Metabolomics or metabolite profiles?

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Authentication of milk thistle commercial products using UHPLC-QTOF-ESI + MS metabolomics and DNA metabarcoding

BACKGROUND

Milk thistle is one of the most popular hepatoprotectants, and is often sold in combination with other ingredients. Botanical supplements are known to be vulnerable to contamination and adulteration, and emerging technologies show promise to improve their quality control.

METHODS

Untargeted and semi-targeted metabolomics based on UHPLC-QTOF-ESI⁺MS techniques, UV spectrometry, and DNA metabarcoding using Illumina MiSeq were used to authenticate eighteen milk thistle botanical formulations (teas, capsules, tablets, emulsion).

RESULTS

Untargeted metabolomics separated 217 molecules and by multivariate analysis the discrimination between the different preparations was established. The semi-targeted metabolomics focused on 63 phytochemicals, mainly silymarin flavonolignans and flavonoids, that may be considered as putative biomarkers of authenticity. All formulations contained molecules from silymarin complexes at different levels. The quantitative evaluation of silybins was done using in parallel UV spectrometry and UHPLC-QTOF-ESI⁺MS and their correlations were compared. DNA metabarcoding detected milk thistle in eleven out of sixteen retained preparations, whereas two others had incomplete evidence of milk thistle despite metabolomics validating specific metabolites, e.g., silymarin complex, identified and quantified in all samples. Meanwhile, the DNA metabarcoding provided insights into the total species composition allowing the interpretation of the results in a broad context.

CONCLUSION

Our study emphasizes that combining spectroscopic, chromatographic, and genetic techniques bring complementary information to guarantee the quality of the botanical formulations.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

Differences in metabolic profiles between bicuspid and tricuspid aortic stenosis in the setting of transcatheter aortic valve replacement

Background

To explore why bicuspid aortic stenosis has certain clinical differences from the tricuspid morphology, we evaluated the metabolomics profile involved in bicuspid aortic valve (BAV) aortic stenosis prior to and after transcatheter aortic valve replacement (TAVR) in comparison with tricuspid aortic valve (TAV).

Methods

In this TAVR cohort with prospectively collected data, blood samples were obtained before TAVR valve deployment and at the 7th day after TAVR, which were then sent for liquid and gas chromatography-mass spectrometry detection. Besides comparisons between BAV and TAV, BAV patients were also divided in subgroups according to baseline hemodynamics (i.e. maximal transaortic velocity, V_max) and post-procedural reverse left ventricular (LV) remodeling (i.e. the change in LV mass index from baseline, ∆LVMI) for further analysis. Metabolic differences between groups were identified by integrating univariate test, multivariate analysis and weighted correlation network analysis algorithm.

Results

A total of 57 patients were enrolled including 33 BAV patients. The BAV group showed lower arginine and proline metabolism both before and post TAVR than TAV represented by decreased expression of L-Glutamine. In BAV subgroup analysis, patients with baseline V_max > 5 m/s (n = 11) or the 4^th quartile of change in ∆LVMI at one-year follow-up (i.e. poorly-recovered LV, n = 8) showed elevated arachidonic acid metabolism compared with V_max < 4.5 m/s (n = 12) or the 1^st quartile of ∆LVMI (i.e. well-recovered LV, n = 8) respectively.

Conclusions

Difference in arginine and proline metabolism was identified between BAV and TAV in TAVR recipients. Elevated arachidonic acid metabolism may reflect more severe baseline hemodynamics and worse LV reserve remodeling after TAVR in BAV.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Application of molecular tools to elucidate the microbiota of seafood

The aim of this review is to present the methodologies currently applied to identify microbiota and pathogens transmitted to humans through seafood consumption, focusing on molecular techniques and pointing out their importance, advantages, disadvantages and applicability. Knowledge of available techniques allows researchers to identify which technique best fits their expectations. With such discernment, it will be possible to infer which disadvantages will be present and, therefore, not interfering with the final result. Two methodologies can be employed for this purpose, dependent and independent cultures. However, the dependent culture has certain limitations that can be solved through the independent cultivation techniques, such as PCR, PFGE and NGS, especially through the sequencing of the 16S rRNA region, providing a complete view of microbial diversity. These have revolutionized microbiological knowledge, mainly because they allow for the identification of uncultivable micro-organisms, which represent a substantial portion of total micro-organisms, making it possible to elucidate not yet described taxa which may display pathogenic potential, besides quantifying microbial communities, microbiota genetics, translated proteins and produced metabolites. In addition, transcriptomic and metabolomic techniques also allow for the evaluation of possible impacts that microbial communities may create in their environment, as well as the determination of potential pathogenicity to humans.

1H NMR Metabolomic Profiling of Human and Animal Blood Serum Samples

Nuclear magnetic resonance (NMR) spectroscopy techniques allow the acquisition of a large amount of data and when combined with multivariate statistical analysis, it is possible to process and interpret the obtained NMR data in accordance with the biological problem being investigated. In this chapter, the search for biologically relevant biomarkers is addressed using NMR spectroscopy-based metabolomics, due to their clinical relevance for either diagnosis or monitoring of diseases and disorders.

Zebrafish Pancreas Development and Regeneration: Fishing for Diabetes Therapies

The zebrafish pancreas shares its basic organization and cell types with the mammalian pancreas. In addition, the developmental pathways that lead to the establishment of the pancreatic islets of Langherhans are generally conserved from fish to mammals. Zebrafish provides a powerful tool to probe the mechanisms controlling establishment of the pancreatic endocrine cell types from early embryonic progenitor cells, as well as the regeneration of endocrine cells after damage. This knowledge is, in turn, applicable to refining protocols to generate renewable sources of human pancreatic islet cells that are critical for regulation of blood sugar levels. Here, we review how previous and ongoing studies in zebrafish and beyond are influencing the understanding of molecular mechanisms underlying various forms of diabetes and efforts to develop cell-based approaches to cure this increasingly widespread disease.

Comparison of peak-picking workflows for untargeted liquid chromatography/high-resolution mass spectrometry metabolomics data analysis

RATIONALE

Data analysis is a key step in mass spectrometry based untargeted metabolomics, starting with the generation of generic peak lists from raw liquid chromatography/mass spectrometry (LC/MS) data. Due to the use of various algorithms by different workflows, the results of different peak-picking strategies often differ widely.

METHODS

Raw LC/HRMS data from two types of biological samples (bile and urine), as well as a standard mixture of 84 metabolites, were processed with four peak-picking softwares: Peakview®, Markerview™, MetabolitePilot™ and XCMS Online. The overlaps between the results of each peak-generating method were then investigated. To gauge the relevance of peak lists, a database search using the METLIN online database was performed to determine which features had accurate masses matching known metabolites as well as a secondary filtering based on MS/MS spectral matching.

RESULTS

In this study, only a small proportion of all peaks (less than 10%) were common to all four software programs. Comparison of database searching results showed peaks found uniquely by one workflow have less chance of being found in the METLIN metabolomics database and are even less likely to be confirmed by MS/MS.

CONCLUSIONS

It was shown that the performance of peak-generating workflows has a direct impact on untargeted metabolomics results. As it was demonstrated that the peaks found in more than one peak detection workflow have higher potential to be identified by accurate mass as well as MS/MS spectrum matching, it is suggested to use the overlap of different peak-picking workflows as preliminary peak lists for more rugged statistical analysis in global metabolomics investigations.

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.

MALDI Mass Spectrometry Imaging for Visualizing In Situ Metabolism of Endogenous Metabolites and Dietary Phytochemicals

Understanding the spatial distribution of bioactive small molecules is indispensable for elucidating their biological or pharmaceutical roles. Mass spectrometry imaging (MSI) enables determination of the distribution of ionizable molecules present in tissue sections of whole-body or single heterogeneous organ samples by direct ionization and detection. This emerging technique is now widely used for in situ label-free molecular imaging of endogenous or exogenous small molecules. MSI allows the simultaneous visualization of many types of molecules including a parent molecule and its metabolites. Thus, MSI has received much attention as a potential tool for pathological analysis, understanding pharmaceutical mechanisms, and biomarker discovery. On the other hand, several issues regarding the technical limitations of MSI are as of yet still unresolved. In this review, we describe the capabilities of the latest matrix-assisted laser desorption/ionization (MALDI)-MSI technology for visualizing in situ metabolism of endogenous metabolites or dietary phytochemicals (food factors), and also discuss the technical problems and new challenges, including MALDI matrix selection and metabolite identification, that need to be addressed for effective and widespread application of MSI in the diverse fields of biological, biomedical, and nutraceutical (food functionality) research.

Metabolic differentiations and classification of Verbascum species by NMR-based metabolomics

The genus Verbascum L. (mulleins) comprises of about 360 species of flowering plants in the Scrophulariaceae family. Mulleins have been used in the traditional folk medicine for centuries, for treatment of a wide range of human ailments, inter alia bronchitis, tuberculosis, asthma, and different inflammations. Despite all applications the knowledge of the metabolites, accumulated in different mullein species, is still limited and based mainly on determination of the major compounds. Here we report the application of 1H NMR metabolic fingerprinting in combination with principal component analyses (PCA) in five different Verbascum species. Based on the obtained results mulleins were divided in two groups: group A (Verbascum phlomoides and Verbascum densiflorum) and group B (Verbascum xanthophoeniceum, Verbascum nigrum and Verbascum phoeniceum). Further it was found that the plants in group B accumulate higher amounts of bioactive iridoid and phenylethanoid glycosides. V. xanthophoeniceum and V. nigrum accumulate higher amounts of the pharmaceutically-important harpagoside (∼0.5% on dry weight basis) and verbascoside, forsythoside B and leucosceptoside B (in total 5.6-5.8% on dry weight basis), which underlines the possibility for their application in pharmaceutical industry. To the best of our knowledge this is the first report on the analyses of Verbascum sp. leaf metabolome.

Exceptional production of both prodigiosin and cycloprodigiosin as major metabolic constituents by a novel marine bacterium, Zooshikella rubidus S1-1

A Gram-negative, red-pigment-producing marine bacterial strain, designated S1-1, was isolated from the tidal flat sediment of the Yellow Sea, Korea. On the basis of phenotypic, phylogenetic, and genetic data, strain S1-1 (KCTC 11448BP) represented a new species of the genus Zooshikella. Thus, we propose the name Zooshikella rubidus sp. nov. Liquid chromatography and mass spectrometry of the red pigments produced by strain S1-1 revealed that the major metabolic compounds were prodigiosin and cycloprodigiosin. In addition, this organism produced six minor prodigiosin analogues, including two new structures that were previously unknown. To our knowledge, this is the first description of a microorganism that simultaneously produces prodigiosin and cycloprodigiosin as two major metabolites. Both prodigiosin and cycloprodigiosin showed antimicrobial activity against several microbial species. These bacteria were approximately 1.5-fold more sensitive to cycloprodigiosin than to prodigiosin. The metabolites also showed anticancer activity against human melanoma cells, which showed significantly more sensitivity to prodigiosin than to cycloprodigiosin. The secondary metabolite profiles of strain S1-1 and two reference bacterial strains were compared by liquid chromatography-mass spectrometry. Multivariate statistical analyses based on secondary metabolite profiles by liquid chromatography-mass spectrometry indicated that the metabolite profile of strain S1-1 could clearly be distinguished from those of two phylogenetically related, prodigiosin-producing bacterial strains.

Correlation between antioxidative activities and metabolite changes during Cheonggukjang fermentation

Liquid chromatography mass spectrometry and multivariate analysis were employed to investigate the correlation between fermentation time-dependent metabolite changes in cheonggukjang, a traditional fermented soybean product, and changes in its antioxidant activity over 72 h. The metabolite patterns were clearly distinguished not by strains but by fermentation time, into patterns I (0-12 h), II (12-24 h), and III (24-72 h), which appeared as distinct clusters on principal component analysis. The compounds that significantly contributed to patterns I, II, and III were soyasaponins, isoflavonoid derivatives, and isoflavonoid aglycons respectively. Partial least square analysis for metabolite to antioxidant effects showed correlations between the ferric reducing/antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay during 24-36 h, and 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) test and total phenol content (TPC) during 36-72 h. Compared with the strong negative correlations of glucosylated-isoflavonoids with DPPH, ABTS and TPC during fermentation, the isoflavonoid aglycon displayed strong positive correlations with these compounds during fermentation.

Using metabolomic analysis to understand inflammatory bowel diseases

Crohn's disease (CD) and ulcerative colitis (UC) are inflammatory bowel diseases (IBD) attributed to a dysregulated immune response towards intestinal microbiota. Although various susceptibility genes have been identified for CD and UC, the exact disease etiology is unclear and complicated by the influence of environmental factors. Metabolomic analysis enables high sample throughput measurements of multiple metabolites in biological samples. The use of metabolomic analysis in medical sciences has revealed metabolite perturbations associated with diseases. This article provides a summary of the current understanding of IBD, and describes potential applications and previous metabolomic analysis in IBD research to understand IBD pathogenesis and improve IBD therapy.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

Analysis of skeletal muscle metabolome: evaluation of extraction methods for targeted metabolite quantification using liquid chromatography tandem mass spectrometry

Functional metabolomics of skeletal muscle involves the simultaneous identification and quantification of a large number of metabolites. For this purpose, the extraction of metabolites from animal tissues is a crucial technical step that needs to be optimized. In this work, five extraction methods for skeletal muscle metabolome analysis using liquid chromatography tandem mass spectrometry (LC-MS/MS) were tested. Bird skeletal muscles sampled postmortem and quenched in liquid nitrogen were used. Three replicates of the same sample were extracted using the following solvent systems of varying polarity: boiling water (BW, +100 degrees C), cold pure methanol (CPM, -80 degrees C), methanol/chloroform/water (MCW, -20 degrees C), boiling ethanol (BE, +80 degrees C), and perchloric acid (PCA, -20 degrees C). Three injections by extraction were performed. The BW extraction showed the highest recovery of metabolites with the lowest variability (<10%) except for creatine-phosphate (creatine-P). Considering yield (area of the peaks), reproducibility, and ease, the current experiment drew a scale for the muscle metabolome extraction starting from the best to the least convenient: BW>MCW>CPM>PCABE. In addition, the semiquantification of metabolites in two muscles showing different metabolic and contractile properties was carried out after BW extraction and showed expected differences in metabolite contents, thereby validating the technique for biological investigations. In conclusion, the BW extraction is recommended for analysis of skeletal muscle metabolome except for creatine-P, which was poorly recovered with this technique.

Challenges in applying chemometrics to LC–MS-based global metabolite profile data

Metabolite profiling can provide insights into the metabolic status of complex living systems through the non-targeted analysis of metabolites in any biological sample. Metabolite profiling is complementary to genomics, transcriptomics and proteomics, and its applications span epidemiology, disease diagnosis, nutrition, pharmaceutical research, and toxicology. Metabolic phenotypes are a reflection of an organism’s environment, lifestyle, diet, gut microfloral composition and are also influenced by genetic factors, with important implications in genome-wide-association studies. Specialized analytical platforms, such as NMR spectroscopy and MS, are required to interrogate such metabolic complexity. The increased sophistication of such techniques has lead to a demand for improved data analysis approaches, including preprocessing and advanced chemometric techniques. This article discusses data generation, preprocessing, multivariate analysis and data interpretation for LC-MS-based metabolite profiling, focusing on challenges encountered and potential solutions.

The metabolomics of carotenoids in engineered cell factory

Carotenoids such as beta-carotene, lycopene, and antheraxanthin have plenty of scientific and commercial value. The comprehensive investigation of carotenoids drives people to improve and develop all kinds of analytical techniques to approach or even achieve "versatile" analysis. The metabolic engineering efforts in plants and algae have progressed rapidly, aiming to enable the use of plants and algae as "cell factories" for producing specific or novel carotenoids, such as beta-carotene (provitamin A) in Gold rice, while the emerging technologies of metabolomics support it by providing comprehensive analysis of carotenoids biochemical characterizations. This review describes metabolomics as a high-throughput platform to study carotenoids, including the engineering methods in the plants or algae, the bioinformatics for metabolomics, and the metabolomics of carotenoids in engineered cell factory. Modern systems biology tools, together with the development of genomics and metabolomics databases, will dramatically facilitate the advancement of our knowledge in gene-to-metabolite networks in plants. Metabolomics accompanying genomics, transcriptomics, and proteomics as well as bioinformatics facilitate metabolic engineering efforts towards designing superior biocatalysts in cell factories. Ongoing advances in biological techniques coupled with crucial metabolic networks will further promote plants and algae as attractive platforms for the production of numerous high-value compounds such as carotenoids.

Inter- and intra-individual variations of urinary endogenous metabolites in healthy male college students using (1)H NMR spectroscopy

BACKGROUND

Most human metabolomics studies have shown that spectral outputs of (1)H nuclear magnetic resonance fingerprinting are strongly influenced by inter- and intra-individual variations; however, few studies have been performed to evaluate the inter- and intra-individual variations in urinary endogenous metabolites.

METHODS

We recruited 30 male college students to evaluate the factors affecting intra- and inter-individual variations in urinary endogenous metabolites. Statistical analysis for variations in urinary metabolites was performed after eliminating outliers found in principal component analysis (PCA) plots.

RESULTS

Inter-individual variations were relatively low for 2-oxoglutarate, succinate, citrate, dimethylglycine, and taurine, but high for trimethylaminoxide (TMAO), hippurate, and lactate. Intra-individual variations for 2-oxoglutarate, citrate, dimethylglycine, and taurine were relatively low, but high for TMAO and hippurate. The factors affecting inter-individual variation of lactate were age, body mass index, beverages, and alcohol, whereas the factors affecting intra-individual variation of lactate were age and fish. Kim Chi intake affected the inter-individual variation of succinate, citrate, TMAO, and hippurate; however, it did not affect the intra-individual variation of endogenous metabolites.

CONCLUSIONS

Our results showed that inter- and intra-individual variations in urinary endogenous metabolites were very large, and significant factors affecting inter- and intra-individual variation were diverse, even after eliminating outliers in PCA analysis.

Metabolomics approach for determining growth-specific metabolites based on Fourier transform ion cyclotron resonance mass spectrometry

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) is the best MS technology for obtaining exact mass measurements owing to its great resolution and accuracy, and several outstanding FT-ICR/MS-based metabolomics approaches have been reported. A reliable annotation scheme is needed to deal with direct-infusion FT-ICR/MS metabolic profiling. Correlation analyses can help us not only uncover relations between the ions but also annotate the ions originated from identical metabolites (metabolite derivative ions). In the present study, we propose a procedure for metabolite annotation on direct-infusion FT-ICR/MS by taking into consideration the classification of metabolite-derived ions using correlation analyses. Integrated analysis based on information of isotope relations, fragmentation patterns by MS/MS analysis, co-occurring metabolites, and database searches (KNApSAcK and KEGG) can make it possible to annotate ions as metabolites and estimate cellular conditions based on metabolite composition. A total of 220 detected ions were classified into 174 metabolite derivative groups and 72 ions were assigned to candidate metabolites in the present work. Finally, metabolic profiling has been able to distinguish between the growth stages with the aid of PCA. The constructed model using PLS regression for OD(600) values as a function of metabolic profiles is very useful for identifying to what degree the ions contribute to the growth stages. Ten phospholipids which largely influence the constructed model are highly abundant in the cells. Our analyses reveal that global modification of those phospholipids occurs as E. coli enters the stationary phase. Thus, the integrated approach involving correlation analyses, metabolic profiling, and database searching is efficient for high-throughput metabolomics.

Mass spectrometry-based metabolomics: accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution

A strategy combining autocorrelation matrices and ultrahigh resolution mass spectrometry (MS) was developed to optimize the characterization of discriminating ions highlighted by metabolomics. As an example, urine samples from rats treated with phenobarbital (PB) were analyzed by ultrahigh-pressure chromatography with two different eluting conditions coupled to time-of-flight mass spectrometric detection in both the positive and negative electrospray ionization modes. Multivariate data analyses were performed to highlight discriminating variables from several thousand detected signals: a few hundred signals were found to be affected by PB, whereas a few tenths of them were linked to its metabolism. Autocorrelation matrices were then applied to eliminate adduct and fragment ions. Finally, the characterization of the ions of interest was performed with ultrahigh-resolution mass spectrometry and sequential MS(n) experiments, by using a LC-LTQ-Orbitrap system. The use of different eluting conditions was shown to drastically impact on the chromatographic retention and ionization of compounds, thus providing a way to obtain more exhaustive metabolic fingerprints, whereas autocorrelation matrices allowed one to focus the identification work on the most relevant ions. By using such an approach, 14 PB metabolites were characterized in rat urines, some of which have not been reported in the literature.

Microbial metabolomics: Toward a platform with full metabolome coverage

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

Cold glycerol–saline: The promising quenching solution for accurate intracellular metabolite analysis of microbial cells

Microbial metabolomics has been seriously limited by our inability to perform a reliable separation of intra- and extracellular metabolites with efficient quenching of cell metabolism. Microbial cells are sensitive to most (if not all) quenching agents developed to date, resulting in leakage of intracellular metabolites to the extracellular medium during quenching. Therefore, as yet we are unable to obtain an accurate concentration of intracellular metabolites from microbial cell cultures. However, knowledge of the in vivo concentrations of intermediary metabolites is of fundamental importance for the characterization of microbial metabolism so as to integrate meaningful metabolomics data with other levels of functional genomics analysis. In this article, we report a novel and robust quenching method for microbial cell cultures based on cold glycerol-saline solution as the quenching agent that prevents significant leakage of intracellular metabolites and, therefore, permits more accurate measurement of intracellular metabolite concentrations in microbial cells.

The Potential of Metabolomics Tools in Bioremediation Studies

As a post-genomics tool, metabolomics is a young and vibrant field of science in its exponential growth phase. Metabolome analysis has become very popular recently, and novel techniques for acquiring and analyzing metabolomics data continue to emerge that are useful for a variety of biological studies. The bioremediation field has a lot to gain from the advances in this emerging area. Thus, this review article focuses on the potential of various experimental and conceptual approaches developed for metabolomics to be applied in bioremediation research, such as strategies for elucidation of biodegradation pathways using isotope distribution analysis and molecular connectivity analysis, the assessment of mineralization process using metabolic footprinting analysis, and the improvement of the biodegradation process via metabolic engineering. We demonstrate how the use of metabolomics tools can significantly extend and enhance the power of existing bioremediation approaches by providing a better overview of the biodegradation process.

Elucidating the mode-of-action of compounds from metabolite profiling studies

Metabolite profiling has been carried out for decades and is as such not a new research area. However, the field has attracted increasing attention in the last couple of years, and the term metabolome is now often used to describe the complete pool of metabolites associated with an organism at any given time. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the best candidates for comprehensive analysis of the metabolome and the application of these technologies is presented in this chapter. In this relation, the importance of efficient metabolite screening for discovery of novel drugs is discussed. Related to metabolite profiling, the principals underlying the application of labeled substrates to quantify in vivo metabolic fluxes are introduced, and the chapter is concluded by discussing the perspectives of metabolite measurements in systems biology.

Fungal metabolite analysis in genomics and phenomics

Metabolomics consists of strategies to quantitatively identify cellular metabolites and to understand how trafficking of these biochemical messengers through the metabolic network influences phenotype. The application of metabolomics to fungi has been strongly pursued because these organisms are widely used for the production of chemicals, are well known for their diverse metabolic landscape and serve as excellent eukaryotic model organisms for studying metabolism and systems biology. Within the context of fungal systems, recent progress has been made in the development of analytical tools and mathematical strategies used in metabolite analysis that have enhanced our ability to crack the code underpinning the cellular inventory, regulatory schemes and communication mechanisms that dictate cellular function. Metabolomics has played a key role in functional genomics and strain classification.

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.