis of intracellular metabolites is essential to delineate metabolic pathways of microbial communities for evaluation and optimization of anaerobic fermentation processes. The metabolomics are reported for a microbial community during two stages of anaerobic fermentation of corn stalk in a biogas digester using GC–MS. Acetonitrile/methanol/water (2:2:1, by vol) was the best extraction solvent for microbial community analysis because it yielded the largest number of peaks (>200), the highest mean summed value of identified metabolites (23) and the best reproducibility with a coefficient of variation of 30 % among four different extraction methods. Inter-stage comparison of metabolite profiles showed increased levels of sugars and sugar alcohols during methanogenesis and fatty acids during acidogenesis. Identification of stage-specific metabolic pathways using metabolomics can therefore assist in monitoring and optimization of the microbial community for increased biogas production during anaerobic fermentation.     

Metabolic Characterization of the Common Marmoset (Callithrix jacchus)

High-resolution metabolomics has created opportunity to integrate nutrition and metabolism into genetic studies to improve understanding of the diverse radiation of primate species. At present, however, there is very little information to help guide experimental design for study of wild populations. In a previous non-targeted metabolomics study of common marmosets (Callithrix jacchus), Rhesus macaques, humans, and four non-primate mammalian species, we found that essential amino acids (AA) and other central metabolites had interspecies variation similar to intraspecies variation while non-essential AA, environmental chemicals and catabolic waste products had greater interspecies variation. The present study was designed to test whether 55 plasma metabolites, including both nutritionally essential and non-essential metabolites and catabolic products, differ in concentration in common marmosets and humans. Significant differences were present for more than half of the metabolites analyzed and included AA, vitamins and central lipid metabolites, as well as for catabolic products of AA, nucleotides, energy metabolism and heme. Three environmental chemicals were present at low nanomolar concentrations but did not differ between species. Sex and age differences in marmosets were present for AA and nucleotide metabolism and warrant additional study. Overall, the results suggest that quantitative, targeted metabolomics can provide a useful complement to non-targeted metabolomics for studies of diet and environment interactions in primate evolution.     

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

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

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

Global Metabolomic Profiling of Mice Brains following Experimental Infection with the Cyst-Forming Toxoplasma gondii

The interplay between the Apicomplexan parasite Toxoplasma gondii and its host has been largely studied. However, molecular changes at the metabolic level in the host central nervous system and pathogenesis-associated metabolites during brain infection are largely unexplored. We used a global metabolomics strategy to identify differentially regulated metabolites and affected metabolic pathways in BALB/c mice during infection with T. gondii Pru strain at 7, 14 and 21 days post-infection (DPI). The non-targeted Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics analysis detected approximately 2,755 retention time-exact mass pairs, of which more than 60 had significantly differential profiles at different stages of infection. These include amino acids, organic acids, carbohydrates, fatty acids, and vitamins. The biological significance of these metabolites is discussed. Principal Component Analysis and Orthogonal Partial Least Square-Discriminant Analysis showed the metabolites’ profile to change over time with the most significant changes occurring at 14 DPI. Correlated metabolic pathway imbalances were observed in carbohydrate metabolism, lipid metabolism, energetic metabolism and fatty acid oxidation. Eight metabolites correlated with the physical recovery from infection-caused illness were identified. These findings indicate that global metabolomics adopted in this study is a sensitive approach for detecting metabolic alterations in T. gondii-infected mice and generated a comparative metabolic profile of brain tissue distinguishing infected from non-infected host.     

Microbial metabolomics: past,present and future methodologies

Microbial metabolomics has received much attention in recent years mainly because it supports and complements a wide range of microbial research areas from new drug discovery efforts to metabolic engineering. Broadly, the term metabolomics refers to the comprehensive (qualitative and quantitative) analysis of the complete set of all low molecular weight metabolites present in and around growing cells at a given time during their growth or production cycle. This review focuses on the past, current and future development of various experimental protocols in the rapid developing area of metabolomics in the ongoing quest to reliably quantify microbial metabolites formed under defined physiological conditions. These developments range from rapid sample collection, instant quenching of microbial metabolic activity, extraction of the relevant intracellular metabolites as well as quantification of these metabolites using enzyme based and or modern high tech hyphenated analytical protocols, mainly chromatographic techniques coupled to mass spectrometry (LC-MSⁿ, GC-MSⁿ, CE-MSⁿ), where n indicates the number of tandem mass spectrometry, and nuclear magnetic resonance spectroscopy (NMR).     

An optimized protocol for metabolome analysis in yeast using direct infusion electrospray mass spectrometry

A method for the global analysis of yeast intracellular metabolites, based on electrospray mass spectrometry (ES-MS), has been developed. This has involved the optimization of methods for quenching metabolism in Saccharomyces cerevisiae and extracting the metabolites for analysis by positive-ion electrospray mass spectrometry. The influence of cultivation conditions, sampling, quenching and extraction conditions, concentration step, and storage have all been studied and adapted to allow direct infusion of samples into the mass spectrometer and the acquisition of metabolic profiles with simultaneous detection of more than 25 intracellular metabolites. The method, which can be applied to other micro-organisms and biological systems, may be used for comparative analysis and screening of metabolite profiles of yeast strains and mutants under controlled conditions in order to elucidate gene function via metabolomics. Examples of the application of this analytical strategy to specific yeast strains and single-ORF yeast deletion mutants generated through the EUROFAN programme are presented.     

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.     

Global metabolic profiling to model biological processes of aging in twins

Aging is intimately linked to system‐wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross‐sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long‐chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R² = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out‐of‐bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age‐related variability in metabolic profiles in response to environmental exposure.     

Global metabolomics characterization of bacteria: pre-analytical treatments and profiling

Pre-analytical treatments of bacteria are crucial steps in bacterial metabolomics studies. In order to achieve reliable samples that can best represent the global metabolic profile in vivo both qualitatively and quantitatively, many sample treatment procedures have been developed. The use of different methods makes it difficult to compare the results among different groups. In this work, E. coli samples were tested by using NMR spectroscopy. Both liquid N₂ and cold methanol quenching procedures reduce the cell membrane integrity and cause metabolites leakage. However, liquid N₂ quenching affected the cell viability and the NMR metabolites’ profile less than cold methanol procedure. Samples obtained by metabolite extraction were significantly superior over cell suspensions and cell lysates, with a higher number of detectable metabolites. Methanol/chloroform extraction proved most efficient at extraction of intracellular metabolites from both qualitative and quantitative points of view. Finally, standard operating procedures of bacterial sample treatments for NMR metabolomics study are presented.     

Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.     

Distinct signatures of host–microbial meta-metabolome and gut microbiome in two C57BL/6 strains under high-fat diet

A combinatory approach using metabolomics and gut microbiome analysis techniques was performed to unravel the nature and specificity of metabolic profiles related to gut ecology in obesity. This study focused on gut and liver metabolomics of two different mouse strains, the C57BL/6J (C57J) and the C57BL/6N (C57N) fed with high-fat diet (HFD) for 3 weeks, causing diet-induced obesity in C57N, but not in C57J mice. Furthermore, a 16S-ribosomal RNA comparative sequence analysis using 454 pyrosequencing detected significant differences between the microbiome of the two strains on phylum level for Firmicutes, Deferribacteres and Proteobacteria that propose an essential role of the microbiome in obesity susceptibility. Gut microbial and liver metabolomics were followed by a combinatory approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and ultra performance liquid chromatography time of tlight MS/MS with subsequent multivariate statistical analysis, revealing distinctive host and microbial metabolome patterns between the C57J and the C57N strain. Many taurine-conjugated bile acids (TBAs) were significantly elevated in the cecum and decreased in liver samples from the C57J phenotype likely displaying different energy utilization behavior by the bacterial community and the host. Furthermore, several metabolite groups could specifically be associated with the C57N phenotype involving fatty acids, eicosanoids and urobilinoids. The mass differences based metabolite network approach enabled to extend the range of known metabolites to important bile acids (BAs) and novel taurine conjugates specific for both strains. In summary, our study showed clear alterations of the metabolome in the gastrointestinal tract and liver within a HFD-induced obesity mouse model in relation to the host–microbial nutritional adaptation.     

基于GC-MS的野生和‘鲁赫’刺蔷薇叶片代谢差异研究

为探究野生刺蔷薇（Rosa acicularis）和‘鲁赫’刺蔷薇（Rosa acicularis ‘Luhe’）叶片的代谢物和代谢通路差异，对2种刺蔷薇叶片进行了GC-MS非靶向代谢组学试验，共检测出6种氨基酸、12种糖类、4种烷烃类化合物、17种有机酸、5种酯类和7种其他类化合物，共计51种代谢物。PCA模型符合预期，OPLS-DA模型筛选出19种显著差异代谢物，差异代谢通路分析共分析出5条主要的差异代谢通路。综合各项结果，2种刺蔷薇叶片在部分有机酸和糖类的代谢上有显著差异，同时也在ABC转运蛋白、苯丙烷的生物合成、苯甲酸的降解和丙氨酸、天冬氨酸和谷氨酸的代谢上有显著差异，推测2种植物可能在叶片光合效率和抗胁迫能力上有显著不同，但仍需进一步结合基因组、蛋白质组、转录组和其他方法进行深入研究。     

Untargeted metabolomics reveals a lack of synergy between nifurtimox and eflornithine against Trypanosoma brucei

A non-targeted metabolomics-based approach is presented that enables the study of pathways in response to drug action with the aim of defining the mode of action of trypanocides. Eflornithine, a polyamine pathway inhibitor, and nifurtimox, whose mode of action involves its metabolic activation, are currently used in combination as first line treatment against stage 2, CNS-involved, human African trypanosomiasis (HAT). Drug action was assessed using an LC-MS based non-targeted metabolomics approach. Eflornithine revealed the expected changes to the polyamine pathway as well as several unexpected changes that point to pathways and metabolites not previously described in bloodstream form trypanosomes, including a lack of arginase activity and N-acetylated ornithine and putrescine. Nifurtimox was shown to be converted to a trinitrile metabolite indicative of metabolic activation, as well as inducing changes in levels of metabolites involved in carbohydrate and nucleotide metabolism. However, eflornithine and nifurtimox failed to synergise anti-trypanosomal activity in vitro, and the metabolomic changes associated with the combination are the sum of those found in each monotherapy with no indication of additional effects. The study reveals how untargeted metabolomics can yield rapid information on drug targets that could be adapted to any pharmacological situation.     

Extracellular metabolomics: a metabolic footprinting approach to assess fiber degradation in complex media

This work reports the implementation and optimization of a method for high-throughput analysis of metabolites produced by the breakdown of natural polysaccharides by microorganisms. Our simple protocol enables simultaneous separation and quantification of more than 40 different sugars and sugar derivatives, in addition to several organic acids in complex media, using 50-mul samples and a standard gas chromatography-mass spectrometry platform that was fully optimized for this purpose. As an implementation proof-of-concept, we assayed extracellular metabolite levels of three bacterial strains cultivated on complex medium rich in polysaccharides and under identical growth conditions. We demonstrate that the metabolic footprinting profile data distinguish among sample types such as typical metabolomics data. Moreover, we demonstrate that the differential metabolite-level data provide insight on specific fibrolytic activity of the different microbial strains and lay the groundwork for integrated proteome-metabolome studies of fiber-degrading microorganisms.     

Metabolic profiling of <Emphasis Type="Italic">Drosophila melanogaster</Emphasis> metamorphosis: a new insight into the central metabolic pathways

Introduction

Metamorphosis is a complicated process in which cell proliferation, differentiation, and death are orchestrated to form the mature structures of insects. In Drosophila, this process is controlled by ecdysone, a steroid hormone responsible for tissue remodeling and organogenesis that gives rise to the adult fly.

Objective

By using a metabolomics approach, this study aimed to elucidate global changes in the central metabolic pathways in Drosophila throughout metamorphosis and then further examine the effects of temperature and origin on metabolic profiles.

Methods

Targeted and non-targeted metabolic profiling of time-course samples from Drosophila were constructed to cover a wide range of cellular metabolites during metamorphosis.

Results

This was the first wide-scale metabolomics study of Drosophila metamorphosis focusing on central metabolism. The abundance of detected metabolites changed drastically and correlated strongly with the development of Drosophila pupae. In non-stress conditions, temperature affected the developmental time, but the metabolic state at a certain stage of metamorphosis remained stable. Between D. melanogaster Canton S and Oregon R, similar metabolic profiles throughout metamorphosis was observed. However, there were still differences in purine and pyrimidine metabolism at an early stage in the pupal period, which was matched by differences in ecdysteroid levels.

Conclusion

This study supported the strength of metabolomics in the field of developmental biology. The results provided a general view on the metabolic profile of Drosophila during metamorphosis, which provides basic 3 knowledge for future metabolomics studies using Drosophila.

     

Serum metabonomics study of pregnant women with gestational diabetes mellitus based on LC-MS

ObjectiveThrough metabolomics method, the objective of the paper is to differentially screen serum metabolites of GDM patients and healthy pregnant women, to explore potential biomarkers of GDM and analyze related pathways, and to explain the potential mechanism and biological significance of GDM.MethodsThe serum samples from 30 GDM patients and 30 healthy pregnant women were selected to conduct non-targeted metabolomics study by liquid chromatography-mass spectrometry. The differential metabolites between the two groups were searched and the metabolic pathway was analyzed by KEGG database.ResultsMultivariate statistical analysis found that serum metabolism in GDM patients was different significantly from healthy pregnant women, 36 differential metabolites and corresponding metabolic pathways were identified in serum, which involved several metabolic ways like, fatty acid metabolism, butyric acid metabolism, bile secretion, and amino acid metabolism.ConclusionThe discovery of these biomarkers provided a new theoretical basis and experimental basis for further study of the early diagnosis and pathogenesis of GDM. At the same time, LC-MS-based serum metabolomics methods also showed great application values in disease diagnosis and mechanism research.